DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to the applicant’s communication filed on 7/1/2024
Claims 1-20 are pending
Claim Objections
Claim 1 objected to because of the following informalities: “in a real-time” in line 5 should be changed to “in real-time”, “network data comprises of” in line 17 should be changed to “network data comprises”, “associated to each bus” in line 17 should be changed to “associated with each bus”, “a voltage stabilizing of” in line 18 should be changed to “a voltage of” or “the voltage of” Appropriate correction is required.
Claim 4 objected to because of the following informalities: “based reactance values” in line 12 should be changed to “based on reactance values”. Appropriate correction is required.
Claim 5 objected to because of the following informalities: “should belongs” in line 18 should be changed to “should belong”. Appropriate correction is required.
Claim 6 objected to because of the following informalities: “should belongs” in line 15 should be changed to “should belong”, and “volage” in line 16 should be changed to “voltage”. Appropriate correction is required.
Claim 8 objected to because of the following informalities: “network data comprises of” in line 13 should be changed to “network data comprises”, “associated to each bus” in line 10 should be changed to “associated with each bus”, “a voltage stabilizing of” in line 12 should be changed to “a voltage of” or “the voltage of”. Appropriate correction is required.
Claim 11 objected to because of the following informalities: “based reactance” in line 5 should be changed to “based on reactance”. Appropriate correction is required.
Claim 12 objected to because of the following informalities: “should belongs” in line 11 should be changed to “should belong”. Appropriate correction is required.
Claim 13 objected to because of the following informalities: “should belongs” in line 6 should be changed to “should belong”, and “volage” in line 7 should be changed to “voltage”. Appropriate correction is required.
Claim 14 objected to because of the following informalities: “a voltage stabilizing of” in line 1 should be changed to “a voltage of” or “the voltage of”. Appropriate correction is required.
Claim 15 objected to because of the following informalities: “in a real-time” in line 1 should be changed to “in real-time”. Appropriate correction is required.
Claim 17 objected to because of the following informalities: “based reactance” in line 14 should be changed to “based on reactance”. Appropriate correction is required.
Claim 18 objected to because of the following informalities: “should belongs” in line 20 should be changed to “should belong”. Appropriate correction is required.
Claim 19 objected to because of the following informalities: “should belongs” in line 16 should be changed to “should belong”, and “volage” in line 16 should be changed to “voltage”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 7, 14, and 20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The claims are generally narrative and indefinite, failing to conform with current U.S. practice. They appear to be a literal translation into English from a foreign document and are replete with grammatical and idiomatic errors.
Regarding claims 7, 14, and 20, “minimizing a non-convex objective function of the non-convex optimization technique and a third constraints set” should be corrected to “minimizing a non-convex objective function of the non-convex optimization technique subject to a third constraint set”. An optimization problem minimizes an objective function, but does not minimize a constraint set. A constraint set defines the feasible conditions subject to which the objective function is minimized. For purposes of the prior-art rejection, the phrase “by minimizing a non-convex objective function … and a third constraints set” is interpreted as determining the control signal by minimizing the non-convex objective function subject to a third constraint set.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu (Data-driven Voltage Regulation in Radial Power Distribution Systems, 2019) (hereinafter Xu) in view of Deaver, SR. et al. US 2012/0022713 A1 (hereinafter Deaver), and further in view of Jereminov et al. US 2018/0158152 A1 (hereinafter Jereminov).
Xu teaches a processor-implemented method (Page 1, Abstract, “we develop a data-driven voltage regulation framework for distributed energy resources (DERs) in a balanced radial power distribution system”; Page 3, Framework Overview, “The voltage controller then computes the set-points for the DER active and reactive power injections that minimize some cost function subject to constraints C1 and C2” – Because the controller performs numerical matrix computations, estimation, and optimization to compute control setpoints, it would have been obvious to implement Xu’s method using one or more hardware processors), comprising the steps of:
obtaining, via one or more hardware processors, a load data (Page 2, Power Distribution System Model, “respectively denote the active and reactive power demanded by load”), a distributed energy resources (DER) data (Page 8, Concluding Remarks, “data-driven voltage regulation framework for DERs in a balanced radial power distribution system”), and a network data of an electrical distribution network (Page 2, Power Distribution System Model, “Consider a three-phase balanced power distribution system represented by a directed graph … is the set of buses (nodes), and … is set of distribution lines (edges) … denote the resistance and reactance of line …”) whose voltage is to be controlled (Page 2, Voltage Regulation Problem, “the problem is to determine the DER active and reactive power injections so that … C1. The active and reactive power injections from each DER … do not exceed its corresponding capacity limits, … and C2. the voltage magnitude at each bus is within the pre-specified interval”), wherein the load data and the DER data are obtained at each timestep of a plurality of time-steps for a predefined time period (Page 3, Voltage Sensitivity Estimator, “Assume at time instant k+1, we have measurements Vo[k’], V[k’], p[k’], q[k’], k’=0,1, …, k, where the index k’ indicates the corresponding measurement is obtained at time instant k’.”), and comprises
a plurality of buses and a plurality of lines connected to the plurality of buses (Fig. 1, Page 2, Power Distribution System Model, “Consider a three-phase balanced power distribution system represented by a directed graph … is the set of buses (nodes), and … is set of distribution lines (edges) … denote the resistance and reactance of line …”),
(ii) one or more distributed energy resources present at one or more buses of the plurality of buses (Page 2, Power Distribution System Model, “pig and qig respectively denote the active and reactive power injected by DER i”), and
(iii) one or more network loads present at one or more buses of the plurality of buses (Page 2, “pid and qid respectively denote the active and reactive power demanded by load i”),
and wherein the load data at each time-step comprises a load active power consumption and a load reactive power consumption (Page 2, Power Distribution System Model, “pid and qid respectively denote the active and reactive power demanded by load i”), at the one or more buses of the plurality of buses (Page 2, Power Distribution System Model, “Consider a three-phase balanced power distribution system represented by a directed graph … is the set of buses (nodes), and … is set of distribution lines (edges) … denote the resistance and reactance of line …”), the DER data at each time-step comprises a DER active power generation and a DER reactive power generation (Page 2, Power Distribution System Model, “pig and qig respectively denote the active and reactive power injected by DER i”; Page 3, Voltage Sensitivity Estimator, “Assume at time instant k+1, we have measurements Vo[k’], V[k’], p[k’], q[k’], k’=0,1, …, k, where the index k’ indicates the corresponding measurement is obtained at time instant k’.”; Page 3, Framework Overview, “The voltage controller then computes the set-points for the DER active and reactive power injections” – DER-generated power is injected/output into the electrical distribution network, and Xu obtains active/reactive power measurements at indexed time instants k’, thereby teaching DER active/reactive power data at each time-step.), and the network data comprises of a line resistance and a line reactance of each line of the plurality of lines (Page 2, Power Distribution System Model, “Let rl and xl denote the resistance and reactance of line … respectively”);
simulating, via the one or more hardware processors, the electrical distribution network based on the load data, the DER data, and the network data (Page 6, Numerical Simulations, “we validate the effectiveness of the proposed framework using a modified single-phase IEEE 123-bus distribution test feeder … loads are simulated … zero-mean Gaussian noise with a standard deviation of 0.01 p.u. is also added to the interpolated loads, which are then scaled to match the active and reactive power load levels in the feeder. DERs are added at buses 76, 97, 105, 112, respectively, with reactive power outputs within [-200, 200] kVAr”) using a non-linear power flow model (Page 6, Numerical Simulations, “the simulation, we use a full nonlinear power flow model that is solved using Matpower”), to obtain a voltage profile at each time-step of the electrical distribution network (Fig. 11-12, Page 8, Voltage Control Performance, “The voltage profiles with the model-based and the proposed voltage regulation schemes are presented in Figs. 11 and 12, respectively” – Figures 11 and 12 show voltage magnitude over time; Page 6, Numerical Simulations, “historical hourly loads of a residential building in San Diego are interpolated to increase the time granularity to 1 second.”), wherein the voltage profile at each time-step comprises a voltage magnitude data at the plurality of buses (Fig. 11-12, Page 8, Voltage Control Performance, “The voltage profiles with the model-based and the proposed voltage regulation schemes are presented in Figs. 11 and 12, respectively” – Figures 11 and 12 show voltage magnitude over time; Page 6, Numerical Simulations, “The minimum and maximum voltage magnitudes are 0.95 p.u. and 1.05 p.u., respectively”; Page 6, Numerical Simulations, “historical hourly loads of a residential building in San Diego are interpolated to increase the time granularity to 1 second.”);
predicting, via the one or more hardware processors, line-parameters of the electrical distribution network, based on the voltage profile (Xu, Page 3, “a parameter estimator that estimates the line parameters (r and x), using measurements of power injections and voltage magnitudes”) using an on-line convex optimization technique (Page 3, Voltage Sensitivity Estimator, “the objective of the parameter estimator is to find the line parameters that can fit the LinDistFlow model best, for the given topology configuration”; Page 4, Par. 1, “(4) can be equivalently formulated in the classical form of a linear regression problem” – Xu teaches the “on-line” aspect because the estimator uses measurements obtained at successive time instants to update the voltage-sensitivity/line-parameter estimates. Xu teaches “convex optimization” because, Xu formulates the line-parameter estimation problem for r and x as a best-fit minimization problem and reduces that parameter-estimation formulation to a classical linear regression problem, which is a convex minimization technique.), wherein the line-parameters of the electrical distribution network comprises a line resistance of each line of the plurality of lines and a line reactance of each line of the plurality of lines (Page 3, Framework Overview, “a parameter estimator that estimates the line parameters (r and x), using measurements of power injections and voltage magnitudes; Page 2, Power Distribution System Model, “Let rl and xl denote the resistance and reactance of line … respectively”);
determining, via the one or more hardware processors, a stable control signal (Page 3, Framework Overview, “the voltage controller then computes the set-points for the DER active and reactive power injections … The DERs will be instructed to inject active and reactive power by the amount determined by the voltage controller” – the DER active/reactive power set-points correspond to the claimed stable control signal because the set-points are computed to control DER power injection so as to maintain the voltage magnitude at each bus within the pre-specified interval.) for each bus of the plurality of buses, that stabilizes a voltage of the electrical distribution network (Page 2, Voltage Regulation Problem, “The objective here is to maintain the voltage magnitude at each bus … of the power distribution system within a pre-specified interval … the voltage magnitude at each bus … is within the pre-specified interval …”; Page 3, Framework Overview, “The DERs will be instructed to inject active and reactive power by the amount determined by the voltage controller … These measurements will be used by the estimator to update ^R and ^X so as to reflect any changes in them.” – Xu stabilizes the voltage by computing DER active/reactive power injection set-points that cause the DERs to modify their power injections, thereby maintaining the voltage magnitude at each bus within the prespecified voltage interval. The updated measurements after the DERs modify their injections are then used to update the controller for subsequent time-steps.), at each time-step (Page 3, Framework Overview, “A new set of measurements will be available once the DERs have modified their power injections. These measurements will be used by the estimator to update R and X so as to reflect any changes in them.”), based on the line-parameters (Page 3, Framework Overview, “The estimated voltage sensitivity matrices, R, and X, are computed using M, r, and x. After that, the estimated R and X, denoted respectively by … are sent to the voltage controller. The voltage controller then computes the set-points for the DER active and reactive power injections…” – Xu’s r and x are the line resistance and line reactance parameters. Xu computes voltage sensitivity matrices from those line parameters and sends them to the voltage controller, which computes DER active/reactive power injection setpoints.);
evaluating, via the one or more hardware processors, a stable voltage (Page 2, Voltage Regulation Problem, “The objective here is to maintain the voltage magnitude at each bus … of the power distribution system within a pre-specified interval” – the voltage maintained within the pre-specified voltage interval corresponds to the claimed stable voltage because the voltage is regulated to remain within acceptable operating bounds.) and a reactive power injection for each bus (Page 3, Framework Overview, “The voltage controller then computes the set-points for the DER active and reactive power injections … The DERs will be instructed to inject active and reactive power by the amount determined by the voltage controller”), at each time-step (Page 3, Voltage Sensitivity Estimator, “Assume at time instant k+1, we have measurements Vo[k’], V[k’], p[k’], q[k’], k’=0,1, …, k, where the index k’ indicates the corresponding measurement is obtained at time instant k’.”), using the stable control signal associated to each bus (Page 3, Framework Overview, “The DERs will be instructed to inject active and reactive power by the amount determined by the voltage controller”); and
stabilizing, via the one or more hardware processors, a voltage stabilizing of the electrical distribution network (Page 2, Voltage Regulation Problem, “The objective here is to maintain the voltage magnitude at each bus … of the power distribution system within a pre-specified interval”) by utilizing the stable voltage evaluated for each bus at each time-step (Page 3, Framework Overview, “The voltage controller then computes the set-points for the DER active and reactive power injections … The DERs will be instructed to inject active and reactive power by the amount determined by the voltage controller”; Page 3, Framework Overview, “A new set of measurements will be available once the DERs have modified their power injections. These measurements will be used by the estimator to update R and X so as to reflect any changes in them”).
Xu does not explicitly teach wherein the electrical distribution network is associated with a plurality of consumers;
using a Gauss-Seidel technique;
using a non-convex optimization technique for determining control signals; and
controlling and stabilizing the electrical distribution network in real time.
However, Deaver teaches wherein the electrical distribution network is associated with a plurality of consumers (Fig. 1 shows a power distribution system including multiple customer premises 118; Par. [0003], “LV power lines typically carry power having a voltage ranging from about 100 V to about 600 V to customer premises.”);
using a Gauss-Seidel technique (Par. [0044], “a Ybus Gauss-Seidel method may be implemented to solve for voltages and phase angles, transformer tap ratios and capacitor status (engaged or not engaged) for a given set of transformer loads and distributed generations … The Ybus Gauss-Seidel method provides effective convergence properties for a highly radial network with high R/X ratios (resistance versus reactance of conductors), such as may be found in distribution feeder (i.e., MV power line) networks”); and
stabilizing the electrical distribution network in real time (Par. [0022], “The availability of such real time data enables the invention to perform not just planning of the construction of the infrastructure, but also provide real time power distribution system performance monitoring and control”).
Xu and Deaver are analogous art because they are from the same field of endeavor and contain functional similarities. They both relate to monitoring, modeling, and controlling voltage/power flow in electrical power distribution networks using measurement data and computational modeling.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above data-driven DER voltage-regulation and line-parameter estimation framework, as taught by Xu, and incorporate a Gauss-Seidel power flow solution technique and real-time power distribution system monitoring and control, as taught by Deaver.
One of ordinary skill in the art would have been motivated to improve “convergence properties for a highly radial network with high R/X ratios” as suggested by Deaver (Par. [0044]).
Xu and Deaver do not explicitly teach using a non-convex optimization technique for determining control signals.
However, Jereminov teaches using a non-convex optimization technique for determining control signals (Par. [0056], “the objective function has to be minimized while satisfying the power flow in the grid … the power balance equations from Equations (2) and (3) used in traditional power flow represent highly nonlinear and non-convex constraints”; Par. [0167] – [0168], “OPF solver 3304 may solve the OPF problem using any one or more of the techniques described herein, including, but not limited to, a direct technique, an iterative technique, the A-stepping homotopy technique, relaxation techniques, and nonlinear optimization techniques, among others … Output 3324 of OPF solver 3304 may include signals based on the solution determined by the OPF solver solving the OPF problem. Such signals can include suitable control signals for controlling generators 3308A and/or for controlling controllable pieces of power grid equipment 3308B.”).
Xu, Deaver, and Jereminov are analogous art because they are from the same field of endeavor and contain functional similarities. They all relate to power flow analysis, voltage regulation, and control of electrical power networks using computational optimization.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above data-driven DER voltage-regulation and line-parameter estimation framework, as taught by Xu and Deaver, and incorporate a non-convex optimization technique for determining control signals, as taught by Jereminov.
One of ordinary skill in the art would have been motivated to improve efficiency and decreasing overall nonlinearities in an OPF approach involving real and reactive generation powers, as suggested by Jereminov (Par. [0068]).
Regarding claim 8, Jereminov teaches a system (Par. [0209], “Computer system 3800 includes a processor 3804 and a memory 3808 that communicate with each other, and with other components, via a bus 3812”) comprising:
a memory storing instructions (Par. [0210], “Memory 3808 may also include (e.g., stored on one or more machine-readable media) instructions (e.g., software) 3820 embodying any one or more of the aspects and/or methodologies of the present disclosure”);
one or more input/output (I/0) interfaces (Par. [0212], “Input device 3832 may be interfaced to bus 3812 via any of a variety of interfaces (not shown) including, but not limited to, a serial interface, a parallel interface, a game port, a USB interface, a FIREWIRE interface, a direct interface to bus 3812, and any combinations thereof.”; Par. [0214], “Such peripheral output devices may be connected to bus 3812 via a peripheral interface 3856”); and
one or more hardware processors coupled to the memory via the one or more I/0 interfaces (Par. [0209], “Computer system 3800 includes a processor 3804 and a memory 3808 that communicate with each other, and with other components, via a bus 3812”; Par. [0209), “Bus 3812 may include any of several types of bus structures including, but not limited to, a memory bus, a memory controller, a peripheral bus, a local bus, and any combinations thereof, using any of a variety of bus architectures”);
using a non-convex optimization technique for determining control signals (Par. [0056], “the objective function has to be minimized while satisfying the power flow in the grid … the power balance equations from Equations (2) and (3) used in traditional power flow represent highly nonlinear and non-convex constraints”; Par. [0167] – [0168], “OPF solver 3304 may solve the OPF problem using any one or more of the techniques described herein, including, but not limited to, a direct technique, an iterative technique, the A-stepping homotopy technique, relaxation techniques, and nonlinear optimization techniques, among others … Output 3324 of OPF solver 3304 may include signals based on the solution determined by the OPF solver solving the OPF problem. Such signals can include suitable control signals for controlling generators 3308A and/or for controlling controllable pieces of power grid equipment 3308B.”).
Jereminov does not explicitly teach wherein the one or more hardware processors are configured by the instructions to:
obtain a load data, a distributed energy resources (DER) data, and a network data of an electrical distribution network whose voltage is to be controlled in a real-time, wherein the load data and the DER data are obtained at each time-step of a plurality of time-steps for a predefined time period, and wherein the electrical distribution network is associated with a plurality of consumers and comprises
(i) a plurality of buses and a plurality of lines connected to the plurality of buses,
(ii) one or more distributed energy resources present at one or more buses of the plurality of buses, and
(iii) one or more network loads present at one or more buses of the plurality of buses, and wherein the load data at each time-step comprises a load active power consumption and a load reactive power consumption at the one or more buses of the plurality of buses, the DER data at each time-step comprises a DER active power generation and a DER reactive power generation, and the network data comprises of a line resistance and a line reactance of each line of the plurality of lines;
simulate the electrical distribution network based on the load data, the DER data, and the network data using a non-linear power flow model, to obtain a voltage profile at each time-step of the electrical distribution network, wherein the voltage profile at each time-step comprises a voltage magnitude data at the plurality of buses;
predict line-parameters of the electrical distribution network, based on the voltage profile using an on-line convex optimization technique and a Gauss-Seidel technique, wherein the line-parameters of the electrical distribution network comprises a line resistance of each line of the plurality of lines and a line reactance of each line of the plurality of lines;
determine a stable control signal for each bus of the plurality of buses, that stabilizes a voltage of the electrical distribution network, for each time-step, based on the line-parameters;
evaluate a stable voltage and a reactive power injection for each bus, at each time-step, using the stable control signal associated to each bus; and
stabilize a voltage stabilizing of the electrical distribution network in real-time, by utilizing the stable voltage evaluated for each bus at each time-step.
However, Xu teaches wherein the one or more hardware processors are configured by the instructions to:
obtain a load data (Page 2, Power Distribution System Model, “respectively denote the active and reactive power demanded by load”), a distributed energy resources (DER) data (Page 8, Concluding Remarks, “data-driven voltage regulation framework for DERs in a balanced radial power distribution system”), and a network data of an electrical distribution network (Page 2, Power Distribution System Model, “Consider a three-phase balanced power distribution system represented by a directed graph … is the set of buses (nodes), and … is set of distribution lines (edges) … denote the resistance and reactance of line …”) whose voltage is to be controlled (Page 2, Voltage Regulation Problem, “the problem is to determine the DER active and reactive power injections so that … C1. The active and reactive power injections from each DER … do not exceed its corresponding capacity limits, … and C2. the voltage magnitude at each bus is within the pre-specified interval”) whose voltage is to be controlled (Page 2, Voltage Regulation Problem, “the problem is to determine the DER active and reactive power injections so that … C1. The active and reactive power injections from each DER … do not exceed its corresponding capacity limits, … and C2. the voltage magnitude at each bus is within the pre-specified interval”), wherein the load data and the DER data are obtained at each time-step of a plurality of time-steps for a predefined time period (Page 3, Voltage Sensitivity Estimator, “Assume at time instant k+1, we have measurements Vo[k’], V[k’], p[k’], q[k’], k’=0,1, …, k, where the index k’ indicates the corresponding measurement is obtained at time instant k’.”), and comprises
(i) a plurality of buses and a plurality of lines connected to the plurality of buses (Fig. 1, Page 2, Power Distribution System Model, “Consider a three-phase balanced power distribution system represented by a directed graph … is the set of buses (nodes), and … is set of distribution lines (edges) … denote the resistance and reactance of line …”),
(ii) one or more distributed energy resources present at one or more buses of the plurality of buses (Page 2, Power Distribution System Model, “pig and qig respectively denote the active and reactive power injected by DER i”), and
(iii) one or more network loads present at one or more buses of the plurality of buses (Page 2, “pid and qid respectively denote the active and reactive power demanded by load i”),
and wherein the load data at each time-step comprises a load active power consumption and a load reactive power consumption (Page 2, Power Distribution System Model, “pid and qid respectively denote the active and reactive power demanded by load i”) at the one or more buses of the plurality of buses (Page 2, Power Distribution System Model, “Consider a three-phase balanced power distribution system represented by a directed graph … is the set of buses (nodes), and … is set of distribution lines (edges) … denote the resistance and reactance of line …”), the DER data at each time-step comprises a DER active power generation and a DER reactive power generation (Page 2, Power Distribution System Model, “pig and qig respectively denote the active and reactive power injected by DER i”; Page 3, Voltage Sensitivity Estimator, “Assume at time instant k+1, we have measurements Vo[k’], V[k’], p[k’], q[k’], k’=0,1, …, k, where the index k’ indicates the corresponding measurement is obtained at time instant k’.”; Page 3, Framework Overview, “The voltage controller then computes the set-points for the DER active and reactive power injections” – DER-generated power is injected/output into the electrical distribution network, and Xu obtains active/reactive power measurements at indexed time instants k’, thereby teaching DER active/reactive power data at each time-step.), and the network data comprises of a line resistance and a line reactance of each line of the plurality of lines (Page 2, Power Distribution System Model, “Let rl and xl denote the resistance and reactance of line … respectively”);
simulate the electrical distribution network based on the load data, the DER data, and the network data (Page 6, Numerical Simulations, “we validate the effectiveness of the proposed framework using a modified single-phase IEEE 123-bus distribution test feeder … loads are simulated … zero-mean Gaussian noise with a standard deviation of 0.01 p.u. is also added to the interpolated loads, which are then scaled to match the active and reactive power load levels in the feeder. DERs are added at buses 76, 97, 105, 112, respectively, with reactive power outputs within [-200, 200] kVAr”) using a non-linear power flow model (Page 6, Numerical Simulations, “the simulation, we use a full nonlinear power flow model that is solved using Matpower”), to obtain a voltage profile at each time-step of the electrical distribution network (Fig. 11-12, Page 8, Voltage Control Performance, “The voltage profiles with the model-based and the proposed voltage regulation schemes are presented in Figs. 11 and 12, respectively” – Figures 11 and 12 show voltage magnitude over time; Page 6, Numerical Simulations, “historical hourly loads of a residential building in San Diego are interpolated to increase the time granularity to 1 second.”), wherein the voltage profile at each time-step comprises a voltage magnitude data at the plurality of buses (Fig. 11-12, Page 8, Voltage Control Performance, “The voltage profiles with the model-based and the proposed voltage regulation schemes are presented in Figs. 11 and 12, respectively” – Figures 11 and 12 show voltage magnitude over time; Page 6, Numerical Simulations, “The minimum and maximum voltage magnitudes are 0.95 p.u. and 1.05 p.u., respectively”; Page 6, Numerical Simulations, “historical hourly loads of a residential building in San Diego are interpolated to increase the time granularity to 1 second.”);
predict line-parameters of the electrical distribution network, based on the voltage profile (Xu, Page 3, “a parameter estimator that estimates the line parameters (r and x), using measurements of power injections and voltage magnitudes”) using an on-line convex optimization technique (Page 3, Voltage Sensitivity Estimator, “the objective of the parameter estimator is to find the line parameters that can fit the LinDistFlow model best, for the given topology configuration”; Page 4, Par. 1, “(4) can be equivalently formulated in the classical form of a linear regression problem” – Xu teaches the “on-line” aspect because the estimator uses measurements obtained at successive time instants to update the voltage-sensitivity/line-parameter estimates. Xu teaches “convex optimization” because, Xu formulates the line-parameter estimation problem for r and x as a best-fit minimization problem and reduces that parameter-estimation formulation to a classical linear regression problem, which is a convex minimization technique.), wherein the line-parameters of the electrical distribution network comprises a line resistance of each line of the plurality of lines and a line reactance of each line of the plurality of lines (Page 3, Framework Overview, “a parameter estimator that estimates the line parameters (r and x), using measurements of power injections and voltage magnitudes; Page 2, Power Distribution System Model, “Let rl and xl denote the resistance and reactance of line … respectively”);
determine a stable control signal (Page 3, Framework Overview, “the voltage controller then computes the set-points for the DER active and reactive power injections … The DERs will be instructed to inject active and reactive power by the amount determined by the voltage controller” – the DER active/reactive power set-points correspond to the claimed stable control signal because the set-points are computed to control DER power injection so as to maintain the voltage magnitude at each bus within the pre-specified interval.) for each bus of the plurality of buses, that stabilizes a voltage of the electrical distribution network (Page 2, Voltage Regulation Problem, “The objective here is to maintain the voltage magnitude at each bus … of the power distribution system within a pre-specified interval … the voltage magnitude at each bus … is within the pre-specified interval …”; Page 3, Framework Overview, “The DERs will be instructed to inject active and reactive power by the amount determined by the voltage controller … These measurements will be used by the estimator to update ^R and ^X so as to reflect any changes in them.” – Xu stabilizes the voltage by computing DER active/reactive power injection set-points that cause the DERs to modify their power injections, thereby maintaining the voltage magnitude at each bus within the prespecified voltage interval. The updated measurements after the DERs modify their injections are then used to update the controller for subsequent time-steps.), for each time-step (Page 3, Framework Overview, “A new set of measurements will be available once the DERs have modified their power injections. These measurements will be used by the estimator to update R and X so as to reflect any changes in them.”), based on the line-parameters (Page 3, Framework Overview, “The estimated voltage sensitivity matrices, R, and X, are computed using M, r, and x. After that, the estimated R and X, denoted respectively by … are sent to the voltage controller. The voltage controller then computes the set-points for the DER active and reactive power injections…” – Xu’s r and x are the line resistance and line reactance parameters. Xu computes voltage sensitivity matrices from those line parameters and sends them to the voltage controller, which computes DER active/reactive power injection setpoints.);
evaluate a stable voltage (Page 2, Voltage Regulation Problem, “The objective here is to maintain the voltage magnitude at each bus … of the power distribution system within a pre-specified interval” – the voltage maintained within the pre-specified voltage interval corresponds to the claimed stable voltage because the voltage is regulated to remain within acceptable operating bounds) and a reactive power injection for each bus (Page 3, Framework Overview, “The voltage controller then computes the set-points for the DER active and reactive power injections … The DERs will be instructed to inject active and reactive power by the amount determined by the voltage controller”), at each time-step (Page 3, Voltage Sensitivity Estimator, “Assume at time instant k+1, we have measurements Vo[k’], V[k’], p[k’], q[k’], k’=0,1, …, k, where the index k’ indicates the corresponding measurement is obtained at time instant k’.”), using the stable control signal associated to each bus (Page 3, Framework Overview, “The DERs will be instructed to inject active and reactive power by the amount determined by the voltage controller”); and
stabilize a voltage stabilizing of the electrical distribution network (Page 2, Voltage Regulation Problem, “The objective here is to maintain the voltage magnitude at each bus … of the power distribution system within a pre-specified interval”), by utilizing the stable voltage evaluated for each bus at each time-step (Page 3, Framework Overview, “The voltage controller then computes the set-points for the DER active and reactive power injections … The DERs will be instructed to inject active and reactive power by the amount determined by the voltage controller”; Page 3, Framework Overview, “A new set of measurements will be available once the DERs have modified their power injections. These measurements will be used by the estimator to update R and X so as to reflect any changes in them”).
Jereminov and Xu are analogous art because they are from the same field of endeavor and contain functional similarities. They all relate to computational analysis, modeling, optimization, and control of electrical power networks.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above computer system, including processor, memory, stored software instructions, and input/output interfaces, as taught by Jereminov, and incorporate a data-driven voltage-regulation and line-parameter estimation method, as taught by Xu.
One of ordinary skill in the art would have been motivated to the accuracy and adaptivity of voltage control setpoint determination when a complete or accurate distribution-system model is unavailable, as suggested by Xu (Page 1, Introduction; Page 8, Concluding Remarks).
Xu and Jereminov do not explicitly teach using a Gauss-Seidel technique; and
controlling and stabilizing the electrical distribution network in real time.
However, Deaver teaches wherein the electrical distribution network is associated with a plurality of consumers (Fig. 1 shows a power distribution system including multiple customer premises 118; Par. [0003], “LV power lines typically carry power having a voltage ranging from about 100 V to about 600 V to customer premises.”);
using a Gauss-Seidel technique (Par. [0044], “a Ybus Gauss-Seidel method may be implemented to solve for voltages and phase angles, transformer tap ratios and capacitor status (engaged or not engaged) for a given set of transformer loads and distributed generations … The Ybus Gauss-Seidel method provides effective convergence properties for a highly radial network with high R/X ratios (resistance versus reactance of conductors), such as may be found in distribution feeder (i.e., MV power line) networks”); and
stabilizing the electrical distribution network in real time (Par. [0022], “The availability of such real time data enables the invention to perform not just planning of the construction of the infrastructure, but also provide real time power distribution system performance monitoring and control”).
Jereminov, Xu, and Deaver are analogous art because they are from the same field of endeavor and contain functional similarities. They both relate to monitoring, modeling, and controlling voltage/power flow in electrical power distribution networks using measurement data and computational modeling.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above data-driven DER voltage-regulation and line-parameter estimation framework, as taught by Xu, and incorporate a Gauss-Seidel power flow solution technique and real-time power distribution system monitoring and control, as taught by Deaver.
One of ordinary skill in the art would have been motivated to improve “convergence properties for a highly radial network with high R/X ratios” as suggested by Deaver (Par. [0044]).
Claim(s) 2-3, 9-10, 15-16, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu (Data-driven Voltage Regulation in Radial Power Distribution Systems, 2019) (hereinafter Xu) in view of Deaver, SR. et al. US 2012/0022713 A1 (hereinafter Deaver) and Jereminov et al. US 2018/0158152 A1 (hereinafter Jereminov), and further in view of Yeh et al. (Online learning for robust voltage control under uncertain grid topology, 2023) (hereinafter Yeh).
Regarding claim 2, the combination of Xu, Deaver, and Jereminov teaches all the limitations of the base claims as outlined above.
Xu, Deaver, and Jereminov do not explicitly teach determining, via the one or more hardware processors, a finite error stability bound of the electrical distribution network, resulted from the stable voltage evaluated for the voltage stabilizing of the electrical distribution network.
However, Yeh teaches determining, via the one or more hardware processors, a finite error stability bound of the electrical distribution network (Page 5, Main result, “We now state our main result, which is a finite-error bound for Algorithm 1 … this result is the first provable stability bound for voltage control in a setting where the network topology is unknown”), resulted from the stable voltage evaluated for the voltage stabilizing of the electrical distribution network (Page 5, Main result, “Algorithm 1 ensures that the voltage limits will be violated at most
PNG
media_image1.png
29
67
media_image1.png
Greyscale
times … this result is the first provable stability bound for voltage control in a setting where the network topology is unknown. It highlights that Algorithm 1 can ensure stability even after unknown changes to the network topology…”).
Xu, Deaver, Jereminov, and Yeh are analogous art because they are from the same field of endeavor and contain functional similarities. They all relate to controlling electrical distribution networks using computational optimization.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above voltage regulation system, as taught by Xu, Deaver, and Jereminov, and incorporate a finite-error stability-bound determination for voltage control, as taught by Yeh.
One of ordinary skill in the art would have been motivated to improve model performance, resulting in quick control, as suggested by Yeh (Page 5, Main result).
Regarding claim 3, the combination of Xu, Deaver, and Jereminov teaches all the limitations of the base claims as outlined above.
Xu, Deaver, and Jereminov do not explicitly teach the simulating further comprising:
representing the electrical distribution network as a tree graph based on the plurality of buses and the plurality of lines, in a parent-child relationship using a simulation model;
defining a set of non-linear power flow equations for each bus of the plurality of buses using the non-linear power flow model, wherein the set of non-linear power flow equations for each bus comprises
(i) an active power injection at the corresponding bus,
(ii) the reactive power injection at the corresponding bus,
(iii) a voltage difference between the corresponding bus and each of one or more neighboring bus, and
(iv) an overall voltage of the electrical distribution network; and
calculating the voltage magnitude data at each bus of the plurality of buses, using the set of non-linear power flow equations, to obtain the voltage profile of the electrical distribution network at each time-step.
However, Yeh teaches the simulating further comprising:
representing the electrical distribution network as a tree graph based on the plurality of buses and the plurality of lines, in a parent-child relationship using a simulation model (Page 2, Model, “We consider a radial (tree-structured) power distribution network represented as a connected directed graph G = (N, E), where N = {0, 1, 2, . . . , n} is the set of buses (nodes) and E ⊂ N ×N is the set of lines (directed edges). Let the network be rooted at bus 0 (the substation or slack bus), and let other buses be branch buses … Because the network is radial and rooted at bus 0, there is a unique path from Pi bus 0 to any other bus i … The DistFlow branch equations [37] for a distribution grid are as follows, for all
PNG
media_image2.png
33
212
media_image2.png
Greyscale
” – in a rooted radial tree-structured directed graph, the directed line (i,j) corresponds to an upstream/downstream parent-child relationship between neighboring buses.; Page 8, Experimental Results, “We test our algorithm under both the linearized system dynamics (5) as well as the more realistic nonlinear balanced AC power flow setting (1) simulated using Pandapower”);
defining a set of non-linear power flow equations for each bus of the plurality of buses using the non-linear power flow model (Page 2, Model, “The DistFlow branch equations [37] for a distribution grid are as follows, for all
PNG
media_image2.png
33
212
media_image2.png
Greyscale
…” – equation 1d is a non-linear power flow equation), wherein the set of non-linear power flow equations for each bus comprises
(i) an active power injection at the corresponding bus (Page 2, Model, “(units W) is the net active power injection”),
(ii) the reactive power injection at the corresponding bus (Page 2, Model, “(units Var) is the net reactive power injection”),
(iii) a voltage difference between the corresponding bus and each of one or more neighboring bus (Page 2, Model, “(1c) represents the voltage drop from bus i to bus j”), and
(iv) an overall voltage of the electrical distribution network (Page 2, Model, “For branch buses, let … be their squared voltage magnitudes…” – voltage magnitudes of the network buses correspond to the overall voltage of the electrical distribution network); and
calculating the voltage magnitude data at each bus of the plurality of buses (Page 2, Model, “For branch buses, let … be their squared voltage magnitudes”), using the set of non-linear power flow equations (Page 2, Model, “The DistFlow branch equations [37] for a distribution grid are as follows…”; Page 7, Case Study, “we show that our algorithm works well on both the linear model and the more realistic nonlinear DistFlow model from Equation (1).” – Yeh’s nonlinear DistFlow equations are used to calculate the squared voltage magnitudes of the buses.), to obtain the voltage profile of the electrical distribution network at each time-step (Page 7, “Fig. 2: Voltage profile of 7 buses without control, simulated with (a) linear dynamics (2) and (b) nonlinear balanced AC dynamics (1).” – voltage is shown over time in Fig. 2.; Page 3, Model, “At each time step, buses may change their reactive power injection in order to regulate the voltage close”).
Xu, Deaver, Jereminov, and Yeh are analogous art because they are from the same field of endeavor and contain functional similarities. They all relate to controlling electrical distribution networks using computational optimization.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above data-driven DER voltage regulation framework, as taught by Xu, Deaver, and Jereminov, and incorporate representing the electrical distribution network as a tree graph using nonlinear power-flow equations, as taught by Yeh.
One of ordinary skill in the art would have been motivated to improve model performance, resulting in quick control, as suggested by Yeh (Page 5, Main result).
Regarding claim 9, the combination of Jereminov, Xu, and Deaver teaches all the limitations of the base claims as outlined above.
Jereminov, Xu, and Deaver do not explicitly teach wherein the one or more hardware processors are further configured to determine a finite error stability bound of the electrical distribution network, resulted from the stable voltage evaluated for the voltage stabilizing of the electrical distribution network.
However, Yeh teaches wherein the one or more hardware processors are further configured to determine a finite error stability bound of the electrical distribution network (Page 5, Main result, “We now state our main result, which is a finite-error bound for Algorithm 1 … this result is the first provable stability bound for voltage control in a setting where the network topology is unknown”), resulted from the stable voltage evaluated for the voltage stabilizing of the electrical distribution network (Page 5, Main result, “Algorithm 1 ensures that the voltage limits will be violated at most
PNG
media_image1.png
29
67
media_image1.png
Greyscale
times … this result is the first provable stability bound for voltage control in a setting where the network topology is unknown. It highlights that Algorithm 1 can ensure stability even after unknown changes to the network topology…”).
Jereminov, Xu, Deaver, and Yeh are analogous art because they are from the same field of endeavor and contain functional similarities. They all relate to controlling electrical distribution networks using computational optimization.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above voltage regulation system, as taught by Jereminov, Xu, and Deaver, and incorporate a finite-error stability-bound determination for voltage control, as taught by Yeh.
One of ordinary skill in the art would have been motivated to improve model performance, resulting in quick control, as suggested by Yeh (Page 5, Main result).
Regarding claim 10, the combination of Jereminov, Xu, and Deaver teaches all the limitations of the base claims as outlined above.
Jereminov, Xu, and Deaver do not explicitly teach the simulating further comprising:
representing the electrical distribution network as a tree graph based on the plurality of buses and the plurality of lines, in a parent-child relationship using a simulation model;
defining a set of non-linear power flow equations for each bus of the plurality of buses using the non-linear power flow model, wherein the set of non-linear power flow equations for each bus comprises
(i) an active power injection at the corresponding bus,
(ii) the reactive power injection at the corresponding bus,
(iii) a voltage difference between the corresponding bus and each of one or more neighboring bus, and
(iv) an overall voltage of the electrical distribution network; and
calculating the voltage magnitude data at each bus of the plurality of buses, using the set of non-linear power flow equations, to obtain the voltage profile of the electrical distribution network at each time-step.
However, Yeh teaches the simulating further comprising:
representing the electrical distribution network as a tree graph based on the plurality of buses and the plurality of lines, in a parent-child relationship using a simulation model (Page 2, Model, “We consider a radial (tree-structured) power distribution network represented as a connected directed graph G = (N, E), where N = {0, 1, 2, . . . , n} is the set of buses (nodes) and E ⊂ N ×N is the set of lines (directed edges). Let the network be rooted at bus 0 (the substation or slack bus), and let other buses be branch buses … Because the network is radial and rooted at bus 0, there is a unique path from Pi bus 0 to any other bus i … The DistFlow branch equations [37] for a distribution grid are as follows, for all
PNG
media_image2.png
33
212
media_image2.png
Greyscale
” – in a rooted radial tree-structured directed graph, the directed line (i,j) corresponds to an upstream/downstream parent-child relationship between neighboring buses.; Page 8, Experimental Results, “We test our algorithm under both the linearized system dynamics (5) as well as the more realistic nonlinear balanced AC power flow setting (1) simulated using Pandapower”);
defining a set of non-linear power flow equations for each bus of the plurality of buses using the non-linear power flow model (Page 2, Model, “The DistFlow branch equations [37] for a distribution grid are as follows, for all
PNG
media_image2.png
33
212
media_image2.png
Greyscale
…” – equation 1d is a non-linear power flow equation), wherein the set of non-linear power flow equations for each bus comprises
(i) an active power injection at the corresponding bus (Page 2, Model, “(units W) is the net active power injection”),
(ii) the reactive power injection at the corresponding bus (Page 2, Model, “(units Var) is the net reactive power injection”),
(iii) a voltage difference between the corresponding bus and each of one or more neighboring bus (Page 2, Model, “(1c) represents the voltage drop from bus i to bus j”), and
(iv) an overall voltage of the electrical distribution network (Page 2, Model, “For branch buses, let … be their squared voltage magnitudes…” – voltage magnitudes of the network buses correspond to the overall voltage of the electrical distribution network); and
calculating the voltage magnitude data at each bus of the plurality of buses (Page 2, Model, “For branch buses, let … be their squared voltage magnitudes”), using the set of non-linear power flow equations (Page 2, Model, “The DistFlow branch equations [37] for a distribution grid are as follows…”; Page 7, Case Study, “we show that our algorithm works well on both the linear model and the more realistic nonlinear DistFlow model from Equation (1).” – Yeh’s nonlinear DistFlow equations are used to calculate the squared voltage magnitudes of the buses.), to obtain the voltage profile of the electrical distribution network at each time-step (Page 7, “Fig. 2: Voltage profile of 7 buses without control, simulated with (a) linear dynamics (2) and (b) nonlinear balanced AC dynamics (1).” – voltage is shown over time in Fig. 2.; Page 3, Model, “At each time step, buses may change their reactive power injection in order to regulate the voltage close”).
Jereminov, Xu, Deaver, and Yeh are analogous art because they are from the same field of endeavor and contain functional similarities. They all relate to controlling electrical distribution networks using computational optimization.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above data-driven DER voltage regulation framework, as taught by Jereminov, Xu, and Deaver, and incorporate representing the electrical distribution network as a tree graph using nonlinear power-flow equations, as taught by Yeh.
One of ordinary skill in the art would have been motivated to improve model performance, resulting in quick control, as suggested by Yeh (Page 5, Main result).
Regarding claim 15, Jereminov teaches one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors (Par. [0014], “machine-readable storage medium containing machine-executable instructions configured to cause one or more processors to perform operations, which includes formulating, for an electrical power system, current and voltage conservation equations from which power flows, currents, and voltages can be derived”) cause:
using a non-convex optimization technique for determining control signals (Par. [0056], “the objective function has to be minimized while satisfying the power flow in the grid … the power balance equations from Equations (2) and (3) used in traditional power flow represent highly nonlinear and non-convex constraints”; Par. [0167] – [0168], “OPF solver 3304 may solve the OPF problem using any one or more of the techniques described herein, including, but not limited to, a direct technique, an iterative technique, the A-stepping homotopy technique, relaxation techniques, and nonlinear optimization techniques, among others … Output 3324 of OPF solver 3304 may include signals based on the solution determined by the OPF solver solving the OPF problem. Such signals can include suitable control signals for controlling generators 3308A and/or for controlling controllable pieces of power grid equipment 3308B.”).
Jereminov does not explicitly teach obtaining a load data, a distributed energy resources (DER) data, and a network data of an electrical distribution network whose voltage is to be controlled in a real-time, wherein the load data and the DER data are obtained at each time-step of a plurality of time-steps for a predefined time period, and wherein the electrical distribution network is associated with a plurality of consumers and comprises
(i) a plurality of buses and a plurality of lines connected to the plurality of buses,
(ii) one or more distributed energy resources present at one or more buses of the plurality of buses, and
(iii) one or more network loads present at one or more buses of the plurality of buses, and wherein the load data at each time-step comprises a load active power consumption and a load reactive power consumption at the one or more buses of the plurality of buses, the DER data at each time-step comprises a DER active power generation and a DER reactive power generation, and the network data comprises of a line resistance and a line reactance of each line of the plurality of lines;
simulating the electrical distribution network based on the load data, the DER data, and the network data using a non-linear power flow model, to obtain a voltage profile at each time-step of the electrical distribution network, wherein the voltage profile at each time-step comprises a voltage magnitude data at the plurality of buses;
predicting line-parameters of the electrical distribution network, based on the voltage profile using an on-line convex optimization technique and a Gauss-Seidel technique, wherein the line-parameters of the electrical distribution network comprises a line resistance of each line of the plurality of lines and a line reactance of each line of the plurality of lines;
determining a stable control signal for each bus of the plurality of buses, that stabilizes a voltage of the electrical distribution network, for each time-step, based on the line-parameters;
evaluating a stable voltage and a reactive power injection for each bus, at each time-step, using the stable control signal associated to each bus;
stabilizing a voltage stabilizing of the electrical distribution network in real-time, by utilizing the stable voltage evaluated for each bus at each time-step; and
determining a finite error stability bound of the electrical distribution network, resulted from the stable voltage evaluated for the voltage stabilizing of the electrical distribution network.
However, Xu teaches obtaining a load data (Page 2, Power Distribution System Model, “respectively denote the active and reactive power demanded by load”), a distributed energy resources (DER) data (Page 8, Concluding Remarks, “data-driven voltage regulation framework for DERs in a balanced radial power distribution system”), and a network data of an electrical distribution network (Page 2, Power Distribution System Model, “Consider a three-phase balanced power distribution system represented by a directed graph … is the set of buses (nodes), and … is set of distribution lines (edges) … denote the resistance and reactance of line …”) whose voltage is to be controlled (Page 2, Voltage Regulation Problem, “the problem is to determine the DER active and reactive power injections so that … C1. The active and reactive power injections from each DER … do not exceed its corresponding capacity limits, … and C2. the voltage magnitude at each bus is within the pre-specified interval”) whose voltage is to be controlled (Page 2, Voltage Regulation Problem, “the problem is to determine the DER active and reactive power injections so that … C1. The active and reactive power injections from each DER … do not exceed its corresponding capacity limits, … and C2. the voltage magnitude at each bus is within the pre-specified interval”), wherein the load data and the DER data are obtained at each time-step of a plurality of time-steps for a predefined time period (Page 3, Voltage Sensitivity Estimator, “Assume at time instant k+1, we have measurements Vo[k’], V[k’], p[k’], q[k’], k’=0,1, …, k, where the index k’ indicates the corresponding measurement is obtained at time instant k’.”), and comprises
(i) a plurality of buses and a plurality of lines connected to the plurality of buses (Fig. 1, Page 2, Power Distribution System Model, “Consider a three-phase balanced power distribution system represented by a directed graph … is the set of buses (nodes), and … is set of distribution lines (edges) … denote the resistance and reactance of line …”),
(ii) one or more distributed energy resources present at one or more buses of the plurality of buses (Page 2, Power Distribution System Model, “pig and qig respectively denote the active and reactive power injected by DER i”), and
(iii) one or more network loads present at one or more buses of the plurality of buses (Page 2, “pid and qid respectively denote the active and reactive power demanded by load i”),
and wherein the load data at each time-step comprises a load active power consumption and a load reactive power consumption (Page 2, Power Distribution System Model, “pid and qid respectively denote the active and reactive power demanded by load i”) at the one or more buses of the plurality of buses (Page 2, Power Distribution System Model, “Consider a three-phase balanced power distribution system represented by a directed graph … is the set of buses (nodes), and … is set of distribution lines (edges) … denote the resistance and reactance of line …”), the DER data at each time-step comprises a DER active power generation and a DER reactive power generation (Page 2, Power Distribution System Model, “pig and qig respectively denote the active and reactive power injected by DER i”; Page 3, Voltage Sensitivity Estimator, “Assume at time instant k+1, we have measurements Vo[k’], V[k’], p[k’], q[k’], k’=0,1, …, k, where the index k’ indicates the corresponding measurement is obtained at time instant k’.”; Page 3, Framework Overview, “The voltage controller then computes the set-points for the DER active and reactive power injections” – DER-generated power is injected/output into the electrical distribution network, and Xu obtains active/reactive power measurements at indexed time instants k’, thereby teaching DER active/reactive power data at each time-step.), and the network data comprises of a line resistance and a line reactance of each line of the plurality of lines (Page 2, Power Distribution System Model, “Let rl and xl denote the resistance and reactance of line … respectively”);
simulating the electrical distribution network based on the load data, the DER data, and the network data (Page 6, Numerical Simulations, “we validate the effectiveness of the proposed framework using a modified single-phase IEEE 123-bus distribution test feeder … loads are simulated … zero-mean Gaussian noise with a standard deviation of 0.01 p.u. is also added to the interpolated loads, which are then scaled to match the active and reactive power load levels in the feeder. DERs are added at buses 76, 97, 105, 112, respectively, with reactive power outputs within [-200, 200] kVAr”) using a non-linear power flow model (Page 6, Numerical Simulations, “the simulation, we use a full nonlinear power flow model that is solved using Matpower”), to obtain a voltage profile at each time-step of the electrical distribution network (Fig. 11-12, Page 8, Voltage Control Performance, “The voltage profiles with the model-based and the proposed voltage regulation schemes are presented in Figs. 11 and 12, respectively” – Figures 11 and 12 show voltage magnitude over time; Page 6, Numerical Simulations, “historical hourly loads of a residential building in San Diego are interpolated to increase the time granularity to 1 second.”), wherein the voltage profile at each time-step comprises a voltage magnitude data at the plurality of buses (Fig. 11-12, Page 8, Voltage Control Performance, “The voltage profiles with the model-based and the proposed voltage regulation schemes are presented in Figs. 11 and 12, respectively” – Figures 11 and 12 show voltage magnitude over time; Page 6, Numerical Simulations, “The minimum and maximum voltage magnitudes are 0.95 p.u. and 1.05 p.u., respectively”; Page 6, Numerical Simulations, “historical hourly loads of a residential building in San Diego are interpolated to increase the time granularity to 1 second.”);
predicting line-parameters of the electrical distribution network, based on the voltage profile (Xu, Page 3, “a parameter estimator that estimates the line parameters (r and x), using measurements of power injections and voltage magnitudes”) using an on-line convex optimization technique (Page 3, Voltage Sensitivity Estimator, “the objective of the parameter estimator is to find the line parameters that can fit the LinDistFlow model best, for the given topology configuration”; Page 4, Par. 1, “(4) can be equivalently formulated in the classical form of a linear regression problem” – Xu teaches the “on-line” aspect because the estimator uses measurements obtained at successive time instants to update the voltage-sensitivity/line-parameter estimates. Xu teaches “convex optimization” because, Xu formulates the line-parameter estimation problem for r and x as a best-fit minimization problem and reduces that parameter-estimation formulation to a classical linear regression problem, which is a convex minimization technique.), wherein the line-parameters of the electrical distribution network comprises a line resistance of each line of the plurality of lines and a line reactance of each line of the plurality of lines (Page 3, Framework Overview, “a parameter estimator that estimates the line parameters (r and x), using measurements of power injections and voltage magnitudes; Page 2, Power Distribution System Model, “Let rl and xl denote the resistance and reactance of line … respectively”);
determining a stable control signal (Page 3, Framework Overview, “the voltage controller then computes the set-points for the DER active and reactive power injections … The DERs will be instructed to inject active and reactive power by the amount determined by the voltage controller” – the DER active/reactive power set-points correspond to the claimed stable control signal because the set-points are computed to control DER power injection so as to maintain the voltage magnitude at each bus within the pre-specified interval.) for each bus of the plurality of buses, that stabilizes a voltage of the electrical distribution network (Page 2, Voltage Regulation Problem, “The objective here is to maintain the voltage magnitude at each bus … of the power distribution system within a pre-specified interval … the voltage magnitude at each bus … is within the pre-specified interval …”; Page 3, Framework Overview, “The DERs will be instructed to inject active and reactive power by the amount determined by the voltage controller … These measurements will be used by the estimator to update ^R and ^X so as to reflect any changes in them.” – Xu stabilizes the voltage by computing DER active/reactive power injection set-points that cause the DERs to modify their power injections, thereby maintaining the voltage magnitude at each bus within the prespecified voltage interval. The updated measurements after the DERs modify their injections are then used to update the controller for subsequent time-steps.), for each time-step (Page 3, Framework Overview, “A new set of measurements will be available once the DERs have modified their power injections. These measurements will be used by the estimator to update R and X so as to reflect any changes in them.”), based on the line-parameters (Page 3, Framework Overview, “The estimated voltage sensitivity matrices, R, and X, are computed using M, r, and x. After that, the estimated R and X, denoted respectively by … are sent to the voltage controller. The voltage controller then computes the set-points for the DER active and reactive power injections…” – Xu’s r and x are the line resistance and line reactance parameters. Xu computes voltage sensitivity matrices from those line parameters and sends them to the voltage controller, which computes DER active/reactive power injection setpoints.);
evaluating a stable voltage (Page 2, Voltage Regulation Problem, “The objective here is to maintain the voltage magnitude at each bus … of the power distribution system within a pre-specified interval” – the voltage maintained within the pre-specified voltage interval corresponds to the claimed stable voltage because the voltage is regulated to remain within acceptable operating bounds) and a reactive power injection for each bus (Page 3, Framework Overview, “The voltage controller then computes the set-points for the DER active and reactive power injections … The DERs will be instructed to inject active and reactive power by the amount determined by the voltage controller”), at each time-step (Page 3, Voltage Sensitivity Estimator, “Assume at time instant k+1, we have measurements Vo[k’], V[k’], p[k’], q[k’], k’=0,1, …, k, where the index k’ indicates the corresponding measurement is obtained at time instant k’.”), using the stable control signal associated to each bus (Page 3, Framework Overview, “The DERs will be instructed to inject active and reactive power by the amount determined by the voltage controller”);
stabilizing a voltage stabilizing of the electrical distribution network (Page 2, Voltage Regulation Problem, “The objective here is to maintain the voltage magnitude at each bus … of the power distribution system within a pre-specified interval”), by utilizing the stable voltage evaluated for each bus at each time-step (Page 3, Framework Overview, “The voltage controller then computes the set-points for the DER active and reactive power injections … The DERs will be instructed to inject active and reactive power by the amount determined by the voltage controller”; Page 3, Framework Overview, “A new set of measurements will be available once the DERs have modified their power injections. These measurements will be used by the estimator to update R and X so as to reflect any changes in them”); and
determining a finite error stability bound of the electrical distribution network, resulted from the stable voltage evaluated for the voltage stabilizing of the electrical distribution network.
Jereminov and Xu are analogous art because they are from the same field of endeavor and contain functional similarities. They all relate to computational analysis, modeling, optimization, and control of electrical power networks.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above computer system, including processor, memory, stored software instructions, and input/output interfaces, as taught by Jereminov, and incorporate a data-driven voltage-regulation and line-parameter estimation method, as taught by Xu.
One of ordinary skill in the art would have been motivated to the accuracy and adaptivity of voltage control setpoint determination when a complete or accurate distribution-system model is unavailable, as suggested by Xu (Page 1, Introduction; Page 8, Concluding Remarks).
Xu and Jereminov do not explicitly teach wherein the electrical distribution network is associated with a plurality of consumers;
using a Gauss-Seidel technique; and
controlling and stabilizing the electrical distribution network in real time; and
determining a finite error stability bound of the electrical distribution network, resulted from the stable voltage evaluated for the voltage stabilizing of the electrical distribution network.
However, Deaver teaches wherein the electrical distribution network is associated with a plurality of consumers (Fig. 1 shows a power distribution system including multiple customer premises 118; Par. [0003], “LV power lines typically carry power having a voltage ranging from about 100 V to about 600 V to customer premises.”);
using a Gauss-Seidel technique (Par. [0044], “a Ybus Gauss-Seidel method may be implemented to solve for voltages and phase angles, transformer tap ratios and capacitor status (engaged or not engaged) for a given set of transformer loads and distributed generations … The Ybus Gauss-Seidel method provides effective convergence properties for a highly radial network with high R/X ratios (resistance versus reactance of conductors), such as may be found in distribution feeder (i.e., MV power line) networks”); and
stabilizing the electrical distribution network in real time (Par. [0022], “The availability of such real time data enables the invention to perform not just planning of the construction of the infrastructure, but also provide real time power distribution system performance monitoring and control”).
Jereminov, Xu, and Deaver are analogous art because they are from the same field of endeavor and contain functional similarities. They both relate to monitoring, modeling, and controlling voltage/power flow in electrical power distribution networks using measurement data and computational modeling.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above data-driven DER voltage-regulation and line-parameter estimation framework, as taught by Xu, and incorporate a Gauss-Seidel power flow solution technique and real-time power distribution system monitoring and control, as taught by Deaver.
One of ordinary skill in the art would have been motivated to improve “convergence properties for a highly radial network with high R/X ratios” as suggested by Deaver (Par. [0044]).
Jereminov, Xu, and Deaver do not explicitly teach determining a finite error stability bound of the electrical distribution network, resulted from the stable voltage evaluated for the voltage stabilizing of the electrical distribution network.
However, Yeh teaches determining a finite error stability bound of the electrical distribution network (Page 5, Main result, “We now state our main result, which is a finite-error bound for Algorithm 1 … this result is the first provable stability bound for voltage control in a setting where the network topology is unknown”), resulted from the stable voltage evaluated for the voltage stabilizing of the electrical distribution network (Page 5, Main result, “Algorithm 1 ensures that the voltage limits will be violated at most
PNG
media_image1.png
29
67
media_image1.png
Greyscale
times … this result is the first provable stability bound for voltage control in a setting where the network topology is unknown. It highlights that Algorithm 1 can ensure stability even after unknown changes to the network topology…”).
Jereminov, Xu, Deaver, and Yeh are analogous art because they are from the same field of endeavor and contain functional similarities. They all relate to controlling electrical distribution networks using computational optimization.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above voltage regulation system, as taught by Jereminov, Xu, and Deaver, and incorporate a finite-error stability-bound determination for voltage control, as taught by Yeh.
One of ordinary skill in the art would have been motivated to improve model performance, resulting in quick control, as suggested by Yeh (Page 5, Main result).
Regarding claim 16, the combination of Jereminov, Xu, Deaver, and Yeh teaches all the limitations of the base claims as outlined above.
Yeh further teaches the simulating further comprising:
representing the electrical distribution network as a tree graph based on the plurality of buses and the plurality of lines, in a parent-child relationship using a simulation model (Page 2, Model, “We consider a radial (tree-structured) power distribution network represented as a connected directed graph G = (N, E), where N = {0, 1, 2, . . . , n} is the set of buses (nodes) and E ⊂ N ×N is the set of lines (directed edges). Let the network be rooted at bus 0 (the substation or slack bus), and let other buses be branch buses … Because the network is radial and rooted at bus 0, there is a unique path from Pi bus 0 to any other bus i … The DistFlow branch equations [37] for a distribution grid are as follows, for all
PNG
media_image2.png
33
212
media_image2.png
Greyscale
” – in a rooted radial tree-structured directed graph, the directed line (i,j) corresponds to an upstream/downstream parent-child relationship between neighboring buses.; Page 8, Experimental Results, “We test our algorithm under both the linearized system dynamics (5) as well as the more realistic nonlinear balanced AC power flow setting (1) simulated using Pandapower”);
defining a set of non-linear power flow equations for each bus of the plurality of buses using the non-linear power flow model (Page 2, Model, “The DistFlow branch equations [37] for a distribution grid are as follows, for all
PNG
media_image2.png
33
212
media_image2.png
Greyscale
…” – equation 1d is a non-linear power flow equation), wherein the set of non-linear power flow equations for each bus comprises
(i) an active power injection at the corresponding bus (Page 2, Model, “(units W) is the net active power injection”),
(ii) the reactive power injection at the corresponding bus (Page 2, Model, “(units Var) is the net reactive power injection”),
(iii) a voltage difference between the corresponding bus and each of one or more neighboring bus (Page 2, Model, “(1c) represents the voltage drop from bus i to bus j”), and
(iv) an overall voltage of the electrical distribution network (Page 2, Model, “For branch buses, let … be their squared voltage magnitudes…” – voltage magnitudes of the network buses correspond to the overall voltage of the electrical distribution network); and
calculating the voltage magnitude data at each bus of the plurality of buses (Page 2, Model, “For branch buses, let … be their squared voltage magnitudes”), using the set of non-linear power flow equations (Page 2, Model, “The DistFlow branch equations [37] for a distribution grid are as follows…”; Page 7, Case Study, “we show that our algorithm works well on both the linear model and the more realistic nonlinear DistFlow model from Equation (1).” - Yeh’s nonlinear DistFlow equations are used to calculate the squared voltage magnitudes of the buses.), to obtain the voltage profile of the electrical distribution network at each time-step (Page 7, “Fig. 2: Voltage profile of 7 buses without control, simulated with (a) linear dynamics (2) and (b) nonlinear balanced AC dynamics (1).” – voltage is shown over time in Fig. 2.; Page 3, Model, “At each time step, buses may change their reactive power injection in order to regulate the voltage close”).
Regarding claim 20, the combination of Jereminov, Xu, Deaver, and Yeh teaches all the limitations of the base claims as outlined above.
Jereminov further teaches determining control signals by minimizing a non-convex objective function using a non-convex optimization technique subject to constraints (Par. [0056], “the objective function has to be minimized while satisfying the power flow in the grid … the power balance equations from Equations (2) and (3) used in traditional power flow represent highly nonlinear and non-convex constraints”; Par [0167], “OPF solver 3304 may solve the OPF problem using any one or more of the techniques described herein, including, but not limited to, a direct technique, an iterative technique, the A-stepping homotopy technique, relaxation techniques, and nonlinear optimization techniques, among others”; Par. [0168], “Output 3324 of OPF solver 3304 may include signals based on the solution determined by the OPF solver solving the OPF problem. Such signals can include suitable control signals for controlling generators 3308A and/or for controlling controllable pieces of power grid equipment 3308B.”).
Jereminov does not explicitly teach wherein the stable control signal for each bus that stabilizes the voltage of the electrical distribution network, at each time-step, based on the line-parameters, is determined, wherein the objective function comprises a voltage violation cost, a control signal for each bus, and a slack variable, and wherein the third constraints set comprises:
(i) the reactive power injection at each bus at the corresponding time-step should be bounded by a second predefined bound value and a third predefined bound value,
(ii) a predicted voltage of the electrical distribution network based on the line-parameters should be equal to the voltage of the electrical distribution network without an external voltage disturbance, and
(iii) the predicted voltage of the electrical distribution network should be bounded within a first predefined voltage bound value and a second predefined voltage bound value.
However, Yeh further teaches wherein the stable control signal for each bus that stabilizes the voltage of the electrical distribution network, at each time-step (Page 3, Model, “At each time step, buses may change their reactive power injection in order to regulate the voltage close”; Page 4, Algorithm 1, “Update qc(t)<- qc(t-1)+u(t). Apply the control action u(t)”), based on the line-parameters, is determined (Page 3, Model, “R*, X* E Sn are computed from the network topology and line parameters”; Page 1, Abstract, “the online controller does not know the true network topology and line parameters, but instead learns them over time by narrowing down the set of network topologies and line parameters that are consistent with its observations and adjusting reactive power generation accordingly to keep voltages within desired safety limits”), wherein the objective function comprises a voltage violation cost, a control signal for each bus, and a slack variable (Page 3, Model, “To drive voltage back to the desired interval and minimize the voltage violation and control costs … one needs the exact system dynamics (5) for choosing the optimal reactive power injections”; Page 4, Algorithm 1, equation 8a -
PNG
media_image3.png
58
185
media_image3.png
Greyscale
is interpreted as the voltage violation cost,
PNG
media_image4.png
48
88
media_image4.png
Greyscale
is interpreted as the control signal cost, and
PNG
media_image5.png
47
52
media_image5.png
Greyscale
is interpreted as the slack variable cost.; Page 3, Algorithm, “One important detail in Algorithm 1 is the inclusion of the slack variable”), and wherein the third constraints set comprises:
(i) the reactive power injection at each bus at the corresponding time-step should be bounded by a second predefined bound value and a third predefined bound value (Page 4, Algorithm 1, “limits on the reactive power injection…”; Page 4, Algorithm 1, equation 8b – qc(t-1)+u is interpreted as the reactive power injection after applying the control signal at the current time-step),
(ii) a predicted voltage of the electrical distribution network based on the line-parameters should be equal to the voltage of the electrical distribution network without an external voltage disturbance (Page 4, Algorithm 1, equation 8c is the predicted voltage based on the model/line-parameter estimate, while the actual transition includes external disturbance/noise w(t). So Yeh’s predicted-voltage equality corresponds to the voltage without the external disturbance.”), and
(iii) the predicted voltage of the electrical distribution network should be bounded within a first predefined voltage bound value and a second predefined voltage bound value (Page 4, Algorithm 1, equation 8e; Page 4, Algorithm 1, “limits on the squared voltage magnitude…”).
Claim(s) 4-6, 11-13, and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu (Data-driven Voltage Regulation in Radial Power Distribution Systems, 2019) (hereinafter Xu) in view of Deaver, SR. et al. US 2012/0022713 A1 (hereinafter Deaver), Jereminov et al. US 2018/0158152 A1 (hereinafter Jereminov), and Yeh et al. (Online learning for robust voltage control under uncertain grid topology, 2023) (hereinafter Yeh), and further in view of Lin et al. (Data-driven Modeling for Distribution Grids Under Partial Observability, 2021) (hereinafter Lin).
Regarding claim 4, the combination of Xu, Deaver, and Jereminov teaches all the limitations of the base claims as outlined above.
Xu further teaches (a) obtaining an initial reactance diagonal matrix and an initial resistance diagonal matrix of the electrical distribution network, based reactance values at each line, and resistance values at each line respectively (Page 3, Voltage Regulation Framework, “Given the topology information M, to estimate R and X is essentially to estimate r and x. Let ˆr and ˆx denote the estimate of r and x, respectively”; Page 3, Voltage Regulation Framework, -In equation 4a, diag(x) corresponds to the claimed reactance diagonal matrix because the diagonal entries are line reactance values, and in equation 4b, diag(b) corresponds to the claimed resistance diagonal matrix because the diagonal entries are line resistance values.);
Xu does not explicitly teach using a random initialization technique;
(b) estimating an intermediate reactance diagonal matrix, by fixing the initial resistance diagonal matrix, based on the voltage profile at a first time-step using the on-line convex optimization technique;
(c) estimating an intermediate resistance diagonal matrix, by fixing the initial reactance diagonal matrix, based on the voltage profile at the first time-step using the on-line convex optimization technique;
(d) estimating a subsequent reactance diagonal matrix, by fixing the intermediate resistance diagonal matrix, based on the voltage profile at next time-step using the on-line convex optimization technique;
(e) estimating a subsequent resistance diagonal matrix, by fixing the intermediate reactance diagonal matrix, based on the voltage profile at the next time-step using the online convex optimization technique;
(f) repeating steps (d) through (e), by considering the subsequent reactance diagonal matrix as the intermediate reactance diagonal matrix and the subsequent resistance diagonal matrix as the intermediate resistance diagonal matrix, at each subsequent step using the Gauss-Seidel technique, until the plurality of time-steps is completed, to obtain a final reactance diagonal matrix and a final resistance diagonal matrix of the electrical distribution network;
(g) determining (i) a line resistance of each line, using the final resistance diagonal matrix, and (ii) a line reactance of each line, using the final reactance diagonal matrix, to predict the line-parameters of the electrical distribution network.
However, Deaver teaches using a Gauss-Seidel technique (Par. [0044], “a Ybus Gauss-Seidel method may be implemented to solve for voltages and phase angles, transformer tap ratios and capacitor status (engaged or not engaged) for a given set of transformer loads and distributed generations … The Ybus Gauss-Seidel method provides effective convergence properties for a highly radial network with high R/X ratios (resistance versus reactance of conductors), such as may be found in distribution feeder (i.e., MV power line) networks”).
Xu and Deaver do not explicitly teach obtaining initial diagonal matrices randomly;
(b) estimating an intermediate reactance diagonal matrix, by fixing the initial resistance diagonal matrix, based on the voltage profile at a first time-step using the on-line convex optimization technique;
(c) estimating an intermediate resistance diagonal matrix, by fixing the initial reactance diagonal matrix, based on the voltage profile at the first time-step using the on-line convex optimization technique;
(d) estimating a subsequent reactance diagonal matrix, by fixing the intermediate resistance diagonal matrix, based on the voltage profile at next time-step using the on-line convex optimization technique;
(e) estimating a subsequent resistance diagonal matrix, by fixing the intermediate reactance diagonal matrix, based on the voltage profile at the next time-step using the online convex optimization technique;
(f) repeating steps (d) through (e), by considering the subsequent reactance diagonal matrix as the intermediate reactance diagonal matrix and the subsequent resistance diagonal matrix as the intermediate resistance diagonal matrix, at each subsequent step, until the plurality of time-steps is completed, to obtain a final reactance diagonal matrix and a final resistance diagonal matrix of the electrical distribution network;
(g) determining (i) a line resistance of each line, using the final resistance diagonal matrix, and (ii) a line reactance of each line, using the final reactance diagonal matrix, to predict the line-parameters of the electrical distribution network.
However, Yeh teaches using a random initialization technique (Page 8, Experimental Setup, “We initialize ^X by adding noise to the true X* in two ways. First, we scale each line impedance xij by a random factor oijiid~ Uniform [0,2]. Second, we randomly permute the bus ordering, so ^X corresponds to a permuted grid topology. Finally, we project ^X into the uncertainty set Xa with a=1”).
Xu, Deaver, Jereminov, and Yeh are analogous art because they are from the same field of endeavor and contain functional similarities. They all relate to controlling electrical distribution networks using computational optimization.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above data-driven DER voltage regulation and line-parameter estimation framework, as taught by Xu, Deaver, and Jereminov, and incorporate a random-initialization technique for uncertain line parameters and to apply that technique to the initial reactance and resistance diagonal-matrix estimates used in the combined line-parameter estimation process, as taught by Yeh.
One of ordinary skill in the art would have been motivated to improve voltage control stability when topology/line parameters are unknown or incorrect, as suggested by Yeh (Page 1, Abstract/Introduction).
Xu, Deaver, Jereminov, and Yeh do not explicitly teach (b) estimating an intermediate reactance diagonal matrix, by fixing the initial resistance diagonal matrix, based on the voltage profile at a first time-step using the on-line convex optimization technique;
(c) estimating an intermediate resistance diagonal matrix, by fixing the initial reactance diagonal matrix, based on the voltage profile at the first time-step using the on-line convex optimization technique;
(d) estimating a subsequent reactance diagonal matrix, by fixing the intermediate resistance diagonal matrix, based on the voltage profile at next time-step using the on-line convex optimization technique;
(e) estimating a subsequent resistance diagonal matrix, by fixing the intermediate reactance diagonal matrix, based on the voltage profile at the next time-step using the online convex optimization technique;
(f) repeating steps (d) through (e), by considering the subsequent reactance diagonal matrix as the intermediate reactance diagonal matrix and the subsequent resistance diagonal matrix as the intermediate resistance diagonal matrix, at each subsequent step, until the plurality of time-steps is completed, to obtain a final reactance diagonal matrix and a final resistance diagonal matrix of the electrical distribution network;
(g) determining (i) a line resistance of each line, using the final resistance diagonal matrix, and (ii) a line reactance of each line, using the final reactance diagonal matrix, to predict the line-parameters of the electrical distribution network.
However, Lin teaches (b) estimating an intermediate reactance diagonal matrix, by fixing the initial resistance diagonal matrix, based on the voltage profile at a first time-step using the on-line convex optimization technique (Page 2, System Modeling, “represent the network effects of respectively resistance r and reactance x, defined as … (2a) … (2b)… where diag(.) returns a diagonal matrix with the input vector as its diagonal entries” – equation 2a includes the diagonal matrix for reactance.; Page 2, System Modeling, “let t E {1, …, T} index the discrete-time samples of the feeder-wide variables. By applying (1) over two consecutive time instances, we obtain the voltage difference
PNG
media_image6.png
27
158
media_image6.png
Greyscale
” – vt corresponds to the voltage profile and ~vt is derived from the voltage profiles at consecutive time steps; Page 2, System Modeling, “the line parameters can be easily estimated by solving a linear regression problem” – linear regression corresponds to an on-line convex optimization technique; Page 1, Introduction, “An alternating minimization (AM) algorithm is developed to solve the resultant bi-linear problem by alternatingly updating the unknown injections and line parameters, both of which are convex sub-problems. As the iterative objective cost is non-increasing during these updates, the proposed AM algorithm is guaranteed to converge.”);
(c) estimating an intermediate resistance diagonal matrix, by fixing the initial reactance diagonal matrix, based on the voltage profile at the first time-step using the on-line convex optimization technique (Page 2, System Modeling, “represent the network effects of respectively resistance r and reactance x, defined as … (2a) … (2b)… where diag(.) returns a diagonal matrix with the input vector as its diagonal entries” – equation 2b includes the diagonal matrix for resistance.; Page 2, System Modeling, “let t E {1, …, T} index the discrete-time samples of the feeder-wide variables. By applying (1) over two consecutive time instances, we obtain the voltage difference
PNG
media_image6.png
27
158
media_image6.png
Greyscale
” – vt corresponds to the voltage profile and ~vt is derived from the voltage profiles at consecutive time steps; Page 2, System Modeling, “the line parameters can be easily estimated by solving a linear regression problem” – linear regression corresponds to an on-line convex optimization technique; Page 1, Introduction, “An alternating minimization (AM) algorithm is developed to solve the resultant bi-linear problem by alternatingly updating the unknown injections and line parameters, both of which are convex sub-problems. As the iterative objective cost is non-increasing during these updates, the proposed AM algorithm is guaranteed to converge.”);
(d) estimating a subsequent reactance diagonal matrix, by fixing the intermediate resistance diagonal matrix, based on the voltage profile at next time-step using the on-line convex optimization technique (Page 2, System Modeling, “to relate ~vt with the unknown line parameters in vector theta := [r;x] E R2L” – Lin defines theta :=[r;x], meaning each updated theta[i] includes updated resistance values r and reactance values x. Because Lin defines R(r) and X(x) using diag(r) and diag(x), each updated theta[i] provides updated values for the resistance and reactance diagonal matrices. Applying Lin’s same fixed-block estimation at the next time-step while fixing the intermediate resistance diagonal matrix produces the claimed subsequent reactance diagonal matrix.; Page 2, System Modeling, “represent the network effects of respectively resistance r and reactance x, defined as … (2a) … (2b)… where diag(.) returns a diagonal matrix with the input vector as its diagonal entries” – equation 2a includes the diagonal matrix for reactance.; Page 2, System Modeling, “let t E {1, …, T} index the discrete-time samples of the feeder-wide variables. By applying (1) over two consecutive time instances, we obtain the voltage difference
PNG
media_image6.png
27
158
media_image6.png
Greyscale
” – vt corresponds to the voltage profile and ~vt is derived from the voltage profiles at consecutive time steps; Page 2, System Modeling, “the line parameters can be easily estimated by solving a linear regression problem” – linear regression corresponds to an on-line convex optimization technique; Page 1, Introduction, “An alternating minimization (AM) algorithm is developed to solve the resultant bi-linear problem by alternatingly updating the unknown injections and line parameters, both of which are convex sub-problems. As the iterative objective cost is non-increasing during these updates, the proposed AM algorithm is guaranteed to converge.”);
(e) estimating a subsequent resistance diagonal matrix, by fixing the intermediate reactance diagonal matrix, based on the voltage profile at the next time-step using the online convex optimization technique (Page 2, System Modeling, “to relate ~vt with the unknown line parameters in vector theta := [r;x] E R2L” – Lin defines theta :=[r;x], meaning each updated theta[i] includes updated resistance values r and reactance values x. Because Lin defines R(r) and X(x) using diag(r) and diag(x), each updated theta[i] provides updated values for the resistance and reactance diagonal matrices. Applying Lin’s same fixed-block estimation at the next time-step while fixing that intermediate reactance diagonal matrix produces the claimed subsequent resistance diagonal matrix.; Page 2, System Modeling, “represent the network effects of respectively resistance r and reactance x, defined as … (2a) … (2b)… where diag(.) returns a diagonal matrix with the input vector as its diagonal entries” – equation 2b includes the diagonal matrix for reactance.; Page 2, System Modeling, “let t E {1, …, T} index the discrete-time samples of the feeder-wide variables. By applying (1) over two consecutive time instances, we obtain the voltage difference
PNG
media_image6.png
27
158
media_image6.png
Greyscale
” – vt corresponds to the voltage profile and ~vt is derived from the voltage profiles at consecutive time steps; Page 2, System Modeling, “the line parameters can be easily estimated by solving a linear regression problem” – linear regression corresponds to an on-line convex optimization technique; Page 1, Introduction, “An alternating minimization (AM) algorithm is developed to solve the resultant bi-linear problem by alternatingly updating the unknown injections and line parameters, both of which are convex sub-problems. As the iterative objective cost is non-increasing during these updates, the proposed AM algorithm is guaranteed to converge.”);
(f) repeating steps (d) through (e), by considering the subsequent reactance diagonal matrix as the intermediate reactance diagonal matrix and the subsequent resistance diagonal matrix as the intermediate resistance diagonal matrix, at each subsequent step, until the plurality of time-steps is completed, to obtain a final reactance diagonal matrix and a final resistance diagonal matrix of the electrical distribution network (Page 3, Distribution Modeling Under Partial Observability, “we develop an alternating minimization (AM) based scheme to solve (7) by iteratively updating the two groups of variables”; Page 5, Conclusion and Future Work, “we developed an alternating minimization (AM) method to update each one of the two groups of unknowns by fixing the other. The proposed AM method leads to efficient updates by solving convex sub-problems and can converge to a stationary point” – once the method converges to a stationary point, the iteration arrives at final reactance and resistance diagonal matrices. Because each update uses the most recently updated value of the other parameter block in the next update, the alternating block update corresponds to a Gauss-Seidel-style iterative update.); and
(g) determining (i) a line resistance of each line, using the final resistance diagonal matrix, and (ii) a line reactance of each line, using the final reactance diagonal matrix, to predict the line-parameters of the electrical distribution network (Page 2, System Modeling, “For each line l, let (rl + jxl) denote the line impedance in p.u. with all line resistances and reactances respectively collected in vectors r and x … respectively resistance r and reactance x, defined as … (2a) … (2b) where diag(.) returns a diagonal matrix with the input vector as its diagonal entries.” – the line resistance and line reactance of each line are determined from the respective final diagonal matrices to predict the line parameters of the electrical distribution network.
Xu, Deaver, Jereminov, Yeh, and Lin are analogous art because they are from the same field of endeavor and contain functional similarities. They all relate to controlling electrical distribution networks using computational optimization.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above data-driven DER voltage regulation and line-parameter estimation framework, as taught by Xu, Deaver, Jereminov, and Yeh, and incorporate an alternating-minimization fixed-block update, as taught by Lin.
One of ordinary skill in the art would have been motivated to improve the accuracy of line-parameter estimation and voltage modeling while taking advantage of efficient convex subproblems that can converge to a stationary point, as suggested by Lin (Page 5, Conclusion and Future work).
Regarding claim 5, the combination of Xu, Deaver, Jereminov, Yeh, and Lin teaches all the limitations of the base claims as outlined above.
Yeh further teaches wherein the on-line convex optimization technique employs a first objective function and a first constraint set (Page 4, Algorithm 1, “Otherwise, query the model chasing algorithm for a new consistent parameter estimate…” – equation 7a corresponds to an objective function and equations 7b and 7d contain constraints.) while
(i)estimating the intermediate reactance diagonal matrix, by fixing the initial resistance diagonal matrix (see claim 4 rejection above) and
(ii) estimating the subsequent reactance diagonal matrix (see claim 4 rejection above), by fixing the intermediate resistance diagonal matrix, wherein the first objective function is to minimize a difference between the intermediate reactance diagonal matrix and the initial reactance diagonal matrix obtained at two consecutive time-steps of the plurality of time-steps (Page 4, Algorithm 1, equation 7a; Page 3, Algorithm, “The model chasing algorithm performs nested CBC, SEL which is the online problem of choosing a sequence of points within sequentially nested convex sets, with the aim of minimizing the sum of distances between the chosen points”), and wherein the first constraint set comprises:
(i) the intermediate reactance diagonal matrix obtained at a current time-step belongs to a predefined reactance convex compact set (Page 4, Algorithm 1, “compact convex uncertainty set for the model parameter…”; Page 4, Algorithm 1, Equation 7b; Page 4, Assumption 2, “The true model X* lies within a known compact, convex uncertainty set…” – Yeh constrains the current estimate ^xt to belong to a known compact convex uncertainty set x. In the combined system, the reactance-related estimate/diagonal matrix being updated is constrained to a corresponding predefined reactance convex compact set.),
(ii) an exogenous noise at each bus of the electrical distribution network should be bounded within a first predefined bound value (Page 4, Algorithm 1, “upper bound for noise…”; Page 4, Assumption 1, “The change in noise is bounded as…” – Yeh’s infinity-norm bound means each component of the noise vector, i.e., the noise associated with each bus, is bounded by the predefined value n.), and
(iii) a voltage of the electrical distribution network obtained at the current time-step should belongs to a predefined voltage convex compact set (Page 3, Model, “For all t >= 2, the voltage control algorithm aims to maintain v(t) within [v,v], ideally as close as possible to a “nominal” value vnom… … At each time step, buses may change their reactive power injection in order to regulate the voltage close to vnom”; Page 4, Algorithm 1, “limits on the squared voltage magnitude…”; Page 4, Algorithm 1, equation 8e – The interval [v, v] c Rn is a predefined voltage set. Because it is a closed bounded box in Rn, it is convex and compact, and Yeh constrains the voltage estimate to remain within that voltage set.).
Regarding claim 6, the combination of Xu, Deaver, Jereminov, Yeh, and Lin teaches all the limitations of the base claims as outlined above.
Yeh further teaches wherein the on-line convex optimization technique employs a second objective function and a second constraint set (Page 4, Algorithm 1, “Otherwise, query the model chasing algorithm for a new consistent parameter estimate…” – equation 7a corresponds to an objective function and equations 7b and 7d contain constraints. The same objective/constraint framework that is applied to the reactance parameter update is applied to the resistance parameter update as well.) while
(i) estimating the intermediate resistance diagonal matrix, by fixing the initial reactance diagonal matrix (see claim 4 rejection above) and
(ii) estimating the subsequent resistance diagonal matrix (see claim 4 rejection above), by fixing the intermediate reactance diagonal matrix, wherein the second objective function is to minimize a difference between the intermediate resistance diagonal matrix and the initial resistance diagonal matrix obtained at two consecutive time-steps of the plurality of time-steps (Page 4, Algorithm 1, equation 7a; Page 3, Algorithm, “The model chasing algorithm performs nested CBC, SEL which is the online problem of choosing a sequence of points within sequentially nested convex sets, with the aim of minimizing the sum of distances between the chosen points” - The same objective/constraint framework that is applied to the reactance parameter update is applied to the resistance parameter update as well.), and wherein the second constraint set comprises:
(i) the intermediate resistance diagonal matrix obtained at a current time-step belongs to a predefined resistance convex compact set (Page 4, Assumption 2, “The true model X* lies within a known compact, convex uncertainty set … X forms the initial “consistent set” (see Definition 1) for our consistent model chasing algorithm SEL” – Yeh constrains the current model estimate to belong to a known compact convex uncertainty set. In the combined system, the resistance diagonal matrix/resistance-related parameter block is the model estimate being updated, so it is constrained to a corresponding predefined resistance convex compact set.),
(ii) an exogenous noise at each bus of the electrical distribution network should be bounded within a first predefined bound value (Page 4, Algorithm 1, “upper bound for noise…”; Page 4, Assumption 1, “The change in noise is bounded as…” – Yeh’s infinity-norm bound means each component of the noise vector, i.e., the noise associated with each bus, is bounded by the predefined value n), and
(iii) a voltage of the electrical distribution network obtained at the current time-step should belongs to a predefined volage convex compact set (Page 3, Model, “For all t >= 2, the voltage control algorithm aims to maintain v(t) within [v,v], ideally as close as possible to a “nominal” value vnom… … At each time step, buses may change their reactive power injection in order to regulate the voltage close to vnom”; Page 4, Algorithm 1, “limits on the squared voltage magnitude…”; Page 4, Algorithm 1, equation 8e – The interval [v, v] c Rn is a predefined voltage set. Because it is a closed bounded box in Rn, it is convex and compact, and Yeh constrains the voltage estimate to remain within that voltage set.).
Regarding claim 11, the combination of Jereminov, Xu, and Deaver teaches all the limitations of the base claims as outlined above.
Xu further teaches (a) obtaining an initial reactance diagonal matrix and an initial resistance diagonal matrix of the electrical distribution network, based reactance values at each line, and resistance values at each line respectively (Page 3, Voltage Regulation Framework, “Given the topology information M, to estimate R and X is essentially to estimate r and x. Let ˆr and ˆx denote the estimate of r and x, respectively”; Page 3, Voltage Regulation Framework, -In equation 4a, diag(x) corresponds to the claimed reactance diagonal matrix because the diagonal entries are line reactance values, and in equation 4b, diag(b) corresponds to the claimed resistance diagonal matrix because the diagonal entries are line resistance values.);
Xu does not explicitly teach using a random initialization technique;
(b) estimating an intermediate reactance diagonal matrix, by fixing the initial resistance diagonal matrix, based on the voltage profile at a first time-step using the on-line convex optimization technique;
(c) estimating an intermediate resistance diagonal matrix, by fixing the initial reactance diagonal matrix, based on the voltage profile at the first time-step using the on-line convex optimization technique;
(d) estimating a subsequent reactance diagonal matrix, by fixing the intermediate resistance diagonal matrix, based on the voltage profile at next time-step using the on-line convex optimization technique;
(e) estimating a subsequent resistance diagonal matrix, by fixing the intermediate reactance diagonal matrix, based on the voltage profile at the next time-step using the online convex optimization technique;
(f) repeating steps (d) through (e), by considering the subsequent reactance diagonal matrix as the intermediate reactance diagonal matrix and the subsequent resistance diagonal matrix as the intermediate resistance diagonal matrix, at each subsequent step using the Gauss-Seidel technique, until the plurality of time-steps is completed, to obtain a final reactance diagonal matrix and a final resistance diagonal matrix of the electrical distribution network;
(g) determining (i) a line resistance of each line, using the final resistance diagonal matrix, and (ii) a line reactance of each line, using the final reactance diagonal matrix, to predict the line-parameters of the electrical distribution network.
However, Deaver teaches using a Gauss-Seidel technique (Par. [0044], “a Ybus Gauss-Seidel method may be implemented to solve for voltages and phase angles, transformer tap ratios and capacitor status (engaged or not engaged) for a given set of transformer loads and distributed generations … The Ybus Gauss-Seidel method provides effective convergence properties for a highly radial network with high R/X ratios (resistance versus reactance of conductors), such as may be found in distribution feeder (i.e., MV power line) networks”).
Xu and Deaver do not explicitly teach obtaining initial diagonal matrices randomly;
(b) estimating an intermediate reactance diagonal matrix, by fixing the initial resistance diagonal matrix, based on the voltage profile at a first time-step using the on-line convex optimization technique;
(c) estimating an intermediate resistance diagonal matrix, by fixing the initial reactance diagonal matrix, based on the voltage profile at the first time-step using the on-line convex optimization technique;
(d) estimating a subsequent reactance diagonal matrix, by fixing the intermediate resistance diagonal matrix, based on the voltage profile at next time-step using the on-line convex optimization technique;
(e) estimating a subsequent resistance diagonal matrix, by fixing the intermediate reactance diagonal matrix, based on the voltage profile at the next time-step using the online convex optimization technique;
(f) repeating steps (d) through (e), by considering the subsequent reactance diagonal matrix as the intermediate reactance diagonal matrix and the subsequent resistance diagonal matrix as the intermediate resistance diagonal matrix, at each subsequent step, until the plurality of time-steps is completed, to obtain a final reactance diagonal matrix and a final resistance diagonal matrix of the electrical distribution network;
(g) determining (i) a line resistance of each line, using the final resistance diagonal matrix, and (ii) a line reactance of each line, using the final reactance diagonal matrix, to predict the line-parameters of the electrical distribution network.
However, Yeh teaches using a random initialization technique (Page 8, Experimental Setup, “We initialize ^X by adding noise to the true X* in two ways. First, we scale each line impedance xij by a random factor oijiid~ Uniform [0,2]. Second, we randomly permute the bus ordering, so ^X corresponds to a permuted grid topology. Finally, we project ^X into the uncertainty set Xa with a=1”).
Jereminov, Xu, Deaver, and Yeh are analogous art because they are from the same field of endeavor and contain functional similarities. They all relate to controlling electrical distribution networks using computational optimization.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above data-driven DER voltage regulation and line-parameter estimation framework, as taught by Jereminov, Xu, and Deaver, and incorporate a random-initialization technique for uncertain line parameters and to apply that technique to the initial reactance and resistance diagonal-matrix estimates used in the combined line-parameter estimation process, as taught by Yeh.
One of ordinary skill in the art would have been motivated to improve voltage control stability when topology/line parameters are unknown or incorrect, as suggested by Yeh (Page 1, Abstract/Introduction).
Jereminov, Xu, Deaver, and Yeh do not explicitly teach (b) estimating an intermediate reactance diagonal matrix, by fixing the initial resistance diagonal matrix, based on the voltage profile at a first time-step using the on-line convex optimization technique;
(c) estimating an intermediate resistance diagonal matrix, by fixing the initial reactance diagonal matrix, based on the voltage profile at the first time-step using the on-line convex optimization technique;
(d) estimating a subsequent reactance diagonal matrix, by fixing the intermediate resistance diagonal matrix, based on the voltage profile at next time-step using the on-line convex optimization technique;
(e) estimating a subsequent resistance diagonal matrix, by fixing the intermediate reactance diagonal matrix, based on the voltage profile at the next time-step using the online convex optimization technique;
(f) repeating steps (d) through (e), by considering the subsequent reactance diagonal matrix as the intermediate reactance diagonal matrix and the subsequent resistance diagonal matrix as the intermediate resistance diagonal matrix, at each subsequent step, until the plurality of time-steps is completed, to obtain a final reactance diagonal matrix and a final resistance diagonal matrix of the electrical distribution network;
(g) determining (i) a line resistance of each line, using the final resistance diagonal matrix, and (ii) a line reactance of each line, using the final reactance diagonal matrix, to predict the line-parameters of the electrical distribution network.
However, Lin teaches (b) estimating an intermediate reactance diagonal matrix, by fixing the initial resistance diagonal matrix, based on the voltage profile at a first time-step using the on-line convex optimization technique (Page 2, System Modeling, “represent the network effects of respectively resistance r and reactance x, defined as … (2a) … (2b)… where diag(.) returns a diagonal matrix with the input vector as its diagonal entries” – equation 2b includes the diagonal matrix for reactance.; Page 2, System Modeling, “let t E {1, …, T} index the discrete-time samples of the feeder-wide variables. By applying (1) over two consecutive time instances, we obtain the voltage difference
PNG
media_image6.png
27
158
media_image6.png
Greyscale
” – vt corresponds to the voltage profile and ~vt is derived from the voltage profiles at consecutive time steps; Page 2, System Modeling, “the line parameters can be easily estimated by solving a linear regression problem” – linear regression corresponds to an on-line convex optimization technique; Page 1, Introduction, “An alternating minimization (AM) algorithm is developed to solve the resultant bi-linear problem by alternatingly updating the unknown injections and line parameters, both of which are convex sub-problems. As the iterative objective cost is non-increasing during these updates, the proposed AM algorithm is guaranteed to converge.”);
(c) estimating an intermediate resistance diagonal matrix, by fixing the initial reactance diagonal matrix, based on the voltage profile at the first time-step using the on-line convex optimization technique (Page 2, System Modeling, “represent the network effects of respectively resistance r and reactance x, defined as … (2a) … (2b)… where diag(.) returns a diagonal matrix with the input vector as its diagonal entries” – equation 2a includes the diagonal matrix for resistance.; Page 2, System Modeling, “let t E {1, …, T} index the discrete-time samples of the feeder-wide variables. By applying (1) over two consecutive time instances, we obtain the voltage difference
PNG
media_image6.png
27
158
media_image6.png
Greyscale
” – vt corresponds to the voltage profile and ~vt is derived from the voltage profiles at consecutive time steps; Page 2, System Modeling, “the line parameters can be easily estimated by solving a linear regression problem” – linear regression corresponds to an on-line convex optimization technique; Page 1, Introduction, “An alternating minimization (AM) algorithm is developed to solve the resultant bi-linear problem by alternatingly updating the unknown injections and line parameters, both of which are convex sub-problems. As the iterative objective cost is non-increasing during these updates, the proposed AM algorithm is guaranteed to converge.”);
(d) estimating a subsequent reactance diagonal matrix, by fixing the intermediate resistance diagonal matrix, based on the voltage profile at next time-step using the on-line convex optimization technique (Page 2, System Modeling, “to relate ~vt with the unknown line parameters in vector theta := [r;x] E R2L” – Lin defines theta :=[r;x], meaning each updated theta[i] includes updated resistance values r and reactance values x. Because Lin defines R(r) and X(x) using diag(r) and diag(x), each updated theta[i] provides updated values for the resistance and reactance diagonal matrices. Applying Lin’s same fixed-block estimation at the next time-step while fixing the intermediate resistance diagonal matrix produces the claimed subsequent reactance diagonal matrix.; Page 2, System Modeling, “represent the network effects of respectively resistance r and reactance x, defined as … (2a) … (2b)… where diag(.) returns a diagonal matrix with the input vector as its diagonal entries” – equation 2b includes the diagonal matrix for reactance.; Page 2, System Modeling, “let t E {1, …, T} index the discrete-time samples of the feeder-wide variables. By applying (1) over two consecutive time instances, we obtain the voltage difference
PNG
media_image6.png
27
158
media_image6.png
Greyscale
” – vt corresponds to the voltage profile and ~vt is derived from the voltage profiles at consecutive time steps; Page 2, System Modeling, “the line parameters can be easily estimated by solving a linear regression problem” – linear regression corresponds to an on-line convex optimization technique; Page 1, Introduction, “An alternating minimization (AM) algorithm is developed to solve the resultant bi-linear problem by alternatingly updating the unknown injections and line parameters, both of which are convex sub-problems. As the iterative objective cost is non-increasing during these updates, the proposed AM algorithm is guaranteed to converge.”);
(e) estimating a subsequent resistance diagonal matrix, by fixing the intermediate reactance diagonal matrix, based on the voltage profile at the next time-step using the online convex optimization technique (Page 2, System Modeling, “to relate ~vt with the unknown line parameters in vector theta := [r;x] E R2L” – Lin defines theta :=[r;x], meaning each updated theta[i] includes updated resistance values r and reactance values x. Because Lin defines R(r) and X(x) using diag(r) and diag(x), each updated theta[i] provides updated values for the resistance and reactance diagonal matrices. Applying Lin’s same fixed-block estimation at the next time-step while fixing that intermediate reactance diagonal matrix produces the claimed subsequent resistance diagonal matrix.; Page 2, System Modeling, “represent the network effects of respectively resistance r and reactance x, defined as … (2a) … (2b)… where diag(.) returns a diagonal matrix with the input vector as its diagonal entries” – equation 2a includes the diagonal matrix for resistance.; Page 2, System Modeling, “let t E {1, …, T} index the discrete-time samples of the feeder-wide variables. By applying (1) over two consecutive time instances, we obtain the voltage difference
PNG
media_image6.png
27
158
media_image6.png
Greyscale
” – vt corresponds to the voltage profile and ~vt is derived from the voltage profiles at consecutive time steps; Page 2, System Modeling, “the line parameters can be easily estimated by solving a linear regression problem” – linear regression corresponds to an on-line convex optimization technique; Page 1, Introduction, “An alternating minimization (AM) algorithm is developed to solve the resultant bi-linear problem by alternatingly updating the unknown injections and line parameters, both of which are convex sub-problems. As the iterative objective cost is non-increasing during these updates, the proposed AM algorithm is guaranteed to converge.”);
(f) repeating steps (d) through (e), by considering the subsequent reactance diagonal matrix as the intermediate reactance diagonal matrix and the subsequent resistance diagonal matrix as the intermediate resistance diagonal matrix, at each subsequent step, until the plurality of time-steps is completed, to obtain a final reactance diagonal matrix and a final resistance diagonal matrix of the electrical distribution network (Page 3, Distribution Modeling Under Partial Observability, “we develop an alternating minimization (AM) based scheme to solve (7) by iteratively updating the two groups of variables”; Page 5, Conclusion and Future Work, “we developed an alternating minimization (AM) method to update each one of the two groups of unknowns by fixing the other. The proposed AM method leads to efficient updates by solving convex sub-problems and can converge to a stationary point” – once the method converges to a stationary point, the iteration arrives at final reactance and resistance diagonal matrices. Because each update uses the most recently updated value of the other parameter block in the next update, the alternating block update corresponds to a Gauss-Seidel-style iterative update.); and
(g) determining (i) a line resistance of each line, using the final resistance diagonal matrix, and (ii) a line reactance of each line, using the final reactance diagonal matrix, to predict the line-parameters of the electrical distribution network (Page 2, System Modeling, “For each line l, let (rl + jxl) denote the line impedance in p.u. with all line resistances and reactances respectively collected in vectors r and x … respectively resistance r and reactance x, defined as … (2a) … (2b) where diag(.) returns a diagonal matrix with the input vector as its diagonal entries.” – the line resistance and line reactance of each line are determined from the respective final diagonal matrices to predict the line parameters of the electrical distribution network.
Jereminov, Xu, Deaver, Yeh, and Lin are analogous art because they are from the same field of endeavor and contain functional similarities. They all relate to controlling electrical distribution networks using computational optimization.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above data-driven DER voltage regulation and line-parameter estimation framework, as taught by Jereminov, Xu, Deaver, and Yeh, and incorporate an alternating-minimization fixed-block update, as taught by Lin.
One of ordinary skill in the art would have been motivated to improve the accuracy of line-parameter estimation and voltage modeling while taking advantage of efficient convex subproblems that can converge to a stationary point, as suggested by Lin (Page 5, Conclusion and Future work).
Regarding claim 12, the combination of Jereminov, Xu, Deaver, Yeh, and Lin teaches all the limitations of the base claims as outlined above.
Yeh further teaches wherein the on-line convex optimization technique employs a first objective function and a first constraint set (Page 4, Algorithm 1, “Otherwise, query the model chasing algorithm for a new consistent parameter estimate…” – equation 7a corresponds to an objective function and equations 7b and 7d contain constraints.) while
(i)estimating the intermediate reactance diagonal matrix, by fixing the initial resistance diagonal matrix (see claim 4 rejection above) and
(ii) estimating the subsequent reactance diagonal matrix (see claim 4 rejection above), by fixing the intermediate resistance diagonal matrix, wherein the first objective function is to minimize a difference between the intermediate reactance diagonal matrix and the initial reactance diagonal matrix obtained at two consecutive time-steps of the plurality of time-steps (Page 4, Algorithm 1, equation 7a; Page 3, Algorithm, “The model chasing algorithm performs nested CBC, SEL which is the online problem of choosing a sequence of points within sequentially nested convex sets, with the aim of minimizing the sum of distances between the chosen points”), and wherein the first constraint set comprises:
(i) the intermediate reactance diagonal matrix obtained at a current time-step belongs to a predefined reactance convex compact set (Page 4, Algorithm 1, “compact convex uncertainty set for the model parameter…”; Page 4, Algorithm 1, Equation 7b; Page 4, Assumption 2, “The true model X* lies within a known compact, convex uncertainty set…” – Yeh constrains the current estimate ^xt to belong to a known compact convex uncertainty set x. In the combined system, the reactance-related estimate/diagonal matrix being updated is constrained to a corresponding predefined reactance convex compact set.),
(ii) an exogenous noise at each bus of the electrical distribution network should be bounded within a first predefined bound value (Page 4, Algorithm 1, “upper bound for noise…”; Page 4, Assumption 1, “The change in noise is bounded as…” – Yeh’s infinity-norm bound means each component of the noise vector, i.e., the noise associated with each bus, is bounded by the predefined value n.), and
(iii) a voltage of the electrical distribution network obtained at the current time-step should belongs to a predefined voltage convex compact set (Page 3, Model, “For all t >= 2, the voltage control algorithm aims to maintain v(t) within [v,v], ideally as close as possible to a “nominal” value vnom… … At each time step, buses may change their reactive power injection in order to regulate the voltage close to vnom”; Page 4, Algorithm 1, “limits on the squared voltage magnitude…”; Page 4, Algorithm 1, equation 8e – The interval [v, v] c Rn is a predefined voltage set. Because it is a closed bounded box in Rn, it is convex and compact, and Yeh constrains the voltage estimate to remain within that voltage set.).
Regarding claim 13, the combination of Jereminov, Xu, Deaver, Yeh, and Lin teaches all the limitations of the base claims as outlined above.
Yeh further teaches wherein the on-line convex optimization technique employs a second objective function and a second constraint set (Page 4, Algorithm 1, “Otherwise, query the model chasing algorithm for a new consistent parameter estimate…” – equation 7a corresponds to an objective function and equations 7b and 7d contain constraints. The same objective/constraint framework that is applied to the reactance parameter update is applied to the resistance parameter update as well.) while
(i) estimating the intermediate resistance diagonal matrix, by fixing the initial reactance diagonal matrix (see claim 4 rejection above) and
(ii) estimating the subsequent resistance diagonal matrix (see claim 4 rejection above), by fixing the intermediate reactance diagonal matrix, wherein the second objective function is to minimize a difference between the intermediate resistance diagonal matrix and the initial resistance diagonal matrix obtained at two consecutive time-steps of the plurality of time-steps (Page 4, Algorithm 1, equation 7a; Page 3, Algorithm, “The model chasing algorithm performs nested CBC, SEL which is the online problem of choosing a sequence of points within sequentially nested convex sets, with the aim of minimizing the sum of distances between the chosen points” - The same objective/constraint framework that is applied to the reactance parameter update is applied to the resistance parameter update as well.), and wherein the second constraint set comprises:
(i) the intermediate resistance diagonal matrix obtained at a current time-step belongs to a predefined resistance convex compact set (Page 4, Assumption 2, “The true model X* lies within a known compact, convex uncertainty set … X forms the initial “consistent set” (see Definition 1) for our consistent model chasing algorithm SEL” – Yeh constrains the current model estimate to belong to a known compact convex uncertainty set. In the combined system, the resistance diagonal matrix/resistance-related parameter block is the model estimate being updated, so it is constrained to a corresponding predefined resistance convex compact set.),
(ii) an exogenous noise at each bus of the electrical distribution network should be bounded within a first predefined bound value (Page 4, Algorithm 1, “upper bound for noise…”; Page 4, Assumption 1, “The change in noise is bounded as…” – Yeh’s infinity-norm bound means each component of the noise vector, i.e., the noise associated with each bus, is bounded by the predefined value n), and
(iii) a voltage of the electrical distribution network obtained at the current time-step should belongs to a predefined volage convex compact set (Page 3, Model, “For all t >= 2, the voltage control algorithm aims to maintain v(t) within [v,v], ideally as close as possible to a “nominal” value vnom… … At each time step, buses may change their reactive power injection in order to regulate the voltage close to vnom”; Page 4, Algorithm 1, “limits on the squared voltage magnitude…”; Page 4, Algorithm 1, equation 8e – The interval [v, v] c Rn is a predefined voltage set. Because it is a closed bounded box in Rn, it is convex and compact, and Yeh constrains the voltage estimate to remain within that voltage set.).
Regarding claim 17, the combination of Jereminov, Xu, Deaver, and Yeh teaches all the limitations of the base claims as outlined above.
Xu further teaches (a) obtaining an initial reactance diagonal matrix and an initial resistance diagonal matrix of the electrical distribution network, based reactance values at each line, and resistance values at each line respectively (Page 3, Voltage Regulation Framework, “Given the topology information M, to estimate R and X is essentially to estimate r and x. Let ˆr and ˆx denote the estimate of r and x, respectively”; Page 3, Voltage Regulation Framework, -In equation 4a, diag(x) corresponds to the claimed reactance diagonal matrix because the diagonal entries are line reactance values, and in equation 4b, diag(b) corresponds to the claimed resistance diagonal matrix because the diagonal entries are line resistance values.);
Xu does not explicitly teach using a random initialization technique;
(b) estimating an intermediate reactance diagonal matrix, by fixing the initial resistance diagonal matrix, based on the voltage profile at a first time-step using the on-line convex optimization technique;
(c) estimating an intermediate resistance diagonal matrix, by fixing the initial reactance diagonal matrix, based on the voltage profile at the first time-step using the on-line convex optimization technique;
(d) estimating a subsequent reactance diagonal matrix, by fixing the intermediate resistance diagonal matrix, based on the voltage profile at next time-step using the on-line convex optimization technique;
(e) estimating a subsequent resistance diagonal matrix, by fixing the intermediate reactance diagonal matrix, based on the voltage profile at the next time-step using the online convex optimization technique;
(f) repeating steps (d) through (e), by considering the subsequent reactance diagonal matrix as the intermediate reactance diagonal matrix and the subsequent resistance diagonal matrix as the intermediate resistance diagonal matrix, at each subsequent step using the Gauss-Seidel technique, until the plurality of time-steps is completed, to obtain a final reactance diagonal matrix and a final resistance diagonal matrix of the electrical distribution network;
(g) determining (i) a line resistance of each line, using the final resistance diagonal matrix, and (ii) a line reactance of each line, using the final reactance diagonal matrix, to predict the line-parameters of the electrical distribution network.
However, Deaver teaches using a Gauss-Seidel technique (Par. [0044], “a Ybus Gauss-Seidel method may be implemented to solve for voltages and phase angles, transformer tap ratios and capacitor status (engaged or not engaged) for a given set of transformer loads and distributed generations … The Ybus Gauss-Seidel method provides effective convergence properties for a highly radial network with high R/X ratios (resistance versus reactance of conductors), such as may be found in distribution feeder (i.e., MV power line) networks”).
Xu and Deaver do not explicitly teach obtaining initial diagonal matrices randomly;
(b) estimating an intermediate reactance diagonal matrix, by fixing the initial resistance diagonal matrix, based on the voltage profile at a first time-step using the on-line convex optimization technique;
(c) estimating an intermediate resistance diagonal matrix, by fixing the initial reactance diagonal matrix, based on the voltage profile at the first time-step using the on-line convex optimization technique;
(d) estimating a subsequent reactance diagonal matrix, by fixing the intermediate resistance diagonal matrix, based on the voltage profile at next time-step using the on-line convex optimization technique;
(e) estimating a subsequent resistance diagonal matrix, by fixing the intermediate reactance diagonal matrix, based on the voltage profile at the next time-step using the online convex optimization technique;
(f) repeating steps (d) through (e), by considering the subsequent reactance diagonal matrix as the intermediate reactance diagonal matrix and the subsequent resistance diagonal matrix as the intermediate resistance diagonal matrix, at each subsequent step, until the plurality of time-steps is completed, to obtain a final reactance diagonal matrix and a final resistance diagonal matrix of the electrical distribution network;
(g) determining (i) a line resistance of each line, using the final resistance diagonal matrix, and (ii) a line reactance of each line, using the final reactance diagonal matrix, to predict the line-parameters of the electrical distribution network.
However, Yeh teaches using a random initialization technique (Page 8, Experimental Setup, “We initialize ^X by adding noise to the true X* in two ways. First, we scale each line impedance xij by a random factor oijiid~ Uniform [0,2]. Second, we randomly permute the bus ordering, so ^X corresponds to a permuted grid topology. Finally, we project ^X into the uncertainty set Xa with a=1”).
Jereminov, Xu, Deaver, and Yeh do not explicitly teach (b) estimating an intermediate reactance diagonal matrix, by fixing the initial resistance diagonal matrix, based on the voltage profile at a first time-step using the on-line convex optimization technique;
(c) estimating an intermediate resistance diagonal matrix, by fixing the initial reactance diagonal matrix, based on the voltage profile at the first time-step using the on-line convex optimization technique;
(d) estimating a subsequent reactance diagonal matrix, by fixing the intermediate resistance diagonal matrix, based on the voltage profile at next time-step using the on-line convex optimization technique;
(e) estimating a subsequent resistance diagonal matrix, by fixing the intermediate reactance diagonal matrix, based on the voltage profile at the next time-step using the online convex optimization technique;
(f) repeating steps (d) through (e), by considering the subsequent reactance diagonal matrix as the intermediate reactance diagonal matrix and the subsequent resistance diagonal matrix as the intermediate resistance diagonal matrix, at each subsequent step, until the plurality of time-steps is completed, to obtain a final reactance diagonal matrix and a final resistance diagonal matrix of the electrical distribution network;
(g) determining (i) a line resistance of each line, using the final resistance diagonal matrix, and (ii) a line reactance of each line, using the final reactance diagonal matrix, to predict the line-parameters of the electrical distribution network.
However, Lin teaches (b) estimating an intermediate reactance diagonal matrix, by fixing the initial resistance diagonal matrix, based on the voltage profile at a first time-step using the on-line convex optimization technique (Page 2, System Modeling, “represent the network effects of respectively resistance r and reactance x, defined as … (2a) … (2b)… where diag(.) returns a diagonal matrix with the input vector as its diagonal entries” – equation 2a includes the diagonal matrix for reactance.; Page 2, System Modeling, “let t E {1, …, T} index the discrete-time samples of the feeder-wide variables. By applying (1) over two consecutive time instances, we obtain the voltage difference
PNG
media_image6.png
27
158
media_image6.png
Greyscale
” – vt corresponds to the voltage profile and ~vt is derived from the voltage profiles at consecutive time steps; Page 2, System Modeling, “the line parameters can be easily estimated by solving a linear regression problem” – linear regression corresponds to an on-line convex optimization technique; Page 1, Introduction, “An alternating minimization (AM) algorithm is developed to solve the resultant bi-linear problem by alternatingly updating the unknown injections and line parameters, both of which are convex sub-problems. As the iterative objective cost is non-increasing during these updates, the proposed AM algorithm is guaranteed to converge.”);
(c) estimating an intermediate resistance diagonal matrix, by fixing the initial reactance diagonal matrix, based on the voltage profile at the first time-step using the on-line convex optimization technique (Page 2, System Modeling, “represent the network effects of respectively resistance r and reactance x, defined as … (2a) … (2b)… where diag(.) returns a diagonal matrix with the input vector as its diagonal entries” – equation 2b includes the diagonal matrix for resistance.; Page 2, System Modeling, “let t E {1, …, T} index the discrete-time samples of the feeder-wide variables. By applying (1) over two consecutive time instances, we obtain the voltage difference
PNG
media_image6.png
27
158
media_image6.png
Greyscale
” – vt corresponds to the voltage profile and ~vt is derived from the voltage profiles at consecutive time steps; Page 2, System Modeling, “the line parameters can be easily estimated by solving a linear regression problem” – linear regression corresponds to an on-line convex optimization technique; Page 1, Introduction, “An alternating minimization (AM) algorithm is developed to solve the resultant bi-linear problem by alternatingly updating the unknown injections and line parameters, both of which are convex sub-problems. As the iterative objective cost is non-increasing during these updates, the proposed AM algorithm is guaranteed to converge.”);
(d) estimating a subsequent reactance diagonal matrix, by fixing the intermediate resistance diagonal matrix, based on the voltage profile at next time-step using the on-line convex optimization technique (Page 2, System Modeling, “to relate ~vt with the unknown line parameters in vector theta := [r;x] E R2L” – Lin defines theta :=[r;x], meaning each updated theta[i] includes updated resistance values r and reactance values x. Because Lin defines R(r) and X(x) using diag(r) and diag(x), each updated theta[i] provides updated values for the resistance and reactance diagonal matrices. Applying Lin’s same fixed-block estimation at the next time-step while fixing the intermediate resistance diagonal matrix produces the claimed subsequent reactance diagonal matrix.; Page 2, System Modeling, “represent the network effects of respectively resistance r and reactance x, defined as … (2a) … (2b)… where diag(.) returns a diagonal matrix with the input vector as its diagonal entries” – equation 2a includes the diagonal matrix for reactance.; Page 2, System Modeling, “let t E {1, …, T} index the discrete-time samples of the feeder-wide variables. By applying (1) over two consecutive time instances, we obtain the voltage difference
PNG
media_image6.png
27
158
media_image6.png
Greyscale
” – vt corresponds to the voltage profile and ~vt is derived from the voltage profiles at consecutive time steps; Page 2, System Modeling, “the line parameters can be easily estimated by solving a linear regression problem” – linear regression corresponds to an on-line convex optimization technique; Page 1, Introduction, “An alternating minimization (AM) algorithm is developed to solve the resultant bi-linear problem by alternatingly updating the unknown injections and line parameters, both of which are convex sub-problems. As the iterative objective cost is non-increasing during these updates, the proposed AM algorithm is guaranteed to converge.”);
(e) estimating a subsequent resistance diagonal matrix, by fixing the intermediate reactance diagonal matrix, based on the voltage profile at the next time-step using the online convex optimization technique (Page 2, System Modeling, “to relate ~vt with the unknown line parameters in vector theta := [r;x] E R2L” – Lin defines theta :=[r;x], meaning each updated theta[i] includes updated resistance values r and reactance values x. Because Lin defines R(r) and X(x) using diag(r) and diag(x), each updated theta[i] provides updated values for the resistance and reactance diagonal matrices. Applying Lin’s same fixed-block estimation at the next time-step while fixing that intermediate reactance diagonal matrix produces the claimed subsequent resistance diagonal matrix.; Page 2, System Modeling, “represent the network effects of respectively resistance r and reactance x, defined as … (2a) … (2b)… where diag(.) returns a diagonal matrix with the input vector as its diagonal entries” – equation 2b includes the diagonal matrix for reactance.; Page 2, System Modeling, “let t E {1, …, T} index the discrete-time samples of the feeder-wide variables. By applying (1) over two consecutive time instances, we obtain the voltage difference
PNG
media_image6.png
27
158
media_image6.png
Greyscale
” – vt corresponds to the voltage profile and ~vt is derived from the voltage profiles at consecutive time steps; Page 2, System Modeling, “the line parameters can be easily estimated by solving a linear regression problem” – linear regression corresponds to an on-line convex optimization technique; Page 1, Introduction, “An alternating minimization (AM) algorithm is developed to solve the resultant bi-linear problem by alternatingly updating the unknown injections and line parameters, both of which are convex sub-problems. As the iterative objective cost is non-increasing during these updates, the proposed AM algorithm is guaranteed to converge.”);
(f) repeating steps (d) through (e), by considering the subsequent reactance diagonal matrix as the intermediate reactance diagonal matrix and the subsequent resistance diagonal matrix as the intermediate resistance diagonal matrix, at each subsequent step, until the plurality of time-steps is completed, to obtain a final reactance diagonal matrix and a final resistance diagonal matrix of the electrical distribution network (Page 3, Distribution Modeling Under Partial Observability, “we develop an alternating minimization (AM) based scheme to solve (7) by iteratively updating the two groups of variables”; Page 5, Conclusion and Future Work, “we developed an alternating minimization (AM) method to update each one of the two groups of unknowns by fixing the other. The proposed AM method leads to efficient updates by solving convex sub-problems and can converge to a stationary point” – once the method converges to a stationary point, the iteration arrives at final reactance and resistance diagonal matrices. Because each update uses the most recently updated value of the other parameter block in the next update, the alternating block update corresponds to a Gauss-Seidel-style iterative update.); and
(g) determining (i) a line resistance of each line, using the final resistance diagonal matrix, and (ii) a line reactance of each line, using the final reactance diagonal matrix, to predict the line-parameters of the electrical distribution network (Page 2, System Modeling, “For each line l, let (rl + jxl) denote the line impedance in p.u. with all line resistances and reactances respectively collected in vectors r and x … respectively resistance r and reactance x, defined as … (2a) … (2b) where diag(.) returns a diagonal matrix with the input vector as its diagonal entries.” – the line resistance and line reactance of each line are determined from the respective final diagonal matrices to predict the line parameters of the electrical distribution network.
Jereminov, Xu, Deaver, Yeh, and Lin are analogous art because they are from the same field of endeavor and contain functional similarities. They all relate to controlling electrical distribution networks using computational optimization.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above data-driven DER voltage regulation and line-parameter estimation framework, as taught by Jereminov, Xu, Deaver, and Yeh, and incorporate an alternating-minimization fixed-block update, as taught by Lin.
One of ordinary skill in the art would have been motivated to improve the accuracy of line-parameter estimation and voltage modeling while taking advantage of efficient convex subproblems that can converge to a stationary point, as suggested by Lin (Page 5, Conclusion and Future work).
Regarding claim 18, the combination of Jereminov, Xu, Deaver, Yeh, and Lin teaches all the limitations of the base claims as outlined above.
Yeh further teaches wherein the on-line convex optimization technique employs a first objective function and a first constraint set (Page 4, Algorithm 1, “Otherwise, query the model chasing algorithm for a new consistent parameter estimate…” – equation 7a corresponds to an objective function and equations 7b and 7d contain constraints.) while
(i)estimating the intermediate reactance diagonal matrix, by fixing the initial resistance diagonal matrix (see claim 4 rejection above) and
(ii) estimating the subsequent reactance diagonal matrix (see claim 4 rejection above), by fixing the intermediate resistance diagonal matrix, wherein the first objective function is to minimize a difference between the intermediate reactance diagonal matrix and the initial reactance diagonal matrix obtained at two consecutive time-steps of the plurality of time-steps (Page 4, Algorithm 1, equation 7a; Page 3, Algorithm, “The model chasing algorithm performs nested CBC, SEL which is the online problem of choosing a sequence of points within sequentially nested convex sets, with the aim of minimizing the sum of distances between the chosen points”), and wherein the first constraint set comprises:
(i) the intermediate reactance diagonal matrix obtained at a current time-step belongs to a predefined reactance convex compact set (Page 4, Algorithm 1, “compact convex uncertainty set for the model parameter…”; Page 4, Algorithm 1, Equation 7b; Page 4, Assumption 2, “The true model X* lies within a known compact, convex uncertainty set…” – Yeh constrains the current estimate ^xt to belong to a known compact convex uncertainty set x. In the combined system, the reactance-related estimate/diagonal matrix being updated is constrained to a corresponding predefined reactance convex compact set.),
(ii) an exogenous noise at each bus of the electrical distribution network should be bounded within a first predefined bound value (Page 4, Algorithm 1, “upper bound for noise…”; Page 4, Assumption 1, “The change in noise is bounded as…” – Yeh’s infinity-norm bound means each component of the noise vector, i.e., the noise associated with each bus, is bounded by the predefined value n.), and
(iii) a voltage of the electrical distribution network obtained at the current time-step should belongs to a predefined voltage convex compact set (Page 3, Model, “For all t >= 2, the voltage control algorithm aims to maintain v(t) within [v,v], ideally as close as possible to a “nominal” value vnom… … At each time step, buses may change their reactive power injection in order to regulate the voltage close to vnom”; Page 4, Algorithm 1, “limits on the squared voltage magnitude…”; Page 4, Algorithm 1, equation 8e – The interval [v, v] c Rn is a predefined voltage set. Because it is a closed bounded box in Rn, it is convex and compact, and Yeh constrains the voltage estimate to remain within that voltage set.).
Regarding claim 19, the combination of Jereminov, Xu, Deaver, Yeh, and Lin teaches all the limitations of the base claims as outlined above.
Yeh further teaches wherein the on-line convex optimization technique employs a second objective function and a second constraint set (Page 4, Algorithm 1, “Otherwise, query the model chasing algorithm for a new consistent parameter estimate…” – equation 7a corresponds to an objective function and equations 7b and 7d contain constraints. The same objective/constraint framework that is applied to the reactance parameter update is applied to the resistance parameter update as well.) while
(i) estimating the intermediate resistance diagonal matrix, by fixing the initial reactance diagonal matrix (see claim 4 rejection above) and
(ii) estimating the subsequent resistance diagonal matrix (see claim 4 rejection above), by fixing the intermediate reactance diagonal matrix, wherein the second objective function is to minimize a difference between the intermediate resistance diagonal matrix and the initial resistance diagonal matrix obtained at two consecutive time-steps of the plurality of time-steps (Page 4, Algorithm 1, equation 7a; Page 3, Algorithm, “The model chasing algorithm performs nested CBC, SEL which is the online problem of choosing a sequence of points within sequentially nested convex sets, with the aim of minimizing the sum of distances between the chosen points” - The same objective/constraint framework that is applied to the reactance parameter update is applied to the resistance parameter update as well.), and wherein the second constraint set comprises:
(i) the intermediate resistance diagonal matrix obtained at a current time-step belongs to a predefined resistance convex compact set (Page 4, Assumption 2, “The true model X* lies within a known compact, convex uncertainty set … X forms the initial “consistent set” (see Definition 1) for our consistent model chasing algorithm SEL” – Yeh constrains the current model estimate to belong to a known compact convex uncertainty set. In the combined system, the resistance diagonal matrix/resistance-related parameter block is the model estimate being updated, so it is constrained to a corresponding predefined resistance convex compact set.),
(ii) an exogenous noise at each bus of the electrical distribution network should be bounded within a first predefined bound value (Page 4, Algorithm 1, “upper bound for noise…”; Page 4, Assumption 1, “The change in noise is bounded as…” – Yeh’s infinity-norm bound means each component of the noise vector, i.e., the noise associated with each bus, is bounded by the predefined value n), and
(iii) a voltage of the electrical distribution network obtained at the current time-step should belongs to a predefined volage convex compact set (Page 3, Model, “For all t >= 2, the voltage control algorithm aims to maintain v(t) within [v,v], ideally as close as possible to a “nominal” value vnom… … At each time step, buses may change their reactive power injection in order to regulate the voltage close to vnom”; Page 4, Algorithm 1, “limits on the squared voltage magnitude…”; Page 4, Algorithm 1, equation 8e – The interval [v, v] c Rn is a predefined voltage set. Because it is a closed bounded box in Rn, it is convex and compact, and Yeh constrains the voltage estimate to remain within that voltage set.).
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu (Data-driven Voltage Regulation in Radial Power Distribution Systems, 2019) (hereinafter Xu) in view of Deaver, SR. et al. US 2012/0022713 A1 (hereinafter Deaver), Jereminov et al. US 2018/0158152 A1 (hereinafter Jereminov), and further in view of Yeh et al. (Online learning for robust voltage control under uncertain grid topology, 2023) (hereinafter Yeh).
Regarding claim 7, the combination of Xu, Deaver, and Jereminov teaches all the limitations of the base claims as outlined above.
Jereminov further teaches determining control signals by minimizing a non-convex objective function using a non-convex optimization technique subject to constraints (Par. [0056], “the objective function has to be minimized while satisfying the power flow in the grid … the power balance equations from Equations (2) and (3) used in traditional power flow represent highly nonlinear and non-convex constraints”; Par [0167], “OPF solver 3304 may solve the OPF problem using any one or more of the techniques described herein, including, but not limited to, a direct technique, an iterative technique, the A-stepping homotopy technique, relaxation techniques, and nonlinear optimization techniques, among others”; Par. [0168], “Output 3324 of OPF solver 3304 may include signals based on the solution determined by the OPF solver solving the OPF problem. Such signals can include suitable control signals for controlling generators 3308A and/or for controlling controllable pieces of power grid equipment 3308B.”).
Xu, Deaver, and Jereminov do not explicitly teach wherein the stable control signal for each bus that stabilizes the voltage of the electrical distribution network, at each time-step, based on the line-parameters, is determined, wherein the objective function comprises a voltage violation cost, a control signal for each bus, and a slack variable, and wherein the third constraints set comprises:
(i) the reactive power injection at each bus at the corresponding time-step should be bounded by a second predefined bound value and a third predefined bound value,
(ii) a predicted voltage of the electrical distribution network based on the line-parameters should be equal to the voltage of the electrical distribution network without an external voltage disturbance, and
(iii) the predicted voltage of the electrical distribution network should be bounded within a first predefined voltage bound value and a second predefined voltage bound value.
However, Yeh teaches wherein the stable control signal for each bus that stabilizes the voltage of the electrical distribution network, at each time-step (Page 3, Model, “At each time step, buses may change their reactive power injection in order to regulate the voltage close”; Page 4, Algorithm 1, “Update qc(t)<- qc(t-1)+u(t). Apply the control action u(t)”), based on the line-parameters, is determined (Page 3, Model, “R*, X* E Sn are computed from the network topology and line parameters”; Page 1, Abstract, “the online controller does not know the true network topology and line parameters, but instead learns them over time by narrowing down the set of network topologies and line parameters that are consistent with its observations and adjusting reactive power generation accordingly to keep voltages within desired safety limits”), wherein the objective function comprises a voltage violation cost, a control signal for each bus, and a slack variable (Page 3, Model, “To drive voltage back to the desired interval and minimize the voltage violation and control costs … one needs the exact system dynamics (5) for choosing the optimal reactive power injections”; Page 4, Algorithm 1, equation 8a -
PNG
media_image3.png
58
185
media_image3.png
Greyscale
is interpreted as the voltage violation cost,
PNG
media_image4.png
48
88
media_image4.png
Greyscale
is interpreted as the control signal cost, and
PNG
media_image5.png
47
52
media_image5.png
Greyscale
is interpreted as the slack variable cost.; Page 3, Algorithm, “One important detail in Algorithm 1 is the inclusion of the slack variable”), and wherein the third constraints set comprises:
(i) the reactive power injection at each bus at the corresponding time-step should be bounded by a second predefined bound value and a third predefined bound value (Page 4, Algorithm 1, “limits on the reactive power injection…”; Page 4, Algorithm 1, equation 8b – qc(t-1)+u is interpreted as the reactive power injection after applying the control signal at the current time-step),
(ii) a predicted voltage of the electrical distribution network based on the line-parameters should be equal to the voltage of the electrical distribution network without an external voltage disturbance (Page 4, Algorithm 1, equation 8c is the predicted voltage based on the model/line-parameter estimate, while the actual transition includes external disturbance/noise w(t). So Yeh’s predicted-voltage equality corresponds to the voltage without the external disturbance.”), and
(iii) the predicted voltage of the electrical distribution network should be bounded within a first predefined voltage bound value and a second predefined voltage bound value (Page 4, Algorithm 1, equation 8e; Page 4, Algorithm 1, “limits on the squared voltage magnitude…”).
Xu, Deaver, Jereminov, and Yeh are analogous art because they are from the same field of endeavor and contain functional similarities. They all relate to controlling electrical distribution networks using computational optimization.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above voltage regulation/control system, as taught by Xu, Deaver, and Jereminov, and incorporate a time-step based, line-parameter-based voltage control objective terms and constraint set for computing the control action, as taught by Yeh.
One of ordinary skill in the art would have been motivated to improve voltage control stability when topology/line parameters are unknown or incorrect, as suggested by Yeh (Page 1, Abstract/Introduction).
Regarding claim 14, the combination of Jereminov, Xu, and Deaver teaches all the limitations of the base claims as outlined above.
Jereminov further teaches determining control signals by minimizing a non-convex objective function using a non-convex optimization technique subject to constraints (Par. [0056], “the objective function has to be minimized while satisfying the power flow in the grid … the power balance equations from Equations (2) and (3) used in traditional power flow represent highly nonlinear and non-convex constraints”; Par [0167], “OPF solver 3304 may solve the OPF problem using any one or more of the techniques described herein, including, but not limited to, a direct technique, an iterative technique, the A-stepping homotopy technique, relaxation techniques, and nonlinear optimization techniques, among others”; Par. [0168], “Output 3324 of OPF solver 3304 may include signals based on the solution determined by the OPF solver solving the OPF problem. Such signals can include suitable control signals for controlling generators 3308A and/or for controlling controllable pieces of power grid equipment 3308B.”).
Jereminov, Xu, and Deaver do not explicitly teach wherein the stable control signal for each bus that stabilizes the voltage of the electrical distribution network, at each time-step, based on the line-parameters, is determined, wherein the objective function comprises a voltage violation cost, a control signal for each bus, and a slack variable, and wherein the third constraints set comprises:
(i) the reactive power injection at each bus at the corresponding time-step should be bounded by a second predefined bound value and a third predefined bound value,
(ii) a predicted voltage of the electrical distribution network based on the line-parameters should be equal to the voltage of the electrical distribution network without an external voltage disturbance, and
(iii) the predicted voltage of the electrical distribution network should be bounded within a first predefined voltage bound value and a second predefined voltage bound value.
However, Yeh teaches wherein the stable control signal for each bus that stabilizes the voltage of the electrical distribution network, at each time-step (Page 3, Model, “At each time step, buses may change their reactive power injection in order to regulate the voltage close”; Page 4, Algorithm 1, “Update qc(t)<- qc(t-1)+u(t). Apply the control action u(t)”), based on the line-parameters, is determined (Page 3, Model, “R*, X* E Sn are computed from the network topology and line parameters”; Page 1, Abstract, “the online controller does not know the true network topology and line parameters, but instead learns them over time by narrowing down the set of network topologies and line parameters that are consistent with its observations and adjusting reactive power generation accordingly to keep voltages within desired safety limits”), wherein the objective function comprises a voltage violation cost, a control signal for each bus, and a slack variable (Page 3, Model, “To drive voltage back to the desired interval and minimize the voltage violation and control costs … one needs the exact system dynamics (5) for choosing the optimal reactive power injections”; Page 4, Algorithm 1, equation 8a -
PNG
media_image3.png
58
185
media_image3.png
Greyscale
is interpreted as the voltage violation cost,
PNG
media_image4.png
48
88
media_image4.png
Greyscale
is interpreted as the control signal cost, and
PNG
media_image5.png
47
52
media_image5.png
Greyscale
is interpreted as the slack variable cost.; Page 3, Algorithm, “One important detail in Algorithm 1 is the inclusion of the slack variable”), and wherein the third constraints set comprises:
(i) the reactive power injection at each bus at the corresponding time-step should be bounded by a second predefined bound value and a third predefined bound value (Page 4, Algorithm 1, “limits on the reactive power injection…”; Page 4, Algorithm 1, equation 8b – qc(t-1)+u is interpreted as the reactive power injection after applying the control signal at the current time-step),
(ii) a predicted voltage of the electrical distribution network based on the line-parameters should be equal to the voltage of the electrical distribution network without an external voltage disturbance (Page 4, Algorithm 1, equation 8c is the predicted voltage based on the model/line-parameter estimate, while the actual transition includes external disturbance/noise w(t). So Yeh’s predicted-voltage equality corresponds to the voltage without the external disturbance.”), and
(iii) the predicted voltage of the electrical distribution network should be bounded within a first predefined voltage bound value and a second predefined voltage bound value (Page 4, Algorithm 1, equation 8e; Page 4, Algorithm 1, “limits on the squared voltage magnitude…”).
Jereminov, Xu, Deaver, and Yeh are analogous art because they are from the same field of endeavor and contain functional similarities. They all relate to controlling electrical distribution networks using computational optimization.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above voltage regulation/control system, as taught by Jereminov, Xu, and Deaver, and incorporate a time-step based, line-parameter-based voltage control objective terms and constraint set for computing the control action, as taught by Yeh.
One of ordinary skill in the art would have been motivated to improve voltage control stability when topology/line parameters are unknown or incorrect, as suggested by Yeh (Page 1, Abstract/Introduction).
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Coffrin et al. [US 2015/0088439 A1] teaches an alternating current (AC) power flow analysis in an electrical power network
Sarwat et al. [US 10,326,280 B1] teaches systems and methods for integrating large-scale distributed gridconnected Renewable Energy Source (RES) power plants across a smart grid and sustaining their benefits, through a holistic controller that comprises predictive and prescriptive computation models to address and mitigate three pressing concerns facing the high-penetration scenarios of the RESs into the grid through three technological modules.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER XU whose telephone number is (571)272-0792. The examiner can normally be reached Monday-Friday 9am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached at (571) 272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/PETER XU/ Examiner, Art Unit 2119
/ZIAUL KARIM/ Primary Examiner, Art Unit 2119
Prosecution Insights