Notice of Pre-AIA or AIA Status
Claims 1-20 are currently presented for Examination.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed on 07/01/2026 has been entered and considered by the examiner. By the
amendment, claims 1, 7, 13 and 17 are amended and claims 19-20 are newly added. In view of the amendment made, the previous 101 rejection is still maintained, and the prior rejection is modified. See office action.
Applicant arguments 101
Here, the claims recite a specific technical solution to the technical problem of computational inefficiency in hosting capacity analysis for distribution systems with distributed energy resources. The claims do not merely recite an abstract idea implemented on a generic computer; rather, they recite specific technical features including: (1) iterative re-evaluation and updating of node clustering based on power flow simulation results, (2) using power flow results to update the feeder model of the distribution system, and (3) iteratively adjusting DER sizing at specific physical nodes using binary search based on detected constraint violations to update the feeder model. These features work together as an integrated technical solution that improves the functioning of distribution system analysis tools. The Ex Parte Desjardins decision emphasized that claims providing a technological improvement to a technological field are not directed to an abstract idea. The present claims provide such a technological improvement by establishing closed-loop feedback mechanisms between simulation results and both node clustering and feeder model updates, enabling more efficient and accurate hosting capacity analysis for physical distribution systems. This is not merely applying mathematical concepts to a technological field, but rather represents a specific technical architecture for managing the complexity of distribution system planning with distributed energy resources.
Regarding Applicant's citation of Ex Parte Desjardins, that case is very distinguished from the present claims. In Desjardins, the claims were found to provide a specific improvement to an underlying technological field. Unlike the claims in Desjardins, which involved the physical transformation and control of tangible technological components, the present claims manipulate data and models. The "closed-loop feedback" argued by Applicant occurs within a software simulation environment between abstract data constructs (clustering logic, a software feeder model, and a mathematical binary search algorithm). It does not involve feedback or control of a physical, real-world electrical grid. Automating a mathematical or mental process using a computer does not change the nature of the underlying abstract idea. Here, the claimed invention improves the computational efficiency of a mathematical model rather than improving the physical distribution system itself. An improvement to the speed or efficiency of a mental process and mathematical concepts on a generic computer does not constitute an improvement to a technological field or computer functionality under MPEP 2106.04(a)(1). Rather, it remains an optimization of an abstract idea and improvement of abstract idea itself. Examiner found claims does not include additional elements that are sufficient to amount into a practical application significantly nor the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. Thus, the claims are directed to the abstract idea of a mental process/mathematical model and fail to recite an inventive concept, the rejection under 35 U.S.C. 101 is still maintained.
Applicant arguments
Applicant argues claim 19 and 20 are eligible.
Examiner response
Regarding claim 19- The recitation of receiving real-world data which is recited at a high level of generality (i.e., as a general means of obtaining data), and fall under the insignificant pre-solution activity. (See MPEP 2106.05(g)) and recognized it as generic computer functions that is well‐understood, routine, and conventional functions See MPEP 2106.05(d)(II) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); The steps of collecting data, updating a model, and calculating a capacity threshold based on operational rules are steps that can be performed entirely in the human mind or by using a pen and paper. The human mind can receive measurements, adjust a conceptual map or formula, and compute an allowable limit. Claim 20 limitation “providing hosting capacity analysis results to support... review procedures." This does not require taking any physical corrective action, dynamically controlling the SCADA hardware, or physically altering the grid's infrastructure. It merely generates information to "support" a human decision-making process ("interconnection review procedures"). limiting an abstract idea to a specific technological environment (DER interconnection review and distribution network operations) Therefore, the claims are directed to an abstract idea.
Applicant prior art arguments
Applicant respectfully submits that none of the cited references, either alone or in combination, teaches or suggests the iterative feedback loop wherein the feeder topology analysis "iteratively re-evaluates and updates the node clustering based on power flow simulation results" as recited by claims 1, 7, and 13 as amended.
Examiner response
In view of applicant amendment and argument regarding the newly added limitation, Examiner withdraw the Conti reference and use the new reference Xiao to teach the limitation “iteratively re-evaluates and updates the node clustering based on power flow simulation results". See office action
Applicant argument
The Examiner has alleged that the combination of Alrushoud, Bletterie, Guddanti, and Conti teaches or suggests all limitations of the claims. Applicant respectfully disagrees. The Federal Circuit has stated that "rejections on obviousness cannot be sustained with mere conclusory statements; instead, there must be some articulated reasoning with some rational underpinning to support the legal conclusion of obviousness." In re Kahn, 441 F.3d 977, 988, 78 USPQ2d 1329, 1336 (Fed. Cir. 2006).
Examiner response
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, Alrushoud’s teaches the generation of testing scenarios based on system loads and renewable generation. Secondary reference Bletterie's teaches using PCA to group similar feeders/scenarios to manage the complexity of this data (see table 2-Bletterie) for hosting capacity studies. It would have been obvious to one of ordinary skill in the art to apply the PCA-based grouping technique of Bletterie to the scenario generation process of Alrushoud’s to improve computational efficiency in determining hosting capacity, as both references are in the same technical field of distribution network analysis. Further, Guddanti used to perform binary search and to calculate final DER maximum integration capacity of Alrushoud’s. Also, all these references are in the same field of invention, and their purpose is same for maximizing hosting capacity for distribution system network. New reference Xiao is added for updating the node clustering to have optimal operation of active distribution networks (ADNs), especially leading to more serious overvoltage problems. Thus, the rejection is maintained in view of Guddanti, Bletterie and Xiao.
Claim Objections
Claims 1, 7, 13 and 19 objected to because of the following informalities: The claims have numerous issues with antecedent basis. The Examiner suggests amending the claims such that the first recitation of each distinct element uses articles such as “a”/”an”, later recitations referring back to the same distinct element uses articles such as “the”/”said”, to use disambiguating modifiers (e.g., first, second, etc.) when there are multiple distinct elements with the same base term, and that the use of modifiers for each distinct element is kept consistent. Below is a non-exhaustive list of examples of these issues: for claims 1, 7 and 13 are “the power flow results” should have been “the power flow simulation results” and claim 19 “the distribution feeder model”. Appropriate correction is required.
Claim Rejections - 35 USC §101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. These claims are directed to an abstract idea without significantly more.
(Step 1) Is the claims to a process, machine, manufacture, or composition of matter?
Claims: 1-6 and 19-20 are directed to method or process that falls on one of statutory category.
Claims: 7-13 are directed to non-transitory computer readable medium that falls on one of statutory category i.e., manufacture.
Claims: 14-18 are directed to apparatus or machine that falls on one of statutory category.
Step 1 Prong one
Claim 1, 7 and 14 recites
executing feeder topology analysis on the topology information to generate output analysis, wherein the feeder topology analysis groups nodes based on DER system integration characteristics to reduce searching dimension for potential DER deployment locations by clustering nodes with similar integration impacts, wherein the feeder topology analysis uses electrical distance from a feeder head, voltage sensitivity parameters comprising sensitivity of node voltage due to integrating DER size for active power dV/dP and the sensitivity of the node voltage due to the integrating DER size for reactive power dV/dQ, and feeder branch topology as clustering parameters to cluster the nodes and wherein the feeder topology analysis iteratively re- evaluates and updates the node clustering based on power flow simulation results; (Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process, then it falls within the “Mental Process” grouping of abstract ideas. The process involves abstracting the physical network, using abstract algorithms to analyze it, and then producing abstract results that represent the network's characteristics. A person can take power flow data, manually group the nodes (clustering), run a manual math formula (simulation), and rewrite the groups on a piece of paper. A process that can be performed in the human mind or using pen and paper, such as organizing data, which is an abstract idea.)
executing scenario management on the system profiles to generate simulation scenario sets, wherein the scenario management includes receiving planning data comprising stochastic system load profiles and renewable energy generation profiles, randomly generating testing scenarios, and processing the scenarios using principal component analysis to group scenarios with similar principal components; (A human first abstracts the real-world power grid into a conceptual system model. The process requires thinking, analyzing, and evaluating system profiles to create diverse scenario sets. Abstract mathematical concepts like stochastic profiles and PCA are applied to the model's data, which is itself an abstraction. PCA is a mathematical technique for converting a set of observations of correlated variables into a set of values of linearly uncorrelated variables called "principal components". The process of grouping scenarios based on "similar principal components" is a clustering of abstract mathematical ideas. Thus, it falls under the combination of mental process and mathematical concepts of abstract ideas.) and
loading and executing a simulation flow from the simulation scenario sets and the output analysis, wherein the simulation flow uses power flow tools to execute quasi-static time-series simulation that provides accurate estimation of system status, outputting node voltages and line current, wherein the power flow results from the simulation flow are used to update a feeder model of the distribution system. (The process described is a combination of mental processes and mathematical concepts of abstract ideas. The simulation of a quasi-static time-series (QSTS) flow, from scenario setup to output analysis, involves both human decision-making and the application of abstract mathematical principles. A human engineer uses abstract thinking to simplify the real-world power grid into a conceptual model. This involves mentally identifying key components like nodes and lines and determining the appropriate level of detail to represent the complex physical system. An engineer must decide what real-world events to model, such as load fluctuations, renewable energy output (e.g., PV), and control actions. This is a mental process of structuring an experiment and setting its parameters. The act of loading, executing, and analyzing the simulation is a mental process because it requires cognitive functions like problem-solving, decision-making, and pattern recognition. It's essentially "running" a mental experiment with the simulated system. At the core of the quasi-static simulation are abstract mathematical models of electrical circuits.)
wherein the simulation flow iteratively adjusts DER sizing at specific physical nodes in the distribution system using binary search based on detected constraint violations, and provides the DER sizing adjustments to update the feeder model, to determine maximum allowable DER installation capacity that can be accommodated on specific distribution system nodes without violating system operation constraints. (The steps of simulation, detecting violations, and adjusting values based on those detections are “mental processes"—concepts that could theoretically be performed in the human mind, or by a human using a pen and paper such as evaluating, observing, and checking constraints. The use of "binary search" to find an optimal value is considered a mathematical algorithm or formula, which falls under the "mathematical concepts" grouping of abstract ideas. Thus, this limitation is considered an abstract idea because it combines mathematical concepts and mental processes The process merely takes the abstract idea and says "do it on a computer" or "update a feeder model”.)
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception
into a practical application?
In accordance with Step 2A, Prong 2, the judicial exception is not integrated into a practical application. In particular, claim 1, 7 and 14 recites the additional elements of receiving data input comprising system profiles and topology information of a distribution system comprising a plurality of distributed energy resource (DER) nodes in an interconnect which is recited at a high level of generality (i.e., as a general means of obtaining data), and fall under the insignificant pre-solution activity. (See MPEP 2106.05(g)) The additional element of a non-transitory computer readable medium, storing instructions for executing a process in claim 7 and a management apparatus, the apparatus comprising: a processor in claim 14 is merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). Thus, a management apparatus configured to manage a distribution system over a network, the distribution system comprising a plurality of distributed energy resource (DER) nodes in an interconnect is no more than generally linking the judicial exception to a field of use as discussed in MPEP 2106.05(h). The claim is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
In view of Step 2B, the claim as a whole does not amount to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. In particular, claim 1, 7 and 14 recites the additional elements of receiving data input comprising system profiles and topology information of a distribution system comprising a plurality of distributed energy resource (DER) nodes in an interconnect which is recited at a high level of generality (i.e., as a general means of obtaining data), and fall under the insignificant pre-solution activity. (See MPEP 2106.05(g)) and recognized it as generic computer functions that is well‐understood, routine, and conventional functions See MPEP 2106.05(d)(II) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs.,
Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); The additional element of a non-transitory computer readable medium, storing instructions for executing a process in claim 7 and a management apparatus, the apparatus comprising: a processor in claim 14 is merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). Thus, a management apparatus configured to manage a distribution system over a network, the distribution system comprising a plurality of distributed energy resource (DER) nodes in an interconnect is simply linking the judicial exception to the field of use as discussed in MPEP 2106.05(h). Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim does not include specific technological steps, use particular tools or data processing techniques that are novel and non-obvious that’s more likely to be considered a concrete application. Thus, claims 1 and 19 are not patent eligible.
Claim 2, 8 and 14 further recites wherein the executing the feeder topology analysis comprises: executing clustering on the topology information to determine feeder system node clustering; and providing the feeder system node clustering as an initial one of the output analysis; for the execution of the simulation flow from the simulation scenario sets and the output analysis: evaluating the feeder system node clustering from power flow simulation provided from the simulation flow; adjusting feeder system node partitioning based on the evaluation; and providing the adjusted partitioning of feeder system nodes as the output analysis. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process, then it falls within the “Mental Process” grouping of abstract ideas. The described process outlines a general strategy for analyzing and optimizing feeder topology using simulations and adaptive partitioning. The core idea involves moving from an initial, abstract representation of the network (clustering) to iteratively refining that representation based on simulated performance, leading to a more efficient or effective network configuration. This entire approach deals with abstract concepts and models. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 3, 9 and 15 further recites wherein the executing the scenario management comprises: randomly generating simulation scenarios from combinations of ones of the system profiles, the system profiles comprising time-series system load profiles and generation profiles of the plurality of DER nodes; and executing clustering on the generated simulation scenarios to generate simulation scenario sets. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process, then it falls within the “Mental Process” grouping of abstract ideas. The generation and clustering of simulation scenarios, especially when involving human interpretation and decision-making, are deeply intertwined with mental processes. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 4, 10 and 16 further recites monitoring system power flow with the plurality of DER nodes on the interconnect from the execution of the simulation flow; and for a detection of a constraint violation in the monitored power flow, noting the constraint violation for hosting capacity output and changing the simulation flow to another simulation flow from the simulation scenario sets. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process, then it falls within the “Mental Process” grouping of abstract ideas. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 5, 11 and 17 further recites for execution of the all-simulation flows from the simulation scenario sets, providing the hosting capacity output. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process, then it falls within the “Mental Process” grouping of abstract ideas. While the actual execution of the simulation flows is performed by a computer, the process of setting up the simulation, interpreting its results, and using the output for decision-making is heavily reliant on mental processes. Specifically, a human is needed to understand the scenario, define the parameters, analyze the output, and translate it into meaningful insights. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 6, 12 and 18 further recites executing a snapshot of performance metrics of the simulation flow to determine whether a constraint violation has occurred; and utilizing a variable-width sliding window on the determined constraint violations to determine whether the constraint violation is false. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process, then it falls within the “Mental Process” grouping of abstract ideas. It essentially mentally simulating the system's behavior and checking if the simulated outcome violates any constraints. The variable-width sliding window and the evaluation of whether a violation is false likely involve mental manipulation of information over time and logical reasoning, which are classic cognitive processes. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 19 further recites updating the feeder model of the distribution system based on the real-world measurement data through a topology mapping module to calibrate the distribution feeder model. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process, then it falls within the “Mental Process” grouping of abstract ideas. Taking measurement data, comparing it to a model, and adjusting the model (topology mapping/calibration) is viewed as a mathematical exercise that a person can do with a pencil, paper, and a calculator. The additional elements of receiving real-world measurement data from the distribution system, the real-world measurement data comprising at least one of sensor measurements, metering data, and supervisory control and data acquisition (SCADA) data from the plurality of DER nodes which is recited at a high level of generality (i.e., as a general means of obtaining data), and fall under the insignificant pre-solution activity. (See MPEP 2106.05(g)) and recognized it as generic computer functions that is well‐understood, routine, and conventional functions See MPEP 2106.05(d)(II) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs. Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 20 further recites providing hosting capacity analysis results to support distributed energy resource interconnection review procedures for distribution network operators, wherein the hosting capacity analysis results comprise maximum allowable DER installation capacity that can be accommodated on specific distribution system nodes without violating system operation constraints. Providing hosting capacity analysis results (the "maximum allowable DER installation capacity") is merely the outputting of a calculated numerical value. It does not recite any physical transformation, nor does it improve the functioning of the underlying computer infrastructure or the physical grid itself. It merely provides information to a human operator (the DNO) to assist them in making regulatory or engineering decisions. The claim recites the mental process of receiving power system data, performing a mathematical/mental calculation to determine a maximum allowable DER installation capacity without violating operational limits, and outputting the numerical result. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process, then it falls within the “Mental Process” grouping of abstract ideas. Claim does not integrate the abstract idea into a practical application because it merely recites generic, conventional computer components (e.g., a distribution network operator) to perform the mathematical calculations. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
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 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.
6. The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
7. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
8. Claim 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Alrushoud, et al. ("Impacts of PV capacity allocation methods on distribution planning studies." 2020 IEEE/PES Transmission and Distribution Conference and Exposition (T&D). IEEE, 2020.) in view of Bletterie et al. ("On the classification of low voltage feeders for network planning and hosting capacity studies." Energies 11.3 (2018): 651.) and further in view of Guddanti et al. ("Better data structures for co-simulation of distribution system with GridLAB-D and Python." 2020 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2020) and still further in view of Xiao et al. ("Multiple Spatiotemporal Characteristics-Based Zonal Voltage Control for High Penetrated PVs in Active Distribution Networks." Energies 13.1 (2020): 249.)
Regarding claim 1
Alrushoud teaches a method, comprising:
receiving data input comprising system profiles and topology information of a distribution system comprising a plurality of distributed energy resource (DER) nodes in an interconnect; (See section II.A- In this study, IEEE 123-bus feeder (see Fig. 1) and an actual feeder (see Fig. 2) have been used. The IEEE 123-bus feeder has 91 residential load nodes at 4.16 kV voltage level and the real life distribution feeder has 48 residential load nodes at 24 kV voltage level. The original feeder data contains only a peak load at each node. Thus, a load allocation method introduced in [7] is applied to generate load profiles at each load node and determine the total number of houses t each node. In order to conduct a time-series study, every load node on the feeder needs to be modeled down to the residential home level at 1-minute resolution.
executing feeder topology analysis on the topology information to generate output analysis; (see section III.B-The IEEE 123-bus feeder is first divided into 10 zones using Proximity Analysis, as shown in Fig. 11. Note that the number of zones can vary depending on the feeder topology, load types, and the length of the feeder. For each zone, 100% customers with PV systems is assumed and the voltage profile within that zone is recorded under different PV capacity allocations and then the voltage change between the case with no PV in the zone with the PV case is presented in Fig. 12. From the results, we made the following observations: • The OCB PV allocation method yields smaller voltage changes compared with random or 1-size PV allocation methods.) Zonal voltage changes are correlated to electrical connection between zones and load characteristics. Criterions can be set up to identify the zones that can host more PVs or less PVs in order to increase the overall hosting capacity on the feeder. As expected, if a zone is located further away from the substation or a voltage regulator, voltage changes increases. Thus, we can use this method to identify the zones that are more vulnerable to voltage violations with concentrated PV installations.)
wherein the feeder topology analysis groups nodes based on DER system integration characteristics to reduce searching dimension for potential DER deployment locations by clustering nodes with similar integration impacts; (See fig 1, 11 and see section III.B-In a community, the installation of rooftop PV systems exhibits zonal characteristics because of the social contacts among neighbors. To model this phenomena in PV technology diffusion process, we applied a zonal PV allocation method in the distribution planning study. The IEEE 123-bus feeder is first divided into 10 zones using Proximity Analysis, as shown in Fig. 11. Note that the number of zones can vary depending on the feeder topology, load types, and the length of the feeder. Zonal voltage changes are correlated to electrical connection between zones and load characteristics. Criterions can be set up to identify the zones that can host more PVs or less PVs in order to increase the overall hosting capacity on the feeder.)
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Examiner note: The use of proximity analysis to create zones for modeling PV diffusion serves same as groups are based on DER system integration characteristics to reduce the search dimension. By dividing the feeder into zones, the complexity of planning can be reduced. The clustering nodes and dividing distribution systems into zones or sub-networks is a strategy used to manage the complexity of integrating distributed energy resources (DERs) and to improve voltage control or system resilience. This approach can help identify areas with specific challenges or opportunities for DER deployment, which can reduce the computational effort for investment optimization.
wherein the feeder topology analysis uses electrical distance from a feeder head; (see section II.D see fig 1, 11-PV Hosting capacity is defined as the maximum amount of PV that a feeder can accommodate before adverse impacts occur on a distribution feeder. This value will be dependent on the feeder characteristics, PV size and location, monitoring criteria, and load. see section III. B-Zonal-Based analysis -Note that the number of zones can vary depending on the feeder topology, load types, and the length of the feeder. For each zone, 100% customers with PV systems is assumed and the voltage profile within that zone is recorded under different PV capacity allocations and then the voltage change between the case with no PV in the zone with the PV case is presented in Fig. 12. From the results, we made the following observations: The OCB PV allocation method yields smaller voltage changes compared with random or 1-size PV allocation methods. Zonal voltage changes are correlated to electrical connection between zones and load characteristics. Criterions can be set up to identify the zones that can host more PVs or less PVs in order to increase the overall hosting capacity on the feeder. As expected, if a zone is located further away from the substation or a voltage regulator, voltage changes increases. Thus, we can use this method to identify the zones that are more vulnerable to voltage violations with concentrated PV installations.)
Examiner note: Fig 11 shows clustering nodes as zones that represent feeder characteristics, PV size and location, monitoring criteria, and load for each node. Zone located away from the substation is considered as electrical distance. Also see Secondary reference (Bletterie-equation 2 and 3 -dk the distance between node k and the secondary substation)
executing scenario management on the system profiles to generate simulation scenario sets; (See section II.B-For every load node, randomly draw weekly load profiles from the load pool and add them up until the aggregated load is bounded by Pi upper and lower ranges as shown in Fig.4. see fig 6 and see section II.D-The methodology to systematically simulate stochastic PV deployment scenarios is described in Fig. 6. We selected 100 PV deployment scenarios (i.e. 𝑀𝑀 = 100). Each scenario is unique in the order that PVs are deployed and the maximum nodal voltage is recorded for each scenario by performing time series power flow. The location of the PV systems is also different in each scenario, which is the main random variable. For a particular PV deployment scenario, the PV penetration level is increased from 0% - 100% in a step of 5% (i.e.𝑁𝑁 = 20). The PV penetration is the ratio between total PV installed capacity in a feeder (kWp) and the maximum apparent power on the feeder)
wherein the scenario management includes receiving planning data comprising stochastic system load profiles and renewable energy generation profiles, randomly generating testing scenarios, (See section I-The stochastic analysis captures the uncertainty in the size and location of future PV installations by populating PV deployment scenarios in a random fashion The primary contribution of this paper is to correlate the residential load characteristics with the rooftop PV capacity selection. We demonstrated that the distribution grid can host more PV systems if households can select PV systems based on their consumption patterns as well as retail rate structures. see section II. A-B- In this study, IEEE 123-bus feeder (see Fig. 1) and an actual feeder (see Fig. 2) have been used. The IEEE 123-bus feeder has 91 residential load nodes at 4.16 kV voltage level and the real life distribution feeder has 48 residential load nodes at 24 kV voltage level. The original feeder data contains only a peak load at each node. Thus, a load allocation method introduced in is applied to generate load profiles at each load node and determine the total number of houses at each node. Because each household has its unique consumption characteristics, the house owner who wants to install PV on his rooftop will most likely consider optimizing the PV systems to either minimizing monthly bill or maximizing revenues when providing grid services. The wide deployment of smart meters makes household yearly load profiles available and accessible to homeowners. Thus, a cost-benefit based method has been conducted to find the optimal PV size for a specific house using yearly household load profiles. SEE FIG 6 and section II.D-The methodology to systematically simulate stochastic PV deployment scenarios is described in Fig. 6. We selected 100 PV deployment scenarios (i.e. 𝑀𝑀 = 100 ). Each scenario is unique in the order that PVs are deployed and the maximum nodal voltage is recorded for each scenario by performing time series power flow.)
loading and executing a simulation flow from the simulation scenario sets and the output analysis. (See section III-The analysis is performed in OpenDSS, an open-source system simulation software developed by EPRI [11]. OpenDSS is controlled through the COM interface by MATLAB. MATLAB is used for creating and iterating through each PV scenario as well as for the analysis of the results. OpenDSS is used to solve the time series power flow for each case. IEEE 123-bus feeder and an actual distribution feeder provided by a local utility were used in the simulation process. See section III.B- In a community, the installation of rooftop PV systems exhibits zonal characteristics because of the social contacts among neighbors. To model this phenomenon in PV technology diffusion process, we applied a zonal PV allocation method in the distribution planning study)
wherein the simulation flow uses power flow tools to execute quasi-static time-series simulation that provides accurate estimation of system status, outputting node voltages unbalances, and thermal loading of distribution transformers and lines can occur. Although several criteria, such as voltage, current, power quality and protection, are used to evaluate PV hosting capacity, the widely used criterion is voltage violations. Thus, over-voltage criterion is adopted in this paper for evaluating hosting capacity because it is one of the major concerns of the utilities and an important limiting factor to how much PV generation can be supported on a feeder. section II.D and step 2- Each scenario is unique in the order that PVs are deployed and the maximum nodal voltage is recorded for each scenario by performing time series power flow. Perform load allocation method for all load nodes to match the feeder head load profile with the household load profiles in the load pool. This step will prepare the nodal load profiles for running quasi-static power flow simulations.)
Alrushoud does not teach processing the testing scenarios using principal component analysis to group scenarios with similar principal components and wherein the simulation flow uses power flow tools to execute quasi-static time-series simulation that provides outputting line current, wherein the power flow results from the simulation flow are used to update a feeder model of the distribution system; wherein the simulation flow iteratively adjusts DER sizing at specific nodes in the distribution using binary search based on detected constraints violations and provides the DER sizing adjustments to update the feeder model, to determine maximum allowable DER installation capacity that can be accommodated on specific distribution system nodes without violating system operation constraints and voltage sensitivity parameters comprising sensitivity of node voltage due to integrating DER size for active power dV/dP and the sensitivity of the node voltage due to the integrating DER size for reactive power dV/dQ and wherein the feeder topology analysis iteratively re- evaluates and updates the node clustering based on power flow simulation results;
In the related field of invention, Bletterie teaches processing the testing scenarios using principal component analysis to group scenarios with similar principal components; (see abstract- The integration of large amounts of generation into distribution networks faces some limitations. By deploying reactive power-based voltage control concepts (e.g., volt/var control with distributed generators), the voltage rise caused by generators can be partly mitigated. As a result, the network hosting capacity can be accordingly increased, and costly network reinforcement might be avoided or postponed. Finally, the results of the clustering and their suitability for the considered problem (reflect the behavior of feeders in terms of hosting capacity) have been analyzed through an external validation. For this, the classes, which had also been used in the classification, are used to measure how close the clustering is to the predetermined classes (voltage or current-constrained feeder). The results of this external validation for a clustering with four clusters is shown on Figure 13 (projection on the first two principal components). See fig 3-4)
electrical distance from the feeder header (Bletterie-equation 2 and 3 dk the distance between node k and the secondary substation)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of managing a distribution system over a network as disclosed by Alrushoud to include processing the testing scenarios using principle component analysis to group scenarios with similar principle components as taught by Bletterie in the system of Alrushoud in order to investigate to which extent it is possible to predict the hosting capacity constraint (voltage or current) of low voltage feeders on the basis of a large network. As a result, the network hosting capacity can be accordingly increased, and costly network reinforcement might be avoided or postponed. (See abstract, Bletterie)
The combination of Alrushoud and Bletterie does not teach wherein the simulation flow uses power flow tools to execute quasi-static time-series simulation that provides outputting line current, wherein the power flow results from the simulation flow are used to update a feeder model of the distribution system; wherein the simulation flow iteratively adjusts DER sizing at specific nodes in the distribution using binary search based on detected constraints violations and provides the DER sizing adjustments to update the feeder model, to determine maximum allowable DER installation capacity that can be accommodated on specific distribution system nodes without violating system operation constraints and voltage sensitivity parameters comprising sensitivity of node voltage due to integrating DER size for active power dV/dP and the sensitivity of the node voltage due to the integrating DER size for reactive power dV/dQ and wherein the feeder topology analysis iteratively re- evaluates and updates the node clustering based on power flow simulation results;
In the related field of invention, Guddanti teaches wherein the simulation flow uses power flow tools to execute quasi-static time-series simulation that provides outputting line current (see section Introduction- this paper uses GridLAB-D, developed by the U.S. Department of Energy, as an example to show how to provide the needed flexibility to speed up the analysis for colleagues based on our codes and framework. The reason to choose GridLAB-D is due to its nice capabilities, such as (3) time-series power flow simulations. And table 1-line currents cannot violate thermal line limits)
wherein the power flow results from the simulation flow are used to update a feeder model of the distribution system; (See fig 2 and section III.B-we allocate hourly load profiles (24 hourly values) to all the loads in the IEEE-123 bus system. For each time instant (each hour), we increase the size of the integrating DER value by a constant up-size step value (α) until any violation from Table. I is recorded. When a violation is recorded then the algorithm returns the previous step integrating DER value with no violation as the hosting capacity value for the DER location. See fig 2 (element-feeder model modification and GridLAB-D power flow simulation))
wherein the simulation flow iteratively adjusts DER sizing at specific nodes in the distribution using binary search based on detected constraints violations and provides the DER sizing adjustments to update the feeder model, to determine maximum allowable DER installation capacity that can be accommodated on specific distribution system nodes without violating system operation constraints. (See section III.B and fig 2- For each time instant (each hour), we increase the size of the integrating DER value by a constant up-size step value (α) until any violation from Table. I is recorded. When a violation is recorded then the algorithm returns the previous step integrating DER value with no violation as the hosting capacity value for the DER location. When a violation is recorded then the algorithm returns the previous step integrating DER value with no violation as the hosting capacity value for the DER location. The binary search is proposed in Fig. 2 for node-by node ICA simulation to improve computational efficiency while maintaining search accuracy. The binary search mainly involves two major steps (red blocks) as shown in Fig. 2: 1) calculate upper and lower bound of the integrating DER value and 2) calculate final DER maximum integration capacity. The system-wide study refers to the simulation of maximum hosting capacity value for each node in the given IEEE-123 base case system. Fig. 3 shows the comparison of 24 hourly ICA values at n = 96 using binary search and linear search approach, where the binary search is significantly better than the linear search due to our algorithm design and framework implementation.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of managing a distribution system over a network as disclosed by Alrushoud and Bletterie to include wherein the simulation flow uses power flow tools to execute quasi-static time-series simulation that provides outputting line current, wherein the power flow results from the simulation flow are used to update a feeder model of the distribution system; wherein the simulation flow iteratively adjusts DER sizing at specific nodes in the distribution using binary search based on detected constraints violations and provides the DER sizing adjustments to update the feeder model, to determine maximum allowable DER installation capacity that can be accommodated on specific distribution system nodes without violating system operation constraints as taught by Guddanti in the system of Alrushoud and Bletterie in order to study the impacts of DERs on distribution networks under various DER control/modeling scenarios. It integrates the two most powerful open-source tools in distribution grid simulation and an extremely popular programming language: GridLAB-D and Python. Specifically, we carefully create (1) an open and flexible design, (2) easy-to-develop analytical application scenarios, and (3) compatibility with a variety of third-party tools. The paper demonstrates (1) and (2) of this co-simulation framework with a use case study on integration capacity analysis (ICA) and we demonstrate feature (3) as an example to conduct graphical analysis in Python for distribution system analysis with a near-zero effort. A highly accurate and fast system-wide ICA result demonstrates the supreme data structure and easy to-extend architecture for speeding renewable integration. (See abstract, Guddanti)
The combination of Alrushoud, Bletterie and Guddanti does not teach voltage sensitivity parameters comprising sensitivity of node voltage due to integrating DER size for active power dV/dP and the sensitivity of the node voltage due to the integrating DER size for reactive power dV/dQ and wherein the feeder topology analysis iteratively re- evaluates and updates the node clustering based on power flow simulation results.
In the related field of invention, Xiao teaches voltage sensitivity parameters comprising sensitivity of node voltage due to integrating DER size for active power dV/dP and the sensitivity of the node voltage due to the integrating DER size for reactive power dV/dQ. (see page 3-4- The reactive/active power partition quality function-based method considers the network topology and groups the nodes with strong couplings of reactive/active power together. Moreover, this partition scheme can keep each sub-network under a reasonable size, as well as prevent an uneven number of PVs, insufficient adjustable power, or excess power in the sub-networks. The reactive/active power partition function proposed in this study is mainly composed of the internal reactive/active power sensitivity function, the external reactive/active power sensitivity function, and the reactive power/active balance function.)
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wherein the feeder topology analysis iteratively re-evaluates and updates the node clustering based on power flow simulation results. (See page 4- Considering the influence of network topology and the output of PVs, this study proposes a reactive/active power-based partition quality function to partition a complex ADN with high penetrated PVs. The reactive/active power partition quality function-based method considers the network topology and groups the nodes with strong couplings of reactive/active power together. Moreover, this partition scheme can keep each sub-network under a reasonable size, as well as prevent an uneven number of PVs, insufficient adjustable power, or excess power in the sub-networks. The reactive/active power partition function proposed in this study is mainly composed of the internal reactive/active power sensitivity function, the external reactive/active power sensitivity function, and the reactive power/active balance function. See section 2.3 and page 6-7- For each node i, a new reactive power sub-network is formed by selecting another node from the remaining nodes. The reactive power partition quality function QQ0 quality of the new partition is calculated. After the calculation of partition quality function variation for all node pairs in the network, the two nodes with the highest partition quality function variation are grouped into one sub-network, and the total value of reactive power partition quality function is updated. Step 4: The newly formed reactive power sub-networks are treated as individual nodes. And by repeating Step 3, the network merge process is conducted, and newly reactive power sub-networks network is updated to form the network. See table 2)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of managing a distribution system over a network as disclosed by Alrushoud, Bletterie and Guddanti to include voltage sensitivity parameters comprising sensitivity of node voltage due to integrating DER size for active power dV/dP and the sensitivity of the node voltage due to the integrating DER size for reactive power dV/dQ and wherein the feeder topology analysis iteratively re- evaluates and updates the node clustering based on power flow simulation results r dV/dQ as taught by Xiao in the system of Alrushoud, Bletterie and Guddanti in order to achieve an economic control and avoid overvoltage caused by errors in the forecast data of the PVs. With the spatial domain, parallel processing could reduce the computing time significantly to simplify the optimization and improve the dynamic response of the voltage control. (See page, Xiao)
Regarding claim 7
Alrushoud teaches a non-transitory computer readable medium, storing instructions for executing a process, the instructions comprising; (See section I-So, in our study, we established a smart meter data base to represent the typical household load profiles in a region. Then, based on the feeder head load profiles, which are normally available to the utilities through SCADA measurements, a load allocation procedure is carried out. This allows us to assign a number of households’ profiles down to each load node.)
Claims 7 is rejected for the same reasons as Claim 1, as they share the same elements and limitations except the preamble.
Regarding claim 13
Alrushoud teaches a management apparatus configured to manage a distribution system over a network, the distribution system comprising a plurality of distributed energy resource (DER) nodes in an interconnect, the apparatus comprising: a processor, configured to: (see section I. In this paper, the PV hosting capacity is calculated using the stochastic analysis framework developed by Electric Power Research Institute (EPRI). The stochastic analysis captures the uncertainty in the size and location of future PV installations by populating PV deployment scenarios in a random fashion. In EPRI studies, for each PV deployment, the size of PV system at each customer location is randomly drawn from the residential or commercial PV distribution curves, depending upon the customer type, and the location is randomly selected from the pool of all load nodes provided by the distribution feeder. Due to the recent installation of smart meters into the residential sector, yearly or longer household profiles has become available describing its consumption characteristics. So, in our study, we established a smart meter data base to represent the typical household load profiles in a region. Then, based on the feeder head load profiles, which are normally available to the utilities through SCADA measurements, a load allocation procedure is carried out. This allows us to assign a number of households’ profiles down to each load node. Then, an optimal PV size selection process is used to determine the PV size for each household based on cost benefit studies. This will allow each household to select a PV system that minimizes their monthly bill.)
Claims 13 is rejected for the same reasons as Claim 1, as they share the same elements and limitations except the preamble.
Regarding claim 2, 8 and 14
Alrushoud, Bletterie, Guddanti and Xiao further teaches the method of claim 1, the non-transitory computer readable medium of claim 7 and the apparatus of claim 13. Alrushoud further teaches wherein the executing the feeder topology analysis comprises: executing clustering on the topology information to determine feeder system node clustering; (See section abstract- We also investigate the zonal allocation method for weak zone identification to address the cluster phenomena in technology diffusion. see section II.A-B- the real-life distribution feeder has 48 residential load nodes at 24 kV voltage level. The original feeder data contains only a peak load at each node. Thus, a load allocation method introduced in is applied to generate load profiles at each load node and determine the total number of houses at each node. A bottom-up approach has been used to allocate weekly residential load profiles to every load node on a test feeder. According to the data sheet, let the number of load nodes is NL and the peak load at the ith load node be Pi. For every load node, randomly draw weekly load profiles from the load pool and add them up until the aggregated load is bounded by Pi upper and lower ranges as shown in Fig.4. See section III.B-In a community, the installation of rooftop PV systems exhibits zonal characteristics because of the social contacts among neighbors. To model this phenomena in PV technology diffusion process, we applied a zonal PV allocation method in the distribution planning study. The IEEE 123-bus feeder is first divided into 10 zones using Proximity Analysis, as shown in Fig. 11. Dividing a feeder into zones can also facilitate the PV hosting capacity study by addressing the clustering phenomena in the technology diffusion process)
providing the feeder system node clustering as an initial one of the output analysis; (section II.D Step 2 and 4-Perform load allocation method for all load nodes to match the feeder head load profile with the household load profiles in the load pool. This step will prepare the nodal load profiles for running quasi-static power flow simulations. See section III.B-The IEEE 123-bus feeder is first divided into 10 zones using Proximity Analysis, as shown in Fig. 11. Note that the number of zones can vary depending on the feeder topology, load types, and the length of the feeder. For each zone, 100% customers with PV systems is assumed and the voltage profile within that zone is recorded under different PV capacity allocations and then the voltage change between the case with no PV in the zone with the PV case is presented in Fig. 12. From the results, we made the following observations: • The OCB PV allocation method yields smaller voltage changes compared with random or 1-size PV allocation methods.) Zonal voltage changes are correlated to electrical connection between zones and load characteristics. Criterions can be set up to identify the zones that can host more PVs or less PVs in order to increase the overall hosting capacity on the feeder. As expected, if a zone is located further away from the substation or a voltage regulator, voltage changes increases. Thus, we can use this method to identify the zones that are more vulnerable to voltage violations with concentrated PV installations)
for the execution of the simulation flow from the simulation scenario sets and the output analysis: evaluating the feeder system node clustering from power flow simulation provided from the simulation flow; (See section III-MATLAB is used for creating and iterating through each PVscenario as well as for the analysis of the results. OpenDSS is used to solve the time series power flow for each case. IEEE 123-bus feeder and an actual distribution feeder provided by a local utility were used in the simulation process. See also section III.B-For each zone, 100% customers with PV systems is assumed and the voltage profile within that zone is recorded under different PV capacity allocations and then the voltage change between the case with no PV in the zone with the PV case is presented in Fig. 12. Zonal voltage changes are correlated to electrical connection between zones and load characteristics. Criterions can be set up to identify the zo)nes that can host more PVs or less PVs in order to increase the overall hosting capacity on the feeder.
adjusting feeder system node partitioning based on the evaluation; (see section II.D- PV Hosting capacity is defined as the maximum amount of PV that a feeder can accommodate before adverse impacts occur on a distribution feeder. This value will be dependent on the feeder characteristics, PV size and location, monitoring criteria, and load. The overvoltage criterion is generally the primary concern of the power system utility. The overvoltage caused by PV output can be a major limiting factor to how much PV generation capacity can be supported on a distribution system. In order to find the PV hosting capacity, we utilized a modified version of the stochastic analysis framework that was developed by EPRI for determining the impacts of PV systems on a distribution circuit as shown In Fig.6. The modification that was added is a step to determine the optimal PV size for each residential house prior performing the hosting capacity. Instead of randomly drawing PV sizes from distribution curves (residential/commercial), a unique and optimal PV size can be found for every house on every load node on a distribution feeder. Note that by selecting an optimal PV size for each household, the reverse flow of PV generation can be reduced. section III.B-number of zones can vary depending on the feeder topology, load types, and the length of the feeder See section IV- Our simulation results demonstrate that it is of great importance for distribution planning engineers to apply optimal PV sizing and zonal allocation methods to identify the PV hosting capacity. Installing large PV systems in weaker zones will decrease the overall PV hosting capacity. Adding voltage regulation devices or letting customers choose an optimal size can alleviate the voltage violations) and
providing the adjusted partitioning of feeder system nodes as the output analysis. (See section III.A- In each PV deployment, a maximum bus voltage was recorded. The PV hosting capacity was calculated based on overvoltage criterion under multiple PV sizing methods. As shown in Figs.7-9, PV hosting capacity under optimal-capacity-based (OCB), randomly assigned, and standardized PV allocation methods are presented for the actual distribution feeder. In Fig.7 (the OCB case), the maximum voltage profile reaches a point where voltage violations decreases as the PV penetration increases until it settled to an acceptable voltage (with no violation) under 100% PV penetration with 6.1 MW of PV installed. As opposed to randomly assigned and standardized PV allocation methods, the maximum voltage rises as the PV penetration increases where it settled to unacceptable voltage under 100% PV penetration. Also, the minimum hosting capacity is 1.6 MW for randomly assigned and standardized PV allocation methods and it is 2.04 MW for OCB approach resulting in an increased capacity for PV without affecting the operational conditions.)
Examiner note: It is the Examiner position that under the broadest interpretation sense, determining the optimal PV size on a feeder while considering changes in feeder topology, load types, and feeder length is a process that involves adjusting the feeder system node partitioning. It's about strategically allocating PV generation to different points on the feeder to maximize the benefits and minimize potential issues. Also, by adding voltage regulators it helps to adjust the voltage levels at various points along a feeder and ensure that the voltage at each node remains within acceptable limits.
Regarding claim 3, 9 and 15
Alrushoud, Bletterie, Guddanti and Xiao further teaches the method of claim 1, the non-transitory computer readable medium of claim 7 and the apparatus of claim 13. Alrushoud further teaches wherein the executing the scenario management comprises: randomly generating simulation scenarios from combinations of ones of the system profiles, the system profiles comprising time-series system load profiles and generation profiles of the plurality of DER nodes; (See section II.A - In this study, IEEE 123-bus feeder (see Fig. 1) and an actual feeder (see Fig. 2) have been used. The IEEE 123-bus feeder has 91 residential load nodes at 4.16 kV voltage level and the real life distribution feeder has 48 residential load nodes at 24 kV voltage level. The original feeder data contains only a peak load at each node. Thus, a load allocation method introduced in [7] is applied to generate load profiles at each load node and determine the total number of houses at each node. In order to conduct a time-series study, every load node on the feeder needs to be modeled down to the residential home level at 1-minute resolution. See section II.B- A bottom-up approach has been used to allocate weekly residential load profiles to every load node on a test feeder. According to the data sheet, let the number of load nodes is NL and the peak load at the ith load node be Pi. For every load node, randomly draw weekly load profiles from the load pool and add them up until the aggregated load is bounded by Pi upper and lower ranges as shown in Fig.4. See section II.D -The modification that was added is a step to determine the optimal PV size for each residential house prior performing the hosting capacity. Instead of randomly drawing PV sizes from distribution curves (residential/commercial), a unique and optimal PV size can be found for every house on every load node on a distribution feeder. Note that by selecting an optimal PV size for each household, the reverse flow of PV generation can be reduced. The methodology to systematically simulate stochastic PV deployment scenarios is described in Fig. 6. We selected 100 PV deployment scenarios (i.e. 𝑀𝑀 = 100 ). Each scenario is unique in the order that PVs are deployed and the maximum nodal voltage is recorded for each scenario by performing time series power flow. The location of the PV systems are also different in each scenario, which is the main random variable) and
executing clustering on the generated simulation scenarios to generate simulation scenario sets. (see section II.D-The methodology to systematically simulate stochastic PV deployment scenarios is described in Fig. 6. We selected 100 PV deployment scenarios (i.e. 𝑀𝑀 = 100). Each scenario is unique in the order that PVs are deployed and the maximum nodal voltage is recorded for each scenario by performing time series power flow. The location of the PV systems are also different in each scenario, which is the main random variable. For a particular PV deployment scenario, the PV penetration level is increased from 0% - 100% in a step of 5% (i.e.𝑁 = 20). The PV penetration is the ratio between total PV installed capacity in a feeder (kWp) and the maximum apparent power on the feeder. See section III-The analysis is performed in OpenDSS, an open-source system simulation software developed by EPRI. OpenDSS is controlled through the COM interface by MATLAB. MATLAB is used for creating and iterating through each PV scenario as well as for the analysis of the results.)
Examiner note: A set of scenarios can be viewed as a cluster of scenarios as shown in fig 6 showing construction M*N PV development scenarios.
Regarding claim 4, 10 and 16
Alrushoud, Bletterie, Guddanti and Xiao further teaches the method of claim 1, the non-transitory computer readable medium of claim 7 and the apparatus of claim 13. Alrushoud further teaches monitoring system power flow with the plurality of DER nodes on the interconnect from the execution of the simulation flow; (see section I- Hosting capacity is normally feeder specific and can assessed by running quasi-static, continuing power flow studies. Utility feeder models are used to determine the feeder topology. Load allocation methods are used to determine the load profiles on each load node. See section II.D- PV Hosting capacity is defined as the maximum amount of PV that a feeder can accommodate before adverse impacts occur on a distribution feeder [1]. This value will be dependent on the feeder characteristics, PV size and location, monitoring criteria, and load. The overvoltage criterion is generally the primary concern of the power system utility and this is the criteria that will be used throughout the paper to determine PV hosting capacity. The methodology to systematically simulate stochastic PV deployment scenarios is described in Fig. 6. We selected 100 PV deployment scenarios (i.e. 𝑀𝑀 = 100 ). Each scenario is unique in the order that PVs are deployed and the maximum nodal voltage is recorded for each scenario by performing time series power flow.)
and for a detection of a constraint violation in the monitored power flow, noting the constraint violation for hosting capacity output (See Section III.A- The PV hosting capacity was calculated based on overvoltage criterion under multiple PV sizing methods. As shown in Figs.7-9, PV hosting capacity under optimal-capacity-based (OCB), randomly assigned, and standardized PV allocation methods are presented for the actual distribution feeder. In Fig.7 (the OCB case), the maximum voltage profile reaches a point where voltage violations decreases as the PV penetration increases until it settled to an acceptable voltage (with no violation) under 100% PV penetration with 6.1 MW of PV installed. As opposed to randomly assigned and standardized PV allocation methods, the maximum voltage rises as the PV penetration increases where it settled to unacceptable voltage under 100% PV penetration. Also, the minimum hosting capacity is 1.6 MW for randomly assigned and standardized PV allocation methods and it is 2.04 MW for OCB approach resulting in an increased capacity for PV without affecting the operational conditions.) and changing the simulation flow to another simulation flow from the simulation scenario sets. (see section II.D-Step 4: Perform stochastic analysis framework to determine PV hosting capacity using the voltage violation criterion. See section IV- Our simulation results demonstrate that it is of great importance for distribution planning engineers to apply optimal PV sizing and zonal allocation methods to identify the PV hosting capacity. Installing large PV systems in weaker zones will decrease the overall PV hosting capacity. Adding voltage regulation devices or letting customers choose an optimal size can alleviate the voltage violations.)
Regarding claim 5, 11 and 17
Alrushoud, Bletterie, Guddanti and Xiao further teaches the method of claim 4, the non-transitory computer readable medium of claim 10 and the apparatus of claim 13. Alrushoud further teaches for execution of the all-simulation flows from the simulation scenario sets, providing the hosting capacity output. (See section II. D step 2 and 4 and fig 6-9-Perform load allocation method for all load nodes to match the feeder head load profile with the household load profiles in the load pool. This step will prepare the nodal load profiles for running quasi-static power flow simulations. Perform stochastic analysis framework to determine PV hosting capacity using the voltage violation criterion.)
Regarding claim 6, 12 and 18
Alrushoud, Bletterie, Guddanti and Xiao further teaches the method of claim 1, the non-transitory computer readable medium of claim 7 and the apparatus of claim 13. Alrushoud further teaches executing a snapshot of performance metrics of the simulation flow to determine whether a constraint violation has occurred; (see section III.A-The PV hosting capacity was calculated based on overvoltage criterion under multiple PV sizing methods. As shown in Figs.7-9, PV hosting capacity under optimal-capacity-based (OCB), randomly assigned, and standardized PV allocation methods are presented for the actual distribution feeder. In Fig.7 (the OCB case), the maximum voltage profile reaches a point where voltage violations decreases as the PV penetration increases until it settled to an acceptable voltage (with no violation) under 100% PV penetration with 6.1 MW of PV installed. As opposed to randomly assigned and standardized PV allocation methods, the maximum voltage rises as the PV penetration increases where it settled to unacceptable voltage under 100% PV penetration. In Fig.10, a comparison between the three PV allocation methods under multiple PV penetration levels is presented. Here, the PV penetration is defined as the percentage of customers with PV systems. Thus, 100% PV penetration represents that all customers on the actual feeder has a PV system. It can be seen that when the PV penetration level is low, all PV allocation method have relatively close maximum voltage profiles. When the PV penetration level is high, the randomly assign and standardized PV capacity allocations will over-estimate the voltage violation limiting possible future PV installation as compared with the OCB approach.)
and
utilizing a variable-width sliding window on the determined constraint violations to determine whether the constraint violation is false. (See fig 12-13 and see section III-C- As mentioned, two load data sources were used to perform load allocation method to generate load profiles at each load node. The data sampling rate from Pecan Street is one minute while the utility provided data is 30 minutes. The hosting capacity results under both data resolution in presented in Table 2. With faster data sampling more benefits can be seen and full potential impacts can be captured. For zonal based analysis, the voltage changes for every zone is presented in Fig.13. It can be seen that for zones (7, 8, 9, and 10) the voltage changes is bigger as compared to Fig.12. It is because the control devices like voltage regulators bring the voltage level down in a short time span, so 1-min data or even faster is required to capture it accurately. See Section IV- When the PV penetration level is high, the ongoing PV capacity allocation methods, i.e. randomly assigning PV capacities or use a fixed PV capacity regardless or residential load characteristics will over-estimate the voltage violation and under-estimate the PV hosting capacity. Such approaches will limit future PV installation as compared with the OCB approach. High resolution end use data will facilitate the hosting capacity study by allowing more accurate estimations on voltage violation events and identifying optimal PV sizes for residential loads. Dividing a feeder into zones can also facilitate the PV hosting)
Examiner note: Using table 2, metric bus voltages are monitored using a n-minute moving window, the window length used here are 1 minutes and 30 minutes.
Regarding claim 19
Alrushoud, Bletterie, Guddanti and Xiao further teaches the method of claim 1. Alrushoud further teaches receiving real-world measurement data from the distribution system, the real-world measurement data comprising at least one of sensor measurements, metering data, and supervisory control and data acquisition (SCADA) data from the plurality of DER nodes; (see section Introduction- So, in our study, we established a smart meter data base to represent the typical household load profiles in a region. Then, based on the feeder head load profiles, which are normally available to the utilities through SCADA measurements, a load allocation procedure is carried out. This allows us to assign a number of households’ profiles down to each load node.)
Alrushoud does not teach updating the feeder model of the distribution system based on the real-world measurement data through a topology mapping module to calibrate the distribution feeder model.
In the related field of invention, Guddanti further teaches updating the feeder model of the distribution system based on the real-world measurement data through a topology mapping module to calibrate the distribution feeder model. (section II.B-. For example, DNM can modify the physical and electrical properties of the nodes, loads, lines, devices, etc by modifying the NDS to NDSmodel in python e.g., NDSmodel = DNM(NDS). Some examples for DNM are, assigning the SCADA/AMI data to the desired load locations, load allocation programs that can identify houses downstream of a transformer and allocate AMI load data to these houses, network reconfiguration problems such as addition or removal of nodes for use cases e.g., GUI, power-rerouting algorithms, etc See fig 2 and section III.B -we allocate hourly load profiles (24 hourly values) to all the loads in the IEEE-123 bus system. For each time instant (each hour), we increase the size of the integrating DER value by a constant up-size step value (α) until any violation from Table. I is recorded. When a violation is recorded then the algorithm returns the previous step integrating DER value with no violation as the hosting capacity value for the DER location. See fig 2 (element-feeder model modification and GridLAB-D power flow simulation))
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of managing a distribution system over a network as disclosed by Alrushoud to include updating the feeder model of the distribution system based on the real-world measurement data through a topology mapping module to calibrate the distribution feeder model as taught by Guddanti in the system of Alrushoud, Xiao and Bletterie in order to study the impacts of DERs on distribution networks under various DER control/modeling scenarios. It integrates the two most powerful open-source tools in distribution grid simulation and an extremely popular programming language: GridLAB-D and Python. Specifically, we carefully create (1) an open and flexible design, (2) easy-to-develop analytical application scenarios, and (3) compatibility with a variety of third-party tools. The paper demonstrates (1) and (2) of this co-simulation framework with a use case study on integration capacity analysis (ICA) and we demonstrate feature (3) as an example to conduct graphical analysis in Python for distribution system analysis with a near-zero effort. A highly accurate and fast system-wide ICA result demonstrates the supreme data structure and easy to-extend architecture for speeding renewable integration. (See abstract, Guddanti)
Regarding claim 20
Alrushoud, Bletterie, Guddanti and Xiao further teaches the method of claim 1. Alrushoud further teaches providing hosting capacity analysis results to support distributed energy resource interconnection review procedures for distribution network operators, (see conclusion- Our simulation results demonstrate that it is of great importance for distribution planning engineers to apply optimal PV sizing and zonal allocation methods to identify the PV hosting capacity. Installing large PV systems in weaker zones will decrease the overall PV hosting capacity. Adding voltage regulation devices or letting customers choose an optimal size can alleviate the voltage violations)
Alrushoud does not teach wherein the hosting capacity analysis results comprise maximum allowable DER installation capacity that can be accommodated on specific distribution system nodes without violating system operation constraints.
In the related field of invention, Guddanti further teaches wherein the hosting capacity analysis results comprise maximum allowable DER installation capacity that can be accommodated on specific distribution system nodes without violating system operation constraints. (See section III.B and fig 2- For each time instant (each hour), we increase the size of the integrating DER value by a constant up-size step value (α) until any violation from Table. I is recorded. When a violation is recorded then the algorithm returns the previous step integrating DER value with no violation as the hosting capacity value for the DER location. When a violation is recorded then the algorithm returns the previous step integrating DER value with no violation as the hosting capacity value for the DER location. The binary search is proposed in Fig. 2 for node-by node ICA simulation to improve computational efficiency while maintaining search accuracy. The binary search mainly involves two major steps (red blocks) as shown in Fig. 2: 1) calculate upper and lower bound of the integrating DER value and 2) calculate final DER maximum integration capacity. The system-wide study refers to the simulation of maximum hosting capacity value for each node in the given IEEE-123 base case system. Fig. 3 shows the comparison of 24 hourly ICA values at n = 96 using binary search and linear search approach, where the binary search is significantly better than the linear search due to our algorithm design and framework implementation.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of managing a distribution system over a network as disclosed by Alrushoud to include wherein the hosting capacity analysis results comprise maximum allowable DER installation capacity that can be accommodated on specific distribution system nodes without violating system operation constraints as taught by Guddanti in the system of Alrushoud, Xiao and Bletterie in order to study the impacts of DERs on distribution networks under various DER control/modeling scenarios. It integrates the two most powerful open-source tools in distribution grid simulation and an extremely popular programming language: GridLAB-D and Python. Specifically, we carefully create (1) an open and flexible design, (2) easy-to-develop analytical application scenarios, and (3) compatibility with a variety of third-party tools. The paper demonstrates (1) and (2) of this co-simulation framework with a use case study on integration capacity analysis (ICA) and we demonstrate feature (3) as an example to conduct graphical analysis in Python for distribution system analysis with a near-zero effort. A highly accurate and fast system-wide ICA result demonstrates the supreme data structure and easy to-extend architecture for speeding renewable integration. (See abstract, Guddanti)
Conclusion
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wong et al. US 20190140455 A1
Discussing a method to enable utilities to determine a dynamic hosting capacity value for each location on the distribution grid, each time interval, and each type of DER asset, and to provide active management of these assets, when calculating interconnection requests, planning distribution grids, and/or when actively managing all DERs connected to the distribution grid.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
10. All claims 1-20 are rejected.
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/PURSOTTAM GIRI/Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186
