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 .
Status of Claims
This Office Action is responsive to communication filed on 6/21/2024.
Claims 1-20 are pending and presented for examination.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim 1, third limitation recites “to measure at least one sub-zonal operational parameter for an assigned sub-zone of the plurality of distribution zones”. However, in view of the claim as a whole, the examiner believes this is a typographical error and that the limitation should recite “an assigned sub-zone of the plurality of
Claim 13 recites “determine a load flexibility index for based on the load …”. Examiner believes there is a typographical error and the claim should recite “determine a load flexibility index
Claim 13 recites “… a value representing a level at which load is able be adjusted.” Examiner believes there is a typographical error and the claim should recite “… a value representing a level at which the load is able to be adjusted.”
Claim 15 recites “… a value representing a level at which generation is able be adjusted in a respective cluster.” Examiner believes there is a typographical error and the claim should recite “… a value representing a level at which generation is able to be adjusted in a respective cluster.”
Claim 17 recites “… a value representing a level at which load is able be adjusted.” Examiner believes there is a typographical error and the claim should recite “… a value representing a level at which the load is able to be adjusted.”
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 1-20 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.
Regarding claim 1
Claim 1, third limitation recites “to measure at least one sub-zonal operational parameter” which introduces at least one sub-zonal operational parameter(s). Claim 1, fourth limitation recites “to adjust one or more sub-zonal operational parameters”. However, it is not clear if the at least one sub-zonal operational parameter adjusted in the fourth limitation is the same as the sub-zonal operational parameter measured in the third limitation.
Claim 1, sixth limitation recites “to measure at least one cluster operational parameter” which introduces at least one cluster operation parameter(s). Claim 1, seventh limitation recites “to adjust one or more cluster operational parameters”. However, it is not clear if the at least one cluster operational parameter adjusted in the seventh limitation is the same as the cluster operational parameter measured in the sixth limitation.
Dependent claims are likewise rejected.
Regarding claim 13
Claim 13 recites to “determine a load flexibility index”. However, claim 7 already introduced “a load flexibility index” and thus it is not clear if the load flexibility index of claim 13 is the same as the load flexibility index of claim 7.
Regarding claim 15
Claim 15 recites to “determine a generation flexibility index”. However, claim 7 already introduced “a generation flexibility index” and thus it is not clear if the generation flexibility index of claim 15 is the same as the generation flexibility index of claim 7.
Claim Rejections - 35 USC § 103
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.
Claims 1-17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over BOARDMAN (US20180107691A1) in view of CHERIAN (US20120029720A1) (hereinafter – “BOARDMAN-CHERIAN”).
Regarding claim 1
BOARDMAN teaches multi-level electrical distribution control system, comprising:
a distribution zonal controller associated with an assigned distribution zone of a plurality of distribution zones of a power grid, each of the plurality of distribution zones having an associated distribution zonal controller (top-level DNNC component Fig. 12 #1210 distribution zonal controller/top-level distribution network node controller (DNNC) [0101]: #1210 zonal controller communicatively coupled to junior level DNNC components 1220 to 1236 “FIG. 12, the interconnections illustrate a basic tree structure topology” [0100]: “Transmission systems are generally presented in the context of delivering high power to regional distribution networks managed by a distribution grid entity”, i.e., the transmission grid of Fig. 12 can deliver power to regional networks each managed by a zonal controller/top-level DNNC thus implying a plurality of distribution zones of a power grid wherein each of the zones have an associated zonal controller specific to that distribution zone), the distribution zonal controller configured to:
divide the assigned distribution zone into a plurality of sub-zones based on one or more assets within the assigned distribution zone (Fig. 12 #1220 & 1221 sub-zonal controllers/mid-level DNNCs [0102]: “two mid-level DNNC components 1220 and 1221 are logically placed between the bottom-level DNNC components and the top-level DNNC component 1210”, examiner annotated Fig. 12 below shows the distribution zone divided into a number of sub-zones corresponding to the number of sub-zonal controllers/mid-level DNNCs, i.e., based on one or more assets such as the two sub-zonal controllers 1220 and 1221, the distribution zone is divided into two sub-zones: sub-zone #1 consists of sub-zonal controller 1220 and bottom-level DNNCs 1230-1231, sub-zone #2 consists of sub-zonal controller 1221 and bottom-level DNNCs 1232-1236), wherein each of the plurality of sub-zones includes:
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BOARDMAN, FIG. 12, examiner annotated, shows distribution zonal controller/top-level DNNC 1210 with assigned zone divided into sub-zones based on sub-zonal controllers/mid-level DNNCs 1220 and 1221
a sub-zonal measurement device in communication with the distribution zonal controller and configured to measure at least one-sub-zonal operation parameter for an assigned sub-zone of the plurality of [0024]: “multi-tier electrical distribution control system can process, aggregate, compress data, etc., such that, for example, data can be used at various levels within the electrical distribution control system, be used more efficiently, and be communicated with more efficiency” [0030]: “by having better control and knowledge of the electrical distribution grid, energy access and distribution of energy resources can be adapted […] Data can be passed by components of an electrical distribution control system, e.g., a DNNC component. As a non-limiting example, a top-level DNNC can dynamically adjust the primary coil tap-voltage at a substation transformer based on data processed from mid- and bottom-level DNNC components. The mid- and bottom-level components can further adaptively configure the topography of the distribution network in accord with configuration rules”, “based on data processed from mid- and bottom-level DNNC components” implies that controllers at each level in the multi-level distribution system are configured to receive and/or generate data and thus comprise a measurement device [0031]: “coordination between the components of an intelligent electrical distribution grid control system can facilitate demand response” [0102]: zonal controller/top-level DNNC communicatively connected to sub-zonal controllers/mid-level DNNCs such that “data and rules can be bubble up or pushed down by way of this path. The bidirectional communication and closed loop control at each level (e.g., top, mid, and bottom) can facilitate improved electrical distribution grid performance”);
divide at least one of the plurality of sub-zones into a plurality of clusters based on one or more assets within the assigned sub-zone, wherein each of the plurality of clusters includes (Fig. 12 #1230-1236 cluster controllers/bottom-level DNNCs [0103]: “mid-level DNNC component 1221 can be associated with bottom-level DNNC components 1232 to 1236 […] bidirectional communication between top-level DNNC component 1210 and bottom-level DNNC components 1232-1236 can be by way of mid-level DNNC component 1221” examiner annotated Fig. 12 below shows the sub-zone associated with sub-zonal controller 1220 divided into a number of clusters corresponding to the number of cluster controllers/bottom-level DNNCs, i.e., based on one or more assets such as the two cluster controllers/bottom-level DNNCs 1230 and 1231, the sub-zone is divided into two clusters: cluster #1 consists of cluster controller/bottom-level DNNC 1230 and a city power plant, cluster #1 consists of cluster controller/bottom-level DNNC 1231 and a plurality of industrial customers), wherein each of the plurality of clusters includes:
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BOARDMAN, FIG. 12, examiner annotated, shows sub-zonal controller/mid-level DNNC 1220 with assigned zone divided into clusters based on cluster controllers/bottom-level DNNCs 1230 and 1231
a cluster measurement device in communication with the distribution zonal controller and configured to measure at least one cluster operation parameter for an assigned cluster of the plurality of[0024], [0030], [0031], [0101]: “top-level DNNC component 1210 can be communicatively coupled to junior level DNNC components(e.g., 1220 to 1236)” the zonal controller/top-level DNNC can communicate with cluster controllers/bottom-level DNNCs) [0102]: “Bottom-level DNNC component 1230 and 1231 can be logically connected to top-level DNNC component 1210 by way of mid-level DNNC component 1220. As such, data and rules can be bubble up or pushed down by way of this path” [0103]: “independent closed loop control can be effected, for example, at bottom-level DNNC component 1234”);
receive sub-zonal operational data for each of the plurality of sub-zones from the sub-zonal measurement devices ([0031]: “coordination between the components of an intelligent electrical distribution grid control system can facilitate demand response, e.g., systematically formulating, applying and solving multi-objective optimization formulas to demand response models” [0102]: “Bottom-level DNNC component 1230 and 1231 can be logically connected to top-level DNNC component 1210 by way of mid-level DNNC component 1220. As such, data and rules can be bubble up or pushed down by way of this path. The bidirectional communication and closed loop control at each level (e.g., top, mid, and bottom) can facilitate improved electrical distribution grid performance” data and rules are communicated between sub-zonal controller/mid-level DNNC and zonal controller/top-level DNNC such that coordinated control instructions can be determined by zonal controller to be implemented by sub-zonal controllers, “solving multi-objective optimization formulas to demand response models” implies the zonal controller receiving sub-zonal operational data for each of the plurality of sub-zonal controllers);
receive cluster operational data for each of the plurality of clusters from the cluster measurement devices ([0031], [0102]: “Bottom-level DNNC component 1230 and 1231 can be logically connected to top-level DNNC component 1210 by way of mid-level DNNC component 1220” data and rules are communicated between cluster controller/bottom-level DNNC and zonal controller/top-level DNNC via sub-zonal controllers/mid-level DNNCs such that coordinated control instructions can be determined by zonal controller to be implemented by cluster controllers, “solving multi-objective optimization formulas to demand response models” implies the zonal controller receiving cluster operational data for each of the plurality of cluster controllers);
determine an adaptive control action for at least one of the sub-zonal control devices or the cluster control devices based on the distributional zonal orchestration index ([0044]: “data that an electrical conductor is damaged can be processed to develop a reconfiguration map of the electrical distribution network control system and the electrical distribution grid to bypass the damage conductor”); and
communicate a command to the at least one sub-zonal control devices or the cluster control devices based on the adaptive control action ([0044]: “data that an electrical conductor is damaged can be processed to develop a reconfiguration map of the electrical distribution network control system and the electrical distribution grid to bypass the damage conductor. This reconfiguration map can then be implemented by dynamically adapting electrical distribution network control system and/or the electrical distribution grid”).
In summary, BOARDMAN teaches a multi-level power grid control system comprising a distribution zonal controller/top-level DNNC, a sub-zonal controller/mid-level DNNC, and a cluster controller/bottom-level DNNC, as outlined above. BOARDMAN also teaches “multi-tier electrical distribution control system can process, aggregate, compress data, etc., such that, for example, data can be used at various levels within the electrical distribution control system, be used more efficiently, and be communicated with more efficiency” ([0024]). BOARDMAN further teaches “coordination between the components of an intelligent electrical distribution grid control system can facilitate demand response, e.g., systematically formulating, applying and solving multi-objective optimization formulas to demand response models” ([0031]), such that a zonal controller/top-level DNNC 120 can facilitate dynamic reconfiguration of the distribution grid in response to identifying that an electrical conductor is damaged ([0044]). While BOARDMAN does disclose solving multi-objective optimization formulas to facilitate demand response, BOARDMAN does is silent to the implementation details associated with solving the multi-objection optimization formulas such that the demand response can be determined and the grid dynamically reconfigured, and thus is not relied on to determine a distribution zonal orchestration index.
However, CHERIAN in an analogous art teaches a method of dynamically reconfiguring a multi-layer distributive and decentralized power grid control system such that the stability and the reliability of the grid is not detrimentally affected ([0062]). CHERIAN teaches to utilize real-time data collected from the multi-level power grid to simulate grid reconfigurations to balance generation and loads ([0076]), such that solutions are determined and ranked such that a solution achieving the global goal is selected for implementation ([0109]-[0125]).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to incorporate CHERIAN’s simulation-based decision process into BOARDMAN’s multi-level distribution control system such that the zonal controller/top-level DNNC determines a distribution zonal orchestration index from sub-zonal and cluster-level operational data and uses that index to select and implement adaptive control actions. CHERIAN teaches using real-time grid data to simulate alternative reconfigurations, evaluate whether they satisfy local and global objectives, and rank candidate solutions so that a configuration achieving the overall objective is determined for implementation. In view of these teachings, one of ordinary skill in the art would have been motivated to reflect the condition and controllability of each distribution zone in an index and to utilize that index in BOARDMAN’s dynamic reconfiguration and demand-response decision, with a reasonable expectation of success regarding improvements to the efficiency, stability and reliability of the distribution grid.
Regarding claims 2-3
BOARDMAN-CHERIAN teaches the elements of claim 1 as outlined above.
Claim 2 recites “each of the plurality of sub-zones is modeled as a digital twin by the distribution zonal controller.” Claim 3 recites “each of the plurality of clusters is modeled as a digital twin by the distribution zonal controller.”
BOARDMAN teaches the multi-level distribution control system with zonal, sub-zonal, and cluster controllers as outlined above under claim 1. CHERIAN teaches developing a simulated power system that “reflects […] a portion of the distribution power grid along with its overlying control system” ([0155]) and that “selection of the control inputs and the iterative process is conducted according to simulation models” ([0158]). These simulation models correspond to the virtual representations of the physical portions of the grid and their controls, i.e., digital twin type models.
In view of CHERIAN’s simulation models and the rejection of claim 1, it would have been obvious to one of ordinary skill in the art to configure BOARDMAN’s zonal controller/top-level DNNC to include such simulation models/digital twins for each sub-zone/mid-level DNNC controlled portion such that the simulation models/digital twins would be used when determining and selecting grid reconfiguration control actions, as recited in claim 2. Regarding claim 3, given BOARDMAN’s bottom-level DNNC controlled portions, it would have been an obvious extension to provide corresponding simulation models/digital twins for each cluster such that control inputs can be iteratively evaluated at the cluster level when determining and selecting grid reconfiguration control actions, as recited in claim 3.
Regarding claim 4
BOARDMAN-CHERIAN teaches the elements of claim 1 as outlined above.
BOARDMAN also teaches at least one of the plurality of sub-zones is a feeder within at least one of the plurality of distribution zones (Fig. 12 shows mid-level DNNC 1221 as a feeder to bottom-level DNNCs 1232-1235).
Regarding claim 5
BOARDMAN-CHERIAN teaches the elements of claim 4 as outlined above.
BOARDMAN also teaches each of the plurality of clusters includes one or more load points connected to the feeder (Fig. 12 shows mid-level DNNC 1221 as a feeder and connected to bottom-level DNNCs 1232-1235 implying at least one load point connected to the feeder).
Regarding claim 6
BOARDMAN-CHERIAN teaches the elements of claim 1 as outlined above.
BOARDMAN also teaches [0103]: “Bottom-level DNNC component 1233 for example can be logically associated with a plurality of transformers service a portion of a city network” Fig. 12 shows city network cluster controlled by cluster controller/bottom-level DNNC as low-voltage network). BOARDMAN does not explicitly teach each of the plurality of clusters are a low voltage network. However, BOARDMAN’s Fig. 12 and corresponding disclosure shows the transmission grid feeding the distribution grid with low voltage networks supplied through substations and transformers. Before the effective filing date of the claimed invention, one of ordinary skill in the art would have recognized that for a city distribution grid designing clusters as low voltage networks is the conventional and predictable way to serve consumers safely and efficiently. Thus, configuring all clusters of a sub-zone to be low voltage networks would have been a routine application of standard distribution design rather than a departure from the teachings of the prior art.
Regarding claim 7
BOARDMAN-CHERIAN teaches the elements of claim 1 as outlined above.
CHERIAN also teaches the distribution index is determined based on at least one of a load flexibility index, a generation flexibility index, or a power quality index ([0066]: regional control module 225 actively manages power production, consumption and distribution of energy within its area of responsibility and is aware of energy consumption and demands [0067]: region control model identifies that a wind turbine begins producing power and as “the wind turbine(s) can provide additional power the regional control module can decrease production requests to the power plant 110 based on its analysis of both the residential 250 and commercial 260 load and adjust the power drawn from the primary grid 205 to maintain the system within operating limits”, [0071]: “regional control module 225 recognizes an increase in power demand”).
Regarding claim 8
BOARDMAN-CHERIAN teaches the elements of claim 7 as outlined above.
BOARDMAN also teaches to determine at least one sub-zonal characteristic based on the sub-zonal operational data for the assigned sub-zone ([0070]: control link 771 able to be identified, Fig. 7 shows the control link 771 logically placed below a sub-zonal controller/mid-level DNNC and above a cluster controller/bottom-level DNNC, [0071]: “where control link 771 can be functioning below expectations, system 700 can be dynamically reconfigured”).
Regarding claim 9
BOARDMAN-CHERIAN teaches the elements of claim 8 as outlined above.
BOARDMAN also teaches to compare the at least one sub-zonal characteristic with baseline operational characteristics, the baseline operational characteristic reflecting operation of the assigned sub-zone under normal operating conditions, wherein normal operating conditions are operating conditions under which there are no violations ([0071]: “where control link 771 can be functioning below expectations, system 700 can be dynamically reconfigured”, “functioning below expectations” implies the a comparison of a real-time operational characteristic to a baseline operational characteristic and also implies that functioning according to expectations would mean functioning under normal operating conditions such as when there are no rules and/or policies being violated).
Regarding claim 10
BOARDMAN-CHERIAN teaches the elements of claim 8 as outlined above.
BOARDMAN also teaches to determine at least one cluster characteristic based on the cluster operational data for the assigned cluster ([0102]: “bottom-level DNNC component 1230 can be associated with a city power plant and bottom-level DNNC component 1231 can be associated with a small group of industrial customers”).
Regarding claim 11
BOARDMAN-CHERIAN teaches the elements of claim 11 as outlined above.
BOARDMAN also teaches to compare the at least one cluster characteristic with baseline operational characteristics, the baseline operational characteristics reflecting operation of the assigned cluster under normal operating conditions ([0081]: “a fault an electrical distribution grid can be related to a low voltage level and the first DNNC data. Based on this low voltage level indicated by the first DNNC data default can be determined, and a fault status can be set”, “based on this low voltage level” implies a comparison against a baseline voltage to determine that it is low, setting a fault status when the comparison determines that the voltage is low implies that no fault is set when the comparisons that the voltage is not low and thus indicates normal operation conditions).
Regarding claim 12
BOARDMAN-CHERIAN teaches the elements of claim 11 as outlined above.
BOARDMAN also teaches to determine a load situational awareness parameter based on the at last one cluster characteristic, the load situational awareness parameter indiciative of load characteristics in a respective cluster ([0043]: “Energy access that is related to lower priority can be delayed until other parameters are met. As a non-limiting example, charging a Plug-in/Hybrid Electric Vehicle (PEV) can be delayed until there are sufficient energy resources, such as, in off-peak hours, so long as the PEV is charged for the morning commute. Thus, the charging of the PEV can be time shifted to a more optimal time period to reduce peak demand on the electrical grid”).
Regarding claim 13
BOARDMAN-CHERIAN teaches the elements of claim 12 as outlined above.
BOARDMAN teaches to determine a load flexibility index based on the load situational awareness parameter, wherein the load flexibility index is a value reparenting a level at which the load is able to be adjusted ([0043]: “sharing of energy resources can be accomplished, such as charging three PEVs either at full rate sequentially one PEV at a time; at a slower rate in parallel where each PEV simultaneously receives ⅓ of the current; at a slower rate in a shared sequential mode where each PEV receives full current for a short period, then the next PEV receives full current for a short period, and so on, cycling through the PEVs until they all reach full charge”).
Regarding claim 14
BOARDMAN-CHERIAN teaches the elements of claim 13 as outlined above.
CHERIAN teaches to determine a generation situational awareness parameter ([0067]: “as each wind turbine beings producing power”). BOARDMAN teaches that a cluster comprises a wind farm (Fig. 12). Thus, the BOARDMAN-CHERIAN combination teaches to determine a generation situational awareness parameter based on the at least one cluster characteristic, the generation situational parameter indicative of generation characteristics in a respective cluster.
Regarding claim 15
BOARDMAN-CHERIAN teaches the elements of claim 14 as outlined above.
CHERIAN also teaches to determine a generation flexibility index based on the generation situational awareness parameter, wherein the generation flexibility index is a value representing a level at which generation is able to be adjusted in a respective cluster ([0067]: “As the wind turbine(s) can provide additional power the regional control module can decrease production requests to the power plant 110 based on its analysis of both the residential 250 and commercial 260 load and adjust the power drawn from the primary grid 205 to maintain the system within operating limits or market based contractual limits”).
Regarding claim 16
BOARDMAN-CHERIAN teaches the elements of claim 15 as outlined above.
BOARDMAN also teaches to determine a power quality situational awareness parameter based on the at least one cluster characteristic, the power quality situational awareness parameter indiciative of power quality characteristics in a respective cluster ([0029]: “Multi-level control systems in the electrical distribution grid can also facilitate reconfiguration of the distribution grid to enable alternate distribution topographies that can be beneficial to efficient energy consumption. In an aspect, where certain sections of a distribution grid are using large amounts of power, this can be associated with higher operating costs and/or lower distribution efficiency, e.g., energy losses associated with heating from high current loads through conductors, running transformers in excess of their ratings, etc”; [0074]: grid data includes efficiency data).
Regarding claim 17
BOARDMAN-CHERIAN teaches the elements of claim 16 as outlined above.
BOARDMAN also teaches to determine a power quality flexibility index based on the power quality situational awareness parameter, wherein the power quality flexibility index is a value representing a level at which the load is able to be adjusted ([0030]: “by having better control and knowledge of the electrical distribution grid, energy access and distribution of energy resources can be adapted which can result in lower levels of energy consumption. Data can be passed by components of an electrical distribution control system, e.g., a DNNC component. As a non-limiting example, a top-level DNNC can dynamically adjust the primary coil tap-voltage at a substation transformer based on data processed from mid- and bottom-level DNNC components. The mid- and bottom-level components can further adaptively configure the topography of the distribution network in accord with configuration rules to better distribute the energy from the substation”).
Regarding claim 19
BOARDMAN-CHERIAN teaches the elements of claim 1 as outlined above.
BOARDMAN also teaches to receive forecast data for at least one of a sub-zonal or a cluster ([0064]: “can illustrate predictive data relating to future energy generation from solar panel 504 as a function of both time, e.g., the location of the sun in relation to tree 530, and local weather”); receive a distribution zonal policy ([0038]: electrical distribution control system data accessed by top-level DNNC includes a rule/policy); and at least suggests to determine a predictive control action based on the sub-zonal operational data, the cluster operational data, the forecast data, and the distribution zonal policy ([0064]: “predictive data can be incorporated into advanced models relating to energy distribution in the region”).
Claims 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over BOARDMAN-CHERIAN in view of ROY (US20220261715A1).
Regarding claim 18
BOARDMAN-CHERIAN teaches the elements of claim 1 as outlined above.
CHERIAN teaches developing a simulated power system that “reflects […] a portion of the distribution power grid along with its overlying control system” ([0155]) and that “selection of the control inputs and the iterative process is conducted according to simulation models” ([0158]). BOARDMAN-CHERIAN are not relied on to determine an adaptive control action based on a machine learning model. However, ROY in analogous art teaches using a machine learning model to determine and implement energy grid reconfigurations in response to collected data (Abstract). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to apply the teachings of ROY to the teachings of BOARDMAN-CHERIAN such that ROY’s machine learning model is trained using sub-zonal and cluster operational data already available in BOARDMAN-CHERIAN’s distribution system, and then used by the distribution zonal controller/top-level DNNC to determine an adaptive control action for the sub-zonal/mid-level DNNC and cluster/bottom-level DNNC controllers, as recited in claim 18.
Regarding claim 20
BOARDMAN-CHERIAN teaches the elements of claim 19 as outlined above.
CHERIAN teaches developing a simulated power system that “reflects […] a portion of the distribution power grid along with its overlying control system” ([0155]) and that “selection of the control inputs and the iterative process is conducted according to simulation models” ([0158]). BOARDMAN-CHERIAN are not relied on to determine the predictive control action based on a predictive control machine learning model. However, ROY in analogous art teaches using machine learning model to determine and implement energy grid reconfigurations (Abstract), and that the machine learning model can include predictive algorithms to determine and instruct a grid reconfiguration ([0085]). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to apply the teachings of ROY to the teachings of BOARDMAN-CHERIAN such that ROY’s predictive machine learning algorithms is trained using sub-zonal and cluster operation data already available in BOARDMAN-CHERIAN’s distribution system, and then used by the distribution zonal controller/top-level DNNC to determine a predictive control action, as recited in claim 20.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
RATNAYAKE (US20220115867A1) teaches to determine control actions in a multi-level power grid in response to constraint violations.
GOUTARD (US20110282508A1) teaches to predictive analysis via simulation to determine control actions in a power grid.
TAFT (US20120310435A1) teaches control disaggregation in a multi-level power grid in response to load demand changes.
Berahmandpour, H, et. al., (“Development a New Flexibility Index Suitable for Power System Operational Planning”, published 2023, retrieved on 7/7/2026, retrieved from https://journals.sbu.ac.ir/article_102237_5bc5621db28b74095626ce0070054bfc.pdf) teaches to develop an index to quantify power system flexibility.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael V Farina whose telephone number is (571)272-4982. The examiner can normally be reached Mon-Thu 8:00-6:00 EST.
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, Kamini Shah can be reached at (571) 272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.V.F./Examiner, Art Unit 2115
/KAMINI S SHAH/Supervisory Patent Examiner, Art Unit 2115
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