Notice of Pre-AIA or AIA Status
Claims 1-18 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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/03/2026 has been entered.
Response to Amendment
The amendment filed on 12/11/2025 has been entered and considered by the examiner. By the
amendment, claims 1, 7, 13 and 17 are amended. 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
The amended claims now recite specific technical parameters and processes that integrate any abstract idea into a practical application of managing actual power distribution systems. Additionally, claim 1 as amended recites "wherein the simulation flow iteratively adjusts DER sizing using binary search based on detected constraint violations to determine maximum allowable DER capacity per node." This limitation specifies a technical process for managing actual power grid constraints by determining the maximum capacity of distributed energy resources that can be safely integrated at specific nodes without violating electrical system constraints. The amended claims recite specific improvements to power distribution technology through the use of concrete electrical engineering parameters and constraint-based optimization.
Examiner response
Examiner respectfully disagrees. While the applicant argues that 'electrical distance', 'voltage sensitivity (dV/dP, dV/dQ)', and 'feeder topology' are specific technical parameters, it is the Examiner position that the claim as a whole is directed to analyzing this data to cluster nodes. The steps of using values of ‘electrical distance,' 'voltage sensitivity,' 'feeder topology' and 'clustering' are actions that can be performed mentally or with pen and paper (i.e., by an electrical engineer looking at a schematic and performing calculations and using that value to cluster nodes)." a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016) See MPEP 2106.05(a)(2)(III). The amendment merely adds further data points to the abstract idea of clustering. The claim does not improve the electrical functioning of the grid itself; it merely improves the analysis of the grid's data. The applicant has not shown that these specific parameters require a specialized, unconventional machine to process. The newly added limitation “The steps of "wherein the simulation flow iteratively adjusts DER sizing using binary search based on detected constraint violations to determine maximum allowable DER capacity per node" describe a process that can be performed in the human mind, or by a human using pen and paper. A human engineer can manually iterate through potential DER sizes and compare them against constraints. The claim relies on a "binary search" algorithm to determine a maximum value. Binary search is a mathematical algorithm for finding an item in a sorted list by repeatedly halving the search space. The recitation of "detecting constraint violations" and "determining maximum allowable DER capacity" are simply mathematical operations applied to constraints. These are mathematical relationships or formulas, which are considered abstract ideas. The claim fails to provide an "inventive concept" that transforms the abstract idea into a patent-eligible application (MPEP § 2106.05).
Applicant arguments
The amended claims recite additional elements that amount to significantly more than any abstract idea. Claims integrate the abstract idea into a practical application by reciting specific technological improvements to distribution system management through intelligent node clustering based on DER integration characteristics, PCA-based scenario reduction, and QSTS simulation that outputs specific technical parameters (node voltages and line current). These elements work together to solve the technical problem of computational inefficiency in hosting capacity analysis while maintaining accuracy, representing a concrete improvement to computer functionality in power grid management rather than merely automating mental processes.
Examiner response
Examiner respectfully disagrees. Claim do not recite the additional elements that are more than judicial exception. The claims are directed to an abstract idea, namely the mental process and mathematical concept and processing of grid data (clustering, PCA reduction, simulation). The overall process of analyzing a grid to determine where to place DERs is characterized as a mental process that a human analyst could perform with pencil and paper, even if a computer does it more efficiently. The process of "grouping nodes based on DER system integration characteristics" is a form of classification or clustering. Algorithms for clustering or organizing data can be considered a mental process or mathematical concept. PCA is a well-known mathematical technique for dimensionality reduction used to identify principal components in data. A claim that merely applies a mathematical algorithm is generally considered abstract. Processing scenarios using Principal Component Analysis (PCA) is a mathematical algorithm. Reducing complexity by grouping similar scenarios is a mathematical concept of data simplification. The process of generating and grouping scenarios can be viewed as organizing human activity or a mental process, as it is a structured approach to planning. QSTS involves performing a series of power flow calculations based on mathematical principles and abstract. Binary search is a mathematical algorithm for finding an item in a sorted list by repeatedly halving the search space. Simply stating that these data analysis steps are performed by a computer does not make an abstract idea patent-eligible. The claimed "output" of parameters (node voltages and line current) is the result of the mental process and mathematical analysis, not a physical transformation or a tangible improvement to the underlying hardware functionality itself. The claim uses standard computational tools to execute the analysis and simulation, and implementing an abstract idea on a generic computer is insufficient to transform it into a patent-eligible invention. Merely applying a known analytical process—in this case, mathematical and mental processes—to a technological field (hosting capacity analysis) does not automatically confer eligibility. Executing a "simulation flow" with "power flow tools" on a generic computer is not "significantly more" than simply running an abstract idea on a machine. It does not describe an improvement to the computer's functionality itself, but merely uses a conventional computer to perform the mathematical calculations and simulation. 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.
Applicant prior art arguments
Alrushoud does not teach or suggest using electrical distance from a feeder head, voltage sensitivity parameters comprising dV/dP and dV/dQ, and feeder branch topology as clustering parameters to cluster the nodes, as recited by amended claim 1. Furthermore, the amended claims require that "the simulation flow iteratively adjusts DER sizing using binary search based on detected constraint violations to determine maximum allowable DER capacity per node." None of the cited references teach or suggest this specific iterative binary search optimization process for DER sizing based on constraint violations. Alrushoud's approach involves recording voltage profiles under different PV capacity allocations but does not disclose the claimed binary search methodology for iteratively adjusting DER sizing.
Examiner response
In view of claim amendment, Examiner withdraw the Teja reference and use the new reference Guddanti and Conti.
Alrushoud’s still teaches wherein the feeder topology analysis uses electrical distance from a feeder head, and feeder branch topology as clustering parameters to cluster the nodes; (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. 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 can also be read as electrical distance. See office action.
In view of newly added limitation, "the simulation flow iteratively adjusts DER sizing using binary search based on detected constraint violations to determine maximum allowable DER capacity per node”. Examiner added the new art 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.) See office action.
For the newly added limitation “voltage sensitivity parameters comprising dV/dP and dV/dQ” Examiner added the new reference. Conti et al. ("Voltage sensitivity analysis in MV distribution networks." Proceedings of the 6th WSEAS/IASME International Conference on Electric Power Systems, High Voltages, Electric Machines, Tenerife, Spain. 2006.). See office action.
Applicant argument
While Bletterie teaches "principal component analysis" with "first two principal components" explaining "about 66%" of variance, this analysis is applied to feeder classification for visualization purposes, not to the scenario management integration required by the claims. Bletterie et al., page 11, Lines 19-24. The purpose of Bletterie's work is "to allow DSOs to easily discriminate between LV feeders in which reactive power-based voltage control can help in increasing the hosting capacity." Bletterie et al., page 3, Lines 1-5. This classification objective differs significantly from the claimed scenario management that processes scenarios using principal component analysis to group scenarios with similar principal components for hosting capacity analysis. The Examiner has not provided adequate reasoning why one of ordinary skill in the art would combine these references, which address different technical problems and employ different methodologies. The alleged combination of the references does not even recognize the problems addressed by the claims, let alone teach or suggest (and thus provides a much different structure than) a solution similar to that of the claims. Therefore, the alleged combination of the references fails to teach or suggest this feature of the claims.
Examiner response
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). For the applicant arguments, 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. Thus, the rejection is maintained in view of Bletterie.
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 is 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; (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 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. (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 using binary search based on detected constraint violations to determine maximum allowable DER capacity per node. (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)
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 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 1-18 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 Conti et al. ("Voltage sensitivity analysis in MV distribution networks." Proceedings of the 6th WSEAS/IASME International Conference on Electric Power Systems, High Voltages, Electric Machines, Tenerife, Spain. 2006.)
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
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 simulation flow iteratively adjusts DER sizing using binary search based on detected constraint violations to determine maximum allowable DER capacity per node 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.
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 simulation flow iteratively adjusts DER sizing using binary search based on detected constraint violations to determine maximum allowable DER capacity per node 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.
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 simulation flow iteratively adjusts DER sizing using binary search based on detected constraint violations to determine maximum allowable DER capacity per node. (see section III.B and fig 2- 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.)
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 simulation flow iteratively adjusts DER sizing using binary search based on detected constraint violations to determine maximum allowable DER capacity per node 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.
In the related field of invention, Conti 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 abstract-The paper presents a study on the assessment of node voltage sensitivity in distribution networks with respect to variations of node active and reactive powers. The work provides an analytical tool to quantify node voltages variation due to injections of node powers at any MV distribution network injection point. See equation 20)
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 as taught by Conti in the system of Alrushoud, Bletterie and Guddanti for the solution of various power system optimization problems, related, for example, to voltage regulation, loss reduction, network expansion planning, optimal placement of reactive sources and generators, etc. (See Introduction, Conti)
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 Conti 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 Conti 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 showing construction M*N PV development scenarios.
Regarding claim 4, 10 and 16
Alrushoud, Bletterie, Guddanti and Conti 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 Conti 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 Conti 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.
Conclusion
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
BAHRAMIRAD et al. US 20200091765 A1
Discussing a method for determining a distribution system's hosting capacity for distributed power generation. Hosting capacity may be assessed on a feeder-by-feeder basis. Performing a hosting capacity assessment may include randomly selecting spot load points in a feeder as candidates for installation of distributed power generation sources. The hosting capacity assessment may be repeated a number of times to ensure optimal results and to correct for any violations of system performance parameters caused by an addition of distributed power generation at a given spot load point.
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.
10. All claims 1-18 are rejected.
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/PURSOTTAM GIRI/Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186
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