# US 10,873,209 B2 · System and method for dynamic energy storage system control > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,873,209 B2 - Document ID: US10873209B2 - Status: Granted - Kind: B2 - Application number: 16/464,947 - Filed: 2017-11-29 - Published: 2019-10-10 - Granted: 2020-12-22 - Earliest claimed: 2016-11-29 - Assignees: Peak Power, Inc. - Applicants: Peak Power Inc. - Inventors: Yiran Li; Peisen Hu; Derek Lim Soo; Matthew Sachs - Web page: https://patentgrep.com/patent/US10873209B2 ## CPC - H02J 13/00: Circuit arrangements for providing remote monitoring or remote control of equipment in a power distribution network - G05B 13/029: {using neural networks and expert systems} - G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO] - G06N 3/02: Neural networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/08: Learning methods - G06N 3/09: Supervised learning - G06N 3/092: Reinforcement learning - G06Q 10/04: Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) - G06Q 50/06: Energy or water supply - H02J 3/003: {Load forecast, e.g. methods or systems for forecasting future load demand} - H02J 3/32: using batteries or super capacitors with converting means - H02J 13/333: the equipment forming part of substations - G06N 20/10: using kernel methods, e.g. support vector machines [SVM] - H02J 2103/30: Simulating, planning, modelling, reliability check or computer assisted design [CAD] of electric power networks - Y02B 90/20: Smart grids as enabling technology in buildings sector (smart grids supporting the management or operation of end-user stationary applications in general, or like technologies with no associated climate change mitigation effect Y04S20/00) - Y02P 90/50: Energy storage in industry with an added climate change mitigation effect - Y04S 10/14: Energy storage units - Y04S 20/00: Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof - H02J: ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - Y02B: CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS - Y02P: CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS - Y04S: SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS ## Abstract A control system for controlling an energy storage system includes a controller including a plurality of layered nodes configured to form an artificial neural network trained to generate a forecasted transmission level load and confidence value for an entire jurisdiction of a utility distribution system. The controller includes at least one memory and at least one processor configured for: identifying a potential coincident peak for the utility distribution system based on the forecasted transmission level load and confidence value generated by the artificial neural network; and upon identifying a potential coincident peak, transmitting signals to cause the electrical infrastructure to consume energy stored at the energy storage system thereby reducing the energy drawn from the utility distribution system during the identified potential coincident peak. ## Claims ### Claim 1 A control system for controlling an energy storage system, the control system comprising: a meter interface for receiving meter data from at least one metering device positioned to track energy drawn by an electrical infrastructure from a utility distribution system; an energy storage system interface for communicating with an energy storage system connected to the electrical infrastructure, the energy storage system having an energy capacity and an output power rating; and a controller including a plurality of layered nodes configured to form an artificial neural network trained to generate a forecasted transmission level load and confidence value for an entire jurisdiction of the utility distribution system; the controller comprising: at least one memory and at least one processor configured for: identifying a potential coincident peak for the utility distribution system based on the forecasted transmission level load and confidence value generated by the artificial neural network; and upon identifying a potential coincident peak, transmitting signals to cause the electrical infrastructure to consume energy stored at the energy storage system thereby reducing the energy drawn from the utility distribution system during the identified potential coincident peak. ### Claim 2 (depends on 1) The control system of claim 1, wherein the transmitted signals cause the electrical infrastructure to consume energy stored at the energy storage system during time periods selected at least in part based on the energy capacity and output power rating of the energy storage system. ### Claim 3 (depends on 1) The control system of claim 1, wherein the plurality of layered nodes are configured to form the artificial neural network based on a forecasting target defined at least in part on load side resources in the jurisdiction of the utility distribution system, and outputs of generators in the jurisdiction of the utility distribution system. ### Claim 4 (depends on 1) The control system of claim 1, wherein the artificial neural network generates a matrix of forecasted transmission level loads for a defined number of upcoming hours. ### Claim 5 (depends on 1) The control system of claim 1, wherein the controller is configured to update parameters of the artificial neural network based on a reinforcement learning model. ### Claim 6 (depends on 5) The control system of claim 5, wherein updating the parameters of the artificial neural network comprises: executing a discrete tabu search of a plurality of time periods with the parameters of the artificial neural network; and updating the parameters of the artificial neural network to minimize an error between forecasted transmission level loads for the discrete tabu search time periods and an actual forecasted transmission level load for the time period. ### Claim 7 (depends on 1) The control system of claim 1, wherein the energy storage system is configured to have one or more energy storage devices to provide the energy capacity and output power rating of the energy storage system based on historical coincident peak data and energy consumption rates of the electrical infrastructure. ### Claim 8 (depends on 1) The control system of claim 1, wherein the controller is configured to transmit the signals to cause the electrical infrastructure to consume energy stored at the energy storage system based on a predicted load of the electrical infrastructure. ### Claim 9 (depends on 1) The control system of claim 1, wherein the artificial neural network has inputs relating to previous and current conditions which affect load side resources and generators. ### Claim 10 (depends on 1) The control system of claim 1, wherein the artificial neural network is trained with data preprocessed through spectral analysis to account for at least one of: frequency, relative correlation or variance of electricity consumption and generation data. ### Claim 11 A method for controlling an energy storage system, the method comprising: receiving meter data from at least one metering device positioned between a utility distribution system and an electrical infrastructure, the at least one metering device for tracking energy drawn from the utility distribution system; using real-time or near real-time historical and current electricity consumption and generator data with a plurality of layered nodes configured to form an artificial neural network, generating a forecasted transmission level load and confidence value for an entire jurisdiction of the utility distribution system; identifying a potential coincident peak for the utility distribution system based on the forecasted transmission level load and confidence value generated by the artificial neural network; and upon identifying a potential coincident peak, transmitting signals to cause the electrical infrastructure to consume energy stored at the energy storage system thereby reducing the energy drawn from the utility distribution system during the identified potential coincident peak. ### Claim 12 (depends on 11) The method of claim 11, wherein the transmitted signals cause the electrical infrastructure to consume energy stored at the energy storage system during time periods selected at least in part based on the energy capacity and output power rating of the energy storage system. ### Claim 13 (depends on 11) The method of claim 11, wherein the plurality of layered nodes are configured to form the artificial neural network based on a forecasting target defined at least in part on load side resources in the jurisdiction of the utility distribution system, and outputs of generators in the jurisdiction of the utility distribution system. ### Claim 14 (depends on 11) The method of claim 11, wherein the artificial neural network generates a matrix of forecasted transmission level loads for a defined number of upcoming hours. ### Claim 15 (depends on 11) The method of claim 11, comprising: updating parameters of the artificial neural network based on a reinforcement learning model. ### Claim 16 (depends on 15) The method of claim 15, wherein updating the parameters of the artificial neural network comprises: executing a discrete tabu search of a plurality of time periods with the parameters of the artificial neural network; and updating the parameters of the artificial neural network to minimize an error between forecasted transmission level loads for the discrete tabu search time periods and an actual forecasted transmission level load for the time period. ### Claim 17 (depends on 11) The method of claim 11, comprising transmitting the signals to cause the electrical infrastructure to consume energy stored at the energy storage system based on a predicted load of the electrical infrastructure. ### Claim 18 (depends on 11) The method of claim 11, wherein the artificial neural network has inputs relating to previous and current conditions which affect load side resources and generators. ### Claim 19 (depends on 11) The method of claim 11, comprising training the artificial neural network with data preprocessed through spectral analysis to account for at least one of: frequency, relative correlation or variance of electricity consumption and generation data. ### Claim 20 A device for controlling an energy storage system, the device comprising: at least one memory and at least one processor providing a plurality of layered nodes configured to form an artificial neural network trained to generate a forecasted transmission level load and confidence value for an entire jurisdiction of the utility distribution system; the at least one processor configured for: identifying a potential coincident peak for the utility distribution system based on the forecasted transmission level load and confidence value generated by the artificial neural network; and upon identifying a potential coincident peak, transmitting signals to cause the electrical infrastructure to consume energy stored at the energy storage system thereby reducing the energy drawn from the utility distribution system during the identified potential coincident peak.