# US 11,909,210 B2 · Systems and methods for load management > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,909,210 B2 - Document ID: US11909210B2 - Status: Granted - Kind: B2 - Application number: 17/085,974 - Filed: 2020-10-30 - Published: 2021-05-06 - Granted: 2024-02-20 - Earliest claimed: 2019-10-30 - Assignees: Enerwise Global Technologies, Inc. - Applicants: Enerwise Global Technologies, LLC - Inventors: Kyle Harbaugh; Yanchun Wang; Peter Bergeron; Ryan Keperling - Web page: https://patentgrep.com/patent/US11909210B2 ## CPC - H02J 3/14: by switching loads on to, or off from, the networks, e.g. progressively balanced loading - G06Q 50/06: Energy or water supply - H02J 3/17: Demand-responsive operation of AC power transmission or distribution networks - H02J 13/1337: {involving the use of Internet protocols} - H02J 13/333: the equipment forming part of substations - H02J 13/13: characterised by the transmission of data to equipment in the power network - Y02B 70/3225: Demand response systems, e.g. load shedding, peak shaving - 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) - Y04S 20/222: Demand response systems, e.g. load shedding, peak shaving - Y04S 40/12: characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment - H02J: ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY - 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 - 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 The present disclosure relates to systems and methods for optimizing resource operations within an energy network. The systems and methods can include retrieving historical interval meter data, profile data, and a curtailment plan, determining, using the historical interval meter data, estimated annual deviation of a customer's load based on average interval load divided by peak load over a given period, calculating a load curtailment for the customer based on the historical interval meter data, the profile data, the curtailment plan, and the estimated annual deviation of the customer's load, and building a report, using the load curtailment, indicating a resource commitment capability for the customer to help inform market participation and operational decision making for load curtailment and distributed energy resources (DER). ## Claims ### Claim 1 A system for optimizing resource operations, the system comprising: a database for aggregating meter data and customer information; a load data analysis module configured to: retrieve historical interval meter data, profile data, and a curtailment plan; determine, using the historical interval meter data, estimated annual deviation of a customer's load based on average interval load in view of peak load over a given period; calculate a load curtailment for the customer based on the estimated annual deviation of the customer's load, the historical interval meter data, the profile data, and the curtailment plan, build a report, using the load curtailment, indicating a resource commitment capability for the customer to help inform market participation and operational decision making for load curtailment and distributed energy resources (DER); receive RT interval meter data from the customer's facility; subtract an expected curtailment capability of the customer's facility from a contracted curtailment volume from the curtailment plan for that delivery season Guaranteed Load Drop (GLD) for a first value; subtract the contracted curtailment volume for the delivery season Guaranteed Load Drop (GLD) from the RT interval meter data for a second value; subtract the expected curtailment capability of the customer's facility from the RT interval meter data for a third value; and determine a maximum value from the first value, the second, value, and the third value to identify a performance risk currently exposed for the customer's facility, wherein the maximum value indicates a performance risk currently exposed for the customer's facility; and a portfolio engine for generating a commitment plan to be used (1) to optimize the customer's resources and to reduce energy consumption cost as a function of data from the load data analysis module and (2) to provide information for resource control system to perform control strategies to be implemented by the customer. ### Claim 2 (depends on 1) The system of claim 1, wherein the load data analysis module is further configured to: receive real-time (RT) interval meter data from the customer's facility; subtract the RT interval meter data from a contracted Firm Service Level (FSL) for the delivery season to calculate a variance of the customer's expected curtailment capability; and calculate an over-commitment or an under-commitment based on the subtraction. ### Claim 3 (depends on 1) The system of claim 1, wherein the portfolio engine for resource portfolio aggregation optimization is configured to: receive an objective function selection; receive resource commitment capability report, resource costs data, resource portfolio data, and resource portfolio commitment requirements; build an optimization object function based on the resource portfolio commitment requirements; build a constraint set for the objective function selection; solve the optimization object function using the constraint set; and build the commitment plan using the solved the optimization object function. ### Claim 4 (depends on 3) The system of claim 3, wherein: building the constraint set comprises using an aggregation of resource commitments, operational limitations, and regulation limitations to build objective functions to maximize portfolio revenues, minimize portfolio operation cost, and maximize portfolio energy usage. ### Claim 5 (depends on 1) The system of claim 1, further comprising a dispatch engine for conducting real-time portfolio operation monitoring and controls, the dispatch engine configured to: receive a dispatch response event for the customer's facility; collect RT interval meter data from the customer's facility; compare the RT interval meter data to an enrolled curtailment commitment; and determine, based on the comparison, whether the RT interval meter data is sufficient to satisfy an obligation for the dispatch response event. ### Claim 6 (depends on 5) The system of claim 5, wherein the dispatch engine is configured to: initialize mitigation procedures to dispatch additional facilities if the RT interval meter data is insufficient to satisfy the obligation; and initialize mitigation procedures to decrease a number of facilities if the RT interval meter data is more than sufficient to satisfy the obligation. ### Claim 7 A method for optimizing resource operations, the method comprising: retrieving, by a load data analysis module, historical interval meter data, profile data, and a curtailment plan; determining using the historical interval meter data, by the load data analysis module, estimated annual deviation of a customer's load based on average interval load in view of peak load over a given period; calculating, by the load data analysis module, a load curtailment for the customer based on the historical interval meter data, the profile data, the curtailment plan, and the estimated annual deviation of the customer's load; building a report using the load curtailment, by the load data analysis module, indicating a resource commitment capability for the customer to help inform market participation and operational decision making for load curtailment and distributed energy resources (DER); receiving, by the load data analysis module, RT interval meter data from the customer's facility; subtracting, by the load data analysis module, an expected curtailment capability of the customer's facility from a contracted curtailment volume from the curtailment plan for that delivery season Guaranteed Load Drop (GLD) for a first value; subtracting, by the load data analysis module, the contracted curtailment volume for the delivery season Guaranteed Load Drop (GLD) from the RT interval meter data for a second value; subtracting, by the load data analysis module, the expected curtailment capability of the customer's facility from the RT interval meter data for a third value; determining, by the load data analysis module, a maximum value from the first value, the second, value, and the third value to identify a performance risk currently exposed for the customer's facility, wherein the maximum value indicates a performance risk currently exposed for the customer's facility; and generating, with a portfolio engine, a commitment plan to be used (1) to optimize the customer's resources and to reduce energy consumption cost as a function of data retrieved by the load data analysis module and (2) to provide information for resource control system to perform control strategies to be implemented by the customer. ### Claim 8 (depends on 7) The method of claim 7, further comprising: receiving, by the load data analysis module, real-time (RT) interval meter data from the customer's facility; subtracting, by the load data analysis module, the RT interval meter data from a contracted Firm Service Level (FSL) for the delivery season to calculate a variance of the customer's expected curtailment capability; and calculating, by the load data analysis module, an over-commitment or an under-commitment based on the subtraction. ### Claim 9 (depends on 7) The method of claim 7, further comprising: receiving, by the portfolio engine, an objective function selection; receiving, by the portfolio engine, resource commitment capability report, resource costs data, resource portfolio data, and resource portfolio commitment requirements; and building, by the portfolio engine, an optimization object function based on the resource portfolio commitment requirements; building, by the portfolio engine, a constraint set for the objective function selection; solving, by the portfolio engine, the optimization object function using the constraint set; and building, by the portfolio engine, the commitment plan using the solved the optimization object function. ### Claim 10 (depends on 9) The method of claim 9, wherein: building the constraint set comprises using an aggregation of resource commitments, operational limitations, and regulation limitations to build objective functions to maximize portfolio revenues, minimize portfolio operation cost, and maximize portfolio energy usage. ### Claim 11 (depends on 9) The method of claim 9, further comprising: receiving, by a dispatch engine, a dispatch response event for the customer's facility; collecting, by the dispatch engine, RT interval meter data from the customer's facility; comparing, by the dispatch engine, the RT interval meter data to an enrolled curtailment commitment; and determining, by the dispatch engine, whether the RT interval meter data is sufficient to satisfy an obligation for the dispatch response event, based on the comparison. ### Claim 12 (depends on 11) The method of claim 11, further comprising: initializing, by the dispatch engine, mitigation procedures to dispatch additional facilities if the RT interval meter data is insufficient to satisfy the obligation; and initializing, by the dispatch engine, mitigation procedures to decrease a number of facilities if the RT interval meter data is more than sufficient to satisfy the obligation.