# US 2017/0256004 A1 · Optimal battery sizing for behind-the-meter applications considering participation in demand response programs and demand charge reduction > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2017/0256004 A1 - Document ID: US20170256004A1 - Status: Published application - Kind: A1 - Application number: 15/416,810 - Filed: 2017-01-26 - Published: 2017-09-07 - Earliest claimed: 2016-03-07 - Assignees: NEC Laboratories America, Inc. - Applicants: NEC Laboratories America, Inc. - Inventors: Ali Hooshmand; Seyyed Ali Pourmousavi Kani; Ratnesh Sharma; Shankar Mohan - Web page: https://patentgrep.com/patent/US20170256004A1 ## CPC - G06Q 50/06: Energy or water supply - G05B 15/02: electric - G06F 1/263: {Arrangements for using multiple switchable power supplies, e.g. battery and AC (G06F1/30 takes precedence)} - H02J 3/28: Arrangements for balancing of the load in networks by storage of energy - Y04S 10/50: Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications - 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 - 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) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - H02J: ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY - 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 system and method are provided. The system includes a processor. The processor is configured to receive power related data relating to power usage of power consuming devices at a customer site from a plurality of sources. The processor is further configured to generate object function inputs from the power related data. The processor is additionally configured to apply the generated object function inputs to an objective function to determine an optimal capacity for a battery storage system powering the power consuming devices at the customer site while minimizing a daily operational power cost for the power consuming devices at the customer site. The processor is also configured to initiate an act to control use of one or more batteries of the battery storage system in accordance with the optimal capacity for the battery storage system. ## Claims ### Claim 1 A method, comprising: receiving, by a processor from a plurality of sources, power related data relating to power usage of power consuming devices at a customer site; generating, by the processor, object function inputs from the power related data; applying, by the processor, the generated object function inputs to an objective function to determine an optimal capacity for a battery storage system powering the power consuming devices at the customer site while minimizing a daily operational power cost for the power consuming devices at the customer site; and initiating, by the processor, an act to control use of one or more batteries of the battery storage system in accordance with the optimal capacity for the battery storage system. ### Claim 2 (depends on 1) The method as recited in claim 1, wherein the objective function utilizes a single Mixed Integer Linear Programming (MILP) technique. ### Claim 3 (depends on 1) The method as recited in claim 1, wherein the objective function simultaneously considers demand response program requirements and demand charge requirements to determine the optimal capacity for the battery storage system. ### Claim 4 (depends on 1) The method as recited in claim 1, wherein the objective function employs different inequality constraints related to power generation and power demand balance, and to a maximum and a minimum battery state-of-charge (SOC). ### Claim 5 (depends on 1) The method as recited in claim 1, wherein the generated object function inputs to the objective function include event-day consumption data and non-event-day consumption data that is simultaneously optimized to determine the optimal capacity for the battery storage system while concurrently minimizing a daily operational power cost for the power consuming devices at the customer site. ### Claim 6 (depends on 1) The method as recited in claim 1, wherein the power related data includes a time of use price, a demand charge price, a load profile, a power generation system output, a demand response program, and a battery storage system cost. ### Claim 7 (depends on 6) The method as recited in claim 6, wherein the power generation system output is generated from a power generation system selected from the group consisting of a photovoltaic system, a wind farm system, a hydroelectric system, a biomass system, and a geothermal system. ### Claim 8 (depends on 6) The method as recited in claim 6, wherein the demand response program is selected from the group consisting of a demand bidding program and a scheduled load reduction program. ### Claim 9 (depends on 1) The method as recited in claim 1, wherein the plurality of sources are selected from the group consisting of a utility provider, a facility and a battery storage system provider. ### Claim 10 (depends on 1) The method as recited in claim 1, wherein the objective function uses the generated object function inputs from a finite period of time. ### Claim 11 (depends on 10) The method as recited in claim 10, wherein the finite period of time includes both demand response event days and demand response non-event days. ### Claim 12 (depends on 1) The method as recited in claim 1, wherein the generated object function inputs for the demand response event days includes a demand response reward. ### Claim 13 (depends on 1) The method as recited in claim 1, wherein the generated object function inputs includes a daily cost of energy from the utility provider, a daily cost of the demand charge from the utility provider, a daily cost of the energy storage capital expenditure, and a daily cost for the facility power generation system. ### Claim 14 (depends on 1) The method as recited in claim 1, wherein the objective function uses a generation and demand balance, a battery state of charge upper and lower limits, a battery state of charge equality at the beginning and end of each day, a power generation maximum limit and curtailment, and no reverse power to the grid. ### Claim 15 (depends on 1) The method as recited in claim 1, further comprising installing the battery storage system with the determined optimal capacity at the customer site to power the power consuming devices at a customer site. ### Claim 16 A system, comprising: a processor configured to: receive, from a plurality of sources, power related data relating to power usage of power consuming devices at a customer site; generate object function inputs from the power related data; apply the generated object function inputs to an objective function to determine an optimal capacity for a battery storage system powering the power consuming devices at the customer site while minimizing a daily operational power cost for the power consuming devices at the customer site; and initiate an act to control use of one or more batteries of the battery storage system in accordance with the optimal capacity for the battery storage system. ### Claim 17 (depends on 16) The system as recited in claim 16, wherein the objective function utilizes a single Mixed Integer Linear Programming (MILP) technique. ### Claim 18 (depends on 16) The system as recited in claim 16, wherein the objective function simultaneously considers demand response program requirements and demand charge requirements to determine the optimal capacity for the battery storage system. ### Claim 19 (depends on 16) The system as recited in claim 16, wherein the objective function employs different inequality constraints related to power generation and power demand balance, and to a maximum and a minimum battery state-of-charge (SOC). ### Claim 20 (depends on 16) The system as recited in claim 16, wherein the generated object function inputs to the objective function include event-day consumption data and non-event-day consumption data that is simultaneously optimized to determine the optimal capacity for the battery storage system while concurrently minimizing a daily operational power cost for the power consuming devices at the customer site.