# US 8,880,233 B2 · Method and apparatus for delivering power using external data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 8,880,233 B2 - Document ID: US8880233B2 - Status: Granted - Kind: B2 - Application number: 14/265,614 - Filed: 2014-04-30 - Published: 2014-08-21 - Granted: 2014-11-04 - Earliest claimed: 2012-08-14 - Assignees: Stem, Inc. - Applicants: STEM Inc. - Inventors: Ben Kearns; Jon Burlinson; Drew Stevens - Web page: https://patentgrep.com/patent/US8880233B2 ## CPC - G05F 1/66: Regulating electric power - G05B 13/026: {using a predictor} - G06Q 50/06: Energy or water supply - H02J 3/00: Circuit arrangements for AC mains or AC distribution networks - H02J 3/32: using batteries or super capacitors with converting means - H02J 4/00: Circuit arrangements for mains or distribution networks not specified as AC or DC; Circuit arrangements for mains or distribution networks combining AC and DC sections or sub-networks (arrangements using intermediate DC-AC-DC conversion H02J1/002; arrangements using high-voltage DC [HVDC] links H02J3/36) - H02J 4/25: for transfer of electric power between AC and DC networks, e.g. for supplying the DC section within a load from an AC mains system - H02J 13/00: Circuit arrangements for providing remote monitoring or remote control of equipment in a power distribution network - H02J 13/12: Monitoring network conditions, e.g. electrical magnitudes or operational status - H02J 13/183: the equipment comprising energy storage systems - H02J 13/333: the equipment forming part of substations - G05B 2219/39361: Minimize time-energy cost - H02J 3/003: {Load forecast, e.g. methods or systems for forecasting future load demand} - Y02E 40/70: Smart grids as climate change mitigation technology in the energy generation sector - Y02E 60/00: Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation - Y02P 80/10: Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier - Y04S 10/14: Energy storage units - Y04S 10/30: State monitoring, e.g. fault, temperature monitoring, insulator monitoring, corona discharge - 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 - G05F: SYSTEMS FOR REGULATING ELECTRIC OR MAGNETIC VARIABLES - 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) - 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 - H02J: ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY - Y02E: REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION - 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 an energy storage system located behind a utility meter uses a unique, feedback-based, communication and control method to reliably and efficiently maximize economic return of the energy storage system. Operating parameters for the energy storage system are calculated at an external, centralized data center, and are selected to prevent electrical power demand of an electric load location from exceeding a specified set-point by discharging energy storage devices, such as DC batteries, through a bidirectional energy converter during peak demand events. The control system can operate autonomously in the case of a communications failure. ## Claims ### Claim 1 A method for generating operating instructions for a distributed energy storage system, the method comprising: iteratively executing a simulation to generate one or more demand set-point curves and one or more battery state-of-charge profiles for an electric bad location associated with the distributed energy storage system, and transmitting the one or more demand set-point curves and the one or more battery state-of-charge profiles to a set-point controller that is included within the distributed energy storage system and is configured to update a demand set point based on the one or more demand set-point curves and to control a rate of energy flow for an energy source associated with the electric bad location based on the one or more battery state-of-charge profiles. ### Claim 2 (depends on 1) The method of claim 1, further comprising: generating a bad forecast for the electric bad location, wherein the bad forecast includes one or more forward-looking forecasted bad profiles for the electric bad location, and wherein iteratively executing the simulation includes iteratively executing the simulation based on the load forecast. ### Claim 3 (depends on 2) The method of claim 2, wherein the load forecast is generated based on historical load data that describes previous load amounts drawn by the electric load location. ### Claim 4 (depends on 3) The method of claim 3, wherein generating the load forecast comprises generating a set of coefficients, wherein each coefficient comprises a weight to be applied to the historical load data and is associated with at least one of weather data, business hours data, maintenance event data, and special events data. ### Claim 5 (depends on 3) The method of claim 3, wherein the simulation comprises simulated operations of the distributed energy storage system based on at least one of a peak billing time, tariff information, current operation conditions associated with the electric bad location, and the load forecast for the electric load location. ### Claim 6 (depends on 1) The method of claim 1, wherein iteratively executing the simulation comprises verifying that the electric load location can maintain operation with the one or more demand set-point curves and one or more battery state-of-charge profiles. ### Claim 7 (depends on 1) The method of claim 1, wherein iteratively executing the simulation comprises determining that the electric bad location cannot maintain operation with the one or more demand set-point curves and one or more battery state-of-charge profiles, and modifying one of the one or more demand set-point curves and one or more battery state-of-charge profiles in response to generate one or more of an adjusted demand set-point curve and an adjusted battery state-of-charge profile. ### Claim 8 (depends on 7) The method of claim 7, wherein iteratively executing the simulation further comprises executing simulated operations of the distributed energy storage system based on one of the one or more adjusted demand set-point curve and the adjusted battery state-of-charge profile. ### Claim 9 (depends on 8) The method of claim 8, further comprising terminating iteratively executing the simulation upon determining that the distributed energy storage system can maintain operation with the one or more adjusted demand set-point curve and the adjusted battery state-of-charge profile. ### Claim 10 A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform the steps of: iteratively executing a simulation to generate one or more demand set-point curves and one or more battery state-of-charge profiles for an electric bad location associated with the distributed energy storage system, and transmitting the one or more demand set-point curves and the one or more battery state-of-charge profiles to a set-point controller that is included within the distributed energy storage system and is configured to update a demand set point based on the one or more demand set-point curves and to control a rate of energy flow for an energy source associated with the electric bad location based on the one or more battery state-of-charge profiles. ### Claim 11 (depends on 10) The non-transitory computer-readable medium of claim 10, further storing instructions that cause the processor to execute the step of: generating a bad forecast for the electric bad location, wherein the bad forecast includes one or more forward-looking forecasted bad profiles for the electric bad location, and wherein iteratively executing the simulation includes iteratively executing the simulation based on the bad forecast. ### Claim 12 (depends on 11) The non-transitory computer-readable medium of claim 11, wherein the bad forecast is generated based on historical load data that describes previous load amounts drawn by the electric load location. ### Claim 13 (depends on 12) The non-transitory computer-readable medium of claim 12, wherein generating the load forecast comprises generating a set of coefficients, wherein each coefficient comprises a weight to be applied to the historical bad data and is associated with at least one of weather data, business hours data, maintenance event data, and special events data. ### Claim 14 (depends on 12) The non-transitory computer-readable medium of claim 12, wherein the simulation comprises simulated operations of the distributed energy storage system based on at least one of a peak buffing time, tariff information, current operation conditions associated with the electric bad location, and the bad forecast for the electric bad location. ### Claim 15 (depends on 10) The non-transitory computer-readable medium of claim 10, wherein iteratively executing the simulation comprises verifying that the electric load location can maintain operation with the one or more demand set-point curves and one or more battery state-of-charge profiles. ### Claim 16 (depends on 10) The non-transitory computer-readable medium of claim 10, wherein iteratively executing the simulation comprises determining that the electric load location cannot maintain operation with the one or more demand set-point curves and one or more battery state-of-charge profiles, and modifying one of the one or more demand set-point curves and one or more battery state-of-charge profiles in response to generate one or more of an adjusted demand set-point curve and an adjusted battery state-of-charge profile. ### Claim 17 (depends on 16) The non-transitory computer-readable medium of claim 16, wherein iteratively executing the simulation further comprises executing simulated operations of the distributed energy storage system based on one of the one or more adjusted demand set-point curve and the adjusted battery state-of-charge profile. ### Claim 18 (depends on 17) The non-transitory computer-readable medium of claim 17, further storing instructions that cause the processor to execute the step of terminating iteratively executing the simulation upon determining that the distributed energy storage system can maintain operation with the one or more adjusted demand set-point curve and the adjusted battery state-of-charge profile. ### Claim 19 A system for generating operating instructions for a distributed energy storage system, the system comprising: a simulation engine configured to: iteratively execute a simulation to generate one or more demand set-point curves and one or more battery state-of-charge profiles for an electric load location associated with the distributed energy storage system, and transmit the one or more demand set-point curves and the one or more battery state-of-charge profiles to a set-point controller that is included within the distributed energy storage system and is configured to update a demand set point based on the one or more demand set-point curves and to control a rate of energy flow for an energy source associated with the electric load location based on the one or more battery state-of-charge profiles. ### Claim 20 (depends on 19) The system of claim 19, further comprising: a forecast engine configured to: generate a load forecast for the electric load location, and transmit the load forecast to the simulation engine for iteratively executing the simulation, wherein the load forecast includes one or more forward-looking forecasted load profiles for the electric load location, and wherein iteratively executing the simulation includes iteratively executing the simulation based on the load forecast.