System and method for optimal control of energy storage system
First Claim
1. A method for controlling an operation of one or more energy storage systems, the method comprising the steps of:
- (a) receiving, at a computer system including at least one computer, live, historical, or forecast data related to the operation of the one or more energy storage systems from one or more data sources;
(b) calculating, by the computer system, one or more forecasts of one or more parameters relating to the operation of the one or more energy storage systems and an associated forecast uncertainty using one or more forecasting techniques based on the received live, historical, or forecast data;
(c) determining, by the computer system, an optimal dispatch schedule for the operation of the one or more energy storage systems based on the one or more forecasts, wherein determining the optimal dispatch schedule includes;
(i) generating, by the computer system, one or more forecast scenarios using at least one of single forecast pass-through technique, Monte-Carlo scenario generation technique and chance constrained optimization constraint generation technique;
(ii) selecting, by the computer system, one or more sets of the generated one or more forecast scenarios for optimization;
(iii) generating, by the computer system, one or more dispatch schedules by applying one or more optimization techniques to the selected one or more sets of the generated one or more forecast scenarios, wherein the one or more optimization techniques comprise at least one of fixed rule scheduler, forecast-based rule scheduler, non-linear multiple rule optimization scheduler, non-linear economic optimization scheduler and neural network scheduler; and
(iv) aggregating, by the computer system, the one or more dispatch schedules using averaging, weighted averaging, time-variable weighted averaging, condition variable weighted averaging, or neural network to produce the optimal dispatch schedule;
(d) using, by the computer system, the optimal dispatch schedule to determine one or more energy storage system parameters; and
(e) sending, by the computer system, the one or more energy storage system parameters to the one or more energy storage systems to control the operation of the one or more energy storage systems.
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Accused Products
Abstract
Systems and methods for optimal control of one or more energy storage systems are provided. Based on live, historical, and/or forecast data received from one or more data sources, one or more forecasts of one or more parameters relating to the operation of the one or more energy storage systems and an associated forecast uncertainty may be calculated by various forecasting techniques. Using one or more optimization techniques, an optimal dispatch schedule for the operation of the one or more energy storage systems may be created based on the forecasts. The optimal dispatch schedule may be used to determine one or more energy storage system parameters, which are used to control the operation of the energy storage systems.
55 Citations
54 Claims
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1. A method for controlling an operation of one or more energy storage systems, the method comprising the steps of:
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(a) receiving, at a computer system including at least one computer, live, historical, or forecast data related to the operation of the one or more energy storage systems from one or more data sources; (b) calculating, by the computer system, one or more forecasts of one or more parameters relating to the operation of the one or more energy storage systems and an associated forecast uncertainty using one or more forecasting techniques based on the received live, historical, or forecast data; (c) determining, by the computer system, an optimal dispatch schedule for the operation of the one or more energy storage systems based on the one or more forecasts, wherein determining the optimal dispatch schedule includes; (i) generating, by the computer system, one or more forecast scenarios using at least one of single forecast pass-through technique, Monte-Carlo scenario generation technique and chance constrained optimization constraint generation technique; (ii) selecting, by the computer system, one or more sets of the generated one or more forecast scenarios for optimization; (iii) generating, by the computer system, one or more dispatch schedules by applying one or more optimization techniques to the selected one or more sets of the generated one or more forecast scenarios, wherein the one or more optimization techniques comprise at least one of fixed rule scheduler, forecast-based rule scheduler, non-linear multiple rule optimization scheduler, non-linear economic optimization scheduler and neural network scheduler; and (iv) aggregating, by the computer system, the one or more dispatch schedules using averaging, weighted averaging, time-variable weighted averaging, condition variable weighted averaging, or neural network to produce the optimal dispatch schedule; (d) using, by the computer system, the optimal dispatch schedule to determine one or more energy storage system parameters; and (e) sending, by the computer system, the one or more energy storage system parameters to the one or more energy storage systems to control the operation of the one or more energy storage systems. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A computer system for controlling an operation of one or more energy storage systems, the computer system comprising:
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one or more processors; and one or more memories operatively connected to the one or more processors and having stored thereon instructions that are executable by the one or more processors to cause the computer system to perform the steps of; (a) receiving, at the computer system, live, historical, or forecast data related to the operation of the one or more energy storage systems from one or more data sources; (b) calculating, by the computer system, one or more forecasts of one or more parameters relating to the operation of the one or more energy storage systems and an associated forecast uncertainty using one or more forecasting techniques based on the received live, historical, or forecast data; (c) determining, by the computer system, an optimal dispatch schedule for the operation of the one or more energy storage systems based on the one or more forecasts, wherein determining an optimal dispatch schedule includes; (i) generating, by the computer system, one or more forecast scenarios using at least one of single forecast pass-through technique, Monte-Carlo scenario generation technique and chance constrained optimization constraint generation technique; (ii) selecting, by the computer system, one or more sets of the generated one or more forecast scenarios for optimization; (iii) generating, by the computer system, one or more dispatch schedules by applying one or more optimization techniques to the selected one or more sets of the generated one or more forecast scenarios, wherein the one or more optimization techniques comprise at least one of fixed rule scheduler, forecast-based rule scheduler, non-linear multiple rule optimization scheduler, non-linear economic optimization scheduler and neural network scheduler; and (iv) aggregating, by the computer system, the one or more dispatch schedules using averaging, weighted averaging, time-variable weighted averaging, condition variable weighted averaging, or neural network to produce the optimal dispatch schedule; (d) using, by the computer system, the optimal dispatch schedule to determine one or more energy storage system parameters; and (e) sending, by the computer system, the one or more energy storage system parameters to the one or more energy storage systems to control the operation of the one or more energy storage systems. - View Dependent Claims (15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31)
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32. A non-transitory computer readable medium having stored thereon program code configured for execution by a computer system comprising at least one computer, wherein, when executed by the computer system, the program code causes the computer system to perform a method for controlling an operation of one or more energy storage systems, the method comprising the steps of:
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(a) receiving, at the computer system, live, historical, or forecast data related to the operation of the one or more energy storage systems from one or more data sources; (b) calculating, by the computer system, one or more forecasts of one or more parameters relating to the operation of the one or more energy storage systems and an associated forecast uncertainty using one or more forecasting techniques based on the received live, historical, or forecast data; (c) determining, by the computer system, an optimal dispatch schedule for the operation of the one or more energy storage systems based on the one or more forecasts, wherein determining an optimal dispatch schedule includes; (i) generating, by the computer system, one or more forecast scenarios using at least one of single forecast pass-through technique, Monte-Carlo scenario generation technique and chance constrained optimization constraint generation technique; (ii) selecting, by the computer system, one or more sets of the generated one or more forecast scenarios for optimization; (iii) generating, by the computer system, one or more dispatch schedules by applying one or more optimization techniques to the selected one or more sets of the generated one or more forecast scenarios, wherein the one or more optimization techniques comprise at least one of fixed rule scheduler, forecast-based rule scheduler, non-linear multiple rule optimization scheduler, non-linear economic optimization scheduler and neural network scheduler; and (iv) aggregating, by the computer system, the one or more dispatch schedules using averaging, weighted averaging, time-variable weighted averaging, condition variable weighted averaging, or neural network to produce the optimal dispatch schedule; (d) using, by the computer system, the optimal dispatch schedule to determine one or more energy storage system parameters; and (e) sending, by the computer system, the one or more energy storage system parameters to the one or more energy storage systems to control the operation of the one or more energy storage systems. - View Dependent Claims (33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45)
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46. A method for controlling an operation of one or more energy storage systems, the method comprising the steps of:
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(a) receiving, at a computer system including at least one computer, live, historical, or forecast data related to the operation of the one or more energy storage systems from one or more data sources, the live, historical, or forecast data including live, historical, or third party forecast data relating to energy price for real-time markets, energy price for day-ahead markets, ancillary services price for real-time markets, ancillary services price for day-ahead markets, weather, electricity demand and power generator availability; (b) calculating, by the computer system, one or more forecasts of one or more parameters relating to the operation of the one or more energy storage systems and an associated forecast uncertainty using one or more forecasting techniques based on the received live, historical, or forecast data, the one or more forecasting techniques including a neural network forecast technique; (c) determining, by the computer system, an optimal dispatch schedule for the operation of the one or more energy storage systems based on the one or more forecasts, wherein determining the optimal dispatch schedule includes; (i) generating, by the computer system, one or more forecast scenarios for the energy price for real-time markets, the energy price for day-ahead markets, the ancillary services price for real-time markets and the ancillary services price for day-ahead markets using Monte-Carlo scenario generation technique; (ii) generating, by the computer system, one or more sets of the generated one or more forecast scenarios that are more similar to one another, more different from one another, or randomly distributed across the generated one or more forecast scenarios; (iii) generating, by the computer system, one or more dispatch schedules by applying non-linear economic optimization scheduler to the selected one or more sets of the generated one or more forecast scenarios; and (iii) averaging, by the computer system, the generated one or more dispatch schedules to produce the optimal dispatch schedule; (d) using, by the computer system, the optimal dispatch schedule to determine one or more energy storage system parameters; and (e) sending, by the computer system, the one or more energy storage system parameters to the one or more energy storage systems to control the operation of the one or more energy storage systems.
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47. A method for controlling an operation of one or more energy storage systems, the method comprising the steps of:
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(a) receiving, at a computer system including at least one computer, live, historical, or forecast data related to the operation of the one or more energy storage systems from one or more data sources, the live, historical, or forecast data including live, historical, or third party forecast data relating to net substation load, solar generation and temperature; (b) calculating, by the computer system, one or more forecasts of one or more parameters relating to the operation of the one or more energy storage systems and an associated forecast uncertainty using one or more forecasting techniques based on the received live, historical, or forecast data, wherein calculating the one or more forecasts includes; creating, by the computer system, at least two forecasts for the net substation load by simultaneously using linear regression with historical average residual forecast technique and neural network forecast technique, and producing, by the computer system, the one or more forecasts by either selecting one of the at least two forecasts based on historically expected uncertainty associated with each of the at least two forecasts based on historically expected uncertainty associated with each of the at least two forecasts, or averaging the at least two forecasts; (c) determining, by the computer system, an optimal dispatch schedule for the operation of the one or more energy storage systems based on the one or more forecasts, wherein determining the optimal dispatch schedule includes; (i) generating, by the computer system, a predetermined number of forecast scenarios for the net substation load using Monte-Carlo scenario generation technique; (ii) selecting, by the computer system, a subset of the generated predetermined number of forecast scenarios based on system configuration, available computational time and resources, or reported forecast uncertainty; (iii) generating, by the computer system, a dispatch schedule for each of the selected subset of the generated predetermined number of forecast scenarios by using non-linear multiple rule optimization scheduler to minimize a peak net substation load after the operation of the one or more energy storage systems; and (iv) averaging, by the computer system, the generated dispatch schedules for the selected subset of the generated predetermined number of forecast scenarios to produce the optimal dispatch schedule; (d) using, by the computer system, the optimal dispatch schedule to determine one or more energy storage system parameters; and (e) sending, by the computer system, the one or more energy storage system parameters to the one or more energy storage systems to control the operation of the one or more energy storage systems.
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48. A method for controlling an operation of one or more energy storage systems, the one or more energy storage systems including a battery system having one or more inverters and one or more battery management controllers, the method comprising the steps of:
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(a) receiving, at a computer system including at least one computer, live, historical, or forecast data related to the operation of the one or more energy storage systems from one or more data sources, the live, historical, or forecast data including live, historical, or third party forecast data relating to weather and load; (b) calculating, by the computer system, one or more forecasts of one or more parameters relating to the operation of the one or more energy storage systems and an associated forecast uncertainty using one or more forecasting techniques based on the received live, historical, or forecast data, the calculating the one or more forecasts including producing, by the computer system, the one or more forecasts by calculating probability of a system peak occurring in each of next twenty-four hours using neural network forecast technique based on the received live, historical, or third party forecast data relating to weather and load; (c) determining, by the computer system, an optimal dispatch schedule for the operation of the one or more energy storage systems based on the one or more forecasts, wherein determining the optimal dispatch schedule includes; determining, by the computer system, the optimal dispatch schedule based on the one or more forecasts using a forecast based rule scheduler to minimize customer load and maximize an output of the one or more energy storage systems during a peak hour; (d) using, by the computer system, the optimal dispatch schedule to determine one or more energy storage system parameters, wherein using the optimal dispatch schedule to determine one or more energy storage system parameters includes; determining, by the computer system, real power and reactive power to input or extract from a grid or a microgrid connected to the battery system based on the optimal dispatch schedule and the live data received from the one or more data sources; and (e) sending, by the computer system, the one or more energy storage system parameters to the one or more energy storage systems to control the operation of the one or more energy storage systems.
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49. A computer system for controlling an operation of one or more energy storage systems, the computer system comprising:
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one or more processors; and one or more memories operatively connected to the one or more processors and having stored thereon instructions that are executable by the one or more processors to cause the computer system to perform the steps of; (a) receiving, at the computer system, live, historical, or forecast data related to the operation of the one or more energy storage systems from one or more data sources, the live, historical, or forecast data including live, historical, or third party forecast forecast data relating to energy price for real-time markets, energy price for day-ahead markets, ancillary services price for real-time markets, ancillary services price for day-ahead markets, weather, electricity demand and power generator availability; (b) calculating, by the computer system, one or more forecasts of one or more parameters relating to the operation of the one or more energy storage systems and an associated forecast uncertainty using one or more forecasting techniques based on the received live, historical, or forecast data, the one or more forecasting techniques including neural network forecast technique; (c) determining, by the computer system, an optimal dispatch schedule for the operation of the one or more energy storage systems based on the one or more forecasts, wherein determining the optimal dispatch schedule includes; (i) generating, by the computer system, one or more forecast scenarios for the energy price for real-time markets, the energy price for day-ahead markets, the ancillary services price for real-time markets and the ancillary services price for day-ahead markets using Monte-Carlo scenario generation technique; (ii) generating, by the computer system, one or more sets of the generated one or more forecast scenarios that are more similar to one another, more different from one another, or randomly distributed across the generated one or more forecast scenarios; (iii) generating, by the computer system, one or more dispatch schedules by applying non-linear economic optimization scheduler to a selected one or more sets of the generated one or more forecast scenarios; and (iv) averaging, by the computer system, the generated one or more dispatch schedules to produce the optimal dispatch schedule; (d) using, by the computer system, the optimal dispatch schedule to determine one or more energy storage system parameters; and (e) sending, by the computer system, the one or more energy storage system parameters to the one or more energy storage systems to control the operation of the one or more energy storage systems.
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50. A computer system for controlling an operation of one or more energy storage systems, the computer system comprising:
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one or more processors; and one or more memories operatively connected to the one or more processors and having stored thereon instructions that are executable by the one or more processors to cause the computer system to perform the steps of; (a) receiving, at the computer system, live, historical, or forecast data related to the operation of the one or more energy storage systems from one or more data sources, the live, historical, or forecast data including live, historical, or third party forecast data relating to net substation load, solar generation and temperature; (b) calculating, by the computer system, one or more forecasts of one or more parameters relating to the operation of the one or more energy storage systems and an associated forecast uncertainty using one or more forecasting techniques based on the received live, historical, or forecast data, wherein calculating the one or more forecasts includes; creating, by the computer system, at least two forecasts for the net substation load by simultaneously using linear regression with historical average residual forecast technique and neural network forecast technique; and producing, by the computer system, the one or more forecasts by either selecting one of at least two forecasts based on historically expected uncertainty associated with each of the at least two forecasts, or averaging the at least two forecasts; (c) determining, by the computer system, an optimal dispatch schedule for the operation of the one or more energy storage systems based on the one or more forecasts, wherein determining the optimal dispatch schedule includes; (i) generating, by the computer system, a predetermined number of forecast scenarios for the next substation load using Monte-Carlo scenario generation technique; (ii) selecting, by the computer system, a subset of the generated predetermined number of forecast scenarios based on system configuration, available computational time and resources, or reported forecast uncertainty; (iii) generating, by the computer system, a dispatch schedule for each of the selected subset of the generated predetermined number of forecast scenarios by using non-linear multiple rule optimization scheduler to minimize peak net substation load after the operation of the one or more energy storage systems; and (iv) averaging, by the computer system, the generated dispatch schedules for the selected subset of the generated predetermined number of forecast scenarios to produce the optimal dispatch schedule; (d) using, by the computer system, the optimal dispatch schedule to determine one or more energy storage system parameters; and (e) sending, by the computer system, the one or more energy storage system parameters to the one or more energy storage systems to control the operation of the one or more energy storage systems.
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51. A computer system for controlling an operation of one or more energy storage systems, the computer system comprising:
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one or more processors; and one or more memories operatively connected to the one or more processors and having stored thereon instructions that are executable by the one or more processors to cause the computer system to perform the steps of; (a) receiving, at the computer system, live, historical, or forecast data related to the operation of the one or more energy storage systems from one or more data sources, the live, historical, or forecast data including live, historical, or third party forecast data relating to weather and load; (b) calculating, by the computer system, one or more forecasts of one or more parameters relating to the operation of the one or more energy storage systems and an associated forecast uncertainty using one or more forecasting techniques based on the received live, historical, or forecast data, the calculating the one or more forecasts including producing, by the computer system, the one or more forecasts by calculating probability of a system peak occurring in each of next twenty-four hours using neural network forecast technique based on the received live, historical, or third party forecast data relating to weather and load; (c) determining, by the computer system, an optimal dispatch schedule for the operation of the one or more energy storage systems based on the one or more forecasts, wherein determining an optimal dispatch schedule includes; determining, by the computer system, the optimal dispatch schedule based on the one or more forecasts using a forecast based rule scheduler to minimize customer load and maximize an output of the one or more energy storage systems during a peak hour; (d) using, by the computer system, the optimal dispatch schedule to determine one or more energy storage system parameters, wherein using the optimal dispatch schedule to determine one or more energy storage system parameters includes; determining, by the computer system, real power and reactive power to input or extract from a grid or microgrid connected to the battery system based on the optimal dispatch schedule and the live data received from the one or more data sources; and (e) sending, by the computer system, the one or more energy storage system parameters to the one or more energy storage systems to control the operation of the one or more energy storage systems.
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52. A non-transitory computer readable medium having stored thereon program code configured for execution by a computer system comprising at least one computer, wherein, when executed by the computer system, the program code causes the computer system to perform a method for controlling an operation of one or more energy storage systems, the method comprising the steps of:
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(a) receiving, at the computer system, live, historical, or forecast data related to the operation of the one or more energy storage systems from one or more data sources, the live, historical, or forecast data including live, historical, or third party forecast data relating to energy price for real-time markets, energy price for day-ahead markets, ancillary services price for real-time markets, ancillary services price for day-ahead markets, weather, electricity demand and power generator availability; (b) calculating, by the computer system, one or more forecasts of one or more parameters relating to the operation of the one or more energy storage systems and an associated forecast uncertainty using one or more forecasting techniques based on the received live, historical, or forecast data, the one or more forecasting techniques including a neural network forecast technique; (c) determining, by the computer system, an optimal dispatch schedule for the operation of the one or more energy storage systems based on the one or more forecasts, wherein determining an optimal dispatch schedule includes; (i) generating, by the computer system, one or more forecast scenarios for the energy price for real-time markets, the energy price for day-ahead markets, the anciallary services price for real-time markets and the ancillary services price for day-ahead markets using Monte-Carlo scenario generation technique; (ii) selecting, by the computer system, one or more sets of the generated one or more forecast scenarios that are more similar to one another, more different from one another, or randomly distributed across the generated one or more forecast scenarios; (iii) generating, by the computer system, one or more dispatch schedules by applying non-linear economic optimization scheduler to a selected one or more sets of the generated one or more forecast scenarios; and (iv) averaging, by the computer system, the generated one or more dispatch schedules to produce the optimal dispatch schedule; (d) using, by the computer system, the optimal dispatch schedule to determine one or more energy storage system parameters; and (e) sending, by the computer system, the one or more energy storage system parameters to the one or more energy storage systems to control the operation of the one or more energy storage systems.
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53. A non-transitory computer readable medium having stored thereon program code configured for execution by a computer system comprising at least one computer, wherein, when executed by the computer system, the program code causes the computer system to perform a method for controlling an operation of one or more energy storage systems, the method comprising the steps of:
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(a) receiving, at the computer system, live, historical, or forecast data related to the operation of the one or more energy storage systems from one or more data sources, the live, historical, or forecast data including live, historical, or third party forecast data relating to net substation load, solar generation and temperature; (b) calculating, by the computer system, one or more forecasts of one or more parameters relating to the operation of the one or more energy storage systems and an associated forecast uncertainty using one or more forecasting techniques based on the received live, historical, or forecast data, wherein calculating the one or more forecasts includes; creating, by the computer system, at least two forecasts for the net substation load by simultaneously using linear regression with historical average residual forecast technique and neural network forecast technique; and producing, by the computer system, the one or more forecasts by either selecting one of at least two forecasts for net substation load based on historically expected uncertainty associated with each of the at least two forecasts, or averaging the at least two forecasts; (c) determining, by the computer system, an optimal dispatch schedule for the operation of the one or more energy storage systems based on the one or more forecasts, wherein determining an optimal dispatch schedule includes; (i) generating, by the computer system, a predetermined number of forecast scenarios for the net substation load using Monte-Carlo scenario generation technique; (ii) selecting, by the computer system, a subset of the generated predetermined number of forecast scenarios based on system configuration, available computational time and resources, or reported forecast uncertainty; (iii) generating, by the computer system, a dispatch schedule for each of the selected subset of the generated predetermined number of forecast scenarios by using non-linear multiple rule optimization scheduler to minimize a peak net substation load after the operation of the one or more energy storage systems; and (iv) averaging, by the computer system, the generated dispatch schedules for the selected subset of the generated predetermined number of forecast scenarios to produce the optimal dispatch schedule; (d) using, by the computer system, the optimal dispatch schedule to determine one or more energy storage system parameters; and (e) sending, by the computer system, the one or more energy storage system parameters to the one or more energy storage systems to control the operation of the one or more energy storage systems.
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54. A non-transitory computer readable medium having stored thereon program code configured for execution by a computer system comprising at least one computer, wherein, when executed by the computer system, the program code causes the computer system to perform a method for controlling an operation of one or more energy storage systems, the one or more energy storage systems including a battery system having one or more inverters and one or more battery managements controllers, the method comprising the steps of:
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(a) receiving, at the computer system, live, historical, or forecast data related to the operation of the one or more energy storage systems from one or more data sources, the live, historical, or forecast data including live, historical, or third party forecast data relating to weather and load; (b) calculating, by the computer system, one or more forecasts of one or more parameters relating to the operation of the one or more energy storage systems and an associated forecast uncertainty using one or more forecasting techniques based on the received live, historical, or forecast data, the calculating the one or more forecasts including producing, by the computer system, the one or more forecasts by calculating probability of a system peak occurring in each of next twenty-four hours using neural network forecast technique based on the received live, historical, or third party forecast data relating to weather and load; (c) determining, by the computer system, an optimal dispatch schedule for the operation of the one or more energy storage systems based on the one or more forecasts, wherein determining the optimal dispatch schedule includes; determining, by the computer system, the optimal dispatch schedule based on the one or more forecasts using a forecast based rule scheduler to minimize customer load and maximize an output of the one or more energy storage systems during a peak hour; (d) using, by the computer system, the optimal dispatch schedule to determine one or more energy storage system parameters, wherein using the optimal dispatch schedule to determine one or more energy storage system parameters includes; determining, by the computer system, real power and reactive power to input or extract from a grid or microgrid connected to the battery system based on the optimal dispatch schedule and the live data received from the one or more data sources; and (e) sending, by the computer system, the one or more energy storage system parameters to the one or more energy storage systems to control the operation of the one or more energy storage systems.
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Specification