Abstract: The present disclosure provides systems and methods for shaping the load of a building for more efficient sizing and/or economics of an energy storage and/or microgrid system. Shaping the load of a building can include identifying a plurality of core hours of a time period increasing a load of the building, a plurality of different increase instances, by interfacing with a BAS and an ESS during the plurality of core hours, and decreasing the load of the building, a plurality of different decrease instances, by interfacing with the BAS and the ESS during the plurality of core hours, wherein each increase instance from the plurality of different increase instances of the load profile is followed by a decrease instance from the plurality of decrease instances of the load profile.

Abstract: The present disclosure provides systems and methods for shaping the load of a building for more efficient sizing and/or economics of an energy storage and/or microgrid system. Shaping the load of a building can include identifying a plurality of core hours of a time period increasing a load of the building, a plurality of different increase instances, by interfacing with a BAS and an ESS during the plurality of core hours, and decreasing the load of the building, a plurality of different decrease instances, by interfacing with the BAS and the ESS during the plurality of core hours, wherein each increase instance from the plurality of different increase instances of the load profile is followed by a decrease instance from the plurality of decrease instances of the load profile.

Abstract: The present disclosure provides systems and methods for shaping the load of a building for more efficient sizing and/or economics of an energy storage and/or microgrid system. Shaping the load of a building can include identifying a plurality of core hours of a time period increasing a load of the building, a plurality of different increase instances, by interfacing with a BAS and an ESS during the plurality of core hours, and decreasing the load of the building, a plurality of different decrease instances, by interfacing with the BAS and the ESS during the plurality of core hours, wherein each increase instance from the plurality of different increase instances of the load profile is followed by a decrease instance from the plurality of decrease instances of the load profile.

Abstract: The present disclosure provides systems and methods for shaping the load of a building for more efficient sizing and/or economics of an energy storage and/or microgrid system. Shaping the load of a building can include identifying a plurality of core hours of a time period increasing a load of the building, a plurality of different increase instances, by interfacing with a BAS and an ESS during the plurality of core hours, and decreasing the load of the building, a plurality of different decrease instances, by interfacing with the BAS and the ESS during the plurality of core hours, wherein each increase instance from the plurality of different increase instances of the load profile is followed by a decrease instance from the plurality of decrease instances of the load profile.

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.

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.

Abstract: The present disclosure provides systems and methods for shaping the load of a building for more efficient sizing and/or economics of an energy storage and/or microgrid system. Shaping the load of a building can include identifying a plurality of core hours of a time period increasing a load of the building, a plurality of different increase instances, by interfacing with a BAS and an ESS during the plurality of core hours, and decreasing the load of the building, a plurality of different decrease instances, by interfacing with the BAS and the ESS during the plurality of core hours, wherein each increase instance from the plurality of different increase instances of the load profile is followed by a decrease instance from the plurality of decrease instances of the load profile.