Abstract: A computer-implemented method, system, and computer program product are provided for demand charge management. The method includes receiving an active power demand for a facility, a current load demand charge threshold (DCT) profile for the facility, and a plurality of previously observed load DCT profiles. The method also includes generating a data set of DCT values based on the current load DCT profile for the facility and the plurality of previously observed load DCT profiles. The method additionally includes forecasting a next month DCT value for the facility using the data set of DCT values. The method further includes preventing actual power used from a utility from exceeding the next month DCT value by discharging a battery storage system into a behind the meter power infrastructure for the facility.

Abstract: A computer-implemented method, system, and computer program product are provided for demand charge management. The method includes receiving an active power demand for a facility, a current load demand charge threshold (DCT) profile for the facility, and a plurality of previously observed load DCT profiles. The method also includes generating a forecast model from a data set of DCT values based on the current load DCT profile for the facility and the plurality of previously observed load DCT profiles. The method additionally includes forecasting a monthly DCT value for the facility using the forecast model. The method further includes preventing actual power used from a utility from exceeding the next month DCT value by discharging a battery storage system into a behind the meter power infrastructure for the facility.

Abstract: Systems and methods for minimizing demand charges, including determining one or more optimal monthly demand charge thresholds based on historical load data, time of use charges, demand charges, and energy storage unit size for one or more end users. A grid power dispatch setpoint is calculated for a particular time step based on a daily load forecast and a daily economic dispatch solution based on the determined optimal monthly demand charge thresholds. A grid power dispatch setpoint for a subsequent time step is determined by iteratively solving the daily energy dispatch for the subsequent time step to determine an optimal grid power dispatch setpoint. Energy and demand charges are minimized by controlling charging and discharging operations for the energy storage unit in real-time based on the determined optimal grid power dispatch setpoint.

Abstract: A method for power management includes applying a decentralized control to manage a large-scale community-level energy system; obtaining a global optimal solution satisfying constraints between the agents representing the energy system's devices as a state-based potential game with a multi-agent framework; independently optimizing each agent's output power while considering operational constraints and assuring a pure Nash equilibrium (NE), wherein a state space helps coordinating the agents' behavior in energy system to deal with system-wide constraints including supply demand balance, battery charging power constraint and satisfy system-wide and device-level (local) operational constraints; and controlling distributed generations (DGs) and storage devices using the agent's output.

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.

Abstract: Systems and methods are disclosed for multi-objective energy management of micro-grids. A two-layer control method is used. In the first layer which is the advisory layer, a Model Predictive Control (MPC) method is used as a long term scheduler. The result of this layer will be used as optimality constraints in the second layer. In the second layer, a real-time controller guarantees a second-by-second balance between supply and demand subject to the constraints provided by the advisory layer.

Abstract: The invention is directed to a method or management system which 1) dispatches high efficiency generators first, 2) charges/discharges energy storage units in a way to enhance efficiency of generators in the system or avoid the necessity of dispatching generators during their low efficiency operations at all. In this way the method or management system utilizes its knowledge about the efficiency characteristics of generators in the system and its ability to change the net demand seen by the generators through charge and discharge of energy storage units to increase the overall efficiency of the energy system.

Abstract: A method and system are provided for managing a power system having a grid portion, a load portion, a storage portion, and at least one of a renewable portion and a fuel-based portion. The method includes generating, by a scheduler responsive to an indication of an occurrence of a power outage, an outage duration prediction. The method further includes solving, by the scheduler, an economic dispatch problem using a long-term energy optimization model. The method also includes generating, by the scheduler based on an analysis of the long-term energy optimization model, an energy management directive that controls, for a time period of the outage duration prediction, the storage portion and at least one of the renewable portion and the fuel-based portion. The method additionally includes controlling, by a controller responsive to the directive, the storage portion and the at least one of the renewable portion and the fuel-based portion.

Abstract: Systems and methods for optimal sizing of one or more grid-scale batteries for frequency regulation service, including determining a desired battery output power for the one or more batteries for a particular period of time. A battery size is optimized for the one or more batteries for the particular period of time, and the optimizing is repeated using different time periods to generate a set of optimal battery sizes based on at least one of generated operational constraints or quality criteria constraints for the one or more batteries. A most optimal battery is selected from the set of optimal battery sizes.

Abstract: Systems and methods are disclosed for providing service based interactions between a utility and a microgrid by adjusting power flow profile at a point of common coupling (PCC) between a microgrid and a utility, wherein the power flow profile is adjusted to achieve a predetermined objective function based on a utility request; delivering different services to the utility at different periods of time by altering its internal operation of distributed generators, energy storage units, and demands as a multi-purpose microgrid; and managing the microgrid to deliver services to the utility and reduce its operational cost simultaneously.

Abstract: A method for power management includes applying a decentralized control to manage a large-scale community-level energy system; obtaining a global optimal solution satisfying constraints between the agents representing the energy system's devices as a state-based potential game with a multi-agent framework; independently optimizing each agent's output power while considering operational constraints and assuring a pure Nash equilibrium (NE), wherein a state space helps coordinating the agents' behavior in energy system to deal with system-wide constraints including supply demand balance, battery charging power constraint and satisfy system-wide and device-level (local) operational constraints; and controlling distributed generations (DGs) and storage devices using the agent's output.

Abstract: A system to manage a power grid includes one or more storage and generator devices coupled to the power grid; and a decentralized management module to control the devices including: a module to perform decentralized local forecasts; and a module to perform decentralized device reconfiguration.

Abstract: A method and system are provided. The method includes co-optimizing a placement, a sizing, and an operation schedule of at least one energy storage system in an energy distribution system. The energy distribution system further has at least one renewable energy resource and at least one distributed energy resource. The co-optimizing step includes generating a placement-sizing-scheduling co-optimization model of the at least one energy storage system by integrating therein a distribution optimal power flow optimization model of the energy distribution system and components thereof. The distribution optimal power flow optimization model integrates therein at least an energy storage system model, a renewable energy resource model, and a distributed energy resource model.

Abstract: Systems and methods to perform multi-objective energy management of micro-grids include determining, by an advisory layer with Model Predictive Control (MPC) using a processor, long-term power management directives that include a charging threshold that characterizes one or more power sources, where the advisory layer provides optimal set points or reference trajectories to reduce a cost of energy; and determining real-time actions based on the charging threshold to adaptively charge a battery from the one or more power sources or to discharge the battery.

Abstract: The invention is directed to a method or management system which 1) dispatches high efficiency generators first, 2) charges/discharges energy storage units in a way to enhance efficiency of generators in the system or avoid the necessity of dispatching generators during their low efficiency operations at all. In this way the method or management system utilizes its knowledge about the efficiency characteristics of generators in the system and its ability to change the net demand seen by the generators through charge and discharge of energy storage units to increase the overall efficiency of the energy system.

Abstract: Systems and methods are disclosed for multi-objective energy management of micro-grids. A two-layer control method is used. In the first layer which is the advisory layer, a Model Predictive Control (MPC) method is used as a long term scheduler. The result of this layer will be used as optimality constraints in the second layer. In the second layer, a real-time controller guarantees a second-by-second balance between supply and demand subject to the constraints provided by the advisory layer.