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: A method for managing demand or load includes, upon receipt of a demand response signal or a load management request from a utility to change the price of electricity, determining the cost of energy from a local battery, and if the cost of energy from battery is higher than the cost of electricity from the utility, shedding power from one or more devices based on the utility price, and otherwise discharging power from the battery to reduce a net load on the grid without any actual load reduction.

Abstract: A system and method for controlling operation of one or more grid scale energy storage systems (GSESSs). The method includes generating at least one time series model to provide forecasted pricing data for a plurality of markets, determining a reserve capacity for the one or more GSESSs to provide one or more real-time operation services, determining battery life and degradation costs for one or more batteries in the one or more GSESSs to provide battery life and degradation costs, and optimizing bids for the plurality of markets to generate optimal bids based on at least one of the forecasted pricing data, the battery life and degradation costs and the reserve capacity.

Abstract: Systems and methods for energy distribution for one or more grid-scale Energy Storage Systems (ESSs), including generating one or more time series models to provide forecasted pricing data for one or more markets, determining battery life and degradation costs for one or more batteries in or more ESSs to provide battery life and degradation costs, optimizing bids for the one or more markets to generate optimal bids based on at least one of the forecasted pricing data or the battery life and degradation costs, and distributing energy to or from the one or more ESSs based on the optimal bids generated.

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 controlling an energy storage unit having a model which tracks each charging instances includes, for each charging event, capturing energy charged into the energy storage unit and an unit energy price of the charging source(s) at the time of charging; updating a unit price of total energy stored in the storage unit at each time-step; during a discharging event, updating the unit price of energy in the storage unit on a selected cost model; comparing the unit price of energy in the storage unit with other generation sources in the microgrid and a utility tariff; selecting a lowest cost combination from generation, storage, and grid and using the lowest cost combination to supply a load; and if a controller decides to use stored energy at any time, sending a discharge command to the energy storage unit.

Abstract: A management framework is disclosed that achieves maximum energy storage device lifetime based on energy storage device life estimation and the price of energy.

Abstract: The implemented controller uses a cost function to find the optimal rate of charge/discharge for all batteries. In other words, this battery management system aims to find the minimum overall operating cost to run the batteries over a specific period of time.

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: A management framework is disclosed that achieves maximum energy storage device lifetime based on energy storage device life estimation and the price of energy. The management framework includes a battery life estimation from a supercycle model, for a time window between two consecutive full charges of a battery, which allows assessing a worst case scenario impact of all partial cycles within a supercycle on the battery life. The battery life estimation then considers each supercycle as a single discharge unit instead of treating each individual discharge period separately.

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: A method for controlling an energy storage unit having a model which tracks each charging instances includes, for each charging event, capturing energy charged into the energy storage unit and an unit energy price of the charging source(s) at the time of charging; updating a unit price of total energy stored in the storage unit at each time-step; during a discharging event, updating the unit price of energy in the storage unit on a selected cost model; comparing the unit price of energy in the storage unit with other generation sources in the microgrid and a utility tariff; selecting a lowest cost combination from generation, storage, and grid and using the lowest cost combination to supply a load; and if a controller decides to use stored energy at any time, sending a discharge command to the energy storage unit.

Abstract: A method for managing demand or load includes, upon receipt of a demand response signal or a load management request from a utility to change the price of electricity, determining the cost of energy from a local battery, and if the cost of energy from battery is higher than the cost of electricity from the utility, shedding power from one or more devices based on the utility price, and otherwise discharging power from the battery to reduce a net load on the grid without any actual load reduction.

Abstract: A management framework is disclosed that achieves maximum energy storage device lifetime based on energy storage device life estimation and the price of energy.