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:
Computer-implemented methods and, a system are provided. A method includes constructing by an Energy Management System (EMS), one or more optimization-based techniques for resilient battery charging based on an optimization problem having an EMS cost-based objective function. The one or more optimization-based techniques are constructed to include a battery degradation metric in the optimization problem. The method further includes charging, by the EMS, one or more batteries in a power system in accordance with the one or more optimization-based techniques.
Abstract:
A computer-implemented method for controlling a distributed energy storage system (ESS) communicating with one or more microgrids is presented. The method includes assigning, via the processor, a weight to a first objective function pertaining to minimizing demand charge (DC) cost, assigning, via the processor, a weight to a second function pertaining to maximizing photovoltaic (PV) utilization, receiving historical demand profiles including demand data and historical PV profiles including PV data, and determining ESS power and capacity. The method further includes employing a multi-objective DC cost and PV utilization optimization module to obtain a plurality of optimal solutions by concurrently processing the assigned weights of the first and second objective functions, the historical demand and PV profiles, and the ESS power and 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:
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:
Aspects of the present disclosure describe a single battery degradation model and methods that considers both CYCLING and CALENDAR aging and useful in both energy management and battery management systems that may employ any of a variety of known battery technologies.
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:
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:
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.