ENERGY STORAGE MODELING AND CONTROL
Systems and methods for optimal planning and real-time control of energy storage systems for multiple simultaneous applications are provided. Energy storage applications can be analyzed for relevant metrics such as profitability and impact on the functionality of the electric grid, subject to system-wide and energy storage hardware constraints. The optimal amount of storage capacity and the optimal operating strategy can then be derived for each application and be prioritized according to a dispatch stack, which can be statically or dynamically updated according to data forecasts. Embodiments can consist of both planning tools and real-time control algorithms.
1 . A method, for energy storage asset management, the method comprising:
providing input data including energy pricing data, energy incentive data, energy storage system configuration data, and an energy storage system cost model;
determining, based on the input data, an optimal allocation of energy storage capacity to each energy-related application from a plurality of energy-related applications of an energy storage system; and
causing the energy storage system to perform multiple energy storage (ES) applications in an application stack with an energy storage asset of the energy storage system based on the determined optimal allocation of energy storage capacity.
2 . The method of claim 1 , wherein the energy pricing data includes a pricing scheme for peak energy rates.
3 . The method of claim 1 , wherein the energy pricing data includes a pricing scheme for off-peak energy rates.
4 . The method of claim 1 , wherein the energy pricing data includes a pricing scheme for peak demand charges.
5 . The method of claim 1 , wherein the energy incentive data includes data associated with a net-metering benefit that is applicable when excess renewable energy is sold back to the power grid.
6 . The method of claim 1 , wherein the energy storage system configuration data includes one of: a capacity, a power rating, a charge rate, a discharge rate, a useful life, or an efficiency loss of the energy storage system.
7 . The method of claim 1 , further comprising updating the optimal allocation of energy storage capacity in response to a data forecast.
8 . The method of claim 1 , wherein the input data further includes a weather forecast.
9 . A method, comprising:
receiving, at a compute device, input data including:
energy pricing data,
energy incentive data,
indications of a plurality of energy storage system configurations,
indications of a plurality of operating strategies for an energy storage system, and
a cost model for the energy storage system;
identifying, via the compute device, an optimal operating strategy from the plurality of operating strategies for the energy storage system based on the input data; and
causing the energy storage system to perform multiple energy storage (ES) applications in an application stack with an energy storage asset of the energy storage system, using a controller operably coupled to the compute device, and based on the identified optimal operating strategy for the energy storage system.
10 . The method of claim 9 , wherein the energy pricing data includes at least one of: a pricing scheme for peak energy rates, a pricing scheme for off-peak energy rates, or peak demand charges.
11 . The method of claim 9 , wherein the energy incentive data includes data associated with a net-metering benefit that is applicable when excess renewable energy is sold back to the power grid.
12 . The method of claim 9 , wherein at least one operating strategy from the plurality of operating strategies is an operating strategy that maximizes an operating life of the energy storage system.
13 . The method of claim 9 , wherein at least one operating strategy from the plurality of operating strategies is an operating strategy that maximizes an economic value of the energy storage system.
14 . The method of claim 9 , wherein the identified optimal operating strategy is the operating strategy from the plurality of operating strategies that is associated with a lowest cost dispatch of available resources to meet a user power demand while complying with a user-defined constraint.
15 . The method of claim 9 , wherein each energy storage system configuration from the plurality of energy storage system configurations includes at least one of: capacity information, power rating information, charge rate information, discharge rate information, efficiency loss information, or useful life information.
16 . The method of claim 9 , wherein the identifying the optimal operating strategy from the plurality of operating strategies is performed using at least one of: list comparison, multivariate regression, local optimization, linear programming, non-linear programming, a Monte Carlo optimization, machine learning, regression fitting, or multivariate optimization.
17 . The method of claim 9 , wherein the input data further includes one of historic energy production data or a physical model of an ES application from the multiple ES applications.
18 . The method of claim 9 , wherein the optimal operating strategy is a first optimal operating strategy, the method further comprising:
receiving, via an interface of the compute device, a modification to the input data; and
identifying, via the compute device, a second optimal operating strategy from the plurality of operating strategies based on the modification to the input data.
19 . The method of claim 9 , further comprising updating the optimal allocation of energy storage capacity in response to one of an external condition or an internal condition.
20 . The method of claim 9 , wherein the input data further includes a weather forecast.