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 a first optimal operating strategy from a plurality of operating strategies for an energy storage system based on the input data, the first optimal operating strategy defining an allocation of energy storage capacity of the energy storage system across a plurality of available energy-related applications;
causing the energy storage system to perform multiple energy storage (ES) applications in an application stack with a single energy storage asset of the energy storage system based on the determined first optimal operating strategy for the energy storage system; and
determining, subsequent to the determining the first optimal operating strategy and the causing the energy storage system to perform the multiple ES applications, a second optimal operating strategy from the plurality of operating strategies based on real-time feedback associated with the energy storage system.
2. The method of claim 1 , wherein determining the first optimal operating strategy is further based on at least one of: weather data, weather forecast data, market data, econometric data, or a system-wide energy constraint.
3. The method of claim 1 , wherein the first optimal operating strategy defines a schedule for charging and discharging the single energy storage asset.
4. The method of claim 1 , wherein the first optimal operating strategy includes an instruction to perform peak load shaving.
5. The method of claim 1 , wherein the first optimal operating strategy includes an instruction to perform energy arbitrage.
6. The method of claim 1 , wherein the first optimal operating strategy includes an instruction to discharge the single energy storage asset at a specified rate during a specified time period.
7. The method of claim 1 , wherein the first optimal operating strategy includes an instruction to charge the single energy storage asset at a specified rate during a specified time period.
8. The method of claim 1 , wherein the first optimal operating strategy includes an instruction to abstain from charging or discharging the single energy storage asset during a specified time period.
9. The method of claim 1 , 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.
10. 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 a power grid.
11. The method of claim 1 , wherein at least one operating strategy from the plurality of operating strategies maximizes at least one of an operating life of the energy storage system or an economic value of the energy storage system.
12. The method of claim 1 , wherein the real-time feedback is associated with an energy storage capacity of the energy storage system.