Optimal battery sizing for behind-the-meter applications considering participation in demand response programs and demand charge reduction
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.
1. A method, comprising:
receiving, by a processor from a plurality of sources, power related data relating to power usage of power consuming devices at a customer site;
generating, by the processor, object function inputs from the power related data;
applying, by the processor, 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; and
initiating, by the processor, 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;
wherein the generated object function inputs to the objective function include event-day consumption data and non-event-day consumption data that is simultaneously optimized to determine the optimal capacity for the battery storage system while concurrently minimizing a daily operational power cost for the power consuming devices at the customer site;
wherein the event-day is a day when the customer is participating in a demand response program (DRP) and the non-event-day is a day when the customer is not participating in a DRP and any customer benefit from the DRP is related to the customer's consumption on non-event-days.
2. The method as recited in claim 1 , wherein the objective function utilizes a single Mixed Integer Linear Programming (MILP) technique.
3. The method as recited in claim 1 , wherein the objective function simultaneously considers demand response program requirements and demand charge requirements to determine the optimal capacity for the battery storage system.
4. The method as recited in claim 1 , wherein the objective function employs different inequality constraints related to power generation and power demand balance, and to a maximum and a minimum battery state-of-charge (SOC).
5. The method as recited in claim 1 , wherein the power related data includes a time of use price, a demand charge price, a load profile, a power generation system output, a demand response program, and a battery storage system cost.
6. The method as recited in claim 5 , wherein the power generation system output is generated from a power generation system selected from the group consisting of a photovoltaic system, a wind farm system, a hydroelectric system, a biomass system, and a geothermal system.
7. The method as recited in claim 5 , wherein the demand response program is selected from the group consisting of a demand bidding program and a scheduled load reduction program.
8. The method as recited in claim 1 , wherein the plurality of sources are selected from the group consisting of a utility provider, a facility and a battery storage system provider.
9. The method as recited in claim 1 , wherein the objective function uses the generated object function inputs from a finite period of time.
10. The method as recited in claim 9 , wherein the finite period of time includes both demand response event days and demand response non-event days.
11. The method as recited in claim 10 , wherein the generated object function inputs for the demand response event days includes a demand response reward.
12. The method as recited in claim 1 , wherein the generated object function inputs includes a daily cost of energy from the utility provider, a daily cost of the demand charge from the utility provider, a daily cost of the energy storage capital expenditure, and a daily cost for the facility power generation system.
13. The method as recited in claim 1 , wherein the objective function uses a generation and demand balance, a battery state of charge upper and lower limits, a battery state of charge equality at the beginning and end of each day, a power generation maximum limit and curtailment, and no reverse power to the grid.
14. The method as recited in claim 1 , further comprising installing the battery storage system with the determined optimal capacity at the customer site to power the power consuming devices at a customer site.
15. A system, comprising:
a processor configured to:
receive, from a plurality of sources, power related data relating to power usage of power consuming devices at a customer site;
generate object function inputs from the power related data;
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; and
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;
wherein the generated object function inputs to the objective function include event-day consumption data and non-event-day consumption data that is simultaneously optimized to determine the optimal capacity for the battery storage system while concurrently minimizing a daily operational power cost for the power consuming devices at the customer site;
wherein the event-day is a day when the customer is participating in a demand response program (DRP) and the non-event-day is a day when the customer is not participating in a DRP and any customer benefit from the DRP is related to the customer's consumption on non-event-days.
16. The system as recited in claim 15 , wherein the objective function utilizes a single Mixed Integer Linear Programming (MILP) technique.
17. The system as recited in claim 15 , wherein the objective function simultaneously considers demand response program requirements and demand charge requirements to determine the optimal capacity for the battery storage system.
18. The system as recited in claim 15 , wherein the objective function employs different inequality constraints related to power generation and power demand balance, and to a maximum and a minimum battery state-of-charge (SOC).