Systems for optimizing battery charging and discharging

Patent metadata

Field	Value
Publication number	US-12394000-B2
Application number	US-202418930560-A
Country	US
Kind code	B2
Filing date	Oct 29, 2024
Priority date	Mar 20, 2020
Publication date	Aug 19, 2025
Grant date	Aug 19, 2025

Abstract

A method includes accessing historic five-minute electricity data for a predetermined number of previous days, determining, using the historic five-minute electricity data for the predetermined number of previous days, a maximum charge price for the predetermined number of previous days, and determining, using the historic five-minute electricity data for the predetermined number of previous days, a minimum discharge price for the predetermined number of previous days. The method further includes determining a current amount of charge of a battery, accessing live five-minute electricity data for a current day, and communicating one or more instructions to discharge a battery when a next live five-minute electricity price is greater than the determined minimum discharge price and the current amount of charge of the battery is greater than zero. The method further includes communicating one or more instructions to charge the battery when the next live five-minute electricity price is less than the determined maximum charge price and the current amount of charge of the battery is greater than a maximum charge amount of the battery.

First claim

The invention claimed is:

1. A system comprising:
a battery;
one or more processors; and
one or more computer-readable non-transitory storage media coupled to one or more of the processors and comprising instructions operable when executed by one or more of the processors to cause the system to:
access historic five-minute electricity data for a predetermined number of previous days;
determine, using the historic five-minute electricity data for the predetermined number of previous days, a matrix of maximum charge price and minimum discharge price combinations for the predetermined number of previous days, the matrix including a respective set number of maximum charge prices and minimum discharge prices for each of the predetermined number of previous days, the set number determined by a number of five-minute periods for the battery to be completely discharged;
determine, using the matrix of maximum charge price and minimum discharge price combinations for the predetermined number of previous days, a minimum discharge price for the predetermined number of previous days;
determine that a current amount of charge of the battery is greater than zero;
access live five-minute electricity data for a current day;
determine that an upcoming next live five-minute electricity price is greater than the determined minimum discharge price using a price prediction model;

in response to the determining that the amount of charge of the battery is greater than zero and that the electricity price is greater than the determined minimum discharge price, electronically communicate to the battery, across a communications network, instructions to discharge the battery during a next five-minute period;
wherein the battery is configured, in response to electronically receiving the instructions across the communications network, to discharge during the next five-minute period.

2. The system of claim 1 , wherein determining the minimum discharge price for the predetermined number of previous days comprises determining a lowest minimum discharge price for the predetermined number of previous days.

3. The system of claim 1 , wherein determining the minimum discharge price for the predetermined number of previous days comprises:
determining a plurality of minimum prices for the predetermined number of previous days, wherein each minimum price of the plurality of minimum prices is a minimum price at which the battery was discharged for a particular day of the predetermined number of previous days; and
calculating an average of the plurality of minimum prices.

4. The system of claim 1 , wherein communicating the instructions to discharge the battery are further based on one or more customer constraints.

5. The system of claim 4 , wherein the one or more customer constraints include a requirement that the battery be discharged to an electrical grid during one or more designated periods during a day.

6. The system of claim 4 , wherein the one or more customer constraints include a requirement that the battery may not be discharged to an electrical grid below a specified percentage of its capacity.

7. The system of claim 1 , wherein the price prediction model is a machine learning model based on ensemble of decision trees.

8. A system comprising:
a battery;
one or more processors; and
one or more computer-readable non-transitory storage media coupled to one or more of the processors and comprising instructions operable when executed by one or more of the processors to cause the system to:
access historic five-minute electricity data for a predetermined number of previous days;
determine, using the historic five-minute electricity data for the predetermined number of previous days, a matrix of maximum charge price and minimum discharge price combinations for the predetermined number of previous days, the matrix including a respective set number of maximum charge prices and minimum discharge prices for each of the predetermined number of previous days, the set number determined by a number of five-minute periods for the battery to be completely discharged;
determine, using the matrix of maximum charge price and minimum discharge price combinations for the predetermined number of previous days, a maximum charge price for the predetermined number of previous days;
determine that a current amount of charge of the battery is less than a maximum charge amount of the battery;
access live five-minute electricity data for a current day;
determine that an upcoming next live five-minute electricity price is less than the determined maximum charge price using a price prediction model;

in response to the determining that the amount of charge of the battery is less than the maximum charge amount of the battery and that the electricity price is less than the determined maximum charge price, electronically communicate to the battery, across a communications network, instructions to charge the battery during the next five-minute period;
wherein the battery is configured, in response to electronically receiving the instructions across the communications network, to charge during the next five-minute period.

9. The system of claim 8 , wherein determining the maximum charge price for the predetermined number of previous days comprises determining a highest maximum charge price for the predetermined number of previous days.

10. The system of claim 8 , wherein determining the maximum charge price for the predetermined number of previous days comprises:
determining a plurality of maximum prices for the predetermined number of previous days, wherein each maximum price of the plurality of maximum prices is a maximum price at which the battery was charged for a particular day of the predetermined number of previous days; and
calculating an average of the plurality of maximum prices.

11. The system of claim 8 , wherein the price prediction model is a machine learning model based on ensemble of decision trees.

12. The system of claim 8 , wherein the price prediction model utilizes classification predictive modeling that defines future time periods as one of a plurality of defined categories based on historical market conditions and forecasted conditions.

13. The system of claim 8 , wherein the price prediction model is a regression predictive model to forecast expected prices.

14. A method comprising:
by a computing device, accessing historic five-minute electricity data for a predetermined number of previous days;
by the computing device, determining, using the historic five-minute electricity data for the predetermined number of previous days, a matrix of maximum charge price and minimum discharge price combinations for the predetermined number of previous days, the matrix including a respective set number of maximum charge prices and minimum discharge prices for each of the predetermined number of previous days, the set number determined by a number of five-minute periods for a battery to be completely discharged;
by the computing device, determining, using the matrix of maximum charge price and minimum discharge price combinations for the predetermined number of previous days, at least one of: a maximum charge price, a minimum discharge price, or combination thereof for the predetermined number of previous days;
by the computing device, determining a current amount of charge of the battery;
by the computing device, accessing live five-minute electricity data for a current day using a price prediction model;
by the computing device, electronically communicating to the battery, across a communications network, instructions to at least one of:
(i) discharging th

Assignees

• Hunt Energy Network L L C
• Hunt Energy Entpr Llc

Inventors

• Allen Jr John Franklin

Classifications

• G06Q30/0283

  Price estimation or determination · CPC title

• G06Q50/06 Primary

  Energy or water supply · CPC title