Demand charge and response management using energy storage

Patent metadata

Field	Value
Publication number	US-10673241-B2
Application number	US-201816185300-A
Country	US
Kind code	B2
Filing date	Nov 9, 2018
Priority date	Nov 13, 2017
Publication date	Jun 2, 2020
Grant date	Jun 2, 2020

Abstract

Systems and methods for controlling battery charge levels to maximize savings in a behind the meter energy management system include predicting a demand charge threshold with a power demand management controller based on historical load. A net energy demand is predicted for a current day with a short-term forecaster. A demand threshold maximizes financial savings using the net energy demand using a rolling time horizon optimizer by concurrently optimizing the demand charge savings and demand response rewards. A load reduction capability factor of batteries is determined with a real-time controller corresponding to an amount of energy to fulfill the demand response rewards. The net energy demand is compared with the demand threshold to determine a demand difference. Battery charge levels of the one or more batteries are controlled with the real time controller according to the demand difference and the load reduction capability factor.

First claim

What is claimed is:

1. A method for controlling battery charge levels to maximize power demand savings in a behind the meter energy management system, the method comprising:
predicting a demand charge threshold with a power demand management controller based on historical load to reduce peak demand charges;
predicting a net energy demand for a current day with a short-term forecaster;
determining a demand threshold for maximizing financial savings using the net energy demand using a rolling time horizon optimizer by concurrently optimizing the savings associated with both the demand charge rates and the time-of-use rates, and the demand response rewards;
determining a load reduction capability factor of one or more batteries with a real-time controller, the load reduction capability factor corresponding to an amount of energy required to fulfill a load reduction corresponding to at least one of the demand response rewards;
comparing the net energy demand with the demand threshold to determine a demand difference with the real-time controller; and
controlling battery charge levels of the one or more batteries with the real time controller according to the demand difference and the load reduction capability factor.

2. The method as recited in claim 1 , wherein predicting the demand charge threshold further includes:
determining a billing cycle demand charge threshold based on historical loads from past billing cycles with a medium-term layer controller; and
optimizing the demand charge threshold for a current period shorter than the billing cycle using the real-time load and the renewable energy source utilization with a short-term layer controller.

3. The method as recited in claim 2 , wherein the billing cycle is one month.

4. The method as recited in claim 2 , wherein the current period is one day.

5. The method as recited in claim 1 , further including recording a power load demand, including a real-time load demanded from an energy distribution network and a renewable energy source utilization.

6. The method as recited in claim 1 , wherein determining the demand threshold includes:
determining an optimum demand charge threshold for reducing demand charges; and
determining firm service levels corresponding to demand response programs issued for a distribution network, wherein the demand threshold is defined by the firm service levels when the optimum demand charge threshold exceeds the firm service levels.

7. The method as recited in claim 1 , further including determining demand response rewards by summing rewards and penalties associated with power demanded from the grid compared to firm service levels of demand response programs issued for a distribution network.

8. The method as recited in claim 1 , wherein concurrently optimizing the savings includes:
summing demand charge savings associated with the demand charge threshold with rewards associated with firm service levels of demand response programs issued for a distribution network to produce a total savings; and
maximizing a reduction in the demand charge threshold according to the total savings.

9. The method as recited in claim 1 , further including determining a battery state of charge to prevent discharge the one or more batteries below a minimum state of charge.

10. A method for controlling battery charge levels to maximize power demand savings in a behind the meter energy management system, the method comprising:
recording a power load demand, including a real-time load demanded from an energy distribution network and a renewable energy source utilization;
predicting a demand charge threshold with a power demand management controller based on historical load to reduce peak demand charges, the predicting a demand charge threshold including:
determining a billing cycle demand charge threshold based on historical loads from past billing cycles with a medium-term layer controller; and
optimizing the demand charge threshold for a current period shorter than the billing cycle using the real-time load and the renewable energy source utilization with a short-term layer controller;

predicting a net energy demand for a current day with a short-term forecaster;
determining a demand threshold for maximizing financial savings using the net energy demand using a rolling time horizon optimizer by concurrently optimizing the savings associated with both the demand charge rates and the time-of-use rates, and the demand response rewards;
determine a load reduction capability factor of one or more batteries with a real-time controller, the load reduction capability factor corresponding to an amount of energy required to fulfill a load reduction corresponding to at least one of the demand response rewards;
comparing the net energy demand with the demand threshold to determine a demand difference with the real-time controller; and
controlling battery charge levels of the one or more batteries with the real time controller according to the demand difference and the load reduction capability factor.

11. The method as recited in claim 10 , wherein the billing cycle is one month.

12. The method as recited in claim 10 , wherein the current period is one day.

13. The method as recited in claim 10 , wherein determining the demand threshold includes:
determining an optimum demand charge threshold for reducing demand charges; and
determining firm service levels corresponding to demand response programs issued for a distribution network, wherein the demand threshold is defined by the firm service levels when the optimum demand charge threshold exceeds the firm service levels.

14. The method as recited in claim 10 , further including determining demand response rewards by summing rewards and penalties associated with power demanded from the grid compared to firm service levels of demand response programs issued for a distribution network.

15. The method as recited in claim 10 , wherein concurrently optimizing the savings includes:
summing demand charge savings associated with the demand charge threshold with rewards associated with firm service levels of demand response programs issued for a distribution network to produce a total savings; and
maximizing a reduction in the demand charge threshold according to the total savings.

16. The method as recited in claim 10 , further including determining a battery state of charge to prevent discharge the one or more batteries below a minimum state of charge.

17. A behind the meter energy management system for controlling battery charge levels to maximize power demand savings, the system comprising:
a power demand management controller that predicts a demand charge threshold based on historical load to reduce peak demand charges;
a short-term forecaster that predicts a net energy demand for a current day;
a rolling time horizon optimizer that determines a demand threshold for maximizing financial savings using the net energy demand by concurrently optimizing the savings associated with both the demand charge rates and the time-of-use rates, and the demand response rewards; and
a real-time controller that controls battery charge levels, controlling the battery charge levels including:
determining a load reduction capability factor of one or more batteries, the load reduction capability factor corresponding to an amount of energy required to fulfill a load reduction corresponding to at least one of the demand response rewards;
comparing the net energy demand with the demand threshold to determine a demand difference; and
controlling battery charge levels of the one

Assignees

• Nec Lab America Inc
• Nec Corp

Inventors

• Nakayama Kiyoshi
• Sharma Ratnesh
• Hooshmand Ali

Classifications

• H02J3/382

  Electricity · mapped topic

• G06Q10/06315

  Needs-based resource requirements planning or analysis · CPC title

• H02J3/003 Primary

  Load forecast, e.g. methods or systems for forecasting future load demand · CPC title

• H02J13/0006

  Electricity · mapped topic

• H02J7/0021

  Electricity · mapped topic