Energy grid control system

Patent metadata

Field	Value
Publication number	US-11462906-B2
Application number	US-202016874247-A
Country	US
Kind code	B2
Filing date	May 14, 2020
Priority date	Mar 31, 2015
Publication date	Oct 4, 2022
Grant date	Oct 4, 2022

Abstract

A method for predicting when energy consumption will exceed normal production capacity for buildings including generating data sets for each of the buildings, where energy consumption values within each set are shifted by one of a plurality of lag values relative to time and temperature values, and where each lag value is different; performing a regression model analysis on each set to yield corresponding regression model parameters and a corresponding residual; determining a least valued residual indicating a corresponding energy lag for each of the buildings; using outside temperatures, regression model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict the time when energy consumption will exceed normal production capacity; and receiving the time when energy consumption will exceed normal production capacity, and preparing and commencing exceptional measures required to manage the energy consumption.

First claim

What is claimed is:

1. A system for predicting a time when energy consumption on a grid will exceed normal production capacity, the system comprising:
a building lag optimizer, configured to receive identifiers for buildings, and configured to generate time shifted versions of energy consumption data for each of said buildings, each of said time shifted versions of energy consumption data comprising energy consumption values along with corresponding time and outside temperature values, wherein said energy consumption values within said each of said time shifted versions of energy consumption data are shifted by one of a plurality of lag values relative to said corresponding time and outside temperature values, and wherein each of said plurality of lag values is different from other ones of said plurality of lag values, and configured to perform a regression analysis on said each of said time shifted versions of energy consumption data to yield corresponding regression model parameters and a corresponding residual, and configured to determine a least valued residual from all residuals yielded, said least valued residual indicating a corresponding energy lag for said each of said buildings, and regression model parameters that correspond to said least valued residual, and wherein said corresponding energy lag describes a transient energy consumption period preceding a change in outside temperature, and wherein said plurality of lag values indicates shifts of said energy consumption values to different time and outside temperature values, and wherein said a cumulative energy consumption for said buildings comprises a future energy consumption timeline as a function of said outside temperatures;
a peak prediction element, coupled to said building lag optimizer and to weather stores, configured to receive, for each of said buildings, outside temperatures, said corresponding energy lag, and said corresponding regression model parameters, and configured to estimate said cumulative energy consumption for said buildings, and configured to predict the time when energy consumption on the grid will exceed normal production capacity; and
peak control, coupled to said peak prediction element, configured to receive the time when energy consumption on the grid will exceed normal production capacity, and configured to prepare and commence exceptional measures required to manage the energy consumption.

2. The system as recited in claim 1 , wherein said identifiers are provided by grid stores.

3. The system as recited in claim 1 , wherein said corresponding time values are less than or equal to said corresponding energy lag for said each of said buildings.

4. The system as recited in claim 1 , wherein said corresponding time values comprise hourly values and said plurality of lag values spans a 24-hour period.

5. The system as recited in claim 1 , wherein said buildings comprise aggregated buildings that are powered by a substation on the grid.

6. The system as recited in claim 1 , wherein said weather stores comprise weather predictions that include said outside temperatures.

7. The system as recited in claim 1 , wherein said each of said time shifted versions of energy consumption data comprises a first portion of a corresponding each of a plurality of baseline energy use data sets, and wherein required energy consumption values resulting from shifts are taken from a second portion of said corresponding each of said plurality of baseline energy use data sets.

8. A system for predicting a time when energy consumption on a grid will exceed normal production capacity for buildings within the grid, the system comprising:
a building lag optimizer, configured to determine an energy lag for one of the buildings, said building lag optimizer comprising:
a thermal response processor, configured to generate a plurality of time shifted versions of energy consumption data for said one of the buildings, each of said plurality of time shifted versions of energy consumption data comprising energy consumption values along with corresponding time and outside temperature values, wherein said energy consumption values within said each of said plurality of time shifted versions of energy consumption data are shifted by one of a plurality of lag values relative to said corresponding time and outside temperature values, and wherein each of said plurality of lag values is different from other ones of said plurality of lag values, and wherein said plurality of lag values indicates shifts of said energy consumption values to different time and outside temperature values; and
a regression model engine, coupled to said thermal response processor, configured to receive said plurality of time shifted versions of energy consumption data, and configured to perform a regression analysis on said each of said plurality of time shifted versions of energy consumption data to yield corresponding regression model parameters and a corresponding residual;
wherein said thermal response processor determines a least valued residual from all residuals yielded by said regression model engine, said least valued residual indicating said energy lag for said one of the buildings, and wherein said energy lag describes a transient energy consumption period preceding a change in outside temperature;
a peak prediction element, coupled to said building lag optimizer and to weather stores, configured to receive, for each of the buildings in the grid, outside temperatures, said corresponding energy lag, and said corresponding regression model parameters, and configured to estimate a cumulative energy consumption for the buildings, and configured to predict the time when energy consumption on the grid will exceed normal production capacity, wherein said cumulative energy consumption for the buildings comprises a future energy consumption timeline as a function of said outside temperatures; and
peak control, coupled to said peak prediction element, configured to receive the time when energy consumption on the grid will exceed normal production capacity, and configured to prepare and commence exceptional measures required to manage the energy consumption.

9. The system as recited in claim 8 , wherein said identifiers are provided by grid stores.

10. The system as recited in claim 8 , wherein said corresponding time values are less than or equal to said corresponding energy lag for the each of the buildings.

11. The system as recited in claim 8 , wherein said corresponding time values comprise hourly values and said plurality of lag values spans a 24-hour period.

12. The system as recited in claim 8 , wherein the buildings comprise aggregated buildings that are powered by a substation on the grid.

13. The system as recited in claim 8 , wherein said weather stores comprise weather predictions that include said outside temperatures.

14. The system as recited in claim 8 , wherein said each of said plurality of time shifted versions of energy consumption data comprises a first portion of a corresponding each of a plurality of baseline energy use data sets, and wherein required energy consumption values resulting from shifts are taken from a second portion of said corresponding each of said plurality of baseline energy use data sets.

15. A method for predicting a time when energy consumption on a grid will exceed normal production capacity for buildings within the grid, the method comprising:
generating a plurality of time shifted versions of energy consumption data for each of the buildings, each of the plurality of time shifted versions of energy consumption data comprising energy consumption values along

Assignees

• Enel X North America Inc

Inventors

• Al-Mohssen Husain
• Bassa Angela S
• Paradis Richard R

Classifications

• H02J3/17

  Demand-responsive operation of AC power transmission or distribution networks · CPC title

• H02J3/003 Primary

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

• H02J3/14

  by switching loads on to, or off from, the networks, e.g. progressively balanced loading · CPC title

• G05B13/026

  using a predictor · CPC title

• H02J3/00 Primary

  Circuit arrangements for AC mains or AC distribution networks · CPC title