Apparatus and method for employing weather induced facility energy consumption characterizations in a demand response dispatch system

• US 9,977,451 B2
• Filed: 12/30/2015
• Issued: 05/22/2018
• Est. Priority Date: 03/31/2015
• Status: Active Grant

• Current Assignee: Enel X North America, Inc. (ENEL SPA)
• Original Assignee: Enernoc
• Inventors: Al-Mohssen, Husain, Paradis, Richard R., Bassa, Angela S.
• Primary Examiner(s): Lee, Thomas
• Assistant Examiner(s): Wang, Zhipeng
• Application Number:
  US14/984,706
• Publication Number:
  US 20160294186A1
• Time in Patent Office: 874 Days
• CPC Class Codes: G05B 13/026 using a predictor
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 2219/2642 Domotique, domestic, home control, automation, smart house
• CPC Class Codes: G05F 1/66 Regulating electric power
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: H02J 2310/12 The local stationary network supplying a household or a building
• CPC Class Codes: H02J 3/00 Circuit arrangements for ac mains or ac distribution networks
• CPC Class Codes: H02J 3/003 Load forecast, e.g. methods or systems for forecasting future load demand
• CPC Class Codes: H04L 12/2816 Controlling appliance services of a home automation network by calling their functionalities arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station; in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom H04Q9/00
• CPC Class Codes: Y02B 70/30 Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y02P 80/10 Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
• CPC Class Codes: Y04S 20/20 End-user application control systems
• CPC Class Codes: Y04S 20/222 Demand response systems, e.g. load shedding, peak shaving

Abstract

A method for dispatching buildings in a demand response program event including retrieving a plurality of baseline energy use data sets for the buildings from a baseline data stores; generating data sets for each of the buildings, each set having energy consumption values along with corresponding time and outside temperature values, where the energy consumption values within each set are shifted by one of a plurality of lag values relative to the corresponding time and outside temperature values, and where each of the plurality of lag values is different from other ones of the plurality of lag values; performing a regression analysis on each set to yield regression model parameters and a residual; determining a least valued residual from all residuals yielded, the least valued residual indicating a corresponding energy lag for the each of the buildings; and using energy lags for all of the buildings to generate a dispatch schedule for the demand response program event according to a prioritization of the energy lags.

Claims

1. A demand response dispatch system, comprising:

baseline data stores, configured to store a plurality of baseline energy use data sets for buildings participating in a demand response program;

a building lag optimizer configured to receive identifiers for said buildings, and configured to retrieve said plurality of baseline energy use data sets from said baseline data stores for said buildings, and configured to generate energy use data sets for each of said buildings, each of said energy use data sets comprising energy consumption values along with corresponding time and outside temperature values, wherein said energy consumption values within said each of said energy use data sets 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 energy use data sets 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;

a dispatch processor, coupled to said building lag optimizer, configured to receive a plurality of energy lags, each associated with a corresponding one of said buildings, and configured to generate a dispatch schedule for a demand response program event according to a prioritization of said plurality of energy lags; and

a dispatch control element, coupled to said dispatch processor, configured to control a specified load shedding by performing load shedding actions in the order provided by said dispatch schedule, wherein said dispatch schedule directs for dispatch of ones of said buildings having greater energy lags prior to others of said buildings having lesser energy lags.

2. The system as recited in claim 1, wherein said plurality of lag values indicates shifts of said energy consumption values to different time and outside temperature values.

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

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 corresponding time values comprise 15-minute values and said plurality of lag values spans a 24-hour period.

6. The system as recited in claim 1, wherein said each of said energy use data sets comprises a first portion of a corresponding each of said 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.

7. A system for dispatching buildings participating in a demand response program event, the system comprising:

baseline data stores, configured to store a plurality of baseline energy use data sets for the buildings;

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 energy use data sets for said one of the buildings, each of said plurality of energy use data sets comprising energy consumption values along with corresponding time and outside temperature values, wherein said energy consumption values within said each of said plurality of energy use data sets 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

a regression engine, coupled to said thermal response processor, configured to receive said plurality of energy use data sets, and configured to perform a regression analysis on said each of said plurality of energy use data sets 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 engine, said least valued residual indicating said energy lag for said one of the buildings, wherein said energy lag describes a transient energy consumption period preceding a change in outside temperature;

a dispatch processor, coupled to said building lag optimizer, configured to receive a plurality of energy lags, each associated with a corresponding one of the buildings, and configured to generate a dispatch schedule for the demand response program event according to a prioritization of said plurality of energy lags; and

a dispatch control element, coupled to said dispatch processor, configured to control a specified load shedding by performing load shedding actions in the order provided by said dispatch schedule, wherein said dispatch schedule directs for dispatch of ones of said buildings having greater energy lags prior to others of said buildings having lesser energy lags.

8. The system as recited in claim 7, wherein said plurality of lag values indicates shifts of said energy consumption values to different time and outside temperature values.

9. The system as recited in claim 7, wherein said corresponding time values are less than or equal to said each of said plurality of energy lags.

10. The system as recited in claim 7, wherein said corresponding time values comprise hourly values and said plurality of lag values spans a 24-hour period.

11. The system as recited in claim 7, wherein said corresponding time values comprise 15-minute values and said plurality of lag values spans a 24-hour period.

12. The system as recited in claim 7, wherein said each of said plurality of energy use data sets comprises a first portion of a corresponding each of said 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.

13. A method for dispatching buildings participating in a demand response program event, the method comprising:

retrieving a plurality of baseline energy use data sets for the buildings from a baseline data stores;

generating a plurality of energy use data sets for each of the buildings, each of the plurality of energy use data sets comprising energy consumption values along with corresponding time and outside temperature values, wherein the energy consumption values within the each of the plurality of energy use data sets are shifted by one of a plurality of lag values relative to the corresponding time and outside temperature values, and wherein each of the plurality of lag values is different from other ones of the plurality of lag values;

performing a regression analysis on the each of the plurality of energy use data sets to yield corresponding regression model parameters and a corresponding residual;

determining a least valued residual from all residuals yielded by the regression engine, the least valued residual indicating a corresponding energy lag for the each of the buildings, wherein the corresponding energy lag describes a transient energy consumption period preceding a change in outside temperature;

using energy lags for all of the buildings to generate a dispatch schedule for the demand response program event according to a prioritization of the energy lags; and

controlling a specified load shedding by performing load shedding actions in the order provided by the dispatch schedule, wherein the dispatch schedule directs for dispatch of ones of the buildings having greater energy lags prior to others of the buildings having lesser energy lags.

14. The method as recited in claim 13, wherein the plurality of lag values indicates shifts of the energy consumption values to different time and outside temperature values.

15. The method as recited in claim 13, wherein the corresponding time values are less than or equal to the energy lags.

16. The method as recited in claim 13, wherein the corresponding time values comprise hourly values and the plurality of lag values spans a 24-hour period.

17. The method as recited in claim 14, wherein the corresponding time values comprise 15-minute values and the plurality of lag values spans a 24-hour period.

18. The method as recited in claim 13, wherein the each of the plurality of energy use data sets comprises a first portion of a corresponding each of the plurality of baseline energy use data sets, and wherein required energy consumption values resulting from shifts are taken from a second portion of the corresponding each of the plurality of baseline energy use data sets.