Automated demand response energy management system

• US 9,581,979 B2
• Filed: 08/07/2014
• Issued: 02/28/2017
• Est. Priority Date: 12/15/2011
• Status: Active Grant

• Current Assignee: REstore NV (Centrica PLC)
• Original Assignee: REstore NV (Centrica PLC)
• Inventors: Gheerardyn, Jos, Rombouts, Jan-Willem
• Primary Examiner(s): Everett, Christopher E
• Application Number:
  US14/454,621
• Publication Number:
  US 20150057824A1
• Time in Patent Office: 936 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G05B 13/02 electric
• 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 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: H02J 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 2310/12 The local stationary network supplying a household or a building
• CPC Class Codes: H02J 3/14 by switching loads on to, or off from, network, e.g. progressively balanced loading
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y02B 90/20 Smart grids as enabling technology in buildings sector smart grids supporting the management or operation of end-user stationary applications in general, or like technologies with no associated climate change mitigation effect Y04S20/00
• CPC Class Codes: Y02E 40/70 Smart grids as climate change mitigation technology in the energy generation sector
• CPC Class Codes: Y02E 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• 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 10/50 Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
• CPC Class Codes: Y04S 20/00 Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
• CPC Class Codes: Y04S 20/222 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 40/20 Information technology specific aspects, e.g. CAD, simulation, modelling, system security
• CPC Class Codes: Y04S 50/16 Energy services, e.g. dispersed generation or demand or load or energy savings aggregation

Abstract

The power flexibility of energy loads is maximized using a value function for each load and outputting optimal control parameters. Loads are aggregated into a virtual load by maximizing a global value function. The solution yields a dispatch function providing: a percentage of energy for each individual load, a time-varying power level for each load, and control parameters and values. An economic term represents the value of the power flexibility to different players. A user interface includes for each time interval upper and lower bounds representing respectively the maximum power that may be reduced to the virtual load and the maximum power that may be consumed. A trader modifies an energy level in a time interval relative to the reference curve for the virtual load. Automatically, energy compensation for other intervals and recalculation of upper and lower boundaries occurs. The energy schedule for the virtual load is distributed to the actual loads.

Claims

1. A method of aggregating a plurality of actual energy loads into virtual energy loads, said method comprising:

receiving a plurality of value functions, each value function of the plurality of value functions representing one of said actual energy loads and including a term describing a difference between a maximal energy consumption of said one of said actual energy loads and a minimal energy consumption of said one of said actual energy loads allowed within load constraints over a plurality of time intervals within a time period;

receiving a first global value function that includes a first term summing said plurality of value functions which is added to a first economic term representing an economic value of a first virtual energy load to a first energy player;

receiving a second global value function that includes a second term summing said plurality of value functions which is added to a second economic term representing an economic value of a second virtual energy load to a second energy player, wherein said first and second economic terms are different;

maximizing said first and second global value functions and creating said first and second virtual energy loads, each of said virtual energy loads being an aggregation of a set of said actual energy loads, and determining first and second dispatch functions respectively;

outputting, for each of said actual energy loads, a percentage of an energy level to be delivered during one of said time intervals, the energy level being divided amongst said actual energy loads; and

controlling actual energy loads of said first and second virtual energy loads using said each percentage and said first and second dispatch functions.

2. The method as recited in claim 1, wherein said first and second economic terms represents the economic value of a power flexibility of said first and second virtual energy load to said first and second energy players.

3. The method as recited in claim 1, wherein said first economic terms is a decreasing function with respect to a response time needed to schedule the first virtual energy load.

4. The method as recited in claim 1, wherein said first economic term represents an amount of energy usage of said first virtual energy load that may be shifted from a first time interval in which energy is most expensive to a second time interval in which energy is least expensive.

5. The method as recited in claim 1, wherein said first economic term represents a negative of volatility of energy available for balancing over said time intervals, wherein the greater said volatility the less the value of said first global value function.

6. The method as recited in claim 1, wherein said first economic term represents the economic value of the power flexibility of said first virtual energy load during demand peak time intervals.

7. The method as recited in claim 1, wherein said first economic term represents a distance to an imbalance along an energy grid servicing said actual energy loads of said first virtual energy load.

8. The method as recited in claim 1, wherein said sets of actual energy loads are distinct sets of said actual energy loads.

9. The method as recited in claim 1, wherein said sets of actual energy loads are overlapping sets of said actual energy loads.

10. The method as recited in claim 1, further comprising:

maximizing said global value functions in order to maximize a potential change in energy usage over said time intervals for all of said actual energy loads.

11. The method as recited in claim 1, wherein said actual energy loads consume electricity or natural gas.

12. The method as recited in claim 1 further comprising:

maximizing each of said value functions by searching through all possible control parameter values and maximizing an expression

E[Σ

_iΔ

E_i/E_peak−

ρ

]

where E is an expectation operator, Δ

E_i is said each difference given a virtual load state during an ith time interval, E_peek is a maximal energy consumption of said each actual energy load in an ith time interval used to make said difference dimensionless, and ρ

is an economic impact of controlling said each actual energy load.

13. The method as recited in claim 1 wherein said actual energy loads are in geographically distributed locations and have different owners.

14. The method as recited in claim 1 wherein said energy level is dictated by an energy player external to and outside control of owners of said actual energy loads.

15. The method as recited in claim 1 wherein said percentage of an energy level for said each actual energy loads is between said maximal energy consumption and said minimal energy consumption of said each actual energy load.

16. The method as recited in claim 1 further comprising:

receiving a set of control parameters and possible values for each of said actual energy loads, wherein each set of control parameters is able to operate its corresponding actual energy load between said maximal energy consumption and said minimal energy consumption of said corresponding actual energy loads.