Automated demand response energy management system

• US 8,417,391 B1
• Filed: 12/15/2011
• Issued: 04/09/2013
• Est. Priority Date: 12/15/2011
• Status: Active Grant

• Current Assignee: REstore NV (Centrica PLC)
• Original Assignee: REstore NV (Centrica PLC)
• Inventors: Gheerardyn, Jos, Mermans, Pieter-Jan, Rombouts, Jan-Willem, Snijers, Luc
• Primary Examiner(s): Gaffin, Jeffrey A
• Assistant Examiner(s): Everett, Christopher E
• Application Number:
  US13/327,411
• Time in Patent Office: 481 Days
• Field of Search: None
• US Class Current: 700/291
• 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 maximizing a power flexibility of an energy load, said method comprising:

receiving load constraints indicating conditions under which said energy load should be operated, said energy load not functioning when said load constraints are not satisfied;

receiving a set of control parameters and possible values that control operation of said energy load and affect power usage of said energy load;

receiving a computer model of said energy load, said model outputting said power usage of said energy load in response to changes in inputs to said model;

receiving a value function having a term indicating a change in energy usage as a function of said control parameters for said energy load during time intervals of a time period;

maximizing said value function using said computer model, said load constraints and said control parameters by maximizing the difference between a maximal energy consumption of said energy load allowed within said load constraints and a minimal energy consumption of said energy load allowed within said load constraints during said time intervals; and

outputting a subset of said control parameters resulting from maximizing said value function, said subset able to operate said energy load between said maximal energy consumption and said minimal energy consumption within said load constraints while maximizing said change in energy usage.

2. A method as recited in claim 1 wherein said energy load consumes electricity or natural gas.

3. A method as recited in claim 1 further comprising:

delivering an energy schedule to said energy load including said subset of said control parameters and values in order to control said energy load within said load constraints during said time intervals within said time period.

4. A method as recited in claim 3 wherein said energy schedule is dictated by an energy player external to and outside control of an owner of said energy load.

5. A method as recited in claim 3 wherein said energy schedule provides energy to said energy load between said maximal energy consumption and said minimal energy consumption.

6. A method as recited in claim 1 further comprising:

maximizing said value function by iterating over all combinations of said set of control parameters using said computer model in order to produce said change in energy usage for said time intervals.

7. A method as recited in claim 1 wherein said value function also includes an economic term indicating an economic impact of controlling said energy load using a first set of said control parameters.

8. A method as recited in claim 1 further comprising:

maximizing said value function by taking an expectation operator over a distribution of allowed load states.

9. A method as recited in claim 1 further comprising:

maximizing said value function by iterating through all combinations of said control parameters and maximizing the expression

E[Σ

_iΔ

E_i/E_peak−

ρ

]

where E is an expectation operator, Δ

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

is an economic impact of controlling said energy load.

10. A method of maximizing a power flexibility of a virtual energy load that represents a plurality of actual energy loads, said method comprising:

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

receiving a set of load constraints for each of said actual energy loads, each of said energy loads not functioning when its said set of load constraints are not satisfied;

receiving a set of control parameters and possible values for each of said actual energy loads;

receiving a computer model for each of said actual energy loads, each computer model outputting a power usage of said each actual energy load in response to changes in inputs to said each model;

receiving a global value function representing said virtual energy load that includes said value functions;

solving for said global value function in order to maximize a potential change in energy usage over said time intervals for all of said actual energy loads using said computer models, said sets of load constraints and said sets of control parameters;

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

wherein each set of control parameters is able to operate said corresponding actual energy load between said maximal energy consumption and said minimal energy consumption of said corresponding actual energy loads.

11. The method as recited in claim 10 wherein said actual energy loads consume electricity or natural gas.

12. The method as recited in claim 10 further comprising:

distributing said energy level over said virtual energy load by scheduling said percentages of said energy level over said actual energy loads; and

operating each of said actual energy loads during one of said time intervals using said percentage of said energy level.

13. The method as recited in claim 10 wherein said global value function also includes an economic term representing the value of the power flexibility of said virtual energy load to a particular energy player that is external to and outside control of said owners of said actual energy loads.

14. The method as recited in claim 10 wherein said global value function also includes an economic term that is a decreasing function with respect to a response time needed to schedule the virtual energy load.

15. The method as recited in claim 10 wherein said global value function also includes an economic term representing an amount of energy usage of said virtual energy load that may be shifted from a first time interval in which energy is most expensive to a second time interval is which energy is least expensive.

16. The method as recited in claim 10 wherein said global value function also includes an economic term representing a negative of volatility of energy usage of said virtual energy load over said time intervals, wherein the greater said volatility the less a value of said global value function.

17. The method as recited in claim 10 wherein said global value function also includes an economic term that includes a plurality of economic values each from a different energy player.

18. A method as recited in claim 10 further comprising:

maximizing each of said value functions by searching through all possible control parameter values and maximizing the 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_peak is a maximal energy consumption of said each energy load in an ith time interval used to make said difference dimensionless, and ρ

is an economic impact of controlling said each energy load.

19. A method as recited in claim 10 wherein said actual energy loads are in geographically distributed locations and have different owners.

20. A method as recited in claim 10 wherein said energy level is dictated by an energy player external to and outside control of said owners of said actual energy loads.

21. A method as recited in claim 10 wherein said percentage of an energy level for said each actual energy load is between said maximal energy consumption and said minimal energy consumption of said each actual energy load.