Automated demand response energy management system

• US 8,825,219 B2
• Filed: 02/27/2013
• Issued: 09/02/2014
• 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): Padmanabhan, Kavita
• Assistant Examiner(s): Everett, Christopher E
• Application Number:
  US13/779,505
• Publication Number:
  US 20130178991A1
• Time in Patent Office: 552 Days
• Field of Search: None
• US Class Current: 700/296
• 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 are maximized using a value function for each load and outputting optimal control parameters per load. These loads are aggregated into a virtual load by maximizing a global value function that includes the value function for each individual load. The solution yields a dispatch function providing: a percentage of energy to be assigned to each individual load, a possible time-varying power level within a time interval for each load, and control parameters and values. An economic term of the global value function represents the value of the power flexibility to different energy 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 by the virtual load. An energy trader modifies an energy level in a time interval relative to the reference curve for the virtual load. Automatically, energy compensation for the other intervals and recalculation of upper and lower boundaries occurs and is displayed. The energy schedule for the virtual load is distributed to the actual loads using the dispatch function.

Claims

1. A method of distributing power over a plurality of actual energy loads, said method comprising:

receiving an energy level for consumption by a virtual load for a particular time interval, said virtual load representing said actual loads;

receiving a percentage of said energy level for each of said actual loads and calculating a portion of said energy level to be distributed to each actual load based upon said percentage for each actual load;

for each of said actual loads, inputting said portion of said energy level to an inverse model of each actual load to determine a set of control parameters and values for each actual load during said time interval;

delivering said sets of control parameters and values to said actual loads;

determining said percentages of said energy level by maximizing a global value function that includes an individual value function for each of said actual loads, each of said individual value functions including a term describing the difference between a maximal energy consumption of said each actual energy load allowed within its load constraints and a minimal energy consumption of said each actual energy load allowed within its load constraints during time intervals of a time period; and

controlling each of said actual loads using said sets of control parameters and values during said time interval while operating each of said actual loads within a set of constraints unique to each actual load in order to consume energy at said energy level, each set of constraints indicating conditions under which each actual load should be operated.

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

controlling at least one of said actual loads by varying said one of said actual loads'"'"' energy consumption over time within said time interval, wherein said at least one actual load consumes energy in said time interval equivalent to a product of said portion of said energy level for said one of said actual loads with a length of said time interval.

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

determining said percentages of said energy level by maximizing a global value function that includes an individual value function for each of said actual loads, each of said individual value functions using a computer model for each actual load that corresponds to said inverse model for each actual load.

4. A method as recited in claim 1 wherein said energy level is requested by an energy player external to and outside control of an owner of said virtual load.

5. A method of distributing power over a plurality of actual energy loads, said method comprising:

receiving an adjustment energy value that adjusts an energy schedule of a virtual load for a designated time interval within a time period, said virtual load representing said actual loads;

scheduling the negative of said adjustment energy value on at least one other time interval within said time period for said virtual load, said at least one other time interval not being said designated time interval;

distributing said adjustment energy value over said actual loads for said designated time interval using a percentage for each of said actual loads, whereby each of said actual loads receives a portion of said adjustment energy value that when added to a corresponding energy value from said energy schedule forms an absolute power level for each of said actual loads in said designated time interval;

distributing said negative of said adjustment energy value over said actual loads for said at least one other time interval using said percentage for each of said actual loads, whereby each of said actual loads receives a portion of said negative adjustment energy value that when added to a corresponding energy value from said energy schedule forms an absolute power level for each of said actual loads in said at least one other time interval;

for each of said actual loads, inputting into an inverse model for each load said absolute power level in said designated time interval and said absolute power level in said at least one other time interval to determine a set of control parameters and values during said time period;

determining said percentages of said adjustment energy value by maximizing a global value function that includes an individual value function for each of said actual loads, each of said individual value functions including a term describing the difference between a maximal energy consumption of said each actual energy load and a minimal energy consumption of said each actual energy load during time intervals of said time period; and

controlling each of said actual loads using said sets of control parameters and values during said time period.

6. A method as recited in claim 5 further comprising:

controlling at least one of said actual loads by varying said one of said actual loads'"'"' power consumption over time within a time interval of said time period.

7. A method as recited in claim 5 further comprising:

operating each of said actual loads within a set of constraints unique to each actual load in response to said controlling, each set of constraints indicating conditions under which each actual load should be operated.

8. A method as recited in claim 5 further comprising:

determining said percentages of said adjustment energy value by maximizing a global value function that includes an individual value function for each of said actual loads, each of said individual value functions using a computer model for each actual load that corresponds to said inverse model for each actual load.

9. A method as recited in claim 5 further comprising:

controlling each of said actual loads such that there is no net change in energy usage over said time period with respect to said energy schedule.

10. A method as recited in claim 5 wherein said designated time interval and said at least one other time interval constitute all time intervals of said time period.

11. A method as recited in claim 5 wherein said adjustment energy value is requested by an energy player external to and outside control of an owner of said virtual load.

12. A method as recited in claim 5 wherein said energy schedule dictates energy that said virtual load shall use during time intervals of said time period.

13. A method as recited in claim 5 wherein said energy schedule includes a plurality of known time intervals within a known period of time.