Virtual power plant system and method incorporating renewal energy, storage and scalable value-based optimization

• US 9,300,141 B2
• Filed: 11/17/2011
• Issued: 03/29/2016
• Est. Priority Date: 11/18/2010
• Status: Active Grant

• Current Assignee: Intelligent Generation, LLC
• Original Assignee: Intelligent Generation, LLC
• Inventors: Marhoefer, John J.
• Primary Examiner(s): Ali, Mohammad
• Assistant Examiner(s): Saavedra, Emilio J
• Application Number:
  US13/299,052
• Publication Number:
  US 20120130556A1
• Time in Patent Office: 1,594 Days
• Field of Search: 700/2
• US Class Current: 1/1
• CPC Class Codes: G05B 15/02 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 50/06 Energy or water supply
• CPC Class Codes: H02J 2300/24 of photovoltaic origin
• CPC Class Codes: H02J 2300/28 The renewable source being wind energy
• CPC Class Codes: H02J 2310/48 for electric vehicles [EV] or hybrid vehicles [HEV]
• CPC Class Codes: H02J 2310/64 The condition being economic, e.g. tariff based load management
• CPC Class Codes: H02J 3/14 by switching loads on to, or off from, network, e.g. progressively balanced loading
• CPC Class Codes: H02J 3/32 using batteries with converting means
• CPC Class Codes: H02J 3/322 the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
• CPC Class Codes: H02J 3/381 Dispersed generators
• CPC Class Codes: H02J 3/40 Synchronising a generator for connection to a network or to another generator
• CPC Class Codes: Y02B 10/10 Photovoltaic [PV]
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y02E 10/56 Power conversion systems, e.g. maximum power point trackers
• CPC Class Codes: Y02E 10/76 Power conversion electric or electronic aspects
• CPC Class Codes: Y02E 70/30 Systems combining energy storage with energy generation of non-fossil origin
• CPC Class Codes: Y02T 90/167 Systems integrating technologies related to power network operation and communication or information technologies for supporting the interoperability of electric or hybrid vehicles, i.e. smartgrids as interface for battery charging of electric vehicles [EV] or hybrid vehicles [HEV]
• CPC Class Codes: Y04S 20/222 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 30/14 Details associated with the interoperability, e.g. vehicle recognition, authentication, identification or billing
• CPC Class Codes: Y04S 50/16 : Energy services, e.g. dispersed generation or demand or load or energy savings aggregation

Abstract

Methods and systems provided for creating a scalable building block for a virtual power plant, where individual buildings can incorporate on-site renewable energy assets and energy storage and optimize the acquisition, storage and consumption of energy in accordance with a value hierarchy. Each building block can be aggregated into a virtual power plant, in which centralized control of load shifting in selected buildings, based on predictive factors or price signals, can provide bulk power for ancillary services or peak demand situations. Aggregation can occur at multiple levels, including developments consisting of both individual and common renewable energy and storage assets. The methods used to optimize the system can also be applied to “right size” the amount of renewable energy and storage capacity at each site to maximize return on the capital investment.

Claims

1. A computer-implemented method for distributing electrical energy to a location in an area having an energy storage device and an electrical load, comprising:

identifying a value hierarchy for the location and at least one other location in the area, the value hierarchy having at least two objectives prioritized relative to one another;

calculating an objective function based on actual and predicted values, the actual and predicted values each comprising at least one of (1) electricity rates, (2) weather data, and (3) electrical load for the location,

identifying periodic and aggregate constraints;

controlling the distribution of electrical energy to the energy storage device for charging based on the objective function; and

controlling the distribution of electrical energy from the energy storage device for discharging based on the objective function;

wherein the objective function is optimized for a lowest priority objective in the value hierarchy based on the periodic and aggregate constraints and then optimized using revised periodic and aggregate constraints determined based on at least one objective prioritized above the lowest priority objective in the value hierarchy.

2. The computer-implemented method of claim 1, wherein the objectives of the value hierarchy are selected from the group consisting of:

(a) storing a static quantity of backup power for the location;

(b) storing a dynamic quantity of backup power for the location;

(c) providing voltage regulation and spinning reserve in aggregation with at least one other location;

(d) maximizing peak power supply in aggregation with at least one other location;

(e) limiting grid power demand in aggregation with at least one other location;

(f) capacity charge reduction for the location;

(g) arbitrage for the location; and

(h) storing a static quantity of power for mobile devices coupled to the location.

3. The computer-implemented method of claim 1, wherein at least one of the periodic and aggregate constraints are dynamically revised by the value hierarchy based on immediate requirements.

4. The computer-implemented method of claim 1, wherein at least one of the periodic and aggregate constraints are dynamically revised by the value hierarchy based on predicted requirements.

5. The computer-implemented method of claim 1, wherein controlling the distribution of electrical energy to the storage device is performed by a communication apparatus operatively coupled to a switch control apparatus.

6. The computer-implemented method of claim 5, wherein the communication apparatus directs the switch control apparatus to distribute electrical energy to the energy storage device or to an electrical load of the location from an electrical grid connected to the location or from a renewable energy source connected to the location.

7. The computer-implemented method of claim 6, wherein the energy storage device and renewable energy source are common to the location and at least one other location.

8. The computer-implemented method of claim 5, wherein the communication apparatus communicates with a computing cloud.

9. The computer-implemented method of claim 6, further comprising the step of:

calculating optimal capacities for the renewable energy source and the energy storage device based on periodic model data for renewable energy production, electrical load and electricity rates, wherein an optimal capacity for each of the renewable energy source and the energy storage device is selected according to a least squares method.

10. The computer-implemented method of claim 2, wherein the electrical storage device comprises a plurality of energy storage devices, wherein each of the plurality of storage devices is independently linked to features of the value hierarchy.

11. A system for distributing electrical energy, comprising:

a renewable energy generator configured to generate electricity;

an energy storage device configured to selectively store electricity from an electrical power grid and the renewable energy generator, the energy storage device being configured to supply electricity to a building structure at a location;

a transfer system configured to direct the flow of electricity between the grid, the energy storage device, and the renewable energy generator;

a controller operably coupled to the transfer system and configured to direct the transfer system to permit the flow of electricity to and from the energy storage device, the controller also being configured to determine when electricity from the renewable energy generator and power grid are to be used to power the building structure or charge the energy storage device based upon an objective function subject to periodic and aggregate constraints determined at least in part based on a value hierarchy for the location and at least one other location in the area and at least in part based on actual and predicted values, the value hierarchy having at least two objectives prioritized relative to one another, the actual and predicted values each comprising at least one of (1) electricity rates, (2) weather data, and (3) electrical load for the location,

wherein the objective function is optimized for a lowest priority objective in the value hierarchy based on the periodic and aggregate constraints and then optimized using revised periodic and aggregate constraints determined based on at least one objective prioritized above the lowest priority objective in the value hierarchy.

12. The system of claim 11, wherein the objectives of the value hierarchy are selected form the croup consisting of:

(a) storing a static quantity of backup power for the building structure;

(b) storing a dynamic quantity of backup power for the building structure;

(c) providing voltage regulation and spinning reserve in aggregation with at least one other building structure;

(d) maximizing peak power supply in aggregation with at least one other building structure;

(e) limiting grid power demand in aggregation with at least one other building structure;

(f) capacity charge reduction for the building structure;

(g) arbitrage for the building structure; and

(h) storing a static quantity of power for mobile devices coupled to the building structure.

13. The apparatus of claim 11, wherein the controller, when a signal is received from the power grid, directs at least a portion of the electricity stored in the battery to the building structure to replace a portion of the energy received from the electrical power grid to reduce the demand placed on the electrical power grid for electricity.

14. The apparatus of claim 13, wherein the controller is configured to direct electricity generated by the renewable energy generator not consumed by the building structure to charge the energy storage device.

15. The apparatus of claim 14, wherein the measured values are selected from the group consisting of metered consumption from the grid, power output from the renewable energy generator, total load of the building structure, and storage capacity.

16. The apparatus of claim 15, wherein the predicted values are selected from the group consisting of market electricity rates, ambient temperature, building load, weather data, and predicted power output from the renewable energy generator.

17. A computer-implemented method for optimizing the acquisition, storage and consumption of electrical energy in an energy storage device at a location where the electrical energy is consumed, comprising:

identifying a value hierarchy for a network of locations including the location, the value hierarchy including at least two objectives prioritized relative to one another;

calculating an objective function based on actual and predicted values for electricity rates and charges for each location in the network of locations;

identifying periodic and aggregate constraints for each location in the network of locations;

identifying an optimized solution for charging or discharging the energy storage device at the location based on the optimized objective function for the location; and

charging or discharging the energy storage device at the location based on the optimized solution;

wherein the objective function is optimized for a lowest priority objective in the value hierarchy based on the periodic and aggregate constraints and then optimized for each location using revised periodic and aggregate constraints determined based on at least one objective prioritized above the lowest priority objective in the value hierarchy such that a benefit to the network of locations is maximized by each location.

18. The computer-implemented method of claim 17, wherein the objectives of the value hierarchy are selected from the group consisting of:

(a) storing a static quantity of backup power for the location;

(b) storing a dynamic quantity of backup power for the location;

(c) providing voltage regulation and spinning reserve in aggregation with at least one other location;

(d) maximizing peak power supply in aggregation with at least one other location;

(e) limiting grid power demand in aggregation with at least one other location;

(f) capacity charge reduction for the location;

(g) arbitrage for the location; and

(h) storing a static quantity of power for mobile devices coupled to the location.

19. The computer-implemented method of claim 17, wherein at least one of the periodic and aggregate constraints are dynamically revised based on immediate requirements.

20. The computer-implemented method of claim 17, wherein at least one of the periodic and aggregate constraints are dynamically revised based on predicted requirements.

21. The computer-implemented method of claim 17, wherein the optimized solution is performed to minimize total energy cost at another location.

22. The computer-implemented method of claim 21, wherein charging and discharging the storage device is performed by a communication apparatus operatively coupled to a switch control apparatus.

23. The computer-implemented method of claim 22, wherein the communication apparatus directs the switch control apparatus to distribute electrical energy to the energy storage device or to an electrical load of the location from an electrical grid connected to the location or from a renewable energy source connected to the location.

24. The computer-implemented method of claim 23, further comprising the step of:

calculating optimal capacities for the renewable energy source and the energy storage device based on periodic model data for renewable energy production, electrical load, and electricity rates, wherein an optimal capacity for each of the renewable energy source and the energy storage device is selected according to a least squares method.

25. The computer-implemented method of claim 17, wherein the periodic constraints are identified for each hour in a 24-hour period.

26. The computer-implemented method of claim 25, wherein at least one periodic constraint comprises the lesser of:

a charge rate of the energy storage device minus a predicted amount of excess electricity provided by a renewable energy source at the location, or

an available unused capacity of the energy storage device for an identified period.

27. The computer-implemented method of claim 26, wherein at least one aggregate constraint comprises the sum of the periodic constraints for all 24 hours in the 24-hour period being greater than or equal to the lesser of:

a cumulative load for all hours in the 24-hour period in which electricity can be stored and used profitably,

a total daily consumption of the location, or

a current available capacity of the energy storage device minus a cumulative amount of predicted excess capacity from the renewable energy source during the 24-hour period.