VIRTUAL POWER PLANT SYSTEM AND METHOD INCORPORATING RENEWAL ENERGY, STORAGE AND SCALABLE VALUE-BASED OPTIMIZATION

• US 20120130556A1
• Filed: 11/17/2011
• Published: 05/24/2012
• Est. Priority Date: 11/18/2010
• Status: Active Grant

• Current Assignee: Intelligent Generation, LLC
• Original Assignee: Intelligent Generation, LLC
• Inventors: Marhoefer, John J.
• Granted Patent: US 9,300,141 B2
• Time in Patent Office: Days
• US Class Current: 700/291
• 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 wind motors F03D
• 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:

computing a value hierarchy for the location and at least one other location in the area;

executing an objective function based on actual and predicted values for at least one of (1) electricity rates, (2) weather data and (3) electrical load for the location, wherein the objective function is subject to periodic and aggregate constraints;

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

revising periodic constraints based on the value hierarchy; and

controlling the distribution of electrical energy at the energy storage device for discharging based on the objective function and revised periodic constraints.

2. The computer-implemented method of claim 1, wherein the value hierarchy comprises at least two of:

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

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

(c) an aggregate ancillary service available for the location and at least one other location;

(d) an aggregate peak power supply for the location and at least one other location;

(e) an aggregate demand-side management function for the location and at least one other location;

(f) capacity charge reduction for the location;

(g) arbitrage for the location; and

(h) static load for a power device 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 the controlling of the electrical energy is performed to minimize cost at the location.

6. 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.

7. The computer-implemented method of claim 6, wherein the communication apparatus directs the switch control apparatus to distribute electrical energy to the energy storage apparatus, the electrical load, and a renewable energy source.

8. The computer-implemented method of claim 7, wherein the renewable energy source is one of a photovoltaic cell and a wind turbine.

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

10. The computer-implemented method of claim 6, wherein the communication apparatus communicates with a computing cloud.

11. The computer-implemented method of claim 7, further comprising the steps of:

calculating optimal capacities for the renewable energy source and the energy storage apparatus 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 apparatus is selected according to a least squares method;

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

13. The computer-implemented method of claim 10, wherein of the plurality of storage devices are configured to dynamically alter linked features.

14. The computer-implemented method of claim 1, wherein the energy storage device is common to the location and the at least one other location.

15. 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;

a transfer system configured to direct the flow of electricity to or from the grid and the energy storage device and from 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 measured values and predicted values, wherein the controller is configured to establish when to transfer a portion of electrical energy from the energy storage device to the building structure based upon a value hierarchy.

16. The system of claim 15, wherein the value hierarchy comprises at least two of:

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

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

(c) an aggregate ancillary service available for the building structure and at least one other building structure;

(d) an aggregate peak power supply for the building structure and at least one other building structure;

(e) an aggregate demand-side management function for the building structure and at least one other building structure;

(f) capacity charge reduction for the building structure;

(g) arbitrage for the building structure; and

(h) static load for a power device coupled to the building structure.

17. The apparatus of claim 15, 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.

18. The apparatus of claim 15, 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.

19. The apparatus of claim 15, wherein the controller is configured to determine a timeframe on a given day when electricity from the grid is at its lowest cost and directing electricity to charge the energy storage unit during the timeframe.

20. The apparatus of claim 15, wherein the value hierarchy is selected from the group consisting of backup power, demand/capacity charge, frequency regulation and arbitrage.

21. The apparatus of claim 20, 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.

22. The apparatus of claim 24, wherein the predicted values are selected from group consisting of market electricity rates, ambient temperature, building load, weather data, and predicted power output from the renewable energy generator.

23. An apparatus comprising:

a computer including a processor and a memory, the memory including computer readable instructions that, when executed by the computer perform the steps of;

determining the state of charge of an energy storage unit;

using a first series of measured values and predicted values to determine a first optimization schedule for selectively charging the energy storage unit;

using a second series of measured and predicted values to determine a second optimization schedule for selectively discharging the energy storage unit;

establishing a value hierarchy of electrical power allocation and reserving more power within the energy storage unit for energy allocations that rank higher in the value hierarchy than those energy allocations that rank lower in the value hierarchy.