Scheduling and control in a power aggregation system for distributed electric resources

• US 7,844,370 B2
• Filed: 08/09/2007
• Issued: 11/30/2010
• Est. Priority Date: 08/10/2006
• Status: Active Grant

• Current Assignee: Gridpoint (Twenty First Century Utilities LLC)
• Original Assignee: Gridpoint (Twenty First Century Utilities LLC)
• Inventors: Bridges, Seth W., Pollack, Seth B., Kaplan, David L.
• Primary Examiner(s): DeCady; Albert
• Assistant Examiner(s): Stevens; Thomas H
• Application Number:
  US11/836,749
• Publication Number:
  US 20080039980A1
• Time in Patent Office: 1,209 Days
• Field of Search: 700/291, 701/22, 705/412, 315/80
• US Class Current: 700/291
• CPC Class Codes: B60L 2240/622 by satellite navigation
• CPC Class Codes: B60L 2270/34 of parts
• CPC Class Codes: B60L 53/305 Communication interfaces
• CPC Class Codes: B60L 53/57 Charging stations without connection to power networks
• CPC Class Codes: B60L 53/64 Optimising energy costs, e.g. responding to electricity rates
• CPC Class Codes: B60L 53/65 involving identification of vehicles or their battery types
• CPC Class Codes: B60L 53/66 Data transfer between charging stations and vehicles
• CPC Class Codes: B60L 53/665 Methods related to measuring, billing or payment
• CPC Class Codes: B60L 53/68 Off-site monitoring or control, e.g. remote control
• CPC Class Codes: B60L 55/00 Arrangements for supplying energy stored within a vehicle to a power network, i.e. vehicle-to-grid [V2G] arrangements
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: H02J 13/00002 characterised by monitoring
• CPC Class Codes: H02J 13/00028 involving the use of Internet protocols
• CPC Class Codes: H02J 3/38 Arrangements for parallely feeding a single network by two or more generators, converters or transformers
• CPC Class Codes: H02J 3/388 Islanding, i.e. disconnection of local power supply from the network
• 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: Y02T 10/70 Energy storage systems for electromobility, e.g. batteries
• CPC Class Codes: Y02T 10/7072 : Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
• CPC Class Codes: Y02T 10/72 : Electric energy management in electromobility
• CPC Class Codes: Y02T 90/12 : Electric charging stations
• CPC Class Codes: Y02T 90/14 : Plug-in electric vehicles
• CPC Class Codes: Y02T 90/16 : Information or communication technologies improving the operation of electric vehicles
• 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 10/126 : the energy generation units being or involving electric vehicles [EV] or hybrid vehicles [HEV], i.e. power aggregation of EV or HEV, vehicle to grid arrangements [V2G]
• 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 30/14 : Details associated with the interoperability, e.g. vehicle recognition, authentication, identification or billing
• CPC Class Codes: Y04S 40/128 : involving the use of Internet protocol
• CPC Class Codes: Y04S 50/16 : Energy services, e.g. dispersed generation or demand or load or energy savings aggregation

Abstract

Systems and methods are described for a power aggregation system. In one implementation, a service establishes individual Internet connections to numerous electric resources intermittently connected to the power grid, such as electric vehicles. The Internet connection may be made over the same wire that connects the resource to the power grid. The service optimizes power flows to suit the needs of each resource and each resource owner, while aggregating flows across numerous resources to suit the needs of the power grid. The service can bring vast numbers of electric vehicle batteries online as a new, dynamically aggregated power resource for the power grid. Electric vehicle owners can participate in an electricity trading economy regardless of where they plug into the power grid.

Claims

1. A method, comprising:

inputting constraints of individual electric resources into a power aggregation system, wherein the power aggregation system signals the electric resources to provide power to or take power from a power grid;

scheduling power aggregation based on the constraints;

classifying the electric resources on lists, the lists including;

a first dynamically prioritized list of electric resources that can be activated for storing power from the power grid and providing a load for the power grid; and

a second dynamically prioritized list of electric resources that can be activated for discharging and providing power to the power grid;

assigning a cost to each resource on the first dynamically prioritized list and the second dynamically prioritized list, wherein a priority order of the lists is directly related to the costs;

wherein assigning a cost includes determining a cost function, the cost function guided by predicting a total system availability;

building a set of models, wherein each model is used to predict a behavior of multiple electric resources;

grouping similar electric resources for creating the models and for assigning the electric resources to each model; and

wherein the assigning includes identifying features of each electric resource, including at least one of a number of unique connections/disconnections per day, typical connection times, average connection duration, and an average state-of-charge at connection time.

2. The method of claim 1, wherein the electric resources include electric storage systems of electric vehicles.

3. The method of claim 1, wherein the constraints include a constraint selected from the group consisting of:

price sensitivity of an owner of an electric resource, a vehicle state-of-charge, a predicted amount of time until the electric resource disconnects from a power grid, a sensitivity of an owner of an electric resource to revenue versus state-of-charge of the electric resource, electrical limits of the electric resource, and manual charging overrides by an owner of an electric resource.

4. The method of claim 1, further comprising scheduling power flows for each of the electric resources based on an optimization of at least some of the constraints of the electric resources.

5. The method of claim 4, further comprising scheduling power flows for each of the electric resources based at least in part on an optimization of at least some constraints on the power aggregation system.

6. The method of claim 1, wherein the constraints on an electric resource are used to assign a cost for activating each available action of the electric resource, wherein the action includes providing power to the power grid, taking power from the power grid, and storing energy from the power grid.

7. The method of claim 1, further comprising comparing two operations that achieve similar results in the power aggregation system by comparing costs on the two lists.

8. The method of claim 7, further comprising selecting a lowest cost operation when there are multiple action choices.

9. The method of claim 7, wherein the power aggregation system selects a cost that maximizes an economic value while minimizing an environmental impact.

10. The method of claim 7, wherein the power aggregation system uses cost as a temporal variable, wherein the power aggregation system predicts a look-ahead cost profile for an action as the action occurs, allowing the power aggregation system to further optimize, adaptively.

11. The method of claim 1, further comprising a third, static list of electric resources with hard constraints, including a constraint of overriding the power aggregation system to force charging the electric resource, wherein an electric resource on the third list takes priority over electric resources on the first and second lists in relation to a degree of hardness of the constraint of the electric resource on the third list.

12. The method of claim 1, wherein building a model further includes creating clusters of electric resources or corresponding users in a full feature space or in a reduced feature space, the full or reduced feature space computed via a dimensionality reduction algorithm, including Principal Components Analysis or Random Projection.

13. The method of claim 12, wherein once the electric resources or the corresponding users have been assigned to a cluster, collective data from all of the electric resources or users in that cluster are used to create the model to be used for predicting a behavior of each electric resource or user in the cluster.

14. The method of claim 13, further comprising using less clusters to increase speed of the power aggregation system or using more clusters to increase an accuracy of the power aggregation system.

15. A method, comprising:

inputting constraints of individual electric resources into a power aggregation system, wherein the power aggregation system signals the individual electric resources to provide power to or take power from a power grid;

scheduling power aggregation based on the constraints;

classifying the electric resources on lists, the lists including;

a first dynamically prioritized list of electric resources that can be activated for storing power from the power grid and providing a load for the power grid; and

a second dynamically prioritized list of electric resources that can be activated for discharging and providing power to the power grid;

assigning a cost to each resource on the first dynamically prioritized list and the second dynamically prioritized list, wherein a priority order of the lists is directly related to the costs;

wherein assigning a cost includes determining a cost function, the cost function guided by predicting a total system availability;

building a set of models, wherein each model is used to predict a behavior of multiple electric resources; and

wherein building a model further includes creating clusters of electric resources or corresponding users in a full feature space or in a reduced full or reduced feature space, the feature space computed via a dimensionality reduction algorithm, including Principal Components Analysis or Random Projection.

16. The method of claim 15, wherein the electric resources include electric storage systems of electric vehicles.

17. The method of claim 15, wherein the constraints include a constraint selected from the group consisting of:

price sensitivity of an owner of an electric resource, a vehicle state-of-charge, a predicted amount of time until the electric resource disconnects from a power grid, a sensitivity of an owner of an electric resource to revenue versus state-of-charge of the electric resource, electrical limits of the electric resource, and manual charging overrides by an owner of an electric resource.

18. The method of claim 17, further comprising scheduling power flows for each of the electric resources based on an optimization of at least some of the constraints of the electric resources.

19. The method of claim 18, further comprising scheduling power flows for each of the electric resources based at least in part on an optimization of at least some constraints on the power aggregation system.

20. The method of claim 15, wherein the constraints on an electric resource are used to assign a cost for activating each available action of the electric resource, wherein the action includes providing power to the power grid, taking power from the power grid, and storing energy from the power grid.

21. The method of claim 15, further comprising comparing two operations that achieve similar results in the power aggregation system by comparing costs on the two lists.

22. The method of claim 21, further comprising selecting a lowest cost operation when there are multiple action choices.

23. The method of claim 21, wherein the power aggregation system selects a cost that maximizes an economic value while minimizing an environmental impact.

24. The method of claim 21, wherein the power aggregation system uses the cost as a temporal variable, wherein the power aggregation system predicts a look-ahead cost profile for an action as the action occurs, allowing the power aggregation system to further optimize, adaptively.

25. The method of claim 15, further comprising a third, static list of electric resources with hard constraints, including a constraint of overriding the power aggregation system to force charging the electric resource, wherein an electric resource on the third list takes priority over electric resources on the first and second lists in relation to a degree of hardness of the constraint of the electric resource on the third list.

26. The method of claim 15, wherein once the electric resources or the corresponding users have been assigned to a cluster, collective data from all of the electric resources or users in that cluster are used to create the model to be used for predicting a behavior of each electric resource or user in the cluster.

27. The method of claim 26, further comprising using less clusters to increase speed of the power aggregation system or using more clusters to increase an accuracy of the power aggregation system.