SYSTEM AND METHOD FOR OPTIMAL LOAD PLANNING OF ELECTRIC VEHICLE CHARGING

• US 20120253531A1
• Filed: 03/30/2011
• Published: 10/04/2012
• Est. Priority Date: 03/30/2011
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Tyagi, Rajesh, Nielsen, Matthew Christian, Marasanapalle, Jayanth Kalle, Black, Jason Wayne
• Granted Patent: US 8,972,074 B2
• Time in Patent Office: Days
• US Class Current: 700/291
• CPC Class Codes: B60L 2240/662 Temperature
• CPC Class Codes: B60L 2240/70 Interactions with external data bases, e.g. traffic centres
• CPC Class Codes: B60L 2240/72 Charging station selection relying on external data
• CPC Class Codes: B60L 2240/80 Time limits
• CPC Class Codes: B60L 2250/14 by input of vehicle departure time
• CPC Class Codes: B60L 2260/58 Departure time prediction
• CPC Class Codes: B60L 53/00 Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
• CPC Class Codes: B60L 53/14 Conductive energy transfer
• CPC Class Codes: B60L 53/63 in response to network capacity
• 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 53/68 Off-site monitoring or control, e.g. remote control
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083
• 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 30/14 : Details associated with the interoperability, e.g. vehicle recognition, authentication, identification or billing

Abstract

A system for optimal planning of electric power demand is presented. The system includes a node comprising one or more smart charging plug-in electric vehicles (SCPEVs), a processing subsystem, wherein the processing subsystem receives relevant data from one or more sources; and determines an optimized SCPEV load and optimal charging schedule for the node by applying an operations research technique on the relevant data.

Claims

1. A system for optimal planning of electric power demand, comprising:

a node comprising one or more smart charging plug-in electric vehicles (SCPEVs);

a processing subsystem, wherein the processing subsystem;

receives relevant data from one or more sources; and

determines an optimized SCPEV load and optimal charging schedule for the node by applying an operations research technique on the relevant data.

2. The system of claim 1, wherein the operations research technique comprises a mathematical programming technique.

3. The system of claim 1, wherein the operations research technique comprises a heuristic technique.

4. The system of claim 1, wherein the relevant data comprises system level data, node level data and vehicle level data.

5. The system of claim 4, wherein the system level data comprises time period and electricity power generation cost curve.

6. The system of claim 4, wherein the node level data comprises node structure data, load characteristics of each node and node overload characteristics.

7. The system of claim 4, wherein the vehicle level parameters comprises vehicle parameters and battery details.

8. The system of claim 1, wherein the one or more sources comprise an operator, a system control data acquisition subsystem (SCADA), an energy management system (EMS), an electric power provider (EPP), or combinations thereof.

9. The system of claim 2, wherein the mathematical programming technique comprises:

dividing the one or more SCPEVs in to one or more groups;

generating an objective function and one or more constraints; and

optimizing the objective function subject to the one or more constraints to determine the optimized SCPEV load and the optimal charging schedule.

10. The system of claim 9, wherein the one or more constraints comprise constraints imposed by an owner of a SCPEV, constraints of a utility grid, constraints due to a rated capacity of a transformer, constraints due to charger and battery specifications, or combinations thereof.

11. The system of claim 1, wherein the processing subsystem further determines an optimized total load by adding the optimized SCPEV load and a forecasted non-SCPEV load.

12. The system of claim 9, wherein the processing subsystem further carries out a check to determine whether the node may be overloaded due to the optimized total load.

13. A method for optimal planning of electric power demand, comprising:

receiving relevant data from one or more sources; and

determining an optimized SCPEV load and optimal charging schedule for a node by applying an operations research technique on the relevant data.

14. The method of claim 13, wherein the operations research technique comprises:

dividing the one or more SCPEVs in to one or more groups;

generating an objective function and one or more constraints; and

optimizing the objective function subject to the one or more constraints to determine the optimized SCPEV load.

15. The method of claim 13, further comprising generating an optimized total load based upon the optimized SCPEV load and a forecasted non-SCPEV load.

16. The method of claim 13, further comprising updating the optimized SCPEV load and the optimal charging schedule based upon an updated relevant data.

17. The method of claim 13, further comprising:

a step of carrying out a check to determine whether the node may be overloaded due to the optimized total load.

18. The method of claim 17, further comprising:

determining a solution to avoid one or more adverse effects due to overloading of the node;

compiling overloading data utilizing the relevant data and the solution;

transmitting the overloading data and the solution to an operator;

receiving an input from the operator based upon the overloading data; and

determining the optimized SCPEV load utilizing the operations research technique based upon the input.