System and method for optimal load planning of electric vehicle charging

• US 8,972,074 B2
• Filed: 03/30/2011
• Issued: 03/03/2015
• 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
• Primary Examiner(s): SHECHTMAN, SEAN P
• Application Number:
  US13/075,287
• Publication Number:
  US 20120253531A1
• Time in Patent Office: 1,434 Days
• Field of Search: None
• US Class Current: 700/296
• 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 system level data, node level data and vehicle level data from one or more sources; and

determines an optimized SCPEV load and optimal charging schedule for the node by applying at least a mathematical programming technique programming technique on the system level data, node level data and vehicle level data,

wherein the node comprises a residential area, a commercial area, or any other area defined by a utility for distribution of electric power, and the node level data comprises load characteristics of the node, wherein the mathematical programming technique comprises;

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

generating an objective function based upon one or more portions of the system level data, node level data and vehicle level data and determining 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 for the one or more groups.

2. The system of claim 1, wherein the system level data comprises time period and electricity power generation cost curve.

3. The system of claim 1, wherein the node level data comprises node structure data, and node overload characteristics, wherein the node structure data comprises a parent node and a child node relationship information.

4. The system of claim 1, wherein the vehicle level parameters comprises vehicle parameters and battery details.

5. 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.

6. The system of claim 1, 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.

7. 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.

8. The system of claim 1, wherein the processing subsystem further carries out a check to determine whether the node may be overloaded due to the optimized total load.

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

receiving system level data, node level data and vehicle level data from one or more sources; and

determining an optimized smart charging plug-in electric vehicles load and optimal charging schedule for a node by applying at least a mathematical programming technique programming technique on the system level data, node level data and vehicle level data,

wherein the node comprises a residential area, a commercial area, or any other area defined by a utility for distribution of electric power, and the node level data comprises load characteristics of the node, wherein the mathematical programming technique comprises;

dividing the one or more smart charging plug-in electric vehicles in to one or more groups;

generating an objective function based upon one or more portions of the system level data, node level data and vehicle level data and determining one or more constraints; and

optimizing the objective function subject to the one or more constraints to determine the optimized smart charging plug-in electric vehicles load for the one or more groups.

10. The method of claim 9, further comprising generating an optimized total load based upon the optimized smart charging plug-in electric vehicles load and a forecasted non-SCPEV load.

11. The method of claim 9, further comprising updating the optimized smart charging plug-in electric vehicles load and the optimal charging schedule based upon an updated system level data, an updated node level data and an updated vehicle level data.

12. The method of claim 9, further comprising:

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

13. The method of claim 12, further comprising:

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

compiling overloading data utilizing the system level data, node level data and vehicle level 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 smart charging plug-in electric vehicles load utilizing the operations research technique based upon the input.

14. The system of claim 9, wherein the one or more constraints comprises one or more conditions that must be satisfied for determination of an optimized smart charging plug-in electric vehicles load and an optimal charging schedule.