ELECTRIC VEHICLE DISTRIBUTED INTELLIGENCE

• US 20130211988A1
• Filed: 02/13/2013
• Published: 08/15/2013
• Est. Priority Date: 02/13/2012
• Status: Active Grant

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture Global Services Limited (Accenture PLC)
• Inventors: Dorn, John Z., Malcolm, Wade P.
• Granted Patent: US 9,766,671 B2
• Time in Patent Office: Days
• US Class Current: 705/35
• CPC Class Codes: B60L 2240/72 Charging station selection relying on external data
• CPC Class Codes: B60L 2260/54 Energy consumption estimation
• CPC Class Codes: B60L 53/62 in response to charging parameters, e.g. current, voltage or electrical charge
• CPC Class Codes: B60L 53/63 in response to network capacity
• CPC Class Codes: B60L 53/65 involving identification of vehicles or their battery types
• CPC Class Codes: G06F 1/26 Power supply means, e.g. regulation thereof for memories G11C
• 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 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: G08G 1/20 Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
• CPC Class Codes: H02J 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 3/003 Load forecast, e.g. methods or systems for forecasting future load demand
• 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: Y02E 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• CPC Class Codes: Y02P 80/10 Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
• 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 40/20 : Information technology specific aspects, e.g. CAD, simulation, modelling, system security
• CPC Class Codes: Y04S 50/14 : Marketing, i.e. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards

Abstract

A system and method for distributed intelligence of power tracking and power allocation may include: receiving data by at least one computer from a plurality of identified charging stations and vehicles of customers at distributed locations throughout a power grid; analyzing, with at least one processor of the at least one computer, the data with respect to available power for those locations and customer historical usage and profiles; and sending commands, with the at least one processor, to reallocate power to assets of the power grid to handle fluctuations or forecasted fluctuations in power demand based on the analysis. Customer preferences may also be considered in predicting power demand issues and need for demand response. Economic rules may be executed to incentivize the customers to comply with demand response requirements where demand is greater than power supply.

Claims

1. A method for distributed intelligence of power tracking and power allocation, the method executable by at least one computer including at least one processor and memory, the method comprising:

receiving data, by the at least one computer, from a plurality of identified charging stations and electric vehicles of customers at distributed locations throughout a power grid;

analyzing, with the at least one processor, the data with respect to available power for those locations and customer historical usage and profiles; and

sending commands, with the at least one processor, to reallocate power to assets of the power grid to handle fluctuations or forecasted fluctuations in power demand based on the analysis.

2. The method of claim 1, where the analyzing also considers one or a combination of a time of day and a day of week.

3. The method of claim 1, where the analysis also considers customer preferences that are included in the customer profiles.

4. The method of claim 1, where the analysis also considers real-time customer inputs regarding planned trips or planned charges.

5. The method of claim 1, further comprising:

receiving a plurality of system and charging infrastructure parameters from the power grid assets;

where analyzing further comprises determining, from the parameters, whether there is sufficient power availability within the power grid to support charging the electric vehicles at locations of the charging stations within the power grid.

6. A system for distributed intelligence of power tracking and power allocation comprising:

at least one computer including at least one processor and memory, the at least one computer configured to receive data from a plurality of identified charging stations and electric vehicles of customers at distributed locations throughout a power grid;

where the processor is configured to;

analyze the data with respect to available power for those locations and customer historical usage and profiles; and

send commands to reallocate power to assets of the power grid to handle fluctuations or forecasted fluctuations in power demand based on the analysis.

7. The system of claim 6, where the at least one processor is further configured to analyze one or a combination of a time of day and a day of week.

8. The system of claim 6, where the at least one processor is further configured to analyze preferences that are included in the customer profiles.

9. The system of claim 6, where the at least one processor is further configured to analyze real-time customer inputs regarding planned trips or planned charges.

10. The system of claim 6, where the assets include transmission, distribution and feeder assets of the power grid, and

where the processor is further configured to:

receive parameters related to transmission and distribution availability within the power grid for performing the charging at the charging stations, and related to a level of feeder utilization of a power feeder that feeds the charging stations; and

analyze the parameters to determine whether there is sufficient power availability to support charging the electric vehicles through the power feeders.

11. A method for distributed intelligence of power tracking and power allocation, the method executable by at least one computer including at least one processor and memory, the method comprising:

receiving, by the at least one computer, first parameters related to system and charging infrastructure within power transmission and distribution assets of a power grid; and

second parameters related to electric vehicle (EV) customers and customer preferences;

analyzing, using the at least one processor, the first parameters to determine available power for charging stations connected to the power grid assets;

analyzing, using the at least one processor, the first and second parameters to determine whether EV charging in relation to the customer preferences can be met by the available power at the charging stations; and

in response to determining that the customer preferences cannot be met by the determined available power, the processor executing instructions to;

execute a demand response in the power grid to compensate for a lack of available power for EV charging; and

execute economic rules drawn to EV charging to incentivize the EV customers to comply with the demand response.

12. The method of claim 11, where executing the demand response comprises reallocating power to and from a plurality of assets of the power grid to handle fluctuations or forecasted fluctuations in power demand based on the customer preferences that affect demand of power at the charging stations.

13. The method of claim 11, where the parameters related to the system and charging infrastructure include whether on-site power storage exits and is sufficient to support EV charging.

14. The method of claim 11, further comprising executing business rules in addition to the economic rules, where parameters related to the business rules include whether an electric vehicle is a new or an existing customer;

and how will payment be made by the customer.

15. The method of claim 11, where parameters related to the economic rules include:

a rate in effect at time of charging at a charging station;

demand response rates in effect at a peak pricing; and

localized rates.

16. The method of claim 11, where parameters related to the economic rules include penalties for opting out of a demand response event that would decrease or stop power available for charging at a desired time;

and applicable rates or tariffs for a transaction during demand response.

17. A system for distributed intelligence of power tracking and power allocation comprising:

at least one computer including at least one processor and memory, the at least one computer configured to receive first parameters related to system and charging infrastructure within power transmission and distribution assets of a power grid; and

second parameters related to electric vehicle (EV) customers and customer preferences;

where the processor is configured to;

analyze the first parameters to determine available power for charging stations connected to the power grid assets;

analyze the first and second parameters to determine whether EV charging in relation to the customer preferences can be met by the available power at the charging stations; and

in response to determining that the customer preferences cannot be met by the determined available power;

execute a demand response in the power grid to compensate for a lack of available power for EV charging; and

execute economic rules drawn to EV charging to incentivize the EV customers to comply with the demand response.

18. The system of claim 17, where to execute the demand response, the processor is configured to reallocate power to and from a plurality of assets of the power grid to handle fluctuations or forecasted fluctuations in power demand based on the customer preferences that affect demand of power at the charging stations.

19. The system of claim 17, where the parameters related to the system and charging infrastructure are selected from the group consisting of:

whether an operational restriction exists with reference to a charging station;

whether a charging station is reserved by a customer;

the presence of an abnormal event; and

a level of infrastructure capability as related to a charging station.

20. The system of claim 17, where the processor is further configured to execute business rules in addition to the economic rules, where parameters related to the business rules include whether an electric vehicle is a new or an existing customer;

and how will payment be made by the customer.

21. The system of claim 17, where the parameters related to the system and charging infrastructure include whether on-site generation is available that contributes to EV charging.

22. The system of claim 17, where the parameters related to the economic rules include:

a rate in effect at time of charging at a charging station;

demand response rates in effect at a peak pricing; and

localized rates.

23. The system of claim 17, where parameters related to the economic rules include penalties for opting out of a demand response event that would decrease or stop power available for charging at a desired time;

and applicable rates or tariffs for a transaction during demand response.