Intelligent electric vehicle recharging

• US 9,225,171 B2
• Filed: 03/30/2012
• Issued: 12/29/2015
• Est. Priority Date: 03/30/2012
• Status: Expired due to Fees

• Current Assignee: Fujitsu Connected Technologies Limited
• Original Assignee: Fujitsu Limited
• Inventors: Chen, Wei-Peng, Majidpour, Mostafa
• Primary Examiner(s): Berhanu, Samuel
• Application Number:
  US13/436,608
• Publication Number:
  US 20130257372A1
• Time in Patent Office: 1,369 Days
• Field of Search: 320/104, 320/109
• US Class Current: 1/1
• CPC Class Codes: B60L 2240/70 Interactions with external data bases, e.g. traffic centres
• 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 53/665 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: H02J 13/00022 using wireless data transmission
• CPC Class Codes: H02J 13/00034 the elements or equipment being or involving an electric power substation
• 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 2310/60 Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving
• CPC Class Codes: H02J 3/144 Demand-response operation of the power transmission or distribution network
• 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 3/381 Dispersed generators
• CPC Class Codes: H02J 7/0013 acting upon several batteries simultaneously or sequentially H02J7/1423 takes precedence
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• 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 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 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 20/222 : Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 30/12 : Remote or cooperative charging
• CPC Class Codes: Y04S 30/14 : Details associated with the interoperability, e.g. vehicle recognition, authentication, identification or billing
• CPC Class Codes: Y04S 40/126 : using wireless data transmission

Abstract

Some embodiments determine, for electric vehicles within an electricity distribution network, optimal recharging schedules based on customer requirements. Some embodiments adjust the optimal recharging schedules to ensure that components of the electricity distribution network operate within their rated limits based on a node sensitivity approach.

Claims

1. A method, performed by one or more computing devices, comprising:

receiving input information associated with an electricity distribution network;

receiving information associated with one or more electric vehicles within the electricity distribution network;

determining, for each of the one or more electric vehicles, a recharging schedule for a specified period of time, the recharging schedule being based on at least the input information; and

for at least one unit of time during the specified period of time;

adjusting the recharging schedule for at least one of the one or more electric vehicles based at least in part on a determination of one or more system constraint violations in the electricity distribution network caused by recharging the one or more electric vehicles; and

wherein the determining comprises;

receiving information associated with one or more customer recharging schedules, each customer recharging schedule being associated with one of the one or more electric vehicles; and

optimizing a total demand of the electricity distribution network based on the one or more customer recharging schedules.

2. The method of claim 1, wherein the system constraint violations comprise violations in voltages or currents in nodes and components of the electricity distribution network.

3. The method of claim 1, further comprising generating a list of components of the electricity distribution network for future upgrade.

4. The method of claim 1, wherein the optimizing comprises:

determining an L-2 norm of the total demand of the electricity distribution network based on the one or more customer recharging schedules.

5. The method of claim 1, wherein the optimizing is further based on fairness among one or more recharging electric vehicles.

6. The method of claim 1, wherein the adjusting comprises:

determining system outputs of the electricity distribution network in response to recharging the one or more electric vehicles; and

for at least one system constraint violation;

determining sensitivity coefficients of the electricity distribution network in response to recharging the one or more electric vehicles;

identifying one or more problematic electric vehicles of the one or more electric vehicles corresponding to the at least one system constraint violation based on sensitivity coefficients; and

adjusting respective recharging schedules of the one or more problematic electric vehicles based at least in part on the corresponding sensitivity coefficients.

7. The method of claim 6, wherein the determining sensitivity coefficients comprises:

determining first system outputs of the electricity distribution network based on a base demand profile;

determining second system outputs of the electricity distribution network based on the base demand profile and additional demand from recharging the one or more electric vehicles; and

calculating the sensitivity coefficients based on a ratio of a difference between the first and the second system outputs to the additional demand from recharging the one or more electric vehicles.

8. The method of claim 6, wherein the adjusting respective recharging schedules of the one or more problematic electric vehicles is also based on a severity of each corresponding system constraint violation.

9. A system comprising:

a memory comprising instructions executable by one or more processors; and

the one or more processors coupled to the memory and operable to execute the instructions, the instructions causing the one or more processors to perform;

receiving input information associated with an electricity distribution network;

receiving information associated with one or more electric vehicles within the electricity distribution network;

determining, for each of the one or more electric vehicles, a recharging schedule for a specified period of time, the recharging schedule being based on at least the input information; and

for at least one unit of time during the specified period of time;

adjusting the recharging schedule for at least one of the one or more electric vehicles based at least in part on a determination of one or more system constraint violations in the electricity distribution network caused by recharging the one or more electric vehicles; and

wherein the system constraint violations comprise violations in voltages or currents in nodes and components of the electricity distribution network.

10. The system of claim 9, wherein the instructions causing the one or more processors to perform:

generate a list of components of the electricity distribution network for future upgrade.

11. The system of claim 9, wherein the instructions causing the one or more processors to perform the determining of the recharging schedule comprise:

receiving information associated with one or more customer recharging schedules, each customer recharging schedule being associated with one of the one or more electric vehicles; and

optimizing a total demand of the electricity distribution network based on the one or more customer recharging schedules.

12. The system of claim 11, wherein the instructions causing the one or more processors to perform the optimizing comprise:

determining an L-2 norm of the total demand of the electricity distribution network based on the one or more customer recharging schedules.

13. The system of claim 11, wherein the optimizing a total demand of the electricity distribution network is further based on fairness among one or more recharging electric vehicles.

14. The system of claim 9, wherein the instructions causing the one or more processors to perform the adjusting comprise:

determining system outputs of the electricity distribution network in response to recharging the one or more electric vehicles; and

for at least one system constraint violation;

determining sensitivity coefficients of the electricity distribution network in response to recharging the one or more electric vehicles;

identifying one or more problematic electric vehicles of the one or more electric vehicles corresponding to the at least one system constraint violation based on sensitivity coefficients; and

adjusting respective recharging schedules of the one or more problematic electric vehicles based at least in part on the corresponding sensitivity coefficients.

15. The system of claim 14, wherein the instructions causing the one or more processors to perform the determining of the sensitivity coefficients comprise:

determining first system outputs of the electricity distribution network based on a base demand profile;

determining second system outputs of the electricity distribution network based on the base demand profile and additional demand from recharging the one or more electric vehicles; and

calculating the sensitivity coefficients based on a ratio of a difference between the first and the second system outputs to the additional demand from recharging the one or more electric vehicles.

16. The system of claim 14, wherein the adjusting is also based on a severity of each corresponding system constraint violation.

17. One or more processors operable to cause an electricity distribution system to:

receive input information associated with an electricity distribution network;

receive information associated with one or more electric vehicles within the electricity distribution network;

determine, for each of the one or more electric vehicles, a recharging schedule for a specified period of time, the recharging schedule being based on at least the input information; and

for at least one unit of time during the specified period of time;

adjust the recharging schedule for at least one of the one or more electric vehicles based at least in part on a determination of one or more system constraint violations in the electricity distribution network caused by recharging the one or more electric vehicles; and

transmit a message to a charging equipment for the at least one electric vehicle, causing the charging equipment to recharge the at least one electric vehicle to recharge based on the adjusted recharging schedule; and

wherein the system constraint violations comprise violations in voltages or currents in nodes and components of the electricity distribution network.