Electrical distribution network improvement for plug-in electric vehicles

• US 9,106,101 B2
• Filed: 03/08/2012
• Issued: 08/11/2015
• Est. Priority Date: 03/10/2011
• Status: Active Grant

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture Global Services Limited (Accenture PLC)
• Inventors: Senart, Aline, Souche, Christian, Kurth, Scott
• Primary Examiner(s): Fernandez Rivas, Omar
• Assistant Examiner(s): KHAN, IFTEKHAR A
• Application Number:
  US13/415,517
• Publication Number:
  US 20120271437A1
• Time in Patent Office: 1,251 Days
• Field of Search: 703/6
• US Class Current: 1/1
• CPC Class Codes: B60L 11/1816 by conductive energy transfer, e.g. connectors
• CPC Class Codes: B60L 11/1824 Details of charging stations, e.g. vehicle recognition or billing B60L11/1811, B60L11/182, B60L11/1822 take precedence
• CPC Class Codes: B60L 11/184 Optimising energy costs, e.g. by charging depending on electricity rates
• CPC Class Codes: B60L 11/1842 Energy stored in the vehicle is provided to the network, i.e. vehicle to grid (V2G) arrangements
• CPC Class Codes: B60L 11/1844 the charging being dependent on network capabilities
• CPC Class Codes: B60L 11/1846 Identification of the vehicle
• CPC Class Codes: B60L 11/1848 Methods related to measuring, billing or payment
• CPC Class Codes: B60L 2230/16 Communication interfaces
• CPC Class Codes: B60L 2230/40 Remote controls for charging stations
• CPC Class Codes: B60L 2240/72 Charging station selection relying on external data
• CPC Class Codes: B60L 2260/44 by parameter estimation
• CPC Class Codes: B60L 3/0046 relating to electric energy storage systems, e.g. batteries or capacitors
• CPC Class Codes: B60L 3/0069 relating to the isolation, e.g. ground fault or leak current
• CPC Class Codes: B60L 3/04 Cutting off the power supply under fault conditions protective devices and circuit arrangements in general H01H; H02H
• CPC Class Codes: B60L 53/14 Conductive energy transfer
• CPC Class Codes: B60L 53/305 Communication interfaces
• 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: 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 2203/20 : Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 2310/48 : for electric vehicles [EV] or hybrid vehicles [HEV]
• CPC Class Codes: H02J 2310/54 : according to a pre-established time schedule
• 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 2310/64 : The condition being economic, e.g. tariff based load management
• CPC Class Codes: H02J 3/003 : Load forecast, e.g. methods or systems for forecasting future load demand
• CPC Class Codes: H02J 3/14 : by switching loads on to, or off from, network, e.g. progressively balanced loading
• CPC Class Codes: Y02B 70/3225 : Demand response systems, e.g. load shedding, peak shaving
• 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 20/222 : Demand response systems, e.g. load shedding, peak shaving
• 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

Abstract

Electrical distribution network (EDN) improvement method for plug-in electric vehicles receives and stores in a database EDN configuration information, demography information and load information for simulating load of the EDN assets. The method dynamically updates the EDN configuration, demography information and/or load information to provide an efficient and customizable method of simulating a PEV load impact on an EDN configuration and apply improvements to the EDN in real time.

Claims

1. An electrical distribution network improvement method comprising:

receiving, by a processor, user input, wherein the user input comprises electrical distribution network (EDN) configuration information, load information, and demographic information of a geographic area to which the EDN distributes electric power;

calculating, by the processor, dynamic plug-in electric vehicle (“

PEV”

) load information in the EDN, the PEV load information calculated based on the demographic information that comprises life-expectancy of plug-in electric vehicles (PEVs) being used in the geographic area, wherein calculating the PEV load information further comprises;

calculating a number of PEVs likely to be recycled based on the life-expectancy of the PEVs currently used in the geographic area; and

calculating an additional number of PEVs likely to be owned by a population in the geographic area based on the demographic information, the demographic information comprising activity profiles of the population in the geographic area;

updating, by the processor, the load information with the PEV load information;

performing, by the processor, a simulation using a simulator based on the EDN configuration information, the demographic information and the load information to obtain a simulation result;

analyzing, by the processor, the simulation result using an analytics engine to obtain an interpretation result;

determining, by the processor, an improvement program for the EDN based on the interpretation result; and

modifying the EDN by applying the improvement program to the EDN.

2. The method of claim 1, wherein applying the improvement program to the EDN comprises:

based on a request for usage allocation to be made in one or more nodes of the EDN, obtaining one or more real-time measurements of the EDN;

determine at least one change to a node comprising at least one plug-in electric vehicle (“

PEV”

) in order to meet the usage allocation; and

modifying the EDN based on the determined at least one change to the node.

3. The method of claim 1, further comprising:

updating, by the processor, at least one of EDN configuration information, demographic information, or load information;

performing, by the processor, a second simulation using the simulator based on the updated at least one of the EDN configuration information, the demography information or the load information to obtain a second simulation result; and

analyzing, by the processor, the second simulation result using the analytics engine to obtain a second interpretation result.

4. The method of claim 1, wherein calculating the PEV load information further comprises:

calculating change in use of load based on expected change in population in a specific range of time based on the demographic information.

5. The method of claim 1, wherein, the improvement program comprises changes to the EDN configuration to encourage altered behavior of a population that receives electric power from the EDN.

6. The method of claim 5, wherein the improvement program is identified based on the demographic information.

7. The method of claim 6, wherein the improvement program comprises at least one of:

a new tariff structure for a specific range of time;

new charging locations within the EDN;

or

an upgrade to equipment in the EDN.

19. The method of claim 1, wherein the PEV load information is calculated based on plug-in electric vehicle (PEV) estimation information comprising, PEV penetration rate, PEV evolution rate, PEV inflexion date, annual increase in load to charge a PEV, and number of PEV sales.

8. A system comprising:

a memory storage device configured to store a database comprising;

EDN configuration information, the EDN comprising multiple nodes;

demographic information of the multiple nodes, the demographic information from a particular time, and the demographic information of a first node comprises information of users within an area of power distribution of the first node;

load information of the multiple nodes, the load information from the particular time; and

plug-in electric vehicle (PEV) estimation information comprising, PEV penetration rate, PEV evolution rate, PEV inflexion date, PEV life expectancy, annual increase in load to charge a PEV, and number of PEV sales;

circuitry configured to calculate PEV load information for the first node at a time different from the particular time based on analysis of the demographic information of the first node, the PEV estimation information, and load information of the first node on the particular time;

circuitry configured to simulate a configuration of the EDN using the calculated PEV load information in addition to the load information of the multiple nodes; and

circuitry configured to analyze the simulated EDN configuration to identify an improvement in the simulated EDN configuration, wherein the improvement prevents overload of equipment in the EDN.

9. The system of claim 8, wherein the improvement in the simulated EDN configuration comprises addition of a control structure for PEV charging, within the area of power distribution of the first node, based on the simulated EDN configuration.

10. The system of claim 9, wherein the control structure comprises a tariff structure, the tariff structure being based on driving patterns of the users within the area of power distribution of the first node.

11. The system of claim 8, wherein the improvement comprises identification of a new PEV charge station location, within the area of power distribution of the first node, based on the simulated EDN configuration and the demographic information of the area of power distribution of the first node.

12. The system of claim 8, further comprising:

circuitry to synchronize power supplied from renewable sources to the first node based on PEV charge time patterns of the users within the area of power distribution of the first node.

20. The system of claim 8, wherein the PEV load information for the first node comprises:

determining a number of PEVs likely to be recycled based on life-expectancy of the PEVs currently used in the area of power distribution of the first node; and

determining an additional number of PEVs likely to be owned by a population in the area of power distribution of the first node.

13. Non-transitory computer readable storage medium comprising instructions executable by a processor, the instructions comprising:

instructions to receive information about an electrical distribution network (EDN), the information comprising an EDN configuration, the EDN configuration comprising a topology of multiple power distribution nodes, each power distribution node responsible to supply power to a respective geographic area;

instructions to receive demographic information of a power distribution node of the EDN, the demographic information comprising demographic information of users within the geographic area receiving power from the power distribution node;

instructions to receive load information of the power distribution node;

instructions to receive plug-in electric vehicle (PEV) information including average energy consumption to charge a PEV, and information related to a number of plug-in electric vehicles (PEVs) sales within the geographic area;

instructions to calculate a number of PEVs estimated to be charged in the geographic area of the power distribution node, the number of PEVs calculated based on the demographic information, the PEV information, and a number of PEVs likely to be recycled based on life-expectancy of the PEVs currently used in the geographic area;

instructions to calculate a change in a power distribution pattern of the power distribution node based on the number of PEVs estimated to be charged within the geographic area of the power distribution node;

instructions to calculate an updated load information of the power distribution node based on the changed power distribution pattern and the load information;

instructions to simulate the EDN with the updated load information; and

instructions to analyze the simulation and output a result that includes a modification to the power distribution node in response to the updated load information of the power distribution node.

14. The non-transitory computer readable storage medium of claim 13, wherein the modification to the power distribution node comprises provision of PEV charging stations at locations different than those in a current topology of the power distribution node.

15. The non-transitory computer readable storage medium of claim 14, wherein the different locations of the PEV charging stations are based on driving patterns, places of interest, and EDN capacity in the geographic area corresponding to the power distribution node.

16. The non-transitory computer readable storage medium of claim 13, wherein the modification to the power distribution node comprises instructions to implement a tariff plan associated with PEV charging within the geographic area of the power distribution node.

17. The non-transitory computer readable storage medium of claim 16, wherein the tariff plan comprises an incentive to connect a PEV to the EDN during a specified time period.

18. The non-transitory computer readable storage medium of claim 17, wherein the tariff plan provides feed-in tariffs as incentives.

21. The non-transitory computer readable storage medium of claim 17, wherein the additional number of PEVs is predicted based on PEV estimation information comprising, PEV penetration rate, PEV evolution rate, PEV inflexion date, annual increase in load to charge a PEV, and number of PEV sales.

22. The non-transitory computer readable storage medium of claim 13, further comprising:

instructions to calculate an additional number of PEVs predicted to be in the geographic area based on the demographic information; and

instructions to calculate the change in the power distribution pattern of the power distribution node based on the additional number of PEVs predicted.