Systems and methods for using electric vehicles as mobile energy storage

• US 8,718,850 B2
• Filed: 10/05/2012
• Issued: 05/06/2014
• Est. Priority Date: 11/30/2011
• Status: Active Grant

• Current Assignee: NEC Corporation
• Original Assignee: NEC Laboratories America Inc (NEC Corporation)
• Inventors: Bozchalui, Mohammad Chehreghani, Sharma, Ratnesh
• Primary Examiner(s): Tran, Khoi
• Assistant Examiner(s): Bendidi, Rachid
• Application Number:
  US13/646,658
• Publication Number:
  US 20130138285A1
• Time in Patent Office: 578 Days
• Field of Search: 701/22, 700/291, 700/292, 700/286, 700/297, 361/500, 361/502, 361/503, 361/504, 307/24, 307/26, 307/64, 307/66, 307/82, 307/104
• US Class Current: 701/22
• CPC Class Codes: B60L 2240/66 Ambient conditions
• CPC Class Codes: B60L 2260/50 by future state prediction
• CPC Class Codes: B60L 2260/56 Temperature prediction, e.g. for pre-cooling
• 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 10/04 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 10/063 Operations research, analysis or management
• 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 2310/64 The condition being economic, e.g. tariff based load management
• CPC Class Codes: H02J 3/008 involving trading of energy or energy transmission rights
• CPC Class Codes: H02J 3/14 by switching loads on to, or off from, network, e.g. progressively balanced loading
• 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: 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 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 50/10 : Energy trading, including energy flowing from end-user application to grid

Abstract

Systems and methods are disclosed for energy management by receiving parameters from commercial building energy system components; optimizing models of system components; optimizing for selected objective functions; generating an optimal operation schedule for the system components; analyzing economic and environmental impacts and optimizing operations of electric vehicles (EVs) for mobile energy storage in commercial buildings.

Claims

1. A method for energy management, comprising:

receiving parameters from management system components;

with a processor, optimizing for one or more selected objective functions optimizing economic and environmental impacts using EVs as mobile energy storage systems for a microgrid powering home or office;

generating an optimal operation schedule for the micro-grid management system components;

optimizing operations of one or more electric vehicles (EVs) for mobile energy storage (MES); and

analyzing economic and environmental impacts;

formulating Maximization of Daily Profit Objective function; and

modeling daily profit of the microgrid as;

2. The method of claim 1, comprising modeling of the EVs for optimal planning, operation, and control purposes.

3. The method of claim 1, comprising determining energy balance of the MES as:

4. The method of claim 1, comprising determining energy storage level of MES is limited by minimum and maximum available capacities of the MES at each time interval, E_mes,t and Ē

_mes,t respectively, as follows;

SOC_mesĒ

_mes,t≦

e_mes,t≦

SOC_mesĒ

_mes,t

where Ē

_mes,t takes into account physically available connected capacity of EVs to the building and the EV owners'"'"' preferences on available energy at the disconnection time of the EV.

5. The method of claim 4, wherein if EV owners do not provide any specific disconnection time energy level of their vehicles, determining Ē

_mes,t of the MES is built based on the rated capacity connected and disconnected EVs'"'"' batteries using the following equation;

Ē

_mes,t=(Ē

_mes,t-1+Ē

_mes,t^conn−

Ē

_mes,t^disc)

E_mes,t=(Ē

_mes,t-1+Ē

_mes,t^conn−

Ē

_mes,t^disc)

where Ē

_mes,t^conn and Ē

_mes,t^disc represent connected and disconnected EV capacities at time t.

6. The method of claim 1, comprising considering charge/discharge constraints of EVs.

7. The method of claim 6, comprising applying constraints to ensure that p_mes,t^chg and p_mes,t^dch are less than maximum charging and discharging power of the MES at each time interval:

0≦

p_mes,t^chg≦

u_mes,t^chgP_mes,t

0≦

p_mes,t^dch≦

u_mes,t^dchP_mes,t

where P_mes,t and P_mes,t are calculated as follows;

P_mes,t=(P_mes,t-1+ P_mes,t^conn−

P_mes,t^disc).

8. The method of claim 6, comprising determining operational and maintenance costs of the MES with degradation costs and charging and discharging cycles capacity loss of the MES.

9. The method of claim 8, wherein the capacity loss is proportional to the number of charging and discharging cycles as follows:

10. The method of claim 1, comprising modeling degradation costs of EV batteries.

11. The method of claim 10, comprising determining

12. The method of claim 1, comprising modeling contribution of EVs in Spinning Reserve requirements.

13. The method of claim 12, comprising determining:

p_ses,t^sp and p_mes,t^sp represent the spinning reserve provided by the SES and MES at time t, respectively, and calculated as follows;

14. The method of claim 1, comprising performing modeling of a grid connection by a microgrid and the grid, wherein a connection between the microgrid and the grid has a maximum power transfer capability limit as follows:

−

P_g>

p_g,t≦

P_g.

15. The method of claim 1, comprising considering peak demand charges for grid connection.

16. The method of claim 1, wherein a grid-connected mode, a microgrid participates in ancillary service markets of the grid market using:

p_sp,t=Σ

_i=1^Ni(P_iu_i,t−

p_i,t)+Σ

_m=1^Nm(P_mu_m,t−

p_m,t)+Σ

_f=1^Nf(P_fu_f,t−

p_f,t)+p_mes,t^sp+p_ses,t^sp−

0.1P_D,t

where p_sp,t is the amount of spinning reserve power that the μ

G can offer in the ancillary service market.

17. The method of claim 1, comprising minimizing GHG Emissions Objective function.

18. The method of claim 17, wherein minimization of GHG emissions of the microgrid in a grid-connected mode comprises: