Systems and methods for stochastically using electric vehicles as mobile energy storage

• US 8,762,189 B2
• Filed: 10/05/2012
• Issued: 06/24/2014
• Est. Priority Date: 02/24/2012
• Status: Expired due to Fees

• Current Assignee: NEC Corporation
• Original Assignee: NEC Laboratories America Inc (NEC Corporation)
• Inventors: Bozchalui, Mohammad Chehreghani, Sharma, Ratnesh
• Primary Examiner(s): Pats, Justin M
• Assistant Examiner(s): Fields, Benjamin S
• Application Number:
  US13/646,616
• Publication Number:
  US 20130226637A1
• Time in Patent Office: 627 Days
• Field of Search: 705/7.11, 705/7.42, 701/22
• US Class Current: 705/7.11
• CPC Class Codes: B60L 2240/66 Ambient conditions
• CPC Class Codes: B60L 2240/70 Interactions with external data bases, e.g. traffic centres
• CPC Class Codes: B60L 2260/50 by future state prediction
• CPC Class Codes: B60L 2260/54 Energy consumption estimation
• CPC Class Codes: B60L 2260/58 Departure time prediction
• 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 53/68 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: 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

Systems and methods for energy management includes receiving parameters from commercial building management system components; generating a stochastic programming model of electric vehicles (EVs) as mobile energy storage (MES) for optimal planning, operation, and control purposes; and controlling operation of EVs according to the stochastic programming model to lower operating cost and carbon emission.

Claims

1. A method for energy management, comprising:

receiving, by electronics in a microgrid, parameters from energy management system components;

generating a stochastic model for electric vehicle (EV) operation as mobile energy storage (MES) for optimal planning, operation, and control purposes;

solving an optimization problem for optimal planning, operation, and control purposes;

controlling operation of EVs according to the optimization model to lower operating cost and emissions;

controlling operation of EVs according to the optimization model to ensure a predetermined level of reliability; and

modeling the EVs for optimal planning, operation, and control using an MES energy balance equation;

2. The method of claim 1, wherein the stochastic programming model comprises a two-stage stochastic formulation and selection of first and second-stage variables.

3. The method of claim 1, comprising generating a probabilistic model of EVs, including arrival and departure times, and charging and discharging energy and power capacities.

4. The method of claim 1, comprising performing probabilistic modeling of contribution of EVs in spinning reserve requirements.

5. The method of claim 1, comprising constructing discretized probability distribution functions for random quantities.

6. The method of claim 1, comprising determining

min_xε

_nc′

x+[Q(x,ξ

)]

s.t. Σ

_ja_ijx_j≦

b_i

where Q(x, ξ

) is the optimal value of a second-stage problem;

min<sub>yε

m</sub>q′

y

s.t. Σ

_kV_ksx_k+Σ

_kw_ksy_s≦

h_s

where ξ

;

=(q, h, V, W) are the data of a second-stage problem, and elements of vector ξ

are random, [Q(x, ξ

)] is the expected operator with respect to probability distribution of ξ

,

where ξ

_s;

=(q_s, h_s, V_s, W_s) with respective probabilities p_s;

[Q(x,ξ

)]=Σ

_s=1^Sp_sQ(x,ξ

_s).

7. The method of claim 1, comprising applying charge/discharge constraints of EVs to ensure that p_mes,t^chg and p_mes,t^dch are less than maximum charging and discharging power of the EVs at each time interval:

0≦

p_mes,t,s^chg≦

u_mes,t,s^chgP_mes,t,s

0≦

p_mes,t,s^dch≦

u_mes,t,s^dchP_mes,t,s

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

P_mes,t,s=(P_mes,t-1,s+ P_mes,t,s^conn−

P_mes,t,s^disc)

and wherein operational and maintenance costs of EVs includes degradation costs and an effect of charging and discharging cycles on capacity loss of the EVS, as follows;

8. The method of claim 1, comprising modeling available charging/discharging energy capacity of EVs is formulated as a random variable.

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

10. The method of claim 1, comprising modeling of the grid connection and peak demand charges for grid connection.

11. The method of claim 1, comprising modeling contribution of EVs is Spinning Reserve requirements as follows:

12. The method of claim 1, comprising modeling uncertainty in energy prices for grid connection.

13. The method of claim 1, comprising modeling stochastic optimization techniques to model electric vehicles as mobile energy storage.

14. The method of claim 1, comprising modeling scenario based stochastic programming approach to model electric vehicles as mobile energy storage.

15. The method of claim 1, comprising modeling stochastic scenario based MILP modeling.

16. The method of claim 1, comprising modeling single objective MILP model with maximization of daily profit, minimization of GHG emissions, and minimization of total costs.

17. The method of claim 1, comprising modeling single objective or multiple objective stochastic scenario based MILP modeling.

18. The method of claim 1, comprising modeling non-linear energy balance equation for EVs as:

19. The method of claim 1, comprising modeling of uncertain parameters in scenario based stochastic programming approach to model electric vehicles as mobile energy storage.

20. The method of claim 1, comprising selecting first and second stage variables in a scenario based stochastic programming approach to model electric vehicles as mobile energy storage.