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Optimal Strategies Modeling in Electricity Market for Electric Vehicles Integration in Presence of Intermittent Resources

  • Research Article - Electrical Engineering
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Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Electric vehicles (EVs) as an alternative to the current fossil fuel vehicles represent the most promising green approach to electrification of an important portion of the global transportation sector. This uncertain load brings new challenges to market-oriented demand response programs (DRPs) specifically in the presence of renewable energy resources (RER). Being a special type of load, EVs are highly capable of providing a significant amount of flexible load demand through participating in various types of DRPs, while using their battery storage potentials allows a higher penetration level of intermittent RER in the grid. Therefore, there is a strong need to increase EV owner’s participation in the market by providing attractive financial benefit-based decision-making tools and simplifying the market process to enhance system reliability and reduce price volatility. In this paper, a novel optimal decision-making methodology is proposed which, unlike previous works, utilizes a grid characteristic’s model within a game-theoretical approach, conflicting and capturing economic interests of both players together and evaluates the optimum strategies for a successful market operation in simplest way. This approach can facilitate both EV owners and utilities to derive their robust bidding strategies, in which they can create a simple business case analysis to weigh their benefits of participation in the market. To evaluate the performance, a simulation framework with uncertain load demands and generation has been developed and compared. The results show that the proposed strategy is appropriate for use in real-time automated DRPs.

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Abbreviations

\({r_k^{\prime}}\) :

Reliability of the MGN at time k

\({{\rm PR}_{\rm CB}^k}\) :

Cost–benefit price at time k

\({{\rm PR}_R^k}\) :

Electricity price at time k given by RA

β′ :

Maximum customer’s gain of profit in the bidding

γ′ :

Maximum customer’s loss in the bidding

δ′ :

Bargain probability

\({U_R^k}\) :

RA’s profit expectation at time k

\({U_D^k}\) :

DA’s profit expectation at time k

\({P\left( {r^{k}|P_{\rm CB}^k}\right)}\) :

Estimation probability

C r :

Restriction curve of market grid operation

C o :

Optimal curve of market grid operation

\({C_{\rm EV}^k}\) :

Cost of energy at time ‘k’ Hr

p :

Starting time of charging/discharging

q :

Ending time of charging/discharging

E k :

Energy Stored in EV battery at time ‘k’ Hr

E k+1 :

Energy stored in EV battery at time ‘k + 1’ Hr

χ charging :

Charging efficiency

χ discharging :

Discharging efficiency

E cons :

Energy consumed in driving/km

\({\emptyset_{\rm travel}^k}\) :

Distance travelled at time ‘k’

E d :

Energy at the time of driving

τ:

Speed of wind in m/s

\({\bar{\tau}}\) :

Mean wind speed

\({P_{\rm avg}^w}\) :

Wind power output average

τ c−i :

Cut-in wind speeds

τ N :

Cutout wind speeds

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Authors and Affiliations

  1. Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia

    N. Zareen, M. W. Mustafa, Saleh Y. I. AbuJarad & M. Moradi

Authors
  1. N. Zareen
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  2. M. W. Mustafa
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  3. Saleh Y. I. AbuJarad
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  4. M. Moradi
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Correspondence to N. Zareen.

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Zareen, N., Mustafa, M.W., AbuJarad, S.Y.I. et al. Optimal Strategies Modeling in Electricity Market for Electric Vehicles Integration in Presence of Intermittent Resources. Arab J Sci Eng 40, 1607–1621 (2015). https://doi.org/10.1007/s13369-015-1620-2

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  • DOI: https://doi.org/10.1007/s13369-015-1620-2

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