Centralized and Distributed Optimization for Vehicle-to-Grid Applications in Frequency Regulation

Mohamed El-Hendawi, Zhanle Wang and Xiaoyue Liu
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Mohamed El-Hendawi: Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada
Zhanle Wang: Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada
Xiaoyue Liu: College of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, China

Energies, 2022, vol. 15, issue 12, 1-22

Abstract: This paper proposes centralized and distributed optimization models for V2G applications to provide frequency regulation in power systems and the electricity market. Battery degradation and dynamic E V usages such as E V driving period, driving distance, and multiple charging/discharging locations are modeled. The centralized V2G problem is formulated into the linear programming (LP) model by introducing two sets of slack variables. However, the centralized model encounters limitations such as privacy concerns, high complexity, and central failure issues. To overcome these limitations, the distributed optimal V2G model is developed by decomposing the centralized model into subproblems using the augmented Lagrangian relaxation (ALR) method. The alternating direction method of multipliers (ADMM) is used to solve the distributed V2G model iteratively. The proposed models are evaluated using real data from the Independent Electricity System Operator (IESO) Ontario, Canada. Simulation results show that the proposed models can aggregate EV s for frequency regulation; meanwhile, the E V owners can obtain monetary rewards. The simulation also shows that including battery degradation and dynamic E V usage increases the model accuracy. By using the proposed approaches, the high cost and the low efficiency power generation units for frequency regulation can be compensated or partially replaced by EVs, which will reduce the generation cost and greenhouse gas emissions.

Keywords: alternating direction method of multipliers; augmented Lagrangian relaxation; electric vehicle; distributed optimization; frequency regulation; vehicle-to-grid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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