Decentralized EV-Based Charging Optimization With Building Integrated Wind Energy

Abstract

Electric vehicles (EVs) have experienced a rapid growth due to the economic and environmental benefits. However, the substantial charging load brings challenging issues to the power grid. Modern technological advances and the huge number of high-rise buildings have promoted the development of distributed energy resources, such as building integrated/mounted wind turbines. The issue to coordinate EV charging with locally generated wind power of buildings can potentially reduce the impacts of EV charging demand on the power grid. As a result, this paper investigates this important problem and three contributions are made. First, the real-time scheduling of EV charging is addressed in a centralized framework based on the ideas of model predictive control, which incorporates the volatile wind power supply of buildings and the random daily driving cycles of EVs among different buildings. Second, an EV-based decentralized charging algorithm (EBDC) is developed to overcome the difficulties due to: 1) the possible lack of global information regarding the charging requirements of all EVs and 2) the computational burden with the increasing number of EVs. Third, we prove that the EBDC method can converge to the optimal solution of the centralized problem over each planning horizon. Moreover, the performance of the EBDC method is

Decentralized scheduling optimization for charging‐storage station considering multiple spatial‐temporal transfer factors of electric vehicles

“…Lagrange relaxation is employed for solving the centralized problem, that is, Equations (12)- (21). The main ingredient of Lagrange relaxation is to multiply the coupling constraint (20) and (21) by ζ and add the multiplication into the centralized objective function (12). Then the Lagrange relaxation function L(χj i, ηj i, μ j , v j , ζ j ) can be obtained.…”

“…However, with the rapid development of EVs, centralized methods may fail due to the lack of scalability and feasibility. 21 Hence, decentralized optimization has been greatly promoted by many researchers for its reinforced robustness and computation efficiency. 22 According to Reference 23, decentralized energy management has been employed for a fast-charging station in scheduling the output power of renewable energy.…”

“…In general, the optimal solutions to these centralized methods can be efficiently implemented when the computational scale is small. However, with the rapid development of EVs, centralized methods may fail due to the lack of scalability and feasibility 21 . Hence, decentralized optimization has been greatly promoted by many researchers for its reinforced robustness and computation efficiency 22 .…”

Optimized Integration of Electric Vehicles in Low Voltage Distribution Grids

“…The work aims at minimizing the load at a charging station with genetic optimization and was able to reduce the peak load of a charging task by up to 4.9%. In Yang et al [25] quadratic programming is used in a centralized framework to schedule EVs so that they are charged with electricity provided by wind power.…”

Decentralized Charging Coordination of Electric Vehicles using Multi-Population Games

“…Corresponding author: Juan Martinez-Piazuelo (jp.martinez10@uniandes.edu.co) the goal is to minimize the charging and battery degradation costs of each PEV within a large fleet. On the other hand, the authors in [5] develop a decentralized dynamic pricing method to coordinate the charging of the PEVs with some renewable energy generation. Finally, in [6], a decentralized gradient projection method is used to shave the peak of the demand of a fleet of PEVs, and hard constraints on the capacity of the feeder lines are considered.…”