Abstract
To improve the computation efficiency of optimally dispatching large-scale cluster electric vehicles (EVs) and to enhance the profit of a charging station (CS) for EVs, this study investigates the optimal dispatch of the CS based on a decentralized optimization method and a time-of-use (TOU) price strategy. With the application of the Lagrange relaxation method (LRM), a decentralized optimization model with its solution is proposed that converts the traditional centralized optimization model into certain sub-problems. The optimization model aims to maximize the profit of CS, but it comprehensively considers the charging preference of EV users, the operation constraints of the distribution network, and the TOU strategy adopted by the CS. To validate the proposed decentralized optimal dispatching method, a series of numerical simulations were conducted to demonstrate its effect on the computation efficiency and stability, the profit of the CS, and the peak-load shifting. The result indicates that the TOU strategy markedly increases the profit of the CS in comparison with the fixed electricity price mechanism, and the computation efficiency and stability are much better than those of the centralized optimization method. Although it does not compensate the load fluctuation completely, the proposed method with the TOU strategy is helpful for filling the valley of power use.
Highlights
Because of energy consumption and environmental protection issues, new energy vehicles [1]have attracted special attention from both industry and academics all over the world, and new energy vehicle technology is regarded as an important future transportation option
To demonstrate the feasibility of the proposed decentralized model and the effectiveness of the decentralized optimization algorithm based on the Lagrange relaxation method (LRM) as well as to obtain a deep insight into the effect of applying the TOU price on the profit of the charging station (CS) and peaking clipping/valley filling of the load curve, various case studies along with analysis and discussion to be conducted based on the variable values and the TOU price [24] are presented in Tables 1 and 2, respectively
The uncontrolled charging [26] refers to the instant charging, i.e., electric vehicles (EVs) are successively charged immediately when they arrive at the CS, and the battery charging continues until their expected charging energy is achieved
Summary
Because of energy consumption and environmental protection issues, new energy vehicles [1]. For optimizing the charging schedule of large-scale BEVs, the central controller is confronted with the cluster of communication and massive data processing burdens It will result in long computation time, especially for the online optimal scheduling. Investigated a hierarchical method, in which a bi-level online interaction procedure from the distribution system operator to the aggregators was presented and a water-filling algorithm in a two-step EV power allocation employed by the aggregators employ was introduced Each of these studies [9,10,11,12,13,14,15] is regarded as an important step forward for dispatching the CS with the decentralized or hierarchical method.
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