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Uncoordinated charging of large-scale electric vehicles can affect the safe and economic operation of distribution power system. As an intermediary between power grid and individual electric vehicle, electric vehicle aggregator can play an important role in the coordination and management of electric vehicle charging. In this paper, we develop a decentralized EV charging control strategy of the electric vehicle aggregator for scheduling the flexible charging demand of plug-in electric vehicles in residential distribution networks. First, the centralized charging control model of the electric vehicle aggregator aiming at the maximization of its revenue under the background of time of use price mechanism is established, meanwhile meeting individual charging requirements and satisfying distribution network constraints. Then a decentralized charging control strategy of the electric vehicle aggregator based on the augmented Lagrangian method and the alternating direction multiplier method is proposed. The electric vehicles can decide their charging plan locally with the decentralized strategy, which can eliminate the problem of high communication cost, low computing efficiency and privacy protection caused by the centralized charging control model in practical application. The effectiveness of the proposed strategy is evaluated with a representative distribution network model.

Based on the current practice of charging peak demand, an expectation-oriented stochastic model is established for the demand contracting decision of electricity users. The resultant optimization problem is proved to be nonlinear but convex. Thus, the first-order optimality condition of the proposed model, which leads to an integral equation founded on the probability density function of the peak load, is used to derive the optimal contracting strategy for consumers. Simulation results support the convexity of the proposed model and the effectiveness of the proposed solution method under different demand charging rules and different consumption patterns of customers.

The expected rise of plug-in electric vehicles (PEVs) will lead to a significant additional demand on low voltage distribution systems, and PEVs can challenge power quality and reliability of power systems if their charging is not coordinated. The PEV aggregator, acting as an intermedium between customers and the grid operator, can solve the charging management problem effectively. In this study, a decentralized scheduling scheme of aggregators for PEV charging based on Lagrange Relaxation method is proposed. At the same time, a centralized model of aggregators is also formulated in the same situation in order to make comparisons with the proposed scheme. The two charging models are both aimed at maximizing the charging profit of PEV aggregators considering constraints including users’ electricity demand, charging time, available capacity of distribution transformers, etc. A number of charging scenarios are simulated to evaluate the proposed method. The economic benefits, computational complexity and efficiency are compared and analyzed between the centralized charging and the decentralized charging. Simulation results verify that the decentralized charging strategy based on Lagrange Relaxation method gains charging profit close to the centralized charging yet is more efficient and realizable than the centralized charging.

Accurate estimation of wind speed probability distributions is a challenging task in wind power planning and operation. Different from the commonly used parametric methods which consist of selecting a suitable parametric model and estimating the parameters, this paper presents an improved non-parametric method to estimate wind speed probability distributions. Based on the diffusion partial differential equation in finite domain, this method accounts for both bandwidth selection and boundary correction of kernel density estimation. Preprocessing techniques are designed to handle data with different recording manners to produce smooth probability density functions. Probability densities of specific grid points are obtained by inverse discrete cosine transformation and are further used to calculate assessment indices of wind resources. The method has been tested to estimate probability densities of parametric distributions and actual wind speed data measured in different places. Simulation results show that the proposed approach is of practical value in fitting wind speed distribution models.

This paper proposes a comprehensive solution methodology for the pricing difficulty when Lagrange multipliers are not unique. A linear optimization model is proposed to solve the congestion-related pricing difficulty. The objective function of the model is set to be minimizing the congestion surplus. In addition, an incentive-based allocation approach is incorporated in the solution procedure for cases when no marginal participant exists. The unique pricing solution obtained through our methodology can achieve proper reallocation of the undetermined surplus. Further, we discuss the reference bus independence property of the proposed pricing methodology. Numerical results are provided to fully test the proposed methodology. Other possible solutions are also presented for comparison.

Uncoordinated charging of large-scale electric vehicles (EVs) will have a negative impact on the secure and economic operation of the power system, especially at the distribution level. Given that the charging load of EVs can be controlled to some extent, research on the optimal charging control of EVs has been extensively carried out. In this paper, two possible smart charging scenarios in China are studied: centralized optimal charging operated by an aggregator and decentralized optimal charging managed by individual users. Under the assumption that the aggregators and individual users only concern the economic benefits, new load peaks will arise under time of use (TOU) pricing which is extensively employed in China. To solve this problem, a simple incentive mechanism is proposed for centralized optimal charging while a rolling-update pricing scheme is devised for decentralized optimal charging. The original optimal charging models are modified to account for the developed schemes. Simulated tests corroborate the efficacy of optimal scheduling for charging EVs in various scenarios., Comment: Preparation of Final Manuscripts Accepted for Journal of Electrical Engineering & Technology

Uncontrolled charging of large-scale electric vehicles (EVs) can affect the safe and economic operation of power systems, especially at the distribution level. The centralized EVs charging optimization methods require complete information of physical appliances and using habits, which will cause problems of high dimensionality and communication block. Given this, an ant-based swarm algorithm (ASA) is proposed to realize the EVs charging coordination at the transformer level, which can overcome the drawbacks of centralized control method. First, the EV charging load model is developed, and the charging management structure based on swarm intelligence is presented. Second, basic data of the EV using habit is sampled by the Monte Carlo method, and the ASA is applied to realize the load valley filling. The load fluctuation and the transformer capacity are also considered in the algorithm. Finally, the charging coordination of 500 EVs under a 12.47 KV transformer is simulated to demonstrate the validity of the proposed method.