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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]