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Decentralized Optimization Charging Strategy Based on Lagrangian Relaxation Method
XU Shaolun， YAN Zheng， ZHANG Liang， FENG Donghan， ZHAO Xiaobo
Electric Power Construction ›› 2015, Vol. 36 ›› Issue (7) : 107-113.
PDF(839 KB)
PDF(839 KB)
Decentralized Optimization Charging Strategy Based on Lagrangian Relaxation Method
As the electric vehicle （EV） charging service provider， aggregator is the important coordinator between the grid and EV users. In this paper， a decentralized optimization charging model based on the Lagrangian relaxation method was formulated from the perspective of EV aggregator， which took the maximum charging profit of the aggregator as target with considering constraints: users’ electricity demand， charging time and available capacity of distribution transformers， etc. The implementation mechanism and process of the decentralized optimization charging strategy were also explained. Then， Monte Carlo method was used to simulate the charging situations of EVs， and based on this simulation， the load curve， economic benefits and computational efficiency under uncoordinated charging， centralized optimization charging and decentralized optimization charging modes were compared and analyzed. The results show that the decentralized optimization charging using the Lagrangian relaxation method can get the approximate charging profit as the centralized optimization charging and the decentralized method possesses higher computing efficiency， so it is suitable for actual application.
electric vehicle / charging strategy / aggregator / economic benefits / Lagrangian relaxation method
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Project Supported by National Natural Science Foundation of China （51377103）； National Key Technology Research and Development Program （2013BAA01B04）.
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