Abstract：Home energy optimization control is one of the important branches of home energy management system (HEMS), but the lack of effective intelligent optimization algorithm has restricted its practical application. In this paper, through analyzing the working characteristics of household appliances, household electric equipment was divided into rigid load, simple adjustable load, battery equipment, HVAC (heating, ventilation, and air conditioning) system equipment, etc. The corresponding load model was then established, taken power grid, photovoltaic power generation and energy storage battery as the energy sources of smart home. With electricity cost and comfort level as the optimization indexes, home energy optimization control model was also establish for the coordinated control of the hybrid energy. In addition, a intelligent solving method was put forward based on improved adaptive particle swarm optimization algorithm (APSOA). Thereafter, the optimal time of each electric appliance, the required power of each time in room temperature control system and the charge and discharge power in each period of the storage battery were obtained. According to electricity situation of one day in summer, this paper built the model and simulated it in Matlab. Compared with the results by particle swarm optimization algorithm (PSOA) and genetic algorithm (GA), the model and algorithm have been verified.
徐建军, 王保娥, 闫丽梅, 李战平. 混合能源协同控制的智能家庭能源优化控制策略[J]. 电工技术学报, 2017, 32(12): 214-223.
Xu Jianjun, Wang Bao’e, Yan Limei, Li Zhanping. The Strategy of the Smart Home Energy Optimization Control of the Hybrid Energy Coordinated Control. Transactions of China Electrotechnical Society, 2017, 32(12): 214-223.
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2017, Vol. 32 
