李雪亮,吴奎华,冯亮,杨波,李昭,李雪,陈豪杰.电动汽车动力电池与风电协同利用的优化调度策略研究[J].电子测量与仪器学报,2017,31(4):501-509 |
电动汽车动力电池与风电协同利用的优化调度策略研究 |
Optimization scheduling strategy research on synergistic utilization of electric vehicle batteries and wind power |
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DOI:10.13382/j.jemi.2017.04.002 |
中文关键词: 电动汽车 风电 粒子群算法 削峰填谷 |
英文关键词:electric vehicles wind power particle swarm optimization (PSO) algorithm peak load shifting |
基金项目:国家自然基金重点项目(61633016, 61533010)、上海市科学技术委员会项目(14ZR1415300, 15JC1401900)资助 |
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Author | Institution |
Li Xueliang | Economic & Technology Research Institute, State Grid Shandong Electric Power Company, Jinan 250021, China |
Wu Kuihua | Economic & Technology Research Institute, State Grid Shandong Electric Power Company, Jinan 250021, China |
Feng Liang | Economic & Technology Research Institute, State Grid Shandong Electric Power Company, Jinan 250021, China |
Yang Bo | Economic & Technology Research Institute, State Grid Shandong Electric Power Company, Jinan 250021, China |
Li Zhao | Economic & Technology Research Institute, State Grid Shandong Electric Power Company, Jinan 250021, China |
Li Xue | Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072, China |
Chen Haojie | Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072, China |
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中文摘要: |
针对如何通过电动汽车与风电协同利用实现负荷曲线削峰填谷问题,主要研究电动汽车动力电池与风电协同优化调度策略。首先建立包含电动汽车、风电和电网负荷需求的电动汽车风电协同利用模型;然后考虑电动汽车作为交通工具和其动力电池的特性,提出了可用时间、电池剩余容量和充放电功率3个约束条件;进一步采用线性递减惯性权值的改进粒子群算法(PSO)进行求解;最后,实验分析了常规、反调峰和正调峰3种风电出力以及不同风电渗透率对模型的影响,以及参与电网互动的电动汽车数量对模型的影响,验证了电动汽车风电协同模型的有效性,实现了对负荷曲线削峰填谷。 |
英文摘要: |
Aiming at the problem of how to collaborate between electric vehicle batteries and wind power to achieve the peak load shifting of load curve, the optimization scheduling strategy of synergistic dispatch between electric vehicle batteries and wind power is mainly investigated in this paper. Firstly, the synergistic dispatch model including electric vehicles, wind power and power load demand is established, and three constraint conditions of the available time, the residual capacity of battery and the charge discharge power are proposed by considering the characteristics of transportation and power battery of electric vehicles. This optimization model is then solved by the improved particle swarm optimization (PSO) algorithm. Finally, the simulation experiments analyze the factors affecting the synergistic optimization model by considering three kinds of wind power outputs including normal and peak regulation and different wind power penetration and the number of electric cars to participate in power grid interaction, and the effectiveness of the synergistic optimization model to achieve of the peak load shifting is also confirmed. |
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