| 引用本文: | 应飞祥,徐天奇,李 琰,等.含电动汽车充电站商业型虚拟电厂的日前调度优化策略研究[J].电力系统保护与控制,2020,48(21):92-100.[点击复制] |
| YING Feixiang,YING Feixiang,YING Feixiang,et al.Research on day-to-day scheduling optimization strategy of a commercial virtual power plant with an electric vehicle charging station[J].Power System Protection and Control,2020,48(21):92-100[点击复制] |
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| 含电动汽车充电站商业型虚拟电厂的日前调度优化策略研究 |
| 应飞祥,徐天奇,李琰,高鑫,贾鉴,汪宇航,何民,田华 |
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| (1.云南民族大学电气信息工程学院，云南 昆明 650500;2.航空工业江西洪都航空工业集团有限责任公司，
江西 南昌 330024;3.中国能源建设集团云南省电力设计院有限公司，云南 昆明 650051;4.国网浙江
建德市供电有限公司，浙江 杭州 311600;5.昆明电器科学研究所，云南 昆明 650221) |
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| 摘要: |
| 研究商业型虚拟电厂运行机制能为新能源电源入网提供一定的技术支撑。将风力发电系统、光伏发电系统、储能系统、电动汽车充电站整合为一个虚拟发电厂，可显著降低因新能源单独并网时的出力不确定性及电动汽车无序充电对电网造成的不良影响，减轻电网压力，并可有效促进新能源消纳。以虚拟电厂经济效益最优为目标，在满足各约束条件的前提下，对其进行调度优化策略研究。通过线性惯性权重粒子群算法及非线性惯性权重粒子群算法对所提模型进行求解，结果表明采用非线性惯性权重粒子群算法不仅能避免过早收敛陷入局部最优而且得到的效益更高。通过算例验证了该模型的合理性及求解方法的有效性。 |
| 关键词: 商业型虚拟电厂 电动汽车充电站 经济运行 调度优化策略 粒子群算法 |
| DOI：DOI: 10.19783/j.cnki.pspc.191516 |
| 修订日期：2020-05-13 |
| 基金项目:国家自然科学基金项目资助(61761049)；云南省教育厅科学研究基金项目资助(2019Y0169) |
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| Research on day-to-day scheduling optimization strategy of a commercial virtual power plant with an electric vehicle charging station |
| YING Feixiang,YING Feixiang,YING Feixiang,YING Feixiang,YING Feixiang,YING Feixiang,YING Feixiang,YING Feixiang |
| (1. School of Electrical and Information Technology, Yunnan Minzu University, Kunming 650500, China;
2. AVIC Jiangxi Hongdu Aviation Industry Group Co., Ltd., Nanchang 330024, China;
3. China Energy Engineering Group Yunnan Electric Power Design Institute Co., Ltd., Kunming 650051, China;
4. State Grid Zhejiang Jiande Power Supply Co., Ltd., Hangzhou 311600, China;
5. Kunming Electric Apparatus Research Institute, Kunming 650221, China) |
| Abstract: |
| Research on the operation mechanism of a commercial virtual power plant provides another possible method for a new energy power source integrating into power grids. Integrating wind power, photovoltaic power, an energy storage systems, and an electric vehicle charging station into a virtual power plant can effectively reduce the power grid's stability risk. This is caused by the separate renewable energy output uncertainty and disordered charging of electric vehicles. Adverse impacts on power systems could be alleviated and the consumption of renewable energy could be promoted. The optimal economic benefit of the virtual power plant is taken as the objective, and under the premise of satisfying various constraints, a scheduling optimization strategy is studied. The proposed model is solved by linear and nonlinear inertia weight particle swarm optimization. The results show that the premature convergence at a local optimum can be avoided and higher benefits can be gained with the nonlinear inertia weight particle swarm optimization. A calculated example is carried out to verify the rationality of the model and the effectiveness of the solution.
This work is supported by National Natural Science Foundation of China (No. 61761049) and Yunnan Provincial Department of Education Science Research Fund Project (No. 2019Y0169). |
| Key words: commercial virtual power plant electric vehicle charging station economic operation scheduling optimization strategy particle swarm optimization |
