| 引用本文: | 张 熙,张 峰,巩乃奇,刘海客,梁 军,杨立滨.基于荷电状态动态调整的储能电站容量规划[J].电力自动化设备,2015,35(11): |
| ZHANG Xi,ZHANG Feng,GONG Naiqi,LIU Haike,LIANG Jun,YANG Libin.BESS capacity planning based on dynamic SOC adjustment[J].Electric Power Automation Equipment,2015,35(11): |
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| 摘要: |
| 储能系统平滑风电功率波动可以有效提高风电输出功率的稳定性，但昂贵的储能成本却制约着储能系统的整体性能，由此储能容量优化成为解决储能成本与平抑波动能力相互制约的方式之一。以储能系统荷电状态（SOC）为参量，提出基于可变功率修正系数的储能系统充放电控制策略，在储能系统有效平抑风电功率波动的同时，避免出现过充过放现象，保证储能系统的运行寿命。以储能系统多种成本之和最小为目标，构建计及风电场投资、运行成本和储能运行寿命的储能容量优化模型，并采用粒子群优化算法对模型进行求解。仿真分析结果验证了所提方法的有效性。 |
| 关键词: 储能 荷电状态 容量 成本 优化 风电 |
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| 基金项目:国家自然科学基金资助项目（51307101，51177091）；山东省优秀中青年科学家科研奖励基金资助项目（BS2013NJ011）；青海省光伏发电并网技术重点实验室项目（2014-Z-Y34A） |
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| BESS capacity planning based on dynamic SOC adjustment |
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ZHANG Xi1, ZHANG Feng1, GONG Naiqi2, LIU Haike3, LIANG Jun1, YANG Libin4
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1.Electric Vehicle Engineering Laboratory of Shandong Province，Shandong University，Ji’nan 250061，China;2.College of Information & Electrical Engineering，Shandong University of Science and Technology，Qingdao 266590，China;3.State Grid of China Technology College，Ji’nan 250061，China;4.State Grid Qinghai Electric Power Research Institute，Xining 810000，China
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| Abstract: |
| Though the BESS（Battery Energy Storage System） may smooth the wind power fluctuation to effectively improve the stability of wind power output，its expensive cost restricts its overall performance，for which，the energy storage capacity optimization is a way to achieve the balance between its cost and performance. With the SOC（State Of Charge） of BESS as an indicator，a strategy of charge／discharge control based on the variable power correction coefficient is proposed，which effectively smoothes the wind power fluctuation while avoids the phenomenon of over-charge or over-discharge to ensure its lifetime. With the minimum total cost of BESS as its objective，an energy storage capacity optimization model considering the wind farm investment，operational cost and BESS lifetime is built and solved by PSO（Particle Swarm Optimization） algorithm. The simulative analysis verifies the effectiveness of the proposed method. |
| Key words: energy storage state of charge capacity costs optimization wind power |
