计及风电消纳和电储能的电力系统优化调度
|
摘要
为保证电力系统备用容量能满足风电出力不确定性对系统安全的要求,建立考虑弃风限制优化的电储能—风电联合鲁棒优化调度模型。通过设置弃风限制来确定安全风电出力区间,在风电出力波动时,保证调度结果能满足电网备用容量等安全约束。模型求解时,运用大M转换将模型中通过求解器难以求解的隐式弃风限制区间转化为可以直接求解的含大M变量的显示表达式。以IEEE 162节点系统进行算例测试,分析了引入电储能后对风电消纳效果的影响,优化结果表明了该方法的可行性。
In order to ensure that the reserve capacity of the system can satisfy the uncertainty of wind power output and the safety of the system, a combined robust optimal dispatching model of electric energy storage and wind power considering the optimization of the abandoned wind is established. To determine the safety of power output by setting the interval of abandoned wind limit, to ensure the wind power output fluctuation, the scheduling results can meet the power grid reserve capacity security constraints. When solving the model, the large M transformation is used to transform the implicit abandon wind constraint which is difficult to solve by the solver in the model to the display representation containing large M variables that can be directly solved.The IEEE 162 node example system test, analyzed the influence of the introduction of electrical energy storage of wind power consumptive effect, the optimization results show that the method is feasible.
In order to ensure that the reserve capacity of the system can satisfy the uncertainty of wind power output and the safety of the system, a combined robust optimal dispatching model of electric energy storage and wind power considering the optimization of the abandoned wind is established. To determine the safety of power output by setting the interval of abandoned wind limit, to ensure the wind power output fluctuation, the scheduling results can meet the power grid reserve capacity security constraints. When solving the model, the large M transformation is used to transform the implicit abandon wind constraint which is difficult to solve by the solver in the model to the display representation containing large M variables that can be directly solved.The IEEE 162 node example system test, analyzed the influence of the introduction of electrical energy storage of wind power consumptive effect, the optimization results show that the method is feasible.
引文
[1]王敏,许建,潘永春.计及风电预测可靠性的含风电电力系统优化调度模型[J].广东电力,2017,30(4):43-49.
[2]张文韬,王秀丽,李言,等.大规模风电并网下多区域互联系统热电综合调度模型[J].电网技术,2018,42(1).154-161.
[3]肖峥,吴云亮,闫秉科.考虑风电波动性的微电源逆变控制器参数优化研究[J].陕西电力,2015,43(3):6-10.
[4]殷桂梁,张雪,操丹丹等.考虑风电和光伏发电影响的电力系统最优旋转备用容量确定[J].电网技术,2015,39(12):3497-3504.
[5]田建芳,毛亚珊,翟桥柱,等.基于风电消纳能力评估的安全约束经济调度方法[J].电网技术,2015,39(9):2398-2403.
[6]李志刚,吴文传,张伯明.消纳大规模风电的鲁棒区间经济调度(一)调度模式与数学模型[J].电力系统自动化,2014(20):33-39.
[7]许玉贞.基于预测误差修正的微电网经济调度的研究[D].西华大学,2015.
[8]泽锋,赵晋泉,魏文辉,等.主动配电网中电池储能系统最优充放电策略[J].电力系统自动化,2016,40(20):47-53.
[9]杨锡运,曹超,李相俊,等.基于模糊经验模态分解的电池储能系统平滑风电出力控制策略[J].电力建设,2016,37(8):134-140.
[10]刘英军,刘畅,王伟,等.储能发展现状与趋势分析[J].中外能源,2017,22(4):80-88.
[11]卢志刚,郭凯,闫桂红,等.考虑需求响应虚拟机组和碳交易的含风电电力系统优化调度[J].电力系统自动化,2017,41(15):58-65.
[12]李砚,杜纲,刘波.线性双层规划的一类鲁棒解[J].统计与决策,2013,373(1):78-80.
[13]杜正春,王延延,王毅,等.获取电力系统运行方式的多平衡机潮流模型[J].电力系统自动化,2014,38(16):41-46.
[14]陈磊,徐飞,王晓,等.储热提升风电消纳能力的实施方式及效果分析[J].中国电机工程学报,2015,35(17):4283-4290.
[2]张文韬,王秀丽,李言,等.大规模风电并网下多区域互联系统热电综合调度模型[J].电网技术,2018,42(1).154-161.
[3]肖峥,吴云亮,闫秉科.考虑风电波动性的微电源逆变控制器参数优化研究[J].陕西电力,2015,43(3):6-10.
[4]殷桂梁,张雪,操丹丹等.考虑风电和光伏发电影响的电力系统最优旋转备用容量确定[J].电网技术,2015,39(12):3497-3504.
[5]田建芳,毛亚珊,翟桥柱,等.基于风电消纳能力评估的安全约束经济调度方法[J].电网技术,2015,39(9):2398-2403.
[6]李志刚,吴文传,张伯明.消纳大规模风电的鲁棒区间经济调度(一)调度模式与数学模型[J].电力系统自动化,2014(20):33-39.
[7]许玉贞.基于预测误差修正的微电网经济调度的研究[D].西华大学,2015.
[8]泽锋,赵晋泉,魏文辉,等.主动配电网中电池储能系统最优充放电策略[J].电力系统自动化,2016,40(20):47-53.
[9]杨锡运,曹超,李相俊,等.基于模糊经验模态分解的电池储能系统平滑风电出力控制策略[J].电力建设,2016,37(8):134-140.
[10]刘英军,刘畅,王伟,等.储能发展现状与趋势分析[J].中外能源,2017,22(4):80-88.
[11]卢志刚,郭凯,闫桂红,等.考虑需求响应虚拟机组和碳交易的含风电电力系统优化调度[J].电力系统自动化,2017,41(15):58-65.
[12]李砚,杜纲,刘波.线性双层规划的一类鲁棒解[J].统计与决策,2013,373(1):78-80.
[13]杜正春,王延延,王毅,等.获取电力系统运行方式的多平衡机潮流模型[J].电力系统自动化,2014,38(16):41-46.
[14]陈磊,徐飞,王晓,等.储热提升风电消纳能力的实施方式及效果分析[J].中国电机工程学报,2015,35(17):4283-4290.