考虑风机接入电能质量的多目标电网规划优化

发布时间：2019-05-24 00:19

【摘要】：大规模风电并网在推动电力行业快速发展,提高国家经济发展水平的同时,也对电网的规划和电力系统的正常运行造成了极大的影响,尤其是风电自身的不确定性等特性所引起的一系列不容忽视的电能质量问题,其中电压波动和闪变是风电所造成的主要负面影响。为了保证规划后的电网其电能质量符合国家的有关标准,这就需要在电网规划中考虑风电场接入后产生的电能质量问题。电网规划已经被证明是一个具有多目标、非线性等特点的相对复杂优化问题,对其求解存在一定的困难。考虑到传统优化算法的目标权重人为选择以及模拟二进制交叉算子的全局搜索性能较弱等问题,本文提出将正态分布交叉(NDX)算子引入到NSGA2算法中。一方面NSGA2算法利用快速非支配排序,拥挤度以及精英策略筛选出能够使优化方案的各目标函数值都比较大(或者比较小)的Pareto最优解集,克服传统多目标优化算法的缺点;另一方面NDX算子借助正态分布和离散重组操作来提高对解空间的探索和开发能力,进一步扩大搜索空间。将这二者结合可以提高算法的全局搜索性能以及收敛性,从而获得高质量且分布均匀的Pareto最优解。由于风电场并网点处的闪变值与在此处电网的等效阻抗有着直接的关系,也就和电网的网络架构有关,本文在考虑风电并网所引起的电能质量的基础上,构建了以线路总长度以及PCC处闪变值为目标的含风电场多目标电网规划模型。考虑到风电以及负荷预测自身的不确定性,借助基于蒙特卡罗的直流概率潮流计算方法以及机会约束规划方法来判断规划方案是否违反支路功率约束,以校验优化方案的可靠性。为了验证本文所提模型以及方法的有效性,以IEEE-6节点以及修改后的IEEE-24节点系统为例,采用传统遗传算法以及两种不同NSGA2算法进行规划优化,对比结果表明NSGA2算法在处理多目标规划优化问题中要比传统遗传算法表现出很大的优越性,而相比经典NSGA2算法,本文所提算法又具有较高的决策效率,其优化得到的含风电场电网的规划方案,在保证经济性的同时,也使风机并网点的电能质量达到更高的品质。
[Abstract]:Large-scale wind power grid connection not only promotes the rapid development of the power industry and improves the level of national economic development, but also has a great impact on the planning of the power grid and the normal operation of the power system. In particular, a series of power quality problems caused by the uncertainty of wind power itself can not be ignored, among which voltage fluctuation and flicker are the main negative effects caused by wind power. In order to ensure that the power quality of the planned power grid conforms to the relevant national standards, it is necessary to consider the power quality problems caused by the access of wind farms in the power grid planning. Power grid planning has been proved to be a relatively complex optimization problem with multi-objective, nonlinear and other characteristics, and it is difficult to solve it. Considering the artificial selection of the target weight of the traditional optimization algorithm and the weak global search performance of the simulated binary cross operator, this paper proposes to introduce the normal distribution cross (NDX) operator into the NSGA2 algorithm. On the one hand, NSGA2 algorithm uses fast non-dominant sorting, congestion and elite strategy to select the Pareto optimal solution set which can make the objective function value of the optimization scheme relatively large (or relatively small), so as to overcome the shortcomings of the traditional multi-objective optimization algorithm. On the other hand, NDX operator improves the ability to explore and develop the solution space by means of normal distribution and discrete recombination operation, and further expands the search space. By combining the two methods, the global search performance and convergence of the algorithm can be improved, and the Pareto optimal solution with high quality and uniform distribution can be obtained. Because the flicker value at the parallel dot of the wind farm is directly related to the equivalent impedance of the power grid here, and it is also related to the network structure of the power grid, this paper considers the power quality caused by the wind power grid connection. A multi-objective power grid planning model with wind farm is constructed, which aims at the total length of the line and the flicker value at PCC. Considering the uncertainty of wind power and load forecasting itself, the DC probabilistic power flow calculation method based on Monte Carlo and the opportunity constrained programming method are used to judge whether the planning scheme violates the branch power constraint. To verify the reliability of the optimization scheme. In order to verify the effectiveness of the model and method proposed in this paper, taking the IEEE-6 node and the modified IEEE-24 node system as examples, the traditional genetic algorithm and two different NSGA2 algorithms are used for planning optimization. The comparison results show that NSGA2 algorithm is superior to the traditional genetic algorithm in dealing with multi-objective programming optimization problems, and compared with the classical NSGA2 algorithm, the proposed algorithm has higher decision efficiency. The optimized planning scheme of wind farm power grid not only ensures the economy, but also makes the power quality of fan parallel network reach higher quality.
【学位授予单位】：天津大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM715

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