含分布式电源的配电网优化规划研究

发布时间：2018-05-23 10:54

本文选题：电力系统 + 优化规划　；参考：《湖南大学》2014年硕士论文

【摘要】：科学合理的电力系统规划是电力系统安全、可靠、经济运行的前提。分布式发电能给用户带来便捷、环保的能源，将分布式电源与主网供电结合是智能电网的发展方向。分布式电源的接入将给传统的配电网规划和运行带来深刻的变革。在这样的背景下，本文从含分布式电源系统负荷预测方法及配电网扩展规划两个方面进行了研究。电力系统负荷预测是电网规划的前提和基础，由于分布式电源的安装容量和位置具有随机性和不确定性，其出力情况也将受到气候等诸多因素的影响，这些都将加大含分布式电源系统负荷预测的难度。本文在分析传统负荷预测方法的基础上，总结了含分布式电源系统负荷预测研究思路。支持向量机技术具有学习能力强，，能处理小样本，并且具有良好的精度，但选择合适的参数具有一定的难度，为此引入自适应粒子群优化算法对支持向量机的参数进行选择，并在算法中加入了极值扰动策略，防止其陷入局部最优。建立了基于自适应粒子群算法和支持向量机的含分布式电源系统负荷预测模型。算例分析结果表明，本文提出的方法与传统的SVM法相比具有更好的预测精度。从分布式电源并网对配电网运行和规划的影响出发，基于双层规划的方法建立了含分布式电源配电网扩展规划的模型，上层规划为电网电源规划，下层规划为配电网网架优化。针对传统规划中没有考虑对分布式电源的种类和运行时间进行选择的问题，提出了一种基于年持续负荷曲线和电源成本特性的方法来选择分布式电源的种类和投入工作的时间。在综合考虑分布式电源接入对网络损耗和电压质量影响的基础上对分布式电源的候选位置进行选择。本文采用改进的遗传算法对配电网规划模型进行优化求解，运用简化的二进制编码方式对染色体进行编码，并对传统的前推回代潮流计算方法做了基于层次关联矩阵的改进。利用图论的知识对配电网规划中可能出现的不可行解问题进行了修复。最后，通过对修改后的IEEE33节点系统进行仿真分析，证明了规划模型及其算法改进的合理性和有效性。
[Abstract]:Scientific and reasonable power system planning is the premise of safe, reliable and economical operation of power system. Distributed generation can bring users convenient and environmentally friendly energy. It is the development direction of smart grid to combine distributed generation with main network power supply. The access of distributed generation will bring profound changes to the traditional distribution network planning and operation. In this context, the load forecasting method and the distribution network expansion planning are studied in this paper. Load forecasting of power system is the premise and foundation of power network planning. Due to the randomness and uncertainty of installation capacity and location of distributed power generation, its output will also be affected by climate and many other factors. All these will increase the difficulty of load forecasting with distributed power system. Based on the analysis of traditional load forecasting methods, this paper summarizes the research ideas of load forecasting in distributed power systems. Support vector machine (SVM) technology has a strong learning ability, can process small samples, and has good precision, but it is difficult to select suitable parameters. Therefore, an adaptive particle swarm optimization algorithm is introduced to select the parameters of support vector machine. The extremum perturbation strategy is added to the algorithm to prevent it from falling into local optimum. A load forecasting model based on adaptive particle swarm optimization (APSO) and support vector machine (SVM) is proposed. The numerical results show that the proposed method has better prediction accuracy than the traditional SVM method. Starting from the influence of distributed power grid connection on distribution network operation and planning, a model of distribution network expansion planning with distributed power generation is established based on bilevel programming method. The upper planning is power planning and the lower planning is the optimization of distribution network frame. In order to solve the problem of not considering the choice of the type and operation time of distributed generation in traditional planning, a method based on the annual continuous load curve and the cost characteristics of power supply is proposed to select the type of distributed power and the time when it is put into operation. On the basis of considering the influence of distributed power access on network loss and voltage quality, the candidate positions of distributed power supply are selected. In this paper, an improved genetic algorithm is used to optimize the distribution network planning model, a simplified binary coding method is used to encode chromosomes, and an improvement based on hierarchical correlation matrix is made for the traditional forward generation power flow calculation method. The infeasible solution problem in distribution network planning is repaired by using the knowledge of graph theory. Finally, through the simulation analysis of the modified IEEE33 node system, the rationality and effectiveness of the improved programming model and its algorithm are proved.
【学位授予单位】：湖南大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM715

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