基于改进量子粒子群算法的智能电网多目标优化规划研究

发布时间：2018-04-02 17:21

本文选题：智能电网　切入点：多目标优化　出处：《兰州理工大学》2014年硕士论文

【摘要】：智能电网是应对全球能源、气候、环境以及经济和可持续发展的综合解决方案,也是未来电网研究与发展的方向。输配电网的智能化是坚强智能电网的重要内容。智能电网规划是电力系统规划的重要组成部分,对其进行科学合理的规划,能够给社会带来巨大的经济效益和社会效益,同时也是发展智能电网的重要基础。电网规划是一个非线性、多目标、多约束的问题,其涉及潮流计算、无功补偿、网络损耗、供电可靠性及经济性等约束。用传统的电网优化方法求解电网规划时,算法往往容易陷入局部最优解,使得最终不能寻到全局最优解。近年来,现代启发式算法被广泛应用于各个领域并且取得很好的效果,这些算法具有全局寻优能力强及通用性强等特点,但是容易产生“维数灾”问题。本文根据粒子群算法的基本思想,结合量子理论,提出一种改进的量子粒子群优化算法,并将其应用在输电网网架规划和含分布式电源的智能电网多目标优化规划中。研究表明,该算法对智能电网多目标优化规划是有效的。本文根据电网规划的多目标性,选择合适的目标建立电网规划的数学模型。对量子粒子群算法进行改进,使其能应用在离散问题的求解中。改进后的量子粒子群算法提高了算法的运行速度和收敛速度。以18节点输电网系统扩展规划和8节点含分布式电源的配电网扩展规划为例,验证该算法求解多目标规划的有效性及高效性。对于多目标Pareto最优解集,采用拥挤距离排序方法进行构造。最后应用MATLAB软件对算例进行仿真研究并得出相应规划结果。结果表明本文应用的量子粒子群算法在智能电网多目标规划时能够在保证计算速度的前提下,很好的完成电网规划的任务。
[Abstract]:Smart grid is a comprehensive solution to global energy, climate, environment, economic and sustainable development. It is also the direction of grid research and development in the future.Intelligent transmission and distribution network is an important content of strong smart grid.Smart grid planning is an important part of power system planning. Scientific and reasonable planning can bring great economic and social benefits to the society, and it is also an important basis for the development of smart grid.Power network planning is a nonlinear, multi-objective, multi-constraint problem, which involves power flow calculation, reactive power compensation, network loss, power supply reliability and economic constraints.When the traditional power network optimization method is used to solve the power network planning, the algorithm is easily trapped in the local optimal solution, so that the global optimal solution can not be found in the end.In recent years, modern heuristic algorithms have been widely used in various fields and achieved good results. These algorithms have the characteristics of strong global optimization ability and strong versatility, but they are easy to produce the problem of "dimension disaster".Based on the basic idea of particle swarm optimization and quantum theory, an improved quantum particle swarm optimization algorithm is proposed in this paper. It is applied to transmission network planning and smart grid multi-objective optimization planning with distributed generation.The research shows that the algorithm is effective for multi-objective optimization planning of smart grid.In this paper, according to the multi-objective of power network planning, the mathematical model of power network planning is established by selecting suitable targets.The quantum particle swarm optimization (QPSO) algorithm is improved so that it can be used to solve discrete problems.The improved Quantum Particle Swarm Optimization (QPSO) algorithm improves the speed of operation and convergence of the algorithm.Taking the expansion planning of 18-node transmission network system and the distribution network expansion planning with 8-node distributed generation as examples, the effectiveness and efficiency of the algorithm for solving multi-objective programming are verified.For the multi-objective Pareto optimal solution set, the congestion distance sorting method is used to construct the optimal solution set.Finally, the MATLAB software is used to simulate the example and the corresponding programming results are obtained.The results show that the quantum particle swarm optimization (QPSO) algorithm applied in this paper can accomplish the task of power network planning well under the premise of ensuring the computing speed when the smart grid multi-objective programming is carried out.
【学位授予单位】：兰州理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM715

【参考文献】