基于量子粒子群算法的含分布式电源的配网规划

发布时间：2018-01-21 20:21

本文关键词： 配网规划分布式电源定容选址综合目标优化量子粒子群　出处：《广东工业大学》2014年硕士论文　论文类型：学位论文

【摘要】：近年来,世界上发生的几次大的停电事故,包括2008年雪灾导致的电力系统事故都充分暴露了单一集中式供电模式的脆弱性。分布式发电技术的发展为集中供电方式提供了有益的补充,风、光和水资源的有机结合是节省投资、降低能耗、提高电力系统稳定性和灵活性的主要方式,是未来能源领域一个重要的发展方向。但是,分布式电源在为大电网提供能源补充和电压支撑的同时,也必然给电力系统的潮流分布、电能质量和继电保护等方面带来影响,因此合理规划配电网的分布式电源显得十分重要。本文根据配电网规划的原则和内容,对分布式电源接入配电网的位置和容量进行优化研究。论文首先详述了各种不同类型的分布式电源技术,并主要介绍了风机、光伏电池和小水电的三种分布式电源的出力模型。其次,介绍了分布式电源接入系统后对配电网系统的影响,包括对潮流计算、电能质量、继电保护系统、系统可靠性和配电规划等的影响；再次,详细介绍了求解含分布式电源的配电网优化规划问题的标准粒子群和量子粒子群算法,并利用算法测试三个非线性带约束条件的函数,验证了量子粒子群算法的有效性和可行性；最后,针对含分布式电源配电网规划问题,建立了以有功网损费用和DG运行费用最小的目标函数,以电压、电流和DG容量限制为约束条件,采用惩罚函数方式将约束条件转换并加入到求解的目标函数中形成综合目标函数,然后以IEEE14节点系统作为该地区模拟测试配电网,利用量子粒子群算法对分布式电源配置进行优化,并与基本粒子群算法进行比较,仿真结果表明本文所提出的算法在配电网规划中具有较好的全局寻优能力和较快的收敛速度。根据某地区配电网规划实际情况,利用量子粒子群算法对三种分布式电源两两接入或是三种同时接入的配电网方案进行对比,以网损费用和DG运行费用之和最小为优化目标,利用DG接入前后的配电网系统节点电压和网损变化来说明合理选择接入DG位置和容量的重要性,为该地区配电系统规划提供参考价值。
[Abstract]:In recent years, there have been several major blackouts in the world. Including the 2008 snow disaster caused by the power system accidents have fully exposed the vulnerability of a single centralized mode of power supply. The development of distributed generation technology provides a useful supplement to the centralized power supply mode wind. The organic combination of light and water resources is the main way to save investment, reduce energy consumption, improve the stability and flexibility of power system, and is an important development direction in the field of energy in the future. Distributed generation not only provides energy supply and voltage support for large power grid, but also influences power flow distribution, power quality and relay protection of power system. Therefore, it is very important to plan the distributed generation of distribution network reasonably. According to the principles and contents of distribution network planning, this paper studies the location and capacity optimization of distributed generation access to distribution network. Firstly, this paper describes various types of distributed power generation technology. And mainly introduces three kinds of distributed power generation model of fan, photovoltaic cell and small hydropower. Secondly, it introduces the influence of distributed power system on distribution network system, including power flow calculation, power quality. The impact of relay protection system, system reliability and distribution planning; Thirdly, the standard particle swarm optimization and quantum particle swarm optimization algorithm are introduced in detail to solve the distribution network optimization problem with distributed generation, and three nonlinear functions with constraints are tested by using the algorithm. The validity and feasibility of quantum particle swarm optimization (QPSO) are verified. Finally, aiming at the distribution network planning problem with distributed generation, an objective function with minimum active power loss cost and DG operation cost is established, with the constraints of voltage, current and DG capacity as constraints. The constraint condition is transformed by penalty function and added to the objective function to form a comprehensive objective function, and then the IEEE14 node system is used as the simulation test distribution network in the region. Quantum Particle Swarm Optimization (QPSO) is used to optimize the distributed power allocation, and compared with the basic PSO. The simulation results show that the proposed algorithm has better global optimization ability and faster convergence rate in distribution network planning. Quantum Particle Swarm Optimization (QPSO) is used to compare the distribution network schemes with two or three kinds of distributed power supply. The optimal goal is to minimize the sum of loss cost and DG operating cost. The change of node voltage and network loss before and after DG access is used to explain the importance of reasonable selection of DG location and capacity, which provides a reference value for distribution system planning in this area.
【学位授予单位】：广东工业大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM715;TP18

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