【作者】 程杉；

【导师】 陈民铀；

【作者基本信息】 重庆大学 ， 电气工程， 2013， 博士

【摘要】 分布式发电（Distributed Generation, DG）与集中式发电相结合是电力系统发展的趋势，更是智能电网的重要组成部分。目前，DG设备研发、制造和设备自身控制方面具有一些较成熟的技术，DG对电力系统的影响已经有大量的研究，但涉及DG并网及并网后的系统优化运行、协调控制等领域的研究大多处于初始阶段。因此，为提高DG并网后配电网运行的安全性、经济性和可靠性，提高配电网对分布式可再生能源的吸纳能力，研究分布式电源在配电网中的优化配置及其并网后的配电网优化运行管理具有重要的理论意义和实际意义。本文以分布式电源的优化配置和含分布式电源的配电网无功优化为对象，围绕含分布式电源的配电网多目标优化问题展开研究，同时，探讨基于群集智能和Pareto支配关系的多目标粒子群优化算法，为求解电力系统多目标优化问题构造更具实用价值的优化算法。主要研究内容包括：（1）围绕智能配电网多目标优化问题，研究基于群集智能理论、能解决多任务、多约束、多目标协同优化问题的智能优化策略，提出一种能够求解复杂多目标优化问题的综合自适应多目标粒子群优化（CAMPSO）算法。CAMPSO算法引入随机黑洞机制和动态惯性权重策略以兼顾粒子群的开拓与探索能力，使算法以较高的精度逼近真实的Pareto前沿；引入基于细菌群体感应机理的扰动机制和动态选择领导粒子策略以保证种群的多样性；采用逐步淘汰策略提高Pareto解的多样性和分布均匀性。（2）针对DG在配电网规划中的优化配置问题，建立带偏好策略的DG多目标优化配置模型，采用CAMPSO算法求解，实现DG容量和位置的全局优化配置，为决策者提供多样化的方案提供支撑。模型兼顾配电网运行的经济性、可靠性、安全性和DG的环境友好性，同时考虑用户对电压质量和供电可靠性的特殊要求，提出电压偏好策略和供电可靠性策略。（3）为了以最少的储能设备投资取得最大的风电输出稳定性，构建以混合储能系统（HESS）安装和运行维护成本最低、风电输出功率合格率最高为目标函数的HESS多目标优化配置模型，采用模糊控制对HESS中的各储能设备进行功率分配，以保证储能设备的循环使用寿命和保障HESS有充足的可用能量平抑风电输出的波动性。（4）将能够提供无功功率的DG与传统的无功调节手段相结合，兼顾系统的经济运行、电能质量和无功补偿设备投资与运行成本等多任务要求，研究含DG的配电网多目标无功优化策略，建立含DG的配电网多目标无功优化数学模型，运用CAMPSO算法求解配电网多目标无功优化问题，为决策人员提供灵活选择的多样化解决方案。更多还原

【Abstract】 Distrubuted generation (DG) is an important portion of smart grid and it’s a trendto combine centralized generation with DG. It’s the fact that DG’s technologies,manufacture and control methods have developed well and the effects to distributionsystem caused by DG have been analyzed deeply. Howerver, DG’s integration todistribution system and the operation and coordinated control of distribution systempenetrated with DG are in development. Therefore, in order to take advantages of DGand decrease its negative effectes caused to power system, studying optimal allocationof DG in distribution system and the optimal operation of distribution system penetratedwith DG is of great theoretical guidance and pratical importance.Focusing on the optimal allocation of DG in distribution system, optimal allocationof hybrid energy storage system (HESS) and reactive power optimization in distributionsystem, the thesis attempts to study the multi-objective optimization problems related todistribution system penetrated with DG. Meanwhile, multi-objective particle swarmoptimization algorithm is studied to provide some effective optimization method formulti-objective optimization problems related to power system.Main points include:(1) Considering the fact that most multi-objective optimization algorithms havelow convergence accuracy and the identified Pareto solutions do not have good diversityand even distribution, a comprenhensively adaptive multi-objective particle swarmoptimization (CAMPSO) algorithm is presented, which can effectively deal withcomplex multi-objective optimization problems. The CAMPSO introduces mechanismof random black hole and dynamic inertia weight to balance the swarm’s capacity ofexploration and exploitation and to convergen to the true Pareto Front with highaccuracy and high speed. Besides, it combines the turbulence mechamism based onquorum sensing and dynamic selection of leader particles to maintain the diversity ofpartice swarm. Moreover, step-by-step elimination is proposed to the diversity anddistribution property of idendtified Pareto solutions.(2) To deal with the optimal placement and capacity of DG integrated intodistribution system, for allocating DG in distribution system, a multi-objectiveoptimization model with preference strategy is estabilished. The CAMPSO is employedto solve the multi-objective problem, which realizes the true multi-objectiveoptimization of allocating DG and provides diverse solutions to decision maker. In the model, the economy, reliability, security of the sytem and environmental advantages ofDG are fully considered, and the multiple objectives include minizing active power lossof the system, maximizing the stability of the system and miniming the environmentalcost. In addition, voltage preference strategy (VPS) and power supply preferencestrategy (PSPS) are presented to meet some special consumers’ requirements.(3) To compensate the fluctuation of wind power better with less investment andmaintance&operation cost of HESS, a multi-objective optimization model withobjectives of minimizing the investment and maintance&operation cost of HESS andmaximizing the probability of satisfying compensated wind power output is developed.Fuzzy controd method is adopted to to allocating power between supercapacitor andbattery, and extend the life of energy storage equipment and to gurantee that HESS hasenough available energy to conpensate for next period.(4) By integrating the reactive power of the distributed generators (DGs) to be thecontrol variables, and concurrently considering the economic operation of distributionsystem, power quality and investment of reactive power equipment, the multi-objectiveoptimization strategy of reactive power in distribution power system penetrated withDGs is discussed. A multi-objective model for optimizing reactive power is establishedand the CAMPSO algorithm is applied the multi-objective reactive power optimizationof distribution system penetrated with DGs. The proposed strategy provides decisionmaker with diverse solutions and facilitates the decision maker to analyze the relationship between the objectives and variables.更多还原