Study On Multi-objective Optimization Of Distribution Network With Distributed Generation

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.