Integration Planning Of Distributed Generation And Distribution Network

As a new way of power generation, Distributed Generation (DG) is flexible, low pollution, high efficiency. Because of its characteristics, DG has caused the wide attention around the world. The stochastic power flow and harmonic problem caused by the presence of DG in the distribution network make workers face many accidents and difficulties in planning, and for any of a single plan can’t meet the demand of the development of power system. Therefore, in order to exert the positive influence and suppress the negative impact, it is important to require a comprehensive understanding of the features and make a comprehensive planning of DG and distribution network. The integration optimization of DG and distribution network was studied in this paper, including probabilistic power flow, reactive power optimization and comprehensive planning under the uncertainty. The specific work is as follows:Firstly, in view of the deterministic flow calculation is no longer suitable for the basis analysis of power system, the Latin Hypercube Sampling principle and implementation process is analyzed in detail after a brief introduction of the probabilistic power flow calculation methods. This sampling method and Monte Carlo simulation is combined for probabilistic power flow calculation, and a specific example is adopted to prove the validity and correctness of the algorithm. Through this provided good theoretical preparation for probabilistic power flow calculation involved in the integration planning of DG and distribution network.Secondly, in allusion to the problems of the presence of DG in the distribution network, we use the reactive power optimization method to realize the economic and reliable operation of the system, and establish a mathematical model for the purpose of improving power quality, reducing network losses and reducing the cost of electricity. An adaptive chaotic particle swarm optimization algorithm is used to solve the multi-objective functions. For enhancing the diversity of particle populations, it introduces the chaos system in the initialization process. This algorithm adopts the adaptive inertia weight and variable learning factors in the iterative process to obtain the more accurate global optimal results. At last, a specific example is adopted to prove the validity and correctness of the reactive power optimization model and algorithm.Finally, in view of the situation that there are fewer studies on the comprehensive planning with strict requirements and diversified indicators, we establish a comprehensive planning model in the case of the expansion of load nodes in the distribution network. The objective function is defined to be the minimization of the total cost including the investment cost, network loss cost, power purchase cost, emission cost, and reliability cost in the planning period. We make a combination of the probabilistic power flow method of Monte Carlo simulation based on Latin Hypercube Sampling, the improved Particle Swarm Optimization algorithm and the simulated annealing algorithm based on branch exchange to deal with the problem. Through the analysis of a concrete example, we not only verify the validity of the model and algorithm, but also highlight the research significance of comprehensive planning.