The Optimization Research Of The Bei'Tun 110 KV Substation Locating In Xian'Yang City

The substation locating is the important part of the power network planning,and has a great influence on the power network development.In this thesis,aiming at problems of the past intelligent optimization algorithm,we obtain the substation size and amount by using the electric power and energy balance model,and present an optimization algorithm by combining the K-means cluster algorithm and the improved ant colony algorithm.We apply the algorithm into the Bei'Tun 110kV substation locating and sizing in Xian'Yang city.First,we establish the substation sizing approach based the electric power and energy balance model.We obtain the electric power model according to the load growth prediction in the planning year,using the relation of the electric power and capacity-load ratio.Here,we consider the investment period and fund.The muti-site continuous locating model is adopted,where the objective function is defined as the construction and operation costs of the substation,and the losses of lines and network.Then,we propose the optimization algorithm by combining the K-means cluster and improved ant colony algorithms.To avoid the in-planning region,we cluster the diffident type loads using the K-means approach.The obtained load center is regarded as the discrete solution space of the ant colony algorithm.To restrain the stagnation behavior,we improve the adaptive control strategy of the pheromone residual coefficient and the pheromone residual update rules.The simulation shows the efficiency of the algorithms.Lastly,we investigate the Bei'Tun 110kV substation locating and sizing in Xian' Yang city.The results show that the improved approach in this thesis has the higher efficiency and precision,compared with the conventional ant colony algorithm.