Distribution Network Expansion Planning Considering Supplying Region Section Adjustments And Load Uncertainty

As the connection between transmission network and consumers facilities, distribution network planning has been paid more and more attentions. A new optimal planning scheme of distribution network is presented. The Genetic Algorithms(GA) is used to carry through the supplying region section adjustments, and then an Improved Minimum-cost Spanning Tree (IMST) is adopted in each section to realize the expansion planning. The main achievements are as follows:A distribution network expansion planning considering supplying region section adjustments is proposed. Minimizing the weighted summation of the construction cost and the operation cost is defined as the index. The GA is adopted to achieve the optimal result under the required correlative constraint. The chromosome is coded by the randomly selected core street divisions. The heuristic partitioning method is used to obtain the partitioning scheme of supplying section. For each partitioning scheme, the distribution network expansion planning is implemented based on IMST. The fitness of each candidate scheme is calculated. The genetic operations of selection, crossover and mutation are used in the optimization.Based on the algorithm mentioned above, the method considering load uncertainty is discussed. The uncertain loads are modeled as the single condition loads and the multi-condition loads and the relativities of the loads are also considered. Based on above modelling, the proposed GA based expansion planning scheme for certain loads is improved to meet the requirements of uncertain loads by introducing the multi-scenario planning approach. In the expansion planning for uncertain loads, the weights of the edges are determined by the second order moments and the relativities of the corresponding currents passing through the edges. The minimum-cost tree planning method is improved to satisfy the uncertain loads.The program is written out. Examples are given showing the feasibility of the proposed methods.