An Optimization Approach for Distribution Network Planning Strategy Based on Combined Improved Cloud Model and Evidence Theory

Abstract

With the continuous promotion of China’s power reform, higher requirements and standards have been put forward for distribution network investment decisions. However, the current work of distribution network project optimization in China has the problems of high subjectivity of investment decisions, fuzzy evaluation method, and high uncertainty of project optimization. In response to the above problems, this paper proposes a distribution network planning scheme optimization method based on the improved cloud model and evidence theory. Firstly, the optimal index system of the planning scheme is constructed from the dimensions of load transfer capacity, grid structure level, load capacity, investment benefit, social benefit, and life-cycle cost. Secondly, for subjectivity and ambiguity problems, the traditional degree of the membership function is replaced by a cloud model optimized by cloud entropy. Thirdly, the problem of uncertainty is addressed through evidence theory, while the game theory approach is used to identify conflicting evidence for evidence revision and fusion in the case of conflicting evidence arising from traditional evidence theory. Moreover, by improving the TOPSIS method and comparing the distances between the basic probability assignments of each planning scheme and the positive and negative virtual optimal solutions, the final decision results can be determined. Finally, the effectiveness and superiority of the proposed method are verified by experiments.