A Reliable Approach for Solving Transmission Network Expansion Planning with Objective of Planning Cost Reduction

Document Type : Original Article

Author

School of Electrical and Mechanical Engineering, Guangdong University of Science & Technology,Dongguan,523083,China

Abstract

This article presents a multi-objective optimization framework for transmission expansion planning using AC optimal power flow to identify the most suitable set of projects and their scheduling along the planning horizon. The candidate plans are evaluated using a fitness function that considered objective function for transmission expansion planning problem is composed of two terms. The first term is related to the sum of investment costs which is the construction cost of new lines; the second term is related to the expected operation costs, which is the expected cost of generation in the power system. The third term is related to the cost of load curtailment. The optimization problem represented in this paper is a large-scale non-convex mixed integer nonlinear programming problem with multiple local minima. The transmission expansion planning procedure is formulated as an optimization problem to overcome the difficulties in solving the non-convex and mixed-integer nature of the optimization problems. The particle swarm optimization algorithm searches for optimal planning to reach the fitness requirement. transmission expansion planning problem involves a decision on the location and number of new transmission lines. In optimization process all constrains are modeled beside problem which should be considered in investment. The proposed transmission expansion planning model has been applied to the well-known IEEE 30-bus test system. In order to illustrated the performance of the proposed method, we consider three scenarios as fix load and generation, fixed load and variable generation and variable load and generation. The detailed results of the case study are presented and thoroughly analyzed. The obtained transmission expansion planning results show the efficiency of the proposed algorithm.

Keywords


  • Receive Date: 05 January 2022
  • Revise Date: 19 February 2022
  • Accept Date: 02 March 2022
  • First Publish Date: 01 April 2022