Abstract
Conference Title: 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) Conference Start Date: 2017, April 30 Conference End Date: 2017, May 3 Conference Location: Windsor, ON, Canada This paper provides a stochastic optimization algorithm for the planning of distribution system (DS) feeders. The algorithm finds the optimal feeder routing considering the stochastic variations of load demands (e.g., electric vehicle charging stations) as well as renewable-based distributed generators (DGs) (e.g., photovoltaic and wind DGs). The stochastic variations are addressed using a Monte-Carlo Simulation technique. The objective of the proposed algorithm is to minimize the net present value of the DS investment, operation, and maintenance costs. The main optimization problem is decomposed into a master problem and a subproblem. The master problem is formulated using a genetic algorithm (GA) that generates different feeder routing scenarios, and the subproblem is used to solve the optimal power flow problem for each GA scenario. The proposed algorithm was employed to find the optimal feeder routing of a medium voltage DS that included different types of loads and DGs.