Stochastic transmission expansion planning incorporating reliability solved using SFLA meta-heuristic optimization technique

Abstract

This paper addresses stochastic transmission expansion planning (TEP) under uncertain load conditions when reliability is taken into consideration. The main objective of the proposed TEP is to minimize the total planning cost by denoting the place, number, and type of new transmission lines subject to safe operation criteria. In this paper, the objective function consists of two terms, namely, investment cost (IC) of new lines and reliability cost. The reliability cost is incorporated as the loss of load cost (LOLC). Network uncertainties in the form of loads are molded as Gaussian probability distribution function (PDF). Monte-Carlo simulation is applied to tackle the uncertainties. The proposed stochastic TEP is expressed as constrained optimization planning and solved using shuffled frog leaping algorithm (SFLA) SFLA is compared to other optimization techniques such as particle swarm optimization (PSO) and genetic algorithms (GA). Finally, stochastic planning (planning including uncertainty) and deterministic planning (planning excluding uncertainty) are compared to demonstrate impacts of uncertainty on the results. Simulation results in different cases and scenarios verify the effectiveness and viability of the proposed stochastic TEP, including uncertainty and reliability.