Differential Evolution with Stochastic Selection for Uncertain Environments: A Smart Grid Application

Abstract

In smart grid, energy resource management is highly complex large-scale optimization problem where the aim is to maximize the incomes while minimizing operational costs. Due to presence of mixed-integer variables and non-linear constraints, recently the use of evolutionary algorithms as a tool to find optimal and near-optimal solutions is becoming popular. The energy resource management problem further gets complicated if the uncertainty associated with the renewable generation, load forecast errors, electric vehicles scheduling and market prices are considered. Therefore, in a real-world scenario, it is essential to address the issues brought by the variability of demand, renewable energy, electric vehicles, and market price variations while maximizing the incomes and minimizing the total operation costs. In this paper, we analyze the performance of Differential Evolution with a stochastic selection on a large-scale energy resource management problem with uncertainty designed for competition at CEC 2018. The system comprises of a 25-bus microgrid representing a residential area with high penetration of Distributed Energy Resources (DER), Electric Vehicles (EVs), Demand Response (DR) programs etc.