A multi-objective optimization approach for efficient energy management in smart grids

Abstract

Smart grid uses bi directional flow of information to create a distributed and efficient energy delivery network. Some of the important objectives of a smart grid include improving energy efficiency, maximizing utility, reducing cost, and controlling emission. Smart grids use demand response as an effective strategy to address this challenge. Demand response uses real time scheduling to enable customers to modify their demand according to energy consumption costs. In this paper, we consider the problem of efficient scheduling of energy consumption of users in a smart grid. Efficient energy management involves tradeoffs between the cost associated with energy consumption and a utility function. The utility function can represent the living comfort of users or gross income of the utility company. The utility function is non decreasing with respect to total utilized power. Hence, it is important to understand the tradeoffs between energy consumption and utility. The main contribution of this work is the development of a multi-objective optimization framework for efficient energy scheduling in smart grids. A recently developed multi-objective evolutionary algorithm called the evolutionary multi-objective crowding algorithm (EMOCA) is adapted for simultaneously optimizing the energy cost and utility function subject to a constraint on the power generation capacity. Simulation results show that EMOCA demonstrates the advantages of multi-objective optimization and outperforms a widely used and well known multi-objective evolutionary algorithm.