Multiobjective optimization using modified game theory for online management of microgrid

This paper presents a generalized formulation for determining the optimal operating strategy and cost optimization scheme as well as reducing the emissions of a microgrid (MG). Multiobjective (MO) optimization based on modified game theory is applied to the environmental economic problem of the MG. The proposed problem is formulated as a nonlinear constrained MO optimization problem. The proposed problem considers the operation and maintenance costs as well as the emissions reduction of NOx, SO2, and CO2. The MG considered in this paper consists of a wind turbine, a micro turbine, a diesel generator, a photovoltaic array, a fuel cell, and a battery storage. The MO formulation is employed to minimize the cost function of the system while constraining it to meet the customer demand and safety of the system. Three typical scenarios of MG operation are investigated. A comparison is made with Multiobjective Sequential Quadratic Programming (MOSQP), Multiobjective Genetic Algorithms (MOGA) and Multiobjective Mesh Adaptive Direct Search (MOMADS). The results demonstrate the efficiency of the proposed approach to satisfy the load and to reduce the cost and the emissions. Copyright (C) 2010 John Wiley & Sons, Ltd.