Multi-Objective Multi-Period Optimal Operation of a Microgrid With Renewable Resources and Energy Storage

Abstract

This paper presents the multi-objective economic-emission dispatch problem of a microgrid (MG) system comprising various types of distributed generation. The proposed MG includes a fuel cell (FC), a micro-gas turbine (MT), renewable energy resources-specifically, a wind turbine (WT) unit and a photovoltaic (PV) unit—and a battery energy storage system to enhance the independence of the MG from the main utility grid. Several operational scenarios are analyzed based on the MG's operating modes and the allowable generation levels from renewable sources. A teaching-learning-based optimization (TLBO) algorithm is employed to manage and optimize the operation of the distributed energy resources within the MG system. The multi-objective problem is addressed using a scalarized weighted metric approach, converting it into a single-objective function through compromise programming. Additionally, the multi-objective feasibility enhanced particle swarm optimization (MOFEPSO) algorithm is applied to further evaluate the effectiveness of the proposed optimization strategy. The best solution is selected from the available Pareto-optimal solutions using the fuzzy satisfying method. Several optimization algorithms are investigated for comparison. The impact of utility-grid emissions is presented in the paper, and a comparative study for all operating scenarios is also conducted. In addition, an initial state of charge (SOC) sensitivity analysis is presented to show its impact on cost and emission, as well as on multi-objective cost–emission optimization problems.