Selecting a Meta-Heuristic Technique for Smart Micro-Grid Optimization Problem: A Comprehensive Analysis

Abstract

In current epoch, the economic operation of micro-grid under soaring renewable energy integration has become a major concern in the smart grid environment. There are several meta-heuristic optimization techniques available under different categories in literature. One of the most difficult tasks in cost minimization of micro-grid is to select the best suitable optimization technique. To resolve the problem of selecting a suitable optimization technique, a rigorous review of six meta-heuristic algorithms (Whale Optimization, Fire Fly, Particle Swarm Optimization, Differential Evaluation, Genetic Algorithm, and Teaching Learning-based Optimization) selected from three categories (Swarm Intelligence, Evolutionary Algorithms, and Teaching Learning) is conducted. It presents, a comparative analysis using different performance indicators for standard benchmark functions and proposed a smart micro-grid (SMG) operation cost minimization problem. A proposed SMG is modeled which incorporates utility connected power resources, e.g., wind turbine, photovoltaic, fuel cell, micro-turbine, battery storage, electric vehicle technology, and diesel power generator. The proposed work will help researchers and engineers to select an appropriate optimization method to solve micro-grid optimization problems with constraints. This paper concludes with a detailed review of micro-grid operation cost minimization techniques based on an exhaustive survey and implementation.