International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 06 | Jun 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 3423 Energy Management System in Smart Microgrid Using Multi Objective Grey Wolf Optimization Algorithm T. Sowmiya 1 , Dr. T. Venkatesan 2 1 M. E. Power Systems Engineering, K. S. Rangasamy College of Technology, Tiruchengode, Tamil Nadu, India. 2 Professor/EEE, K. S. Rangasamy College of Technology, Tiruchengode, Tamil Nadu, India. ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Nowadays the population is increasing day by day, so that the demand for energy becomes high which in turn increases the demand for coal. This rapid increase in demand of electricity becomes uneconomical, detrimental and high in power losses. Also the conventional grid is unable to adjust to the growing energy demands and locating grid failures. Hence there is the need for other energy resources like renewable sources and this integration may cause unbalanced power flow to the grid which needs an energy management system. This proposed work aims at maximizing the use of local generation, minimizing the consumption price and reducing the emission of greenhouse gases. This efficient energy management system is achieved with the help of two controllers: Energy Market Management Controller (EMMC) and Home Energy Management Controller (HEMC). HEMC shares the information about load and energy storage systems to EMMC which will contain all details about the energy providers, local generation and its price details. The problems in smart grid can be solved using the strategies that were followed in demand response. Among various optimization methods, Multi Objective Grey Wolf Optimization (MOGWO) is preferred due to its fast converging capability compared to other optimization techniques. The simulation result shows the reduction in pollution and consumption price in this work. Key Words: Microgrid, smart grid, energy market management controller, home energy management controller, multi objective grey wolf optimization, energy providers, renewable energy resources. 1. INTRODUCTION Energy plays a crucial part in a country's growth of its social and economic position. Because it has a direct impact on the economy and is linked to raising the country's living standards. As the world's population grows, more energy is required to meet the growing demand for energy. As a result of these energy constraints in emerging countries, smart energy management (SEM) can help to alleviate both technical and economic issues. SEM is concerned with integrating local generation, such as photovoltaic (PV), wind, and fuel cells, as well as effective energy trading between energy providers and customers. By combining both generation and consumption, researchers are attempting to design improved structures for optimal energy and market management. Consumer-based energy management to increase profit for consumers by employing a stochastic game strategy that combines prosumer decision and the stochastic nature of renewable energy is proposed in [1]. [2] provides task classification-based home energy management, which identifies the best activation task within device restrictions. The ideal activation time for each type of work is determined using a quadratic utility function. [3] proposes a decision-making controller that optimizes generation, load, and storage. To make decisions more intelligent, intelligent fuzzy logic is offered. In [4,] the integration of a storage system is proposed in order to achieve high energy independence in an SMG that is based on home load control. [5] investigates data-driven home energy management (HEM), which is optimized using a Bayesian algorithm and includes renewable energy resources (RER) and an energy storage system. Within micro-grid (MG) and multi-MG environments, the energy market management system in [6] executes day-ahead optimization of distribution network addressing (MMG). The goals are to reduce costs by using two operators in a dynamic games function. Researchers in [7] developed a power loss-based energy transaction inside the MG and MMG paradigms to minimize power loss. The Multi Energy Router System is used to achieve this strategy (MERS). [8] proposes a market mechanism for average pricing that is utilized in distribution networks. The goal is to decentralize the formulation of the average price market mechanism in order to spread the cost production of energy resources with a zero margin. Using Mixed Integral Linear Programming, [10] proposes a multi-objective optimization to handle the energy management-based social and ecological problem for microgrid (MILP). Approach for maximum utilization of renewable distribution is proposed in [11], and the same concept is addressed in [12] to reduce energy loss in order to recognize the economic benefits. [13] presents a quick overview of various control strategies. In addition, the authors recommended intelligent and IoT-based control solutions for future clustered microgrids. According to a survey of related literature, researchers have solved technological challenges for SMG, such as user