Sustainability 2022, 14, 10158. https://doi.org/10.3390/su141610158 www.mdpi.com/journal/sustainability Article A Multi-Objective Demand/Generation Scheduling Model-Based Microgrid Energy Management System Ali M. Jasim 1, *, Basil H. Jasim 1 , Habib Kraiem 2, * and Aymen Flah 3 1 Electrical Engineering Department, University of Basrah, Basrah 61001, Iraq 2 Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 73222, Saudi Arabia 3 National Engineering School of Gabès, Processes, Energy, Environment and Electrical Systems, University of Gabès, LR18ES34, Gabes 6072, Tunisia * Correspondence: e.alim.j.92@gmail.com (A.M.J.); habib.kraiem@yahoo.fr (H.K.) Abstract: In recent years, microgrids (MGs) have been developed to improve the overall manage- ment of the power network. This paper examines how a smart MG’s generation and demand sides are managed to improve the MG’s performance in order to minimize operating costs and emissions. A binary orientation search algorithm (BOSA)-based optimal demand side management (DSM) pro- gram using the load-shifting technique has been proposed, resulting in significant electricity cost savings. The proposed optimal DSM-based energy management strategy considers the MG’s eco- nomic and environmental indices to be the key objective functions. Single-objective particle swarm optimization (SOPSO) and multi-objective particle swarm optimization (MOPSO) were adopted in order to optimize MG performance in the presence of renewable energy resources (RERs) with a randomized natural behavior. A PSO algorithm was adopted due to the nonlinearity and complex- ity of the proposed problem. In addition, fuzzy-based mechanisms and a nonlinear sorting system were used to discover the optimal compromise given the collection of Pareto-front space solutions. To test the proposed method in a more realistic setting, the stochastic behavior of renewable units was also factored in. The simulation findings indicate that the proposed BOSA algorithm-based DSM had the lowest peak demand (88.4 kWh) compared to unscheduled demand (105 kWh); addi- tionally, the operating costs were reduced by 23%, from 660 USD to 508 USD , and the emissions decreased from 840 kg to 725 kg, saving 13.7%. Keywords: microgrid; binary orientation search algorithm; demand side management; real-time pricing; energy management; multi-objective management; generation power uncertainty; operating Cost 1. Introduction 1.1. Motivation Stability and proper management of power system networks are crucial for societies and countries. Optimal power network operation significantly affects economic perfor- mance and consumer satisfaction [1]. Smart microgrids (SMGs) facilitate two-way com- munication between producers and consumers. Consequently, to encourage consumers to control their demand, various costs of electrical energy may have to be applied in what is known as a DSM program, which improves the load profile of consumers [2]. Three DSM categories—environmentally motivated type, market-driven type, and network- driven type—are commonly used, according to the literature. The environmental-driven DSM focuses primarily on environmental and social standards, such as greenhouse gas emission reduction. The network-driven type seeks to maintain system reliability, while the market-driven type seeks to save money for providers and customers [3]. Smart pric- ing tools for the DSM implementation process include dynamic pricing policies such as Citation: Jasim, A.M.; Jasim, B.H.; Kraiem, H.; Flah, A. A Multi- Objective Demand/Generation Scheduling Model Based Microgrid Energy Management System. Sustainability 2022, 14, 10158. https://doi.org/10.3390/su141610158 Academic Editor: Luis Hernández- Callejo Received: 25 July 2022 Accepted: 12 August 2022 Published: 16 August 2022 Publisher’s Note: MDPI stays neu- tral with regard to jurisdictional claims in published maps and institu- tional affiliations. Copyright: © 2022 by the authors. Li- censee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and con- ditions of the Creative Commons At- tribution (CC BY) license (https://cre- ativecommons.org/licenses/by/4.0/).