  Citation: Nourianfar, H.; Abdi, H. Environmental/Economic Dispatch Using a New Hybridizing Algorithm Integrated with an Effective Constraint Handling Technique. Sustainability 2022, 14, 3173. https:// doi.org/10.3390/su14063173 Academic Editor: Thanikanti Sudhakar Babu Received: 8 February 2022 Accepted: 6 March 2022 Published: 8 March 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). sustainability Article Environmental/Economic Dispatch Using a New Hybridizing Algorithm Integrated with an Effective Constraint Handling Technique Hossein Nourianfar and Hamdi Abdi * Electrical Engineering Department, Engineering Faculty, Razi University, Kermanshah 67144-14971, Iran; norianfar.hossein@stu.razi.ac.ir * Correspondence: hamdiabdi@razi.ac.ir; Tel.: +98-833-434-3200 Abstract: This work tackles a relatively new issue in power system operation, known as the Environ- mental/Economic Dispatch problem. For this purpose, the combination of two powerful heuristic algorithms, namely, the Exchange Market Algorithm (EMA) and Adaptive Inertia Weight Particle Swarm Optimization (AIWPSO), was employed. Additionally, the Multiple Constraint Ranking (MCR) technique was used to address the system constraints such as prohibited operating zones and ramp rate limits. Furthermore, the mutation operator was used to improve the performance of the global search mechanism. The main purpose of combining these two algorithms was utilizing the EMA’s high performance to explore the global optimum and local exploitation ability of AIWPSO. The algorithm performance was evaluated on six standard benchmark functions and was scrutinized on several different test systems, including 6–40 units. By using the proposed method, the minimum values of the reduction in annual costs, with equal or less emissions, compared to other methods, were USD 17,520, 8760 and 10,801,080, respectively, for the 6-unit, 10-unit, and 40-unit test systems (assuming the same load profile throughout the year). Similarly, in the 14-unit test system for 1750, 2150, and 2650 (MW) load demands, these values were USD 229,879, 148,438, and 4483, respectively. Keywords: Environmental/Economic Dispatch; hybridizing algorithm; prohibited operating zones; constraint handling technique; ramp rate limits 1. Introduction 1.1. General The economic load dispatch (ELD) problem has always been a fundamental issue in the control and operation of power system. The purpose of this problem is specifying the optimal outputs of generation units somehow that the total fuel cost is minimized considering different technical, economic, and environmental limitations and constraints in an hourly time scale [1]. The cornerstone of the ELD problem is to recognize the opti- mal operation cost, mainly the fuel cost, in a power network from different standpoints, supplying the load and satisfying the generation limits [2]. Several techniques and algo- rithms have been suggested for addressing the ELD problem, such as: hybridizing BA with ABC [3], Social Cognitive Optimization with tent map [4], the piece-wise linearization approach [5], Society-based Grey Wolf Optimizer [6], and adaptive cuckoo search with differential evolution mutation [7]. Nowadays, with increasing environmental concerns, reducing the generated emissions by generators has also become a great purpose. This is mainly due to the hazardous gases and particulates from these units. This behavior gives rise to a new complex multi-objective optimization problem called a combined Environmental/Economic Dispatch (CEED). The CEED problem is one of the most essential issues in power system optimization, which specifies the minimum operating cost and the emission created by thermal generation units simultaneously, somehow such Sustainability 2022, 14, 3173. https://doi.org/10.3390/su14063173 https://www.mdpi.com/journal/sustainability