Citation: Tiwari, V.; Dubey,H.M.; Pandit, M.; Salkuti, S.R. CHP-Based Economic Emission Dispatch of Microgrid Using Harris Hawks Optimization. Fluids 2022, 7, 248. https://doi.org/10.3390/ fluids7070248 Academic Editors: Ioannis K. Chatjigeorgiou, Dimitrios N. Konispoliatis and Mehrdad Massoudi Received: 28 June 2022 Accepted: 13 July 2022 Published: 18 July 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/). fluids Article CHP-Based Economic Emission Dispatch of Microgrid Using Harris Hawks Optimization Vimal Tiwari 1 , Hari Mohan Dubey 2 , Manjaree Pandit 1 and Surender Reddy Salkuti 3, * 1 Department of Electrical Engineering, MITS, Gwalior 474005, India; vimaltiwari01@gmail.com (V.T.); manjaree_p@mitsgwalior.in (M.P.) 2 Department of Electrical Engineering, BIT, Sindri 828123, India; hmdubey.ee@bitsindri.ac.in 3 Department of Railroad and Electrical Engineering, Woosong University, Daejeon 34606, Korea * Correspondence: surender@wsu.ac.kr Abstract: In this paper, the economically self-sufficient microgrid is planned to provide electric power and heat demand. The combined heat and power-based microgrid needs strategic placement of distributed generators concerning optimal size, location, and type. As fossil fuel cost and emission depend mainly on the types of distributed generator units used in the microgrid, economic emission dispatch is performed for an hour with a static load demand and multiple load demands over 24 h of a day. The TOPSIS ranking approach is used as a tool to obtain the best compromise solution. Harris Hawks Optimization (HHO) is used to solve the problem. For validation, the obtained results in terms of cost, emission, and heat are compared with the reported results by DE and PSO, which shows the superiority of HHO over them. The impact of renewable integration in terms of cost and emission is also investigated. With renewable energy integration, fuel cost is reduced by 18% and emission is reduced by 3.4% for analysis under static load demand, whereas for the multiple load demands over 24 h, fuel cost is reduced by 14.95% and emission is reduced by 5.58%. Keywords: microgrid; combined heat and power; economic emission dispatch; renewable integra- tion; TOPSIS 1. Introduction In the past decades, the attention toward microgrid (MG) operation has increased with the integration of distributed generation (DG) units near the consumer end to fulfill the power demand. The MG has been characterized as a small-scale, self-sustaining cluster distribution power system architecture that combines multiple DG, combined heat and power (CHP) units, energy storage systems (ESSs), and load, acting as a single and controllable entity [1]. Integrating CHP units in the MG has attracted more attention with the motivation to provide thermal energy with electric power by using the waste heat generated during electricity generation [2]. The successful implementation of bio-inspired evolutionary optimization techniques in solving many complex engineering problems has attracted researchers to apply different optimization algorithms to solve the load dispatch problems using several test cases of power systems. The combined heat and power dispatch (CHPED) problem has been realized using a real coded genetic algorithm [3], improved group search algorithm [4], oppositional teaching-learning based optimization [5], modified particle swarm optimization (PSO) [6], self-regulating PSO [7], cuckoo search algorithm (CSA) [8], gravitational search algo- rithm [9], exchange market algorithm [10], group search algorithm [11], and grey wolf optimization (GWO) [12] using different test cases. The demand-side management and the optimal operational problem of the MG were studied using a hybrid genetic algo- rithm (GA) and artificial bee colony (ABC) algorithm. Here, the objective is to minimize overall running costs of the MG, demand-side management costs, and costs due to load shifting [13]. A hybrid artificial neural network (ANN) and PSO model were used to Fluids 2022, 7, 248. https://doi.org/10.3390/fluids7070248 https://www.mdpi.com/journal/fluids