## 1 Introduction
### The Literature Review
In research article [26], the researchers suggest a cooperative optimization approach for demand side management (DSM)-assisted grid-tied residential microgrid (MG) planning and operation. A combined model of dynamic economic and emission dispatch (DEED) and DSM with the inclusion of renewable energy resources (RERs) is presented by the researcher in study [27]. Research [28] introduces a novel multi-objective evolutionary algorithm dubbed the improved multi-objective exchange market algorithm to solve the MDEEDP when electric vehicles (EVs) are present, when EV drivers exhibit random behavior, and when WP uncertainty exists. A MOEED model that takes solar power variability into account is presented in [29]. Both underestimating and overestimating solar power are modeled, with the latter case incurring additional costs in operation. The authors of article [30] propose a multi-objective optimization technique to address the problem related to economic emission dispatch in integrated energy and heating systems (IEHS). This strategy considers system uncertainties and incorporates multi-energy demand response (MEDR) and carbon capture power plants (CCPPs). The influence of errors in the investigational source of the input/output characteristics of conventional power plants is studied by the authors of [31]. Taking into account LCA and risk cost, research [32] suggests a low-carbon economic schedule for IES. In article [33], the authors develop a crow search optimization algorithm (CSA) based on swarm intelligence to deal with the difficult restricted MEEDP with the modified predictive element of RES. The author of article [34] conducts an analysis to examine how the inclusion of renewable energy sources, an electric vehicle parking lot, and an integrated demand response program affects the economic emission dispatch of a multi-county combined heat and power system. Using a real-world complex three-county test system, researchers employ the strength pareto evolutionary algorithm 2 and the nondominated sorting genetic algorithm-III. In study [35], the authors present an ECSO method, which stands for extended crisscross search optimization. In order to address the combined heat and power economic emission dispatch (CHPEED) issue in a nonlinear and nonconvex area, the authors of research [36] offer a multi-objective multi-criterion decision-making (MCDM) technique with constraint-handling rules tailored to this specific situation. Study [37] introduces a novel method for incorporating loss prediction using artificial neural networks into the dynamic economic emission dispatch model. Economic power dispatch over a whole 24 h period is posed as an optimization issue in [38]. The load dispatch simulation incorporates the modeling of thermal, water resources, and renewable for demand-side management, aiming to create a realistic situation. To tackle the MO-CHPEED problem in a fuzzy environment, a new developed algorithm is developed by [39]--dynamically controlled whale optimization algorithm (DCWOA)--through which problems can be solved like multi-objective nonconvex optimization. The benchmark functions in line with CEC-BC-2017 is outlined in work [40]. The authors' first step involves comparing the original AOA with five enhanced versions of AOA in order to choose the most optimal improved strategy. Subsequently, the authors use simulations to compare the improved method with other intelligent optimization algorithms, therefore confirming its effectiveness. In [41], the three-layer optimization issue is solved using the column-and-constraint generation technique (CCG) and, with this, the best scheduling plan and worst case operating domain are achieved. Paper [42] examines the economic emission dispatch issue for many zones of combined heat and power generation and uses a simulated system to test the efficacy of the method. In [43], researchers optimize the energy costs and then compare the outcomes to choose the best hybrid system.