# An Improved Artificial Ecosystem Algorithm for Economic Dispatch with Combined Heat and Power Units
### Literature Survey  
For this reason, a systematic learned PSO has been blended with a sequential quadratic programming technique and employed for ED optimization of the power system [6]. However, the primary objective task that was taken into consideration was the reduction of gasoline expenses. In [7], a multi-objective pigeon-inspired algorithm was used to solve the ED problem involving emissions minimization; however, in three scenarios explored, only 6-unit and 14-unit systems were considered in detail. The ED problem was solved using a dispersed fixed step-size optimizer in [8] while taking into account the cost function of the distributed energy resources. However, the traditional quadratic model was applied while excluding the actual effects of the valve point loadings. In standard thermal power plants, a substantial amount of thermal energy is wasted and released into the environment via cooling towers, flue gas, or other methods. The efficiency of converting carbon fuels into electrical energy is therefore just 50% to 60%, despite the most efficient contemporary combined cycle plants. By gathering and using waste heat, CHPUs raise the energy conversion efficiency of these typical units from 50-60% to the order of 90% [9]. An essential issue for managing the operation of these units is the ED model combined with CHPUs [10]. Traditionally, an ED incorporating CHPUs manages the Power-Only Unit, CHP, and HOUs to save fuel expenditures. However, the production and use of energy are closely related to environmental concerns. The integrated hybrid energy systems can meet a variety of energy demands with increasing productivity and efficiency. This lays the groundwork for creating a low-carbon, sustainable method of economic and social advancement. Additionally, during the past few decades, combined heat and power systems have been associated with energy savings and reduced environmental impact. Such systems have alerted the scientific community to further research and developments of renewable-based combined heat and power configurations in the domestic and commercial sectors, which served the objective [11; 12]. By managing Power-Only Units, CHPs, and HOUs, the CHPEED challenge seeks to reduce fuel costs and emissions [13]. Moreover, in order to maintain the performance of the power, heat, and CHPUs, certain inequality limits must be satisfied. Additionally, the mutual dependence of the CHPUs must be maintained because it may have an impact on how the CHPEED problem is solved [14]. The challenging ED problem with CHPUs has been addressed using a wide variety of MAs. According to the primary objectives, the published research on the ED problem using CHPUs that has used metaheuristic methodologies to solve this problem can be split into two types. To achieve the lowest operating costs, the first category involves creating efficient optimization methods for systems incorporating thermal plants, CHPUs, and boilers. The investigation of all practically pertinent restrictions, such as transmission loss, valve-point impacts, and environmental difficulties with the heat and power supply of the ED issue with CHPUs, falls under the second category. Some of the most intriguing works in the first category include the following: as shown in [15], the ED problem with CHPUs was solved using the GSA by examining network system losses and the valve-point effect of POUs. To address the production cost reduction of the ED problem with the CHPU problem, specifically examining the valve-point effect of POUs, the CSA was employed in [16]. Both investigations looked at network losses and valve-point consequences. The environmental concerns, however, were not considered. To solve the ED issue under varying CHPU operating conditions with minimal computational effort, [17] implements a DRL approach. Artificial neural networks have also been used to try to fix the ED issue using CHPUs [18]. Practical limitations including valve-point influence, transmission power loss, and environmental factors were not considered in [17; 18].