Evaluating the efficiency of CCHP systems in Xinjiang Uygur Autonomous Region: An optimal strategy based on improved mother optimization algorithm

Abstract

A novel approach is presented in this research to improve the design of a combined cooling, heating, and power (CCHP) system. The focus of the study is on a gas turbine system that can provide efficient power, cooling, and heating. The efficacy of the proposed technique is evaluated based on four parameters, namely energetic, exergetic, economic, and environmental features. To achieve superior and optimal results, an Improved Mother Optimization Algorithm (IMOA), a recently developed metaheuristic algorithm, is employed. This study provides a comprehensive guide for enhancing the performance, efficiency, and sustainability of CCHP systems. A practical case study was conducted to evaluate the proposed methodology in rural areas of Xinjiang Uygur Autonomous Region, China, over a period of one year. The simulation results reveal that the Gas Engine (GE) and the boiler components play a significant role in utilizing fuel energy, contributing 65 % and 35 %, respectively to the overall utilization. The analysis of the destruction rate of profile highlights that heat dissipation, mechanical losses, and electrical losses are the primary sources of energy losses within the system. When comparing the proposed IMOA with existing techniques, it is evident that the IMOA achieves a noteworthy 10 % reduction in energy consumption and a remarkable 15 % increase in overall efficiency of system. Furthermore, in terms of environmental impact, the IMOA leads to a substantial 20 % reduction in carbon dioxide (CO2) emissions compared to traditional optimization methods. The economic analysis results demonstrate that the IMOA-based approach not only improves cost-effectiveness by 25 %, but also yields an estimated return on investment (ROI) that is 18 % higher than alternative optimization techniques. Moreover, the proposed method with minimum fuel usage of 0.200 L/h, compared with Improved Butterfly Optimizer (IBO) with 0.201 L/h, Modified Mayfly (MM) algorithm with 0.205 L/h, Developed Owl Search (DOS) algorithm with 0.211 L/h, and Improved Owl Search (IOS) algorithm methods with 0.207 L/h, provides better results.