Enhancing energy efficiency in rural CCHP systems with optimal gas engine size selection and Improved Coyote Optimizer

• Published in: Energy reports Vol. 10; pp. 3146 - 3157
• Main Authors: Kong, Mei, Gou, Xiaogui, Fathi, Gholamreza
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2023; Elsevier
• Subjects: Combined cooling; Coyote optimizer; Efficient sizing; Gas engines; Heating, and power system; Improved algorithms; Rural areas; Heating, and power system; Improved algorithms; Coyote optimizer; Efficient sizing; Rural areas; Gas engines; Combined cooling
• ISSN: 2352-4847; 2352-4847
• DOI: 10.1016/j.egyr.2023.09.092

This study introduces a novel tactic to efficiently size the selection of gas engines for Combined Cooling, Heating, and Power (CCHP) systems in rural areas. This method is based on an adapted bio-inspired method, called Improved Coyote Optimizer (ICO). The proposed ICO technique uses a combination of metaheuristic optimization techniques and the system model to determine an optimal overall engine size while satisfying given operating constraints and minimizing the total cost. The analysis revealed that the fuel consumption of the gas engine and boiler in the proposed system is dependent on the power level. At 40 kW, the engine consumed 1558 units of fuel, while the boiler consumed 1430 units, with this difference increasing to 411 units at 110 kW. These findings emphasize the importance of improving the fuel efficiency of the engine, particularly at higher power levels. Furthermore, the study assessed the system’s CO2 emissions, finding that the minimum yearly emissions of 220,600 kg occurred at an engine power of 1 kW, while the maximum CO2 reduction rate of 40% was achieved at 120 kW. This suggests that larger engines are more efficient, resulting in lower CO2 emissions due to reduced fuel consumption. In terms of financial considerations, the engine power dimension of 118.9 kW yielded the highest cash flow value of 49,840 $. However, variations in engine power between 60 and 100 kW indicate a wider range of production costs and output quantities. To accurately evaluate the return on investment, more detailed information on production costs and output within this range is necessary. Lastly, the study compared different optimization methods and found that the Improved Coyote Optimizer (ICO) demonstrated the fastest convergence, reaching its ideal values in 3000 iterations. Both the ICO-based method and the ABC-based technique successfully minimized the cost function, with the ICO-based method achieving the best value of 0.20. Furthermore, the proposed algorithm is practical to a wide variety of CCHP system configurations, making it a valuable tool for practitioners.