Nan Jiangping, Xiao Qi, Teimourian Milad
Xi'an Traffic Engineering Institute, Xi'an, 710300, Shaanxi, China.
CCTEG Xi'an Research Institute, Xi'an, 710076, Shaanxi, China.
Heliyon. 2024 May 14;10(11):e31208. doi: 10.1016/j.heliyon.2024.e31208. eCollection 2024 Jun 15.
This paper aims to enhance the design and operation of a Combined Cooling, Heating, and Power (CCHP) system utilizing a gas engine as the primary energy source for a residential building in China. An Energy, Exergy, Economic, and Environment (4E) analysis is employed to assess the system's performance and impact based on energy, exergy, economic, and environmental criteria. The effectiveness of the DNGO algorithm is evaluated on a case study site and compared with Northern Goshawk Optimization (NGO) and Genetic Algorithm (GA). The findings demonstrate that the DNGO algorithm identifies the optimal gas engine size of 130 kW. The algorithm's search capabilities are greatly enhanced by this unique blend, surpassing what traditional methods can offer. The DNGO algorithm brings several advantages, including unparalleled energy efficiency, reduced exergy destruction, and a substantial decrease in emissions. This not only supports environmental sustainability but also aligns with global standards. Economically, the algorithm enhances the performance of the CCHP system, evident through a reduced payback period and increased annual profit. Additionally, the algorithm's rapid convergence rate allows it to reach the optimal solution faster than its counterparts, making it advantageous for time-sensitive applications. Incorporating innovative methods like chaos theory, the DNGO algorithm effectively avoids local optima, enabling a broader search for the best solution. The utilization of Lévy flight further enhances the algorithm's ability to escape local optima and navigate the search space more efficiently. Additionally, swarm intelligence is employed to simulate the collective behavior of decentralized systems, aiding in problem-solving. This research represents a significant advancement in optimization techniques for CCHP systems and offers a fresh perspective to the field of swarm-based optimization algorithms.
本文旨在改进以燃气发动机作为中国某住宅建筑主要能源的冷热电联产（CCHP）系统的设计与运行。采用能量、㶲、经济和环境（4E）分析方法，基于能量、㶲、经济和环境标准评估该系统的性能和影响。在一个案例研究地点评估了DNGO算法的有效性，并与苍鹰优化算法（NGO）和遗传算法（GA）进行了比较。研究结果表明，DNGO算法确定的最佳燃气发动机尺寸为130千瓦。这种独特的融合极大地增强了该算法的搜索能力，超越了传统方法。DNGO算法具有诸多优势，包括无与伦比的能源效率、减少的㶲损耗以及大幅降低的排放。这不仅支持环境可持续性，还符合全球标准。在经济方面，该算法提高了CCHP系统的性能，通过缩短投资回收期和增加年利润得以体现。此外，该算法的快速收敛速度使其能够比其他算法更快地找到最优解，这使其在对时间敏感的应用中具有优势。结合混沌理论等创新方法，DNGO算法有效地避免了局部最优，能够更广泛地搜索最佳解决方案。Lévy飞行的运用进一步增强了该算法逃离局部最优并更有效地在搜索空间中导航的能力。此外，采用群体智能来模拟分散系统的集体行为，有助于解决问题。这项研究代表了CCHP系统优化技术的重大进步，并为基于群体的优化算法领域提供了新的视角。
