Energy-saving optimal control of secondary district cooling system based on tribal intelligent evolution optimization algorithm

With the significant increase in energy consumption for large central air conditioning systems, optimal control of district cooling systems is crucial for energy conservation and CO2 emission reduction. This study focuses on the energy-saving optimal control of secondary district cooling systems (SDCSs). Firstly, energy models for SDCSs utilizing distribution manifolds or plate heat exchangers are presented, with the goal of establishing global optimal control models for energy conservation. Next, a novel metaheuristic algorithm called Tribal Intelligent Evolution Optimization (TIEO) is proposed, which innovatively introduces human intelligent behavioral characteristics into the tribal evolution process. The TIEO and seven other optimization algorithms have been tested for optimizing the SDCSs. The test results demonstrated that the TIEO surpassed other algorithms in terms of optimization effectiveness, stability, and computational efficiency. Additionally, this study has conducted engineering validation of the TIEO algorithm on the SDCSs with distribution manifolds or plate heat exchangers. Compared to the traditional control strategies, the TIEO algorithm improved the energy efficiency ratio of the system by 13.56 % and 11.40 %, respectively. Therefore, the TIEO algorithm has the potential to serve as an optimization tool for contributing to energy conservation and promoting the sustainable development of large-scale district cooling systems.