Optimal design of hybrid cooling/heating system using TLBO: Techno-economic evaluation for office buildings

This research investigates the optimization of a novel integrated cooling and heating system through a mathematical model implemented in MATLAB, employing the Teaching-Learning-Based Optimization (TLBO) algorithm. The initial analysis focuses on identifying the optimal capacities of key system components, including air conditioning units, ice storage, ground source heat pumps (GSHP), gas boilers, thermal storage, electric chillers, and electric heaters. The gas boiler and absorption chiller are highlighted for their high nominal capacities, underscoring their importance in fulfilling system demands. A comprehensive Net Present Cost (NPC) analysis reveals that the GSHP, gas boiler, and ice storage are the primary cost drivers, reflecting their significant operational and maintenance expenses. Environmental impacts are also considered, with higher NPC components likely contributing more to carbon emissions. The optimization results are corroborated using the Firefly Algorithm (FA) and Harmony Search Algorithm (HSA), confirming the TLBO method as a cost-effective and time-efficient approach. This validation ensures the minimization of total costs and demonstrates the economic feasibility and suitability of the TLBO algorithm for optimizing integrated heating and cooling systems.