Multi-Objective Optimization and Performance Analysis of BCHP Systems Using Genetic Algorithms

The performance assessment criteria of the BCHP (Building Cooling Heating and Power) system include thermodynamic parameters (primary energy rate, exergy efficiency) and economic parameters (economic exergy rate, payback periods) and environmental factors (emission). These criteria are affected by many factors such as the performance of power equipment, unit initial cost, energy demand, primary and second energy price, annual interest rate, operation hours. The scheme with minimum primary energy rate may also have high equipment cost which leads to longer payback periods, so that it is impossible to find a solution that simultaneously satisfies all of them. A genetic algorithm is then chosen to carry out the search for the optimal solution in this paper. The set of optimal solutions lead to the minimum values of the primary energy rate at fixed payback periods, or to the lowest payback periods at fixed primary energy rate. The optimization results are obtained under various load conditions (yearly average energy demand, changeable thermal-power rate, cooling-power rate and typical monthly load demand). The optimal unit capacity choice and operational strategy are discussed which is very important in design and operation process.