Assessment of new operational strategy in optimization of CCHP plant for different climates using evolutionary algorithms

• Proposing a new operational strategy.
• Simultaneous selection of 29 design parameters.
• Applying PSO algorithm to find the optimum design parameters.
• Performing the above procedure for different climates.
• Comparison of PSO with Genetic Algorithm.

Optimal design of combined cooling, heating and power (CCHP) generation systems is presented in this paper. The goal of this study is comparison of a new operational strategy named variable electric cooling ratio (VER) with constant electric cooling ratio (CER) for different climates including hot, cold and moderate. In VER strategy, the share of absorption and electrical chillers supply load could vary during a year while in CER strategy it is constant. The gas engine is selected as prime mover and Particle Swarm Optimization (PSO) method is used to select the optimum CCHP equipments by maximizing the Relative Annual Benefit (RAB) as a new objective function. Optimization Results show that VER strategy, provides more benefit in comparison with CER strategy in all the studied climates. VER strategy shows 12.71%, 5.84% and 10.92% growth in optimum value of RAB in comparison with CER in the case of hot, cold and moderate climates, respectively. Furthermore, the optimum results demonstrate that a gas engine with higher nominal capacity is needed in VER compared with CER strategy. Results show that the VER strategy is a good alternative for following the cooling load in the CCHP operational strategy since it gives a good increment in RAB. Finally the optimum results of PSO algorithm is compared with Genetic Algorithm and differences are reported.