Abstract
Energy saving is an important issue for multiple-chiller systems. Optimal chiller loading (OCL) in multiple-chiller systems has been investigated with many optimization algorithms to save energy. Particle swarm optimization (PSO) algorithm has been successful in solving this problem in some cases, but not in all. This study innovatively added a team evolution to the original particle swarm optimization algorithm, called team particle swarm optimization (TPSO). The TPSO enhances the effectiveness of original particle swarm optimization to better solve the OCL problem. The TPSO algorithm is composed of two evolutions: particle evolution and team evolution. The partial load ratio (PLR) of each operating chiller and the on-off state of each chiller are the particle evolution parameters and team evolution parameters, respectively. To evaluate the performance of the proposed method, this paper adopts three case studies so the results generated from the proposed algorithm TPSO, the original particle swarm optimization (PSO) and other recently published algorithms can be compared. In these three case studies, the optimal results generated by using TPSO algorithm are the same as those by other compared algorithms. In case 1 under 5717 RT and 5334 RT cooling load, the results generated using the TPSO are lower than those by the original PSO in the amounts of 63.35 and 79.33 kW, respectively. The results indicated that the TPSO algorithm not only enabled the optimal solution in minimizing energy consumption, but also demonstrated the best stability when compared to other algorithms. In conclusion, the presented TPSO algorithm is an efficient and promising new algorithm for solving the OCL problem.
Highlights
Air conditioning systems contributes considerably to the energy consumption in buildings—with their chiller systems being the main cause of energy consumption
This study proposed an improved particle swarm optimization algorithm, called team particle swarm optimization (TPSO), to enhance the performance of the original PSO to better solve the optimal chiller loading (OCL) problem
To verify the effectiveness of the TPSO algorithm, figures from three case studies are adopted so comparison can be made on the TPSO algorithm, the PSO algorithm, and other recently published algorithms
Summary
Air conditioning systems contributes considerably to the energy consumption in buildings—with their chiller systems being the main cause of energy consumption. CITRONI et al [27] studied on an array configuration of rectified optical nanoantennas for energy harvesting application Based on these studies, the DE, IFOA, DCEDA and EIWO algorithms have shown their efficacy on finding the best solution and stability in three well-known cases on 3-, 4- and 6-chiller systems. Under the two cases of 5717 RT and 5334 RT cooling load on the 6-chiller system, the best solutions of energy consumption generated by using the PSO [3] are higher than those by the IFOA [18] and EIWO [22] in the amounts of 63.35 and 79.33 kW, respectively In this current study, an improved particle swarm optimization algorithm, called team particle swarm optimization (TPSO), is proposed to enhance the performance of the original particle swarm optimization to best solve the OCL problem.
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