Abstract
In this study, the multi-objective optimization of an indirect forced-circulation solar water heating (SWH) system was performed to obtain the optimal configuration that minimized the life cycle cost (LCC) and maximized the life cycle net energy saving (LCES). An elitist non-dominated sorting genetic algorithm (NSGA-II) was employed to obtain the Pareto optimal solutions of the multi-objective optimization. To incorporate the characteristics of practical SWH systems, operation-related decision variables as well as capacity-related decision variables were included. The proposed method was used to conduct a case study wherein the optimal configuration of the SWH system of an office building was determined. The case study results showed that the energy cost decreases linearly and the equipment cost increases more significantly as the LCES increases. However, the results also showed that it is difficult to identify the best solution among the Pareto optimal solutions using only the correlation between the corresponding objective function values. Furthermore, regression analysis showed that the energy and economic performances of the Pareto optimal solutions are significantly influenced by the ratio of the storage tank volume to the collector area (RVA). Therefore, it is necessary to simultaneously consider the trade-off and the effect of the RVA on the Pareto optimal solutions while selecting the best solution from among the optimal solutions.
Highlights
Over the past several decades, a number of design methods have been developed for solar water heating (SWH) systems, ranging from correlation-based methods such as the φ method [1], φ method [2], f -chart method [3], and φ, f -chart method [4] to simulation-based methods such as transient system simulation (TRNSYS) [5] and pre-design and optimization tool for solar heating systems with seasonal storage (SOLCHIP) [6]
The proposed multi-objective optimization method is developed to determine the optimal design of an indirect forced-circulation SWH system based on the minimization of the life cycle cost (LCC) and maximization of the life cycle net energy saving (LCES)
By performing the regression technique on the Pareto frontier obtained from the optimization, it is found that th1e9energy cost decreases linearly and the equipment cost increases more significantly as the LCES increases
Summary
Over the past several decades, a number of design methods have been developed for solar water heating (SWH) systems, ranging from correlation-based methods such as the φ method [1], φ method [2], f -chart method [3], and φ, f -chart method [4] to simulation-based methods such as transient system simulation (TRNSYS) [5] and pre-design and optimization tool for solar heating systems with seasonal storage (SOLCHIP) [6]. Some design methods using optimization techniques such as linear and nonlinear optimization and evolutionary algorithms have been proposed. In this study, the multi-objective optimization of an indirect forced-circulation SWH system is carried out to determine the optimal configuration based on energy and economic aspects. An elitist non-dominated sorting genetic algorithm (NSGA-II) is used to obtain the Pareto optimal solutions by minimizing the LCC and maximizing the life cycle net energy saving (LCES) of an SWH system. The proposed multi-objective optimization method is applied to determine the optimal configuration of the SWH system of an office building. The effect of the ratio of the storage tank volume to the collector area (RVA) on the energy and economic performance of the non-dominated solutions is assessed
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