Elsevier

Renewable Energy

Volume 150, May 2020, Pages 878-890
Renewable Energy

Techno-economic optimization of open-air swimming pool heating system with PCM storage tank for winter applications

https://doi.org/10.1016/j.renene.2020.01.029Get rights and content

Highlights

  • A techno-economic design optimization approach for open-air swimming pool heating system is proposed.

  • Response surface approach is used to develop surrogated system models for enhancing computational efficiency

  • Non-dominated sorting genetic algorithm II is used to conduct double-objective optimization of the system.

  • Performance analysis of the system with optimal system configuration is performed.

Abstract

Feasible heating systems have been designed to increase the availability of open-air swimming pools in winter in subtropical climate regions. However, the approach to optimally size the main components of the system from multiple aspects is lacking. A techno-economic optimization method for swimming pool heating systems is proposed here. Minimizing the lifecycle cost of the system while ensuring the thermal comfort of the pool are considered as the optimization objectives. The volume of phase change material storage tank and the heating capacity of air-source heat pumps are selected as design variables. To improve computational efficiency, surrogate models are developed using the response surface approach, in which the dataset is generated from the simulation platform established using MATLAB and TRNSYS. Generic algorithm and non-dominated sorting genetic algorithm II are adopted to conduct single-objective and double-objective optimizations, respectively. Case studies indicate that optimal combinations for the size of main components can be identified using the proposed optimization approach. The energy and economic performance of the heating system are enhanced after optimization. The proposed techno-economic optimization method provides an instructive guideline for the optimal design of swimming pool heating systems.

Introduction

Most open-air swimming pools in subtropical regions are closed in winter because the energy demand required for satisfying the thermal comfort of the pool is high. If conventional heating approaches such as electrical heaters are adopted to supply heat to pools, the expense will be extremely high [1]. Therefore, various techniques have been used to increase the availability of pools in winter. These techniques can be divided into two roles: passive and active approaches. The passive approach primarily uses a thermal-insulation cover to prevent heat losses when the pool is closed. Different studies regarding the passive approach have been conducted. For example, Yadav et al. [2] modeled the water temperature variation of an Australian swimming pool with thermal-insulation cover. They concluded that the heat losses of the pool could be significantly reduced when the cover was used. In the study by Francey et al. [3], thermal insulation properties with transparent and opaque covers used in pools were compared by analyzing the in-situ measured water temperature of the pool. They discovered that the transparent cover was more effective in improving the water temperature than the opaque cover because more solar energy could be obtained by the pool water when the transparent cover was used.

The active approach is developed to provide heat for satisfying the heat demand of pools. One typically used method is air-source heat pumps (ASHPs). Lam et al. [4,5] used ASHPs to supply heat to a swimming pool in a hotel in Hong Kong. The surface area and volume of the pool were 35 m2 and 52 m3, respectively. The energy saving analysis of the system was performed, and the lifecycle energy cost of the system was calculated. They concluded that, compared with traditional heating technologies, the energy cost with a lifecycle period of 10 years could be reduced by HK$275,700 if ASHPs with a coefficient of performance (COP) of 3.5 was installed. However, the designed heating capacity of the ASHPs should be identified by the peak heating load of the pool. For pools with a large peak heating load (e.g., pools with large surface area), the designed heat capacity of the ASHPs should be large, which results in a high capital cost for installing ASHPs. To tackle with this problem, Li et al. [6] proposed a heating system with PST (i.e., storage tank with phase change material (PCM)) to completely shift the energy use from on-peak to off-peak periods, which could reduce the operating cost significantly. The ASHPs were not used to supply heat to the pool during the on-peak period but to charge the PST during the off-peak period. Therefore, the designed heat capacity of the ASHPs was not based on the peak heat load of the pool, and it could be reduced.

The approach that adopts the PST to shift the electricity use from on-peak to off-peak periods has been extensively investigated for building energy systems. For instance, Comodi et al. [7] conducted the economic analysis of a cold energy storage system with a PST in different tariff scenarios, and the economic benefits was estimated. They discovered that a shorter payback period of the system could be obtained if the electricity tariff difference between the on-peak and off-peak periods was larger. Burno et al. [8] utilized a PST in a chiller cooling system, and they reported that 85% of the energy consumed by the system could be shifted from the on-peak to off-peak periods when a PST was used. In addition, a 13.5% energy reduction could be obtained when the PCM had a melting temperature of 10 °C. Najafian et al. [9] conducted the optimal design of a domestic hot water system with PST and determined the minimum amount of PCM by the generic algorithm (GA). It was concluded that the energy consumed during the on-peak period could be completely shifted to the off-peak period with the optimal amount of PCM. Nkwetta et al. [10] investigated the performance of a residential hot water system with PST and discovered that the energy performance of the system could be improved using the proposed control strategy. However, the approach where the PST is used to shift the electricity consumed from on-peak to off-peak periods is rarely applied in swimming pool heating systems. It should be mentioned that even though the PST was used in the study of Zsembinszki et al. [11] to provide heat for the pool, the approach has not been adopted.

From the abovementioned swimming pool heating techniques, it can be concluded that a thermal-insulation cover is efficient for preventing heat loss when a pool is closed; additionally, ASHPs integrated with a PST can effectively enhance the economic performance of the system. Hence, it will be meaningful to develop a swimming pool heating system that comprehensively utilizes these techniques for a better performing system. However, it is challenging to optimize the size of the main components in complex heating systems to satisfy multiple objectives (e.g., reliability and economic performance).

The techno-economic optimization (TEO) can effectively improve the reliability and economic performance of the system [12,13]. Kaabeche and Ibtiouen [14] performed TEO for an energy system comprising photovoltaic (PV) panels, wind turbines, diesel, and batteries. Amrollahi and Bathaee [15] performed the TEO of a stand-alone grid system with PV panels, wind turbines, and batteries, considering the effect of a demand response program. It was discovered that the capacity of PV panels and the number of batteries could be reduced when the demand response program was adopted. Jamshidi and Askarzadeh [16] conducted the TEO of a power generation system comprising PV panels, diesel generators, and fuel cells. They reported that the total expense of the system could be reduced when the hydrogen energy technique was adopted. Although TEO methods for various building energy systems have been proposed, the approach for conducting the TEO of the swimming pool heating system is still lacking.

Therefore, a TEO method for swimming pool heating systems is proposed here. The optimization objective is to minimize the system’s lifecycle cost while ensuring the desired thermal comfort of the pool. The volume of the PST and the heat capacity of the ASHPs were selected as the design variables. To enhance computational efficiency, the response surface approach (RSA) was adopted to develop the surrogate models. The simulation platform of the system was constructed using TRNSYS and MATLAB. The GA and non-dominated sorting genetic algorithm II (NSGA-II) were utilized to perform single-objective and double-objective optimizations, respectively. The control, energy, and economic performance of the system with the optimal system configuration are analyzed.

The novelty of this study is presented as follows: (1) The proposed TEO method fills the knowledge gap pertaining to the optimal design of open-air swimming pool heating systems, which considers lifecycle cost as the economic indicator and the desired thermal comfort of the pool as the reliability indicator; (2) The surrogated model of the complex heating system, which is developed using the RSA, can effectively enhance computational efficiency and is highly reliable. Its application can be extended to other building heating or cooling systems; (3) Single-objective and double-objective optimizations of the system are performed using optimization algorithms, i.e., GA and NSGA-II, respectively, and the optimal design solutions can be effectively identified; (4) The case study of an advanced heating system for open-air swimming pools in winter in subtropical climates is conducted to demonstrate the applicability and efficiency of the proposed TEO method.

Section snippets

Methodology for techno-economic optimization

The framework for the methodology of the TEO is depicted in Fig. 1. This methodology comprises three primary steps: development of surrogate models for objective functions, TEO, and performance analysis. In the first step, professional software (e.g., DESIGN EXPERTS) can be used to design a set of simulated experiments. Based on predefined upper and lower bound values of the design variables, the design dataset will be determined. The generated dataset of the design variables will be used as

Open-air swimming pool heating system

The schematic of the proposed heating systems is shown in Fig. 4, which includes a PST, ASHPs, thermal-insulation cover, heat exchangers, valves, and pumps. The PST is adopted to store the heat provided by the ASHPs during the off-peak period and release it to the pool during the on-peak period. Hence, the electricity consumed is shifted from the on-peak to off-peak periods, which efficiently reduces the operating cost. The ASHPs were adopted to not only charge the PST, but also preheat the

Case study

The swimming pool located at the City University of Hong Kong, where the climate is subtropical, was selected as the application object of the proposed heating system. The total volume of the pool is 1,963.5 m3. Its width and length are 22 and 50 m, respectively. Its minimum depth is 1.2 m, which appears on both sides of the pool; its maximum depth is 2.5 m, which appears in the middle of the pool. The pool cannot be used for swimming from December to next April because the water temperature is

Development and validation of surrogate models

To develop surrogate models of different objective functions, the central composite design (CCD) method was adopted to generate the design of experiments (DOE) scheme, which was realized using the DESIGN EXPERTS software. The maximum thermal energy requirement of the pool during the open period that was identified at the design day (occurring on February 26th, 2005) was 5.2 × 107 kJ. The factor for determining the minimum size of the system configuration (ϕn) was set to 10%. Hence, the range

Conclusions

A TEO approach for a heating system was proposed in this study to minimize the lifecycle cost of the system while ensuring the desired thermal comfort. The design variables were the PST volume and the heating capacity of ASHPs. A case study of a typical swimming pool in Hong Kong that used the proposed heating system to extend the time available to use it in winter was presented to illustrate the proposed optimization approach. The DESIGN EXPERTS software was utilized for generating a dataset

CRediT authorship contribution statement

Yantong Li: Conceptualization, Methodology, Software, Writing - original draft. Zhixiong Ding: Visualization. Yaxing Du: Writing - review & editing, Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

The authors sincerely thank the anonymous reviewers for their time and effort. In addition, the authors appreciate the support of Dr. Gongsheng Huang.

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