Abstract
This chapter presents the details of the performance optimization of an Ice Thermal Energy Storage (ITES) system carried out using TLBO algorithm, Jaya and self-adaptive Jaya algorithms . The results achieved by using Jaya and self-adaptive Jaya algorithms are compared with those obtained by using the GA and TLBO techniques for ITES system with phase change material (PCM). In ITES system, two objective functions including exergy efficiency (to be maximized) and total cost rate (to be minimized) of the whole system are considered. The Jaya and self-adaptive Jaya algorithms are proved superior to GA and TLBO optimization algorithms in terms of robustness of the results. The self-adaptive Jaya takes less computational time and the function evaluations as compared to the other algorithms.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Dincer, I. (2002). On thermal energy storage systems and applications in buildings. Energy Buildings, 34, 377–388.
Khudhiar, A. M., & Farid, M. M. (2004). A review on energy conservation in building applications with thermal storage by latent heat using phase change materials. Energy Conversion and Management, 45, 263–275.
Koca, A., Oztop, H. F., Koyun, T., & Varol, Y. (2008). Energy and exergy analysis of a latent heat storage system with phase change material for a solar collector. Renewable Energy, 33, 567–574.
Lefebvre, D., & Tezel, F. H. (2017). A review of energy storage technologies with a focus on adsorption thermal energy storage processes for heating applications. Renewable and Sustainable Energy Reviews, 67, 116–125.
MacPhee, D., & Dincer, I. (2009). Performance assessment of some ice TES systems. International Journal of Thermal Sciences, 48, 2288–2299.
Navidbakhsh, M., Shirazi, A., & Sanaye, S. (2013). Four E analysis and multi-objective optimization of an ice storage system incorporating PCM as the partial cold storage for air conditioning applications. Applied Thermal Engineering, 58, 30–41.
Ooka, R., & Ikeda, S. (2015). A review on optimization techniques for active thermal energy storage control. Energy and Buildings, 106, 225–233.
Palacio, S. N., Valentine, K. F., Wong, M., & Zhang, K. M. (2014). Reducing power system costs with thermal energy storage. Applied Energy, 129, 228–237.
Rahdar, M. H., Emamzadeh, A., & Ataei, A. A. (2016a). Comparative study on PCM and ice thermal energy storage tank for air-conditioning systems in office buildings. Applied Thermal Engineering, 96, 391–399.
Rahdar, M. H., Heidari, M., Ataei, A., & Choi, J. K. (2016b). Modeling and optimization of R-717 and R-134a ice thermal energy storage air conditioning systems using NSGA-II and MOPSO algorithms. Applied Thermal Engineering, 96, 217–227.
Saito, A. (2001). Recent advances in research on cold thermal energy storage. International Journal of Refrigeration, 25, 177–189.
Sanaye, S., & Shirazi, A. (2013a). Four E analysis and multi-objective optimization of an ice thermal energy storage for air-conditioning applications. International Journal of Refrigeration, 36, 828–841.
Sanaye, S., & Shirazi, A. (2013b). Thermo-economic optimization of an ice thermal energy storage system for air-conditioning applications. Energy and Buildings, 60, 100–109.
Shu, W., Longzhe, J., Zhonglong, H., Yage, L., Shengnan, O., Na, G., et al. (2017). Discharging performance of a forced-circulation ice thermal storage system for a permanent refuge chamber in an underground mine. Applied Thermal Engineering, 110, 703–709.
Stritih, U., & Butala, V. (2007). Energy saving in building with PCM cold storage. International Journal of Energy Research, 31, 1532–1544.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Venkata Rao, R. (2019). Multi-objective Design Optimization of Ice Thermal Energy Storage System Using Jaya Algorithm and Its Variants. In: Jaya: An Advanced Optimization Algorithm and its Engineering Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-78922-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-78922-4_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-78921-7
Online ISBN: 978-3-319-78922-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)