Risk-Constrained Scheduling of a Solar Ice Harvesting System Using Information Gap Decision Theory

  • Farkhondeh JabariEmail author
  • Behnam Mohammadi-ivatloo
  • Hadi Ghaebi
  • Mohammad-Bagher Bannae-Sharifian


In summer, air conditioning systems with high electricity requirement are large consumers, which may lead to load-generation mismatch, cascaded outages, and wide-area blackouts. To avoid them, on-peak electrical demand of air conditioning units can be supplied via renewable energy resources such as solar. By increasing rate of solar radiations and ambient temperature, total cooling demand of residential buildings increases causing a rise in electricity consumption. Hence, use of solar energy for making ice and building space cooling not only reduces CO2 footprints of fossil fuel-based power generation facilities and electricity usage in residential sector but also increases the coefficient of performance of the ice harvesting cycle. Meanwhile, hourly fluctuations of solar irradiance lead to uncertainty of cooling demand. Therefore, this chapter presents an information gap decision theory (IGDT)-based framework for robust scheduling of an ice storage system, which consists of air source heat pump (ASHP). In ASHP’s cooling cycle, R134a absorbs heat from inside air at evaporator coil and extracts it to ambience, while producing a cooled air with temperature of −8 °C entering a water tank for making ice crystals. Uncertain nature of building cooling load affects optimum operating point of this refrigeration process. Hence, IGDT is implemented on cooling demand to minimize total energy cost of ice storage system and assess both robustness and opportunistic aspects of optimal operating strategies for making two risk-averse and risk-seeker decisions under uncertain operating conditions, respectively.


Air source heat pump (ASHP) Ice storage system Information gap decision theory (IGDT) 


  1. 1.
    Jabari, F., Mohammadi-Ivatloo, B., Li, G., & Mehrjerdi, H. (2018). Design and performance investigation of a novel absorption ice-making system using waste heat recovery from flue gases of air to air heat pump. Applied Thermal Engineering, 130, 782–792.CrossRefGoogle Scholar
  2. 2.
    Su, B., Han, W., & Jin, H. (2017). An innovative solar-powered absorption refrigeration system combined with liquid desiccant dehumidification for cooling and water. Energy Conversion and Management, 153(Supplement C), 515–525.CrossRefGoogle Scholar
  3. 3.
    Ali, S. M., Chakraborty, A., & Leong, K. C. (2017). CO2-assisted compression-adsorption hybrid for cooling and desalination. Energy Conversion and Management, 143(Supplement C), 538–552.CrossRefGoogle Scholar
  4. 4.
    Hogerwaard, J., Dincer, I., & Naterer, G. F. (2017). Solar energy based integrated system for power generation, refrigeration and desalination. Applied Thermal Engineering, 121(Supplement C), 1059–1069.CrossRefGoogle Scholar
  5. 5.
    Wang, J., Lu, Y., Yang, Y., & Mao, T. (2016). Thermodynamic performance analysis and optimization of a solar-assisted combined cooling, heating and power system. Energy, 115(Part 1), 49–59.CrossRefGoogle Scholar
  6. 6.
    Ozcan, H., & Akyavuz, U. D. (2017). Thermodynamic and economic assessment of off-grid portable cooling systems with energy storage for emergency areas. Applied Thermal Engineering, 119, 108–118.CrossRefGoogle Scholar
  7. 7.
    Fong, K. F., Lee, C. K., & Zhao, T. F. (2017). Effective design and operation strategy of renewable cooling and heating system for building application in hot-humid climate. Solar Energy, 143, 1–9.CrossRefGoogle Scholar
  8. 8.
    Yan, C., Shi, W., Li, X., & Zhao, Y. (2016). Optimal design and application of a compound cold storage system combining seasonal ice storage and chilled water storage. Applied Energy, 171, 1–11.CrossRefGoogle Scholar
  9. 9.
    Sehar, F., Pipattanasomporn, M., & Rahman, S. (2018). Coordinated control of building loads, PVs and ice storage to absorb PEV penetrations. International Journal of Electrical Power & Energy Systems, 95, 394–404.CrossRefGoogle Scholar
  10. 10.
    Song, X., Liu, L., Zhu, T., Chen, S., & Cao, Z. (2018). Study of economic feasibility of a compound cool thermal storage system combining chilled water storage and ice storage. Applied Thermal Engineering, 133, 613–621.CrossRefGoogle Scholar
  11. 11.
    Carbonell, D., Philippen, D., Haller, M. Y., & Brunold, S. (2016). Modeling of an ice storage buried in the ground for solar heating applications. Validations with one year of monitored data from a pilot plant. Solar Energy, 125, 398–414.CrossRefGoogle Scholar
  12. 12.
    Mammoli, A., & Robinson, M. (2018). Numerical analysis of heat transfer processes in a low-cost, high-performance ice storage device for residential applications. Applied Thermal Engineering, 128, 453–463.CrossRefGoogle Scholar
  13. 13.
    Ruan, Y., Liu, Q., Li, Z., & Wu, J. (2016). Optimization and analysis of building combined cooling, heating and power (BCHP) plants with chilled ice thermal storage system. Applied Energy, 179, 738–754.CrossRefGoogle Scholar
  14. 14.
    Gholamian, E., Zare, V., & Mousavi, S. M. (2016). Integration of biomass gasification with a solid oxide fuel cell in a combined cooling, heating and power system: A thermodynamic and environmental analysis. International Journal of Hydrogen Energy, 41(44), 20396–20406.CrossRefGoogle Scholar
  15. 15.
    Jabari, F., Mohammadi-Ivatloo, B., & Rasouli, M. (2017). Optimal planning of a micro-combined cooling, heating and power system using air-source heat pumps for residential buildings. In Energy harvesting and energy efficiency (pp. 423–455). Cham: Springer.CrossRefGoogle Scholar
  16. 16.
    Jabari, F., Nojavan, S., & Mohammadi-Ivatloo, B. (2016). Designing and optimizing a novel advanced adiabatic compressed air energy storage and air source heat pump based μ-combined cooling, heating and power system. Energy, 116, 64–77.CrossRefGoogle Scholar
  17. 17.
    Jabari, F., Nojavan, S., Mohammadi-Ivatloo, B., & Sharifian, M. B. (2016). Optimal short-term scheduling of a novel tri-generation system in the presence of demand response programs and battery storage system. Energy Conversion and Management, 122, 95–108.CrossRefGoogle Scholar
  18. 18.
    Pandey, S., Hindoliya, D. A., & Mod, R. (2012). Artificial neural networks for predicting indoor temperature using roof passive cooling techniques in buildings in different climatic conditions. Applied Soft Computing, 12(3), 1214–1226.CrossRefGoogle Scholar
  19. 19.
    Kiran, T. R., & Rajput, S. P. S. (2011). An effectiveness model for an indirect evaporative cooling (IEC) system: Comparison of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS) and fuzzy inference system (FIS) approach. Applied Soft Computing, 11(4), 3525–3533.CrossRefGoogle Scholar
  20. 20.
    Ghaebi, H., Parikhani, T., Rostamzadeh, H., & Farhang, B. (2018). Proposal and assessment of a novel geothermal combined cooling and power cycle based on Kalina and ejector refrigeration cycles. Applied Thermal Engineering, 130, 767–781.CrossRefGoogle Scholar
  21. 21.
    Karami, R., & Sayyaadi, H. (2015). Optimal sizing of Stirling-CCHP systems for residential buildings at diverse climatic conditions. Applied Thermal Engineering, 89, 377–393.CrossRefGoogle Scholar
  22. 22.
  23. 23.
    Jiang, Q., & Wen, Z. (2011). Fundamentals of thermodynamics. In Thermodynamics of materials (pp. 1–35). Berlin, Heidelberg: Springer.CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Farkhondeh Jabari
    • 1
    Email author
  • Behnam Mohammadi-ivatloo
    • 1
  • Hadi Ghaebi
    • 2
  • Mohammad-Bagher Bannae-Sharifian
    • 1
  1. 1.Faculty of Electrical and Computer EngineeringUniversity of TabrizTabrizIran
  2. 2.Department of Mechanical EngineeringUniversity of Mohaghegh ArdabiliArdabilIran

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