Research on Optimal Control Algorithm of Ice Thermal-Storage Air-Conditioning System
The constraint-based nonlinear multivariate function optimization algorithm was used to optimize the distribution of cooling load between chillers and ice-storage tanks. The goal is to minimize the cooling load and system running costs of the air-conditioning system. Based on the peak-valley price principle of the power grid system, the most economical running of the ice-storage air-conditioning system is achieved. The results show that compared with the traditional ice-storage air-conditioning system control algorithm, the proposed method can reduce the power consumption of the system by 10.32% and reduce the system operating cost by 12.07% under the premise of satisfying the demand for terminal cooling capacity.
KeywordsOptimal control Ice-storage air-conditioning Building energy conservation
This work is supported by the National Key Research and Development Project of China with the grant number: 2017YFC0704100 (entitled New Generation Intelligent Building Platform Techniques) and Xi’an Beilin District Science and Technology Plan Project with the grant number: GX1603 (entitled An Energy-Saving Optimized Operation Strategy for an Ice Storage Air Conditioning System in Xi’an, China).
- 1.Building energy conservation research center, Tsinghua University, Annual Report on China Building Energy Efficiency. Beijing, China Architecture & Building Press (2018)Google Scholar
- 3.Sehar, F., Rahman, S., Pipattanasomporn, M.: Impacts of ice storage on electrical energy consumptions in office buildings. Energy Build. 51(51), 255–262 (2012)Google Scholar
- 4.Lin, H., Lia, X., Cheng, P.: Study on chilled energy storage of air conditioning system with energy saving. Energy Build. 79(4), 41–46 (2015)Google Scholar
- 5.Yau, Y.H., Hasbi, S.: A Comprehensive case study of climate change impacts on the cooling load in an air-conditioned office building in Malaysia. Energy Procedia 143, 295–300 (2017)Google Scholar
- 6.Rawlings, L.K.: Ice storage system optimization and control strategies. ASHRAE Trans. 91(2), 12–23 (1985)Google Scholar
- 7.Arcuri, B., Spataru, C., Barrett, M.: Evaluation of ice thermal energy storage (ITES) for commercial buildings in cities in Brazil. Sustain. Cities Soc. 2(29), 178–192 (2017)Google Scholar
- 9.Sanaye, S., Shirazi, A.: Four E analysis and multi-objective optimization of an ice thermal energy storage for air conditioning applications. Int. J. Refrig. 36(3), 828–841 (2013)Google Scholar
- 10.Sanaye, S., Hekmatian, M.: Ice thermal energy storage (ITES) for air conditioning application in full and partial load operating modes. Int. J. Refrig. 66, 181–197 (2016)Google Scholar
- 11.Sanaye, S., Shirazi, A.: Thermo-economic optimization of an ice thermal energy storage system for air conditioning applications. Energy Build. 60(1), 100–109 (2013)Google Scholar
- 12.Cai, Z.Y.: Analysis of the principle and operating characteristics of the ice storage air conditioning system. J. Green Sci. Technol. 2, 164–165 (2016)Google Scholar
- 14.Henze, G.P., Krarti, M.: Guidelines for improved performance of ice storage systems. Energy Build. 35(2), 111–127 (2003)Google Scholar
- 15.King Dion, J., Potter, Robert A.: Description of a steady-state cooling plant model developed for use in evaluating optimal control of ice thermal energy storage systems. ASHRAE Trans. 104(1), 42–53 (1998)Google Scholar