Performance Estimation and Optimal Operation of a CO2 Heat Pump Water Heating System

Abstract

The daily performance of a CO2 heat pump water heating system with a hot water storage tank is affected by the history of daily hot water demand and heat pump operating conditions. To attain the maximum system performance, it is important to estimate the daily changes in the system performance values accurately in relation to those in hot water demand and heat pump operating conditions, and determine the operating conditions optimally based on the estimation. In this paper, neural network models are used for this estimation, and the values of model parameters are identified by a global optimization method. In addition, the outlet water temperature for during operation and the inlet water temperature for shutdown are determined to maximize the system efficiency subject to a lower limit for the volume of unused hot water. The validity and effectiveness of this approach are ascertained by a numerical study using a simulated hot water demand.

Keywords

• Heat pump; Water heater; Thermal storage; Natural refrigerant; Carbon dioxide; System performance; Estimation; Optimization

References

1. Cecchinato, L., Corradi, M., Fornasieri, E. and Zamboni, L. (2005). Carbon Dioxide as Refrigerant for Tap Water Heat Pumps: a Comparison With the Traditional Solution. International Journal of Refrigeration, 28 (8), 1250–1258.
2. Hashimoto, K. (2006). Technology and Market Development of CO2Heat Pump Water Heaters (ECO CUTE) in Japan. IEA Heat Pump Centre Newsletter, 24 (3), 12–16.
3. Hwang, Y. and Radermacher, R. (1998). Theoretical Evaluation of Carbon Dioxide Refrigeration Cycle. International Journal of Heating, Ventilating, Air- Conditioning and Refrigerating Research, 4(3), 1–20.
4. Japanese Industrial Standards Committee (2011). Residential Heat Pump Water Heaters. Standard No. JIS C 9220: 2011, Japanese Standards Association (in Japanese).
5. Laipvadit, P., Tiansuwan, J., Kiatsiriroat, T. and Aye, L. (2008). Theoretical Performance Analysis of Heat Pump Water Heaters Using Carbon Dioxide as Refrigerant. International Journal of Energy Research, 32(4), 356–366.
6. Minetto, S. (2011). Theoretical and Experimental Analysis of a CO2Heat Pump for Domestic Hot Water. International Journal of Refrigeration, 34(4), 742–751.
7. Nekså, P. (2002). CO2Heat Pump Systems. International Journal of Refrigeration, 25(4), 421–427.
8. Nekså, P., Rekstad, H., Zakeri, R. and Schiefloe, P.A. (1998). CO2-Heat Pump Water Heater: Characteristics, System Design and Experimental Results. International Journal of Refrigeration, 21(3), 172–179.

9. Richter, M.R., Song, S.M., Yin, J.M., Kim, M.H., Bullard, C.W. and Hrnjak, P.S. (2003). Experimental Results of Transcritical CO2Heat Pump for Residential Application. Energy, 28(10), 1005–1019.
10. Saikawa, M. (2004). CO2Heat Pump Water Heater. Energy and Resources, 25(2), 101–105 (in Japanese).
11. Saikawa, M. and Hashimoto, K. (2001). Evaluation on Efficiency of CO2Heat Pump Cycle for Hot Water Supply (Evaluation on Theoretical Efficiency and Characteristics). Transactions of the JSRAE, 18 (3), 217–223 (in Japanese).
12. Saikawa, M., Hashimoto, K., Hasegawa, H. and Iwatsubo, T. (1999). Study on Efficiency and Control Method of CO2Heat Pump. Report No. W98004, Central Research Institute of Electric Power Industry (in Japanese).
13. Sarkar, J., Bhattacharyya, S. and Ram Gopal, M. (2010). Performance of a Transcritical CO2Heat Pump for Simultaneous Water Cooling and Heating. ASHRAE Transactions, 116(1), 534–541.
14. Skaugen, G., Nekså, P. and Pettersen, J. (2002). Simulation of Trans-Critical CO2Vapour Compression Systems. Proceedings of the 5th IIR-Gustav Lorentzen Conference on Natural Working Fluids, 82–89.
15. Stene, J. (2005). Residential CO2Heat Pump System for Combined Space Heating and Hot Water Heating. International Journal of Refrigeration, 28 (8), 1259–1265.
16. Ukaji, M., Sawachi, T., Akimoto, T., Hori, Y., Kuwasawa, Y., Mae, M. and Hosoi, A. (2004). Study on Low Energy and Resource Saving Technologies for Autonomous Housing (Part 6, Basic Schedule for the Verification of Energy Consumption in Daily Human Activities). Proceedings of the SHASE Annual Conference, 209–212 (in Japanese).
17. White, S.D., Yarrall, M.G., Cleland, D.J. and Hedley, R.A. (2002). Modelling the Performance of a Transcritical CO2Heat Pump for High Temperature Heating. International Journal of Refrigeration, 25(4), 479–486.
18. Yamaguchi, S., Kato, D., Saito, K. and Kawai, S. (2011). Development and Validation of Static Simulation Model for CO2Heat Pump. International Journal of Heat and Mass Transfer, 54(9–10), 1896–1906.
19. Yan, J.L., Ma, Y.T., Li, M.X. and Hua, J. (2010). Modeling and Simulating the Transcritical CO2Heat Pump System. Energy, 35(12), 4812–4818.
20. Yokoyama, R. and Ito, K. (2005). Capability of Global Search and Improvement in Modal Trimming Method for Global Optimization. JSME International Journal, Ser. C, 48(4), 730–737.
21. Yokoyama, R., Kohno, Y., Wakui, T. and Takemura, K. (2010a). Performance Analysis of a CO2Heat Pump Water Heating System Under a Daily Change in a Simulated Demand. Transactions of the JSRAE, 27 (4), 355–364.
22. Yokoyama, R., Okagaki, S., Wakui, T. and Takemura, K. (2008). Influence of Operation Temperatures on Performance of a CO2Heat Pump Water Heating System. Journal of Environment and Engineering, 3(1), 61–73.
23. Yokoyama, R., Shimizu, T., Ito, K. and Takemura, K. (2007). Influence of Ambient Temperatures on Performance of a CO2Heat Pump Water Heating System. Energy, 32(4), 388–398.
24. Yokoyama, R., Shimizu, T., Takemura, K. and Ito, K. (2006). Performance Analysis of a Hot Water Supply System With a CO2Heat Pump by Numerical Simulation (1st Report, Modeling and Analysis of Heat Pump). JSME International Journal, Ser. B, 49 (2), 541–548.
25. Yokoyama, R., Wakui, T., Kamakari, J. and Takemura, K. (2010b). Performance Analysis of a CO2Heat Pump Water Heating System Under a Daily Change in a Standardized Demand. Energy, 35(2), 718–728.
26. Yokoyama, R., Wakui, T. and Satake, R. (2009). Prediction of Energy Demands Using Neural Network With Model Identification by Global Optimization. Energy Conversion and Management, 50(2), 319–327.