A Study on Operational Strategy of Ground–source Heat Pump System Based on Variation of Building Load. (August 2015)

• Record Type: Journal Article
• Authors: Tian, X.; Yang, M.J.; Zhao, J.W.; He, S.M.; Zhao, Jun
• Is Part Of: Energy procedia. Volume 75(2015)
• Page Start: 1508
• Page End: 1513
• Publication Date: 2015-08
• Subjects: Distributed energy system -- dynamic simulation -- energy storage -- load forecast.; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2015.07.298
• Languages: English
• ISSNs: 1876-6102

Abstract

Abstract: Compared with the air-source air-conditioning system, heat pump with renewable energy has advantages of higher energy efficiency, environmental-friendly, etc. However, most operation strategies of existing renewable energy systems are based on maintenance experience or semi-empirical strategies at the current status. Such operation is commonly lack of optimizing, especially which is based on a comprehensive quantitative analysis. Therefore, it is difficult for renewable energy system, which employed such operation strategy, has a reasonable response to the fluctuation of building load with an optimized object, e.g. a dynamic match between supply and demand. In this paper, a renewable energy station, which is located in Sino-Singapore Tianjin Eco-city, is studied for a centralized heating and cooling based on ground-source heat pump (GSHP) and water energy storage. The GSHP system is comprised of two heat pump units with 3550 kW cooling capacity and 4100 kW heating capacity, and 1400 boreholes with a depth of 120m and a covering area of 40000 m 2 . Water energy storage system is comprised of four tanks with the capacity of 750 m 3 (The total volume is 3000 m 3 ). The renewable energy system provides cooling and heating for six office buildings. The heating and cooling load are obtained via simulation, and dynamic operational performance is analysed. The load variation curve and fitting formula are presented for typical design days. Also, a building heating/cooling load prediction algorithm, which is used to meet the daily average load and the daily maximum load, is developed based on the weather information and historical operational data. Furthermore, the combined operational performance of GSHP and water, energy storage is analysed within the framework of local peak-valley price. Finally, an optimized combined operational strategy of the distributed energy system was obtained for a dynamic match between the demand and supply.