Low-cost optimization of geothermal heating system with thermal energy storage for an office building

https://doi.org/10.1016/j.tsep.2023.101918Get rights and content

Highlights

  • The geothermal heating system including thermal energy storage is optimized by two-stage approach.
  • The introduction of thermal energy storage increases the capital investment of the geothermal system.
  • The time-of-use tariffs is conducive to the economic operation of the geothermal system.
  • A reduction occurs in the comprehensive cost of the optimized geothermal system.
  • The optimal operation strategy and equipment selection parameters are determined.

Abstract

Using geothermal energy to meet office building heating is an effective measure to reduce energy consumption. The heat demand of an office building features a significant intermittent profile; however, the geothermal submersible pump should not be started and stopped frequently within a heating season, which makes it difficult to match the continuity of heat source with the intermittent heat load and thus resulting in a serious waste of geothermal resources. The utilization of thermal energy storage (TES) technology can solve the problem of the mismatch between the supply and demand of the geothermal heating system for an office building. In this paper, aiming at obtaining a low-cost system while promoting the energy supply and demand balance, the geothermal heating system with TES for an office building located in Cangzhou, Hebei province, China is optimized based on a two-stage approach considering the time-of-use tariffs. The results show that the geothermal heating system including TES can effectively meet the heat load of office buildings without a backup facility. Compared with the benchmark system, although the addition of TES leads to an increase in the system capital investment, the system operation cost reduces by 38.10% or so under the optimal operation strategy. Besides, reasonable operation control of the TES as well as the heat pump is the key measure to reduce the system cost. Accordingly, the optimal operation strategy and corresponding equipment selection parameters of the geothermal heating system with TES are determined.

Introduction

In recent years building energy consumption has attracted increased attention around the world. As for China, the building sector accounts for about 30% of the primary energy consumption [1], in which the energy used for space heating in Northern China occupies one-quarter or so [2]. Among all types of buildings, the number of office buildings has increased rapidly with economic development, which leads to office buildings becoming the largest energy consumption buildings [3].
Using renewable energy to meet building heating is an effective and wise alternative. As one of the main renewable energy sources, geothermal energy can play an important role in building space heating, which is generally unaffected by geography, climate and season [4]. The geothermal energy for building heating can be generally classified into two types, i.e. the shallow and medium-deep geothermal systems. Although the shallow one is popular, besides the cooling and heat loads are not easy to be balanced, the average temperature of soil is relatively low, which restricts its application for building heating; Besides, a large area is needed for the shallow to meet the heat load of building due to the weak performance of borehole heat exchangers [5]. Comparatively, the medium-deep geothermal heating system can overcome the aforementioned shortcomings and has an outstanding advantage in a heating supply stability and carbon reduction [6]. Also, it is believed that with the progress of exploration technology and the reduction of drilling costs, the medium-deep geothermal heating system has a promising prospect. Currently, in view of engineering applications, the medium-deep geothermal systems are mainly dominated by hydrothermal heating technology. By the end of 2020, the heating area supported by the hydrothermal geothermal source in China has exceeded 580 million square meters, including over 7.0 million square meters in Xiongan, Hebei province, China [7], which meets approximately 95% of the heating demand in the county. Considering its rich resources equivalent to 853 × 103 million tons of standard coal [8], the development of hydrothermal geothermal for building heating has become a significant measure for China to achieve its energy conservation and emission reduction goals.
The hydrothermal geothermal heating system commonly consists of a submersible pump, a plate heat exchanger or an addition of water source heat pump, which in general provides the baseload heat supply while a backup system covers any excess demand [9]. To date, the heat production only from the geothermal heating system to satisfy the heating demand has not been studied extensively. What is more, with the advancement of technology, more and more geothermal heating systems use variable frequency pumps, but some investigation assumes the geothermal water flow rate is constant, see for example [10]. It is well known that the heat demand of an office building features a significant intermittent profile; however, the submersible pump of geothermal well should not be started and stopped frequently within a heating season [11], which makes it difficult to match the continuity of heat source with the discontinuity of heat load and thus resulting in a serious waste of geothermal resources. Therefore, some researchers proposed to employ thermal energy storage (TES) devices to bridge the discrepancy between the heat supply and demand [10]. For instance, Daniilidis et al. [9] designed a model predictive control strategy for the heat production of a geothermal system including a storage device in relation to the demand from a district-heating network; Guo and Li [11] taking a public building in Xianyang, China as an example, suggested a method to improve the efficiency and benefit of geothermal heating by using heat storage technology; Lee et al. [12] compared the energy consumption of a single well-circulating heat pump system before and after optimization, and showed that the system energy consumption decreased by 14% after adding a thermal storage tank; Manente et al.[13] used short-term thermal energy storage to decouple the heat production sector from the distribution network of Ferrara in Italy, and showed that adding a thermal storage system combined with time-of-use (TOU) optimization control can save 11% of the system energy consumption; Kyriakis and Younger [10] investigated a geothermal heating system including two storage tanks, one on the supply line and one on the return line of the system that will store hot and cold water respectively, which showed that adding a thermal storage tank could reduce both heating cost by 4.2% to 14.9% and carbon emissions by 54.2%. These researches prove the effectiveness of thermal energy storage in geothermal heating.
However, it is well known that the introduction of TES will increase the capital investment of the geothermal system. Besides, concerning the electric supply side, a smart grid with TOU tariffs is conducive to the system’s economic operation [14], but to the best of the authors’ knowledge, this has not previously been addressed well in the geothermal heating system. Aiming at obtaining a low-cost system while promoting the energy supply and demand balance, the present paper introduces the TOU tariffs into the geothermal heating system with TES for an office building and two-stage optimization is performed. With these analyses, some suggestions are given as resources for the design of the geothermal heating system with TES for office buildings.

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Section snippets

System description

At present, most of the geothermal heating systems are mainly composed of primary plate heat exchanger (PPHE), secondary plate heat exchanger (SPHE), water source heat pump (HP) and circulation pumps, which is denoted as the benchmark system (BS) hereafter, as shown in Fig. 1. The geothermal heating system includes a hot water storage tank as TES unit (GHSTES) to shift peak heat load to off-peak hours is illustrated in Fig. 2. In the GHSTES, the geothermal water from the production well

Mathematical model

This chapter introduces various component models of the GHSTES system, and elaborates the objectives and constraints of the first-stage optimization model and second-stage optimization model.

Results and discussion

This paper takes an office building in Cangzhou, Hebei Province, China as the research object. The simulation period is 24 h [20] and the daily heating period is from 7:00 to 20:00 on working days [11]. Fig. 3, Fig. 4 show the building heat load curve of typical day in the heating season and the local TOU tariffs, respectively. The input parameters of the simulation are shown in Table 1. According to the characteristics of the heat load of the building and time-of-use price, four operation

Conclusions

This paper introduces the TOU tariffs into the geothermal heating system with TES and has built a two-stage optimization model taking an office building in Cangzhou, Hebei province, China as a case study. The two-stage optimization of the GHSTES is solved by Gurobi solver. The main conclusions drawn from the investigation are summarized as follows:
(1) The introduction of the TOU tariffs into the geothermal heating system with TES is conducive to obtain a low-cost system while promoting the

CRediT authorship contribution statement

Sihao Huang: Conceptualization, Methodology, Investigation, Software, Visualization, Data curation, Writing – original draft. Xiaoshuang Zhao: Investigation, Software, Formal analysis, Writing – original draft. Lingbao Wang: Investigation, Visualization, Software. Xianbiao Bu: Investigation, Visualization, Writing – review & editing. Huashan Li: Methodology, Investigation, Software, Writing – review & editing, Funding acquisition, Supervision.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The authors gratefully acknowledge the financial support of the National Key Research and Development Program of China (No. 2019YFB1504105).

References (23)

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