Comparative life cycle assessment of geothermal power generation systems in China

Resources, Conservation & Recycling 155 (2020) 104670

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Comparative life cycle assessment of geothermal power generation systems in China

T

Yongzhen Wanga,b, Yanping Duc, Junyao Wangd,*, Jun Zhaob, Shuai Dengb, Hongmei Yinb a

Department of Electrical Engineering, Energy Internet Research Institute, Tsinghua University, Beijing, 100084, China Key Laboratory of Efficient Utilization of Low and Medium Grade Energy, Tianjin University, Tianjin, 300350, China c China-UK Low Carbon College, Shanghai Jiao Tong University, Shanghai, 200240, China d Guangdong Research Center for Climate Change, Sun Yat-Sen University, Guangdong, 510006, China b

ARTICLE INFO

ABSTRACT

Keywords: Geothermal power generation system Environmental impacts Life cycle assessment Geothermal reservoir

This study concerns the assessment of the environmental impacts of geothermal power generation systems using life cycle assessment approach. Particularly, four types of typical geothermal power generation systems in China based on different technologies (double flash, single flash, binary and enhanced geothermal system) are involved in the case study, and critical environmental impacts of acidification potential, global warming potential and eutrophication potential are evaluated for the above geothermal power generation systems based on their energy system analysis models. Analytical results reveal that environmental impacts of geothermal power generation systems are significantly affected by well drilling process. In general, construction process contributes more than 60 % of acidification potential while running process is the major source of eutrophication potential. Environmental impacts vary for each geothermal power generation system due to their different configurations as well as reservoir conditions (namely geothermal gradient). Acidification potential, global warming potential and eutrophication potential of the cases are among 30.43∼250.05 mgSO2/kWh, 3.88∼80.49 gCO2/kWh, 4.78∼32.50 mgPO43−/kWh. In particular, environmental impacts of geothermal power generation systems can be largely reduced with a larger geothermal gradient, and it’s the reason that South West double Flash geothermal power generation system is with the lowest environmental impacts.

1. Introduction In 2017, hot dry rock (HDR) with a temperature of 236 °C was exploited in Gonghe basin of Qinghai province in China (Anon, 2019). This represents a significant breakthrough of drilling and exploitation with HDR and enables the use of new methods for geothermal power generation in China, namely geothermal power generation based on enhanced geothermal system (Luo et al., 2012; Li et al., 2014; Cheng et al., 2014, 2016; Noorollahi et al., 2017). Until now, there are four configurations of geothermal power generation system (GPGS) available in China: the double stage flash power plant exploiting high-temperature geothermal energy, located in South West; the single stage flash power plant, adopted for utilization of medium-temperature geothermal energy, located in South East; the binary power system based on organic Rankine cycle (ORC), suits for utilizing the low-temperature geothermal energy, located in North East; and the enhanced geothermal system (EGS) that will be constructed in North West. In fact, geothermal energy is enormously distributed all over the world, and it

⁎

can be converted into electric energy as a base load for its advantage of stability (Siouane et al., 2017). Compared to other renewables, such as solar energy and wind energy, geothermal energy is more stable as it uses water, steam as energy medium (Bertani, 2016; Lund, 2011; RubioMaya et al., 2015). Therefore, the capacity factor of GPGS can be as high as 74 % or more and the total capacity of geothermal power generation system (GPGS) is expected to be more than 160 GW in 2050 (Goldstein, 2020). However, GPGS is currently challenging for larger scale application as its high cost (Zhao et al., 2016). As a consequence, solar energy and wind energy are preferably utilized as the primary renewable energy in most countries, while the total capacity of geothermal power plants in the world is only 12 GW in 2015 (Martinot et al., 2005; Bajpai and Dash, 2012; Roland, 2012). Thus, methods for improving the energy efficiency and economic and environmental impact of GPGS need to be developed. Numbers of researchers have conducted systematic optimization studies on different GPGSs. For example, Clarke and McLeskey (2015) performed the multi-objective optimization for a binary geothermal

Corresponding author at: Guangdong Research Center for Climate Change, Sun Yat-Sen University, Guangdong, 510006, China. E-mail address: [email protected] (J. Wang).

https://doi.org/10.1016/j.resconrec.2019.104670 Received 7 September 2019; Received in revised form 25 November 2019; Accepted 26 December 2019 Available online 07 January 2020 0921-3449/ © 2020 Elsevier B.V. All rights reserved.

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Nomenclature AP DFPS EGS EP GWP GPGS HDR ORC

HB-SO NW-FO SW-DF SE-SF Pgf mgf Wnet Tinj Tgf

Acidification potential Double stage flash power system Enhanced geothermal system Eutrophication potential Global warming potential Geothermal power generationsystem Hot dry rock Organic Rankine cycle

power plant based on ORC. Cakici et al. (2017) completed a thermodynamic analysis for the supercritical ORC driven by the geothermal energy and solar energy. Ergun et al. (2017), Aali et al. (2017) and Zhu et al. (2017) conducted the exergy analysis and multi-objective optimization for geothermal power system based on ORC, combined Flash and ORC, and enhanced geothermal system, respectively. Hu et al. (2014) studied the performance of ORC driven by geothermal energy using R245fa as working fluid. Li et al. (2016) made an optimization for the two-stage organic Rankine cycle with R245fa driven by geothermal water. Alzaharani et al. (2013) evaluated the performance of an integrated system for power, hydrogen and heat generation driven by geothermal energy. While, techno-economic optimization of the binary power plants for the exploitation of medium–low temperature geothermal sources was carried out by Menon et al. (2014). Although the techno-economic performances of different GPGSs have been largely investigated in numerous studies as above-mentioned, only the ‘aboveground’ system and its running stage are analyzed and optimized. The objects of most works are restrained as systematic optimization and the working fluids selection of the ‘aboveground’ system, without considering the geothermal reservoir conditions and influence. Meanwhile, there are a few researches that

Huabei Plain-Single ORC North West-Flash ORC South West-Double Flash South East-Single flash Phase of geothermal fluid Mass flow rate of geothermal fluid Network of the system Temperature of injection Temperature of geothermal fluid

addressed the environmental impacts of GPGS through life cycle assessment (LCA), considering various conditions of geothermal reservoirs worldwide. Therefore, interactions of ‘underground’ system with environmental impacts of GPGSs need to be further studied. Recently, with application of LCA to energy systems (Ripa et al., 2017; Hennig and Gawor, 2012; Wang et al., 2017), some researchers have extended LCA to GPGSs. For example, Frick et al. (2010) assessed the binary power generation system based on EGS using LCA. Heberle et al. (2016a) adopted LCA to analyze the environmental impacts of GPGSs based on ORC when using different organic working fluids. Karlsdóttir et al. (2015a) completed a detailed inventory data of LCA for analyzing the combined heat and power system driven by geothermal energy. Takahashi et al. (2013) calculated the additional load of emitted greenhouse gases from the steam and land used by GPGS. In detail, Tomasini et al. (2017) performed an updated review of life cycle environmental studies for GPGSs, where environmental impacts are evaluated considering eighteen kinds of impact categories commonly used in LCA. Parisi et al. (2019) performed an environmental impact assessment for the calculation of atmospheric emissions profiles connected with the operational stage of the geothermal power plants in Italy. Pratiwi et al. (2018) analyzed greenhouse gas emissions of

Fig. 1. The systematic diagrams of the power plants and the corresponding T-S diagrams. 2

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Rittershoffen geothermal plant in France, in which five different scenarios comprising a heat plant, power plants and cogeneration plants are developed respecting LCA. Hanbury and Vasquez (2017) conducted a life cycle analysis of geothermal energy for power and transportation by a stochastic approach, due to the large variations of hydrothermal reservoir chemistry and thermodynamic conditions between geothermal plants. Martínez et al. (2017) proposed a multi-objective methodology for the optimal selection of the working fluids for a geothermal ORC, which simultaneously considers the economic, environmental and safety aspects. The environmental function is based on carbon dioxide emissions, considering carbon dioxide mitigation due to the use of cooling water as well as emissions due to material release. Paulillo et al. (2019) conducted a LCA research on a combined heat and power double flash geothermal plant located in Iceland, and the analysis identifies consumption of diesel for drilling and use of steel for wells casing and construction of the power plant as the main hot spots. It can be observed that environmental impacts studies on GPGS have been carried out worldwide. However, there is no research in this field in China, which is rich in low-medium temperature geothermal resources and has a great potential in geothermal utilization as mentioned-above. At the same time, the study of the environmental impact of GPGS is closely related to its thermodynamic modeling and performance. As a result, this study aims to present LCA focusing on research of environmental impacts for four different of typical GPGSs located in discrete regions of China (i.e. double flash, single flash, binary and EGS). Critical environmental impacts of acidification potential (AP), eutrophication potential (EP) and global warming potential (GWP) are analyzed quantitatively across all the life cycle stages of GPGSs. Particularly, aboveground systems and underground reservoir as well as auxiliary systems for geothermal energy exploitation are taken into considerations. In addition, the environmental impacts of GPGSs in the view of the geothermal gradient are also investigated in the study.

plant in Gonghe basin is also analyzed, where, a combined flash and ORC system is assumed (Liu et al., 2013). The systematic diagrams of the above four power plants and their corresponding T-S diagrams are illustrated in Fig. 1, respectively. 2.2. Thermodynamic models of GPGSs The thermodynamic characteristics of GPGSs are the premise for environmental impacts assessment of GPGSs. Therefore, some fundamental parameters are calculated as below: The heat that is supplied for GPGS from geothermal fluids is quantified as:

Qgeo = mgeo*(hgeo, in

(1)

h geo, out )

The power output of GPGS is quantified as: (2)

Wexp = Wexp, FLASH + Wexp, ORC The thermal efficiency of GPGS is defined as: I

=

Wexp (3)

Qgeo

The mass flow rate of the cooling water of GPGS is defined as:

m water =

Qcon Cp* T

Qgeo*(1

I)

(4)

Cp* T

Where Qgeo, mgeo are heat rate and mass flow rate of geothermal fluid, respectively; hgeo,in, hgeo,out are enthalpies of geothermal fluid at inlet and outlet of the power plant, respectively; Wexp, FLASH and Wexp, ORC represent the power of expander in flash and ORC subsystems, respectively; Qcon is heat release in condensation; while T is the temperature rise of cooling water, which is assumed as 8 °C to abtain a higher thermal efficiency of GPGS in the analysis. Table 1 shows the general information of the four typical GPGSs in China as mentioned in the last section, including geographic locations, categories of the reservoir, construction time and the running time of the installed systems, etc. It is worth mentioning that the parameters for the North West power plant are the designed parameters, as it is not a real power plant in running. The maximum electricity power generation is the object of GPGSs in the view of thermodynamics. In the North West power plant, which adopts the combined Flash and ORC power system, the flashing temperature and the evaporation temperature in the subsystems are the two critical variables that need to be quantified in the system optimization. It is assumed that the temperature of the injection geothermal fluid of the system is above 50 °C, Meanwhile, the optimized flashing temperature and the evaporation temperature are calculated at 140 °C and 85 °C, respectively. In the case study, cooling tower is used in the four power plants. Based on the above assumptions and the calculated parameters, the maximum electricity power generation can be

2. Geothermal power plants in China and thermodynamic models Currently, there are three geothermal power plants in operation and one system will be constructed in China, which are Yangbajing power plant in Tibet, Fengshun power plant in Guangdong, Huabei oil field power plant, and the enhanced geothermal system in Gonghe basin (An et al., 2016). 2.1. System description As mentioned above, Fengshun and Yangbajing geothermal power plants use a single stage and double stage flash system for power generation, respectively. While Huabei oil field geothermal power plant adopts the binary cycle based on ORC for power generation. In addition, hot dry rock was found in Gonghe basin and he geothermal fluid can be acquired by hydro-fracture. Thus, in the case study, the power Table 1 General information of geothermal power plants. Item

Unit

South West

South East

Hua Bei Plain

North West

Plant Location and Cycle type Plant abbreviation Geographic location Construction time Reservoir categories

/

South East- single Flash SE-SF Fengshun 1982 low temperature convection type

Hua Bei- single ORC HB-SO Huabei oil field 2014 low temperature conduction type

North West-Flash ORC NW-FO Qinghaic Planning HDR

Number of production/ injection wells Production well deep

/ m

South West- double Flash SW-DF Yangbajing 1979 high temperature convection type 8/8 300

1/0a 800

1/1b 3216

1/1 3705

/ year /

a. There is no reinjection of the geothermal fluid in Fengshun power plant as it is a synthesized energy system in terms of grade of the thermal energy. b. Huabei oil field power plant uses a geothermal well reformed from an exhausted oil well. c. The basic parameters in Gonghe basin plant in Qinghai are obtained based on the well depth and the measured temperatures in the scenarios of the design parameters. 3

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evaluated. The basic thermodynamic parameters of the four power plants in the case study are summarized in Table 2, such as temperature of geothermal fluid, phase of geothermal fluid, mass flow rate of geothermal fluid, injection temperature of the geothermal fluid, output work and network of the system.

3.2. Life cycle inventory (LCI) Source of Life cycle inventory of GPGS in this paper is shown as Table 3. Detailed inventory data of GPGSs are acquired through inventory data surveys from published papers. 3.2.1. Well drilling and casing The inventory data in the processes of the well drilling, installation and washing, and wellhead equipment are illustrated in Table 4.

3. Application of life cycle assessment for GPGSs in China Life Cycle Assessment is an established method that takes into account environmental impacts along the entire life cycle of products and services (Naser and Timothy, 2008). Specifically, LCA consists of four phases: i) the Goal and scope definition, ii) the Life Cycle Inventory (LCI), iii) the Life Cycle Impact Assessment (LCIA), and iv) the Interpretation. In this study, the process-based LCA method is used to assess the environmental impacts of GPGS, covering three typical stages (i.e. construction, operation and maintenance, and decommissioning), which includes six processes (i.e. well drilling, building construction, generator producing, plant operating, plant maintenance and decommissioning). The input resource, material and energy consumption accruing along each process is considered to complete LCI of GPGSs. Fig. 2 shows the schematic flow-chart of LCA for assessing the environmental impacts of GPGSs in this paper. It is shown that the objects include both the characteristic parameters of aboveground and underground subsystems. Background inventory data are collected based on Chinese Life Cycle Database (CLCD), technical investigations and literature reviews. It is noted that the CLCD is the only publicly available comprehensive database in China, which represents the Chinese market average technology. Up to now, there are more than 600 datasets in CLCD 0.8 version and is still expanding (IKE&SCU-ISCP, 2015). The environmental impact categories considered in this study are Acidification potential (AP), Eutrophication potential (EP) and Global warming potential (GWP). As mentioned above, the potential values of the environmental impacts are significantly affected by the characteristic parameters of aboveground and underground subsystems. Therefore, correlations of the key parameters (i.e. the geothermal gradient) and the potential values are established to quantify the environmental impacts of the geothermal systems. This is to be elaborated in the next sections.

3.2.2. Construction of power plant buildings The inventory data in the process of the power plant buildings’ construction are shown in Table 5. It is noted that, the inventory data for NW-FO power plant is calculated proportionately from values of single ORC (HB-SO power plant) and single Flash (SE-SF power plant) on basis of their scales. 3.2.3. Production of power plant machineries The materials consumptions of four different GPGSs are revealed in Table 6. It is pointed out the inventory data is acquired based on surveys and equivalence in terms of the installed capacity of the power systems (Council N A O S. America’s Energy Future, 2009; Pehnt, 2006). 3.2.4. Operation and maintenance In the operation of system, the replenishment quantity of water occupies 0.5 % of the demand of cooling water. In terms of the maintenance, the diesel consumption for maintenance is set as 0.05 t/MW/yr and the supplement ratio of the steel of the system is set as 2 % of the system per year, while the escape ratio of the working fluid in the binary-cycle based power plants is assumed to be 2 % (Liu et al., 2013). 3.2.5. Decommissioning In the process of decommissioning of the power plant, the disposal is assumed as 0.6 t/MW. The break stone and cement consumption are 51.1 kg/m and 4.9 kg/m (Naser and Timothy, 2008), respectively. Meanwhile, environmental impacts in the decommissioning process of aboveground system is assumed as 10 % of its impacts of construction and operating stages (Bravi and Basosi, 2014). 3.3. Life cycle impact analysis (LCIA)

3.1. Goal and scope of LCA

Three main environmental impacts indicators of Acidification potential (AP), Eutrophication potential (EP) and Global warming potential (GWP) are considered in this study as showed in Table 7. The

The main motivation of this work is to investigate the performance of environmental impacts of four typical GPGSs in China, integrating the methods of thermodynamic analysis and LCA. The system boundary for LCA will cover GPGSs from the construction, operation and maintenance, and decommissioning stages. Functional unit of LCA in this paper is set as 1 kW h. Fig. 3 shows the spatial and temporal boundaries of a geothermal power plant in the environmental impacts assessment using LCA. Materials used in well drilling, plant construction and electricity generation processes are taken into account. The geothermal fluid is the heat source of GPGS, while the net power is regarded as the output of GPGS. The exhausted fluids include the gas and liquids eliminated in the processes of construction, operation and maintenance stages. Some assumptions used in LCA in this paper are summarized as below: 1) the average radius of the transportation for materials is set as 69 km, the capacity of the truck is 46 tons; 2) life period of GPGSs is 30 years with the exception of South West power plant, and the annual running ratio is 0.65; 3) cooling water used in the drilling and running processes is regarded as industrial water; 4) energy consumption and the environmental impacts in the rock cracking process in the North West power plant is neglected; 5) geothermal fluid is not accounted in the inventory data as the reinjection of the geothermal fluid is adopted in the close system.

Table 2 Thermodynamic modeling of the plants. Item

Unit

South West

South East

Hua Bei Plain

North West

Thermodynamic cycle

/

Temperature of geothermal fluid Phase of geothermal fluid mgf

℃

Double Flash 160a

Single Flash 91

Single ORC 110

Combined Flash and ORC 236

/

wet steam

water

water

hydro fracture

kg/s MW

125

64

33

28

Wexp

MW ℃

16.00 81

0.225 72

0.15 87

2.80 57

Wnet Tinj I

mwater

% t/h

24.00

9.39 8914.96

0.30

4.43 530.59

0.40

4.83 322.61

3.30

13.37 1982.42

Note: a. the inlet temperature of the geothermal fluid in South West geothermal power plant is in the range of 155∼165 °C, therefore, the saturated pressure is obtained under the average temperature of 160 °C. The vapor quality is assumed as 0.15 in the calculations. 4

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Fig. 2. The schematic flow-chart of LCA approach for GPGSs in this paper.

Fig. 3. The system boundary of GPGS.

5

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Table 3 Data source of life cycle inventory assessment of this paper. Item

Data

Source

Reference

1

Survey

(An et al., 2016)

2 3 4

General details of geothermal power plants Thermodynamic parameters of the plants Background life cycle data Life cycle inventory

Survey and calculation Chinese Core Life Cycle Database Literature

5

Inventory data of material transport

(Luo et al., 2012; An et al., 2016; Liu et al., 2013) IKE (Heberle et al., 2016a; Karlsdóttir et al., 2015a; Takahashi et al., 2013; Tomasini et al., 2017; Naser and Timothy, 2008), N/A

China’s traffic statistic year book publications

Table 4 Inventory data for drilling and casing (Naser and Timothy, 2008). Item

Amount

Unit

Diesela Steel Cement Bentonite Silica flour Waterb Stainless steel Aluminum Excavation Fill Concrete

53.100 100.200 36.969 27.110 13.118 26.682 6.625 0.554 1.363 0.454 0.008

kg/m kg/m kg/m kg/m kg/m kg/m kg/m kg/m m³/m m³/m m³/m

Table 6 Inventory data for main electricity generation machinery (Council N A O S. America’s Energy Future, 2009; Pehnt, 2006).

SW-DF

SE-SF

HB-SO

NW-FO

Excavationa Fillb Concrete Steelc Stainless steeld Aluminume Copperf Mineral Wool Plasticg Water

m³/MW m³/MW m³/MW kg/MW kg/MW kg/MW kg/MW kg/MW kg/MW kg/MW

2136 2443 91 13057 738 577 150 594 729 45500

2165 2432 86 11943 517 578 152 567 702 43000

2151 2438 89 12500 628 578 151 581 716 44250

2581 2925 106 15000 753 693 181 697 859 53100

SW-DF

SE-SF

HB-SO

NW-FO

Steel Stainless steela Aluminum Copper Titanium Mineral wool Plasticb Reinforced plastic Organic fluid

kg/MW kg/MW kg/MW kg/MW kg/MW kg/MW kg/MW kg/MW t/MW

9015 2114 255 377 465 264 9 2142 0

8616 2343 242 363 523 246 8 2116 0

10579 2674 298 444 593 306 10 2555 11.56d

9371 2470 264 394 550 269 9 2285 12

Table 7 The impact indicators and their calculation in LCA.

Table 5 Inventory data for construction of power plant (Karlsdóttir et al., 2015b; Heberle et al., 2016b). Unit

Unit

Note: a. 316 L grade stainless steel; b. 100 % PE plastic.

Note: All the inventory data are scaled to the total meters drilled or the number of wells, the use of sulphate is neglected in the assessment. a. the consumed diesels in the excavating, filling and freezing are equivalent values; b. the water used in the well drilling is considered as the consumption in the process of cement-water mixing.

Item

Item

Impact categories

Unit

Impact assessment methods

Acidification potential (AP) Eutrophication potential (EP) Global warming potential (GWP)

mg SO2 eq. mg PO43−eq. g CO2 eq.

CML2002a CML2002b IPCC2007c

Note: a. H+ from the acidic gases uses the SO2 as the benchmark, the featured factor represents the effect of the acidic gases on AP; b. In the assumption that other nutrition is abundant, the effect of the N+3, P+5 regards the PO3−1 as the bench mark; c. thermal radiation raised by the greenhouse gases regards the CO2 as the benchmark, the corresponding featured factor denotes the effect of the greenhouse gases on the global warming (Karlsdóttir et al., 2015b).

4. Results and discussion 4.1. Overall environmental impacts and the key influence factor 4.1.1. Overall environmental impacts of the GPGS in China Fig. 4 provides results of AP, EP and GWP values of four GPGSs. In addition, two more published LCA results are compared simultaneously, to verify the correctness of the results of the paper roughly. It is seen that values of the evaluated AP, EP and GWP are of the same magnitude with the published data in literatures (Frick et al., 2010; Pehnt, 2006). However, the case study in China also shows distinguished features. Contributing factors could include different conditions of characteristic reservoirs, the radius of the transportation and the technologies adopted in the power plants. For example, in the case study, the radius of transportation is defined as 69 km in China, which is smaller than the values in the case studies in other countries (i.e. 100 km). Meanwhile, drilling depths in SW-DF and SE-SF are 300 m and 800 m, respectively, which are obviously smaller than the values in the case studies in the others (i.e. 3000 m). In addition, accompanied amounts of CO2 and CH4 during the drilling process are not considered in this paper due to the lack of data, and this might be the reason that GWP in the cases is less than that in reference (Bravi and Basosi, 2014). On the other hand, to make an integrated evaluation between plants in this study and other papers, multi-criteria analysis which combines

Note: a. 7 % for construction of roads and preparation of land, 90 % for power house, 2 % for cold water works, and 1 % for staff facilities; b. 23 % for construction of roads and preparation of land, 76 % for power house, 1 % for staff facilities; c. 316 L grade stainless steel; d. For reinforcement of concrete, support beams, and machinery supports; e. Sheets for wall and roof cladding; f. In electrical wires, calculated from length, cross-sectional area, and density of 8790 kg/m3; g. 60 % polyethylene plastic and 40 % polyvinylchloride plastic for piping.

detailed characterization factors, which convert the emissions from certain substance into the environmental impact, e.g. converting CH4 emissions to GWP with unit of gCO2eq., are based on the instruction of ISO14040/44. Specifically, the CML 2002 characterization methods are employed for AP and EP calculation and IPCC2007 method is used for GWP calculation, which is employed by eBalance LCA software. Therefore, CML 2002 and IPCC2007 are feasible for life cycle impact assessment in this study. 6

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Fig. 4. The overall AP, EP and GWP of GPGS in the case study.

all possible criteria into one indicator is employed in this section (Wei et al., 2010; Wang et al., 2008). Among the multiple multi-criteria evaluation methods, Grey Relational Analysis (GRA) is superior in the rigorous comparison among multiple alternative alternatives in the complex and grey system. GRA solves multi-criteria decision making problems by combining the entire range of performance criterion values considered for each alternative into a quantified value of grey relational grade. The alternative that has the highest grey relational grade among all alternatives will be the best choice. In addition, the priority of all alternatives can be easily sequenced by their grey relational grade value after GRA. Therefore, GRA has been successfully implemented in solving a variety of multiple criteria decision making problems in economics, control and engineering. The main procedures of GRA include grey relational generating, reference sequence definition, grey relational coefficient calculation, and grey relational grade calculation (Liu et al., 2015). Where, the goal of grey relational generating is to translate the performance of all candidates into a comparability sequence (Xi); Reference sequence definition aims to determine the alternative whose comparability sequence is the closest to the reference sequence (X0); Grey relational coefficient calculation is used to determine how close (Xi) is to (X0); Finally, in the process of grey relational grade calculation, if a comparability sequence translated from an alternative has the highest grey relational grade between the reference sequence and itself, then that alternative will be the optimal choice. Details can be found in (Luo et al., 2016). With application of GRA, Fig. 5 reveals the comparison of the overall environmental impacts between the geothermal power plant in this paper and the other two geothermal power plants mentioned above. It can be seen from Fig. 5, in the view of AP, GWP and EP with the same weight (namely, the weights of each environmental indicator are set as one third for the equal trade-off), the grey correlation coefficient of SW-DF on integrated environmental impact is the largest with 1.00, followed by NW-FO with 0.89 and SE-SF with 0.81. Namely, among the six geothermal power systems in papers condition, SW-DF has the least comprehensive environmental impacts, NW-FO is the second least. The results indicate that SW-DF power plant shows the best advantages in terms of environmental impacts, and HB-SO power plant is the worst in terms of AP, EP and GWP. In detail, it is shown from Fig. 4, HB-SO results in AP value of 250.05 mgSO2 eq./kWh, GWP value of 80.49 gCO2 eq./kWh and EP value of 32.50 mgPO43− eq./kWh, which are the largest amongst the four power plants. It is caused by its worse quality geothermal reservoirs in different power plants in the case

study. For instance, as shown in Fig. 6, the geothermal gradient is as small as 1.8∼3.5 °C /100 m in HB-SO power plant. In comparison, the geothermal gradient in SW-DF power plant is more than 5.0 °C /100 m. In fact, the advantage of the larger geothermal gradient of geothermal reservoir causes a smaller consumption of well drilling (i.e. smaller drilling depth is needed under the same conditions), and a large system energy efficiency of the power plant, resulting in the smallest environmental impacts compared with the other three power plants in the case study. 4.1.2. The influence of geothermal gradient Fig. 7 reveals the influence of geothermal gradient on the environmental impacts of NW-FO. Where, NW-FO power plant is set as the benchmark, in which the well depth varies from 3705 m to 7000 m. As a consequence, the geothermal gradient is approximately 3.0∼6.0 °C/ 100 m, which covers 65 % of the geothermal gradients of the exploited 1160 geothermal wells in China (Jiang et al., 2016). It is seen that the smaller geothermal gradient results in a bigger environmental impacts of the system. Furthermore, AP, GWP and EP values are an approximately linear function of the temperature gradient. For NW-FO power plant, with the increase of the well depth from 3705 m to 7000 m, the geothermal gradient changes from 5.94 °C/100 m to 3.25 °C/100 m. Consequently, AP, GWP and EP increase from 43.56 mgSO2 eq./kWh, 12.74 gCO2eq./kWh and 6.77 mgPO43−eq./kWh, to 62.57 mgSO2eq./kWh, 17.39 gCO2eq./kWh and 8.61mgPO43−eq./kWh, respectively.

Fig. 5. The multi-criteria evaluation between cases in this study and other papers. 7

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Fig. 6. Distribution of geothermal gradient data of China (revised from (Jiang et al., 2016)). Fig. 8. Ratio of three stages on the environmental impacts of GPGSs.

Fig. 9. Ratio of six processes on the environmental impacts of GPGSs.

On the contrary, from Fig. 8, it is also shown that the contributions of the operation and maintenance stage are the maximum to EP, which are 57.23 %, 70.84 %, 34.01 % and 50.92 % in the four geothermal power plants, respectively. This is due to the massive use of the cooling water in the operation and maintenance stage. While, the contributions of the construction stage to EP are 29.91 %, 19.84 %, 56.73 % and 39.60 %, respectively. It is worth mentioning that the effect of the construction is more significant than that of the operation and maintenance stage for HB-SO power plant. This is caused by the substantial water consumption in the well drilling process in HB-SO power plant. 4.3. LCIA of GPGS in the view of processes Fig. 7. The influence of geothermal gradient on environmental impacts of NWFO.

Fig. 9 displays the environmental impacts by the different processes in the four GPGSs. The typical processes include well drilling, building construction, generator producing, plant operating, plant maintenance and decommissioning of the plant, etc. Original data also can be found in Appendix Table A1–A3. From Fig. 9, it is shown that AP caused by HB-SO power plant is mainly caused by the well drilling process, which occupies 62.35 %. While, for SE-SF and SW-DF power plants, the contribution ratios are 44.57 % and 33.35 %, respectively. The contributions of the plant operating process to AP are 15.75 %, 28.20 %, 5.91 % and 11.21 % in SW-DF, SE-SF, HB-SO and NW-FO power plants, respectively. In consideration of the aboveground subsystems including the building construction, generator producing, plant operating and plant maintenance processes, AP becomes 54.34 %, 45.18 %, 27.94 % and 26.27 %, respectively. This indicates that the contribution of the aboveground subsystems of AP is lower than that in the underground subsystems except that in SW-DF. For GWP, it is concluded that the well drilling process contributes

4.2. LCIA of GPGS in the view of stages Fig. 8 compares the ratio of contribution to AP, EP and GWP in three different stages in the four GPGSs. Original data can be found in Appendix Tables A1–A3. It is shown in Fig. 8, the construction stage contributes the most to AP and GWP, with a contribution of more than 60 % amongst the whole life cycle. Specifically, contributions to AP are 71.12 %, 61.87 %, 79.27 % and 73.95 % in SW-DF, SE-SF, HB-SO and NW-FO power plants, respectively. For GWP, they are 63.38 %, 57.93 %, 71.94 % and 66.12 %, respectively. The reason for the major contributions of the construction stage to AP and GWP is that there are lots of materials and energy consumption in the construction stage, which includes the use of diesel and metal in the processes of drilling and building of the system. 8

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The copper consumption is far less than that of the steel. However, the specific contribution (i.e. per unit weight) of copper consumption to AP is much larger than that of the steel. Therefore, its contribution to AP plays the dominant role in environmental impact of power plants. For example, the contributions of copper consumption in SW-DF and NW-FO power plants are 31.41 % and 28.78 %, while the contributions of copper in SE-SF and HB-SO power plants are 13.96 % and 12.95 %. However, for EP and GWP, the copper contributes less than 2 % in SWDF and NW-FO power plants, and less than 0.5 % in SE-SF and HB-SO power plants. The water is consumed in the well drilling process for cooling of the drill and cleaning of the soil, stone powder and the plant operating process for cooling of the systems in the whole life. Due to the massive use of water, the contributions of the water consumption to the environment are also analyzed. While, contributions to EP are as high as 56.78 % and 77.40 % in SW-DF and SE-SF power plants. However, the contributions of water consumption to AP and GWP are relatively small. For instance, the contributions to AP and GWP in HB-SO power plant are 6.44 % and 4.67 %, respectively. For HB-SO and NW-FO power plants, due to the use of organics, the effect of organics to GWP is relatively higher that contributes to AP and EP. For example, in HB-SO and NW-FO power plants, the contributions of organics to GWP are 40.77 % and 43.86 %, respectively, while these values become approximately 15 % in the consideration of the effect of the organics on AP and EP.

more than 40 % in SW-DF, SE-SF, HB-SO power plants. Especially for SE-SF power plant, the contribution is as high as 53.35 %. However, the plant operating process only dedicates 24.32 %, 31.75 %, 4.27 % and 8.91 %, respectively. The entire aboveground subsystem contributes 43.09 %, 36.76 %, 42.98 % and 53.49 % in the four power plants, respectively, which are approximately the same as that by the underground subsystem. However, for EP, operating process represents 54.54 %, 70.10 %, 28.97 % and 45.83 %, respectively. In comparison, the well drilling process contributes only 19.21 %, 16.99 %, 46.37 % and 27.50 % to EP, respectively. It is seen that the contribution of operating process is superior to the well drilling process. This is because EP is more sensitive to the amount of cooling water in operating process. From Fig. 9, amongst the six intermediate processes, well drilling process represents 46.28 %, 45.90 % and 27.52 % to AP, EP and GWP, in average respectively, it is concluded that underground subsystem plays a dominant role in the contribution to AP and GWP, while the aboveground subsystem plays a dominant role in the contribution to EP compared with the underground subsystem. 4.4. LCIA of GPGS in the view of materials In general, there are more than 16 main materials that are consumed in the life of GPGS. Fig. 10 shows the environmental impacts of the consumption of each different materials. Orginal data can be found in Appendix Table B1–B3. It is seen in Fig. 10 that the consumption of diesel, steel, copper and water affects the most to AP, EP and GWP values in GPGSs. The diesel is consumed mainly in the drilling process for driving the diesel engine. The contributions of the diesel consumption of AP, EP and GWP in SWDF power plant are the most significant, which are 29.08 %, 37.31 % and 15.98 %, respectively. In contrast, in SE-SF power plant, these contributions become the least, which are 5.28 %, 2.93 % and 2.92 %, respectively. The consumption of steel covers the drilling process, building construction and electricity generation processes. Therefore, the steel consumption contributes the most in the life of the power plants. For example, the contribution of the steel contributes 57.52 % to AP in HB-SO power plant, 43.19 % to GWP in SE-SF power plant and 42.90 % to EP in HB-SO power plant. Although steel contributes only 8.32 % to EP in SW-DF power plant, it is still the most contributions amongst the 16 materials in view of AP and GWP.

5. Conclusion Energetic and environmental impacts of GPGSs in China are analyzed in this paper. Where, four typical geothermal power generation plants in China are involved, such as double flash, single flash, binary, and EGS. While, AP, EP and GWP are regard as environmental impact indicators and quantified by LCA, including life subsystems, stages, processes, materials. The following conclusions are obtained: 1) HB-SO power plant and SW-DF power plant have the worst and the best environmental impacts amongst the four typical power plants in China. The maximum AP, EP and GWP values are 250.05 mgSO2 eq./kWh, 32.50 mgPO43− eq./kWh and 80.49 gCO2 eq./kWh, respectively. 2) Geothermal gradient is a crucial factor affecting environmental

Fig. 10. Ratio of consumption of different materials on the environmental impacts of GPGSs. 9

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impacts of GPGSs. With a large geothermal gradient, the environmental impacts of the geothermal power plants tend to be smaller. 3) The contribution of the construction stage represents more than 60 % of the total impacts to AP and GWP, indicating a dominant role amongst all stages in the lifetime of the geothermal power plants. The operating stage contributes mainly to EP values, which are 57.23 %, 70.84 %, 34.01 % and 50.92 % in SW-DF, SE-SF, HB-SO and NW-FO power plants, respectively. 4) Environmental impacts of underground subsystem are approximately the same as that of the aboveground subsystem. It is highlighted that the drilling of geothermal wells contributes 46.28 %, 45.90 % and 27.52 % in average to AP, GWP and EP, respectively. 5) Material consumption including diesel, steel, copper and water has significant effect on environmental impacts of power plants. Organics contribute 40.77 % and 43.86 % to GWP in HB-SO and NW-FO power plants. However, the contribution of organics to AP and EP in the same power plants (i.e. HB-SO and NW-FO power plants) is as low as 15 %.

aims to make a rounded analysis of the environmental impacts; 2) the sensitive analysis of LCI to the environmental impacts of GPGS should be performed in the further research, such as non-condensable gas; 3) auxiliary equipment, like submersible pump, will be developed and applied for the environmental impact evaluation. Author contribution Yongzhen Wang contributed to drafting the article and revising it critically; Junyao Wang contributed to conception of this work and revising it critically; Yanping Du contributed to data collection and language reversion of the article. Jun zhao and Shuai Deng contributed to design of the work; Hongmei Yin contributed data collection and interpretation Acknowledgements This work was supported by the China Postdoctoral Science Foundation (No. 2019M660634), the National Natural Science Foundation of China (No. 51406212). And thanks to the IKE Co. Ltd for its support of LCA on the e-Balance (http://www.ike-global.com/).

Some more future work need to be conducted to address the issues as follows: 1) more environmental impact indicators such as human toxicity potential and waste solids need to be analyzed with LCA, in Appendix A

Table A1 Orginal data on AP of GPGS in the view of stages and processes (mgSO2 eq./kWh). Stage

Process

SW-DF

SE-SF

HB-SO

NW-FO

Construction

Drilling Building Generator Operating Maintenance Decommission

10.1455224 4.04838177 7.44560313 4.79115103 0.24821481 3.7466498

25.85671317 3.350523876 6.139939811 16.10793212 0.212325582 5.45801465

155.9159804 6.771980877 35.51780902 14.78212425 12.80359204 24.26209946

19.24535939 3.591568429 9.377397021 4.885230431 2.302954348 4.16019527

Opera.& Main. Decommission

Table A2 Orginal data on GWP of GPGS in the view of stages and processes (mgCO2 eq./kWh). Stage

Process

SW-DF

SE-SF

HB-SO

NW-FO

Construction

Drilling Building Generator Operating Maintenance Decommission

1791.59325 365.566525 304.179490 944.615695 59.1380510 418.646548

6310.410296 286.6046525 255.4994844 3756.087006 50.59327948 1169.55347

38051.77408 596.9564111 19259.36164 3440.452196 11300.49872 7842.559561

4696.889092 316.6000966 3412.807416 1135.661379 1951.204021 1230.118714

Opera.& Main. Decommission

Table A3 Orginal data on EP of GPGS in the view of stages and processes (mgPO43− eq./kWh). Stage

Process

SW-DF

SE-SF

HB-SO

NW-FO

Construction

Drilling Building Generator Operating Maintenance Decommission

0.91824060 0.24396777 0.26734053 2.60631266 0.12858202 0.614153

2.499789038 0.187047079 0.232057273 10.31365659 0.1100244 1.371287622

15.0737279 0.389259663 2.858349919 9.414042886 1.638167533 3.127580858

1.860613068 0.206446643 0.612109179 3.100694969 0.344751502 0.641277634

Opera.& Main. Decommission

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Appendix B

Table B1 Orginal data on AP of GPGS in the view of materials (mgSO2 eq./kWh). Materials

SW-DF

SE-SF

HB-SO

NW-FO

Diesel Transport diessel Steel Stain steel Cement Bentonite Silica flour Water Aliminum Copper Mineral wool Plastics Titanium GRP Work fluid Gravel

8.036505879 2.286995758 3.697549821 0.287299749 0.184357826 0.000970339 0.003695392 3.786702681 0.297034947 8.676896733 0.065751998 0.100987882 0.144317209 0.057329096 0 0.002400641

2.778003318 0.000521364 22.63118563 0.246816576 2.184981646 0.011500315 0.043797242 16.02514031 0.339508836 7.235686688 0.053165625 0.082906763 0.138511416 0.048327019 0 0.028452042

16.25391855 0.002275929 130.7003184 0.573746199 13.17543933 0.069346899 0.264097368 14.61960535 1.101104949 16.71935371 0.115944222 0.169480108 0.313994521 0.116697417 32.85867853 0.171565812

2.086930296 0.001097612 17.30694124 0.245625725 1.626299397 0.008559777 0.032598639 4.811969142 0.322401994 7.145369909 0.055819944 0.089534685 0.128718877 0.046124732 5.585523995 0.021177083

Table B2 Orginal data on GWP of GPGS in the view of materials (mgCO2 eq./kWh). Materials

SW-DF

SE-SF

HB-SO

NW-FO

Diesel Transport Steel Stain Steel Cement Bentonite Silica Flour Water Aliminum Copper Mineral Wool Plastics Titanium Grp Work Fluid Gravel

1304.461703 111.9658856 893.9088634 71.77274643 69.25147556 0.116554957 2.771544209 884.3998757 66.17756863 9.621714378 8.829195205 21.96621444 19.38690947 31.95617097 0 0.005730128

450.9172262 0.025524756 5471.249449 61.65930751 820.7582288 1.381392087 32.84793137 3742.736964 67.70127691 8.023572572 7.139093783 18.03332943 18.60698591 26.9382664 0 0.067912631

2638.287658 0.111424077 31597.72787 143.3323233 4949.17212 8.329794287 198.0730262 3414.468535 195.0845304 18.53990555 15.56902002 36.86418921 42.18057839 65.04903875 29826.90239 0.409513162

338.7443111 0.053736457 4184.07565 61.36181094 610.8969451 1.028181231 24.44897927 1123.855045 66.09394157 7.923421299 7.495516536 19.47499099 17.29150137 25.71067588 5070.163697 0.050547917

Table B3 Orginal data on EP of GPGS in the view of materials (mgPO43− eq./kWh). Materials

SW-DF

SE-SF

HB-SO

NW-FO

Diesel Transport Steel Stain Steel Cement Bentonite Silica Flour Water Aliminum Copper Mineral Wool Plastics Titanium Grp Work Fluid Gravel

0.685674278 0.410496357 0.357135092 0.16776201 0.019889418 9.88908E-05 0.000731476 2.435582644 0.11117134 0.032817952 0.009700342 0.006586489 0.035941067 0.015891876 0 0.000204795

0.237019104 9.35805E-05 2.185877394 0.144122803 0.23572643 0.001172039 0.008669342 10.30726648 0.106730558 0.027366975 0.007843484 0.005407228 0.03449518 0.013396461 0 0.002427198

1.386783515 0.00040851 12.62394627 0.335025756 1.421430376 0.007067395 0.052276131 9.403235497 0.283426502 0.063236311 0.017105163 0.011053592 0.078197869 0.032349035 3.692992077 0.014636007

0.178056788 0.000197012 1.671624821 0.143427432 0.175453076 0.000872358 0.006452661 3.095027393 0.105973764 0.027025377 0.008235074 0.005839505 0.032056425 0.012785977 0.627757925 0.001806583

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