Integrating life cycle assessment and emergy synthesis for the evaluation of a dry steam geothermal power plant in Italy

Energy xxx (2015) 1e12

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Integrating life cycle assessment and emergy synthesis for the evaluation of a dry steam geothermal power plant in Italy Elvira Buonocore a, *, Laura Vanoli b, Alberto Carotenuto b, Sergio Ulgiati a a b

Department of Science and Technology, Parthenope University of Naples, Centro Direzionale-Isola C4, 80143, Napoli, Italy Department of Engineering, Parthenope University of Naples, Centro Direzionale-Isola C4, 80143, Napoli, Italy

a r t i c l e i n f o

a b s t r a c t

Article history: Received 23 September 2014 Received in revised form 13 March 2015 Accepted 12 April 2015 Available online xxx

Greenhouse gas emissions, climate change and the rising energy demand are currently seen as most crucial environmental concerns. With the exploration of renewable energy sources to meet the challenges of energy security and climate change, geothermal energy is expected to play an important role. In this study a LCA (Life Cycle Assessment) and an EMA (Emergy Assessment) of a 20 MW dry steam geothermal power plant located in the Tuscany Region (Italy) are performed and discussed. The plant is able to produce electricity by utilizing locally available renewable resources together with a moderate support by non-renewable resources. This makes the geothermal source eligible to produce renewable electricity. However, the direct utilization of the geothermal fluid generates the release into the atmosphere of carbon dioxide, hydrogen sulfide, mercury, arsenic and other chemicals that highly contribute to climate change, acidification potential, eutrophication potential, human toxicity and photochemical oxidation. The study aims to understand to what extent the geothermal power plant is environmentally sound, in spite of claims by local populations, and if there are steps and/or components that require further attention. The application of the Emergy Synthesis method provides a complementary perspective to LCA, by highlighting the direct and indirect contribution in terms of natural capital and ecosystem services to the power plant construction and operation. The environmental impacts of the geothermal power plant are also compared to those of renewable and fossil-based power plants. The release of CO2-eq calculated for the investigated geothermal plant (248 g kWh
1) is lower than fossil fuel based power plants but still higher than renewable technologies like solar photovoltaic and hydropower plant. Moreover, the SO2-eq release associated to the geothermal power plant (3.37 g kWh
1) is comparable with fossil fuel based power plants. Results suggest the need for further investigation of other geothermal options (e.g. binary systems) in order to reduce the environmental impacts while taking the maximum advantage of the geothermal resource. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Geothermal energy Life cycle assessment Emergy accounting

1. Introduction Greenhouse gas emissions, climate change and the rising energy demand are currently seen as most crucial environmental concerns. Renewable energy use is claimed to be at least a partial solution in order to reduce fossil energy consumption and related environmental impact as well as capital and operating and maintenance costs [1e4].

* Corresponding author. E-mail address: [email protected] (E. Buonocore).

The discontinuous nature of most renewable resources calls for storage devices [5e8] and smart grids [9] capable to constantly manage electricity supply and demand. With the exploration of renewable energy sources to meet the challenges of energy security and climate change, geothermal energy is expected to play an important role [10,11]. Over the past ten years geothermal uses increased in many parts of the world, both in countries that have traditional interests in conventional geothermal resources and countries without historical interests in geothermal energy [12]. Geothermal resources have been identified in approximately 90 countries, and their use was quantified in 72 countries, with 24

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Nomenclature ISO ILCD CED CML LCA LCI LCIA EMA R N F U seJ ELR ESI EYR UEV EROEI ORC

international organization for standardization international reference life cycle data system cumulative energy demand centre of environmental sciences of the Leiden University life cycle assessment life cycle inventory life cycle impact assessment emergy assessment locally renewable emergy flows locally nonrenewable or slow-renewable emergy flows emergy flows imported from outside (purchased) or supplied as feedback total emergy supporting the process or system under investigation. solar equivalent Joule: unit used to quantify emergy flows environmental loading ratio emergy sustainability index emergy yield ratio unit emergy value energy returned on energy invested organic rankine cycle

countries relying on geothermal power for electricity generation [13,14]. Significant geothermal energy capacity is being developed across Europe. The total installed capacity for geothermal energy in Europe is 1600 MW, producing 10,900,000 MWh of electric power through 59 geothermal power plants, 47 of which are in European Union (EU) member states. In addition, in EU member states there are 109 new power plants under construction or under investigation. By 2015, Europe is expected to have about 2600 MW of installed geothermal energy capacity, with an additional 800 MW to be under development or investigation by 2018 [15]. Within Europe, Italy has installed over 50 percent of the European capacity. Geothermal electricity generation only occurs in the Tuscany region (central Italy), while direct uses are spread all along the country, mainly for bathing and district heating purposes. The total installed capacity is about 882 MW, with 35 plants and a production of 5315 GWh yr
1. The heat delivered to direct uses is around 3500 GWh from 1000 MWth plants, 50% of this installed capacity being used by heat pumps [16]. Geothermal energy is mainly utilized in three technological categories: (i) heating and cooling of buildings via geothermal heat pumps that utilize shallow sources, (ii) heating structures with direct-use devices, and (iii) generating electricity through indirect use [17]. The basic types of geothermal power plants operating today are steam condensing turbines and binary cycle units. Steam condensing turbines can be used in flash or dry-steam plants operating at sites with intermediate and high temperature resources (generally higher than 150  C) while binary-cycle plants, typically ORC (Organic Rankine Cycle) units, are commonly installed to extract heat from low and intermediate temperature geothermal fluids (generally from 70 to 170  C). The environmental impacts of geothermal uses differ depending on the technology. As for other renewable technologies, the

environmental burden of geothermal energy use must be assessed based on a life cycle approach [18,19]. LCA (Life Cycle Assessment) investigates environmental impacts of systems or products from cradle to grave throughout the full life cycle, from the exploration and supply of materials and fuels, through the production and operation of the investigated objects, to their final disposal or recycling [20]. LCA has been widely used to investigate renewable energy technologies and it has already been used in various applications to reveal the directions for future greenhouse emission reduction [21e24]. For geothermal power plants, all the impacts directly and indirectly related to the construction, operation and decommissioning of the plant need to be considered in LCA. Geothermal power plants consist of numerous components such as production and reinjection boreholes, pipelines, intermediate equipment, turbines, generators, and cooling towers and each of them has environmental effects and adds to life cycle contributions. The distinction between open-loop and closed-loop systems is important with respect to air emissions. In closed-loop systems, gases from the well are not released to the atmosphere and are injected back into the ground after releasing their heat, so that air emissions are minimal. In contrast, open-loop systems release into the atmosphere emissions as hydrogen sulfide, carbon dioxide, ammonia, methane, among others. As a consequence, direct and indirect emissions from geothermal sources use and their contribution to environmental impact categories need to be assessed. LCA has already been used in a few papers to evaluate the environmental impact of geothermal power plants. Some studies focused on conventional flash or dry-steam plants [25e27] while others investigated binary power plants [18,28,29]. Bayer et al. [30] published a review on LCA of geothermal electricity production. They found that LCA studies on geothermal electricity production are scarce and often focused on a more qualitative description or analysis of environmental burdens and benefits. According to these authors, some studies are focused only on a specific aspect of LCA, such as a global warming potential, water use, or on a selected life cycle stage and in some cases transparent reporting and assessment of local and regional environmental consequences are lacking. More recently, Martín-Gamboa et al. [31] addressed the LCA of power generation in a binary-cycle power plant using high-enthalpy geothermal resources, and heat generation in a closed-loop geothermal heat pump system using low-enthalpy resources. Bravi and Basosi [32] evaluated the environmental impact of selected geothermal power plants in Italy with a special focus on emissions of non-condensable gases of geothermal fluids, pointing out nonnegligible emissions of CO2, H2S, NH3, and CH4. However, only the power production phase of the investigated geothermal power plants has been considered in this paper while the consumption of material and energy resources associated with the drilling, construction, and operation of geothermal plants were not included. The aim of this study is to present and discuss the environmental performance of a geothermal power plant located in the Tuscany region of Italy from a cradle to grave perspective, applying the LCA methodology. In addition, the Emergy Synthesis method is used to expand the perspective of LCA and provide the added value of a comprehensive donor-side assessment, namely an estimate of the total environmental support to the investigated process. 2. Materials and methods 2.1. LCA mechanism and characteristics LCA is a methodological framework to assess the potential environmental impacts and resources used throughout a product's

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E. Buonocore et al. / Energy xxx (2015) 1e12

lifecycle, from raw material acquisition, via production and use phases, to waste management. All activities and processes result in environmental impacts due to consumption of resources, emissions of substances into the natural environment, and other environmental exchanges. In other words, LCA looks at the process relation with the environment as a source and as a sink, and provides indicators related to many different environmental impact categories, such as climate change, stratospheric ozone depletion, depletion of resources, toxicological effects, among others [33]. In the evaluation of a process, identifying “hot spots” facilitates prioritization of activities to improve its environmental performance. LCA allows technology comparisons in terms of environmental burden, providing valuable insights about the environmental performances of different technologies across categories [34]. Although developments of the tool continue to be achieved, International Standards of the ISO 14000 series provide a consensus framework for standardized LCAs [35,36]. The ILCD Handbook [37,38], stemming from the ISO 14040-44 standards, confirms the importance and the role of LCA as a decision-supporting tool in contexts ranging from product development to policy making [39]. The Handbook provides clear and goal-specific methodological recommendations, specific terminology and nomenclature, an accurate verification and review frame and other supporting documents and tools. The ILCD Handbook offers the basis for comparable and reliable LCA applications in business and public decision-making. According to the ILCD handbook, an LCA consists of four phases (Fig. 1):  Goal and scope definition phase, where the final goal of the LCA is stated and the central assumptions and choices in the assessment are identified. The goal definition is of paramount importance for all the other phases of the LCA, in that a clear, initial goal definition is essential for a correct later interpretation of the results.  LCI (Life Cycle Inventory) phase, where input and output flows of matter and energy are quantified for the investigated process. For an LCA study, two types of data are usually required: specific

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inventory data on the foreground system, and average or generic data for the background system. It is important that all foreground and background data used in a LCA study are methodologically consistent and that the overall quality requirements for the analysed system are met.  LCIA (Life Cycle Impact Assessment) phase, where input and output flow data that have been collected and reported in the inventory are translated into indicators that reflect the pressure on environment and human health as well as the potential or actual resource scarcity. Calculation is based on factors which represent the contribution to an impact as emission or resource consumption per unit of product or service. The impact assessment analyses the potential environmental impacts caused by interventions that cross the border between technosphere and ecosphere and act on the natural environment and humans, often only after fate and exposure steps. The results of LCIA can be interpreted as environmentally relevant impact potential indicators.  Interpretation phase, where the results of the LCIA are interpreted in accordance with the goal of the study to answer questions posed in the goal definition. In this phase the significant issues are identified and evaluated in relation to their influence on the overall results of the LCA. Comparison among two or more systems may be involved. The interpretation is used to develop conclusions and recommendations. The aim of an LCA study is to calculate the amounts of material and energy resources required, the emissions and waste generated, and the contribution to environmental impact categories per functional unit. The functional unit, that is a quantitative identification of the function/product of the studied system providing a reference to which the inputs and outputs can be related, is a key element of LCA that has to be clearly specified, so that all input and output flow can make reference to it. Another important aspect of LCA is the distinction between attributional and consequential LCA. Attributional LCAs describe the environmental exchanges that are ‘attributed to’ the delivery of a specified amount of the functional unit. In contrast, consequential LCA refers to a description of the expected consequences of a change in the process or flow. It is an estimate of the system-wide change in pollution and resource flows that may result from a change in the investigated process. In this case, results may heavily depend on the magnitude of the change [20]. Among the impact assessment methods, the CML 2001 (Centre of Environmental Sciences of the Leiden University) and CED (Cumulative Energy Demand) were chosen. The CML 2001 method is used to assess the environmental impacts in different impact categories (e.g. global warming, abiotic depletion, acidification, eutrophication). The method provides characterization factors to quantify the contribution to impact categories and normalization factors to allow a comparison across categories. Table 1 lists the normalization factors used in this study. The CED method is applied to investigate the use of nonrenewable (fossil, nuclear, biomass from primary forests) and renewable (biomass from agriculture, wind, solar, geothermal, water) sources supporting the investigated process. Finally, since a crucial limitation for a proper interpretation of LCA results is the existence of uncertainties and variations in the used data, Monte Carlo analysis was performed to address the uncertainty related to data collection and processing. 2.2. EMA framework

Fig. 1. LCA framework [35].

LCA studies are focused on matter and energy flows used under human control, while flows outside the market dynamics and flows

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The main steps followed to perform the EMA of the power plant were:

Table 1 Normalization factors used within CML method for selected impact categories. Impact category

Normalization factors

Climate change Human toxicity Eutrophication potential Acidification potential Depletion of abiotic resources Land use Photochemical oxidation Stratospheric ozone depletion

2.08E-13 1.34E-13 3.11E-11 3.66E-11 6.74E-11 3.06E-15 1.21E-10 1.05E-08

which are not associated to significant matter and energy carriers (such as labor) are generally disregarded. Moreover, the time needed for resource generation within natural cycles (that is a fundamental parameter for their renewability) is not accounted for in LCAs. In order to better explore the performance and sustainability of a production process, such flows need to be also included. In the present study, the Emergy Synthesis method is used to expand the perspective of LCA by accounting for free renewable inputs, different forms of energy, materials, human labor and economic services on a common basis (solar energy), offering larger potentiality to explore the sustainable interplay of environment and economy [40]. Emergy arguably offers the added value of a comprehensive donor-side assessment capable of providing an estimate of the total environmental support to a process [41]. The EMA method [42e44] is an environmental assessment procedure aimed at evaluating the performance of a system on the global scale of biosphere, also taking into account free environmental inputs (e.g., solar radiation, wind, rain, and geothermal flows) as well as indirect environmental support embodied in human labor and services. While LCA includes in the assessment of CED only the renewable energy flows that are captured through technological devices (e.g., photovoltaic modules), EMA also accounts for the broader ecosystem services that indirectly support the human society and economy, but are not generally included in economic and biophysical accounts. According to this method, inputs are accounted for in terms of their solar emergy, defined as the total amount of solar available energy (exergy) directly or indirectly required to make a given product or support a given flow, and measured as seJ (solar equivalent joules). The emergy required to generate one unit of each product or service is referred to as its UEV (Unit Emergy Value) or emergy intensity (seJ J
1, seJ g
1, seJ V
1 etc.). UEVs are used to convert matter and energy input flows into emergy units. Raw data on mass, energy, labor, and money flows are converted into emergy units by using appropriate UEVs and then summed into a total amount emergy (U) used by the investigated system. The following generic emergy equation is used:

U¼

X ðSi  UEVi Þ

i ¼ 1; /n

(1)

i

where U is the total emergy supporting the system, Si is the amount of the i-th flow (mass, energy, labor and money) and UEVi is the Unit Emergy Value of the i-th flow. The ratio of U to the energy or mass of the product yields the new UEV of the product. The UEVs that result are useful for other emergy evaluations. The UEV is a measure of how much activity of the environment was required to provide a product: the higher the UEV of a product the greater the environmental work to produce it. So transformity is an indicator of past environmental contributions to a resource and future load on the environment from its use.

I. Identification of the boundaries (spatial and temporal) of the study area. II. Modeling of the investigated system through an emergy system diagram according to Odum's diagramming language [45] (Fig. 2). III. Calculation of matter, energy and money flows supporting the system. IV. Conversion of the above flows into emergy units by using suitable UEVs. V. Assessment of the total emergy used by the system. VI. Calculation and interpretation of emergy-based indicators of environmental performance and sustainability. According to the emergy procedure input sources are divided into three categories: renewable local resources that come from the environment (R), nonrenewable local resources (N) and imported resources and services (F). Several emergy indices have been defined to assess the sustainability of investigated processes. The main emergy-based indicators used in this study to describe the environmental performance and sustainability of the investigated process are the ELR (Environmental Loading Ratio), the EYR (Emergy Yield Ratio), and the ESI (Emergy Sustainability Index) [44]. The ELR compares the amount of locally nonrenewable (N) and imported emergy (F, mainly nonrenewable) to the amount of locally available renewable emergy (R). It is an indicator of the pressure of a transformation process on the environment and can be considered a measure of ecosystem stress due to a production (transformation activity). The EYR is a measure of the ability of a process to exploit and make available local resources by investing outside resources. The ratio is a measure of the potential contribution of the process to the main economy, due to the exploitation of local resources. The ESI is an aggregated indicator calculated as the ratio of the EYR (sensitive to the outside-versus-local emergy use) and the ELR (sensitive to the nonrenewable-versus-renewable emergy use). It measures the potential contribution of a resource or process to the economy per unit of environmental loading. The ELR, EYR and ESI are calculated as follows: ELR ¼ (F þ N)/R EYR ¼ (R þ N þ F)/F ESI ¼ EYR/ELR where: ELR ¼ Environmental Loading Ratio EYR ¼ Emergy Yield Ratio ESI ¼ Emergy Sustainability Index F ¼ Imported nonrenewable energy and resources N ¼ Local nonrenewable energy resources R ¼ Local renewable energy and resources

2.3. Added value of approach integration LCA and EMA share many similarities in the way they are applied: they start from model definition, are based on the same data inventory, and provide indicators that may help choices and improvement. LCA indicators can be divided into two broad

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Fuels

Rain

5

Goods & Machinery

Materials

Labor

Services

$ Ground Water Wind

Chemicals

Infrastructure

Electricity Steam Deep heat

Chemicals

gaseous

Power plant

M a r k e t

solid Waste Geothermal Power Plant

Fig. 2. System diagram of the geothermal power plant.

categories: those that are focused on the assessment of the resources used per unit of product (indicators of ‘upstream’ impact), and those that deal with the consequences of the system's emissions (indicators of ‘downstream’ impact). Emergy indicators can be proposed as an upstream complement of LCA indicators, being focused on the environmental performance of a process on the global scale, also taking into account all the free environmental inputs such as sunlight, wind, rain, as well as the indirect environmental support embodied in human labor and services, which are not usually included in traditional LCAs. Moreover, the emergy accounting is extended back in time to include the environmental

work needed for resource formation. Such a time dimension is a very unique feature in the emergy method compared to others. The added value of the emergy method as such relies in the generation of indicators of performance that assess the efficiency and the “size” of the process on the scale of the biosphere (respectively UEV and U), and compare local versus imported flows (EYR), nonrenewable versus renewable flows (ELR), and also provide a sustainability evaluation based on the supply side of resources (ESI) instead of the usual user-side impacts (emissions). When the two methods (LCA and EMA) are suitably integrated and jointly applied, the following added values are also achieved:

Fig. 3. Flowchart for the geothermal power plant. Dashed lines do not indicate real matter or energy flows but virtual allocation with reference to the functional unit.

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2.4. Case study description, main choices and assumptions

Table 2 Inventory of input flows (a), output and main emissions (b) (unit yr
1). a) Input flows

Unit (unit yr
1)

Amount

J J

2.25Eþ15 2.53Eþ12

g g g g g g g g g g g g g g g g g g g g J g J J

5.71Eþ08 2.85Eþ08 2.79Eþ07 4.53Eþ06 1.40Eþ06 2.43Eþ06 1.57Eþ08 3.14Eþ09 7.27Eþ04 4.14Eþ06 1.14Eþ05 3.64Eþ04 2.36Eþ07 7.53Eþ05 3.59Eþ05 1.36Eþ06 1.36Eþ05 8.98Eþ03 1.22Eþ05 3.83Eþ03 5.41Eþ10 4.61Eþ05 1.64Eþ10 4.95Eþ11

J J g g g g

2.53Eþ13 4.61Eþ10 5.00Eþ05 8.00Eþ05 3.00Eþ05 2.00Eþ05

J g g g g g g g

4.29Eþ10 1.80Eþ06 1.01Eþ09 2.97Eþ06 5.50Eþ07 3.64Eþ04 1.10Eþ06 2.17Eþ06

Elementary flows Geothermal heat Underground water used up Construction Extraction wells, cement Reinjection wells, cement Steel Thermal insulating materials Aluminium cover Glass-fiber reinforced plastic Concrete Inert stuffing materials Epoxivinyl paints Carbon and inox steel Aluminium Glasses Steel components of the plant Copper Copper Pig-iron Chromium Molybdenum Nickel Manganese Lube and insulating oil Electricity insulating material (plastics) Electricity Diesel Operation Annual electricity input Lube oil Epoxivinyl paints Steel Aluminium Thermal insulating materials Dismantling and disposal Diesel Disposal of alluminium Disposal of cement Disposal of plastics Disposal of steel Disposal of glass Disposal of copper Disposal of lube oil b) Output flows Electricity CH4 CO2 H2S As Hg H3BO3 NH3

Unit (unit yr
1)

Amount

J g g g g g g g

3.28Eþ14 8.64Eþ07 1.49Eþ10 5.33Eþ08 7.22Eþ02 1.73Eþ04 7.77Eþ05 1.77Eþ08

 Consistency of results: the same set of data is used for both approaches;  Comprehensiveness of investigated aspects: by adding a second set of input flows (environmental, labor and services) that are not included in the LCA the assessment does not limit to conventional resources such as fossil fuels and materials, but acknowledges the importance and the role of other resource categories, sometimes not even included in market economy evaluations;  Expanded focus: by developing indicators of performance and sustainability that refer to different “questions” and scales, the joint assessment provides a much more complete picture of the entire process.

The geothermal power plant investigated in this study, “Cornia 2”, is located in the municipality of Castelnuovo di Val di Cecina in the Province of Pisa in the Tuscany Italian region. The power plant construction started in 1994; the plant covers a total area of 7120 m2. It is a 20 MW dry steam power plant utilizing steam piped from eight production wells. About 80 ton/hour of hot fluid, at a temperature of 238  C, is extracted from an average depth of 1000 m. Each kg of fluid supplies about 2.8 MJ of heat to the plant. After the heat has been extracted, most of the water is evaporated through cooling towers, while only 15 ton hour
1 are re-injected at a temperature of 25  C. Plant operating time is about 7850 h per year. Chemicals extracted with underground fluid are also released to the atmosphere through the cooling towers. The annual electricity production is 92 GWh yr
1 [46,47]. The choices and assumptions made during system modeling, especially with respect to the system boundaries and what processes to include within these boundaries, are often decisive for the result of an LCA study and need to be clearly stated. In this study the environmental impacts are analysed with reference to one kWh of electricity yearly produced (functional unit). Direct and indirect environmental impacts of the construction, operation, dismantling and disposal of the plant are accounted for, taking into account the life time and needed maintenance of each component. The flowchart in Fig. 3 shows the processes and their interconnections and the main input and output flows of the investigated power plant. During the inventory phase, local data were collected for each of the above-mentioned phases: all different materials (e.g. concrete, steel, glass), machinery, as well as the energy consumption for drilling the wells, for buildings construction, and plant operation. The construction and delivery of the major components of the power plant as well as plant dismantling and disposal of waste were also included. Table 2 shows the inventory of the input flows (a), the output and the main emissions (b) of the investigated system. Construction inputs have been divided by plant life time (20 years) [47]; transportation of plant materials and machinery was assessed assuming the average distance of 200 km. The local direct emissions from the investigated power plant were taken from Arpat [46]. Background data over the supply chain of energy and materials were derived from the Ecoinvent library [48]. The environmental inputs (geothermal heat, water and wind) and labor and services were also calculated for the implementation of the emergy synthesis. The data used for this study refer to the year 2007. 3. Results 3.1. LCA results Table 3 displays the impacts associated to the reference flow of electricity generated (1 kWhel) with reference to the different energy sources (CED). The contribution of elementary flows (i.e. geothermal heat and water) was also accounted for. These values allow the comparison of the LCA performance of the investigated system with other different typologies of power plant mainly powered by sources other than geothermal. Geothermal energy, converted to power generation, accounted for 96% of the total CED, while only minor contributions were from fossil energy (3%) and other sources, mainly for plant construction (electricity from the grid and fuels). Moreover, since underground water is provided to the power plant for electricity generation (about 80 tons/hour, out of which only 15 tons h
1 reinjected into the ground), the net water used up and released into the atmosphere through the cooling

Please cite this article in press as: Buonocore E, et al., Integrating life cycle assessment and emergy synthesis for the evaluation of a dry steam geothermal power plant in Italy, Energy (2015), http://dx.doi.org/10.1016/j.energy.2015.04.048

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Table 3 Breakdown of CED impact categories referring to life cycle stages of the power plant. CED category

Construction

Operation

Decommissioning

Disposal

Total by category

Unit

Geothermal Fossil Water (hydro, geo, etc) Nuclear Renew. Biomass Wind Solar Nonrenew. biomass Total by step

0.00Eþ00 1.06E-01 3.00E-03 1.53E-02 2.73E-03 1.40E-04 3.14E-06 1.88E-07 1.27E-01

2.47Eþ01 6.70E-01 7.57E-02 9.10E-03 2.29E-03 2.09E-03 5.98E-06 5.07E-07 2.55Eþ01

0.00Eþ00 6.30E-04 1.51E-06 1.26E-05 5.28E-07 2.31E-07 8.38E-10 5.28E-07 6.45E-04

0.00Eþ00 4.00E-03 2.19E-05 1.40E-04 4.79E-06 1.33E-06 2.91E-08 6.66E-09 4.17E-03

2.47Eþ01 7.81E-01 7.87E-02 2.46E-02 5.03E-03 2.23E-03 9.15E-06 1.23E-06 2.56Eþ01

MJ-eq kWh
1 MJ-eq kWh
1 MJ-eq kWh
1 MJ-eq kWh
1 MJ-eq kWh
1 MJ-eq kWh
1 MJ-eq kWh
1 MJ-eq kWh
1 MJ-eq kWh
1

towers was added to the “Water - renewable energy resources” category, using a characterization factor equal to 1. Table 3 displays the breakdown of CED impact categories over the life cycle stages of the power plant. These last values are useful to identify which steps contribute more to a given impact category (more than 99% of CED refers to the operation step, with less than 1% for plant construction and disposal). Fig. 4 shows the percentage contributions of each source to the different process steps. The plant operation step represents the largest share in all CED categories, followed by the construction step, while dismantling and disposal are negligible. The demand for nuclear, wind, biomass and primary forest resources is very small, being linked to the use of electricity, fuels and materials for the construction of the buildings and, to a smaller extent, for the operation step: in fact, the Italian electric mix includes small fractions of nuclear electricity imported from abroad and minor percentages of other sources. A total direct geothermal energy of 24.7 MJ and a total cumulative energy supply of 25.6 MJ are needed per kWh of electricity generated: these figures translate into efficiencies of about 15% and 14% respectively, indicating a low ability of the entire system (plant þ supply chain) to convert high temperature heat into electricity. If the geothermal source is not included, only about 1 MJ are needed in the form of imported energy investment to generate 1 kWh. Tables 4 and 5 show the total contribution to selected CML impact categories per kWh of generated electricity as well as the contribution of the each process step, respectively. Fig. 5 displays the percentage contribution of each process to the impact categories. The plant operation step results the largest share of the climate change category (95%); a minor contribution is due to the construction step (5%) while the contribution of the disposal and decommissioning steps is negligible. Likewise, the contribution of the operation step is also high in the acidification potential,

Table 4 Contribution to selected CML impact categories per unit of generated electricity. Impact category

Total

Unit

Climate change Human toxicity Eutrophication potential Acidification potential Depletion of abiotic resources Land use Photochemical oxidation Stratospheric ozone depletion

2.48E-01 1.12E-02 8.51E-03 3.37E-03 4.05E-04 9.05E-04 1.65E-05 5.62E-09

kg CO2-eq kWh
1 kg 1,4-DCB-eq kWh
1 kg NOx-eq kWh
1 kg SO2-eq kWh
1 kg antimony-eq kWh
1 m2a kWh
1 kg ethylene-eq kWh
1 kg CFC-11-eq kWh
1

eutrophication potential, human toxicity and photochemical oxidation categories, being mainly linked to the direct emissions from the utilization of the geothermal flow. Fig. 6 shows the normalized impacts. Normalization is an optional step in LCIA that is used to better understand the relative importance and magnitude of the impact category indicator results. The overall advantage of normalisation is the possibility of making comparison across categories. Further, it also provides an option of control by unveiling extreme indicator results. A normalized score for a certain impact category is obtained by determining the ratio of the category indicator result of the product and that of a reference system. However a lack of emission data and/or characterisation factors could affect the normalized results. For this reason, complete and updated databases and tables of characterisation factors should be used. According to the normalized impacts, the most affected categories are eutrophication potential, acidification potential and climate change. The breakdown of the normalized impacts of each step to impact categories confirms that the highest impacts in all categories come from the operation step, with construction playing a minor role.

3.2. Uncertainty analysis

Fig. 4. Percentage contribution of the different process steps to CED categories (decommissioning and disposal impacts are so small that they do not show in the diagram).

A crucial limitation for a proper interpretation of LCA results is the existence of uncertainties in the used data. All the input and output data used within an LCA are uncertain to some extent, sometimes because of their being average values, sometimes because of estimated or calculated data related to some flows, and finally because of uncertainties on conversion coefficients used. Uncertainty and its consequences on results were addressed in our study through a Monte Carlo analysis. A log-normal distribution was chosen as probability distribution. The log-normal distribution is more appropriate for data in many environmental disciplines and it is typically used for uncertainty analysis in LCA studies [49]. A pedigree matrix was generated to estimate uncertainties for each single parameter. The pedigree matrix is a way to add qualitative uncertainty to existing uncertainty distributions. In the pedigree

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E. Buonocore et al. / Energy xxx (2015) 1e12

Table 5 Breakdown of CML impact categories referring to life cycle stages of the power plant. Impact category

Operation

Construction

Decommissioning

Disposal

Unit

Climate change Human toxicity Eutrophication potential Acidification potential Depletion of abiotic resources Land use Photochemical oxidation Stratospheric ozone depletion

2.36E-01 9.41E-03 8.47E-03 3.34E-03 3.50E-04 3.40E-04 1.49E-05 4.82E-09

1.13E-02 1.67E-03 3.90E-05 3.68E-05 5.28E-05 5.50E-04 1.52E-06 7.60E-10

4.30E-05 2.41E-05 6.10E-07 3.30E-07 2.83E-07 1.25E-07 8.34E-09 4.85E-12

2.10E-04 5.39E-05 1.89E-06 1.11E-06 1.81E-06 1.50E-05 3.78E-08 3.81E-11

kg CO2-eq kWh
1 kg 1,4-DCB-eq kWh
1 kg NOx-eq kWh
1 kg SO2-eq kWh
1 kg antimony-eq kWh
1 m2a kWh
1 kg ethylene-eq kWh
1 kg CFC-11-eq kWh
1

CML impact categories, showing the mean, the median, the standard deviation values and the coefficients of variation. As clearly pointed out by Tables 6 and 7, results appear robust against uncertainty and with small deviations. The variation coefficient of energy resource depletion (CED method) ranges between 7% of the water requirement and 59% of the primary forest energy requirement. Regarding the CML impact categories, the lowest coefficient of variation (10%) resulted for the photochemical oxidation category, while the highest value (34%) resulted for the human toxicity potential category. 3.3. Emergy results Fig. 5. Percentage contribution of the different process steps to CML categories (decommissioning and disposal impacts are so small that they do not show in the diagram).

matrix qualitative criteria (very good, good, fair, and poor) are assigned to quality indicators (reliability, completeness, temporal correlation, geographical correlation, technological correlation) to change the basic uncertainty. Once the single parameter uncertainties were determined using this approach, they were propagated by using the Monte Carlo simulation through the LCA software. The Monte Carlo analysis was carried out with a confidence interval of 95% and a fixed number of runs (1000). Tables 6 and 7 summarize the outcomes of the Monte Carlo analysis for CED and

Table 8 displays the EMA of the whole geothermal power plant. The input flows supporting the electricity generation are on a yearly base. The UEVs refer to the 15.2Eþ24 seJ yr
1 biosphere emergy baseline calculated by Brown and Ulgiati [50]. The locally available renewable resources are largely dominant as drivers of the power plant operation, that is only moderately supported by non-renewable resources imported from outside the system. The contribution of geothermal heat and underground water to the total emergy supporting the annual electricity production was 63% and 10% while a minor contribution was associated to the construction and operation phases (11% and 9%). Similarly, the emergy fraction of labor and services was 7%. The application of the Emergy Synthesis method provides a complementary perspective to LCA, highlighting the large environmental support in terms of free renewable resources on which the geothermal power plant is based, for comparison with other typologies of power plant (be they fossil powered or driven by other renewables, such as wind or solar). Selected emergy-based indicators calculated for the investigated system are summarized in Table 9. The solar transformity of electricity (a measure of intensity of the cumulative environmental support) resulted 2.18Eþ05 seJ J
1. The ELR equal to 0.59 revealed that although dealing with an industrial process, the generation of electricity from renewable geothermal resources does not imply a large loading in terms of unbalance between nonrenewable sources (local and invested) and locally renewables. The EYR (3.73) proved that the process is capable to exploit local resources in a profitable way compared to the investment from outside. The ESI, an aggregated indicators of performance and loading, resulted 6.31 thus appearing to be indicative of a process that is globally sustainable in resource uptake and use, in spite of specific problems related to local emissions. 4. Discussion

Fig. 6. Normalized impacts of the different process steps to CML categories (decommissioning and disposal impacts are so small that they do not show in the diagram).

Table 3 (CED results) shows that one kWh of geothermal electricity requires 24.7 MJ of input energy (including geothermal enthalpy), which translates into an efficiency around 14%, (an average value for the majority of geothermal power plants). However, the energy returned on energy invested (EROEI ¼ electricity

Please cite this article in press as: Buonocore E, et al., Integrating life cycle assessment and emergy synthesis for the evaluation of a dry steam geothermal power plant in Italy, Energy (2015), http://dx.doi.org/10.1016/j.energy.2015.04.048

E. Buonocore et al. / Energy xxx (2015) 1e12

9

Table 6 Results of Monte Carlo analysis to address uncertainties of CED categories. Impact category

Unit

Mean

Median

Standard deviation

Coefficient of variation %

Biomass Fossil Geothermal Nuclear Primary forest Solar Water Wind

MJ-eq MJ-eq MJ-eq MJ-eq MJ-eq MJ-eq MJ-eq MJ-eq

9.53E-03 8.18E-01 2.48Eþ01 2.81E-02 9.94E-07 9.70E-06 8.03E-02 2.30E-03

9.03E-03 8.12E-01 2.47Eþ01 2.74E-02 8.53E-07 9.60E-06 8.01E-02 2.25E-03

2.66E-03 9.58E-02 2.63Eþ00 5.83E-03 5.86E-07 1.01E-06 5.80E-03 4.65E-04

28% 12% 11% 21% 59% 10% 7% 20%

generated/energy invested other than geothermal [57]) is relatively high, around 4, which proves the profitability of the investigated technology considering that the EROEI calculated for oil-fired thermal electricity ranges from 3.7 to 10.6 [58]. Fig. 4 displays that the plant operation phase has the largest impact to all CED categories except for the geothermal one. This large dependence on the geothermal heat is a default consequence of the plant typology and is not a problem, considering the relative renewability of this energy source. Of course, it places a constraint on the feasibility of geo-electricity, by requiring high enthalpy flows and large reservoirs, and therefore limiting the applicability of the investigated technology. Moreover, the contribution of the power plant to LCIA categories reveals interesting findings (Fig. 5). The amount of CO2-eq emissions calculated for the investigated geothermal plant (248 g kWh
1, Table 4) was lower than the CO2 release characterizing fossil fuel-based power plants (ranking from 1300 gCO2 kWh
1 of a lignite power plant to 380 gCO2 kWh
1 of a natural gas power plant) but higher than other renewable technologies (ranking from 190 gCO2 kWh
1 of a solar photovoltaic to 20 gCO2 kWh
1 of a hydropower plant) [59]. Regarding the amount of SO2-eq associated to the geothermal power plant (3.37 g kWh
1, Table 4) it resulted comparable with fossil powered plants [59]. This last finding is due to the non-negligible release of hydrogen sulfide associated with the geothermal flow utilization, that largely contributes to the acidification potential category. The contribution of the investigated power plant to climate change resulted lower than the average value calculated by Bravi and Basosi [32] for other geothermal power plants in Italy (3801045 gCO2-eq kWh
1), while the contribution to acidification potential (3.37 gSO2-eq kWh
1) and human toxicity (11.2 g1,4 DCBeq kWh
1) fall within the same range calculated by these authors (0.1e44.8 g SO2-eq kWh
1 and 1.1e31.6 g1.4-DCB-eq kWh
1). The issue of hydrogen sulfide and other chemicals release from geothermal power plant in Italy is well discussed in literature with many warnings for appropriate controls required to reduce those emissions, especially if growth of exploitation of geothermal resources is to be expected [60e62]. Innovative technologies have been also installed to reduce geothermal emissions (e.g., the AMIS

technology-Abbattimento Mercurio ed Idrogeno SolforatoAbatement of mercury and hydrogen sulphide). Results highlight that geothermal fluids also contain high levels of arsenic and mercury. The direct release of mercury and arsenic from the use of the geothermal flow is another crucial issue since they can potentially harm human health. The contribution of these two toxic elements to the human toxicity category accounts for 37.29% (Fig. 5). Unlike the case of dry and flash steam geothermal plants, closed-cycle ORC (or binary) plants produce near-zero emissions during geothermal flow utilization since the geothermal water utilized is injected back into the reservoir. According to a comprehensive study on geothermal binary power plants [28], typical binary plants have CO2-eq in the range of 33e62 g kWh
1 while the amount of SO2-eq release is lesser than 0.5 g kWh
1. Similarly, according to Lacirignola and Blanc [29] emissions of greenhouse gases from enhanced geothermal systems are in the range of 16.9/ 49.8 gCO2-eq kWh
1. These values are much lower than those calculated in the present study (248 g CO2-eq kWh
1 and 3.37 g SO2-eq kWh
1). Tables 3e5 list selected LCA indicators, while Table 9 summarizes the main emergy indicators. The emergy indicators calculated for the geothermal power plant offer interesting findings. The calculated transformity of electricity (2.18 105 seJ J
1, Table 9) falls unexpectedly within the range of transformity values calculated for fossil fuels-based power plants [44], while the ELR (0.59, Table 9) is lower than for other fossil fuels-based electricity productions (characterized by a range of 10e14), and much more similar to electricity generated by other renewable sources such as hydro and wind power. This indicates at the same time a high convergence of emergy for geo-electricity production even though more than 70% of the required inputs to the plant operation comes from renewable sources. The emergy indicators calculated for the geothermal power plant confirm that the system is mainly driven by locally available renewable sources and only moderately supported by non-renewable sources imported from outside the system. This is an important point when considering the sustainability of

Table 7 Results of Monte Carlo analysis to address uncertainties of selected CML impact categories. Impact category

Unit

Mean

Median

Standard deviation

Coefficient of variation %

Acidification potential Climate change Eutrophication potential Human toxicity Land use e competition Photochemical oxidation Depletion of abiotic resources Stratospheric ozone depletion

kg SO2-eq kg CO2-eq kg PO4-eq kg 1,4-DCB-eq m2a kg ethylene-eq kg antimony-eq kg CFC-11-eq

3.44E-03 2.54E-01 7.11E-04 1.56E-02 1.09E-03 1.80E-05 4.21E-04 6.66E-09

3.40E-03 2.52E-01 7.02E-04 1.42E-02 1.05E-03 1.79E-05 4.18E-04 6.47E-09

4.80E-04 2.80E-02 1.05E-04 5.36E-03 2.90E-04 1.77E-06 4.75E-05 1.64E-09

14% 11% 15% 34% 27% 10% 11% 25%

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E. Buonocore et al. / Energy xxx (2015) 1e12

Table 8 EMA of the geothermal power plant. #

Item

Environmental inputs 1 Geothermal heat 2 Underground water used up 3 Wind Plant construction phase 4 Extraction wells, cement 5 Reinjection wells, cement 6 Steam lines: Steel Thermal insulating materials Aluminium cover 7 Water reinjection lines Glass-fiber reinforced plastic 8 Buildings and assets of plant: Concrete Inert stuffing materials Epoxivinyl paints Carbon and inox steel Aluminium Glasses Thermal insulating materials 9 Steel components of the plant 10 Electric wires: Copper Electricity insulating material (plastics) 11 Electricity 12 Diesel fuel for construction machinery 13 Major components of the plant Iron and steel Copper Pig-iron Chromium Molybdenum Nickel Manganese Electricity insulating material (plastics) Thermal insulating materials Lube and insulating oil Epoxivinyl paints 14 Diesel fuel used for transport Plant operating phase 15 Annual electricity input 16 Labor 17 Maintenance materials Lube oil Epoxivinyl paints Steel Aluminium Thermal insulating materials Services 18 Total investment for the plant Other technical services Electricity production 19 Electricity production with services Electricity production without services

Unit

Amount

UEV (sej unit
1)

Ref.

Emergy (sej)

Emergy %

J J J

2.25Eþ15 2.53Eþ12 1.06Eþ16

2.00Eþ04 2.93Eþ06 2.42Eþ03

[50] [43] [43]

4.50Eþ19 7.42Eþ18 2.56Eþ19

63.84% 10.36% 0.00%

g g

5.71Eþ08 2.85Eþ08

2.48Eþ09 2.48Eþ09

[51] [51]

1.42Eþ18 7.08Eþ17

1.98% 0.99%

g g g

2.79Eþ07 3.63Eþ06 1.40Eþ06

3.03Eþ09 2.42Eþ09 2.01Eþ10

[52] [a] [51]

8.46Eþ16 8.78Eþ15 2.82Eþ16

0.12% 0.01% 0.04%

g

2.43Eþ06

9.45Eþ09

[51]

2.30Eþ16

0.03%

g g g g g g g g

1.57Eþ08 3.14Eþ09 3.18Eþ04 4.14Eþ06 1.14Eþ05 3.64Eþ04 8.73Eþ05 8.28Eþ06

2.48Eþ09 1.61Eþ09 4.83Eþ09 3.03Eþ09 2.01Eþ10 3.48Eþ09 2.42Eþ09 3.03Eþ09

[51] [43] [a] [52] [51] [51] [a] [52]

3.90Eþ17 5.05Eþ18 1.54Eþ14 1.25Eþ16 2.29Eþ15 1.27Eþ14 2.11Eþ15 2.51Eþ16

0.55% 7.06% 0.00% 0.02% 0.00% 0.00% 0.00% 0.04%

g g J J

7.53Eþ05 4.51Eþ05 1.64Eþ10 1.94Eþ11

3.22Eþ09 9.45Eþ09 2.52Eþ05 1.81Eþ05

[53] [51] [44] [54]

2.42Eþ15 4.26Eþ15 4.12Eþ15 3.51Eþ16

0.00% 0.01% 0.01% 0.05%

g g g g g g g g g J g J

1.54Eþ07 3.59Eþ05 1.36Eþ06 1.36Eþ05 8.98Eþ03 1.22Eþ05 3.83Eþ03 9.09Eþ03 1.82Eþ04 5.41Eþ10 4.09Eþ04 3.00Eþ11

3.03Eþ09 3.22Eþ09 2.40Eþ09 1.61Eþ09 1.61Eþ09 1.61Eþ09 1.09Eþ11 9.45Eþ09 2.42Eþ09 1.81Eþ05 4.83Eþ09 1.81Eþ05

[52] [53] [52] [43] [43] [43] [53] [51] [a] [54] [a] [54]

4.65Eþ16 1.16Eþ15 3.27Eþ15 2.19Eþ14 1.45Eþ13 1.96Eþ14 4.20Eþ14 8.59Eþ13 4.39Eþ13 9.78Eþ15 1.98Eþ14 5.44Eþ16

0.07% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.01% 0.00% 0.08%

J €

2.53Eþ13 1.39Eþ05

2.52Eþ05 9.60Eþ11

[41] [b]

6.39Eþ18 1.33Eþ17

8.92% 0.19%

J g g g g

4.61Eþ10 5.00Eþ05 8.00Eþ05 3.00Eþ05 2.00Eþ05

1.81Eþ05 4.83Eþ09 3.03Eþ09 2.01Eþ10 2.42Eþ09

[54] [a] [52] [51] [a]

8.34Eþ15 2.42Eþ15 2.42Eþ15 6.04Eþ15 4.83Eþ14

0.01% 0.00% 0.00% 0.01% 0.00%

€ €

3.64Eþ06 1.28Eþ06

9.6Eþ11 9.60Eþ11

[b] [b]

3.49Eþ18 1.23Eþ18

4.88% 1.72%

J J

3.28Eþ14 3.28Eþ14

2.18Eþ05 2.03Eþ05

[c] [c]

7.16Eþ19 6.67Eþ19

100.00% 93.22%

[a] Our estimate; [b] Our calculation from [55] and [56]; [c] From calculation performed in this work.

electricity production processes indicating that the exploitation of local sources does not require too much investment of imported technology and infrastructure. 5. Conclusions The life cycle perspective adopted in this study allowed a comprehensive assessment of the environmental impacts of the geothermal power plant, from both a source and a sink sides, exploring constraints and potentialities of the investigated system. The inclusion of the Emergy Synthesis method within a LCA framework allowed the proper evaluation of the global

Table 9 Emergy-based indicators calculated for the geothermal power plant. Emergy indicators

Value

Unit

UEV of electricity UEV of electricity (without labor & services) R N F U ELR EYR ESI

2.18Eþ05 2.03Eþ05 4.50Eþ19 7.42Eþ18 1.92Eþ19 7.16Eþ19 0.59 3.73 6.31

seJ seJ seJ seJ seJ seJ

J
1 J
1 yr
1 yr
1 yr
1 yr
1

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E. Buonocore et al. / Energy xxx (2015) 1e12

environmental support provided by biosphere, over the time frame needed for resource generation, in so adding a time and evolutionary perspective to the thermodynamic and burden assessments provided by energy analysis and LCA. The geothermal power plant resulted to be able to generate electricity driven by locally available renewable resources and only moderately supported by non-renewable resources imported from outside the system. This makes the geothermal source eligible to produce renewable electricity being mainly run on local and renewable resources. The investigated power plant shows an efficiency of 14%, which is in line of average geothermal dry-steam technology. Its EROEI around 4 indicates that the geothermal technology compares quite well with traditional fossil powered plants. The generation of 1 kWh of geoelectricity releases about 248 g CO2, much less than fossil powered electricity. However, the direct utilization of the geothermal fluid causes the release into the atmosphere of hydrogen sulfide, mercury, arsenic and other chemicals that have environmental impacts and can potentially harm human health. These emissions, coupled to a relatively low heat-to-electricity conversion efficiency, calls for improved technology in the operation phase. The low efficiency makes the environmental impact much higher in all impact categories, compared to other renewable sources. Further studies should be performed, according to a life cycle perspective, to explore the environmental performance and sustainability of other geothermal options for electricity production (e.g. binary cycles, and direct thermal uses for industry and domestic heating) in order to utilize the geothermal flows with higher efficiency and lower impacts.

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Please cite this article in press as: Buonocore E, et al., Integrating life cycle assessment and emergy synthesis for the evaluation of a dry steam geothermal power plant in Italy, Energy (2015), http://dx.doi.org/10.1016/j.energy.2015.04.048