The Dynamics of the Water-Electricity Nexus Vaca Jiménez, Santiago

The Dynamics of the Water-Electricity Nexus Vaca Jiménez, Santiago

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10.33612/diss.135589228

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2020

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Vaca Jiménez, S. (2020). The Dynamics of the Water-Electricity Nexus: How water availability affects electricity generation and its water consumption. University of Groningen.

https://doi.org/10.33612/diss.135589228

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Echo-chambers in Science

A.1 Additional Introduction

Water is required for electricity generation (Meldrum et al. 2013). Power plants and their fuels use water throughout the production process (Meldrum et al. 2013). The assessment of water use by power plants, and the fuels they use, is paramount as elec- tricity demand increases and water becomes scarce. This importance was observed by Peter Gleick, who was one of the first authors to assess and compile water intensities of different fuels and electricity technologies with a series of publications, e.g., Gleick (1992, 1993, 1994). Since then, many studies followed suit, especially after the official introduction of the water-energy-food (WEF) nexus concept in 2011 (Hoff 2011). The integrative approach of the water-energy-food (WEF) nexus concept, introduced at the Bonn Conference in 2011, aims to bridge disciplines dealing with interactions between water, energy and food production (Hoff 2011), e.g. water needs of electricity genera- tion, the water-electricity-nexus (WEN). The WEF nexus provides a framework to iden- tify trade-offs and synergies requiring systems thinking, aiming to achieve sustainable water, energy and food systems. Water and electricity encounter considerable opportu- nities to optimize their use using the WEN approach.

The WEN can consider both directions of the water-electricity relationship. How- ever, water use, and especially freshwater use, for power plants have been one of its most studied topics. Part of it, the definition of water intensities, ratios of water use against electricity output, has been a major part of WEN assessments, e.g., Macknick et al. (2011, 2012), Meldrum et al. (2013), Spang et al. (2014), or Jin et al. (2019).

Despite a large number of publications about water intensities for power plants and the fuels they use, concerned voices in the area have indicated the lack of reliable data sources. For instance, a recent article published by the World Resources Institute about the importance of understanding power plants water consumption describes the lack of data sources as one of the most critical limitations in the subject (Schleifer and Luo 2018).

The lack of appropriate data sources has made researchers use the few available sources,

mostly from previous case studies, to assess new cases despite being framed for differ- ent settings and operational conditions, creating echo-chambers.

In social sciences, an echo-chamber is described as a situation in which certain con- cepts or beliefs are amplified or reinforced by repetition inside closed networks (Barber´a et al. 2015). In scientific literature, one may encounter echo-chambers in terms of concepts or values that might not be factual but that have perpetuated in the academic discussion based on repetition. We hypothesize that due to the lack of data sources in the subject, there are currently several echo-chambers in WEN literature. Particularly, echo-chambers form when water intensities, which were reported for specific case studies, are used for other case studies regardless of the differences between them, or the data sources limitations. Data sources that fall in echo-chambers may use these different case studies as generalizations, considering their water intensities as independent and comparable.

With time, as papers use these data sources, their citation gets higher, increasing their reputation, and making them more likely to be used in future publications.

The pressure on finite water resources for electricity generation is expected to in- crease as the population grows, and societies move towards more electricity-based life- styles (IEA 2019c). Nonetheless, if echo-chambers are fundamentally shaping the WEN discussion, solutions may be ineffective or even counterproductive. Some technologies that are currently described water-intensive may be water-efficient depending on the circumstances. This cannot be observed while echo-chambers are present. Thus, there is the need for an extensive, comprehensive, and detailed analysis of the water-electricity literature to identify the echo-chambers.

The study answers five research questions:

• What is the main water-electricity literature of electricity generation systems, their fuels, and applied water sources?

• What are the main data sources of the water-electricity nexus literature for differ- ent fuels, e.g., coal, gas, nuclear, hydropower, solar, wind, and bioenergy?

• What are the characteristics of available publications, i.e., origin, quality, and the use of first or second-hand data?

• What are the most important echo-chambers?

• What are the implications of echo-chambers in science?

A.1.1 Relevance and Contribution

With the use of a hybrid approach of conventional literature review and network analy- sis tools, this study exposes the existing echo-chambers. It also provides discussion points into how to recognize them. We assessed 2426 papers, 854 in detail, published between 1981 to 2020, regarding the water usage for electricity generation and their fuels. These type of hybrid approaches that include network analysis tools, e.g., Liu and Mei (2016), are useful to identify research fronts, collaboration networks, and influential authors.

We created a citation network, including grey literature, classifying them by topic to identify papers assessing water intensities for different electricity-generating technolo- gies and their fuel use. Next, we categorized them into original source papers, reviews, and case studies. Finally, we identified the original data sources using the references of the citation network. By addressing the extensive WEN literature, this study provides a critical assessment of the currently available data sources. Thus, providing essential information regarding the available data sources to future studies.

Furthermore, the WEN community must recognize and solve the echo-chambers to provide specific solutions to future water and energy sectors in a resource-constrained world. As time passes, and the WEN and WEF discussions go farther, unidentified echo-chambers may get more challenging to spot as the connection with original data gets fussier and blurred.

A.2 Background Information

A.2.1 Water use in power generation

Power plants, and the fuels they use, require water in different ways throughout their operation and life cycle. The quantity and quality of the water required for power gen- eration depend on the electricity generating technology, the fuel applied and operating conditions. In the case of fuels, fossil fuels require water for the exploration, extrac- tion, processing, and transportation phases (Meldrum et al. 2013). Energy crops and biofuels also require water, especially during the crops growing stages (Gerbens-Leenes et al. 2009b). In the case of power plants, their water use depends on the technology and operating conditions (Meldrum et al. 2013, Mekonnen et al. 2015, Vaca-Jim´enez et al. 2019a).

Thermal power plants (TPPs) include coal, gas, nuclear, concentrated solar power,

and some types of biomass power plants. They produce more than 75% of the global

annual electricity production (IEA 2019b). These power plants use water in two ways:

in the cooling system and the operation of the power plant. Cooling is required for Rankine and Internal Combustion Engine (ICE) machines that are used as power plants (Vaca-Jim´enez et al. 2019a). In the first case, to cool the working fluid (steam), in the second to cool the engine (Vaca-Jim´enez et al. 2019a). The latter was considered as negligible in previous publications, e.g., (Sanders 2015). However, recent publications made with a higher level of detail, have shown that the water used in this case is sig- nificant (Vaca-Jim´enez et al. 2019a). In both cases, water is the most common cooling agent applied. Depending on the type of cooling system, water is mostly only with- drawn (once-through system) or withdrawn and evaporated (wet-tower). The first sys- tem withdraws large but evaporates small volumes of water. The second is the opposite (Williams et al. 2013). Power plant operation also requires water. Rankine power plants use water as the working fluid, while other TPPs require water to prepare the fuel they use, especially if they are using heavy oil derivates (Vaca-Jim´enez et al. 2019a).

Likewise, other renewable energy power plants also require water. The obvious users are hydropower plants (HPPs), which is the most deployed renewable energy technology, producing around 16% of global electricity (IEA 2019b). These power plants use water directly for the electricity generation process (Gleick 1992). In the beginning, HPPs water use was not considered consumptive as water is diverted from the river, passed through the turbines, and then returned to the river. Nonetheless, several stud- ies have indicated that HPPs do consume water, especially in terms of water evapora- tion from their reservoirs (Mekonnen and Hoekstra 2012). Nowadays, scholars are still discussing the nature of water consumption by HPPs, especially in terms of the method to estimate these volumes (Scherer and Pfister 2016, Bakken et al. 2015). Solar photo- voltaics and wind power require water, but contrary to previous cases, most of its use is during the construction of the solar panels or wind turbines (Meldrum et al. 2013).

Some technologies are more water-intensive than others, e.g., thermal power plants (TPPs) with wet tower cooling systems require more water than solar plants (Meldrum et al. 2013).

Power plants, and their fuels, may use different sources of water for different pur-

poses. For instance, heavy oil extraction requires enhanced oil recovery methods, which

can be water-intensive (Williams et al. 2013). Nonetheless, energy companies usually

use wastewater from their processes instead of freshwater (Williams et al. 2013). The

same is relevant for TPPs plants. Saline and wastewater do not have the quality to be

used in other processes but are suitable for cooling (Jiang and Ramaswami 2015). The

definition of the water source is paramount for the WEN discussion, as different sources

have different environmental impacts and operational implications. For instance, fresh-

water is scarce, and its use promotes competition with other sector (Mekonnen et al.

2016). Saline water is not scarce, and therefore, there is no volumetric limit (Zhang and Dzombak 2010). However, its use in power plants may have an impact on its quality, and affect local biodiversity (EPRI 2007). Wastewater is not scarce, but its volume is limited, and the power generation system does not contribute to its treatment. It only poises an additional step of use for wastewater, but it does not help to discharge it safely.

Water intensities of electricity, also known as water footprints, are defined as the ratio between the volume of freshwater used and electricity output of the system. If the system considers the fuel production phase, water intensities are water volumes per unit of thermal energy embedded in the fuel, e.g., m

per MWh. For power plants, water intensities are defined as the volume of water per unit of electricity produced, e.g., m

per GJ.

A.2.2 Limitations of water intensities reported in the literature

Water use by power plants shows temporal and spatial variation. Different climates, re- sources and energy management options are unique, generating water use differences among power plants (Vaca-Jim´enez et al. 2019b). In terms of temporal limitations, wa- ter intensities reported in the past (decades-old) may not have validity in the present as power plants, and their fuels have improved their technologies, increasing yields, out- puts, and operating time. Besides, some of them may have shifted towards lower GHG and pollutant emissions, e.g., NOx. All of these variables affect water usage (Williams et al. 2013, Mekonnen et al. 2015, Vaca-Jim´enez et al. 2019a).

Data sources also have a spatial limitation, as the location-based differences between technologies and fuels are significant. Fuels water usage is affected by location, e.g., oil extraction depends on its viscosity. The higher the viscosity, the more water is used as it is employed in most of the Enhanced Oil Recovery Methods (Williams et al. 2013).

In terms of electricity generation, most technologies experience significant variations

of water intensities in different locations. HPPs and bioenergy technologies are loca-

tion dependent. Their water consumption is defined by climate variables, which are

locally bound (Gerbens-Leenes et al. 2009a, Vaca-Jim´enez et al. 2020). TPPs plants like

coal, oil, nuclear, and concentrated solar power plants are also likely to vary largely due

to spatial differences, especially for those using wet-tower cooling technologies as the

evaporative process depends on climatic conditions (EPRI 2008); or using oil as their

main fuel (Vaca-Jim´enez et al. 2019b). However, they are more challenging to assess

than HPPs or bioenergy as the relation between water use, and climate is complex. It

is likely, that water intensity differences of TPPs using these technologies in different

locations is significant.

In principle, past case studies that refer to specific system boundaries do not pro- vide relevant information for present case studies, especially those involving different system boundaries described in different regions.

A.2.3 Types of papers and their use in the WEN literature

Authors cite previous work for different reasons: (i) to define concepts, (ii) to provide context to their work, (iii) to adopt methods, and (iv) to use their data. To contrast ones work with previous publications is fundamental to improve the knowledge of the subject. Therefore, the connectivity between papers on a specific topic is paramount to describe the study and define its relevance. The problem resides in the use of data sources where echo-chambers are likely to appear.

Three types of data sources exist in WEN literature (i) grey literature data, (ii) case study data, and (iii) review paper data. Grey literature, including reports and books, can contain first or second-hand sources. First-hand data originate necessarily from en- ergy companies. They produce internal or public reports in which they compile and disclose information regarding water use of their activities, e.g., the BP report (Williams et al. 2013). However, these sources are scarce as energy companies do not usually log or compile this information, and in some cases, they may be reluctant to provide them publicly. Second-hand data usually come from interested parties like Governmental Agencies or Watchdogs, which have oversight over energy companies and log, or es- timate, several operational data. They usually produce grey literature compiling this information for power plants in a regional or national scope. Nonetheless, these infor- mation sources rely on reporting from third parties that are seldom independent, and are rarely publishable in scientific journals, and therefore, not peer-reviewed.

Case studies assess water use by power plants for specific cases constrained by well- defined system boundaries, which can be published in scientific journals or conference proceedings. They use (i) first-hand data sources, (ii) grey literature with first or second- hand data, or (iii) water use estimations based on mathematical models. Coal, gas, oil, and nuclear technologies are some of the ones that can be estimated based on math- ematical models regarding the water and energy balance in the cooling system, e.g., Bouckaert et al. (2014). However, these models are not commonly accessible and usu- ally involve great uncertainty as energy and water balances include many complexities.

Additionally, these models usually do not discriminate between water sources, so there

is uncertainty into whether it considers freshwater, saline, or wastewater.

Finally, there are review papers that are published in scientific journals. These papers compile different data sources of different backgrounds, including different regions, us- ing first-hand data, grey literature, and case studies. They cluster power plants, usually based on their energy source, e.g., coal, gas or solar, and the specific characteristics of each energy source, i.e., cooling system. Finally, they compare all technologies in- ventoried, giving water intensity ranges and median or average values per source and technology. When first-hand data are not accessible, modelling is uncertain, and grey literature is not peer-reviewed, review papers are the preferred ones to be used as data sources. These papers are considered reliable due to the peer-reviewing process that they are subject to before publication. However, for most of the cases, the review pro- cess does not imply that the data sources are precise, reliable, or updated, but that the method to compile them and the conclusions reached thereafter are scientific and repli- cable.

The three data source types are usually related to each other, and their appearance is often consecutive. First, grey literature appears based on first-hand data, which is picked up by a case study. When there are a few case studies published, with more grey literature connected to them, a review paper comes and compiles that information. In this process, both case studies and reviews may include additional first-hand data. The process continues until a new generation of reviews appears. These are larger reviews that use the previous reviews as their main input, along with new available first-hand data, grey literature, and case studies. As time passes, the following generation of re- views concentrate on compiling reviews from previous generations, seldomly including more first-hand data.

A.3 Additional discussion

WEN literature has a loud echo and chamber effect. The echo effect indicates that data sources and publications providing water intensities have been used repeatedly, jump- ing from one publication to another, without any awareness of original data sources.

Some data sources can even be traced back to publications over four decades old. In broad and multidisciplinary fields, experts in one field rely on the knowledge from ex- perts of another field. In this case, water intensities might be used by experts on water or environmental science fields, which may overlook the technical conditions of the en- ergy system, and thus, is prone to fall into an echo.

The chamber effect shows that data sources are dependent as they use data from

each other. Following a similar rationale as Stovel (2019), current scientific databases as Scopus

and Web of Science

may be contributing to echo-chambers in science as their search engines prioritize between papers. These databases sort papers based on rele- vance, making the most cited papers appear in the top of the searches, affecting the way people are using available information, as in most of the cases, the first hits are the ones used.

A.3.1 Understanding temporal and spatial limitations of water intensities

Our study shows how WEN literature, and especially data sources used in the WEN, are mostly Macknick, Newmark, Heath and Hallett (2012) based and defined inside the boundaries of the U.S. Nonetheless, as we have already discussed in the previous sec- tions, water intensities of power plants have spatial and temporal conditions. Previous research, e.g., EPRI (2008), Vaca-Jim´enez et al. (2019a), has shown that climate and wa- ter availability have a large influence on energy systems water intensities. In theory, data sources should be considered only inside the temporal and spatial boundaries of the cases used for their estimation.

Efficiency differences are an example of temporal limitations. In the last decades, power plant efficiencies have improved, decreasing water use of a power plant. For instance, in the case of coal-fired power plants, water intensities provided by Gleick (1993, 1994) considered a thermal efficiency of 35%. Twenty years later, Meldrum et al.

(2013) considered an efficiency of 38.5%. Case studies and review papers use Gleick’s and Meldrum’s values indistinctively, not considering the improvement of power plant efficiencies, or harmonizing them. This creates a disparity in terms of water intensities as the electricity output is higher, and volumes of fuel these plants use is smaller.

In terms of spatial limitations, oil is an example of large spatial water intensity vari- ation, while natural gas has small variation. Oil’s global distribution is uneven, and oil quality varies (Williams et al. 2013). Depending on oil viscosity, water intensities differ.

Studies should differentiate between heavy and light oils and chose the data source ac-

cordingly, but this is seldom considered in recent literature. Conversely, conventional

natural gas production requires small water volumes (Meldrum et al. 2013) (except-

ing shale gas, which is water intensive (Ali and Kumar 2016)) and global differences

among extraction processes are smaller than for oil. In the case of the power plants op-

eration, water intensities have large spatial variation among power plants, as each site

has a different climate, regulations, water sources and in-plant water management sys-

tems (EPRI 2008). The same type of power plant, with the same installed capacity, may have different water requirement depending on the site, e.g., due to climatic conditions, which significantly affect the operation of cooling systems (EPRI 2008).

Overall, there are a few cases in which the water intensity of power plants may not significantly vary in terms of the source of the data used. Gas combustion turbines, pho- tovoltaic systems, and wind turbines are technologies that do not require large volumes of water (Meldrum et al. 2013). Therefore, their water intensity may not be significantly different from place to place. However, besides HPPs and bioenergy technologies, wa- ter intensities for wet-tower cooled TPPs, e.g., coal, oil, nuclear, geothermal, and con- centrated solar-powered technologies, are likely to vary largely due to climatic spatial differences.

A.3.2 Moving forward

We have shown how interwoven, and complex are the data sources used in WEN litera- ture. As the WEN, and WEF discussion continues, this will only get more complex and interconnected, and likely accentuates the echo-chambers as the original data gets fussier and blurred. The WEN community must recognize and solve these echo-chambers.

Our analysis can be used as an example of how to identify echo-chambers in the cur- rent literature. Based on the examples in the WEN, we identified two main ways authors may avoid falling in the echo. First, there is the need to be explicit about the nature of the data source, and arguments to justify its use. Authors should be aware of the con- text and limitations of available data sources and choose the most appropriate data for their case studies. If this is impossible, authors should use data papers that present the disaggregation of the different water intensities concerning the life cycle of the energy system, as Meldrum et al. (2013). In this way, they reduce the uncertainty of generaliza- tion by picking individual aspects of the assessed energy technology.

However, future works should aim to address and solve these echo-chambers by pro-

viding reliable, spatially bound data of the water use of energy systems. A possible

example is the publically available, free of charge, Water Footprint Networks compre-

hensive database of water footprints of agricultural products (Mekonnen and Hoekstra

2011b). That database provides values of water footprints (intensities) per crop type,

country and province of origin, and type of water source. We acknowledge that this

is not entirely applicable to the WEN. Nonetheless, the WEN community should aim

for an updated and comparable database that aggregates water intensities of fuels and

power plants technologies for countries or regions. This database should involve new

estimations, quantifications, and construction of first-hand databases, with clear system boundaries.

A set of existing tools that could address this is the LCA databases (e.g., Ecoinvent (2019)). These databases are formed by a large body of scholars that aim to compile, homogenize, and purge data sources and provide them with different levels of detail, making regional differentiation. However, in our perspective, there is one main issue that prevents these to fulfil this role. This is the lack of ease of access to these LCA databases, original data sources. In our study, we were only able to trace the original source until the database, but due to several limitations, we could not go further to as- sess their data sources. Based on the method they use to compile their database, it is unlikely that they contain echo-chambers, but we cannot know for sure.

Finally, we identify that a large improvement in the data sources of water intensities should be made for certain fuels and technologies. In the case of the former, it is still required to make full assessments from well (fuel source) to end-user, for oil and oil- derived fuels. In the case of the latter, there are still knowledge gaps regarding the water intensity for key technologies like geothermal power plants, concentrated solar power plants, or new nuclear technologies like thorium-based reactors. Moreover, emission reduction technologies should also improve. These technologies, as Carbon Capture and Storage (CCS), are relatively new concepts for energy systems, and therefore should be addressed as those technologies could largely impact the future of the discussion.

A.3.3 The importance of a detailed hybrid approach

The hybrid method used was effective in improving the articles database as it included a large fraction of literature that was missed by the keyword search. For instance, pub- lications by Gleick (1992, 1993, 1994) were not part of the keyword search, despite being fundamental for the subject. Without the hybrid approach, we may have missed the most influential papers, showcasing the importance of the network analysis.

Additionally, our results should have been different if we had not considered such a detailed approach, especially in terms of the homogenization of the literature database.

If these publications would have been considered independent, despite reporting es-

sentially the same water intensities, the analysis would not have shown how influential

these publications are in the subject and the extension that their water intensities have

been used repeatedly throughout the WEN literature. Similar methods can be used for

other topics as any article may transfer findings from one context to another without

considering the validity of such assumptions.

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