Carbon Mitigation in the Power Sector As a Solution to Global Climate Change, a Good Idea but How Much Water Will It Cost? THESI

Carbon mitigation in the power sector as a solution to global climate change, a good idea but how much water will it cost?

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

James A. Datko, B.S.C.E.

Graduate Program in Geological Sciences

The Ohio State University

2010

Master's Examination Committee:

Motomu Ibaraki, Advisor

Garry D. McKenzie

Frank W. Schwartz

James A. Datko

2010

Abstract

Issues of water unsustainability with respect to future energy projections are discussed in order to enhance our understanding of the feedback loop between water and energy and explore potential approaches to meet future energy needs without compromising water resources. Energy projections to 2050 from the International

Energy Agency (IEA) are compiled, modified, and then used to model-calculate operational water requirements for electric-power generation. Water withdrawal statistics from the U.S. Geological Survey are used as model validation. Results show that the water needed for power generation in 2050 is expected to be much greater than current

(2007) demands, whether in a carbon mitigating energy setting (average global increase of operational water withdrawal of 107%) due to the use of carbon capture and storage

(CCS) technology, or a setting where energy policy remains “business-as-usual” (average global increases of 158%) because of the high water demand of thermoelectric power plants. In order to determine what future action would be most beneficial, a sensitivity analysis is performed to examine what factors of power generation have the greatest potential to alter future water needs. Analysis indicates that if a global average of 77.7% of coal, natural gas, and biomass power plants are equipped with CCS by 2050 (following

IEA’s future projection), then operational water withdrawal and consumption will respectively be 66% and 32% greater than in an equivalent energy scenario without CCS.

The sensitivity analysis also shows that the most effective (and feasible) potential methods to combat these future increases in water needs from CCS are through the implementation (or modified use) of four fundamental factors of power generation.

These include (1) the use of closed-loop cooling towers and not open-loop cooling at thermoelectric power plants, (2) the use of combined cycle technology at all coal and natural gas power plants, especially those being installed with CCS technology, (3) the implementation of as much renewable energy (wind, solar photovoltaic, and ocean energy) as allowed for by other resources, and (4) the conservation of energy.

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Dedication

Dedicated to Dolly and Carl Kleinhenz The ever-present and unifying link of our family

Acknowledgments

My deepest gratitude goes to my advisor Dr. Motomu Ibaraki who guided me though this difficult path. I would also like to thank Dr. Berry Lyons, Dr. Frank Schwartz, and Dr.

Garry McKenzie, who all played very important parts contributing to my successful completion of this work and the SES master’s program. Finally, I would like to thank my parents Karen and Dave, brother Eric, sister Stephanie, and girlfriend Andi, all of whose support and encouragement I could not have done without.

Vita

March 7, 1986 ...... Born – Cleveland, Ohio

2008 ...... B.S.C.E. The Ohio State University

2008 – present ...... Graduate Teaching Associate,

School of Earth Sciences,

The Ohio State University

Publications

Datko, James A., Ibaraki, Motomu, 2009. Using strontium isotopes as a tracer to study snail population dynamics for schistosomiasis transmission control. Abstracts with Programs, Geol. Soc. Am., 41, T139.

Fields of Study

Major Field: Geological Sciences

Studies in Hydrogeology, Geology and Health, Environmental Geology, Sustainability

Table of Contents

Abstract ...... ii

Dedication ...... iv

Acknowledgments ...... v

Vita ...... vi

List of Tables...... ix

List of Figures ...... x

List of Abbreviations ...... xii

Chapters:

1. Introduction ...... 1

2. Water requirements for electric-power generation ...... 3 2.1. Thermoelectric generation ...... 4 2.1.1. Open-loop cooling ...... 5 2.1.2. Closed-loop cooling ...... 7 2.1.3. Dry cooling ...... 8 2.1.4. Combined cycle power ...... 9 2.1.5. Carbon capture and storage technology ...... 10 2.1.6. Salt versus freshwater ...... 11 2.2. Hydroelectric generation ...... 12 2.3. Generation from renewables ...... 12 2.4. Summary...... 12

3. Projections of electric-power generation ...... 16 3.1. Combining IEA datasets ...... 17 3.1.1. Combining global scenarios ...... 18 3.1.2. Modifications to the 2050 global projections ...... 19 vii

3.1.3. Country-level data to 2050 ...... 20 3.2. Breakdown of fuel types in the 450 scenario ...... 21 3.3. Power generation trends ...... 22

4. Methodology, assumptions, and results ...... 31 4.1. 2005 model ...... 32 4.1.1. General assumptions ...... 32 4.1.1.1. Distribution of cooling technologies ...... 32 4.1.1.2. Solar power generation ...... 33 4.1.1.3. Combined cycle generation ...... 33 4.1.1.4. Coal-fired generation...... 34 4.1.2. Current water withdrawal and consumption factors ...... 34 4.1.2.1. Fossil fuel generation with open-loop cooling ...... 35 4.1.2.2. Nuclear generation with recirculated cooling ...... 36 4.1.2.2. Dry cooling ...... 36 4.1.2.3. IGCC generation ...... 37 4.2. Future models ...... 37 4.2.1. Updates to solar power generation ...... 38 4.2.2. Inclusion of carbon capture and storage ...... 38 4.3. Additional model assumptions ...... 39 4.4. Discussion of results ...... 39

5. Sensitivity analysis ...... 45 5.1. Sensitivity analysis inputs and outputs ...... 46 5.1.1. Ranges for the x-axis ...... 48 5.1.2. Water multiplier ...... 50 5.2. Sensitivity analysis results ...... 50 5.2.1. Comparison of trend line slopes ...... 51 5.2.2. Feasible range for water demand factors ...... 52 5.2.3. Possible changes in future water demand ...... 54 5.3. Comparing other countries ...... 56

6. Conclusions ...... 63

Bibliography ...... 66

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List of Tables

Table 4.2. Operational water withdrawal and consumption according to generation type (used in the 2005 model) ...... 43

Table 4.3. Modifications of the 2005 model applied to the future models. Compiled and adjusted from the IEA (IEA, 2008 and 2009) ...... 44

List of Figures

Figure 2.1. Quantity of water withdrawn per unit of power generated (in m3/MWh). Modified from the USDOE (USDOE, 2006). A logarithmic scale is used due to the significant variance of water withdrawal values. It should be noted that the very narrow shaded ranges (NGCC recirculated and geothermal) represent average values and not ranges ...... 14

Figure 2.2. Same as Figure 2.1 except now displaying statistics for water consumption ...... 15

Figure 3.1. Global energy and water projections to 2050 by scenario and fuel type: (A) annual power generation, (B) daily water withdrawal, and (C) daily water consumption. Energy data is modified from the IEA (IEA, 2008 and 2009). Water demand values are model-calculated estimates (see Chapter 4). The “Other Renewables” category shown in white shading is the IEA’s category of other renewable (see Section 3.2) ...... 24

Figure 3.2. Same as Figure 3.1 except now showing data for the United States ...... 25

Figure 3.3. Same as Figure 3.1 except now showing data for China ...... 26

Figure 3.4. Same as Figure 3.1 except now showing data for India ...... 27

Figure 3.5. Same as Figure 3.1 except now showing data for Japan ...... 28

Figure 3.6. Same as Figure 3.1 except now showing data for Russia ...... 29

Figure 3.7. Global power generation (in 103 TWh/year) versus time. This figure includes all IEA datasets: historical (IEA, 1998, 2000, 2004, 2006, and 2007), WEO scenarios to 2030 (IEA, 2008), and ETP scenarios to 2050 (IEA, 2009). The lines fit through the data are smoothed lines connecting available data points ...... 30

Figure 5.1. The results of a model sensitivity analysis for (A) power demand reduction, (B) renewables and hydropower, (C) closed-loop cooling towers, (D) combined cycle technology for coal and natural gas, and (E) CCS technology for coal, natural gas, and biomass. The sensitivity analyses are based on the REFS in 2050 for the United States. For power demand reduction the withdrawal and consumption lines coincide ...... 59

Figure 5.2. Trend line slopes for the model sensitivity analyses corresponding to Figures 5.1.A through 5.1.E. Negative values indicate that implementation of those water demand factors cause decreases in operational water withdrawal and/or consumption (positive indicates increases)...... 60

Figure 5.3. Feasible ranges (possible x-axis values) for the five major water demand factors of power generation (corresponding to Figures 5.1.A through 5.1.E) ...... 61

Figure 5.4. The potential change of future operational water demand (according to a water multiplier, see Section 5.1.2) using the maximum feasible input values (from Figure 5.2) for each of the five respective water demand factors. Note that 1.0 is used as a zero-point for the y-axis because a WM of 1.0 is equivalent to no change in future water needs. WM values between 0 and 1.0 indicated future water need decreases, and values greater than 1.0 indicate increases ...... 62

List of Abbreviations

450S — 450 scenario (future energy scenario from the IEA trying to cap total atmospheric GHG emissions at 450 ppm carbon dioxide equivalent).

ACC — Air-cooled condenser (used in dry cooling systems).

CCS — Carbon capture and storage (also known as carbon sequestration).

CL — Closed-loop [cooling].

CSP — Concentrated solar power.

ETP — Energy Technology Perspectives (energy statistics published by IEA).

FGD — Flue gas desulfurization.

GHG — Greenhouse gas [emissions].

IEA — International Energy Agency.

IGCC — Integrated gasification combined cycle.

MWh — Mega-watt hour.

NETL — [United States] National Environmental Technology Laboratory.

NGCC — Natural gas combined cycle.

OL — Open-loop [cooling]. ppm — Parts per million.

PV — [Solar] photovoltaic.

REFS — Reference scenario (future energy scenario from the IEA following a “business- as-usual” policy where energy practices in the future mimic current practices).

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TWh — Terra-watt hours.

USDOE — United States Department of Energy.

USGS — United States Geological Survey.

WEO — World Energy Outlook (energy statistics published by IEA).

WM — Water multiplier (used to describe future changes in operational water demand).

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Chapter 1: Introduction

Energy and water depend on each other, and their relationship can be viewed in the form of a feedback loop. Energy is needed to extract, treat, and deliver water, and many aspects of energy production (e.g. fuel mining/refining, growing crops for biofuels, cooling systems at thermoelectric power plants, etc.) require water. Growing concerns related to climate change from greenhouse gas (GHG) emissions will have significant impact on global energy production in the next fifty years (IEA, 2008 and 2009). The

International Energy Agency’s (IEA) annual reports and future projections show that drastic changes from current energy practices must be made if the increase in average global temperature by 2050 is to be minimized (IEA, 2008 and 2009). The energy sector is a typical target in considerations of GHG emission reduction, with discussion topics focusing on limiting fossil fuel use, increasing the use of biofuels and renewable forms of electricity, and many other aspects of energy production such as improving system/distribution efficiency or implementing new technology such as carbon capture and storage (CCS). However, the issue of availability of water resources linked to energy production is typically overlooked, which compromises the sustainability of future energy projections from the IEA (and many other organizations).

Certain (key) techniques of reducing GHG emissions (like CCS) cause significant increases in water demand, which can potentially pose problems in urban or agricultural

1 settings where freshwater resources are already strained. Currently over 2 of the 6.8 billion people in the world are considered to live in high water stress areas (which includes the middle to western U.S., northern China, northern India and Pakistan, and parts of the Middle East) (Graedel, 2010; NETL, 2009b; Shen, 2008; WRI, 2005 and

2008). In regions like these, changing water needs in the energy sector could potentially force the agriculture, industrial, or domestic sectors to compete for water, in which devastating economic impacts could result; such as increasing prices for food and/or water.

This research focuses on one component of energy production, electric-power generation. The sustainability of future electric-power generation projections from the

IEA are evaluated from an operational water requirement perspective (IEA, 2008 and

2009). These results are used in combination with the collective sustainability research edited by Graedel and van der Voet (stressing the multidisciplinary links between energy, water, land, and [nonfuel] nonrenewable resources to achieve true sustainability) to provide more feasible energy solutions into 2050, which address concerns of climate change (from GHG emissions) while still accounting for water resources (Graedel, 2010).

Chapter 2: Water requirements for electric-power generation

Almost all types of electric-power generation have some form of operational water requirement. When viewed from a water use perspective, while also considering the resource exploited and method of generation, electric-power generation can be divided into three categories; (1) thermoelectric (includes fossil fuels [coal, oil, natural gas, and biomass and waste], nuclear, geothermal, and solar thermal generation), (2) hydroelectric, and (3) renewables (includes wind, solar photovoltaic, and tide and wave generation). All forms of thermoelectric power generation are water dependent, as water is needed in cooling systems in order to condense exhaust from steam-driven turbine generators. Conversely, generating power with one of the three fuel sources grouped in the renewables category has no operational water requirement (not considering water used in facility construction which is beyond the scope of this paper). Hydroelectric power generation is considered separately because of its unique interaction with water resources (see Section 2.2). One thing that should be noted is that biomass and waste power generation comes from the combustion of wood waste or municipal refuse. This is not the same as what is commonly referred to as “biofuels,” which are typically transportation fuels and only in very rare instances combusted to generate power.

Biofuels are therefore not discussed because of their negligible contribution to current electric-power generation.

In 2007 approximately 84% of global electric-power was generated by water- dependent thermoelectric systems, which stresses the importance of considering the links between power generation and water availability (IEA, 2009). However, more details concerning the operational water needs associated with power generation must first be understood. The water requirements for a power plant can be viewed from two perspectives: withdrawal and consumption. A water withdrawal is considered any diversion or extraction from a surface water or groundwater source. The majority of withdrawn water is returned to the source after use, however a small portion is lost to the atmosphere through evaporation, and this is known as water consumption. Water withdrawal and consumption rates vary by the generation fuel source (i.e., power plant type), the type of cooling system used (for thermoelectric power generation), the operating climate for thermoelectric plants (cooling water sources change in temperature according to climate), and by variations in technology (even for similar types of power plants).

This chapter explains the water requirements for electric-power generation.

Discussion is split into the three categories described above; (1) thermoelectric, (2) hydroelectric, and (3) renewables. Included within the thermoelectric section is an overview of how operational water demand is affected by cooling system type, combined cycle power generation, and the use of CCS technology.

2.1. Thermoelectric generation

The cooling procedure itself is an essential part of the thermoelectric power generation process. Typical thermoelectric power plants burn a fuel source to supply heat

4 to water in order to generate steam. The steam is used to drive a turbine, which in turn powers an electricity-producing generator. Cooling systems are required in order to condense the exhaust steam from these turbine generators (Torcellini, 2003). The condensation process is necessary because it helps drive the movement of steam through a turbine by creating a necessary pressure differential (Dziegielewski, 2006). The heated steam approaching a turbine has a high pressure, and the cooling system at the turbine exhaust condenses the steam back into water in order to reduce pressure and create a vacuum-like system. In this manner, cooling systems are responsible for creating pressure drops across turbines, and thus control and drive the movement of steam.

There are three different types of cooling systems utilized in thermoelectric power plants. Open-loop and closed-loop are the two most common types and dry cooling is a third type which is currently rarely used. Water requirements vary by cooling system type. Additionally, variations in technology as well as system efficiency cause the water requirements for each type of cooling system to change based on fuel source and power plant type. Thermoelectric system efficiency is controlled by the temperature difference between heated generation steam and its corresponding cooling media, which is the controlling factor in determining the level of pressure drop available in the system

(USDOE, 2006). The following sections offer a brief overview of the three different styles of thermoelectric cooling systems.

2.1.1. Open-loop cooling

Open-loop cooling is also known as once-through cooling because these systems intake water and utilize it for the cooling process only once before discharging it back to

5 the withdrawal source. A condensing unit is used to change heated turbine exhaust steam back into water by piping cooling water through the condenser while it is filled with steam (Torcellini, 2003). Cooling water increases in temperature as it moves through a condenser, thus the water is discharged from power plants in a heated form (causing some environmental degradation). The “once-though” nature of open-loop systems necessitates large volumes of cooling water withdrawal. However, water consumption

(atmospheric losses) is only due to enhanced downstream evaporation from heated discharge water, and is much smaller than corresponding withdrawals.

Use of open-loop cooling in industrialized nations has decreased significantly since the 1970’s, however, some developing economies such as China and India still rely heavily upon it because of the relatively lower cost (in comparison to other cooling options) (Graedel, 2010). In the United States only ten new thermoelectric power plants with once-through cooling have been constructed since 1980 (USDOE, 2006).

Nevertheless, according to a 2009 report from the U.S. National Environmental

Technology Laboratory (NETL) approximately 42.7% of existing thermoelectric power generating capacity utilizes once-thorough cooling (NETL, 2009). This is likely due to the fact that there are many older plants still in operation that were built when open-loop cooling was the standard (as the typical operating life of a power plant can be 30 or 40 years). The decreasing trend for open-loop cooling systems is expected to continue in the future for the U.S. due to recent revisions of the 1972 Clean Water Act (specifically

Sections 316(a and b) and 303(d) (Dziegielewski, 2006). The revisions place more stringent regulations on thermal discharges and the performance of cooling water intake

6 structures, to minimize potential negative impacts on wildlife and ecosystems. Indirectly, these revisions push for the use of recirculated cooling (in place of open-loop) because of the large water withdrawal and thermal discharge associated with once-through systems.

2.1.2. Closed-loop cooling

In closed-loop cooling water is used multiple times for the cooling process, thus it is commonly referred to as recirculated cooling. Power plants utilizing closed-loop cooling function similar to those with open-loop installations. However, thermally degraded water is pumped to on-site cooling towers or ponds, allowed to decrease in temperature, and then used for cooling again, instead of being discharged back to its withdrawal source. This methodology of reuse for closed-loop cooling systems significantly decreases water withdrawal by as much as 95% in comparison to open-loop systems, although much of this water is evaporated to the atmosphere (unlike open-loop systems which have a low ratio of consumption to withdrawal) (Sovacool, 2009;

USDOE, 2006). However, even with these small increases in consumption, recirculated cooling is widely considered more sustainable with respect to water (than once-through cooling), because of the significant reduction in withdrawal amounts (Graedel, 2010;

NETL, 2009). The lower consumption values for open-loop cooling may seem appealing, however water withdrawal is more important than water consumption because withdrawal quantities are a true representation of how much water a power plant needs to operate as opposed to what is lost to evaporation (and has potential to re-enter the local watershed through precipitation).

Allowing for contact between thermally degraded water and air is the general principle behind recirculated cooling. In a cooling tower, hot water is pumped from ground level to a slightly higher elevation in the lower portion of the tower. The water is then released (my means of a sprinkler system or something similar) and allowed to fall back to ground level into a holding basin beneath the cooling tower. Contact with air from the tower sides, as well as from below, cools water (by means of evaporation) during the falling process (Dziegielewski, 2006). The use of cooling ponds is based on similar principles, however ponds (not towers) are utilized (hence the name) to store heated water, allowing it to cool over time by means of air contact. In pond cooling, the need to store water for extended time periods can significantly increase demands for water withdrawal (more comparable to open-loop than recirculated towers).

Approximately 41.9% of current U.S. thermoelectric generating capacity utilizes systems with cooling towers, and cooling ponds account for around 14.5% (NETL, 2009). The frequency of closed-loop cooling is expected to increase in the future (for the U.S.) because of recent revisions to the 1972 Clean Water Act (as described in Section 2.1.1).

2.1.3. Dry cooling

Dry cooling systems (also known as air-cooled systems) utilize air, either directly or indirectly, in place of water to dissipate heat at power generating facilities. Direct dry cooling is based around the use of an air-cooled condenser (ACC). Heated turbine exhaust steam moves through tubes inside an ACC while transmitting heat to surrounding ambient air, by means of conductive heat transfer (NETL, 2009). Indirect dry cooling utilizes a very similar procedure. The main difference is that the turbine exhaust steam is

8 first condensed before the use of an ACC, thus heat is conductively transferred from water (not air) to ambient air. There is a small water requirement for indirect dry cooled systems (extremely small in comparison to open and closed-loop), and direct dry cooling systems have zero operational water demand because the steam condensation process is avoided.

The NETL reports that dry cooling installations only account for approximately

0.9% of current U.S. power generating capacity (NETL, 2009). Problems associated with the operational efficiency of these systems prevent them from being implemented at more power-generating facilities, as inefficient systems can lead to increased fuel consumption and emissions. Similar to cooling systems which utilize water, dry cooling system efficiency is still based on the principle of temperature differences. The problem is that in most environments ambient air temperature is greater than that of water so there is less potential for a temperature drop (and therefore reduced efficiency) in a cooling system utilizing air. The issue of system end temperature associated with air-cooling is worst in warm, dry regions due to higher ambient air temperatures (up to 25% decreases in operational efficiency have been observed), which is unfortunate because the low water need associated with dry cooling would otherwise be optimal in such climates with limited water resources (USDOE, 2006).

2.1.4. Combined cycle power generation

Combined cycle technology is beginning to set a new standard for fossil fuel generation. This technology combines gas turbine and steam turbine generation, increasing the total possible electric-power output at a fossil fuel plant. Gas turbine

9 generation is the mechanical driving of turbines from fossil fuel combustion gases (not steam from heated water). The heat from the combustion process is then captured in a heat recovery steam generator to produce additional electricity in the typical steam-driven turbine manner. Contributions of steam turbine power account for approximately one- third of the total power production in a combined cycle plant, with gas turbines generating the other two-thirds (NETL, 2009). Water demand is therefore reduced for combined cycle power because more electricity is being generated by burning the same amount fuel (compared to a corresponding non-combined cycle power plant), resulting in less water required per unit of electricity generated. Combined cycle technology is common for natural gas generation, with natural gas combined cycle (NGCC) power accounting for approximately one third of total natural gas generation in the U.S. (IEA,

2009; NETL, 2009). However, coal-fired generation coupled with combined cycle technology, known as integrated gasification combined cycle (IGCC) power, is still categorized as an emerging technology with only trial plants in operation (Dziegielewski,

2006; USDOE, 2006).

2.1.5. Carbon capture and storage technology

Coinciding with growing concerns of climate change, the proposed use of CCS technologies at coal-fired (as well as natural gas and biomass) power plants is becoming more popular (IEA, 2008 and 2009). This further stresses the importance of considering issues relating power generation and water needs, as the addition of CCS equipment at power plants can significantly increase water demand. When discussing a typical coal- fired (pulverized coal) power plant, the “carbon capture” part of the CCS process requires

10 additional water for the cooling of flue gas and the removal of heat from carbon dioxide compressor intercoolers and auxiliary power units (NETL, 2009). Additionally, the carbon “storage” part of the CCS process increases water demand because up to 30% of a plant’s generated electricity is consumed by transforming and pumping the captured carbon dioxide into the subsurface (NETL, 2009). The extra power that must be generated as an offset to this 30% “parasitic” electric load creates an increased water demand. Use of CCS technology coupled with IGCC generation creates similar water demand increases; however increases from the “carbon capture” part of the CCS process are mainly due to steam requirements in the water gas shift reaction (NETL, 2009). More details discussing specific values of water demand increases associated with CCS (and what is selected for use in this paper’s model) can be found in Chapter 4.

2.1.6. Salt versus freshwater

The water requirements for power generation discussed throughout this research are in reference to freshwater resources. Using saltwater (sea water or saline groundwater) for cooling is possible (and actually accounted for 28% of U.S. thermoelectric water withdrawals in 2005); however, saltwater is rarely used (less than

0.25 of that 28%) for recirculated cooling because it can cause severe (and costly) equipment corrosion (Kenny, 2009). Therefore as closed-loop cooling is becoming more the standard (see Sections 2.1.1 and 2.1.2), the future potential use of saltwater for electric-power generation will be minimal. Although, in some coastal or desert-like regions (like the western U.S. and Middle East) saltwater use may still play an integral

11 role due to its availability (coupled with severe limitations in freshwater resources as well as increasing population and water demand) (Graedel, 2010; Kenny, 2009).

2.2. Hydroelectric generation

Water acts as the mechanical driver for electricity-generating turbines in hydroelectric power instead of heated steam. Therefore no cooling system is required, and the water withdrawal is considered to be zero (even though the presence of water is necessary for generation). On the other hand, hydroelectric generation is considered to consume water because of induced evaporation as a result of creating water storage reservoirs behind dams (USDOE, 2006). It should be noted that hydropower is the only type of electricity generation that is considered to consume water while reporting zero withdrawals (for all other types water consumption is a portion of the withdrawal).

2.3. Generation from renewables

This paper classifies wind, solar photovoltaic (PV), and tide and wave (ocean) electric-power as “renewable” because not only are the energy sources renewable (i.e., non fossil fuel) but their corresponding generation processes have no operational water needs. Wind and ocean energy use mechanical force to drive turbines (like hydropower), thus no water is required because a cooling system is not used. Similarly, power generation at a solar PV plant requires no water for operational purposes because PV cells (semiconductor devices) are used to directly convert solar energy into electricity

(IEA, 2008).

2.4. Summary

The general conclusion to be drawn from Chapter 2 is that the operational water demand for electric-power generation has potential to be very diverse. Figures 2.1 and

2.2 respectively illustrate typical water withdrawal and consumption ranges (and averages) for power generation modified from a 2006 report from the U.S. Department of

Energy (USDOE) (USDOE, 2006). The figures exemplify how different fuel sources, cooling systems, and technological variations can all alter the water requirements of power plants. These USDOE statistics will be combined with multiple other datasets to estimate the water withdrawal and consumption factors for different power plant types in order to calculate a general water demand associated with power generation into the future (described in detail in Chapter 4). However, before calculations of water demand can begin a reliable dataset projecting future power generation must first be obtained.

Figure 2.2. Same as Figure 2.1 except now displaying statistics for water consumption.

Chapter 3: Projections of electric-power generation

The potential distribution of electric-power generation by fuel type in 2050 has very different outlooks based on how society, governments, and ultimately policy change in response to growing concerns about GHG emissions and climate change. This paper selects future energy statistics from the IEA, which are mainly based on population and

GDP growth (IEA, 2008 and 2009). The IEA has annually published its World Energy

Outlook (WEO) series since 1998 focusing on projections of future energy demand

(which includes electricity generation) for the world as a whole, and selected countries of focus. Every other year the WEO focuses discussion on carbon dioxide emissions related to energy, and future projections are split into two different scenarios. One is usually used to represent an extension of current energy practices from that time, while the second embodies the recommendations necessary to combat changes in the global climate.

The IEA’s most recent WEO makes use of the following two scenarios: (1)

Reference scenario and (2) 450 scenario (IEA, 2009). The Reference scenario (from now on abbreviated as REFS) is “a baseline picture of how global energy markets would evolve if governments make no changes to their existing policies and measures” (IEA,

2009). The 450 scenario (henceforth 450S) is a carbon-active energy initiative based on

“a coordinated global commitment to ultimately stabilize the concentration of GHG

16 emissions in the atmosphere at 450 parts per million (ppm) of carbon dioxide equivalent”

(IEA, 2009).

The compiled energy data used throughout this paper is shown in Figures 3.1.A through 3.6.A (IEA, 1998, 2000, 2004, 2006, 2007, 2008, and 2009). Included is current electric-power generation statistics (for 2007), and the two scenarios of future projections

(REFS and 450S) for 2020, 2030, and 2050. Figure 3.1 displays global projections and

Figures 3.2 to 3.6 correspond (respectively) to country-level projections for the United

States, China, India, Japan, and Russia, which were chosen based on the availability of data. This paper will use these future energy projections in order to estimate the water demand for electric-power generation into 2050, however, some alterations must be made such that the data is complete enough to be used as a model input. The following sections discuss the modifications (and associated assumptions) made to the original IEA energy data necessary in obtaining the finalized power generation statistics presented in

Figures 3.1.A through 3.6.A.

3.1. Combining IEA datasets

Future energy projections from WEO publications do not extend beyond the year

2030. The IEA publishes another annual series, Energy Technology Perspectives (ETP), which projects energy to 2050. These ETP statistics are not adequate as a sole data source for this research because they are only available at the global (or other large-scale) level and the country-level values desired are unavailable (IEA, 2008). To provide country-specific energy projections to 2050 as accurately as possible, the ETP data to

2050 (IEA, 2008) is used in combination with the WEO country-level data to 2030 (IEA,

2009). Nevertheless, combining these datasets poses a problem because their energy scenarios are slightly different.

3.1.1. Combining global scenarios

The ETP makes projections of future global energy by means of the Baseline,

Act, and Blue scenarios, which all differ from the WEO's REFS and 450S (IEA, 2008 and 2009). The Baseline scenario corresponds well to the REFS, as the Baseline scenario is described as being “consistent with the World Energy Outlook 2007 Reference scenario for the period 2005 to 2030” (IEA, 2008). Since the 2009 WEO is described as an update of the 2007 edition (IEA, 2009), it would make justifiable sense to use the 2050

Baseline scenario projections (IEA, 2008) as a continuation of the 2030 REFS projections

(IEA, 2009).

The Act and Blue scenarios from the 2008 ETP are more difficult to handle because both are carbon-active energy projections concerned with limiting global climate change (IEA, 2008), however, neither is directly related to the WEO 's 450S (IEA, 2009).

The Act scenario is based on the goal of “bringing global energy carbon dioxide emissions in 2050 back to 2005 levels,” while the goal of the Blue scenario is to half the

2005 emission levels by the year 2050 (IEA, 2008). These scenario descriptions differ from the 450S's goal, which is to “restrict the global temperature increase to two degrees

Celsius” (IEA, 2009). However, IEA further describes the Blue scenario as allowing for a maximum “global rise in temperature of two to three degrees Celsius” (IEA, 2008).

Therefore, due to similarities in the scenario descriptions concerning allowable global atmospheric temperature increases, the Blue scenario projections to 2050 (IEA, 2008) are

18 used as an extension of the 450S projections to 2030 (IEA, 2009). Figure 3.7 illustrates the total world power generation for all five scenarios discussed above (REFS and 450S from the WEO, and the Baseline, Act, and Blue from the ETP). This figure exemplifies the general similarities between scenarios (specifically the 450S and Blue, as well as the

REFS and Baseline), and provides further justification for the data extensions from 2030 to 2050 described in this section.

3.1.2. Modifications to the 2050 global projections

Before it could be used as an extension of the 2030 data, the 2050 global power generation statistics had to be modified due to limitations in the available IEA datasets

(IEA, 2008 and 2009). For some fuel types, the Baseline scenario (to 2050) was not an accurate extension of the 450S. This is likely because the Baseline scenario data does not reflect updates made to the WEO REFS from 2007 to 2009, because (as cited earlier) the

2008 ETP derived its Baseline scenario from the 2007 WEO, not the updated 2009 version (IEA, 2008 and 2009). Discrepancies are seen for oil, hydroelectric, wind, solar, and tide and wave power generation. For example, the REFS shows wind power generation increasing at an average rate of 57.1 TWh/year between 2015 and 2030 (from

678 to 1,535 TWh), however, the 2008 ETP Baseline scenario only projects wind generation in 2050 to be 1,208 TWh (a lower value compared to the updated 2030 projection) (IEA, 2008 and 2009). Due to obvious discrepancies (like this wind example) in future data trends, manual alterations were made to the 2050 Baseline scenario projections by extending the average yearly generation increases seen from 2015 to 2030 out to 2050.

To demonstrate one of these manual alterations we can continue the wind example previously mentioned. If wind power generation continued to increase at an average of 57.1 TWh/year (the rate from 2015 to 2030) by 2050 power generation from wind would be approximately 2,678 TWh (and this value is used in place of the non- updated ETP projection of 1,208 TWh) (IEA, 2008 and 2009). This procedure of manually altering 2050 projections is applied to oil, hydroelectric, wind, solar, and tide and wave power generation in the REFS, and hydroelectric generation in the 450S.

3.1.3. Country-level data to 2050

Sections 3.1.1 and 3.1.2 basically describe how energy projections to 2050

(published by IEA in 2008) were used to extend updated projections to 2030 (published by IEA in 2009) out to 2050, focusing on the global level. These methods cannot be used for the country-level statistics (from IEA’s 2009 WEO) used in this research because projections to 2050 (from IEA’s 2008 ETP) are not available in a country-specific form

(only global data and selected country groupings are available). Instead, observed percentile changes of fuel types in the global data from 2030 to 2050 (seen in the WEO and ETP datasets from IEA as described in Section 3.2.1) are used as a model to extend country-level projections from 2030 to 2050. For example, in the REFS for global data coal generation increased by about 69% from 2030 to 2050 (IEA, 2008), so this 69% increase in coal usage is applied to the 2030 coal generation projections for each country of focus in order to estimate their coal demand in 2050. This methodology is applied to each fuel type separately for both future energy scenarios (REFS and 450S). One exclusion is IEA’s category of other renewable (explained in Section 3.2) which was left

20 grouped together to extend country-level projections out to 2050 and then split into individual fuel types by utilizing the same method for global data described next in

Section 3.2.

The main problem with relating country-level changes to global data is that some countries do not exactly mimic the global trends for future energy changes. One example is the Japan data for the 450S in 2050 (Figure 3.5.A). In extending power generation from 2030 to 2050 using global trends, Japan is the only country with larger values for the 450S in comparison to the REFS. This discrepancy is most likely observed because

Japan’s projected energy breakdown by fuel type in 2030 has some significant differences from the world as a whole (such as only 5% coal but nearly 45% nuclear in comparison to 24% and 18% respectively for the world) (IEA, 2009). This creates a poor extension of the 450S from 2030 to 2050 when using average global increases (resulting in the 450S having a greater power demand than the REFS in 2050).

3.2. Breakdown of fuel types in the 450 Scenario

The 450S statistics groups power generation from multiple fuel types into one larger category, thus some assumptions must be made in order to separate this aggregated data because of differences in water demand. Specifically, a breakdown of data is not available for power generation coming from IEA’s category of other renewables, which includes biomass and waste, geothermal, solar, and tide and wave (note this is different from the “renewables” category used throughout this paper which encompasses solar PV, wind, and tide and wave generation) (IEA, 2009). However, the Blue scenario (from

IEA’s 2008 ETP) does contain specific values for each fuel type within IEA’s “other

21 renewables” category. Therefore, due to the similarities between the Blue scenario and the 450S (as described in Section 3.1), the Blue scenario data breakdown can be used to estimate the distribution of IEA’s “other renewables” category in the 450S.

Following is an example of one of these estimations: viewing global data for the

Blue scenario in 2050, geothermal power is projected to make up 13.5% of the generation from IEA’s aggregate category of other renewables (IEA, 2008). This 13.5% from the

Blue scenario (fraction of other renewables generation in the form of geothermal) is therefore used to separate the aggregate data of other renewables in the 450S. The general methodology demonstrated in this example is applied to all four fuels grouped in

IEA’s other renewables category (biomass and waste, geothermal, solar, and tide and wave).

3.3. Power generation trends

Observing trends in IEA's energy projections (Figures 3.1.A through 3.6.A) it can be seen that power generation is expected to significantly increase by 2050 in both the

REFS and 450S. For example, the global average increase in electricity generation

(Figure 3.1.A) from 2007 to 2050 is projected to range from 52% (450S) to 74% (REFS)

(IEA, 2008 and 2009). The country level data follows similar trends; however, countries can be divided into categories based on the severity of their expected increases in energy demand. The developing nations of China and India have more room for growth

(currently lower standard of living), so their projected range of increased power generation is very high (167-322% and 293-523% respectively) (IEA, 2008 and 2009).

Conversely, the United States, Japan, and Russia, being more developed countries, have

22 less potential for future growth and therefore have lower ranges of expected energy increases (68-88%, 60-75%, and 86-103% respectively) (IEA, 2008 and 2009). These energy trends are important because quantities of electric-power are directly proportional to associated operational water requirements, and now with finalized future power generation projections work can progress forward to begin model-calculating values of respective water demand.

Figure 3.2. Same as Figure 3.1 except now showing data for the United States. 25

Figure 3.3. Same as Figure 3.1 except now showing data for China. 26

Figure 3.4. Same as Figure 3.1 except now showing data for India. 27

Figure 3.5. Same as Figure 3.1 except now showing data for Japan. 28

Figure 3.6. Same as Figure 3.1 except now showing data for Russia. 29

Chapter 4: Methodology, assumptions, and results

The water requirements for electric-power generation are estimated to 2050 by using water withdrawal (and consumption) averages for different power plants (see

Chapter 2) in combination with future projections of energy (see Chapter 3). Both the

REFS and 450S are used to estimate potential future water demand in order to observe how water use can change based on carbon-active energy decisions and to assess the sustainability of IEA’s power generation projections (IEA, 2008 and 2009). However, prior to estimating water requirements in 2050, 2005 statistics of U.S. electric-power generation are used to estimate corresponding water needs, and then comparisons are made to actual water withdrawal data from the United States Geological Survey (USGS)

(Kenny, 2009). This is done in order to develop and validate the power-to-water calculation model used throughout this research.

Figures 3.1.B (and C) through 3.6.B (and C) show the model-calculated results of operational water requirements, which correspond respectively to the power generation datasets shown in Figures 3.1.A through 3.6.A. In these figures the years 2020, 2030, and 2050 are the results of the future models (described in this chapter) and the 2007 water values represent an application of the 2005 model (also described in this chapter) to

2007 power generation statistics in order to approximate current operational water needs.

The sections that follow describe the methods and assumptions used to obtain these

31 model-calculated results of operational water requirements for electric-power generation.

Discussion is split into two sections: (1) 2005 model, explaining the selection of current water withdrawal and consumption factors (and other necessary statistics) and validation with data from the USGS, and (2) future models, explaining modifications to the 2005 model used to estimate water demand to 2050 for the REFS and 450S.

4.1. 2005 model

4.1.1. General assumptions

Due to the variability of water withdrawal and consumption factors at different power-generating facilities the first step necessary in setting up a model to theoretically estimate the water demand of power generation is to establish some general assumptions, such as what cooling technologies are utilized with different source fuels or how much natural gas generation uses combined cycle technology. The following sections outline the general assumptions used in establishing the 2005 model.

4.1.1.1. Distribution of cooling technologies

Table 4.1 displays a percentage distribution of cooling technology utilization based on fuel type (NETL, 2009; USDOE, 2006). Use of this data is based on the assumption that power generating capacity has a similar breakdown to that of actual power generation, which is necessary because the NETL statistics are based on potential power generation capacity, and not actual power generation. This assumption should not compromise the validity of obtained results because research from the NETL comparing

U.S. regional breakdowns of thermoelectric (and coal-fired) power in 2005 (and 2030 projections) display very few instances where capacity and generation have significantly

32 different distributions (NETL, 2009). As shown in Table 4.1, dry cooling is factored into this analysis by accounting for it as a small percentage of each generation type (which has no associated water demand). It should be noted that the NETL explains that their

59% dry generation reported for combined cycle power is likely an over-estimate due to missing data from surveyed NGCC plants; however, this value is used nonetheless based on availability (NETL, 2009).

4.1.1.2. Solar power generation

CSP or concentrated solar power generation (also called solar thermal) and solar

PV systems have different operational water demands. CSP is a form of thermoelectric generation requiring cooling water while PV systems directly convert sunlight into electricity and are considered renewable electricity. The country-level datasets from IEA aggregates solar power into one category (IEA, 2009). Therefore, for the 2005 model all solar power generation is considered to be CSP because it is the most conservative standpoint when considering potential water usage. This assumption may produce small over-estimates in 2005 water demand calculations, however these changes are negligible due to the fact that solar power accounts for such a minimal portion of current total power generation (see Figures 3.1.A through 3.6.A) (IEA, 2008 and 2009).

4.1.1.3. Combined cycle generation

The IEA’s energy data does not specify how much natural gas generation is in the form of combined cycle. This value is estimated by dividing the current U.S. combined cycle generating capacity (NETL, 2009), by the total U.S. natural gas generating capacity

(IEA, 2009). Using the assumption of interchangeability between capacity and

33 generation statistics (see Section 4.1.1.1), this calculation yields the following: approximately one-third of current natural gas generation utilizes combined cycle technology.

4.1.1.4. Coal-fired generation

Coal-fired power plants vary in water demand based on subcritical versus supercritical boiler operation, and differences in flue gas desulfurization (FGD) installations. Variations from boiler operation are factored into the 2005 model by using weighted averages based on the percentage breakdown for subcritical versus supercritical boiler utilization. According to the NETL, 73% of current U.S. coal-fired power facilities utilize subcritical boiler operation, and the remainder is supercritical (NETL,

2009). Differences in FGD setups (wet, dry, or none present) are accounted for simply by averaging, partially due to a lack of detailed statistics providing a percentile breakdown of types of FGD installations, but mostly because fluctuations in water demand based on FGD factors are negligible compared to variations from boiler operations or cooling system type (NETL, 2009).

4.1.2. Current water withdrawal and consumption factors

With all general assumptions established, the second (and final) step in setting up the 2005 model to theoretically estimate the current operational water demand of power generation, is to select representative water requirement factors (in volume of water per unit of power generated, this paper uses m3/MWh) for different power-generating facilities. Due to the variability of water needs at power plants according to source fuel and installed technologies (see Chapter 2), water withdrawal factors are chosen using a

34 result optimization methodology such that error is minimized between theoretically calculated values (produced by the 2005 model) and actual water withdrawal statistics

(from the USGS).

Table 4.2 displays the chosen water withdrawal and consumption factors for the

2005 model which produced minimal error (3.5% based on total) when comparing theoretical water withdrawal (7.90 x 108 m3/year total; 7.19 x 108 for open-loop and 0.71 x 108 for closed-loop) to 2005 water withdrawal statistics (7.62 x 108 m3/year total; 7.00 x 108 for open-loop and 0.62 x 108 for closed-loop) from the USGS (Kenny, 2009). As denoted in the table, some values were selected from one specific source, while other values were chosen by comparing and averaging data from multiple sources. It should be noted that by changing certain withdrawal/consumption values in Table 4.2 it is possible to have other data combinations that result in 3.5% error (or less), however, all values selected were picked for their consistency of use in other published work. Additionally, the water withdrawal ranges in Figures 2.1 (USDOE, 2006) were used as a guideline to ensure that values selected from other datasets (Dziegielewski, 2006; NETL, 2009) were accurate, and other sources were also consulted to ensure that chosen values were representative when comparing similar power plants (Feeley, 2008; Yang, 2007). The following is a brief explanation of some additional procedures (beyond the result optimization method just described) that were necessary in selecting the values found in

Table 4.2.

4.1.2.1. Fossil fuel generation with open-loop cooling

The operational water demand factors for open-loop cooling for coal, oil, natural gas, and biomass and waste generation from Dziegielewski el al. are reported as one aggregate category of fossil fuels (Dziegielewski, 2006). However, according to the

NETL and Feeley et al. non-coal fossil fuel plants typically have reduced water demand in comparison to coal-fired plants (Feeley, 2008; NETL, 2009). To account for these differences, the ratio of water demand for coal power plants compared to fossil-non-coal from the NETL was used as a conversion factor to reduce the reported value for fossil fuel generation from Dziegielewski el al. (Dziegielewski, 2006; NETL, 2009). Excluded from this ratio reduction rule is biomass and waste generation, which is assumed to require the same amount of water as coal-fired power, and not a reduced value like oil and natural gas (consistent with the NETL and USDOE) (NETL, 2009; USDOE, 2006).

4.1.2.2. Nuclear generation with closed-loop cooling

For most power generation types, closed-loop cooling towers withdraw and consume different amounts of water in comparison to cooling pond installations, however, there is some discrepancy for nuclear power in published datasets. This research assumes no difference in water demand for nuclear cooling towers versus ponds.

This assumption is based on compatibility with the 2005 model, and remains consistent with research from numerous sources (Feeley, 2008; NETL, 2009; USDOE, 2006).

4.1.2.3. Dry cooling

The NETL reports a very small operational water requirement at air-cooled power plants (NETL, 2009). One example is that a NGCC plant with dry cooling still requires some water for gasification processes. Values such as these are negligibly small in

36 comparison to annual water use totals, so they are excluded from this paper and all dry cooling facilities are assumed to have zero water demand.

4.1.2.4. IGCC generation

IGCC power is not factored into the 2005 model because there are no facilities in the U.S. currently used for commercial generation (Dziegielewski, 2006; IEA, 2008;

USDOE, 2006). However, water demand factors for IGCC generation shown in Table

4.2 are included for reference purposes, because they will be needed for later analysis.

4.2. Future models

It is nearly impossible to accurately quantify the water demand for power generation in 2050. Not only will future actual power generation likely be different from projected data (used as calculation inputs), it is also extremely difficult to accurately estimate future power plant changes in cooling technology use or efficiency improvements. Nevertheless, numerous published papers have attempted to approximate future water withdrawal and/or consumption by creating a variety of cases or scenarios each based on a specific set of assumptions (Dziegielewski, 2006; Feeley, 2008; Kenny,

2009; Shen, 2008; EPRI, 2002).

This research tries a different approach and attempts to determine what factors of power generation have the greatest influence on future water requirements, with a goal of ultimately providing an outlook of power generation that considers sustainability holistically and not just from a climate change (GHG emissions) perspective. To achieve this, water demand in 2050 must be estimated in some sort of a typical or “baseline” setting (something comparable to current water use policy), and then (later in Chapter 5)

37 a sensitivity analysis can be performed to reveal what parameters of power generation have the greatest impact on water demand.

The future models (REFS and 450S) of water withdrawal and consumption (to

2050) are calculated using assumptions very similar to those used in the 2005 model

(distribution of cooling types by fuel source from Table 4.1, water withdrawal and consumption factors from Table 4.2, etc.), which represents a “business-as-usual” water policy setting. However, as explained in the following sections, some changes to the

2005 model were necessary in order to ensure the validity of the results obtained from the future models.

4.2.1. Updates to solar power generation

Table 4.3 shows the average global statistics for solar energy from the IEA, which are used for the future models (IEA, 2008 and 2009). These updates for solar energy are included because the assumption made for solar power in the 2005 model, described in

Section 4.1.1.2 (all solar considered CSP), produces increasing error as more solar power is utilized. For the 450S model, in addition to the updates for solar energy (which are the same as the REFS because GHG emissions are not affected by changes in CSP versus solar PV power), operation of CCS equipment also has to be considered.

4.2.2. Inclusion of carbon capture and storage

The inclusion of CCS operations at power plants as part of the future models is integral for this paper because of the significant increase in associated operational water requirements (as discussed in Section 2.1.5). To quantify these increases, data from the

NETL was analyzed and the following two conclusions concerning CCS technologies

38 were obtained: (1) on average operational water demand (withdrawal and consumption) doubles for coal, natural gas, and biomass fired power plants, and (2) on average water demand increases 1.5 times for combined cycle power plants (NGCC and IGCC) (NETL,

2009). Displayed in Table 4.3 are the average global projections for CCS usage from the

IEA which are used as input for the future models (IEA, 2008 and 2009). As seen in the table, projected use of CCS technologies is extremely large in the 450S and negligibly small (or nonexistent) in the REFS, and therefore any CCS associated with the REFS is excluded from analysis.

4.3. Additional model assumptions

Based on the availability and quality of data, all water use factors for power plants

(and other water demand statistics) used as model parameters/inputs are in reference to power generation facilities in the United States (unless it was noted that global data was used). It is likely that this will create some error in the model results for the other countries included in this research (China, India, Japan, and Russia) due to differences in energy and/or water policy and the availability of cost-effective technology. However, as discussed next in Section 4.4, model results are intended for use as water demand indicators and not actual future predictions, therefore applying U.S. statistics to other countries is an acceptable methodology.

4.4. Discussion of results

The model results for both scenarios show that water demand is expected to significantly increase from 2007 to 2050. For the REFS increasing water requirements closely match trends of increasing power generation (especially thermoelectric). On the

39 other hand, growing water needs in the 450S can be attributed to the use of CCS technology. However, one key item that should be pointed out about the future model results is that the calculated water withdrawal (Figures 3.1.B through 3.6.B) and consumption (Figures 3.1.C through 3.6.C) values should not be used as a strict interpretation of actual future water use (see Section 4.2 for discussion of problems associated with calculating values into 2050). These values coincide with IEA’s energy projection scenarios (REFS and 450S), which are slightly extreme (and unlikely) future predictions where technological advancement either ceases to exist (REFS) or is expected to exponentially increase in order to minimize GHG emissions (450S) (IEA, 2008 and

2009).

To be specific with respect to the future model results, problems arise with the

REFS as it likely overestimates values for more developed countries, like the United

States, Japan, and Russia, because their distribution of cooling technologies will probably move to a more water conservative breakdown by 2050 (in comparison to the current

U.S. data in Table 4.1). Similarly, there are problems with the 450S being that it is an unrealistic scenario for developing nations, like China and India, because they will likely not be able to maintain usage of CCS technology according to the average global future projections in Table 4.3. However, even with problems such as these, the future model results still serve as effective indicators to show that the water needed for power generation in 2050 is expected to be much greater than current (2007) demands, whether in a carbon mitigating energy setting (450S) or one where energy policy remains

“business-as-usual” (REFS). Therefore the remainder of this paper focuses on addressing

40 the following question: what must be done in order to meet future energy demands, while attempting to minimize GHG emissions, without putting strain on available water resources (or other potentially limiting resources like land or trace metals)?

Table 4.1. Percentage breakdown of cooling technologies according to the type of electric-power generation (used in the 2005 model). Data is modified from the NETL and the USDOE (NETL, 2009; USDOE, 2006). a As reported by the USDOE nearly all CSP and geothermal power plants use recirculated cooling towers for their cooling system. b Includes oil, natural gas (non-combined cycle), and biomass and waste generation.

Cooling Technology Distribution (2005 model) Percentage (%) Closed-loop Generation Type Pond Tower Open-loop Dry Coal 12.7 48.0 39.1 0.2 Fossil non-coalb 17.1 23.8 59.2 0 NGCC 1.7 30.8 8.6 59.0 Nuclear 18.3 43.6 38.1 0 Geothermala - 100 - - Solar thermala - 100 - -

Table 4.2. Operational water withdrawal and consumption according to generation type (used in the 2005 model).

a OL and CL stand for open-loop and closed-loop respectively. b Data is from the USDOE (USDOE, 2006). c Data is from the NETL (NETL, 2009). d Data is from Dziegielewski et al. (Dziegielewski, 2006).

Water Withdrawal and Consumption by Generation Type Water Use Factor Generation Type (m3/MWh) Energy Source Cooling Systema Withdrawal Consumption Coal OLc,d 166.6 0.4 CL Towerc 2.0 1.7 CL Pondc 64.8 2.2 IGCC (Tower)c 0.9 0.7 Oil OLc,d 146.4 0.3 CL Towerc 0.9 0.6 CL Pondc 29.9 0.4 Natural Gas OL (fired)c,d 146.4 0.3 CL Tower (fired)c 0.9 0.6 CL Pond (fired)c 29.9 0.4 NGCC OLc 34.1 0.1 NGCC CL Towerc 0.6 0.5 NGCC CL Pondc 22.5 0.9 Nuclear OLd 182.1 1.1 CL Towerb,c 4.2 2.4 CL Pondb,c 4.2 2.4 Hydrob - - 17.0 Windb - - - Biomass & Waste OLc,d 166.6 0.4 CL Towerc 2.2 1.7 CL Pondc 64.8 2.2 Geothermalb CL Tower 7.6 5.3 Solar Thermalb CL Tower 3.0 3.0 Solar Photovoltaicb - - - Tide and Waveb - - -

Table 4.3. Modifications of the 2005 model applied to the future models. Compiled and adjusted from the IEA (IEA, 2008 and 2009).

a Value is excluded from analysis because it is negligibly small. b Value is an extension based on data from 2007 to 2030 (IEA, 2009).

Modifications of the 2005 Model Applied to the Future Models Item of Measurement 2020 2030 2050 % of coal plants with CCS (REFS) - - 0.01a % of coal plants with CCS (450S) 1.18 17.80 100.0 % of natural gas plants with CCS (REFS) - - 0.78a % of natural gas plants with CCS (450S) 0.52 4.20 75.71 % of biomass plants with CCS (REFS) - - - % of biomass plants with CCS (450S) - - 34.05 % of solar that is PV (REFS and 450S) 30.10 38.24 50.00b

Chapter 5: Sensitivity analysis

In theory trying to calculate the water requirements of power generation can be thought of as a very complex equation with potentially hundreds of variables. From observation of the model-calculated results of operational water requirements associated with power production (Chapter 4), as well as a little common knowledge of the water- energy feedback loop (Chapters 1, 2, and 3), it can be concluded that certain aspects of power generation (a few variables in that complex equation) play the most significant roles in determining water demand. For example, as described in Section 2.1.4, the use of combined cycle technology can reduce water needs by one-third through the use of gas turbines in combination with steam-driven turbines; while on the other hand changes in

FGD installations play a negligible role in determining water demand at coal-fired power plants (see section 4.1.1.4). The model results of the REFS and 450S both display two more factors of power generation that greatly impact water needs: for the REFS selection of fuel type (thermoelectric fuels demand more water than renewables, except for hydropower which has high level of consumption) and type of cooling system (open-loop cooling systems require large amounts of water), and for the 450S use of CCS technology

(more water is needed due to the creation of a “parasitic” electric load) and reduction of power demand (water demand can be lowered simply by generating less power).

The first section in this chapter presents the results of a model sensitivity analysis for the five above-listed factors of power generation which have significant impact on water demand: (1) power demand reduction (variable x1), (2) selection of fuel type (x2),

(3) type of cooling system (x3), (4) combined cycle technology (x4), and (5) CCS technology (x5). The sensitivity analyses will display how water requirements fluctuate with each of these factors/variables. Using the complex equation analogy, a sensitivity analysis can be thought of as an attempt to simplify this complex equation by holding all variables constant, except for one, and observing how the water needed for power generation is affected by altering one water demand factors (fluctuations of each of the five water demand factors are considered separately). United States energy statistics for the REFS will be used to perform a sensitivity analysis for each water demand factor and display the trending of results, and at the end of this chapter comparisons to other country-specific results will be made. The remainder of Chapter 5 will present the idea of a feasibility range associated with each of the five water-important-factors of power generation, and then, a maximum feasible value (for each variable) will be used to show how IEA’s future energy scenario’s can be altered (in a realistic manner) to be more water sustainable.

5.1. Sensitivity analysis inputs and outputs

Figures 5.1.A through 5.1.E illustrate the results of the model sensitivity analysis.

Each of these figures provides a display of how water requirements can change by making hypothetical modifications to one of the input parameters used in the power-to- water calculation model (see Chapter 4). In the figures, the x-axis shows the changing

46 percent value of a water demand factor (x1, x2, etc…) and the y-axis shows how water withdrawal (and consumption) varies accordingly (by use of a water multiplier, explained in more detail in Section 5.1.2).

To better understand the sensitivity analysis figures let’s take a closer look at

Figure 5.1.D as an example. One item that should stand out is the fact that the trend lines for withdrawal and consumption intersect at a water multiplier value of 1.0. This represents a “zero point”, in that if future energy use follows all the assumptions used in the REFS model for 2050 (see Section 4.2) then water demand will be as projected by the model calculations. Therefore, a water multiplier value of 1.0 is used to indicate no change from the original model value, meaning that if the original model-calculated water demand in 2050 is multiplied by 1.0 the same water demand value will be obtained.

In simple terms this point of “no change” in water demand represents that the x-axis value, amount of combined cycle usage, is identical to the value used as a model input for the 2050 REFS. This x-axis value for Figure 5.1.D is 12.5%, which is obtained by using a weighted average to balance out the original model input values of 33% NGCC for natural gas generation and 0% IGCC for coal generation (see Section 4.1.1.3).

The remainder of the figure moving away from the “zero point” (or point of no water demand change) displays how water requirements can hypothetically vary if the

[weighted average] x-axis value is altered (by using more or less combined cycle technology). For example, in 2050 if 75% of natural gas generation is NGCC (instead of

33%) and 25% of coal generation is IGCC (instead of 0%) the new x-axis value under these conditions would be a weighted average of 39.6% combined cycle usage. Using

47 this 39.6% x-axis input, the resultant water multipliers for withdrawal and consumption are both less than the “no change” value of 1.0 indicating that less water would be required in a scenario with 39.6% combined cycle usage in comparison to 12.5%. The exact amount of change in water demand can be determined from the water multiplier value (further explained in Section 5.1.2).

Each of the other water demand factors in Figure 5.1 can be interpreted in similar fashion to the combined cycle example just described. “Zero points” can be observed where the trend lines have a water multiplier value of 1.0 and this will correspond to the original model input values, like one third of natural gas as NGCC and no IGCC (for the combined cycle example). The original input values (using the REFS in 2050 for the

United States) for each of the other water demand variables are as follows: 8,137 TWh’s of total power required (for x1), a breakdown of fuel types matching Figure 3.2.A (for x2), a distribution of cooling technologies matching Table 4.1 (for x3), and zero CCS usage

(for x5).

5.1.1. Ranges for the x-axis

With all original input values being different for x1 through x5, each water demand factor’s corresponding 0 to 100 percent range on the x-axis also represents a different set of values. A simple example is power demand reduction (Figure 5.1.A), in which the range of model input values is 8,137 TWh (0% power reduction) to 0 TWh

(100% power reduction). Another simple example is CCS technology (Figure 5.1.E), where the range of model input values is no CCS usage (0% CCS) to total CCS usage at all coal, natural gas, and biomass power plants (100% CCS).

The range for fuel type selection (Figure 5.1.B) is more complicated because the 0 to 100% range of the x-axis is shown for a grouped category of renewables (wind, solar

PV, and tide and wave) and hydroelectric. For any given value of x2, like 80%, the breakdown of that 80% is not evenly distributed (at 20% each) between the four fuel types just listed. Instead, the fuel ratios from IEA’s original datasets are used to keep the distribution of renewables and hydroelectric (within that 80%) relative with respect to each other and corresponding to the original data (IEA, 2008 and 2009).

Another complicated range can be seen when considering types of cooling systems (Figure 5.1.C) because as exemplified by Table 4.l, every fuel type has a different breakdown of cooling technologies and (as described in Section 2.1) only thermoelectric generation can be considered for cooling system variations (because other fuels do not require cooling systems). Therefore the range of the x-axis for x3 is displayed according to how much (percent) closed-loop cooling towers are utilized within the category of thermoelectric power plants. In attempt to maintain a realistic scenario for this specific sensitivity analysis any given value of x3 (0 to 100%) is applied to all thermoelectric fuels, as it is unknown which power plant types would be favored for cooling system conversions in a future scenario where closed-loop systems are being used to replace open-loop ones. For the cooling system type sensitivity analysis dry cooling was excluded from consideration because it is already generally understood that this cooling technology is the most water conservative (and should therefore be used where conditions and finances allow), however, frequency of use is limited due to efficiency issues related to climate (see Section 2.1.3) (NETL, 2009). The last of the five

49 water demand factors, combined cycle technology (Figure 5.1.D), has an x-axis range showing what percentage of coal and natural gas plants are in the form of IGCC and

NGCC, respectively. Similar to the previous example (cooling system type) any given value of x4 is applied to both coal and natural gas generation, so an x4 value of 50% would represent that half of natural gas generation would be NGCC and half of coal generation would be IGCC.

5.1.2. Water multiplier

As each water demand factor fluctuates the resulting water withdrawal and consumption changes can be interpreted from the y-axis values of a so called “water multiplier” (WM). The WM is a relative value which is calculated by dividing the model results of the sensitivity analysis based on a specific value of an x variable, by the originally calculated value of water withdrawal (or consumption) for the United States

REFS in 2050 (Figures 3.2.B and 3.2.C). In simple terms the WM is what the original operational water needs (calculated from IEA’s scenario projections) is multiplied by to determine what water demand would hypothetically be with a different value of any water demand factor (x1 through x5). Therefore, a WM equal to one would mean equivalent to or no change from the 2050 REFS projected water needs, and a WM of two means double water requirements.

5.2. Sensitivity analysis results

As it can be seen from Figure 5.1, of the five water demand factors considered four of them show decreases in water withdrawal with greater values on the x-axis. Only use of CCS technology (Figure 5.1.E) illustrates a positive correlation where operational

50 water withdrawal increases as more CCS is used. Comparing the four water demand factors with decreasing trends of withdrawal (Figures 5.1.A through 5.1.D) a distinctive pattern of water consumption emerges: two factors cause water consumption to also decrease, while the other two actually cause it to increase. The decrease of water consumption observed for power demand reduction and use of combined cycle technology (Figures 5.1.A and 5.1.D respectively) can be attributed to the fact that these water demand factors improve power generation efficiency (by using less power or using less water to generate equivalent power levels).

The other two water demand factors, which display increases in water consumption (selection of fuel and type of cooling system, Figures 5.1.B and 5.1.C respectively), do so because closed-loop cooling towers and use of hydropower both act as tradeoffs where withdrawal changes inversely with consumption. Water withdrawal lessens as more recirculated cooling towers and renewable fuels (including hydroelectric) are used. However, more water is lost to consumption in closed-loop cooling towers

(compared to open-loop) because of water contacting air during the on-site cooling process (see Sections 2.1.1 and 2.1.2), and use of hydropower greatly increases water consumption due to evaporation at water storage reservoirs behind dams (see Section

2.2).

5.2.1. Comparison of trend line slopes

Aside from having a different relationship between water withdrawal and consumption, each water demand factor sensitivity analysis (Figures 5.1.A through 5.1.E) displays different linear slopes, meaning that each variable changes future operational

51 water requirements at different rates. For example, comparing Figures 5.1.B and 5.1.C, it can be seen that alterations of cooling system types can decrease water withdrawal faster than changing fuel types, but on the other hand using more renewables and hydropower increases consumption at a faster rate than the use of closed-loop cooling towers. Figure

5.2 provides a comparison summary of all trend line slopes (withdrawal and consumption) from the five sensitivity analyses figures (5.1.A through 5.1.E).

Figure 5.2 essentially allows for ranking of the five major factors of power generation (by comparing slopes) to show that selection of cooling systems has the greatest influence on water demand. Using closed-loop cooling towers (Figure 5.1.C) has potential to reduce water withdrawal at the greatest rate (slope of -0.021, negative to indicate decreasing); however, this decision also causes the second fastest increase in water consumption (slope of 0.005) behind fuel type selection (Figure 5.1.B, consumption slope of 0.027). Additionally, Figure 5.2 shows that IEA’s 450S may be problematic with respect to water requirements because of CCS technology, which shows increasing trend line slopes for withdrawal and consumption of 0.009 and 0.004, respectively (IEA, 2009). However, as also displayed by Figure 5.2 many other water demand factors have potential to offset the operational water increase rates from CCS

(such as the use of combined cycle technology, with trend lines slopes of –0.008 and –

0.002 for withdrawal and consumption, respectively).

5.2.2. Feasible range for water demand factors

In viewing the model sensitivity analyses (Figures 5.1.A through 5.1.E) it should be apparent that some sections of the water withdrawal and/or consumption trend lines

52 represent unattainable results because the input values from the x-axis are not feasible.

For example, when considering power demand reduction (Figure 5.1.A) it is not possible for any country to reduce power demand 100% (meaning zero power required) by 2050.

Therefore, to continue using the complex equation analogy, each water demand factor

(variables x1 through x5) can be thought of as having a restricted domain (within the 0 to

100% range described in Section 5.1.1), which can change based on future development and energy decisions. By using the IEA’s 450S power generation statistics as a source to restrict the domains for the water demand factors, the sensitivity analysis results can be used to predict how much water needs will increase from future CCS usage, and how each of the other water demand factors (power demand reduction, fuel selection, cooling system type, and use of combined cycle technology) can be utilized to offset these increases.

Based on global averages, the IEA’s 450S projects that the percent use of CCS for coal, natural gas, and biomass generation in 2050 will respectively be 100, 75.7 and 34.1

(IEA, 2009). In using a weighted average to account for the generation from each fuel type it can be approximated that up to 77.7% of all coal, natural gas, and biomass generation will be equipped for CCS in 2050, and therefore the maximum feasible x-axis value for Figure 5.1.E (CCS usage) is considered to be 77.7. Figure 5.3 displays the feasible range for each water demand factor’s x-axis (like 0 to 77.7 for the CCS example just used). The feasible ranges for fuel selection and power reduction are both directly from IEA’s 450S (like the range for CCS). IEA’s global statistics indicate that the use of

36% renewables and hydropower (12% hydroelectric, 12% wind, 11% solar, and 1% tide

53 and wave) and an average total power reduction of 15.5% are both feasible by 2050 (IEA,

2008 and 2009). Therefore, the maximum x-axis values for power reduction (x1) and fuel selection (x2) are 15.5 and 36, respectively. The feasible x-axis range for use of combined cycle technology (x4) is modeled to match the feasible range used for CCS

(x1), because it is reasonable to assume that any coal or natural gas plant able to be equipped for CCS should also be able to use combined cycle technology (IEA, 2008 and

2009; NETL, 2009). The only difference is that biomass is excluded from the weighted average procedure, and therefore the maximum feasible value for x4 is 86.2 (and not identical to the 77.7 used for CCS) (IEA, 2009). A future potential range for the distribution of cooling system types does not come from the IEA as water resources are not considered in their energy projections. Instead, the maximum percent value for use of closed-loop cooling towers (x3) is considered to be 100, based on the idea that current high usage rates and relatively low system costs indicate that complete use of recirculated cooling for all thermoelectric power plants (meaning a total elimination of once-through cooling) is feasible by 2050 (Dziegielewski, 2006; Hoffmann, 2004; IEA, 2008 and 2009;

NETL, 2009 and 2009b; Yang, 2007).

5.2.3. Possible changes in future water demand

When the maximum feasible value of any water demand factor is selected (see

Section 5.2.2), the resultant WM (as would be observed from each trend line in Figure

5.1) is an indication of the potential that each water demand factor has to increase or decrease future operational water needs. To continue the CCS example used in Section

5.2.2, when 77.7 (max CCS value by 2050) is used as an input value WM’s of 1.66 and

1.32 for water withdrawal and consumption (respectively) are obtained. This indicates that if usage of CCS technology in the future follows IEA predictions (according to a carbon-active energy scenario) water withdrawal in 2050 will be 66 percent greater (and

32 percent for consumption) than for an equivalent energy scenario (fuels, cooling, power needs all the same) without CCS. This poses severe threat to countries or regions with limited water resources and/or large expected population growth in the near future (see

Chapter 1 for examples).

Figure 5.4 depicts the possible changes in future water requirements (for each water demand factor) based on the maximum feasible value for each variable from Figure

5.3. The information in Figure 5.4 is displayed in a very similar manner to Figure 5.2, except now instead of seeing a rate (with no limitation) at which each variable can affect water demand, Figure 5.4 allows for observation of the total possible increases and/or decreases that are potentially achievable through realistic usage rate changes of each water demand factor. As explained by the previous example, the results show that according to IEA’s projected demand for CCS technology (in the 450S) water withdrawal and consumption in 2050 could be (respectively) as much as 66% and 32% greater than without CCS. However, this should not be a problem considering that complete use of recirculated cooling towers can lower future water withdrawal by up to 97% (and even if total use of recirculated cooling towers is not possible the use of combined cycle technology has potential to decrease water withdrawal and consumption by up to 64% and 15.6%, respectively). The use of renewable fuels and hydropower and reducing power demand are other effective methods to lower future power-associated water

55 withdrawals, however, both methods have drawbacks. Changing fuel types has potential to increase future water consumption by 53.4%, and power demand reduction has a very limited feasible x-axis range (methods of energy conservation can only go so far).

The simultaneous alteration of multiple water demand factors moving into the future provides the most effective route in reducing operational water requirements. This idea is especially important because not all countries will be able to achieve the maximum feasible value for each water demand factor (see Figure 5.3), therefore decreases in future water demand will have to come from a mixture of alterations in the energy sector. However, it should be noted that when multiple water demand factors are modified together the resultant changes in water needs cannot always be determined by summing the percentage results from each water demand factor. For example, if the 36% use of renewable fuels is accomplished this effects the extent that other water demand factors can change future water needs; changes in cooling systems would be less effective at reducing withdrawals because using more renewables would mean that less power is being produced by cooling-system-dependent thermoelectric systems, and CCS usage would increase water needs at a lower rate for similar reasons (there would be less fossil fuel generation available for CCS use).

5.3. Comparing other countries

To briefly summarize, Chapter 5 has thus far shown (by use of a model sensitivity analysis) that multiple different factors which affect the operational water demanded by power generation have potential for changes such that future water withdrawals and consumption can be significantly reduced (to near negligible levels), and this would

56 eliminate concern of any potential future increases in water needs associated with CCS usage. These factors include power demand reduction, the use of renewables and hydropower, the use of closed-loop cooling towers at thermoelectric power facilities, and the use of combined cycle technology.

By repeating the sensitivity analyses procedures using 2050 REFS energy statistics for other countries (see Figures 3.1.A through 3.6.A) similar data to that found in Figure 5.2 (and projected to Figures 5.3 and 5.4) can be obtained so that comparisons can be made to determine if the importance of each of the five water demand factors (x1 through x5) differ country-by-country. When this process is completed, trend line results end up with identical slopes to those in Figure 5.2 when considered out to one significant digit (two decimal places) and very minor differences when another significant digit

(three decimal places) is considered, which could incorrectly portray the idea that different countries can change their future operational water demand at the same rate by making identical changes in their respective power generation sector. In actuality, trend line slopes do not change because the WM values for each country are not equivalent, meaning that a 10% reduction in water demand in China is more significant than a 10% reduction in Japan because China’s initial water demand is larger (due to China having more power demand and fossil fuel use, see Figures 3.3 and 3.5).

Similarities in trend line slopes are also observed because the model input values for most of the sensitivity analysis factors (x3, x4, and x5) do not change country-by- country (the same global and/or U.S. data from the IEA, USDOE, and NETL for cooling systems, combined cycle, and CCS was applied to all countries in the model calculations,

57 see Section 3.1.3) (IEA, 2008 and 2009; NETL, 2009; USDOE, 2006). In addition, when a water demand factor does change (x1: power demand changes by country), this does not affect the trend line slopes in the sensitivity analyses because the WM is a relative value that compensates for input value changes (a country’s original water needs will correspond to changes in power demand). For the final of the five water demand factors, variations in selection of fuel type (x2), the WM does not compensate for the diversity of fuel distributions for the different country-specific datasets, and this may explain the minor difference in trend line slope values observed when considering the results out to two significant digits.

Figure 5.3. Feasible ranges (possible x-axis values) for the five major water demand factors of power generation (corresponding to Figures 5.1.A through 5.1.E).

Figure 5.4. The potential change of future operational water demand (according to a water multiplier, see Section 5.1.2) using the maximum feasible input values (from Figure 5.2) for each of the five respective water demand factors. Note that 1.0 is used as a zero-point for the y-axis because a WM of 1.0 is equivalent to no change in future water needs. WM values between 0 and 1.0 indicate future water need decreases, and values greater than 1.0 indicate increases.

Chapter 6: Conclusions

The energy sector has become target for future change such that GHG emissions are minimized to limit global climate change. However, many future “carbon-limiting” projections of power generation are short sighted in the respect that limitations in water resources are completely overlooked. Whether in a “business-as-usual” or “carbon- limiting” future energy scenario, model calculated values of water requirements show that water will be a necessary component for power generation for a long time into the foreseeable future (with average global increases of operational water withdrawal from now to 2050 ranging from 107% to 158%). It will be very difficult to break the link between water and energy because the energy sector will likely never be completely composed of what this paper classifies as renewable fuels (wind, solar PV, and ocean energy). Land (space for wind farms) and material needs for turbine construction constrict the use of wind power, while diminishing trace metal resources limit the unlimited building of solar power cells.

With these points in mind, the proposed implementation of CCS at power facilities (from now to 2050) should raise serious concerns because this technology has potential to increase future operational water withdrawal and consumption in the power generation sector by 66% and 32%, respectively. Due to the importance of carbon mitigation in the fight to prevent global climate change, it is not recommended that CCS

63 be avoided due to a lack of water resources. Instead, this research presents the idea that other fundamental factors of power generation be altered such that water requirements are significantly reduced.

The most effective way to lower water demand is through the conversion (and use in new plants) of all open-loop and pond cooling systems to closed-loop cooling towers.

Complete use of recirculated tower cooling at thermoelectric power plants alone has potential to decrease future operational water withdrawals by 97%; however, future water consumption may increase by as much as 20%. Another very effective way to conserve water during power generation is through the use of combined cycle technology (NGCC and IGCC), which can reduce future operational water withdrawal by up to 64% due to the currently high use of natural gas and coal fuels. Combined cycle technology is a very promising option for offsetting the water need increases from CCS because the implementation of NGCC and IGCC has potential to decrease future water withdrawal at approximately the same rate of increase caused by CCS and the technologies are compatible with the same fuel types, not to mention that combined cycle technology also has potential to significantly lower future water consumption. Reducing power demand

(energy conservation) and changing fuel types (less thermoelectric and more renewables) are two additional factors that play less important, but still very significant, roles in helping to reduce the future water requirements associated with power generation.

Freshwater resources are a priceless commodity which humanity requires for existence for direct consumption and the growth of agricultural products. With climate change altering the availability of freshwater resources in many regions across the globe,

64 as well as brining about the use of CCS which increases water demand at power plants, future limitations in water resources are becoming more of a threat. However, the water needed by the energy sector has potential to become a negligible portion of total human water demand (while carbon mitigation goals set forth to limit global climate change are still met) through the alteration of four fundamental water-demand-factors of power generation: (1) use closed-loop cooling towers and not open-loop cooling at thermoelectric power plants, (2) use combined cycle technology at all coal and natural gas power plants, especially those being installed with CCS technology, (3) implement as much wind, solar PV, and ocean energy as allowed for by other resources, and (4) make attempts to conserve energy by improving power plant system/transmission efficiency as well as reducing person energy consumption.

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