Conditions for natural gas to become an effective bridge fuel to a low-carbon future

CONDITIONS FOR NATURAL GAS TO BECOME AN EFFECTIVE BRIDGE

FUEL TO A LOW-CARBON FUTURE

by Yet Feng Mak

B.Eng. Chemical & Bimolecular Engineering National University of Singapore, 2010

SUBMITTED TO THE SYSTEM DESIGN AND MANAGEMENT PROGRAM IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF SCIENCE IN ENGINEERING AND MANAGEMENT AT THE

MASSACHUSETTS INSTITUTE OF TECHNOLOGY

February 2016

2015 Yet Feng Mak. All rights reserved.

MASSACHUSETTS INSTITUTE OF TECHNOLOGY

OCT 2

6

2016

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Christopher R. Knittel William Barton Rogers Professor of Energy Economics MIT Sloan School of Management

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CONDITIONS FOR NATURAL GAS TO BECOME AN EFFECTIVE BRIDGE

FUEL TO A LOW-CARBON FUTURE

by

Yet Feng Mak

Submitted to the System Design and Management Program on December 31, 2015 In Partial Fulfillment of the Requirements for the Degree Of

Master of Science in Engineering and Management

ABSTRACT

Natural gas has commonly been described as a 'bridge fuel' that could transition U.S. from fossil fuels to a low-carbon energy system by 2050 in order to reach the internationally agreed target of limiting the global mean surface temperature to about 2 degrees Celsius (*C) above pre-industrial levels. This natural gas resource has grown tremendously over the last decade, as its production has been fueled by the use of more advanced hydraulic fracturing and horizontal drilling technologies. Being a cleaner form of fossil fuel, burning natural gas emits about half as much carbon dioxide as coal and is thought to aid in decarbonizing the nation by displacing coal as a fuel for power generation.

However, the increased supply of cheap natural gas could also have an effect of delaying the advancement of renewable resources such as solar and wind. Nonetheless, optimal conditions could be explored on how natural gas can become an effective 'bridge fuel' towards a low carbon energy system. This thesis developed a system dynamics model to analyze these required conditions and found that high natural prices that rise to $26.45/mmBTU in 2050 are necessary to hit the 20C target. An efficient

policy that could drive these high natural gas prices is the carbon tax. Furthermore, another important role that natural gas serves is as a backup power source for intermittent renewable energy resources.

Thesis Supervisor: Professor Christopher R. Knittel

Title: William Barton Rogers Professor of Energy Economics, MIT Sloan School of Management

ACKNOWLEDGEMENT

This thesis has given me a lot of opportunities to pursue the multiple energy topics that I am interested in. As climate change issues become more and more critical, the understanding of the energy market dynamics through the completion of this thesis has helped me recognize important changes that need to be made to create a more sustainable future. In this fulfilling knowledge acquisition journey, I will like to express my heartfelt gratitude to some persons who have advised and supported me.

First and foremost, I would like to thank my thesis supervisor, Professor Chris Knittel, who has imparted me with valuable knowledge on the energy market through his energy economics class as well as his guidance on this thesis. MIT Energy Economics and Policy is one of the most useful classes for any student passionate about energy. I deeply appreciate his efforts to review my thesis thoroughly and work on it even during the holidays. I am also grateful for having a thesis advisor who is more concerned with whether the student is able to achieve his or her learning objectives than what use the thesis can serve to him.

Next, I would like to thank all the System Design and Management (SDM) faculty and staff including Pat Hale (Director), Joan Rubin (Co-director), Professor Oliver de Weck (Chief Instructor), John Helferich (Head TA) and many others. I know I am able to pursue my graduate studies in this

extraordinary institution because of you and I am thankful for your continued support and the great department that you have created. The vastly diverse group of students that you have admitted to SDM have become supportive friends of mine, and the community spirit you have fostered among us is remarkable.

To my classmates and schoolmates whom I have met through SDM, Sloan classes, Energy Ventures class, MIT Energy Conference, MIT Clean Earth Hackathon and MIT Energy Hackathon, I thank you for being there in my learning journey and inspiring my thoughts on energy matters.

Last but not least, I would like to thank my family and friends back home in Singapore for their support and encouragement from 9407 miles away.

TABLE OF CONTENTS

1

1.1 M otivation... 13

1.2 Thesis Objectives...14

1.3 Research Approach and M ethodology ... 14

1.4 Organization of the Thesis ... 15

2 LITERATURE REVIEW ... 16

2.1 Review of Current Energy M arket Dynam ics ... 16

2.1.1 U.S. Electricity M arket... 16

2.1.2 Natural Gas Power M arket Dynam ics ... 20

2.1.3 Renew ables M arket Dynam ics... 23

2.2 Ene rgy Policies ... 30

2.2.1 Production Tax Credit (PTC)... 30

2.2.2 Investm ent Tax Credit (ITC)... 31

2.2.3 Renew able Portfolio Standards (RPS)... 32

2.2.4 Clean Power Plan ... 35

2.3 Energy Projections and Scenarios modelling ... 36

2.3.1 International Energy Agency ... 36

2.3.2 Deep Decarbonization Pathway Project (DDPP)... 39

2.3.3 Energy Inform ation Adm inistration Annual Energy Outlook... 41

3 NATURAL GAS AS A BRIDGE FUEL ... 43

3.1 Current Context of Natural Gas as a Bridge Fuel ... 43

3.2 Im pact of Natural Gas Growth on Coal... 44

3.3 Environm ental Im pact of Using Natural Gas... 45

3.4 Synergy between Natural Gas and Renewables ... 46

4 SYSTEM DYNAM ICS M ODEL DEVELOPM ENT ... 48

4.1 System Dynam ics M ethodology... 48

4.2 Causal Loop Analysis ... 49

4.3 Data Collection ... 51

4.4 Relationships and Assum ptions ... 52

4.4.2 Effect of Rate of Return ... 54

4.4.3 Effect of Subsidy...56

4.4.4 Effect of Capacities on W holesale Prices ... 56

4.4.5 Other Assum ptions ... 56

4.5 Sim ulation Scenarios... 58

5 RESULTS AND DISCUSSIONS ... 61

5.1 Sim ulations Results Analysis ... 61

5.1.1 Electricity Generation ... 61

5.1.2 Costs of Solar and W ind ... 63

5.2 Com parison w ith Other M odels... 65

5.3 Sensitivity Analysis ... 67

5.3.1 Varying Learning Rates... 67

5.3.2 Varying Initial LCOE ... 69

5.4 Conditions for Natural Gas to be an Effective Bridge Fuel ... 70

5.4.1 High Natural Gas Prices... 70

5.4.2 Subsidies for Solar and W ind Development ... 71

5.4.3 Other Conditions...72

5.5 M odel Im provem ents and Future W ork... 74

6 CONCLUSION ... 76

7 REFERENCES ... 78

LIST OF FIGURES

Figure 2.1.1: Decom position of the U.S. electricity market system ... 17

Figure 2.1.2: Total U.S. electricity net generation from 1950 - 2014 (Source: Statistica, EIA [5])...18

Figure 2.1.3: U.S. electricity generaton by fuel (Source: EIA [6])... 19

Figure 2.1.4: Electricity dispatch curve (Source: EIA [9]) ... 20

Figure 2.1.5: Natural gas prices and production (Data Source: EIA)... 22

Figure 2.1.6: Natural gas prices and wholesale electricity prices (Source: Linn et al., 2014 [15]) ... 23

Figure 2.1.7: U.S. renewable energy m ix (Source: EIA [6]) ... 24

Figure 2.1.8: Solar PV prices and installed capacity (Source: SEIA [22])... 25

Figure 2.1.9: Wind cost and annual installed capacity (Source: U.S. Department of Energy, LBNL [26]) .. 26

Figure 2.1.10: Levelized Cost of Electricity (LCOE)-Wind/Solar PV (Source: Lazard [27]) ... 27

Figure 2.1.11: Levelized Cost of Electricity (LCOE) for all generating technologies (Source: Lazard [27]) .28 Figure 2.1.12: Projected LCOE in the U.S. by 2020 (Source: EIA [28]) ... 29

Figure 2.2.1: Impact of PTC expiration and extension on installed wind capacity (Source: [33]) ... 31

Figure 2.2.2: Impact of ITC extension on US solar industry (Source: BNEF [34])... 32

Figure 2.2.3: Renewable Portfolio Standards (RPS) by states (Source: DSIRE [35]) ... 33

Figure 2.2.4: Average retail electricity price for renewables leaders and laggards (Source: DBL, EIA [36]) ... 3 4 Figure 2.2.5: Annual retail electricity prices in RPS and non-RPS states (Source: DBL, EIA [36])... 34

Figure 2.2.6: Projections of electricity generation mix under Clean Power Plan rule (Source: EIA [39]) ... 35

Figure 2.3.1: Energy mix of the high renewables case (Source: DDPP [44])... 41

Figure 2.3.2: Global electricity generation m ix (Source: IEA [45])... 37

Figure 2.3.3: Regional production of wind electricity in the 2DS and hiRen (Source: IEA [46])...38

Figure 2.3.4: Total electricity generation in Reference Case (Source: EIA [2])... 41

Figure 2.3.5: Cumulative additions to electricity generation capacity by fuel in six cases (Source: EIA [2]) ... 4 2 Figure 3.2.1: U.S. net electricity generation by energy source (Source: EIA [51])... 44

Figure 3.3.1: Emissions reduction from switch to natural gas with varying methane leak rates [55] ... 45

Figure 4.2.1: System dynamics model for utility solar PV technology... 50

Figure 4.2.2: System dynam ics model for wind technology ... 50

Figure 4.4.1: Aggregated wholesale electric price and natural gas price ... 53

Figure 4.4.2: Aggregated Wholesale Prices regressed against natural gas prices... 54

Figure 4.4.3: Annual utility solar PV installations against ROR ... 55

Figure 4.4.4: Establish effect of ROR for w ind energy ... 55

Figure 4.4.5: Learning rate of Solar in U .S. ... 57

Figure 4.5.1: Natural gas price in mm/BTU under the different scenarios... 59

Figure 5.1.1: Electricity generation from utility solar PV ... 62

Figure 5.1.2: Electricity generation from w ind ... 62

Figure 5.1.3: Unsubsidized cost for utility solar PV technology... 63

Figure 5.2.1: Comparisons of simulation results for solar with other models. *Assumed that 40% of the

total electricity generation from solar is from utility solar PV installations ... 65

Figure 5.2.2: Comparisons of simulation results for wind with other models ... 66

Figure 5.2.3: Slowing wind power generation growth under High NG Price, Standard Subsidy scenario . 67 Figure 5.3.1: Solar power generation at varying learning rates ... 68

Figure 5.3.2: Simulation results comparing cases when different initial LCOE values are used ... 69

Figure 5.4.1: Federal subsidies and support for electricity (Source: IER, EIA [85])...72

LIST OF TABLES

Table 2.3.1: Total electricity generation in 2050 by DDPP Hi-renewables scenario model (Source: DDPP

[43])...-- ... .. . -- -- - -- - - --... 40

Table 2.3.2: PV capacities by region in 2030 and 2050 in the hi-Ren Scenario (GW) (Source: lEA [25]).... 37

Table 2.3.3: Projections for LCOE for new-built utility-scale PV plants under the hi-Ren Scenario (Source: lEA [25])... ... . ... ... ... 38

Table 4 .3 .1: D ata so urces ... . . ... 52

Table 4.4.1: Values for fixed input param eters ... 57

Table 4.5.1: Six sim ulation scenarios analyzed in this paper ... 60

1INTRODUCTION

Climate change has been one of the most discussed topics in this decade. The world is increasingly feeling the effects of climate change including rapidly-rising temperatures, shrinking glaciers, rising sea

levels, more droughts and floods, stronger and more intense storms, and other extreme weather conditions. The consequences of these effects will lead to further depletion of resources including water, food and energy and this depletion of resources may lead or had even led to conflicts and wars.

People are concerned with building a sustainable future for ourselves and our future generations, and this year, 2015, is an even more important year, as the world leaders congregated in Paris to make decisions and agree on actions that prevent further climate catastrophes at the United Nations Climate

Change Conference (COP21). The main goal of COP21 is to reduce greenhouse gas (GHG) emissions to limit global temperature increase to 2 degrees Celsius ("C) above pre-industrial levels, a target that is considered to be in a safe threshold for global warming.

In the United States (U.S.), the electric power sector is the largest contributor to GHG emissions and currently accounts for approximately one-third of the nation's total emissions. This motivates the author to identify issues that could affect the environmental impacts of the power industry and one such issue is how natural gas could help bridge the gap between more carbon intensive fossil fuels and zero-emissions renewables.

Natural gas has commonly been described as a 'bridge fuel' that could transition U.S. from fossil fuels to a low-carbon energy system by 2050 in order to reach the 2*C target. This resource is rapidly growing in importance as the use of more advanced hydraulic fracturing and horizontal drilling technologies has increased natural gas production tremendously. Being a cleaner form of fossil fuel, burning natural gas emits about half as much carbon dioxide as coal and is thought to aid in

decarbonizing U.S. by displace coal as a fuel for electricity production.

However, the increasing supply of cheap natural gas has an effect of delaying the advancement of renewables such as solar and wind. As natural gas prices fall, consumers will demand more of natural gas and be less willing to switch to renewables. This will also reduce investment dollars in renewables, and potentially continuing our reliance on fossil fuels. Nonetheless, optimal conditions could be

Several literature have studied the effects of natural gas on electricity generation and the environment, but few have examined what the needed conditions are to meet low-carbon future targets. This thesis explores the conditions in terms of pricing and policies that could help natural gas in becoming an effective 'bridge fuel'.

1.2 Thesis Objectives

The thesis seeks to satisfy the following objectives:

1. Understand the effects of natural gas prices on the growth of utility solar photovoltaics (PV) and wind technologies for power generation in the U.S. using a system dynamics modelling

approach, and

2. analyze the conditions in terms of natural gas pricing and government policies that can promote increased use of solar and wind technologies to meet low-carbon future targets.

1.3 Research Approach and Methodology

This thesis achieves its objectives by developing a simplified system dynamics model that could sufficiently project the growth of utility solar PV and wind capacities until 2050 by varying values of parameters such as natural gas prices and government subsidies. Empirical methods and results from past work were used to calibrate the parameters used in the model. Using different values of natural gas prices and its growth rates into 2050, effects of natural gas on the development of solar and wind technologies were determined. Discussions and recommendations were then made on how policies could affect natural gas prices to promote growth of solar and wind to the extent of reaching the 2*C target. The thesis focuses on utility solar because natural gas prices have a strong direct relationship with wholesale electricity prices which in turn affect the demand for utility solar technologies.

Residential and commercial solar installations, however, are more influenced by retail electricity prices which are not directly impacted by natural gas prices. In terms of the solar technologies, this paper focused on solar PV as PV comprised the majority share of the solar power generation, as compared to solar thermal technology. In EIA's annual energy outlook 2015, projections into 2040 showed that solar thermal generating capacity is less than 0.2% of the total generating capacity by all resources [1]. There is also a greater amount of data and work done on solar PV than solar thermal technology which we can extract and use.

1.4 Organization of the Thesis

The thesis is organized as follows:

* Chapter 1 introduces the thesis topic, and defines the objectives and approach.

* Chapter 2 reviews the current energy market dynamics in the U.S., energy policies and energy projections conducted by other prominent studies.

* Chapter 3 covers the current context of natural gas as a bridge fuel in the U.S. * Chapter 4 presents the approach to develop the system dynamics model.

* Chapter 5 analyzes the simulation results and discusses the conditions for natural gas to be an effective bridge fuel.

2 LITERATURE REVIEW

This chapter reviews the current energy market dynamics within the U.S. and how these have evolved from the past. It provides a background of the electricity market trends, how wholesale electricity market works in the U.S., how changes in the natural gas industry have affected the power market and highlights trends in renewables, specifically on solar and wind. This review further

elaborates on the energy policies that could affect the growth of renewables in the future and studies existing energy projections and models conducted by several reputable institutions which are important as a lead up to the discussion of this paper.

2.1 Review of Current Energy Market Dynamics

2.1.1 U.S. Electricity Market

Decomposition of U.S. Electricity Market System

The U.S. electricity market is a huge system, serving a population of 320 million people, across 50 states and 3.8 million square miles. In 2014, the total electricity generation was 4093 million MWh and the market received a revenue of $390 billion [2]. The electricity market intertwines a wide range of political, economic, environmental, and social factors, and involves numerous stakeholders, making this sector one of the most complex systems in the world. To better understand this complex market, the system is decomposed into subjects of interest in this paper and boundaries are set as illustrated in

Figure 2.1.1. The main focus of this thesis will be on the wholesale electricity market as well as the inter-relationships between natural gas and renewables, specifically, utility solar and wind, which are the fastest-growing renewable resources and make up a larger proportion of the electricity generation from renewables relative to geothermal and biomass.

Electricity Generation

In comparison to an average growth rate of about 30% between 1950 and 2000, the electricity demand growth has slowed down significantly over the years and has been flat since 2007 (See Figure 2.1.2). Although the U.S. economy has grown 8% since 2007, the annual electricity generation growth

has been between -1% and 1%. This decoupling between electricity growth and economic growth is largely driven by energy efficiency improvements, weather patterns and the Great Recession [3].

- Economics

-- Resources

-Competitive /

Regulated

- Market Types Wholesale Market

Retail Market Demand/ P -Suply_, Coal Fossil Natural Gas Solar Wind - Renewables Geothermal Biomass Nuclear -- Others Hydroelectric

0 Electric Generating Resources

Receiving Station SCommo rc tAI Customer - Supply Chain

-0

0

Figure 2.1.1: Decomposition of the U.S. electricity market system

U.S. Electricity Market

(WIT

Commercial facilities move towards better efficiency with the establishment of policies for commercial building energy benchmarking; home appliances and consumer electronics also became more energy efficient. Nonetheless, rising population, increasing use of electronic devices, and limits to energy efficiency improvement may continue to push the growth of electricity demand. In the Annual Energy outlook 2015 (AEO 2015), the Energy Information Administration (EIA) projected that electricity

consumption will increase at an annual rate of 0.8% from 2013 to 2040 [4] (See Section 2.3.3). However, electricity consumption may increase even more if massive electrification of end uses (e.g. electric vehicles) occurs. This scenario is further elaborated in the U.S. Pathways to Deep Decarbonization projections in Section

2.3

2*C target.

Total U.S. electricity net generation from 1950 to 2014 (in billion kilowatt hours) 5.000 4,055 4,125 4,100 4,048 4,066 4,093 4.000 3,802 3,354 3,038 3.000 2,473 2,290 000 1921 1,535 1,0001000 ₇₅₉ 550 334

1950

Source Additional Information

Figure 2.1.2: Total U.S. electricity net generation from 1950 - 2014 (Source: Stotistica, EIA J4])

In terms of electricity mix, over the last decade, there has been a steady rise in natural gas as the fuel source while a decline in coal as a result of fuel switching to the more flexible and lower cost natural gas for firing power plants. In 2014, natural gas produced 27% of total electricity generated, while the share of coal was 39%, nuclear at 19% and renewables contributed 7% (4.4% by wind; 0.4% by solar) as shown in Figure 2.1.3 (5].

U.S. Electricity Generation by Fuel, All Sectors

thousand megawatthours per day

14.000 Forecast 12.000 10.000 _8.5%

~

4

ePa

m

%I

nd natural gas.

Figure 2.1.3: U.S. electricity generaton by fuel (Source: EIA [5])

Wholesale Electricity Market

As illustrated in the decomposition diagram (See Figure 2.1. 1), the electricity markets consist of wholesale and retail components. The retail market involves the sale of electricity to end-users while the wholesale market involves the buying and selling of electricity between the power generators and the

resellers who can be electric utilities or electricity traders. There are also large end-users who

participate in the wholesale market and buy electricity directly from the generators to cut down their electricity costs. In this thesis, wholesale electricity market will be the main focus.

Wholesale electricity markets were formed in the late 1970s and they expanded through a succession of federal rules [61. Major organized competitive electricity markets are operated by

Independent System Operators (ISO) or Regional Transmission Organization (RTO) which are responsible for reliable electricity transmission between states and for offering a trading platform for the sale of electricity [7]. The 9 RTO/ISOs include Electric Reliability Council of Texas (ERCOT), New York ISO

(NYISO), Midcontinent ISO (MISO), ISO New England (ISO-NE), California ISO (CAISO), Alberta Electric System Operator (AESO), Independent Electricity System Operator (IESO), Southwest Power Pool (SPP), and Pennsylvania-New Jersey-Maryland ISO (PJM-ISO).

The ISO/RTOs conduct day-ahead and real-time electricity markets using a uniform clearing price auction system. The electricity generators submit their price bids for a certain amount of electricity to the system operators who will in turn, dispatch the generators starting from the lowest bids to the

highest bids until all electricity demand is met. Baseload generating units are the ones to be dispatched first as they have the lowest marginal costs and appear on the left of the dispatch curve as seen in Figure 2.1.4. These units typically include nuclear, hydroelectric and sometimes, renewables (solar, wind) which have very high startup and shutdown costs and therefore have to run virtually all the time. Towards the right of the dispatch curve, peaking units which generally include combustion turbines and diesel engines, have the highest marginal costs and only operate when the power demand is high. Between baseload and peaking units are the intermediate generating units that tend to be coal-fired, gas-fired or combined-cycle power plants. These power units are usually able to adapt to changes in electricity demand over the course of a day and year [8]. Under a uniform pricing system, when the

market clears, all generating units are paid the same price as the price offered to the marginal power plant that provides the marginal power unit to satisfy all demand.

Hypothetical dispatch curve for summer 2011 variable operating cost (dollars per megawatthours)

300 _demand= 67 GW;: 250 renewables early . nuclear morning hydro hours 150 -coal

-natural gas - combined cycle

100 natural gas - other petroleum

50

0-."Von"-0 20 40 60 80 100

system capacity available to meet electric demand

Figure 2.1.4: Electricity dispatch curve (Source: EiA [8])

Cit demand = 114 GW: afternoon on a hot day 120 (GW) 140

4

2.1.2 Natural Gas Power Market Dynamics

Demand of natural gas for power generation has risen significantly over the last decade, from 710 TWh in 2004 or 18% of total net power generation to 1122 TWh in 2014 or 27% of the power mix [2]. In fact, the EIA recently reported that the monthly natural gas share of the total power generation in July (2015) had surpassed coal for the second time with natural gas providing 35.0% of the electricity mix compared to coal at 34.9% [9]. The rising natural gas-fired generation is mainly driven by the lower

natural gas prices. The monthly average Henry Hub natural gas price in July 2015 was $2.91/MMBtu, which had declined from $4.14 per million Btu (MMBtu) in July 2014 [9]. The EIA article also compared a coal price of $2.31/MMBtu at Central Appalachian to the lower wholesale natural gas price in New York City at $2.06/MMBtu in July [9]. As natural gas prices drop relative to coal prices, demand from the

power sector for natural gas rises. This increase in demand also reflects the environmental benefits of natural gas as well as the operating flexibility of natural gas-fired generators. A natural gas combined-cycle power plant produces about 60% less CO2 compared to a coal plant for the same amount of electricity generated 110]. Composed primarily of methane, natural gas, when combusted, also releases lower levels of nitrogen oxides and sulfur dioxide than coal which are made up of more complex molecules [11]. Furthermore, natural gas-fired power plants are relatively easier to build and they can alter their generation output more flexibly to match changing loads than other baseload generators including nuclear and coal.

The natural gas industry has undergone many transitions due to the shale gas boom in the U.S. The technological advancements of hydraulic fracturing and horizontal drilling have enabled producers to access unconventional resources in the shale formation. As a result, shale gas production had grown over 750% from 1,293 billion cubic feet (bcf) in 2007 to 11,415 bcf in 2013, making shale gas the largest share of U.S. natural gas production in 2013 [12]. The EIA also forecasted that the U.S. will become a net exporter of natural gas by 2017 [13]. The popularity of natural gas is further boosted by environmental regulations that provided strong incentive for power companies to switch from coal to natural gas.

Natural Gas Prices and Production 13 12 o 11 a 10 a- M 9 B C: 0_.g 8 7 _ 6 5 2 7 z ₄ .0 T_ 3 C 2 0

/

V.

Henry Hub Natural Gas Spot Price, Monthly (Dollars per Million Btu) Natural Gas Production (Dry) Monthly (Billion Cubic Feet)

Figure 2.1.5: Natural gas prices and production (Data Source: EIA [2])

0 '0 00 0-.0. 2015

Natural gas prices are affected by several major factors impacting both its supply and demand. On the supply side, these include production levels of natural gas, net imports, and gas storage levels while on the demand side, the factors include weather, economic growth and prices of competing fuels [141. The plummeting of natural gas prices since 2008 from the highest price of over $12 per million British thermal unit (BTU) to below $3 in 2015 (See Figure 2.1.5) was primarily driven by increased shale gas production which led to an oversupply in the natural gas market. Since the shale gas boom, the natural gas prices have also begun to decouple from the crude oil prices.

Wholesale electricity prices are influenced by multiple factors that include: 1) economic activity which affects load demand; 2) weather which also affects demand; 3) fuel costs; and 4) transmission constraints. Nonetheless, wholesale electricity prices in the U.S. correlate strongly with natural gas prices as natural gas-fired plants are often the marginal plants that submit the highest bid and set the spot market electricity price. In the Resources for the Future (RFF) discussion paper, Linn et al. conducted empirical studies and concluded that natural gas prices have direct effects on wholesale electricity prices, with an average elasticity of 0.94 (off-peak) to 0.96 (peak) [15]. The strong correlation between natural gas prices and electricity prices is also illustrated in Figure 2.1.6 [15]. This correlation will be further discussed in Section 4.4 as we assume a relationship between natural gas prices and wholesale electricity prices in our model.

I

I

E

1_0L

2001q1

2004q1

2007ql

2010q1

2013q1

Year-Quarter

--Electricity Price

Gas Price

Figure 2.1.6: Natural gas prices and wholesale electricity prices (Source: Linn et of., 2014 [15])

2.1.3 Renewables Market Dynamics

In the U.S., renewables (excluding hydroelectric) accounted for 7% of the total electricity generated in 2014 [5]. Wind took up a significant share at 4.4%, while solar was around half a percent of total power supplied [5]. Nonetheless, the growth of solar and wind has been rapid in the last few years. Net generation from wind has tripled while solar has grown by 21-fold since 2008 [5]. Comparing between 2013 and 2014, solar had doubled to 18 TWh while wind had grown by 8% to 182 TWh [5]. In fact, a new solar project was installed every two minutes for the first half of 2015 [16]. Meanwhile, for wind, capacity additions slowed in 2013, with just 1.1GW added, but the additions rebounded in 2014 with $8.3 billion invested in 4.9 GW of new installations [17][18].

According to a report by the Federal Energy Regulatory Commission (FERC), renewables have

overtaken natural gas to provide more new electric generating capacity in 2014 [19]. Renewables

including hydroelectric accounted for 49.81% of the new electrical generating capacity while natural gas

provided 48.65% in 2014 [19]. In Figure 2.1.7, it is also shown that electricity generation of renewables

has grown steadily over the last decade while solar and wind have been taking up an increasing share in the renewables mix. At the global scale, investments in renewables had surged by 17 % in 2014 to $270

billion after a two-year decline [20]. U.S. contributed $38.3 billion while China invested $83.3 billion on

U.S. Renewable Energy Supply projections 4 3-2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 0Solar G Geothermal Other biomass Wind power Liquid bioluels Wood biomass Hydropower eia I It

~

~

~

Figure 2.1.7: U.S. renewable energy mix (Source: EIA [5])

Cost of solar and wind

The costs of solar and wind as resources for electricity have fallen tremendously especially in the last 5 years. In some regions such as the Great Plains and Southwest where wind and sunlight are abundant, renewable energy, when subsidies are considered, can be even cheaper than coal or natural gas [21]. Figure 2.1.8 shows the average solar (PV) price fell from $7.50/watt in 2009 to $2.80/watt in 2014 as the installed capacity rose about 60% each year [22]. Utility-scale solar contracts had even reached prices below $50/MWh in 2015 from $200/MWh seven years ago [23]. According to a utility solar market report done by the Solar Electric Power Association (SEPA), three low-price power purchase agreements (PPA) announcements made in recent two years were highlighted [24]:

" Austin Energy (Texas) signed a PPA for less than $50/ MWh,

" TVA (Alabama) signed a PPA for $61 per MWh,

" Salt River Project (Arizona) signed a PPA for roughly $53 per MWh.

The sharp drop in solar prices is mainly driven by the lower installed costs which are in turn a result of the rapidly declining module price especially over the last 5 years due to an oversupply of solar glass

from China. The soft costs of solar including customer acquisition, permitting, installation, connection, and financing however, have not decreased much in the U.S. [25].

0. M-(U M) $9.00 $8.00 $7.00 $6.00 $S.00 $4.00 $300 $2.00 $1.00

As Industry Scales, Prices Fall

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Solar PV Installations Solar PV Prices

4 4

@RESEARCH

SEIA

Figure 2.1.8: Solar PV prices and installed capacity (Source: SEIA [22])

As for wind, according to a study conducted by the Lawrence Berkeley National Laboratory (LBNL), average levelized long-term price from wind power purchase agreements (PPA) in 2014 was at $23.50/MWh, decreased from $70/MWh in 2009 [17]'18]. The boom in wind development has largely been fueled by generous government subsidies such as the Production Tax Credit (PTC) which will be discussed in the Energy Policies Section 2.2. Figure 2.1.9 further illustrates a longer term relationship between levelized cost of electricity (LCOE) for wind and its annual capacity additions since 1980 [26]. The growth in wind capacity has followed a general positive trend but has also seen a few irregularities due to multiple changes to PTC as will be discussed later.

I

600 15.0 500 12.5 4001 10

3

U Annual installed wind capacity - Wind LCOE

Figure 2.1.9: Wind cost and annual installed capacity (Source: U.S. Department of Energy, LBNL [26])

Cost Comparisons across Technologies

Levelized cost of electricity (LCOE) is a common metric used to compare costs of various generating technologies which will also be used in this thesis for cost analysis. It is calculated as the net present value of all costs over the lifetime of the generating asset divided by the total electricity output

from it. Main inputs for LCOE calculations thus include investment costs, fuel costs, variable operations

and maintenance (O&M) costs, and utilization rates of each technology. The LCOE equation is give as:

Discounted total life cycle cost

LCOE =

Discounted total lifetime electricity production

n Investment costs + O&M costs + Fuel costs

t=0

(+

n Electricity generated

t=O (1 + r)t

LITLAIURE REVIE

2-Lazard is an asset management firm that publishes reports on LCOE on an annual basis. This thesis used data from Lazard as their LCOE studies are comprehensive and they list out all key

assumptions made in their analyses. According to Lazard's analysis (See Figure 2.1.10), LCOE of utility-scale solar PV had come down by 78% since 2009 to $79/MWh in 2014 while that of wind had dropped

by 58% since 2009 to $59/MWh in 2014 [27].

WIND LCOE SOLAR PV LCOE)

LCOE $/MWh $ 169 c e $148 e $92 $95 $95 $101 $99 % $50 $48 $45 $81 $37 $450 400 350 300 250 200 150 100 50 0 2009 2010 2011 2012 2013 2014 $394 $323 %

_~$270

- - - LCOE LCOE Range

Figure 2.1.10: Levelized Cost of Electricity (LCOE)-Wind/Solar PV (Source: Lazard [27])

Figure 2.1.11 provides the unsubsidized LCOE on all generating technologies in the U.S. [27]. Wind was on par with natural gas CCGT plant which was at about $74/MWh in 2014. LCOE of utility solar PV was also quite close to that of natural gas plants but residential, and commercial and industrial solar PV LCOE were still rather high between $126/MWh and $265/MWh.

LCOE $/NMWh $250 200 150 100 50 0

ITERATUIEJRE REVIEW 2

Unsubsidized Levelized Cost of Energy Comparison

Certain Alternative Energy generation technologies are cost-competitive with conventional generation technologies under some scenarios; such observation does not take into account potential social and environmental externalities (e.g., social costs of distributed generation, environmental consequences of certain conventional generation technologies, etc.) or reliabilit% -related considerations (e.g., transmission and back-top generation costs associated with certain Alternative Energy generation technologies)

Solar PV--Rtsfzop Rcsidctial I $180 $265

Slar PV-Rtmfrop C&T t $126 $177

Solar P*--C

5

rtalle Utility Scak lv $6 0" $72 $86

SrLa l'V-T hi Fim Uulir Scal lv 160 " $72 $86

SolarThcrmal %ah Storaze l

$118 $130 mFu ( l t $115 $176 l lcrurbinr $102 $135 Gcorhernil $89 $142 liomass lhruct $87 S116 \md $37 $81 $162" Fnergv Eftcincy $0 $50

litim StorV . ki; t46 $69 265 $324

Dic,,, I Ge-rAtir, :$297 $332 Gas Peaking $179 $230 IG(A: W $102 _$16 L $171 LNuclear $92 $124'" $132 Coal $66 $151 Gas Cbmhmd Cyck $61 $87 $127' So S54 slm $150 S2rt 5250 S34N) S350 ULvelized Cost (/MWh)

Figure 2.1.11: Levelized Cost of Electricity (LCOE) for all generating technologies in 2014 (Source: Lazard [27])

The EIA has also released their LCOE estimates for generating technologies that are brought online in 2020. The unsubsidized LCOE of solar PV, wind and natural gas CCGT are $125.30/MWh, $73.60/MWh and $72.60/MWh respectively [28] (See Figure 2.1.12). The 10% investment tax credit (ITC) subsidy will further reduce solar's LCOE by $11/MWh [28]. In comparison with Lazard's analysis, Lazard seems to present much lower estimates for solar ($79/MWh) and wind ($59/MWh) than EIA while the estimates for natural gas are comparable. For solar, this could be explained as Lazard's $79/MWh is just for utility-scale solar. Looking at Lazard's analysis for residential solar as well as commercial and industrial solar, the range is between $126/MWh to $265/MWh which is higher than

EIA's aggregated solar PV LCOE estimate. For wind, while Lazard has presented a lower cost value than EIA, as noted earlier, price of PPA for wind has indeed come down to as low as $23.50/MWh which could justify Lazard's optimism.

Despite the falling costs of solar and wind, it is important to note that due to the intermittent nature of solar and wind, there may be additional costs needed to ensure utilities have other energy sources such as natural gas that can respond to fluctuations in demand quickly.

*

I

Projected LCOE in the U.S. by 2020 (as of 2015)

41.0 400 IMO 250 200 100 so

Solar Thermal Wind .ffshore Mnomum 174 170 O Average 240 197 Ma-murn 383 270 Natural Ga Conventional Combuslion Turbine 107 142 156 IGCC Jintegrated Solar PV Coal Gasification Combined Cyce) 98 105. 125 116 193 136 iomas Advnced Nuciear 90 92 101 95 117 101 Conventonal Coal 87 95 119 Hydro 69 84 107 Natural Ga Wind Advanced onshore Combined Cyt e 66 69 74 13 82 82 Geothermal 44 48 52

Figure 2.1.12: Projected LCOE in the U.S. by 2020 (Source: EIA [28])

Learning Rates of Solar and Wind

The learning rate refers to the percentage reduction in the cost of a technology with every

doubling of the cumulative production or installation of the given technology. This concept is very popular in evaluating how costs of wind and solar technologies have fallen due to their proliferation and will be used as a parameter in this thesis' model. The reduction in costs could be a result of economies of scale, increase in worker productivity as workers get more familiar with the repeated production

tasks, improvements in the production process, as well as technological innovations. The learning effect

is expressed as the following formula:

Y = aXb

where Y = unit cost of technology

a = unit cost of the first unit X = cumulative installed capacity

b = learning rate exponent

The expression of the learning rate (LR) is given by:

LR = 1 - 2b

The learning rate exponent, b, is also known as the rate of cost reduction and can be found by taking the slope of the learning effect equation on a log-log scale:

logY = a+b(logX)

Many studies have been done to find the learning rates for solar PV and wind generation technologies. A review of learning rates studies conducted by Rubin et al. has found that out of 13 studies on solar PV learning rates, the range falls between 10 and 47% with a mean of 23% learning rate [29]. For wind, 12 studies were reviewed for onshore wind power units and 2 on offshore power units [29]. The range of learning rates is between -11 and 32% for onshore wind, and 5 to 19% for offshore wind, with a mean learning rate of 12% for both types of wind technologies [29].

In the U.S., while the solar PV modules have a learning rate of about 20%, a study using solar PPA prices against cumulative installed solar PV capacity gave a learning rate of 16%, which is more

representative of the solar PV system as a whole [30]. In the 2013 Wind Technologies Market Report done by the LBNL, the wind power learning rate was calculated as 6.9% using installed cost and global

cumulative wind power installations data dating from 1982 to 2013 [31].

2.2 Energy Policies

Renewables development is highly driven by policies that promote their uses since these newer technologies are relatively more costly than their conventional counterparts. Federal incentives and policies that promote the deployment of solar and wind projects include production tax credit (PTC), investment tax credit (ITC), renewable portfolio standards (RPS), Clean Power Plan (CPP) as well as R&D funding.

2.2.1 Production Tax Credit (PTC)

The PTC is an inflation-adjusted tax credit that provides $0.023/kWh credit for wind, geothermal, closed-loop biomass and $0.011/kWh for other technologies such as opened-loop biomass, landfill gas and municipal solid waste, for the first ten years of a renewable energy facility's operation. Originally enacted in 1992, the PTC had been renewed 6 times and expired on 31 December, 2014. Wind development had been heavily reliant on PTC for its rapid growth in the last decade. The actual generation cost of a wind project is about $50/MWh and the PTC covers $23/MWh, about half of a project's cost, as commented by a researcher in Texas wind industry [32]. Texas is the U.S.' largest wind producer, accounting for 20% of the country's wind power [32]. However, the unpredictability in the PTC

policy has caused difficulties in the industry and as shown in Figure 2.2.1, wind installations have dropped between 76 and 93% each time PTC expires [33].

Impact of Production Tax Credit Expiration and Extension on U.S. Annual Installed Wind Capacity

14,000 12,000 8,000 PTC PTC ,E xtension Expiration L E xtension 6. 00 0 4,000

C 'IC Expiration & Extension

2,000

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Figure 2.2.1: Impact of PTC expiration and extension on installed wind capacity (Source: UCSUSA [33])

2.2.2 Investment Tax Credit (ITC)

The ITC was created under the Energy Policy Act of 2005 and has been an important driver of renewables development especially for solar. The credit amount for solar is a 30% tax credit on residential and commercial properties with no maximum cap. The commercial ITC is used for utility-scale, commercial and residential sized projects where the company that installs, develops and finances the project claims the credit. For the residential ITC, it is the homeowners who will claim the credit through their income tax rebates. The ITC has been a hotly-discussed issue in the solar industry as it is due to expire on 31 December, 2016. According to the current policy, in 2017, the residential credit will

be eliminated and the commercial credit will reduce from 30% to 10%. The ITC had previously gone through several extensions and expansions, and now, the industry is pushing for a further extension of

ITC beyond 2016.

Bloomberg New Energy Finance (BNEF) has conducted an independent analysis on the impact of

ITC extension on the solar industry [34]. Under the current policy, the additional installed solar capacity

ii

L .~ V 1ff VV

2015 and 2022 will be 54GW. However, under a 5-year extension of the ITC at 30% for both residential and commercial properties, the total addition to solar capacity will be 76 GW between 2015 and 2022, an increase of 22 GW from the current policy scenario (See Figure 2.2.2). The utility-scale installations will add 31 GW to the solar capacity between 2015 and 2022, 10 GW more than the current policy case with no extension. The ITC extension will also smooth out the economic impact on the solar industry and will prevent a sudden major loss of jobs in the industry after ITC expiration in 2016.

POLICY AS USUAL - NEW BUILD (GW)

12 10 8 6 0.8 ITC 2.0 expires 1.7 1.2-1.7 19 22 2.3

ITC EXTENSION - NEW BUILD (GW)

ITC expires 12 10 8. 6 42 20 23 3.9 2 'i~4i.33 2.4 1 28 08 4 2 06 4 06 04 0 0 20112012 2013 2014 2015 2016 2017 2018 2019 2020 20212022 20112012 2013 2014 2015 2016 2017 2018 2019 2020 20212022 a Utility Residential Commercial and industrial

Notes 'ITC extension scenano considers a 5-yr extension to both the personal and business investment

tax credits. and commence construction language added to the business credit Source Bloomberg New Energy Finance EIA 826. 860 and 861

Figure 2.2.2: Impact of ITC extension on U.S. solar industry (Source: BNEF [34])

4

2.2.3 Renewable Portfolio Standards (RPS)

Renewable portfolio standards are requirements that set a certain minimum percentage of electricity sales coming from selected eligible renewable power resources by a specific time period. As of October 2015, 29 U.S. states, Washington D.C., and 3 U.S. territories have implemented mandatory RPS requirements while 8 states and 1 territory have voluntary goals for renewable generation (See Figure 2.2.3) [35]. An associated feature of RPS is the renewable energy certificates (REC), also known as

green tags, which allows state regulators to track compliance with RPS targets. One REC is created by renewable electricity generators for every 1 MWh of renewable electricity they placed on the grid. These RECs can be traded to help other electricity suppliers meet the requirements of RPS.

Renewable Portfolio Standard Policies

www.dsireusa.org I October 2015 MT: 15%x2015 ND 10%x2015 3 % SID 10%x201,' MN.26 5 2025 (IOUs,, Mi: 1 0< WI: 10% 20151t 2015 IA: 105 MW _{IN OH} NV: 25% x IL: 2025. UT 20% o 30% by 202 x

CA: 50% IOUs) *t KS: 20%x 2020 MO:15% x

x 2030 2021 AZ: 15% x NM: 20%x 2020 2025' (OUs) 25% 10 x x 2o26 20251 OK: 15% x 2015 VT: 75% x201W2 CT: 27% x 2020 :12 5% NJ: 20.38% RE x 2W 2026 +4t%*dlbV2W PA: 18% x 2021t x 201 DE: 25% x 2026* Mk 20% x 2022 12 5% x 2021 (OUs) SC: 2% 2021 TX: 5 880 MW x 2015'

HI, 100% x 2045 _{NMI 20%} U.S. Territories_{x 2016 Guam 25%}

x 2035

PR: 20% x 2035

Renewable portfolio standard * Extra credit for solar or customer-sited renewables

D

29 States + Washington

DC + 3 territories have a Renewable Portfolio Standard

(8 states and 1 territories have renewable portfolio goals)

Figure 2.2.3: Renewable Portfolio Standards (RPS) by states (Source: DSIRE [35])

While RPS has been an effective driver in the growth of renewables investments, one of the key issues raised by RPS critics is that the renewables requirements cause end-consumers to bear a higher electricity price. However, a study conducted by DBL investors has shown that the average annual rate of increase in electricity prices has been lower in leading renewable states (See Figure 2.2.4) [36]. Also, RPS states experienced a lower annual price increase of an average of 3.02% compared to non-RPS states which experienced price increase of an average of 3.52% (See Figure 2.2.5) [36]. A National Renewable Energy Laboratory (NREL) report also found that RPS has resulted in wholesale price reductions of about $1/MWh or less in some markets, or price suppression benefits of $2-$50/MWh of renewable energy generation in other markets [37].

WA: 15% x 2020*

OR: 25%x 2025*

(large U14*

, L RAITUIRE RE VIEW I

Renewable Leaders and Laggards:*

Average Retail Electricity Prices 2001

2013

12 11 10 1z 9 8 7 S6 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

0 Top 10 Leading Renewable States 0 National Average * Bottom 10 Lagging Renewable States

Source: US. Energy Information Adminiaralion * The Top 10 Renewable States have experlenced low retail prices for a variety of reasons, Including, in many cases, abundant wind resources.

Figure 2.2.4: Average retail electricity price for renewables leaders and laggards (Source: DBL, EIA [36])

Meeting in the Middle:

Annual Retail Electricity Prices in RPS and Non-RPS States

10% _{10% AFigure 7} 8% 6% 4% 2% 0% -2% Q' I -4% 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

* RPS States 0 Non-RPS States

Source: US. Energy Infrrmation Administration

2.2.4 Clean Power Plan

In August of 2015, President Obama announced the release of the final Clean Power Plan (CPP)

which sets limits on carbon dioxide emissions from power plants, the largest source of carbon emissions

in the U.S. The CPP standards are developed under the Clean Air Act by the Environmental Protection

Agency (EPA) which target carbon emissions from power sector to drop by 32% below 2005 levels by 2030 [38]. Under the CPP, each state is required to implement plans within a flexible framework set by

EPA to meet its carbon emissions performance rates targets by 2030. The measures that states can use

to meet their emissions goals consist of wide-ranging options that include energy efficiency improvements, employing the use of demand-side energy management, coal-to-gas switching for electricity generation, investing in zero-emitting renewable energy sources (like wind and solar), and

emissions trading.

The EIA found that under the CPP rule, natural gas will substitute coal as the main source of fuel

for electricity generation [39]. More wind and solar capacity will also be added and renewables generation will overtake coal-fired generation between 2030 and 2040 (See Figure 2.2.6) [39]. EIA also modelled the effect of the CPP using the High Oil and Gas Resource (HOGR) scenario as baseline. Under the HOGR scenario in which higher domestic production of oil and gas and therefore lower natural gas

prices are assumed, natural gas-fired share of the total generation is much larger than the other cases

considered, rising to 47% by 2040 while renewables grow at a slower rate, making up 19% of the energy mix compared to 27% in the case using Reference scenario as baseline (See Figure 2.2.6) [39].

U.S. total electricity generation in four cases, 1990-2040

trillion kilowatthours

histor, Reference Clean Power Plan High 2!I CPP + Base Policy and Gas High al and Gas

6 CPP) Resource Resource percent of total coal * * nuclear 0h _ i I I 90 2000 10 2020 30 40 2020 30 40 2020 30 40 2020 30 40

Anass o'e Irauso of the Ce, P ,-

The Union of Concerned Scientists (UCS) also analyzed the CPP and concluded that all states are

able to comply with the new standards, with 20 states on track to be more than halfway toward

achieving their 2030 benchmarks and 16 states expected to surpass their 2030 targets [401. The UCS

commented that the CPP is able to limit the overreliance on natural gas for two reasons [41]. Firstly, the

final CPP increases the role of renewable energy in setting state emissions targets and encourages

investments in renewables through the Clean Energy Incentive Program which rewards early

investments in renewable energy generation and demand-side energy efficiency measures [41]. Second,

in EPA's state targets calculations, limits on natural gas generation growth rates are made to allow a

more gradual coal-to-gas switch, preventing a rush to natural gas [41].

In conclusion, the Clean Power Plan, being the latest policy that can impact the power sector to

a great extent, will change the energy mix of the country considerably. The coal plants may be phased

out at a faster rate; the reliance on natural gas may reduce; and the growth of renewables may become

much more rapid. In fact, when President Obama announcement this policy, he called the plan "the

single most important step that America has ever made in the fight against global climate change." [42]

2.3 Energy Projections and Scenarios modelling

2.3.1 International Energy Agency

Several papers have modeled future low-carbon scenarios and ways to achieve them. One of the more comprehensive work is done by the International Energy Agency (IEA) which has published

multiple reports that portray different pathways to attain low-carbon future targets on a global scale

through the use of detailed quantitative modelling analyses. These publications include Energy

Technology Perspectives, Solar Photovoltaic (PV) Energy Technology Roadmap and Wind Energy

Technology Roadmap which we will review some of the key findings in this section.

The Energy Technology Perspectives 2015 highlighted the requirements to attain the 2*C scenario (2DS) in which the global temperature rise is limited to 2*C. These requirements include a

reduction of 60% of both the energy and carbon intensity of global gross domestic product (GDP); reducing 28% of global final energy demand in 2050 by energy efficiency measures; increasing electrification in end-use sectors; switching to renewables electricity for 40% of heating and cooling needs; as well as an additional US$ 40 trillion investments between now and 2050 [43]. The 6*C scenario

(6DS) is a reference scenario in which no further GHG mitigation efforts beyond the current policies are

LI TERTURE REVIEW

the global electricity generation mix in which the share of fossil fuels is entirely replaced by renewables at about 68% [43]. For the U.S., the projection in 2050 shows that solar PV and wind will take up about 30% of the total electricity generation [43].

6 50000 40000 30000 20000 10000 01 201 a Natural ga Os 2DS 50000 - - - 40 000-30000 20 000

10000

s 0 Natural gas with CCS m Od t Coal M Coal with CCS Nudear Bocmass and waste wHydro Solar Wind a Other

Figure 2.3.1: Global electricity generation mix (Source: lEA [43])

In the Solar PV Technology Roadmap released in 2014, the IEA dived deeper into defining key actions for the solar PV industry to take in order to meet low-carbon future targets. The hi-Ren scenario modelled is a variant of the 2DS with more rapid deployment of renewables, especially in solar and wind energy, and slower progress in nuclear and CCS technologies compared to 2DS. Under this hi-Ren Scenario, PV capacity in U.S. will grow to 246 GW and 599 GW in 2030 and 2050 respectively (See Table 2.3.1) [25]. Projections for LCOE for utility-scale PV plants were also performed and solar PV cost could go as low as $40/MWh in 2050 (See Table 2.3.2).

2013 12.5 1.3 78 18 18 2.3 0.3 0.1 1.4 3 0.2 135 2030 246 29 192 157 634 142 85 94 93 12 38 1721 2050 599 62 229 292 1738 575 169 268 526 67 149 4674

LIi I HAIURE REVIEW

Minimum 119 96 71 56 48 45 42 40

Average 177 133 96 81 72 68 59 56

Maximum 318 250 180 139 119 109 104 97

Table 2.3.2: Projections for LCOE for new-built utility-scale PV plants under the hi-Ren Scenario (Source: lEA [25])

The IEA has also published technology roadmaps on wind energy with the latest issue in 2013. In this roadmap, the IEA forecasts that wind generation in the U.S. will grow to 1000 TWh in the 2DS and 1200 TWh in the hi-Ren scenario by 2050 (See Figure 2.3.2) [44]. This equates to about 20-25 % of total electricity demand in U.S. In terms of capacities, both land-based and offshore wind installations will increase to 380 GW in 2050, 6 times of what it is today [44]. The LCOE of wind energy is also estimated to be as low as $44/MWh for land-based wind turbine in 2050 [45].

2DS TWh hiRen 20%- 8000 - 20% 18%- - 7000 -18% 16% - 16% 14%- 14% 5 000 _12% 10% - 4000 10% 8%- 3 000 8% 6%- 6% 2 000-4%- -4% 2% 1000 2% 0% 0 0% 2009 2015 2020 2025 2030 2035 2040 2045 2050 2009 2015 2020 2025 2030 2035 2040 2045 2050

* China U OECD Europe U United States Other developing Asia Middle East U Eastern Europe and FSU

* India N Other OECD North America U OECD Asia Oceanic U Africa U Latin America - Share of total

Source: 1EA, 2012a.