The impacts on climate mitigation costs of considering curtailment and storage of variable renewable energy in a general equilibrium model

    The impacts on climate mitigation costs of considering curtailment and storage of variable renewable energy in a general equilibrium model Hancheng Dai, Shinichiro Fujimori, Diego Silva Herran, Hiroto Shiraki, Toshiko Masui, Yuzuru Matsuoka PII: DOI: Reference:

S0140-9883(16)30039-1 doi: 10.1016/j.eneco.2016.03.002 ENEECO 3286

To appear in:

Energy Economics

Received date: Revised date: Accepted date:

16 September 2015 6 March 2016 6 March 2016

Please cite this article as: Dai, Hancheng, Fujimori, Shinichiro, Herran, Diego Silva, Shiraki, Hiroto, Masui, Toshiko, Matsuoka, Yuzuru, The impacts on climate mitigation costs of considering curtailment and storage of variable renewable energy in a general equilibrium model, Energy Economics (2016), doi: 10.1016/j.eneco.2016.03.002

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ACCEPTED MANUSCRIPT The impacts on climate mitigation costs of considering curtailment and storage of variable renewable energy in a

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general equilibrium model

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Hancheng Daia, *, Shinichiro Fujimorib, Diego Silva Herranc, Hiroto Shirakid, Toshihiko Masuie,

Hancheng Dai,

[email protected],

Corresponding

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a

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Yuzuru Matsuokaf

author,

Center

for

Social

&

Environmental Systems Research, National Institute for Environmental Studies, 16-2 Onogawa,

Shinichiro Fujimori, [email protected], Center for Social & Environmental Systems

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b

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Tsukuba-City, Ibaraki, 305-8506, Japan

Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba-City, Ibaraki,

c

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305-8506, Japan

Diego Silva Herran, [email protected], Center for Social & Environmental Systems Research,

National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba-City, Ibaraki, 305-8506, Japan d

Hiroto Shiraki, [email protected], Center for Social & Environmental Systems Research,

National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba-City, Ibaraki, 305-8506, Japan e

Toshihiko Masui, [email protected], Center for Social & Environmental Systems Research,

National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba-City, Ibaraki, 305-8506, Japan f

Yuzuru Matsuoka, [email protected], Graduate School of Engineering, Kyoto

University, Kyoto-Daigaku Katsura, Nishikyo-Ku, Kyoto 606-8530, Japan

Abstract

ACCEPTED MANUSCRIPT The curtailment and storage associated with the fluctuation of electricity supplied by variable renewable energy (VRE) may limit its penetration into electricity systems. Therefore, these factors need to be explicitly treated in the integrated assessment models (IAMs). This study improves the representation of curtailment and storage of VRE in a computable general equilibrium (CGE) model.

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With the data generated from an hourly power sector model, curtailment and storage of VRE

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electricity are treated as a function of the shares of solar and wind in the electricity mix. This relationship is incorporated into a CGE model and we also updated the VRE costs and resource

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potential. The results show that with such improvement, by 2100, in a 450-ppm atmospheric CO2 equivalent concentration (henceforth ppm) scenario, some electricity generated from VRE is either

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curtailed (2.1%) or needs to be stored (2.9%). In contrast, if VRE fluctuation is not considered, the long-term global economic cost of carbon mitigation is significantly underestimated (by 52%) in the

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same scenario. Conversely, updating the VRE costs and resource potential leads to a decrease in mitigation costs. Our simulation implies that the fluctuation of VRE cannot be ignored and needs to be incorporated in CGE models. Moreover, in addition to storage with battery, many other options

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are available to reduce curtailment of VRE. Top-down type CGE model has limitations to fully

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incorporate all aspects due to its limited spatial, temporal and technological resolution.

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Keywords: Curtailment and storage; variable renewable energy; computable general equilibrium model; Asia-Pacific Integrated Modeling/Computable General Equilibrium (AIM/CGE)

ACCEPTED MANUSCRIPT Highlights Curtailment and storage of variable renewable energy are represented in a CGE model.

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Some curtailment and storage are evident in climate change mitigation scenarios.

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If curtailment and storage are not considered, climate change mitigation costs may be

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underestimated.

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ACCEPTED MANUSCRIPT 1

Introduction In the past decade, energy supply from variable renewable energy (VRE), which mainly refers

to wind and solar power, has experienced rapid development. Global power generation from solar

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rose from 0.035 exajoules (EJ) in 2000 to 0.22 EJ in 2011, accounting for 0.04% of global primary energy. More noticeably, global wind power generation increased from 0.11 EJ (accounting for

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merely 0.027% of the total global primary energy) to 1.56 EJ (accounting for 0.28%) over the same period (IEA, 2013). The majority of VRE capacity installed to date has been in Organisation for

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Economic Co-operation and Development (OECD) countries, especially in Europe; however, recent growth has been driven more by emerging economies such as China. Moreover, considering the rich

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resource abundance (Error! Reference source not found.) and importance of cutting emissions, many countries and regions have set ambitious VRE targets in their climate mitigation portfolios.

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For example, in 2009 the European Union (EU) adopted the Renewable Energy Directive (European Union 2009), which endorses a mandatory target of a 20% share of energy from renewables by 2020. Such a target implies that 34% of the EU’s electricity consumption will come from renewable

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energy sources (Martínez-Anido, de Vries et al. 2012). In Germany, renewable energy sources must

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account for at least 35%, 50%, 65%, and 80% of its gross electricity consumption by 2020, 2030, 2040, and 2050, respectively (BMWi and BMU, 2010). In Denmark, a target of 30% of energy from

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renewable sources for the year 2025 has been proposed, with further plans to decarbonize the energy system completely by supplying all energy from renewables (Lund and Mathiesen 2009). Most EU member states are on track to achieve their targets for the electricity sector – the share of renewables in electricity consumption in 2013 was 43% in Denmark, 36% in Spain, and 25.6% in Germany (Eurostat 2015). In addition to its Copenhagen commitment to raise the share of non-fossil energy in primary energy to 15% by 2020, China recently announced another more ambitious target of 20% by 2030 (US White House Press 2014). Wind and solar photovoltaic (PV) power differ from conventional power generation in many respects. Power production from VRE fluctuates, with the hourly generation capacity changing over time, and being strongly dependent on weather and season, as well as the time of day (Olson, Jones et al. 2014). Moreover, depending on the region and type of VRE, generation may be only weakly correlated with hourly load profiles (Schill 2014). Consequently, it sometimes becomes necessary to disconnect VRE generators from the grid, which leads to the phenomenon of curtailment of VRE. Many measures are available to reduce the amount of curtailed VRE; for example, installing dispatchable generators that can ramp output up and down very quickly, demand-side management,

ACCEPTED MANUSCRIPT or interconnections to adjacent power systems for trade. Storage of excess VRE power is a commonly used approach. As the VRE share of total power generation increases, integration will become more challenging and costly (Ueckerdt, Brecha et al. 2015). Moreover, the challenges of system integration could play a significant role in determining the future deployment of renewable

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energy sources (Luderer, Krey et al. 2014).

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Global integrated assessments also consider VRE to be a promising option to achieve a deep

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reduction in greenhouse gas (GHG) emissions (Krey and Clarke 2011; Akashi, Hijioka et al. 2012; Knopf, Chen et al. 2013; Luderer, Krey et al. 2014; Pietzcker, Stetter et al. 2014). However, due to their large time horizons and limited temporal detail, integrated assessment models have difficulty in

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explicitly representing key features of VRE. In particular, curtailment and storage of VRE have been treated adequately in only a few of the existing top-down general equilibrium models. Krey and

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Clarke (2011) found that existing medium- to long-term scenarios, with high percentages of wind and solar PV, implicitly assume that any barriers to grid management are largely overcome; for

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example, through electricity storage technologies, demand-side management options and general

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advances in grid management. Many previous integration studies have treated VRE as a “must-take” resource, assuming that all VRE output is delivered to the grid. The system is then dispatched to

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meet the ‘‘netload’’ signal, i.e., load minus VRE output (Olson, Jones et al. 2014). Previously, 37 different computer tools developed before 2010, most of which were of the bottom-up type, have been reviewed (Connolly, Lund et al. 2010). It was reported that the choice of an ideal model to evaluate VRE strongly depends on the objective such as time frame, target sector and technology. Models with time step of 1 hour or less would be appropriate if the objective is to optimize the energy-system to accommodate the fluctuations of renewable energy, whereas if the objective is generate a long-term

storyline other models with annual time frame would be more suitable.

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also pointed out that computable general equilibrium (CGE) models, which focus on economic interactions rather than technological details, have difficulty representing power sector details such as variability in loads and VRE. It is uncertain whether the challenges of VRE integration will prevent VRE gaining a large share of the total power generation capacity in the future, and the impacts of VRE integration challenge on the climate mitigation costs are also unknown. As a starting point, the latest Intergovernmental Panel on Climate Change (IPCC) Assessment Report 5 (AR5) states that compared with the baseline scenario, the consumption loss in 2100 due to the achievement of 5

ACCEPTED MANUSCRIPT atmospheric CO2 equivalent concentration targets of 650, 550, and 450 parts per million (ppm) would be 2.3%, 4.7%, and 4.8%, respectively (IPCC 2013). However, in most top-down type CGE models, such as the Asian-pacific Integrated Model (AIM)/CGE (Fujimori, Masui et al.(2012; 2014)) and the EPPA model (Chen, Paltsev et al. 2015), penetration of VRE is assumed to be

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restricted by a fixed factor of natural resource in the production function, which still have not

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appropriately considered curtailment and storage of VRE, and there may therefore be non-ignorable

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consequences in assessing the economic costs of long-term climate mitigation scenarios. Given this context, the aim of this study is to explicitly incorporate curtailment and storage of VRE in a top-down global CGE model. Using the latest VRE resource data and best available data

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for estimating curtailment and storage of VRE obtained from bottom-up models, we have identified specific functional form and used the regression method to parameterize it, which could yield a

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more reliable and robust method of estimating intermittency-related costs. Furthermore, this methodology is applied to revisit the economic impacts of achieving different climate targets

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throughout this century. It should be noted that, due to limited data availability, only short-term

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storage with batteries is considered as a measure to mitigate curtailment. The remainder of the paper is structured as follows. Section 2 describes the data and methods

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used to incorporate curtailment and storage into a CGE model. The section is further divided into: a) how curtailment and storage are estimated as a function of the shares of wind and solar power; b) how the model is modified based on findings from a); and c) scenario definitions for the 21st century. Section 3 presents the main findings concerning the additional economic costs of carbon mitigation that result from consideration of curtailment and storage of VRE and their underlying causes. Section 4 discusses the results of the sensitivity analysis. We conclude this study in Section 5.

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Methodology As shown in Figure 1, this study combines a database of renewable energy resources and

curtailment

and

storage

of

renewable

energy

using

the

Asia-Pacific

Integrated

Modeling/Computable General Equilibrium (AIM/CGE) model (Fujimori, Masui et al. (2012; 2014)). The role of the database is to obtain data regarding the global resources of onshore wind (NREL, 2014) and solar PV (Pietzcker, Stetter et al. 2014) (see Error! Reference source not found.) and to estimate the relationship between the share of VRE and its curtailment and storage. This information is then fed into the CGE model to simulate future economic and energy systems in this century. By comparing scenarios with and without consideration of curtailment and storage in

ACCEPTED MANUSCRIPT the model, we evaluate how consideration of these factors may affect the economic costs of achieving different climate targets.

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Figure 1: Research framework of the study

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2.1.1 Regional load duration curve data

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2.1 Estimation of the shares of curtailment and storage

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We obtained data that explicitly represent the relationship between the shares of curtailment and storage, and the shares of solar and wind, in the overall power mix. The data, as shown in Figure 2

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and Figure 3, are based on regional load duration curve (RLDC) data generated for Brazil, China, the EU, India, Japan, the Middle East, Africa, and the USA by the one-node hourly dispatch model DIMES (Ueckerdt, Brecha et al., 2015, this issue). The hourly model internally optimizes the

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endogenous deployment and use of short-term storage to smoothen the RLDC, assuming a carbon

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price of 150 $/tCO2 in 2050. This produces a well-interconnected region so that both load and

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VRE-production can be aggregated to one curve, with basic storage costs for redox-flow batteries of 300 $/kW for a converter and 100 $/kWh for a reservoir, and with carbon capture and storage (CCS) technology available.

The RLDC data in Figure 2 and Figure 3 show that the shares of curtailment (and storage), which are defined as the curtailed (and stored) energy divided by the actual electricity consumption, are closely related to the theoretical shares of wind and solar PV, which are defined as power generation from wind or solar (including the curtailed power generation) divided by the actual electricity consumption. As shown in Figure 2, when the theoretical shares of wind and solar PV are low, there is almost no curtailment in any region considered (Brazil, China, the EU, India, Japan, and the USA). However, in most regions, curtailment starts to appear when the total theoretical share of VRE reaches 20‒30%, albeit from a very small base (less than 1%), which is negligible. Moreover, when the total VRE share reaches 40‒50%, a level seen in most moderate climate mitigation scenarios, curtailment of VRE is no longer negligible (more than 5%). On the other hand, storage is more dependent on solar PV. As shown in Figure 3, storage is needed only if the share of solar PV is high, whereas there is little demand for storage when there is a high proportion of wind energy. This may be because short-term (less than 1 hour) fluctuations of wind power are greater 7

ACCEPTED MANUSCRIPT than solar power, while long-term (seasonal or daily) fluctuations of solar power are greater than wind power. Curtailment is a cost-effective means of balancing short-term fluctuations in wind because it requires no additional investment. In contrast, storage is cost-effective for balancing long-term fluctuations in solar power, although it requires additional investment. It should be noted

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that, because storage is relatively expensive, only a small amount of total electricity (less than 30%)

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is supplied by stored energy, especially when the share of solar PV is below 80%.

Figure 2: The relationship between curtailment share and the shares of theoretical wind and solar power in

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annual electricity demand. Note: dots represent individual observation.

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Figure 3: The relationship between storage output share and the shares of theoretical wind and solar power in

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annual electricity demand. Note: dots represent individual observation.

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2.1.2 Parameterization of RLDC data Using the above information, the shares of curtailment and storage are approximated as a function of the theoretical wind share and solar PV share in Equation ( 1 ). The parameters of the equation are estimated using the least squares method and are shown in Table 2. It should be noted that this equation was chosen because it provides a better fit after testing different equation forms. The function

Equation ( 1 ) where: :

Share of curtailment or storage in annual electricity demand (%); i indicates a set of curtailment or storage,

:

:

:

Theoretical wind share in the overall power mix (%)

:

Theoretical solar PV share in the power mix (%) Theoretical energy source e share in the power mix (%); e indicates a set of power energy sources consisting of solar (“sol”)

ACCEPTED MANUSCRIPT and wind (“win”) ,

, Parameters to be estimated for curtailment and storage.

,

: A set of regions

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:

2.2 Representing curtailment and storage in the AIM/CGE model

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The AIM/CGE [Global] model has been widely used in the assessment of climate mitigation and adaptation (Fujimori, Kainuma et al. 2014; Hasegawa, Fujimori et al. 2014; Ishida, Kobayashi et al. 2014;

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Fujimori, Masui et al. 2015b; Hasegawa, Fujimori et al. 2015). It is a recursive dynamic general equilibrium

model, extended from the “Standard CGE model” (Lofgren, Harris et al. 2002). The model represents

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the world as 17 regions and 42 industrial classifications (Error! Reference source not found. and S2, Appendix). Details of the model structure and mathematical formulas have been described by

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Fujimori, Masui et al. (2012; 2014).

Similar to most energy technology-rich CGE models, the model inputs include population,

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gross domestic product (GDP), energy service demand, the extraction cost of fossil fuels, and the

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availability and costs of renewable energy. Model outputs include energy demand and supply structure, GHG emissions, and the prices of energy and carbon emissions. More technically, the

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production sectors are assumed to maximize profits subject to multi-nested constant elasticity substitution functions and the relative prices of inputs. Household expenditures on each commodity are described by a linear expenditure system function. The saving ratio is determined endogenously to balance saving and investment, and capital formation for each good is determined by a fixed coefficient. The Armington assumption is used for international trade, and the current account is assumed to be balanced. In addition to energy-related CO2 emissions, CO2 from other sources, CH4, and N2O (including changes resulting from land use and non-energy related emissions) are treated as GHG emissions in the model. In the case of the power sector, the fired power plants have the option to utilize CCS. The costs of the CCS technology range from 50 US$/tCO2 for coal fired power and 120 US$/tCO2 for biomass fired power (Fujimori, Masui et al. 2015a), which are taken from median value of IEA (2008). We assume maximum annual penetration rate for CCS technology differentiated across the penetration stage. More specifically, at the early stage the new installation is limited (if the share of CCS power is under 10%, 2% per annual is the maximum) but as the technology matures (if the share of CCS power is over 10%), the penetration speed gets higher (4% per annual is the maximum). This study improves the representation of curtailment and storage of onshore wind and solar PV 9

ACCEPTED MANUSCRIPT power generation in the CGE model. First, total curtailment and storage are estimated based on the parameters estimated in Equation (1) and the shares of wind and solar power are endogenously generated by the CGE model. The second step is to divide the total curtailment and total storage of VRE into those of wind and solar, as shown in Equation (2), which are assumed to be in proportion

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to the share of wind and solar in the overall power mix. In this way, the additional costs of

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curtailment and storage can be reflected in the production functions of power generation. The curtailed VRE is disconnected from the grid and therefore not used in the energy system, whereas

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the stored energy will be supplied to the energy system again, but with additional costs to the economy by assuming that another sector provides a storage service. This sector, as shown in

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(stcostr) and the amount of electricity stored.

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Equation (3), requires an additional intermediate input that is dependent on the unit cost of storage

Equation (2)

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Curtailment and storage rates of wind and solar power

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Storage service as an intermediate input

where

Equation ( 3 )

share of curtailment or storage in annual electricity demand (%); i indicates a set of curtailment or storage,

: : : : stcostr:

the ratio of i (i

curtailment or storage) of VRE

the power generation from technology vre intermediate input of commodity c by the storage service sector storage cost conversion factor (which consists of a capacity factor; the unit cost in $/MWh, taking into account the lifetime and discount rate; and the MWh to ktoe conversion. The value is 205,900 USD per MW. Data source: METI (2009))

e and e’ are VRE sectors, including solar PV and wind energy, i is storage or curtailment, r is region, and c is a commodity

2.3 Scenario Two considerations are taken into account for the scenario setting: first, and most importantly,

ACCEPTED MANUSCRIPT whether curtailment and storage are represented; and second, three targets of 650, 550, and 450 ppm CO2 equivalent concentration by the end of this century. All six climate mitigation scenarios are compared with the respective baseline scenarios without GHG constraints. In total, there are eight

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scenarios, as shown in Table 1.

Results

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Table 1: Scenario assumptions

Here, we will show the regression results of estimated parameters first, how considering

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curtailment and storage in the CGE would affect economic cost of mitigation.

3.1 Estimated RDLC parameters from regression

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The estimated parameters for Equation (1) are shown in Table 2. It can be seen that, for curtailment, αsol is higher than αwin in all regions. For storage, αsol is positive, while αwin is negative,

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suggesting that the share of solar PV also plays a dominant role in determining the level of storage.

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In addition, it is also found that the absolute T-values for both curtailment and storage in all regions are higher than 2, indicating that the zero hypothesis can be rejected and the parameters are

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significant. Furthermore, the estimated values of both curtailment and storage fit the observed values from the DIMES model well (Error! Reference source not found.).

Table 2: Estimated parameters

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3.2.1 Socio-economic conditions toward 2100

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3.2 Assessment with the CGE model

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Before showing the main results, here we describe the broad picture of socio-economic

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circumstances from 2005 to 2100. As shown in Figure 4, the main underlying socio-economic assumptions of this study are that, under the SSP2 scenario assumptions (Luderer, Krey et al., 2015

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this issue), the global population will peak in around 2070 at 9.47 billion and then fall to around

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9.02 billion, an increase of 1.38 times the 2005 level, by the end of this century. The global GDP will increase ~7.5-fold from 2005 to 2100, equivalent to an annual growth rate of 2.15%. As a result,

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energy demand will grow dramatically. If there is no climate policy, the business as usual (BaU)

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scenario, the world’s total primary energy and electricity demand will increase 2.91- and 4.62-fold,

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to 1,368 and 368 EJ/year, respectively. In contrast, in the 450-ppm scenario, the primary energy will fall to 1,037 EJ/year, while the electricity demand will increase significantly to 433 EJ/year compared with the BaU scenario, due to the increased cost of fossil energy and relatively cheaper price of low-carbon electricity.

Figure 4:

World gross domestic product (Trillion USD in 2005), primary energy consumption

(EJ/year), electricity demand (EJ/year), GHG emissions (Gt/year), and radiative forcing (W/m2) in four representative scenarios up to 2100

3.2.2 Shares of VRE, curtailment, and storage Stringent climate mitigation targets promote the penetration of VRE in the energy system. In

ACCEPTED MANUSCRIPT our simulation, the global carbon shadow prices required to achieve 650-, 550-, and 450-ppm targets are 9, 56, and 270 USD/ton-CO2 in 2050 and 143, 392, and 1,700 USD/ton-CO2 in 2100, respectively. As a result of the carbon price signal, the share of VRE increases over time in most regions (Figure 5). The global shares of wind and solar power in the overall power mix increase

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from marginal levels to 19.4% and 15.6% in 2100, respectively, even under the modest atmospheric

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CO2 equivalent concentration target of 650 ppm. Moreover, more stringent targets result in higher shares of VRE. In the 450-ppm scenario, the shares of wind and solar power increase to 25.0% and

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24.2% in 2100, respectively. The global shares of total VRE in 2100 under the 650-, 550-, and 450-ppm scenarios are 35.0%, 37.8%, and 49.2%, respectively. The regional details reveal that the

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share of VRE is relatively high in Latin America (LAM) and the Middle East and Africa (MAF), and relatively low in Eastern Europe and the former Soviet Union (REF) countries. In addition, the

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lower cost of wind power results in a share higher than that of solar PV despite the large solar resource potential (Error! Reference source not found.). However, a more stringent atmospheric CO2 equivalent concentration target (e.g., 450 ppm) could reduce this difference or even overturn

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the relationship.

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Figure 5: Shares of solar and wind in the overall power mix. OECD90 = United Nations Framework Convention on Climate Change (UNFCCC) Annex I countries, REF = Eastern Europe and former Soviet Union, ASIA = Asia excl. OECD90 countries, MAF = Middle East and Africa, LAM = Latin America and the Caribbean (IIASA, 2012).

As implied by Equation 1, and Figure 2 and Figure 3, a high VRE share in mitigation scenarios causes a noticeable amount of curtailment and storage in the future. Figure 6 shows that curtailment and storage increases over time and with tighter atmospheric CO2 equivalent concentration targets. Before 2020, the share of VRE is too small to induce curtailment and storage. However, by 2030, as the world attempts to cut absolute GHG emissions with strong policy interventions, such as those recently adopted by China and the United States (US White House Press 2014), VRE becomes a key option for cutting GHG emissions, making curtailment unavoidable. As shown in Figure 6, the global VRE either curtailed or stored in 2030 is 0.87, 1.03, 1.94 EJ in the 650-, 550-, and 450-ppm scenarios, respectively, of which 0.38, 0.43, and 0.98 EJ are stored and supplied to the grid again. Their corresponding shares of the total power generated in 2030 (Figure 7) are 0.57%, 0.68%, and 1.30%, which does not create a serious issue. However, curtailment and storage challenge the power supply system in the long-term. In 2050 (2100), the global curtailed and stored VRE would increase 13

ACCEPTED MANUSCRIPT to 2.2 (9.1) EJ under the 650-ppm scenario, accounting for 1.0% (2.5%) of total electricity consumption. Furthermore, these numbers would increase to 6.2 (21.6) EJ under the more stringent target of 450 ppm, equivalent to 2.6% (5.0%) of total electricity consumption, and a storage capacity of 1,284 (4683) GW, or 3.9% (6.1%) of the total installed power generation capacity in 2050 (2100).

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Without reforming the existing grid system, the penetration of VRE would be seriously hindered.

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The regional results indicate that curtailment would mainly occur in the OECD and Asian

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countries in all years, and in 2100 there is also a significant curtailment in the Middle East and

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Africa. In contrast, there is relatively little curtailment in the REF and LAM regions.

Figure 6: Curtailment and storage output of variable renewable energy. OECD90 = UNFCCC Annex I countries,

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REF = Eastern Europe and former Soviet Union, ASIA = Asia excl. OECD90 countries, MAF = Middle East and

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Africa, LAM = Latin America and the Caribbean (IIASA, 2012).

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Figure 7: Ratio of curtailment and storage output to total power generation. OECD90 = UNFCCC Annex I countries, REF = Eastern Europe and former Soviet Union, ASIA = Asia excl. OECD90 countries, MAF = Middle East and

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Africa, LAM = Latin America and the Caribbean (IIASA, 2012).

3.2.3 Economic cost of mitigation The main findings of this study are shown in Figure 8. They reveal that when curtailment and storage of VRE is taken into account, the economic costs of mitigation, which are represented by a loss in GDP and household consumption (i.e., welfare) compared to the baseline scenario, increase significantly in all regions, years, and for all atmospheric CO2 equivalent concentration targets. For example, in a counter-factual scenario, where curtailment and storage of VRE are not considered, the global GDP (and welfare) losses in 2100 when achieving the 650-, 550-, and 450-ppm targets would be 1.1 (1.5% for welfare loss), 1.9% (2.5%) and 2.8% (3.6%), respectively. In contrast, if curtailment and storage are considered, the corresponding losses in GDP (and welfare) would be 1.7% (2.2%), 2.7% (3.6%) and 4.2% (5.5%), equivalent to relative increases of 51%, 42% and 52% in the 650-, 550-, and 450-ppm scenarios, respectively. The regional results show a similar increasing trend in mitigation costs. For example, the GDP losses in 2100 under the 450-ppm scenario in the OECD, REF, Asia, MAF, and LAM regions

ACCEPTED MANUSCRIPT increase from 1.2%, 7.2, 1.9%, 4.9%, and 2.0% in the no-curtailment scenario to 2.2%, 9.2%, 4.0%, 6.1%, and 3.1%, respectively. The relative increases are particularly large for the OECD (rising by an additional 92%) and Asian (112%) countries, followed by the LAM (55%), MAF (25%), and REF (29%) regions. The order of the relative increases partly reflects the role of VRE in mitigating

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carbon emissions in these regions. OECD and Asian countries may depend more on VRE for cutting

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carbon emissions than the rest of the world. This implication is consistent with that of a bottom-up study by (Akashi, Hijioka et al. 2012), who showed that VRE in Asia would account for 27% of the

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effort required to meet the deep carbon reduction target in 2050, which is higher than the world average of 23%. Therefore, the mitigation costs in these regions could be more sensitive to

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fluctuation in VRE. On the other hand, regions with a large resource base of fossil fuels, such as the Middle East and the former Soviet Union, can switch more easily to other CCS technologies to cut

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emissions even if there is curtailment of VRE; thus, fluctuations in VRE would not impact mitigation costs as much as in other regions.

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Conceptually, the increased mitigation costs can be explained by the following mechanism.

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First, as a result of stringent atmospheric CO2 equivalent concentration targets, the carbon shadow price is high. Second, the high carbon price makes renewable energy cost-effective compared to

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fossil energy alternatives, which in turn leads to a continuous increase in VRE penetration into the market. Third, a high share of VRE in the overall power mix induces a significant amount of VRE curtailment and storage as demonstrated in Equation 1, and Figure 2 and 3 Consequently, power generation systems have to use either more fossil-fired power or more stored energy due to the decreased availability of VRE. Both options lead to additional costs, and thus result in an increase in the price of power. In summary, the additional increase in mitigation costs of achieving climate targets can be attributed to two main factors. The first is the additional storage cost originating from the introduction of batteries, while the second is the value of the curtailed VRE that is measured as the power price multiplied by the curtailed VRE. Table 3 compares the GDP loss with the cost of storage and the value of curtailment. It demonstrates that the additional curtailment and storage costs explain a large part of the GDP loss in most cases. The residuals are caused by secondary effects due to the price response mechanism of the CGE model.

Figure 8: Additional economic impact due to consideration of curtailment and storage. OECD90 = UNFCCC Annex I countries, REF = Eastern Europe and former Soviet Union, ASIA = Asia excl. OECD90 countries, MAF = Middle

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ACCEPTED MANUSCRIPT East and Africa, LAM = Latin America and the Caribbean (IIASA, 2012).

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Table 3: GDP loss and additional costs of curtailment and storage

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3.2.4 Change in the price of electricity due to fluctuations in VRE As explained in previous sections, curtailment limits the effective VRE supply. As a result, this gap in supply has to be filled using other energy sources such as fossil-fired power generation

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coupled with CCS technology, or by paying an additional cost to store some of the curtailed VRE. Because these alternatives may not be as cost effective as low-carbon VRE, the price of electricity

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will increase. As illustrated in Figure 9, when curtailment and storage are taken into account, the

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global average electricity prices in 2050 increase by 13.2%, 15.7%, and 13.8% under the 650-, 550-, and 450-ppm scenarios, respectively. The increase in prices shows a trend in regional disparity

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similar to that of mitigation costs. Larger increases are projected in OECD and Asian countries (ranging from 16%–21%), followed by the LAM region (11%–14%). In contrast, electricity prices change less in the REF and MAF regions (ranging from 4.5%–8.7%, mostly around 5%). This outcome explains why the mitigation costs increase when curtailment and storage are considered. Furthermore, the order of the price increases is consistent with that of the additional mitigation costs increase, as shown in Figure 8.

Figure 9: Impacts of considering curtailment and storage on the price of electricity. OECD90 = UNFCCC Annex I countries, REF = Eastern Europe and former Soviet Union, ASIA = Asia excl. OECD90 countries, MAF = Middle East and Africa, LAM = Latin America and the Caribbean (IIASA, 2012).

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Discussion

4.1 The role of VRE in climate mitigation

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This study suggests that VRE development and climate change mitigation targets are

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complementary to each other. On the one hand, developing VRE could achieve a large reduction in GHG emissions in the long term. Similarly, Akashi, Hijioka et al. (2012) showed that solar and wind

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energy represent two of the top five key carbon reduction technologies for Asia and the world to achieve significant GHG reductions by 2050. Together, they contribute 23% and 27% of the

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reduction required to achieve a 2-degree target increase in the world and Asia, respectively. Knopf et al. (2013) concluded from a model comparison study that to meet the European goal of reducing

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GHG emissions by 80% by 2050, wind and solar PV would on average need to contribute 27% of the future electricity mix by 2050. In addition, by reviewing 162 recent medium- to long-term

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2010 to 2050 would be nearly 10%.

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scenarios, Krey and Clarke (2011) found that the annual growth rates of wind and solar energy from

On the other hand, climate mitigation targets will promote penetration of VRE. Our model

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shows that even if curtailment and storage are included, the aggregated share of VRE in the power mix in 2050 would be 23%, 32%, and 39% for the 650-, 550-, and 450-ppm scenarios, respectively, and by 2100 these percentages would increase to 35%, 38%, and 49%, respectively. This is in line with most IAM projections, which show that VRE will expand in future climate mitigation scenarios. In an assessment of the role of renewable energy in climate stabilization, Luderer, Krey et al. (2014) found that the use of renewable energy for electricity supply would expand considerably in mitigation scenarios. A more comprehensive investigation of nearly 200 scenarios included in the IPCC AR51 database reveals that in stringent climate mitigation policy scenarios, total global deployment of wind and solar energy would account for a considerable portion of primary energy. For example, the shares of wind and solar in 2100 under the 450-ppm target would be over 5% and nearly 20%, respectively (Figure 10).

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I Fifth Assessment Report of the Intergovernmental Panel on Climate Change

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ACCEPTED MANUSCRIPT Figure 10: Shares of wind and solar in the primary energy mix in 2050 and 2100 in the Intergovernmental Panel on Climate Change Assessment Report 5 database. Data source: IIASA (2014).

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4.2 The need to improve the representation of VRE in economic models Given the importance of VRE in climate mitigation assessment, it makes sense to improve the

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representation of VRE further in CGE models by taking into account the distinguishing features of VRE. Our experimental simulation reveals that the “must-take” assumption embedded in the

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original AIM/CGE model may be over-optimistic with regard to the penetration of VRE, and therefore underestimate climate mitigation costs. By explicitly considering curtailment and storage

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of VRE, it is found that a noticeable amount of VRE will be curtailed or stored, especially when the share of VRE reaches a high level in the power mix. One of our key findings is that, in the moderate target scenario of 550 ppm, global curtailed (stored) VRE would account for 0.89% (0.87%) of total

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electricity consumption in 2050 and 1.4% (1.6%) in 2100. As explained below, this leads to

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ignored.

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additional significant climate mitigation costs that make curtailment and storage too important to be

Furthermore, without considering curtailment and storage, the global consumption loss and mitigation costs would be significantly underestimated. Compared to the mitigation costs reported in the AR5 scenario database (IIASA, 2014), accounting for curtailment and storage of VRE is equivalent to the use of scenarios with a limited availability of bioenergy, and is secondary only to suppressing CCS availability. The AR5 database shows that, with restrictions on the availability of CCS and bioenergy, mitigation costs will vary by factors of 138% and 64%, respectively; while our study suggests that the additional mitigation costs in 2100 resulting from curtailment and storage of VRE would be 52% and 42% for the 450- and 550-ppm scenarios, respectively, in the AIM/CGE model. To test the range of our results, a sensitivity analysis was performed by lowering or increasing the key parameters of resource potential, investment cost, and storage cost of VRE by 50%. It was found that the aforementioned 52% of additional mitigation cost for the 450-ppm target would vary from 47% to 103%, while for the 550-ppm target would vary from 37% to 81%, which agrees with the main message of our study.

4.3 Implications for mitigating the impacts of curtailment and storage

ACCEPTED MANUSCRIPT After realizing the importance of considering curtailment and storage of VRE, it is clear that to achieve a high penetration of VRE the impact of the fluctuating electricity supply from VRE must be mitigated by improving the flexibility of power systems.

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No two power systems are alike. Some systems have a very large installed generation capacity that covers several countries or states. Conversely, some are very small systems that cover small islands.

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In addition, some systems may be closely interconnected with adjacent areas and able to trade,

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whereas some may be isolated and unable to trade with neighboring areas. Some may have strong storage resources, such as pumped-storage hydroelectricity, while others may contain no storage

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facilities.

Therefore, as discussed in IEA (2011; 2012) and Table 4, the options to increase the flexibility of

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power systems will vary from one area to another, depending on the characteristics of not only the power system but also other sectors. The most important factor is the availability of dispatchable generators that can ramp output up and down very quickly. In one area, flexibility may feature

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predominantly in the hydroelectric power plants installed. In contrast, in another area maximum

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flexibility may be achieved by a combination of gas plants and demand-side management. In addition, other resources that may potentially be used for energy balancing are storage, demand-side

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management or response, and interconnection to adjacent power systems for trade. For example, during times of shortfall in high-demand periods, storage technologies can act as a generation source to reduce or eliminate a deficit. Storage contributes to the secure and reliable operation of the electricity system by assuring manageable provision of reserve power and deferring the need for additional generation capacity. This option will also be available in different areas to greater or lesser extents. Furthermore, sources outside the electricity sector, such as the heating and transport sectors, can also contribute to flexibility. In the heating sector, space heating using electric thermal storage systems can create opportunities to manage surplus VRE output. In the transport sector, electric vehicle fleets may provide a valuable opportunity to use surplus VRE output. Using the Danish energy system as a case, Mathiesen and Lund (2009) compared the performance of seven technologies to facilitate VRE integration. They found that large-scale heat pumps are the most promising technology to absorb the excess electricity generated from VRE. Flexible power demand management and electric boilers are cost effective. In the transport sector battery electric vehicles are more promising than fuel cell vehicles. They also argue that transition from fossil fuels towards the integration of high share of VRE requires rethinking and redesigning the energy system both on 19

ACCEPTED MANUSCRIPT the generation and consumption side. In addition to focusing on the electricity sector alone, they propose to establish a more systematic Smart Energy Systems by involving the electricity, heating and transport sectors in combination with storage options, which would facilitate even higher

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penetration of VRE (Mathiesen, Lund et al. 2015).

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Table 4: Countermeasures to address fluctuation in variable renewable energy

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4.4 Contributions and limitations of the assessment

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The main contribution of this study is the explicit representation of curtailment and storage of VRE in a top-down type CGE model. Using the data for curtailment and storage of VRE obtained

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from bottom-up models, we have identified specific functional form and used the regression method to parameterize it, which could yield a more reliable and robust method of estimating

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intermittency-related costs. Using the improved model, it is found that consideration of curtailment

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and storage of VRE will noticeably affect the future energy supply system and climate mitigation costs. Once data are available for other options to facilitate penetration of VRE, similar improvement could be made in a CGE model following our method. However, this is a preliminary study and the assessment includes various limitations that require improvement. First, there is uncertainty in the estimation of the parameters describing the shares of curtailment and storage in terms of VRE shares (equation (1)), which is crucial for this study. The power sector model could be improved by the use of a more reliable dataset covering more regions, which is beyond the scope of this study. In the current dataset, the RDLC data is obtained by simulating a single historical base year, implying that it carries information from the existing grid infrastructure and demand of each region. In such a case, using these parameters derived of a present-day base year is a source of uncertainty in the projections, particularly for regions where the power sector is growing by an order of magnitude from present-day to the end of the century. More specifically speaking, if the grid infrastructure is improved to integrate VRE, the parameters in Equation 1 and Table 2 would reduce, reflecting the possibility that less curtailment and storage will be necessary as a result of improved infrastructure and demand side management. It is also important to select an appropriate regression function form (if any) to best capture how curtailment

ACCEPTED MANUSCRIPT and storage are affected by other factors. Second, in this study only storage using batteries is considered as an option to address curtailment. Other options such as the inter-regional trading of excess VRE production (Martínez-Anido, de Vries et al. 2012), the production of hydrogen from the excess power generated from VRE (Edwards, Kuznetsov et al. 2008; van Ruijven, Hari et al. 2008;

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Neef 2009; Bleischwitz and Bader 2010; Houghton and Cruden 2011; Wang 2011; Andrews and

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Shabani 2012), pumped-storage hydroelectricity coupled with a VRE power plant (Vieira and Ramos 2009; Duque, Castronuovo et al. 2011; Dursun, Alboyaci et al. 2011), and demand-side

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responses such as the charging of electric vehicles at night (Soares M.C. Borba, Szklo et al. 2012) are not considered. Third, the regional resolution is also important. In the CGE model, the world is

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divided into 17 economic regions and markets. There is an implicit assumption that wind power is freely tradable within a region, but untradeable between regions. This may overestimate VRE power

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generation in some large countries with uneven distributions of VRE resources, where there is a lack of transmission capacity. For example, the best Chinese wind resources are located in the north and west, far from the principal demand centers in the south and east of the country. In such cases, the

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cost of connecting to and reinforcing the existing grid may be high. Trade between regions may also

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be underestimated, especially in the long term. For example, if a transmission network is constructed between Europe and resource-rich regions, such as North Africa and the former Soviet Union, trade

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in wind power could be possible. Furthermore, the balancing timescale is considered to stretch from 15 minutes to 36 hours ahead of the time the electricity is used (IEA, 2011). However, the time resolution in the CGE model is 1 year, which does not address short-term integration challenges, such as balancing the peak and base loads. Last but not least, besides cutting or storing power generation from VRE when supply exceeds demand, recent model development also concerns with ensuring adequate electricity generation when demand exceeds supply, and the costs associated with investing in backup generators with low capacity factors or maintaining spinning reserves. Such kinds of short-term balancing costs are not explicitly considered in the CGE model, implying that there is no separate backup capacity or capital to balance the short-term imbalance. As a result, the levelized capital cost and the total electricity system costs (as shown in Figure 9) may be underestimated.

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Conclusion This study took a novel approach to integrated assessment modeling by explicitly representing

curtailment and storage of VRE in a top-down type CGE model. We also evaluated the impacts of 21

ACCEPTED MANUSCRIPT curtailment and storage of VRE supply on climate mitigation costs as calculated by a CGE model. Data obtained from a power sector model reveal that curtailment and storage are closely related to the share of VRE in the overall power mix, especially the share of solar PV power. We applied the improved CGE model under different climate policy scenarios and compared the mitigation cost

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with the cost without considering curtailment and storage. This study provides a better

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representation of VRE in economic IAMs and an improved assessment of climate mitigation costs.

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This study also demonstrates that the “must-take” assumption embedded in the original AIM/CGE model may be over-optimistic with regard to the penetration of VRE, and therefore the climate mitigation costs are likely to have been underestimated. The results show that, as VRE

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comprises an increasing share of power generation in carbon mitigation scenarios, a remarkable amount of VRE will be curtailed and stored, leading to a significant decrease in global VRE

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availability by a factor of over 19%, and an increase in power supply cost by a factor of more than 5.0% in 2100 with the 450-ppm target scenario. Consequently, without considering the fluctuation

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of VRE in AIM/CGE, the long-term mitigation costs of achieving climate stabilization targets may

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be significantly underestimated by a factor of 52% in 2100. Moreover, underestimation would be especially large in OECD and Asian countries and moderate in the LAM region, whereas in the

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MAF and REF regions the impacts would be relatively small. On the other hand, updating the VRE costs and resource potential in the light of recent developments leads to a decrease in mitigation costs of the same magnitude.

Our simulation highlights the importance of improving existing top-down economic models and IAMs in which VRE treatment has not yet been improved and which use outdated cost assumptions. In addition to storage with battery, a more systematic so-called Smart Energy Systems is needed to incorporate many other options available to facilitate penetration of VRE, e.g. inter-regional trading of excess VRE production, producing hydrogen from the excess VRE power, pumped-storage hydroelectricity coupled with a VRE power plant, and demand-side management such as the charging of electric vehicles. However, top-down type CGE model has limitations to fully incorporate all these aspects due to its limited spatial, temporal and technological resolution.

Acknowledgments This study was supported by the Environment Research and Technology Fund 2-1402 of the

ACCEPTED MANUSCRIPT Ministry of the Environment, Japan. The research leading to these results has received funding from the European Union’s Seventh Programme FP7/2007-2013 under grant agreement No. 308329

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(ADVANCE). We are also grateful for the valuable comments from the reviewers.

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Note

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http://www.textcheck.com/certificate/k9L4hl

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speakers of English. For a certificate, please see:

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The English in this document has been checked by at least two professional editors, both native

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Resource • VRE supply curve

AIM/CGE • A global general equilibrium model

Storage • With/without

• Economic impact on - GDP - Welfare • Power price change • Curtailment • Storage

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• VRE curtailment and storage • VRE resource data

Curtailment • With/without

Outputs

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CO2-eq target • 650/550/450ppm

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Socio-economic pathways • Population • Technology improvement • Energy service • Fossil fuel extraction cost demand

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Figure 11: Research framework of the study

Figure 12: The relationship between curtailment share and the shares of theoretical wind and solar power in annual electricity demand. Note: dots represent individual observation.

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Figure 13: The relationship between storage output share and the shares of theoretical wind and solar power in

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annual electricity demand. Note: dots represent individual observation.

Figure 14:

World gross domestic product (Trillion USD in market exchange rate in 2005),

primary energy consumption (EJ/year), electricity demand (EJ/year), GHG emissions (Gt/year), and radiative forcing (W/m2) in four representative scenarios up to 2100.

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Figure 15: Shares of solar and wind in the overall power mix. OECD90 = United Nations Framework Convention

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on Climate Change (UNFCCC) Annex I countries, REF = Eastern Europe and former Soviet Union, ASIA = Asia excl.

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OECD90 countries, MAF = Middle East and Africa, LAM = Latin America and the Caribbean (IIASA, 2012).

Figure 16: Curtailment and storage output of variable renewable energy. OECD90 = UNFCCC Annex I countries, REF = Eastern Europe and former Soviet Union, ASIA = Asia excl. OECD90 countries, MAF = Middle East and Africa, LAM = Latin America and the Caribbean (IIASA, 2012).

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Figure 17: Ratio of curtailment and storage output to total power generation. OECD90 = UNFCCC Annex I countries, REF = Eastern Europe and former Soviet Union, ASIA = Asia excl. OECD90 countries, MAF = Middle East

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and Africa, LAM = Latin America and the Caribbean (IIASA, 2012).

Figure 18: Additional economic impact due to consideration of curtailment and storage. OECD90 = UNFCCC Annex I countries, REF = Eastern Europe and former Soviet Union, ASIA = Asia excl. OECD90 countries, MAF = Middle East and Africa, LAM = Latin America and the Caribbean (IIASA, 2012).

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Figure 19: Impacts of considering curtailment and storage on the price of electricity. OECD90 = UNFCCC Annex I countries, REF = Eastern Europe and former Soviet Union, ASIA = Asia excl. OECD90 countries, MAF = Middle East

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and Africa, LAM = Latin America and the Caribbean (IIASA, 2012).

Figure 20: Shares of wind and solar in the primary energy mix in 2050 and 2100 in the Intergovernmental Panel on Climate Change Assessment Report 5 database. Data source: IIASA (2014).

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Table 5: Scenario assumptions

Climate target

Without

Baseline

No GHG emission constraints

650ppm

w/o curtail 650 ppm

w curtail 650 ppm

550ppm

w/o curtail 550 ppm

w curtail 550 ppm

450ppm

w/o curtail 450 ppm

w curtail 450 ppm

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Coefficient

0.335 0.327 0.316 0.310 0.244 0.290 0.259 0.282

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AC CE P 0.222 0.212 0.181 0.311 -0.001 0.000 0.327 0.153

0.193 0.255 0.227 -0.008 0.278 0.248 0.367 0.315

T-value 1.795 1.665 1.858 1.797 1.470 1.716 1.416 1.742

D

storage

MA

Table 6: Estimated parameters

Africa Brazil USA India China EU Japan Middle East curtailment Africa Brazil USA India China EU Japan Middle East

RI

With

PT

Consideration of curtailment and storage

3.824 2.776 2.657 1.087 -1.578 -2.074 3.023 2.995

2.626 2.902 2.890 -1.036 1.755 2.341 2.879 3.578

13.104 13.552 13.415 18.831 13.074 11.621 12.825 12.278 3.457 6.978 5.283 6.496 -0.224 -0.082 10.927 3.488

5.334 7.914 5.969 -0.388 5.797 4.110 13.170 5.618

13.538 13.641 14.152 19.595 12.441 11.839 12.094 12.511 4.637 7.939 5.814 2.851 -0.877 -0.411 13.056 4.193

6.095 9.178 6.837 -1.084 3.878 3.377 15.412 7.389

Table 7: GDP loss and additional costs of curtailment and storage

Year

Target

Cost

World

OECD90

REF

Asia

MAF

LAM

9923

3960

0

4812

801

350

(bilUSD) 2050

450ppm

storage cost

ACCEPTED MANUSCRIPT

550ppm

5982 1939 2956 3868 736 1340 2439 201 11915 17619 3577 6753 11709 2101 5333 9430 1115

336 986 158 124 409 0 45 202 15878 4404 5943 7739 1852 4225 5584 1567 2932

142 210 203 62 62 0 34 31 2116 1099 838 892 494 372 654 322 159

MA

650ppm

PT

450ppm

128 691 0 48 299 0 20 164 2362 643 1438 70 180 838 54 116 552

RI

2100

1502 975 2902 957 409 2046 577 253 8687 3790 2297 5506 2017 1532 4527 1576 945

SC

650ppm

8090 4801 6219 5058 1915 3386 3115 850 40958 27556 14093 20961 16251 9068 16152 13011 5702

NU

550ppm

curtail cost GDP loss storage cost curtail cost GDP loss storage cost curtail cost GDP loss storage cost curtail cost GDP loss storage cost curtail cost GDP loss storage cost curtail cost GDP loss

D

Table 8: Countermeasures to address fluctuation in vairable renewable energy

Supply side

Short-term

LFC, Curtailment, Secondary battery

Long-term

EDC, Secondary battery, Pumped-storage hydroelectricity plant, Interregional transmission

AC CE P

TE

Fluctuation

Demand side Secondary large-scale heat electric boilers Secondary battery

Cost battery, pump,

Battery cost Output reduction from renewables Increase of part load operation

Electric vehicle, flexible power demand management, Other demand response

LFC: load frequency control; EDC: economic dispatching control

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ACCEPTED MANUSCRIPT

MA

NU

SC

RI

PT

Appendices

D

Figure A 1: Resource potential of wind and solar energy compared with primary energy demand in 2005. Data

TE

source: Resource data from NREL (2014) and the primary energy demand data from (IEA, 2013). OECD90 = UNFCCC Annex I countries, REF = Eastern Europe and former Soviet Union, ASIA = Asia excl. OECD90 countries,

AC CE P

MAF = Middle East and Africa, LAM = Latin America and the Caribbean (IIASA, 2012)

Figure A 2: Fitness of curtailment share

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

AC CE P

TE

D

Figure A 3: Fitness of storage output share

35