Least-cost adaptation options for global climate change impacts on the Brazilian electric power system

Global Environmental Change 20 (2010) 342–350

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Least-cost adaptation options for global climate change impacts on the Brazilian electric power system Andre Frossard Pereira de Lucena *, Roberto Schaeffer, Alexandre Salem Szklo Energy Planning Program, Graduate School of Engineering, Federal University of Rio de Janeiro (PPE/COPPE/UFRJ), Centro de Tecnologia, Bloco C, Sala 211, Cidade Universita´ria 21941-972 Ilha do Funda˜o, Rio de Janeiro, RJ, Brazil

A R T I C L E I N F O

A B S T R A C T

Article history: Received 27 June 2009 Received in revised form 21 January 2010 Accepted 22 January 2010

Global climate change induced by the emission of greenhouse gases may pose challenges to energy security. The vulnerability of energy sources, in particular of renewable sources, to climate change raises the need to identify adaptation measures. This paper applies an integrated resource planning approach to calculate least-cost adaptation measures to a set of projected climate impacts on the Brazilian power sector. The methodology used has the advantage of finding optimal solutions that take into consideration the whole energy chain and the interactions between energy supply and demand. Results point in the direction of an increased installed capacity based, mostly, on natural gas, but also sugarcane bagasse, wind power and coal/nuclear plants, to compensate for a lower reliability of hydroelectric production, amongst other impacts. The indirect effect of these results is the displacement of natural gas from other consuming sectors, such as industry, in favor of its use for power generation. Results obtained are, however, based on the techno-economic premises used in the simulation, which may vary in the long term. ß 2010 Elsevier Ltd. All rights reserved.

Keywords: Global climate change impacts Adaptation Integrated energy modeling

1. Introduction The Brazilian energy sector relies heavily on renewable energy sources. Some 45% of all energy produced in the country comes from renewable energy sources. In the power sector, this reliance is even higher. Hydroelectric power plants accounted for 80% of Brazil’s electric power generation in 2008 (MME, 2009). Bioenergy has also become increasingly important in the Brazilian energy sector, both for electricity generation (e.g. sugarcane bagasse) and liquid biofuels production (e.g. sugarcane ethanol). The availability and reliability of these renewable sources, however, depend on climate conditions, which can vary in light of global climate changes related to the emission of greenhouse gases and their increasing concentration in the atmosphere. Long-term energy planning in Brazil has not yet analyzed or assessed the possible impacts of global climate change scenarios on the vulnerability of the Brazilian energy system in general, and in its power system in particular. Energy demand and gas-fired thermoelectric generation can also be affected by changing climate conditions. Higher temperatures may lead to increased electricity consumption, for example,

* Corresponding author. Tel.: +55 21 2562 8775; fax: +55 21 2562 8775. E-mail addresses: [email protected] (A.F.P. de Lucena), [email protected] (R. Schaeffer), [email protected] (A.S. Szklo). 0959-3780/$ – see front matter ß 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.gloenvcha.2010.01.004

for air conditioning. Also, the electricity conversion nominal (or ISO) rated-efficiency of natural gas turbines diminishes with higher temperatures. The impacts of global climate changes imply in socioeconomic costs (and benefits) that are difficult to assess and measure. These costs include not only the direct damage caused by impacts but also adaptation costs; in other words, the efforts to attenuate or avoid those impacts (Kundzewicz et al., 2007). For instance, for impacts in terms of reduced electricity production, the adaptation measures could include the need for an extra installed generation capacity. Identifying an energy sector’s vulnerability is essential for the formulation of adaptation policies, and the concern about the impacts of global climate changes can affect the perception and evaluation of the technological alternatives and the formulation of energy policies in a country (Wilbanks et al., 2007). The ways in which adaptation is investigated in the climate impact literature can be classified into four categories (Tol et al., 1998): no adaptation, arbitrary adaptation, observed adaptation, modeled adaptation. In many studies, no adaptation at all is considered. In other studies, an arbitrary set of adaptation policies is proposed to cope with the projected impacts. The observation of spatial or time analogues can be used to assess how human societies can adapt to varying climatic conditions. Finally, in some studies adaptation is determined in economic decision models, in which rational agents optimize welfare. In this paper, this latter approach is adopted by using an integrated energy supply

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optimization model to simulate the least-cost adaptation options to climate change for the power sector in Brazil. Therefore, the objective of this paper is to show how an integrated framework of energy models can be applied to calculate least-cost adaptation options for a given set of climate impacts on an electric power system, using the Brazilian case as an example. By using a parametric long-term energy demand projection model (MAED – model for analysis of energy demand), and an energy supply optimization model (MESSAGE – model for energy supply strategy alternatives and their general environmental impact), it was possible to identify the least-cost options for compensating the projected impacts, based on premises about each energy technology’s technical-economic characteristics and the country’s resource endowments. The remaining of this paper is organized as follows: in Section 2, the methodology, as well as the basic scenario assumptions, are explained; in Section 3, a brief presentation of the climate impacts used in this study is made; in Section 4, least-cost adaptation costs are portrayed, in Section 5, these latter results are discussed; and, finally, in Section 6, some concluding remarks to this work are presented. 2. Methodology for calculating least-cost adaptation options using integrated energy system models An integrated approach based on the coupling of the MAED– MESSAGE models is used in this work. These models were developed by the International Atomic Energy Agency and were applied in different energy studies1 (e.g. IPCC, 2000; Messner and Schrattenholzer, 2000; Nakicenovic and Riahi, 2003; IAEA, 2006). This approach is used in this study to make scenario projections for the evolution of the energy sector in Brazil with and without impacts resulting from climate change. The basic premises, as well as the central structure of the models, are derived from the study Brazil: A Country Profile on Sustainable Energy Development (IAEA, 2006), in which a comprehensive assessment of different energy development paths for the country was made. These premises were updated in Schaeffer et al. (2007) and Lucena (2010). 2.1. The MAED–MESSAGE adapted methodology The approach combines top-down assumptions, such as economic and population growth, bottom-up disaggregated sectoral information and constraints related to energy resources availability to produce energy demand and optimal energy supply scenarios. The demand component (MAED) provides detailed sectoral energy demand projections while a linear programming energy supply optimization model (MESSAGE) provides the leastcost energy and electricity supply mix scenario. Fig. 1 illustrates the integrated approach undertaken, relating each model’s input and output variables, as well as the interactions between the models (IAEA, 2006). The demand module (MAED) is a parametric bottom-up longterm energy demand simulation model based on premises about demographic evolution, economic development, technological advances and lifestyle changes. It systematically relates the specific energy demand for producing various goods and services to the corresponding social, economic and technological factors that affect that demand. Then, aggregating from the lowest level, it calculates each end use’s total useful energy demand, which serves as an input for the supply model (MESSAGE). The residential and the transportation sectors were modeled apart, using MAED only as a central interface for the interaction 1 For a review of different energy models, see Jebaraj and Iniyan (2006) and Urban et al. (2007).

Fig. 1. Conceptual MAED–MESSAGE framework (IAEA, 2006).

with MESSAGE. The structure of those specific sectors in MAED is rigid and does not fit well to the Brazilian case. Therefore, the residential sector was modeled using the LEAP model (long-range energy alternatives planning system: COMMEND, 2008), developed by the Stockholm Environment Institute, which is a flexible parametric long-term energy demand model. The transportation sector was modeled using a parametric model developed by Borba (2008), which calculates long-term energy consumption from transportation based on the growth and characteristics of the fleet and on alternative modes of transportation. MESSAGE is an integrated optimization model which minimizes the total cost of expanding the energy system to attend the useful energy demand projected by MAED, given restrictions related to the availability of resources, infrastructure, import possibilities, environmental restrictions, etc. It uses technicaleconomic characteristics of different technologies along the whole energy chain (from primary energy to secondary, etc., up to choosing the final source that will be supplied to attend the useful energy demand) to arrive at an optimal energy mix for the system (i.e. at the lowest cost, considering the interactions between different energy sources’ chains and the resources/infrastructure restrictions). Finally, in the MAED–MESSAGE approach, an interactive process is conducted, where the feedbacks between models allow checking for the consistency of the modeling of the supply and demand equilibrium. The Intergovernmental Panel on Climate Change Special Report on Emission Scenarios (IPCC SRES) created four families of emission scenarios (A1, A2, B1 and B2) which are widely used in future climate impacts analysis. These scenarios are based on qualitative storylines characterized by different economical and energy development paths in an attempt to cover a significant portion of the underlying uncertainties in the key driving forces for

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greenhouse gases emissions (IPCC, 2000). These emission scenarios were used in future climate simulations based on which climate impacts on the energy sector were projected (Lucena, 2010; Schaeffer et al., in press). The idea behind the approach used to calculate least-cost adaptation options is to compare the optimal evolution for the Brazilian energy system with and without incorporating the projected climate impacts. In other words, for each Intergovernmental Panel on Climate Change (IPCC) emissions scenario considered (A2 and B2), two variants were simulated (see Section 2.2 for scenario premises) and compared: Scenario A2: Scenario A2a: evolution of the energy system based on general IPCC SRES A2 scenario premises. Scenario A2b: evolution of the energy system based on general IPCC SRES A2 scenario premises and incorporating the projected climate impacts based on IPCC SRES A2 climate premises. Scenario B2: Scenario B2a: evolution of the energy system based on general IPCC SRES B2 scenario premises. Scenario B2b: evolution of the energy system based on general IPCC B2 SRES scenario premises and incorporating the projected climate impacts based on IPCC SRES B2 climate premises. Firstly, the base scenarios (A2a and B2a) were adjusted, giving the electricity generation capacity and mix and the sectoral final energy consumption by source up to 2035. Secondly, those scenarios were reproduced including the projected climatic impacts (A2b and B2b), which were inserted in the models through (see Section 3): a lower capacity factor for hydropower generation; a lower conversion efficiency for gas-fired thermoelectric power plants; a higher demand for electricity in the residential and service sectors. Finally, the differences in the electric power generation system projected for the A2a and A2b scenarios and the differences between the B2a and B2b scenarios represent the least-cost adaptation options to cope with the projected global climate change impacts for the A2 and B2 scenarios, respectively. The main advantage of using an energy system integrated approach (rather than a specific electric sector optimization model) for simulating adaptation to global climate change impacts in electricity generation is that it allows the analyst to understand second-order impacts in the entire energy supply and demand system. Although an electric system optimization model would also produce optimal adaptation scenarios,2 it would not consider the interactions between the power sector and other economic sectors in terms, for example, of competition for resources. Neither would it allow for an assessment of the extent to which other energy consuming sectors would be affected by some of the power sector adaptation policies. 2.2. Scenario premises The scenario premises used in the MAED–MESSAGE modeling were based on the general premises of the A2 and B2 scenarios of the IPCC SRES (IPCC, 2000). In the A2 scenario, thus, a business-asusual evolution for the energy system was adopted, since this scenario assumes final energy intensities declining slower than the historical experience. In the B2 scenario, environmental concerns and lower economic and demographic growth, combined with an increased technical development, lead to an energy intensity decrease rate in line with the historical trend since the nineteenth century. 2 For example, Schaeffer and Szklo (2001) conducted a power sector optimization analysis for the expansion of electricity generation in Brazil.

The economic and demographical premises were those used in Lucena (2010). The economic growth rate used in the modeling of energy demand in MAED was projected by an interactive process between the MAED–MESSAGE results and a computable general equilibrium modeling effort carried out by the University of Sa˜o Paulo’s Economic Research Institute (FIPE/USP – Fundac¸a˜o Instituto de Pesquisas Econoˆmicas/Universidade de Sa˜o Paulo), in which sectoral economic growth rates take into account the projected changes in the technical coefficients of energy inputs.3 Besides sectoral economic growth rates, the major differences in the premises between both scenarios are, basically, greater energy efficiency and homogeneity of financing incentives in the B2 scenario. The latter has to do with the fact that the business-asusual evolution for the Brazilian energy system includes incentives to certain sources prioritized by the government’s energy policy. In the most recent official long-term energy plan for the country (EPE, 2007a), nuclear power generation is scheduled to increase its share in the generation mix at the cost of some incentives. However, this option is not competitive in Brazil solely in terms of generation costs (Cavalho and Sauer, 2009). Only with financial incentives, modeled as an analogue for lower capital costs resulting from lower discount rates, does the nuclear option become competitive4 (as modeled in the A2 scenario). Altering the economic parameters of nuclear power generation does not undermine the least-cost optimization approach. Instead, it favors the competitiveness of that option in relation to other power generation alternatives. Specifically, the premises of demand for the industrial, agricultural and service sectors were derived from IAEA (2006), with the A2 scenario resembling a business-as-usual scenario and a B2 the alternative scenario. The demand premises for the residential sector are summarized in Aguiar et al. (2008). The technical-economic premises for the electricity generation options were based on IAEA (2006). The remaining exploitable hydropower potential considered is that estimated in EPE (2007a). The domestic production of oil and gas was based on Szklo et al. (2007), including the recent, large offshore discoveries. The premises for the production of liquid biofuels are described in Schaeffer et al. (in press). The energy prices scenario adopted is that of (EIA, 2008). Finally, some of the premises regarding the power sector are exposed in the annex. For a more complete description of the scenario premises adopted, see IAEA (2006) and Lucena (2010).

3. Impacts of global climate change on the Brazilian energy system The projection of global climate change impacts is not in the scope of this paper. Therefore, it was used a set of climatic impacts on the Brazilian energy sector estimated in Lucena (2010) and Schaeffer et al. (in press). These impacts were projected using the dynamically downscaled (the PRECIS regional model: Marengo, 2007; Jones et al., 2004) results of the HadCM3 general circulation model (GCM) for the A2 and B2 scenarios of the IPCC SRES (IPCC, 2000). The above-mentioned study did not assess impacts associated to extreme weather events. 3 For a thorough explanation of this interactive process, see Schaeffer et al. (in press). 4 Different generation alternatives may face different financing conditions (Du and Parsons, 2009). The differentiation done for nuclear power could have been done for other power generation sources, such as renewables. However, unlike nuclear power, some of these sources are competitive in a straight forward leastcost analysis and are not favored in the country’s official energy planning (EPE, 2007a). Only by altering the economic parameters of nuclear power generation could a business-as-usual scenario for Brazil that resembles the official planning for the electric sector – which emphasizes nuclear power – be created.

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Fig. 2. Projected impacts on hydropower production Lucena (2010). Note: *Projected installed capacity in 2017 (EPE, 2007b).

The impacts of global climate changes on the Brazilian Energy Sector were projected for: hydropower production; natural-gasfired thermoelectric conversion efficiency; and energy demand. Although other sources were investigated,5 hydropower accounts for most of the country’s electricity production and should, therefore, be the focus of global climate change impacts and adaptation assessments in Brazil. The projected impacts are summarized below (Lucena, 2010; Schaeffer et al., in press): The operation of the Brazilian hydropower system was simulated for a synthetic 75-year (2025–2100) time series6 of flow at each plant, projected based on the climate simulations for temperature and precipitation. For hydroelectric production in Brazil, the aggregate projected impacts show a loss in the reliability of electricity generation from hydraulic sources (Lucena et al., 2009; Lucena, 2010). The firm power – defined as the greatest amount of energy the hydroelectric system can provide 100% of the 5 Schaeffer et al. (in press) also investigated the impacts on the country’s wind power potential and liquid biofuel production. These, however, were not relevant for adaptation policies, since the projected global climate change impacts on them were not restrictive. 6 Although the planning horizon in this study stretches up to 2035, it is assumed here that the power system in 2035 would already be adapted to the hydrological conditions of the 2025–2100 period. Given the pluriannual reservoir capacity of the majority of the hydro installed capacity, the planning of the hydropower operation is based on a long period of hydrological series, in this case, based on future climate. While it can be argued that the system should not adapt to a condition that may yet come, this may turn out to be a risky decision since the planning horizon intersects with the projected impact horizon.

time or given the worst or critical hydrological conditions – of the country’s hydroelectric generation system falls by 31% and 29% in the A2 and B2 scenarios, respectively.7 However, there was no aggregate relevant impact on average electricity generation – produced by the system given the average hydrological condition – although significant regional impacts have been projected. According to the climate projections, the north and northeast regions’ water availability will decrease dramatically, affecting hydroelectricity generation in these regions very negatively. In some places, like in the Parnaı´ba and Atlaˆntico Leste Basins, the loss in average electricity generation is higher than 80%. Fig. 2 summarizes the results of Lucena (2010), showing the projected firm capacity factor for hydropower production according to subsystem and plant size. Natural gas turbines are also vulnerable to climate change, particularly temperature increases. Increased temperatures of the admission air to gas turbines mean a lower specific volume, which, in turn, increases the power demand of the air compressor. As a result, the conversion efficiency of the gas turbine is reduced (Tolmasquim et al., 2003). Based on the simulated temperature for

7 The small difference between scenarios can be attributed to the fact that: there are no big differences in the projections for precipitation and temperature between the two emission scenarios; the biggest differences among scenarios occur in the basins located in the north and northeast regions of the country where the installed capacity is smaller; and large pluriannual reservoirs are predominant in the Brazilian hydroelectric system, which can buffer some climatic variability.

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Table 1 Projected capacity factors by plant size and sub-system (Lucena, 2010). Sub-system

Table 2 Adaptation results, scenario A2: electricity generation and installed capacity variation between A2a and A2b scenarios.

Capacity factor Historical

A2

B2

S/SE/MW <30 MW >30; <300 MW >300 MW

58.0% 48.5% 44.6%

40.2% 31.6% 38.7%

39.7% 32.1% 39.5%

N/NE <30 MW >30; <300 MW >300 MW

58.0% 42.4% 49.6%

43.4% 21.5% 25.5%

48.8% 23.3% 26.8%

Note: S/SE/MW – south, southeast and midwest; N/NE – north and northeast.

the 2005–2035 period,8 the projected efficiency of natural-gasfired thermoelectric generation would decrease by 1.8% in both A2 and B2 scenarios, on average for the country, at the end of the period. Although this is not a big impact given the magnitude of the gas-fired installed capacity, it is taken into account in our adaptation analysis. Increasing temperatures may also affect the demand for electricity in different sectors. The impacts on demand used in this study were calculated as a result of an estimated increase in air conditioning in the residential and service sectors given the higher temperatures projected for the 2005–2035 period.9 The increased demand was estimated as the combination of two effects: (1) the increased energy consumption by the appliances themselves, given a higher average temperature, and (2) a more often use of air conditioning as the result of more frequent hot days. Results show that the projected higher temperatures would increase electricity consumption in the residential and service sectors by 6% and 5%, respectively, in the worst case scenario. Since these projections were made using a bottom-up parametric model, they already account for the effect of wealth and distributional aspects on air conditioning ownership and use. Of the impacts described above, the most relevant is the loss in hydroelectric reliability, given the country’s high dependence on this particular source (80% in 2008 – MME, 2009). In planning the expansion of a power system, the reliability of a source is of extreme importance. Hydroelectric-based systems must be dimensioned (or complemented by other sources) to guarantee supply in the worst hydrological condition. Therefore, in the MAED–MESSAGE simulation for least-cost adaptation, the firm power is the relevant variable, although, on average, the aggregate impacts may not be significant. Supply-side impacts (hydro and thermo power) were inserted in the integrated MAED–MESSAGE modeling of the least-cost adaptation options as changes in the technical parameters of these technologies. For hydropower, the loss in reliability was measured as a decrease in the system’s capacity factor (i.e. the ratio of actual power production of the system to the power which it would produce if it worked full time at full capacity). A lower capacity factor indicates that the system would yield a lower amount of energy (guaranteed energy) for a given installed capacity. In other words, the amount of confidence, and thus energy, the system can expect from hydropower would be lower, given the projected future river flow conditions. Table 1 shows the projected capacity factors per sub-system and plant size. For natural gas, in turn, the projected loss in conversion efficiency was incorporated in the technical characteristics of power plants. Lastly, the projected

8 Unlike hydropower operation, where reliability is an issue, the efficiency of thermal generation depends on the temperature at that specific moment. Therefore it was possible to match the climate projections with the optimization horizon. 9 Similarly to thermal power generation, increased demand for air conditioning was projected for the same time horizon as the optimization process.

Sugarcane bagasse BP 22 bar BP 42 bar Cascade cogeneration CEST BIG-GT Municipal solid waste Wind power Natural gas Nuclear Coal Diesel oil Oil

Energy variation

Capacity change

TWh

%

GW

0 0 20 99 0

– – 57% 143% –

– – 3.7 13.2 –

0 21 133 45 0 0 0

– 39% 135% 58% – – –

– 10.0 32.9 6.1 – – –

Note: BP – backpressure steam turbines; CEST – condensing extraction steam turbines; BIG-GT – biomass integrated gasification steam turbines. For a review of the technical characteristics of these technologies, see Coelho et al. (2003).

increased electricity demand for air conditioning was inserted in the energy demand module of the respective sectors. Finally, there is a great deal of uncertainty on the impacts of global climate changes on energy systems. The cases of A2 and B2 scenarios for a single GCM projection are not enough to assess the uncertainties and the possible range of impacts on climate and, consequently, on energy. However, this paper focuses mainly on a methodological procedure to calculate least-cost adaptation options for an energy system, using a single set of impacts as a case study. Although further development of the study on the impacts on the energy sector is needed, the methodological approach proposed in this study for calculating adaptation costs can be used for different sets of impact projections. 4. Least-cost adaptation results Tables 2 and 3 show the variation in electricity generation and installed capacity by source resulting from the introduction of the impacts of global climate changes in the integrated energy modeling. They show, respectively, for the A2 and B2 scenarios, the difference in the MAED–MESSAGE runs for the scenarios with (A2b and B2b) and without (A2a and B2a) the impacts on hydropower capacity factor, natural gas efficiency and electricity demand. In other words, they portray what electricity generation Table 3 Adaptation results, scenario B2: electricity generation and installed capacity variation between B2a and B2b scenarios.

Sugarcane bagasse BP 22 bar BP 42 bar Cascade cogeneration CEST BIG-GT Municipal solid waste Wind power Natural gas Nuclear Coal Diesel oil Oil

Energy variation

Capacity change

TWh

GW

%

0 0 12 77 0

– – 100% 49% –

– – 2.3 10.3 –

0 24 124 0 53 0 0

– 26% 147% – 134% – –

– 11.5 30.2 – 8.6 – –

Note: BP – backpressure steam turbines; CEST – condensing extraction steam turbines; BIG-GT – biomass integrated gasification steam turbines. For a review of the technical characteristics of these technologies, see Coelho et al. (2003).

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options the power sector would optimally choose to respond to the impact of decreased energy generation capacity resulting from new climate conditions. Results indicate the extra installed capacity that would have to be installed by 2035 to prevent the system from failing due to the projected lack of reliability of hydroelectricity, as well as the other considered impacts. The power system should be dimensioned to generate additional 162 TWh and 153 TWh per year in the A2 and B2 scenarios, respectively. As can be observed from the results, this installed capacity would be composed of natural-gas-fired power plants, higher efficiency sugarcane bagasse burning technologies (Condensing Extraction Gas Turbines–CEST), wind power and nuclear or coal (depending on financial premises10). It should be noted that the decrease in sugarcane bagasse cascade cogeneration is actually a shift to the CEST and not an impact of global climate changes. This shift allows an increase in the amount of electricity generated for a given quantity of bagasse. According to the technical and economic premises, the necessary capital investments to build the projected increased capacity are 51 and 48 billion dollars in the A2 and B2 scenarios, respectively. This represents almost 10 years of capital expenditures in expanding the country’s power generation system, according to Brazil’s long-term energy plan (EPE, 2007a). The variable operational and fuel costs would depend on the extent to which the hydrological scenario approaches the worstcase scenario. Since the actual realization of the worst-case scenario does not happen all the time, the operation of the hydropower system will not be normally based on its firm capacity factor. From the simulation results, the annual variable operational costs and fuel costs for each year in which the worst-case scenario happens is 6.9 and 7.2 billion dollars in the A2 and B2 scenarios, respectively, given the adopted technical and economic premises. Therefore, this can be regarded as an upper limit for variable costs of adaptation for the decreased reliability of hydroelectric generation. 5. Discussion Simulation results show that an additional electricity generation capacity would be necessary to compensate for a loss of reliability of Brazil’s power generation system, amongst other impacts. This capacity would be mainly based on natural gas, but also advanced sugarcane bagasse burning technologies, wind power and coal/nuclear power plants. The advantage of using an energy sector integrated modeling approach is that indirect impacts on different energy consuming sectors can also be identified. The main consequence of the projected least-cost adaptation strategy is the decreased availability of natural gas for the industrial sector given the expansion of the gas-fired electric generating capacity. As a result, the demand for heat in industrial processes would have to be supplied by oil. According to the projected refining infrastructure, this would not be a problem. On the other hand, the previously exported oil surplus would now be domestically consumed, which would affect the country’s international energy trade. To ensure a reliable expansion of the power system, the hydropower capacity factor used in the MESSAGE optimization is based on the hydroelectric system’s firm power. The idea is to 10 In the A2 scenario, nuclear power was chosen by the model as mean of increasing electricity generation as the result of favorable financing conditions to this option. In the official National Energy Plan (EPE, 2007a) nuclear power generation is projected to increase in the country up to 2030. The country’s current energy policy has indeed favored this option by restarting to build the Angra 3 nuclear power plant and announcing many others. However, only with favorable financing conditions nuclear power becomes competitive. In a purely lower cost analysis, like in the B2 scenario where the financing conditions are not differentiated, the nuclear option is not competitive in the Brazilian energy sector.

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guarantee electricity supply considering the worst-case hydrologic scenario. However, the operation of the hydroelectric system should be more close to the average capacity factor, since the critical hydrological period does not happen all the time. This means that the additional installed capacity would operate mostly as back up, staying idle most of the time. For the case of natural gas this is not a technical problem, since the operation of natural gas turbines is flexible enough so that they can run with a low capacity factor. However, this implies that the projected reallocation of natural gas from the industrial sector should be intermittent. For that reason, a resulting adaptation policy would be assuring the supply of natural gas to electricity generation without unnecessarily depriving the industrial sector of this fuel. Since the large reservoir capacity of the Brazilian hydroelectric system allows to predict, within the short-mid term, the amount of hydroelectric energy that can be generated, it would be necessary to set an institutional and operational background for the trade of natural gas between sectors. Sugarcane bagasse-based electricity generation would also permit some level of flexibility. On the other hand, for nuclear and, to some extent, coal power generation, operating on a low capacity factor is not technically or economically reasonable. These technologies do not have the operational flexibility to easily vary the load or, as for the case of nuclear power, the high capital investments must be compensated by generating (and selling) as much energy as possible. In this sense, the optimization model’s result for nuclear power may not be a good alternative. It should be emphasized, however, that this option was the result of incorporating the country’s current energy policy of supporting nuclear generation – not considering adaptation – in the first place. From a purely economic perspective, this option is not viable. In a modeling approach like the one used in this paper, results reflect economically optimal solutions. However, there are significant market barriers that obstruct the adoption of leastcost adaptation options. Although in the energy integrated framework some market distortions can be modeled (for example the differentiated financing conditions for nuclear power in the A2 scenario), this approach also allows identifying potential barriers to some sources or technologies. Thus, economically optimal results are an important way to help direct energy policies aimed at reducing market barriers. For the case presented in this paper, market barriers in Brazil affect mostly wind and bagasse for power generation. For specific policies for fomenting these renewable energy sources see Schaeffer et al. (2008). 6. Concluding remarks This paper introduced energy sector climate impacts into an integrated energy modeling framework in order to arrive at optimal adaptation options and calculate their costs. Specifically, three kinds of impacts were introduced: a lower reliability of hydroelectric production, modeled through a lower capacity factor; a lower conversion efficiency in gas-fired thermal power plants; and a higher electricity demand for air conditioning in the residential and service sectors. Although only three types of impacts were analyzed in this paper, the coupled models used are a flexible tool, which permits analyzing a broad range of climate impacts on the energy sector, as well as a broad range of energy policies and market distortions. The integrated approach used in this study has the benefit of, besides calculating least-cost adaptation options for a given set of climate change impacts, analyzing the indirect impacts on other energy consuming sectors as well. This would have not been the case if a power sector optimization model were used. A single sector model would not be able to look at the whole energy chain or the interactions with other energy consuming sectors.

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The least-cost adaptation alternatives calculated in this study, however, are based on the technical and socioeconomic premises used in the MAED–MESSAGE simulation, which are likely to evolve and change in the future. The A2 and B2 emission scenarios represent qualitative storylines which have a wide range of local specific trajectories for future energy development. Changes in energy prices, technology costs and technological advances can lead to another set of optimal adaptation options. Different socioeconomic premises and changes in consumer behavior – induced by demand side management, for example, like in the B2 scenario – also influence the modeling results. This paper, however, tried to focus on the methodological aspects of calculating least-cost adaptation strategies. This methodology can be used to produce a number of scenarios that resemble different development paths and different energy/environmental policy environments, as well as testing specific measures through sensitivity analysis. For example, although the MESSAGE does not endogenously account for learning and the consequent changes in relative costs, this can be done exogenously. A good opportunity for future development resides in testing the extent to which technological development may influence the least-cost adaptation strategies. Similarly to the technical-economic premises adopted, the incorporation of energy and environmental policies in the analysis could also affect optimal results. In an effort to reduce greenhouse gas emissions, mitigation policies, such as cap and trade regimes and carbon taxation could also lead to different optimum adaptation policies. Although not explored in this work, this possibilities could be modeled using the approach proposed here and is an interesting topic for future research. The adaptation alternatives calculated depend on specific impacts of global climate changes on the energy sector for which there is a great deal of uncertainty. Besides its own limitations, the adaptation modeling is subjected to a cascade of uncertainties regarding the emission scenarios, the translation of those into changes in global climate (GCM results), the downscaling into regional climate and, finally, the modeling of the impacts on energy production and consumption. Finally, the approach used in this study did not include adaptation of the energy system to possible impacts from extreme weather events. These events could affect particular sites at specific points in time, which could affect the reliability of the energy sector. The vulnerability of the Brazilian energy sector to extreme events is an important topic for future research development. Moreover, by looking at the energy system as a whole, the approach used in this study did not include site-specific adaptation measures, which could help cope with the challenges raised by global climate changes.

Appendix A. Scenario premises The methodology and premises used in this study derived from those of IAEA (2006), which were updated in Schaeffer et al. (2007) and Lucena (2010). Some of the main methodological considerations and premises related to the power sector are summarized below. For a complete review of the model’s structures and detailed premises, see IAEA (2006) and Lucena (2010). A.1. Demand side The MAED is a parametric bottom-up long-term useful energy demand projection model. This annex presents some of the parameters applied for forecasting the demand for electricity, as well as some of the aggregate results arrived. For references to other fuel sources, see Lucena (2010). The projection of the industrial electricity demand was divided into 11 industrial sub-sectors, The bottom-up premises related to each of the 11 industrial sub-sectors end-use efficiency and production mix lead to final energy intensities that were used to project long-term electricity demand. The same methodology was applied to the agriculture sector. In the services sector, the floor area and the electricity requirement of floor space were the key variables for the energy projection. For further details on the premises of the industrial, agriculture and services sectors, see IAEA (2006). The bottom-up projections of MAED resulted in the aggregated energy intensities and electricity demands for each sector as show in Tables A.1 and A.2. The demand projections for the residential sector were made using bottom-up premises regarding ownership, frequency of use and efficiency of electric appliances as well as other energy uses (such as heat for cooking), considering wealth and distributional aspects. For further details, see Aguiar et al. (2008). The B2 scenario assumes a higher rate of penetration of more efficient appliances than the A2 scenario. Also, in the B2 scenario, the ownership of some appliances (especially personal computers and air conditioners) grows at a higher rate as the result of a better income distribution. Finally, the substitution of electric showers (by natural gas or liquefied petroleum gas) in the B2 scenario is higher. Combining these premises in the LEAP model, the average electricity consumption per household was calculated, with which, given the growth in the number of households, the final energy consumption of the residential sector was projected (Table A.3).

Table A.1 Energy intensities and electricity consumption per sector: A2 scenario. Sector

Unit

2005

2010

2015

2020

2025

2030

2035

Agriculture Sectoral productiona Energy intensity Final electricity consumption

106 US$ (2004) kWh/US$ GWh

48,614 0.32 15,684

64,908 0.34 22,278

78,510 0.37 29,360

94,963 0.40 38,428

117,420 0.44 51,113

145,187 0.47 67,641

177,091 0.50 87,920

Industry Sectoral productiona Energy intensity Final electricity consumption

106 US$ (2004) kWh/US$ GWh

169,087 0.86 146,019

217,703 0.84 182,093

267,872 0.81 218,138

329,647 0.79 261,467

404,857 0.79 318,372

497,252 0.78 386,140

603,098 0.77 464,332

Services Sectoral productiona Energy intensity Final electricity consumption

106 US$ (2004) kWh/US$ GWh

403,563 0.21 86,221

483,782 0.21 103,706

596,503 0.20 120,906

735,488 0.19 140,413

905,846 0.18 163,167

1,115,664 0.17 188,625

1,357,858 0.16 217,050

a

Sectoral gross value added.

A.F.P. de Lucena et al. / Global Environmental Change 20 (2010) 342–350

349

Table A.2 Energy intensities and electricity consumption per sector: B2 scenario. Sector

Unit

2005

2010

2015

2020

2025

2030

2035

Agriculture Sectoral productiona Energy intensity Final electricity consumption

106 US$ (2004) kWh/US$ GWh

48,589 0.32 15,684

64,707 0.34 22,215

78,125 0.37 29,206

94,327 0.40 38,133

115,472 0.43 50,183

141,357 0.46 65,709

171,962 0.50 85,129

Industry Sectoral productiona Energy intensity Final electricity consumption

106 US$ (2004) kWh/US$ GWh

169,052 0.86 146,019

217,433 0.84 181,741

267,945 0.81 217,794

330,238 0.79 261,143

406,240 0.78 318,000

499,758 0.77 385,829

607,533 0.76 464,081

Services Sectoral productiona Energy intensity Final electricity consumption

106 US$ (2004) kWh/US$ GWh

403,673 0.21 86,221

484,572 0.22 104,750

600,439 0.21 124,176

744,011 0.20 146,603

920,095 0.19 172,943

1,137,851 0.18 199,600

1,397,480 0.16 228,313

a

Sectoral gross value added.

Table A.3 Electricity consumption in the residential sector. Unit

2005

2010

2015

2020

2025

2030

2035

Scenario A2 Population Households Consumption per household Electricity consumption

Thousands Thousands GWh/household GWh

180,965 51,753 1.6 83,198

191,759 59,925 2.0 118,491

201,532 67,177 2.4 159,075

209,962 69,987 2.7 191,287

217,349 72,450 3.2 229,341

223,642 74,547 3.7 274,128

228,807 76,269 4.3 325,432

Scenario B2 Population Households Consumption per household Electricity consumption

Thousands Thousands GWh/household GWh

180,965 51,753 1.6 83,198

191,759 59,925 2.0 117,568

201,532 67,177 2.3 152,220

209,962 69,987 2.6 180,456

217,349 72,450 2.9 210,501

223,642 74,547 3.3 244,937

228,807 76,269 3.8 292,098

Source: Aguiar et al. (2008).

A.2. Supply side

MESSAGE uses the technical and economic parameters of each technology in the cost minimization process. The model’s choice of technology is based on the total costs along the whole energy chain (from the primary to the final energy source) to supply the useful energy demand projected by MAED. For a complete summary of the premises for the whole energy sector, see Lucena (2010). For the

The premises in MESSAGE do not vary across scenarios except for: the demand projected in MAED; the discount rate (10% a year for all technologies in all scenarios except nuclear power generation in the A2 scenario – 6%); the projected impacts from global climate changes.

Table A.4 Costs of technologies considered in the MESSA for the power sector. Overnight investment costs (US$/kW)

Variable O&M costs (US$/MWh)

Fixed O&M costs (US$/kW)

Capacity (factor)

Hydropower Small (<30 MW) Medium (>30 MW; <300 MW) Large (>300 MW)

1570 1230 800

4.41 1.54 1.29

– – –

a

Sugarcane bagasse Backpressure 22 bar Backpressure 42 bar Cascade cogeneration CESTb BIG-CCGTc

325 500 750 1250 2300

0.50 0.50 0.50 3.00 3.65

10.00 10.00 10.00 50.00 44.71

0.60 0.60 0.60 0.85 0.85

Municipal waste Wind

1560 1000

6.67 10.00

– –

0.60 0.25

Natural gas Open cycle Combined cycle

450 800

8.70 7.00

– –

0.85 0.85

Nuclear Coal Diesel Residual oil-fired plants

2000 1350 1000 1070

0.42 15.00 7.99 11.00

56.00

0.85 0.85 0.85 0.85

Note: Does not include fuel costs. Source: Lucena (2010). a See Table 1. b Condensation and extraction steam turbine. c Biomass integrated gasification combined cycle gas turbine.

– –

a a

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Table A.5 Remaining exploitable hydropower potential per region (GW). South/southeast/midwest

Operational

Being constructed

Remaining exploitable potential

Small (<30) Med (30 < x < 300) Grd (>300)

3.17 8.53 46.22

0.87 1.17 1.55

2.96 27.93 –

North/northeast

Operational

Being constructed

Remaining exploitable potential

Small (<30) Med (30 < x < 300) Grd (>300)

1.04 1.46 19.97

0.10 0.07 7.54

0.18 3.54 26.19

Source: EPE (2007a) and ANEEL (2009).

power sector, Table A.4 presents the costs for each power generation alternative used in MESSAGE. The choice of technology in MESSAGE also depends on the fuel availability or renewability capacity. For the case of hydropower, the renewability is given by its capacity factor. The expansion of the hydropower system, however, was limited to the exploitable potential estimated in EPE (2007a), excluding the potential that interferes with environmental protection areas, Indian Territory and high environmental impact areas. Table A.5 shows the remaining exploitable hydropower potential assumed in MESSAGE, besides the potential that is already operational or in construction (also included in MESSAGE). References Aguiar, A.C.J., Szklo, A.S., Schaeffer, R., Cohen, C., 2008. Projec¸a˜o do consumo energe´tico brasileiro no setor residencial: 2005–2035. In: Proceeding of XII Congresso Brasileiro de Energia – XII CBE, Rio de Janeiro, 2008. ANEEL – Ageˆncia Nacional de Energia Ele´trica, 2009. Banco de Informac¸a˜o de Gerac¸a˜o. , Available at:http://www.aneel.gov.br/. Borba, B.S.M.C., 2008. Metodologia de Regionalizac¸a˜o do Mercado de Combustı´veis Automotivos no Brasil. Rio de Janeiro, 2008. M.Sc. Dissertation – PPE/COPPE/UFRJ. Cavalho, J.F., Sauer, I.L., 2009. Does Brazil need new nuclear power plants? Energy Policy 37, 1580–1584. Coelho, S., Braunbeck, O., Cortez, L., Hoffmann, R., Macedo, I., Moreira, J., Paletta, C., Pretz, R., Walter, A., 2003. Gerac¸a˜o de energia a partir da biomassa (exceto resı´duos do lixo e o´leos vegetais). In: Tolmasquim, M. (Ed.), Fontes Reonva´veis de Energia no Brasil. Editora Intercieˆncias, Rio de Janeiro. COMMEND (Community for Energy Environment and Development), 2008. An Introduction to LEAP. , Available at:http://www.energycommunity.org/. Du, Y., Parsons, J.E., 2009. Update on the Cost of Nuclear Power. MIT Center for Energy and Environmental Policy Research, Working Paper: WP-2009-004. EIA (Energy Information Administration), 2008. Annual Energy Outlook 2008. U.S. Department of Energy, Washington, DC. EPE (Empresa de Pesquisa Energe´tica), 2007a. Plano Nacional de Energia – PNE 2030, Rio de Janeiro, 2007. Empresa de Pesquisa Energe´tica, Available at:http:// www.epe.gov.br. EPE (Empresa de Pesquisa Energe´tica), 2007b. Plano Decenal de Expansa˜o de Energia–2007/2016. Rio de Janeiro, 2007. , Available at:http://www.epe.gov.br. IAEA (International Atomic Energy Agency), 2006. Brazil: A Country Profile on Sustainable Energy Development, Vienna, 2006. . IPCC (Intergovernmental Panel on Climate Change), 2000. Intergovernmental Panel on Climate Change Special Report on Emission Scenarios, Vienna, 2000. . Jebaraj, S., Iniyan, S., 2006. A review of energy models. Renewable and Sustainable Energy Reviews 10, 281–311. Jones, R.G., Noguer, M., Hassel, D., Hudsson, D., Wilson, S., Jenkins, G., Mitchel, J., 2004. Generating High resolution climate change scenarios using PRECIS. Report. Met Off. Hadley Centre, Exeter, UK.

Kundzewicz, Z.W., Mata, L.J., Arnell, N.W., Do¨ll, P., Kabat, P., Jime´nez, B., Miller, K.A., Oki, T., Sen, Z., Shiklomanov, I.A., 2007. In: Parry, M.L., Canziani, O.F., Palutikof, J.P., van der Linden, P.J., Hanson, C.E. (Eds.), Freshwater resources and their management. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, pp. 173–210. Lucena, A.F.P., Szklo, A.S., Schaeffer, R., Souza, R.R., Borba, B.S.M.C., Costa, I.V.L., Pereira Jr., A.O., Cunha, S.H.F., 2009. The vulnerability of renewable energy to climate change in Brazil. Energy Policy 37, 879–889. Lucena, A.F.P., 2010. Proposta Metodolo´gica para Avaliac¸a˜o da Vulnerabilidade a`s Mudanc¸as Clima´ticas no Setor Hidrele´trico. D.Sc. Thesis, PPE/COPPE/UFRJ, Rio de Janeiro. Marengo, J.A., 2007. Integrating Across Spatial and Temporal Scales in Climate Projections: Challenges for Using RCM Projections to Develop Plausible Scenarios for Future Extreme Events in South America for Vulnerability and Impact Studies IPCC TGICA Expert Meeting: Integrating Analysis of Regional Climate Change and Response Options. Nadi, Giji, 2007. Messner, S., Schrattenholzer, L., 2000. MESSAGE-MACRO: linking an energy supply model with a macroeconomic module and solving it iteratively. Energy 25, 267– 282. MME (Ministe´rio de Minas e Energia), 2009. Balanc¸o Energe´tico Nacional – Resultados Preliminares Empresa de Pesquisa Energe´tica. EPE, Rio de Janeiro. , Available at:http://www.ben.epe.gov.br. Nakicenovic, N., Riahi, K., 2003. Model Runs with MESSAGE in the Context of the Further Developments of the Kyoto-Protocol. WBGU Special Assessment Report. IIASA, Laxenburg, Austria. Schaeffer, R., Lucena, A.F.P., Szklo, A.S., 2008. Climate Change and Energy Security – Technical Report. PPE/COPPE/UFRJ, Available at:www.ppe.ufrj.br. Schaeffer, R., Szklo, A.S., 2001. Future electric power technology choices of Brazil: a possible conflict between local pollution and global climate change. Energy Policy 29, 355–369. Schaeffer, R., Szklo, A.S., Nogueira, L.A.H., Santos, A.H.M. (org), 2007. Matriz Energe´tica do Estado de Minas Gerais 2030. Technical Report – Programa de Planejamento Energe´tico. COPPE/UFRJ. Available at: http://www.conselhos. mg.gov.br/coner/page/publicacoes/matriz-energtica-de-mg. Schaeffer, R., Szklo, A.S., Lucena, A.F.P., Souza, R.R., Borba, B.S.M.C., Costa, I.V.L., Pereira Jr., A.O., Cunha, S.H.F., in press. Seguranc¸a Energe´tica – Relato´rio Te´cnico. In: Margulis, S., Marcovitch J., Dubeaux, C.B.S. (org), Economia das Mudanc¸as do Clima no Brasil: custos e oportunidades (www.economiado clima.org.br). Szklo, A.S., Machado, G.V., Schaeffer, R., 2007. Future oil production in Brazil – estimates based on a Hubbert model. Energy Policy 35, 2360–2367. Tol, R.S.J., Fankhauser, S., Smith, J.B., 1998. The scope for adaptation to climate change: what can we learn from the impact literature? Global Environmental Change 8 (2), 109–123. Tolmasquim, M., Szklo, A., Soares, J., 2003. Mercado de Ga´s natural na Indu´stria Quı´mica e no Setor Hospitalar Brasileiro Edic¸o˜es CENERGIA, Rio de Janeiro. Urban, F., Bendersa, R.M.J., Molla, H.C., 2007. Modelling energy systems for developing countries. Energy Policy 35, 3473–3482. Wilbanks, T.J., et al., 2007. Introduction in Effects of Climate Change on Energy Production and Use in the United States. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research, Washington, DC.