Modeling framework for estimating impacts of climate change on electricity demand at regional level: Case of Greece

Energy Conversion and Management 48 (2007) 1737–1750 www.elsevier.com/locate/enconman

Modeling framework for estimating impacts of climate change on electricity demand at regional level: Case of Greece S. Mirasgedis *, Y. Sarafidis, E. Georgopoulou, V. Kotroni, K. Lagouvardos, D.P. Lalas Institute for Environmental Research and Sustainable Development, National Observatory of Athens, Lofos Nimfon, Thesion, Athens 11810, Greece Received 11 April 2006; accepted 29 October 2006 Available online 19 December 2006

Abstract This paper focuses on the potential upcoming impacts of climate change in the 21st century on electricity demand at regional/national levels for regions where topography and location result in large differences in local climate. To address this issue, a regional climate model, PRECIS, has been used to predict future climatic conditions under different emissions scenarios (namely A2 and B2 of the IPCC special report on emissions scenarios (SRES)) as an input to a multiple regression model of the sensitivity of electricity demand in the Greek interconnected power system to climate and socio-economic factors. The economic development input to the multiple regression model follows the same storylines of the SRES scenarios upto 2100 and includes sub-scenarios to cover larger and smaller economic development rates. The results of the analysis indicate an increase of the annual electricity demand attributable solely to climate change of 3.6–5.5% under all scenarios examined, most of which results from increased annual variability with substantial increases during the summer period that outweighs moderate declines estimated for the winter period. This becomes more pronounced if inter-annual variability, especially of summer months, is taken into consideration. It was also found that in the long run, economic development will have a strong effect on future electricity demand, thus increasing substantially the total amount of energy consumed for cooling and heating purposes. This substantial increase in energy demand with strong annual variability will lead to the need for inordinate increases of installed capacity, a large percentage of which will be underutilized. Thus, appropriate adaptation strategies (e.g. new investments, interconnections with other power systems, energy saving programmes, etc.) need to be developed at the state level in order to ensure the security of energy supply.  2006 Elsevier Ltd. All rights reserved. Keywords: Climate change; Electricity demand; Regional impacts; Regional climate model

1. Introduction The energy sector contributes to global climate change through the combustion of fossil fuels that produce greenhouse gases (GHG) and is itself sensitive to climate on both the demand side (e.g. heating, cooling, etc.) and the supply side through the exploitation of renewable energy sources [1]. To date, the bulk of climate change research in this sector of the economy has mainly concentrated on policies and measures to mitigate GHG emissions, while only recently did researchers start to investigate the relevant *

Corresponding author. Tel.: +30 210 3256218; fax: +30 210 3256212. E-mail address: [email protected] (S. Mirasgedis).

0196-8904/$ - see front matter  2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.enconman.2006.10.022

potential impacts and adaptation strategies. On the energy supply side, most of the studies examine the climate change impacts on renewable energy sources and particularly on hydropower generation [2,3] and wind energy availability [4,5]. Yet, future climate change is also expected to influence the total demand for energy in a specific region, both directly as a result of differentiations on heating and cooling needs and indirectly due to changes on the level of economic activity. This paper focuses on the potential impacts of climate change on electricity demand and explores demand changes in the broader context of climatic, economic, technology and population changes. The link between weather/climatic variables and electricity demand has been widely investigated in the past

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[6–9] and utilized in short and mid term electricity forecasting [10–12]. The weather elements that influence electricity demand are, in decreasing order of importance, temperature, humidity, wind and precipitation. Fig. 1 presents the relationship between electricity demand and temperature for the Greek interconnected electricity system in the last decade (1993–2003). Specifically, a normalized electricity demand obtained by dividing the actual observed electricity demand with the demand nominally attributed to economic development, expressed by a linear trend line, is plotted versus the mean daily temperature. The variation of electricity demand with temperature is non-linear, increasing, albeit asymmetrically, both for decreasing and increasing temperatures (reflecting mainly the use of electric heating appliances in winter and air conditioners in summer), with a minimum at around 18.5 C. Despite the fact that many studies in the past have explored the link between weather variability and electricity demand, only a few address the longer term implications of climate change for electricity consumption patterns and investment decisions. Some of the studies in this area focus on energy and electricity consumption in relation to climate variability at the level of the individual building [13–15]. Sailor [16] developed a multiple regression model for relating electricity consumption to climate parameters and applied this model to eight states of the US, using specific temperature change scenarios resulting from three different global climate model (GCM) simulations. The results indicated a wide range of electricity demand impacts, with one state experiencing decreased loads (about 7.2% in residential sector and 0.3% in commercial sector for a temperature increase of 2 C) and others experiencing significant increases (ranging between 0.9% and 11.6% in residential sector and 1.6– 5.0% in commercial sector for a temperature increase of 2 C). Parkpoom et al. [17] also implemented a multiple

linear regression model (using the temperature and two dummy variables assigned to the day of the week and hour of the day as predictors) for estimating the sensitivity of electricity demand to climate change in Thailand, assuming uniform temperature increases of upto 4 C in steps of 1 C. Both studies conclude that for estimating the climate change impacts on electricity demand in the long run, additional non-climate factors should be incorporated into the models’ structure. Ruth and Lin [18] also implemented a multiple regression model for estimating the impacts of climate change on energy demand in the state of Maryland, incorporating both climate (heating and cooling degree days) and socio-economic (energy prices, population, etc.) factors. The study concluded that although there are noticeable seasonal and annual impacts of climate change, future energy prices and regional population changes may have much larger impacts on future energy use in Maryland. A similar approach was adopted by Amato et al. [19] for the Commonwealth of Massachusetts, finding notable changes with respect to overall energy consumption in the residential and commercial sectors attributable to climate change. Mansur et al. [20] developed a multinomial discrete–continuous fuel choice model of both households and firms in the US in order to determine the sensitivity of national energy demand to climate change. The study analyzed the potential impacts for changes in temperature of 1 C and 2 C with a 7% increase in precipitation in 2050, as well as for changes in temperature of 2.5 C and 5 C with a 7% increase in precipitation in 2100. One of its major findings is that consumers switch from natural gas, oil, and other fuels to electricity as climate warms and that overall energy demand, especially electricity demand, increases. The study concludes that for a 5 C increase in temperature by 2100, the annual financial loss will reach $40 billion.

y = 4E-05x3 - 0.0006x2 - 0.0165x + 1.1835 R2 = 0.5058

Normalized electricity demand

1.40 1.30 1.20 1.10 1.00 0.90 0.80 0.70 0.60 -2

3

8

13

18

23

28

33

Average temperature (C) Fig. 1. Relationship between normalized electricity demand (obtained by dividing the actual observed electricity demand with the demand nominally attributed to economic development) and temperature for the Greek interconnected power system based on daily data for the period 1/1/1993–31/12/2003.

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The majority of studies examining the consequences of climate change on energy demand typically quantify impacts at a national/regional level on the basis of the historic relationship between energy demand and weather/economic factors. The input data that specify climate variability are either the predictions of climate change from global climate change models (GCM) or uniform changes in temperature and/or precipitation (e.g. a temperature increase of 4 C). However, these representations of future climate are not detailed enough to apply regionally, let alone locally, particularly in areas where coasts and mountains have a significant effect on the weather. In such areas, the differences of mean temperature are considerable. Even more, areas and cities 150 km apart, which is the typical horizontal grid size of GCMs, resulting in a horizontal resolution of the order of 250 km, yet the consumption patterns are clearly driven by local conditions as they are perceived by the public, which also consciously and sometimes subconsciously takes into account economic and social factors in its behavior. The goal of this study was to develop a modeling framework for providing, to the extent possible, robust estimates of climate change impacts on electricity demand at a regional/national level, using the mainland area of Greece, which is serviced by one interconnected grid, as a case study. To this end, it attempts to incorporate both drivers of electricity demand, socio-economic factors and changes in climate parameters at a local, more meaningful, level through the use of a regional climate model and local economic forecasts while, at the same time, ensuring that the future evolution of these prime drivers are consistent with the global trends that shape them. The structure of this paper is as follows: Section 2 presents the methodological framework developed for estimating the impacts of climate change on electricity demand, while Section 3 describes the data used in the study. Section 4 addresses the possible future scenarios for electricity demand in Greece under a range of climate and socio-economic assumptions. Finally, in Section 5, the main findings of the study are summarized and conclusions are drawn. 2. Methodology 2.1. Overview Climate change is expected to influence a range of climatic variables in the long run, and as electricity demand is closely related to some of them, it is likely to impact demand patterns. The methodological framework developed and implemented in this study for estimating the impacts of climate change on electricity demand at a regional level is graphically depicted in Fig. 2 and comprises three basic stages. First, the sensitivity of electricity demand in the examined power generation system with respect to climate and

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socio-economic factors is identified. The analysis is performed using a statistical approach (multiple regression models) based on historical data of the last 11 years. Second, a regional climate model (PRECIS) is used to estimate the future climatic conditions with sufficient spatial and temporal resolution in the corresponding geographical area. This model, which has a significantly higher horizontal analysis (with grid size of the order of 25 km) than global models, is based on the latest version of the Hadley center global climate model (HadCM3) and uses its forecasts as boundary conditions. Two different emissions scenarios produced by the Intergovernmental Panel for Climate Change [21] have been analyzed, namely A2 and B2. Third, the regression model developed previously is used to estimate the future electricity demand in the mainland part of Greece under the climate change scenarios developed previously and taking into account the changes in socio-economic factors that the corresponding global scenarios adopt. A more detailed presentation of the stages of the analysis is given in the following: 2.2. The electricity demand model In the context of this study, a multiple regression model has been developed in order to quantify the influence of various climatic and socio-economic factors on the monthly electricity demand in the Greek interconnected power system. In a previous study for medium term electricity demand forecasting for the Greek system [12], the influence of economic activity on energy demand was incorporated in the form of a linear increasing trend with time, with the slope determined by historical data. Such a simplified structure of the model implies certain limitations on the analysis undertaken as it implies that electricity consumption in the reference system attributed to economic development will continue to change with the same rates as in the near past, while the sensitivity of electricity demand to climatic conditions will remain unchanged. These assumptions are not valid for extremely long term predictions, since economic development will be affected by several factors, such as more efficient technologies combined with increased use of devices as a result of the improving standards of living. At the same time, the uncertainties of economic development over decades are such that bottom up modeling would not be feasible. As a compromise, only a limited number of robust economic drivers will be incorporated in this study, namely population, gross domestic product and energy intensity. The equation of this model is given by Et ¼ c þ b  P t þ d  GDPt þ g  EIt þ m  ðGDPt  HDDt Þ þ n  ðGDPt  CDDt Þ þ

12 X j¼2

fj M jt þ et

ð1Þ

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Scenarios for population, economic development, technologies, etc.

Emissions Carbon cycle and chemistry models Global Climate Model

Downscaling at regional level

clim Projection of climatic variables at global level Regional Climate Model Projection of climatic variables at regional level

Historic data on electricity demand, climate and economic activity

Multiple regression models for estimating electricity demand

Impacts on electricity demand

Fig. 2. The main stages of the methodological framework implemented for assessing the impacts of climate change on electricity demand at regional level.

where c, b, d, g, m, n and fj (j from 2 to 12) are the coefficients to be estimated from the regression analysis and et is the residual term. The dependent variable (total electricity demand E in the month t) appears in Eq. (1) directly rather than as the natural log of its value or any other mathematical transformation, as the model does not present significant serial correlation or heteroscedasticity (see Section 4). Thus, possible loss of forecast accuracy sometimes caused when the independent variables of a forecast model take in future values, which differ substantially from those in the past on which the regression of the model has been based, is avoided [22]. The explanatory variables used in the model are defined as follows: • The population (Pt) that lives in the region covered by the power system under consideration during the month t. • The gross domestic product (GDPt) produced in the region during the month t, serving as a measure of the size of economic activity and the social welfare. • The energy intensity (EIt) of the reference energy system defined as the final energy consumption on a monthly basis per unit of GDP produced and serving as a measure of the penetration of efficient energy technologies. • Eleven dummy variables (Mjt) in the model account for the monthly seasonality of electricity demand not related to weather conditions. Monthly seasonality of electricity demand is influenced by a number of economic and social factors such as summer vacations, the seasonal character of some industrial activities (e.g. food industries), etc. The index j values in the interval [2,12] representing correspondingly all months in a year (j = 2 for February, j = 3 for March, . . ., j = 12 for December) except the base month of January (the selection of January as the base month of the model is arbitrary). Each dummy variable has only two allowable values, 0 or 1.

The variable Mjt takes the value of 1 if the t observation belongs to the month j and 0 otherwise. We use one dummy variable less than the number of periods (12 months per year) to avoid multicollinearity problems [22]. • The number of monthly heating and cooling degree days (HDDt and CDDt) multiplied by the GDPt values produced in month t. The meteorological parameters have been incorporated into the model in combination with economic factors in order to reflect more realistically the situation that irrespective of climatic conditions, the final consumers adjust to some extent their long term energy consumption for heating and cooling to their personal financial circumstances and the prevailing standards of living. For example, heating and cooling needs in the residential sector have increased substantially during the last decades in Greece as a result of the increased average dwelling surface. Furthermore, the penetration and usage of air conditioners is strongly related to the available income of households. It should be pointed out that this model, though able to produce, given the simplifications adopted and the uncertainty in the future inputs, only approximate estimates of the future long term electricity demand, can provide considerable insight on the potential changes attributable to climate variability. 2.3. The regional climate model PRECIS In the context of this study, the PRECIS model has been used in order to estimate the future climate variability in the examined area (Fig. 3). PRECIS is a regional climate model (henceforth RCM) developed by the Hadley centre for climate prediction and research that can be applied to any area of the globe to generate detailed climate change predictions. In general, RCMs have a much higher resolution than global climate models (GCM) and, as a result,

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Fig. 3. Schematic presentation of the geographical area included in the PRECIS domain for present analysis. The inner grey border delineates the useful prediction area, which is unaffected by computational boundary effects.

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southeast Europe for a recent period (1961–1990) as a check on its accuracy and for a future period (2071–2100) under two different socio-economic scenarios, both characterized by regionally focused development but with priority to economic issues in one (A21) and to environmental issues in the other (B22). For all simulations, a horizontal analysis of 25 km has been used in PRECIS, which is deemed sufficient to encapsulate in its results the local variability in the eastern Mediterranean area. In Fig. 4, the results of PRECIS for the maximum average monthly temperature in Athens for the period 1961– 1990 are presented together with the corresponding measurements from the Thesion station in the center of the city where a 1st class meteorological station is in operation for over 100 years. The comparison shows that PRECIS results reproduce fairly accurately the present day climate and provides confidence in the results that it provides for the future 2071–2100 period as the level of agreement is approximately the same for other climatic parameters such as minimum temperature and rainfall. 2.4. Estimation of future electricity demand

provide climate information with finer detail, including generally more realistic, local extreme events [23]. GCMs provide predictions of changes in climate down to scales of a few hundred kilometers or so at best. These predictions may be adequate where the terrain is reasonably flat and uniform and away from coasts. However, in areas where coasts and mountains have a significant effect on weather (e.g. in the wider Mediterranean area), scenarios based on global models will fail to capture the local detail needed for impacts assessments at a national and regional level, hence the need to utilize RCMs with their much finer spatial resolution. Like other RCMs, PRECIS is driven by boundary conditions computed by GCMs, and specifically by HadCM3. The model has a horizontal resolution of 25–50 km with 19 levels in the atmosphere (from the surface to 30 km in the stratosphere) and four levels in the soil. In addition to a comprehensive representation of the physical processes in the atmosphere and land surface, it also includes a model for the sulphur cycle. To predict future climate change, a projection of how anthropogenic emissions of the GHG and other constituents will evolve in the future is necessary. Since the long term evolution of GHG emissions is one with great uncertainties, a range of emissions scenarios has been generated by the Intergovernmental Panel for Climate Change [21] on the basis of explicitly different packages of socio-economic and technological conditions (‘‘storylines’’). Specifically, four qualitative storylines yield four sets of scenarios called ‘‘families’’: A1, A2, B1 and B2. They cover a wide range of key future characteristics such as demographic change, economic development and technological change, resulting in a substantial variance of GHG emissions. In the present study, PRECIS has been used to reproduce the climate in

The multiple regression model developed in the first stage of the analysis is then used to estimate future electricity demand in Greece under the potential impacts of climate change, that is the percent changes that the climatic scenarios examined in the previous stage of the analysis induce to electricity demand for the period 2071–2100 in comparison with a reference case, which assumes that the historic climatic conditions of the period 1961–1990 will remain unchanged. To this end, the background information provided by the special report on emissions scenarios (SRES) [21] for each of the two marker3 Scenarios of the corresponding scenario families A2 and B2 are exploited to formulate three sets of data regarding future socio-economic conditions in Greece. The first sub-scenario assumes that future changes in population, GDP and energy intensity in Greece for the period 2000–2100 will follow the general trends that the two examined marker scenarios adopt for the OECD countries (henceforth the ‘OECD general trend’ sub-scenario). The other two sub-scenarios assume 1 The A2 storyline and scenario family describes a very heterogeneous world. The underlying theme is self reliance and preservation of local identities. Fertility patterns across regions converge very slowly, which results in continuously increasing global population. Economic development is primarily regionally oriented and per capita economic growth and technological change are more fragmented and slower than in other storylines [21]. 2 The B2 storyline and scenario family describes a world in which the emphasis is on local solutions to economic, social and environmental sustainability. It is a world with continuously increasing global population at a rate lower than A2, intermediate levels of economic development and less rapid and more diverse technological change than in the B1 and A1 storylines. While the scenario is oriented toward environmental protection and social equity, it focuses on local and regional levels [21]. 3 In the context of the IPCC SRES for the each family scenario, one was selected for illustrative proposes and is characterized as ‘marker’ scenario.

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Tmax ( oC)

45 40 35 30 25 20 15 10

Jan

Feb

Mar

Apr

May

June

July

Thesion meteorological station

Aug

Sep

Oct

Nov

Dec

PRECIS results for 1961-1990

PRECIS results for 2071-2100 Fig. 4. Comparison between 1961–1990 data and PRECIS maximum temperature results for Athens.

that during the 21st century, the Greek economy will continue the high rates of growth relative to the OECD average that it has enjoyed the last 10 years and converge with or even surpass the average OECD levels. More specifically, the second sub-scenario assumes that the GDP per capita and the energy intensity in Greece will converge to the average OECD levels projected by the two IPCC scenarios by 2070 (henceforth the ‘convergence with OECD2070 0 sub-scenario), that is the average for the 2070–2100 period is equal to that of the 2070 OECD. This implies that the Greek economy will reach 80% (A2) and 87% (B2) of the OECD average for the whole period. Finally, the third sub-scenario assumes that the convergence is achieved earlier, and the Greek economy continues to improve so that the average for the 2070–2100 period equals the projected average OECD levels in 2100 (hence the ‘convergence with OECD-2100’ sub-scenario). This implies that the Greek economy, from about 50% of the OECD average that it is today, will reach 114% (A2) to 115% (B2) for the 2070–2100 period. Table 1 summarizes the future evolution of the relevant socio-economic factors for Greece under the

two scenarios A2 and B2 and for all three sub-scenarios formulated. Specifically, all sub-scenarios formulated under scenario A2 assume that the population in Greece will increase by almost 50% during the coming century, implying substantial pressures to the energy system, while the sub-scenarios formulated under scenario B2 assume that population in Greece will remain practically constant during the same period. With respect to economic development, all sub-scenarios under both scenarios A2 and B2 project that GDP will increase substantially during the reference period. In general, the GDP rates of growth are higher for scenario A2 compared to those projected for B2 as well as for sub-scenarios that assume a convergence of the Greek economy with the average OECD levels compared to those that assume that the development of the Greek economy will follow the OECD average rates of growth. Finally, all sub-scenarios under both A2 and B2 scenarios project significant improvements in the energy intensity of the electricity system (ranging between 24 and 48% for A2 scenario and 36–50% for B2 scenario) as a result of the penetration of more efficient technologies

Table 1 Long term demographic and economic forecasts for Greece applicable in the period 2071–2100 under the A2 and B2 IPCC SRES scenarios and for the three differentiated local growth sub-scenarios examined Variable

Population (million) GDP (M€, in 1995 prices) Energy intensity (MJ/€ in 1995 prices)

Historic data (2000)

10.92 106 7.36

A2 scenario OECD general trend 16.25 400 3.82

B2 scenario Convergence OECD-2070 16.25 580 5.56

Convergence OECD-2100 16.25 835 4.58

OECD general trend 11.19 253 4.28

Convergence OECD-2070 11.19 458 4.69

Convergence OECD-2100 11.19 599 3.66

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and the implementation of energy conservation techniques. As already mentioned, the analysis has been performed on a monthly basis to allow for the assessment of non-uniform seasonal impacts on electricity consumption [18] that would be lost if only yearly estimates are used. Additionally, since monthly analysis leads to larger number of observations as well as variability, the determination of the model parameters is usually more robust, leading to improved performance. 3. Data The regression model developed in this paper has been applied to the historical data of electricity consumption available for the Greek interconnected power system from 1993 to 2003. This power system supplies electricity to almost 10 million people in the mainland of Greece, including the two main urban centers of Athens and Thessaloniki. The data set used comprises monthly data of electricity consumption (in GWh) for the entire period under consideration and was provided by the Greek Public Power Corporation (PPC). The monthly demand data are the total of all sectors of economic activity (industrial, commercial, residential and agriculture), as sectoral disaggregated data were not available on a monthly basis. The socio-economic parameters included in the developed model comprise population, GDP and energy intensity. Annual population data have been derived from the National Statistical Service of Greece. To coincide with the time step of the monthly electricity data, monthly population data were estimated through linear interpolation from the corresponding annual figures. GDP data were obtained on a quarterly basis from the EUROSTAT database. To disaggregate these data on a monthly basis, we assumed that the GDP data are held constant throughout each month of the corresponding quarter. Last, energy intensity data were available only on an annual basis, and again, a linear interpolation was implemented in order to estimate the corresponding monthly values. Historical weather data required by the proposed models for the period 1993–2003 were obtained from a number of meteorological stations in representative locations in various regions of the country. Taking into account that the available electricity consumption data used in the study correspond to the whole Greek mainland, while the meteorological parameters vary at different geographical regions (especially between northern and southern Greece), it is necessary to calculate weighted indices for the meteorological parameters, representative of the entire geographical region under consideration. In this study, mainland Greece has been subdivided into two parts, northern and southern. Attempts to divide the country into more regions either proved impossible because of unavailability of disaggregated electricity demand data (division of the country into 10–12 regions) or because the increase in accuracy was neg-

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ligible (division of the country into 4–5 regions). Meteorological data from two 1st class meteorological stations (so classified according to the criteria set by the World Meteorological Organization) in the main urban centers of Athens (central southern) and Thessaloniki (northern) was used. The meteorological data from the two regions were weighted on the basis of electricity consumption in the corresponding geographical sub-regions during the last 5 years. The experience of many utilities indicates that the weather elements that influence electricity demand consist of temperature, humidity, wind and precipitation in decreasing order of importance [10]. A study elaborated recently by the Public Power Corporation of Greece [24] analyzed the influence of a considerable number of meteorological parameters on monthly electricity demand of the Greek interconnected system. Its major conclusion is that of the various meteorological parameters, temperature is the most significant of them, while for the Greek mainland, humidity, wind speed and direct solar radiation do not influence demand noticeably. Therefore, in the present analysis, only the mean daily outdoor temperature (C) is considered in estimating the meteorological influence on electricity demand. As is clearly depicted in Fig. 1, the non-linear influence of temperature on electricity demand allows the use of two temperature derived functions (the heating degree days and the cooling degree days), thus separating winter from summer. Based on that, the heating degree days (HDDi) and the cooling degree days (CDDi) of the day i are estimated on the basis of the following equations: HDDi ¼ maxðT ref  T i ; 0Þ

ð2Þ

CDDi ¼ maxðT i  T ref ; 0Þ

ð3Þ

where Ti is the weighted average temperature for the day i and Tref is a reference temperature that should be selected so as to separate adequately the heat and cold branches of the demand-temperature relationship. In the context of this study, the reference temperature has been selected to be equal to 18.5 C, which is the temperature at which (as shown in Fig. 1) the influence of temperature is minimized, and electricity demand is inelastic to temperature changes. The monthly heating and cooling degree days were estimated as the sum of the corresponding daily figures calculated previously. In computing the values for HDDs and CDDs, the data from the two meteorological stations chosen to represent the two climatic regions of mainland Greece were used, weighted on the basis of electricity consumption in the corresponding geographical sub-regions during the last 5 years. The same weighting approach was used in computing HDDs and CDDs in the 2071–2100 period where the temperatures for Athens and Thessaloniki were obtained by interpolation of the results at the four nearer grid points of the computational grid whose grid size was 25 km.

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4. Results and discussion 4.1. Sensitivity of electricity demand to climate and economy Using n = 132 monthly observations (covering the period January 1993 upto December 2003), the monthly multiple regression coefficients were estimated, and the results are presented in Table 2. The regression run gave an adjusted R2 of 98.5%, showing that the model has a high predictive power. The constant and all the economic parameters (i.e. population, GDP and energy intensity) were found to be very significant. The estimated coefficient for the modified CDD is larger than that of HDD, showing the strong influence of weather conditions in electricity demand during the summer. This is mainly attributed to the fact that final consumers can use a variety of energy sources for heating (e.g. diesel oil, natural gas, electricity, etc.) and, practically, only electricity for cooling. For a given level of economic activity, the higher number of either HDDs or CDDs leads to higher levels of electricity demand. The results also show the importance of the monthly variability in the electricity consumption. The coefficients for July, October and November are not significant. This implies that electricity consumption in these months is similar to that of January. All the dummy variables for the other months (with the exception of December) are negative and significant, which shows that less electricity is consumed during the corresponding months than in January. The Durbin–Watson statistic, which is widely used for testing the serial correlation of residuals in regression models, is estimated at 2.116, showing indepen-

Table 2 Values of the coefficients of the multiple regression model chosen and its performance statistics for the period January 1993–December 2003 Variable

Coefficients

Sig.

c Pt GDPt EIt GDPt HDDt GDPt CDDt M2t M3t M4t M5t M6t M7t M8t M9t M10t M11t M12t

12049.7 1549.284 119.433 361.715 0.205 0.365 260.007 60.460 193.897 155.653 184.987 47.382 248.422 283.592 59.717 35.370 70.204

0.000 0.000 0.009 0.000 0.000 0.000 0.000 0.053 0.000 0.004 0.007 0.559 0.002 0.000 0.224 0.346 0.018

Adjusted R2 Durbin–Watson

98.5% 2.116

dence of the residuals. In addition, the residuals were found to have the same variance throughout, and therefore, the model does not violate the assumption of homoscedasticity.

4.2. Climate projections for Greece A detailed analysis of the climate characteristics derived by PRECIS for southeast Europe for the 30 year period 2071–2100 are presented elsewhere [25]. In this study, predictions of future climate (2071–2100) under emissions scenarios A2 and B2, as well as of recent past climate (1961–1990) for validation, were used to construct scenarios of climate change for the two representative climatic regions in Greece with a view to estimating the potential impacts on electricity demand. Since the developed multiple regression model uses only the heating and cooling degree days for estimating the influence of climate variability on electricity demand, we focus on changes in mean temperature. Under A2 emissions scenarios, PRECIS predicts an increase in mean annual temperature for the period 2071– 2100 of 4.8 C for Athens and 5.1 C for Thessaloniki, compared to the corresponding values for the period 1961–1990. On a monthly basis, temperature increases are more pronounced in summer periods and particularly in July, when the average monthly temperature is projected to increase by 7.5 C in Athens and 8.2 C in Thessaloniki. During winter, spring and fall, mean monthly temperature increases range in the interval 3.3–5.9 C for Athens and 3.7–6.1 C for Thessaloniki. Substantial increases in mean annual temperature are also predicted under B2 emissions scenario, estimated at 3.3 C for Athens and 3.6 C for Thessaloniki. Again, July is the month of the year for which the largest increases in mean temperature are predicted, reaching 5.5 C in Athens and 6.2 C in Thessaloniki. On the basis of these future temperature scenarios, future heating and cooling degree-days (weighted on the basis of regional electricity consumption) for the mainland area of Greece to be used in the multiple regression models were obtained. PRECIS predicts mean temperature on a daily basis for the whole 2071–2100 period, as well as for the historic period 1961–1990. HDDs and CDDs, estimated on a daily basis, are then aggregated on a monthly and yearly basis. In order to investigate the impacts of climate change on electricity demand, we compare the average number of HDDs and CDDs estimated on a monthly basis for 2071–2100 under A2 and B2 emissions scenarios with the corresponding average number of HDDs and CDDs estimated by PRECIS for the historic period 1961–1990. Fig. 5 shows the heating and cooling degree days as calculated using the results of PRECIS for the 1961–1990 period as well as for the future 2071–2100 period under the emissions scenarios A2 and B2 for Greece.

S. Mirasgedis et al. / Energy Conversion and Management 48 (2007) 1737–1750

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400 350 300

HDDs

250 200 150 100 50 0 Jan

Feb

Mar

Apr

May

Jun

Jul

Recent climate (1961-1990)

Aug

Sep

Oct

Nov

Dec

Future climate A2 (2071-2100)

Future climate B2 (2071-2100)

600 500

CDDs

400 300 200 100 0 Jan

Feb

Mar

Apr

May

Jun

Jul

Recent climate (1961-1990)

Aug

Sep

Oct

Nov

Dec

Future climate A2 (2071-2100)

Future climate B2 (2071-2100)

Fig. 5. Estimated heating and cooling degree days in Greece for the period 1961–1990 and for the period 2071–2100 under A2 and B2 IPCC SRES emission scenarios, weighted on the basis of regional electricity consumption.

4.3. Climate change impacts on electricity demand The empirical relationship developed previously to estimate the effect of weather conditions on electricity demand is now applied to estimate how electricity demand in Greece might be affected in the future by climate change. The results obtained for all examined scenarios and data sets suggest that economic growth is expected to have a strong effect on future electricity demand for the reference system (Fig. 6). Specifically, it is estimated that future growth of GDP and regional population and penetration of energy technologies alone may increase future electricity demand in Greece by 4.7–6.4 times under A2 emissions scenario and 2.0–3.4 times under B2 emissions

scenario compared to 2000 levels, even if climate were not to change in the future. This is the result of economic growth on the amount of energy consumed for heating and cooling purposes (i.e. the two energy uses that are primarily associated with climatic conditions) as a result of the increases in average dwelling area, the acceleration of penetration of air conditioning systems, the increase of the average area per employee in the tertiary sector, etc., thus making the mainland grid more vulnerable to climate change. Fig. 6 also shows clearly the changes in electricity demand by 2071–2100 attributable to climate change under all scenarios examined. On an annual basis, the A2 future climate results in an increase of electricity demand by 3.6–5.5% (depending on the economic assumptions

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Future Climate

GWh

Historic data

Projections for 2071-2100

250000 200000 150000 100000 50000 0 2000 Data

A2, OECD general trend

A2, convergence with OECD2070

A2, convergence with OECD2100

B2, OECD general trend

B2, convergence with OECD2070

B2, convergence with OECD2100

Fig. 6. Projected annual electricity demand for Greece under alternative climatic and economic assumptions.

adopted), while the B2 future climate leads to a total increase of 3.9–5.3%. Yet, the estimated changes attributable to climate change are seasonally not uniform (see Figs. 7 and 8). Most of the annual increase results from the projected increases during the summer period (i.e. May–September) and particularly in June and July, which is above 13% under all scenarios examined. On the other hand, the higher average temperatures projected for both scenarios A2 and B2 result in relatively small decreases in electricity consumption during the winter period (i.e. November–April), ranging between 3.6 and 6.6%, while a small increase is projected only for October. It is worth mentioning that under all scenarios examined, electricity demand reaches its highest value in July. Consequently, although the results of the analysis suggest that future electricity demand in the reference power system is primarily affected by economic factors, there are also noticeable seasonal and annual impacts of climate change. On a percentage basis (see Fig. 8), it seems that the system presents a similar sensitivity to the two future climate change scenarios (A2 and B2) examined as the difference between scenarios does not exceed 3%. However, the economic assumptions of the A2 scenario result in higher levels of electricity demand compared to the B2 scenario, and therefore, in absolute terms, the climate change impacts on electricity demand are more pronounced for A2 than for B2. The differences, percentage wise, though are more noticeable between the three subscenarios that differentiate between the local economic growth rates as compared to the OECD ones. Here, the differences between the slowest growth rate of the local economy (OECD general trend) and the fastest (OECD2100) exceed 7% (for scenario A2 in June). Table 3 presents the projected future monthly changes of electricity demand attributable to climate change in absolute terms (i.e. in GWh) for the period 2071–2100. The current lev-

els (for 2000) of total monthly electricity demand are also presented for comparison purposes. It is worth mentioning that increases in monthly electricity demand attributable to climate change during the summer range from 28% (for the B2 with OECD general trend) to 128% (for A2 with OECD-2100 convergence) of the current levels of electricity demand, exceeding for specific scenarios and months even the current levels of total electricity consumption. On the other hand, decreases in electricity demand attributable to climate change during the winter period are as low as 1% and do not exceed 43% of the today’s monthly consumption. The effect on demand due to climate change presented this far was estimated on the basis of an ‘‘average’’ future climate as it was projected by PRECIS under A2 and B2 emissions scenarios for the period 2071–2100. The electricity system though has to try to cover the needs for electricity demand for all the period 2071–2100 and, thus, for years with the more unfavorable climatic conditions of the 30 year period. Table 4 shows the fluctuations of the projected electricity demand for the summer months (i.e. for June, July and August) of the period 2071–2100 under all economic scenarios for the warmest and the coldest summers of the period as they have been projected by PRECIS. A warmer summer compared to the average projected by PRECIS for 2071–2100 under all scenarios may lead to an increase of the monthly electricity demand of upto 14% (B2 scenario with the fastest growth rates, i.e. OECD-2100) with respect to the monthly electricity demand projected for this period under average climatic conditions for the period 2071–2100. The very high increases of projected electricity demand in the long run, attributable primarily to economic reasons, make appropriate adaptation strategies and particularly the installation of additional generation units unavoidable. However, the fact that climate change results in substantial additional increases in electricity

S. Mirasgedis et al. / Energy Conversion and Management 48 (2007) 1737–1750

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35000

30000

Electricity Demand (GWh)

25000

20000

15000

10000

5000

0 Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Nov

Dec

2000 Data Recent climate / Economy A2, OECD general trend Climate A2/ Economy A2, OECD general trend Recent climate / Economy A2, convergence with OECD-2070 Climate A2/ Economy A2, convergence with OECD-2070 Recent climate / Economy A2, convergence with OECD-2100 Climate A2/ Economy A2, convergence with OECD-2100

20000 18000

Electricity Demand (GWh)

16000 14000 12000 10000 8000 6000 4000 2000 0 Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

2000 Data Recent climate / EconomyB2, OECD general trend Climate B2 / Economy B2, OECD general trend Recent climate / Economy B2, convergence with OECD-2070 Climate B2 / Economy B2, convergence with OECD-2070 Recent climate / Economy B2, convergence with OECD-2100 Climate B2 / Economy B2, convergence with OECD-2100

Fig. 7. Projected monthly electricity demand for Greece under alternative climatic and economic assumptions. (a) Scenario A2; (b) Scenario B2.

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(%) Change on electricity demand due to climate change

25.0%

20.0%

15.0%

10.0%

5.0%

0.0%

-5.0%

-10.0%

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Climate A2 / Economy A2, OECD general trend

Climate A2 / Economy A2, convergence with OECD-2070

Climate A2 / Economy A2, convergence with OECD-2100

Climate B2 / Economy B2, OECD general trend

Climate B2 / Economy B2, convergence with OECD-2070

Climate B2 / Economy B2, convergence with OECD-2100

Fig. 8. Percentage change of electricity demand in Greece attributable to climate change as applied to the model.

Table 3 Projected changes in future monthly electricity demand in mainland Greece (in GWh) attributable solely to climate change under all economic and climate scenarios examined Month

January February March April May June July August September October November December Total

Electricity demand in 2000

Projected changes in electricity demand attributable to climate change Scenario A2 for climate and economy

Scenario B2 for climate and economy

OECD general trend

Convergence with OECD-2070

Convergence with OECD-2100

OECD general trend

Convergence with OECD-2070

Convergence with OECD-2100

3857 3371 3572 3228 3417 3909 4572 4118 3507 3413 3440 3824

768 685 683 339 1402 2348 2860 2568 1920 327 644 761

1138 1016 1013 494 2045 3427 4083 3668 2742 468 922 1090

1637 1461 1458 710 2941 4928 5873 5276 3944 673 1326 1567

374 280 275 202 504 1153 1342 1170 940 42 307 283

692 518 509 368 918 2099 2390 2084 1676 76 547 506

904 677 665 480 1199 2742 3122 2722 2189 99 715 661

44229

7545

10760

15475

3431

6103

7972

The historic levels of total electricity demand for 2000 are also given for comparison purposes.

demand during the summer and simultaneously in lower levels of load during the winter will affect negatively the economic performance of these units, exacerbating further the summer–winter gap. A significant part of the installed capacity will operate only a few hundreds of hours per year to cover the summer peak loads, thus lengthening substantially the pay back period of the corresponding units. It is, therefore, obvious that the decision makers should examine supplementary adaptation strategies in order to safeguard the security of supply of the reference energy system. To this end, the intercon-

nection of the reference system with the electricity grids of neighboring countries as well as the intensification of conservation policies and measures are of primary importance. In Greece, as in many other developed countries, the residential sector presents the most significant electricity conservation potential through renovation of the existing building stock, strengthening building construction codes, increasing the efficiency of white goods, lighting and other appliances etc. This is a long term process, but luckily, it is within the climate change time frame provided that its implementation starts soon.

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Table 4 Estimated monthly electricity demand in mainland Greece during the summer months of the period 2071–2100 under all the economic/climatic scenarios examined, assuming average, coldest and warmest climatic conditions projected for the corresponding months by PRECIS Electricity demand for the average summer (GWh)

Electricity demand for the coldest summer

Electricity demand for the warmest summer

(GWh)

(% Change)

(GWh)

(% Change)

June A2 OECD general trend A2 convergence with OECD-2070 A2 convergence with OECD-2100 B2 OECD general trend B2 convergence with OECD-2070 B2 convergence with OECD-2100

19 670 22 767 28 309 8346 12 142 15 191

18 386 20 892 25 613 7898 11 327 14 126

6.5 8.2 9.5 5.4 6.7 7.0

20 692 24 257 30 453 9220 13 732 17 268

5.2 6.5 7.6 10.5 13.1 13.7

July A2 OECD general trend A2 convergence with OECD-2070 A2 convergence with OECD-2100 B2 OECD general trend B2 convergence with OECD-2070 B2 convergence with OECD-2100

21 977 25 701 32 425 9758 14 280 17 912

20 928 24 204 30 272 9214 13 311 16 646

4.8 5.8 6.6 5.6 6.8 7.1

22 958 27 103 34 441 10 218 15 098 18 981

4.5 5.5 6.2 4.7 5.7 6.0

August A2 OECD general trend A2 convergence with OECD-2070 A2 convergence with OECD-2100 B2 OECD general trend B2 convergence with OECD-2070 B2 convergence with OECD-2100

21 318 24 884 31 377 9247 13 594 17 105

19 840 22 773 28 341 8744 12 699 15 936

6.9 8.5 9.7 5.4 6.6 6.8

22 292 26 274 33 376 9864 14 694 18 542

4.6 5.6 6.4 6.7 8.1 8.4

5. Conclusions In this study, the potential impacts of the upcoming climate change in the 21st century on the electricity demand at regional level, that is on the Greek mainland electricity grid system has been investigated. To this end, a modeling framework has been developed comprising a high spatial resolution regional climate model (PRECIS) to predict future local climate parameters with input from a global climate model, and a multiple regression model of the electricity demand that is driven by economic, social and climatic parameters. The global climate change drivers are the A2 and B2 storylines of the IPCC SRES with variations of the local socio-economic parameters differentiating local economic growth rates. The results of the analysis, the first that regional climate models results are utilized for Greece, clearly show that future electricity demand in Greece could be affected significantly from future warming predicted from both optimistic (B2) and pessimistic (A2) emission evolution scenarios. These changes, attributed solely to climate, are of the order of 3–6% on an annual basis. They refer though to the much larger future values of demand expected from increased economic growth even with decoupling induced by improvements in energy efficiency taken into account, which are 100–550% higher than present values. Assuming a typical average electricity cost of 50€/MWh to the utility, which is an average electricity price in Greece at present, the climate change scenarios lead to additional expendi-

tures for electricity generation in the range of €170–770 million compared to the corresponding future scenarios that assume the historic climatic conditions. What is more troubling is the increase in seasonal variability, both in percentage and in absolute terms, caused by climate change, which may reach 34% of average yearly values. This seasonal variability would result in a large increase in unused installed capacity mainly to power air conditioning in the warmer summers expected in the future. In this respect, the use of a small enough time step, i.e. 1 month, was crucial in bringing out the consequences of the seasonal variability. In addition, the use of heating and cooling degree days, rather than average or maximum temperatures, in the demand prediction model also enhanced accuracy. The monthly time step also enabled investigation of the consequences of the extreme rather than the average months expected to appear in the 2071–2100 period. The variation for the same summer month is of the order of 13%, reaching as high as 22% for some sub-scenarios. As loss of load is usually caused by hourly rather than monthly peaks, and since PRECIS produces predictions for each day of the 30 year period, it would be of interest to investigate further the increased probability of load loss under the future climate regime. Such an extension of this work, to be attempted next, will provide more accurate estimations of the idle, unproductive capacity and the associated extra capital expenditure that may be required. An additional aspect that would be of interest to investigate when introducing further details in the modeling is

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the breakdown of the region into more than two parts, as was the case in the present study. Given the spatial resolution of the climatic parameter predictions, it becomes possible, if the disaggregated data of electricity consumption by climatic rather than administrative geographic division become available. Then, the effect of climate change will become clearer, and the additional cost in unused infrastructure will be more accurately estimated. In either case, it becomes evident that mitigation measures are necessary to enhance further the decoupling of energy use and economic growth but also to address the other side of the supply-demand balance, namely to reduce average energy and particularly air conditioning electricity consumption with special attention to peak shaving measures. References [1] IPCC. Climate change 2001. Impacts, adaptation and vulnerability. Intergovernmental Panel of Climate Change. Geneva: 2001. [2] Munoz JR, Sailor DJ. A modeling methodology for assessing the impact of climate variability and climatic change on hydroelectric generation. Energ Convers Manage 1998;39:1459–69. [3] Lehner B, Czisch G, Vassolo S. The impact of global change on the hydropower potential of Europe: a model based analysis. Energ Policy 2005;33(7):839–55. [4] Breslow PB, Sailor DJ. Vulnerability of wind power resources to climate change in the continental United States. Renew Energ 2002;27:585–98. [5] Pryor SC, Barthelmie RJ, Kjellstrom EP. Potential climate change impact on wind energy resources in northern Europe: analyses using a regional climate model. Clim Dynam 2005;25:815–35. [6] Lam CJ. Climatic and economic influences on residential electricity consumption. Energ Convers Manage 1998;39:623–9. [7] Ranjan M, Jain VK. Modeling of electrical energy consumption in Delhi. Energy 1999;24:351–61. [8] Lam CJ, Tsang CL, Li D. Long term ambient temperature analysis and energy use implications in Hong Kong. Energ Convers Manage 2004;45:315–27. [9] Moral-Carcedo J, Vicens-Otero J. Modeling the non-linear response of Spanish electricity demand to temperature variations. Energ Econ 2005;27:477–94.

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