Modelling energy demand and optimizing local energy systems in mountain regions: Insights from mountainous Greece

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1 Modelling energy demand and optimizing local energy systems in mountain regions: Insights from mountainous Greece Dimitris Kaliampakos Professor, NTUA MIRC

2 Metsovion Interdisciplinary Research Center (MIRC) MIRC is a research unit of the National Technical University of Athens dedicated to the research on mountains. It houses the MSc Environment and Development of Mountainous Areas, one of the few in Europe addressing this issue. One of its main research areas is energy problems of mountainous areas.

3 Energy and mountains Mountains have been characterized as sources of energy (Agenda 21). Due to high-altitude climate and high relief, mountains are rich in renewable energy sources. But, mountainous people, worldwide, suffer from energy supply problems.

4 The AENAOS project AENAOS is the greek word for perpetual. The AENAOS project aims at covering mountainous energy needs mainly with the use of renewable resources and decentralized systems. First, the project deals with the quantification of the impact of altitude in energy needs. Then, it optimizes the mix of energy sources according to altitude. Energy savings in mountainous areas proved to be of decisive importance.

5 Modelling energy needs: the use of degree-days The method of degree-days (DD): DDs are quantitative indicators, based on temperature, which are proportional to the energy heating and cooling demand of a building. Heating Degree-Days [HDD = (1day) (T b -T m ) + ] Cooling Degree-Days [CDD = (1day) (T m -T b ) + ] (where T m : mean daily temperature and T b : base temperature) Q D = DD*H tot *24/1000 Main factors affecting temperature / degree-days: Latitude Local parameters (altitude, distance from the sea, prevailing winds etc.)

6 The impact of altitude on energy needs We tested a daring hypothesis: what if we ignore, in a mountainous area like a small country or region, the impact of all the other parameters except altitude on DDs?

7 The impact of altitude on energy needs We do know that higher altitudes present colder climatic conditions. We do know that there is a decrease of air temperature vertically approximately 6,5 ο C/km known as lapse rate. But, there is no specific method quantifying the different energy needs between areas let s say within a country or region at different altitudes.

8 Quantifying energy needs vs. altitude Collection and processing of long-term meteorological data Calculation of the annual HDD and CDD for all the meteorological stations Statistical analysis of the correlations between degree days, altitude and latitude Regression of HDD and CDD with altitude Case studies: Four mountainous countries/regions: Greece, Austria, Switzerland and Northern Italy

9 Greece HDD = 1.484*h Independent variables Adjust. R² Altitude 85,7% Altitude, Latitude 91,7% Comparing two similar buildings lying at 1200 m and 200 m respectively: Heating needs: + 126%

10 Energy needs of households in Greece Mountain households have much greater energy needs up to three times higher. High vulnerability to energy poverty.

11 Austria HDD = 1.492*h Independent variables Adjust. R² Altitude 95,1% Altitude, Latitude 95,3% Comparing two similar buildings lying at 1200 m and 200 m respectively: Heating needs: %

12 Switzerland HDD = 1.615*h Independent variables Adjust. R² Altitude 95,5% Altitude, Latitude 96,0% Comparing two similar buildings lying at 1200 m and 200 m respectively: Heating needs: %

13 Northern Italy HDD = 1.719*h Independent variables Adjust. R² Altitude 90,5% Altitude, Latitude 93,3% Comparing two similar buildings lying at 1200 m and 200 m respectively: Heating needs: %

14 Energy needs and altitude: To sum up In mountainous areas of small latitudinal variation, such as a country level or a large region within a country, altitude is the predominant factor affecting HDDs and CDDs. It should be noted, though, that this scale is the usual for implementing energy policies. Models of HDDs and CDDs as simple functions of altitude are effective policy tools, providing a simple and reliable way of estimating the variation of heating and cooling energy demand of the mountainous areas. Thus, AENAOS will provide a useful tool in the hands of mountainous local authorities.

15 The optimization methodology nn nn nn nn CC tttttttttt = CC ii BB ii = CC ii,iiiiiiii + CC ii,oo/mm + CC ii,eeeeee BB ii,ssssss ii ii ii=1 ii=1 ii=1 ii=1 Objective function with monetary dimensions, including investment, O/M and environmental costs, as well as employment benefits Decision variables covering a great variety of energy technologies: Renewables (wind, small hydro, pv etc.) Conventional (diesel oil heaters, gas heaters etc.) Energy saving interventions in buildings A theoretical settlement was created, with similar characteristics to Greek mountain towns The optimal mixes for heat and electricity were determined with respect to altitude

16 Optimum energy mix versus altitude (Heat) Significant differentiations with respect to altitude Energy saving possibilities are exhausted at high altitudes Solar thermal systems and firewood boilers substitute the greatest proportion of gas boilers over 800m

17 Optimum energy mix versus altitude (Electricity) The share of renewable resources increases versus altitude

18 The crucial role of biomass in mountain areas Availability of biomass influences the total cost of the solutions twice as much as wind energy potential Special attention should be paid to over-exploitation of forests

19 The importance of energy saving interventions Without the inclusion of energy saving interventions the operational costs of optimal mixes becomes more than two times higher

20 Lets continue the discussion in Metsovo On 26 September the final results of AENAOS will be presented in MIRC s congress hall in Metsovo. You are all welcome!