Evaluation of wind, solar and hydro energy potential using GRASS

Geomatics Workbooks n° 12 – "FOSS4G Europe Como 2015"

Evaluation of wind, solar and hydro energy potential using GRASS Giulia Garegnani, Pietro Zambelli, Gianluca Grilli, Daniele Vettorato European Academy of Bozen/Bolzano, Italy

Abstract One of the aim of the European directive 2009/28/EC is the promotion of the use of energy from renewable sources in order to decrease the fossil fuel dependency. In the Alpine region the application of this directive is particularly relevant because of the complex morphology of the territory with a high renewable energy potential due to the presence of several renewable sources (Vettorato & al., 2011). On the other hand, the production of energy from renewable sources creates new environmental pressures on the Alps that are sensible and fragile from the biodiversity, the landscape value and in general for their ecosystems services preservation point of view. The understanding of the trade-off between renewable energy potential and ecosystem services is needed in order to support decision makers in the sustainable energy planning phases . Usually the spatial information about energy potential is missed or data are inhomogeneous and depend on specific study of each alpine country. The aim of this article is to present 3 GRASS modules (r.green.wind; r.green.solar; r.green.hydro) that help to obtain a clear understanding of the spatial distribution of hydro, solar and wind-power in complex territories like the Alps. We evaluated the theoretical potential with a transparent methodology starting from physical parameters describing the availability of the renewable source (i.e. wind velocity, irradiation, discharge etc.). The methodology, thanks to the customizable GRASS modules, can fit also other regions . The main outputs of the r.green package is given by energy potential maps that consider theoretical, economic, technical and legislative aspects as well as the ecosystem services preservation. In this paper only the first step of the process (theoretical potential calculation) is presented.

Keywords Energy potential maps, Alpine region, Hydro-power, Solar-power, Wind-power

1 Introduction Several studies have been done in order to compute the energy potential from renewable energy sources (REs) by means of GIS systems. Studies are often not comparable, they differently define the potential and they regard only one of the REs available in the Alpine region. Several authors suggest a definitions of energy theoretical and technical potential, especially (Chatzimouratidis & Pilavachi, 2009; de Vries, van Vuuren, & Hoogwijk, 2007; Resch et al., 2008; 25


Sacchelli, Zambelli, Zatelli, & Ciolli, 2013; Sacchelli et al., 2013). From the theoretical potential, i.e. all the available energy in the nature, the technical, sustainable and economical potentials can be derived by applying different constrains, i.e. by excluding too steepest areas, natural parks, etc. Assumptions on average values and trends can strongly influence the potential for this reason a transparent definition of potential and a clear description of the methodology should be mandatory. This work focuses on the methodology to compute the theoretical potential and some technical constrains in a quite large region as the Alps. The first step is to define the physical variables necessary to compute theoretical potential maps. Regarding the hydro-power, it is necessary to know the availability of water and the gross head, i.e the elevation model and discharge data in order to compute the potential energy. In the case of wind power, the energy is related to the wind velocity, the annual irradiation is instead necessary to compute the solar power. It is not the aim of the article a deeper analysis of sector studies to collect, compute and recalibrate all these physical parameters. In the literature, (Huld, Müller, & Gambardella, 2012; Šúri, Huld, Dunlop, & Ossenbrink, 2007) develop an European solar radiation database. Several studies are related to hydrological models and computation of the run-off (Formetta, Antonello, Franceschi, David, & Rigon, 2014; Formetta, David, & Rigon, 2012; Norbiato, Borga, Merz, Blöschl, & Carton, 2009; Sauquet & Leblois, 2001; Schmidt & Morche, 2006). In the case of wind velocity, data for the alpine region are available and analyzed (Schaffner, Remund, Cattin, & Kunz, 2006). We try to use a unique information source covering the entire Alpine region for each physical parameters. The resolution, precision and accuracy of the data are coarser than in the case of a limited area but the methodology is still suitable for more specific studies and results provide a useful description of a more general Alps potential without entering in the details of a local case study. The second step is the choice of the algorithms to compute the energy potential of the REs. The hydro-power potential depends on the river discharge and the gross head (Bódis, Monforti, & Szabó, 2014; Kusre, Baruah, Bordoloi, & Patra, 2010; Palomino Cuya, Brandimarte, Popescu, Alterach, & Peviani, 2013; Punys & Pelikan, 2007). The potential usually refers to a single water basin and should be considered has a mean value. Palomino Cuya et al. (2013) investigate also the possibility to transfer water, i.e. energy power, from one river-basin to another ones. In the case of wind power, (Baban & Parry, 2001; Draxl & Mayr, 2011; Mari et al., 2011; van Haaren & Fthenakis, 2011) build a decision support system starting from wind velocity maps. Draxl & Mayr (2011) and Mari et al. (2011) compute the energy potential starting from wind distribution functions and the power curve of available turbines. The aim of this contribution is then to identify a methodology that can be applied in other areas in order to provide an essential input, i.e. the theoretical potential maps, for decision support systems (DSS) in the field of REs planning. In fact, DSS are very important multi-disciplinary tool to plan a very complex territory as the alpine area and to find alternative energy solutions. In this work, firstly we clarify the terminology based on the literature (Chatzimouratidis & Pilavachi, 2009; de Vries, van Vuuren, & Hoogwijk, 2007; Resch et al., 2008; Sacchelli et al., 2013) in order to have comparable results 26


for the three sources. After reviewing available data in the alpine area for the REs, we report the algorithms developed to compute the theoretical potential in the first section. The methodology is applied for the Alpine region and results are analyzed in order to provide first recommendation to decision makers and stakeholders in the last section. The paper presents an innovative methodology to identify where and what kind of energy potential it is available at regional scale, considering not only the geographical and physical factors, but also the constraints due to the current technology available on the market and their exploitability in the Alpine region.

2 Methodology and GIS algorithms In the first section, we define the theoretical potential based on the work of Resch et al. (2008), we clearly define the terminology and the criteria to evaluate the potential. We consider the theoretical limit as the maximum energy that can be theoretically converted in the ideal case. For the theoretical potential general physical parameters are taken into account. Then, theoretical limits cannot be reach in practice. Secondly, another limit can be defined by the actual technology and by the efficiency limit that can be reached with the actual technology. Besides, overall technical limitations (i.e. the available land area, steepest areas, etc. ), planning and legislation constrains have to be added. Ecological constrains and sustainable criteria can limit the technical potential to the sustainable potential. Finally, we can derive the economical potential, i.e the market and the realization cost. In this work we focus on wind velocity, solar irradiation and water availability and their theoretical based on physical law and current technology without any other constrains. The methodology presented in this paper is based on GRASS GIS 7. The algorithms are implemented in Python and split in several sub-modules to preserve the granularity and independence of task for each renewable energy source. All the developed modules use and combine whenever possible the existing GRASS modules, but for some specific tasks, especially when we need to use other python libraires such as Numpy or Scipy, the PyGrass library were used (Zambelli, Gebbert, & Ciolli, 2013). The physical parameters considered to assess the energy potential for the four REs are reported in table 1. RES

Physical parameters

Hydro-power

Gross head Discharge

ΔH Q

m m3s-1

Wind-power

Mean velocity

v

m s-1

PV

Variable

Unit

Esol Solar irradiation KW m-2 Table 1: Physical parameters for theoretical potential estimation.

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2.1 Hydro-power In the case of hydro-power, the maximum potential is the energy that can be produced under the assumption that all the water resource is used. The potential does not consider the environmental flow and other use of water (aqueducts, irrigation, etc.). The base unit for hydro-power estimation is the river basin. All the physical variables necessary for the potential estimation refers to the base unite and they are the afferent discharge Qaff , the total discharge at the closure point of the basin Qclosure , the mean elevation hmean and the closure elevation hclosure as shown in figure 1. In the case of referent basin 3, the discharge Q3,aff is equal to the difference between the discharge at the closure point of the basin 3 and the sum of the closure discharges of basins 1 and 2, Figure 1: Q3,aff =Q3,closure −(Q 1,closure +Q2, closure ) The hydro-power potential of a referent basin i is evaluated in a qualitative way as Pi ,aff =g Qi ,aff Δ h where P is the power, g the gravity acceleration, Δ h=hi , mean−hi , closure the gross head equal to the difference between the average elevation of the basin and the elevation at the river basin closure. The power Pi ,aff is the gross theoretical potential, i.e. the annual energy potentially available in the catchment if all natural flows were turbined down to the closure point of the catchment.

Figure 1: Sketch of variables for hydro-power evaluation. 28


In order to calculate the gross head we assume that it depends on the difference between the mean elevation of the referent basin and the elevation at the closure point. This gross head can be an average of several run-of the river power station. Following Mari et al. (2011) we don't consider only the own power of the catchment but also the interaction we upper basins as shown in figure 1. The hydro-power potential depends on two variables, the discharge and the gross head. The discharge coming from upper basins can be used to produce energy in the lower basin. This can be done by means of a system of weirs and reservoirs. In this case the potential related to the base unit and due to the upper discharge is equal to: Pi ,up= ∑ (g Q j, closure ( h j , closure−hi ,closure )) j∈UP

where UP is the set of the upper basin, we set a maximum number equal 3. In order to identify the network of basins in the Alps region and their relationships, a search algorithm is developed in python language starting from the output of r.watershed by means of PyGrass library (Zambelli, Gebbert, & Ciolli, 2013). Finally, the total power of the basin is given by: Pi=Pi ,up + Pi ,aff 2.2 Solar-power The photovoltaic (PV) effect is the basis of conversion of light to electricity in photovoltaic, or solar, cells. Hersch & Zweibel (1982) simply describe the PV effect as follows: light, which is pure energy, enters a PV cell and imparts enough energy to some electrons to free them. A built-in-potential barrier in the cell acts on these electrons to produce voltage which can be used to drive a current through a circuit. According to european norm EN 15316-4-6 for the solar photovoltaic, the electricity produced by the photovoltaic system is P pk Eel , pv , out =E sol f perf I ref where Esol is the annual solar irradiation on the photovoltaic system, P pk the peak power, represents the electrical power of a photovoltaic system with a given surface and for a solar irradiance of 1 kWm 2 on this surface (at 25°C); f perf is the system performance factor; I ref is the reference solar irradiance equal to 1 kWm2. With actual technologies the performance factor f perf can reach value of 0.80. Most solar cells on the market are based on silicon wafers with typical efficiency of 10-15\% ( P pk 100-150 Wpm-2). Conversion efficiency needs to be increased in the future. According to the aim of the article the upper theoretical limit has to be considered. Shockley & Queisser (1961) studied this limit for or a standard solar cell. An optimal cell with a band gap of 1.3 eV is limited by transmission losses of photons to 31% (310 Wpm-2). According to thermodynamic law, by considering the sun as a black body at 5760 K and a solar cell as another black body at 300 K the conversion 29


efficiency is related to the Carnot efficiency limit, which is nearly 95% (Green, 2002). Notice that the Carnot limit is only a theoretical limit and cannot be built in practice. The large difference between the Shockley-Queisser and thermodynamic (Carnot) limits arises from the fact that a single material is characterized by a single energy gap, whereas the solar spectrum contains photons with a wide range of energies. Several methods have been studied to increase the power conversion efficiency of solar cells, Razykov et al. (2011) review the future prospect of photovoltaic devices. 2.3 Wind-power The mean power production for a wind turbine assuming 100 percent availability is equal to: ∞

E=∫ P(v )ϕ( v )dv 0

where P(v ) is the power curve of a wind turbines, ϕ(v) is the statistical distribution of the wind velocity v . The wind velocity depends on the elevation that we consider for the hub. The potential is then strictly related to this assumptions. The distribution of the wind velocity is usually a Weibull (Celik, 2003): v −( ) k ϕ( v)= k v k−1 e k λ where the scale parameter λ can be calculated from the following equation: 1 v¯=λ Γ (1+ ) k with Γ gamma function and v¯ mean yearly velocity. The power P(v ) of a wind turbine is a function of the wind velocity v : 1 P(v )= c p ρ A v 3 2 where c p is the power coefficient, ρ is the air density and A the swept area. The power coefficient has a theoretical maximum value of 0.593, the so-called Betz limit (Bergey, 1979). It represents the maximum limit to the amount of energy that can be converted in usable power. We consider the Betz limit has the theoretical maximum value for the estimation of the wind potential. The wind turbine converts 70% of the Betz limit into electricity. Consequently, the wind turbine converts the 41% of the available wind energy into electricity. This module has been developed using PyGrass. k

3 Estimation of RE sources in the Alps The availability of high quality GIS data played the strongest role in determining the results of this research. GIS data is often inconsistent across large regions due to the different policies in each alpine countries. It is not the aim of this article to analyzed the input data but a critical review it is necessary in order to know the uncertainty and the limits of input data. We summarize the input data-set for the physical parameters in table 2. 30


RES

Variable

Input Source

Output Unit

Hydro-power

ΔH Q

STRM GRDC, MeteoSwiss

Basin (area ~ 150 km2)

Wind-power

v

WINDHARVEST

300 m/px

PV

Esol

PVGIS 1 km/px Table 2: Physical parameters for theoretical potential estimation.

As shown in the previous section, the unit base for hydro potential maps is the river basin. By processing elevation data, rivers and watershed basins in the alpine area can be computed (Ehlschlaeger, 1989; Holmgren, 1994). The algorithm is available as a GRASS GIS software extension. We set the threshold parameter equal to 10000 and we use CIAT data-set derived from the USGS/NASA SRTM elevation data with a raster resolution of 60m. Jarvis, Reuter, Nelson, & Guevara (2008) have processed this data to provide seamless continuous topography surfaces. The GRDC repository of river discharge data provides annual mean values for some gauging stations in the Alps. The database comprises discharge data of nearly 9.000 gauging stations from all over the world but does not obviously cover all the basins in the alpine region. For the basins without gauging stations, we use a rain data-set to compute the afferent discharge of each river basin as: N

Qaff =∑ Ai pi c d i=1

where A i is the cell area, pi is the rain precipitation in the i-th cell, c d is the runoff coefficient and N is the number of cells in the each basin. The rainfall dataset has been provided by the federal office of meteorology and climatology MeteoSwiss and was developed as part of EU project EURO4M (Isotta et al., 2014). The daily precipitation is derived from a pan-Alpine high resolution raingauge dataset of More than 8500 time series in total, approximately 5500 measurements each data. The precipitation considers rainfall plus snow water equivalent. The cover period is January-December, 1971-2008. The resolution in grid spacing is 5km/px, but the effective resolution is approximately 10-20 km, depending on local station density. Since we do not know the runoff coefficients of each basin in the Alps we set them equal to 0.5. We report in Figures 2, 3 and 4 the potential maps obtained by considering the whole potential energy and the 80% of the all available energy. The Photovoltaic Geographical Information System (PVGIS) provides an inventory of solar energy resource in Europe (Šúri, Huld, Dunlop, & Ossenbrink, 2007). The map resolution is 1km/px. The data used to estimate solar potential are the global irradiation maps for horizontal surface and for surface inclined at an optimum angle with clear sky condition. The computation accounts for sky obstruction by local terrain features. As reported in the previous section, we consider the 95% of the solar radiation for the Carnot limit, the 30% for the Shockley-Queisser limit and 15% for the actual technology, Figure 3.

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Figure 2: Hydro-power potential maps.

Figure 3: Solar-power potential maps. Data of wind velocity at different elevation (50m, 70 m, 100m) are provided by the Alpine Space project Alpine Windharvest. Schaffner, Remund, Cattin, & Kunz (2006) use a statistical modeling methodology to compile wind map based on climatological data collected by 592 permanent or temporary meteorological stations. Regarding the accuracy of the data, they test the interpolation for 14 randomly chosen station and the standard deviation of this test is ±1.5ms-1. The computed values can underestimate the wind speed but big overestimations do not occur. The resolution is 250m/px. 32


We assume a Weibull distribution (Celik, 2003) with shape parameter k equal to 1.5 (Casale, 2009). In the Alpine area, difficulties of access limit the size of turbines and we consider only two different elevations, 50m and 70m. We choose two sample wind turbines with about 800-900 kW rated wind generator (ENERCON). Figure 4 shows the potentials respectively based on the Betz limit and the current technology.

Figure 4: Wind-power potential maps.

4 Results and discussion The algorithms can be applied to different scales. The quality and the resolution of the input data determine the accuracy of the output data. All the three potential definitions are the starting point to evaluate the residual potential in a region by considering existing plants and different constrains. Considering wind and hydro potential, Figure 2 and Figure 4, we observe that the difference between the theoretical and the technical potential for wind and hydro-power is not so big. Consequently, an improvement of technology should not improve the energy potential. Secondly, the elevation above the sea level in a mountain area is related to the population and energy consumption. For this reason, we compare the elevation with the energy potential by considering the current technology. Especially, for wind and solar the full-load equivalent working hours can be derived by knowing the installable power P pk and the annual energy production E . In the case of hydro-power, data about the discharge annual distribution are not available and we plot the mean annual power P versus the elevation of the closure point of each basin. In figures 5, 6 and 7, we exclude hypothetical plants behind a maximum elevation of 1500m. Above 1500m the costs to set up an energy plant increase considerably In the case of hydro-power we highlight the more productive basins (> 10MW), while for solar and wind-power we consider respectively capacity factor greater the 13% and 20%. The variables are plotted for each 33


alpine country.

Figure 5: Annual mean power versus elevation in each unit catchment for hydro-power.

Figure 6: Full-load equivalent working hours versus elevation for solar-power.

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Figure 7: Full-load equivalent working hours versus elevation for wind-power.

5 Conclusion A clear and coherent definition of the renewable energy potentials is necessary to compute energy potential maps. In this work, the theoretical limit of hydro, solar and wind-power is evaluated for the alpine region. The produced maps show the REs distribution and these data can be analyzed as shown in the previous section in order to find useful information for decision makers and stakeholders. From the discussed analysis it can be noticed that all the alpine states have a great hydro-power potential also for basins with lower elevation, Figure 5, with no linear correlation between elevation and potential. This can be consideredone of the reasons why hydro power has been the most used renewable energy in the Alps. In the case of photovoltaic ground system the linear correlation is observed in each alpine country except for Italy and French due to the south exposition of their valley. Indeed, these two countries have the greater solar potential. Finally, for wind a linear correlation between working hours and elevation is observed in each country. The distribution of the potential is then in disagreement with the distribution of the population and the energy needs. After further testing the final source code used to produce the data and the graphs presented on this work will be available in the r.green module, available in the GRASS-addons repository. The final map of the energy potential for each source analyzed on this article will be published within the INSPIRE standard. To assure a higher visibility of the results to policy and decision makers at a supranational scale. 35


Next steps of this work will be to consider not only the renewable energy potential variables, but combine them to consider also economic, environmental and social variables.

Acknowledgments This study was conducted in the frame of the Recharge.Green project “Balancing Alpine Energy and Nature” (http://www.recharge-green.eu), which is carried out within the Alpine Space Programme, and is co-financed by the European Regional Development Fund.The Recharge.Green project is focused on the analysis of how to reconcile biodiversity conservation of ecosystems and renewable energy production.

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