Application of Remote Sensing-GIS to renewable energy

resource assessment

María del Rosario Iglesias

Octubre 2013

Maestría en Aplicaciones Espaciales de Alerta y Respuesta Temprana a

Emergencias

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Index

Introduction 3

Kind of renewable energy and GIS application 3

Solar power 4

The use of remote sensing and GIS in solar energy 4

Other Data base and web page 7

Bioenergy 7

GIS and bioenergy 8

The case of Argentina 10

Wind Power 11

Gis and wind power 12

Conclusion 13

Reference 14

Introduction

Maestría en Aplicaciones Espaciales de Alerta y Respuesta Temprana a Emergencias

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There are two driving forces behind renewable energy development: the threat of

climate change and the need for countries to secure their own energy production

(Warren et al., 2005). A significant part of most countries’ energy production

comes from burning fossil fuels. This process releases carbon dioxide (CO2),

contributing to the greenhouse effect and global warming. Thus, governments are

under pressure to promote and encourage new forms of energy production with

zero CO2 emissions. Renewable energy is generally defined as energy that comes

from resources that are constantly replaced in nature on a human timescale such

as sunlight, wind, rain, tides, waves and geothermal heat, this is why is commonly

accepted as the key for future life in the world (Ramachandra, 2007).

The diverse natural environments found on the earth surface shows different

potential uses of this kind of energy. In order to tap the potential of renewable energy sources, there is a need to assess the availability of resources spatially as

well as temporally. Geographic Information Systems (GIS) along with Remote Sensing (RS) helps in mapping on spatial and temporal scales of the resources and

demands and are well suited for identifying of these potential zones (Ramachandra, 2007). Different tools as decision support system presented for

forest biomass exploitation for energy, or finding future areas suitable for others renewable energy exploitation, are amply used according to the bibliography. For

these reasons, the main objective of this review is to realize an integration of the

information for the application of remote sensing and GIS, in some renewable energy.

Kind of renewable energy and GIS application

In this review I will focus only in three kind of renewable energy: wind power, solar power and bioenergy.

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Solar power

Energy generated by the sun is radiated outwards in all directions, and only two

thousand-millionths of it is intercepted by the earth as light and infrared (heat) radiation. The intensity of the sun's radiation (irradiance) at the top of the earth's

atmosphere at the mean distance of the earth from the sun is roughly constant (solar constant) with an observed value of 1366 Watts/m2 ± 0.3%. However, on

average, only about half of this energy reaches the earth's surface (Ramachandra, 2007) because it is the result of complex interactions of energy between the

atmosphere and surface. At a global scale, the latitudinal gradients of radiation are caused by the geometry of the earth and its rotation and revolution about the sun.

At regional and local scales, terrain (relief) is the major factor modifying the distribution of radiation. Variability in elevation, surface inclination (slope) and

orientation (aspect) and shadows cast by terrain features create strong local gradients.

The spatial and temporal heterogeneity of incoming solar energy determines the

dynamics of many landscape processes (e.g. air and soil temperature and moisture, snowmelt, photosynthesis and evapotranspiration) with direct impacts

on human society. Accurate and spatially distributed solar radiation data are desired for various applications (environmental sciences, building design, remote

sensing, photovoltaics, land management, etc.)

The use of remote sensing and GIS in solar energy

Using remote sensing data to assess the solar energy potential is not a completely

new idea. In order to obtain direct normal irradiation (DNI) of solar energy, Wang et al. (2010) used the moderate resolution imaging spectroradiometer Terra/Aqua

satellite to obtain the water vapor data and aerosols transmittance data, and obtained the cloud data from the National Oceanic and Atmospheric Administration

(NOAA) data, whereas the DNI can be calculated. Broesamle et al. (2001) used the cloud index data from visible and infrared image channel of the METEOSA

weather satellite, aerosols transmittance data from the global aerosol data set by Kopke et al. (1997), water vapor and ozone data sets taken from the National

Aeronautics and Space Administration (NASA) vapor project (NVAP) and NASA ozone mapping spectrometer project (TOMS) to calculate the DNI.

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Also Wang and Koch (2010) determine the profits for solar energy integrating

remote sensing data and the optimal locations of photovoltaic and the base price of electricity resulting from solar energy in five European countries. They used the

solar irradiation data obtained from NASA (http://eosweb.larc.nasa.gov/sse/). The data are latitude- longitude grid data with the spatial resolution of 1x1 degrees.

On the other hand Ramachandra (2007) analyzed the regions suitable for solar energy based of global solar radiation data from a climatological station and GIS

tools. The study identifies that Coastal parts of Karnataka with the higher global solar radiation is ideally suited for harvesting solar energy.

HELIO- SAT method convert acquired by meteorological geostationary satellites,

such as Meteosat (europe), Goes(USA) or GMS (Japan), into data and maps of solar radiation received at ground level. The method calls upon libraries describing

the solar geometry, the satellite geometry and clear-skies models. These libraries are freely available. It has been tested with ground data by Hammer (2000). In a

first step the clear sky irradiance for a given location and time is calculated. In a second step a cloud index is derived from METEOSAT imagery to take into account

the cloud extinction. This step uses the reflected radiance measured by the satellite is approximately proportional to the amount of cloudiness characterized

by the cloud index. This value then is correlated to the cloud transmission. Finally,

the clear sky irradiance is reduced due to the cloud transmission to infer the surface irradiance.

There are many models proposed for the estimation of solar radiation. Súri and Hofierka, (2004) perform a summary of the developed models that calculate solar

radiation. One of the first GIS-based solar radiation models was SolarFlux (Dubayah and Rich 1995, Hetrick et al. 1993), developed for the Arc/Info GIS. A

series of solar radiation algorithms was also implemented in the Genasys GIS, using AML script (Kumar et al. 1997). Another approach for computing all three

components of radiation was realised in a standalone model Solei under MS

Windows that was linked to the GIS software IDRISI by means of data format (Mészáro 1998, Miklánek 1993). All of the above three models use rather simple

empirical formulas. Some of their parameters are spatially averaged (lumped) and therefore not suitable for calculations over large areas. More advanced methods

for ecological and biological applications are implemented in Solar Analyst, developed as an ArcView GIS extension (Fu and Rich 2000). In the pre-processing

phase, the model generates an upward-looking hemispherical viewshed based on a digital terrain model. Generating a sunmap for every raster cell makes calculation

of solar radiation considerably faster. The model is suitable for fine scale studies.

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It is not flexible enough for calculations of atmospheric transmissivity and the

diffuse proportion because it allows to set the available parameters only for the nearest weather stations or just typical values (typical???). This makes its use for

larger areas rather than limited. The SRAD model (Wilson and Gallant 2000, McKenney et al. 1999) was designed to model a complex set of short-wave and

long-wave interactions of solar energy with the earth’s surface and atmosphere. Although based on a simplified representation of the underlying physics, the main

solar radiation factors are considered and the model is able to characterize the spatial variability of the landscape processes. However, it is designed for the

modelling of topo- and mesoscale processes and the calculation of solar radiation over large territories is also limited.

Finally, Súri and Hofierka, (2004) proposed the model r.sun as a flexible and

efficient tool for the estimation of solar radiation for clear-sky and overcast atmospheric conditions. In contrast to other models, r.sun considers all relevant

input parameters as spatially distributed entities to enable computations for large areas with complex terrain. Conceptually the model is based on equations

published in the European Solar Radiation Atlas (ESRA). The r.sun model was applied to estimate the solar potential for photovoltaic systems in Central and

Eastern Europe. The overcast radiation was computed from clear-sky values and a clear-sky index. The raster map of the clear-sky index was computed using a

multivariate interpolation method to account for terrain effects, with interpolation parameters optimized using a cross-validation technique. The incorporation of

terrain data improved the radiation estimates in terms of the model’s predictive error and the spatial pattern of the model outputs. Comparing the results of r.sun

with the ESRA database demonstrates that integration of the solar radiation model

and the spatial interpolation tools in a GIS can be especially helpful for data at higher resolutions and in regions with a lack of ground measurements

The r.sun finally is a complex and flexible solar radiation model, fully integrated

within the open source environment of the GRASS GIS. It calculates all three

components of solar irradiance/ irradiation (beam, diffuse and reflected) for clear-skies as well as overcast conditions. The implemented equations follow the latest

European research in solar radiation modelling. Integration in the GRASS GIS enables the use of interpolation tools that are necessary for data preparation. The

r.sun is especially appropriate for modelling large areas with complex terrain because all the spatially variable input parameters can be defined as raster maps.

The model can be used easily for long-term calculations at different map scales – from continental to local. Two operational modes take into account the temporal

variability of solar radiation within a selected time interval.

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Other Data base and web page

Energy National Ministry Of Chile has a solar explorer on line (http://ernc.dgf.uchile.cl/Explorador/E3S/) where, from a specific location, provide

a report of the solar energy. Also, the company 3 tier (http://www.3tier.com/), brings a global mean solar irradiace map, in his webpage.

The means of the methods Heliosat, the databases HelioClim of solar radiation (irradiation or irradiance) were and are created by processing Meteosat images.

These databases run since 1985. They can be accessed through the SoDa service.

The SoDa service delivers information on solar radiation (data, databases, algorithms, advanced applications). Open solar data

base(http://www.opensolardb.org) is another website , where users can download information for different location in the world. In this database, it can be found

solar radiation information for Azul city, Buenos Aires province, Argentina.

Bioenergy

Bioenergy is renewable energy made available from materials derived from

biological sources. Biomass is any organic material which has stored sunlight in the form of chemical energy. As a fuel it may include wood, wood waste, energy

crops, agricultural residues, forest residues, aquatic plants, human and animal wastes, municipal wastes.

A biofuel is a fuel that contains energy from geologically recent carbon fixation.

These fuels are produced from living organisms. Examples of this carbon fixation occur in plants and microalgae. These fuels are made by a biomass conversion

(biomass refers to recently living organisms, most often referring to plants or plant-derived materials). This biomass can be converted to convenient energy

containing substances in three different ways: thermal conversion, chemical conversion, and biochemical conversion. This biomass conversion can result in fuel

in solid, liquid, or gas form. This new biomass can be used for biofuels. Biofuels have increased in popularity because of rising oil prices and the need for energy

security. However, according to the European Environment Agency, biofuels do not necessarily mitigate global warming.

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Bioethanol is an alcohol made by fermentation, mostly from carbohydrates produced in sugar or starch crops such as corn or sugarcane. Cellulosic biomass,

derived from non-food sources, such as trees and grasses, is also being developed as a feedstock for ethanol production. Ethanol can be used as a fuel for vehicles in

its pure form, but it is often used as a gasoline additive to increase octane and

improve vehicle emissions. Bioethanol is widely used in the USA and in Brazil. Current plant design does not provide for converting the lignin portion of plant raw

materials to fuel components by fermentation.

Biodiesel is an alternative fuel made from virgin vegetable oil or used vegetable oil. It can be used as a fuel for vehicles in its pure form, but it is usually used as a

diesel additive to reduce levels of particulates, carbon monoxide, and hydrocarbons from diesel-powered vehicles. Biodiesel is produced from oils or fats

using trans esterification and is the most common biofuel in Europe.

GIS and bioenergy

Some studies (inta, Voivontas el al 2001, Panichelli & Gnansounou, 2008, Beccali et al 2009), use a GIS-based methodology to present energy potential of biomass

coming from the agricultural and forestry sectors. In general, the authors made a

land cover map for potential bioenergy use. The methodology adopted to assess the quantity and the distribution of the potential resources is based on the

development of a GIS. The GIS was implemented with a database containing information about land cover, land use, regional cartography, administrative

boundaries, populated areas, road network, a digital terrain model, lithological map, climatic data, industry, and a civil census have been stored in the GIS to

define potential areas for gathering the residues coming from the pruning of olive groves, vineyards, and other agricultural crops, and to assess biomass available

for energy cultivation. According to the considered variables, they identified the most exploitable biomass sources and then planned actions to implement them for

the suggested purpose. It was possible to quantify the potential results of the planned actions (for immediate biomasses uses, including home heating and heat

production for businesses), considering the environmental issues.

Panichelli & Gnansounou, 2008 present a GIS-based decision support system for selecting least-cost bioenergy locations when there is a significant variability in

biomass farmgate price and when more than one bioenergy plant with a fixed capacity has to be placed in the region. The methodology tackles the resources

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competition problem between energy facilities through a location-allocation model

based on least-cost biomass quantities. The whole system least delivery cost including intermediate bioenergy products is estimated. The methodology is based

on a case study where forest wood residues (FWR) from final cuttings are used to produce torrefied wood in two torrefaction plants that supply a gasification unit in

order to produce electricity. A GIS-based approach combined with a biomass allocation algorithm is effective for selecting suitable energy facilities location. The

procedure it is used was as follows:

Extracted from Panichelli & Gnansounou, 2008

Voivontas et al 2001, also proposed a GIS decision support system to identify the geographic distribution of the economically exploited biomass potential based in

four level analysis to determine the theoretical, available, technological and economically exploitable potential.

The case of Argentina

In Argentina there is a specific regulatory framework laws for all types of bioenergy. INTA started a series of studies and actions for the geographical

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evaluation of the resource as an initial stage of feasibility analysis and life cycle

analysis of each biofuel, focusing in ethanol, biodiesel, forest biomass, biogas and waste agricultural and agribusiness

There are some studies that use bioenergies evidence in Argentina. In Argentina, the waste produced by the forestry sector, represent a large biomass potential for

bioenergy production (Uasuf & Hilbert, 2012). Anschau & Carballo, 2010 show that the region of Salta has great potential to develop projects for utilization of biomass

for energy purposes. To quantify this potential it has been implemented using WISDOM methodology, developed by FAO and based on Geographical Information

Systems and spatial analysis techniques, which allows visualizing, quantifying and

contextualizing both the Supply and Demand areas for woody biomass. In applying WISDOM analysis,the methodology involves five main steps (FAO, 2003): 1-

Definition of the administrative unit / minimum spatial analysis. 2. Development of the demand module. 3- Module Development offer. 4 Integration Module

Development. 5-Selection of priority areas or hot spots of biomass under different scenarios. Besides, it allows detecting hot spots or points of action, in which,

either a negative or a surplus balance exists and it’s necessary a more detailed analysis. It has been incorporated into the analysis the Land Use Planning

(Provincial Law 7543/09) enacted by the province in compliance with the Forest National Law (National Law 26.331/07) to quantify the resources to be available

(in a sustainable management of the territory) for a potential bio-energy use in the coming years. From this analysis it appears that currently exist in the province

nearly 11 million tons of woody biomass renewable annually, while consumption of firewood in the province does not reach half a million tons., leaving a surplus of

approximately 10, 5 million tons annually potentially available for power

generation, a figure that will vary annually depending on the rate of allowed deforestation

On the other hand, The first energy balance of biofuel production in Argentina has been developed, using principally soybean, sunflower, rapeseed (biodiesel), maize

and sorghum (bioethanol). They mapped agroecological, legal and economical issues for each of the 11 crops that make up the project, 1:500,000. Work has

begun on a larger scale mapping of different crops with a drop at the provincial level with information from provincial agencies.

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Estracted from: INTA. Residuos y cultivos agrícolas para la producción de bioenergía. PNEG 1411. 2009 – 2011

Wind Power

Wind power is the conversion of wind energy into a useful form of energy. Is an alternative to fossil fuels, is plentiful, renewable, widely distributed, clean,

produces no greenhouse gas emissions during operation and uses little land. The effects on the environment are generally less problematic than those from other

power sources. Wind power is the conversion of wind energy into a useful form of energy, such as using wind turbines to make electrical power. Large wind farms

consist of hundreds of individual wind turbines which are connected to the electric power transmission network. For new constructions, onshore wind is an

inexpensive source of electricity, competitive with or in many places cheaper than fossil fuel plants. Small onshore wind farms provide electricity to isolated

locations.

Gis and wind power

There are some studies using GIS and strategic planning of wind farms location

(Hansen,2005 ; Simao et al 2009). For example, Simao et al (2009), presents a

conceptual system framework for web-based GIS that supports public participation in collaborative planning. The framework combines an information area, a Multi-

Criteria Spatial Decision Support System (MC-SDSS) and an argumentation map

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to support distributed and asynchronous collaboration in spatial planning. After

analysing the novel aspects of this framework, the paper describes its implementation, as a proof of concept, in a system for Web-based Participatory

Wind Energy Planning (WePWEP). Details are provided on the specific implementation of each of WePWEP’s four tiers, including technical and structural

aspects. Throughout the paper, particular emphasis is placed on the need to support user learning throughout the planning process

As a web-based system, WePWEP employs a client–server architecture. On the client side, a web browser that includes the Java Runtime Environment is required

to access WePWEP because it embeds several Java applets (e.g., the

argumentation map itself and slider bars). On the server-side, WePWEP integrates several technologies. A wealth of geographical information, both in raster and

vector format is used: the backdrop in the second and third tiers, for instance, consists of three different scales of Ordnance Survey raster maps that are selected

and displayed automatically to reflect the current map viewer scale. All geographical information is stored in an Oracle database managed by ArcSDE. This

information is accessed by two products: ArcIMS and ArcGIS Server. ArcIMS publishes all geographical information backdrop layers in the argumentation map’s

viewer. Any other Web Map Server (WMS) compliant with the Open Geospatial Consortium’s (OGC) specifications could be used instead of, or in combination

with, ArcIMS . ArcGIS Server publishes all geographical information in the MC-SDSS (backdrop layers and decision criteria maps).

Similary, Hansen (2005) described how to use fuzzy based multi-criteria analysis

for the evaluation of new wind farms. This system is able to handle the complexity, uncertainty, multiple and sometimes conflicting management

objectives, which is characteristic for environmental planning generally. Additionally, the developed system is capable to support the integration of

numerous and different data types. By using other combinations of weights various scenario building is possible. The output maps can be used easily to assist

in making informed decisions. Additional relevant layers of information, such as stake holder opinion, could be quantified and easily integrated into the GIS and,

consequently, be taken into consideration when locating new wind farms.

Remote sensing data contributes to applied wind energy resource mapping both onshore and offshore. So far most wind turbines are located on land, yet the

offshore wind industry is in rapid growth. The tendency is that large arrays of wind farms are constructed.

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Offshore wind farms are either constructed or in development in several countries.

The offshore wind resource needs to be quantified prior to financing a wind farm.

Satellite remote sensing can contribute to the mapping of wind resources offshore

using imaging SAR. Advantages are that SAR scenes are readily available from archives and the spatial resolution is sufficient for wind resource mapping

(Hasager et al., 2004a). Software for applied use has been developed by Risoe National Laboratory (Nielsen et al., 2004; Hasager et al., 2004) in collaboration

with the Nansen Environmental and Remote Sensing Centre (NERSC) in the EU-WEMSAR project and in the ongoing ESA EOMD EO-windfarm project.Offshore

wind resources can be calculated from satellite SAR, yet the accuracy is limited

and only useful in feasibility studies or in combination with classical meteorological observations and wind resource mapping tools (Hasager et al., 2005).

For land-sites the topography and surface roughness needs to be known for calculation of the wind resource statistics. Wind resources is possible to be

calculated with the Wind Atlas and Application Program (WASP) (MORTENSEN et al., 2000) and Topography maps from the SRTM data. Software WASPContouring

allows to easy transfer of the SRTM data to the vector format used in WASP. The WASPContouring also allow a land cover map to be used for production of a

roughness map. (http:///www.wasp.dk)

Conclusion

This review has demonstrated that Geographic Information Systems (GIS) helps in

mapping on spatial and temporal scales of the resources and demand of energy and this would help in the regional energy planning. GIS provided the means for

identifying, and quantifying the factors affecting the available solar and wind energy potential. Argentina has great potential for renewables, mainly due to its

varied geography and climate throughout the country. An example would be that wind power farms could be installed in south of the country where the winds are

constant year round. The country has also high solar energy potential, because the region of the earth between the latitude of 40°N and 40°S is generally known as

the solar belt and this region is supposed to be with an abundant amount of solar

radiation.

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