Díaz, P., Masó, J. (2013). The importance of geospatial data to calculate the optimal distribution of renewable energies. Poster in EGU General Assembly 2013, Session ERE – Energy, Resources and the Environment, Vienna, April 2013.

1. The new renewable energies are revolutioning the international trade while are geographically diversifying markets, specially during last three years
The importance of geospatial data
to calculate the efficiency of renewables
Difficulties of finding optimal
locations for the establishment of
installations for renewables
Paula Díaz (paula.diaz@uab.cat) and Joan Masó (joan.maso@uab.cat)
In 2011
EU&Eurasia accounted for about 43% of
the total renewables production, the
BRICS nations 26%, and the US 23%.
Spatialized Energy Return on Investment
• Spatialized EROIs can be presented as maps that will show the best productive zones and
the optimal position in terms of both energy production and associated costs.
European Geosciences Union
General Assembly 2013
• The energy return ratios are commonly used to
calculate the efficiency of the traditional energy sources.
• The energy return on investment (EROI) compares the
energy returned for a certain source and the energy used
to get it (explore, find, develop, produce, extract,
transform, harvest, grow, process, etc.).
• The energy return ratios show a general decrease of
efficiency of fossil fuels and gas.
• Some recent studies give new, but sometimes
contradictory, results on the real energy return for
renewables (Grassi, 2012; Prieto, 2013).
• Due to an ever increasing scarce of the appropriate
• Initial considerations
To define the areas where it is
technically and economically feasible to develop
renewable energy installations is a challenge.
The locations where it is easier to install
renewable farms reduces over time.
The optimal distribution should consider the
various kinds of technology (different
efficiency) for renewable sources and the costs
of the initial installation and energy
interconnection.
There is a need for Spatial Data Infrastructure
(SDI) of official and open access energy
datasets. OGC is designing a three year
interoperability experiment to demonstrate the
feasibility of a global Energy SDI.
• Natural constraints
The Energy Return on Investment
(EROI) concept
the optimal position in terms of both energy production and associated costs.
• Geographic Information Systems (GIS) tools characteristics
GIS tools facilitate the identification of the eligible areas.
Combine large number of variables by automatic and reproducible processes.
The calculations can be applied to different territories using appropriate geographic data.
Facilitate the access to the data, so, increase the knowledge and social acceptance of renewable installation
projects.
• Spatialized EROI is effective for calculating best productive zones in renewables
Spatialized EROI will enable the optimal position in terms of both energy production and associated costs
Spatialized EROI analysis could include any indirect costs that the source of energy could produce (such as
visual impacts, food market impacts and land price).
It calculates the places of maximum energy return at minimum cost and the less impact of the production.
• Spatialized EROI maps are a decision making tool that can have policy implications
Conclusions
• Due to an ever increasing scarce of the appropriate
territories, an spatialized EROI becomes more relevant in
renewables.
• There is a lack of agreement in literature. We propose
the Raugei’s (2012) formula (show bellow).
• Natural constraints
Characteristics of the topography (lands with
slopes>20% are excluded (Grassi, 2012)) and
natural variables (wind, sun, etc.).
Conflict with other activities such as agriculture
or Natural Parks.
• Anthropological constraints
Lack of infrastructure (roads for accessing of
cranes).
Presence of buildings and the ownership of the
lands (private/public).
Lack of network for transmission of the energy
produced.
Distances and transport costs Topography and connexions Environmental variables
Between production and
consumption:
transportation of energy
from farms to cities.
Energy cost of infrastructure
maintenance and
development.
Accessibility.
Easiness of construction of
new farms (slope
ownership).
System of connection to
existing grids.
Solar radiation.
Wind speed.
Ocean speed.
Environmental impact.
Greenhouse emissions.
Variables proposed to be included in the calculation of the EROI
EROI=
EOUT/(EED+EPP) It does not show
the overall
amount of non-
renewable
Conclusions
Investment in energy is experiencing an increase, but also manufactures and policies. However, in
the coming years, further efforts will be need to provide more energy access, and increasingly
reduce carbon emissions and waste generation. Determine the efficient areas that maximize the
energy returned on investment will be crucial in renewables. The authors propose a spatialization
strategy for the EROI as a reliable solution to facilitate the efficiency in energy investment, taking
into consideration the appropriate geospatial variables. Spatialized information has to be easily
available to make possible calculation of the EROI. Spatialized EROI technique described here is
one of the use cases where a standardized Energy SDI, containing official georeferenced
information will allow the development of a milliard of new applications in science research and the
energy industry and utilities. In that sense, OGC has started a domain working group that aims to
create a global Energy SDI.
References
BP (2012). BP Statistical Review of World Energy. June, 2012.
Grassi, S., Chokani, N., Abhari, R.S. (2012) Large scale technical and economical assessment of wind energy potential with a GIS
tool, Case study Iowa. Energy Policy 45(2012), pp. 73–85.
Prieto, P. A. and Hall, C.A.S. (2013) Spain's Photovoltaic Revolution: The Energy Return on Investment. Springer. ISBN: 978-1-
4419-9436-3
Raugei, M., Fullana, P., Fthenakis, V. (2012). The energy return on energy investment (EROI) of photovoltaics: Methodology and
comparisons with fossil fuel life cycles. Energy Policy, 45 (2012) 576–582
REN=
EFEED &EED= 0
• EFF= non-renewable PES in the ground (e.g.crude oil)
• EFeed= energy of extracted and delivered EC (e.g. heavy fuel oil)=direct non-renewable energy input for
electricity production (feedstock)
• EED= Feed/EROIF =energy for the supply chain (extraction and delivery) of the feed stock, expressed in
terms of (renewable+non-renewable) Primary Energy
• EPP= energy for the construction and end-of-life (EoL) of the power plant, expressed in terms of
(renewable+non-renew-able) Primary Energy
• ER= direct renewable Primary Energy input for electricity production (usually excluded from the EROI
calculations)
• EOUT= net electricity (EC) output
Based on gross generation from renewable sources including wind, geothermal,
solar, biomass and waste, and not accounting for cross border electricity supply.
Source: BP Statistical Review of World Energy. June, 2012