Distributed Generation with Renewable Energy Systems: The spatial dimension for an autonomous Grid
ABSTRACT The global requirement for sustainable energy provision will become increasingly important over the next years as the environmental effects of fossil fuels become apparent. Distributed Generation (DG) based on Renewable Energy Technologies (solar, wind, hydro and biomass) is becoming a more important energy option in the future generation system. Depending on the local conditions and energy potential, one or more of the widely used renewable energy sources can be exploited locally. Wind energy for electricity production, biogas from solid waste for heat and electricity production, solar for heat and electricity production, hydro for electricity production. As other energy facilities, DG facilities require a siting review process to acquire the permits and approval needs for construction and operation. Locating optimal sites for power generation facilities is a complex task involving many environmental, economic, and social constraints and factors associated with existing central power plants, substations, transmission and distributions lines, networks of power system, etc. A Geographic Information System (GIS) is an appropriate tool to address this issue, since it efficiently stores, retrieves, analyses, and displays geographically referenced information (i.e., data identified regarding to their locations) according to user-defined specifications. Thus, once a GIS database is developed, it can provide an efficient and affordable means of analyzing potential DG facility site attributes. This paper presents an outline of a Spatial Decision Support System (SDSS) to select optimal sites to install DG facilities on the island of Lesvos, Greece, where various renewable energy sources can be found. A variety of constraints and factors were identified that address environmental, energy, social, political and economic considerations. The results may help build a developmental vision for sustainable energy systems based on locally available natural resources, and facilitate a transition of national energy and environmental policies towards sustainability.
- [show abstract] [hide abstract]
ABSTRACT: This research discusses the implementation of a geographic information system (GIS) for the simultaneous discovery of multiple types of renewable energy sources. In particular, the GIS model analyzes wind, solar, and biomass potential within the greater southern Appalachian region, an area which is currently very heavily dependent on coal for electricity generation. The location and availability of biomass is specifically considered in the context of potential co-fire generation within existing coal plants, while the availability of wind and solar power are based on both resource strength and the geographic, topographic, and regulatory constraints that provide limits on their use. The model determines potential wind and solar sites within the region, and potential biomass co-fire locations, based on these input constraints and it calculates the cost and generation characteristics for each site.Highlights► We discuss a geographic information system for analyzing renewable energy potential. ► The system is focused on solar, wind, and biomass resources, at the regional level. ► The chosen region for application is the greater southern Appalachian mountains. ► Potential sites for each type of resource are identified, given regional constraints. ► Costs and potential impacts on regional electricity generation are discussed.Renewable Energy. 01/2011; 36(11):2785-2798.
Distributed Generation with Renewable Energy Systems: The spatial dimension
for an autonomous Grid
Paper presented at the 47th conference of the European Regional Science Association
‘Local governance and sustainable development,
Thematic Stream M: Environment, natural resources and sustainability’
ERSA 2007, Paris, France, August 29th – September 2nd 2007
Leda-Ioanna Tegou*, Heracles Polatidis, Dias A. Haralambopoulos
University of the Aegean, Dept. of Environment, Energy Management Laboratory,
University Hill, Mytilene 81100, Greece,
Tel. +30-22510-36283, Fax. +30-22510-36209
*corresponding author, email: firstname.lastname@example.org
The global requirement for sustainable energy provision will become increasingly
important over the next years as the environmental effects of fossil fuels become apparent.
Distributed Generation (DG) based on Renewable Energy Technologies (solar, wind, hydro
and biomass) is becoming a more important energy option in the future generation system.
Depending on the local conditions and energy potential, one or more of the widely used
renewable energy sources can be exploited locally. Wind energy for electricity production,
biogas from solid waste for heat and electricity production, solar for heat and electricity
production, hydro for electricity production.
As other energy facilities, DG facilities require a siting review process to acquire the
permits and approval needs for construction and operation. Locating optimal sites for power
generation facilities is a complex task involving many environmental, economic, and social
constraints and factors associated with existing central power plants, substations, transmission
and distributions lines, networks of power system, etc.
A Geographic Information System (GIS) is an appropriate tool to address this issue,
since it efficiently stores, retrieves, analyses, and displays geographically referenced
information (i.e., data identified regarding to their locations) according to user-defined
specifications. Thus, once a GIS database is developed, it can provide an efficient and
affordable means of analyzing potential DG facility site attributes.
This paper presents an outline of a Spatial Decision Support System (SDSS) to select
optimal sites to install DG facilities on the island of Lesvos, Greece, where various renewable
energy sources can be found. A variety of constraints and factors were identified that address
environmental, energy, social, political and economic considerations. The results may help
build a developmental vision for sustainable energy systems based on locally available natural
resources, and facilitate a transition of national energy and environmental policies towards
Key words: Energy Systems, Distributed Generation, Spatial Decision Support Systems,
GIS, Sustainability, Greece
This paper is part of the 03ED375 research project, implemented within the
framework of the "Reinforcement Programme of Human Research Manpower" (PENED) and
co-financed by National and Community Funds (25% from the Greek Ministry of
Development- General Secretariat of Research and Technology and 75% from E.U.-
European Social Fund).
Fund, Operational Programme for Education and Initial Vocational Training
PYTHAGORAS, ‘The contribution of landfill biogas to climate change, air pollution and the
possibilities for renewable energy development’, Contract No: 1362
This work has been partly financed by the Ministry of Education, European Social
The issue of climate change is becoming a great challenge which the
international community must face in this century. Over the last decade, the EU has
put significant effort into developing a common strategy in the energy sector.
Substituting fossil fuels with Renewable Energy Sources (RES) is regarded as a
significant measure for cutting global carbon emissions.
The environmental benefits that go along with the increased use of RES for
electricity generation are widely acknowledged. Various life cycle assessment studies
in particular point out the large potential of renewable energy technologies for
reducing greenhouse gas emissions, as well as of emissions that contribute to regional
environmental problems like acidification. As a consequence, quite ambitious targets
for increasing the use of RES were specified by the EU, as well as by various national
governments (Krewitt and Nitsch, 2003). The EU strategy is that 22.1% of the total
electricity consumption in 2010 should stem from RES. Particularly Greece aims to
increase the contribution of RES to an indicative 20.1% (European Directive
Furthermore, a rapid growth for distributed electricity generation is foreseen
(IEA, 2002). It is expected that the annual distributed electricity output will grow by
4.2% between 2000 and 2030 reaching 35 GWh by the year 2030 (Soderman and
Pettersson, 2006). The use of renewable energy (solar, biogas, wind and hydro) and
Combined Heat and Power (CHP) to limit Greenhouse Gases (GHG) emissions is one
of the main drivers for Distributed Generation (DG) (Tagaris et al., 2004; Pecas Lopes
et al., 2007).
This paper presents an outline of a Spatial Decision Support System (SDSS)
for siting DG facilities. Section 2 discusses the definition of distributed generation.
Section 3 focuses on the problem of locating DG facilities. Section 4 commences on
the relevance of Geographic Information Systems (GIS) for environmental planning
and, in particular, for locating RES facilities. Section 5 introduces Spatial Decision
Support Systems and discusses their usage as tools in integrated regional planning. In
Section 6 we present an outline of an integrated methodological framework, which
necessitates a SDSS to identify suitable areas to install wind parks and in Section 7
we present an application of this framework to the Island of Lesvos, Greece. Finally
we discuss the results of our methodology and application and provide conclusions.
2. Distributed Generation
In today’s open energy market, distributed energy systems have an
increasingly important role. Different definitions regarding Distributed Generation
(DG) are used in the literature. According to Ackerman et al. (2001) DG is an electric
power source connected directly to the distribution network or on the customer side of
the meter. Soderman and Pettersson (2006) consider that a distributed energy system
is a complex system comprising of a number of energy suppliers and consumers,
district heating pipelines, heat storage facilities and power transmission lines in a
region. DG should not be exclusively confused with renewable energy generation.
Renewables can be exploited in DG and are very much encouraged by certain
lobbying groups, though non-renewable technologies could also be considered in DG
systems (Puttgen et al., 2003).
Traditionally, electricity is generated in large power stations, located near
resources or at logistical optima; it is transported through a high-voltage transmission
grid and is locally distributed through medium-voltage distribution grids. DG aims to
add versatility of energy sources and reliability of supply and reduce emissions and
dependence on fossil fuels (Figure 1). The goals of DG include the minimization of
the environmental impacts of energy production and introduction of RES to the
distribution network. In addition, DG can contribute to the reduction of transmission
losses and help introduce new developments such as fuel cells and super-conducting
devices (Hartikainen et al., 2007).
Figure 1: Schematic diagram of traditional central-plant model and DG-model
Certain DG technologies are not new (e.g., internal combustion engines, gas
turbines, etc.). On the other hand, due to the changes in the utility industry, several
new technologies are being developed or advanced toward commercialization (e.g.,
fuel cells, photovoltaics, etc.). Figure 2 presents the different distributed generation
technologies (Poullikkas, 2007).
Figure 2: Distributed Generation technologies for power generation
3. Siting DG facilities
Although enthusiasm for renewable energy has grown, siting major energy
facilities has become increasingly difficult. DG facilities require, like other energy
projects, a siting process geographically constrained by laws or conditioned by the
acceptability or unacceptability of the several entities involved in the process
(Monteiro et al., 2001; Polatidis and Haralambopoulos, 2004).
Energy and electricity industry professionals and policy groups have
developed a variety of studies and strategies for mitigating siting difficulty for a range
of new facilities for new power plants (NCEP, 2006). However, siting remains a
broad and complex problem, affecting both conventional and alternative energy
facilities, for which solutions are not obvious or well-understood (Vajjhala, 2006).
The most commonly identified causes of siting difficulty fall into three main
categories: (a) public opposition, (b) regulatory roadblocks, and (c) environmental
constraints (Vajjhala and Fischbeck, 2007). Together these categories encompass any
combination of obstacles to the process of finding locations for new facilities. This
includes factors such as public opposition; environmental, topographic, and
geographic constraints; inter-agency coordination problems; local and federal
regulatory barriers to permitting, investment, and/or construction. In addition to these
constrains, financing of new facilities remains insecure and growing siting issues have
only compounded investment uncertainty (Vajjhala, 2006).
Small Gas Turbines
Public opposition to siting is now so commonplace that it is frequently
disparaged as the primary barrier to any new development. The term NIMBY (Not In
My BackYard) has become part of the national vocabulary to the point where it has
been replaced by the new term BANANA (Build Absolutely Nothing Anywhere Near
Anything). The transition from NIMBY to BANANA marks a turning point in public
influence on siting projects.
Public opposition to new power pants is perhaps the most well-known
constraint on siting processes. However, the most fundamental and yet least discussed
siting constraint is the physical or environmental characteristics of the site itself.
Technical and engineering criteria provide the basic guidelines for the earliest stages
of project decision-making as well as the identification of site alternatives. In other
words, in order to be economically viable, renewable facilities must be located close
to RES potential. Furthermore, the specific physical conditions of a potential site (i.e.
topography, local ecology, type of land cover, etc.) affect the overall project design,
and related economic profitability. Particularly for the case of RES, these conditions
include the availability and predictability of the resource itself. As a result, project
planners tradeoff between project attributes and site characteristics, since rarely one
alternative can dominate others (Vajjhala, 2006).
Siting facilities in general is a difficult task, and renewable facilities face even
greater challenges. Three of the most important obstacles for siting in particular RES
facilities are as follows: (1) renewable resources are inflexible, (2) renewable energy
facilities and transmission lines are tightly coupled systems, and (3) renewable
resource sites have limited overlap. The first of these constraints is the most intuitive.
Renewable resources are immobile: natural gas or coal can be readily stored and
shipped to a wide range of locations, but the wind is blowing where and when it is
blowing. Kahn (2000) captures this dilemma in the statement: ‘in developing a
renewable power plant, it is the site that chooses the project, not the reverse’.
4. GIS in Environmental Planning
Because RES tend to be highly site-specific, it is important to know where
they are available in addition to numerical assessment. A Geographic Information
System (GIS), a computer system capable of assembling, storing, analyzing, and
displaying geographically referenced information, is an appropriate tool to address
this issue (Ma et al., 2005). Generally, a GIS is a computerized data base which
allows one to integrate and to process information coming from different sources. As
a toolbox, a GIS allows planners to perform spatial analysis by using its geo-
processing or cartographic modelling functions, such as map overlay, selection SQL
(structured query language) and thematic analysis. Among all the geo-processing
functions, the map overlay is probably the most useful (Figure 3) where the different
layers could represent real world features such as urban settlements, roads, land type
terrain, water features, electric network, RES potential, etc. (Muselli et al., 1999;
Quininez-Varela et al., 2007).
Figure 3: Map overlay function in GIS
Some people refer to GIS as a spatial database used to collect, store and
retrieve information about the location and shape of, and relationships among,
geographic features. Indeed, GIS is a particular form of information system applied
to geographical data. Geographical data include those which are spatially referenced
(location-based data) and contain four integrated components: (1) location, (2)
attribute, (3) spatial relationship, and (4) time (Zeng, 2002).
The application of GIS with renewable energies in distributed electricity
generation has been the focus of a number of research projects (Domingues and
Amador, 2007 in press). In this field, studies for wind farm siting, photovoltaic
electrification, or biomass evaluation stand out (Yapa, 1991; Voivontas et al., 1998;
Gadsden et al., 2003; Ma et al., 2005; Masera et al., 2006; Yue and Wang, 2006;
The appropriateness of a GIS for locating RES facilities is portrayed in Baban
and Parry (2001):
1. it can manage and analyse the volumes of diverse multidisciplinary data;
2. it has the functionality to perform “what if” scenarios in order to evaluate the
effects of different planning policies or to uncover the optimum RES (e.g.,
wind farm) site among a number of potential alternatives;
3. it is capable to be used for modelling the adverse impacts of proposed projects
and suggest modifications to mitigate or minimized them.
5. Spatial Decision Support Systems - SDSS
The evaluation of a large number of possible site solutions for siting DG
facilities is facilitated by the use of a SDSS (Monteiro et al., 2001). Malczewski
(1999) defines SDSS as an interactive, computer-based system designed to support
users in achieving effective decision making by solving semi-structured spatial
In recent years, there has been a rapid expansion of interest and research on
spatial decision support systems. To varying degrees, these approaches attempt to
(Store and Kangas, 2001; Dragan et al., 2003; Arampatzis et al., 2004):
? capture system dynamics;
? deliver outputs as spatial data that define biophysical, economic and social
? use new methods for translating factor layers into standardised inputs for
problem criteria definition;
? use new methods for capturing uncertainty in ranking of alternatives;
? explore options for quantitative optimisation with or without spatial
Spatial multi-criteria decision problems typically involve a set of
geographically-defined alternatives from which a choice of one or more alternatives is
made with respect to a given set of evaluation criteria (Jankowski, 1995; Malczewski,
1999). Spatial multi-criteria analysis differs from conventional Multi-Criteria
Decision Aid (MCDA) techniques due to the inclusion of an explicit geographic
component. In contrast to conventional MCDA analysis, spatial multi-criteria analysis
requires information on criterion values and the geographical locations of alternatives
in addition to the Decision Makers’ (DMs) preferences with respect to a set of
evaluation criteria. Therefore, two considerations are of paramount importance for
spatial multi-criteria decision analysis (Ascough et al., 2002):
1. the GIS component (e.g., data acquisition, storage, retrieval, manipulation, and
2. the MCDA analysis component (e.g., aggregation of spatial data and DMs’
preferences into discrete decision alternatives).
It is common practice, in multi-criteria analysis, to distinguish criteria in two
categories: factors and constraints. In a SDSS context, criteria are represented in
separate map layers. The aggregation phase is eventually carried out to combine the
information from the various factors and constraints (Jankowski, 1995).
The decision making process can be represented as a three-stage hierarchy of
intelligence, design and choice (Figure 4) (Ascough et al., 2002). The three stages of
decision making do not necessarily follow a linear path from intelligence to design
and to choice (Malczewski, 1999).
Figure 4: Decision flowchart for spatial multi-criteria analysis
6. An outline of the integrated framework
As other energy facilities, DG facilities require a siting review process to
acquire the permits and approval needs for construction and operation. In this process
different groups and individuals with different roles, interests and priorities are
involved. The integrated framework, outlined in this work, is based on a SDSS that
helps to identify permissible areas to install DG facilities. Wind energy facilities are
used in this paper to exemplify the use of the SDSS.
One of the most significant obstacles in exploiting wind power is land use
restrictions. Development of wind power plants requires land with sufficient wind
resources, proximity to the power grid, and compatibility with environmental and
Public resistance to wind farms is another challenge. Strong opposition to
wind turbine placement is encountered in and around communities concerned with
visual-, noise-, or environmental impacts. It is essential that these diverse factors
should be examined so that site suitability is understood before construction (Rodman
and Meentemeyer, 2006).
The siting process is a multi-criteria decision problem requiring consideration
of several criteria (Table 1) (Baban and Parry, 2001; Krewitt and Nitsch, 2003;
Gamboa and Munda, 2007; Ma et al., 2005; Monteiro et al., 2001; Hansen, 2005;
Ramachandra et al., 2005). These criteria involve bounding constraints that comprise
of physical, planning, economic, environmental and cultural issues, and factors that
represent a yardstick or means by which a particular option can be evaluated as more
desirable than another. These constraints and factors influence the selection of
potential sites. The constraints are based on the Boolean criteria (true/false), which
limit the analyses to specific regions. The factors are criteria, which define some
degree of suitability for all the geographic regions. They define areas or alternatives
according to a continuous measure of suitability, enhancing or diminishing the
importance of an alternative under consideration in the geographic space resulting
after the exclusion of the areas defined by the restrictions (Hansen, 2005). Their type
could be identified as (Monteiro et al., 2001):
? quantitative, where the attributes are a set of ranges of measurable values
(e.g. distance to the electricity grid)
? qualitative, where the attributes are qualitative classes (e.g. types of land
? zonal, representing multiple geographical zones, each one influenced by
specific local interest groups – actors (e.g. representation of local policies
of the municipalities).
Table 1: Examples of criteria for siting wind farms
summits of large hills
existing road infrastructures
areas of ecological value/ special scientific interest
land owner's income
return on investment
number of jobs
social acceptance (NIMBY phenomenon)
GHGs emissions reduce
cohesion with local policies
Other aspects of the problem include the involvement of several actors in the
decision process. Governments, utilities, private investors and local authorities
become active participants in the energy planning procedure. Governments, as the
major energy policy makers, are usually in favour of large scale integration of RES
into local energy systems. Utilities, as the traditional energy administrators, put more
interest on the reliability of the energy system and production cost. Investors are
mainly interested in the profits that can be obtained by RES investments and local
authorities focus on the harmonisation of local needs with the proposed actions
(Voivontas et al., 1998). Consequently, the objectives and interests of these actors
could be in conflict. Examples of actors that could be involved on the process are:
? DG technology developers and investors,
? local and national government and agencies,
? community groups and
? environmental organizations.
In the first stage of the methodology outlined here, the evaluation criteria are
defined. A buffer zone in a GIS software is usually required for every constraint to
define the minimum distance of development sites to the selected geographic feature.
The width of buffer zones varies in relation to the specific constraint. By aggregating
all constraint layers, a final constraint map is calculated, which represent the areas
that are restricted from development of (wind) power facilities. Furthermore,
additional selective criteria are defined that are used to further explore the suitability
of the remaining sites.
More specifically, under a MCDA approach, for each actor there is a set of
criteria represented by geographic thematic maps. The thematic maps represent issues
directly valuable by the actors. The alternative solutions, to be evaluated, are the
possible locations. The aggregation phase is eventually carried out to combine the
information from the various factors and constraints.
Figure 5 illustrates the integrated framework for siting DG facilities. Data and
observations are the primary input for the GIS. An initial portfolio of constraint maps
is generated showing the various potential areas according to first input.
Subsequently, several evaluation criteria are introduced to operationalize the various
constraints and aspirations. A multi-criteria analytical exercise is then unfolded to
provide an overall evaluation of potential sites. New, more complex, maps are being
generated leading to the identification of the most promising sites that meet the
induced constraints and fulfill (as close as possible) the aspirations. The GIS software
package used in this study is ArcMap® developed by the Environmental Systems
Research Institute (EPRI).
Figure 5: A schematic diagram of the outlined integrated framework
In the next section an application regarding the siting of wind projects in the
island of Lesvos is unfolded.
Lesvos Island (Figure 6) is located to the northeast of Greece, in the Aegean
Sea. Its total area is 1636 km2, with 109,000 inhabitants. Tourism is the main
economic activity in the island (apart from agriculture) and its seasonal characteristic,
coupled with hot summers, are the main culprits of the annual fluctuations in
electricity demand (Eleftheriadou et al., 2004).
Figure 6: Map of Lesvos Island
Electricity production is based on an autonomous grid powered by a
conventional diesel station, owned by the Public Power Corporation (PPC) and is
located in the outskirt of Mytilene. It is fired by crude and diesel oil. Wind potential
on the island is high (Figure 7) and PPC and other private and municipal investors
have employed it for electricity generation, but these projects have so far managed to
exploit only a small fraction of the island’s full wind capacity. Other RES, geothermal
and solar, have also been developed but on a very limited scale. Table 2 presents the
installed electricity capacity in Lesvos in 2003.
Table 2: Installed electricity capacity in Lesvos in 2003*
Conventional power station capacity
Lesvos 66.464 12.825
*source: Primary data, PPC plant, Mytilene
Figure 7: Wind Potential Map of Lesvos Island, Greece.
Source: Laboratory of Natural Disasters, Department of Geography, University of the Aegean
Due to the variable nature of RES, until recently, the Ministerial Decision
8395/95 (Government Gazette Β 385, 1995) prohibited licensing of a RES power
station when the installed RES power exceeded 30% of the peak hourly demand of the
previous year, to protect the stability of the electricity grid. Although this restriction
has been currently abolished, it is still assumed to represent a technically feasible
upper limit for the maximum RES penetration to the electricity production
(Ntziachristos et al., 2005).
An assessment of suitable locations for wind power projects includes the
examination of various geographic limitations and is considered essential for an
effective process of energy planning. Targeting the most suitable sites will minimize
controversy and improve public perception of wind power. Local, regional and
national stakeholders participated in the decision-making process to identify suitable
sites for placing wind turbines. Table 2 shows the relevant interest groups and Table 3
the selected criteria. Most criteria used in this case-study were based in regulations
specified by central government (Hellenic Ministry of the Environment, Physical
Planning and Public Works, 2007).
Table 3: Interest Groups (Stakeholders)
Interest Groups (Stakeholders)
Environmental Group – Naftilos (local non-governmental organization)
Wind farm investors – Hellenic Wind Energy Association
Regional Authorities of the North Aegean
Central Government – Representative of the Ministry of Development
Table 4: Criteria for siting wind farms in the island of Lesvos, Greece
areas of special scientific interest (Petrified Forest)
areas of ecological value (NATURA 2000)
existing road infrastructures
land owner's income
return on investment
number of jobs
social acceptance (NIMBY phenomenon)
GHGs emissions reduce
cohesion with local policies
Figure 8 presents the various thematic maps that operationalize the constraints
to be taken into account in locating wind farms in the island of Lesvos. This
procedure results into the final constraint map that will be the primary input for the
subsequent phase of the decision process, where each interest group selects a set of
evaluation factors/criteria represented by GIS layers, and a multi-criteria analysis is