23-05-2012, 04:09 PM
GIS-based Multi-Criteria Analysis of Wind Farm Development
GIS-based Multi-Criteria Analysis of Wind Farm Development.pdf (Size: 437.86 KB / Downloads: 104)
Introduction
An increase in public awareness regarding the negative impact on the environment
of traditional power-generating methods, especially coal and oil-fired power stations,
has created a demand for developing and using environmentally friendly renewable
energy. Wind power is a popular and safe form of renewable energy, and in Europe,
the demand for wind energy is increasing. Achieving the goal set by the EU due to the
implementation of the Kyoto protocol will require further expansion, and in order to
facilitate this process around the Baltic Sea a project – Wind Energy in the Baltic Sea
Region - financed by EU / INTERREG III B was launched in January 2003. One aim
of this project is to develop methods and tools to support spatial planning in relation
to wind energy. An important outcome of the project will be identify best practices in
wind energy planning and disseminate these methods to Member States with no or
little experiences with wind energy.
Project objectives and background
Climate change is one of the greatest environmental, social and economic threats
facing our planet. During the last century, the Earth's average surface temperature
rose by around 0.6°C. Evidence is getting stronger that most of the global warming
that has occurred over the last 50 years is attributable to human activities. Human
activities that contribute to climate change include in particular the burning of fossil
fuels and deforestation, both of which cause emissions of carbon dioxide (CO2), the
main gas responsible for climate change.
Methods
Decision-making on environmental issues is often a process characterised by
complexity, uncertainty, multiple and sometimes conflicting management objectives,
as well as integration of numerous and different data types. A decision is a choice
between alternative actions, hypotheses, locations, and so on (Eastman et al. 1993),
and a decision support system should aid and strengthen the process of choice (Sauter,
1997). A decision is therefore derived from an assessment of suitability, the degree,
which a location belongs to the suitable or not suitable set. Generally, the not suitable
set is assumed to be the complement of the suitable set. Most decision-making
processes consider multiple criteria to assess the degree of suitability each location
bears to the allocation under consideration. Therefore, suitability is generally not
Boolean in character, but expresses varying degrees of set membership.
Multi-criteria decision support
Two types of criteria support the decision-making: constraints and factors. These
criteria represent conditions possible to be quantified and contribute for the decisionmaking
(Eastman et al., 1993). 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. The factors
indicate continuous degrees of fuzzy membership in the range between one and zero,
whereas the Boolean factor criteria can be considered as a special case of fuzzy sets.
Uncertainty and fuzziness
Measurement uncertainty is that which resides in the data. This error is often
assumed to be random with a normal distribution and can be handled by identifying
levels of risk based on standard deviations, and accepting a certain risk in the
decision. Human conceptual uncertainty stems from difficulties in setting precise
(numerical) thresholds. For instance, if we identify tall as more than 2 meter, does that
make 2.01 meter “tall” and 1.99 meter “not tall”? This type of uncertainty can be
resolved by applying continuous classifications based on fuzzy logic. Finally we have
decision rule specification uncertainty. A decision rule might be biased towards one
single factor, which was not intended by the decision maker. To resolve this the
robustness of the decision rule should be evaluated carefully. Incorporating risk and
uncertainty in the decision-making is sometimes referred to as going from a hard to a
soft decision (Gumbricht and McCarthy, 2003).