Changing wind energy landscapes:
Interplay between energy policy,
technology development and
landscape impact in Denmark
Bernd Möller, Ph.D.
Sustainable Energy Planning & Management Group
Geography Research Group
Department of Development and Planning
Aalborg University, Denmark
Source: European Wind Energy Association, 2009
Emerging wind energy…
160
PJ
140
Wind Power
Electricity demand
120
100
80
60
40
20
1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006
About 5,300 turbines produce 20 % of the
national electricity demand.
Decommissioning of ageing turbines
currently decreases production.
Data source: Danish Energy Authority, 2008
3.500
…and fading.
7.000
6.000
2.500
5.000
2.000
4.000
1.500
3.000
1.000
2.000
500
1.000
-
19
77
19
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19
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19
83
19
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19
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19
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91
19
93
19
95
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20
01
20
03
20
05
20
07
20
09
20
11
20
13
20
15
20
17
20
19
[no]
[MW]
3.000
Capacity [MW]
Number [no]
The number of turbines had peaked in the year 2000. Decommissioning of old plants
will greatly reduce the number, potentially freeing large areas from visual impact.
Data source: Danish Energy Authority, 2008
Problems associated to wind energy in DK
• Most land-based locations occupied or unsuitable
• Planning requirements are tightened
• Increasing turbine size aggravates visibility problem
• Growing local resistance against wind power projects
• Structural changes (tariffs, ownership etc)
The advantage of upscaling
(EWEA, 2006)
Wind energy planning in DK
Areas exempted for
development by
environmental protection
and conservation, national
and EU
Areas designated for
wind energy
development after 2005
Locations of wind
turbines by size, 2007
Public regulation
• Until 2002: fixed feed in tariff, now market model
• Until 2000: preference of co-operative ownership
• Three forms of ownership and owners:
– Individual, mostly farmers
– Utilities, who until 1996 were made to invest
– Co-operatives, with limited, local shares
• Municipalities and regions to lay out designated
wind energy development areas.
Wind energy and landscapes
• Connotation with the environment / being green
• Iconic for sustainable development; used e.g. for attracting tourists
• Valuable landscapes deemed unsuitable for visibility reasons
• On a local scale neighbours play a certain role (NIMBY)
• There is no analytical assessment of landscape visibility or visual
openness on national or regional scales
Increasing size
140
Average total height [m]
Bubble area proportional to number of turbines
120
100
80
60
40
20
0
1977
1982
1987
1992
1997
2002
2007
Year of installation
Data source: Danish Energy Authority, 2008
7.000
6.000
5.000
4.000
3.000
2.000
1.000
0
<1
99
0
19
90
19
91
19
92
19
93
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19
97
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98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
Number of turbines
Ownership
8.000
n
Other/unknow
Pow er utilities and
Individual/industry
Cooperative ownership involves Cooperatives
local people
economically
hence
improves local acceptance.
The good experiences with neighbour ownership point towards
commonly managed wind resources as a good solution.
Data source: EMD International, 2007
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Wind energy development
in Denmark through the
times
1985:
1990:
1995:
2005:
2000:
774
2,570
3,553
5,286
6,236
turbines
turbines
44
317
589
3,127
2,389
MW
MW
MW
20%
0.2
2
4
13%%
%
of
of
of
of
power
power
power
power
production
production
production
production
^^
^
^^
^
^
Data source: Danish
Energy Authority, 2007
Spatial analyses of wind energy economy
and the environment
•
•
•
•
Temporal cumulative viewshed analysis of wind turbines
Intervisibility analysis of landscape openness.
Wind energy production and its spatial distribution
The costs to society (socio-economic costs) of utilising
the wind resource
• Turbine ownership and proximity
• A combination of wind power production, wind power
economy and environmental impact is carried out on a
regional / national scale.
The study area
Data sources: EEA, 2005; KMS, 2007
Wind energy data
Wind energy potential is calculated by Wind Resource Mapper
(www.emd.dk), based on the Wind Atlas method by RISØ National
Laboratory (www.wasp.dk). The original grid resolution is 200 m.
Cumulative temporal
viewshed analysis
1995
2005
2000
1990
1985
Intervisibility to model landscape openness
How much of a landscape can be seen from everywhere else? Cut-off
radii of different size effectively influence the output.
Data sources: KMS, 2007; Danish Energy Authority, 2008
Intervisibility and wind energy economy
Intervisibility
low
high
Costs
low
high
The best locations for wind energy are not necessarily the most visible.
Data sources: KMS, 2007; EMD International, 2001
Intervisibility and proximity
Intervisibility
low
high
Weighted
proximity
low
high
Most wind turbines are located in areas with low intervisibility; few
large utility-owned parks cause most cumulative weighted impact.
Data sources: KMS, 2007; Danish Energy Authority, 2008
Distributed investments
Until now, investments in wind energy (left) are well distributed in the country and
not necessarily in rural areas alone, which is positive for social acceptance in
population (right). The maps show statistics on a 10 x 10 km grid.
Offshore: the solution?
Good things:
Better wind conditions
Less visual impact
Bad things:
Possibly influencing wildlife to a
higher degree
Higher investment costs
Massive infrastructure requirements
Multinational rather than local
National policy requirements
Summary of results
• Reversibility of visual impact in fading wind
energy landscapes
• Technological development is sometimes faster
than getting accustomed to landscape change
• Heterogeneity may lead to alienation as wind
energy transforms from low impact, everyday
experience to exceptional high impact
Conclusions
• By means of spatial analysis it can be quantified:
– landscape value proxies such as intervisibility
– wind energy economy and controversy
– and the geography of ownership and alienation.
• It can be seen from the work presented that:
– new cartographical representations and
– a quantitative basis for decision making may improve and
revive the planning process
• However, great care has to be taken to support these
analyses by appropriate empirical ground data.
Please visit
www.energyplanning.aau.dk
for information on the international M.Sc. Programme
Sustainable Energy Planning & Management
and www.unigeo.dk
for information on the Geography Programme
at Aalborg University, Denmark