Optimal Spatial Allocation of Wind Turbines Taking Externalities into Account
Figure 2: Welfare-optimal Levels of the Three Relevant Attributes as Functions of the Price Factors fL and fD* Red kite loss (% in 20 years)
Minimum distance (m) 10 10 1
0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6
0.1 0.1 1 Price factor red kite loss fL Red kite loss important 10
800 850 900 950
1
1,000 1,050 1,100
1 10
690 700 710 720 730 740
Production cost (€ million)
0.1 0.1 1 Price factor red kite loss fL Red kite loss important 10
0.1 0.1 1 Price factor red kite loss fL Red kite loss important
The three relevant attributes are red kite population loss (left panel), minimum distance between wind turbines and settlements (centre panel) and total production cost (right panel), indicated by colour. *See text for details.
Welfare-optimal Allocation and Extent of Externalities The optimisation algorithm identifies those sites that produce a desired amount of electricity at the lowest social cost, where social cost is defined as the sum of production and external costs. For the considered study region, the welfare-optimal allocation of wind turbines for an electricity production rate of 690GWh per annum is characterised by the following attributes:
• minimum distance between wind turbines and settlements: 1,025m; • •
red kite loss rate: 1.2% over 20 years; and production costs: €730 million over 20 years.
These levels are indicated by blue circles in Figure 2, which shows how the welfare-optimal levels of these attributes depend on the magnitude of the associated MWTP. To explore this dependency we
multiplied the measured MWTP for red kite protection with a factor fL, which varies from 0.1 (left) to 10 (right), and the measured MWTP for
the minimum distance to settlements with a factor fD, which varies from 0.1 (bottom) to 10 (top). This means that the lower left corner of
each panel (red circles: fL=fD=0.1) corresponds to a case of very low MWTP. At this point, the optimal red kite loss rate is 2.6% over 20 years, the minimum distance between wind turbines and settlements is 800m and the production costs are €690 million over 20 years.
Low MWTP means that externalities are regarded as unimportant, so the red circles in Figure 2 represent a scenario where the allocation of the wind turbines is determined by minimising the production cost and ignoring the externalities. As a consequence, the production costs for the red scenario are smaller than those for the blue scenario, in which the allocation of wind turbines is determined by minimising the sum of production and external costs. The difference is €40 million over 20 years (see Figure 2, right panel). However, since the externalities are ignored in the red scenario, the resulting externalities are higher than
1. 2.
Meyerhoff J, Ohl C, Hartje V, Landscape externalities from onshore wind power, Energ Pol, 2010;38(1): 82–92.
Ladenburg J, Onshore and offshore locations for wind power development – what does the public prefer and should
in the scenario indicated by the blue circle. In monetary terms, Figure 1 shows that the external costs increase from the blue scenario to the red scenario by €210 million over 20 years. Altogether, ignoring the externalities saves €40 million of production costs but increases external costs by €210 million. This means that ignoring the externalities reduces social welfare by €170 million.
Are Externalities Decisive?
He argued that knowledge of the externalities of renewables such as wind power provides important information for energy planners. Not taking them into account could lead to welfare losses and negative effects on long-term acceptance of wind power generation. On the other hand, he stressed that knowledge of externalities gained by stated preference studies alone does not point out the optimal location of wind turbines. The approach presented here integrates information about externalities from stated preference studies into an ecological–economic modelling framework. It allows the optimal spatial location of wind turbines to be determined, taking externalities into consideration. As the results for Westsachsen show, the externalities caused by the turbines are significant and clearly influence the spatial allocation of turbines. This is despite the fact that the level of wind power generation within the Westsachsen planning region is still modest compared with regions that are much more intensively used for wind power generation, such as coastal areas. Thus, in regions with more wind power production facilities, higher levels of externalities would be expected. Whether this leads to a greater influence of the externalities on the optimal spatial allocation in these regions is an open question, because the influence depends on the spatial variation of the two considered cost factors: the larger the spatial variation of the external costs compared with the spatial variation of the production costs, the higher the relevance of externalities for optimal spatial allocation. n
Recently, Ladenburg raised the question of whether preferences should matter.2
it matter?, Modern Energy Review, 2009;1:32–4. 3.
Drechsler M, Ohl C, Meyerhoff J, et al., A modelling approach for allocating land-use in space to maximise social welfare, exemplified on the problem of wind power generation, UFZ Discussion Paper, Leipzig, June 2010. Available at:
4.
www.ufz.de/index.php?de=14487
Holmes TP, Adamowicz WL, Attribute-Based Methods. In: Champ PA, Boyle KJ, Brown TC (ed.), A Primer on Nonmarket Valuation, Dordrecht: Kluwer, 2003;171–219.
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MODERN ENERGY REVIEW – VOLUME 2 ISSUE 2
Minimum distance important Price factor minimum distance fL
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