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A GIS-Based Decision Support System for the Optimal Siting of Wind Farm Projects Yasin Sunak, Tim Höfer, Hafiz Siddique, Reinhard Madlener, Rik W

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A GIS-Based Decision Support System for the Optimal Siting of Wind Farm Projects Yasin Sunak, Tim Höfer, Hafiz Siddique, Reinhard Madlener, Rik W E.ON Energy Research Center Series A GIS-based Decision Support System for the Optimal Siting of Wind Farm Projects Yasin Sunak, Tim Höfer, Hafiz Siddique, Reinhard Madlener, Rik W. De Doncker Volume 7, Issue 2 E.ON Energy Research Center Series ISSN: 1868-7415 First Edition: Aachen, June 2015 E.ON Energy Research Center, RWTH Aachen University Mathieustraße 10 52074 Aachen Germany T +49 (0)241 80 49667 F +49 (0)241 80 49669 [email protected] www.eonerc.rwth-aachen.de E.ON Energy Research Center Series A GIS-based Decision Support System for the Optimal Siting of Wind Farm Projects Yasin Sunak, Tim Höfer, Hafiz Siddique, Reinhard Madlener, Rik W. De Doncker Volume 7, Issue 2 Table of Contents 1 INTRODUCTION ...............................................................................................................................2 1.1 Goals of the project .............................................................................................................................. 3 1.2 Positioning of the project within the E.ON ERC strategy ....................................................................... 4 2 WIND FARM SITING USING A SPATIAL ANALYTIC HIERARCHY PROCESS APPROACH: A CASE STUDY OF THE STÄDTEREGION AACHEN, ...........................................5 2.1 Background ........................................................................................................................................... 5 2.2 Multi-Criteria Decision-Making ............................................................................................................. 8 2.3 Data and AHP Framework ................................................................................................................... 10 2.3.1 Data and study area .......................................................................................................................... 10 2.3.2 Methodological framework ............................................................................................................... 11 2.3.3 Exclusion area.................................................................................................................................... 12 2.3.4 AHP criteria and rated area ............................................................................................................... 12 2.3.5 AHP weights ...................................................................................................................................... 20 2.3.6 Suitable area ..................................................................................................................................... 21 2.4 Results ................................................................................................................................................ 22 2.4.1 AHP .................................................................................................................................................... 22 2.4.2 Sensitivity analysis ............................................................................................................................. 27 3 MICROSITING OF ONSHORE WIND FARMS – ECONOMICALLY OPTIMAL LAYOUT AND DESIGN ................................................................................................................................ 30 3.1 Background ......................................................................................................................................... 30 3.2 Parametrization of Optimization Model ............................................................................................. 31 3.2.1 Location and wind data ..................................................................................................................... 31 3.2.2 Wind turbine specifications and installation costs............................................................................ 34 3.2.3 Wake model ...................................................................................................................................... 38 3.3 Optimization Model ............................................................................................................................ 40 3.3.1 Optimization criterion ....................................................................................................................... 40 3.3.2 Optimization algorithm ..................................................................................................................... 41 3.3.3 Implementation of optimization process .......................................................................................... 45 3.4 Results ................................................................................................................................................ 46 4 CONCLUSIONS ............................................................................................................................. 51 REFERENCES ...................................................................................................................................... 53 DATA DESCRIPTION ........................................................................................................................... 60 APPENDIX ............................................................................................................................................ 62 List of Figures ................................................................................................................................................. 62 List of Tables .................................................................................................................................................. 62 Publications generated by the project ............................................................................................................ 63 Short CV of scientists involved in the project ................................................................................................. 63 Project timeline .............................................................................................................................................. 64 Activities within the scope of the project ....................................................................................................... 64 Executive Summary The ambitious expansion targets for the use of renewable energies of the German federal government also foster the further diffusion of wind farms throughout Germany. However, although this further spread of wind turbines for the generation of renewable electricity is widely supported on the more general level, it is nevertheless frequently confronted with low acceptance, and sometimes even severe resistance and public protests, on the local level. This increasing gap between the social acceptance on the more general and the more local level stems from various adverse effects on landscape, noise level, and wildlife. As a matter of fact, however, the currently prevailing wind farm siting assessments and processes do not adequately incorporate the social acceptance dimension of this technology. In this research project, a holistic multi-criteria spatial decision support system has been developed which specifically emphasizes social acceptance-related issues. The analysis is subdivided into two parts. The aim of the first part is to find the most suitable sites for wind farms on a macro level, considering economic, socio-political, and environmental criteria. In order to achieve this objective, a GIS-based Analytic Hierarchy Process (AHP) approach is used in which local experts from wind energy-related fields are asked to pairwise compare the incorporated criteria in order to derive the relative importance of each criterion. A comparison with the location of existing wind farms and a sensitivity analysis validate the reliability and accuracy of the model results. The aim of the second part of the analysis is to optimize the wind farm layout on the micro level in terms of maximizing the technical efficiency and the profitability. Therefore, in the micro-siting model developed, the gradient- based optimization algorithm is applied which optimizes the number and arrangement of wind turbines of a wind farm. The most important optimization criteria for the micro-siting model are the prevalent wind direction and the wake effects among wind turbines. The best wind farm layout is, thereby, determined by calculating the levelized cost of electricity (LCOE), which is the ratio between the total discounted costs and the expected yearly energy yield of the entire wind farm. This micro-siting model is applied to the most suitable areas, which are identified in the first part of this report. The merit of the presented methodology is that a systematic, very comprehensive and consistent siting assessment is developed, which combines the pre-assessment of potential wind farm sites on the macro level with an algorithm-based optimization approach for the micro siting of wind turbines. The successful application of the methodology to the Städteregion Aachen, a region in western Germany, demonstrates the effectiveness of this approach and illustrates that an adaptation and application to wind farm projects in other regions, and even the adaptation to other energy conversion technologies, is both feasible and promising. 1 1 Introduction In the light of climate change, rising energy prices, and increasing concerns about the security of energy supply, countries such as Germany
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