Assessing the Optimized Spatial Allocation of Wind Turbines in the German Electricity System S¨onke Bohm August 6, 2016 Doctoral Thesis A thesis submitted in fulfillment of the requirements for the degree of Dr. rer. pol. at the Interdisciplinary Institute of Environmental, Social and Human Sciences of Europa-Universit¨atFlensburg Supervisors: Prof. Dr. Olav Hohmeyer Europa-Universit¨atFlensburg, Germany Prof. Dr. Benjamin K. Sovacool Aarhus University, Denmark University of Sussex, England Centre for Sustainable Energy Systems, Flensburg Europa-Universit¨atFlensburg Assurance according to §5 (3) of the PhD Statutes of Europa-Universit¨atFlensburg Versicherung nach §5 (3) der Promotionsordnung der Europa-Universit¨atFlensburg Ich erkl¨arehiermit an Eides Statt, dass ich die vorliegende Arbeit selbst¨andigund ohne Hilfsmittel angefertigt habe; die aus fremden Quellen (einschließlich elektro- nischer Quellen, dem Internet und m¨undlicher Kommunikation) direkt oder indirekt ¨ubernommenen Gedanken sind ausnahmslos unter genauer Quellenangabe als solche kenntlich gemacht. Insbesondere habe ich nicht die Hilfe sogenannter Promotions- beraterinnen / Promotionsberater in Anspruch genommen. Dritte haben von mir weder unmittelbar noch mittelbar Geld oder geldwerte Leistungen f¨urArbeiten erhalten, die im Zusammenhang mit dem Inhalt der vorgelegten Dissertation stehen. Die Arbeit wurde bisher weder im Inland noch im Ausland in gleicher oder ¨ahnlicher Form einer anderen Pr¨ufungsbeh¨orde vorgelegt. S¨onke Bohm Flensburg, 2016{08{06 i Acknowledgements I would like to express my sincere gratitude to Olav Hohmeyer and Benjamin Sovacool for supervising this thesis and for their valuable and constructive comments during the genesis of this work. I would also like to thank former and current members of the EEM staff at Europa- Universit¨atFlensburg for their valuable inputs to this piece. Particular thanks are due to Dr. Jonathan Mole for his indispensable support. And I would like to thank Anja for her continued support, her exceptional patience and every minute we spend together, and Rune for making me happy and proud. S¨onke Bohm Flensburg, August 2016 iii For Lisa. In loving memory. v The sands of time were eroded by the river of constant change. Genesis, Firth of Fifth vii Executive Summary Scope In energy models a usual approach to simulate the electricity generation from wind power is to relate wind power capacity to measured wind speed time series and a power curve of wind turbine generators (WTGs). In recent work, emphasis has been increasingly put on the spatial distribution of wind power capacity in order to improve modeling results. In this thesis a new approach to model the allocation of WTGs and their power output in future power systems is presented. A new model with a high spatial and temporal resolution has been developed in which the area potentially available for WTGs, space requirements of WTGs and development trajectories of the installed capacity are incorporated in an integrated approach. In the new model, wind power capacity is allocated to the next best locations available, on an annual basis, under consideration of the size development of WTGs and temporal interdependencies of the installed capacity. The approach can be regarded as a vintage model of the WTG stock. For the year of analysis the spatially distributed capacity is then used to model the electricity generation from wind power in a high temporal and spatial resolution. In the context of this work the question was what is the impact of pre-defined area restrictions for wind power installations { given as percentages of the total federal state areas and of the district areas, respectively { on the allocation of the capacity, on its corresponding power production, on its levelized cost of electricity (LCOE) and on the residual load in a specific year of analysis. With the newly developed model, possible development trajectories, i.e. scenarios, were calculated for the showcase of Germany. Although the focus was put on wind power, other variable renewable energies (VRE), namely photovoltaics and run-of-the- river hydro power, were also included in the considerations and it was analyzed how much of Germany's future electricity demand can be covered by power generation from those sources. The scenarios were modeled in several variants. They did not only differ in the total capacity to be allocated but also in area restrictions defined for the allocation of wind power capacity and in the capacity allocation mode applied. Model calculations were conducted for the national and sub-national level { i.e. districts, federal states and transmission grid regions { in order to detect potential regional differences in power pro- duction, in the residual load and in transmission requirements. The modeling approach can be applied as an input to other research activities and the scenarios and scenario ix variants modeled show how renewable energy sources can contribute to Germany's future power supply. Methodology In this thesis the newly developed model is presented. It consists of two main parts (cf. figure 0.1) that are run sequentially: the capacity allocation part and the electricity generation part. In the model, technical data (WTG size development, power curves), economical data (capital expenditures (CAPEX), operation expenditures (OPEX)), me- teorological data (long-term mean wind speeds, wind speed time series), geographical data (areas potentially excluded from wind power use) and scenario data (installed ca- pacity over time) are utilized as fixed inputs. Additionally, variable inputs such as the spacing of WTGs and additional area restrictions for wind power installations in the federal states and in the districts need to be defined. In the model the area potentially available for wind power installations can be further restricted by additional variable model inputs that set a limit of the usable area at the federal state and at the district level. This model feature represents potential limitations set by political decision and it can substantially reduce the area potentially available for wind power installations. Intermediate model results are available for the district level and at the end of each model run results are aggregated for defined transmission grid regions and nationally. Figure 0.1: Basic flow chart of the new model x The model includes three important new features of wind power modeling. First, future wind power installations as defined in scenarios are allocated to the expectedly next best locations available, year by year in a sequential order until 2050. This approach generates an age structure of WTGs in all scenario years, i.e. also in the year of analysis. Second, in the model development trajectories of onshore wind power are considered not only at the national level but also at a sub-national level, i.e. in the showcase of Germany for the federal states. This again allows to detect potential differences between two capacity allocation modes incorporated in the model, meaning an allocation of wind power capacity envisaged at the sub-national level ("state-by-state allocation") and an allocation of the same total capacity amount installed without such sub-national installation targets ("nationwide allocation"). Third, in the model potential additional area restrictions for onshore WTGs can be taken into account, i.e. percentages of the federal state areas and of the district areas to be available for wind power installations at maximum. In the new model, these issues are simultaneously taken into account in an integrated approach. In the first core part of the model the potentially available area in every federal state and district is either limited to the remaining areas as found in the geographical analysis or, if resulting in a lower value, to the maximum area percentages as defined as additional model inputs. The pre-defined wind power capacity from a selected scenario is allocated year by year, i.e. additional new capacity plus repowered capacity, on a square kilometer basis to available areas. Those locations with the highest expected EFLH (onshore) and lowest cost (offshore), respectively, are utilized first and marked as unavailable for the following twenty years. More capacity to be installed is then allocated to the next best locations and so on. Based on an assumed limited service life of WTGs, after twenty years a location is regarded to be available for new installations again. The so-derived allocated capacity acts as the key input to the second core part of the model. In that model part, the capacity newly installed in the years prior to and in the year of analysis year is related to wind speed time series recorded at measuring stations that represent the wind speed conditions in the respective districts and combined with a multi-turbine power curve of a WTG and the respective hub height in the year of installation. The so-derived electricity production time series in all districts can also be spatially and temporally aggregated. Besides wind power, the model includes a simplified simulation module of solar PV and run-of-the-river hydro power, based on historical and future installation figures and historical power production patterns, and the electricity load. xi Table 0.1: Benchmark data of the scenarios modeled Scenario Scenario Target Capacity* Capacity* Capacity* Capacity allocation no. name year mode onshore offshore PV wind wind [GW] [GW] [GWp] 1 Offshore wind leads 2050 39.50 73.20 59.54 nationwide 2 PV leads 2050 54.27 35.50 79.03 nationwide 3 The anticipated 2035 82.40 17.52 60.70 nationwide 3 The anticipated 2035 82.40 17.52 60.70 state-by-state 4 Beyond the anticipated 2050 115.70 73.20 79.03 nationwide 4 Beyond the anticipated 2050 115.70 73.20 79.03 state-by-state *) in the respective year of analysis Results With the new model the technical potential of onshore wind power without further area limitations was detected to range between 401 GW and 702 GW in Germany, depending on the assumed spacing of WTGs.