To reduce emissions in the energy sector and reach worldwide climate goals, further expansion of renewable energy sources (RES) is inevitable. Local opposition has increased in recent years, resulting in the need for more consideration of acceptance issues in the planning process of RES projects. To fill this gap, a method is introduced to consider the dimension of social acceptance in a holistic approach and at an early project stage. In a two-step procedure, a municipal interest profile is created, followed by an examination of possible expansion projects based on the municipal profile. Both hard and soft characteristics of a given project are assessed in combination. Using the example of two potential scenarios for biomass expansion in a given municipality in Germany, the methodology is put to the test. The results show that with the new method House of municipal Energy (HomE), the interest profile of a municipality can be quantified in a comprehensible and transparent way. It is further shown that, depending on the initial objective function of the municipality, different expansion scenarios can be advantageous. In the examined case, the larger biogas plant achieves a higher utility value, since a clearly higher local added value can be generated. A smaller plant, which is only operated with waste materials, is preferable with regard to the required area and lower environmental impact. However, the advantages of the larger plant outweigh those of the smaller plant for the investigated example.

Abstract. Fixed feed-in tariffs based on the Renewable Energy Act grant secure revenues from selling electricity for wind turbine operators in Germany. Anyhow, the level of federal financial support is being reduced consecutively. Plant operators must trade self-sufficiently in the future and hence generate revenue by selling electricity directly on electricity markets. Therefore, uncertain future market price developments will influence investment considerations and may lead to stagnation in the expansion of renewable energies. This study estimates future revenue potentials of non-subsidized wind turbines in Germany to reduce this risk. The paper introduces and analyses a forecasting model that generates electricity price time series suited for revenue estimation of wind turbines based on the electricity exchange market. Revenues from the capacity market are neglected. The model is based on openly accessible data and applies a merit-order approach in combination with a simple agent-based approach to forecast long-term day-ahead prices at an hourly resolution. The hourly generation profile of wind turbines can be mapped over several years in conjunction with fluctuations in the electricity price. Levelized revenue of energy is used to assess both dynamic variables (electricity supply and price). The merit-order effect from the expansion of renewables as well as the phasing out of nuclear energy and coal are assessed in a scenario analysis. Based on the assumptions made, the opposing effects could result in a constant average price level for Germany over the next 20 years. The influence of emission prices is considered in a sensitivity analysis and correlates with the share of fossil generation capacities in the generation mix. In a brief case study, it was observed that current average wind turbines are not able to yield financial profit over their lifetime without additional subsidies for the given case. This underlines a need for technical development and new business models like power purchase agreements. The model results can be used for setting and negotiating appropriate terms, such as energy price schedule or penalties for those agreements.

Abstract. Thanks to the German Renewable Energy Act todays wind turbine operator are dealing with low risk on the revenue side in Germany. Fixed feed-in compensation ensures planning security and high system utilisation. Anyhow, the level of financial support is being reduced consecutively. Therefore, tomorrow’s plant operators have to trade self-sufficiently on European electricity markets hence generate revenue only by marketing electricity. Against the background of uncertain future market developments as well as stagnation in the expansion of renewable energies in Germany, it is of interest to estimate future revenue potentials of those non-subsidized wind turbines. This way investment risks can be reduced and development goals for tomorrow’s wind turbine technology can be deduced. To address this topic, a model has been developed using a modified merit-order approach to forecast long-term day-ahead prices on European electricity markets at an hourly resolution. The model is solely based on open access data. The results show how changes in the German power generation landscape like dismantling of coal and nuclear power plants as well as different emission prices impact the wind turbines potential revenue. A scenario analysis highlights that most of today’s wind turbines are not able to yield financial profit over their lifetime without guaranteed subsidies in Germany. This underlines an urgent need for technical development and new business models. Possible business models could be Power Purchase Agreements (PPA) for which the model results can be used for setting and negotiating appropriate terms, such as an energy price schedule or penalties. Moreover, the results can be used as input for investment calculation and analysis. Hence, the given forecasting model can help to reduce risks on revenue side for plant operators and finally support the expansion of wind energy as a whole.

Thank you for this detailed and very specific review. All points raised are clearly understandable and will be discussed below. The general scope and purpose of the presented model is to deliver macroscopic longterm trend estimates for a given electricity exchange market at a comparatively slim data demand and low computational cost. Along other (partly strong) simplifications there is no spatial resolution of the generation units. Ultimately, this makes it possible to make a statement on the development of electricity exchange prices without having to solve an optimization problem first.

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