Browsing by Person "Butemann, Hendrik"

Now showing 1 - 1 of 1

Modelle und Lösungsverfahren zur langfristigen Planung der Stromproduktion einer flexiblen Biogasanlage unter Berücksichtigung von Verschleiß
(2021) Butemann, Hendrik; Schimmelpfeng, Katja
One of the most important measures against climate change is the shift from fossil to renewable energies. Many countries have therefore made it their goal to increase the share of renewable energies for electricity generation. In Germany, the share in 2019 was 40.2%, of which biomass accounted for 20.6%. This category includes biogas plants, which, unlike other sources of renewable energy, have the advantage of not being dependent on certain weather conditions. They are considered a flexible option for electricity generation because they can produce electricity when neither the sun is shining nor the wind is blowing. When the first biogas plants were put into operation, revenues from electricity production could be maximized by having the combined heat and power unit (CHP) associated with the biogas plant generate electricity continuously. To take advantage of the flexibility of biogas plants, German legislators introduced premiums that contained incentives to produce electricity during periods of low supply from other renewable energy sources. Since then, biogas plant operators have been able to maximize their revenues when the CHP produces electricity on demand, i.e., in start-stop mode. However, a large number of starts and stops of the CHP causes altered wear and tear and must be taken into account in the long-term planning of the electricity production of a biogas plant. The aim of this dissertation is therefore to use operations research methods to develop cyclical electricity production plans for biogas plants that take into account the wear and tear of the CHP and the timing and costs of maintenance activities in order to support biogas plant operators in maximizing their revenues. For this purpose, first a classification of electricity production planning of biogas plants into the planning tasks along the biomass-based supply chain is given. Subsequently, the basics of biogas plants are explained, which include their relevance in Germany, their way of operation, service and maintenance as well as the legal framework for their operation. The research gap, which is filled by this dissertation, results from the literature review on quantitative approaches for the operation of biogas plants. It shows that there is still no research work that sufficiently addresses the wear and tear of CHP in flexible operation and the planning of maintenance activities in connection with electricity production. Therefore, a conceptual optimization model is developed that accurately replicates the non-linear wear that occurs in reality and thus enables simultaneous planning of electricity production and maintenance activities. For better applicability with standard solvers, the model is additionally linearized. A case study based on real-world data reveals that a flexible biogas plant achieves higher total revenues than a continuously operated biogas plant under the conditions prevailing in Germany, even when maintenance costs are taken into account. The conceptual optimization model is then extended to produce a cyclical plan that biogas plant operators can apply on a weekly basis. In the following chapter, a greedy heuristic for generating a starting solution as well as a genetic algorithm and a tabu search are developed with the goal of reducing the computation time when solving the extended model. For this purpose, the basics of the individual solution methods are first explained and the input data are adapted to the problem with the help of parameter tuning. An extensive numerical study, in which the input parameters electricity prices, costs for maintenance activities, wear and tear of the CHP and biogas storage capacity are varied, compares the performance of the methods with that of the extended optimization model. In all scenarios, the tabu search determines the best result in low runtime. A summary and an outlook on further research opportunities conclude the dissertation.