A Two-Stage Heuristic Procedure for Solving the Long-Term Unit Commitment Problem with Pumped Storages and Its Application to the German Electricity Market

Franz, Alexander; Zimmermann, Jürgen

doi:10.1007/978-3-319-55702-1_21

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“…A two-stage hierarchical methodology is used in contrast to the often monolithic solution methods for pure UCPs (e.g., Kazarlis et al 1996;Carrión and Arroyo 2006;Delarue et al 2013;Morales-España et al 2013, or the survey in Saravanan et al 2013. The problem is divided into the optimization of thermal plants as well as the planning and coordination of hydro storages (see Franz and Zimmermann 2018b).…”

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confidence: 99%

A long-term unit commitment problem with hydrothermal coordination for economic and emission control in large-scale electricity systems

2020

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The paper describes a long-term scheduling problem for thermal power plants and energy storages. In addition, renewable energy sources are integrated by considering the residual demand. Besides the classical minimization of the production costs, emission-related costs are taken into account. Thereby, emission costs are determined by market prices for CO 2 emission certificates (i.e., using the EU emissions trading system). For the proposed unit commitment problem with hydrothermal coordination for economic and emission control, an enhanced mixed-integer linear programming model is presented. Moreover, a new heuristic approach is developed, which consists of two solution stages. The heuristic first performs an isolated dispatching of thermal plants. Then, a re-optimization stage is included in order to embed activities of energy storages into the final solution schedule. The considered approach is able to find outstanding schedules for benchmark instances with a planning horizon of up to one year. Furthermore, promising results are also obtained for large-scale real-world electricity systems. For the German electricity market, the relationship of CO 2 certificate prices and the optimal thermal dispatch is illustrated by a comprehensive sensitivity analysis.

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