Scheduling in concentrating solar power plants based on mixed-integer optimization and binary-regularization

In a one-day ahead energy market scenario, power plant owners have to provide a power generation profile in advance. This power generation problem is addressed in this paper where a new scheduling strategy for concentrating solar power (CSP) plants with thermal energy storage (TES) is proposed. The scheduling method is based on a mixed-integer programming formulation. The main novelty of the proposal is the inclusion of a power block protection method based on a binary-regularization term that penalizes for changes of the power block output and binary constraints that limit the number of daily power block startups. Binary variables are used to avoid penalization of the power block start-up and shutdown. An interesting question is if this protection mechanism affect the economic results of the CSP plant. An evaluation of the economical impact of reducing generation schedule variability due to energy prices and weather forecast is included. The economic study shows that the proposed scheduling method provides a good trade-off between the economic profits obtained from energy sales and the protection of the power block. The study is based on a realistic simulation of a 50 MW parabolic trough collector-based CSP with TES under the assumption of participation in the Spanish day-ahead energy market scenario.