Comparison of three different approaches for the optimization of the CSP plant scheduling

Mario Petrollese, Daniele Cocco, Giorgio Cau, Euro Cogliani

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

Concentrating Solar Power (CSP) is a promising technology for the exploitation of solar energy. The coupling with Thermal Energy Storage (TES) systems allows to shift the generation of CSP plants toward more convenient periods, increasing their capacity factor and dispatch flexibility. The participation of CSP plants in the day-ahead electricity market is therefore potentially feasible with the advantage of increasing their revenues. However, inappropriate control strategies could be adopted due to the uncertainties in solar energy availability and market prices. A comparative analysis is therefore carried out in this paper with the aim of investigating the best approach to deal with the solar energy uncertainty. In particular, three different approaches are compared: deterministic, robust and stochastic. Two different weather forecast services are considered referring to the meteorological conditions occurring in a location near Rome. A 50 MW CSP plant is evaluated as a case study and the Italian electricity spot market is considered. The results show that both robust and stochastic approaches increase the revenues of the CSP plant and minimize the risk of occurrence of unmet energy compared to a deterministic approach. The stochastic approach is strongly influenced by the weather forecast modeling and the consequent distribution of weather forecast uncertainty. The stochastic approach attains the highest profits by adopting weather forecast services characterized by a robust statistical inference. On the other hand, robust optimization achieves highest profits if weather forecast is characterized by low accuracy and more distributed errors.
Original languageEnglish
Pages (from-to)463 - 476
Number of pages14
JournalSolar Energy
Volume150
DOIs
Publication statusPublished - 2017

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All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Materials Science(all)

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