 A MPC approach for optimal generation scheduling in CSP plantsManuel Jesús Vasallo and José Manuel Bravo Applied Energy, 2016, vol. 165, issue C, 357-370 Abstract: Thermal energy storage (TES) systems allow concentrated solar power (CSP) producers to participate in a day-ahead market. Therefore, the optimal power scheduling problem can be posed, whose objective is the maximization of profits derived from electricity sales. The daily generation schedule has to be offered in advance, usually the previous day before a certain time, thus an electricity price and weather forecast must be carried out. This paper proposes a model-based predictive control (MPC) approach for optimal scheduling in CSP plants. This approach has a dual purpose: (1) the periodic update of the generation schedule to track the schedule that has been committed to by means of the most recent electricity price and weather forecast and information about the plant state and (2) the generation of the optimal schedule for the next day. As these two tasks are related, they are performed simultaneously. Therefore, the MPC sliding window is composed of a first time interval to track the committed schedule and a second time interval to generate the next schedule for the following hours. This is then offered as the generation schedule for the next day at the appropriate time. The proposed approach is applied, in a simulation context, to a 50MW parabolic trough collector-based CSP plant with molten-salt-based TES. The chosen criterion to track the committed schedule is the even distribution of the possible generation error within the first interval. A case-study with overestimated initial DNI forecast is undertaken. The results show that the MPC control with short-term DNI forecast significantly improves the above-mentioned objective and allows for a reduction of the deviation from the scheduled generation, when compared with the case without short-term DNI forecast. Keywords: Concentrating solar power plant; Thermal energy storage; Electricity market; Optimized operation strategy; Model-based predictive control; Mixed-integer programming (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:165:y:2016:i:c:p:357-370 Ordering information: This journal article can be ordered from DOI: 10.1016/j.apenergy.2015.12.092 Access Statistics for this article Applied Energy is currently edited by J. Yan More articles in Applied Energy from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.