Calculating the profits of an economic MPC applied to CSP plants with thermal storage system

Abstract

Electricity producers participating in a day-ahead energy market aim to maximize profits derived from electricity sales. The daily generation schedule has to be offered in advance, usually the previous day before a certain moment in time. The development of an economically-optimal generation schedule is the core of the generation scheduling problem. To solve this problem, renewable energy plant owners need, besides energy prices forecast, weather prediction. Among renewable energy sources, concentrated solar power (CSP) plants with thermal energy storage (TES) may find it easier to participate in electricity markets due to their semi-dispatchable generation. In any case, the limited accuracy of forecasting solar resource brings about the risk of penalties that may be imposed to CSP plants for deviation from the submitted schedule. This paper proposes a model-based predictive control (MPC) approach with an economic objective function to tackle the scheduling problem in CSP plants with TES. By this approach, the most recent forecast and the current status of plant can be used by the proposed economic MPC approach to reschedule the generation conveniently at regular time intervals. On the other hand, a more feasible generation schedule for the next day is performed at the appropriate time thanks to the use of short-term forecast. The proposed approach is applied, in a simulation context, to a 50 MW parabolic trough collector-based CSP plant with TES under the assumptions of perfect price forecasts and participation in the Spanish day-ahead energy market. A case study based on a half-year period to test several meteorological conditions is performed. In this study, an economic analysis is carried out using actual values of energy price, penalty cost, solar resource data and its day-ahead forecast. Results show an economic improvement in comparison with a traditional day-ahead scheduling strategy, especially in periods with a bad weather forecast. To overcome the lack of short-term weather forecast data for this study, a synthetic short-term predictor, whose accuracy level can be tuned by means of a parameter, is used. Sweeping this accuracy level between the situation with no forecast improvement and perfect shortterm forecast, the MPC strategy reaches an improvement in total profits during the six months period between 13.9% and 33.3% of the maximum room for improvement. This maximum ideal improvement is defined as the difference in profits between the MPC strategy with perfect forecasts and the dayahead scheduling strategy.