José Manuel Bravo

Summary: This paper proposes the use of model predictive control (MPC) with binary‐regularization to manage the electric power generation problem in concentrating solar power plants with thermal energy storage. The main advantage of the of MPC with binary‐regularization formulation is the inclusion of a power block protection method based on a binary‐regularization term that penalizes power generation variation (also called generation cycling) differently according to the power block situation, ie, normal operation, startup, or shutdown. This distinction simplifies the choice of schedules with reduced variation and high energy sale profits. The interest in this reduction is the achievement of a higher lifetime of the power block elements, lower maintenance costs, and easier plant operability. A benefit of the generation scheduling based on MPC is the capacity of rescheduling the power generation at regular periods, taking advantage of the most recent energy prices and weather forecast, and of the plant's current state. An interesting question is if the proposed protection mechanism affects the economic results of the MPC black strategy. In this regard, an economic study based on a realistic simulation of a 50 MW parabolic trough collector‐based concentrating solar power plant with thermal energy storage, under the assumption of participation in the Spanish day‐ahead energy market scenario, is included. Realistic values for actual and forecasted solar resource and for energy price are used, and for penalties for deviation from the committed generation schedule. The economic study shows that the proposed scheduling method provides an important reduction of the generation cycling without decreasing energy sales profits. Another advantage of the proposed method is the possibility of estimating the highest level of power block protection, which maintains the profits by means of historical data, which favors its practical implementation.

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.

This paper proposes to use model predictive control with binary-regularization (MPC ^BR ) to manage the generation problem in concentrating solar power (CSP) plants with thermal energy storage (TES). The main advantage of the proposed method is to allow rescheduling the generation at regular periods taking advantage of the most recent energy prices and weather forecast, and of the current plant's state. A second benefit of the MPC ^BR formulation is the inclusion of a turbine protection method based on a binary-regularization term that penalizes for changes of the turbine output and binary constraints that limit the number of daily turbine startups. Binary variables are used to avoid penalization of the turbine start-up and shutdown. These extensions of the classical MPC approach aim to preserve the turbine lifetime. An interesting question is if this protection mechanism affect the economic results of the CSP plant. An included 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 generation 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.

In this work, a model-based predictive control (MPC) approach with turbine protection is proposed to face the scheduling problem in concentrating solar power (CSP) plants with thermal energy storage (TES). This approach allows rescheduling the generation at regular periods taking advantage of the most recent energy prices and weather forecast, and of the current plant's state. Moreover, a more workable generation schedule for the next day is carried out as a result of that rescheduling. The turbine protection is based on the penalization for changes of the turbine output and the limitations of the number of daily turbine startups. These mechanisms aim to preserve the turbine lifetime. However, these constraints limit the flexibility of the electricity generation, therefore they could worsen the economic profits derived from sales of electricity. In conclusion, the proposed scheduling method provides a trade-off between the economic profits obtained from energy sales and the protection of the generation block. The proposed approach is applied, in a simulation context, to a 50 MW parabolic trough collector-based CSP with TES under the assumption of participation in the Spanish day-ahead energy market. A case study based on a one-month period is performed. The simulation results show, for several levels of turbine protection, the changes in generation profiles and the reduction of economic profits. It can also be observed that the reduction of economic profits is not significant. This result encourages the use of the turbine protection in the MPC approach.

Concentrated solar power (CSP) plants that include thermal energy storage (TES) are an emerging renewable energy source due their dispatch capabilities similar to fossil fuel power plants. As such, it can be of interest to introduce students to its work mechanism and particularities. A simulator for a CSP plant with TES has been built as a teaching tool in order to provide a clear picture of the system. The simulation is based on the model of a 50 MW parabolic trough collector-based CSP plant with molten-salt-based TES. The plant is modeled as a mixed-integer linear-program, with the software implementation in Matlab.

The present paper aims at presenting the methodology and first results of a detection system of risk of diabetic macular edema (DME) in fundus images. The system is based on the detection of retinal exudates (Ex), whose presence in the image is clinically used for an early diagnosis of the disease. To do so, the system applies digital image processing algorithms to the retinal image in order to obtain a set of candidate regions to be Ex, which are validated by means of feature extraction and supervised classification techniques. The diagnoses provided by the system on 1058 retinographies of 529 diabetic patients at risk of having DME show that the system can operate at a level of sensitivity comparable to that of ophthalmological specialists: it achieved 0.9000 sensitivity per patient against 0.7733, 0.9133 and 0.9000 of several specialists, where the false negatives were mild clinical cases of the disease. In addition, the level of specificity reached by the system was 0.6939, high enough to screen about 70% of the patients with no evidence of DME. These values show that the system fulfils the requirements for its possible integration into a complete diabetic retinopathy pre-screening tool for the automated management of patients within a screening programme.