This paper analyses the degradation that is experienced by different types of Li-ion batteries when used as home solar storage systems controlled to minimize the electricity bill of the corresponding household. Simulating the annual operation of photovoltaic (PV) residential systems with batteries at different locations was undertaken to perform the study and it uses actual consumption values and real PV production profiles, as well as validated semi-empirical ageing models of the batteries. Therefore, the work provides a realistic prognosis around the lifetime expectancies for the different Li-ion chemistries.

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This work analyses the minimum energy capacity requirements to be demanded to battery energy storage systems used in megawatt-range merchant solar PV plants to grant capacity firming. The operation of such a plant is simulated (with a 2-minute time step, at three different locations of the Iberian Peninsula, and for different battery sizes) after solving a quadratic programming optimization problem. The control algorithm takes into account the irradiance forecast and the intraday electricity market configuration, which presents certain peculiarities in the Iberian region with regard to other European markets. The analysis has been performed in an annual basis and current irradiance measured values have been used.

This paper introduces deep learning-based forecasting models for the continuous prediction of the aggregated production generated by CSP plants in Spain. These models use as inputs the expected top of atmosphere irradiance values and available weather conditions forecasts for the locations where the main CSP power plants are installed. The performances of the forecast models are analysed and compared by means of the most extended metrics in the literature for a whole year of CSP energy production.

This article considers the introduction of Li‐ion batteries in photovoltaic power plants to firm their energy production and analyzes the dependence of their degradation on the structure of the electricity market where the power production is traded. The operation of the batteries is decided as a result of successive optimization problems that benefit from the use of deep‐learning‐based irradiance forecasting tools with low prediction error, which allows the batteries to keep a small size. In addition, state‐of‐the‐art battery aging models are handled to derive a realistic lifetime prognosis. The simulation results obtained by using real data from three European locations, which have different irradiance patterns and market structures, show how the proposed control strategy makes it possible to decouple the saturation rate of the batteries from the climatic conditions of the plant location. Furthermore, regarding the market structure, the results show that the shorter the energy block and the closer the lead time, the lower is the degradation of the batteries.

Este trabajo define una estrategia de operación basada en Control Predictivo basado en Modelo (Model Predictive Control, MPC) para instalaciones fotovoltaicas con baterías instaladas en el sector terciario/comercial. La propuesta incluye modelos de predicción de la irradiancia a futuro y modelos de consumo de las cargas basados en técnicas de agrupamiento. La operación del sistema se simula durante un año entero con datos de irradiancia y consumo reales para un centro comercial situado al sur de España. Finalmente, se analiza la rentabilidad del sistema, en términos de vida útil de las baterías requeridas para lograr el retorno de la inversión realizada, para diferentes combinaciones de sistema FV, tamaño de las baterías, y precios de la energía.