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International audienceThis study explores the possibility to calibrate the estimation of the global horizontal irradiation provided by HelioClim-3, a satellite-based surface solar irradiation database (available at www.soda-is.com). The main objective of this work is to refine such an estimation whose performances differ from one site to another. A first processing of the long-term measurements provided by nine weather stations located in Provence-Alpes-Côte d'Azur Region (South France) leads to the characterization of the clearness index error variability for that Region: this parameter is made up of a bias, a drift and 3 sinusoids with periods respectively equal to the astronomical year, half a year and one third of a year. We show that the phase of the dominant frequency (365 days) is similar whatever the tested site. We propose a simple calibration procedure based on a linear regression whose performances, in terms of mean bias error and root mean square error, depend on the beginning and the duration of the measurement campaign; to illustrate this point, the mean bias error on the global horizontal irradiation for nine sites considered systematically goes below 3% when considering a 6-month measurement campaign starting in May. We also show that the performances of the proposed calibration are also applicable to another site in the same Region for which the initial error exceeds 13%. A graphical representation allows visualizing the characterization of these measurement campaigns depending on the expected accuracy

The increasing share of photovoltaic (PV) power in the global energy mix presents a great challenge to power grid operators. In particular, PV power's intermittency caused by varying weather conditions can lead to mismatches between energy production and expectation. Battery Energy Storage Systems (BESS) are often put forward as a good technological solution to these problems, as they are able to mitigate PV power forecast errors. However, the investment cost of such systems is still high, which questions the benefits in relation to the cost of using these systems in operational contexts. In this paper, we compare several strategies to manage a PV power plant coupled with a BESS in a market environment. They are obtained by stochastic optimization using a Model Predictive Control (MPC) approach. This paper proposes an approach that takes into account the aging of the BESS, both at the day-ahead level and in the real-time control of the BESS, by modeling the cost associated with BESS usage. As a result, the BESS arbitrates between compensating forecast errors and preserving its own life expectancy, based on both PV production and price scenarios derived from probabilistic forecasts. A sensitivity analysis is also carried out to provide guidelines on the optimal sizing of the BESS capacity, depending on market characteristics and BESS prospective costs.

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International audienceThis paper presents the benchmarking of different Typical Meteorological Year (TMY) datasets applied to a Concentrated-PV (CPV) system. Using 18-years of high quality meteorological and pyranometric ground measurements, five types of TMY datasets were generated using variable time period and following different methods: the standard Sandia method or only considering the Direct Normal Irradiation (DNI) or a more sophisticated DNI-based driver considering the characteristics of the CPV system. The results show that the Sandia method is not suitable for CPV systems. The TMY datasets obtained using dedicated drivers are more representative to derive TMY datasets from limited long-term meteorological dataset