Lithium-ion batteries play a vital role in many systems and applications, making them the most commonly used battery energy storage systems. Optimizing their usage requires accurate state-of-health (SoH) estimation, which provides insight into the performance level of the battery and improves the precision of other diagnostic measures, such as state of charge. In this paper, the classical machine learning (ML) strategies of multiple linear and polynomial regression, support vector regression (SVR), and random forest are compared for the task of battery SoH estimation. These ML strategies were selected because they represent a good compromise between light computational effort, applicability, and accuracy of results. The best results were produced using SVR, followed closely by multiple linear regression. This paper also discusses the feature selection process based on the partial charging time between different voltage intervals and shows the linear dependence of these features with capacity reduction. The feature selection, parameter tuning, and performance evaluation of all models were completed using a dataset from the Prognostics Center of Excellence at NASA, considering three batteries in the dataset.

The push for renewable energy and sustainable development has led to an ever-increasing integration of grid-tied photovoltaic (PV) systems. To maximize revenue, this resource generally operates in maximum power point tracking (MPPT) mode. However, to ensure grid stability and reliability, system operators will continue to introduce new requirements, eventually forcing PV plants to adhere to primary frequency regulation. To perform this task, PV plants will have to be capable of operating outside the MPP and varying their power production, to maintain an active power reserve, according to grid request. This paper presents an innovative model-based (MB) tracking algorithm devoted to supporting power network regulation. Thanks to the updated formulation, the algorithm can vary the power curtailment according to a reduction factor given by the power system operators. Results show the remarkable performance and accuracy of the new algorithm, providing power regulation capability in the range 20% - 100% of the maximum available power. Moreover, the impact of the employment of constant reduction coefficients on the algorithm performances has been evaluated. Validation has been performed on the base of data collected over an observation interval of more than six months. Thanks to its flexibility, this could be the basis for the participation of PV systems to frequency regulation.

Battery aging is a complex phenomenon, and precise state of health (SoH) monitoring is essential for effective battery management. This paper presents a data-driven method for SoH estimation based on support vector regression (SVR), utilizing features built from both full and partial discharge capacity curves, as well as battery temperature data. It provides an in-depth discussion of the novel features constructed from different voltage intervals. Moreover, three combinations of features were analyzed, demonstrating how their efficacy changes across different voltage ranges. Successful results were obtained using the full discharge capacity curves, built from the full interval of 2 to 3.4 V and achieving a mean R² value of 0.962 for the test set, thus showcasing the adequacy of the selected SVR strategy. Finally, the features constructed from the full voltage range were compared with ones built from 10 small voltage ranges. Similar success was observed, evidenced by a mean R² value ranging between 0.939 and 0.973 across different voltage ranges. This indicates the practical applicability of the developed models in real-world scenarios. The tuning and evaluation of the proposed models were carried out using a substantial dataset created by Toyota, consisting of 124 lithium iron phosphate batteries.

Grid-connected photovoltaic (PV) systems are commonly designed for maximum energy production. However, as their presence grows, revised grid regulations increasingly require these systems to partially adhere to the primary frequency regulation (PFR) by reducing their power production when the grid frequency exceeds the nominal value. Nevertheless, full participation in PFR of PV systems, without battery storage, would require operating with a dedicated active power reserve to be utilized in the event of underfrequency conditions. This paper presents a model-based (MB) flexible power point tracking (FPPT) algorithm for PV systems. Results of a microgrid simulation show that the proposed algorithm enables a PV system with a nominal power of 100 kW to maintain a 20 kW active power reserve under various irradiation and temperature conditions, with a relative error lower than 3%. Furthermore, a simulation of the PV system assisting in primary frequency regulation is presented. Thanks to the proposed strategy, when employing the algorithm, the frequency deviation is reduced both in the event of underfrequency and overfrequency conditions. By enabling operation with a constant active power reserve, as well as a quick adjustment of the produced active power, in the event of frequency imbalance, the proposed MB algorithm is an interesting candidate for achieving complete PFR capabilities of PV systems.

This research investigates the economic viability of photovoltaic (PV) systems operating with an active power reserve to provide frequency containment reserve (FCR) services in the Netherlands. For that reason, the potential revenue of a PV system on the day‐ahead (DA) market is compared to the FCR market using market price data from the period of January 1st to October 31st, 2024. Results show that there are 71 out of 304 days in which it is profitable for the considered 49.5 MWp PV system to provide FCR during the selected interval of 12:00–16:00 h. The PV system increased its revenue by €53 661 or 11.34% during these 71 days, which corresponds to a 2.87% increase compared to the total revenue earned on the DA market over the 10‐month interval. Furthermore, aggregating the production of three PV plants reduces variability and increases minimum power output during the same 4‐hour window. The minimum aggregated PV output power is higher than 10% of the nominal value for 85% of considered days, as opposed to 44%–53% for individual sites, while a delta power analysis shows a 22.9%–31.6% reduction in short‐term variability through aggregation, demonstrating its potential to improve FCR provision viability.