Increased renewable energy penetration in isolated power systems has a clear impact on the quality of system frequency. The flywheel energy storage system (FESS) is a mature technology with a fast frequency response, high power density, high round-trip efficiency, low maintenance, no depth of discharge effects, and resilience to withstand continuous charge-discharge cycling without lifetime degradation. These FESS properties allows to effectively address the frequency quality problem. This study analyzes the contribution of a FESS to reducing frequency deviations in an isolated system that combines a diesel plant, wind farm, and pump-storage hydropower plant based on the El Hierro power system. This study approaches this analysis by comparing six different FESS governor control schemes (GCSs). Of these six GCSs, the nonlinear proportional variant (NLP $_{\mathrm {V}}$ ) is a singular contribution based on the NLP scheme previously developed by the same researchers. Different governor’s parameter settings for the FESS GCSs were also compared, obtained from the proposed tuning methodology that considers the renewable energy generation distribution, frequency impact, and lifetime degradation of diesel, hydraulic groups, and flywheels. The GCSs were compared in terms of average frequency deviation, Zenith and Nadir frequency difference, wear and tear of diesel electromechanical elements and Pelton turbine nozzles, flywheels cycles per hour, and FESS average state of charge. The results show that including a FESS plant considerably improves frequency regulation. The tuning criteria and GCSs have a clear influence on the results, with NLP and NLP _V GCSs offering relevant improvements in frequency deviations.

Over the last two decades, variable‐speed wind turbines (VSWTs) have gradually replaced conventional generation. However, the variable and stochastic nature of wind speed may lead to large frequency deviations, especially in isolated power systems with high wind energy integration, where this integration causes a lack of inertia. This paper proposes a hybrid hydro‐wind‐flywheel frequency control strategy for isolated power systems with 100% renewable energy generation, considering both variable wind and a generator tripping. VSWTs and flywheels include a conventional inertial frequency control. The frequency control strategy involves VSWTs rotational speed and State of Charge (SOC) of flywheels variations that may affect the wear and tear of mechanical elements and reduce the efficiency of the frequency control action. The hydro‐power controller also tracks the VSWTs' rotational speed deviation and the flywheel SOC to modify the generated power accordingly. This hybrid frequency strategy significantly reduces frequency excursions, the rotational speed deviations of VSWTs and the SOC of flywheels. To reduce the hydro‐power plants' wear, an additional control strategy is proposed by the authors and evaluated. Results from a case study based on an isolated power system located in El Hierro (Canary Islands, Spain) are included, and extensively discussed in the paper.

The efficient use of the photovoltaic power requires a good estimation of the PV generation. That is why the use of good techniques for forecast is necessary. In this research paper, Long Short-Term Memory, Bidirectional Long Short-Term Memory and the Temporal convolutional network are studied in depth to forecast the photovoltaic power, voltage and efficiency of a 1320 Wp amorphous plant installed in the Technology Support Centre in the University Rey Juan Carlos, Madrid (Spain). The accuracy of these techniques are compared using experimental data along one year, applying 1 timestep or 15 min and 96 step times or 24 h, showing that TCN exhibits outstanding performance, compared with the two other techniques. For instance, it presents better results in all forecast variables and both forecast horizons, achieving an overall Mean Squared Error (MSE) of 0.0024 for 15 min forecasts and 0.0058 for 24 h forecasts. In addition, the sensitivity analyses for the TCN technique is performed and shows that the accuracy is reduced as the forecast horizon increases and that the 6 months of dataset is sufficient to obtain an adequate result with an MSE value of 0.0080 and a coefficient of determination of 0.90 in the worst scenarios (24 h of forecast).