Power electronic-interfaced renewable energy sources (RES) exhibit lower inertia compared to traditional synchronous generators. The large-scale integration of RES has led to a significant reduction in system inertia, posing significant challenges for maintaining frequency stability in future power systems. This issue has garnered considerable attention in recent years. However, the existing research has not yet achieved a comprehensive understanding of system inertia and frequency stability in the context of low-inertia systems. To this end, this paper provides a comprehensive review of the definition, modeling, analysis, evaluation, and control for frequency stability. It commences with an exploration of inertia and frequency characteristics in low-inertia systems, followed by a novel definition of frequency stability. A summary of frequency stability modeling, analysis, and evaluation methods is then provided, along with their respective applicability in various scenarios. Additionally, the two critical factors of frequency control—energy sources at the system level and control strategies at the device level—are examined. Finally, an outlook on future research in low-inertia power systems is discussed.

In this paper, a solar PV (Photovoltaic) array, a battery energy storage (BES), a diesel generator (DG) set and grid-based EV charging station (CS) is utilized to provide the incessant charging in islanded, grid connected and DG set connected modes. The charging station is primarily designed to use the solar photovoltaic PV array and a BES to charge the electric vehicle (EV) battery. However, in case of exhausted storage battery and unavailable solar PV array generation, the charging station intelligently takes power from the grid or DG (Diesel Generator) set. However, the power from DG set is drawn in a manner that, it always operates at 80-85% loading to achieve maximum fuel efficiency under all loading conditions. Moreover, in coordination with the storage battery, the charging station regulates the generator voltage and frequency without a mechanical speed governor. It also ensures that the power drawn from the grid or the DG set is at unity power factor (UPF) even at nonlinear loading. Moreover, the PCC (Point of Common Coupling) voltage is synchronized to the grid/ generator voltage to obtain the ceaseless charging. The charging station also performs the vehicle to grid active/reactive power transfer, vehicle to home and vehicle to vehicle power transfer for increasing the operational efficiency of the charging station. The operation of the charging station is experimentally validated using the prototype developed in the laboratory.

In this paper, an implementation of solar photovoltaic (PV) array powered grid connected, residential electric vehicle (EV) charger is presented, which caters the need of an EV, household loads and the grid. The charger is enabled to operate autonomously using PV array for providing an uninterruptible charging and power to household loads. However, in the absence of the PV array or insufficient PV array generation, the grid connected mode of operation is presented. Moreover, the charger is supported with the synchronization and seamless mode switching control, so that the charger automatically connects/disconnects from the grid without disturbing the EV charging and household supply. The charger is also enabled with the vehicle-to-grid (V2G) active/reactive power support to the grid and vehicle-to-home (V2H) power transfer for supporting the local loads in an islanded condition. The charger is also controlled to operate as an active power filter for achieving the unity power factor (UPF) operation and total harmonic distortion (THD) of grid current within 5%. Moreover, for achieving energy management, a dc link voltage regulation based energy management strategy is used and a sliding mode control (SMC) is used for regulating the dc link voltage. For satisfactory operation under distorted voltage condition, a second order generalized integrator frequency locked loop with dc offset rejection (SOGI-FLL-DR), is used to generate the sinusoidal reference grid current. The charger is designed for a single phase 230V, 50Hz grid and it is experimentally validated in the laboratory.

This paper deals with a comprehensive study of configurations of microgrid (MG) based on variable speed generators. The selection criteria for these configurations along with the design of required control algorithms, are presented in detail. Simulation results and experimental validation of selected MG configurations are given to demonstrate their suitability. It is aimed to enhance applications of MGs with main emphasis on renewable energy sources to ensure sustainable development in rural and remote areas.

This paper proposes a fast repetitive control (FRC) scheme with harmonic correction loops for the three-phase three-wire shunt active power filter (APF) applied in weak power grid. The FRC scheme consists of a repetitive control loop which is designed in the synchronous rotational frame (SRF) and a fractional delay (FD) filter for approximating the fractional delay caused by the fixed sampling rate. It can significantly improve the dynamic performance for the harmonic compensation. In weak grid situation, the grid frequency, voltage and then harmonic currents would vary rapidly with disturbances. A cumulative error cancellation loop is introduced into the FRC to improve the harmonic detection accuracy when grid frequency drifts. And the harmonic correction loops are proposed to correct the harmonic references with selected orders when they vary rapidly with the grid voltage. With such loops, the compensation precision of the shunt APF can be highly improved. Simulation and experiment results verified the effectiveness of the proposed scheme.

The intermittent and stochastic nature of solar energy generation systems, climate change, and the inefficiency of modern power systems due to zero inertia have created many challenges for on-grid operators. Solar forecasting systems based on machine learning algorithms are an emerging and effective solution that uses Big Data (historical data) related to weather phenomena. However, the predictive ability of these algorithms is hampered by the sporadic nature of solar energy generation. In this paper, a robust hybrid machine learning system that utilizes multiple linear regression (MLR) and a Pearson correlation coefficient (PCC) was tested on solar power plant sites of varying capacities in Germany (100-8500 kW). The volume of big-data features can be reduced by focusing on the features that significantly improve the reliability of the mid-term forecasting system. In this way, drastic fluctuations in the prediction of photovoltaic (PV) power generation can be avoided. The results of our approach are evaluated regarding real-world data using the extreme gradient boosting (XGBoost) with feature engineering, and principal component analysis (PCA), in order to forecast PV energy, rank and track the importance of feature engineering for different PV capacities. Furthermore, we found that the need for selectivity and reduction of performance error was supported by ridge regression (L2 regularization). In addition, the proposed novel XGBoost forecast system decreased the root mean square error (RMSE) and mean absolute error (MAE) by 30% and 18%, respectively, compared to the Autoencoder and long short-term memory (LSTM) neural networks for same data sets. Furthermore, the CoD determination coefficient ($R^{2}$) increased by 85% compared to the statistical model's autoregressive integrated moving average (ARIMA).