Renewable energy systems (RESs), such as photovoltaic (PV) systems, are providing increasingly larger shares of power generation. PV systems are the fastest growing generation technology today with almost ~30% increase since 2015 reaching 509.3 GW_p worldwide capacity by the end of 2018 and predicted to reach 1000 GW_p by 2022. Due to the fluctuating and intermittent nature of PV systems, their large-scale integration into the grid poses momentous challenges. This paper provides a review of the technical challenges, such as frequency disturbances and voltage limit violation, related to the stability issues due to the large-scale and intensive PV system penetration into the power network. Possible solutions that mitigate the effect of large-scale PV system integration on the grid are also reviewed. Finally, power system stability when faults occur are outlined as well as their respective achievable solutions.

Recently, the rapid increase in the adoption of electric vehicles (EVs) has been driven by considerable technological advancements and a growing focus on environmental sustainability. As consumers and governments increasingly recognize EVs as a viable alternative to traditional internal combustion engine vehicles, the demand for a reliable and accessible charging infrastructure has surged. However, establishing a robust network of charging stations is no longer crucial only to fulfill the demands of EV proprietors but also to relieve range anxiety and improve user convenience, thereby facilitating wider EV adoption. This paper provides a comprehensive global analysis of charging station infrastructure, exploring international standards and regulations, various charging modes, the key parameters of leading electric vehicles, and the importance of RE deployment and ES solutions.

The challenges of global warming and other environmental concerns have prompted governments worldwide to transition from fossil-fuel vehicles to low-emission electric vehicles (EVs). The energy crisis, coupled with environmental issues like air pollution and climate change, has been a driving force behind the development of EVs. In recent years, EVs have emerged as one of the most innovative and vital advancements in clean transportation. According to recent reports, EVs are gradually replacing traditional automobiles, offering benefits such as pollution reduction and the conservation of natural resources. This research focuses on analyzing and reviewing the impact of EV integration on electrical networks, with particular attention to photovoltaic (PV) energy as a sustainable charging solution. It examines both current and anticipated challenges, especially those related to power quality, harmonics, and voltage imbalance. A special emphasis is placed on Tunisia, a country with high solar energy potential and increasing interest in EV deployment. By exploring the technical and infrastructural readiness of Tunisia for PV-based EV charging systems, this paper aims to inform regional strategies and contribute to the broader goal of sustainable energy integration in developing countries as part of future work.

This paper investigates the feasibility of hydrogen-powered hybrid electric vehicles as a solution to transportation-related pollution. It focuses on optimizing energy use to improve efficiency and reduce emissions. The study details the creation and real-time performance assessment of a hydrogen hybrid electric vehicle (HHEV)system using an STM32F407VG board. This system includes a fuel cell (FC) as the main energy source, a battery (Bat) to provide energy during hydrogen supply disruptions and a supercapacitor (SC) to handle power fluctuations. A multi-agent-based artificial intelligence tool is used to model the system components, and an energy management algorithm (EMA) is applied to optimize energy use and support decision-making. Real Global Positioning System (GPS) data are analyzed to estimate energy consumption based on trip and speed parameters. The EMA, developed and implemented in real-time using Matlab/Simulink(2016), identifies the most energy-efficient routes. The results show that the proposed vehicle architecture and management strategy effectively select optimal routes with minimal energy use.

The research presented in this paper explains how the complex-vector-filter (CVF) method can help in minimizing the current harmonic of a grid-tied photovoltaic system. In fact, the harmonic-free positive sequence (HFPS) load current is used to produce referential sinusoidal currents. This control stabilizes the grid’s currents under unbalanced load currents, as well as mitigates undesirable harmonic load currents, while feeding clean active power to the grid. Thanks to the proposed controller, the performance, such as robustness, as well as the stability and dynamics of the CVF are more effective compared to the proportional-integral (PI) with phase-locked-loop (PLL) controller. Moreover, the CVF ensures robustness and stability during the synchronization between the photovoltaic (PV) generator and the utility grid system. The PI&PLL control presents higher active and reactive power fluctuations during synchronization. On the other hand, the CVF ensures the elimination of the reactive power fluctuations during synchronization. The performance of the proposed CVF is validated by simulation through MATLAB software. Under all conditions, the grid current, considering harmonics, is within the limits set by the IEEE-519 power quality standard, where a total harmonic distortion (THD) of 1.56% was achieved in the case of feeding a non-linear load.

This paper presents an intelligent hybrid framework for short-term energy consumption forecasting and real-time energy management in photovoltaic (PV)-integrated smart homes with Vehicle-to-Home (V2H) systems, tailored to the Tunisian context. The forecasting module employs an Attention-based Long Short-Term Memory (LSTM) neural network, whose hyperparameters (learning rate, hidden units, temporal window size) are optimized using the Harris Hawks Optimization (HHO) algorithm. Simulation results show that the proposed LSTM-HHO model achieves a Root Mean Square Error (RMSE) of 269 Wh, a Mean Absolute Error (MAE) of 187 Wh, and a Mean Absolute Percentage Error (MAPE) of 9.43%, with R² = 0.97, substantially outperforming conventional LSTM (RMSE: 945 Wh, MAPE: 51.05%) and LSTM-PSO (RMSE: 586 Wh, MAPE: 28.72%). These accurate forecasts are exploited by the Energy Management System (EMS) to optimize energy flows through dynamic appliance scheduling, HVAC load shifting, and coordinated operation of home and EV batteries. Compared with baseline operation, PV self-consumption increased by 18.6%, grid reliance decreased by 25%, and household energy costs were reduced by 17.3%. Cost savings are achieved via predictive and adaptive control that prioritizes PV utilization, shifts flexible loads to surplus periods, and hierarchically manages distributed storage (home battery for short-term balancing, EV battery for extended deficits). Overall, the proposed LSTM-HHO-based EMS provides a practical and effective pathway toward smart, sustainable, and cost-efficient residential energy systems, contributing directly to Tunisia’s energy transition goals.