Tahar Tafticht

Hybrid renewable energy systems (HRES) now sit at the centre of efforts to decarbonize electricity supply. The literature describing them has grown faster than anyone has organized it. Work on storage integration, optimization, artificial intelligence (AI) and digital energy management has accumulated largely in parallel, and existing reviews tend to treat each strand on its own. This paper takes on that fragmentation through a structured, bibliometrically grounded synthesis instead of new experimental work. What it offers is a maturity–momentum framework that places every research theme on two axes at once: accumulated citation influence and current publication growth. The position a theme occupies separates settled knowledge from frontiers that are busy but still empirically thin. The framework was applied to a corpus of 18,420 Scopus-indexed documents published between 2000 and 2024, retrieved through eight pre-specified TITLE-ABS-KEY queries and screened against documented eligibility criteria. From that corpus, 150 studies were read in full and synthesized thematically. The corpus spans 1840 source journals and 142 countries, carries 312,640 citations, yields a field-level h-index of 214, and grew at a compound annual rate of 18.3% between 2015 and 2024. One relationship runs through the whole analysis: citation maturity and publication momentum move in opposite directions. Early HRES design and pumped hydro storage record the highest citation intensity, at 48.2 and 42.1 citations per document, while their output decelerates. AI-driven operation and future-oriented themes grow fastest, with AI-related output rising roughly 65-fold between 2010 and 2024, yet they rest on comparatively thin citation foundations. Reported minimum levelized cost of energy across successive families of optimization methods fell from USD 0.091 kWh⁻¹ in 2019 to USD 0.037 kWh⁻¹ in 2024, and the reductions arrived in steps that coincide with changes in the method family, not with incremental algorithmic refinement. Eight gaps persist: large-scale experimental validation, multi-energy interoperability, uncertainty-aware optimization, cybersecurity of AI-enabled control, Digital Twin standardization, explainable AI, lifecycle sustainability assessment, and integrated electricity–hydrogen market design. Taken together, they indicate that the weakest point in the field is no longer algorithmic capability. It is the shortage of long-horizon field evidence against which simulated gains can be tested.

Power generation from Renewable Energy Sources (RESs) is unpredictable due to climate or weather changes. Therefore, more control strategies are required to maintain the proper power supply in the entire microgrid. This paper presents a simulation scheme utilizing a solar system instanced by Photovoltaic (PV) panels coupled to the grid, loads, and an energy storage device. At first, modeling the PV panels cell and their operation were analyzed. The synthesis of the adaptive notch filter was designed to compensate for the input currents. The power converter’s/inverter’s efficiency and control facility allowed us to link the energy storage system with an electrical grid. Several simulations were accomplished consistently with nonlinear control techniques for the simple inverter, multi-variable filter, notch filter, and without a filter. Finally, the performances of the nonlinear controller with various filters were carried out to regulate the DC bus of the proposed grid. The advantage of these controllers is compensating the reactive power and harmonic currents to obtain a disturbance-free power network. The overall installations and simulations were established using the Matlab/Simulink software.

Major changes in the technologies of power generation and distribution systems have been introduced in recent years due to concern over rapid climate change. Therefore, disturbances in the large-scale generation, transmission, and distribution of energy are expected to occur in the near future. This is due to the difficulty in controlling the transmission and distribution of energy produced from renewable energy sources (RESs), caused by the instability of these sources and the intermittent nature of their energy. As a result, maintaining the dynamic stability of wind power flow and control of the network frequency is becoming more challenging due to the high penetration impacts of RESs. In this paper, a control algorithm using the power-sharing method is proposed for a wind-based energy storage system to maintain the dynamic stability of wind power flow and control of frequency in the power network. To maintain the network stability, a storage system (battery) was installed to store the excess wind power without throwing it into the Secondary/Dump Load (SL) and minimize losses in power generated by the wind turbine. The results show, the transient time of wind power flow and the fluctuation rate of frequency are reduced significantly using a Fuzzy Logic (FL) controller compared to the Proportional Integral Derivative (PID) controller.