This paper describes an energy management strategy for a DC microgrid that utilizes a hybrid renewable energy system (HRES) composed of a photovoltaic (PV) module, a wind turbine based on a permanent magnetic synchronous generator (PMSG), and a battery energy storage system (BESS). The strategy is designed to provide a flexible and reliable system architecture that ensures continuous power supply to loads under all conditions. The control scheme is based on the generation of reference source currents and the management of power flux. To optimize the supply–demand balance and ensure optimal power sharing, the strategy employs artificial intelligence algorithms that use previous data, constantly updated forecasts (such as weather forecasts and local production data), and other factors to control all system components in an optimal manner. A double-input single-output DC–DC converter is used to extract the maximum power point tracking (MPPT) from each source. This allows the converter to still transfer power from one source to another even if one of the sources is unable to generate power. In this HRES configuration, all the sources are connected in parallel through the common DC–DC converter. The strategy also includes a long short-term memory (LSTM) network-based forecasting approach to predict the available energy production and the battery state of charge (SOC). The system is tested using Matlab/Simulink and validated experimentally in a laboratory setting.

The overexploitation of non-renewable fossil resources has led to dangerous warming of our planet due to greenhouse gas emissions. The main reason for this problem is the increase in global energy demand. The rising prices of oil and gas have pushed governments around the world to turn to renewable energy, especially solar and wind power. For this reason, the present paper aimed to focus on photovoltaic and wind energy systems. However, exploitation of these two sources individually is not always easy because of their intermittent and irregular characters. Therefore, the obvious solution is the hybridisation of these two sources, which, when used alongside other systems such as batteries, increases the reliability, availability, and efficiency of these renewable sources. The main objective of this paper is to give an overview of different configurations of hybrid solar and wind energy conversion systems. First, the behaviour of each system, as well as their mathematical models, characteristics, and existing topologies, is presented. Then, the control strategies, optimal configurations, and sizing techniques, as well as different energy management strategies, of these hybrid PV–wind systems are presented.

The present paper exhibits a real time assessment of a robust Energy Management Strategy (EMS) for battery-super capacitor (SC) Hybrid Energy Storage System (HESS). The proposed algorithm, dedicated to an electric vehicular application, is based on a self-gain scheduled controller, which guarantees the H performance for a class of linear parameter varying (LPV) systems. Assuming that the duty cycle of the involved DC-DC converters are considered as the variable parameters, that can be captured in real time, and forwarded to the controller to ensure both; the performance and robustness of the closed-loop system. The subsequent controller is therefore time-varying and it is automatically scheduled according to each parameter variation. This algorithm has been validated through experimental results provided by a tailor-made test bench including both the HESS and the vehicle traction emulation system. The experimental results demonstrate the overall stability of the system, where the proposed LPV supervisor successfully accomplishes a power frequency splitting in an adequate way, respecting the dynamic of the sources. The proposed solution provides significant performances for different speed levels.

Energy efficiency and ratio performance are two key parameters for the analysis of the performance of photovoltaic (PV) modules. The present paper focusses on the assessment of the efficiency of four different photovoltaic module technologies based on energy efficiency and ratio performance. These PV modules were installed at the Applied Research Unit in Renewable Energy (URAER) in Algeria and were used to provide experimental data to help local and international economical actors with performance enhancement and optimal choice of different technologies subject to arid outdoor conditions. The modules studied in this paper are: two thin-film modules of copper indium selenide (CIS), hetero-junction with intrinsic thin-layer silicon (HIT) and two crystalline silicon modules (polycrystalline (poly-Si), monocrystalline (mono-Si)). These technologies were initially characterized using a DC regulator based on their measured I-V characteristics under the same outdoor climate conditions as the location where the monitoring of the electrical energy produced from each PV module was carried out. The DC regulator allows for extracting the maximum electrical power. At the same time, the measurements of the solar radiation and temperature were obtained from a pyranometer type Kipp & ZonenTM CMP21 and a Pt-100 temperature sensor (Kipp & Zonen, Delft, Netherlands). These measurements were performed from July 2020 to June 2021. In this work, the monthly average performance parameters such as energy efficiency are given and analyzed. The average efficiency of the modules over 12 months was evaluated at 4.74%, 7.65%, 9.13% and 10.27% for the HIT, CIS, mono-Si and poly-Si modules, respectively. The calculated percentage deviations in the efficiency of the modules were 8.49%, 18.88%, 19.74% and 23.57% for the HIT, CIS, mono-Si and poly-Si modules, respectively. The low variation in the efficiency of the HIT module can be attributed to the better operation of this module under arid outdoor conditions, which makes it a promising module for adaptation to the region concerned.