In this paper, we present a high performance approach to smart energy management (SEM) of energy flow between the various components of an isolated microgrid (IMG), comprising a hybrid (photovoltaic-wind) system with battery storage. This intelligent management is based on artificial intelligence (AI) techniques such as fuzzy logic controller (FLC). The SM is designed to optimize the management of energy flows throughout the isolated microgrid, maximizing the extraction of available energy despite the constraints of meteorological conditions. The proposed approach adjusts the system output to supply the load demand, offering benefits of precise control of the battery’s state of charge (SOC) across five states. This is a marked improvement over traditional management systems that rely on two SOC states, namely SOCmin-SOCmax.

Following significant advances in power electronics, electronic devices are becoming increasingly common in everyday life. However, these devices consume non-sinusoidal currents, which degrades the quality of voltage and current in distribution systems. Now, with advances in control theory and signal processing, these harmonic current generators, or static converters, can be used to mitigate harmonic contamination. However, the efficiency of these active power filters (APF) is largely dependent on the control strategy adopted. APF control strategies are classified into two main categories: time-domain strategies and frequency-domain strategies. This paper investigates three time-domain control techniques, namely the PQ method, the SRF-ICC method, and the SRF-DCC method, that have been used to control three-phase SAPF connected to a polluted power grid. These approaches aim to reduce system harmonics and enhance power quality. Meanwhile, an anti-windup PI controller-based back-calculation technique ensures DC bus voltage stability. The investigation was conducted in Matlab/Simulink, and the results of merging these approaches with the AW-PI controller were satisfactory. Furthermore, the results reveal that the SRF-DCC method outperforms the other two techniques, with a THD of 0.87%.

This research focuses on the analysis of power quality and its improvement in three-phase, four-wire Photovoltaic grid-connected inverters using a three-phase, two-level inverter with four legs that performs as an active power filter connected in parallel with the perturbing load. This powerful electronic circuit is designed to attenuate harmonic currents from unbalanced non-linear loads. This research paper compares the efficiencies of the synchronous reference method (SRF) with both low pass filters and high pass filters inside the control by the park’s transformation this control based on hysteresis modulation used to generate harmonic currents on hysteresis regeneration. For the three-phase, four-leg, two-level shunt active power filter (SAPF) to improve the power quality of four-wire grid-connected photovoltaic systems and on the opposite side a non-linear load designed, using MATLAB/Simulink, the performance analysis of the two techniques is based on response time and harmonic distortion ratio (THD). The results show that the SRF theory is significantly more accurate and dynamic with the use of high pass filters than the low pass filter adapted in our studied system it means the choice of pass filters in the SRF control should be taken in consideration to have better results of power quality. Simulation results in MATLAB Simulink demonstrate the effectiveness of our choice of high pass filter in improving power quality of the system using SRF theory.

With the widespread use of DC Microgrid, operation safety is important factor that requires careful consideration. Indeed, unexpected sensor faults can seriously affect the Microgrid stability, which highly impairs the system performance if they are not compensated. This paper aims to increase the reliability of a DC Microgrid by isolating and tolerating sensor faults at the primary level of DC-DC converters, operated in a decentralized DC Microgrid without the use of communications. The simultaneous fault isolation and mitigation are based on a single sliding mode observer. To verify the effectiveness of this approach, a simulation of a DC Microgrid with multiple distributed generation units subjected to faulty sensors is implemented in Matlab/Simulink. The obtained results validate the ability of the proposed method to isolate and tolerate sensor faults.

In practical photovoltaic grid-tied four-leg inverter systems (PV-GTFLIs), changes in radiation, temperature, and grid voltage magnitude cause significant disturbances, including DC-link voltage disturbance and power unbalance, which can impact the system’s dynamic responses, control performance, grid power quality, efficiency, and reliability. To deal with these problems, this article proposes an improved active disturbance rejection control (ADRC) methodology for optimizing DC-link voltage regulation in PV-GTFLIs. The proposed ADRC approach incorporates a nonlinear high-gain observer (NHGO) within the external DC-link voltage control loop to estimate and mitigate disturbances. The suggested ADRC approach adopts the NHGO instead of the traditional extended state observer due to its superior characteristics, which include excellent dynamic responses, rapid and accurate disturbance estimation and rejection, and enhanced resilience against measurement noise. Thus, it enhances the stability of the DC bus voltage, enhances the system’s dynamic responses, improves its steady-state performance, increases its ability to reject voltage disturbances, and improves the reliability of the PV-GTFLI, as well as reducing the cost and size. The effectiveness of the proposed ADRC approach based on the NHGO is confirmed through software-in-the-loop real-time validation tests, including changes in irradiation and PV cell temperature, sag in grid voltage amplitude, and internal uncertainties, using the OPAL real-time digital simulator.

This research presents a study on a hybrid renewable energy system that integrates battery storage with photovoltaic panels, wind turbines, and a diesel generator. The study was conducted in Bejaia, Algeria, which possesses substantial potential for wind and solar energy resources. The integration of the diesel generator is essential as an auxiliary source to compensate for the intermittency of renewable resources, but it requires precise sizing according to daily peak load demands. The stand-alone system architecture is simulated under two different optimization algorithms using HOMER Pro (open-source version): combined dispatch (CD) and predictive dispatch (PS), to determine the optimal system cost. The impact of various component prices on the net present cost (NPC) and the cost of energy (COE), along with the emissions of polluting gases following diesel generator integration, were evaluated using sensitivity analysis. The results demonstrate that the predictive dispatch algorithm clearly outperforms the combined dispatch approach in optimizing investment costs, while the combined dispatch strategy achieves greater reductions in emissions and fuel consumption by the diesel generator.