The aging of PV cells reduces their electrical performance i.e., the parasitic parameters are introduced in the solar panel. The shunt resistance (R_Sh), series resistance (R_S), photo current (I_Ph), diode current (I_d), and diffusion constant (a₁) are known as parasitic or extraction parameters. Cracks and hotspots reduce the performance of PV cells and result in poor V–I characteristics. Certain tests are carried out over a long period of time to determine the quality of solar cells; for example, 1000 h of testing is comparable to 20 years of operation. The extraction of solar parameters is important for PV modules. The Tabu Search Optimization (TSO) algorithm is a robust meta-heuristic algorithm that was employed in this study for the extraction of parasitic parameters. Particle Swarm Optimization (PSO) and a Genetic lgorithm (GA), as well as other well-known optimization methods, were used to test the proposed method’s correctness. The other approaches included the lightning search algorithm (LSA), gravitational search algorithm (GSA), and pattern search (PS). It can be concluded that the TSO approach extracts all six parameters in a reasonably short period of time. The work presented in this paper was developed and analyzed using a MATLAB-Simulink software environment.

Phased out conventional energy sources, the urge to shift to clean energy sources because of climate change, ever increased population necessitates for the incorporation of renewable energy sources into the microgrid (MG). Moreover, the presence of renewable energy sources in the MG introduces uncertainty and uncontrollability. Energy management is crucial for a microgrid for an assortment of reasons, including cost minimization, sustainability, efficiency, and resilience of the microgrid system. A blend of conventional and renewable energy sources is common in microgrids which reduces carbon emissions and its adverse impacts on the environment can be achieved by emphasizing the use of greener energy sources through energy management systems. This paper addresses a two-stage scheduling of MG for energy management. In the first stage, the objective is to minimize the operational cost in the day-ahead stage and in the real-time stage, the objective is to minimize the imbalance cost in the MG.

The main objective of this study is to reduce disturbances, such as voltage sags, swells, and fluctuations, increase stability of voltage and Power Quality (PQ) in distributed power systems using an optimized Dynamic Voltage Restorer (DVR) control strategy. These objectives are achieved through the design and optimal tuning of a Cascaded Adaptive Neuro-Fuzzy Inference System (ANFIS) controller using the Lemurs optimization algorithm (LOA). The proposed controller is verified in the MATLAB/Simulink environment systematically for nonlinear and sensitive load conditions, which is a complete verification and testing to establish controller performance for voltage stability, improving dynamic responses, and reliably operating the system under disturbance operating conditions. According to the simulation study, the results show that the DVR controller is able to maintain a consistent load voltage under disturbance, therefore the connected loads are effectively and efficiently utilized within the controlled system. The proposed solution achieves a performance effectiveness of 96%, ensure effective and consistent operation of the system under changing load conditions. The proposed central controller enhances the dynamic performance of the DVR by reducing the settling time to 0.08 s, thus ensuring that performance is better than conventional approaches with fast transient response and higher accuracy. The significance of the results lies in validate that the cascaded ANFIS controller with LOA optimization is a legitimate and computationally efficiency substitute for real-time voltage compensation. This enhances the robustness of sensitive loads, minimizes possible consequences of economic losses and enhances the reliability of distributed networks in modern smart grid infrastructure.