The expanding use of photovoltaics (PV) as a green energy resource has been rising in these years, mostly due to the possibility of being incorporated with traditional power systems, to meet the world’s energy needs and reduce carbon emissions. However, providing green electricity from this renewable generator is frequently vulnerable to power quality (PQ) disruptions resulting from the PV’s intermittent nature and other factors associated with the electric grid, power converters, and linked loads. These disruptions need to be reduced to keep the investigated system’s PQ from deteriorating. The investigated system includes PV, DC-DC, and DC-AC converters, filter, power grid, and control schemes. If the DC-DC converter is not managed, a deviation from the maximum power point (MPP) extrapolated from the PV system will take place. In order to maximize the energy harvested from the PV system by managing the DC-DC converter, this research developed two MPP tracking (MPPT) algorithms: artificial neural networks (ANN) and cuckoo search (CS). Additionally, a design and implementation for a shunt active power filter (LCL) using genetic algorithm and GRG is provided to lower the injected total harmonic distortion (THD) and thereby enhance the PQ. To achieve the smallest size of the LCL components, the generalized reduced gradient (GRG) was the best compared to genetic algorithms GA. The results of the simulation showed that ANN performed better at tracking maximum power than CS. With the designed LCL, the THD is reduced by 99.78% compared to without a filter. To verify the simulation’s findings, a practical configuration is implemented.

This Power quality problem is a vital issue in current-day power system with the increased penetration of distributed generation (DG) systems which greatly affects the stability and reliability of the system. This study presents a comprehensive analysis of detection and classification strategies for monitoring power quality (PQ) disturbances. Specifically, the focus is on PQ disturbances caused as a result of load switching and variations in environmental factors, such as changes in solar irradiation and wind speed. Twelve different types of PQ disturbances under various operating scenarios are considered for the detection and classification study. Twelve different statistical features are extracted by passing the disturbances through robust H-infinity filter in order to minimize the data size and computational complexities. The statistical properties of PQ disturbances are initially categorized using the Fast Generalized Fuzzy-C means (FGFCM) clustering approach. Further, the disturbances are correctly classified by pattern classification approach like support vector machines (SVM) in order to distinctly classify them. The validity of the classification analysis is reinforced by the inclusion of experimental data gathered from a prototype wind energy system and photovoltaic (PV) system setup. Again, Genetic algorithm (GA) is used for extracting optimal features and Particle swarm optimization (PSO) is applied for optimising the heuristic parameters of the SVM in order to improvise the classification accuracy under normal, noisy as well as increased penetration of wind and photovoltaic (PV) systems.

Active power losses have the potential to affect the distribution of power flows along transmission lines as well as the mix of energy used throughout power networks. Grey wolf optimization algorithms (GWOs) are used in electrical power systems to reduce active power losses. GWOs are straightforward algorithms to implement because of their simple structure, low storage and computing needs, and quicker convergence from the constant decrease in search space. The electrical power system may be separated into three primary components: generation, transmission, and distribution. Each component of the power system is critical in the process of distributing electricity from where it is produced to where it is used by customers. By using the GWO, it is possible to regulate the active power delivered by a high-voltage direct current network based on a multi-terminal voltage-source converter. This review focuses on the role of GWO in reducing the amount of active power lost in power systems by considering the three major components of electrical power systems. Additionally, this work discusses the significance of GWO in minimizing active power losses in all components of the electrical power system. Results show that GWO plays a key role in reducing active power losses and consequently reducing the impact of power losses on the performance of electrical components by different percentages. Depending on how the power system is set up, the amount of reduction can be anywhere from 12% to 65.5%.

The world energy sector is experiencing many challenges, such as maintaining a demand–supply balance with continuous increases in demand, reliability issues, and environmental concerns. Distributed energy resources (DERs) that use renewable energy sources (RESs) have become more prevalent due to environmental challenges and the depletion of fossil fuel reserves. An increased penetration of RESs in a microgrid system facilitates the establishment of a local independent system. However, these systems, due to the uncertainties of RESs, still encounter major issues, like increased operating costs or operating constraint violations, optimal power management, etc. To solve these issues, this paper proposes a stochastic programming model to minimize the total operating cost and emissions and improve the operational reliability with the help of a generalized normal distribution optimization (GNDO). A day-ahead demand response is scheduled, aiming to shift loads to enhance RES utilization efficiency. Demand-side management (DSM) with RESs is utilized, and battery energy storage systems in low-voltage and medium-voltage microgrids are shown. Mathematical formulations of each element in the microgrids were performed. Optimal and consumer-friendly solutions were found for all the cases. Environmental concerns based on the amount of harmful emissions were also analyzed. The importance of demand response is demonstrated vividly. The aim is to optimize energy consumption and achieve optimum cost of operation via DSM, considering several security constraints. A comparative analysis of operating costs, emission values, and the voltage deviation was carried out to prove and justify their potential to solve the optimal scheduling and power flow problem in AC/DC microgrids.

The modern power systems incorporate high penetration of renewable is a large, composite, interconnected network with dynamic behavior. The small disturbances occurring in the system may induce low-frequency oscillations (LFOs) in the system. If the (LFOs) are not suppressed within a stipulated time, it may cause system islanding or even blackouts. Hence, it is essential to investigate the behavior of the system under various levels of disturbances and control action must be taken to damp these oscillations. The established approach to damping the LFOs is by installing power system stabilizers (PSS). PSS uses the local signals from generators to control the oscillations. The dominant source of inter-area oscillations in power systems is due to overloaded weak interconnected lines, converter-interfaced generation, and the action of the high gain exciter present in the system. Consequently, wide area control is needed to control the inter-area oscillations existent in the system. This paper developed a coordinated design of conventional PSS, static compensator, renewable converters, and wide area controller for damping the local and inter-area oscillations in renewable incorporated power systems. The performance of the developed controller is evaluated through the time domain analysis and eigenvalue analysis. A comparison of the introduced controller has been done with other standard conventional methods. The choice of input signals for the wide area controller from the wide-area measurement system is done based on the controllability index. Additionally, the location of the controller must be identified to dampen the inter-area oscillations in the system. In this paper, the controllability index is calculated to find out the highly affected wide area signals for considering it as the feedback signal to a developed controller. The location of the controller is recognized by computing the participation factor. The developed controller has experimented on renewable incorporated large study power systems when time delay and noise are present in wide area signals.

Microgrid (MG) technologies assist the power grid in evolving to become more efficient, less polluting, and more resilient by addressing the requirements of energy users. However, several technological issues arise as a result of the unpredictability and difficulty in estimating the efficacy and regulation of the many renewable energy resources (RERs) incorporated in MGs. Two of the most significant of these issues are maintaining system stability and power quality, which necessitate to get better the performance of the MGs. The most difficult challenge, system stability, can be achieved with successful Power Management System (PMS). This paper proposes an effective PMS for an AC MG equipped with a diesel generator (DG), a permanent magnet wind generator (PMWG), and a solar photovoltaic (PV) panel Based on an adaptable Artificial Neural Network (ANN). The ANN weights are properly tuned via the Enhanced Bald Eagle Search (EBES) optimization algorithm to produce a stable system during the whole training period, achieve MG energy balance, reduce the usage of fossil fuel DG and maintain MG voltage stability. In addition, for keeping power quality, an adaptive series shunt compensator (ASSC) is described in this work, along with a developed integrative PID controller, where the latter’s controller gains are ideally set utilizing the EBES optimization algorithm to perform adaptably with self-tuning when the operational circumstances of an MG change. various cases are displayed to test the strong of offered ASSC on harmonic mitigation, dynamic voltage stabilization, reactive power control and power factor correction. Moreover, comprehensive case study based on realistic on-site location for Zafarana region, Suez Gulf region of Egypt is proposed. Taking into account The changing nature of weather-related renewable energy, actual loads states and transient faults.