The operating performance of a PV module/array is extremely reliant on the weather (temperature/irradiation) and non-linear. Thus, to ensure that the PV array produces the maximum possible power at any time and regardless of the external conditions, maximum power point tracking (MPPT) techniques are required. The solution suggested in this paper involves taking into account two cascaded controllers as follows; the incremental conductance (INC) controller, which is intended to provide a reference proportional to the PV array's optimal power PMPP, and the sliding mode control (SMC), which is in charge of controlling the GPV voltage. The strategy of the SMC is to design a sliding surface that defines the operating point. The SMC combined with the INC aims to achieve fast MPPT action on PV systems using cascade control. The proposed controller is robust to changing weather conditions. In order to evaluate what is done, the results are compared with the INC+PI controller. When an abrupt change occurs, the SMC has a low transient and arrives to equilibrium sooner than the INC+PI controller. the results are presented by the PSIM software, and demonstrate the SMC controller's performance while confirming that the new approach has increased both production and energy efficiency.

There are two significant issues with the incorporation of smart grid technology in power system operating studies including the economic emission, unit commitment problem (UCP). Economic dispatch problem (EDP) is a UCP sub-problem which find the optimum output for a given combination of running units. When using electro-energy systems to strategically distribute the power produced by all plants, the power economic dispatch problem is especially important. Pumped storage units that have the capacity to store energy can provide spinning reserves, which will lower overall costs and emissions. The general goal of this study is to develop control and optimization algorithms that are appropriate for managing new generation electrical networks. In this research work, the economic dispatch issue in a ten-unit smart grid system is resolved using the crow search algorithm (CSA), which acts as a local optimizer of the eagle strategy (ES). The outcomes of the ES-CSA program are compared to those found in the literature. The results of simulations suggest that adopting ES-CSA can lead to the generation of reliable and enough power that can meet the needs of both civil and industrial areas.

Partial shading, from obstacles such as buildings or trees, is a major challenge for photovoltaic systems, causing unpredictable fluctuations in solar energy production and underlining the need for advanced energy management strategies. In this paper, we propose an innovative approach that combines hybrid metaheuristic optimization with maximum power point tracking control (MPPT), using particle swarm optimization (PSO) in conjunction with the incremental conductance (IC) algorithm. We compare the proposed method with the conventional Perturb and Observation (P&O) algorithm. The choice of P&O as a comparison method is due to its simplicity, its familiarity with the scientific literature, its low cost of implementation. The main objective of swarm optimization combined with the IC algorithm lies in its ability to overcome the challenges posed by partial shading, ensuring accurate and efficient tracking of the point of maximum power, thanks to dynamic adaptation to variations in solar irradiation, thus enhancing the performance and resilience of the photovoltaic system. This approach is of crucial importance, offering considerable potential for solving the complex challenges associated with partial shading. Our results show that this hybrid MPPT algorithm offers superior tracking efficiency > 98% , faster convergence 500ms , better stability and increased robustness compared to traditional MPPT approaches. The system is composed of a PV and a boost converter that connects the input to the resistive load. The algorithms were implemented with MATLAB/Simulink as the simulation tool. These results not only reinforce the viability of sustainable energy solutions, but also open the way for the development of more sustainable energy solutions.The perspectives of this work are oriented towards a practical and extended integration of the proposed hybrid approach in real photovoltaic systems, with a particular emphasis on experimental validation.

In recent years, smart grid security has gained considerable attention. Numerous studies have proposed techniques to detect cyber-attacks using sensor data; however, limited attention has been given to distinguishing cyber intrusions from physical faults in the power grid. In this paper, we present a supervised intrusion–disturbance classification pipeline to accurately differentiate physical faults from cyber-attacks. First, we augment raw channels with relation-centered features to emphasize relative contrasts and suppress common-mode effects, then we apply embedded feature selection via LightGBM to retain a compact, informative subset. Class imbalance is addressed through class weighting, and an Extremely Randomized Trees classifier serves as the core learner. Experiments on 15 datasets cover both binary (Attack vs. Natural) and multiclass (Attack/Natural/NoEvents) regimes. The approach attains 98.44% mean accuracy for the binary task and 98.22% for the multiclass task, demonstrating consistent discrimination between cyber-attacks, physical faults, and normal operation. The results indicate that relational features combined with embedded selection and a tree ensemble offer a practical, accurate alternative to heavier deep models for smart-grid monitoring.

This paper proposes a hybrid eagle strategy with crow search algorithm (ES-CSA) as local optimizer to solve the unit commitment problem (UCP) in power systems. The algorithm aims to minimize total operational costs while considering pumped storage units as spinning reserves. The proposed methodology combines the exploration capability of ES with CSA's local search efficiency to determine optimal generator scheduling and power dispatch. The approach is validated using standard test cases from the literature, demonstrating improved convergence and cost reduction compared to existing methods. Results confirm the effectiveness of integrating pumped storage units in reducing overall system costs while maintaining reliable operation.

The smart grid is an advanced evolution of the traditional electrical power grid, developed to meet the increasing energy demands and requirements of the 21st century by incorporating digital technologies and data management systems to improve efficiency and reliability. Unlike conventional grids, the smart grid relies on a network of interconnected digital devices, sensors, and computerized controls that enable real-time monitoring and management of electricity distribution across vast geographic areas. However, the growing dependence on digital technologies also brings heightened cyber security concerns, since their integration can expose the grid to an increased risk of malicious intrusions. This systematic literature review investigates the nature and scope of cyber-attacks and cyber defense strategies in smart grids, which are critical to modern energy infrastructure. Following established research guidelines, this review rigorously examines existing studies by focusing on peer-reviewed articles and conference papers to understand the range of cyber security threats and defense mechanisms that smart grids face. The review uses a structured methodology to identify, evaluate, and synthesize key findings, revealing trends and gaps in current knowledge about smart grid security. The outcomes of this analysis offer valuable clarity on the specific weaknesses and operational challenges that affect smart grid infrastructures, contributing to the ongoing efforts to enhance cyber security measures and guide future research in this vital field.