The primary goal of enhancing automatic generation control (AGC) in interconnected multi-area power systems is to ensure high-quality power generation and reliable distribution during emergencies. These systems still struggle with consistent stability and effective response under dynamic load conditions despite technological advancements. This research introduces a secondary controller designed for load frequency control (LFC) to maintain stability during unexpected load changes by optimally tuning the parameters of a Proportional–Integral–Derivative (PID) controller using pelican optimization algorithm (POA). An interconnected power system for ith multi-area is modeled in this study; meanwhile, for determining the optimal PID gain settings, a four-area interconnected power system is developed consisting of thermal, reheat thermal, hydroelectric, and gas turbine units based on the ith area model. A sensitivity analysis was conducted to validate the proposed controller’s robustness under different load conditions (1%, 2%, and 10% step load perturbation) and adjusting nominal parameters (R, T_p, and T_ij) within a range of ±25% and ±50%. The performance response indicates that the POA-optimized PID controller achieves superior performance in frequency stabilization and oscillation reduction, with the lowest integral time absolute error (ITAE) value showing improvements of 7.01%, 7.31%, 45.97%, and 50.57% over gray wolf optimization (GWO), Moth Flame Optimization Algorithm (MFOA), Particle Swarm Optimization (PSO), and Harris Hawks Optimization (HHO), respectively.

This paper presents the development and deployment of an IoT-based monitoring and automatic control system for power substations to address equipment failures, energy losses, and safety concerns. This system utilizes a network of sensors to collect voltage, current, and temperature data from various substation components in real-time. The collected data is then sent to a centralized control center and a user-friendly Blynk IoT application that authorized personnel can access. Utilizing sophisticated analytics and algorithms enables proactive maintenance by identifying potential malfunctions and initiating prompt actions. In addition, the system maximizes resource utilization by monitoring voltage levels and balancing loads. Integration with the Blynk application enables remote monitoring, real-time alerts, and remote-control capabilities, thereby improving accessibility and expediting responses to urgent situations. Results show that the proposed system compared to existing ones, improves the substation’s efficiency, minimizes downtime, and increases grid reliability, thereby contributing to more resilient and sustainable power infrastructure.

The increasing use of renewable energy sources necessitates advanced energy management in DC microgrid systems for their stability, efficiency, and reliability. This paper presents a design and simulation of a DC microgrid incorporating renewable energy sources and energy storage. The system integrates a 10.5 kW PV array and 0.65 kW battery energy storage system, managed by bidirectional and boost converters. The goal is to demonstrate the effective use of the DC bus for balancing generation and load demands while optimizing energy flows within the microgrid. The simulation results show the system's capacity to handle variable renewable generation and load profiles, ensuring a constant and uninterrupted power supply. Strategic control of power electronic converters is crucial for maintaining DC bus voltage within desired thresholds. This study contributes to sustainable energy solutions by showcasing the potential of integrated renewable resources in a DC microgrid.

The Internet of Things (IoT) is becoming increasingly important for global civilization, as it can achieve various goals. As global temperatures rise, people are turning to alternative energy sources, such as solar energy, which can be used efficiently in various areas. Regular maintenance and development of solar systems can save on electricity costs. The IoT allows for real-time monitoring of solar energy systems from any location, improving efficiency and reliability. This study aims to improve solar power generation efficiency and reliability by using a combination of sensors, microcontrollers, and IoT technologies to monitor solar panel performance, track sun movement, and calculate energy production. The system also includes a user interface providing real-time data on solar panel performance and energy production. The proposed system offers higher efficiency, increased energy production, and improved reliability, making it a promising option for widespread adoption in the renewable energy industry.

This study presents a novel approach in the form of a fully automated car parking management system, integrating IoT, computer vision, and database technologies to alleviate urban mobility challenges. Employing vehicle presence sensors and computer vision techniques for Bengali number-plate detection, the system enables real-time monitoring and management of parking spaces. Upon vehicle detection, the system cross-checks the license plate against a centralized database to confirm parking eligibility. Additionally, the integration of a mobile app and website interface provides drivers with instant updates on parking slot availability. This dual-layered IoT system not only restricts unauthorized access, minimizing illegal parking but also significantly reduces the time spent searching for parking, enhancing user experience. Consequently, this innovative solution contributes to reduced traffic congestion, fostering seamless urban mobility. By amalgamating automation, user-centric design, and efficient management, this system promises a transformative parking experience for both drivers and facility operators, revolutionizing urban parking paradigms.

This paper presents a comprehensive comparative analysis of three battery types—Lead-acid (LA), Lithium-ion (Li-ion), and Nickel–iron (Ni-Fe)—within microgrid configurations. The performance of these batteries is evaluated using four advanced optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Archimedes Optimization Algorithm (AOA). Key metrics for comparison include the number of photovoltaic panels, wind turbines, and batteries required, Total Annual Fuel Consumption (TAFCIL), Total Annual CO2 Emission (TACO2), Cost of Energy (COE), and Life Cycle Cost (LCC). The results indicate that the AOA configuration with Lead-acid batteries provides the most cost-effective solution, achieving the lowest LCC ${\$}$8342528 and COE ${\$}$0.1080/kwh, despite higher fuel consumption 7283 liters and CO2 emissions 29132 kg. In contrast, Li-ion and Ni-Fe batteries demonstrate better environmental performance but at higher costs. This study highlights the trade-offs between economic efficiency and environmental impact, offering valuable insights for optimizing microgrid systems.