Billions of IoT devices increasingly function as gateways to cloud infrastructures, making them an inevitable target of cyber threats because of the limited resources and low processing capabilities of IoT devices. This paper proposes a lightweight decision tree-based intrusion detection framework suitable for real-time anomaly detection in a resource-constrained IoT environment. Finally, the model also makes use of a novel leaf-cut feature optimization strategy and tight adaptive cloud edge intelligence to achieve high accuracy while minimizing memory and computation demand. In terms of memory, they also use only 12.5 MB in it and evaluated on benchmark datasets including NSL-KDD and Bot-IoT, it gives an accuracy of 98.2% and 97.9%, respectively, and less than 1% false positives, thereby giving up to 6.8% accuracy over some traditional models such as SVM and Neural Networks and up to 78% less energy. It is deployed on Raspberry Pi nodes and can do real-time inference in less than 1 ms and 1,250 samples/sec. Due to the energy efficient, scalable, and interpretable architecture of the proposed solution, it can be implemented as a security solution for IoT use cases in Smart cities, industrial automation, health care, and autonomous vehicles.

This paper introduces a robust and adaptive control framework that integrates a Proportional–Integral–Derivative (PID) controller with the bio-inspired Grey Wolf Optimization (GWO) algorithm for real-time tuning of controller parameters in grid-connected photovoltaic (PV) inverter systems. Conventional controllers such as P and PI are widely used in PV applications due to their simplicity, but they exhibit notable limitations in dynamic environments, including increased Total Harmonic Distortion (THD), slower transient response, and poor voltage regulation—particularly under variable irradiance conditions. The proposed GWO-PID method overcomes these limitations by leveraging the GWO algorithm’s global search capability to dynamically optimize the PID gains (K_p, K_i, K_d) based on a composite fitness function that minimizes Mean Squared Error (MSE) and THD. The system architecture, simulated in MATLAB/Simulink, comprises a 50 kW PV array with a boost converter employing an Incremental Conductance (INC) Maximum Power Point Tracking (MPPT) algorithm, a three-phase voltage source inverter, RLC filters, and a dual-loop (voltage and current) control system synchronized with the utility grid through a Phase-Locked Loop (PLL). The GWO algorithm iteratively refines PID parameters to achieve real-time adaptation to environmental fluctuations. Under standard irradiance (1000 W/m²), the GWO-PID controller achieved a rise time of 0.025 s, settling time of 0.035 s, THD of 3.7%, and MSE of 0.25 kW², while maintaining a stable DC-link voltage of 500 V, thereby ensuring compliance with IEEE 519–2014 power quality standards. Across irradiance levels ranging from 400 W/m² to 1000 W/m², the GWO-PID controller consistently maintained DC-link voltage stability and minimized oscillations in PV voltage and current. Compared to traditional PI and P controllers, the proposed method reduced settling time by over 45%, improved power tracking accuracy, and significantly lowered harmonic distortion. Furthermore, it ensured a power factor close to unity and exhibited excellent frequency stability under transient disturbances. Simulation results also confirm the superior performance of the GWO-PID controller in managing active and reactive power exchange, minimizing overshoot, and maintaining synchronization with the grid, even during rapid environmental transitions. By embedding intelligent metaheuristic optimization into a classical PID framework, this work advances the state of inverter control strategies for PV systems. The proposed GWO-PID technique provides a scalable, efficient, and real-time solution that enhances grid compliance, energy quality, and system stability, marking a key advancement in adaptive control for smart grid and microgrid applications.

This paper presents a method for refining the forecast schedule of renewable energy sources (RES) generation by its intraday adjustment and investigates the measures for reserving RES with unstable generation in electric power systems (EPSs). Owing to the dependence of electricity generation by solar and wind power plants (PV and WPPs, respectively) on natural conditions, problems arise with their contribution to the process of balancing the power system. Therefore, the EPS is obliged to keep a power reserve to compensate for deviations in RES from the planned generation amount. A system-wide reserve (mainly the shunting capacity of thermal and hydroelectric power plants) is used first, followed by other means of power reserve: electrochemical, hydrogen, or biogas plants. To analyze the technical and economic efficiency of certain backup means, mathematical models based on the theory of similarity and the criterion method were developed. This method is preferred because it provides the ability to compare different methods of backing up RES generation with each other, assess their proportionality, and determine the sensitivity of costs to the capacity of backup methods with minimal available initial information. Criterion models have been formed that allow us to build dependencies of the costs of backup means for unstable RES generation on the capacity of the backup means. It is shown that, according to the results of the analysis of various methods and means of RES backup, hydrogen technologies are relatively the most effective. The results of the analysis in relative units can be clarified if the current and near-term price indicators are known.

Wireless Sensor Networks (WSNs) are considered essential to distributed sensing in agricultural, health and industrial domains. Although WSNs have several advantages, they encounter profound cybersecurity threats owing to their processing capacities and small energy sources. In this research work, an intrusion detection framework based on deep learning is designed: a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) with an adversarial-aware optimization model. The benchmark datasets of NSL-KDD, CICIDS2017, UNSW-NB15, and CTU-13 are analyzed in a number of ways based on structure, diversity, and deep learning requirements. We propose a compound objective to optimize all of these simultaneously, maximizing detection accuracy, minimizing adversarial vulnerability and ensuring model generalizability. Synthetic oversampling with SMOTE is employed to deal with this. Cross-dataset and intra-dataset experiments are implemented when testing the proposed framework, and it outperforms in terms of robustness and transferability. Our efforts are practical in terms of the deployment of a lightweight and resilient IDS that is suitable for WSN settings.

This paper gives information about Renewable Energy Generation and its impacts on E-Mobility. By 2050, the two pillars of modern transformation are E-Mobility and Renewable Energy. This transformation requires adaptation to meet demographic and economic growth without increasing pollution and congestion. The modern world needs affordable, secure and inclusive, sustainable and integrated with customer-centric infrastructure and services. There is a great fundamental change needed in road transportation sector, which is to achieve its objective of a long term transition to a low-carbon economy. Electric Vehicles when charged with the electricity generated from Renewable Energy Sources can reduce future emissions of greenhouse gases and air pollutants from road transport. Electric Vehicles combined with renewable energy paint a different picture from the oversimplification of gas miles versus electric miles.Smart Mobility and Smart Grid technologies make people live more sustainable and efficiently.

The paper analyzes the methods of stabilizing generation schedules of photovoltaic stations (PV) in electric power systems (EPS) in the process of balancing electricity. Since PV is characterized by the instability of electricity production due to dependence on weather conditions, an automatic system for forecasting their generation schedules (ASFG) for the next day has been created to increase the energy efficiency of PV. The process of automating the prediction of the power stations as part of the balancing group of the power stations and the algorithm for adjusting the prediction of power plant generation are considered. The criterion for managing the forecasting process is the minimization of the difference between the values ​ of forecasted and actual generation for the same period of time. Checking the performance and tuning of the ASFG PV in order to evaluate its functioning and the effectiveness of its application in the task of balancing the states of the EPS is possible only by means of simulation. It is shown that based on the nature of the process of forecasting the generation of PV using ASFG, it is advisable to use simulation modeling. Since the actual value of generation is constantly monitored during balancing using ASFG, it is possible to use these values ​​during simulation and proceed to real-time simulation. In this case, modeling is considered as an experimental method of research, according to which it is not the object itself that is subjected to perturbations and research, but the software-implemented computer model of the object. The real-life simulation model of the operation of the PV makes it possible to more fully consider the various modes of their operation in the process of balancing the modes of the EPS as part of the balancing group and to more reasonably choose decisions regarding the participation of the PV in generation, taking into account weather conditions and the limitations of the system operators of transmission and distribution of electricity.