Power, Control, and Data Processing Systems

Abstract Energy plays a critical role in the growth and development of societies, and universities are recognized as key drivers of economic progress. In light of declining fossil fuel reserves and global warming, adopting renewable energy sources, such as photovoltaic (PV) systems, has become increasingly essential. Furthermore, the advantages of distributed generation are now more apparent than ever. Many countries have developed various scenarios to eliminate fossil fuels from electricity generation, an approach that warrants serious consideration in Iran as well. This study investigates the techno-economic feasibility and optimal design of a hybrid photovoltaic/diesel/battery power system intended to supply electricity to an academic center located in western Iran. The technical aspects and economic viability of implementing such a system were evaluated using the HOMER (Hybrid Optimization of Multiple Energy Resources) simulation tool. Various configurations combining solar energy, battery storage, and diesel generators were analyzed and compared. The simulation results presented in this study demonstrate that, given the subsidized nature of energy carriers in Iran, the implementation of hybrid energy systems is not highly recommended at this time.

Abstract This paper presents a novel finite-time adaptive dynamic sliding mode controller (SMC) for regulating the power of a brushless double-fed induction generator (BDFIG). Wind turbines equipped with BDFIGs present unique challenges due to the system's nonlinear dynamics, external disturbances, and inherent uncertainties. To address these issues, SMC is employed for its robustness and effectiveness in uncertain environments. The proposed controller integrates a dynamic SMC, ensuring a smooth control signal and mitigating the chattering effect commonly associated with conventional SMCs. A key innovation of this work is the development of an adaptive gain mechanism that eliminates the need for prior knowledge of uncertainty bounds. This adaptive gain dynamically converges to the upper bound of uncertainties, enhancing the system's adaptability and robustness. Using the Lyapunov stability theorem, finite-time convergence is rigorously proven, ensuring that the sliding variable reaches zero within a finite time, and the adaptive gain aligns with the uncertainty bound.

Abstract This paper introduces a novel approach to improving voltage profiles in a 22-bus radial distribution network by integrating distributed generation (DG) sources with advanced optimization techniques. By leveraging multi-objective particle swarm optimization (MOPSO) alongside soft open points (SOP) devices, this study effectively reduces power losses, total harmonic distortion (THD), and voltage imbalance. The proposed method not only optimizes the allocation and sizing of DG units but also strategically deploys SOP devices to enhance operational flexibility. Simulation results conducted in MATLAB environment demonstrate that the integration of SOPs significantly improves the overall voltage profile, minimizes active power losses, and substantially reduces THD levels across the network. Furthermore, the coordinated optimization approach enhances the resilience and stability of the distribution system under varying load conditions. The improvement in power quality indices, including voltage regulation and harmonic performance, highlights the practical viability and technical effectiveness of combining MOPSO optimization techniques with SOP deployment for resilient and efficient distribution network management.

Abstract This review article provides a comprehensive examination of power system optimization and smart grids, analyzing the pivotal role of emerging technologies in energy management. It begins with foundational concepts of power system optimization and smart grids, followed by a detailed discussion of artificial intelligence and data-driven techniques in energy management, including a review of recent literature on AI applications in future energy networks. The integration of renewable energy sources and distributed energy systems, stochastic energy scheduling in multi-microgrid systems, and multi-objective decision-making frameworks for energy hubs are also explored. Additionally, advanced optimization methods, cybersecurity challenges, and resilience in modern power networks are examined. Emerging trends in smart grid technologies and future outlooks on intelligent power systems are presented in the final sections, including a roadmap for intelligent multi-energy systems. This review offers an up-to-date and comprehensive perspective on advancements and challenges in the optimization and development of smart energy networks.

Abstract The intermittent nature of wind energy generation poses substantial challenges to the stability of electrical grids and the efficiency of energy management systems. Accurate and reliable wind power forecasting is therefore critical for a multitude of reasons: to optimize the operational efficiency of grids, to effectively balance energy supply and demand, to enhance the planning and execution of energy storage strategies, to minimize the reliance on backup power sources, and ultimately, to reduce operational costs within renewable energy infrastructures. This study introduces a novel hybrid deep learning approach designed to improve the accuracy of wind power forecasting through the integration of Long Short-Term Memory (LSTM) networks with an attention mechanism. The model's efficacy was rigorously evaluated using high-resolution data, recorded at 10-minute intervals, from two distinct meteorological stations located in Khomein, Saveh and Tafresh, Iran. The performance of the hybrid model was benchmarked against traditional machine learning methodologies, including Random Forest (RF), XGBoost, and standalone LSTM networks. The results of the evaluation demonstrate the superior performance of the hybrid LSTM-Attention model, which achieved notable coefficient of determination (R²) values of 0.9812, 0.9911 and 0.9842 at the Khomein, Saveh and Tafresh stations, respectively, indicating significant advancements in forecasting accuracy compared to the other models. These enhanced forecasting capabilities have significant implications for facilitating the efficient integration of wind energy into electrical grids, thereby enabling more effective grid management practices and supporting optimized energy distribution strategies.

Abstract This study proposes a stochastic mixed-integer linear programming model for the optimal operation of a microgrid incorporating multiple energy carriers. Due to the inherent uncertainty in renewable energy generation, sudden high power generation from renewable sources may occur that the system cannot utilize directly. Nonetheless, these surplus energies can be transformed into other carriers. In this study, a probabilistic distribution function has been utilized to implement the stochastic model. The analysis results demonstrate that this approach effectively captures the stochastic behavior of the system. The research models and evaluates electric and hydrogen vehicle parking lots to assess their roles within the system. The approach is tested through two scenarios, demonstrating significant reductions in operational costs by effectively leveraging the sudden renewable energy surges. Operational costs for the first and second scenarios are 19,526 and 15,671 MU, respectively. A sensitivity analysis is also performed to confirm the robustness of the findings, ensuring the model’s reliability under various conditions.