Grid-tied microgrids play a crucial role by connecting renewable energy sources to the main power grid, contributing to sustainability and resilience in a balanced and effective manner. However, the dynamic interplay between the intermittent nature of renewable energy sources and the volatility of load fluctuations presents a multifaceted array of intricate energy management complexities. This study aims to formulate optimization techniques for energy management systems based on renewable energy resources and standalone diesel systems. The proposed system consists of a wind turbine, a photovoltaic system, a standalone diesel generator, and a battery energy storage system, along with flexible and non-flexible loads tied to the local grid. Battery energy storage acts as a primary backup system, while diesel generators act as a standalone secondary backup system. The performance of the proposed optimization technique is validated using Matlab/Simulink, substantiating its performance and robustness, thus affirming its pragmatic suitability for real-world implementation. A comparison has been made with other optimization techniques and found that the proposed technique gives enhanced efficiency, improved resource allocation, load scheduling, and greater adaptability to varying demand and supply dynamics. Moreover, the proposed system exhibits a superior ability to achieve optimal energy utilization and realize noteworthy cost savings in comparison to the alternatives that underwent evaluation.

Accurate load forecasting is essential for ensuring the stability and efficiency of modern power systems, particularly in the context of increasing renewable energy integration. This study proposes an advanced forecasting approach using Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) to enhance electricity demand prediction. The research analyzes key temporal patterns in historical load, renewable energy generation, and weather data to improve forecasting precision. RNN-based LSTM, with its gated memory structure, effectively captures long-term dependencies and mitigates vanishing gradient issues, making it highly suitable for modeling complex time-series data in power systems. Its ability to retain crucial past information across extended sequences enables precise load forecasting, even in the presence of fluctuations and intermittent renewable energy sources. To validate the effectiveness of the proposed technique, the model was implemented and tested using MATLAB/Simulink, ensuring realistic simulation of power system dynamics. Through a comprehensive comparative analysis, RNN-LSTM is found to exhibit superior predictive accuracy, achieving the lowest RMSE (2.2889), MAE (1.1041), and MAPE (1.538%), while also demonstrating faster convergence, lower inference time, and higher stability. Furthermore, LSTM demonstrates superior convergence speed, lower inference time (0.00098 s), and high stability (0.9091), which confirms its robustness. Compared to other deep learning techniques such as GRU, standard RNN, and CNN, RNN-LSTM outperforms in capturing long-range dependencies and learning from temporal patterns effectively. The convergence analysis further confirms the robustness of RNN-LSTM, making it the most suitable model for dynamic and complex power system environments. These findings underscore the significance of RNN-based LSTM in enhancing load forecasting reliability, ultimately contributing to improved grid stability, energy management, and operational efficiency.

The purpose of this research is to present a thorough evaluation of energy management systems that consist of hybrid energy storage systems and their control algorithms, which may be used in electric vehicles. This paper outlines the characteristics of electric vehicles, research methods, an analysis of the hybrid energy storage system architecture, the converter topology, and energy management techniques. The strength and co-occurrence of keywords over the past ten years are shown in this study using a systematic research framework for performing a literature review and using keyword analysis techniques. This study reveals a pattern of recently and frequently used terms in works of literature. Consequently, their suitability, benefits, and drawbacks are assessed. In this study, the hybrid energy storage system and converter circuit architecture are evaluated and rated. A non-isolated DC-DC converter connected to an SC is a suitable configuration for the hybrid converter because it is simple to build, is reliable, and has minimal loss/weight/cost, which all improve vehicle performance. In terms of the application of control strategies, it is shown that deterministic and fuzzy-rule-based control techniques are successfully assessed using real-scale vehicle experiments and can be selected for manufacturing. On the other hand, real-time optimization-based energy management techniques have been effectively shown in lab-scale tests and may be used in a future real-scale vehicle.

Grid-forming inverters represent a transformative advancement in power grid management, providing a flexible solution for optimizing energy systems and enhancing grid stability amid the increasing integration of renewable energy and growing grid complexity. This control strategy focuses on regulating voltage and frequency, enabling decentralized energy sources, such as renewables, to synchronize effectively with the island grid, ensuring a stable power supply. This study aims to develop a control algorithm for decentralized grid-forming inverters utilizing renewable energy resources and diesel generators. The proposed system incorporates five distributed generators: a fuel cell, wind turbine, photovoltaic panel, battery energy storage system, and diesel generator. An adaptive fuzzy-based droop controller is employed to meet the study’s objectives, with one component tracking reference voltage and frequency and another addressing the load’s active and reactive power demands. Validation through Matlab/Simulink demonstrates effective voltage and frequency regulation at the point of common coupling, supported by compelling simulation results. The study’s alignment with expected outcomes highlights the system’s reliability and robustness in maintaining grid stability amid dynamic load conditions and renewable energy integration. Furthermore, practical assessments on a test bed validate consistent active and reactive power sharing across all microgrids, reinforcing the proposed control strategy’s effectiveness in optimizing energy management and enhancing power distribution system performance.

Sustainable microgrids (MGs) provide a decentralized and environmentally sustainable solution to contemporary energy challenges, offering improved resilience and reduced carbon emissions. The effective optimization of renewable energy-based MGs requires the precise calibration of energy generation, storage, and distribution systems to maximize efficiency, reliability, and sustainability. This research presents a priority-based renewable energy system with a backup supply designed for grid-tied operation, aiming to optimize renewable resource utilization while ensuring system reliability and contributing to grid stability. The primary objective of this study is to develop an advanced optimization algorithm tailored to renewable energy-based MGs, with the goals of enhancing energy efficiency, maintaining grid stability, and minimizing operational costs. We introduce the Compact Cyclone-Based Manta Ray Foraging Optimization (C-CMRFO) algorithm, specifically designed for application in sustainable MGs to improve energy distribution efficiency, optimize resource allocation, and facilitate the integration of renewable energy sources. The proposed algorithm ensures precise voltage and frequency regulation, thereby supporting grid stability, and is characterized by rapid convergence, allowing for quick adaptation to dynamic grid conditions and efficient real-time operations. The effectiveness of the C-CMRFO algorithm is validated through extensive MATLAB/Simulink simulations, which demonstrate significant improvements in energy distribution efficiency, optimized resource utilization, and accurate voltage and frequency tracking. Additionally, the algorithm’s fast convergence rate is confirmed, enabling swift response to fluctuating grid conditions. Practical validation on a test bed further corroborates the consistency and reliability of the proposed control strategy in optimizing energy management and enhancing power distribution system performance. The close alignment between experimental results and simulation outcomes underscores the robustness and dependability of the proposed approach.