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.

The growing diversity of consumer loads emphasizes the need for efficient operation of photovoltaic generation systems (PVGSs). This paper presents a novel control framework for maximum power point tracking (MPPT) in PVGSs, designed to enhance efficiency, robustness, and adaptability under dynamic environmental conditions. The proposed methodology integrates optimized Artificial Neural Network (ANN) with nonlinear backstepping controller. Hybrid particle swarm optimization (PSO) and genetic algorithm (GA) approach is used to optimize ANN, which leverages the global search capabilities of PSO and the local refinement strengths of GA to optimize the ANN’s weights and biases, ensuring accurate prediction of the PV reference voltage that is critical for MPPT. Additionally, the nonlinear backstepping controller ensures system stability and precise tracking of the predicted reference voltage by recursively constructing Lyapunov functions to guarantee asymptotic stability. The effectiveness of this control framework is evaluated through MATLAB/Simulink and validated with hardware testing. It is observed from simulation and hardware testing results, compared to existing ANN and conventional techniques, the proposed approach yielded significant improvements by achieving root mean square error of 0.103%, tracking accuracy of 99. 8%, and reduction of power loss to 0. 2%. The proposed control framework significantly enhances MPPT robustness and efficiency in dynamic environments and enables higher integration of renewable energy. This advancement supports scalable, resilient PV systems critical for sustainable energy transitions.

In this paper, an energy management system (EMS) using intelligent Lyapunov based adaptive fuzzy controller is designed for standalone microgrid having photovoltaic and wind turbines as primary sources, while battery storage system and diesel generator are available as secondary sources. Moreover, any remnant power available in the system is guided by the controller towards the dump load. The centralized controller based on intelligent adaptive fuzzy system helps to optimize the power flow in the system during high and low penetration of renewable energy resources under variable load conditions. Additionally, voltage regulation is carried out using nonlinear Lyapunov controller which kept the output voltages in desired range. The proposed control system helps in increasing the efficiency and reliability of standalone microgrid. Furthermore, the proposed system is validated by comparing the simulation and hardware results with the PID and sliding mode controller, which are consider as benchmark controller techniques for standalone microgrid. The outcome of the system validates the superiority of proposed techniques and achieved global asymptotic stability under variable load conditions.

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.