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In the scope of energy management systems (EMSs) for microgrids, the forecasting module stands out as an essential element, significantly influencing the efficacy of optimal solution policies. Forecasts for consumption, generation, and market prices play a crucial role in both day-ahead and real-time decision-making processes within EMSs. This paper aims to develop a machine learning-based multivariate forecasting methodology to account for the intricate interplay pertaining to these variables from the perspective of day-ahead energy management. Specifically, our approach delves into the dynamic relationship between load demand variations and electricity price fluctuations within the microgrid EMSs. The investigation involves a comparative analysis and evaluation of recurrent neural networks' performance to recognize the most effective technique for the forecasting module of microgrid EMSs. This study includes approaches based on Long Short-Term Memory Neural Networks (LSTMs), with architectures ranging from Vanilla LSTM, Stacked LSTM, Bi-directional LSTM, and Convolution LSTM to attention-based models. The empirical study involves analyzing real-world time-series data sourced from the Australian Energy Market (AEM), specifically focusing on historical data from the NSW state. The findings indicate that while the Triple-Stacked LSTM demonstrates superior performance for this application, it does not necessarily lead to more optimal operational costs, with forecast inaccuracies potentially causing deviations of up to forty percent from the optimal cost. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative framework for wind power prediction that focuses on the future of energy forecasting utilizing intelligent deep learning and strategic feature engineering. This research investigates the application of a state-of-the-art deep learning model for wind energy prediction to make extremely short-term forecasts using real-time data on wind generation from New South Wales, Australia. In contrast with typical approaches to wind energy forecasting, this model relies entirely on historical data and strategic feature engineering to make predictions, rather than relying on meteorological parameters. A hybrid feature engineering strategy that integrates features from several feature generation techniques to obtain the optimal input parameters is a significant contribution to this work. The model's performance is assessed using key metrics, yielding optimal results with a Mean Absolute Error (MAE) of 8.76, Mean Squared Error (MSE) of 139.49, Root Mean Squared Error (RMSE) of 11.81, R-squared score of 0.997, and Mean Absolute Percentage Error (MAPE) of 4.85%. Additionally, the proposed framework outperforms six other deep learning and hybrid deep learning models in terms of wind energy prediction accuracy. These findings highlight the importance of advanced data analysis for feature generation in data processing, pointing to its key role in boosting the precision of forecasting applications. [ABSTRACT FROM AUTHOR]

An enormous number of domestic and international tourists visit Saint Martin's Island in Bangladesh annually. Unfortunately, the lack of proper planning as well as severe electricity shortages are hampering its development towards a smart city. This study proposes a smart city model for the remote area with a grid-independent microgrid to meet the rising load demand. It demonstrates that implementation of the Internet of things can effectively utilize the resources of Saint Martin following the smart city criteria. The distributed energy resources have been optimized to identify the best microgrid configuration that complies with Sustainable Development Goal 7. Finally, nonlinear simulations are carried out to compare the stability of the proposed systems. The study outlines the benefits of employing eco wave power and second-life batteries, as well as the advantages of using a supercapacitor as a speedy responder to disturbances. The research ultimately gives the multidisciplinary knowledge to policymakers that they require to transform a small island like Saint Martin into a tourist-intensive smart city. [ABSTRACT FROM AUTHOR]

Distribution network operators face technical and operational challenges in integrating the increasing number of distributed energy resources (DER) with the distribution network. The hosting capacity analysis quantifies the level of power quality violation on the network due to the high penetration of the DER, such as over voltage, under voltage, transformer and feeder overloading, and protection failures. Real-time monitoring of the power quality factors such as the voltage, current, angle, frequency, harmonics and overloading that would help the distribution network operators overcome the challenges created by the high penetration of the DER. In this paper, different conventional hosting capacity analysis methods have been discussed. These methods have been compared based on the network constraints, impact factors, required input data, computational efficiency, and output accuracy. The artificial intelligence approaches of the hosting capacity analysis for the real-time monitoring of distribution network parameters have also been covered in this paper. Different artificial intelligence techniques have been analysed for sustainable integration, power system optimisation, and overcoming real-time monitoring challenges of conventional hosting capacity analysis methods. An overview of the conventional hosting capacity analysis methods, artificial intelligence techniques for overcoming the challenges of distributed energy resources integration, and different impact factors affecting the real-time hosting capacity analysis has been summarised. The distribution system operators and researchers will find the review paper as an easy reference for planning and further research. Finally, it is evident that artificial intelligence techniques could be a better alternative solution for real-time estimation and forecasting of the distribution network hosting capacity considering the intermittent nature of the DER, consumer loads, and network constraints. [ABSTRACT FROM AUTHOR]

In electric vehicle (EV) applications, hybrid excitation of switched reluctance motors (HESRMs) are gaining popularity due to their advantages over other EV motors. The benefits include control flexibility, simple construction, high torque/power density, and the ability to operate over a broad speed range. However, modern HESRMs are constructed by increasing the air gap flux density with permanent magnets (PMs) in the excitation system in order to generate more electromagnetic torque. This study aims to investigate a new topology for increasing the torque capabilities of HESRM without the use of permanent magnets (PMs) or other rare-earth components. This paper provides a comprehensive evaluation of the static and dynamic characteristics, software analysis using the Ansys 2D finite element method (FEM), and an experimental demonstration of the real-time motor with an advanced control strategy in MATLAB/Simulink. Our simulation and experimental results for a machine with 12/8 poles and a machine rating of 1.2 kW indicate that the HESRM designed without PMs has greater torque capability and efficiency than the conventional SRM. The proposed HESRM without PMs has a high torque/power density and a higher torque per ampere across the entire speed range, making it suitable for EV applications. [ABSTRACT FROM AUTHOR]

Automotive applications often experience conflicting-objective optimization problems focusing on performance parameters that are catered through precisely developed cost functions. Two such conflicting objectives which substantially affect the working of traction machine drive are maximizing its speed performance and minimizing its energy consumption. In case of an electric vehicle (EV) powertrain, drive energy is bounded by battery dynamics (charging and capacity) which depend on the consumption of drive voltage and current caused by driving cycle schedules, traffic state, EV loading, and drive temperature. In other words, battery consumption of an EV depends upon its drive energy consumption. A conventional control technique improves the speed performance of EV at the cost of its drive energy consumption. However, the proposed optimized energy control (OEC) scheme optimizes this energy consumption by using robust linear parameter varying (LPV) control tuned by genetic algorithms which significantly improves the EV powertrain performance. The analysis of OEC scheme is conducted on the developed vehicle simulator through MATLAB/Simulink based simulations as well as on an induction machine drive platform. The accuracy of the proposed OEC is quantitatively assessed to be 99.3% regarding speed performance which is elaborated by the drive speed, voltage, and current results against standard driving cycles. [ABSTRACT FROM AUTHOR]

In recent years, photovoltaic (PV) systems have emerged as economical solutions for irrigation systems in rural areas. However, they are characterized by low voltage output and less reliable configurations. To address this issue in this paper, a promising inverter configuration called Impedance (Z)-source inverter (ZSI) is designed and implemented to obtain high voltage output with single-stage power conversion, particularly suitable for irrigation application. An improved and efficient modulation scheme and design specifications of the network parameters are derived. Additionally, a suitable fault-tolerant strategy is developed and implemented to improve reliability and efficiency. It incorporates an additional redundant leg with an improved control strategy to facilitate the fault-tolerant operation. The proposed fault-tolerant circuit is designed to handle switch failures of the inverter modules due to the open-circuit and short-circuit faults. The relevant simulation and experimental results under normal, faulty and post-fault operation are presented. The post-fault operation characteristics are identical to the normal operation. The motor performance characteristics such as load current, torque, harmonic spectrum, and efficiency are thoroughly analysed to prove the suitability of the proposed system for irrigation applications. This study provides an efficient and economical solution for rural irrigation utilized in developing countries, for example, India. [ABSTRACT FROM AUTHOR]