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This paper presents a cutting-edge Sustainable Power Management System for Light Electric Vehicles (LEVs) using a Hybrid Energy Storage Solution (HESS) integrated with Machine Learning (ML)-enhanced control. The system's central feature is its ability to harness renewable energy sources, such as Photovoltaic (PV) panels and supercapacitors, which overcome traditional battery-dependent constraints. The proposed control algorithm orchestrates power sharing among the battery, supercapacitor, and PV sources, optimizing the utilization of available renewable energy and ensuring stringent voltage regulation of the DC bus. Notably, the ML-based control ensures precise torque and speed regulation, resulting in significantly reduced torque ripple and transient response times. In practical terms, the system maintains the DC bus voltage within a mere 2.7% deviation from the nominal value under various operating conditions, a substantial improvement over existing systems. Furthermore, the supercapacitor excels at managing rapid variations in load power, while the battery adjusts smoothly to meet the demands. Simulation results confirm the system's robust performance. The HESS effectively maintains voltage stability, even under the most challenging conditions. Additionally, its torque response is exceptionally robust, with negligible steady-state torque ripple and fast transient response times. The system also handles speed reversal commands efficiently, a vital feature for real-world applications. By showcasing these capabilities, the paper lays the groundwork for a more sustainable and efficient future for LEVs, suggesting pathways for scalable and advanced electric mobility solutions. [ABSTRACT FROM AUTHOR]

Using recent information and communication technologies for monitoring and management initiates a revolution in the smart grid. These technologies generate massive data that can only be processed using big data tools. This paper emphasizes the role of big data in resolving load forecasting, renewable energy sources integration, and demand response as significant aspects of smart grids. Meters data from the Low Carbon London Project is investigated as a case study. Because of the immense stream of meters' readings and exogenous data added to load forecasting models, addressing the problem is in the context of big data. Descriptive analytics are developed using Spark SQL to get insights regarding household energy consumption. Spark MLlib is utilized for predictive analytics by building scalable machine learning models accommodating meters' data streams. Multivariate polynomial regression and decision tree models are preferred here based on the big data point of view and the literature that ensures they are accurate and interpretable. The results confirmed the descriptive analytics and data visualization capabilities to provide valuable insights, guide the feature selection process, and enhance load forecasting models' accuracy. Accordingly, proper evaluation of demand response programs and integration of renewable energy resources is accomplished using achieved load forecasting results. [ABSTRACT FROM AUTHOR]

Accurate forecasting of electricity generation from renewable energy sources is crucial for the operation, planning and management of smart grids. For reliable planning and operation of photovoltaic (PV) systems in grid-connected or islanded utilities, an hourly day-ahead forecast of PV output is critical. The forecast of PV power can be done indirectly by estimating solar irradiance. For forecasting day-ahead hourly global horizontal irradiance (GHI), two forecasting models with different multivariate inputs are proposed in this paper, and the results are compared. These models use a hybrid algorithm of discrete wavelet decomposition and bidirectional long short-term memory (BILSTM). The inputs of the first model contain GHI and weather type data. The other model allows for observation of the effect of meteorological values including GHI, temperature, humidity, wind speed, and weather type data. The forecasting performance of deep learning algorithms which contain recurrent neural network (RNN), long short-term memory (LSTM), and BILSTM algorithms for day ahead hourly solar irradiance forecasting problems are also compared. To evaluate the performance of proposed models, two datasets are used for Model 1 and one dataset is used for Model 2. An experiment is also done to demonstrate that the proposed Model 1 is applicable in datasets collected in the vicinity of the city of Trabzon. On the other hand, BILSTM algorithm outperforms RNN and LSTM algorithms. It is seen that the test successes of both proposed models are better than the results given in the literature. [ABSTRACT FROM AUTHOR]

Due to favourable economic and environmental concerns, reduced power losses, and technological advancements in data science, large-scale renewable energy sources (RESs) are being inserted into smart microgrids (MGs). Generally, these resources are inserted at the distribution level and feed consumers locally with better reliability. The planning and stable operation of the power networks are complicated at numerous levels as a result of large-scale renewable integration, as the infeed from renewable sources like wind and solar is highly intermittent and uncertain, significantly affecting the resilient, intelligent, and optimal operation of the power system. The creation of scenarios is an essential step in the planning and stable operation of renewable-rich power systems. Scenarios can be generated based on seasonal events to mitigate growing complexity and intermittency. In this paper, time-series solar data is used for forecasting solar irradiance using long-short-term memory (LSTM). Besides, a cost-based optimisation model has been developed by using stochastic programming to obtain both the optimal sizing and optimal switching sequence of distributed units as well as the optimal number of required units. The stochastic nature of solar and load demand is considered in beta (β ) distribution. The objective function formulated covers different costs, like capital, operational, and switching costs associated with required units. The running cost incorporated with the capital cost gives us a broader design for planning the MG, thus making it both planning and operational problem. The entire proposed MG planning problem has been developed in a Python and MATLAB ® environment. [ABSTRACT FROM AUTHOR]

This study presents a meticulous examination of the solar energy potential of five selected metropolitan cities (Abakaliki, Awka, Enugu, Owerri, and Umuahia) in Eastern part of Nigeria using deep learning algorithm, specifically the Long Short-Term Memory (LSTM) model. These cities, despite being characterized by extended rainy seasons and a high level of cloudiness, are suitable environment for solar power generation and investment opportunities. The employed methodology capitalized on the LSTM deep learning approach to analyze and predict energy generation, utilizing comprehensive hourly weather data from the National Airspace Agency (NASA). The data set comprised various parameters, such as date/time, solar azimuth angle, temperature, humidity, wind speed, wind direction, cloud cover, and power, enabling a thorough analysis of each city. To ensure accuracy, energy prediction capabilities were benchmarked against real-time datasets from a solar power plant in Ulsan, South Korea, thereby training and fine-tuning the model for precision. The LSTM model's performance metrics were maintained at a learning rate of 0.07, a batch size of 150, and a train-test split ratio of 0.8 to 0.2. Data validation exhibited a mean square error (MSE) of 0.01, demonstrating the model's reliability. Results showed Enugu as having the highest solar energy potential, averaging 6.25 kWh/day, while Awka registered the most substantial electricity demand across various sectors. These findings highlight the substantial potential for photovoltaic (PV) power systems and advocate for the immediate implementation of renewable energy policy in the selected cities. These are expected to bring about significant implications for future renewable energy environmentally friendly investments in Nigeria and globally. [ABSTRACT FROM AUTHOR]