Arafat Ibne Ikram

Microgrids are designed to utilize renewable energy resources (RER) that are revolutionary choices in reducing the environmental effect while producing electricity. The RER intermittency poses technical and economic challenges for the microgrid systems that can be overcome by utilizing the full potential of hybrid energy storage systems (HESS). A microgrid comprising of a solar photovoltaic panel, wind turbine, lead-acid battery, electrolyzer, fuel cell, and hydrogen (H ) tank is considered for techno-economic feasibility and environmental impact assessment on a grid integration scenario. Mathematical functions are utilized to model the components for estimating annual hourly renewable generation and energy storage behavior. The load consumption model for 50 homes is generated using Gaussian distribution to incorporate the uncertainty. Optimal sizing of the microgrid components is determined using the particle swarm optimization (PSO) algorithm to minimize the upfront installation cost and levelized cost of energy (LCOE). Different energy storage penetration scenarios, e.g., 25%, 50%, 75%, and 100% for the microgrid system, are considered where 100% penetration level stands for maintaining the load demand using the available resources without depending on the grid energy supply. The lowest LCOE is found between 0.06 /kWh and 0.11 /kWh, and the highest annual GHG is reduced to half compared to the grid emission. GHG is imposed around 62.14 (tCO2e/yr) - 73.57 (tCO2e/yr) for Madrid and Seville, respectively.

Rising fuel prices, global warming, and environmental damage are leading to increased demand for rooftop solar energy systems connected to the power grid. The development of smart grids, modern metering systems, and energy management could promote energy conservation in households. In this study, two different meta-heuristic optimization techniques were employed to schedule the shiftable load in suitable hours for decreasing electricity costs and minimizing peak to average ratio in a smart home while maintaining optimum user comfort. For power generation and storing energy, a grid-connected residential load with rooftop solar panels, a battery, and an inverter is considered. First, the problem is theoretically described using a load model and an objective function, with the primary goal of reducing power costs by moving the time of usage for certain home appliances. Simulations validate the proposed strategies, which effectively reduce power costs by 4.5% by shifting the time of use, with both optimization algorithms showing similar output. The residential electricity cost before optimization was 507.12 BDT/day, which decreased to 484.33 BDT/day after optimization without compromising load turn-off.

A smart grid is integral to the digitalized transformation of the electricity sector, employing self-sufficient systems that integrate information, telecommunication, and advanced power technologies. Artificial Intelligence (AI), particularly Machine Learning (ML), plays a crucial role in overcoming the limitations of traditional modeling techniques. ML enables intelligent decision-making in response to dynamic factors like changing customer energy demands or disruptions in power supply within smart grids. This paper compares Machine Learning-Based Traditional and Ensemble techniques for predicting smart grid stability, utilizing an augmented dataset from Kaggle. For optimization, various classifiers and Ensemble Techniques, including Bagging, Boosting, Stacking, and Voting, were implemented with hyperparameter tuning. The experimental study highlights that ML-based Ensemble Techniques, with optimized parameters, outperform individual traditional techniques, showcasing higher accuracy and overall superior performance. A comprehensive comparative analysis based on evaluation metrics such as accuracy, precision, recall, F1-score, and ROC emphasizes the potential of these techniques to enhance the efficiency of predicting smart grid stability.

There are increasing concerns about power quality disturbances (PQDs) at many phases of energy generation, transformation, distribution, and consumption due to the increasing interconnection of various energy systems. The basis for addressing PQDs is the automatic categorization of voltage or phase angle disturbances. According to a usual standpoint, the three distinct steps of signal analysis, feature selection, and classification should be used to separate the detection of issues with power quality. Nevertheless, signal analysis possesses several inherent deficiencies, mostly stemming from the laborious and inaccurate process of human feature selection. Consequently, this results in diminished classification accuracy when dealing with many disturbances and a compromised ability to withstand disruptive interference. This study focuses on the identification and categorization of PQD using a machine learning-based classifier, taking into account the features of the power quality problems problem, eight different features are taken from the voltage data and used to figure out what caused each PQD. Various types of machine learning models are employed to analyze the dataset, and the effectiveness of the machine learning classifier is assessed by validating its performance using a separate test dataset. Once the machine learning classifier model can classify the disturbances types with 96% accuracy. The proposed classifiers can effectively detect disturbances in the transmission line.

Renewable energy holds enormous potential in Bangladesh, but significant obstacles must be overcome to fully harness its benefits. With natural gas being the primary source of power generation in the country, there is a growing need to explore alternative sources of energy to reduce emissions and relieve the pressure on limited fuel supplies. Wind turbines (WT) and solar photovoltaic (PV) systems are currently being deployed to harness renewable energy, and Bangladesh’s unique geographical features, including being a riverine country with a modest land area, make it an ideal candidate for floating PV and WT installations. However, the intermittency of renewable generation and high upfront costs pose significant challenges to the technical and economic feasibility of microgrid operation. The use of Energy Management Systems (EMS) and Grey Wolf Optimization (GWO) can help optimize the use of renewable resources and minimize capital costs, respectively. In this research, a methodology is proposed to assess the economic feasibility of floating PV and WT in the Korean Export Processing Zone (KEPZ), a critical hub of the country’s economic activity, while minimizing renewable energy costs using the GWO technique. Mathematical models are used to design the hybrid renewable system, and load-demand data is collected from KEPZ. Based on hourly resource data, annual hourly floating PV and WT energy generation is estimated, and the electricity cost (LCEO) is evaluated, yielding a cost of 0.099$/kWh for the system, taking into account project lifetime, interest rate, and inflation rate.

The rising popularity of renewable energy sources (RES) has escalated due to limited fossil fuel and environmental concerns. However, due to weather dependency, renewable energy generation is uncertain. In order to ensure maximum renewable generation and store the energy for later use, a methodology was shown in this paper. Maximum power point tracking (MPPT) controllers can significantly improve Solar photo-voltaic (PV) performance. Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller was implemented for a grid-connected PV system considering different levels of load demand, and weather conditions such as solar insolation and temperature. In a time of no renewable generation and peak energy demand, Energy storage can be a sustainable solution. A hybrid energy storage (HES) system consisting of both battery (BAT) and super-capacitor (SC) performs the best in time of peak load demand, and this was implemented in the system. In the time of no renewable generation, HES is able to provide the deficit energy to the grid. A smart energy management system (EMS) is proposed in this study which can effortlessly manage the energy between generation, energy storage, load demand, and existing grid. The proposed model was tested considering real hourly data. The output response of each component was analyzed to measure the performance and redundancy of the system.

Electric vehicles (EVs) have rapidly gained popularity in numerous countries, surpassing other transportation forms. The swift ascent of EV adoption in Bangladesh has exacerbated the prevailing energy crisis. Amid its growth, Bangladesh faces energy-related challenges stemming from excessive consumption. While electric vehicles offer benefits such as cost-effective transportation and reduced greenhouse gas emissions, the substantial energy demand for daily battery charging poses a formidable challenge. This equilibrium will shift shortly despite the current lower EV count compared to conventional vehicles in Bangladesh. Due to the impending increase in EV numbers, the high energy consumption required for frequent battery recharging has significant ramifications for power grids and distribution networks. This paper centers on comprehending the repercussions of EV charging on distribution transformers. Integrating a substantial number of EV chargers into the distribution grid engenders harmonics, which, in turn, influence voltage profiles, contribute to power losses, and ultimately impact electricity quality. This paper’s contribution lies in its investigation of the challenges posed by the increasing adoption of electric vehicles in Bangladesh, particularly concerning the energy demand for EV charging and its impact on the distribution grid, with a specific focus on the role of distribution transformers and harmonics. The paper aims to provide insights and potential solutions for mitigating these challenges in the context of EV adoption.

Microgrids (MG) are innovative in lowering GHG emissions from electricity production by using renewable energy sources. The technical and economic feasibility of MG operation with hybrid energy storage systems is challenged by the intermittent nature of renewable generation. The hybrid energy storage system has been investigated for decades. We provide a hybrid energy storage system with a Grid-connected MG integration model to assess its technological, economic, and environmental impacts. The MG model included photovoltaic panels, wind turbines, lead-acid batteries, electrolyzer modules, fuel cells, and H2 cylinder tanks. The mathematical function for each component used in the system is developed individually to estimate the annual hourly energy generation and consumption. Annual hourly data sets of load consumption are used as load models. The number of components needed for the MG operation to run economically feasible is achieved using the Genetic algorithm (GA) optimization technique thereafter reducing the Levelized cost of energy (LCOE) of the system. An energy dispatching technique is employed to efficiently distribute energy across the hybrid storage and load models. We examined different MG energy penetration levels of 25%, 50%, 75%, and 100% in terms of peak power distribution capacities to load demand relative to the existing grid. MG with 100% integration strives to maintain full load demand without buying energy from the grid. The LCOE and GHG emissions for each Grid-MG integration scenario are calculated. At 100% of the penetration scenario, the LCOE was found 0.0611 ($/kWh) which was best among all the other penetration scenarios.