As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these increases. With increase in data accessibility advancements computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers analyzing, optimizing, modernizing systems. This paper presents a comprehensive review over 440 articles published through 2022, emphasizing application K-means clustering,...

The recent advances in computing technologies and the increasing availability of large amounts data smart grids cities are generating new research opportunities application Machine Learning (ML) for improving observability efficiency modern power grids. However, as number diversity ML techniques increase, questions arise about their performance applicability, on most suitable method depending specific application. Trying to answer these questions, this manuscript presents a systematic review...

Climate change and global warming drive many governments scientists to investigate new renewable green energy sources. Special attention is on solar panel technology, since considered one of the primary sources panels can be installed in domestic neighborhoods. Photovoltaic (PV) power prediction essential match supply demand ensure grid stability. However, PV system has assertive stochastic behavior, requiring advanced forecasting methods, such as machine learning deep learning, predict...

DC microgrids have gained significant attention in recent years due to their potential enhance energy efficiency, integrate renewable sources, and improve the resilience of power distribution systems. However, reliable operation relies on early detection location faults ensure an uninterrupted supply. This paper aims develop fast fault mechanisms for microgrids, thereby enhancing operational minimizing environmental impact, contributing resource conservation sustainability goals. The method...

Climate disruptions have prompted institutions to invest in zero-emissions technologies, recent years. As a result, the transportation sector has witnessed shift from internal combustion engines electric. Several public transport companies initiated "Zero-emissions" project introduce alternatives diesel their bus service. This paper delves into impacts of transitioning diesel-powered electric buses. It starts by estimating emissions produced buses and comparing them. Subsequently, analysis...

The Mediterranean region is a hot spot for climate change, with transportation accounting quarter of global CO2 emissions. To meet the 2030 Sustainable Development Goals (SDGs), sustainable urban transport network needed to cut carbon emissions and improve air quality. This study aims investigate electrification public in both developed underdeveloped countries by examining existing two modes (buses trams) across region. suggests that could result significant additional demand more than 200...

The integration of Artificial Intelligence (AI) in Energy Storage Systems (ESS) for Electric Vehicles (EVs) has emerged as a pivotal solution to address the challenges energy efficiency, battery degradation, and optimal power management. capability such systems differ from theoretical modeling enhances their applicability across various domains. vast amount data available today enabled AI be trained predict behavior complex with high degree accuracy. As we move towards more sustainable...

Recent advances in computing technologies and the availability of large amounts heterogeneous data power grids are opening way for application state-of-art machine learning techniques. Compared to traditional computational approaches, algorithms could gain an advantage from their intrinsic generalization capability, by also providing accurate short-term flow forecasts distributed measurement units, with greater efficiency scalability. Several studies literature investigated use suitable...

Transportation is considered as the largest contributor to greenhouse gas emissions. Recently, many European countries and World Health organization (WHO) have passed laws reduce road vehicles emissions, which are responsible for 60.7% of transport air pollution. The electrification required various charging infrastructure options. One state-of-the-art technologies dynamic wireless systems (to deliver energy EV in motion). Thus, this paper summarizes distinct static electric vehicles....

The unpredictable nature of photovoltaic solar power generation, caused by changing weather conditions, creates challenges for grid operators as they work to balance supply and demand.As continues become a larger part the energy mix, managing this intermittency will be increasingly important.This paper focuses on identifying daily production patterns gain new knowledge generation throughout year based unsupervised learning algorithms.The proposed data-driven model aims extract typical...

The ubiquitous influence of E-mobility, especially electrical vehicles (EVs), in recent years has been considered the power system which CO2 reduction is primary concern. Having an accurate and timely estimation total energy demand EVs defines interaction between customers grid, considering traffic flow, demand, available charging infrastructures around a city. existing EV prediction methods mainly focus on single electric vehicle demand; to best our knowledge, none them address that all...

This paper proposes an unsupervised learning schema for seeking the patterns in rms voltage variations at time scale between 1 s and 10 min, a rarely considered studies but could be relevant incorrect operation of end-user equipment. The proposed framework employs Kernel Principal Component Analysis (KPCA) followed by k-means clustering. is applied on 10-min series with 1-s resolution obtained from 44 different periods location south Sweden. Then, ten are reconstructing each cluster center....

Recognition and analysis of voltage sags (dips) allow network operators to predict prevent problems in real-life applications. Clearing the sag source by direction detection methods is most effective way solve improve their related problems. However, existing analytical use single or two input features as phasor-based (PB) instantaneous-based (IB) values. Hence, limited maximum accuracy given at 93% 84% when using PB for noiseless high-level noise signals, respectively. To increase accuracy,...

This paper presents a comprehensive study of winter temperatures in Norway and northern Sweden, covering period 50 to 70 years. The analysis utilizes Singular Spectrum Analysis (SSA) investigate temperature trends at six selected locations. results demonstrate an overall long-term rise temperatures, which can be attributed global warming. However, when investigating variations highest, lowest, average for December, January, February, 50% the cases exhibit significant decrease recent years,...

Transportation is one of the sectors with highest CO2 emissions, accounting for 23% globally and significantly contributing to climate change. To address this challenge, authorities have proposed new stringent policies that lead decarbonization. From perspective, work proposes a multi-scenario analysis electrification fleet private users. The scenarios differ on type charging mode adopted: slow (charging modes 1 2) fast 3 4). model aims identify percentage potential users who can shift from...

This paper addresses the issue of seeking sub-10-min patterns in fast rms voltage variations from time-limited measurement data at multiple locations worldwide. is a rarely considered time scale studies that could be important for incorrect operation end-user equipment. Moreover, measurements significant view pattern methods. To learn more about this scale, we propose an unsupervised learning method employs Kernel Principal Component Analysis (KPCA) with Cosine kernel to extract principal...

The Electric Vehicle (EV) market has been growing exponentially in recent years, which is why the distribution network of public charging stations will be subject to expansion and upgrading. In order improve infrastructure, this paper aims develop a model capable analyzing current situation stretch highway, identifying congestion points, created by formation queues at points. A specific section highway Spain was selected as case study evaluate performance model, allowing for rigorous testing...

An accurate prediction of the electric vehicles (EVs) energy consumption is crucial requirement to deliver promise green solution for relieving concerns from fossil depletion and vehicle emissions. To solve problem, most substantial facing challenges are laying down on heterogeneous data insight, modelling non-linear lack supporting technologies provide primary model problem. In this paper, latent pattern extracted presented as a new set metadata. Statistical features crowdsourced EVs...

In recent years, Photovoltaic System (PV) have been installed in parking lots order to provide the green energy Electric vehicles (EVs). Energy Synchronizing between PV generations and EVs demand is a function of different variables, it very challenging. Having an accurate prediction generation helps ease complexity this problem. Although various Machine Learning (ML) techniques applied resulted well, traditional ML approaches need years history make prediction. many cases, or houses...

Electric Vehicles (EVs) play an important role to reach the global ambitious climate and air quality goals. The timely implementation of adequate EV charging infrastructure is critical success ecological revolution. However, at same time, adoption main driver for business case infrastructure. In order implement optimal infrastructure, it consider all relevant factors which influence demand EVs. This paper aims evaluate on highways by considering several that impact electric vehicle...