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 solar power ...continues to become a larger part of the energy mix, managing this intermittency will be increasingly important. This paper focuses on identifying daily photovoltaic power production patterns to gain new knowledge of the generation patterns throughout the year based on unsupervised learning algorithms. The proposed data-driven model aims to extract typical daily photovoltaic power generation patterns by transforming the high dimensional temporal features of the daily PV power output into a lower latent feature space, which is learned by a deep learning autoencoder. Subsequently, the Partitioning Around Medoids (PAM) clustering algorithm is employed to identify the six distinct dominant patterns. Finally, a new algorithm is proposed to reconstruct these patterns in their original subspace. The proposed model is applied to two distinct datasets for further analysis. The results indicate that four out of the identified patterns in both datasets exhibit high correlation (over 95%) and temporal trends. These patterns correspond to distinct weather conditions, such as entirely sunny, mostly sunny, cloudy, and negligible power generation days, which were observed approximately 61% of the analyzed period. These typical patterns can be expected to be observed in other locations as well. Identified PV power generation patterns can improve forecasting models, optimize energy management systems, and aid in implementing energy storage or demand response programs and scheduling efficiently.
In recent years, the Electric Vehicles (EVs) industry has experienced rapid growth, driven by advancements in battery technology, environmental awareness, and government incentives. However, ...traditional charging infrastructure's limited availability and long charging times pose significant challenges, especially for long-distance travel and public service vehicles like taxis, buses, and law enforcement vehicles. This work explores the innovative concept of Battery Swap Stations (BSSs), an emerging technology poised to transform the EV charging landscape. It specifically focuses on electric taxis operating in Chicago's urban environment, highlighting the substantial benefits this technology can offer. BSSs demonstrated to dramatically reduce charging times, improving taxi service efficiency and increasing revenue potential. Instead, conductive charging impacts the working time of taxis across all case studies (as observed in the Level 2 charger scenario) While BSS technology has its drawbacks, such as optimal location challenges and battery management complexities, it has the potential to significantly enhance service quality. Additionally, these stations hold the promise of not only increasing urban transportation system efficiency but also contributing to their sustainability.
The present paper proposes a new methodology to aid the electrification process of local public transport (LPT). In more detail, real drive cycles of traditional buses currently in use are evaluated ...together with other data to simulate the consumption of equivalent e-buses (electric buses) with similar characteristics. The results are then used in order to design the best charging infrastructure. The proposed methodology is applied to the case study of Algeciras Bay, where a specific line of LPT is considered. Real measurements are used as data for the simulation model, and the average consumption of an equivalent e-bus is obtained for different operating conditions. Based on these results, different sizes and locations for fast-charging infrastructure are proposed, and the size of the depot charging system is defined trying to maintain the current buses timetable. Finally, some future developments of the present work are presented by considering other bus lines that may benefit from the introduction of the defined charging systems.
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The ubiquitous influence of E-mobility, especially electrical vehicles (EVs), in recent years has been considered in the electrical power system in which CO2 reduction is the primary concern. Having ...an accurate and timely estimation of the total energy demand of EVs defines the interaction between customers and the electrical power grid, considering the traffic flow, power demand, and available charging infrastructures around a city. The existing EV energy prediction methods mainly focus on a single electric vehicle energy demand; to the best of our knowledge, none of them address the total energy that all EVs consume in a city. This situation motivated us to develop a novel estimation model in the big data regime to calculate EVs’ total energy consumption for any desired time interval. The main contribution of this article is to learn the generic demand patterns in order to adjust the schedules of power generation and prevent any electrical disturbances. The proposed model successfully handled 100 million records of real-world taxi routes and weather condition datasets, demonstrating that energy consumptions are highly correlated to the weekdays’ traffic flow. Moreover, the pattern identifies Thursdays and Fridays as the days of peak energy usage, while weekend days and holidays present the lowest range.
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In this paper a general model for the estimation of the uncoordinated charging costs of Electric Vehicles (EVs) in the presence of distributed and intermittent generation, and variable electricity ...tariffs is presented. The proposed method aims at estimating the monthly average cost of uncoordinated charging of a single EV depending on the hour at which the EV is plugged into the EV Supply Equipment (EVSE). The feasibility and relevance of the proposed model is verified by applying the considered cost estimation method to a suitable use case. A single EV charging service offered at a public building equipped with a Photovoltaic (PV) system has been considered as reference case. The proposed model has been applied to the PV production and loads consumption data collected during one year, and the results of the study compared with the Time-Of-Use (TOU) electricity tariff. The application of the proposed model identified noticeable deviations among the computed EV charging costs and the reference TOU profile, with differences up to 40%, depending on the considered month and on the time of charging during the day. It can be concluded that such model could be used to properly detect opportunities of energy savings, and to define dedicated EV price signals that could help to promote the optimal use of distributed energy resources.
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In recent years, market trends are confirming the increasing use of electric vehicles for private mobility. The use of such vehicles is inevitably affecting highway contexts as well. Therefore, ...highway network operators need to plan for the installation of adequate infrastructure to enable and manage the growing demand for fast charging expected in the coming years. This paper aims to assess the impact that this charging demand may have on the service areas (SAs) of the highway network operated by Autostrade per l'Italia. Starting from available traffic data, this paper proposes a methodology to forecast, for each service area, the future charging needs of EVs on a daily, monthly, and yearly basis. The analysis considers both the energy and the power that needs to be made available to ensure charging during daily traffic peaks. The results show that the impact generated by EVs will be generally significant, especially in terms of power demands with peaks between two and three megawatts. The methodology developed is entirely general and therefore applicable for similar planning in other highway or suburban roadway contexts. The validity of the developed methodology and the made assumptions have been preliminary confirmed through an initial set of data collected from one of the charging stations installed in one representative service area.
The objective of this paper is to assess the probable effect that electric vehicles (EVs), already in wide circulation and likely to increase exponentially in the near future, will have on ...distribution networks. Analyses are conducted on the necessary interventions and evolutions that the distribution grid will have to undergo in order to manage this new and progressively increasing heavy load of energy. Thus, in order to understand the technical limitations of the current infrastructure and how transformers and lines will be able to withstand the increasing penetration of EVs, urban and rural grid models have been studied, to highlight the differences between the impacts on high- and low-density networks. In addition, an analysis of fast charging station impact has been carried out. MATLAB software was used to perform the simulations for the creation of scripts, which were then exploited within the DIgSILENT PowerFactory software. This allowed evaluation of the networks under examination and verification of the effectiveness of the proposed solutions. In concluding based on findings, some methods of managing the distribution network to optimise the network parameters analysed in the study and a solution involving electric vehicles are recommended.
Increasing problems of air pollution caused by petrol-fueled vehicles had a positive impact on the expanded use and acceptance of the electric vehicles (EVs). Currently, both academic and ...institutional researchers are conducting studies to explore alternative methods of charging vehicles in a fast, reliable, and safe way that would compensate for the drawbacks of the otherwise beneficial and sustainable EVs. The wireless power transfer (WPT) systems are now offered as a possible option. Another option is the dynamic wireless charging (DWC) system, which is considered the best application of a WPT system by many practitioners and researchers because it enables vehicles to increase their driving ranges and decrease their battery sizes, which are the main problems of the EVs. A DWC system is composed of many sub-systems that require different approaches for their design and optimization. The aim of this work is to find the most functional and optimal configuration of magnetic couplers for a DWC system. This was done by performing an investigation of the main magnetic couplers adopted by the system using Ansys® Maxwell as a finite element method software. The results were analyzed in detail to identify the best option. The values of the coupling coefficients have been obtained for every configuration examined. The results disclosed that the best trade-off between performance and economic feasibility is the DD–DDQ pad, which is characterized by the best values of coupling coefficient and misalignment tolerance, without the need for two power converters for each side, as in the DDQ–DDQ configuration.
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As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases. With the increase in data ...accessibility and advancements in computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers in analyzing, optimizing, and modernizing power systems. This paper presents a comprehensive review of over 440 articles published through 2022, emphasizing the application of K-means clustering, a widely recognized and frequently used algorithm, along with its alternative clustering methods within modern power systems. The main contributions of this study include a bibliometric analysis to understand the historical development and wide-ranging applications of K-means clustering in power systems. This research also thoroughly examines K-means, its various variants, potential limitations, and advantages. Furthermore, the study explores alternative clustering algorithms that can complete or substitute K-means. Some prominent examples include K-medoids, Time-series K-means, BIRCH, Bayesian clustering, HDBSCAN, CLIQUE, SPECTRAL, SOMs, TICC, and swarm-based methods, broadening the understanding and applications of clustering methodologies in modern power systems. The paper highlights the wide-ranging applications of these techniques, from load forecasting and fault detection to power quality analysis and system security assessment. Throughout the examination, it has been observed that the number of publications employing clustering algorithms within modern power systems is following an exponential upward trend. This emphasizes the necessity for professionals to understand various clustering methods, including their benefits and potential challenges, to incorporate the most suitable ones into their studies.
The installation of ultra-fast charging stations (UFCSs) is essential to push the adoption of electric vehicles (EVs). Given the high amount of power required by this charging technology, the ...integration of renewable energy sources (RESs) and energy storage systems (ESSs) in the design of the station represents a valuable option to decrease its impact on the grid and the environment. Therefore, this paper proposes a multi-objective optimization problem for the optimal sizing of photovoltaic (PV) system and battery ESS (BESS) in a UFCS of EVs. The proposed multi-objective function aims to minimize, on one side, the annualized cost of the station, and on the other side, the produced pollutant emissions. The decision variables are the number of PV panels and the capacity of the ESS to be installed. The optimization problem is reduced to a single-objective problem by applying the linear scalarization method. Then the equivalent single-objective function is optimized through a genetic algorithm (GA). The proposed optimization framework is applied to a study case and the results prove that PV and ESS could lead to a significant reduction of both the annualized cost and the pollutant emissions. Finally, a sensitivity analysis is also presented to validate the effectiveness of the proposed solution.
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