6,230 research outputs found
Big Data in Power Systems: Leveraging grid optimization and wave energy integration
Power systems have been through different challenges and technological innovations in the last years and are rapidly evolving into digital systems through the deployment of the smart grids concept. Producing large amounts of data, power systems can benefit from the application of big data analytics which can help leveraging the optimization processes going on in power grids nowadays. The whole value of chain of electric power can benefit from the application of big data techniques. This paper presents a short overview of possible applications and challenges that still need to be considered for this synergy to grow. Under the framework of an H2020 funded project named BigDataOcean, a case study will be described, showing how a data-driven approach can foster the development of offshore renewable sources using the example of wave energy
Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
This systematic review meticulously explores the transformative impact of digital technologies on the grid planning, grid operations, and energy market dynamics of power distribution grids. Utilizing a robust methodological framework, over 54,000 scholarly articles were analyzed to investigate the integration and effects of artificial intelligence, machine learning, optimization, the Internet of Things, and advanced metering infrastructure within these key subsections. The literature was categorized to show how these technologies contribute specifically to grid planning, operation, and market mechanisms. It was found that digitalization significantly enhances grid planning through improved forecasting accuracy and robust infrastructure design. In operations, these technologies enable real-time management and advanced fault detection, thereby enhancing reliability and operational efficiency. Moreover, in the market domain, they support more efficient energy trading and help in achieving regulatory compliance, thus fostering transparent and competitive markets. However, challenges such as data complexity and system integration are identified as critical hurdles that must be overcome to fully harness the potential of smart grid technologies. This review not only highlights the comprehensive benefits but also maps out the interdependencies among the planning, operation, and market strategies, underlining the critical role of digital technologies in advancing sustainable and resilient energy systems
What are the key multidimensional success criteria required for reducing LCOE through digital transformation in offshore wind farms?
Formålet med denne studien er å undersøke de flerdimensjonale suksesskriteriene som er avgjørende for å redusere energikostnaden også kjent som Levlized cost of Energy (LCOE) gjennom digital transformasjon innenfor offshore vind prosjekter. For å besvare problemstilling vil studien sette søkelys på fire underspørsmål som omhandler:
(1) For å sikre operational excellence og tilpasning til FNs bærekraftsmål gjennom digital transformasjon: Hvilke suksessfaktorer må være på plass? (2) Er data tilgjengelig for bruk til den digital transformasjon? (3) Hvordan kan man muliggjør optimal Grid Integration av vindparken? (4) Kan man utnytte digitale verktøy for å redusere LCOE i en havvindpark?
Studien fremhever den uunnværlige rollen av teknologi i form av digitale verktøy og data, som spiller som katalysatorer for å styrke operasjonell effektivitet og maksimere verdiskaping i offshore vindenergisektoren.
Studien er gjennomført som kvalitativ Case-studier analyse i form av ti individuelle dybdeintervjuer med deltakere fra ulike selskaper i verdikjeden til offshore vind industri. Studien undersøker den betydelige påvirkningen FNs bærekraftsmål har på utviklingen av offshore vindprosjekter, samt den vitale rollen operational excellence har for å lykkes. Den vurderer om offshore vind industrien er klar for Industri 5.0, dens evne til å redusere LCOE, og dens innflytelse på sektorens fremtid. Funnene understreker betydningen av tilgjengelig data, optimalisert effektivitet, og bruk av sanntidsdata for å forbedre sikkerhet, bærekraft og effektiv energiproduksjon i vindparker.
Videre dykker studien ned i implementeringen av digital transformasjon, og viser til hvordan digitale verktøy og automatisering, sammen med menneskelig inngripen, driver informert beslutningstaking. Funnene legger vekt på nødvendigheten av datasamarbeid, kunnskapsdeling, og kompetent personell for å fremme industriell vekst, samtidig som det opprettholdes en balanse mellom kompleksitet og kompetanse, og utforsker avansert digital tvilling-teknologi og hvordan det kan påvirke i redusering av LCOE.
Studien tilbyr verdifull innsikt for interessenter og hjelper til med å håndtere utfordringer og muligheter i digital transformasjon av offshore vindparker. Den fremhever offshore vinindustriens avgjørende rolle i utviklingen av renere, effektive energisystemer, og støtter en bærekraftig og fremgangsrik fremtid.This purpose of this study is to thoroughly examine the multidimensional success criteria crucial in reducing the levelized cost of energy (LCOE) through digital transformation within the context of offshore wind farm projects. To help answer the research question, this study will focus on four preliminary research questions: (1) To ensure Operational Excellence and Alignment with UN SDGs through Digital Transformation: What success factors need to be in place? (2) Is Data available to be used to enable Digital Transformation? (3) How do you enable optimal Grid Integration of the wind park? (4) Can you leverage digital tools to reduce LCOE in an offshore wind farm? The research spotlights the indispensable role of technology in form of digital tools and data, as catalysts for bolstering operational efficiency and maximizing value creation in the offshore wind energy sector.
The study has been carried out as a qualitative case study analysis in the form of ten individual in-depth interviews with participants from various companies in the value chain of the offshore wind industry. The study investigates the substantial impact of United Nations (UN) sustainability goals on offshore wind project development and the vital role of operational excellence. It evaluates the industry's preparedness for Industry 5.0, its capacity to reduce LCOE, and its influence on the sector's future. The research and findings underscore the significance of accessible data, optimized efficiency, and real-time data utilization to enhance safety, sustainability, and energy production in wind farms.
Additionally, the research delves into Industry 5.0's implementation, demonstrating how digital tools and automation, combined with human input, drive informed decision-making. The findings emphasize the necessity for data collaboration, knowledge sharing, and skilled personnel to foster industry growth while maintaining a balance between complexity and competence and explores advanced digital twin technology and how it can influence in reducing LCOE.
The study offers valuable insights for stakeholders and aids in addressing challenges and opportunities in offshore wind farm digital transformation. It accentuates the offshore wind industry's pivotal role in advancing cleaner, efficient energy systems, promoting a sustainable and prosperous future
Enhancing Energy Production with Exascale HPC Methods
High Performance Computing (HPC) resources have become the key actor for achieving more ambitious challenges in many disciplines. In this step beyond, an explosion on the available parallelism and the use of special purpose
processors are crucial. With such a goal, the HPC4E project applies new exascale HPC techniques to energy industry simulations, customizing them if necessary, and going beyond the state-of-the-art in the required HPC exascale
simulations for different energy sources. In this paper, a general overview of these methods is presented as well as some specific preliminary results.The research leading to these results has received funding from the European Union's Horizon 2020 Programme (2014-2020) under the HPC4E Project (www.hpc4e.eu), grant agreement n° 689772, the Spanish Ministry of
Economy and Competitiveness under the CODEC2 project (TIN2015-63562-R), and
from the Brazilian Ministry of Science, Technology and Innovation through Rede
Nacional de Pesquisa (RNP). Computer time on Endeavour cluster is provided by the
Intel Corporation, which enabled us to obtain the presented experimental results in
uncertainty quantification in seismic imagingPostprint (author's final draft
Empowering Distributed Solutions in Renewable Energy Systems and Grid Optimization
This study delves into the shift from centralized to decentralized approaches
in the electricity industry, with a particular focus on how machine learning
(ML) advancements play a crucial role in empowering renewable energy sources
and improving grid management. ML models have become increasingly important in
predicting renewable energy generation and consumption, utilizing various
techniques like artificial neural networks, support vector machines, and
decision trees. Furthermore, data preprocessing methods, such as data
splitting, normalization, decomposition, and discretization, are employed to
enhance prediction accuracy.
The incorporation of big data and ML into smart grids offers several
advantages, including heightened energy efficiency, more effective responses to
demand, and better integration of renewable energy sources. Nevertheless,
challenges like handling large data volumes, ensuring cybersecurity, and
obtaining specialized expertise must be addressed. The research investigates
various ML applications within the realms of solar energy, wind energy, and
electric distribution and storage, illustrating their potential to optimize
energy systems. To sum up, this research demonstrates the evolving landscape of
the electricity sector as it shifts from centralized to decentralized solutions
through the application of ML innovations and distributed decision-making,
ultimately shaping a more efficient and sustainable energy future
Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions
Traditional power grids are being transformed into Smart Grids (SGs) to
address the issues in existing power system due to uni-directional information
flow, energy wastage, growing energy demand, reliability and security. SGs
offer bi-directional energy flow between service providers and consumers,
involving power generation, transmission, distribution and utilization systems.
SGs employ various devices for the monitoring, analysis and control of the
grid, deployed at power plants, distribution centers and in consumers' premises
in a very large number. Hence, an SG requires connectivity, automation and the
tracking of such devices. This is achieved with the help of Internet of Things
(IoT). IoT helps SG systems to support various network functions throughout the
generation, transmission, distribution and consumption of energy by
incorporating IoT devices (such as sensors, actuators and smart meters), as
well as by providing the connectivity, automation and tracking for such
devices. In this paper, we provide a comprehensive survey on IoT-aided SG
systems, which includes the existing architectures, applications and prototypes
of IoT-aided SG systems. This survey also highlights the open issues,
challenges and future research directions for IoT-aided SG systems
Electrical and Computer Engineering Annual Report 2016
Faculty Directory Faculty Highlights Faculty Fellow Program Multidisciplinary Research Fills Critical Needs Better, Faster Technology Metamaterials: Searching for the Perfect Lens The Nontraditional Power of Demand Dispatch Space, Solar Power\u27s Next Frontier Kit Cischke, Award-Winning Senior Lecturer Faculty Publications ECE Academy Class of 2016 Staff Profile: Michele Kamppinen For the Love of Teaching: Jenn Winikus Graduate Student Highlights Undergraduate Student Highlights External Advisory Committee Contracts and Grants Department Statistics AAES National Engineering Awardhttps://digitalcommons.mtu.edu/ece-annualreports/1002/thumbnail.jp
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