6,245 research outputs found
Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants
Within the field of soft computing, intelligent optimization modelling techniques include
various major techniques in artificial intelligence. These techniques pretend to generate new business
knowledge transforming sets of "raw data" into business value. One of the principal applications of
these techniques is related to the design of predictive analytics for the improvement of advanced
CBM (condition-based maintenance) strategies and energy production forecasting. These advanced
techniques can be used to transform control system data, operational data and maintenance event data
to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation.
One of the systems where these techniques can be applied with massive potential impact are the
legacy monitoring systems existing in solar PV energy generation plants. These systems produce a
great amount of data over time, while at the same time they demand an important e ort in order to
increase their performance through the use of more accurate predictive analytics to reduce production
losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of
the problems to address. This paper presents a review and a comparative analysis of six intelligent
optimization modelling techniques, which have been applied on a PV plant case study, using the
energy production forecast as the decision variable. The methodology proposed not only pretends
to elicit the most accurate solution but also validates the results, in comparison with the di erent
outputs for the di erent techniques
Key Technical Performance Indicators for Power Plants
In this chapter, we will underline the importance of the key performance indicators (KPIs) computation for power plants’ management. The main scope of the KPIs is to continuously monitor and improve the business and technological processes. Such indicators show the efficiency of a process or a system in relation with norms, targets or plans. They usually provide investors and stakeholders a better image regarding location, equipment technology, layout and design, solar and wind exposure in case of renewable energy sources and maintenance strategies. We will present the most important KPIs such as energy performance index, compensated performance ratio, power performance index, yield, and performance, and we will compare these KPIs in terms of relevance and propose a set of new KPIs relevant for maintenance activities. We will also present a case study of a business intelligence (BI) dashboard developed for renewable power plant operation in order to analyze the KPIs. The BI solution contains a data level for data management, an analytical model with KPI framework and forecasting methods based on artificial neural networks (ANN) for estimating the generated energy from renewable energy sources and an interactive dashboard for advanced analytics and decision support
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CleanTX Analysis on the Smart Grid
The utility industry in the United States has an opportunity to revolutionize its electric grid system by utilizing emerging software, hardware and wireless technologies and renewable energy sources. As electricity generation in the U.S. increases by over 30% from today’s generation of 4,100 Terawatt hours per year to a production of 5,400 Terawatt hours per year by 2030, a new type of grid is necessary to ensure reliable and quality power. The projected U.S. population increase and economic growth will require a grid that can transmit and distribute significantly more power than it does today. Known as a Smart Grid, this system enables two- way transmission of electrons and information to create a demand-response system that will optimize electricity delivery to consumers. This paper outlines the issues with the current grid infrastructure, discusses the economic advantages of the Smart Grid for both consumers and utilities, and examines the emerging technologies that will enable cleaner, more efficient and cost- effective power transmission and consumption.IC2 Institut
AI-driven approaches for optimizing the energy efficiency of integrated energy system
To decarbonize the global energy system and replace the unidirectional architecture of existing grid networks, integrated and electrified energy systems are becoming more demanding. Energy integration is critical for renewable energy sources like wind, solar, and hydropower. However, there are still specific challenges to overcome, such as their high reliance on the weather and the complexity of their integrated operation. As a result, this research goes through the study of a new approach to energy service that has arisen in the shape of data-driven AI technologies, which hold tremendous promise for system improvement while maximizing energy efficiency and reducing carbon emissions.
This research aims to evaluate the use of data-driven AI techniques in electrical integrated energy systems, focusing on energy integration, operation, and planning of multiple energy supplies and demand. Based on the formation point, the main research question is: "To what extent do AI algorithms contribute to attaining greater efficiency of integrated grid systems?". It also included a discussion on four key research areas of AI application: Energy and load prediction, fault prediction, AI-based technologies IoT used for smart monitoring grid system optimization such as energy storage, demand response, grid flexibility, and Business value creation. The study adopted a two-way approach that includes empirical research on energy industry expert interviews and a Likert scale survey among energy sector representatives from Finland, Norway, and Nepal. On the other hand, the theoretical part was from current energy industry optimization models and a review of publications linked to a given research issue.
The research's key findings were AI's significant potential in electrically integrated energy systems, which concluded AI's implication as a better understanding of energy consumption patterns, highly effective and precise energy load and fault prediction, automated energy management, enhanced energy storage system, more excellent business value, a smart control center, smooth monitoring, tracking, and communication of energy networks. In addition, further research directions are prospects towards its technical characteristics on energy conversion
A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency
In this paper, we address the problem of asset performance monitoring, with the intention
of both detecting any potential reliability problem and predicting any loss of energy consumption
e ciency. This is an important concern for many industries and utilities with very intensive
capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an
approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically
with Association Rule (AR) Mining. The combination of these two techniques can now be done
using software which can handle large volumes of data (big data), but the process still needs to
ensure that the required amount of data will be available during the assets’ life cycle and that its
quality is acceptable. The combination of these two techniques in the proposed sequence di ers
from previous works found in the literature, giving researchers new options to face the problem.
Practical implementation of the proposed approach may lead to novel predictive maintenance models
(emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of
performance and help manage assets’ O&M accordingly. The approach is illustrated using specific
examples where asset performance monitoring is rather complex under normal operational conditions.Ministerio de Economía y Competitividad DPI2015-70842-
Cleantechs and Digital Solutions for Sustainability in the Brazilian Energy Sector
This paper aims to map the main digital solutions practiced by entrepreneurs of Cleantech companies. Entrepreneurs from 12 Cleantech (Clean Technology) companies in the Brazilian energy sector were interviewed, using a qualitative research approach. Big Data & Data Analytics, Internet of Things and Artificial Intelligence were identified as digital technologies enablers of a sustainable energy transition for the sector. The study presents the relation and behavior of each technology with the sustainability triple bottom line and introduces a framework on how digital solutions contribute to solving the main bottlenecks in the electricity sector in a sustainable way. Research shown that Cleantechs also operationalize digital technologies like Blockchain and 5G to enable energy transformation as it has emerged as research findings. Additionally, technology and digitalization in conjunction with entrepreneur's capacity for innovation are driving mechanisms for companies in the initial stage of the energy sector, exploring regulatory loopholes and putting their business models into practice.Este artigo tem como objetivo mapear as principais soluções digitais praticadas por empresários de empresas de tecnologia limpa. Foram entrevistados empresários de 12 empresas de tecnologia limpa (Cleantech) do setor de energia brasileiro, por meio de uma abordagem de pesquisa qualitativa. Big Data & Data Analytics, Internet das Coisas e a Inteligência Artificial foram identificadas como facilitadores das tecnologias digitais de uma transição energética sustentável para o setor. O estudo apresenta a relação e o comportamento de cada tecnologia com o triple bottom line da sustentabilidade e apresenta um quadro de como as soluções digitais contribuem para resolver os principais gargalos do setor elétrico de forma sustentável. A pesquisa mostra que as Cleantechs também operacionalizam tecnologias digitais como Blockchain e 5G para permitir a transformação de energia conforme emergiu como resultados de pesquisa. Além disso, a tecnologia e a digitalização em conjunto com a capacidade de inovação do empreendedor são mecanismos motrizes para empresas em estágio inicial do setor de energia, explorando brechas regulatórias e colocando em prática seus modelos de negócios
Smart Grid for the Smart City
Modern cities are embracing cutting-edge technologies to improve the services they offer to the citizens from traffic control to the reduction of greenhouse gases and energy provisioning. In this chapter, we look at the energy sector advocating how Information and Communication Technologies (ICT) and signal processing techniques can be integrated into next generation power grids for an increased effectiveness in terms of: electrical stability, distribution, improved communication security, energy production, and utilization. In particular, we deliberate about the use of these techniques within new demand response paradigms, where communities of prosumers (e.g., households, generating part of their electricity consumption) contribute to the satisfaction of the energy demand through load balancing and peak shaving. Our discussion also covers the use of big data analytics for demand response and serious games as a tool to promote energy-efficient behaviors from end users
Use, Operation and Maintenance of Renewable Energy Systems:Experiences and Future Approaches
The aim of this book is to put the reader in contact with real experiences, current
and future trends in the context of the use, exploitation and maintenance of renewable
energy systems around the world. Today the constant increase of production
plants of renewable energy is guided by important social, economical, environmental
and technical considerations. The substitution of traditional methods of
energy production is a challenge in the current context. New strategies of exploitation,
new uses of energy and new maintenance procedures are emerging naturally
as isolated actions for solving the integration of these new aspects in the current
systems of energy production. This book puts together different experiences in
order to be a valuable instrument of reference to take into account when a system
of renewable energy production is in operation
Distributed energy resources and the application of AI, IoT, and blockchain in smart grids
Smart grid (SG), an evolving concept in the modern power infrastructure, enables the two-way flow of electricity and data between the peers within the electricity system networks (ESN) and its clusters. The self-healing capabilities of SG allow the peers to become active partakers in ESN. In general, the SG is intended to replace the fossil fuel-rich conventional grid with the distributed energy resources (DER) and pools numerous existing and emerging know-hows like information and digital communications technologies together to manage countless operations. With this, the SG will able to “detect, react, and pro-act” to changes in usage and address multiple issues, thereby ensuring timely grid operations. However, the “detect, react, and pro-act” features in DER-based SG can only be accomplished at the fullest level with the use of technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and the Blockchain (BC). The techniques associated with AI include fuzzy logic, knowledge-based systems, and neural networks. They have brought advances in controlling DER-based SG. The IoT and BC have also enabled various services like data sensing, data storage, secured, transparent, and traceable digital transactions among ESN peers and its clusters. These promising technologies have gone through fast technological evolution in the past decade, and their applications have increased rapidly in ESN. Hence, this study discusses the SG and applications of AI, IoT, and BC. First, a comprehensive survey of the DER, power electronics components and their control, electric vehicles (EVs) as load components, and communication and cybersecurity issues are carried out. Second, the role played by AI-based analytics, IoT components along with energy internet architecture, and the BC assistance in improving SG services are thoroughly discussed. This study revealed that AI, IoT, and BC provide automated services to peers by monitoring real-time information about the ESN, thereby enhancing reliability, availability, resilience, stability, security, and sustainability
General Information About the Design of Smart Grids in Universities
Until recently, the dominant paradigm in the electrification consisted of universal service and its centralization, and for loor modern times think of the power grid of the future where a qualitative and radical leap is required because of the need to manage better energy resources, promote environmental protection and meet the increasingly demanding requirements of quality of service. A power distribution network becomes intelligent acquiring data, communicating, processing information and exercising control through a feedback that allows you to adjust to changes that may arise in actual operation. Ecuador aimed at energy efficiency through smart grids, which allow the dealer to maintain absolute monitoring of energy flow and the elements of the power grid. Thus, it is possible that service companies can efficiently manage their assets and the end user to manage consumption rationally, requiring to enhance the energy efficiency of power grids, one management timely and efficient energy
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