Future smart energy software houses

Software is the key enabling technology (KET) as digitalization is cross-cutting future energy systems spanning the production sites, distribution networks, and consumers particularly in electricity smart grids. In this paper, we identify systematically what particular software competencies are required in the future energy systems focusing on electricity system smart grids. The realizations of that can then be roadmapped to specific software capabilities of the different future software houses' across the networks. Our instrumental method is software competence development scenario path construction with environmental scanning of the related systems elements. The vision of future software-enabled smart energy systems with software houses is mapped with the already progressing scenarios of energy systems transitions on the one hand coupled with the technology foresight of software on the other hand. Grounding on the Smart Grid Reference Architecture Model (SGAM), it tabulates the distinguished software competencies and attributes them to the different partiesincluding customers/consumers (Internet of People, IoP)involved in future smart energy systems. The resulting designations can then be used to recognize and measure the necessary software competencies (e.g., fog computing) in order to be able to develop them in-house, or for instance to partner with software companies, depending on the future desirability. Software-intensive systems development competence becomes one of the key success factors for such cyber-physical-social systems (CPSS). Further futures research work is chartered with the Futures Map frame. This paper contributes preliminarily toward that by identifying pictures of the software-enabled futures and the connecting software competence-based scenario paths.Peer reviewe

Smart Grids: A Comprehensive Survey of Challenges, Industry Applications, and Future Trends

With the increased energy demands of the 21st century, there is a clear need for developing a more sustainable method of energy generation, distribution, and transmission. The popularity of Smart Grid continues to grow as it presents its benefits, including interconnectivity, improved efficiency, the ability to integrate renewable energy sources, and many more. However, it is not without its challenges. This survey aims to provide an introductory background of smart grids, detail some of the main aspects and current challenges, and review the most recent papers and proposed solutions. It will also highlight the current state of implementation of the smart grid by describing various prototypes, as well as various countries and continents implementation plans and projects.Comment: Paper has been submitted for review to the journal Energy Reports (January 23, 2024). 58 pages, 7 figures, 7 table

Software-Defined Networking for Smart Grid Resilience: Opportunities and Challenges

Software-defined networking (SDN) is an emerging networking paradigm that provides unprecedented flexibility in dynamically reconfiguring an IP network. It enables various applications, such as network management, quality of service (QoS) optimization, and system resilience enhancement. Pilot studies have investigated the possibilities of applying SDN on smart grid communications, while the specific benefits and risks that SDN may bring to the resilience of smart grids against accidental failures and malicious attacks remain largely unexplored. Without a systematic understanding of these issues and convincing validations of proposed solutions, the power industry will be unlikely to embrace SDN, since resilience is always a key consideration for critical infrastructures like power grids. In this position paper, we aim to provide an initial understanding of these issues, by investigating (1) how SDN can enhance the resilience of typical smart grids to malicious attacks, (2) additional risks introduced by SDN and how to manage them, and (3) how to validate and evaluate SDN-based resilience solutions. Our goal is also to trigger more profound discussions on applying SDN to smart grids and inspire innovative SDN-based solutions for enhancing smart grid resilience.Agency for Science, Technology and Research; National Science Foundation (OCI-1032889); Department of Energy (DE-OE0000097)Ope

Decision system for incremental upgrades of the Power Distribution System for Electric Vehicles

The growing demand of Electric energy powered vehicles has some unwanted side effects on the current power grid system. Some of these side effects are wires not being able to sustain such high currents for prolonged periods of time, transformers not being able to sustain stable voltages [1], to sustain the added energy demands. Thus, we have to solve how and where we can provide a feasible incremental upgrade approach for the energy distribution companies. This requires us to monitor voltage and current output of various nodes in the electrical distribution system, which is able to collect data and identify patterns. Thus, a real-time monitoring and decision making system is proposed that is able to forecast future needs as well. The simulated environment created in this thesis verifies that such monitoring system can provide valuable information to distribution companies and identify problems in the grid for immediate and future upgrades

Applying Trust for Operational States of ICT-Enabled Power Grid Services

Digitalization enables the automation required to operate modern cyber-physical energy systems (CPESs), leading to a shift from hierarchical to organic systems. However, digitalization increases the number of factors affecting the state of a CPES (e.g., software bugs and cyber threats). In addition to established factors like functional correctness, others like security become relevant but are yet to be integrated into an operational viewpoint, i.e. a holistic perspective on the system state. Trust in organic computing is an approach to gain a holistic view of the state of systems. It consists of several facets (e.g., functional correctness, security, and reliability), which can be used to assess the state of CPES. Therefore, a trust assessment on all levels can contribute to a coherent state assessment. This paper focuses on the trust in ICT-enabled grid services in a CPES. These are essential for operating the CPES, and their performance relies on various data aspects like availability, timeliness, and correctness. This paper proposes to assess the trust in involved components and data to estimate data correctness, which is crucial for grid services. The assessment is presented considering two exemplary grid services, namely state estimation and coordinated voltage control. Furthermore, the interpretation of different trust facets is also discussed.Comment: Preprint of the article under revision for the ACM Transactions on Autonomous and Adaptive Systems, Special Issue on 20 Years of Organic Computin

Sustainable modular IoT solution for smart cities applications supported by machine learning algorithms

The Internet of Things (IoT) and Smart Cities are nowadays a big trend, but with the proliferation of these systems several challenges start to appear and put in jeopardy the acceptance by the population, mainly in terms of sustainability and environmental issues. This Thesis introduces a new system composed by a modular IoT smart node that is self-configurable and sustainable with the support of machine learning techniques, as well as the research and development to achieve a innovative solution considering data analysis, wireless communications and hardware and software development. For all these, concepts are introduced, research methodologies, tests and results are presented and discussed as well as the development and implementation. The developed research and methodology shows that Random Forest was the best choice for the data analysis in the self-configuration of the hardware and communication systems and that Edge Computing has an advantage in terms of energy efficiency and latency. The autonomous communication system was able to create a 65% more sustainable node, in terms of energy consumption, with only a 13% decrease in quality of service. The modular approach for the smart node presented advantages in the integration, scalability and implementation of smart cities projects when facing traditional implementations, reducing up to 45% the energy consumption of the overall system and 60% of messages exchanged, without compromising the system performance. The deployment of this new system will help Smart Cities, in a worldwide fashion, to decrease their environmental issues and comply with rules and regulations to reduce CO2 emission.A Internet das Coisas (IoT) e as Cidades Inteligentes são hoje uma grande tendência, mas com a rápida evolução destes sistemas são vários os desafios que põem em causa a sua aceitação por parte das populações, maioritariamente devido a problemas ambientais e de sustentabilidade. Esta Tese introduz um novo sistema composto por nós de IoT inteligentes que são auto-configuáveis e sustentáveis suportados por de aprendizagem automática, e o trabalho de investigação e desenvolvimento para se obter uma solução inovadora que considera a análise de dados, comunicações sem fios e o desenvolvimento do hardware e software. Para todos estes, os conceitos chave são introduzidos, as metodologias de investigação, testes e resultados são apresentados e discutidos, bem como todo o desenvolvimento e implementação. Através do trabalho desenvolvido mostra-se que as Árvores Aleatórias são a melhor escolha para análise de dados em termos da autoconfiguração do hardware e sistema de comunicações e que a computação nos nós tem uma vantagem em termos de eficiência energética e latência. O sistema de configuração autónoma de comunicações foi capaz de criar um nós 65% mais sustentável, em termos en- ergéticos, comprometendo apenas em 13% a qualidade do servi ̧co. A solução modular do nó inteligente apresentou vantagens na integração, escalabilidade e implementação de projectos para Cidades Inteligentes quando comparado com soluções tradicionais, reduzindo em 45% o consumo energético e 60% a troca de mensagens, sem comprometer a qualidade do sistema. A implementação deste novo sistema irá ajudar as cidades inteligentes, em todo o mundo, a diminuir os seus problemas ambientais e a cumprir com as normas e regulamentos para reduzir as emissões de CO2

Advanced Metering Infrastructure Based on Smart Meters in Smart Grid

Due to lack of situational awareness, automated analysis, poor visibility, and mechanical switches, today\u27s electric power grid has been aging and ill‐suited to the demand for electricity, which has gradually increased, in the twenty‐first century. Besides, the global climate change and the greenhouse gas emissions on the Earth caused by the electricity industries, the growing population, one‐way communication, equipment failures, energy storage problems, the capacity limitations of electricity generation, decrease in fossil fuels, and resilience problems put more stress on the existing power grid. Consequently, the smart grid (SG) has emerged to address these challenges. To realize the SG, an advanced metering infrastructure (AMI) based on smart meters is the most important key

Systematic review of energy theft practices and autonomous detection through artificial intelligence methods

Energy theft poses a significant challenge for all parties involved in energy distribution, and its detection is crucial for maintaining stable and financially sustainable energy grids. One potential solution for detecting energy theft is through the use of artificial intelligence (AI) methods. This systematic review article provides an overview of the various methods used by malicious users to steal energy, along with a discussion of the challenges associated with implementing a generalized AI solution for energy theft detection. In this work, we analyze the benefits and limitations of AI methods, including machine learning, deep learning, and neural networks, and relate them to the specific thefts also analyzing problems arising with data collection. The article proposes key aspects of generalized AI solutions for energy theft detection, such as the use of smart meters and the integration of AI algorithms with existing utility systems. Overall, we highlight the potential of AI methods to detect various types of energy theft and emphasize the need for further research to develop more effective and generalized detection systems, providing key aspects of possible generalized solutions

CINELDI Annual Report 2020

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