Data Challenges and Data Analytics Solutions for Power Systems

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What Ukraine Taught NATO about Hybrid Warfare

Russia’s invasion of Ukraine in 2022 forced the United States and its NATO partners to be confronted with the impact of hybrid warfare far beyond the battlefield. Targeting Europe’s energy security, Russia’s malign influence campaigns and malicious cyber intrusions are affecting global gas prices, driving up food costs, disrupting supply chains and grids, and testing US and Allied military mobility. This study examines how hybrid warfare is being used by NATO’s adversaries, what vulnerabilities in energy security exist across the Alliance, and what mitigation strategies are available to the member states. Cyberattacks targeting the renewable energy landscape during Europe’s green transition are increasing, making it urgent that new tools are developed to protect these emerging technologies. No less significant are the cyber and information operations targeting energy security in Eastern Europe as it seeks to become independent from Russia. Economic coercion is being used against Western and Central Europe to stop gas from flowing. China’s malign investments in Southern and Mediterranean Europe are enabling Beijing to control several NATO member states’ critical energy infrastructure at a critical moment in the global balance of power. What Ukraine Taught NATO about Hybrid Warfare will be an important reference for NATO officials and US installations operating in the European theater.https://press.armywarcollege.edu/monographs/1952/thumbnail.jp

Big Data Analytics in Smart Grids for Renewable Energy Networks: Systematic Review of Information and Communication Technology Tools

El desarrollo industrial y económico de los países industrializados, a partir del siglo XIX, ha ido de la mano del desarrollo de la electricidad, del motor de combustión interna, de los ordenadores, de Internet, de la utilización de datos y del uso intensivo del conocimiento centrado en la ciencia y la tecnología. La mayoría de las fuentes de energía convencionales han demostrado ser finitas y agotables. A su vez, las diferentes actividades de producción de bienes y servicios que utilizan combustibles fósiles y energía convencional, han aumentado significativamente la contaminación del medio ambiente, y con ello, han contribuido al calentamiento global. El objetivo de este trabajo fue realizar una aproximación teórica a las tecnologías de análisis de datos e inteligencia de negocio aplicadas a las redes de sistemas eléctricos inteligentes con energías renovables. Para este trabajo se realizó una revisión bibliométrica y bibliográfica sobre Big Data Analytics, herramientas TIC de la industria 4.0 y Business intelligence en diferentes bases de datos disponibles en el dominio público. Los resultados del análisis indican la importancia del uso de la analítica de datos y la inteligencia de negocio en la gestión de las empresas energéticas. El trabajo concluye señalando cómo se está aplicando la inteligencia de negocio y la analítica de datos en ejemplos concretos de empresas energéticas y su creciente importancia en la toma de decisiones estratégicas y operativasThe industrial and economic development of the industrialized countries, from the nineteenth century, has gone hand in hand with the development of electricity, the internal combustion engine, computers, the Internet, data use and the intensive use of knowledge focused on science and the technology. Most conventional energy sources have proven to be finite and exhaustible. In turn, the different production activities of goods and services using fossil fuels and conventional energy, have significantly increased the pollution of the environment, and with it, contributed to global warming. The objective of this work was to carry out a theoretical approach to data analytics and business intelligence technologies applied to smart electrical-system networks with renewable energies. For this paper, a bibliometric and bibliographic review about Big Data Analytics, ICT tools of industry 4.0 and Business intelligence was carried out in different databases available in the public domain. The results of the analysis indicate the importance of the use of data analytics and business intelligence in the management of energy companies. The paper concludes by pointing out how business intelligence and data analytics are being applied in specific examples of energy companies and their growing importance in strategic and operational decision makinghttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000192503https://scholar.google.com/citations?user=9HLAZYUAAAAJ&hl=eshttps://scienti.minciencias.gov.co/gruplac/jsp/visualiza/visualizagr.jsp?nro=00000000005961https://orcid.org/0000-0003-1166-198

Big data analytics in smart grids: a review

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An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Secure data sharing and analysis in cloud-based energy management systems

Analysing data acquired from one or more buildings (through specialist sensors, energy generation capability such as PV panels or smart meters) via a cloud-based Local Energy Management System (LEMS) is increasingly gaining in popularity. In a LEMS, various smart devices within a building are monitored and/or controlled to either investigate energy usage trends within a building, or to investigate mechanisms to reduce total energy demand. However, whenever we are connecting externally monitored/controlled smart devices there are security and privacy concerns. We describe the architecture and components of a LEMS and provide a survey of security and privacy concerns associated with data acquisition and control within a LEMS. Our scenarios specifically focus on the integration of Electric Vehicles (EV) and Energy Storage Units (ESU) at the building premises, to identify how EVs/ESUs can be used to store energy and reduce the electricity costs of the building. We review security strategies and identify potential security attacks that could be carried out on such a system, while exploring vulnerable points in the system. Additionally, we will systematically categorize each vulnerability and look at potential attacks exploiting that vulnerability for LEMS. Finally, we will evaluate current counter measures used against these attacks and suggest possible mitigation strategies

Short-term forecast techniques for energy management systems in microgrid applications

A Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Doctor of Philosophy in Sustainable Energy Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyIn the 2015 Paris Agreement, 195 countries adopted a global climate agreement to limit the global average temperature rise to less than 2°C. Achieving the set targets involves increasing energy efficiency and embracing cleaner energy solutions. Although advances in computing and Internet of Things (IoT) technologies have been made, there is limited scientific research work in this arena that tackles the challenges of implementing low-cost IoT-based Energy Management System (EMS) with energy forecast and user engagement for adoption by a layman both in off-grid or microgrid tied to a weak grid. This study proposes an EMS approach for short-term forecast and monitoring for hybrid microgrids in emerging countries. This is done by addressing typical submodules of EMS namely: load forecast, blackout forecast, and energy monitoring module. A short-term load forecast model framework consisting of a hybrid feature selection and prediction model was developed. Prediction error performance evaluation of the developed model was done by varying input predictors and using the principal subset features to perform supervised training of 20 different conventional prediction models and their hybrid variants. The proposed principal k-features subset union approach registered low error performance values than standard feature selection methods when it was used with the ‘linear Support Vector Machine (SVM)’ prediction model for load forecast. The hybrid regression model formed from a fusion of the best 2 models (‘linearSVM’ and ‘cubicSVM’) showed improved prediction performance than the individual regression models with a reduction in Mean Absolute Error (MAE) by 5.4%. In the case of the EMS blackout prediction aspect, a hybrid Adaptive Similar Day (ASD) and Random Forest (RF) model for short-term power outage prediction was proposed that predicted accurately almost half of the blackouts (49.16%), thereby performing slightly better than the stand-alone RF (32.23%), and ASD (46.57%) models. Additionally, a low-cost EMS smart meter was developed to realize the implemented energy forecast and offer user engagement through monitoring and control of the microgrid towards the goal of increasing energy efficiency

Component degradation and system deterioration: An overview of early termination of PV-DG microgrid system

Degradation of components and system failure within the microgrid system is deteriorating the performance of electrification. The aim of this study is to discuss the relationship and connections between issues resulting from degradation and deterioration in the microgrid system, in addition to introducing the prominent impacts which may eventually lead to the premature termination of the microgrid system. This study explored the microgrid degradation and deterioration issues within four microgrid sections: generation section, storage section, transmission section, and distribution section. Subsequently, this study analyzes, derives, and classifies all emerging issues into four types of prominent impacts. The degradation and deterioration invoked many component performance issues into four main damaging outcomes, namely (i) deteriorated transmission line yielded issues regarding expected energy not achieved; (ii) energy deficit and unpredicted blackout come after the depth of discharge (DOD) reduction and invoke a loss of power supply; (iii) a shorter battery life cycle, shorter transformer lifespan, and decreased DG lifetime concluded as a shorter microgrid life expectancy; and (iv) rapid microgrid broke down and the crash of the key component inadvertently fastened the time to failure and gave rise to the early failure of a microgrid system. It is envisaged that the discussion in this study can provide useful mapped information for the researcher, stakeholder, operator, and other parties for thoroughly addressing various degradation and deterioration issues and anticipating the early termination of the microgrid system