Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives

Consumer's privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services. These data-driven models often require huge amounts of data to achieve acceptable performance leading in most cases to risks of privacy leakage. By pushing the training to the edge, Federated Learning (FL) offers a good compromise between privacy preservation and the predictive performance of these models. The current paper presents an overview of FL applications in SGs while discussing their advantages and drawbacks, mainly in load forecasting, electric vehicles, fault diagnoses, load disaggregation and renewable energies. In addition, an analysis of main design trends and possible taxonomies is provided considering data partitioning, the communication topology, and security mechanisms. Towards the end, an overview of main challenges facing this technology and potential future directions is presented

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

A Federated learning model for Electric Energy management using Blockchain Technology

Energy shortfall and electricity load shedding are the main problems for developing countries. The main causes are lack of management in the energy sector and the use of non-renewable energy sources. The improved energy management and use of renewable sources can be significant to resolve energy crisis. It is necessary to increase the use of renewable energy sources (RESs) to meet the increasing energy demand due to high prices of fossil-fuel based energy. Federated learning (FL) is the most emerging technique in the field of artificial intelligence. Federated learning helps to generate global model at server side by ensemble locally trained models at remote edges sites while preserving data privacy. The global model used to predict energy demand to satisfy the needs of consumers. In this article, we have proposed Blockchain based safe distributed ledger technology for transaction of data between prosumer and consumer to ensure their transparency, traceability and security. Furthermore, we have also proposed a Federated learning model to forecast the energy requirements of consumer and prosumer. Moreover, Blockchain has been used to store excess energy data from prosumer for better management of energy between prosumer and grid. Lastly, the experiment results revealed that renewable energy sources have produced better and comparable results to other non-renewable energy resources.Comment: 14 figures, 7 tables, 15 page

Situational Intelligence for Improving Power System Operations Under High Penetration of Photovoltaics

Nowadays, power grid operators are experiencing challenges and pressures to balance the interconnected grid frequency with rapidly increasing photovoltaic (PV) power penetration levels. PV sources are variable and intermittent. To mitigate the effect of this intermittency, power system frequency is regulated towards its security limits. Under aforementioned stressed regimes, frequency oscillations are inevitable, especially during disturbances and may lead to costly consequences as brownout or blackout. Hence, the power system operations need to be improved to make the appropriate decision in time. Specifically, concurrent or beforehand power system precise frequencies simplified straightforward-to-comprehend power system visualizations and cooperated well-performed automatic generation controls (AGC) for multiple areas are needed for operation centers to enhance. The first study in this dissertation focuses on developing frequency prediction general structures for PV and phasor measurement units integrated electric grids to improve the situational awareness (SA) of the power system operation center in making normal and emergency decisions ahead of time. Thus, in this dissertation, a frequency situational intelligence (FSI) methodology capable of multi-bus type and multi-timescale prediction is presented based on the cellular computational network (CCN) structure with a multi-layer proception (MLP) and a generalized neuron (GN) algorithms. The results present that both CCMLPN and CCGNN can provide precise multi-timescale frequency predictions. Moreover, the CCGNN has a superior performance than the CCMLPN. The second study of this dissertation is to improve the SA of the operation centers by developing the online visualization tool based on the synchronous generator vulnerability index (GVI) and the corresponding power system vulnerability index (SVI) considering dynamic PV penetration. The GVI and SVI are developed by the coherency grouping results of synchronous generator using K-Harmonic Means Clustering (KHMC) algorithm. Furthermore, the CCGNN based FSI method has been implemented for the online coherency grouping procedure to achieve a faster-than-real-time grouping performance. Last but not the least, the multi-area AGCs under different PV integrated power system operating conditions are investigated on the multi-area multi-source interconnected testbed, especially with severe load disturbances. Furthermore, an onward asynchronous tuning method and a two-step (synchronous) tuning method utilizing particle swarm optimization algorithm are developed to refine the multi-area AGCs, which provide more opportunities for power system balancing authorities to interconnect freely and to utilize more PV power. In summary, a number of methods for improving the interconnected power system situational intelligence for a high level of PV power penetration have been presented in this dissertation

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

Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care