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

Smart home energy management

The new challenges on Information and Communication Technologies (ICT) in Automatic Home Systems (AHS) focus on the methods useful to monitor, control, and optimize the data management flow and the use of energy. An AHS is a residential dwelling, in some cases with a garden or an outdoor space, equipped with sensors and actuators to collect data and send controls according to the activities and expectations of the occupants/users. Home automation provides a centralized or distributed control of electrical appliances. Adding intelligence to the home environment, it would be possible to obtain, not only excellent levels of comfort, but also energy savings both inside and outside the dwelling, for instance using smart solutions for the management of the external lights and of the garden

A critical analysis of an IoT—aware AAL system for elderly monitoring

Abstract A growing number of elderly people (65+ years old) are affected by particular conditions, such as Mild Cognitive Impairment (MCI) and frailty, which are characterized by a gradual cognitive and physical decline. Early symptoms may spread across years and often they are noticed only at late stages, when the outcomes remain irrevocable and require costly intervention plans. Therefore, the clinical utility of early detecting these conditions is of substantial importance in order to avoid hospitalization and lessen the socio-economic costs of caring, while it may also significantly improve elderly people's quality of life. This work deals with a critical performance analysis of an Internet of Things aware Ambient Assisted Living (AAL) system for elderly monitoring. The analysis is focused on three main system components: (i) the City-wide data capturing layer, (ii) the Cloud-based centralized data management repository, and (iii) the risk analysis and prediction module. Each module can provide different operating modes, therefore the critical analysis aims at defining which are the best solutions according to context's needs. The proposed system architecture is used by the H2020 City4Age project to support geriatricians for the early detection of MCI and frailty conditions

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

Internal report cluster 1: Urban freight innovations and solutions for sustainable deliveries (1/4)

Technical report about sustainable urban freight solutions, part 1 of

Internal report cluster 1: Urban freight innovations and solutions for sustainable deliveries (2/4)

Technical report about sustainable urban freight solutions, part 2 of

Road2CPS priorities and recommendations for research and innovation in cyber-physical systems

This document summarises the findings of the Road2CPS project, co-financed by the European Commission under the H2020 Research and Innovation Programme, to develop a roadmap and recommendations for strategic action required for future deployment of Cyber-Physical Systems (CPS). The term Cyber-Physical System describes hardware-software systems, which tightly couple the physical world and the virtual world. They are established from networked embedded systems that are connected with the outside world through sensors and actuators and have the capability to collaborate, adapt, and evolve. In the ARTEMIS Strategic Research Agenda 2016, CPS are described as ‘Embedded Intelligent ICT Systems’ that make products smarter, more interconnected, interdependent, collaborative, and autonomous. In the future world of CPS, a huge number of devices connected to the physical world will be able to exchange data with each other, access web services, and interact with people. Moreover, information systems will sense, monitor and even control the physical world via Cyber-Physical Systems and the Internet of Things (HiPEAC Vision 2015). Cyber-Physical Systems find their application in many highly relevant areas to our society: multi-modal transport, health, smart factories, smart grids and smart cities amongst others. The deployment of Cyber-Physical Systems (CPS) is expected to increase substantially over the next decades, holding great potential for novel applications and innovative product development. Digital technologies have already pervaded day-to-day life massively, affecting all kinds of interactions between humans and their environment. However, the inherent complexity of CPSs, as well as the need to meet optimised performance and comply with essential requirements like safety, privacy, security, raises many questions that are currently being explored by the research community. Road2CPS aims at accelerating uptake and implementation of these efforts. The Road2CPS project identifying and analysing the relevant technology fields and related research priorities to fuel the development of trustworthy CPS, as well as the specific technologies, needs and barriers for a successful implementation in different application domains and to derive recommendations for strategic action. The document at hand was established through an interactive, community-based approach, involving over 300 experts from academia, industry and policy making through a series of workshops and consultations. Visions and priorities of recently produced roadmaps in the area of CPS, IoT (Internet of Things), SoS (System-of-Systems) and FoF (Factories of the Future) were discussed, complemented by sharing views and perspectives on CPS implementation in application domains, evolving multi-sided eco-systems as well as business and policy related barriers, enablers and success factors. From the workshops and accompanying activities recommendations for future research and innovation activities were derived and topics and timelines for their implementation proposed. Amongst the technological topics, and related future research priorities ‘integration, interoperability, standards’ ranged highest in all workshops. The topic is connected to digital platforms and reference architectures, which have already become a key priority theme for the EC and their Digitisation Strategy as well as the work on the right standards to help successful implementation of CPSs. Other themes of very high technology/research relevance revealed to be ‘modelling and simulation’, ‘safety and dependability’, ‘security and privacy’, ‘big data and real-time analysis’, ‘ubiquitous autonomy and forecasting’ as well as ‘HMI/human machine awareness’. Next to this, themes emerged including ‘decision making and support’, ‘CPS engineering (requirements, design)’, ‘CPS life-cycle management’, ‘System-of-Systems’, ‘distributed management’, ‘cognitive CPS’, ‘emergence, complexity, adaptability and flexibility’ and work on the foundations of CPS and ‘cross-disciplinary research/CPS Science’

Internet of things (IoT) based adaptive energy management system for smart homes

PhD ThesisInternet of things enhances the flexibility of measurements under different environments, the development of advanced wireless sensors and communication networks on the smart grid infrastructure would be essential for energy efficiency systems. It makes deployment of a smart home concept easy and realistic. The smart home concept allows residents to control, monitor and manage their energy consumption with minimal wastage. The scheduling of energy usage enables forecasting techniques to be essential for smart homes. This thesis presents a self-learning home management system based on machine learning techniques and energy management system for smart homes. Home energy management system, demand side management system, supply side management system, and power notification system are the major components of the proposed self-learning home management system. The proposed system has various functions including price forecasting, price clustering, power forecasting alert, power consumption alert, and smart energy theft system to enhance the capabilities of the self-learning home management system. These functions were developed and implemented through the use of computational and machine learning technologies. In order to validate the proposed system, real-time power consumption data were collected from a Singapore smart home and a realistic experimental case study was carried out. The case study had proven that the developed system performing well and increased energy awareness to the residents. This proposed system also showcases its customizable ability according to different types of environments as compared to traditional smart home models. Forecasting systems for the electricity market generation have become one of the foremost research topics in the power industry. It is essential to have a forecasting system that can accurately predict electricity generation for planning and operation in the electricity market. This thesis also proposed a novel system called multi prediction system and it is developed based on long short term memory and gated recurrent unit models. This proposed system is able to predict the electricity market generation with high accuracy. Multi Prediction System is based on four stages which include a data collecting and pre-processing module, a multi-input feature model, multi forecast model and mean absolute percentage error. The data collecting and pre-processing module preprocess the real-time data using a window method. Multi-input feature model uses single input feeding method, double input feeding method and multiple feeding method for features input to the multi forecast model. Multi forecast model integrates long short term memory and gated recurrent unit variations such as regression model, regression with time steps model, memory between batches model and stacked model to predict the future generation of electricity. The mean absolute percentage error calculation was utilized to evaluate the accuracy of the prediction. The proposed system achieved high accuracy results to demonstrate its performance

The Role of Plug-In Electric Vehicles with Renewable Resources in Electricity Systems

Deux voies technologiques, la génération d’électricité renouvelable et l’électrification des véhicules, sont souvent avancées comme solution à deux des plus grands défis de notre époque : satisfaire à une demande énergétique croissante tout en réduisant les émissions de gaz à effet de serre. La réalisation de ces deux objectifs implique le besoin de transférer une partie de la demande de combustibles fossiles vers d’autres sources d’énergie primaire. La diffusion des énergies renouvelables et des véhicules électriques rechargeables (VER) a été entravée par des obstacles importants, malgré leur potentiel reconnu d’améliorer la durabilité énergétique dans les secteurs de l’électricité et du transport. Les deux technologies ont des synergies naturelles entre elles : les VER sont une source inhérente de flexibilité du côté de la demande aussi bien que de l’offre, qui pourraient aider à mitiger les effets négatifs de la variabilité de la génération d’électricité renouvelable. Dans cet article nous examinons les obstacles au déploiement des renouvelables et des VER, ainsi que les synergies entre les deux voies technologiques. Nous soulevons des questions autour de l’implémentation ainsi que des mesures d’incitation et des modèles d’affaires qui pourraient empêcher ou aider à réaliser la valeur de ces synergies. Nous proposons enfin de nouvelles problématiques de recherche qui pourraient amener à résoudre ces questions d’implémentation.Two technology options, renewable electricity generation and vehicle electrification, are being promoted to achieve two of the greatest objectives of this century: meeting growing global energy demand while reducing greenhouse gas emissions. Addressing both objectives implies shifting part of this energy demand away from fossil fuels to other primary energy sources. Renewables and plug-in electric vehicle (PEV) adoption has been hindered by significant challenges despite their known potential to improve energy sustainability in electric power systems and transportation. The two technologies have natural synergies between them, however: PEVs are a natural source of demand -and supply-side flexibility, which can help mitigate the negative ancillary effects of renewable variability and uncertainty. In this paper we discuss the issues hindering renewable and PEV adoption and the synergies between these two technology pathways. Finally, we raise some issues with implementation and challenges with incentive and business plan design that may hinder fully realizing these synergies. We also propose some important research questions that would help address these implementation issues

Autonomous Demand Side Management of Electric Vehicles

There is an error in the table of content, where publication A and B have swiched places.Demand-side management approaches that exploit the temporal flexibility of electric vehicles have attracted much attention in recent years due to the increasing market penetration. These demand-side management measures contribute to alleviating the burden on the power system, especially in distribution grids where bottlenecks are more prevalent. Electric vehicles can be defined as an attractive asset for distribution system operators, which have the potential to provide grid services if properly managed. In this thesis, first, a systematic investigation is conducted for two typically employed demand-side management methods reported in the literature: A voltage droop control-based approach and a market-driven approach. Then a control scheme of decentralized autonomous demand side management for electric vehicle charging scheduling which relies on a unidirectionally communicated grid-induced signal is proposed. In all the topics considered, the implications on the distribution grid operation are evaluated using a set of time series load flow simulations performed for representative Austrian distribution grids. Droop control mechanisms are discussed for electric vehicle charging control which requires no communication. The method provides an economically viable solution at all penetrations if electric vehicles charge at low nominal power rates. However, with the current market trends in residential charging equipment especially in the European context where most of the charging equipment is designed for 11 kW charging, the technical feasibility of the method, in the long run, is debatable. As electricity demand strongly correlates with energy prices, a linear optimization algorithm is proposed to minimize charging costs, which uses next-day market prices as the grid-induced incentive function under the assumption of perfect user predictions. The constraints on the state of charge guarantee the energy required for driving is delivered without failure. An average energy cost saving of 30% is realized at all penetrations. Nevertheless, the avalanche effect due to simultaneous charging during low price periods introduces new power peaks exceeding those of uncontrolled charging. This obstructs the grid-friendly integration of electric vehicles.publishedVersio