The circular economy aims to decouple growth from environmental impacts by optimizing resource use, minimizing waste and pollution. The European Union (EU) has the ambition to lead a circular economy transition on a global level. Realizing the transition is complex, because it requires substantial and interconnected changes in the current system. Previous literature has identified the main drivers and barriers to a circular economy, categorizing them as technical, economic, regulatory and cultural. Despite its relevance, this literature has a broad focus, not taking into account the characteristics of specific industry sectors. More granular insight is essential to overcome barriers, while leveraging drivers. To this end, research focusing on these drivers and barriers within specific sectors is emerging. However, to date, this research is recent and limited, leaving a large and critical knowledge gap still to be addressed. In this study, we focus on the EU automotive sector. Specifically, in the context of the on-going review of the end-of-life vehicles (ELV) directive, we are carrying out an investigation into the barriers and drivers for increasing the uptake of recycled plastics embedded in new vehicles put on the EU market. Through the analysis of literature and in-depth interviews with key stakeholders (including vehicle manufacturers, suppliers, recyclers, experts and industry associations) we outline the value chain of plastics in the sector, while identifying and explaining specific drivers and barriers to recycling. In this paper we present some initial results of this ongoing effort. From a practice perspective, these results contribute to a better understanding on how to advance circularity in the EU automotive sector. From a theory perspective, the results illustrate how circularity barriers and drivers may be identified at a sectoral level. This may provide future studies with a methodological blueprint to replicate this work in other sectors, as a way to continue addressing the aforementioned knowledge gap.@en

Given the ongoing transition towards a more decentralised and adaptive energy system, the potential of blockchain-enabled smart contracts for the energy sector is being increasingly recognised. Due to their self-executing, customisable and tamper-proof nature, they are seen as a key technology for enabling the transition to a more efficient, transparent and transactive energy market. The applications of smart contracts include coordination of smart electric vehicle charging, automated demand-side response, peer-to-peer energy trading and allocation of the control duties amongst the network operators. Nevertheless, their use in the energy sector is still in its early stages as there are many open challenges related to security, privacy, scalability and billing. In this paper, we systematically review 178 peer-reviewed publications and 13 innovation projects, providing a thorough analysis of the strengths and weaknesses of smart contracts used in the energy sector. This work offers a broad perspective on the opportunities and challenges that stakeholders using this technology face, in both current and emergent markets, such as peer-to-peer energy trading platforms. To provide a roadmap for researchers and practitioners interested in the technology, we propose a systematic model of the smart contracting process, by developing a novel 6-layer architecture, as well as presenting a sample energy contract in pseudocode form and as open-source code. Our analysis focuses on the two mainstream application areas we identify for smart contract use in this area: energy and flexibility trading, and distributed control. The paper concludes with a comprehensive, critical discussion of the advantages and challenges that must be addressed in the area of smart contracts and blockchains in energy, and a set of recommendations that researchers and developers should consider when applying smart contracts to energy system settings.@en

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time decisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and preferences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area.@en

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time decisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and preferences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area.@en

Recent years have seen an increasing interest in Demand Response (DR), as a means to satisfy the growing flexibility needs of modern power grids. This increased flexibility is required due to the growing proportion of intermittent renewable energy generation into the energy mix, and increasing complexity in demand profiles from the electrification of transport networks. Currently, less than 2% of the global potential for demand-side flexibility is currently utilised, but a more widespread adoption of residential consumers as flexibility resources can lead to substantially higher utilisation of the demand-side flexibility potential. In order to achieve this target, acquiring a better understanding of how residential DR participants respond in DR events is essential – and recent advances in novel machine learning and statistical AI provide promising tools to address this challenge. This study provides an in-depth analysis of how residential customers have responded in incentive-based DR, utilising household-related data from a large-scale, real-world trial: the Smart Grid, Smart City (SGSC) project. Using a number of different machine learning approaches, we model the relationship between a household’s response and household-related features. Moreover, we examine the potential effects of households’ features on the residential response behaviour, and highlight a number of key insights which raise questions about the reported level of consumers’ engagement in DR schemes, and the motivation for different customers’ response level. Finally, we explore the temporal structure of the response – and although we found no supporting evidence of DR responders learning over time for the available data from this trial, the proposed methodologies could be used for longer-term longitudinal DR studies. Our study concludes with a broader discussion of our findings and potential paths for future research in this emerging area.@en