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Local energy markets and energy communities have emerged as novel solutions to efficiently coordinate demand for locally generated renewable energy. This study presents a hierarchical multi-stage optimisation framework for the day-ahead scheduling of demand and determination of energy transactions within an energy community. Special pricing when trading with peers in the community is extended to vulnerable households facing energy poverty. The research study emphasizes the importance of mitigating energy poverty and evaluates the effect of various local energy market approaches on the reduction of energy costs through simulation analysis. Application of the proposed methodology is demonstrated through a case study involving a UK-based community of 200 households. Substantial reductions in electricity costs exceeding 13% are achieved across all market structures explored in this paper, particularly when flexibility and optimisation are combined with community trading. Prosumers experience the greatest savings exceeding 32.2%, while energy-poor households gain over 11.1% on average benefiting from reduced tariffs at the community trading stage, however, this is accompanied with diminished gains on behalf of prosumers.

Autonomous robots used in infrastructure inspection, space exploration and other critical missions operate in highly dynamic environments. As such, they must continually verify their ability to complete the tasks associated with these missions safely and effectively. Here we present a Bayesian learning framework that enables this runtime verification of autonomous robots. The framework uses prior knowledge and observations of the verified robot to learn expected ranges for the occurrence rates of regular and singular (e.g., catastrophic failure) events. Interval continuous-time Markov models defined using these ranges are then analysed to obtain expected intervals of variation for system properties such as mission duration and success probability. We apply the framework to an autonomous robotic mission for underwater infrastructure inspection and repair. The formal proofs and experiments presented in the paper show that our framework produces results that reflect the uncertainty intrinsic to many real-world systems, enabling the robust verification of their quantitative properties under parametric uncertainty.

Access to affordable energy has become a major concern in many European countries due to significant increases in energy prices. Concurrently, energy communities have emerged as a potential solution to mitigate this issue. In this paper, we propose a Local Energy Market (LEM) mechanism based on automated negotiations designed to reduce electricity bills for community members, particularly those experiencing fuel poverty (i.e. limited ability to meet rising energy costs). This LEM necessitates consumers to submit energy requests, including specific ones for their essential needs. It also supports consumers flexibility to maximise access to cheaper electricity offered by local producers. The advantages of the proposed P2P automated negotiation process between consumers and producers are showcased through a small stylized use case followed by an extension of the findings to the context of the Orkney Islands in the UK, an area significantly affected by fuel poverty. Results indicate that a maximum bill reduction of 30% can be achieved with such LEM, showing the potential of the method to improve access to affordable energy.

Peer-to-peer (P2P) energy trading and energy communities have garnered much attention over in recent years due to increasing investments in local energy generation and storage assets. Much research has been performed on the mechanisms and methodologies behind their implementation and realisation. However, the efficiency to be gained from P2P trading, and the structure of local energy markets raise many important challenges. To analyse the efficiency of P2P energy markets, in this work, we consider two different popular approaches to peer-to-peer trading: centralised (through a central market maker/clearing entity) vs. fully decentralised (P2P), and explore the comparative economic benefits of these models. We focus on the metric of Gains from Trade (GT), given optimal P2P trading schedule computed by a schedule optimiser. In both local market models, benefits from trading are realised mainly due to the diversity in consumption behaviour and renewable energy generation between prosumers in an energy community. Both market models will lead to the most promising P2P contracts (the ones with the highest Gains from Trade) to be established first. Yet, we find diversity decreases quickly as more peer-to-peer energy contracts are established and more prosumers join the market, leading to significantly diminishing returns. In this work, we aim to quantify this effect using real-world data from two large-scale smart energy trials in the UK, i.e. the Low Carbon London project and the Thames Valley Vision project. Our experimental study shows that, for both market models, only a small number of P2P contracts i.e. less than 10% of the possible P2P contracts are required to achieve the majority of the maximal potential Gains from Trade. Similarly, only a fraction of prosumers are required to participate in energy trading to realise significant GT; namely we found that 60% of the maximal GT can be realised with only 30% of prosumers’ participation, with the percentage of maximal

AI technologies offer significant opportunities for the media sector, including content production, distribution, and audience engagement. This case study aims to bridge the technological expertise of the AI, Media & Democracy lab in the Netherlands to stakeholders in the media sector, identifying current technology implementation practices and outlining the potential of our technological expertise. First, a visual portfolio of the expertise of the researchers was created. Then, focus group workshops were held with broadcasters and media institutions, using an interactive online Miro environment. Results include insights on current implementation of the presented technologies by the media organizations, and opportunities for further implementation. Key takeaways are the broadcaster need for short-term implementation possibilities and the importance of having both B2C (broadcasters) and a B2B (the entire sector) perspectives present in focus groups to provide confirmation of findings and offer a broader viewpoint on the media landscape.

Autonomous robots used in infrastructure inspection, space exploration and other critical missions operate in highly dynamic environments. As such, they must continually verify their ability to complete the tasks associated with these missions safely and effectively. Here we present a Bayesian learning framework that enables this runtime verification of autonomous robots. The framework uses prior knowledge and observations of the verified robot to learn expected ranges for the occurrence rates of regular and singular (e.g., catastrophic failure) events. Interval continuous-time Markov models defined using these ranges are then analysed to obtain expected intervals of variation for system properties such as mission duration and success probability. We apply the framework to an autonomous robotic mission for underwater infrastructure inspection and repair. The formal proofs and experiments presented in the paper show that our framework produces results that reflect the uncertainty intrinsic to many real-world systems, enabling the robust verification of their quantitative properties under parametric uncertainty.

This extended abstract introduces the work of the Netherlands AI Media and Democracy lab, especially focusing on the research performed from an AI/computer science perspective at CWI, the Netherlands National Research Center for Mathematics and Computer Science in Amsterdam. We first provide an overview of the general aims and set-up of the lab, and then focuses in on the research areas of the 3 research groups at CWI, outlining there are of research and expected research contributions in the areas between AI and media & democracy.

The transition towards a more decarbonised, resilient and distributed energy system requires local initiatives, such as Smart Local Energy Systems (SLES), which lead communities to gain self-sufficiency and become electricity islands. Although many SLES projects have been recently deployed, only a few of them have managed to be successful, mostly due to an initial knowledge gap in the SLES planning and deployment phases. This paper leverages the knowledge from the UK's largest SLES demonstrator in the Orkney Islands, named the Responsive FLEXibility (ReFLEX) project, to propose a framework that will help communities to successfully implement a SLES. First, this paper describes how the multi-services electrical SLES implemented in Orkney reduces the impact of the energy transition on the electrical infrastructure. We identify and discuss the main enablers and barriers to a successful SLES, based on a review of SLES projects in the UK. Second, to help future communities to implement SLES, we extend the Smart Grid Architecture Model (SGAM) into a comprehensive multi-vector Smart Local Energy Architecture Model (SLEAM) that includes all main energy services, namely power, heat and transport. This extended architecture model describes the main components and interaction layers that need to be addressed in a comprehensive SLES. Next, to inform successful deployment of SLES, an extensive list of key performance indicators for SLES is proposed and implemented for the ReFLEX project. Finally, we discuss lessons learnt from the ReFLEX project and we list required future technologies that enable communities, energy policy makers and regulatory bodies to best prepare for the energy transition.

Given the fundamental role of renewable energy assets in achieving global temperature control targets, new energy management methods are required to efficiently match intermittent renewable generation and demand. Based on analysing various designed cases, this paper explores a number of heuristics for a smart battery scheduling algorithm that efficiently matches available power supply and demand. The core of improvement of the proposed smart battery scheduling algorithm is exploiting future knowledge, which can be realized by current state-of-the-art forecasting techniques, to effectively store and trade energy. The performance of the developed heuristic battery scheduling algorithm using forecast data of demands, generation, and energy prices is compared to a heuristic baseline algorithm, where decisions are made solely on the current state of the battery, demand, and generation. The battery scheduling algorithms are tested using real data from two large-scale smart energy trials in the UK, in addition to various types and levels of simulated uncertainty in forecasts. The results show that when using a battery to store generated energy, on average, the newly proposed algorithm outperforms the baseline algorithm, obtaining up to 20–60% more profit for the prosumer from their energy assets, in cases where the battery is optimally sized and high-quality forecasts are available. Crucially, the proposed algorithm generates greater profit than the baseline method even with large uncertainty on the forecast, showing the robustness of the proposed solution. On average, only 2–12% of profit is lost on generation and demand uncertainty compared to perfect forecasts. Furthermore, the performance of the proposed algorithm increases as the uncertainty decreases, showing great promise for the algorithm as the quality of forecasting keeps improving.

The emergence of peer-to-peer, collective or community self-consumption, and transactive energy concepts gives rise to new configurations of business models for local energy trading among a variety of actors. Much attention has been paid in the academic literature to the transition of the underlying energy system with its macroeconomic market framework. However, fewer contributions focus on the microeconomic aspects of the broad set of involved actors. Even though specific case studies highlight single business models, a comprehensive analysis of emerging business models for the entire set of actors is missing. Following this research gap, this paper conducts a systematic literature review of 135 peer-reviewed journal articles to examine business models of actors operating in local energy markets. From 221 businesses in the reviewed literature, nine macro-actor categories are identified. For each type of market actor, a business model archetype is determined and characterised using the business model canvas. The key elements of each business model archetype are discussed, and areas are highlighted where further research is needed. Finally, this paper outlines the differences of business models for their presence in the three local energy market models. Focusing on the identified customers and partner relationships, this study highlights the key actors per market model and the character of the interactions between market participants.

Peer-to-peer, community or collective self-consumption, and transactive energy markets offer new models for trading energy locally. Over the past five years, there has been significant growth in the amount of academic literature examining how these local energy markets might function. This systematic literature review of 139 peer-reviewed journal articles examines the market designs used in these energy trading models. A modified version of the Business Ecosystem Architecture Modelling framework is used to extract market model information from the literature, and to identify differences and similarities between the models. This paper examines how peer-to-peer, community self-consumption and transactive energy markets are described in current literature. It explores the similarities and differences between these markets in terms of participation, governance structure, topology, and design. This paper systematises peer-to-peer, community self-consumption and transactive energy market designs, identifying six archetypes. Finally, it identifies five evidence gaps which require future research before these markets could be widely adopted. These evidence gaps are the lack of: consideration of physical constraints; a holistic approach to market design and operation; consideration about how these market designs will scale; consideration of information security; and, consideration of market participant privacy.

Electromagnetic Relays (EMRs) are omnipresent in electrical systems, ranging from mass-produced consumer products to highly specialised, safety-critical industrial systems. Our detailed literature review focused on EMR reliability highlighting the methods used to estimate EMR-State of Health (SOH) or -Remaining Useful Life (RUL) emphasises the limited analysis and understanding of expressive EMR-Degradation Indicators (DIs), as well as accessibility and use of EMR life cycle data sets. Our research prioritises an analysis of the state-of-the-art literature and how to overcome these open challenges. A Deep Learning (DL) pipeline is presented in a prognostic context, termed Electromagnetic Relay Useful Actuation Pipeline (EMRUA). Leveraging the attributes of causal convolution, a Temporal Convolutional Network (TCN) based architecture integrates an arbitrary long sequence of multiple features to produce a Remaining Useful Switching Actuations (RUA) forecast. These features are extracted from raw, high volume life cycle data sets, namely EMR switching data (Contact Voltage (CV), Contact Current (CI) and Coil Current (CC)). Monte-Carlo Dropout (MCD) is utilised to estimate uncertainty during inference. The TCN hyperparameter space, as well as various methods to select and analyse long sequences of multivariate time series data are investigated. Subsequently, our results demonstrate improvements using the developed statistical feature-set over traditional, time-based features, commonly found in literature. EMRUA achieves an average forecasting Mean Absolute Percentage Error (MAPE) of ±12 % over the course of the entire EMR life.

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.

The UK has set plans to increase offshore wind capacity from 22GW to 154GW by 2030. With such tremendous growth, the sector is now looking to Robotics and Artificial Intelligence (RAI) in order to tackle lifecycle service barriers as to support sustainable and profitable offshore wind energy production. Today, RAI applications are predominately being used to support short term objectives in operation and maintenance. However, moving forward, RAI has the potential to play a critical role throughout the full lifecycle of offshore wind infrastructure, from surveying, planning, design, logistics, operational support, training and decommissioning. This paper presents one of the first systematic reviews of RAI for the offshore renewable energy sector. The state-of-the-art in RAI is analyzed with respect to offshore energy requirements, from both industry and academia, in terms of current and future requirements. Our review also includes a detailed evaluation of investment, regulation and skills development required to support the adoption of RAI. The key trends identified through a detailed analysis of patent and academic publication databases provide insights to barriers such as certification of autonomous platforms for safety compliance and reliability, the need for digital architectures for scalability in autonomous fleets, adaptive mission planning for resilient resident operations and optimization of human machine interaction for trusted partnerships between people and autonomous assistants. Our study concludes with identification of technological priorities and outlines their integration into a new ‘symbiotic digital architecture’ to deliver the future of offshore wind farm lifecycle management.

With the emergence of energy communities, where a number of prosumers invest in shared renewable generation capacity and battery storage, the issue of fair allocation of benefits and costs has become increasingly important. The Shapley value has attracted increasing interest for redistribution in energy settings - however, computing it exactly is intractable beyond a few dozen prosumers. In this paper, we examine a number of methods for approximating the Shapley value in realistic community energy settings, and propose a new one. To compare the performances of these methods, we also design a novel method to compute the Shapley value exactly, for communities of up to several hundred agents by clustering consumers into a smaller number of demand profiles. We compare the methods in a large-scale case study of a community of up to 200 household consumers in the UK, and show that our method can achieve very close redistribution to the exact Shapley values but at a much lower (and practically feasible) computation cost.

With the emergence of energy communities, where a number of prosumers invest in shared renewable generation capacity and battery storage, the issue of fair allocation of benefits and costs has become increasingly important. The Shapley value has attracted increasing interest for redistribution in energy settings - however, computing it exactly is intractable beyond a few dozen prosumers. In this paper, we examine a number of methods for approximating the Shapley value in realistic community energy settings, and propose a new one. To compare the performances of these methods, we also design a novel method to compute the Shapley value exactly, for communities of up to several hundred agents by clustering consumers into a smaller number of demand profiles. We compare the methods in a large-scale case study of a community of up to 200 household consumers in the UK, and show that our method can achieve very close redistribution to the exact Shapley values but at a much lower (and practically feasible) computation cost.

With the emergence of energy communities, where a number of prosumers invest in shared generation and storage, the issue of fair allocation of benefits is increasingly important. The Shapley value has attracted increasing interest for redistribution in energy settings — however, computing it exactly is intractable beyond a few dozen prosumers. In this paper, we first conduct a systematic review of the literature on the use of Shapley value in energy-related applications, as well as efforts to compute or approximate it. Next, we formalise the main methods for approximating the Shapley value in community energy settings, and propose a new one, which we call the stratified expected value approximation. To compare the performance of these methods, we design a novel method for exact Shapley value computation, which can be applied to communities of up to several hundred agents by clustering the prosumers into a smaller number of demand profiles. We perform a large-scale experimental comparison of the proposed methods, for communities of up to 200 prosumers, using large-scale, publicly available data from two large-scale energy trials in the UK (UKERC Energy Data Centre, 2017, UK Power Networks Innovation, 2021). Our analysis shows that, as the number of agents in the community increases, the relative difference to the exact Shapley value converges to under 1% for all the approximation methods considered. In particular, for most experimental scenarios, we show that there is no statistical difference between the newly proposed stratified expected value method and the existing state-of- the-art method that uses adaptive sampling (O’Brien et al., 2015), although the cost of computation for large communities is an order of magnitude lower.

Electromagnetic Relays (EMRs) are omnipresent in electrical systems, ranging from mass-produced consumer products to highly specialised, safety-critical industrial systems. Our detailed literature review focused on EMR reliability highlighting the methods used to estimate EMR-State of Health (SOH) or -Remaining Useful Life (RUL) emphasises the limited analysis and understanding of expressive EMR-Degradation Indicators (DIs), as well as accessibility and use of EMR life cycle data sets. Our research prioritises an analysis of the state-of-the-art literature and how to overcome these open challenges. A Deep Learning (DL) pipeline is presented in a prognostic context, termed Electromagnetic Relay Useful Actuation Pipeline (EMRUA). Leveraging the attributes of causal convolution, a Temporal Convolutional Network (TCN) based architecture integrates an arbitrary long sequence of multiple features to produce a Remaining Useful Switching Actuations (RUA) forecast. These features are extracted from raw, high volume life cycle data sets, namely EMR switching data (Contact Voltage (CV), Contact Current (CI) and Coil Current (CC)). Monte-Carlo Dropout (MCD) is utilised to estimate uncertainty during inference. The TCN hyperparameter space, as well as various methods to select and analyse long sequences of multivariate time series data are investigated. Subsequently, our results demonstrate improvements using the developed statistical feature-set over traditional, time-based features, commonly found in literature. EMRUA achieves an average forecasting Mean Absolute Percentage Error (MAPE) of ±12 % over the course of the entire EMR life.

Peer-to-peer, community or collective self-consumption, and transactive energy markets offer new models for trading energy locally. Over the past five years, there has been significant growth in the amount of academic literature examining how these local energy markets might function. This systematic literature review of 139 peer-reviewed journal articles examines the market designs used in these energy trading models. A modified version of the Business Ecosystem Architecture Modelling framework is used to extract market model information from the literature, and to identify differences and similarities between the models. This paper examines how peer-to-peer, community self-consumption and transactive energy markets are described in current literature. It explores the similarities and differences between these markets in terms of participation, governance structure, topology, and design. This paper systematises peer-to-peer, community self-consumption and transactive energy market designs, identifying six archetypes. Finally, it identifies five evidence gaps which require future research before these markets could be widely adopted. These evidence gaps are the lack of: consideration of physical constraints; a holistic approach to market design and operation; consideration about how these market designs will scale; consideration of information security; and, consideration of market participant privacy.

The UK has set plans to increase offshore wind capacity from 22GW to 154GW by 2030. With such tremendous growth, the sector is now looking to Robotics and Artificial Intelligence (RAI) in order to tackle lifecycle service barriers as to support sustainable and profitable offshore wind energy production. Today, RAI applications are predominately being used to support short term objectives in operation and maintenance. However, moving forward, RAI has the potential to play a critical role throughout the full lifecycle of offshore wind infrastructure, from surveying, planning, design, logistics, operational support, training and decommissioning. This paper presents one of the first systematic reviews of RAI for the offshore renewable energy sector. The state-of-the-art in RAI is analyzed with respect to offshore energy requirements, from both industry and academia, in terms of current and future requirements. Our review also includes a detailed evaluation of investment, regulation and skills development required to support the adoption of RAI. The key trends identified through a detailed analysis of patent and academic publication databases provide insights to barriers such as certification of autonomous platforms for safety compliance and reliability, the need for digital architectures for scalability in autonomous fleets, adaptive mission planning for resilient resident operations and optimization of human machine interaction for trusted partnerships between people and autonomous assistants. Our study concludes with identification of technological priorities and outlines their integration into a new ‘symbiotic digital architecture’ to deliver the future of offshore wind farm lifecycle management.

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.

Recent years have seen a surge of interest in distributed residential batteries for households with renewable generation. Yet, assuring battery assets are profitable for their owners requires a complex optimisation of the battery asset and additional revenue sources, such as novel ways to access wholesale energy markets. In this paper, we propose a framework in which wholesale market bids are placed on forward energy markets by an aggregator of distributed residential batteries that are controlled in real time by a novel Home Energy Management System (HEMS) control algorithm to meet the market commitments, while maximising local self-consumption. The proposed framework consists of three stages. In the first stage, an optimal day-ahead or intra-day scheduling of the aggregated storage assets is computed centrally. For the second stage, a bidding strategy is developed for wholesale energy markets. Finally, in the third stage, a novel HEMS real-time control algorithm based on a smart contract allows coordination of residential batteries to meet the market commitments and maximise self-consumption of local production. Using a case study provided by a large UK-based energy demonstrator, we apply the framework to an aggregator with 70 residential batteries. Experimental analysis is done using real per minute data for demand and production. Results indicate that the proposed approach increases the aggregator’s revenues by 35% compared to a case without residential flexibility, and increases the self-consumption rate of the households by a factor of two. The robustness of the results to uncertainty, forecast errors and to communication latency is also demonstrated.

Given the pressing need for assuring algorithmic transparency, Explainable AI (XAI) has emerged as one of the key areas of AI research. In this paper, we develop a novel Bayesian extension to the LIME framework, one of the most widely used approaches in XAI – which we call BayLIME. Compared to LIME, BayLIME exploits prior knowledge and Bayesian reasoning to improve both the consistency in repeated explanations of a single prediction and the robustness to kernel settings. BayLIME also exhibits better explanation fidelity than the state-of-the-art (LIME, SHAP and GradCAM) by its ability to integrate prior knowledge from, e.g., a variety of other XAI techniques, as well as verification and validation (V&V) methods. We demonstrate the desirable properties of BayLIME through both theoretical analysis and extensive experiments.

Given the pressing need for assuring algorithmic transparency, Explainable AI (XAI) has emerged as one of the key areas of AI research. In this paper, we develop a novel Bayesian extension to the LIME framework, one of the most widely used approaches in XAI – which we call BayLIME. Compared to LIME, BayLIME exploits prior knowledge and Bayesian reasoning to improve both the consistency in repeated explanations of a single prediction and the robustness to kernel settings. BayLIME also exhibits better explanation fidelity than the state-of-the-art (LIME, SHAP and GradCAM) by its ability to integrate prior knowledge from, e.g., a variety of other XAI techniques, as well as verification and validation (V&V) methods. We demonstrate the desirable properties of BayLIME through both theoretical analysis and extensive experiments.

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.

A novel modelling paradigm for online diagnostics and prognostics for autonomous systems is presented. A model for the autonomous system being diagnosed is designed using a logic-based formalism. The model supports the run-time a

Off-grid PV systems are providing critical access to energy services for millions of people throughout the globe. However, optimum sizing of these PV systems still poses a challenge, as inadequate system sizing could result in low system reliability and/or high cost of electricity generated. This paper presents a hybrid method of sizing off-grid PV systems for undefined electricity consumption. It then compares this sizing with off-grid PV systems installed in Nigeria – ranked the most populous electricity-deficit country in the world. The yields of the installed off-grid PV systems are also simulated for four major cities in Nigeria. Results show that for the over 1.5MWp of off-grid PV systems installed in the country, there is a potential 1.11 – 3.04 MWh of unutilised surplus electricity, which can supply 2hours of green electricity to at least 2,000 Tier-2 households during peak demand in the dry hot season. Thus, our hybrid model provides design and operational insight to off-grid PV system optimization.

Industrial-scale component maintenance is shifting towards novel Predictive Maintenance (PdM) strategies supported by Big Data Analytics (BDA). This has resulted in an increased effort to implement Artificial Intelligence (AI) decision making into new maintenance paradigms. The transition of AI into industry faces significant challenges due to the inherent complexities of industrial operations, such as variability in components due to manufacturing, integration, dynamic operating environments and variable loading conditions. Therefore, AI in critical industrial systems requires more advanced capabilities such as robustness, scalability and verifiability. This paper presents the first Deep Learning (DL) based strategy for the classification of the State-Of-Health (SOH) of Electromagnetic Relays (EMR). The DL strategy scales with high-volumes of multivariate time-series data whilst automating labour intensive feature extraction requirements. The method proposed in our paper, combines a Convolutional-Auto-Encoder (CAE) with a Temporal Convolutional Neural Network (TCN), referred to as EMR-SOH CAE- TCN pipeline. Model uncertainty and SOH confidence bounds are approximated by Monte-Carlo dropout. Our pipeline is trained and evaluated on data generated from EMR life-cycle tests. We report a high classification accuracy and discriminatory power of the EMR-SOH classifier. The findings from our paper demonstrate the potential of AI pipelines for maintenance decision making of components in critical applications, providing a transferable AI based PdM solution that scales with large data quantities.

To reduce Operation and Maintenance (OM) expenditure on offshore wind farms, wherein 80% of the cost relates to deploying personnel, the offshore wind sector looks to advances in Robotics and Artificial Intelligence (RAI) for solutions. Barriers to residential Beyond Visual Line of Sight (BVLOS) autonomy as a service, include operational challenges in run-time safety compliance, reliability and resilience, due to the complexities of dealing with known and unknown risk in dynamic environments. In this paper we incorporate a Symbiotic System Of Systems Approach (SSOSA) that uses a Symbiotic Digital Architecture (SDA) to provide a cyber physical orchestration of enabling technologies. Implementing a SSOSA enables Cooperation, Collaboration and Corroboration (C3), as to address run-time verification of safety, reliability and resilience during autonomous missions. Our SDA provides a means to synchronize distributed digital models of the robot, environment and infrastructure. Through the coordinated bidirectional communication network of the SDA, the remote human operator has improved visibility and understanding of the mission profile. We evaluate our SSOSA in an asset inspection mission within a confined operating environment. Demonstrating the ability of our SSOSA to overcome safety, reliability and resilience challenges. The SDA supports lifecycle learning and co-evolution with knowledge sharing across the interconnected systems. Our results evaluate both sudden and gradual faults, as well as unknown events, that may jeopardize an autonomous mission. Using distributed and coordinated decision making, the SSOSA enhances the analysis of the mission status, which includes diagnostics of critical sub-systems within the resident robot. This evaluation demonstrates that the SSOSA provides enhanced run-time operational resilience and safety compliance to BVLOS autonomous missions. The SSOSA has the potential to be a highly transferable methodology to other mission scenarios

Lithium-ion batteries power numerous systems from consumer electronics to electric vehicles, and thus undergo qualification testing for degradation assessment prior to deployment. Qualification testing involves repeated charge–discharge operation of the batteries, which can take more than three months if subjected to 500 cycles at a C-rate of 0.5C. Accelerated degradation testing can be used to reduce extensive test time, but its application requires a careful selection of stress factors. To address this challenge, this study identifies and ranks stress factors in terms of their effects on battery degradation (capacity fade) using half-fractional design of experiments and machine learning. Two case studies are presented involving 96 lithium-ion batteries from two different manufacturers, tested under five different stress factors. Results show that neither the individual (main) effects nor the two-way interaction effects of charge C-rate and depth of discharge rank in the top three significant stress factors for the capacity fade in lithium-ion batteries, while temperature in the form of either individual or interaction effect provides the maximum acceleration.

Lithium-ion batteries are ubiquitous in modern day applications ranging from portable electronics to electric vehicles. Irrespective of the application, reliable real-time estimation of battery state of health (SOH) by on-board computers is crucial to the safe operation of the battery, ultimately safeguarding asset integrity. In this paper, we design and evaluate a machine learning pipeline for estimation of battery capacity fade - a metric of battery health - on 179 cells cycled under various conditions. The pipeline estimates battery SOH with an associated confidence interval by using two parametric and two non-parametric algorithms. Using segments of charge voltage and current curves, the pipeline engineers 30 features, performs automatic feature selection and calibrates the algorithms. When deployed on cells operated under the fast-charging protocol, the best model achieves a root mean squared percent error of 0.45\%. This work provides insights into the design of scalable data-driven models for battery SOH estimation, emphasising the value of confidence bounds around the prediction. The pipeline methodology combines experimental data with machine learning modelling and can be generalized to other critical components that require real-time estimation of SOH.

The trend of decentralization of energy services has given rise to community energy systems. These energy communities aim to maximize the self-consumption of local renewable energy generated and stored in assets that are typically connected to low-voltage (LV) distribution networks. Energy community schemes often involve jointly owned assets such as community-owned solar photo-voltaic panels (PVs), wind turbines and/or shared battery storage. This raises the question of how these assets should be controlled in real-time, and how the energy outputs from these jointly owned assets should be shared fairly among heterogeneous community members. Crucially, such real-time control and fair sharing of energy must also consider the technical constraints of the community, such as the local LV network characteristics, voltage limits and power ratings of electric cables and transformers. In this paper, we design and analyze a heuristic-based battery control algorithm that considers the influence of battery life degradation, and the resultant increase in local renewable energy consumption within local operating constraints of the LV network. We provide a model that first studies the techno-economic benefits of community-owned versus individually-owned energy assets considering the network/grid constraints. Then, using the methodology and principles from cooperative game theory, we propose a redistribution model for benefits in a community based on the marginal contribution of each household. The results from our study demonstrate that the redistribution mechanism is fairer and computationally tractable compared to the existing state-of-the-art me

The energy landscape for the Low-Voltage (LV) networks is undergoing rapid changes. These changes are driven by the increased penetration of distributed Low Carbon Technologies, both on the generation side (i.e. adoption of micro-renewables) and demand side (i.e. electric vehicle charging). The previously passive ‘fit-and-forget’ approach to LV network management is becoming increasing inefficient to ensure its effective operation. A more agile approach to operation and planning is needed, that includes pro-active prediction and mitigation of risks to local sub-networks (such as risk of voltage deviations out of legal limits). The mass rollout of smart meters (SMs) and advances in metering infrastructure holds the promise for smarter network management. However, many of the proposed methods require full observability, yet the expectation of being able to collect complete, error free data from every smart meter is unrealistic in operational reality. Furthermore, the smart meter (SM) roll-out has encountered significant issues, with the current voluntary nature of installation in the UK and in many other countries resulting in low-likelihood of full SM coverage for all LV networks. Even with a comprehensive SM roll-out privacy restrictions, constrain data availability from meters. To address these issues, this paper proposes the use of a Deep Learning Neural Network architecture to predict the voltage distribution with partial SM coverage on actual network operator LV circuits. The results show that SM measurements from key locations are sufficient for effective prediction of the voltage distribution, even without the use of the high granularity personal power demand data from individual customers.

Electrochemical capacitors (ECs) have only recently been considered as an alternative power source for telemetry sensors of drilling equipment for geothermal or oil and gas exploration. The lifecycle analysis and modelling of ECs is underrepresented in literature in comparison to other storage devices e.g. Li-ion batteries. This paper investigates the degradation of ECs when cycled outside the manufacturer-specified operating temperature envelope and proposes a machine learning-based approach for modelling the degradation. Experimental results show that end of life, defined as a 30% decrease in capacitance, occurs at 1,000 cycles when the environmental temperature exceeds the maximum operating temperature by 30%. The life-cycle test data is then used as an input to a Gaussian process regression (GPR) algorithm to predict the capacitance fade trend. The GPR is validated on a total of nine commercial cells from two different manufacturers, achieving an average root mean squared percent error of less than 2% and a mean calibration score of 93% when referenced to a 95% confidence interval. The model can be utilized to determine the EC degradation rate at a range of operating temperature values.

Given the widespread adoption of renewable generation, storage and new loads like electric vehicle charging, there has been a growing effort to enhance local energy resilience, particularly at the community level. This has led to increasing interest in the development of local or community energy projects, in which individual prosumers are able to generate, store and trade energy within the community — enabling a shift in market power from large utility companies to individual prosumers. Such schemes often involve a group of consumers investing in community-owned asset such as community-owned wind turbines or shared battery storage. Yet, developing methods to enable efficient control and fair sharing of jointly-owned assets is a key open question, of both research and practical importance. In this paper, we provide a method inspired from game theory concepts to fairly redistribute the benefits from community owned energy-assets such as community wind turbines and storage. We propose a heuristic-based battery control algorithm for maximization of behind-the-meter self-consumption, which considers the effect of battery life degradation. Using real consumption and production data to model a community of two hundred households, we assess and compare technical and economic benefits of investment in individually-owned or community-owned assets such as chemical storage. We show that battery storage simple pay-back period can be considerably reduced by sharing the asset within a community. Finally, we compare several redistribution and benefit allocation schemes for community-owned assets, and show that the proposed scheme based on principles from cooperative game theory achieves the fairest redistribution.

With increasing decarbonisation and accessibility to our energy systems and markets, there is a need to understand and optimise the value proposition for different stakeholders. Game-theoretic models represent a promising approach to study strategic interactions between self-interested private energy system investors. In this work, we design and evaluate a game-theoretic framework to study strategic interactions between profit-maximising players that invest in network, renewable generation and storage capacity. Specifically, we study the case where grid capacity is developed by a private renewable investor, but line access is shared with competing renewable and storage investors, thus enabling them to export energy and access electricity demand. We model the problem of deducing how much capacity each player should build as a non-cooperative Stackelberg-Cournot game between a dominant player (leader) who builds the power line and renewable generation capacity, and local renewable and storage investors (multiple followers), who react to the installation of the line by increasing their own capacity. Using data-driven analysis and simulations, we developed an empirical search method for estimating the game equilibrium, where the payoffs capture the realistic operation and control of the energy system under study. A practical demonstration of the underlying methodology is shown for a real-world grid reinforcement project in the UK. The methodology provides a realistic mechanism to analyse investor decision-making and investigate feasible tariffs that encourage

Lithium-ion batteries are ubiquitous in applications ranging from portable electronics to electric vehicles. Irrespective of the application, reliable real-time estimation of battery state of health (SOH) by on-board computers is crucial to the safe operation of the battery, ultimately safeguarding asset integrity. In this Article, we design and evaluate a machine learning pipeline for estimation of battery capacity fade—a metric of battery health—on 179 cells cycled under various conditions. The pipeline estimates battery SOH with an associated confidence interval by using two parametric and two non-parametric algorithms. Using segments of charge voltage and current curves, the pipeline engineers 30 features, performs automatic feature selection and calibrates the algorithms. When deployed on cells operated under the fast-charging protocol, the best model achieves a root-mean-squared error of 0.45%. This work provides insights into the design of scalable data-driven models for battery SOH estimation, emphasizing the value of confidence bounds around the prediction. The pipeline methodology combines experimental data with machine learning modelling and could be applied to other critical components that require real-time estimation of SOH.

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.

To reduce Operation and Maintenance (OM) expenditure on offshore wind farms, wherein 80% of the cost relates to deploying personnel, the offshore wind sector looks to advances in Robotics and Artificial Intelligence (RAI) for solutions. Barriers to residential Beyond Visual Line of Sight (BVLOS) autonomy as a service, include operational challenges in run-time safety compliance, reliability and resilience, due to the complexities of dealing with known and unknown risk in dynamic environments. In this paper we incorporate a Symbiotic System Of Systems Approach (SSOSA) that uses a Symbiotic Digital Architecture (SDA) to provide a cyber physical orchestration of enabling technologies. Implementing a SSOSA enables Cooperation, Collaboration and Corroboration (C3), as to address run-time verification of safety, reliability and resilience during autonomous missions. Our SDA provides a means to synchronize distributed digital models of the robot, environment and infrastructure. Through the coordinated bidirectional communication network of the SDA, the remote human operator has improved visibility and understanding of the mission profile. We evaluate our SSOSA in an asset inspection mission within a confined operating environment. Demonstrating the ability of our SSOSA to overcome safety, reliability and resilience challenges. The SDA supports lifecycle learning and co-evolution with knowledge sharing across the interconnected systems. Our results evaluate both sudden and gradual faults, as well as unknown events, that may jeopardize an autonomous mission. Using distributed and coordinated decision making, the SSOSA enhances the analysis of the mission status, which includes diagnostics of critical sub-systems within the resident robot. This evaluation demonstrates that the SSOSA provides enhanced run-time operational resilience and safety compliance to BVLOS autonomous missions. The SSOSA has the potential to be a highly transferable methodology to other mission scenarios

Off-grid PV systems are providing critical access to energy services for millions of people throughout the globe. However, optimum sizing of these PV systems still poses a challenge, as inadequate system sizing could result in low system reliability and/or high cost of electricity generated. This paper presents a hybrid method of sizing off-grid PV systems for undefined electricity consumption. It then compares this sizing with off-grid PV systems installed in Nigeria – ranked the most populous electricity-deficit country in the world. The yields of the installed off-grid PV systems are also simulated for four major cities in Nigeria. Results show that for the over 1.5MWp of off-grid PV systems installed in the country, there is a potential 1.11 – 3.04 MWh of unutilised surplus electricity, which can supply 2hours of green electricity to at least 2,000 Tier-2 households during peak demand in the dry hot season. Thus, our hybrid model provides design and operational insight to off-grid PV system optimization.

Recent years have seen a surge of interest in distributed residential batteries for households with renewable generation. Yet, assuring battery assets are profitable for their owners requires a complex optimisation of the battery asset and additional revenue sources, such as novel ways to access wholesale energy markets. In this paper, we propose a framework in which wholesale market bids are placed on forward energy markets by an aggregator of distributed residential batteries that are controlled in real time by a novel Home Energy Management System (HEMS) control algorithm to meet the market commitments, while maximising local self-consumption. The proposed framework consists of three stages. In the first stage, an optimal day-ahead or intra-day scheduling of the aggregated storage assets is computed centrally. For the second stage, a bidding strategy is developed for wholesale energy markets. Finally, in the third stage, a novel HEMS real-time control algorithm based on a smart contract allows coordination of residential batteries to meet the market commitments and maximise self-consumption of local production. Using a case study provided by a large UK-based energy demonstrator, we apply the framework to an aggregator with 70 residential batteries. Experimental analysis is done using real per minute data for demand and production. Results indicate that the proposed approach increases the aggregator’s revenues by 35% compared to a case without residential flexibility, and increases the self-consumption rate of the households by a factor of two. The robustness of the results to uncertainty, forecast errors and to communication latency is also demonstrated.

Industrial-scale component maintenance is shifting towards novel Predictive Maintenance (PdM) strategies supported by Big Data Analytics (BDA). This has resulted in an increased effort to implement Artificial Intelligence (AI) decision making into new maintenance paradigms. The transition of AI into industry faces significant challenges due to the inherent complexities of industrial operations, such as variability in components due to manufacturing, integration, dynamic operating environments and variable loading conditions. Therefore, AI in critical industrial systems requires more advanced capabilities such as robustness, scalability and verifiability. This paper presents the first Deep Learning (DL) based strategy for the classification of the State-Of-Health (SOH) of Electromagnetic Relays (EMR). The DL strategy scales with high-volumes of multivariate time-series data whilst automating labour intensive feature extraction requirements. The method proposed in our paper, combines a Convolutional-Auto-Encoder (CAE) with a Temporal Convolutional Neural Network (TCN), referred to as EMR-SOH CAE- TCN pipeline. Model uncertainty and SOH confidence bounds are approximated by Monte-Carlo dropout. Our pipeline is trained and evaluated on data generated from EMR life-cycle tests. We report a high classification accuracy and discriminatory power of the EMR-SOH classifier. The findings from our paper demonstrate the potential of AI pipelines for maintenance decision making of components in critical applications, providing a transferable AI based PdM solution that scales with large data quantities.

Electrochemical capacitors (ECs) have only recently been considered as an alternative power source for telemetry sensors of drilling equipment for geothermal or oil and gas exploration. The lifecycle analysis and modelling of ECs is underrepresented in literature in comparison to other storage devices e.g. Li-ion batteries. This paper investigates the degradation of ECs when cycled outside the manufacturer-specified operating temperature envelope and proposes a machine learning-based approach for modelling the degradation. Experimental results show that end of life, defined as a 30% decrease in capacitance, occurs at 1,000 cycles when the environmental temperature exceeds the maximum operating temperature by 30%. The life-cycle test data is then used as an input to a Gaussian process regression (GPR) algorithm to predict the capacitance fade trend. The GPR is validated on a total of nine commercial cells from two different manufacturers, achieving an average root mean squared percent error of less than 2% and a mean calibration score of 93% when referenced to a 95% confidence interval. The model can be utilized to determine the EC degradation rate at a range of operating temperature values.

A novel modelling paradigm for online diagnostics and prognostics for autonomous systems is presented. A model for the autonomous system being diagnosed is designed using a logic-based formalism. The model supports the run-time ability to verify that the autonomous system is safe and reliable for operation within a dynamic environment. The paradigm is based on the philosophy that there are different types of semantic relationships between the states of different parts of the system. A finite state automaton is devised for each sensed component and some of the non-sensed components. To capture the interdependencies of components within such a complex robotic platform, automatons were related to each other by semantic relationships. Modality was utilised by the formalism to abstract the relationships and to add measures for the possibility and uncertainty of the relationships. The complexity of the model was analysed to evaluate its scalability and applicability to other systems. The results demonstrate that the complexity is not linear and a computational time of 10 ms was required to achieve run-time diagnostics for 2200 KB of knowledge for complex system interdependences. The ability to detect and mitigate hardware related failures was demonstrated within a confined space autonomous operation. Our findings provide evidence of the applicability of our approach for the significant challenge of run-time safety compliance and reliability in autonomous systems.

Lithium-ion batteries power numerous systems from consumer electronics to electric vehicles, and thus undergo qualification testing for degradation assessment prior to deployment. Qualification testing involves repeated charge–discharge operation of the batteries, which can take more than three months if subjected to 500 cycles at a C-rate of 0.5C. Accelerated degradation testing can be used to reduce extensive test time, but its application requires a careful selection of stress factors. To address this challenge, this study identifies and ranks stress factors in terms of their effects on battery degradation (capacity fade) using half-fractional design of experiments and machine learning. Two case studies are presented involving 96 lithium-ion batteries from two different manufacturers, tested under five different stress factors. Results show that neither the individual (main) effects nor the two-way interaction effects of charge C-rate and depth of discharge rank in the top three significant stress factors for the capacity fade in lithium-ion batteries, while temperature in the form of either individual or interaction effect provides the maximum acceleration.

The energy landscape for the Low-Voltage (LV) networks is undergoing rapid changes. These changes are driven by the increased penetration of distributed Low Carbon Technologies, both on the generation side (i.e. adoption of micro-renewables) and demand side (i.e. electric vehicle charging). The previously passive ‘fit-and-forget’ approach to LV network management is becoming increasing inefficient to ensure its effective operation. A more agile approach to operation and planning is needed, that includes pro-active prediction and mitigation of risks to local sub-networks (such as risk of voltage deviations out of legal limits). The mass rollout of smart meters (SMs) and advances in metering infrastructure holds the promise for smarter network management. However, many of the proposed methods require full observability, yet the expectation of being able to collect complete, error free data from every smart meter is unrealistic in operational reality. Furthermore, the smart meter (SM) roll-out has encountered significant issues, with the current voluntary nature of installation in the UK and in many other countries resulting in low-likelihood of full SM coverage for all LV networks. Even with a comprehensive SM roll-out privacy restrictions, constrain data availability from meters. To address these issues, this paper proposes the use of a Deep Learning Neural Network architecture to predict the voltage distribution with partial SM coverage on actual network operator LV circuits. The results show that SM measurements from key locations are sufficient for effective prediction of the voltage distribution, even without the use of the high granularity personal power demand data from individual customers.

Given the pressing need for assuring algorithmic transparency, Explainable AI (XAI) has emerged as one of the key areas of AI research. In this paper, we develop a novel Bayesian extension to the LIME framework, one of the most widely used approaches in XAI – which we call BayLIME. Compared to LIME, BayLIME exploits prior knowledge and Bayesian reasoning to improve both the consistency in repeated explanations of a single prediction and the robustness to kernel settings. BayLIME also exhibits better explanation fidelity than the state-of-the-art (LIME, SHAP and GradCAM) by its ability to integrate prior knowledge from, e.g., a variety of other XAI techniques, as well as verification and validation (V&V) methods. We demonstrate the desirable properties of BayLIME through both theoretical analysis and extensive experiments.