Blockchains or distributed ledgers are an emerging technology that has drawn considerable interest from energy supply firms, startups, technology developers, financial institutions, national governments and the academic community. Numerous sources coming from these backgrounds identify blockchains as having the potential to bring significant benefits and innovation. Blockchains promise transparent, tamper-proof and secure systems that can enable novel business solutions, especially when combined with smart contracts. This work provides a comprehensive overview of fundamental principles that underpin blockchain technologies, such as system architectures and distributed consensus algorithms. Next, we focus on blockchain solutions for the energy industry and inform the state-of-the-art by thoroughly reviewing the literature and current business cases. To our knowledge, this is one of the first academic, peer-reviewed works to provide a systematic review of blockchain activities and initiatives in the energy sector. Our study reviews 140 blockchain research projects and startups from which we construct a map of the potential and relevance of blockchains for energy applications. These initiatives were systematically classified into different groups according to the field of activity, implementation platform and consensus strategy used.¹ Opportunities, potential challenges and limitations for a number of use cases are discussed, ranging from emerging peer-to-peer (P2P) energy trading and Internet of Things (IoT) applications, to decentralised marketplaces, electric vehicle charging and e-mobility. For each of these use cases, our contribution is twofold: first, in identifying the technical challenges that blockchain technology can solve for that application as well as its potential drawbacks, and second in briefly presenting the research and industrial projects and startups that are currently applying blockchain technology to that area. The paper ends with a discussion of challenges and market barriers the technology needs to overcome to get past the hype phase, prove its commercial viability and finally be adopted in the mainstream.

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Electricity-Access is a growing concern in developing countries of Sub-Saharan Africa where approximately 563million people (8% of the global population) lack electricity. Many use fossil-fuel generators and/or solar-home-systems (SHSs) with storage. Given the observed excess and unutilized electricity generated from these SHSs, a novel community peer-To-peer (P2P) electricity-market using automated negotiations is presented as a solution to improve access to electricity. Software agents representing World Bank Tiers 1-4 electricity-Access households bilaterally negotiate electricity price and quantities applying five different negotiations heuristics. Simulation results show 65-100% trade of the excess generation; with Tiers 1-2 (low-income households) negotiating sufficient electricity (1-1.1kWh/day) at the least market-price of £0.35/kWh to move to Tier 3. Likewise, the Tier 3 household agent negotiated 1.5kWh/day sufficient to meet its needs. This novel electricitymarket model can help drive the United Nations goal of affordable, sustainable and modern energy for all by 2030.

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Subsea power cables are essential assets for the electrical transmission and distribution networks. They are crucial in ensuring the security of electricity supply and supporting the global expansion in offshore renewable energy generation. After reviewing historical data on subsea cable failure modes, we established that existing monitoring systems do not account for over 70% of subsea cable failure modes. The current technologies focus on electrical failure modes and subsea cable asset management strategies are typically reactive or time based, with inspection limited to diver and/or ROV supported video footage which has several limitations, such as requiring good visibility, access to the cable, challenges in locating the cable and inability to identify failure modes at the interface of the seabed. To overcome these limitations, we propose an innovative sensor technology that can provide the in-situ integrity analysis of the subsea cable. In this paper, we applied low frequency sonar technology to undertake detailed and in-situ assessment of subsea cable integrity. Specifically, in our work, a wideband low frequency (LF) sonar scanning system is manufactured to collect acoustic response from different subsea power cable samples with different inner structure and external failure modes. In addition, accelerated life cycle testing was conducted by manually introduce controlled stages of corrosion and abrasion to the cables to obtain integrity data at various cable degradation levels. Seminal results provide a detailed library of LF sonar responses to cable type and failure mode variations. The results of preliminary data analysis demonstrate the ability to distinguish subsea cables by differences in diameter and cable types and achieve an overall 95%+ accuracy rate to detect different cable degradation stages.

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There is an urgent societal need to assess whether autonomous vehicles (AVs) are safe enough. From published quantitative safety and reliability assessments of AVs, we know that, given the goal of predicting very low rates of accidents, road testing alone requires infeasible numbers of miles to be driven. However, previous analyses do not consider any knowledge prior to road testing - knowledge which could bring substantial advantages if the AV design allows strong expectations of safety before road testing. We present the advantages of a new variant of Conservative Bayesian Inference (CBI), which uses prior knowledge while avoiding optimistic biases. We then study the trend of disengagements (take-overs by human drivers) by applying Software Reliability Growth Models (SRGMs) to data from Waymo's public road testing over 51 months, in view of the practice of software updates during this testing. Our approach is to not trust any specific SRGM, but to assess forecast accuracy and then improve forecasts. We show that, coupled with accuracy assessment and recalibration techniques, SRGMs could be a valuable test planning aid.

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The increase of solar PV installations at the distribution grid level causes new challenges that Distribution System Operators have to resolve. Amongst those challenges, one is the voltage fluctuation at the distribution grid level due to intermittent active power injection from solar PV systems. A well-known solution to regulate the voltage is the local consumption or production of reactive power, that is extensively used at the transmission grid level by generators or Flexible AC Transmission Systems. This paper discusses the impacts of low-voltage control by reactive power injection from commercialized rooftop PV solar inverters. Using a fully decentralized approach, several state of the art strategies for PV inverters' power factor control are compared, showing that some of these decentralized strategies are a viable solution to control voltage in distribution grids. The methodology used for the assessment of these control strategies includes the impact on the inverters' life time (measured as the annual cost for each strategy) as well as the impact on the voltage profiles of a real grid representing an Indian community with more than 200 households.

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This paper examines the major challenges associated with evaluating energy demand in the residential building sector in an integrated energy system modelling environment. Three established modelling fields are examined to generate a framework for assessing the impact of energy policy: energy system models, building stock models and dynamic building simulation. A set of profound challenges emerge when attempting to integrate such models, due to distinct differences in their intended applications, operational scales, formulations and computational implementations. Detailed discussions are provided on the integration of temporally refined energy demand, based on thermodynamic processes and socio-technical effects which may stem from new policy. A detailed framework is discussed for generating aggregate residential demands, in terms of space heating demand, domestic hot water demand, and lighting, appliance and consumer electronics demand. The framework incorporates a pathway for interpreting the effects of changes in household behaviour resulting from prospective policy measures. When long-term planning exercises are carried out using this framework, the cyclic effects between behavioural change and policy implementation are also considered. This work focused specifically on the United Kingdom energy system, however parallels can be drawn with other countries, in particular those with a mature privatised system, dominated by space heating concerns.

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The battery is a key component of autonomous robots. Its performance limits the robot’s safety and reliability. Unlike liquid-fuel, a battery, as a chemical device, exhibits complicated features, including (i) capacity fade over successive recharges and (ii) increasing discharge rate as the state of charge (SOC) goes down for a given power demand. Existing formal verification studies of autonomous robots, when considering energy constraints, formalise the energy component in a generic manner such that the battery features are overlooked. In this paper, we model an unmanned aerial vehicle (UAV) inspection mission on a wind farm and via probabilistic model checking in PRISM show (i) how the battery features may affect the verification results significantly in practical cases; and (ii) how the battery features, together with dynamic environments and battery safety strategies, jointly affect the verification results. Potential solutions to explicitly integrate battery prognostics and health management (PHM) with formal verification of autonomous robots are also discussed to motivate future work.

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Autonomous systems are increasingly being used in safety-and mission-critical domains, including aviation, manufacturing, healthcare and the automotive industry. Systems for such domains are often verified with respect to essential requirements set by a regulator, as part of a process called certification. In principle, autonomous systems can be deployed if they can be certified for use. However, certification is especially challenging as the condition of both the system and its environment will surely change, limiting the effective use of the system. In this paper we discuss the technological and regulatory background for such systems, and introduce an architectural framework that supports verifiably-correct dynamic self-certification by the system, potentially allowing deployed systems to operate more safely and effectively.

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The increasing penetration of low carbon technologies and electric vehicles is expected to place a considerable burden to Low Voltage (LV) power distribution networks. The previously passive management of LV networks will no longer be sustainable to cope with this growth. A more proactive approach is required to manage LV networks, usually achieved by using Power System State Estimation (PSSE) tools. The effective use of PSSE tools however, requires accurate reporting of each LV network's topology and configuration of assets (cables) that compose it. This paper presents a method for ongoing accuracy improvement of the LV networks topologies, by automating the process of approximating missing cable information and validating the network architectures. The method proposed was derived based on the assumptions that a specific network infrastructure was selected to meet a certain distributed customer loading requirement. The results obtained agrees with the assumptions, providing also a mechanism that scores the confidence level for the choices made to approximate any missing asset information.

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Renewable energy is increasingly being curtailed, due to oversupply or network constraints. Curtailment can be partially avoided by smart grid management, but the long term solution is network reinforcement. Network upgrades, however, can be costly, so recent interest has focused on incentivising private investors to participate in network investments. In this paper, we study settings where a private renewable investor constructs a power line, but also provides access to other generators that pay a transmission fee. The decisions on optimal (and interdependent) renewable capacities built by investors, affect the resulting curtailment and profitability of projects, and can be formulated as a Stackelberg game. Optimal capacities rely jointly on stochastic variables, such as the renewable resource at project location. In this paper, we show how Markov chain Monte Carlo (MCMC) and Gibbs sampling techniques, can be used to generate observations from historic resource data and simulate multiple future scenarios. Finally, we validate and apply our game-Theoretic formulation of the investment decision, to a real network upgrade problem in the UK.

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In this paper the challenges of creating accurate, scalable and usable energy demand models are discussed, in the context of existing simulation and data driven energy demand models. Results from high resolution bottom-up data and simulation-based energy demand analysis from a community energy project are provided. A novel Hidden Markov Modelling and Generalised Pareto (HMM-GP) methodology for simulating synthetic electrical demand profiles is validated for residential buildings at a temporal resolution of five minutes. The corresponding dynamic thermal demands for the various building archetypes within the community are also modelled. This is achieved using automated externally driven IES-VE (building simulation) models for arrays of control profiles, and is also compared against in-situ thermal measurements.

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In this paper we present a review into data driven prognostics and its relevance to resilience in energy systems. A data driven remaining useful life prediction for Li-ion batteries utilizing data analysis via a relevance vector machine (RVM) model is shown to be within 5% accuracy when applied to large lifecycle datasets. Results demonstrate that due to the agile nature of prognostic models and their accuracy, prognostics and health management methods will be vital to resilient and sustainable energy systems.

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In this research we present the state of the art in prognostics and health management, demonstrating opportunities and challenges in designing and implementing prognostic models via three case studies. The remaining useful life prediction for Li-ion batteries utilising data analysis is shown to be within 5% accuracy and in fusion prognostic instance 3-5% accuracy when applied to subsea cables. Empirical data gathered in electromagnetic relay lifecycle analysis demonstrated how low rate sampling can classify failure modes with abnormal resistance spikes representing a precursor, 1.5 million actuations, prior to failure. Results demonstrate that due to the agile nature of PHM models and their accuracy, PHM will be vital to resilient and sustainable complex systems.@en

Renewable energy has achieved high penetration rates in many areas, leading to curtailment, especially if existing network infrastructure is insufficient and energy generated cannot be exported. In this context, Distribution Network Operators (DNOs) face a significant knowledge gap about how to implement curtailment rules that achieve desired operational objectives, but at the same time minimise disruption and economic losses for renewable generators. In this work, we study the properties of several curtailment rules widely used in UK renewable energy projects, and their effect on the viability of renewable generation investment. Moreover, we propose a new curtailment rule which guarantees fair allocation of curtailment amongst all generators with minimal disruption. Another key knowledge gap faced by DNOs is how to incentivise private network upgrades, especially in settings where several generators can use the same line against the payment of a transmission fee. In this work, we provide a solution to this problem by using tools from algorithmic game theory. Specifically, this setting can be modelled as a Stackelberg game between the private transmission line investor and local renewable generators, who are required to pay a transmission fee to access the line. We provide a method for computing the equilibrium of this game, using a model that captures the stochastic nature of renewable energy generation and demand. Finally, we use the practical setting of a grid reinforcement project from the UK and a large dataset of wind speed measurements and demand to validate our model. We show that charging a transmission fee as a proportion of the feed-in tariff price between 15% and 75% would allow both investors to implement their projects and achieve desirable distribution of the profit. Overall, our results show how using game-theoretic tools can help network operators to bridge the knowledge gap about setting the optimal curtailment rule and determining transmission charges for private network infrastructure.@en

A key challenge for distribution and transmission system operators is to relate the retrofitting of monitoring systems to support asset management aligned with the continuity of service within the electrical network. The research within this study demonstrates how a Smart System Integration approach, utilising a wireless sensor network (WSN), can provide a low cost and scalable sensor platform for in situ sensing of SF6 within substations. In this study, the design and manufacturing stages of an ultra-low power WSN are outlined. The WSN is evaluated within a high-voltage laboratory and deployed within a 400 kV substation. Results indicated that the system can reliably transmit data within a noise environment, recover when there is a mote failure without data loss, can operate on batteries for 1.5 years or 5 years taking 1 SF6 density measurement every 60 or 300 s, respectively. The findings of this research demonstrated the advantageous features of WSNs, namely low cost, rapid deployment, reliable and secure data transfer, adaptive and scalable sensor platform.@en

Battery energy storage systems can assist distribution network operators (DNOs) to face the challenges raised by the substantial increase in distributed renewable generation. A challenge is that these resources are intermittent and often 'invisible'to the DNO. If not monitored, the aggregate size of small embedded generation resources can cause thermal wearing of distribution assets and voltage excursions, especially in sunny/windy periods with insufficient local demand. Several developers of energy storage solutions, with technologies such as lithium-ion (Li-ion) batteries, offer their products to address peak shaving, frequency and voltage control needs within the network. Once deployed within the energy network batteries experience capacity degradation with usage, these companies will need to incorporate methods from prognostics and health management (PHM) in order to better manage their products. The main deliverable of this project is validation of data analysis, based on relevance vector machine, to predict the remaining useful life of Li-ion batteries. The accuracy of the predictions for different batteries is all within 10 cycles (within 8.5% relative error). These results confirm the importance of PHM methods within a distribution system operator model, where lifecycle management of critical sub-systems and systems will become increasingly important to network operators.@en

In recent years there has been a surge of interest in hybrid propulsion technology for commercial and domestic maritime vessels. Factors driving this growth include high maintenance cost of diesel engines and the need for compliance to more stringent environmental regulations. Although electric and hybrid propulsion techniques present an opportunity to address this challenge, today's batteries for large vessels are still expensive and prone to unexpected failure. Therefore, the cost and reliability of batteries still represent risks to the maintenance in hybrid energy systems. In this paper, we first introduce the challenges of asset health management within hybrid energy systems for maritime vessels. We explore the feasibility of data-driven methods to evaluate Remaining Useful Life (RUL) of the system assets to inform a preventative and cost efficient management system. An overview of our proposed solution is presented along with preliminary results of our data-driven model applied to large battery data sets to estimate the battery RUL. The model is based on a state-of-art machine learning technique, Relevance Vector Machine (RVM), which is a powerful tool for resolving uncertainty in large data sets. Initial training of our machine learning algorithm utilizes a public battery life cycle testing dataset, provided from NASA Ames Research Center. Next, we use life cycle analysis of batteries designed for hybrid vessels to evaluate the performance of the algorithm in predicting battery remaining useful life (RUL). The accuracy of the predictions for different batteries are all within 10 cycles (within 8.5% relative error) which encourages us to adopt the same approach in future health management works for other power assets on the hybrid maritime vessels.@en