• Energy Research

La tendance à la décentralisation des services énergétiques a donné naissance à des systèmes énergétiques communautaires. Ces communautés énergétiques visent à maximiser l'autoconsommation d'énergie renouvelable locale produite et stockée dans des actifs généralement connectés à des réseaux de distribution basse tension (BT). Les projets de la communauté de l'énergie impliquent souvent des actifs en copropriété tels que des panneaux photovoltaïques (PV) solaires appartenant à la communauté, des éoliennes et/ou des batteries de stockage partagées. Cela soulève la question de savoir comment ces actifs devraient être contrôlés en temps réel et comment les rendements énergétiques de ces actifs détenus conjointement devraient être partagés équitablement entre les membres hétérogènes de la communauté. Fondamentalement, un tel contrôle en temps réel et un partage équitable de l'énergie doivent également tenir compte des contraintes techniques de la communauté, telles que les caractéristiques du réseau BT local, les limites de tension et les puissances nominales des câbles électriques et des transformateurs. Dans cet article, nous concevons et analysons un algorithme de contrôle de batterie basé sur l'heuristique qui prend en compte l'influence de la dégradation de la durée de vie de la batterie et l'augmentation résultante de la consommation locale d'énergie renouvelable dans les contraintes d'exploitation locales du réseau BT. Nous fournissons un modèle qui étudie d'abord les avantages technico-économiques des actifs énergétiques détenus par la communauté par rapport à ceux détenus individuellement compte tenu des contraintes du réseau/réseau. Ensuite, en utilisant la méthodologie et les principes de la théorie coopérative des jeux, nous proposons un modèle de redistribution des avantages dans une communauté basé sur la contribution marginale de chaque ménage. Les résultats de notre étude démontrent que le mécanisme de redistribution est plus juste et calculable par rapport aux méthodes de pointe existantes. Ainsi, notre méthodologie est plus évolutive en ce qui concerne la modélisation du partage économique des actifs communs dans les systèmes énergétiques communautaires. La tendencia a la descentralización de los servicios energéticos ha dado lugar a sistemas energéticos comunitarios. Estas comunidades energéticas tienen como objetivo maximizar el autoconsumo de energía renovable local generada y almacenada en activos que normalmente están conectados a redes de distribución de baja tensión (BT). Los esquemas comunitarios de energía a menudo involucran activos de propiedad conjunta, como paneles solares fotovoltaicos (PV) de propiedad comunitaria, turbinas eólicas y/o almacenamiento compartido de baterías. Esto plantea la cuestión de cómo se deben controlar estos activos en tiempo real y cómo se deben compartir equitativamente las salidas de energía de estos activos de propiedad conjunta entre los miembros heterogéneos de la comunidad. Fundamentalmente, dicho control en tiempo real y el reparto justo de la energía también deben tener en cuenta las limitaciones técnicas de la comunidad, como las características de la red local de BT, los límites de tensión y las potencias nominales de los cables y transformadores eléctricos. En este documento, diseñamos y analizamos un algoritmo de control de batería basado en la heurística que considera la influencia de la degradación de la vida útil de la batería y el aumento resultante en el consumo local de energía renovable dentro de las restricciones operativas locales de la red de BT. Proporcionamos un modelo que primero estudia los beneficios tecnoeconómicos de los activos energéticos de propiedad comunitaria frente a los de propiedad individual teniendo en cuenta las limitaciones de la red/red. Luego, utilizando la metodología y los principios de la teoría de juegos cooperativos, proponemos un modelo de redistribución de beneficios en una comunidad basado en la contribución marginal de cada hogar. Los resultados de nuestro estudio demuestran que el mecanismo de redistribución es más justo y manejable computacionalmente en comparación con los métodos de vanguardia existentes. Por lo tanto, nuestra metodología es más escalable con respecto a la modelización del reparto económico de los activos conjuntos en los sistemas energéticos comunitarios. 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 methods. Thus, our methodology is more scalable with respect to modeling the economic sharing of joint assets in community energy systems. أدى اتجاه اللامركزية في خدمات الطاقة إلى ظهور أنظمة الطاقة المجتمعية. تهدف مجتمعات الطاقة هذه إلى زيادة الاستهلاك الذاتي للطاقة المتجددة المحلية المولدة والمخزنة في الأصول التي ترتبط عادةً بشبكات توزيع الجهد المنخفض (LV). غالبًا ما تتضمن مخططات مجتمع الطاقة أصولًا مملوكة بشكل مشترك مثل الألواح الكهروضوئية الشمسية المملوكة للمجتمع (PVs) وتوربينات الرياح و/أو تخزين البطارية المشترك. ويثير هذا السؤال حول كيفية التحكم في هذه الأصول في الوقت الفعلي، وكيف ينبغي تقاسم مخرجات الطاقة من هذه الأصول المشتركة بشكل عادل بين أفراد المجتمع غير المتجانسين. والأهم من ذلك، يجب أن يأخذ هذا التحكم في الوقت الفعلي والتقاسم العادل للطاقة في الاعتبار القيود الفنية للمجتمع، مثل خصائص شبكة الجهد المنخفض المحلية وحدود الجهد ومعدلات الطاقة للكابلات والمحولات الكهربائية. في هذه الورقة، نقوم بتصميم وتحليل خوارزمية التحكم في البطارية القائمة على الاستدلال والتي تأخذ في الاعتبار تأثير تدهور عمر البطارية، والزيادة الناتجة في الاستهلاك المحلي للطاقة المتجددة ضمن قيود التشغيل المحلية لشبكة الجهد المنخفض. نحن نقدم نموذجًا يدرس أولاً الفوائد التقنية والاقتصادية لأصول الطاقة المملوكة للمجتمع مقابل المملوكة للأفراد مع مراعاة قيود الشبكة/الشبكة. ثم، باستخدام المنهجية والمبادئ من نظرية اللعبة التعاونية، نقترح نموذج إعادة توزيع للمنافع في المجتمع على أساس المساهمة الهامشية لكل أسرة. تُظهر نتائج دراستنا أن آلية إعادة التوزيع أكثر عدلاً وقابلة للتتبع الحسابي مقارنة بالطرق الحديثة الحالية. وبالتالي، فإن منهجيتنا أكثر قابلية للتوسع فيما يتعلق بنمذجة المشاركة الاقتصادية للأصول المشتركة في أنظمة الطاقة المجتمعية.

Abstract 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.

This paper introduces a novel intention-aware routing system (IARS) for electric vehicles. This system enables vehicles to compute a routing policy that minimizes their expected journey time while considering the policies, or intentions , of other vehicles. Considering such intentions is critical for electric vehicles, which may need to recharge en route and face potentially significant queueing times if other vehicles choose the same charging stations. To address this, the computed routing policy takes into consideration predicted queueing times at the stations, which are derived from the current intentions of other electric vehicles. The efficacy of IARS is demonstrated through simulations using realistic settings based on real data from The Netherlands, including charging station locations, road networks, historical travel times, and journey origin–destination pairs. In these settings, IARS is compared with a number of state-of-the-art benchmark routing algorithms and achieves significantly lower average journey times. In some cases, IARS leads to an over 80% improvement in waiting times at charging stations and a more than 50% reduction in overall journey times.

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 empirical 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%-75% would allow both investors to implement their projects and achieve desirable distribution of the profit. Preprint of final submitted version

Power companies such as Southern California Edison (SCE) uses Demand Response (DR) contracts to incentivize consumers to reduce their power consumption during periods when demand forecast exceeds supply. Current mechanisms in use offer contracts to consumers independent of one another, do not take into consideration consumers' heterogeneity in consumption profile or reliability, and fail to achieve high participation. We introduce DR-VCG, a new DR mechanism that offers a flexible set of contracts (which may include the standard SCE contracts) and uses VCG pricing. We prove that DR-VCG elicits truthful bids, incentivizes honest preparation efforts, and enables efficient computation of allocation and prices. With simple fixed-penalty contracts, the optimization goal of the mechanism is an upper bound on probability that the reduction target is missed. Extensive simulations show that compared to the current mechanism deployed by SCE, the DR-VCG mechanism achieves higher participation, increased reliability, and significantly reduced total expenses.

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. Peer review, pre-print to be published in Nature Machine Intelligence - 32 pages and 24 figures (including supplementary material)

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.