• Energy Research

Recent development of renewable generation and increasing penetration of electric vehicles have led to large volumes of residential battery storage systems connected at distribution networks. In this paper, we propose a control algorithm for residential batteries that determines optimal day-ahead battery scheduling and operation with the aim to minimize household energy bills and in the context of dynamic Time of Use (ToU) electricity tariffs. The proposed formulation of the optimization problem takes into consideration the battery’s depreciation cost, which is determined by the accurate enumeration of battery cycles, including partial cycling i.e. battery cycles that do not start or end at 100% of State of Charge (SoC). A key advantage of the proposed formulation is that the problem can be solvable by use of linear programming. In addition, we study and compare the benefits of the optimisation-based algorithm with lifespan consideration to a simple heuristic-based battery control scheme and an optimisation-based algorithm without battery lifecycle consideration. Results show that battery lifespan consideration in the optimization algorithm does not necessarily yield to lower prosumer energy bills, when compared to other approaches, but it can lead to a lower depreciation cost of the battery.

Abstract Isolated renewable energy system offers promising options to electrify communities located in remote areas where the utility grid is not available or extension of the grid is not economical. Proper sizing of renewable energy conversion system along with storage capacity is the key element to achieve the technical and economical feasibility of such renewable-based isolated system. In this paper, a methodology, based on the concept of Power Pinch Analysis, is proposed to determine the minimum renewable generator area, its extreme limits, and the corresponding storage capacity. The proposed methodology accounts for the uncertainties associated with the renewable resource to size the overall system with a predefined reliability. The concept of the chance constrained programming is applied within the Pinch Analysis framework to incorporate stochastic nature of the renewable energy resources. The applicability of the methodology is demonstrated with an illustrative example of photovoltaic-battery system and verified using sequential Monte-Carlo simulation approach as well as through annual simulation of the system with actual isolation data.

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.

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 distributed renewable investment, with sharing of grid access.

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.

Compte tenu de l'adoption généralisée de la production et du stockage d'énergie renouvelable et de nouvelles charges telles que la recharge des véhicules électriques, des efforts croissants ont été déployés pour améliorer la résilience énergétique locale, en particulier au niveau communautaire. Cela a suscité un intérêt croissant pour le développement de projets énergétiques locaux ou communautaires, dans lesquels les prosommateurs individuels sont en mesure de générer, de stocker et d'échanger de l'énergie au sein de la communauté, ce qui permet de passer du pouvoir de marché des grandes entreprises de services publics aux prosommateurs individuels. De tels systèmes impliquent souvent un groupe de consommateurs investissant dans des actifs appartenant à la communauté tels que des éoliennes appartenant à la communauté ou des batteries de stockage partagées. Pourtant, le développement de méthodes permettant un contrôle efficace et un partage équitable des actifs détenus conjointement est une question ouverte clé, à la fois de recherche et d'importance pratique. Dans cet article, nous fournissons une méthode inspirée des concepts de la théorie des jeux pour redistribuer équitablement les avantages des actifs énergétiques appartenant à la communauté tels que les éoliennes communautaires et le stockage. Nous proposons un algorithme de contrôle de batterie basé sur l'heuristique pour la maximisation de l'autoconsommation derrière le compteur, qui prend en compte l'effet de la dégradation de la durée de vie de la batterie. En utilisant des données de consommation et de production réelles pour modéliser une communauté de deux cents ménages, nous évaluons et comparons les avantages techniques et économiques de l'investissement dans des actifs appartenant à des particuliers ou à des communautés, tels que le stockage de produits chimiques. Nous montrons que la période de récupération simple du stockage de la batterie peut être considérablement réduite en partageant l'actif au sein d'une communauté. Enfin, nous comparons plusieurs schémas de redistribution et d'allocation d'avantages pour les actifs appartenant à la communauté, et montrons que le schéma proposé basé sur les principes de la théorie coopérative des jeux réalise la redistribution la plus équitable. Dada la adopción generalizada de generación renovable, almacenamiento y nuevas cargas como la carga de vehículos eléctricos, ha habido un esfuerzo creciente para mejorar la resiliencia energética local, particularmente a nivel comunitario. Esto ha llevado a un creciente interés en el desarrollo de proyectos energéticos locales o comunitarios, en los que los prosumidores individuales pueden generar, almacenar y comercializar energía dentro de la comunidad, lo que permite un cambio en el poder de mercado de las grandes empresas de servicios públicos a los prosumidores individuales. Tales esquemas a menudo involucran a un grupo de consumidores que invierten en activos de propiedad comunitaria, como turbinas eólicas de propiedad comunitaria o almacenamiento compartido de baterías. Sin embargo, el desarrollo de métodos que permitan un control eficiente y un reparto justo de los activos de propiedad conjunta es una cuestión clave abierta, tanto de investigación como de importancia práctica. En este documento, proporcionamos un método inspirado en los conceptos de la teoría de juegos para redistribuir de manera justa los beneficios de los activos energéticos de propiedad comunitaria, como las turbinas eólicas comunitarias y el almacenamiento. Proponemos un algoritmo de control de batería basado en la heurística para maximizar el autoconsumo detrás del medidor, que considera el efecto de la degradación de la vida útil de la batería. Utilizando datos reales de consumo y producción para modelar una comunidad de doscientos hogares, evaluamos y comparamos los beneficios técnicos y económicos de la inversión en activos de propiedad individual o comunitaria, como el almacenamiento de productos químicos. Mostramos que el período de amortización simple del almacenamiento de la batería se puede reducir considerablemente al compartir el activo dentro de una comunidad. Finalmente, comparamos varios esquemas de redistribución y asignación de beneficios para activos de propiedad comunitaria, y mostramos que el esquema propuesto basado en principios de la teoría de juegos cooperativos logra la redistribución más justa. 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. نظرًا للاعتماد الواسع النطاق لتوليد الطاقة المتجددة وتخزينها والأحمال الجديدة مثل شحن المركبات الكهربائية، كان هناك جهد متزايد لتعزيز مرونة الطاقة المحلية، لا سيما على مستوى المجتمع. وقد أدى ذلك إلى زيادة الاهتمام بتطوير مشاريع الطاقة المحلية أو المجتمعية، حيث يتمكن الأفراد من توليد الطاقة وتخزينها وتداولها داخل المجتمع — مما يتيح التحول في القوة السوقية من شركات المرافق الكبيرة إلى الأفراد. غالبًا ما تتضمن هذه المخططات مجموعة من المستهلكين يستثمرون في الأصول المملوكة للمجتمع مثل توربينات الرياح المملوكة للمجتمع أو تخزين البطارية المشترك. ومع ذلك، فإن تطوير طرق لتمكين الرقابة الفعالة والتقاسم العادل للأصول المشتركة هو سؤال رئيسي مفتوح، ذو أهمية بحثية وعملية على حد سواء. في هذه الورقة، نقدم طريقة مستوحاة من مفاهيم نظرية الألعاب لإعادة توزيع الفوائد إلى حد ما من أصول الطاقة المملوكة للمجتمع مثل توربينات الرياح المجتمعية والتخزين. نقترح خوارزمية للتحكم في البطارية قائمة على الاستدلال لتعظيم الاستهلاك الذاتي وراء العداد، والتي تأخذ في الاعتبار تأثير تدهور عمر البطارية. باستخدام بيانات الاستهلاك والإنتاج الحقيقية لنمذجة مجتمع مكون من مائتي أسرة، نقوم بتقييم ومقارنة الفوائد الفنية والاقتصادية للاستثمار في الأصول المملوكة للأفراد أو المملوكة للمجتمع مثل تخزين المواد الكيميائية. نوضح أنه يمكن تقليل فترة الاسترداد البسيطة لتخزين البطارية بشكل كبير من خلال مشاركة الأصل داخل المجتمع. أخيرًا، نقارن العديد من خطط إعادة التوزيع وتخصيص المزايا للأصول المملوكة للمجتمع، ونظهر أن المخطط المقترح القائم على مبادئ من نظرية اللعبة التعاونية يحقق إعادة التوزيع الأكثر إنصافًا.

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. 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, and only a fraction of total prosumers in the community are required to achieve the majority of the maximal potential Gains from Trade. We also study the effect that diversity in consumption profiles has on overall trading potential and dynamics in an energy community.