• Energy Research

Technological advances in residential-scale batteries are paving the way towards self-sufficient communities to make the most use of their photovoltaic systems to support local energy consumption needs. To effectively utilize capabilities of batteries, the community can participate in the provision of short term operating reserve (STOR) services. To do so, adequate energy reserves in batteries are maintained during prescribed time windows to be utilized by electricity system operators. However, this may reduce energy sufficiency of the community. Further, the actual delivery of reserve could create distribution network congestions. To adequately understand the capability of a community to provide reserve, this work proposed a residential community energy management system formulated as a Mixed-Integer Linear Programming (MILP) model. This model aims to maximize energy sufficiency by optimal scheduling of batteries whilst considering reserve constraints. The model also maintains the aggregate power of houses within export/import limits that are defined offline using an iterative approach to ensure that the reserve provision does not breach distribution network constraints. The model is demonstrated on a residential community. The maximum committed reserve power with minimal impact on energy sufficiency is determined. Results also show that the capability of a community to provide reserve could be overestimated unless distribution network constraints are adequately considered.

The increasing Photovoltaic (PV) penetration in residential Low Voltage (LV) networks is likely to result in a voltage rise problem. One of the potential solutions to deal with this problem is to adopt a distribution transformer fitted with an On-Load Tap Changer (OLTC). The control of the OLTC in response to local measurements reduces the need for expensive communication channels and remote measuring devices. However, this requires developing an advanced decision-making algorithm to estimate the existence of voltage issues and define the best set point of the OLTC. This paper presents a decentralized data-driven control approach to operate the OLTC using local measurements at a distribution transformer (i.e., active power and voltage at the secondary side of the transformer). To do so, Monte Carlo simulations are utilized offline to produce a comprehensive dataset of power flows throughout the distribution transformer and customers’ voltages for different PV penetrations. By the application of the curve-fitting technique to the resulting dataset, models to estimate the maximum and the minimum customers’ voltages are defined and embedded into the control logic to manage the OLTC in real time. The application of the approach to a real UK LV feeder shows its effectiveness in improving PV hosting capacity without the need for remote monitoring elements.

This paper presents a comprehensive and general optimization-based home energy management controller, incorporating several classes of domestic appliances including deferrable, curtailable, thermal, and critical ones. The operations of the appliances are controlled in response to dynamic price signals to reduce the consumer’s electricity bill whilst minimizing the daily volume of curtailed energy, and therefore considering the user’s comfort level. To avoid shifting a large portion of consumer demand toward the least price intervals, which could create network issues due to loss of diversity, higher prices are applied when the consumer’s demand goes beyond a prescribed power threshold. The arising mixed integer nonlinear optimization problem is solved in an iterative manner rolling throughout the day to follow the changes in the anticipated price signals and the variations in the controller inputs while information is updated. The results from different realistic case studies show the effectiveness of the proposed controller in minimizing the household’s daily electricity bill while preserving comfort level, as well as preventing creation of new least-price peaks.

La formulation de la tarification dynamique est l'une des solutions émergentes pour guider la demande résidentielle pour les avantages du système d'alimentation en vrac.Toutefois, le calendrier de la demande résidentielle en réponse aux prix de l'énergie différenciés dans le temps pourrait provoquer des congestions dans les réseaux de distribution à la fois au prix le plus bas et aux intervalles de temps les plus élevés.Pour permettre l'adoption de la tarification dynamique, ce travail présente un nouveau cadre pour gérer les contraintes des réseaux de distribution basé sur le concept de système énergétique transactif (tes).Le cadre basé sur tes produit des incitations lors des problèmes de réseau pour débloquer la flexibilité des clients services pour reprogrammer les actifs contrôlables (par exemple, les batteries).En exécutant les systèmes de gestion de l'énergie domestique (HEMS), la flexibilité des clients pour modifier les horaires est quantifiée par rapport à un ensemble prédéfini d'incitations. Pour chaque incitation, les montants de changement de la demande nette par client sont agrégés et soumis par le biais d'agrégateurs au gestionnaire de réseau de distribution (DSO) sous la forme d'offres de production (réduction de la demande) et d'offres de demande (augmentation de la demande). Ces dernières sont cruciales pour répondre aux problèmes de réseau liés à la génération. Les courbes d'enchères en escalier des agrégateurs résultantes sont intégrées à un système avancé Modèle de flux de puissance optimal (OPF) pour identifier les offres réussies pour gérer les contraintes du réseau tout en minimisant les incitations versées aux agrégateurs. Cela permet de définir directement les incitations et les quantités sans itérations étendues entre le GRD et les agrégateurs. L'application du cadre à une alimentation urbaine de 11 kV montre son efficacité pour gérer les congestions. Cependant, les fortes variations des prix dynamiques augmentent les montants des incitations, en particulier lorsque des services de flexibilité sont demandés le soir et la nuit. La formulación de precios dinámicos es una de las soluciones emergentes para guiar la demanda residencial de los beneficios del sistema de energía a granel. Sin embargo, el programa de demanda residencial en respuesta a los precios de la energía diferenciados en el tiempo podría causar congestiones en las redes de distribución tanto en los intervalos de tiempo de precio más bajo como en los de precio más alto. Para permitir la adopción de precios dinámicos, este trabajo presenta un marco novedoso para gestionar las restricciones de las redes de distribución basado en el concepto de Sistema de Energía Transactiva (tes). El marco basado en tes produce incentivos durante los problemas de la red para desbloquear la flexibilidad de los clientes servicios para reprogramar activos controlables (por ejemplo, baterías). Al ejecutar los Sistemas de Gestión de Energía Doméstica (HEMS), la flexibilidad de los clientes para modificar los horarios se cuantifica frente a un conjunto predefinido de incentivos. Para cada incentivo, las cantidades de cambio de demanda neta por cliente se agregan y envían a través de agregadores al Operador del Sistema de Distribución (DSO) en forma de ofertas de generación (reducción de la demanda) y ofertas de demanda (aumento de la demanda). Estas últimas son cruciales para atender los problemas de red impulsados por la generación. Las curvas de licitación de escalera de los agregadores resultantes están integradas en un Modelo de flujo de energía óptimo (OPF) para identificar las ofertas exitosas para administrar las restricciones de la red y minimizar los incentivos pagados a los agregadores. Esto permite definir incentivos y cantidades directamente sin iteraciones extensas entre DSO y agregadores. La aplicación del marco a un alimentador urbano de 11kV muestra su efectividad para administrar las congestiones. Sin embargo, las variaciones en los precios dinámicos aumentan las cantidades de incentivos, particularmente cuando los servicios de flexibilidad se solicitan en intervalos de tiempo nocturnos y nocturnos. The formulation of dynamic pricing is one of the emerging solutions to guide residential demand for the benefits of the bulk power system.However, the schedule of residential demand in response to time-differentiated energy prices could cause congestions in distribution networks at both the lowest-price and highest-price time intervals.To enable the adoption of dynamic pricing, this work presents a novel framework to manage the constraints of distribution networks based on the concept of Transactive Energy System (TES).The TES-based framework produces incentives during network issues to unlock customers' flexibility services to reschedule controllable assets (e.g., batteries).By running Home Energy Management Systems (HEMS), the flexibility of customers to modify schedules are quantified against predefined set of incentives.For each incentive, the amounts of net-demand change per customer are aggregated and submitted through aggregators to the Distribution System Operator (DSO) in the forms of both generation offers (reducing demand) and demand offers (increasing demand).The latter are crucial to cater for generationdriven network issues.The resulting aggregators' staircase bidding curves are embedded to an advanced Optimal Power Flow (OPF) model to identify the successful offers to manage network constraints whilst minimizing incentives paid to aggregators.This allows defining incentives and quantities directly without extensive iterations between DSO and aggregators.The application of the framework to an urban 11kV feeder shows its effectiveness to manage congestions.However, the highly variations in dynamic prices increase the amounts of incentives particularly when flexibility services are requested at evening and night time intervals. تعد صياغة التسعير الديناميكي أحد الحلول الناشئة لتوجيه الطلب السكني على فوائد نظام الطاقة السائبة. ومع ذلك، يمكن أن يتسبب جدول الطلب السكني استجابةً لأسعار الطاقة المتباينة زمنيًا في حدوث ازدحام في شبكات التوزيع بأقل الأسعار وأعلاها على حد سواء. لتمكين اعتماد التسعير الديناميكي، يقدم هذا العمل إطارًا جديدًا لإدارة قيود شبكات التوزيع بناءً على مفهوم نظام الطاقة التفاعلي (TES). ينتج الإطار القائم على TES حوافز أثناء مشكلات الشبكة لفتح مرونة العملاء خدمات لإعادة جدولة الأصول التي يمكن التحكم فيها (على سبيل المثال، البطاريات). من خلال تشغيل أنظمة إدارة الطاقة المنزلية (HEMS)، يتم تحديد مرونة العملاء في تعديل الجداول الزمنية مقابل مجموعة محددة مسبقًا من الحوافز. لكل حافز، يتم تجميع مبالغ صافي تغيير الطلب لكل عميل وتقديمها من خلال المجمعين إلى مشغل نظام التوزيع (DSO) في أشكال كل من عروض الجيل (تقليل الطلب) وعروض الطلب (زيادة الطلب). هذه الأخيرة حاسمة لتلبية مشكلات الشبكة التي يحركها الجيل. يتم تضمين منحنيات مناقصات درج المجمعين الناتجة إلى نموذج تدفق الطاقة الأمثل (OPF) لتحديد العروض الناجحة لإدارة قيود الشبكة مع تقليل الحوافز المدفوعة للمجمعات. وهذا يسمح بتحديد الحوافز والكميات مباشرة دون تكرارات مكثفة بين DSO والمجمعات. يُظهر تطبيق الإطار على وحدة التغذية الحضرية 11 كيلو فولت فعاليته في إدارة الازدحام. ومع ذلك، فإن الاختلافات الكبيرة في الأسعار الديناميكية تزيد من كميات الحوافز خاصة عندما يتم طلب خدمات المرونة في فترات المساء والليل.

The transition towards low-carbon energy systems requires increasing the contribution of residential Photovoltaic (PV) in the energy consumption needs (i.e., PV self-consumption). For this purpose, the adoption of PV self-consumption policies as alternatives to the current net-metering policy may support harnessing batteries to improve PV self-consumption. However, the technical impacts of PV policies on distribution networks have to be adequately assessed and mitigated. To do so, a two-stage planning framework is proposed. The first stage is an optimization approach that determines the best sizes of PV and batteries based on the adopted PV policy. The second stage assesses the impacts of the resulting sizes on distribution networks using Monte-Carlo simulations to cope with uncertainties in demand and generation. The framework is applied on real medium and low voltage distribution networks from the south of Jordan. For the net-metering, the results show that the uptake of residential PV penetration above 40% will result in voltage issues. It is also found that the adoption of batteries for the benefits of customers (i.e., reduce electricity bills) will not mitigate the PV impacts for PV penetration above 60%. Further, the results demonstrate the important role of distribution network operators to manage the uptake of batteries for the benefits of customers and distribution networks. Network operators can support customers to adopt larger sizes of batteries to achieve the desired PV self-consumption in return of controlling the batteries to solve network issues. This facilitates the uptake of 100% PV penetration and improves PV self-consumption to 50%.