11 research outputs found
Integration of Legacy Appliances into Home Energy Management Systems
The progressive installation of renewable energy sources requires the
coordination of energy consuming devices. At consumer level, this coordination
can be done by a home energy management system (HEMS). Interoperability issues
need to be solved among smart appliances as well as between smart and
non-smart, i.e., legacy devices. We expect current standardization efforts to
soon provide technologies to design smart appliances in order to cope with the
current interoperability issues. Nevertheless, common electrical devices affect
energy consumption significantly and therefore deserve consideration within
energy management applications. This paper discusses the integration of smart
and legacy devices into a generic system architecture and, subsequently,
elaborates the requirements and components which are necessary to realize such
an architecture including an application of load detection for the
identification of running loads and their integration into existing HEM
systems. We assess the feasibility of such an approach with a case study based
on a measurement campaign on real households. We show how the information of
detected appliances can be extracted in order to create device profiles
allowing for their integration and management within a HEMS
GREEND: An Energy Consumption Dataset of Households in Italy and Austria
Home energy management systems can be used to monitor and optimize
consumption and local production from renewable energy. To assess solutions
before their deployment, researchers and designers of those systems demand for
energy consumption datasets. In this paper, we present the GREEND dataset,
containing detailed power usage information obtained through a measurement
campaign in households in Austria and Italy. We provide a description of
consumption scenarios and discuss design choices for the sensing
infrastructure. Finally, we benchmark the dataset with state-of-the-art
techniques in load disaggregation, occupancy detection and appliance usage
mining
Smart Microgrids: Optimizing Local Resources toward Increased Efficiency and a More Sustainable Growth
Smart microgrids are a possibility to reduce complexity by performing local optimization of power production, consumption and storage. We do not envision smart microgrids to be island solutions but rather to be integrated into a larger network of microgrids that form the future energy grid. Operating and controlling a smart microgrid involves optimization for using locally generated energy and to provide feedback to the user when and how to use devices. This chapter shows how these issues can be addressed starting with measuring and modeling energy consumption patterns by collecting an energy consumption dataset at device level. The open dataset allows to extract typical usage patterns and subsequently to model test scenarios for energy management algorithms. Section 3 discusses means for analyzing measured data and for providing detailed feedback about energy consumption to increase customersâ energy awareness. Section 4 shows how renewable energy sources can be integrated in a smart microgrid and how energy production can be accurately predicted. Section 5 introduces a self-organizing local energy system that autonomously coordinates production and consumption via an agent-based energy auction system. The final section discusses how the proposed methods contribute to sustainable growth and gives an outlook to future research
Assisting energy management in smart buildings and microgrids
Die wachsende Nutzung von erneuerbaren Energien stellt das Stromnetz vor neue Herausforderungen die geeignete MaĂnahmen fĂr die stabile Energieversorgung im Stromnetz erfordern. Der massive Einsatz von SpeichermĂglichkeiten ist hierbei eine vergleichsweise teure und ineffiziente LĂsung. Mechanismen zur Nachfragesteuerung kĂnnen das Ungleichgewicht zwischen Energieangebot und -nachfrage verbessern. Dies inkludiert sowohl direkte als auch indirekte Regelmechanismen, die abhĂsse und ĂÂberschĂsse durch einen verĂhlern zunehmend genauere Strommessdaten zur VerfĂgung. Solche Daten sind auch fĂr das GebĂglichen Energieverbrauch zu erfassen und auf dieser Grundlage Effizienzverbesserungen zu planen. Diese Arbeit befasst sich mit verschiedene Anwendungen von hochauflĂsenden Stromverbrauchsdaten fĂr das Energiemanagement in Smart Microgrids. Zu diesem Zweck wurde in der ersten Phase eine Messkampagne in ausgewĂlt mehr als 1 Jahr an detaillierten Verbrauchsdaten die im Sekundenabstand gemessen wurden. Der GREEND-Datensatz wurde verĂffentlicht und fĂr die Forschungsgemeinschaft freigegeben. Er wurde ebenfalls durchgehend in dieser Dissertation verwendet. Diese Arbeit stellt auĂerdem eine Dateninfrastruktur vor, welche das ZusammenfĂhren von Daten aus unterschiedlichen Quellen in dynamischen Umgebungen erlaubt. Im Besonderen werden Architekturanforderungen identifiziert welche die InteroperabilitĂte- und Datenebene ermĂglicht. Die vorgestellte LĂsung bietet eine einheitliche Schnittstelle um DatenĂtzlich wird ein Ontologiemodell vorgestellt welche statische und dynamische Information zu HaushaltsgerĂten, so dass das Verhalten eines GerĂge an den Kunden zurĂckgegeben. Unter Verwendung von GREEND konnte fĂr diesen Ansatz ein Einsparungspotential von bis zu 34% ermittelt werden. Die EffektivitĂngt jedoch stark von automatisiert steuerbaren GerĂtesteuerungen ermĂglicht. Da dieser Ansatz auf einem kompetitiven Ansatz beruht liefert er nur bedingt kooperative LĂsung zur GerĂtze sind unabhĂge zum Stand der Forschung. Die Ergebnisse bieten eine Basis fĂr zukĂnftige Forschung im Bereich Energiemanagement und in der intelligenten GebĂ$udetechnik.The increasing exploitation of renewable energy sources for power generation introduces a significant instability into the power grid, which has to be addressed with appropriate management strategies. Energy storage is a costly and inefficient solution. Demand-side control mechanisms can help mitigating the unbalance between available supply and demand. This includes both direct and indirect control, depending on the degree of controllability of demand-side loads. In the latter, congestion on the shared resource is managed using a price signal, exchanged throughout the power grid and reflecting the resource availability. This requires the timely exchange of information between energy consumers and producers, namely power and phase measurements to be used for the resource pricing. Furthermore, more fine-grained usage data is progressively becoming available to utilities thanks to the deployment of smart meters. Such an information is also relevant to facility managers and users, to become aware of the energy footprint of daily activities and seek a more efficient usage process. This thesis deals with different applications of high-resolution power usage data for energy management in smart microgrids. To this end, the first stage included a measurement campaign in selected households in Italy and Austria. The resulting dataset, named GREEND, contains more than 1 year consumption data at 1 Hz. GREEND was released to the research community for open use, as well as used throughout the thesis. We elaborate on the design of a data infrastructure capable of collecting data from heterogeneous data sources in highly dynamic environments. Specifically, architectural requirements are identified to achieve interoperability at the level of electrical devices as well as exchanged data. The proposed solution offers a single interface to query for status changes, which eases the application development process. In addition, we propose an ontology modeling both static and dynamic information of household appliances. This allows for the full integration of smart and non-smart devices, whose behavior can be tracked and recorded in a sort of datasheet to be exchanged across the network. The availability of energy usage data allows for the provisioning of value-added services to both end-users and utilities. To this end, we investigate on the possibility of an interactive system to timely inform users on their energy usage, in order to promote an efficient use of local resources. In particular, advices are returned to consumers based on their usage behavior and building occupance. Using the GREEND, this solution alone was quantified as potentially yielding up to 34% of savings. However, the effectiveness of demand response programs is greatly affected by the possibility to automate specific devices. Towards this vision, we introduced the HEMS market simulator, which allows for training appliance controllers. Because of the strictly competitive setting, pure market mechanisms do not offer a complete solution for automatic load management. Accordingly, competition is limited to a specific trading day, and has the potential effect of yielding service interruption. To solve this issue, we propose a microgrid power broker that acts as a retailer of available supply. The broker seeks profit by forecasting the price of different power provisioning durations. The three different approaches are independent and give an individual contribution to the research community. The results provide the basis for future research in the field of energy management systems for microgrids and smart buildings.Andrea MonacchiZusammenfassung in deutscher und in italienischer SpracheAlpen Adria UniversitĂ€t Klagenfurt, Dissertation, 2016OeBB(VLID)241232