• Energy Research

IT systems can quickly and effectively evaluate complex relations. Therefore, their usage could also contribute to the reduction of CO2 emissions in the complete energy system. Here, a concept for an overall IT ecosystem is presented which supports the minimization of CO2 emissions. It includes the measurement of material flows by sensors, the storage of the collected data, the combination of IT systems and a supervision system. Furthermore, a CO2 monitoring tool is presented which displays the current CO2 emissions and can provide a 24-hour CO2 emission prognosis. By including modern energy system approaches, like sector coupling, a more realistic consideration of CO2 emissions is possible. The usage is designed on three separate layers. By data aggregation of the lower layers, given by industries and energy suppliers, action recommendations for reducing CO2 emissions can be made by the supervision layer.

Regulatory measures under Redispatch 2.0 put forth new requirements on energy data management software. At the same time, district energy management poses very similar challenges with somewhat different intentions. In this paper we present these similarities with their arising tasks and an energy data management tool to solve them. Based on a field-tested software solution, we implement an asset management component, which can meet the requirements of both domains, and outline the advantages for further prevalence of renewable energy production and optimized consumption in the district scope.

Regulatory measures under Redispatch 2.0 put forth new requirements on energy data management software. At the same time, district energy management poses very similar challenges with somewhat different intentions. In this paper we present these similarities with their arising tasks and an energy data management tool to solve them. Based on a field-tested software solution, we implement an asset management component, which can meet the requirements of both domains, and outline the advantages for further prevalence of renewable energy production and optimized consumption in the district scope.

Electrical power grids around the world are currently being equipped with an increasing number of monitoring and actuation components, as part of their conversion to “smart grids”. This results in an improved visibility of the power grid’s real-time status as well as a fine-grained tracking of events. These features represent enabling elements for the adaptive and fine-grained decentralized control, targeting to sustain or even increase the power grid’s reliability. Besides catering to this purpose, an unprecedented volume of voltage, current, and/or power data becomes available from the distributed sensing devices. The analysis of such data enables a variety of use cases. These include the disaggregation of household consumption, the prediction of future electricity demand, or the detection of anomalies in the use of electrical power. The methodological collection and analysis of energy-related data is one of the major research challenges of the emerging “energy informatics” discipline. This thesis proposes a delineation of the scope of energy informatics, with a focus on electrical power grids. It furthermore presents selected contributions to the field of energy informatics, including approaches for the methodological data collection, energy analytics algorithms for the data analysis and interpretation, as well as ways to provide user-centric services based on such data. Lastly, the thesis highlights possible directions for future research in the field of energy informatics.

As smart grids gained relevance in the renewable energy landscape, the demand for new data management models became evident. Independently, whether emerging models are centralised on the Transmission System Operators (TSO), Distribution System Operators (DSO), or even on a third-party entity/aggregator (under proper regulatory supervision), the complexity of integration and interoperability of heterogeneous systems are worsened by the increase of renewable energy sources - given its potential reach and the diversity of solution providers. This work provides a detailed study of the main systems concerns present on TSO and DSO platforms alike, and main concerns in relation to renewable energy sources, sensor devices (e.g., smart meters and Internet of Things (IoT) devices) and processes. Furthermore, this work also carries out an analysis of the available integration studies - including a study case. Finally, this work proposes a top-level ontology-based standardisation for smart grid integration to support energy trading initiatives.

Provenance, the derivation history of data objects, records how, when, and by whom a piece of data was created and modified. Provenance allows users to understand the context of derived data, estimate its quality for use, locate data of interest, and determine datasets affected by erroneous processes. Thus it is playing an important role in scientific experiments and business processes for data quality control, audit trail, and ensuring regulatory compliance. ��� While most of the previous works only study provenance in a closed and well-controlled environment (e.g., a workflow engine), challenges still exist for holistic provenance management in practical and open environments, where provenance can be distributed, dynamic and diverse. For example, in the Energy Informatics domain, provenance is often collected from large-scale workflows across disciplines and organizations and thus is usually stored in distributed repositories. However, there has been limited research on reconstruction of and query over distributed provenance information. Meanwhile, recurrent and stream processing workflows can generate fine-grained provenance with overwhelming size that can be larger than the original dataset. Provenance storage approaches for efficiently managing such metadata volumes do not have adequate focus in literature. And lastly, the fact that legacy tools without automatic provenance collection functionalities are still widely used leads to the requirement of manual provenance annotation operations, which causes provenance to be incomplete. ��� In this thesis, by using Energy Informatics as an exemplar domain, we design and develop algorithms and systems for managing provenance in dynamic, distributed and dataflow environments, that are motivated by real world challenges. In particular, we make the following contributions: (1) template-based algorithms that can efficiently store provenance information for dynamic datasets, (2) algorithms for reconstructing and querying provenance graphs from distributed provenance repositories, (3) semantic-based approaches for predicting incomplete provenance. We evaluate our research contributions with use cases from the Energy Informatics domain, including both Smart Oilfield and Smart Grid. The evaluation results demonstrate that our work can achieve efficient and scalable provenance management. As future work, we also discuss key challenges and initial solutions for presenting provenance across different granularities based on its usage context information.