Transformative Translations? Challenges and tensions in territorial innovation governance

Since the 1990s, changing ways of producing and circulating knowledge have been accompanied by debates that diagnose and call for change in the relationship between science, society, politics, and innovation. Most recently in Europe, some of these debates emphasize the concept of responsible research and innovation (RRI). In this paper, we present a comparative analysis of different territorial RRI-pilots within the Horizon 2020-funded project TRANSFORM. In these pilots, different translations of RRI become visible. RRI (1) gets translated as participatory and deliberative modes of innovation governance aimed at transformative change, (2) takes the shape of citizen science projects; and (3) is enacted as participatory agenda setting and (plans for a) citizen assembly. We argue that it is the often-invisible work of establishing, nurturing, and caring for relationships within the territorial R&I ecosystems – what can the thought of as ongoing “maintenance work” – that creates the conditions for more responsive modes of innovation governance, and thus a shift towards transformative change in innovation policy. Through describing these translations and the related practices we will direct attention to the potential, challenges, and systemic barriers of this kind of work

Translating tools and indicators in territorial RRI

Introduction: By a series of calls within the Horizon 2020 framework programme, the EU funded projects intended to deploy Responsible Research and Innovation (RRI) at a territorial level, in regional research and innovation ecosystems. This paper presents efforts to document and evaluate the achievements in TRANSFORM, one of these projects. Methods: Evaluative inquiry and theoretical reasoning. Results: Noting the need for a general principle to be interpreted, adapted and translated in order to be rendered meaningful at a local level, we studied precisely these multiple territorial translations of RRI, the organizational and institutional orderings with which they co-emerge and the challenges that come with these translations. An important shared feature is that RRI work does not start from zero, but rather builds on pre-existing relationships and repertoires of collaboration. The RRI project is hence a way to continue ongoing work and follow pre-set purposes, aims and objectives, as a form of “maintenance work”. In this very human sense, RRI is deployed with a logic of care in the regional context, while the Horizon 2020 calls and proposals above all are formulated in a logic of choice, to be assessed by indicators. Discussion: We warn against undue standardization of RRI by toolification and use of quantitative indicators, and recommend that RRI performance is monitored by methods of evaluative inquiry.publishedVersio

Forecast of Renewable Curtailment in Distribution Grids Considering Uncertainties

Renewable energies curtailment induced by grid congestions increase due to grown renewable energies integration and the resulting mismatch of grid expansion. Short-term predictions for curtailment can help to increase the efficiency of its management. This paper proposes a novel, holistic approach of a short-term curtailment prediction for distribution grids. The load flow calculations for congestion detection are realized by taking different operational security criteria into account, whereas the models for the node-injections are adjusted to the characteristic of each grid node specifically. The determination of required curtailment based on the resulting congestions considers uncertainties of component loading and its corresponding probability. The forecast model is validated using an actual 110 kV distribution grid located in Germany. In order to meet the requirements of a forecast model designed for operational business, prediction accuracy, and its greatest source of error are analyzed. Furthermore, a suitable length of training data is investigated. Results indicate that a six month time period for maintenance gains the highest accuracy. Curtailment prediction accuracy is better for transmission system operator components than for distribution system operator components, but the Słrensen Dice factor for the aggregated grid shows a high match of historic and predicted curtailment with a value of 0.84 and a low error for curtailed energy, which makes 2.23% of the historic curtailed energy. The model is a promising approach, which can contribute to improvement of curtailment strategies and enable valuable insight into distribution grids

Predicting Renewable Curtailment in Distribution Grids Using Neural Networks

The growing integration of renewable energies into electricity grids leads to an increase of grid congestions. One countermeasure is the curtailment of renewable energies, which has the disadvantage of wasting energy. Forecasting congestion provides valuable information for grid operators to prepare and instruct countermeasures to reduce these energy losses. This paper presents a novel approach for congestion prediction in distribution grids (i.e. up to 110 kV) considering the n-1 security criterion. For this, our method considers node injections and power flow and combines three artificial neural network models. The analysis of study results shows that the implemented neural networks within the presented approach perform better than naive forecasts models. In the case of vertical power flow, the artificial neural networks also show better results than comparable parametric models: average values of the mean absolute errors relative to the parametric models range from 0.89 to 0.21. A high level of accuracy can be achieved for the neural network that predicts the loading of grid components with a F1 score of 0.92. Further, also with a F1 score of 0.92, this model shows higher accuracy for the distribution grid components than for those of the transmission grid, which achieve a F1 score of 0.84. The presented approaches show good potential to support grid operators in congestion management

A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape

Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways

Are standard load profiles suitable for modern electricity grid models?

Demand data is as necessary for energy system analysis as generation data and grid information. When it comes to time series analysis, load profiles are required. In Germany standard load profiles (SLPs) are widely used by network operators and electricity grid modelers. Multiple research and also business offers show that advanced techniques can improve forecasted small consumers loads. Hence, our contribution reviews SLPs and suggested improvements along with other available data. In a case study for a rural region in north-west Germany, assumed load profiles of a grid model are compared to real demand derived measurement data. Additionally, available forecasted and realised power of SLP customers by the distribution system operator are evaluated. The creation of a regional synthetic load profile from this data using a simple approach improved relative root mean square error by approx. 10 compared to usage of SLPs

Einspeisemanagement in der enera Region in 2030

Im Rahmen von enera wird die Flexibilisierung von dezentralen Erzeugungsanlagen, Verbrauchern und Speichern erprobt. Dieser Beitrag liefert eine Abschätzung des zukünftig benötigten Einspeisemanagements im Hochspannungsnetz in der enera Region im Jahr 2030. Dazu wurde ein Szenario für den Windleistungsausbau entwickelt und der Netzausbau im Höchstspannungsnetz bis 2030 einbezogen. Die Ergebnisse des Ausbauszenarios 2030 zeigen hohe Leitungsüberlastungen in der Nähe der neuen HöS/HS-Übergabepunkte im Norden der Region. Aufgrund der hohen Überlastungen und Überlastungszeiten wird davon ausgegangen, dass zusätzlich zum Höchstspannungsnetzausbau weitere Maßnahmen ergriffen werden müssen, um Netzengpässe im Hochspannungsnetz zu verhindern

Validation of an open source high voltage grid model for AC load flow calculations in a delimited region

Large shares of renewable energy production in the electricity grid make grid expansion and new technologies necessary. The unavailability of grid models to address upcoming research questions led to the development of open source grid models. Our work contributes to establish the open_eGo model for grid simulations by validating its assumptions and results for a rural region with high share of wind energy. In particular, assumptions on electrical parameters and the graph structure of the model are compared to the grid owner's model along with a validation of AC load flow results at the model boundaries. It was found that the graph structure deviates in the degree of nodes and connection characteristic. These deviations are less exterior nodes and a lower maximum degree of nodes as well as a higher number of parallel lines in the open_eGo model. The AC load flow results differ slightly in active power and significantly in reactive power, but are more reliable than an aggregation of loads and generation to the extra high voltage (EHV) nodes. Concluding, the open_eGo model has a limited usability for simulating, understanding and optimising DSO grid operation but can enhance EHV-only analysis in large area contexts

Optimierungspotentiale für die operative Umsetzung des Einspeisemanagements

Mit ca. 6,5 TWh Ausfallarbeit wurde im Jahr 2019 ein neuer Höchstwert der engpassbedingten Abregelung von Erneuerbaren-Energien-Anlagen (EE-Anlagen) zur Stromerzeugung erreicht. Da der zur Integration größerer EE-Mengen notwendige Netzausbau lediglich mit großer zeitlicher Verzögerung umsetzbar ist, gilt es vermehrt, bestehende Übertragungskapazitäten möglichst effizient auszunutzen. Diese Arbeit untersucht drei verschiedene Ansätze zur Weiterentwicklung des Einspeisemanagements mit dem Ziel, die Menge an notwendiger Ausfallarbeit bei gleichzeitiger Gewährleistung der Netzsicherheit zu reduzieren. Die betrachteten Ansätze umfassen eine feinere Granularität der Abregelungsstufen, die Implementierung netzebenenübergreifender Optimierungsansätze sowie eine kurative Netzbetriebsführung. Sämtliche Ansätze werden hinsichtlich der resultierenden Ausfallarbeit mit dem derzeitigen State-of-the-Art der Einspeisemanagementumsetzung verglichen