City district concepts and Smart City

In the Berlin, Dresden and Zwickau model regions, WindNODE is realising Smart City designs with a focus on flexibilisation and regionalisation of loads and on decentralised own generation. The objective of this workstream is to test different designs, comprehend the potentials of city district concepts in the scope of the energy transition – for instance with regard to grid and system support offers – and work out transfer possibilities in other regions.

Model region Zwickau / Marienthal district

In cooperation with ZEV and SenerTec, WH Zwickau connects renewable energy producers, energy storage units and consumers of the Zwickau model region together in a smart ICT infrastructure (smart grid). We use the data from this ICT infrastructure to develop and apply forecast methods and to develop control algorithms for energy storage systems and the power supply grid. This enables us to cover relevant opportunities in the energy market, test connections to a comprehensive ICT network and evaluate user acceptance. Moreover, we provide the simulation and big data tool ‘Smart Heat’ and use it to monitor the evolution of heating in the model districts Marienthal and Prenzlauer Berg. The project is presented to interested citizens in the ‘ubineum’ show house.


Hommel (2017): "Experiences from an e-mobility Project in a local power grid with redox-flow energy storage system". In: IEEE, San Francisco, August 2017.


Hempel, Fischer, Veit, Hommel, Kretz, Dziurzik, Bodach (2017): "Energiewende im Niederspannungsnetz – Modellregion Zwickau“. In: International ETG Congress 2017 Die Energiewende – Blueprints for the new energy age, S. 538-543, Bonn, November 2017.


Teich, Kretz, Neumann, Hermann (2018): "Blockchain in dezentralisierten Energiemärkten“. In: 25th Interdisciplinary Scientific Conference Mittweida (IWKM), Mittweida, October 2018.


Hermann, Teich, Kassel, Kretz, Neumann, Leonhardt, Junghans (2018): "Blockchain in decentralized local energy markets“. In: Popplewell, Thoben, Knothe, Poler (Hrsg.), Enterprise Interoperability VIII – Proceedings of the I-ESA Conferences 9, Springer, Berlin, April 2019.


Model region Berlin / Prenzlauer Berg district

In two Berlin districts, the ‘Hosemannstraße’ in Prenzlauer Berg and the ‘Lindenhof II’ in Schöneberg, the Borderstep Institute is working together with Dr. Riedel Automatisierungstechnik and the DAI laboratory of TU Berlin to test how more flexible, current-controlled operating modes of residential quarters can be achieved through various combinations of energy generation and storage in residential buildings. The technologies that are used range from modular cogeneration units, buffer and thermal storage in building mass over Power-to-Heat aggregates to smart building techniques. This enables us to create decentralised flexibility to respond to an excess supply of green power. Integrating external signals from the market and the grid, we therefore develop fitting business and calculation models for the energy and housing sector as well as a software application for end consumers.


Beucker (2017): "Vorhaben ProSHAPE: Optimierung von Energiekosten im Quartier durch dezentrales Energiemanagement“. In: Jürgen Pöschk (Hrsg.), Energieeffizienz in Gebäuden - Jahrbuch 2017, VME Verlag und Medienservice Energie, Berlin, May 2017.


Schabel, Fichter (2018): "Inkubationsprogramme in der Energiewirtschaft. Merkmale, Erfolgseinschätzungen und Gestaltungsansätze". Borderstep Institut, Berlin, Februar 2018.


Bender-Saebelkampf (2018): "Short-Term Load Forecasting of Residential Electricity Consumption Using Convolutional Neural Networks”. Masterarbeit am Fachgebiet Distributed Artificial Intelligence Laboratory, Technische Universität Berlin, Berlin, April 2018.


Berardo (2018): "Integrating Flexibility in Smart Grids via Aggregation - Modeling and Optimization of a Virtual Power Plant as a Service Provider”. Masterarbeit am Fachgebiet Distributed Artificial Intelligence Laboratory, Technische Universität Berlin, Berlin, April 2018.


Elvers (2018): "Short-Term Probabilistic Residential Load Forecasting using CNN“. Masterarbeit am Fachgebiet Distributed Artificial Intelligence Laboratory, Technische Universität Berlin, Berlin, October 2018.


Haja (2018): "Towards Accurate Short-Term Residential Load Forecasting: Employing Local Permutation-Based Error Metrics and Machine Learning Techniques“. Master's thesis at the Distributed Artificial Intelligence Laboratory, Technische Universität Berlin, Berlin, October 2018.


Voß, Bender-Saebelkampf, Albayrak (2018): "Residential Short Term Load Forecasting Using Convolutional Neural Networks”. In: IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Aalborg, October 2018.


Voß, Haja, Albayrak (2018): "Adjusted Feature-Aware k-Nearest Neighbors: Utilizing Local Permutation-Based Error for Short-Term Residential Building Load Forecasting”. In: IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGrid-Comm), Aalborg, October 2018.


Beucker, Hinterholzer (2019): "Building Energy Management Systems and Their Role in the Energy Transition Results from Research Projects and Applications in Germany”. In: ICT4S 2019 – 6th International Conference on ICT for Sustainability, Lappeenranta, June 2019.


Elvers, Voß, Albayrak (2019): "Short-Term Probabilistic Load Forecasting at Low Aggregation Levels using Convolutional Neural Networks". In: 13th IEEE PowerTech 2019, Mailand, June 2019.


Voß, Jain, Albayrak (2019): "Subgradient Methods for Averaging Household Load Profiles under Local Permutations". In: 13th IEEE PowerTech 2019, Mailand, June 2019.


Beucker (2019): "Reduktion von CO2-Emissionen im Gebäudebestand durch digitales Energiemanagement“. Stellungnahme für die Industrieinitiative Smart Living (White-Paper), Borderstep Institut, Berlin, August 2019.


Voß (2020): „Datenanalyse von Haushalts- und Gebäudelastprofilen: Distanzmaße, Prognosefehler und Mittelwerte im Kontext von Smart Meter Daten im Niederspannungsnetz“. In: Tagungsunterlagen des SINTEG Science Lab 2020, S.33-37, Conexio GmbH, January 2020, Berlin.


Selim (2020): "Local Shift-Invariant Distance Measure for Individual Households". Master thesis at the Distributed Artificial Intelligence Laboratory, Technische Universität Berlin, Berlin, February 2020.


Beucker, Hinterholzer (2020): "Effects of ICT-Enabled Flexible Energy Consumption on the Reduction of CO2 Emissions in Buildings". ICT4S 2020 - 7th International Conference on ICT for Sustainability, 2020, Bristol.


Voß (2020): "Permutation-Based Residential Short-term Load Forecasting in the Context of Energy Management Optimization Objectives". In: Proceedings of the Eleventh ACM International Conference on Future Energy Systems, Melbourne, June 2020.


Kleinjung: "Anbindung von Elektrofahrzeugen an Regelleistungsmärkte durch Aggregatoren - Eine Monte Carlo Simulation zur Quantifizierung der Flexibilität von halb-öffentlicher Ladeinfrastruktur". Master thesis at the Fachgebiet Distributed Artificial Intelligence Laboratory, Technische Universität Berlin, Berlin, December 2020.


Model region Dresden / municipal load shifting potentials

Municipal properties, undertakings and institutions offer a large potential for energy saving and for the short- and medium term load balancing in power grids with a high share of solar and wind power. In the areas of flexibilisation of energy generation and consumers, energy efficiency and smart grids, the state capital Dresden will prepare the results from WindNODE for municipal institutions, companies and citizens' associations, evaluate them with regard to their implementation and use them for the development of pilot projects. At the same time, we are studying municipal flexibilisation options using the example of Dresden’s waste water disposal. The results and conclusions are to be incorporated into an overall study that deduces options, recommendations and a guideline for action for the municipality.


Anke, Möst, Kupke, Graebig, Franke (2018): "Bewertung lokaler und kommunaler Flexibilitätspotenziale: Verbundprojekt WindNODE“. In: BWK Das Energie-Fachmagazin, Ausgabe 12/2018, VDI Fachmedien, Düsseldorf, December 2018.


Anke, Dierstein, Hladik, Möst (2018): "Begleitstudie WindNODE – Lastverschiebungspotentiale in Dresden“. In: Schriftenreihe des Lehrstuhls für Energiewirtschaft der Technischen Universität Dresden, Band 16, Technische Universität Dresden, Dresden, September 2019.


Comparison and transfer of city district concepts

In the scope of this project, the Borderstep Institute brings together the results from the workstream with regard to the potential usefulness of districts, the possible contribution of the building sector to the energy transition and the use of renewable energy in districts including its conversion to heat (Power-to-Heat). We then make comparisons with other research areas to study the transferability of the developed solutions to districts of varying technical complexity. The comparison projects are selected to represent different approaches, including a comprehensive district design with Power-to-Heat installations to reduce the primary energy need and a district with a cogeneration unit, innovative high temperature storage units and a steam turbine for CO2-free reconversion into electricity.

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