Generalized Operational FLEXibility for Integrating Renewables in the Distribution Grid (GOFLEX)
Objective
The GOFLEX project will innovate, integrate, further develop and demonstrate a group of electricity smart-grid technologies, enabling the cost-effective use of demand response in distribution grids, increasing the grids’ available adaptation capacity and safely supporting an increasing share of renewable electricity generation. The GOFLEX smart grid solution will deliver flexibility that is both general (across different loads and devices) and operational (solving specific local grid problems). GOFLEX enables active use of distributed sources of load flexibility to provide services for grid operators, balance electricity demand and supply, and optimize energy consumption and production at the local level of electricity trading and distribution systems. Building on top of existing, validated technologies for capturing and exploiting distributed energy consumption and production flexibility, GOFLEX enables flexibility in automatic trading of general, localized, device-specific energy as well as flexibility in trading aggregated prosumer energy. Generalized demand-response services are based on transparent aggregation of distributed, heterogeneous resources to offer virtual-power-plant and virtual-storage capabilities. The sources of load flexibility include thermal (heating/cooling) and electric storage (electric vehicles charging/discharging). A backbone data-services platform offers localised estimation and short-term predictions of market and energy demand/generation, and flexibility in order to support effective data-driven decisions for the various stakeholders. Smart-grid technologies, such as increased observability and congestion management, contribute to the platform. Over 36 months, GOFLEX will demonstrate the benefits of the integrated GOFLEX solution in three use-cases, covering a diverse range of structural and operational distribution grid conditions in three European countries.
Mission of Technische Universität Dresden
The Technische Universität Dresden has developed the benchmark framework ECAST for energy forecasts that allows transparent comparison of forecasting models for different application scenarios. Our task is the integration of ECAST as part of the GOFLEX cloud service and the integration of additional forecasting models, especially those coming from IBM’s energy forecasting solution. Furthermore, we will integrate maintenance functionality into ECAST, in order to keep predictive models up-to-date in an efficient way.
Acknowledgment
This research project receives funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 731232.
Related Publications
@conference{,
author = {Lars Kegel and Martin Hahmann and Wolfgang Lehner},
title = {Feature-based Comparison and Generation of Time Series},
booktitle = {Proceedings of the 30th International Conference on Scientific and Statistical Database Management (SSDBM'18), Bozen-Bolzano, Italy, July 9-11, 2018},
year = {2018},
month = {7},
location = {Bozen-Bolzano, Italy},
numpages = {12},
url = {https://doi.org/10.1145/3221269.3221293}
}@conference{,
author = {Lars Kegel and Martin Hahmann and Wolfgang Lehner},
title = {Generating What-If Scenarios for Time Series Data},
booktitle = {Proceedings of the 29th International Conference on Scientific and Statistical Database Management (SSDBM’17), Chicago, IL, USA, June 27-29, 2017},
year = {2017},
month = {6},
location = {Chicago, IL, USA},
numpages = {12},
url = {https://doi.org/10.1145/3085504.3085507}
}@conference{,
author = {Lars Kegel and Martin Hahmann and Wolfgang Lehner},
title = {Feature-driven Time Series Generation},
booktitle = {Proceedings of the 29th Workshop on Foundations of Databases (GvDB'17), 30.05.2017 - 02.06.2017, Blankenburg/Harz, Germany},
year = {2017},
month = {5},
numpages = {6},
keywords = {time series analysis, data generation, business analytics}
}Systematic Validation of Time Series Generation Methods
Christian Kabelitz November 5th, 2017 until April 7th, 2018
Diplom ThesisSupervision: Wolfgang Lehner, Maik Thiele
Feature Selection for the Classification of Energy Time Series
Till Ilić April 6th, 2017 until June 21st, 2017
Bachelor ThesisSupervision: Wolfgang Lehner, Dirk Habich