In electrical power systems, operational flexibility is crucial for the balancing of long- and shortterm disparities between load and non-dispatchable generation. This flexibility thus plays a critical role in the security and reliability of modern smart power systems and affects their overall costs and efficiency. Due to the increasing share of fluctuating power generation from renewable resources, this demand for flexibility is going to drastically increase in the oncoming years. At the same time, the dismantling of conventional power plants leads to a shortfall of today’s main flexibility providers, causing a need for new sources of operational flexibility.
Conventionally, this flexibility gap is closed by installing cost-intensive and regressive technologies such as fossil power plants or pumped storage system. In order to avoid these large-scale investments, other ways of using the power system’s inherent flexibility have been developed. Many of them work with present technical units and use an existing technical degree of freedom. These approaches are called demand-side or supply-side management. Both forms include the operation of distributed technical units in accordance with the requirements of the respective electrical power system. Due to the distributed character of the aggregates involved (e.g. electric vehicles, heat pumps, CHP) these approaches are called distributed flexibility. This form of flexibility has a high potential with regard to its technical properties and cost efficiency, but requires a high effort in modeling and simulation due to its heterogeneous technical components.
In order to tackle this conflict, the applicants strive to develop a unified modelling approach for distributed flexibility. While recent approaches fail at common understanding of flexibility in the different layers of the power system, the applicants will develop a modelling framework that allows the detailed quantification of flexibility potentials with a distribution-oriented perspective as well as on a system-wide view. In this approach, detailed technical optimisation models for the dispatch of distributed flexibility are implemented and enhanced by means of an extensive stochastic simulation in a first step. The systematic behaviour of this model is in a second step analysed, learned and finally reproduced by methods of artificial intelligence and machine learning. The resulting multilevel model of distributed flexibility subsequently allows a much more accurate quantification of the distributed flexibility together and novel analyses of the cross-impact on distribution and transmission systems.
In practice, such modelling approaches will be crucial for an efficient planning of European and Russian transmission and distribution grids. The improved quantification of distributed flexibility will allow a more secure and stable grid operation. Additionally, the multilevel consideration of distributed flexibility is important for minimising the overall grid expansion demand and efficient planning of power plant capacity and energy markets.
Project duration: 02/2020 - 01/2023
Institut für Energiesysteme, Energieeffizienz und Energiewirtschaft (ie3), TU Dortmund Prof. Dr.-Ing. Christian Rehtanz | Russian Academy of Sciences
Melentiev Energy Systems Institute of Siberian Branch, Prof. Dr.-Ing. Nikolai Voropai