Resilience and efficiency of smart and sustainable power grids: mesoscopic modelling and stochastic simulation

Description

Modern power grids are currently affected by major changes in terms of technology, resiliency and efficiency, among others, as exemplified by the use of renewable energy sources and smart technology. Modelling and optimisation of large energy grids will be complicated by the changes mentioned above. To address and achieve the future goals of grid operation the project develops further analysis of power grids by a two-stage modelling and simulation process.The focus is on mesoscopic power grids and their stochastic modelling. On the one hand, these are still closely related to power production and consumption, and on the other hand, they are not far away from the modelling of global grids. The modelling approach covers two specific and innovative modelling and assessment levels. The first approach is a quantitative resilience assessment and results in Resilience Priority Values (RePV). To compute the network RePVs we plan to work on two major problem areas: (1) provision of a tailored resilience metric, and (2) simplified approach for grid resilience audits.On the second level we expand the basic entities (buses) with stochastic power flow modelling based on the bottom-up architecture and performance of real power grids using a description of individual power consumers and producers. In the next step we use the specific (near to scale-free) network architecture, DC approximation and fast linear solver to provide a rapid optimal power flow prototyping which can be used to refine grid stability (resiliency) and to analyse the impact of any system optimisation measures in the grid architecture.Hence, these modelling steps lead from a simple overview model to an advanced stochastic power flow model on the mesoscopic level. The results can be industrially used by grid providers, energy suppliers and regulatory institutions in determining the resilience levels and in optimising the design, evaluation and optimisation of modern grids.