A transition towards a low carbon energy system increases net load variability and uncertainty in electric grids and can cause a potential threat for its stability and reliability of supply. Therefore, new operational practices for system operators and market actors are necessary to handle the complexity of modern electric grids. By applying optimization theory, power system modeling, and big data analytics, this research aims to extend the current understanding of the opportunity and challenges of leveraging the value of end-user energy resources to adequately and cost-effectively mitigate the forthcoming challenges. In particular, a key aspect of this research is focusing on control and optimization of flexibility of these distributed energy resources from perspectives of uncertainty and risks in flexibility estimation as well as multi-actor coordination. The research target is to develop an approach that prevents a counter productive procurement of flexibility from system operators and market actors while pursuing end-user preferences and contributing to a local network support and power system stability. As a result, sensitivity analysis of flexibility coordination decisions under the effects of forecast uncertainty in the multi-actor optimization value chain is assessed. Moreover, potential economic and grid benefits of the such method are evaluated with real-world grid and end-user data.

2020

2021