Stochastic flexibility needs assessment: learnings from H2020 EUniversal's German demonstration
Operational flexibility needs assessment (FNA) is crucial for system operators to plan/procure flexible resources in order to avoid probable network issues. We implemented an FNA tool in the framework of the H2020 EUniversal project for the German demonstration. In this work, we summarize our learnings from the demo implementation to cope with the limited availability of measurement data. Using a reduced network model and key performance indicators, we evaluate the digital-twin results with real-world implementations. The paper aims to motivate future research directions by duly considering real-world limitations in their modelling and developing innovative tailor-made solutions for an improved decision support framework for system operators.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Chance constrained day-ahead robust flexibility needs assessment for low voltage distribution network
For market-based procurement of low voltage (LV) flexibility, DSOs identify the amount of flexibility needed for resolving probable distribution network (DN) voltage and thermal congestion. A framework is required to avo…
Perspectives on distribution network flexible and curtailable resource activation and needs assessment
{A curtailable and flexible resource activation framework for solving distribution network (DN) voltage and thermal congestions is used to quantify three important aspects with respect to modelling low voltage networks.}…
Assessment of Continuous-Time Transmission-Distribution-Interface Active and Reactive Flexibility for Flexible Distribution Networks
With the widespread use of power electronic devices, modern distribution networks are turning into flexible distribution networks (FDNs), which have enhanced active and reactive power flexibility at the transmission-dist…
An Adaptive Random Fourier Features approach Applied to Learning Stochastic Differential Equations
This work proposes a training algorithm based on adaptive random Fourier features (ARFF) with Metropolis sampling and resampling \cite{kammonen2024adaptiverandomfourierfeatures} for learning drift and diffusion component…
Dynamically Addressing Unseen Rumor via Continual Learning
Rumors are often associated with newly emerging events, thus, an ability to deal with unseen rumors is crucial for a rumor veracity classification model. Previous works address this issue by improving the model's general…
Continual LearningVeracity Classification