Monitoring & control automation architecture for Hybrid AC/DC Distribution
Creators: name: Dognini, Alberto
name: Reisenbauer, Sarah
name: Nagaraja, Shreya
Publisher: name: The HYPERRIDE Consortium
Year: 2023
Subjects: Energy service
AC/DC
Distribution networks
Service restoration
State estimation
Deliverable
European Union (EU)
H2020
Project
HYPERRIDE
GA 95778

description: The AC-DC distribution networks require dedicated approaches for the monitoring and optimal management. System operators have to upgrade their Distribution Management System (DMS) in order to account for the new DC-based components and their optimal utilisation. In particular, the presence of AC-DC and DC converters, as well as DC based loads and generators, introduces new challenges and possibilities for the optimal automation of distribution systems. This document presents the energy services that have been developed within the Task T4.5 "Monitoring and control automation architecture for Hybrid DC/AC Distribution Network" of HYPERRIDE project.

To upgrade the protection strategies of system operator in presence of DC sub-networks, the Service Restoration (SR) algorithm dedicated for AC-DC distribution networks have been proposed. This algorithm, independent by grid topology and network parameters, categorises the priorities of disconnected nodes to form the set of candidate solutions for the network reconfiguration. Each candidate solution is tested with an Optimal Power Flow (OPF) that, in addition to the real time measurements, accounts for the forecasts of load and generation. Multiple criteria are pursued and prioritised according to user preferences. Multiple test cases demonstrates its applicability in different scenarios as well as test grids, providing noticeable information about the contribution of DC sub-networks in case of fault occurrences.

To perform accurate and advanced monitoring of AC-DC distribution networks, the Weighted Least-Squares (WLS) State Estimation (SE) is deployed and tested. This algorithm is based on a two-steps SE that relies on separated SE for each sub-network (AC and DC) and the exchange of converter power flow measurements. Additionally, the role of measurement on converters modulation index has been accounted and investigated. The algorithm has been integrated in a real time simulation setup and the included test results shows the performances of the algorithm in the different grid conditions and with various measurements sets.

Another approach to state estimation is the use of machine learning algorithms as predictors of relevant quantities at different locations in the grid from both sparse and redundant measurements, in the form of a generalised view on state estimation. Two machine learning algorithms are employed to answer questions related to observability and ideal measurement unit placement in a hybrid AC-DC grid. Machine learning models are very fast predictors, making them ideally suited for real-time (control) applications. However, they are sensitive to noisy or unexpected data. Therefore methods were investigated to make the models more robust and to find the most suitable algorithm for different application cases.

The deployed energy services constitute key elements for the HYPERRIDE ICT platform, integrating additional software components for the management of data communication protocols, measurements and commands in accordance with cybersecurity aspects, data storages and user interfaces. The proposed energy algorithm will constitute the baseline, providing specific knowledge about peculiar monitoring and operation requirements, for the deployment and tests in HYPERRIDE pilots.