The ongoing changes in modern power systems towards increasingly decentralized systems render the coordination of generation assets and the corresponding dependency on Information and Communication Technology highly relevant. This work demonstrates the impact of three types of ICT errors, namely delayed data, data loss and data corruption, on the control of distributed energy resources in an active distribution network. The settling time of the active power response at the interconnection point between the distribution and transmission system is investigated in the simulations. Additionally, two fallback strategies to mitigate the impact of data loss are proposed and evaluated with regard to their impact on the controller’s response. Finally, a generalized, aggregated service state description is proposed in order to capture the performance of the active distribution network service. It is meant to improve the interpretability of the results, which can be used to compare service designs and setups.

The ongoing changes in modern power systems towards increasingly decentralized systems render the coordination of generation assets and the corresponding dependency on Information and Communication Technology highly relevant. This work demonstrates the impact of three types of ICT errors, namely delayed data, data loss and data corruption, on the control of distributed energy resources in an active distribution network. The settling time of the active power response at the interconnection point between the distribution and transmission system is investigated in the simulations. Additionally, two fallback strategies to mitigate the impact of data loss are proposed and evaluated with regard to their impact on the controller's response. Finally, a generalized, aggregated service state description is proposed in order to capture the performance of the active distribution network service. It is meant to improve the interpretability of the results, which can be used to compare service designs and setups.

One of the significant challenges linked with the massive integration of distributed energy resources (DER) in the active distribution grids is the uncertainty it brings along. The grid operation becomes more arduous to avoid voltage or thermal violations. While the Optimal Power Flow (OPF) algorithm is vastly discussed in the literature, little attention has been given to the robustness of such centralised implementation, such as the provision of redundant control solutions during a communication failure. This paper aims to implement a machine learning-based algorithm at each Intelligent Electronic Device (IED) that mimics the centralised OPF used during communication failures using IEC 61850 data models. Under normal circumstances, the IEDs communicate for centralised OPF. In addition, the system is trained offline for all operational conditions and the individual look-up tables linking the actual voltages to the DER setpoints are sent to the respective controllers. The regression models allow for the local reconstruction of the DER setpoints, emulating the overall OPF, in case of a communication failure. In addition to the regression control, the paper also explains an offline learning approach for periodic re-training of the regression models. The implementation is experimentally verified using a Hardware-in-the-loop test setup. The tests showed promising results compared to conventional control strategies during communication failures. When properly trained and coordinated, such an intuitive local control approach for each DER could be very beneficial for the bulk power system. This machine learning-based approach could also replace the existing Q(V) control strategies, to better support the bulk power system.

Future power systems are expected to depend more on ICT for essential grid services such as voltage and frequency control, increasing the interdependencies between both systems. Therefore, disturbances from one system could propagate and impact the other, degrading the state of the interconnected system. This paper proposes a formalised hybrid model for analysing the impact and propagation of disturbances in a cyber–physical energy system. The states representing the performance of ICT-enabled grid services are modelled using a finite-state automaton. The impact of power system operational decisions in response to disturbances using these grid services are modelled using an optimisation considering situational awareness. The output from both models is used as input to a hybrid automaton that determines the state of the overall cyber–physical energy system. The model is verified by a proof of concept using state estimation and congestion management as exemplary grid services.