Abstract
Active distribution networks (ADNs) are a typical cyber–physical system (CPS), which consist of two kinds of interdependent sub-networks: power networks (PNs) and communication networks (CNs). The combination of typical characteristics of the ADN includes (1) a large number of distributed generators contained in the PN, (2) load redistribution in both the PN and CN, and (3) strong interdependence between the PN and CN, which makes ADNs vulnerable to cross-domain cascading failures (CCFs). In this paper, we focus on the robustness analysis of the ADN against the CCF. Rather than via the rate of the clusters with size greater than a predefined threshold, we evaluate the robustness of the ADN using the rate of the clusters containing generators after the CCF. Firstly, a synchronous probabilistic model is derived to calculate the proportions of remaining normal operational nodes after the CCF. With this model, the propagation of the CCF in the ADN can be described as recursive equations. Secondly, we analyze the relationship between the proportions of remaining normal operational nodes after the CCF and the distribution of distributed generators, unintentional random initial failure rate, the interdependence between the sub-networks, network topology, and tolerance parameters. Some results are revealed which include (1) the more distributed generators the PN contains, the higher ADN robustness is, (2) the robustness of the ADN is negatively correlated with the unintentional random initial failure rate, (3) the robustness of the ADN can be improved by increasing the average control fan in of each node in the PN and the average power fan in of each node in the CN, (4) the robustness of the ADN with Erdos–Renyi (ER) network topological structure is greater than that with Barabasi–Albert (BA) network topological structure under the same average node degree, and (5) the robustness of the ADN is greater, when the tolerance parameters increase. Lastly, some simulation experiments are conducted and experimental results also demonstrate that the conclusions above are effective to improve the robustness of the ADN against the CCF.
Highlights
In recent years, many countries strongly support the access of a great many distributed generators to low/medium voltage distribution networks [1,2], which are called active distribution networks (ADNs)
(1) A synchronous probabilistic percolation model of the ADN is proposed, and the cascading failures (CCFs) simulation algorithm is proposed based on the model, which can describe the propagation process of the CCF. (2) We propose a method to analyze the relationship between the robustness of the ADN and distribution of generators, unintentional random initial failure, interdependence between the sub-networks, network topology and tolerance parameters
Our main aim was to find the relations between the distribution of the generators, unintentional random initial failure rate, interdependence, the network topology, tolerance parameters, and the ADN robustness against the CCF
Summary
Many countries strongly support the access of a great many distributed generators to low/medium voltage distribution networks [1,2], which are called active distribution networks (ADNs). It is necessary to comprehensively study the relationship between the steady state behavior of the CCF of the ADN and the distribution of generators, unintentional random initial failure (e.g., the terminal voltage of the node is zero or the current flowing through this node is zero), interdependence, network structure, and load redistribution. We needed a model to analyze the relationship between the proportions of remaining normal operational nodes after the CCF and the distribution of generators, unintentional random initial failure, the interdependence between the sub-networks, network topology, and load redistribution. How to characterize the influence of the distribution of generators, unintentional random initial failure, the interdependence between the sub-networks, network topology, and load redistribution on the robustness of the ADN based on Ai. Thirdly, how to simulate the propagation process of the CCF. The last one is that the robustness of the ADN was greater, when the tolerance parameters increased. (3) We conducted extensive simulation experiments to verify our five conclusions based on the ER network topological structure and the BA network topological structure
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