Dynamic Topology Awareness in Active Distribution Networks Using Blockchain-Based State Estimations

Abstract

With growing complexity and uncertainty from distributed generations and active customers, distribution networks are facing more unexpected events and topology changes; while very limited measurements may degrade the observability of distribution system states and endanger the security of the system. Meanwhile, massive data from various types of users (if they can be used), can be valuable for power utilities. This makes privacy and data security a more critical issue, while being endowed with operation and market flexibilities. Thus, we propose a blockchain-based privacy-protecting approach for Dynamic Topology Awareness (DTA) to distinguish topology changes from cyber anomalies, including physical, cyber and business layers. At the bottom layer (physical layer), the distribution network is partitioned into subareas and tie-line areas, coping with local data from measurement devices or users. Estimated border values from parallel local estimations would be examined through data consistency checking. And DTA consensus algorithm might be triggered, querying historical status stored as blockchain contracts for anomalies detections and new block formations. Voting mechanism is then adopted to verify new blocks from data tampering. The performance of the proposed DTA approach is validated through simulations to identify topology changes and cyber anomalies, with additional discussions on the trade-off between DTA sensitivity and estimation accuracy.