Multi-Stage Restoration Strategy to Enhance Distribution System Resilience with Improved Conditional Generative Adversarial Nets

Abstract

In the scenario of a large-scale power outage after an extreme disaster, such as a severe ice storm, the distribution system with multiple distributed generations (DGs) is of great value for post-disaster load restoration. However, due to the uncertainty of renewable energy output and the controllability of different DGs, effective utilization of these DGs becomes an urgent issue. To address the uncertainty of renewable energy output under disasters, this paper proposes a multi-stage optimization restoration strategy for a distribution system with distributed resources, such as a mobile energy storage system (MESS), integrated energy system (IES), and photovoltaic (PV). In particular, this study extracts historical data features by utilizing improved conditional generative adversarial nets (CGAN) to generate PV output scenarios. Subsequently, according to the dynamic and static characteristics of the power supply, the time sequence model of each distributed resource is established. On the premise of meeting the constraints of emergency microgrids and load characteristics, the optimal MESS configuration scheme and controllable DGs output are achieved to maximize the restoration of the power supply for critical loads. Finally, the case studies of IEEE 33-bus and PE&G 69-bus systems demonstrate the effectiveness of the proposed model in enhancing the resilience of the distribution system.

Keywords

• Conditional generative adversarial nets
• distributed resources
• distribution system
• multi-stage optimization
• post-disaster restoration
• system resilience