Integration of Preventive and Emergency Responses to Boost Distribution System Resilience - (:unkn) unknown (My University, 2019)
Recent years have seen a series of large-scale blackouts due to extreme weather events around the world. These high impact, lower probability events have caused great economic losses to modern society. Therefore, it is urgent to study the resilience improvement measures of power systems to mitigate the effects of adverse extreme events. Current research mainly focuses on the hardening measures where robust optimization is used to solve the problems. However, due to the consideration of worst case of uncertain parameters, the robust optimization method is usually too conservative and uneconomical in many situations. In this thesis, operational measures are deployed to boost the distribution system resilience considering all possible scenarios. An integrated resilience response framework is proposed, which provides distribution system operators solutions to address the resilience enhancement problem in both preventive state and emergency states. The key of the framework is a two-stage stochastic mix-integer linear optimization model. The mathematical formulation and the solving method, progressive hedging algorithm, are presented in this thesis as well. Preventive response includes topology reconfiguration and generator redispatch, while topology reconfiguration, generator redispatch and load curtailment are allowed in emergency response. Case study on IEEE 33 bus system and a modified 69 bus system validates the correctness and effectiveness of the proposed framework and model. Integrated response solution is obtained by solving the model and sensitivity analysis is performed to study the performance of integrated response under different system parameters. The key conclusions include the following: 1) integrated response improve distribution system resilience in a minimum cost; 2) integrated response is preferable to either individual preventive or emergency response; 3) system parameters and abilities such as unit load shedding cost, ramping ability and generator availability influence the system resilience and expected total cost in different degrees.