Resilience-driven planning and operation of networked microgrids featuring decentralisation and flexibility

High-impact and low-probability extreme events including both man-made events and natural weather events can cause severe damage to power systems. These events are typically rare but featured in long duration and large scale. Many research efforts have been conducted on the resilience enhancement of modern power systems. In recent years, microgrids (MGs) with distributed energy resources (DERs) including both conventional generation resources and renewable energy sources provide a viable solution for the resilience enhancement of such multi-energy systems during extreme events. More specifically, several islanded MGs after extreme events can be connected with each other as a cluster, which has the advantage of significantly reducing load shedding through energy sharing among them. On the other hand, mobile power sources (MPSs) such as mobile energy storage systems (MESSs), electric vehicles (EVs), and mobile emergency generators (MEGs) have been gradually deployed in current energy systems for resilience enhancement due to their significant advantages on mobility and flexibility. Given such a context, a literature review on resilience-driven planning and operation problems featuring MGs is presented in detail, while research limitations are summarised briefly. Then, this thesis investigates how to develop appropriate planning and operation models for the resilience enhancement of networked MGs via different types of DERs (e.g., MGs, ESSs, EVs, MESSs, etc.). This research is conducted in the following application scenarios: 1. This thesis proposes novel operation strategies for hybrid AC/DC MGs and networked MGs towards resilience enhancement. Three modelling approaches including centralised control, hierarchical control, and distributed control have been applied to formulate the proposed operation problems. A detailed non-linear AC OPF algorithm is employed to model each MG capturing all the network and technical constraints relating to stability properties (e.g., voltage limits, active and reactive power flow limits, and power losses), while uncertainties associated with renewable energy sources and load profiles are incorporated into the proposed models via stochastic programming. Impacts of limited generation resources, load distinction intro critical and non-critical, and severe contingencies (e.g., multiple line outages) are appropriately captured to mimic a realistic scenario. 2. This thesis introduces MPSs (e.g., EVs and MESSs) into the suggested networked MGs against the severe contingencies caused by extreme events. Specifically, time-coupled routing and scheduling characteristics of MPSs inside each MG are modelled to reduce load shedding when large damage is caused to each MG during extreme events. Both transportation networks and power networks are considered in the proposed models, while transporting time of MPSs between different transportation nodes is also appropriately captured. 3. This thesis focuses on developing realistic planning models for the optimal sizing problem of networked MGs capturing a trade-off between resilience and cost, while both internal uncertainties and external contingencies are considered in the suggested three-level planning model. Additionally, a resilience-driven planning model is developed to solve the coupled optimal sizing and pre-positioning problem of MESSs in the context of decentralised networked MGs. Internal uncertainties are captured in the model via stochastic programming, while external contingencies are included through the three-level structure. 4. This thesis investigates the application of artificial intelligence techniques to power system operations. Specifically, a model-free multi-agent reinforcement learning (MARL) approach is proposed for the coordinated routing and scheduling problem of multiple MESSs towards resilience enhancement. The parameterized double deep Q-network method (P-DDQN) is employed to capture a hybrid policy including both discrete and continuous actions. A coupled power-transportation network featuring a linearised AC OPF algorithm is realised as the environment, while uncertainties associated with renewable energy sources, load profiles, line outages, and traffic volumes are incorporated into the proposed data-driven approach through the learning procedure.Open Acces

Integration of Preventive and Emergency Responses to Boost Distribution System Resilience

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