Stochastic Resilience-Oriented Smart Power Distribution System Planning and Operation Against Natural Disasters
Creators: Shi, Wenlong
Publisher: name: University of Alberta Library
Year: 2024

<p>Climate change has become an urgent global concern in the 21st century. Such environmental variation has led to an increasing occurrence of natural disasters. For example, the continuing rises in global temperatures can bring about severe storms and wildfires. Consequently, electrical infrastructures can be damaged, inducing large-scale blackouts and considerable economic losses. Therefore, power grid resilience against natural disasters has become a hot topic in both industry and academia. Benefiting from smart power distribution systems (PDSs), advanced techniques such as distributed generation and distributed automation can enhance power grid resilience effectively. However, one of the greatest challenges is how to efficiently utilize the emerging smart devices in a resilience-oriented manner considering the randomness of natural disasters. Therefore, in this thesis, the stochastic resilience-oriented smart PDS planning and operation against natural disasters is investigated. Four main research topics are studied.</p>

<p>Firstly, the stochastic planning for PDS resilience enhancement against earthquakes is investigated. Specifically, the portfolio of resilient measures including hardening distribution lines (DLs), and investing in Mobile Emergency Generators (MEGs) and Mobile Energy Storage Systems (MESSs) are studied in a stochastic environment. A spatial seismic damage model is developed to geographically characterize the random damages of earthquakes. The stochastic PDS planning problem is formulated as a risk-averse two-stage stochastic bi-level programming problem. The upper-level minimizes the total investment cost and the expected interruption cost. The lower-level minimizes the expected loss of load through MEG and MESS coordination, including co-allocation and energy exchange. To solve this problem, a decomposition method is proposed to break up the problem into two separate subproblems to speed up the computation. Case studies based on IEEE 37-Node and 123-Node Test Feeders demonstrate that the co-optimization of DL hardening and MEG and MESS investment considering MEG and MESS coordination including co-allocation and energy exchange is necessary. It can enhance the PDS resilience against earthquakes in a cost-effective manner.</p>

<p>Some types of natural disasters can impose destructive impact over a period of time, resulting in post-restoration failures. In the second work, the resilient restoration against uncertain multi-shocks of earthquakes and post-restoration failures is investigated. A data-driven PDS resilience enhancement strategy is proposed against multi-shocks of earthquakes considering the underlying uncertainties. A resistibility index (RI) is developed based on hierarchical hidden Markov models (HHMMs) for stochastic resilience evaluation. The historical earthquake data are incorporated into the HHMM as observed information of multi-shocks of earthquakes. Based on the RI metric, the problems of pre-positioning and reallocation of MEGs are formulated as mixed-integer programming problems. The problem of repair scheduling is formulated as an adaptive two-stage multi-period stochastic programming problem, for which a revision period is introduced to allow the decisions to adapt to the underlying uncertainties after the revision. Also, to reduce the computational complexity, an iterative algorithm is presented based on linear relaxation. Case studies based on the modified IEEE 123-Node Test Feeder and historical earthquake data of the 1994 Northridge earthquake demonstrate the efficiency of the proposed strategy.</p>

<p>The existing MG formation approaches based on the Distflow model always demand MG roots and their corresponding topologies. This can result in an increased number of variables and constraints in the optimization problem. In the third work, the dynamic microgrid (MG) formation considering large-scale deployment of mobile energy resources (MERs) is studied. Specifically, an adaptive linearized Distflow model is proposed based on the single commodity flow model in graph theory. The active and reactive powers are represented as commodities, which are sent from one node to each of its adjacent nodes in a graph. Accordingly, the power flow and nodal voltage calculation based on the commodity flow only requires adjacent node information of the original topology rather than various MG topologies caused by the dynamic deployment of MERs. Moreover, the dynamic MG formation problem is formulated as a mixed-integer nonlinear programming problem by incorporating the adaptive LinDistflow model as constraints. A linearization technique is proposed based on propositional logic constraints. The effectiveness of the proposed dynamic MG formation approach is evaluated based on the IEEE 37-Node, IEEE 123-Node and IEEE 8500-Node Test Feeders. The evaluation results also indicate that the large-scale MER deployment can lead to a lower average total load shed.</p>

<p>Modern power systems are undergoing a paradigm shift from traditional grids towards smart grids. New challenges arise in terms of grid resilience, because natural disasters can cause damages to both cyber and physical systems. In the forth work, we propose a stochastic sequential restoration scheme for cyber-physical power distribution systems (CPDSs) considering resilience. The sequential restoration problem is formulated as an uncertain Markov decision process (UMDP) with hurricanes incorporated as natural disasters. Different wind velocities and directions are considered as hurricane scenarios, which are used to obtain the fragility of DLs. The fragility functions are further used for the derivation of uncertain state transition functions of the UMDP. The minimax regret optimization considering the sample weights of UMDP is presented. The robust sequential actions are determined, such that the loads can be restored in a timely manner. To improve computational efficiency, a minimax regret policy iteration algorithm is presented based on the regret Bellman equation. Case studies are conducted based on the IEEE 123-Node Test Feeder and historical data of Hurricane Bonnie to demonstrate the effectiveness of the proposed scheme.</p>