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Abstract
The rapid proliferation and widespread adoption of microgrids (MG) necessitate the development of new methodologies to holistically model all the active components within MGs. It’s crucial to focus on specific islanding requirements, especially when the primary grid power is unavailable. In order to ensure a high level of reliability in an interconnected MG network, this dissertation presents an optimal scheduling model designed to minimize the day-ahead costs of the MGs while taking into account the existing operational constraints.This problem is thoughtfully decomposed using Bender’s Decomposition method into two key operating conditions: grid-connected and resilient operations. The ultimate goal is to ensure that each MG within the network maintains sufficient online capacity in the event of an emergency islanding situation, such as during extreme weather events. These events often come with uncertainties regarding their timing and duration, necessitating the consideration of multiple potential islanding scenarios for each event. The primary objective of this thesis is to establish optimal scheduling that guarantees the feasibility of islanding for all conceivable scenarios of such events, with load shedding as a last resort. The optimization model has been put into practice across different layouts of the modified IEEE 123-bus test system, encompassing various events over a 24-hour period. In addition to proposing a day-ahead scheduling approach oriented towards resiliency for multiple MGs, a comprehensive cost analysis and comparisons among all the test cases are also offered. The results convincingly demonstrate the utility of the proposed day-ahead scheduling algorithm, particularly for MG owners looking to foster collaborations with neighboring MGs. Lastly, after comparing with the traditional Single Stage MILP approach, the proposed method has proven to be computationally faster for practical usage. It has been shown that decomposing the problem using the proposed model makes it possible to combat real life events with thousand scenarios, where the single stage approach may fail.