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Microgrids are one important lever to increase power system resilience and to tightly integrate renewable energies at the same time. Commonly, an optimization-based proactive scheduling controls assets in advance in a cost-effective way and ensures that contingencies may be successfully mitigated. However, often strong simplifications are introduced to manage the high computational complexity of scheduling, which can adversely impact fault mitigation. To consider essential phenomena such as power flow limitations and low-level control capabilities in detail, a novel hybrid scheduling approach is presented that integrates mathematical programming and arbitrary nonlinear constraint models via decision trees. A detailed case study compares the new method to an extended hybrid scheduling approach from literature. It is demonstrated that hybrid optimization can efficiently solve proactive resilient scheduling problems and that the tree-based algorithm provides a feasible solution, even in case the reference algorithm fails. Details on the convergence of both algorithms give further insights into the working principles and show that the novel method quickly finds a feasible solution that is successively improved afterwards. By the novel combination of highly-developed solvers for both mathematical programming and detailed asset models it is expected that this study further supports the operation of power systems and reduces costly reserve requirements.

Hybrid Optimization Toward Proactive Resilient Microgrid Scheduling