A comparative study on the robust optimal design of microgrids under random contingency
Résumé
This study presents a comparative analysis of robust optimization approaches for the optimal design of microgrids under random contingency. We consider a radial distribution system with distributed energy resources (DERs) and battery energy storage systems (BESS) to minimize the overall system investment cost while ensuring reliability and resilience. The optimal design problem is formulated as a mixed-integer linear programming (MILP) problem, incorporating uncertainties in renewable generation and demand profiles. We compare three robust optimization approaches: robust optimization (RO), distributionally robust optimization (DRO), and robust satisficing (RS). The RS approach is particularly effective in capturing the fragility of the system and achieving a targeted level of cost or acceptable loss of optimality. We highlight the trade-offs between optimality and robustness and explain the rationale of RS in providing good enough decisions with reasonable computational costs. We also discuss the challenges in large-scale integration of DERs, including siting and sizing, and the importance of considering uncertainty in the generation and demand profiles.
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