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Affine arithmetic meets robust optimization in scheduling microgrids-based flexibility services
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AI
DOI：10.1016/j.segan.2025.101839.png)
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Abstract
En 中文
Enabling distributed and dispersed flexible sources to offer advanced dispatching services in the grid-balancing markets is considered one of the most challenging issues to address in decarbonized power systems. In this context, microgrids could play a strategic role by enabling the provision of valuable grid-balancing services through the optimal operation scheduling of their components. For this purpose, reliable decision-support methods should be deployed to identify feasible and profitable microgrid operation strategies, considering the effects of the large and correlated uncertainty sources affecting the input data. To try and address this problem, this paper proposes a new formulation of the affine arithmetic counterpart of the microgrid-based flexibility scheduling problem that allows overcoming the over-conservativism of conventional range-based operators by computing a family of solutions that include the solution computed by robust optimization. Detailed simulation results obtained on a realistic case study are presented and discussed to assess the benefits of the proposed formulation and to define its connection with robust optimization.
Keywords:
microgrid
flexibility scheduling
grid-balancing services
affine arithmetic
robust optimization
Journal
IF:
5.6
Papers:
614
Citations:
5.1K
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