2026-06-05T12:51:29Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/324602026-02-03T10:59:51Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Porras, Álvaro author Roald, Line author Morales-González, Juan Miguel author Pineda-Morente, Salvador author 2024 Uncertainty in renewable energy generation has the potential to adversely impact the operation of electric networks. Numerous approaches to manage this impact have been proposed, ranging from stochastic and chance-constrained programming to robust optimization. However, these approaches either tend to be conservative or leave the system vulnerable to low probability, high impact uncertainty realizations. To address this issue, we propose a new formulation for stochastic optimal power flow that explicitly distinguishes between “normal operation”, in which automatic generation control (AGC) is sufficient to guarantee system security, and “adverse operation”, in which the system operator is required to take additional actions, e.g., manual reserve deployment. The new formulation has been compared with the classical ones in a case study on the IEEE-118 and IEEE-300 bus systems. We observe that our consideration of extreme scenarios enables solutions that are more secure than typical chance-constrained formulations, yet less costly than solutions that guarantee robust feasibility with only AGC. Á. Porras, L. Roald, J. M. Morales and S. Pineda, "Unifying Chance-Constrained and Robust Optimal Power Flow for Resilient Network Operations," in IEEE Transactions on Control of Network Systems, doi: 10.1109/TCNS.2024.3432188. https://hdl.handle.net/10630/32460 10.1109/TCNS.2024.3432188 Optimización matemática Recursos energéticos renovables Ingeniería eléctrica - Matemáticas Unifying Chance-Constrained and Robust Optimal Power Flow for Resilient Network Operations.