Distributed Robust Low-Carbon Economic Dispatch of Power Systems Considering Extreme Scenarios - HU Heng, QIN Jianru, LI Haibo, LIU Jiefeng (Dianli jianshe)
[Objective] Under the advancing energy transition driven by China's national "dual-carbon" strategy， the escalating penetration of renewable energy （RE） sources has not only heightened power system spinning reserve requirements due to their inherent stochasticity and volatility in generation patterns， but has also precipitated a marked surge in peak-shaving and frequency regulation expenditures necessary for maintaining power supply reliability. This dynamic further exacerbates the fundamental multi-objective conflict between economic operation costs and reliability assurance in modern power systems. Particularly， tail risks triggered by extreme weather and the dynamic mismatches between stochastic RE fluctuations and conventional unit regulation rates invalidate conventional deterministic scheduling models reliant on typical scenarios. [Methods] To address this， this paper first constructs RE generation scenarios using Latin hypercube sampling （LHS） and modified k-means clustering， verifying their reserve feasibility， while transforming reserve-infeasible scenarios into extreme scenario sets. A two-stage distributionally robust optimization （DRO） model is proposed， minimizing day-ahead operational costs and intraday costs including carbon trading， rescheduling expenses， and risk penalties. A discrete probability ambiguity set with comprehensive norm constraints is established to rigorously characterize RE uncertainty by incorporating extreme scenarios. [Results] Case studies on an improved IEEE 39-node system using the column-and-constraint generation （C&CG） algorithm demonstrate that， compared with traditional deterministic and DRO models based on typical scenarios， the proposed approach increases scheduling costs by 7.11% and 14.37% respectively， but reduces renewable curtailment rates by 8.28% and 34.65%， and load shedding rates by 8.19% and 33.32%. [Conclusions] This methodology effectively resolves the limitations of conventional approaches in coordinating economic efficiency， reliability， and low-carbon requirements while ensuring system robustness， offering a viable solution for secure operations in renewable-dominated power systems.