Risk-Aware Tie-Line Exchange Optimization for Probabilistic Production Simulation and Sustainable Renewable Energy Accommodation in Interconnected Power Systems

The transition toward sustainable and low-carbon power systems increasingly depends on the efficient accommodation of high shares of renewable energy while maintaining secure and reliable grid operation. In interconnected power systems, this challenge is intensified by strong cross-regional coupling, tie-line flow violation risks, and the high computational burden of fully coupled probabilistic assessments. To support the sustainable operation of renewable-rich interconnected systems, this paper proposes a probabilistic production simulation method that incorporates risk-aware tie-line exchange optimization. Sequential random sample paths are constructed by considering load fluctuations, renewable energy output uncertainty, and random outages of conventional units. Using cross-regional exchange power as coupling variables, a conditional value-at-risk (CVaR)-based pre-scheduling model is established to control tie-line and interface flow tail risks. Given the scheduled exchange power, cross-regional exchanges are transformed into regional boundary power injections, enabling decoupled sequential probabilistic production simulation for each region. The exchange schedule is then iteratively updated through marginal-value feedback. A four-region interconnected system is used for case-study validation. Results show that the proposed method improves renewable energy accommodation, reduces renewable curtailment, suppresses tie-line flow violation risk, and maintains high reliability assessment accuracy. Compared with the region-decoupled benchmark with fixed exchange power, the proposed method increases the renewable energy accommodation rate from 93.82% to 95.41% and reduces renewable curtailment from 312,162 MWh to 231,284 MWh, while also lowering expected energy not served and loss of load expectation. In addition, under the reported case-study setting, the proposed RC-IEF-PPS reduces the computation time from 5216.24 s for Full-PPS to 4074.63 s, i.e., by 21.9%, while maintaining comparable reliability assessment accuracy. These results indicate that the proposed framework can support the sustainable integration of high-penetration renewable energy by improving clean-energy utilization, operational reliability, and computational tractability in interconnected power systems.