In recent years, extreme events have frequently occurred, and the extreme uncertainty of the source-demand side of high-ratio renewable energy systems poses a great challenge to the safe operation of power systems. Accurately generating extreme scenarios related to the source-demand side under a high percentage of new power systems is vital for the safe operation of power systems and the assessment of their reliability. However, at this stage, methods for extreme scenario generation that fully consider the correlation between wind power, solar power, and load are lacking. To address these problems, this paper proposes a method for extreme scenario generation based on information-maximizing generative adversarial networks (InfoGANs) for high-proportion renewable power systems. The example analysis shows that the method for extreme scenario generation proposed in this paper can fully explore the correlation between historical wind–solar–load data, greatly improve the accuracy with which extreme scenarios are generated, and provide effective theories and methodologies for the safe operation of a new type of power system.

Incorporating reliability constraints in economic dispatch is challenging considering massive system states resulting from random component failures and renewable energy (RE) uncertainties. Traditionally, only N-1 or N-2 failures are considered to reduce complexity, and the consequence is remarkable accuracy losses. This paper aims to achieve low complexity and precise characterization of reliability constraints with fewer system states. First, the initial system state space is constructed, and the limit state function is proposed to define the boundary between reliable and unreliable domains. Then, the limit state function is converted to constraints on a particular set of system states defined as limit system states (LSS). Moreover, by revealing the distribution regulations of system states, the “worst of the best” principle is proposed to identify LSS. In the first step, a chance-constrained optimal power flow model is established to screen the better system states (BSS) considering RE uncertainties. In the second step, the worst ones among the BSS are targeted based on the capacity of failed components and the tolerance to RE uncertainties. Finally, the reliability constraint is converted to the condition that none of the LSS suffers load loss, which is convenient to formulate and incorporate in the dispatch model. Case studies have been conducted to verify the validity of the proposed methods.

Multi-energy coupled low-carbon distribution networks (MEC-LCDNs) face growing risks from extreme weather and high-order contingencies. Traditional deterministic criteria (e.g., N-1) often overlook these low-probability, high-impact events, while existing simulation-based probabilistic methods suffer from excessive computational burdens and a lack of intuitive visualization. To address these challenges, this paper proposes an explicit representation method for MEC-LCDN operational reliability constraints based on the probabilistic reliability region (PRR). This approach transforms the abstract probabilistic reliability criterion—loss of load probability (LOLP)—into a visualizable geometric space. Specifically, a fast contingency screening technique (FCST) is developed to identify a minimal set of boundary scenarios that anchor the target reliability threshold. Subsequently, complex probabilistic constraints are decoupled into deterministic N-k security constraints under these boundary scenarios, enabling the analytical construction of the PRR boundary. A case study demonstrates that the proposed method reduces the number of required contingency scenarios by over 90% and slashes computation time from 78.8 s to 3.1 s compared to traditional N-k truncation methods. Furthermore, the method accurately quantifies the system’s total supply capability (TSC) at 44.501 MW while providing intuitive visualizations of reliability boundaries that satisfy stringent LOLP criterion.