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| 面向极端风险防御的园区综合能源系统场景生成与规划 |
| Scenario generation and planning of park integrated energy systems for extreme risk prevention |
| 投稿时间：2026-06-03 修订日期：2026-07-03 |
| DOI： |
| 中文关键词: 综合能源系统规划 极端场景 WGAN-GP |
| 英文关键词: integrated energy system planning extreme scenarios WGAN-GP |
| 基金项目:国家自然科学基金项目(52577119) |
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| 中文摘要: |
| 针对园区综合能源系统规划中极端场景覆盖不足、高风险边界特征易在聚类过程中被弱化以及风险偏好缺乏动态调节能力等问题，提出一种面向极端风险防御的场景构建与规划框架。首先，构建基于带梯度惩罚的Wasserstein生成对抗网络的场景生成模型，通过学习历史数据的分布特性，实现对极端场景扩展。其次，构建五维极端性指标体系，并结合Pareto-TOPSIS实现极端场景与常规场景的综合评估。在此基础上，提出极端与常规场景双轨分离聚类策略，利用最大最小距离分析对极端场景进行独立聚类，以保留高风险边界特征。进一步地，引入极端场景占比指数，实现规划模型中安全性与经济性的权衡。算例结果表明，所提方法能够拓展场景边界并提高未知极端场景覆盖能力。与仅采用历史场景的规划方案相比，在相同极端测试场景下，系统切负荷量与切负荷时长均明显降低，验证了所提框架在提升系统供能韧性和极端风险适应能力方面的有效性。 |
| 英文摘要: |
| To address the issues of insufficient coverage of extreme scenarios, the tendency of high-risk boundary features to be weakened during the clustering process, and the lack of dy-namic risk-preference adjustment in park-level integrated energy system (PIES) planning, an extreme risk-oriented sce-nario generation and planning framework is proposed. First, a scenario generation model based on a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is developed to expand extreme scenarios by learning the distri-bution characteristics of historical data. Second, a five-dimensional extremeness evaluation index system is es-tablished, and a Pareto-TOPSIS-based approach is employed to comprehensively evaluate both extreme and conventional scenarios. On this basis, a dual-track clustering strategy is proposed, in which extreme and conventional scenarios are clustered separately. Extreme scenarios are independently clustered using a max–min distance analysis method to pre-serve high-risk boundary features. Furthermore, an extreme scenario proportion index is introduced to balance system security and economic performance within the planning mod-el. Case studies demonstrate that the proposed method can effectively expand scenario boundaries and improve the cov-erage of unknown extreme scenarios. Compared with the planning scheme using only historical scenarios, the proposed method significantly reduces both load shedding amount and load shedding duration under the same extreme test scenarios, thereby verifying its effectiveness in enhancing energy supply resilience and adaptability to extreme risks. |
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