闭环抽水蓄能水电站选址优化
Optimization of Closed-Loop Pumped-Storage Hydropower Siting
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中文总结 AI 辅助
针对可再生能源并网的电网挑战，提出HERA-S框架优化闭环抽水蓄能选址，经里约热内卢案例验证可提升预可行性规划敏捷性。
中文摘要 AI 辅助
加速可变可再生能源并网给现代电网带来了重大运行挑战，过剩可再生能源的频繁弃用凸显了对长时储能的迫切需求，这类储能可将发电量转移至高需求时段，同时提供必要的辅助服务和电网惯性。社会环境约束日益限制传统水电开发，而闭环抽水蓄能（PSH）是可行替代方案，因其库区位于河流外，可避开天然水道。本文介绍由PSR开发的HERA-S计算建模框架（获EDF、CTG、Brookfield和Rio Light支持），用于简化和标准化区域PSH选址勘探。HERA-S可自动化空间筛选、大坝优化、成本估算及社会环境影响评分。将其应用于巴西里约热内卢的案例研究，该框架采用整数规划模型优化大坝填筑与开挖的质量平衡，同时考虑库区间距，显著提升了预可行性规划的敏捷性。
英文摘要
Accelerating variable renewable energy integration introduces substantial operational challenges to modern power grids. Frequent curtailment of surplus renewable generation highlights the pressing need for long-duration energy storage capable of shifting generation to high-demand periods while providing essential ancillary services and grid inertia. Socio-environmental constraints increasingly limit conventional hydropower development; however, closed-loop pumped-storage hydropower (PSH) presents a viable alternative because its off-river reservoirs avoid natural watercourses. This paper introduces HERA-S, a computational modeling framework developed by PSR (supported by EDF, CTG, Brookfield, and Rio Light) to streamline and standardize regional PSH site prospecting. HERA-S automates spatial screening, dam optimization, cost estimation, and socio-environmental impact scoring. Applied to a case study in Rio de Janeiro, Brazil, the framework employs an integer programming model to optimize dam-fill and excavation mass balances against inter-reservoir distance, significantly improving pre-feasibility planning agility.
