GENCO - A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysis· GENCO - 统一神经求解器
Foundation models are transforming business workflows and boosting productivity, yet they remain largely absent from engineering domains such as power system analysis, where strict physical consistency must be enforced. We present GENCO (GEometric Neural Corrective Optimizer), a unified neural solver for steady-state transmission grid analysis that handles power flow (PF), optimal power flow (OPF), and state estimation (SE) within a single architecture and shared network representation. To support advances in neural power system solvers, we introduce the open-source GridFM Development Framework, which standardizes synthetic data generation and training in a low-code environment. We also release large-scale datasets with millions of PF and OPF scenarios across diverse grid topologies to sup
提出 GENCO 统一神经求解器，用于稳态电网分析。
- 核心方法
- GENCO 采用几何神经校正优化器，通过单个架构和共享网络表示同时解决潮流、最优潮流和状态估计问题。引入 GridFM 开源开发框架，标准化合成数据生成和低代码环境训练。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 现有方法在电力系统分析中无法同时处理潮流、最优潮流和状态估计问题，且缺乏高效的标准化数据生成和训练框架。
- 关键实验
- 实验包括数百万个潮流和最优潮流场景，涉及多种电网拓扑结构。
- 主要贡献
- 首次提出统一神经求解器，解决多种电网分析问题；提供大规模数据集和开源框架，推动领域发展。
- 意义与局限
- 该方法能够提高电力系统分析的效率和准确性，对电力行业有重大影响；但可能需要更多实际应用验证其稳定性和可靠性。