发表机构
Tsinghua Shenzhen International Graduate School, Tsinghua University; Hong Kong Polytechnic University（清华大学深圳国际研究生院; 香港理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于条件随机神经生成器的端到端生成框架，通过生成多个候选调度轨迹并选择可行解，高效求解交流潮流约束下的随机模型预测控制问题，在IEEE 14和118节点系统上实现100%可行性和低于2%的最优性差距。
AI 中文摘要
本文提出了一种端到端生成框架，用于高效求解在非线性交流潮流约束下的多周期、多场景随机模型预测控制（SMPC）问题。传统的确定性神经替代模型依赖单次预测，由于一条调度轨迹必须同时满足所有场景和时段下的非线性约束，因此难以实现可靠的可行性。为解决这一局限，开发了条件随机神经生成器（CSNG），为每个不确定性实例生成多个候选调度轨迹，通过候选选择能够恢复可行且经济的解。进一步引入了可行性感知的自监督分布整形方案，以促进约束满足、候选多样性和运行经济性，无需计算昂贵的SMPC解标签，同时缓解生成式ACOPF学习中的候选坍缩问题。为支持高效的端到端训练，引入了约束感知的可微架构。该架构采用投影机制精确执行箱式约束和爬坡约束，同时在活跃边界附近保留信息丰富的梯度，并配备可微等式补全替代模型以实现高效的交流潮流重构。在IEEE 14节点和118节点系统上的案例研究表明，可行性达到100%，最优性差距低于2%，计算效率适用于日内调度。该实现已在https://this https URL公开提供。
英文摘要
This paper proposes an end-to-end generative framework for efficiently solving multi-period and multi-scenario stochastic model predictive control (SMPC) problems under nonlinear AC power-flow constraints. Conventional deterministic neural surrogates rely on a single-shot prediction, which makes reliable feasibility difficult to achieve because one dispatch trajectory must simultaneously satisfy nonlinear constraints across all scenarios and time periods. To address this limitation, a conditional stochastic neural generator (CSNG) is developed to produce multiple candidate dispatch trajectories for each uncertainty instance, enabling the recovery of a feasible and economical solution through candidate selection. A feasibility-aware self-supervised distribution-shaping scheme is further introduced to promote constraint satisfaction, candidate diversity, and operating economy without requiring computationally expensive SMPC solution labels, while mitigating candidate collapse in generative ACOPF learning. To support efficient end-to-end training, a constraint-aware differentiable architecture is introduced. It employs a projection mechanism to exactly enforce box and ramping constraints while preserving informative gradients near active bounds, together with a differentiable equality-completion surrogate for efficient AC power-flow reconstruction. Case studies on the IEEE 14- and 118-bus systems demonstrate $100\%$ feasibility, optimality gaps below $2\%$, and computational efficiency suitable for intraday dispatch. The implementation is publicly available at https://github.com/JieZhu6/Generative_SMPC.
