端到端电池调度：基于混合整数可微预测控制的精确雨流退化建模
End-to-End Battery Dispatch with Exact Rainflow Degradation via Mixed-Integer Differentiable Predictive Control
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中文总结 AI 辅助
针对电池调度中雨流退化不可微的难题，提出混合整数可微预测控制框架，通过可微雨流层实现端到端训练，在真实数据上性能差距小且计算加速显著。
中文摘要 AI 辅助
电池储能系统的最优调度需要在能量套利与循环引起的退化之间取得平衡，而退化通过雨流循环计数得以精确量化。然而，雨流算法具有组合性和不可微性，与凸优化和基于梯度的神经网络训练均不兼容。我们提出了一种自监督的混合整数可微预测控制框架，通过新颖的可微雨流层直接在精确雨流退化上训练神经策略，该层在荷电状态极值处提供精确梯度，并在增量变化上提供密集代理梯度，从而实现稳定的端到端训练，同时保留真实的退化物理机制。混合整数可微架构强制执行功率平衡、动态约束和模式互斥性，并配有安全滤波器以保证约束满足。我们在3,650个真实电池日场景（10个电池组成的车队运行365天）上评估了该框架，这些场景涵盖三个公用事业区域（加州SDG&E、科罗拉多州Xcel Energy、亚利桑那州APS），具有多样化的分时电价结构。单电池训练的策略在其训练分布上实现了0.35%的性能差距，并带来超过200倍的加速；而车队级训练则泛化到所有家庭和公用事业区域，实现了3.33%的性能差距，计算加速达564倍（62秒对比9.7小时），且可行性为100%。毫秒级推理相比混合整数求解器将计算量减少了两个数量级以上，使得大规模实际部署成为可能。
英文摘要
Optimal dispatch of battery energy storage systems requires balancing energy arbitrage against cycle-induced degradation, which is accurately quantified through rainflow cycle counting. However, rainflow's combinatorial, nondifferentiable algorithm is incompatible with both convex optimization and gradient-based neural network training. We present a self-supervised mixed-integer differentiable predictive control framework that trains neural policies directly on exact rainflow degradation through a novel differentiable rainflow layer combining exact gradients at state-of-charge extrema with dense proxy gradients on incremental changes, enabling stable end-to-end training while preserving true degradation physics. A mixed-integer differentiable architecture enforces power balance, dynamics, and mode exclusivity, with a safety filter guaranteeing constraint satisfaction. We evaluate the framework on 3,650 real battery-day scenarios (a 10-battery fleet over 365 days) spanning three utility regions (SDG&E California, Xcel Energy Colorado, APS Arizona) with diverse time-of-use pricing structures. A single-battery trained policy achieves a 0.35% performance gap on its training distribution with over 200x speedup, while fleet-wide training generalizes across all households and utility regions, achieving a 3.33% performance gap with 564x computational speedup (62 seconds vs. 9.7 hours) and 100% feasibility. The millisecond-scale inference reduces computation by over two orders of magnitude compared to mixed-integer solvers, enabling practical deployment at scale.
发表机构
- The Johns Hopkins University（约翰斯·霍普金斯大学）
- University of Nevada, Reno（内华达大学里诺分校）
机构由 AI 辅助整理，请以论文原文为准。
