安全滚动时域混合整数可微预测控制在考虑退化电池调度中的应用
Safe Receding Horizon Mixed-Integer Differentiable Predictive Control for Degradation-Aware Battery Dispatch
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中文总结 AI 辅助
提出一种安全滚动时域混合整数可微预测控制方法，结合神经网络与递归可行性保证，用于电池调度，实现6.9%成本差距和25倍加速。
中文摘要 AI 辅助
我们提出了一种用于住宅电池储能调度的安全滚动时域混合整数可微预测控制方法，该方法结合了神经网络的速度与递归可行性的保证。与开环学习优化方法不同，它纳入了实时荷电状态反馈和正弦时间调节，使得在每个时间步都能进行闭环重新规划，而无需重新求解混合整数规划。一个可微的雨流循环计数层使得基于精确退化物理的混合整数策略能够进行自监督训练。该控制器是一个混合闭环系统，将神经模式选择和连续动作策略与二次规划安全滤波器配对，该滤波器保证递归可行性，且不依赖于网络权重或模式最优性。我们建立了模式条件下的Lipschitz连续性和条件遗憾分解，包括训练质量、模式不匹配和预测误差项。在为期7天的净计量评估中，该方法与闭环混合整数MPC基准相比，实现了6.9%的成本差距，并获得了25倍的加速（每步0.11秒对比2.7秒），而平均遗憾在0-30%的预测噪声下上升不到4%。学习到的模式和基准模式在每一步都一致，因此界限简化为其训练质量和预测误差项，两者均经过实证验证。
英文摘要
We present a safe receding-horizon mixed-integer differentiable predictive control methodology for residential battery energy storage dispatch that combines neural-network speed with recursive feasibility guarantees. Unlike open-loop learning-to-optimize methods, it incorporates real-time state-of-charge feedback and sinusoidal time-of-day conditioning, enabling closed-loop re-planning at every timestep without re-solving a mixed-integer program. A differentiable rainflow cycle-counting layer enables self-supervised training of the mixed-integer policy on exact degradation physics. The controller is a hybrid closed-loop system pairing a neural mode-selection and continuous-action policy with a quadratic-programming safety filter that guarantees recursive feasibility independent of network weights or mode optimality. We establish mode-conditioned Lipschitz continuity and a conditional regret decomposition into training-quality, mode-mismatch, and forecast-error terms. On a 7-day net-metering evaluation, the method attains a 6.9% cost gap versus the closed-loop mixed-integer MPC benchmark with a 25x speedup (0.11 s vs 2.7 s per step), while average regret rises by under 4% across 0-30% forecast noise. The learned and benchmark modes agree at every step, so the bound reduces to its training-quality and forecast-error terms, both empirically validated.
发表机构
- Johns Hopkins University（约翰斯·霍普金斯大学）
机构由 AI 辅助整理，请以论文原文为准。
