基于在线学习的自适应混合Benders分解用于风险规避的最优容量配置
Online Learning-Based Adaptive Hybrid Benders Decomposition for Risk-Averse Optimal Sizing
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- Aix Marseille Université（艾克斯马赛大学）
- LIS Lab CNRS UMR 7020（CNRS LIS实验室）
- The University of Sheffield（谢菲尔德大学）
- Autonex Systems Limited（Autonex系统有限公司）
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中文总结 AI 辅助
针对电池储能系统最优容量配置的两阶段随机规划求解慢问题，提出基于在线学习的自适应混合Benders分解算法，通过场景选择加速收敛，减少80%计算量。
中文摘要 AI 辅助
在不确定输入下，电池储能系统（BESS）的最优容量配置问题（OSP）通常被建模为两阶段随机规划（2SP），这在大规模场景集下往往会带来计算和RAM（内存）瓶颈。Benders分解（BD）是解决该问题的常用方法，但其收敛到精确解的速度较慢。为解决这一问题，我们针对风险规避的2SP模型提出了一种加速的基于在线学习的自适应混合BD算法。所提方法通过在主问题中显式嵌入一个精心选择的场景，避免了早期迭代中陷入不可行区域，同时基于在线学习的尾部相关场景选择器有助于避免在每次迭代中求解整个场景集。OSP被构建为在具有现有可再生能源的多站点能源社区中选择表后BESS。该用例探索了确定性加速方法与训练密集型机器学习模型之间的有趣中间地带，展示了机器学习辅助决策的潜力。与原始BD相比，在相同容差设置下，OLAH-BD将子问题评估次数和总墙钟时间减少了高达80%。
英文摘要
Optimal sizing problem (OSP) for battery energy storage system (BESS) under uncertain inputs is often formulated as a two-stage stochastic program (2SP), which typically introduces computational and RAM (memory) bottlenecks for large scenario sets. Benders Decomposition (BD) is a popular approach for tackling this problem, but it suffers from slow convergence to the exact solution. To address this problem, we propose an accelerated online learning-based adaptive hybrid BD algorithm for a risk-averse 2SP formulation. The proposed method avoids getting stuck in the infeasible region during early iterations by explicitly embedding a carefully selected scenario in the master problem, while a tail-relevant scenario selector based on online learning helps to avoid solving the entire scenario set at every iteration. The OSP is formulated to select a behind-the-meter BESS in a multi-site energy community with existing renewables. The use case explores an interesting middle-ground between deterministic acceleration methods and training-heavy ML models, showcasing the potential of ML-assisted decision-making. Compared with vanilla BD, OLAH-BD reduces subproblem evaluations and total wall time by up to 80\% under the same tolerance settings.
