AI 中文总结
该研究提出ARBO-DART算法，通过自适应细化贝叶斯优化联合优化电池储能的日前承诺与实时灵活性，在实际日前价格曲线实验中，该算法能恢复有经济意义的投标结构且速度显著更快。
AI 中文摘要
我们提出了用于日前与实时（ARBO-DART）市场的自适应细化贝叶斯优化算法，这是一种用于电池储能系统（BESS）日内调度联合优化的算法，其中日前（DA）承诺配置是针对由黑盒随机控制求解器计算的实时（RT）追索权价值进行优化的。在我们的框架中，日前价格曲线被视为外生变量，而实时价格则围绕其以均值回复过程演化。实时追索层执行动态闭环控制，同时考虑分段线性的荷电状态动态以及日前驱动的可行控制集。通过将贝叶斯优化（BO）包裹在实时求解器周围，ARBO-DART可联合优化日前承诺与动态实时灵活性，无需解析梯度、闭式价值函数或有限场景近似。为克服固定分辨率日前配置中的维度诅咒，ARBO-DART从日前承诺的粗略划分开始，并根据相应实时策略的判断，在最需要额外时间分辨率的地方逐步细化充放电块。针对实际日前价格曲线的数值实验表明，ARBO-DART在恢复具有经济意义的日前投标结构方面是有效的，且相对于固定分辨率方法快数倍。
英文摘要
We propose Adaptive Refinement Bayesian Optimization for Day-Ahead and Real-Time (ARBO-DART) markets, an algorithm for BESS intraday dispatch co-optimization in which day-ahead (DA) commitment profiles are optimized against value of real-time (RT) recourse computed by a black-box stochastic control solver. In our framework, the DA price curve is taken as exogenous and RT prices evolve as a mean-reverting process around it. The RT recourse layer performs dynamic closed-loop control while accounting for the piecewise-linear state-of-charge dynamics and the DA-driven feasible control set. By wrapping Bayesian Optimization (BO) around the RT solver, ARBO-DART jointly optimizes DA commitments and dynamic RT flexibility without requiring analytic gradients, closed-form value functions, or finite-scenario approximations. To overcome the curse of dimensionality in fixed-resolution DA profiles, ARBO-DART starts from a coarse partition of DA commitments and progressively refines charge and discharge blocks where additional temporal resolution is most needed, as judged by the corresponding RT policy. Numerical experiments across realistic DA price curves reveal the effectiveness of ARBO-DART in recovering economically meaningful DA bidding structures while being several times faster relative to fixed-resolution
Comments33 pages
