论文标题
随机搜索参数成本函数近似值：滚动预测的能量存储
Stochastic Search for a Parametric Cost Function Approximation: Energy storage with rolling forecasts
论文作者
论文摘要
在可再生能源存储文献中，几乎忽略了滚动预测。在本文中，我们提供了一种新的方法来处理不确定性，不仅是预测的准确性，而且是随着时间的推移的预测的演变。我们的方法将重点从对LookAhead模型中的不确定性进行建模转变为在随机基本模型中的准确模拟。我们通过创建一个参数修改的lookahead模型来制定强大的策略，以制定储能决策，其中参数在随机基本模型中被调整。由于计算无偏的随机梯度相对于参数需要限制性假设，因此我们根据数值衍生物提出了一种基于模拟的随机近似算法，以优化这些参数。尽管基于嘈杂函数评估计算的数值衍生物提供了偏见的梯度估计，但在我们提出的算法的框架内建立的一种在线差异降低技术将使我们能够控制累积的偏见错误并建立算法的有限时间算法。我们的数值实验表明，该算法在寻找策略方面的性能优于确定性基准策略。
Rolling forecasts have been almost overlooked in the renewable energy storage literature. In this paper, we provide a new approach for handling uncertainty not just in the accuracy of a forecast, but in the evolution of forecasts over time. Our approach shifts the focus from modeling the uncertainty in a lookahead model to accurate simulations in a stochastic base model. We develop a robust policy for making energy storage decisions by creating a parametrically modified lookahead model, where the parameters are tuned in the stochastic base model. Since computing unbiased stochastic gradients with respect to the parameters require restrictive assumptions, we propose a simulation-based stochastic approximation algorithm based on numerical derivatives to optimize these parameters. While numerical derivatives, calculated based on the noisy function evaluations, provide biased gradient estimates, an online variance reduction technique built in the framework of our proposed algorithm, will enable us to control the accumulated bias errors and establish the finite-time rate of convergence of the algorithm. Our numerical experiments show the performance of this algorithm in finding policies outperforming the deterministic benchmark policy.
