论文标题
近似动态编程，以估算连接的水库的利润估算
Approximate dynamic programming for profit estimation of connected hydro reservoirs
论文作者
论文摘要
在本文中，我们研究了连接的水电站的操作问题，涉及在不确定和动态环境中进行连续决策。传统上，这个问题是作为随机动态计划的理解，该计划占了电价和储层流入的不确定性。随着国家空间的数量以及价格和流入的历史，这种表述遭受了维度的诅咒。为了避免计算对未来价值函数的期望，提出的模型利用了所谓的决策后状态。为了进一步解决维度问题，我们提出了一种近似动态编程方法，该方法使用线性近似结构来估算水的未来价值。当价格和流入的时间序列遵循自回归过程时，我们的近似值为未来价值函数提供了上限。我们根据价格和流入的历史数据使用离线培训算法，并在样本外和样本外模拟中运行。级联和网络连接的储层的两个现实测试系统可证明我们方法的计算障碍性。特别是，我们提供了解决方案收敛和质量的数值证据。对于我们的测试系统，我们的结果表明，在线性近似中包括流入时，利润估计提高了20％。
In this paper, we study the operational problem of connected hydro power reservoirs which involves sequential decision-making in an uncertain and dynamic environment. The problem is traditionally formulated as a stochastic dynamic program accounting for the uncertainty of electricity prices and reservoir inflows. This formulation suffers from the curse of dimensionality, as the state space explodes with the number of reservoirs and the history of prices and inflows. To avoid computing the expectation of future value functions, the proposed model takes advantage of the so-called post-decision state. To further tackle the dimensionality issue, we propose an approximate dynamic programming approach that estimates the future value of water using a linear approximation architecture. When the time series of prices and inflows follow autoregressive processes, our approximation provides an upper bound on the future value function. We use an offline training algorithm based on the historical data of prices and inflows and run both in-sample and out-of-sample simulations. Two realistic test systems of cascade and network connected reservoirs serve to demonstrate the computational tractability of our approach. In particular, we provide numerical evidence of convergence and quality of solutions. For our test systems, our results show that profit estimation is improved by 20% when including inflows in the linear approximation.
