Approximate dynamic programming with policy-based exploration for microgrid dispatch under uncertainties
Approximate dynamic programming with policy-based exploration for microgrid dispatch under uncertainties
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DOI：
10.1016/j.ijepes.2022.108359
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发表时间：
2022-05-31
影响因子：
5.2
通讯作者：
Ni, Zhen
中科院分区：
文献类型：
--
作者：
Das, Avijit;Wu, Di;Ni, Zhen
近似动态规划（ADP）是不确定性下电力系统调度和调度的一种有前景的方法。本文提出了一种基于 ADP 的创新调度方法，适用于具有间歇性可再生能源发电、电池储能系统和可控分布式发电机的微电网。所提出的 ADP 算法基于双遍值迭代方法，并利用了微电网调度问题的基本特性。在前向传递中，决策变量使用������贪婪策略及时更新，以平衡利用和探索。特别是，提出了一种近似优化方法来加速开发。除了随机探索之外，还设计了策略来引导算法以概率方式探索一些有希望的解决方案空间。在后向传递中，价值函数使用前向传递中样本路径的状态、决策和结果的轨迹及时向后更新。通过在确定性和随机环境中进行数值实验来评估所提出的方法。案例研究结果表明，与传统方法相比，所提出的方法在优化间隙和计算时间方面表现出改进的性能。
Approximate dynamic programming (ADP) is a promising approach for power system scheduling and dispatch under uncertainties. This paper presents an innovative ADP-based dispatch method for a microgrid with intermittent renewable generation, battery energy storage systems, and controllable distributed generators. The proposed ADP algorithm is based on a double-pass value iteration approach and takes advantage of the underlying properties of the microgrid dispatch problem. In the forward pass, decision variables are updated moving forward in time using an ������-greedy strategy to balance exploitation and exploration. In particular, an approximate optimization method is proposed to speed up exploitation. In addition to random exploration, a policy is designed to guide the algorithm to explore some promising solution space in a probabilistic manner. In the backward pass, the value function is updated moving backward in time using the trajectory of states, decisions, and outcomes of the sample path in the forward pass. The proposed method is evaluated through numerical experiments in both deterministic and stochastic environments. Case study results show that the proposed method demonstrates improved performance in both optimization gap and computation time in comparison to conventional methods.