基于贝叶斯深度强化学习的主动配电网电压控制

Voltage Control for Active Distribution Network Based on Bayesian Deep Reinforcement Learning

多分布式源荷接入导致配电网电压波动性增强。同时,上级主网电压的不确定性波动也会对配电网电压特性造成影响。为有效应对主配电网电压波动,文中提出一种基于数据驱动与模型求解结合的主动配电网多时间尺度电压控制框架。在慢时间尺度下,考虑主网电压波动,构建了上级主网非无穷大系统多馈线环境,对该环境下的电压控制问题进行了对抗性马尔可夫过程建模。在训练过程中,以投影梯度下降算法使主网电压发生扰动。利用贝叶斯深度Q网络算法感知上级主网电压波动,实现有载调压变压器挡位快速控制。在快时间尺度下,基于传统二阶锥优化方法控制光伏逆变器无功输出。算例结果表明,该方法可准确感知上级主网电压波动,在极短时间实现配电网无模型电压控制,保证各节点电压维持在安全范围内。

The access of multiple distributed sources and loads leads to enhanced voltage volatility in the distribution network.Meanwhile,the uncertainty fluctuation in the voltage of the upper main grid also affect the voltage characteristics of the distribution network.In order to effectively deal with the voltage fluctuations of the main grid and the distribution network,this paper proposes a multi-timescale voltage control framework for active distribution networks based on the combination of data-driven and model solving.In the slow time scale,considering the voltage fluctuation of the main grid,a multiple-feeder environment with a noninfinity system of the upper main grid is constructed,and the voltage control problem in this environment is modeled as an adversarial Markov process.During the training process,the voltage of the main grid is perturbed with a projected gradient descent algorithm.The Bayesian deep Q network algorithm is utilized to sense the voltage fluctuation of the upper main grid and realize the fast control of taps of the on-load tap changer.In the fast time scale,the reactive power output of the photovoltaic inverter is controlled based on the traditional second-order cone optimization method.The case results show that the method can accurately sense the voltage fluctuation of the upper main grid,realize model-free voltage control of the distribution network in a very short time,and ensure that the voltage of each node is maintained within the safety range.