Renewable energy resources (RERs) have been increasingly integrated into large-scale distributed power systems. Considering uncertainties and voltage fluctuation issues introduced by RERs, in this paper, we propose a deep reinforcement learning (DRL)-based strategy leveraging spatial-temporal (ST) graphical information of power systems, to dynamically search for the optimal operation, i.e., optimal power flow (OPF), of power systems with a high uptake of RERs. Specifically, we formulate the OPF problem as a multi-objective optimization problem considering generation cost, voltage fluctuation, and transmission loss, and employ deep deterministic policy gradient (DDPG) to learn an optimal allocation strategy for OPF. Moreover, given that the nodes in power systems are self-correlated and interrelated in temporal and spatial views, we develop a multi-grained attention-based spatial-temporal graph convolution network (MG-ASTGCN) for extracting ST graphical correlations and features, aiming to provide prior knowledge of power systems for its sequential DDPG algorithm to more effectively solve OPF. We validate our algorithm on modified IEEE 33, 69, and 118-bus radial distribution systems and demonstrate that our algorithm outperforms other benchmark algorithms. Our experimental results also reveal that our MG-ASTGCN can significantly accelerate DDPG's training process and performance in solving OPF.
翻译:可再生能源已日益被纳入大规模分布式电力系统;考虑到可再生能源带来的不确定性和电压波动问题,我们在本文件中提议采用基于深度强化学习(DRL)战略,利用电力系统的空间时空图形信息,动态地寻找最佳操作,即最佳电流(OPF),利用高度吸收RER的动力系统;具体地说,我们将OPF问题作为一个多目标优化问题,考虑到发电成本、电压波动和传输损失,并采用深度确定性政策梯度(DDPG)来学习OPF的最佳分配战略;此外,鉴于电力系统中的节点与时间和空间观点自相联且相互关联,我们开发了一个多加关注的基于空间时空图动态网络(MG-ASTGCN),以提取ST图形的关联性和特征,目的是为电力系统提供先前的知识,以便按顺序对DDPG进行算法,从而更有效地解决OPFF。 我们验证了我们在经修订的 IEEE-ST A 69 和118 GLA 实验性算法中的数据分析方法,也显著地展示了我们的IEE-G ASVA-RA-Risalmagal Exgalmagal sal sal sal sal sal sal resulationsmal sal sal salpal sal sal sal salsmalpal sal sal sal salsmalpalpalpalpalpalpals sals sal salsmals sals salsmalsmalsmalsmalsmalsmalsmalsmalsmalsmalsmals.