融合注意力机制与SAC算法的虚拟电厂多能流低碳调度

Optimizing multi-energy fow scheduling of hydrogen-inclusive virtual powerplants based on deep reinforcement learning under dual-carbon targets

虚拟电厂(virtual power plant,VPP)作为多能流互联的综合能源网络,已成为中国加速实现双碳目标的重要角色。但VPP内部资源协同低碳调度面临多能流的耦合程度紧密、传统碳交易模型参数主观性强、含高维动态参数的优化目标在线求解困难等问题。针对这些问题,文中提出一种融合注意力机制(attention mechanism,AM)与柔性动作评价(soft actor-critic,SAC)算法的VPP多能流低碳调度方法。首先,根据VPP的随机碳流特性,面向动态参数建立基于贝叶斯优化的改进阶梯型碳交易机制。接着,以经济效益和碳排放量为目标函数构建含氢VPP多能流解耦模型。然后,考虑到该模型具有高维非线性与权重参数实时更新的特征,利用融合AM的改进SAC深度强化学习算法在连续动作空间对模型进行求解。最后,对多能流调度结果进行仿真分析和对比实验,验证了文中方法的可行性及其相较于原SAC算法较高的决策准确性。

Virtual power plants,as a comprehensive energy network with multi-energy flow interconnection,have become an important player in China′s accelerated pursuit of its dual carbon goals.However,it is difficult to coordinate internal resources with low-carbon emission when facing challenges such as tight coupling of multi-energy flows,subjectivity of traditional carbon trading model parameters and difficulty of online optimization with high-dimensional dynamic parameters.To address these issues,this paper proposes a virtual power plant multi-energy low-carbon dispatching method that integrates the attention mechanism(AM)and soft actor-critic(SAC)algorithm.Firstly,based on the random carbon flow characteristics of virtual power plants,an improved stepped carbon trading mechanism based on Bayesian optimization is established for dynamic parameters.Next,an economic benefit and carbon emission-based objective function is constructed for the decoupling model of multi-energy flows in virtual power plants.Considering the high-dimensional nonlinearity and real-time updating of weight parameters in this model,the improved SAC deep reinforcement learning algorithm with integrated attention mechanism is used to solve it in a continuous action space.Finally,simulation analysis and comparative experiments are conducted to verify the feasibility of the proposed method and its efficiency compared with SAC algorithm.