基于信息松弛的多态能源协调控制方法研究

“双碳”目标驱动以新能源为主体的新型电力系统快速发展，大规模新能源接入给电力系统带来强随机扰动，传统控制方法无法有效解决强随机扰动下电力系统稳定性变差的问题。从自动发电控制(automatic generation control, AGC)角度，提出了一种具有信息松弛的多态能源协调控制策略，以获取多态能源系统的最优协调控制。所提策略在“控制”部分采用具有完全信息松弛特性的前瞻有界Q学习(lookahead-bounded Q-learning, LQ)来预测未来Q值的上下界，以提高强随机环境下Q学习的快速收敛能力及控制性能；在“分配”部分利用新颖的分层双Q学习强一致性(hierarchical double Q-learning based multi paxos, HDQMP)策略来解决机组激增而产生的“维度灾难”问题。通过对改进的IEEE标准两区域负荷频率控制模型和大规模新能源接入的多态能源系统模型仿真，验证了所提方法的有效性。且与其他方法相比，所提方法具有更优的控制性能和更快的收敛速度。

A polymorphic energy-coordinated control strategy based on information relaxation

The "dual carbon" goal drives the rapid development of new power systems with new energy. Large-scale new energy access brings strongly random disturbance to the power system. Traditional control methods cannot cope with the issue the power system becoming unstable because of the strongly random disturbance. In this paper, from the perspective of automatic generation control (AGC), a polymorphic energy-coordinated control strategy with information relaxation is proposed to obtain the optimal cooperative control of the polymorphic energy system. In the "control" part, the proposed strategy uses lookahead-bounded Q-learning (LQ) with complete information relaxation characteristics to predict the upper and lower bounds of future Q values, so as to improve the fast convergence ability and control performance of Q learning in a strong random environment. In the "allocation" part, the proposed strategy uses a novel hierarchical double Q-learning based multi paxos (HDQMP) strategy to solve the "dimensional disaster" problem caused by the surge of units. Through the simulation of the improved IEEE standard two-area load frequency control model and the multi-state energy system model of large-scale new energy access, the effectiveness of the proposed method is verified. Compared with other methods, it has better control performance and faster convergence speed.