Smart generation control based on deep reinforcement learning with the ability of action self-optimization

Abstract

Smart generation control based on multi-agent reinforcement learning with the idea of the time tunnel SCIENTIA SINICA Technologica 48, 441 (2018); Automatic generation control of ubiquitous power Internet of Things integrated energy system based on deep reinforcement learning SCIENTIA SINICA Technologica 50, 221 (2020);RLO: a reinforcement learning-based method for join optimization SCIENTIA SINICA Informationis 50, 637 (2020); Q-learning based heterogenous network self-optimization for reconfigurable network with CPC assistance

A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems

“…Among these, the theoretical framework for a new D‐ADP algorithm proposed in Yin et al contains three networks, prediction network, evaluation network, and execution network, which are improved by adopting the DQNs. These above‐mentioned HL algorithms have been preliminarily used to good effect in the SG and EI fields …”

“…For instance, a win‐or‐learn fast policy hill‐climbing (WoLF‐PHC) algorithm was combined with the idea of the time tunnel–based RL algorithm (ie, the SARSA algorithm) to construct an integrated WoLF‐PHC(λ) algorithm in Xi et al This algorithm was used to acquire the equilibrium solution of multiregional smart generation control to solve the problem of stochastic disturbances caused by the integration of large‐scale and high‐penetration new energy resources that traditional centralized AGC methods cannot solve. Based on this, Xi et al further combined DL with RL to form a new DRL capable of action self‐optimization to solve the random disturbances caused by the large‐scale integration of new energy and distributed energy into an interconnected power grid. This approach can effectively improve the safety and economic operation of the power grid.…”

“…These above-mentioned HL algorithms have been preliminarily used to good effect in the SG and EI fields. 12,[59][60][61]65,68…”

Deep learning and reinforcement learning approach on microgrid

“…The advanced control scheme on an integrated multi-area system is proposed. 105,106 The smart generation control is proposed using the DQL algorithm under a multiagent grid system to attain robustness. 107,108 The smart generation control proposed 109 to prevent large disturbance in the power system.…”

Automatic generation control of ubiquitous power Internet of Things integrated energy system based on deep reinforcement learning

“…然而, 随着泛在电力物联网的提出, 万物互联, 且 分布式能源及负荷将继续超大规模的增加, 如风 电 [16] 、光伏发电 [17] 、生物质发电 [18] 以及冷热电联产 系统 [19] 等不断地大规模接入电网中, 基于泛在电力物 联网的综合能源系统也将逐渐完善. 此模式下, 分布 式能源及负荷的多元化与间歇性, 将给电网带来强随 机扰动问题, 导致电网的频率控制难度急剧变大 [20] . (1) 若CPS1≥200%, 且CPS2为任意值, CPS指标 合格;…”