Abstract
Because of the high penetration of renewable energies and the installation of new control devices, modern distribution networks are faced with voltage regulation challenges. Recently, the rapid development of artificial intelligence technology has introduced new solutions for optimal control problems with high dimensions and dynamics. In this paper, a deep reinforcement learning method is proposed to solve the two-timescale optimal voltage control problem. All control variables are assigned to different agents, and discrete variables are solved by a deep Q network (DQN) agent while the continuous variables are solved by a deep deterministic policy gradient (DDPG) agent. All agents are trained simultaneously with specially designed reward aiming at minimizing long-term average voltage deviation. Case study is executed on a modified IEEE-123 bus system, and the results demonstrate that the proposed algorithm has similar or even better performance than the model-based optimal control scheme and has high computational efficiency and competitive potential for online application.
Highlights
The high penetration of distributed generation (DG) energy sources, such as photovoltaic (PV), has made distribution networks faced with the problem of voltage regulation
While these regulators are all applied to adjust the distribution of reactive power in the grid, the real power flow can impact the nodal voltages in distribution networks [1,2]
The voltage control problem is first formulated as an Markov decision process (MDP), and a model-free solution based on deep reinforcement learning is put forward, in which the control variables of different controllers are assigned to different agents
Summary
The high penetration of distributed generation (DG) energy sources, such as photovoltaic (PV), has made distribution networks faced with the problem of voltage regulation. The voltage profiles in distribution networks are regulated by the control of slow regulation devices (e.g., on-load tap changers (OLTCs) and shunt capacitors) and fast regulation devices (e.g., PV inverters and static var compensators (SVCs)) While these regulators are all applied to adjust the distribution of reactive power in the grid, the real power flow can impact the nodal voltages in distribution networks [1,2]. Heuristic algorithms which are less dependent on the model are applied to solve these problems (e.g., particle swarm optimization (PSO) [5] and genetic algorithm (GA) [6]) These algorithms have the shortcomings of high randomness and long search time, and fall into local optimal solutions, and as a result cannot meet the requirement of real-time voltage control in a fast time scale. The existing voltage control methods using DRL only focus on the reactive power control, and cannot deal with the discrete and continuous control variables simultaneously
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