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Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systems
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DOI：10.1109/TSG.2020.3010130.png)
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Abstract
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This paper develops a model-free volt-VAR optimization (VVO) algorithm via multi-agent deep reinforcement learning (DRL) in unbalanced distribution systems. This method is novel since we cast the VVO problem in distribution networks to an intelligent deep Q-network (DQN) framework, which avoids solving a specific optimization model directly when facing time-varying operating conditions in the systems. We consider statuses/ratios of switchable capacitors, voltage regulators, and smart inverters installed at distributed generators as the action variables of the agents. A delicately designed reward function guides these agents to interact with the distribution system, in the direction of reinforcing voltage regulation and power loss reduction simultaneously. The forward-backward sweep method for radial three-phase distribution systems provides accurate power flow results within a few iterations to the DRL environment. The proposed method realizes the dual goals for VVO. We test this algorithm on the unbalanced IEEE 13-bus and 123-bus systems. Numerical simulations validate the excellent performance of this method in voltage regulation and power loss reduction.
Keywords:
Voltage control
Inverters
Load modeling
Computational modeling
Optimization
Machine learning
Adaptation models
Volt-VAR optimization
deep reinforcement learning
artificial intelligence
voltage regulation
unbalanced distribution systems
smart inverter
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