AC power

Electrical engineering

Physics

Control engineering

Mathematics

Capacitor

Reinforcement learning

Voltage regulator

Mathematical optimization

Reduction (mathematics)

Engineering

Voltage

Artificial intelligence

Power (physics)

Control (management)

Geometry

Voltage reduction

Computer science

Quantum mechanics

Electric power system

Control theory (sociology)

IEEE Transactions on Smart Grid

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.

Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systems