Abstract
The growing integration of distributed solar photovoltaic (PV) in distribution systems could result in adverse effects during grid operation. This paper develops a two-agent soft actor critic-based deep reinforcement learning (SAC-DRL) solution to simultaneously control PV inverters and battery energy storage systems for voltage regulation and peak demand reduction. The novel two-stage framework, featured with two different control agents, is applied for daytime and nighttime operations to enhance control performance. Comparison results with other control methods on a real feeder in Western Colorado demonstrate that the proposed method can provide advanced voltage regulation with modest active power curtailment and reduce peak load demand from feeder's head.
Original language | American English |
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Number of pages | 5 |
DOIs | |
State | Published - 2023 |
Bibliographical note
See NREL/CP-5D00-84637 for preprintNREL Publication Number
- NREL/CP-5D00-88236
Keywords
- deep reinforcement learning
- distribution system
- peak load management
- voltage regulation