Two-Stage Deep Reinforcement Learning for Distribution System Voltage Regulation and Peak Load Management: Preprint
Conference
·
OSTI ID:1992819
The growing integration of distributed solar photovoltaic (PV) in distribution systems could result in adverse effects during grid operation. This paper develops a 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 load demand shaving. The novel two-stage framework, featured with two different control agents, is applied for daytime and nighttime operation to enhance the 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 for peak demand reduction.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1992819
- Report Number(s):
- NREL/CP-5D00-84637; MainId:85410; UUID:7622bc0b-d2b9-433e-92c6-25839feea4b1; MainAdminID:69038
- Country of Publication:
- United States
- Language:
- English
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