LLM-Based Adaptive Distribution Voltage Regulation Under Frequent Topology Changes: An In-Context MPC Framework
Journal Article
·
· IEEE Transactions on Smart Grid
- Texas A&M University
- National Renewable Energy Lab., Golden, CO (United States)
- Harvard University
This paper proposes a large language model (LLM) based adaptive inverter control for distribution voltage regulation under frequent topology changes. We leverage the ability of the LLM to perform in-context learning and create a topology-adaptive surrogate model for power flow calculation. The surrogate model is then integrated with a long short-term memory-based load forecaster and a model predictive control (MPC) scheme to achieve the optimal inverter control that adapts to frequent topology changes. Unlike many existing works that assume fixed-topology grids or require the knowledge of all possible topologies when training a model, the proposed in-context MPC method tackles the distribution voltage control problem under various topologies and adapts to unknown topologies with limited data requirement for fine-tuning. The effectiveness of our method is demonstrated on a modified IEEE 123-bus test system.
- 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:
- 2574748
- Report Number(s):
- NREL/JA-5D00-93237
- Journal Information:
- IEEE Transactions on Smart Grid, Journal Name: IEEE Transactions on Smart Grid Journal Issue: 5 Vol. 16
- Country of Publication:
- United States
- Language:
- English
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