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Found 9 papers, 0 papers with code
Multi-Feeder Restoration using Multi-Microgrid Formation and Management
This papers highlights the benefit of coordinating resources on mulitple active distribution feeders during severe long duration outages through multi-microgrid formation.
A Novel Feeder-level Microgrid Unit Commitment Algorithm Considering Cold-load Pickup, Phase Balancing, and Reconfiguration
This paper presents a novel 2-stage microgrid unit commitment (Microgrid-UC) algorithm considering cold-load pickup (CLPU) effects, three-phase load balancing requirements, and feasible reconfiguration options.
Design Considerations of a Coordinative Demand Charge Mitigation Strategy
This paper presents a coordinative demand charge mitigation (DCM) strategy for reducing electricity consumption during system peak periods.
An ICA-Based HVAC Load Disaggregation Method Using Smart Meter Data
The residual load profiles are processed using ICA for HVAC load extraction.
Feeder Microgrid Management on an Active Distribution System during a Severe Outage
The management of such feeder-level microgrid has however many challenges, such as limited resources that can be deployed on the feeder quickly, and the limited real-time monitoring and control on the distribution system.
SA-HMTS: A Secure and Adaptive Hierarchical Multi-timescale Framework for Resilient Load Restoration Using A Community Microgrid
Distribution system integrated community microgrids (CMGs) can partake in restoring loads during extended duration outages.
Reinforcement Learning for Volt-Var Control: A Novel Two-stage Progressive Training Strategy
In Stage 1(individual training), while holding all the other agents inactive, we separately train each agent to obtain its own optimal VVC actions in the action space: {consume, generate, do-nothing}.
Hierarchical Multi-timescale Framework For Operation of Dynamic Community Microgrid
The intermediate near real-time scheduling stage updates the DA schedule closer to the dispatch time, followed by the RT dispatch stage.
A Meta-learning based Distribution System Load Forecasting Model Selection Framework
This paper presents a meta-learning based, automatic distribution system load forecasting model selection framework.
