Unsupervised Learning for Equitable DER Control
arXiv:2403.11068 · doi:10.1016/j.epsr.2024.110634
Abstract
In the context of managing distributed energy resources (DERs) within distribution networks (DNs), this work focuses on the task of developing local controllers. We propose an unsupervised learning framework to train functions that can closely approximate optimal power flow (OPF) solutions. The primary aim is to establish specific conditions under which these learned functions can collectively guide the network towards desired configurations asymptotically, leveraging an incremental control approach. The flexibility of the proposed methodology allows to integrate fairness-driven components into the cost function associated with the OPF problem. This addition seeks to mitigate power curtailment disparities among DERs, thereby promoting equitable power injections across the network. To demonstrate the effectiveness of the proposed approach, power flow simulations are conducted using the IEEE 37-bus feeder. The findings not only showcase the guaranteed system stability but also underscore its improved overall performance.
To appear at 23rd Power Systems Computation Conference
References in corpus (7)
- Adam: A Method for Stochastic Optimization
- Options for Control of Reactive Power by Distributed Photovoltaic Generators
- On the Properties of the Power Systems Nodal Admittance Matrix
- Learning Provably Stable Local Volt/Var Controllers for Efficient Network Operation
- Reducing the Unfairness of Coordinated Inverter Dispatch in PV-Rich Distribution Networks
- Bridging Transient and Steady-State Performance in Voltage Control: A Reinforcement Learning Approach with Safe Gradient Flow
- Constraints on OPF Surrogates for Learning Stable Local Volt/Var Controllers