Name
Abstract | ||
|---|---|---|
Coordinating inverters at scale under uncertainty is the desideratum for integrating renewables in distribution grids. Unless load demands and solar generation are telemetered frequently, controlling inverters given approximate grid conditions or proxies thereof becomes a key specification. Although deep neural networks (DNNs) can learn optimal inverter schedules, guaranteeing feasibility is largely elusive. Rather than training DNNs to imitate already computed optimal power flow (OPF) solutions, this work integrates DNN-based inverter policies into the OPF. The proposed DNNs are trained through two OPF alternatives that confine voltage deviations on the average and as a convex restriction of chance constraints. The trained DNNs can be driven by partial, noisy, or proxy descriptors of the current grid conditions. This is important when OPF has to be solved for an unobservable feeder. DNN weights are trained via back-propagation and upon differentiating the AC power flow equations. An alternative gradient-free variant is also put forth, which requires only a power flow solver and avoids computing gradients. Such variant is practically relevant when calculating gradients becomes cumbersome or prone to errors. Numerical tests compare the DNN-based inverter control schemes with the optimal inverter setpoints in terms of optimality and feasibility. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TSG.2021.3132029 | IEEE Transactions on Smart Grid |
Keywords | DocType | Volume |
Stochastic gradient descent,deep neural networks,primal-dual updates,inverter control,inverse function theorem,reactive power compensation,stochastic optimization,chance constraints | Journal | 13 |
Issue | ISSN | Citations |
2 | 1949-3053 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sarthak Gupta | 1 | 1 | 3.41 |
| Vassilis Kekatos | 2 | 249 | 27.11 |
| Ming Jin | 3 | 0 | 1.69 |