Pith. sign in

REVIEW 1 cited by

Bi-level Volt/VAR Optimization in Distribution Networks with Smart PV Inverters

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2201.05267 v1 pith:4MC3QG62 submitted 2022-01-14 eess.SY cs.SY

Bi-level Volt/VAR Optimization in Distribution Networks with Smart PV Inverters

classification eess.SY cs.SY
keywords invertersoptimizationbi-leveldevicesdistributionnetworkssetpointssmart
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

Optimal Volt/VAR control (VVC) in distribution networks relies on an effective coordination between the conventional utility-owned mechanical devices and the smart residential photovoltaic (PV) inverters. Typically, a central controller carries out a periodic optimization and sends setpoints to the local controller of each device. However, instead of tracking centrally dispatched setpoints, smart PV inverters can cooperate on a much faster timescale to reach optimality within a PV inverter group. To accommodate such PV inverter groups in the VVC architecture, this paper proposes a bi-level optimization framework. The upper-level determines the setpoints of the mechanical devices to minimize the network active power losses, while the lower-level represents the coordinated actions that the inverters take for their own objectives. The interactions between these two levels are captured in the bi-level optimization, which is solved using the Karush-Kuhn-Tucker (KKT) conditions. This framework fully exploits the capabilities of the different types of voltage regulation devices and enables them to cooperatively optimize their goals. Case studies on typical distribution networks with field-recorded data demonstrate the effectiveness and advantages of the proposed approach.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. NEO-Grid: A Neural Approximation Framework for Optimization and Control in Distribution Grids

    eess.SY 2025-09 unverdicted novelty 6.0

    NEO-Grid trains ReLU networks as power-flow surrogates and applies deep equilibrium models for closed-loop volt-var optimization and control, reporting better voltage regulation than linear and heuristic baselines on ...