A survey of distributed optimization and control algorithms for electric power systems

DK Molzahn, F Dörfler, H Sandberg… - … on Smart Grid, 2017 - ieeexplore.ieee.org
Historically, centrally computed algorithms have been the primary means of power system
optimization and control. With increasing penetrations of distributed energy resources …

[HTML][HTML] Optimization algorithms as robust feedback controllers

A Hauswirth, Z He, S Bolognani, G Hug… - Annual Reviews in Control, 2024 - Elsevier
Mathematical optimization is one of the cornerstones of modern engineering research and
practice. Yet, throughout all application domains, mathematical optimization is, for the most …

Precision micro-synchrophasors for distribution systems: A summary of applications

A Von Meier, E Stewart, A McEachern… - … on Smart Grid, 2017 - ieeexplore.ieee.org
This paper describes high-level findings from an innovative network of high-precision
phasor measurement units (PMUs), or micro-PMUs (μPMUs), designed to provide an …

Optimal power flow pursuit

E Dall'Anese, A Simonetto - IEEE Transactions on Smart Grid, 2016 - ieeexplore.ieee.org
This paper considers distribution networks featuring inverter-interfaced distributed energy
resources, and develops distributed feedback controllers that continuously drive the inverter …

Hierarchically-coordinated voltage/VAR control of distribution networks using PV inverters

C Zhang, Y Xu - IEEE Transactions on Smart Grid, 2020 - ieeexplore.ieee.org
Photovoltaic (PV) inverters can provide fast and flexible reactive power support for voltage
regulation and power loss reduction in distribution networks. Conventionally, central and …

Real-time optimal power flow

Y Tang, K Dvijotham, S Low - IEEE Transactions on Smart Grid, 2017 - ieeexplore.ieee.org
Future power networks are expected to incorporate a large number of distributed energy
resources, which introduce randomness and fluctuations as well as fast control capabilities …

Model-free voltage control of active distribution system with PVs using surrogate model-based deep reinforcement learning

D Cao, J Zhao, W Hu, F Ding, N Yu, Q Huang, Z Chen - Applied Energy, 2022 - Elsevier
Accurate knowledge of the distribution system topology and parameters is required to
achieve good voltage control performance, but this is difficult to obtain in practice. This paper …

Two-stage deep reinforcement learning for inverter-based volt-var control in active distribution networks

H Liu, W Wu - IEEE Transactions on Smart Grid, 2020 - ieeexplore.ieee.org
Model-based Vol/VAR optimization method is widely used to eliminate voltage violations
and reduce network losses. However, the parameters of active distribution networks (ADNs) …

Decentralized charging control of electric vehicles in residential distribution networks

M Liu, PK Phanivong, Y Shi… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
Electric vehicle (EV) charging can negatively impact electric distribution networks by
exceeding equipment thermal ratings and causing voltages to drop below standard ranges …

Toward distributed energy services: Decentralizing optimal power flow with machine learning

R Dobbe, O Sondermeijer… - … on Smart Grid, 2019 - ieeexplore.ieee.org
The implementation of optimal power flow (OPF) methods to perform voltage and power flow
regulation in electric networks is generally believed to require extensive communication. We …