Voltage regulation in distribution grids: A survey

P Srivastava, R Haider, VJ Nair… - Annual Reviews in …, 2023 - Elsevier
Environmental and sustainability concerns have caused a recent surge in the penetration of
distributed energy resources into the power grid. This may lead to voltage violations in the …

Distributed optimization in distribution systems: Use cases, limitations, and research needs

N Patari, V Venkataramanan… - … on Power Systems, 2021 - ieeexplore.ieee.org
Electric distribution grid operations typically rely on both centralized optimization and local
non-optimal control techniques. As an alternative, distribution system operational practices …

A data-driven multi-agent autonomous voltage control framework using deep reinforcement learning

S Wang, J Duan, D Shi, C Xu, H Li… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
The complexity of modern power grids keeps increasing due to the expansion of renewable
energy resources and the requirement of fast demand responses, which results in a great …

Safe off-policy deep reinforcement learning algorithm for volt-var control in power distribution systems

W Wang, N Yu, Y Gao, J Shi - IEEE Transactions on Smart Grid, 2019 - ieeexplore.ieee.org
Volt-VAR control is critical to kee** distribution network voltages within allowable range,
minimizing losses, and reducing wear and tear of voltage regulating devices. To deal with …

A multi-agent deep reinforcement learning based voltage regulation using coordinated PV inverters

D Cao, W Hu, J Zhao, Q Huang, Z Chen… - … on Power Systems, 2020 - ieeexplore.ieee.org
This paper proposes a multi-agent deep reinforcement learning-based approach for
distribution system voltage regulation with high penetration of photovoltaics (PVs). The …

Distributed transactive energy trading framework in distribution networks

J Li, C Zhang, Z Xu, J Wang, J Zhao… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
In this paper, we propose a novel transactive energy trading (TET) framework to deal with
the economic issues in energy trading and the technical issues in distribution system …

Deep reinforcement learning enabled physical-model-free two-timescale voltage control method for active distribution systems

D Cao, J Zhao, W Hu, N Yu, F Ding… - … on Smart Grid, 2021 - ieeexplore.ieee.org
Active distribution networks are being challenged by frequent and rapid voltage violations
due to renewable energy integration. Conventional model-based voltage control methods …

A deep reinforcement learning-based multi-agent framework to enhance power system resilience using shunt resources

M Kamruzzaman, J Duan, D Shi… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Existing power system resilience enhancement methods, such as proactive generation
rescheduling, movable sources dispatch, and network topology reconfiguration, do not …

MPC-based decentralized voltage control in power distribution systems with EV and PV coordination

L Wang, A Dubey, AH Gebremedhin… - … on Smart Grid, 2022 - ieeexplore.ieee.org
Distribution systems are growing rapidly in size and complexity with increased penetrations
of distributed energy resources (DER) and electric vehicles (EVs), leading to operational …

Online multi-agent reinforcement learning for decentralized inverter-based volt-var control

H Liu, W Wu - IEEE Transactions on Smart Grid, 2021 - ieeexplore.ieee.org
The distributed Volt/Var control (VVC) methods have been widely studied for active
distribution networks (ADNs), which is based on perfect model and real-time P2P …