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Voltage regulation in distribution grids: A survey
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 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
Electric distribution grid operations typically rely on both centralized optimization and local
non-optimal control techniques. As an alternative, distribution system operational practices …
non-optimal control techniques. As an alternative, distribution system operational practices …
A data-driven multi-agent autonomous voltage control framework using deep reinforcement learning
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 …
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
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 …
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
This paper proposes a multi-agent deep reinforcement learning-based approach for
distribution system voltage regulation with high penetration of photovoltaics (PVs). The …
distribution system voltage regulation with high penetration of photovoltaics (PVs). The …
Distributed transactive energy trading framework in distribution networks
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 …
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
Active distribution networks are being challenged by frequent and rapid voltage violations
due to renewable energy integration. Conventional model-based voltage control methods …
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
Existing power system resilience enhancement methods, such as proactive generation
rescheduling, movable sources dispatch, and network topology reconfiguration, do not …
rescheduling, movable sources dispatch, and network topology reconfiguration, do not …
MPC-based decentralized voltage control in power distribution systems with EV and PV coordination
Distribution systems are growing rapidly in size and complexity with increased penetrations
of distributed energy resources (DER) and electric vehicles (EVs), leading to operational …
of distributed energy resources (DER) and electric vehicles (EVs), leading to operational …
Online multi-agent reinforcement learning for decentralized inverter-based volt-var control
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 …
distribution networks (ADNs), which is based on perfect model and real-time P2P …