Reinforcement learning and its applications in modern power and energy systems: A review
With the growing integration of distributed energy resources (DERs), flexible loads, and
other emerging technologies, there are increasing complexities and uncertainties for …
other emerging technologies, there are increasing complexities and uncertainties for …
Operational planning steps in smart electric power delivery system
This paper presents a comprehensive review of advanced technologies with various control
approaches in terms of their respective merits and outcomes for power grids. Distributed …
approaches in terms of their respective merits and outcomes for power grids. Distributed …
Two-stage volt/var control in active distribution networks with multi-agent deep reinforcement learning method
The high penetration of intermittent renewable energy resources in active distribution
networks (ADN) results in a great challenge for the conventional Volt-Var control (VVC). This …
networks (ADN) results in a great challenge for the conventional Volt-Var control (VVC). This …
Data-driven multi-agent deep reinforcement learning for distribution system decentralized voltage control with high penetration of PVs
This paper proposes a novel model-free/data-driven centralized training and decentralized
execution multi-agent deep reinforcement learning (MADRL) framework for distribution …
execution multi-agent deep reinforcement learning (MADRL) framework for distribution …
Stochastic-weighted robust optimization based bilayer operation of a multi-energy building microgrid considering practical thermal loads and battery degradation
This paper discusses a bilayer coordinated operation scheme for the multi-energy building
microgrid (MEBM) with comprehensive uncertainty sources. First, a building model …
microgrid (MEBM) with comprehensive uncertainty sources. First, a building model …
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 …
Attention enabled multi-agent DRL for decentralized volt-VAR control of active distribution system using PV inverters and SVCs
This paper proposes attention enabled multi-agent deep reinforcement learning (MADRL)
framework for active distribution network decentralized Volt-VAR control. Using the …
framework for active distribution network decentralized Volt-VAR control. Using the …
Physics-informed graphical representation-enabled deep reinforcement learning for robust distribution system voltage control
The anomalous measurements and inaccurate distribution system physical models cause
huge challenges for distribution system optimization. This paper proposes a robust voltage …
huge challenges for distribution system optimization. This paper proposes a robust voltage …
Interval optimization based coordination of demand response and battery energy storage system considering SOC management in a microgrid
Microgrids can effectively integrate distributed generation (DG) to supply power to local
loads. However, uncertainties from renewable DG and loads may lead to increased …
loads. However, uncertainties from renewable DG and loads may lead to increased …
Model-free voltage control of active distribution system with PVs using surrogate model-based deep reinforcement learning
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 …
achieve good voltage control performance, but this is difficult to obtain in practice. This paper …