[HTML][HTML] Profit maximization for large-scale energy storage systems to enable fast EV charging infrastructure in distribution networks
Large-scale integration of battery energy storage systems (BESS) in distribution networks
has the potential to enhance the utilization of photovoltaic (PV) power generation and …
has the potential to enhance the utilization of photovoltaic (PV) power generation and …
Data-driven energy storage scheduling to minimise peak demand on distribution systems with pv generation
The growing adoption of decentralised renewable energy generation (such as solar
photovoltaic panels and wind turbines) and low-carbon technologies will increase the strain …
photovoltaic panels and wind turbines) and low-carbon technologies will increase the strain …
Deep-reinforcement-learning-based capacity scheduling for PV-battery storage system
Investor-owned photovoltaic-battery storage systems (PV-BSS) can gain revenue by
providing stacked services, including PV charging and frequency regulation, and by …
providing stacked services, including PV charging and frequency regulation, and by …
Deep reinforcement learning assisted co-optimization of Volt-VAR grid service in distribution networks
With the increasing penetration of distributed energy resources in distribution networks, Volt-
VAR control and optimization (VVC/VVO) have become very important to ensure an …
VAR control and optimization (VVC/VVO) have become very important to ensure an …
Reinforcement learning-based distributed BESS management for mitigating overvoltage issues in systems with high PV penetration
M Al-Saffar, P Musilek - IEEE Transactions on Smart Grid, 2020 - ieeexplore.ieee.org
High levels of penetration of distributed photovoltaic generators can cause serious
overvoltage issues, especially during periods of high power generation and light loads …
overvoltage issues, especially during periods of high power generation and light loads …
Volt-var optimization in distribution networks using twin delayed deep reinforcement learning
Modern distribution grids are undergoing new challenges due to the stochastic nature of
distributed energy resources (DERs). High penetration of DERs has a significant impact on …
distributed energy resources (DERs). High penetration of DERs has a significant impact on …
[HTML][HTML] Optimization of a photovoltaic-battery system using deep reinforcement learning and load forecasting
Abstract Home Energy Management Systems (HEMS) are increasingly relevant for demand-
side management at the residential level by collecting data (energy, weather, electricity …
side management at the residential level by collecting data (energy, weather, electricity …
[HTML][HTML] Constrained large-scale real-time EV scheduling based on recurrent deep reinforcement learning
The rapid growth of electric vehicles (EVs) is an unstoppable worldwide development trend.
An optimal charging strategy for large-scale EVs is able to deal with the randomness of EVs …
An optimal charging strategy for large-scale EVs is able to deal with the randomness of EVs …
Multi-agent DRL-based data-driven approach for PEVs charging/discharging scheduling in smart grid
This paper studies the charging/discharging scheduling problem of plug-in electric vehicles
(PEVs) in smart grid, considering the users' satisfaction with state of charge (SoC) and the …
(PEVs) in smart grid, considering the users' satisfaction with state of charge (SoC) and the …
Optimal dynamic power allocation for electric vehicles in an extreme fast charging station
With the ever-increasing penetration of electric vehicles (EVs), extreme fast charging
stations (XFCSs) are being widely deployed, wherein battery energy storages (BESs) are …
stations (XFCSs) are being widely deployed, wherein battery energy storages (BESs) are …