Multi-agent deep reinforcement learning for resilience-driven routing and scheduling of mobile energy storage systems
Extreme events are featured by high impact and low probability, which can cause severe
damage to power systems. There has been much research focused on resilience-driven …
damage to power systems. There has been much research focused on resilience-driven …
Uncertainty-aware deployment of mobile energy storage systems for distribution grid resilience
With the spatial flexibility exchange across the network, mobile energy storage systems
(MESSs) offer promising opportunities to elevate power distribution system resilience …
(MESSs) offer promising opportunities to elevate power distribution system resilience …
[HTML][HTML] Hierarchical multi-agent reinforcement learning for repair crews dispatch control towards multi-energy microgrid resilience
Extreme events are greatly impacting the normal operations of microgrids, which can lead to
severe outages and affect the continuous supply of energy to customers, incurring …
severe outages and affect the continuous supply of energy to customers, incurring …
Resilient load restoration in microgrids considering mobile energy storage fleets: A deep reinforcement learning approach
Mobile energy storage systems (MESSs) provide mobility and flexibility to enhance
distribution system resilience. The paper proposes a Markov decision process (MDP) …
distribution system resilience. The paper proposes a Markov decision process (MDP) …
Multi-service provision for electric vehicles in power-transportation networks towards a low-carbon transition: A hierarchical and hybrid multi-agent reinforcement …
In order to achieve the target of carbon peak and carbon neutrality, electric vehicles (EVs)
have increasingly received a prominent interest to electrify the transportation sector due to …
have increasingly received a prominent interest to electrify the transportation sector due to …
Data-driven stochastic energy management of multi energy system using deep reinforcement learning
Y Zhou, Z Ma, J Zhang, S Zou - Energy, 2022 - Elsevier
The multi energy system (MES) is promising in the process of carbon neutrality, such that
multi energy resources are utilized comprehensively to reduce the operation cost. Another …
multi energy resources are utilized comprehensively to reduce the operation cost. Another …
A novel model-free deep reinforcement learning framework for energy management of a PV integrated energy hub
A Dolatabadi, H Abdeltawab… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
This paper utilizes a fully model-free and data-driven deep reinforcement learning (DRL)
framework to develop an intelligent controller that can exploit information to optimally …
framework to develop an intelligent controller that can exploit information to optimally …
Hybrid multiagent reinforcement learning for electric vehicle resilience control towards a low-carbon transition
In responseto low-carbon requirements, a large amount of renewable energy resources
(RESs) have been deployed in power systems; nevertheless, the intermittency of RESs …
(RESs) have been deployed in power systems; nevertheless, the intermittency of RESs …
A multiagent deep reinforcement learning based approach for the optimization of transformer life using coordinated electric vehicles
The uncertainties of charging behavior of electric vehicle (EV) owners have a negative
impact on the loss of life (LOL) of distribution transformer. This article proposes a …
impact on the loss of life (LOL) of distribution transformer. This article proposes a …
Routing and scheduling of mobile power sources for distribution system resilience enhancement
Mobile power sources (MPSs), including electric vehicle fleets, truck-mounted mobile energy
storage systems, and mobile emergency generators, have great potential to enhance …
storage systems, and mobile emergency generators, have great potential to enhance …