Dynamic Feasibility Assessment of Ship-to-Grid Interconnection by DC-Link

Badakhshan, Sobhan; Senemmar, Soroush; Zhang, Jie

doi:10.1109/tpec54980.2022.9750767

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…In [68], the authors developed an RL-based model to facilitate intentional islanding in power systems, offering real-time switching control and adaptability to changing system conditions. They framed the intentional islanding process as an MDP and utilized RL to learn the optimal transmission switching policy.…”

Section: Dynamic Responsementioning

confidence: 99%

Deep Reinforcement Learning for Resilient Power and Energy Systems: Progress, Prospects, and Future Avenues

Gautam

2023

Electricity

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In recent years, deep reinforcement learning (DRL) has garnered substantial attention in the context of enhancing resilience in power and energy systems. Resilience, characterized by the ability to withstand, absorb, and quickly recover from natural disasters and human-induced disruptions, has become paramount in ensuring the stability and dependability of critical infrastructure. This comprehensive review delves into the latest advancements and applications of DRL in enhancing the resilience of power and energy systems, highlighting significant contributions and key insights. The exploration commences with a concise elucidation of the fundamental principles of DRL, highlighting the intricate interplay among reinforcement learning (RL), deep learning, and the emergence of DRL. Furthermore, it categorizes and describes various DRL algorithms, laying a robust foundation for comprehending the applicability of DRL. The linkage between DRL and power system resilience is forged through a systematic classification of DRL applications into five pivotal dimensions: dynamic response, recovery and restoration, energy management and control, communications and cybersecurity, and resilience planning and metrics development. This structured categorization facilitates a methodical exploration of how DRL methodologies can effectively tackle critical challenges within the domain of power and energy system resilience. The review meticulously examines the inherent challenges and limitations entailed in integrating DRL into power and energy system resilience, shedding light on practical challenges and potential pitfalls. Additionally, it offers insights into promising avenues for future research, with the aim of inspiring innovative solutions and further progress in this vital domain.

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Section: Dynamic Responsementioning

confidence: 99%

Deep Reinforcement Learning for Resilient Power and Energy Systems: Progress, Prospects, and Future Avenues

Gautam

2023

Electricity

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“…T denotes the succeeding state in which the environment transitions after the agent executes a specific action, as described in [42]. The descriptors of the base station are updated to reflect the effects of the action, which could be an increase in the number of connected users and a new mean utility, as well as the impact of time, which could be a decrease in the number of connected users if a user's demand has been satisfied.…”

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confidence: 99%

DQN-GNN-Based User Association Approach for Wireless Networks

Alablani,

Alenazi

2023

Mathematics

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In the realm of advanced mobile networks, such as the fifth generation (5G) and beyond, the increasing complexity and proliferation of devices and unique applications present a substantial challenge for User Association (UA) in wireless systems. The problem of UA in wireless networks is multifaceted and requires comprehensive exploration. This paper presents a pioneering approach to the issue, integrating a Deep Q-Network (DQN) with a Graph Neural Network (GNN) to enhance user-base station association in wireless networks. This novel approach surpasses recent methodologies, including Q-learning and max average techniques, in terms of average rewards, returns, and success rate. This superiority is attributed to its capacity to encapsulate intricate relationships and spatial dependencies among users and base stations in wireless systems. The proposed methodology achieves a success rate of 95.2%, outperforming other methodologies by a margin of up to 5.9%.

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