Multi-agent deep reinforcement learning based real-time planning approach for responsive customized bus routes

Wu, Binglin; Zuo, Xingquan; Chen, Gang; Ai, Guanqun; Wan, Xing

doi:10.1016/j.cie.2023.109840

Cited by 4 publications

(1 citation statement)

References 41 publications

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“…Nevertheless, when power networks experience substantial changes, marked by the incorporation of renewable energy sources and the emergence of intelligent grids, the constraints of centralized management become evident. [21][22][23][24][25] Traditional hierarchical control structures have difficulties in adjusting to the dynamic and unpredictable characteristics of contemporary power systems. Researchers have been inspired to examine alternative paradigms due to the need for control systems that are more flexible, adaptable, and robust.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Agent Reinforcement Learning for Power System Operation and Control

Jain,

Sridevi,

Dabral

et al. 2024

E3S Web Conf.

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This study investigates the use of Multi-Agent Reinforcement Learning (MARL) to enhance the efficiency of power system operation and control. The simulated power system environment is represented as a multi-agent system, where intelligent agents are used to mimic generators and loads. The MARL framework utilizes Q-learning algorithms to allow agents to independently adjust their activities in accordance with changing operating circumstances. The resulting simulated data represents a wide-ranging power grid scenario, including buses with different generator capacity, load needs, and transmission line capacities. The findings indicate a significant improvement in the stability of the system via Multi-Agent Reinforcement Learning (MARL), since the agents’ capacity to learn and adapt enables them to quickly alter the outputs of generators and meet the needs of the load, so ensuring that voltage and frequency levels remain within acceptable limits. The MARL framework significantly improves economic efficiency by enabling actors to optimize their behaviors in order to reduce the total costs of the system. The agility of the MARL-based control method is emphasized by the decrease in response time to dynamic disturbances, as agents demonstrate quick and efficient reactions to unforeseen occurrences. The favorable results highlight the potential of MARL as a decentralized decision-making model in power systems, providing advantages in terms of stability, economic efficiency, and the capacity to respond to disruptions. Although the research uses artificial data in a controlled setting, the observed enhancements indicate the flexibility and efficacy of the MARL framework. Future research should prioritize the integration of more practical situations and tackling computational obstacles to further confirm the suitability and expandability of Multi-Agent Reinforcement Learning (MARL) in actual power systems.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-Agent Reinforcement Learning for Power System Operation and Control

Jain,

Sridevi,

Dabral

et al. 2024

E3S Web Conf.

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Demand-Responsive Transport Dynamic Scheduling Optimization Based on Multi-agent Reinforcement Learning Under Mixed Demand

Wang,

Li,

Sun

et al. 2024

Lecture Notes in Computer Science

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Optimizing customized bus services for multi‐trip urban passengers: A bi‐objective approach

Guan,

Wang,

Guo

et al. 2024

IET Intelligent Trans Sys

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Customized bus services typically focus on single‐trip requests, which often struggle to accommodate the growing needs for varied multiple trips in urban daily travel. This paper addresses the customized bus routing problem for passengers with multiple trips. A bi‐objective mathematical model is established for maximizing the operational profit and minimizing the travel costs by considering the characteristics of the multi‐trip requests and time‐dependent travel time. Besides, a novel profit objective function is proposed considering the service's completion status and the starting price. Since the proposed mixed integer linear programming model is an NP‐hard problem, a non‐dominated sorting genetic algorithm II‐based method is proposed to handle different sizes of instances. Finally, the instances with multi‐trip requests are carried out to test the accuracy of the model and the effectiveness of our method compared with Gurobi and the local search‐based multi‐objective algorithm approach.

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Multi-agent deep reinforcement learning based real-time planning approach for responsive customized bus routes

Cited by 4 publications

References 41 publications

Multi-Agent Reinforcement Learning for Power System Operation and Control

Multi-Agent Reinforcement Learning for Power System Operation and Control

Demand-Responsive Transport Dynamic Scheduling Optimization Based on Multi-agent Reinforcement Learning Under Mixed Demand

Optimizing customized bus services for multi‐trip urban passengers: A bi‐objective approach

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