A Data-Driven Multi-Agent Autonomous Voltage Control Framework Using Deep Reinforcement Learning

Wang, Shengyi; Duan, Jiajun; Shi, Di; Xu, Chunlei; Li, Haifeng; Diao, Ruisheng; Wang, Zhiwei

doi:10.1109/tpwrs.2020.2990179

Cited by 171 publications

(87 citation statements)

References 19 publications

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“…Implementing machine learning or artificial intelligence into HVAC control system is an effective way to enable the controllers with the ability to learn and improve their decision-making. Reinforcement Learning (RL) was implemented in a wide range of power system economic problems [15]- [17], which proves the capability and potential of RL. In this paper, we propose a novel multi-agent reinforcement learning algorithm to optimize the control of HVAC and planning of power system.…”

Section: Introductionmentioning

confidence: 89%

Reinforcement Learning for Building Energy Optimization Through Controlling of Central HVAC System

Hao

Gao

Zhang

2020

IEEE Open J. Power Energy

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This paper presents a novel methodology to control HVAC system and minimize energy cost on the premise of satisfying power system constraints. A multiagent architecture based on game theory and reinforcement learning is developed so as to reduce the cost and computational complexity of the microgrid. The multi-agent architecture comprising agents, state variables, action variables, reward function and cost game is formulated. The paper fills the gap between multi-agent HVAC systems control and power system optimization and planning. The results and analysis indicate that the proposed algorithm is beneficial to deal with the problem of "curse of dimensionality" for multi-agent microgrid HVAC system control and speed up learning of unknown power system conditions.

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Section: Introductionmentioning

confidence: 89%

Reinforcement Learning for Building Energy Optimization Through Controlling of Central HVAC System

Hao

Gao

Zhang

2020

IEEE Open J. Power Energy

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“…Centralized training and decentralized execution have the general MADRL framework which employs centralized critics to guarantee the Markov property utilizing the global information during training. Reference [109] proposes an MADRL-based approach to solve the autonomous voltage control problem. The voltage control problem with several zones is first modeled as a microgrid.…”

Section: ) Centralized Training and Decentralized Executionmentioning

confidence: 99%

Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review

Cao

Zhao

et al. 2020

Journal of Modern Power Systems and Clean Energy

205

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With the growing integration of distributed energy resources (DERs), flexible loads, and other emerging technologies, there are increasing complexities and uncertainties for modern power and energy systems. This brings great challenges to the operation and control. Besides, with the deployment of advanced sensor and smart meters, a large number of data are generated, which brings opportunities for novel data-driven methods to deal with complicated operation and control issues. Among them, reinforcement learning (RL) is one of the most widely promoted methods for control and optimization problems. This paper provides a comprehensive literature review of RL in terms of basic ideas, various types of algorithms, and their applications in power and energy systems. The challenges and further works are also discussed.

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“…e MADDPG method introduces a training regimen utilizing an ensemble of policies for each agent, resulting in more robust multi-agent policies. MADDPG has been used in many applications such as Wang et al [33] that proposed a data-driven multiagent power grid control scheme using MADDPG for the large-scale energy system with more control options and operating conditions. Zhu et al [34] applied MADDPG to solve the flocking control problem of multi-robot systems in complex environments with dynamic obstacles.…”

Section: Multiagent Reinforcement Learningmentioning

confidence: 99%

Autonomous Bus Fleet Control Using Multiagent Reinforcement Learning

Wang

Chang

2021

Journal of Advanced Transportation

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Autonomous buses are becoming increasingly popular and have been widely developed in many countries. However, autonomous buses must learn to navigate the city efficiently to be integrated into public transport systems. Efficient operation of these buses can be achieved by intelligent agents through reinforcement learning. In this study, we investigate the autonomous bus fleet control problem, which appears noisy to the agents owing to random arrivals and incomplete observation of the environment. We propose a multi-agent reinforcement learning method combined with an advanced policy gradient algorithm for this large-scale dynamic optimization problem. An agent-based simulation platform was developed to model the dynamic system of a fixed stop/station loop route, autonomous bus fleet, and passengers. This platform was also applied to assess the performance of the proposed algorithm. The experimental results indicate that the developed algorithm outperforms other reinforcement learning methods in the multi-agent domain. The simulation results also reveal the effectiveness of our proposed algorithm in outperforming the existing scheduled bus system in terms of the bus fleet size and passenger wait times for bus routes with comparatively lesser number of passengers.

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A Data-Driven Multi-Agent Autonomous Voltage Control Framework Using Deep Reinforcement Learning

Cited by 171 publications

References 19 publications

Reinforcement Learning for Building Energy Optimization Through Controlling of Central HVAC System

Reinforcement Learning for Building Energy Optimization Through Controlling of Central HVAC System

Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review

Autonomous Bus Fleet Control Using Multiagent Reinforcement Learning

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