Constrained EV Charging Scheduling Based on Safe Deep Reinforcement Learning

Li, Hepeng; Wan, Zhiqiang; He, Haibo

doi:10.1109/tsg.2019.2955437

Cited by 291 publications

(100 citation statements)

References 20 publications

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“…Besides supervised and unsupervised learning, reinforcement learning (RL) [92] is the third category of ML, that allows the model to learn a behavior through trial-and-error interactions with the environment through notions of reward and punishment. There are a few recent works utilizing RL-based concepts for scheduling of EV charging [93], [94], [95], [96] and therefore has great potential for further research.…”

Section: Recommendations and Future Workmentioning

confidence: 99%

Machine Learning Approaches for EV Charging Behavior: A Review

et al. 2020

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As the smart city applications are moving from conceptual models to development phase, smart transportation is one of smart cities applications and it is gaining ground nowadays. Electric Vehicles (EVs) are considered one of the major pillars of smart transportation applications. EVs are ever growing in popularity due to their potential contribution in reducing dependency on fossil fuels and greenhouse gas emissions. However, large-scale deployment of EV charging stations poses multiple challenges to the power grid and public infrastructure. To overcome the issue of prolonged charging time, the simple solution of deploying more charging stations to increase charging capacity does not work due to the strain on power grids and physical space limitations. Therefore, researchers have focused on developing smart scheduling algorithms to manage the demand for public charging using modeling and optimization. More recently, there has been a growing interest in data-driven approaches in modeling EV charging. Consequently, researchers are looking to identify consumer charging behavior pattern that can provide insights and predictive analytics capability. The purpose of this paper is to provide a comprehensive review for the use of supervised and unsupervised Machine Learning as well as Deep Neural Networks for charging behavior analysis and prediction. Recommendations and future research directions are also discussed.

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Section: Recommendations and Future Workmentioning

confidence: 99%

Machine Learning Approaches for EV Charging Behavior: A Review

et al. 2020

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“…Reference [80] proposes a DQN-based approach for the optimization of EV charging while [81] extends this work by integrating the DQN algorithm with the long-short term memory (LSTM) NN. To ensure that the battery operates within the allowable ranges, [82] models the charging of EV as a constrained MDP and solves it by the safe DRL.…”

Section: B Demand-side Managementmentioning

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

263

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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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“…The basic idea is to introduce some penalty terms corresponding to the security constraints, and minimize them in priority during the learning process. Reference [61] adopted this idea to consider charging constraints of electric vehicle batteries.Reference [62] optimized voltage and reactive power by a safe off-policy deep reinforcement learning algorithm to avoid voltage violations.…”

Section: Category 3 Surrogate Modelmentioning

confidence: 99%

Review of Learning-Assisted Power System Optimization

Ruan

Zhong

Zhang

et al. 2020

Preprint

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Machine learning, with a dramatic breakthrough in recent years, is showing great potential to upgrade the power system optimization toolbox. Understanding the strength and limitation of machine learning approaches is crucial to answer when and how to integrate them in various power system optimization tasks. This paper pays special attention to the coordination between machine learning approaches and optimization models, and carefully evaluates to what extent such data-driven analysis may benefit the rule-based optimization. A series of typical references are selected and categorized into four kinds: the boundary parameter improvement, the optimization option selection, the surrogate model and the hybrid model. This taxonomy provides a novel perspective to understand the latest research progress and achievements. We further discuss several key challenges and provide an in-depth comparison on the features and designs of different categories. Deep integration of machine learning approaches and optimization models is expected to become the most promising technical trend.

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Constrained EV Charging Scheduling Based on Safe Deep Reinforcement Learning

Cited by 291 publications

References 20 publications

Machine Learning Approaches for EV Charging Behavior: A Review

Machine Learning Approaches for EV Charging Behavior: A Review

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

Review of Learning-Assisted Power System Optimization

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