Real-time optimal energy management of microgrid with uncertainties based on deep reinforcement learning

Guo, Chenyu; Wang, Xin; Zheng, Yongjun; Zhang, Feng

doi:10.1016/j.energy.2021.121873

Cited by 131 publications

(40 citation statements)

References 25 publications

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“…The optimization problem includes the grid components and characteristics related to achieve the required technical constraints with the minimum investment cost [48]. An optimal substation model can consider all the costs associated, which will provide the optimal selection and schedule multistage transformer installations in the substation, taking into account the constraints in the substation system [49,50].…”

Section: Discussion and Analysismentioning

confidence: 99%

A Review on Optimal Control for the Smart Grid Electrical Substation Enhancing Transition Stability

et al. 2021

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This paper is a research article for finding the optimal control of smart power substations for improving the network parameters and reliability. The included papers are the most essential and main studies in the field, which propose a different approach to reach the best performance in electrical power systems. The parameters for improvement are the ability for tracking of the reference signal, stabilizing the system, reducing the error in steady state and controlling the behavior in transient state. The research focuses with the reaching a better transient stability considering voltage and frequency dynamic parameters. The optimal model for the control is focused on minimizing energy consumption but maintaining the controllable parameters, exploring some optimization techniques to find the optimal control, with of aim of minimizing the response time, the energy consumption, and maximizing the reliability by means of improving the controller to be more robust.

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Section: Discussion and Analysismentioning

confidence: 99%

A Review on Optimal Control for the Smart Grid Electrical Substation Enhancing Transition Stability

et al. 2021

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“…In the literature, DQN has been applied to enhance the energy management system of an MG that coordinates different flexible sources [24]. To further represent the continuous action space, the policy-based RL methods such as deep deterministic policy gradient (DDPG) [25] and proximal policy optimization (PPO) [26] have been successfully applied to the MG energy management problems. Regarding the application of RL methods in MEMGs, a real-time autonomous energy management strategy combining the DDPG method with prioritized experience replay is proposed in [7] for the energy management of a residential MEMG.…”

Section: Literature Review On Model-free Rl Approachesmentioning

confidence: 99%

Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach

et al. 2023

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“…Application Objective Building Type Algorithm [141], [142] Other/Mixed Cost Residential DQN [143] Cost & Load Balance [94] EV, ES, and RG Cost [144] Other [145] Cost & Comfort [146] HVAC, Fans, WH Cost [147] Other/Mixed Commercial [148] Cost & Comfort [149], [150] HVAC, Fans, WH Mixed/NA [151], [152] Other/Mixed Cost [153], [154] P2P Trading Other Mixed/NA [163] EV, ES, and RG [164] Other/Mixed Cost [165] Cost & Comfort Residential TRPO [51], [168], [169], [170] Other/Mixed [171], [172] Cost & Load Balance [173] Cost [174] EV, ES, and RG [175] Other/Mixed Cost & Comfort Academic [176] Other [177], [178] EV, ES, and RG Commercial [179], [180], [181] HVAC, Fans, WH Cost & Comfort Mixed/NA [182], [183], [184] EV, ES, and RG Other [185], [186] Other/Mixed Cost & Load Balance Residential SAC [187], [188] HVAC, Fans, WH Cost Commercial [189], [190],…”

Section: Referencementioning

confidence: 99%

Reinforcement Learning: Theory and Applications in HEMS

Alani¹,

Das²

2022

Preprint

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The twin capabilities of learning from experience and learning at higher levels of abstraction, set reinforcement learning apart from other areas of machine learning and (within the broader context) all of artificial intelligence. It allows algorithmic agents to replace human beings in the real world, including in homes and buildings, in application domains that had hitherto been considered to be beyond today’s capabilities. This goal, specifically aimed at home energy automation that forms the backdrop of this article, which surveys the use of deep reinforcement learning in various HEMS applications. The article provides an overview of generic reinforcement learning. This is followed with discussions on the state-of-the-art methods for value based, policy gradient, and actor-critic methods in deep reinforcement learning. In order to make published literature in reinforcement learning more accessible to HEMS researchers, verbal descriptions are accompanied with explanatory figures as well as mathematical expressions using the same terminology as the machine learning community. Next, a detailed survey of how reinforcement learning is used in different HEMS domains is described. The survey also considers what kind of reinforcement learning algorithms are used in each HEMS application. The survey suggests that this research is still in its infancy.

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Real-time optimal energy management of microgrid with uncertainties based on deep reinforcement learning

Cited by 131 publications

References 25 publications

A Review on Optimal Control for the Smart Grid Electrical Substation Enhancing Transition Stability

A Review on Optimal Control for the Smart Grid Electrical Substation Enhancing Transition Stability

Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach

Reinforcement Learning: Theory and Applications in HEMS

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