Dynamic pricing for smart grid with reinforcement learning

Kim, Byung Gook; Schaar, Mihaela van der; Lee, Jang-Won

doi:10.1109/infcomw.2014.6849306

Cited by 31 publications

(11 citation statements)

References 10 publications

(21 reference statements)

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“…Q-learning also used for optimization of energy consumption and the delay in [286]. AIOT systems for energy storage management and energy trading process are discussed in [287], [288] and ( [289], [290], [291], [292], [293], [294]) respectively. Some other applications of Qlearning for AIOT may be seen in ( [295], [296], [297], [298], [299], [300], [301], [302], [303], [304], [305]).…”

Section: E Autonomous Iotmentioning

confidence: 99%

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

Naeem

Rizvi

Coronato³

2020

IEEE Access

124

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Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has become one of the most important and useful technology. It is a learning method where a software agent interacts with an unknown environment, selects actions, and progressively discovers the environment dynamics. RL has been effectively applied in many important areas of real life. This article intends to provide an in-depth introduction of the Markov Decision Process, RL and its algorithms. Moreover, we present a literature review of the application of RL to a variety of fields, including robotics and autonomous control, communication and networking, natural language processing, games and self-organized system, scheduling management and configuration of resources, and computer vision.

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Section: E Autonomous Iotmentioning

confidence: 99%

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

Naeem

Rizvi

Coronato³

2020

IEEE Access

124

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“…Paper 74 proposed RL‐based dynamic algorithm for pricing without a feed of required data about the dynamic system. Paper 81 uses the RL algorithm with nash equilibrium (NE) to develop trade energy games among different players (consumers) for a dynamic pricing approach. Q learning with broker agent architecture 82 found its equal play in dynamic pricing in the grid market.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep learning and reinforcement learning approach on microgrid

Kumar¹,

Kandasamy

Ramanathan

2020

Int Trans Electr Energ Syst

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Microgrid is a new era in the power system and it has more scope of investigation on research. Due to an increase in demand and future expansion of the power system, analyzing the complexities of the network becomes a challenging task. Artificial intelligence plays a vital role in resolving such issues in a microgrid in various aspects. Owing to the rapid growth of periodical update in computational cost reduction, enhanced data analysis-based algorithm artificial intelligence enters into new epoch Artificial Intelligence AI 2.0. Based on such approach, machine learning has been evolved as AI 2.0 initially. Now, it develops branches like deep learning, reinforcement learning, and a combination of both deep reinforcement learning algorithms. These algorithms are precise to attain higher priority in decision-making under a complex network. This paper deals with numerous challenges of the above algorithm to state the significance of AI 2.0 and summarization of their application toward microgrid is useful to analyze the power system. K E Y W O R D S deep learning, deep reinforcement learning, microgrid, reinforcement learning 1 | INTRODUCTION Microgrid is a cluster of distributed generators (DG), stored energy system, local loads along with protecting and monitoring devices. Microgrid has its own specialty in operating under two modes of operation: autonomous and grid-connected mode, mode selection attained by the use of static transfer switch that bridges operation selection in microgrid. The presence of multiple DGs creates a distribution access problem in multipoint is transformed into a concentrated

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“…Wu et al (Wu, Joseph, and Russell 2016) use tabular Q-learning to set prices for Uber-type on-demand economies, by discretizing prices into ranges as actions. Kim et al (Kim et al 2014) use tabular Q-learning to decide the electricity prices in smart grids, for charging users' energy consumption. These studies mostly consider simpler models and use tabular RL to handle the relatively small state and action spaces.…”

Section: Background and Related Workmentioning

confidence: 99%

Learning Resource Allocation and Pricing for Cloud Profit Maximization

Huang

2019

AAAI

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Cloud computing has been widely adopted to support various computation services. A fundamental problem faced by cloud providers is how to efficiently allocate resources upon user requests and price the resource usage, in order to maximize resource efficiency and hence provider profit. Existing studies establish detailed performance models of cloud resource usage, and propose offline or online algorithms to decide allocation and pricing. Differently, we adopt a blackbox approach, and leverage model-free Deep Reinforcement Learning (DRL) to capture dynamics of cloud users and better characterize inherent connections between an optimal allocation/pricing policy and the states of the dynamic cloud system. The goal is to learn a policy that maximizes net profit of the cloud provider through trial and error, which is better than decisions made on explicit performance models. We combine long short-term memory (LSTM) units with fully-connected neural networks in our DRL to deal with online user arrivals, and adjust the output and update methods of basic DRL algorithms to address both resource allocation and pricing. Evaluation based on real-world datasets shows that our DRL approach outperforms basic DRL algorithms and state-of-theart white-box online cloud resource allocation/pricing algorithms significantly, in terms of both profit and the number of accepted users.

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Dynamic pricing for smart grid with reinforcement learning

Cited by 31 publications

References 10 publications

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

Deep learning and reinforcement learning approach on microgrid

Learning Resource Allocation and Pricing for Cloud Profit Maximization

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