Deep reinforcement learning framework for dynamic pricing demand response of regenerative electric heating

Zhong, Shengyuan; Wang, Xiaoyuan; Zhao, Jun; Li, Wenjia; Li, Hao; Wang, Yongzhen; Deng, Shuai; Zhu, Jiebei

doi:10.1016/j.apenergy.2021.116623

Cited by 45 publications

(16 citation statements)

References 48 publications

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“…• Several articles addressed non-battery energy storage systems such as fuel cells e.g. [33], ultracapacitors [34], natural gas storage tanks [35] and thermal storages such as hot water tanks [36], boilers [37], chilled water tanks [38], [39] and ice storage [40] or by exploiting the building structures themselves as a passive thermal energy storage [41]. Such works were not selected, unless these storages were used in addition to a battery.…”

Section: Literature Review Methodologymentioning

confidence: 99%

Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

2022

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The transition to renewable production and smart grids is driving a massive investment to battery storages, and reinforcement learning (RL) has recently emerged as a potentially disruptive technology for their control and optimization of battery storage systems. A surge of papers has appeared in the last two years applying reinforcement learning to the optimization of battery storages in buildings, energy communities, energy harvesting Internet of Things networks, renewable generation, microgrids, electric vehicles and plug-in hybrid electric vehicles. This article reviews these applications through 4 different perspectives. Firstly, the type of optimization problem is analyzed; the literature can be divided to approaches that optimize either financial targets or energy efficiency. Secondly, the approaches for handling user comfort are analyzed for applications that may impact a human user. Thirdly, this paper discusses the approach to model and reduce battery degradation. Fourthly, the articles are categorized by application context and applications likely to attract a high amount of research are identified. The paper concludes with a list of unresolved challenges.

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Section: Literature Review Methodologymentioning

confidence: 99%

Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

2022

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“…Nowadays, the most widely used methods are deep learning approaches. They are appropriate for the problem of energy-related fields [33].…”

Section: Problem Statementmentioning

confidence: 99%

A Fault Diagnosis Design Based on Deep Learning Approach for Electric Vehicle Applications

2021

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Diagnosing faults in electric vehicles (EVs) is a great challenge. The purpose of this paper is to demonstrate the detection of faults in an electromechanical conversion chain for conventional or autonomous EVs. The information and data coming from different sensors make it possible for EVs to recover a series of information including currents, voltages, speeds, and so on. This information is processed to detect any faults in the electromechanical conversion chain. The novelty of this study is to develop an architecture for a fault diagnosis model by means of the feature extraction technique. In this regard, the long short-term memory (LSTM) approach for the fault diagnosis is proposed. This approach has been tested for an EV prototype in practice, is superior in accuracy over other fault diagnosis techniques, and is based on machine learning. An EV in an urban context is modeled, and then the fault diagnosis approach is applied based on deep learning architectures. The EV and the fault diagnosis model is simulated in Matlab software. It is also revealed how deep learning contributes to the fault diagnosis of EVs. The simulation and practical results confirm that higher accuracy in the fault diagnosis is obtained by applying the LSTM.

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“…Moreover, they develop another pricing model using a policy gradient algorithm named soft actor-critic (SAC). Furthermore, the work developed by Zhong et al (2021) applies deep reinforcement learning to dynamic pricing in regenerative electric heating.…”

Section: Studies Of the Exploration-exploitation Trade-offmentioning

confidence: 99%

“…Furthermore, the work developed by Zhong et al. ( 2021 ) applies deep reinforcement learning to dynamic pricing in regenerative electric heating.…”

Section: Related Workmentioning

confidence: 99%

Novel pricing strategies for revenue maximization and demand learning using an exploration–exploitation framework

2021

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The price demand relation is a fundamental concept that models how price affects the sale of a product. It is critical to have an accurate estimate of its parameters, as it will impact the company's revenue. The learning has to be performed very efficiently using a small window of a few test points, because of the rapid changes in price demand parameters due to seasonality and fluctuations. However, there are conflicting goals when seeking the two objectives of revenue maximization and demand learning, known as the learn/earn trade-off. This is akin to the exploration/exploitation trade-off that we encounter in machine learning and optimization algorithms. In this paper, we consider the problem of price demand function estimation, taking into account its exploration-exploitation characteristic. We design a new objective function that combines both aspects. This objective function is essentially the revenue minus a term that measures the error in parameter estimates. Recursive algorithms that optimize this objective function are derived. The proposed method outperforms other existing approaches.

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Deep reinforcement learning framework for dynamic pricing demand response of regenerative electric heating

Cited by 45 publications

References 48 publications

Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

A Fault Diagnosis Design Based on Deep Learning Approach for Electric Vehicle Applications

Novel pricing strategies for revenue maximization and demand learning using an exploration–exploitation framework

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