An Edge-Cloud Integrated Solution for Buildings Demand Response Using Reinforcement Learning

Zhang, Xiangyu; Biagioni, Dave; Cai, Mengmeng; Gräf, Peter; Rahman, Saifur

doi:10.1109/tsg.2020.3014055

Cited by 80 publications

(29 citation statements)

References 26 publications

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“…DL has the required potential to automatically investigate the data from edge devices for quick real-time predictive decision-making [148]. The authors in [149] introduced an edge-cloud integrated solution using RL. The proposed solution tracks the demand response with high exactitude.…”

Section: Edge Intelligencementioning

confidence: 99%

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

et al. 2021

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The current electric power system witnesses a significant transition into Smart Grids (SG) as a promising landscape for high grid reliability and efficient energy management. This ongoing transition undergoes rapid changes, requiring a plethora of advanced methodologies to process the big data generated by various units. In this context, SG stands tied very closely to Deep Learning (DL) as an emerging technology for creating a more decentralized and intelligent energy paradigm while integrating high intelligence in supervisory and operational decision-making. Motivated by the outstanding success of DL-based prediction methods, this article attempts to provide a thorough review from a broad perspective on the state-of-the-art advances of DL in SG systems. Firstly, a bibliometric analysis has been conducted to categorize this review's methodology. Further, we taxonomically delve into the mechanism behind some of the trending DL algorithms. We then showcase the DL enabling technologies in SG, such as federated learning, edge intelligence, and distributed computing. Finally, challenges and research frontiers are provided to serve as guidelines for future work in the futuristic power grid domain. This study's core objective is to foster the synergy between these two fields for decision-makers and researchers to accelerate DL's practical deployment for SG systems. INDEX TERMSSmart grid, deep learning, deep neural networks, edge computing, distributed and federated learning, power systems. NOMENCLATURE Abbreviations DDL Distributed deep learning DL Deep learning DRL Deep reinforcement learning DRN Deep residual network EI Edge intelligence EPS Electric power systems FL Federated learning IoT Internet of things LSTM Long short-term memory neural network The associate editor coordinating the review of this manuscript and approving it for publication was Shadi Alawneh . NN Neural network PVPF Photovoltaic power forecasting D. RESEARCH METHODOLOGY AND SYSTEMATIC REVIEW PROTOCOLStarting from September 2019, the multiple-methods approach was conducted [24]. The collection of the mainstream research papers on SG/AI from Web of Science (WoS), Scopus, IEEE Xplore, Science Direct, and Google scholar was conducted as the largest databases of peerreviewed articles. Only peer-reviewed articles written in English, providing experimental results, and having a unique identifier from the mentioned databases were taken into consideration, including reviews, research articles, patent reports, and conference proceedings. The adopted methodology for conducting this review article employs a combination of keywords categorized into three main groups, specifically, 'Deep Learning', 'Smart Grid', and 'Prediction'. The search methodology focuses on the recent research articles from 2015-2020 to identify the comprehensive statues of the AI applications on SG. The filtering process results in 220 research papers from 600 related papers selected based on their relevance by reading the title, abstract, conclusion,

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Section: Edge Intelligencementioning

confidence: 99%

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

et al. 2021

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“…The users create a proper scheduling pattern with DR strategy. Different machine learning algorithms [36] - [43] are involved in literature embarking on the effective DR strategy with proper scheduling patterns. And also different algorithms are considered for the DR strategy.…”

Section: Introductionmentioning

confidence: 99%

Real Time Demand Response Modeling for Residential Consumers in Smart Grid Considering Renewable Energy With Deep Learning Approach

et al. 2021

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Demand response modelling have paved an important role in smart grid at a greater perspective. DR analysis exhibits the analysis of scheduling of appliances for an optimal strategy at the user's side with an effective pricing scheme. In this proposed work, the entire model is done in three different steps. The first step develops strategy patterns for the users considering integration of renewable energy and effective demand response analysis is done. The second step in the process exhibits the learning process of the consumers using Robust Adversarial Reinforcement Learning for privacy process among the users. The third step develops optimal strategy plan for the users for maintaining privacy among the users. Considering the uncertainties of the user's behavioral patterns, typical pricing schemes are involved with integration of renewable energy at the user' side so that an optimal strategy is obtained. The optimal strategy for scheduling the appliances solving privacy issues and considering renewable energy at user' side is done using Robust Adversarial Reinforcement learning and Gradient Based Nikaido-Isoda Function which gives an optimal accuracy. The results of the proposed work exhibit optimal strategy plan for the users developing proper learning paradigm. The effectiveness of the proposed work with mathematical modelling are validated using real time data and shows the demand response strategy plan with proper learning access model. The results obtained among the set of strategy develops 80 % of the patterns created with the learning paradigm moves with optimal DR scheduling patterns. This work embarks the best learning DR pattern created for the future set of consumers following the strategy so privacy among the users can be maintained effectively.INDEX TERMS Demand response, best strategy, robust adversarial reinforcement learning, renewable energy. NOMENCLATURE n i S -Strategy for set of users In -Incentives announced () n  -cost function for the model and   -Best policy parameters for learning , tt hl -reward and incentive function in learning i   -Policy strategy ( , ) n v  -Optimization function for pricing and cost analysis RL-Reinforcement Learning ADP -Approximate Dynamic Programming RARL -Robust Adversarial Reinforcement learning GNI -Gradient Based Nikaido -Isoda Function

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“…© 2021 The Authors. IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology concept of virtual power plant (VPP) [5,6] is by natural an ideal tool to management the commercial buildings as a whole to participate in various DR programs, with the help of advanced information and communication technology (ICT) platforms [7]. The optimal operation and demand response strategy of such a VPP should consider the electricity price, extra incentive and residents' utility at the same time.…”

Section: Introductionmentioning

confidence: 99%

Optimal demand response strategy of commercial building‐based virtual power plant using reinforcement learning

Chen

Cui

Gao

et al. 2021

IET Generation Trans & Dist

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In this paper, the optimal demand response strategy of a commercial building-based virtual power plant with real-world implementation in heavily urbanised area is studied. Instead of modelling the decision-making process as an optimisation problem, a reinforcement learning method is used to seek the optimal strategy, which could update its performance with minimal manpower manipulation. Specifically, the data collection from several commercial buildings, including hotel, shopping mall and office, in Huangpu district, Shanghai city is analysed to deploy the demand response program. Compared with the conventional demand response strategy based on optimisation, the learnt strategy does not rely on the forecasting information as input and could adapt to the changing demand response incentive automatically. It may not produce the best result every time, but can guarantee the benefit in a non-deterministic way in long-term operation. The real-world deployment of the Huangpu virtual power plant involving hardware and software platform is also introduced, as well as its future development projection.

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An Edge-Cloud Integrated Solution for Buildings Demand Response Using Reinforcement Learning

Cited by 80 publications

References 26 publications

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

Real Time Demand Response Modeling for Residential Consumers in Smart Grid Considering Renewable Energy With Deep Learning Approach

Optimal demand response strategy of commercial building‐based virtual power plant using reinforcement learning

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