Quantum-Enhanced Grid of the Future: A Primer

Eskandarpour, Rozhin; Ghosh, Kumar; Khodaei, Amin; Paaso, Aleksi; Zhang, Liuxi

doi:10.1109/access.2020.3031595

Cited by 35 publications

(20 citation statements)

References 60 publications

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“…In terms of computational power, quantum computers represent a significant leap forward, perhaps outperforming even the most powerful supercomputers on the market today. The laws of quantum physics allow a quantum computer to acquire massive processing power over multi-state capacity and simultaneously execute numerous operations [297]. In recent years, quantum information processing has grown into a substantial interdisciplinary field at the junction of quantum physics and engineering.…”

Section: Quantum Algorithmsmentioning

confidence: 99%

Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods

et al. 2022

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In view of scarcity of traditional energy resources and environmental issues, renewable energy resources (RERs) are introduced to fulfill the electricity requirement of growing world. Moreover, the effective utilization of RERs to fulfill the varying electricity demands of customers can be achieved via demand response (DR). Furthermore, control techniques, decision variables and offered motivations are the ways to introduce DR into distribution network (DN). This categorization needs to be optimized to balance the supply and demand in DN. Therefore, intelligent algorithms are employed to achieve optimized DR. However, these algorithms are computationally restrained to handle the parametric load of uncertainty involved with RERs and power system. Henceforth, this paper focuses on the limitations of intelligent algorithms for DR. Furthermore, a comparative study of different intelligent algorithms for DR is discussed. Based on conclusions, quantum algorithms are recommended to optimize the computational burden for DR in future smartgrid.

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Section: Quantum Algorithmsmentioning

confidence: 99%

Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods

et al. 2022

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“…In the first step of the UC reformulation, we relax binary variable y i to vary continuously as 0 ≤ y i ≤ 1. We then introduce a new auxiliary binary variable z i , a new auxiliary continuous variable r i , and a new constraint (2c) to preserve the characteristics of problem (1).…”

Section: Three-block Decompositionmentioning

confidence: 99%

“…Computing plays a pivotal role in power system modeling and analysis. As the mathematical challenges of real-world problems increase, progress in advanced computing technologies becomes more crucial [1]. Though still in the early stages, algorithms performed on quantum computers promise to complete important computing tasks faster than they could ever be done on traditional computers [2].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Quantum-Classical Unit Commitment

Mahroo,

Kargarian

2022

Preprint

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This paper proposes a hybrid quantum-classical algorithm to solve a fundamental power system problem called unit commitment (UC). The UC problem is decomposed into a quadratic subproblem, a quadratic unconstrained binary optimization (QUBO) subproblem, and an unconstrained quadratic subproblem. A classical optimization solver solves the first and third subproblems, while the QUBO subproblem is solved by a quantum algorithm called quantum approximate optimization algorithm (QAOA). The three subproblems are then coordinated iteratively using a three-block alternating direction method of multipliers algorithm. Using Qiskit on the IBM Q system as the simulation environment, simulation results demonstrate the validity of the proposed algorithm to solve the UC problem.

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“…Despite the inherent potential of QDL, there are few pieces of evidence of its practical application in power systems. Paper [183] reveal that the electric power grid can significantly make use of Quantum computing to tackle the EPSs challenges. QDL employs Big data to speed up the training and make the computation process more efficient [184].…”

Section: B Tailoring Quantum Deep Learning In Smart Gridmentioning

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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Quantum-Enhanced Grid of the Future: A Primer

Cited by 35 publications

References 60 publications

Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods

Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods

Hybrid Quantum-Classical Unit Commitment

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

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