CLC number: TM73;TP18
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
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Gang HUANG, Fei WU, Chuangxin GUO. Smart grid dispatch powered by deep learning: a survey[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2000719 @article{title="Smart grid dispatch powered by deep learning: a survey", %0 Journal Article TY - JOUR
深度学习驱动的智能电网调度:综述1之江实验室,中国杭州市,311121 2浙江大学计算机科学与技术学院,中国杭州市,310027 3浙江大学电气工程学院,中国杭州市,310027 摘要:电力调度是智能电网运行的一大核心问题,其目的是在满足时空变化的电力负荷条件下提供电网的最优运行点。这一功能需要在一天内每隔几分钟运行一次,因此快速、准确的调度决策方法至关重要。但是,由于问题的复杂性,可靠且高效的决策方法仍在不断探索的过程中。随着可再生能源的大规模并网以及灾害性气候的不断恶化,智能电网对调度决策方法提出了更为严苛的要求。近年来,以深度学习为代表的人工智能方法在不少领域取得巨大成功,因此深度学习也被电气工程领域寄予厚望,国内外研究者开始重新思考智能电网的调度决策问题。本文即从深度学习这一角度对智能电网调度决策相关研究进行综述,旨在促进智能电网领域发展的同时促进人工智能生态的发展。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
Reference[1]Ardakani AJ, Bouffard F, 2018. Prediction of umbrella constraints. Power Systems Computation Conf, p.1-7. ![]() [2]Babaeinejadsarookolaee S, Birchfield A, Christie RD, et al., 2021. The power grid library for benchmarking AC optimal power flow algorithms. https://arxiv.org/abs/1908.02788 ![]() [3]Baker K, 2019. Learning warm-start points for AC optimal power flow. IEEE 29th Int Workshop on Machine Learning for Signal Processing, p.1-6. ![]() [4]Baker K, 2020. A learning-boosted quasi-Newton method for AC optimal power flow. Workshop on Machine Learning for Engineering Modeling, Simulation and Design, p.1-7. ![]() [5]Biagioni D, Graf P, Zhang XY, et al., 2020. Learning-accelerated ADMM for distributed DC optimal power flow. IEEE Contr Syst Lett, 6:1-6. ![]() [6]Blundell C, Cornebise J, Kavukcuoglu K, et al., 2015. Weight uncertainty in neural networks. Proc 32nd Int Conf on Machine Learning, p.1613-1622. ![]() [7]Bojarski M, Del Testa D, Dworakowski D, et al., 2016. End to end learning for self-driving cars. https://arxiv.org/abs/1604.07316v1 ![]() [8]Bose BK, 2017. Artificial intelligence techniques in smart grid and renewable energy systems—some example applications. Proc IEEE, 105(11):2262-2273. ![]() [9]Buchanan BG, 2005. A (very) brief history of artificial intelligence. AI Mag, 26(4):53-60. ![]() [10]Cambria E, White B, 2014. Jumping NLP curves: a review of natural language processing research. IEEE Comput Intell Mag, 9(2):48-57. ![]() [11]Capitanescu F, Wehenkel L, 2013. Experiments with the interior-point method for solving large scale optimal power flow problems. Electr Power Syst Res, 95:276-283. ![]() [12]Carpentier J, 1979. Optimal power flows. Int J Electr Power Energy Syst, 1(1):3-15. ![]() [13]Changpinyo S, Chao WL, Gong BQ, et al., 2016. Synthesized classifiers for zero-shot learning. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.5327-5336. ![]() [14]Chatzimparmpas A, Martins RM, Jusufi I, et al., 2020. A survey of surveys on the use of visualization for interpreting machine learning models. Inform Visual, 19(3):207-233. ![]() [15]Chatzos M, Fioretto F, Mak TWK, et al., 2020. High-fidelity machine learning approximations of large-scale optimal power flow. https://arxiv.org/abs/2006.16356 ![]() [16]Chen LJ, Tate JE, 2020. Hot-starting the AC power flow with convolutional neural networks. https://arxiv.org/abs/2004.09342 ![]() [17]Chen YZ, Zhang BS, 2020. Learning to solve network flow problems via neural decoding. https://arxiv.org/abs/2002.04091 ![]() [18]Chen YZ, Tan YS, Deka D, 2018. Is machine learning in power systems vulnerable? IEEE Int Conf on Communications, Control, and Computing Technologies for Smart Grids, p.1-6. ![]() [19]Coffrin C, Gordon D, Scott P, 2019. NESTA, the NICTA energy system test case archive. https://arxiv.org/abs/1411.0359 ![]() [20]Deka D, Misra S, 2019. Learning for DC-OPF: classifying active sets using neural nets. IEEE Milan PowerTech, p.1-6. ![]() [21]Diehl F, 2019. Warm-starting AC optimal power flow with graph neural networks. Proc 33rd Conf on Neural Information Processing Systems, p.1-6. ![]() [22]Dror R, Baumer G, Bogomolov M, et al., 2017. Replicability analysis for natural language processing: testing significance with multiple datasets. Trans Assoc Comput Linguist, 5:471-486. ![]() [23]Duchesne L, Karangelos E, Sutera A, et al., 2020a. Machine learning for ranking day-ahead decisions in the context of short-term operation planning. Electr Power Syst Res, 189:106548. ![]() [24]Duchesne L, Karangelos E, Wehenkel L, 2020b. Recent developments in machine learning for energy systems reliability management. Proc IEEE, 108(9):1656-1676. ![]() [25]Eskandarpour R, Khodaei A, 2017. Machine learning based power grid outage prediction in response to extreme events. IEEE Trans Power Syst, 32(4):3315-3316. ![]() [26]Fioretto F, Mak TWK, van Hentenryck P, 2019. Predicting AC optimal power flows: combining deep learning and Lagrangian dual methods. https://arxiv.org/abs/1909.10461 ![]() [27]Gandhi O, Rodríguez-Gallegos CD, Srinivasan D, 2016. Review of optimization of power dispatch in renewable energy system. IEEE Innovative Smart Grid Technologies-Asia, p.250-257. ![]() [28]Gharavi H, Ghafurian R, 2011. Smart grid: the electric energy system of the future. Proc IEEE, 99(6):917-921. ![]() [29]Glasmachers T, 2017. Limits of end-to-end learning. Proc Mach Learn Res, 77:17-32. ![]() [30]Goodfellow I, Bengio Y, Courville A, et al., 2016. Deep Learning. MIT Press, Cambridge, USA. ![]() [31]Guha N, Wang ZC, Wytock M, et al., 2019. Machine learning for AC optimal power flow. Climate Change Workshop at Int Conf on Machine Learning, p.1-4. ![]() [32]Gurobi Optimization, 2019. Gurobi optimizer reference manual. Available from https://www.gurobi.com/wp-content/plugins/hd_documentations/documentation/9.0/refman.pdf [Accessed on Dec. 24, 2020]. ![]() [33]Haridas AV, Marimuthu R, Sivakumar VG, 2018. A critical review and analysis on techniques of speech recognition: the road ahead. Int J Knowl-Based Intell Eng Syst, 22(1):39-57. ![]() [34]Hasan F, Kargarian A, Mohammadi A, 2020. A survey on applications of machine learning for optimal power flow. IEEE Texas Power and Energy Conf, p.1-6. ![]() [35]He KM, Zhang XY, Ren SQ, et al., 2016. Deep residual learning for image recognition. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.770-778. ![]() [36]Hey T, Tansley S, Tolle K, 2009. The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research, Redmond, WA, USA. ![]() [37]Hossain E, Han Z, Poor HV, 2012. Smart Grid Communications and Networking. Cambridge University Press, Cambridge, UK. ![]() [38]Hu XY, Hu HJ, Verma S, et al., 2021. Physics-guided deep neural networks for power flow analysis. IEEE Trans Power Syst, 36(3):2082-2092. ![]() [39]Huang G, Wen YF, Bao YK, et al., 2015. Comprehensive decoupled risk-limiting dispatch. IEEE Power & Energy Society General Meeting, p.1-5. ![]() [40]Huang G, Wang JH, Chen C, et al., 2017a. Integration of preventive and emergency responses for power grid resilience enhancement. IEEE Trans Power Syst, 32(6):4451-4463. ![]() [41]Huang G, Wang JH, Chen C, et al., 2017b. System resilience enhancement: smart grid and beyond. Front Eng Manag, 4(3):271-282. ![]() [42]Huang G, Wang JH, Chen C, et al., 2019. Cyber-constrained optimal power flow model for smart grid resilience enhancement. IEEE Trans Smart Grid, 10(5):5547-5555. ![]() [43]Huang G, Wu C, Hu YF, et al., 2021. Serverless distributed learning for smart grid analytics. Chinese Phys B, 30:088802. ![]() [44]Ibrahim MR, Haworth J, Cheng T, 2020. Understanding cities with machine eyes: a review of deep computer vision in urban analytics. Cities, 96:102481. ![]() [45]Jaller M, Otero-Palencia C, Pahwa A, 2020. Automation, electrification, and shared mobility in urban freight: opportunities and challenges. Transport Res Procedia, 46:13-20. ![]() [46]Jamei M, Mones L, Robson A, et al., 2019. Meta-optimization of optimal power flow. Climate Change Workshop at Int Conf on Machine Learning, p.1-3. ![]() [47]Kairouz P, McMahan HB, Avent B, et al., 2019. Advances and open problems in federated learning. https://arxiv.org/abs/1912.04977v3 ![]() [48]King RTFA, Tu XP, Dessaint LA, et al., 2016. Multi-contingency transient stability-constrained optimal power flow using multilayer feedforward neural networks. IEEE Canadian Conf on Electrical and Computer Engineering, p.1-6. ![]() [49]Kingma DP, Ba J, 2015. Adam: a method for stochastic optimization. Int Conf for Learning Representations, p.1-15. ![]() [50]Kundur P, 1994. Power System Stability and Control. McGraw-Hill, New York, USA. ![]() [51]Le QV, Ngiam J, Coates A, et al., 2011. On optimization methods for deep learning. Proc 28th Int Conf on Machine Learning, p.265-272. ![]() [52]LeCun Y, Bengio Y, Hinton G, 2015. Deep learning. Nature, 521(7553):436-444. ![]() [53]Li T, Sahu AK, Talwalkar A, et al., 2020. Federated learning: challenges, methods, and future directions. IEEE Signal Process Mag, 37(3):50-60. ![]() [54]Liu TJ, Liu YB, Liu JY, et al., 2020. A Bayesian learning based scheme for online dynamic security assessment and preventive control. IEEE Trans Power Syst, 35(5):4088-4099. ![]() [55]Liu ZF, 2020. Research on emergency response processing model of thermal power enterprise based on epidemic situation. Int Conf on Wireless Communications and Smart Grid, p.171-174. ![]() [56]Mai TT, Jadun P, Logan JS, et al., 2018. Electrification Futures Study: Scenarios of Electric Technology Adoption and Power Consumption for the United States. NREL/TP-6A20-71500, National Renewable Energy Laboratory, United States. ![]() [57]Mathis A, Mamidanna P, Cury KM, et al., 2018. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat Neurosci, 21(9):1281-1289. ![]() [58]McMahan HB, Ramage D, 2017. Federated learning: collaborative machine learning without centralized training data. Google Research Blog. Available from https://ai.googleblog.com/2017/04/federated-learning-collaborative.html [Accessed on Dec. 24, 2020]. ![]() [59]Misra S, Roald L, Ng Y, 2019. Learning for constrained optimization: identifying optimal active constraint sets. https://arxiv.org/abs/1802.09639 ![]() [60]Mohamed MA, Eltamaly AM, 2018. Modeling and Simulation of Smart Grid Integrated with Hybrid Renewable Energy Systems. Springer, Switzerland. ![]() [61]Oughton EJ, Skelton A, Horne RB, et al., 2017. Quantifying the daily economic impact of extreme space weather due to failure in electricity transmission infrastructure. Space Wea, 15(1):65-83. ![]() [62]Owerko D, Gama F, Ribeiro A, 2020. Optimal power flow using graph neural networks. IEEE Int Conf on Acoustics, Speech and Signal Processing, p.5930-5934. ![]() [63]Padhy NP, 2004. Unit commitment—a bibliographical survey. IEEE Trans Power Syst, 19(2):1196-1205. ![]() [64]Pan X, Zhao TY, Chen MH, 2019. DeepOPF: deep neural network for DC optimal power flow. IEEE Int Conf on Communications, Control, and Computing Technologies for Smart Grids, p.1-6. ![]() [65]Pan X, Zhao TY, Chen MH, et al., 2021a. DeepOPF: a deep neural network approach for security-constrained DC optimal power flow. IEEE Trans Power Syst, 36(3):1725-1735. ![]() [66]Pan X, Chen MH, Zhao TY, et al., 2021b. DeepOPF: a feasibility-optimized deep neural network approach for AC optimal power flow problems. https://arxiv.org/abs/2007.01002 ![]() [67]Papernot N, McDaniel P, Jha S, et al., 2016. The limitations of deep learning in adversarial settings. IEEE European Symp on Security and Privacy, p.372-387. ![]() [68]Prabhu VU, Birhane A, 2020. Large image datasets: a pyrrhic win for computer vision? https://arxiv.org/abs/2006.16923 ![]() [69]Rahman J, Feng C, Zhang J, 2020. Machine learning-aided security constrained optimal power flow. IEEE Power & Energy Society General Meeting, p.1-5. ![]() [70]Ravi S, Larochelle H, 2016. Optimization as a model for few-shot learning. Int Conf for Learning Representations, p.1-11. ![]() [71]Robson A, Jamei M, Ududec C, et al., 2020. Learning an optimally reduced formulation of OPF through meta-optimization. https://arxiv.org/abs/1911.06784 ![]() [72]Ruan GC, Zhong HW, Zhang GL, et al., 2021. Review of learning-assisted power system optimization. CSEE J Power Energy Syst, 7(2):221-231. ![]() [73]Rudin C, Waltz D, Anderson RN, et al., 2012. Machine learning for the New York City power grid. IEEE Trans Patt Anal Mach Intell, 34(2):328-345. ![]() [74]Sheble GB, Fahd GN, 1994. Unit commitment literature synopsis. IEEE Trans Power Syst, 9(1):128-135. ![]() [75]Shorten C, Khoshgoftaar TM, 2019. A survey on image data augmentation for deep learning. J Big Data, 6(1):60. ![]() [76]Silver D, Schrittwieser J, Simonyan K, et al., 2017. Mastering the game of go without human knowledge. Nature, 550(7676):354-359. ![]() [77]Sun DI, Ashley B, Brewer B, et al., 1984. Optimal power flow by Newton approach. IEEE Power Eng Rev, PER-4(10):39. ![]() [78]Tejada-Arango DA, Lumbreras S, Sánchez-Martín P, et al., 2020. Which unit-commitment formulation is best? A comparison framework. IEEE Trans Power Syst, 35(4):2926-2936. ![]() [79]Venzke A, Chatzivasileiadis S, 2021. Verification of neural network behaviour: formal guarantees for power system applications. IEEE Trans Smart Grid, 12(1):383-397. ![]() [80]Venzke A, Qu GN, Low S, et al., 2020a. Learning optimal power flow: worst-case guarantees for neural networks. IEEE Int Conf on Communications, Control, and Computing Technologies for Smart Grids, p.1-7. ![]() [81]Venzke A, Viola DT, Mermet-Guyennet J, et al., 2020b. Neural networks for encoding dynamic security-constrained optimal power flow to mixed-integer linear programs. https://arxiv.org/abs/2003.07939 ![]() [82]Venzke A, Molzahn DK, Chatzivasileiadis S, 2021. Efficient creation of datasets for data-driven power system applications. Electr Power Syst Res, 190:106614. ![]() [83]Wächter A, Biegler LT, 2006. On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Math Program, 106(1):25-57. ![]() [84]Walsh B, 2013. The surprisingly large energy footprint of the digital economy. Time Magazine. Available from https://science.time.com/2013/08/14/power-drain-the-digital-cloud-is-using-more-energy-than-you-think/ [Accessed on Dec. 24, 2020]. ![]() [85]Wen GH, Yu XH, Liu ZW, 2021. Recent progress on the study of distributed economic dispatch in smart grid: an overview. Front Inform Technol Electron Eng, 22(1):25-39. ![]() [86]Wen YF, Li WY, Huang G, et al., 2016. Frequency dynamics constrained unit commitment with battery energy storage. IEEE Trans Power Syst, 31(6):5115-5125. ![]() [87]Wood AJ, Wollenberg BF, Sheblé GB, 2013. Power Generation, Operation, and Control (3rd Ed.). John Wiley & Sons, New York, USA. ![]() [88]Wu C, Xiao J, Huang G, et al., 2019. Galaxy learning—a position paper. https://arxiv.org/abs/1905.00753 ![]() [89]Wu F, Lu CW, Zhu MJ, et al., 2020. Towards a new generation of artificial intelligence in China. Nat Mach Intell, 2(6):312-316. ![]() [90]Wu ZH, Pan SR, Chen FW, et al., 2021. A comprehensive survey on graph neural networks. IEEE Trans Neur Netw Learn Syst, 32(1):4-24. ![]() [91]Xiang YM, Wang LF, Liu N, 2018. A robustness-oriented power grid operation strategy considering attacks. IEEE Trans Smart Grid, 9(5):4248-4261. ![]() [92]Xu Y, Dong ZY, Zhang R, et al., 2014. Solving preventive-corrective SCOPF by a hybrid computational strategy. IEEE Trans Power Syst, 29(3):1345-1355. ![]() [93]Yan ZM, Xu Y, 2020. Real-time optimal power flow: a Lagrangian based deep reinforcement learning approach. IEEE Trans Power Syst, 35(4):3270-3273. ![]() [94]Yang Y, Yang ZF, Yu J, et al., 2020a. Fast calculation of probabilistic power flow: a model-based deep learning approach. IEEE Trans Smart Grid, 11(3):2235-2244. ![]() [95]Yang Y, Yang ZF, Yu J, et al., 2020b. Fast economic dispatch in smart grids using deep learning: an active constraint screening approach. IEEE Int Things J, 7(11):11030-11040. ![]() [96]Yin LF, Yu T, Zhang XS, et al., 2018. Relaxed deep learning for real-time economic generation dispatch and control with unified time scale. Energy, 149:11-23. ![]() [97]Yin LF, Gao Q, Zhao LL, et al., 2020. Expandable deep learning for real-time economic generation dispatch and control of three-state energies based future smart grids. Energy, 191:116561. ![]() [98]Zamzam AS, Baker K, 2020. Learning optimal solutions for extremely fast AC optimal power flow. IEEE Int Conf on Communications, Control, and Computing Technologies for Smart Grids, p.1-6. ![]() [99]Zeng B, Ge SY, Kong XY, et al., 2014. Study for economic dispatch considering network loss in power pool market. Int Conf on Power System Technology, p.1754-1759. ![]() [100]Zhang DX, Han XQ, Deng CY, 2018. Review on the research and practice of deep learning and reinforcement learning in smart grids. CSEE J Power Energy Syst, 4(3):362-370. ![]() [101]Zhao TY, Pan X, Chen MH, et al., 2020. DeepOPF+: a deep neural network approach for DC optimal power flow for ensuring feasibility. IEEE Int Conf on Communications, Control, and Computing Technologies for Smart Grids, p.1-6. ![]() [102]Zhou Y, Tuzel O, 2018. VoxelNet: end-to-end learning for point cloud based 3D object detection. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.4490-4499. ![]() [103]Zhou YH, Zhang B, Xu CL, et al., 2020. Deriving fast AC OPF solutions via proximal policy optimization for secure and economic grid operation. https://arxiv.org/abs/2003.12584v1 ![]() [104]Zimmerman R, Zhu QY, Dimitri C, 2016. Promoting resilience for food, energy, and water interdependencies. J Environ Stud Sci, 6(1):50-61. ![]() [105]Zimmerman RD, Murillo-Sánchez CE, Thomas RJ, 2011. MATPOWER: steady-state operations, planning, and analysis tools for power systems research and education. IEEE Trans Power Syst, 26(1):12-19. ![]() Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou
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