Similar articles

Abstract: power dispatch is a core problem for smart grid operations. It aims to provide optimal operating points within a transmission network while power demands are changing over space and time. This function needs to be run every few minutes throughout the day; thus, a fast, accurate solution is of vital importance. However, due to the complexity of the problem, reliable and computationally efficient solutions are still under development. This issue will become more urgent and complicated as the integration of intermittent renewable energies increases and the severity of uncertain disasters gets worse. With the recent success of artificial intelligence in various industries, deep learning becomes a promising direction for power engineering as well, and the research community begins to rethink the problem of power dispatch. This paper reviews the recent progress in smart grid dispatch from a deep learning perspective. Through this paper, we hope to advance not only the development of smart grids but also the ecosystem of artificial intelligence.

深度学习驱动的智能电网调度：综述

[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 29^th 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 32^nd 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 33^rd 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 28^th 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 (3^rd 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.