[ 1 ] Hu J, Zhou H, Li Y, Hou P, Yang G. Multi-time scale energy management strategy of aggregator characterized by photovoltaic generation and electric vehicles. J Mod Power Syst Clean Energy 2020;8(4):727–36. 链接1

[ 2 ] Kumar A, Sekhar C. Demand response based congestion management in a mix of pool and bilateral electricity market model. Front Energy 2012;6(2):164–78. 链接1

[ 3 ] Hu J, Yang G, Ziras C, Kok K. Aggregator operation in the balancing market through network-constrained transactive energy. IEEE Trans Power Syst 2019;34(5):4071–80. 链接1

[ 4 ] Xue Y, Yu X. Beyond smart grid—cyber–physical–social system in energy future. Proc IEEE 2017;105(12):2290–2. 链接1

[ 5 ] International energy agency. Harnessing variable renewables: a guide to the balancing challenge. Paris: OECD Publishing; 2011. 链接1

[ 6 ] Chen Z, Sun Y, Ai X, Malik SM, Yang L. Integrated demand response characteristics of industrial park: a review. J Mod Power Syst Clean Energy 2020;8(1):15–26. 链接1

[ 7 ] Fattahi J, Samadi M, Erol-Kantarci M, Schriemer H. Transactive demand response operation at the grid edge using the IEEE 2030.5 standard. Engineering 2020;6(7):801–11. 链接1

[ 8 ] Li Z, Guo Q, Sun H, Wang J. Storage-like devices in load leveling: complementarity constraints and a new and exact relaxation method. Appl Energy 2015;151:13–22. 链接1

[ 9 ] Sanandaji BM, Vincent TL, Poolla K. Ramping rate flexibility of residential HVAC loads. IEEE Trans Sustain Energy 2016;7(2):865–74. 链接1

[10] Hu K, Li W, Wang L, Cao S, Zhu F, Shou Z. Energy management for multimicrogrid system based on model predictive control. Front Inf Technol Electron Eng 2018;19(11):1340–51. 链接1

[11] Vivekananthan C, Mishra Y, Ledwich G, Li F. Demand response for residential appliances via customer reward scheme. IEEE Trans Smart Grid 2014;5 (2):809–20. 链接1

[12] Wu J, Xue Y, Xie D, Yue D, Wen F, Zhao J. Evaluation and simulation analysis of reserve capability for electric vehicles. Autom Electr Power Syst 2018;42 (13):101–7. Chinese. 链接1

[13] Wang J, Jia Y, Mi Z, Chen H, Fang H. Reserve service strategy of electric vehicles based on double-incentive mechanism. Autom Electr Power Syst 2020;44 (10):68–76. Chinese. 链接1

[14] Yao W, Zhao J, Wen F, Xue Y, Chen F, Li L. Frequency regulation strategy for electric vehicles with centralized charging. Autom Electr Power Syst 2014;38 (9):69–76. Chinese. 链接1

[15] Zhang B, Xu G. Rolling horizon optimization for grid-connected electric vehicles considering demand difference. Autom Electr Power Syst 2020;44 (13):106–14. Chinese. 链接1

[16] Han B, Lu S, Xue F, Jiang L. Day-ahead electric vehicle aggregator bidding strategy using stochastic programming in an uncertain reserve market. IET Gener Transm Distrib 2019;13(12):2517–25. 链接1

[17] Wang Yi, Hug G, Liu Z, Zhang N. Modeling load forecast uncertainty using generative adversarial networks. Electr Power Syst Res 2020;189:106732. 链接1

[18] Chen K, Chen K, Wang Q, He Z, Hu J, He J. Short-term load forecasting with deep residual network. IEEE Trans Smart Grid 2019;10(4):3943–52. 链接1

[19] Hong T, Pinson P, Wang Yi, Weron R, Yang D, Zareipour H. Energy forecasting: a review and outlook. IEEE Open Access J Power and Energy 2020;7:376–88. 链接1

[20] Divshali PH, Evens C. Behaviour analysis of electrical vehicle flexibility based on large-scale charging data. In: 2019 IEEE Milan PowerTech; 2019 Jun 23–27; Milan, Italy; 2019.

[21] Sajjad IA, Chicco G, Napoli R. Definitions of demand flexibility for aggregate residential loads. IEEE Trans Smart Grid 2016;7(6):2633–43. 链接1

[22] Paridari K, Nordström L. Flexibility prediction, scheduling and control of aggregated TCLs. Electr Power Syst Res 2020;178:106004. 链接1

[23] Hochreiter S, Bengio Y, Frasconi P, Schmidhuber J. Gradient flow in recurrent nets: the diffificulty of learning long-term dependencies. In: John FK, Stefan CK, editors. A field guide to dynamical recurrent networks. New York: IEEE Press; 2001. p. 237–44. 链接1

[24] Bengio Y, Simard P, Frasconi P. Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw 1994;5(2):157–66. 链接1

[25] Lu Z, Li H, Qiao Y. Power system flexibility planning and challenges considering high proportion of renewable energy. Autom Electr Power Syst 2016;40 (13):147–57. Chinese. 链接1

[26] Parlos AG, Rais OT, Atiya AF. Multi-step-ahead prediction using dynamic recurrent neural networks. Neural Netw 2000;13(7):765–86. 链接1

[27] Bao Y, Xiong T, Hu Z. Multi-step-ahead time series prediction using multipleoutput support vector regression. Neurocomputing 2014;129:482–93. 链接1

[28] Sutskever I, Vinyals O, Le QV. Sequence to sequence learning with neural networks. 2014. arXiv:1409.3215v3.

[29] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. 2017. arXiv:1706.03762v5.

[30] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput 1997;9 (8):1735–80. 链接1

[31] Chung J, Gulcehre C, Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. 2014. arXiv:1412.3555v1.

[32] Bai S, Kolter JZ, Koltun V. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. 2018. arXiv:1803.01271.

[33] He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition; 2016 Jun 27–30; Las Vegas, NV, USA; 2016.

[34] Salimans T, Kingma DP. Weight normalization: a simple reparameterization to accelerate training of deep neural networks. 2016. arXiv:1602.07868. 链接1

[35] Kingma DP, Ba JL. Adam: a method for stochastic optimization. 2017. arXiv:1412.6980v9.

[36] Luo Z, Hu Z, Song Y, Xu Z, Lu H. Optimal coordination of plug-in electric vehicles in power grids with cost-benefit analysis—part II: a case study in china. IEEE Trans Power Syst 2013;28(4):3556–65. 链接1

[37] Sandels C, Widén J, Nordström L. Forecasting household consumer electricity load profiles with a combined physical and behavioral approach. Appl Energy 2014;131:267–78. 链接1