IEEE/CAA Journal of Automatica Sinica
Citation:  Chentao Xu and Xing He, "A Fully Distributed Approach to Optimal Energy Scheduling of Users and Generators Considering a Novel Combined Neurodynamic Algorithm in Smart Grid," IEEE/CAA J. Autom. Sinica, vol. 8, no. 7, pp. 13251335, July 2021. doi: 10.1109/JAS.2021.1004048 
[1] 
Y. Kabalci, “A survey on smart metering and smart grid communication,” Renewable and Sustainable Energy Reviews, vol. 57, pp. 302–318, May 2016. doi: 10.1016/j.rser.2015.12.114

[2] 
S. A. Arefifar, M. Ordonez, and Y. A. I. Mohamed, “Energy management in multimicrogrid systemsdevelopment and assessment,” IEEE Trans. Power Systems, vol. 32, no. 2, pp. 910–922, Mar. 2017. doi: 10.1109/TPWRD.2016.2578941

[3] 
J. Ferdous, M. P. Mollah, M. A. Razzaque, M. M. Hassan, A. Alamri, G. Fortino, and M. Zhou, “Optimal dynamic pricing for tradingoff user utility and operator profit in smart grid,” IEEE Trans. Systems,Man,and Cybernetics:Systems, vol. 50, no. 2, pp. 455–467, Nov. 2017.

[4] 
C. K. Lee, H. Liu, D. Fuhs, A. Kores, and E. Waffenschmidt, “Smart lighting systems as a demand response solution for future smart grids,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 8, no. 3, pp. 2362–2370, Jan. 2019.

[5] 
H. T. Haider, O. H. See, and W. Elmenreich, “A review of residential demand response of smart grid,” Renewable and Sustainable Energy Reviews, vol. 59, pp. 166–178, Jun. 2016. doi: 10.1016/j.rser.2016.01.016

[6] 
A. Ihsan, M. Jeppesen, and M. J Brear, “Impact of demand response on the optimal, technoeconomic performance of a hybrid, renewable energy power plant,” Applied Energy, vol. 238, pp. 972–984, Mar. 2019. doi: 10.1016/j.apenergy.2019.01.090

[7] 
C. Xu, X. He, T. Huang, and J. Huang, “A combined neurodynamic approach to optimize the realtime pricebased demand response management problem using mixed zeroone programming,” Neural Computing and Applications, vol. 32, pp. 8799–8809, May 2019.

[8] 
E. L. Karfopoulos and N. D. Hatziargyriou, “Distributed coordination of electric vehicles providing V2G services,” IEEE Trans. Power Systems, vol. 31, no. 1, pp. 329–338, Jan. 2016. doi: 10.1109/TPWRS.2015.2395723

[9] 
C. Luo, Z. Shen, S. Evangelou, G. Xiong, and F.Y. Wang, “The combination of two control strategies for series hybrid electric vehicles,” IEEE/CAA Journal of Automatica Sinica, vol. 6, no. 2, pp. 596–608, Mar. 2019. doi: 10.1109/JAS.2019.1911420

[10] 
S. Qin, J. Feng, J. Song, X. Wen, and C. Xu, “A onelayer recurrent neural network for constrained complexvariable convex optimization,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 3, pp. 534–544, Dec. 2016. doi: 10.1109/TNNLS.2016.2635676

[11] 
Y. Xia, J. Wang, and W. Guo, “Two projection neural networks with reduced model complexity for nonlinear programming,” IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 6, pp. 2020–2029, Aug. 2019.

[12] 
Y. Xia and J. Wang, “A general methodology for designing globally convergent optimization neural networks,” IEEE Trans. Neural Networks, vol. 9, no. 6, pp. 1331–1343, Jan. 1998. doi: 10.1109/72.728383

[13] 
X. Gao and L. Z. Liao, “A novel neural network for generally constrained variational inequalities,” IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 9, pp. 2062–2075, Jun. 2016. doi: 10.1109/TNNLS.2016.2570257

[14] 
W. Han, S. Yan, X. Wen, and S. Qin, “An artificial neural network for solving quadratic zeroone programming problems,” in Proc. Int. Symposium Neural Networks, Springer, Cham, pp. 192–199, 2018.

[15] 
T. Yang, X. Yi, J. Wu, Y. Yuan, D. Wu, Z. Meng, Y. Hong, H. Wang, Z. Lin, and K. H. Johansson, “A survey of distributed optimization,” Annual Reviews in Control, vol. 47, pp. 278–305, May 2019. doi: 10.1016/j.arcontrol.2019.05.006

[16] 
P. Yi, Y. Hong, and F. Liu, “Distributed gradient algorithm for constrained optimization with application to load sharing in power systems,” Systems and Control Letters, vol. 83, pp. 45–52, Sep. 2015. doi: 10.1016/j.sysconle.2015.06.006

[17] 
X. He, D. W. C. Ho, T. Huang, J. Yu, H. AbuRub, and C. Li, “Secondorder continuoustime algorithms for economic power dispatch in smart grids,” IEEE Trans. Systems,Man,and Cybernetics:Systems, vol. 48, no. 9, pp. 1482–1492, Sep. 2018. doi: 10.1109/TSMC.2017.2672205

[18] 
W. Jia, S. Qin, and X. Xue, “A generalized neural network for distributed nonsmooth optimization with inequality constraint,” Neural Networks, vol. 119, pp. 46–56, Nov. 2019. doi: 10.1016/j.neunet.2019.07.019

[19] 
F. Guo, G. Li, C. Wen, L. Wang, and Z. Meng, “An accelerated distributed gradientbased algorithm for constrained optimization with application to economic dispatch in a largescale power system,” IEEE Trans. Systems,Man,and Cybernetics:Systems, vol. 51, no. 4, pp. 2041–2053, Sep. 2019.

[20] 
Z. Chen, R. Xiong, and J. Cao, “Particle swarm optimizationbased optimal power management of plugin hybrid electric vehicles considering uncertain driving conditions,” Energy, vol. 96, pp. 197–208, Feb. 2016. doi: 10.1016/j.energy.2015.12.071

[21] 
R. Jensi and G. W. Jiji, “An enhanced particle swarm optimization with levy flight for global optimization,” Applied Soft Computing, vol. 43, pp. 248–261, Jun. 2016. doi: 10.1016/j.asoc.2016.02.018

[22] 
M. Nouiri, A. Bekrar, A. Jemai, S. Niar, and A. C. Ammari, “An effective and distributed particle swarm optimization algorithm for flexible jobshop scheduling problem,” Journal of Intelligent Manufacturing, vol. 29, no. 3, pp. 603–615, Mar. 2018. doi: 10.1007/s1084501510393

[23] 
S. Das, S. S. Mullick, and P. N. Suganthan, “Recent advances in differential evolutionCan updated survey,” Swarm and Evolutionary Computation, vol. 27, pp. 1–30, Apr. 2016. doi: 10.1016/j.swevo.2016.01.004

[24] 
L. Cui, G. Li, Q. Lin, J. Chen, and N. Lu, “Adaptive differential evolution algorithm with novel mutation strategies in multiple subpopulations,” Computers &Operations Research, vol. 67, pp. 155–173, Mar. 2016. doi: 10.1016/j.cor.2015.09.006

[25] 
A. W. Mohamed, “A novel differential evolution algorithm for solving constrained engineering optimization problems,” Journal of Intelligent Manufacturing, vol. 29, no. 3, pp. 659–692, Mar. 2018. doi: 10.1007/s1084501712946

[26] 
Z. Yan, J. Fan, and J. Wang, “A collective neurodynamic approach to constrained global optimization,” IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 5, pp. 1206–1215, May 2017. doi: 10.1109/TNNLS.2016.2524619
