Multi-timescale Collaborative Optimization of Distribution, Distributed Generation and Load in Microgrid


The distribution loads, output of distributed generations (DGs) and dynamic power price present obvious time-sequence property, the typical property is studied in this paper. The model of microgrid (including adjustable load, DGs, storage and dynamic power price) is studied. A multi-timescale collaborative optimization model is built towards microgrid; main measures in different timescale optimization are realized. An improved adaptive genetic algorithm is used to solve the optimization problem, which improved the efficiency and reliability. The proposed optimization model is simulated in IEEE 33 node system; the results show it’s effective.

Share and Cite:

W. Hu, Y. Sun, Y. Wang, Y. Zhou and M. Wang, "Multi-timescale Collaborative Optimization of Distribution, Distributed Generation and Load in Microgrid," Open Journal of Applied Sciences, Vol. 3 No. 2B, 2013, pp. 12-17. doi: 10.4236/ojapps.2013.32B003.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] R. H. Lasseter, “Smart Distribution: Coupled Microgrids,” Proceedings of the IEEE, Vol. 13, No. 8, 2011, pp. 1074-1082. doi:10.1109/JPROC.2011.2114630
[2] Y. X. Yu and W. P. Luan, “Smart Grid and Its Implementations,” Proceedings of the CSEE, Vol. 29, No. 34, 2009, pp. 1-6.
[3] F. Bin, “Research of Electricity Price Regulation Method and Application,” Ph. D Thesis, North China Electric Power University, Beijing, 2010.
[4] L. LI, W. Tang and M. K. Bai, “Multi-objective Location and Sizing of Distributed Generators based on Time-sequence Characteristics,” Automation of Electric Power, Vol. 37, No. 3, 2013, pp. 58-63.
[5] G. Chen, P. Dai and H. Zhou “Distribution System Recon-figuration Considering Distributed Generators and Plug-in Electric Vehicles,” Power System Technology, Vol. 37, No. 1, 2013, pp. 82-88.
[6] J. Li, J. Y. Liu, L. F. Xie, H. Quan and Y. B. Liu, “Dynamic Game Linkage of TOU Pricing Between Generating Side and Retail Side,” Electric Power Automation Equipment, Vol. 32, No. 4, 2012, pp. 16-19.
[7] J. Arabas, Z. Michalewicz and J. Mulawka, “GAVaPS:A Genetic Algorithm with Varying Population Size,” Proceedings of the 1st IEEE Conference on Evolutionary Computation, 1994, pp. 73-78.
[8] X. Jin, “The Application of Genetic Algorithm with Adaptive Population Size in Distribution Network,” North China Electric Power University, Beijing, 2011.
[9] H. Zhang, “Study of Distribution Network Fault Restoration Based on Genetic and Particle Swarm Mixed Algorithm,” Wuhan University, Wuhan, 2009.
[10] S. Q. Sheng, Z. G. Ma and J. Wu, “Distribution Network Fault Restoration Based on Improved Adaptive Genetic Algorithm,” Second Conference on Intelligent Computation Technology and Automation, 2009.

Copyright © 2022 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.