AI Chat Paper
Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Search articles, authors, keywords, DOl and etc.
{{expandStatus?'Exit ':''}}Advanced Search
(
With the increasing presence of intermittent energy resources in microgrids, it is difficult to precisely predict the output of renewable resources and their load demand. In order to realize the economical operations of the system, an energy management method based on a model predictive control (MPC) and dynamic programming (DP) algorithm is proposed. This method can reasonably distribute the energy of the battery, fuel cell, electrolyzer and external grid, and maximize the output of the distributed power supply while ensuring the power balance and cost optimization of the system. Based on an ultra-short-term forecast, the output power of the photovoltaic array and the demand power of the system load are predicted. The off-line global optimization of traditional dynamic programming is replaced by the repeated rolling optimization in a limited period of time to obtain power values of each unit in the energy storage system. Compared with the traditional DP, MILP-MPC and the logic based real-time management method, the proposed energy management method is proved to be feasible and effective.
C. Essayeh, M. R. E. Fenni, and H. Dahmouni, “Optimization of energy exchange in microgrid networks: a coalition formation approach,” Protection and Control of Modern Power Systems, vol. 4, no. 1, 2019. DOI: 10.1186/s41601-019-0141-5.
V. V. S. N. Murty and A. Kumar, “Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems,” Protection and Control of Modern Power Systems, vol. 5, no. 1, 2020. DOI: 10.1186/s41601-019-0147-z.
M. Farokhian Firuzi, A. Roosta, and M. Gitizadeh, “Stability analysis and decentralized control of inverter-based ac microgrid,” Protection and Control of Modern Power Systems, vol. 4, no. 1, 2019. DOI: 10.1186/s41601-019-0120-x.
H. F. Qiu, W. Gu, Y. L. Xu, Z. Wu, S. Y. Zhou, and G. S. Pan, “Robustly multi-microgrid scheduling: stakeholder-parallelizing distributed optimization,” IEEE Transactions on Sustainable Energy, vol. 11, no. 2, pp. 988–1001, Apr. 2020.
H. F. Qiu, W. Gu, Y. L. Xu, Z. Wu, S. Y. Zhou, and J. H. Wang, “Interval-partitioned uncertainty constrained robust dispatch for AC/DC hybrid microgrids With uncontrollable renewable generators,” IEEE Transactions on Smart Grid, vol. 10, no. 4, pp. 4603–4614, Jul. 2019.
L. J. Chen, X. Zhu, J. L. Cai, X. H. Xu, and H. X. Liu, “Multi-time scale coordinated optimal dispatch of microgrid cluster based on MAS,” Electric Power Systems Research, vol. 177, pp. 105976, Dec. 2019.
W. Gu, Z. H. Wang, Z. Wu, Z. Luo, Y. Y. Tang, and J. Wang, “An online optimal dispatch schedule for CCHP microgrids based on model predictive control,” IEEE Transactions on Smart Grid, vol. 8, no. 5, pp. 2332–2342, Sep. 2017.
D. Xu, J. Liu, X. Yan, and W. Yan, “A Novel Adaptive Neural Network Constrained Control for a Multi-Area Interconnected Power System With Hybrid Energy Storage,” IEEE T. Ind. Electron., vol. 65, no. 8, pp. 6625–6634, 2018. DOI: 10.1109/TIE.2017.2767544.
E. Gallestey, A. Stothert, M. Antoine, and S. Morton, “Model predictive control and the optimization of power plant load while considering lifetime consumption,” IEEE Power Engineering Review, vol. 21, no. 11, pp. 54, Nov. 2001.
A. Parisio, E. Rikos, G. Tzamalis, and L. Glielmo, “Use of model predictive control for experimental microgrid optimization,” Applied Energy, vol. 115, pp. 37–46, Feb. 2014.
A. M. Elaiw, X. Xia, and A. M. Shehata, “Application of model predictive control to optimal dynamic dispatch of generation with emission limitations,” Electric Power Systems Research, vol. 84, no. 1, pp. 31–44, Mar. 2012.
G. N. Lou, W. Gu, Y. L. Xu, M. Cheng, and W. Liu, “Distributed MPC-based secondary voltage control scheme for autonomous droop-controlled microgrids,” IEEE Transactions on Sustainable Energy, vol. 8, no. 2, pp. 792–804, Apr. 2017.
F. Garcia-Torres and C. Bordons, “Optimal economical schedule of hydrogen-based microgrids with hybrid storage using model predictive control,” IEEE Transactions on Industrial Electronics, vol. 62, no. 8, pp. 5195–5207, Aug. 2015.
Z. L. Zhao, P. Yang, J. M. Guerrero, Z. R. Xu, and T. C. Green, “Multiple-time-scales hierarchical frequency stability control strategy of medium-voltage isolated microgrid,” IEEE Transactions on Power Electronics, vol. 31, no. 8, pp. 5974–5991, Aug. 2016.
X. L. Jin, T. Jiang, Y. F. Mu, C. Long, X. Li, H. J. Jia, and Z. N. Li, “Scheduling distributed energy resources and smart buildings of a microgrid via multi-time scale and model predictive control method,” IET Renewable Power Generation, vol. 13, no. 6, pp. 816–833, Apr. 2019.
D. Fares, R. Chedid, F. Panik, S. Karaki, and R. Jabr, “Dynamic programming technique for optimizing fuel cell hybrid vehicles,” International Journal of Hydrogen Energy, vol. 40, no. 24, pp. 7777–7790, Jun. 2015.
H. Zhang, J. Zhang, G. Yang, and Y. Luo, “Leader-Based Optimal Coordination Control for the Consensus Problem of Multiagent Differential Games via Fuzzy Adaptive Dynamic Programming,” IEEE T. Fuzzy Syst., vol. 23, no. 1, pp. 152–163, 2015. doi:10.1109/TFUZZ.2014.2310238.
L. Xu and D. Chen, “Control and operation of a DC microgrid with variable generation and energy storage,” IEEE Transactions on Power Delivery, vol. 26, no. 4, pp. 2513–2522, Oct. 2011.
Y. C. Pu, Q. Li, W. R. Chen, and H. Liu, “Hierarchical energy management control for islanding DC microgrid with electric-hydrogen hybrid storage system,” International Journal of Hydrogen Energy, vol. 44, no. 11, pp. 5153–5161, Feb. 2019.
I. Prodan and E. Zio, “A model predictive control framework for reliable microgrid energy management,” International Journal of Electrical Power & Energy Systems, vol. 61, pp. 399–409, Oct. 2014.
Q. Li, B. Su, Y. C. Pu, Y. Han, T. H. Wang, L. Z. Yin, and W. R. Chen, “A state machine control based on equivalent consumption minimization for fuel cell/supercapacitor hybrid tramway,” IEEE Transactions on Transportation Electrification, vol. 5, no. 2, pp. 552–564, Jun. 2019.
A. Parisio, E. Rikos, and L. Glielmo, “A model predictive control approach to microgrid operation optimization,” IEEE Transactions on Control Systems Technology, vol. 22, no. 5, pp. 1813–1827, Sep. 2014.
G. Cau, D. Cocco, M. Petrollese, S. K. Kær, and C. Milan, “Energy management strategy based on short-term generation scheduling for a renewable microgrid using a hydrogen storage system,” Energy Conversion and Management, vol. 87, pp. 820–831, Nov. 2014.
H. Ibrahim, A. Ilinca, and J. Perron, “Energy storage systems–-Characteristics and comparisons,” Renewable and Sustainable Energy Reviews, vol. 12, no. 5, pp. 1221–1250, Jun. 2008.
L. Valverde, F. Rosa, and C. Bordons, “Design, planning and management of a hydrogen-based microgrid,” IEEE Transactions on Industrial Informatics, vol. 9, no. 3, pp. 1398–1404, Aug. 2013.
R. Dufo-López, J. L. Bernal-Agustín, and J. Contreras, “Optimization of control strategies for stand-alone renewable energy systems with hydrogen storage,” Renewable Energy, vol. 32, no. 7, pp. 1102–1126, Jun. 2007.
A. Parisio, E. Rikos, and L. Glielmo, “A model predictive control approach to microgrid operation optimization,” IEEE Transactions on Control Systems Technology, vol. 22, no. 5, pp. 1813–1827, Sep. 2014.
A. Parisio, C. Wiezorek, T. Kyntäjä, J. Elo, K. Strunz, and K. H. Johansson, “Cooperative MPC-based energy management for networked microgrids,” IEEE Transactions on Smart Grid, vol. 8, no. 6, pp. 3066–3074, Nov. 2017.
356
Views
10
Downloads
12
Crossref
N/A
Web of Science
26
Scopus
0
CSCD
Altmetrics
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
京公网安备11010802044758号