Authors

Thanh Hung Pham, Ionela Prodan, Denis Genon-Catalot, Laurent Lefèvre

Abstract

This paper considers a discrete-time scheduling method for the power balancing of a continuous-time DC microgrid system. A high-order dynamics and a resistor network are used for modelling the electrical storage unit and the DC bus of the centralized microgrid system, respectively. A PH (Port-Hamiltonian) formulation on graphs is employed to explicitly describe the microgrid topology. This modelling approach allows us to derive a discrete-time model which preserves the power and energy balance of the physical system. Next, a constrained economic MPC (Model Predictive Control) using the proposed control model is formulated for efficiently managing the microgrid operation. The systematic combination of the network modelling method and optimization-based control allows us to generate the appropriate power profiles. Finally, the benefits of the proposed approach are validated through simulation and comparison results over a particular DC microgrid elevator system under different scenarios and using real numerical data.

Keywords

DC microgrid; Port-Hamiltonian systems on graphs; Model Predictive Control

Citation

  • Journal: International Journal of Electrical Power & Energy Systems
  • Year: 2020
  • Volume: 118
  • Issue:
  • Pages: 105753
  • Publisher: Elsevier BV
  • DOI: 10.1016/j.ijepes.2019.105753

BibTeX

@article{Pham_2020,
  title={{Economic constrained optimization for power balancing in a DC microgrid: A multi-source elevator system application}},
  volume={118},
  ISSN={0142-0615},
  DOI={10.1016/j.ijepes.2019.105753},
  journal={International Journal of Electrical Power & Energy Systems},
  publisher={Elsevier BV},
  author={Pham, Thanh Hung and Prodan, Ionela and Genon-Catalot, Denis and Lefèvre, Laurent},
  year={2020},
  pages={105753}
}

Download the bib file

References

  • Yin, C., Wu, H., Locment, F. & Sechilariu, M. Energy management of DC microgrid based on photovoltaic combined with diesel generator and supercapacitor. Energy Conversion and Management vol. 132 14–27 (2017) – 10.1016/j.enconman.2016.11.018
  • Barreiro-Gomez, J., Ocampo-Martinez, C., Bianchi, F. D. & Quijano, N. Data-Driven Decentralized Algorithm for Wind Farm Control with Population-Games Assistance. Energies vol. 12 1164 (2019) – 10.3390/en12061164
  • Siniscalchi-Minna, S., Bianchi, F. D., De-Prada-Gil, M. & Ocampo-Martinez, C. A wind farm control strategy for power reserve maximization. Renewable Energy vol. 131 37–44 (2019) – 10.1016/j.renene.2018.06.112
  • Conejo, A. J. & Sioshansi, R. Rethinking restructured electricity market design: Lessons learned and future needs. International Journal of Electrical Power & Energy Systems vol. 98 520–530 (2018) – 10.1016/j.ijepes.2017.12.014
  • Iovine, A., Rigaut, T., Damm, G., De Santis, E. & Di Benedetto, M. D. Power management for a DC MicroGrid integrating renewables and storages. Control Engineering Practice vol. 85 59–79 (2019) – 10.1016/j.conengprac.2019.01.009
  • Kou, P., Liang, D. & Gao, L. Distributed Coordination of Multiple PMSGs in an Islanded DC Microgrid for Load Sharing. IEEE Transactions on Energy Conversion vol. 32 471–485 (2017) – 10.1109/tec.2017.2649526
  • Kou, P., Liang, D., Wang, J. & Gao, L. Stable and Optimal Load Sharing of Multiple PMSGs in an Islanded DC Microgrid. IEEE Transactions on Energy Conversion vol. 33 260–271 (2018) – 10.1109/tec.2017.2755461
  • Parisio, A., Rikos, E. & Glielmo, L. Stochastic model predictive control for economic/environmental operation management of microgrids: An experimental case study. Journal of Process Control vol. 43 24–37 (2016) – 10.1016/j.jprocont.2016.04.008
  • Touretzky, C. R. & Baldea, M. A hierarchical scheduling and control strategy for thermal energy storage systems. Energy and Buildings vol. 110 94–107 (2016) – 10.1016/j.enbuild.2015.09.049
  • Trélat, E. Optimal Control and Applications to Aerospace: Some Results and Challenges. Journal of Optimization Theory and Applications vol. 154 713–758 (2012) – 10.1007/s10957-012-0050-5
  • Siad, S. B., Malkawi, A., Damm, G., Lopes, L. & Dol, L. G. Nonlinear control of a DC MicroGrid for the integration of distributed generation based on different time scales. International Journal of Electrical Power & Energy Systems vol. 111 93–100 (2019) – 10.1016/j.ijepes.2019.03.073
  • Alamir, M., Rahmani, M. A. & Gualino, D. Constrained control framework for a stand-alone hybrid (Stirling engine)/supercapacitor power generation system. Applied Energy vol. 118 192–206 (2014) – 10.1016/j.apenergy.2013.12.044
  • Lagorse, J., Paire, D. & Miraoui, A. A multi-agent system for energy management of distributed power sources. Renewable Energy vol. 35 174–182 (2010) – 10.1016/j.renene.2009.02.029
  • Sechilariu, M., Wang, B. C. & Locment, F. Supervision control for optimal energy cost management in DC microgrid: Design and simulation. International Journal of Electrical Power & Energy Systems vol. 58 140–149 (2014) – 10.1016/j.ijepes.2014.01.018
  • Lifshitz, D. & Weiss, G. Optimal Control of a Capacitor-Type Energy Storage System. IEEE Transactions on Automatic Control vol. 60 216–220 (2015) – 10.1109/tac.2014.2323136
  • Trigueiro dos Santos, L., Sechilariu, M. & Locment, F. Optimized Load Shedding Approach for Grid-Connected DC Microgrid Systems under Realistic Constraints. Buildings vol. 6 50 (2016) – 10.3390/buildings6040050
  • Maciejowski, (2002)
  • Rawlings, (2009)
  • Grüne, L. Economic receding horizon control without terminal constraints. Automatica vol. 49 725–734 (2013) – 10.1016/j.automatica.2012.12.003
  • Ellis, (2017)
  • Prodan, I., Zio, E. & Stoican, F. Fault tolerant predictive control design for reliable microgrid energy management under uncertainties. Energy vol. 91 20–34 (2015) – 10.1016/j.energy.2015.08.009
  • Lefort, A., Bourdais, R., Ansanay-Alex, G. & Guéguen, H. Hierarchical control method applied to energy management of a residential house. Energy and Buildings vol. 64 53–61 (2013) – 10.1016/j.enbuild.2013.04.010
  • van der Schaft, A. & Jeltsema, D. Port-Hamiltonian Systems Theory: An Introductory Overview. Foundations and Trends® in Systems and Control vol. 1 173–378 (2014)10.1561/2600000002
  • Pham, Power balancing in a DC microgrid elevator system through constrained optimization. (2017)
  • van der Schaft, A. J. & Maschke, B. M. Port-Hamiltonian Systems on Graphs. SIAM Journal on Control and Optimization vol. 51 906–937 (2013)10.1137/110840091
  • Manwell, J. F. & McGowan, J. G. Lead acid battery storage model for hybrid energy systems. Solar Energy vol. 50 399–405 (1993) – 10.1016/0038-092x(93)90060-2
  • Lifshitz, Optimal energy management for grid-connected storage systems. Opt Control: Appl Methods (2015)
  • Desdouits, Multisource elevator energy optimization and control. (2015)
  • Paire, A real-time sharing reference voltage for hybrid generation power system. (2010)
  • Zonetti, D., Ortega, R. & Benchaib, A. Modeling and control of HVDC transmission systems from theory to practice and back. Control Engineering Practice vol. 45 133–146 (2015)10.1016/j.conengprac.2015.09.012
  • Zhao, J. & Dörfler, F. Distributed control and optimization in DC microgrids. Automatica vol. 61 18–26 (2015) – 10.1016/j.automatica.2015.07.015
  • Kotyczka, Discrete-Time Port-Hamiltonian Systems Based on Gauss-Legendre Collocation. (2018)
  • Duran, M. A. & Grossmann, I. E. An outer-approximation algorithm for a class of mixed-integer nonlinear programs. Mathematical Programming vol. 36 307–339 (1986) – 10.1007/bf02592064
  • Prodan, I., Stoican, F., Olaru, S. & Niculescu, S.-I. Enhancements on the Hyperplanes Arrangements in Mixed-Integer Programming Techniques. Journal of Optimization Theory and Applications vol. 154 549–572 (2012) – 10.1007/s10957-012-0022-9
  • Biegler, L. T. & Zavala, V. M. Large-scale nonlinear programming using IPOPT: An integrating framework for enterprise-wide dynamic optimization. Computers & Chemical Engineering vol. 33 575–582 (2009) – 10.1016/j.compchemeng.2008.08.006
  • Hovd, Handling state and output constraints in MPC using timedependent weights. (2001)
  • Christofides, P. D., Scattolini, R., Muñoz de la Peña, D. & Liu, J. Distributed model predictive control: A tutorial review and future research directions. Computers & Chemical Engineering vol. 51 21–41 (2013) – 10.1016/j.compchemeng.2012.05.011
  • Löfberg, YALMIP: A toolbox for modeling and optimization in MATLAB. (2004)
  • Wächter, (2002)
  • Büngeler, J., Cattaneo, E., Riegel, B. & Sauer, D. U. Advantages in energy efficiency of flooded lead-acid batteries when using partial state of charge operation. Journal of Power Sources vol. 375 53–58 (2018) – 10.1016/j.jpowsour.2017.11.050
  • Hairer, (2006)