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Uncertainty-resistant stochastic MPC approach for optimal operation of CHP microgrid

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  • Zhang, Yan
  • Meng, Fanlin
  • Wang, Rui
  • Kazemtabrizi, Behzad
  • Shi, Jianmai

Abstract

The combined heat and power (CHP) microgrid can work both effectively and efficiently to provide electric and thermal power when an appropriate schedule and control strategy is provided. This study proposes a stochastic model predictive control (MPC) framework to optimally schedule and control the CHP microgrid with large scale renewable energy sources. This CHP microgrid consists of fuel cell based CHP, wind turbines, PV generators, battery/thermal energy storage system (BESS/TESS), gas fired boilers and various types of electrical and thermal loads scheduled according to the demand response policy. A mixed integer linear programming based energy management model with uncertainty variables represented by typical scenarios is developed to coordinate the operation of the electrical subsystem and thermal subsystem. This energy management model is integrated into an MPC framework so that it can effectively utilize both forecasts and newly updated information with a rolling up mechanism to reduce the negative impacts introduced by uncertainties. Simulation results show that the approach proposed in this paper is more efficient when compared with an open loop based stochastic day-ahead programming (S-DA) strategy and a MPC strategy. In addition, the impacts of fuel cell capacity and TESS capacity on microgrid operations are investigated and discussed.

Suggested Citation

  • Zhang, Yan & Meng, Fanlin & Wang, Rui & Kazemtabrizi, Behzad & Shi, Jianmai, 2019. "Uncertainty-resistant stochastic MPC approach for optimal operation of CHP microgrid," Energy, Elsevier, vol. 179(C), pages 1265-1278.
    • Handle: RePEc:eee:energy:v:179:y:2019:i:c:p:1265-1278
    DOI: 10.1016/j.energy.2019.04.151
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    References listed on IDEAS

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    1. Bornapour, Mosayeb & Hooshmand, Rahmat-Allah & Parastegari, Moein, 2019. "An efficient scenario-based stochastic programming method for optimal scheduling of CHP-PEMFC, WT, PV and hydrogen storage units in micro grids," Renewable Energy, Elsevier, vol. 130(C), pages 1049-1066.
    2. Arandian, B. & Ardehali, M.M., 2017. "Effects of environmental emissions on optimal combination and allocation of renewable and non-renewable CHP technologies in heat and electricity distribution networks based on improved particle swarm ," Energy, Elsevier, vol. 140(P1), pages 466-480.
    3. Erdinc, Ozan, 2014. "Economic impacts of small-scale own generating and storage units, and electric vehicles under different demand response strategies for smart households," Applied Energy, Elsevier, vol. 126(C), pages 142-150.
    4. Kia, Mohsen & Nazar, Mehrdad Setayesh & Sepasian, Mohammad Sadegh & Heidari, Alireza & Siano, Pierluigi, 2017. "Optimal day ahead scheduling of combined heat and power units with electrical and thermal storage considering security constraint of power system," Energy, Elsevier, vol. 120(C), pages 241-252.
    5. Xiaogang Guo & Zhejing Bao & Zhijie Li & Wenjun Yan, 2018. "Adaptively Constrained Stochastic Model Predictive Control for the Optimal Dispatch of Microgrid," Energies, MDPI, vol. 11(1), pages 1-17, January.
    6. Kriett, Phillip Oliver & Salani, Matteo, 2012. "Optimal control of a residential microgrid," Energy, Elsevier, vol. 42(1), pages 321-330.
    7. Menon, Ramanunni P. & Maréchal, François & Paolone, Mario, 2016. "Intra-day electro-thermal model predictive control for polygeneration systems in microgrids," Energy, Elsevier, vol. 104(C), pages 308-319.
    8. Bischi, Aldo & Taccari, Leonardo & Martelli, Emanuele & Amaldi, Edoardo & Manzolini, Giampaolo & Silva, Paolo & Campanari, Stefano & Macchi, Ennio, 2014. "A detailed MILP optimization model for combined cooling, heat and power system operation planning," Energy, Elsevier, vol. 74(C), pages 12-26.
    9. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Multi-objective self-scheduling of CHP (combined heat and power)-based microgrids considering demand response programs and ESSs (energy storage systems)," Energy, Elsevier, vol. 55(C), pages 1044-1054.
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