Multi-Objective Stochastic Programming in Microgrids Considering Environmental Emissions

Document Type : Research paper

Authors

Electrical Engineering Department, Engineering Faculty, Razi University, Kermanshah, Iran.

Abstract

This paper deals with day-ahead programming under uncertainties in microgrids (MGs). A two-stage stochastic programming with the fixed recourse approach was adopted. The studied MG was considered in the grid-connected mode with the capability of power exchange with the upstream network. Uncertain electricity market prices, unpredictable load demand, and uncertain wind and solar power values, due to intrinsically stochastic weather changes, were also considered in the proposed method. To cope with uncertainties, the scenario-based stochastic approach was utilized, and the reduction of the environmental emissions generated by the power resources was regarded as the second objective, besides the cost of units’ operation. The ɛ-constraint method was employed to deal with the presented multi-objective optimization problem, and the simulations were performed on a sample MG with one month of real data. The results demonstrated the applicability and effectiveness of the proposed techniques in real-world conditions.

Keywords

Main Subjects

References

[1]    The U.S. Department of Energy, “DOE Microgrid Workshop Report”, The microgrid workshop, San Diego, California. 2011
[2]    A. Einaddin, A. Yazdankhah and R. Kazemzadeh, “Power management in a utility connected micro-grid with multiple renewable energy sources”, J. Oper. Autom. Power Eng., vol. 5, no. 1, pp. 1-9, 2017.
[3]    D. Olivares, C. Cañizares and M. Kazerani, “A centralized energy management system for isolated microgrids”, IEEE Trans. Smart Grid, vol. 5, no. 4, pp. 1864-1875, 2014.
[4]    P. Li, D. Xu, Z. Zhou, W. Lee and B. Zhao, “Stochastic optimal operation of microgrid based on chaotic binary particle swarm optimization”, IEEE Trans. Smart Grid, vol. 7, no. 1, pp. 66-73, 2016.
[5]    M. Sadeghi and M. Kalantar, “Clean and polluting DG types planning in stochastic price conditions and DG unit uncertainties”, J. Oper. Autom. Power Eng., vol. 4, no. 1, pp. 1-15, 2016.
[6]    A. Moghaddam, A. Seifi, T. Niknam and M. Alizadeh Pahlavi, “Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source”, Energy, vol. 36, no. 11, pp. 6490-6507, 2011.
[7]    M. Marzband, E. Yousefnejad, A. Sumper and J. Domínguez-Garcí, “Real time experimental implementation of optimum energy management system in standalone microgrid by using multi-layer ant colony optimization”, Int. J. Electr. Power Energy Syst., vol. 75, pp. 265-274, 2016.
[8]    H. Morais, P. Kádár, P. Faria, Z. Vale and H. Khodr, “Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming”, Renew. Energy, vol. 35, no. 1, pp.151-156, 2010.
[9]    O. Hafez and K. Bhattacharya, “Optimal planning and design of a renewable energy based supply system for microgrids”, Renew. Energy, vol. 45, pp. 7-15, 2012.
[10]    M. Alilou, D. Nazarpour and H. Shayeghi, “Multi-objective optimization of demand side management and multi DG in the distribution system with demand response”, J. Oper. Autom. Power Eng., vol. 6, no. 2, pp. 230-242, 2018.
[11]    S. Chakraborty, M. Weiss and M. Simoes, “Distributed intelligent energy management system for a single-phase high frequency AC microgrid”, IEEE Trans. Ind. Electron., vol. 51, no. 1, pp. 97-109, 2007.
[12]    C. Chen, S. Duan, T. Cai, B. Liu and G. Hu, “Smart energy management system for optimal microgrid economic operation”, IET Renew. Power Gener., vol. 5, no. 3, 2011.
[13]    A. Dukpa, I. Dugga, B. Venkatesh and L. Chang, “Optimal participation and risk mitigation of wind generators in an electricity market”, IET Renew. Power Gener., vol. 4, no. 2, pp. 165-175, 2010.
[14]    J. Zhu, “Optimization of power system planning”, John WILEY & sons, Inc., publication, 2009.
[15]    T. Niknam, F. Golestaneh and A. Malekpour, “Probabilistic energy and operation management of a microgrid containing wind/photovoltaic/fuel cell generation and energy storage devices based on point estimate method and self-adaptive gravitational search algorithm”, Energy, vol. 43, no. 1, pp. 427-437, 2012.
[16]    V. Mohan, J. Singh and W. Ongsakul, “An efficient two stage stochastic optimal energy and reserve management in a microgrid”, Appl. Energy, vol. 160, pp. 28-38, 2015.
[17]    S. Mohammadi, S. Soleymani and B. Mozafari, “Scenario-based stochastic operation management of microgrid including wind, photovoltaic, micro-turbine, fuel cell and energy storage devices”, Int. J. Elec. Power Energy Syst., vol. 54, pp. 525-535, 2014.
[18]    T. Niknam, R. Abarghooee and M. Narimani, “An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation”, Appl. Energy, vol. 99, pp. 455-70, 2012.
[19]    M. Sedighizadeh, M. Esmaili, A. Jamshidi and M. Ghaderi, “Stochastic multi-objective economic-environmental energy and reserve scheduling of microgrids considering battery energy storage system”, Electr. Power Energy Syst., vol. 106, pp. 1-16, 2019.
[20]    F. Najibi and T. Niknam, “Stochastic scheduling of renewable micro-grids considering photovoltaic source uncertainties”, Energy Convers. Manage., vol. 98, pp. 484-99, 2015.
[21]    A. Samimi, M. Nikzad, P. Siano, “Scenario-based framework for coupled active and reactive power market in smart distribution systems with demand response program,” Renew. Energy, vol. 109, pp. 22-40, 2017.
[22]    M. Bornapour, R Hooshmand and M. Parastegari, “An efficient scenario-based stochastic programming method for optimal scheduling of CHP-PEMFC, WT, PV and hydrogen storage units in Micro Grids”, Renewable Energy, vol. 120, pp. 1049-66, 2019.
[23]    P. Firouzmakan, R Hooshmand, M. Bornapour and A. Khodabakhshian, “A comprehensive stochastic energy management system of micro-CHP units, renewable energy sources and storage systems in microgrids considering demand response programs”, Renew. Sustain. Energy Rev., vol. 108, pp. 355-368, 2019.
[24]    M. Elsied, A. Oukaour, T. Youssef, H. Gualous and O. Mohammed, “An advanced real time energy management system for microgrids”, Energy, vol. 114, pp. 742-752, 2016.
[25]    M. Elsied, A. Oukaour, H. Gualou and R. Hassan, “Energy management and optimization in microgrid system based on green energy,” Energy, vol. 84, pp. 139-151, 2015.
[26]    G. C. Liao, “A novel evolutionary algorithm for dynamic economic dispatch with energy saving and emission reduction in power system integrated wind power”, Energy, vol. 36, no, 2, pp. 1018-29, 2011.
[27]    A. Zakariazadeh, S. Jadid, P. Siano, “Economic-environmental energy and reserve scheduling of smart distribution systems: A multiobjective mathematical programming approach”, Energy Conv. Manag., vol. 78, pp. 151-164, 2014.
[28]    G. Mavrotas, “Effective implementation of the ɛ-constraint method in multi-objective mathematical programming problems”, Appl. Math. Comput., vol. 213, no. 2, pp. 455-465, 2009.
[29]    K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, “A fast and elitist multiobjective Genetic algorithm: NSGA-2”, IEEE Tran. Evol. Comput., vol. 6, no. 2, pp. 182-197, 2002.
[30]    J. R. Birge and F. Louveaux, “Introduction to stochastic programming”, Springer series in operations research and financial engineering. New York, Springer-Verlag, 1997.
[31]    H. Li, C. Zang, P. Zeng, H. Yu and Z. Li, “A stochastic programming strategy in microgrid cyber physical energy system for energy optimal operation”, IEEE/CAA J Autom. SINICA, vol. 2, no. 3, 2015.
[32]    M. Majidi, S. Nojavan and K. Zare, “Optimal sizing of energy storage system in a renewable-based microgrid under flexible demand side management considering reliability and uncertainties”, J. Oper. Autom. Power Eng., vol. 5, no. 2, pp. 205-214, 2017.
[33]    B. Borow and Z. M.Salameh, “Methodology for optimally sizing the combination of a battery bank and PV array in a wind/PV hybrid system”, IEEE Trans. Energy Conver., vol. 11, no. 2, pp. 367-75, 1996.
[34]    M. Ehrgott, “Multicriteria optimization”, 2nd edition, Springer, 2005.
[35]    C. Soares and Microturbines, “Applications for distributed energy systems”, First edition, Butterworth-Heinemann, 2007.
[36]    aerodyn SCD 8.0/168 turbine datasheet [online], Available: http://en.wind-turbine-models.com/turbines/1116-aerodyn-scd-8.0-168.
[37]    SIEMENS solar module SM 50/H technical data, [online], Available: http://www.siemen.co.uk/sm50_h.html.
[38]    The ELIA, Belgium’s electricity transmission system operator website [Online], Available: http://elia.be/en/grid-data/data-download/.
[39]    University of Massachusetts, Amherst, “Wind energy center”, [Online], Available: http://www.umass.edu/windenergy/resourcedata/Nantucket.
[40]    The NSRDB solar and filled meteorological fields data set, NCDC [Online], Available: FTP://ftp.ncdc.noaa.gov/pub/data/nsrdb-solar/solaronly. Site ID: 725063, Nantucket Memorial AP, MA, US.
[41]    EPEX SPOT,[Online], Available: https://www.epexspot.com/en/market-data/.
Journal of Operation and Automation in Power Engineering
Volume 8, Issue 2
August 2020
Pages 141-151

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History

  • Receive Date: 22 June 2019
  • Revise Date: 25 August 2019
  • Accept Date: 15 September 2019
  • First Publish Date: 01 August 2020

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