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A Cost- and Environmental-Based Energy Procurement

  • Mehrdad Khaksar
Chapter

Abstract

In the recent years, considering environmental issues, generated emission by power-generating units has turned to be a challenge in the power system industry. However, financial issues have been always the first priority in power generation and procurement process, which conflicts with environmental objects. Therefore, a trade-off should be made between financial and environmental objects. In this chapter, a multi-objective model is presented to solve the power procurement problem of a large consumer, to take both environmental and economic objectives into account. The ε-constraint and max-min fuzzy satisfying methods are employed to solve and select the trade-off solution, respectively. In addition, to reduce power procurement cost, demand response programs are implemented flattening the load demand curve of the large consumer. The model is formulated as a mixed-integer linear programming and solved using GAMS optimization software. Considered sample of large consumer in Chap.  2 is used to validate efficiency of utilized techniques.

Keywords

Multi-objective optimization Epsilon constraint method Min-max fuzzy satisfying technique Power procurement of large consumer Economic-environmental power procurement 

References

  1. 1.
    M. Hamian, A. Darvishan, M. Hosseinzadeh, M.J. Lariche, N. Ghadimi, A. Nouri, A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on mixed integer genetic algorithm. Eng. Appl. Artif. Intell. 72, 203–212 (2018)CrossRefGoogle Scholar
  2. 2.
    M. Majidi, S. Nojavan, N. Nourani Esfetanaj, A. Najafi-Ghalelou, K. Zare, A multi-objective model for optimal operation of a battery/PV/fuel cell/grid hybrid energy system using weighted sum technique and fuzzy satisfying approach considering responsible load management. Sol. Energy 144, 79–89 (2017)CrossRefGoogle Scholar
  3. 3.
    S. Nojavan, M. Majidi, A. Najafi-Ghalelou, M. Ghahramani, K. Zare, A cost-emission model for fuel cell/PV/battery hybrid energy system in the presence of demand response program: ε-constraint method and fuzzy satisfying approach. Energy Convers. Manag. 138, 383–392 (2017)CrossRefGoogle Scholar
  4. 4.
    A. Bal, S.I. Satoglu, A goal programming model for sustainable reverse logistics operations planning and an application. J. Clean. Prod. 201, 1081–1091 (2018)CrossRefGoogle Scholar
  5. 5.
    H. Khodaei, M. Hajiali, A. Darvishan, M. Sepehr, N. Ghadimi, Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming. Appl. Therm. Eng. 137, 395–405 (2018)CrossRefGoogle Scholar
  6. 6.
    G. Aghajani, N. Ghadimi, Multi-objective energy management in a micro-grid. Energy Rep. 4, 218–225 (2018)CrossRefGoogle Scholar
  7. 7.
    S. Pazouki, M.-R. Haghifam, A. Moser, Uncertainty modeling in optimal operation of energy hub in presence of wind, storage and demand response. Int. J. Electr. Power Energy Syst. 61, 335–345 (2014)CrossRefGoogle Scholar
  8. 8.
    M. Majidi, S. Nojavan, K. Zare, Optimal stochastic short-term thermal and electrical operation of fuel cell/photovoltaic/battery/grid hybrid energy system in the presence of demand response program. Energy Convers. Manag. 144, 132–142 (2017)CrossRefGoogle Scholar
  9. 9.
    S. Nojavan, H. Qesmati, K. Zare, H. Seyyedi, Large consumer electricity acquisition considering time-of-use rates demand response programs. Arab. J. Sci. Eng. 39(12), 8913–8923 (2014)MathSciNetCrossRefGoogle Scholar
  10. 10.
    S. Nojavan, H. Ghesmati, K. Zare, Robust optimal offering strategy of large consumer using IGDT considering demand response programs. Electr. Power Syst. Res. 130, 46–58 (2016)CrossRefGoogle Scholar
  11. 11.
    S. Nojavan, M. Majidi, N.N. Esfetanaj, An efficient cost-reliability optimization model for optimal siting and sizing of energy storage system in a microgrid in the presence of responsible load management. Energy 139, 89–97 (2017)CrossRefGoogle Scholar
  12. 12.
    A.S.M. Rabiee, S. Jalilzadeh, B. Mohammadi-Ivatloo, S. Nojavan, Probabilistic multi objective optimal reactive power dispatch considering load uncertainties using Monte Carlo simulations. J. Oper. Autom. Power Eng. 3(1), 83–93 (2015)Google Scholar
  13. 13.
    A. Brooke, D. Kendrick, A. Meeraus, R. Raman, R.E. Rosenthal, GAMS A User’s Guide Introduction 1 (GAMS Development Corporation, Washington, DC, 1998)Google Scholar
  14. 14.
    CPLEX 12. [Online]. Available: https://www.gams.com/latest/docs/S_CPLEX.html. Accessed 15 Jul 2018

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mehrdad Khaksar
    • 1
  1. 1.Young Researchers and Elite Club, Islamshahr BranchIslamic Azad UniversityIslamshahrIran

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