Power Flow Constrained Short-Term Scheduling of CHP Units



The electric power system consists of units for electricity production, devices that make use of the electricity, and a power grid that connects them. The aim of the power grid utilities is to enable the reliable transportation of electrical energy from the production to the consumption, while satisfying system constraints, and all these for the lowest possible price. Conventional power system is facing the problems of gradual depletion of fossil fuel resources, poor energy efficiency, and negative environmental effects. These problems have persuaded system utilities to a new trend of power generation. The new trend incorporates power production at distribution voltage level by using non-conventional or renewable energy sources such as natural gas, biogas, wind power, solar photovoltaic cells, fuel cells, combined heat and power (CHP) systems, and micro turbines. Microgrids (MGs) are accounted as the building blocks of the future power systems known as smart girds. This chapter presents the power flow constrained short-term hourly scheduling of DG units. In the most of the MG scheduling literature, the physical constraints of electric power transmission, known as power flow constraints, has not been taken into account. This simplification may result in a solution that is not technically acceptable. In this study, a MG incorporating cogeneration facilities, conventional power units, and heat-only units are considered. The optimal scheduling determines the performance of units in order to supply whole electrical and thermal demand of the MG as well as determining the amount of exchanging power between main and microgrid. In addition, the heat–power dual dependency characteristic in different types of CHP units are considered, and all technical constraints of generation units have been satisfied as well. A mixed-integer linear formulation has been employed to model the non-convex feasible operation region of CHP unit. In this study, a heat buffer tank, with the ability of heat storage, has been incorporated in the proposed framework. Moreover, in order to consider realistic model of the problem, network operation constraints such as voltage magnitude of buses and line flow limits are taken into account.


Microgrids Distributed generation (DG) Short-Term scheduling Combined heat and power (CHP) 


  1. 1.
    H. Jiayi, J. Chuanwen, and X. Rong, “A review on distributed energy resources and MicroGrid,” Renewable and Sustainable Energy Reviews, vol. 12, pp. 2472–2483, 2008.Google Scholar
  2. 2.
    A. Zangeneh, S. Jadid, and A. Rahimi-Kian, “Promotion strategy of clean technologies in distributed generation expansion planning,” Renewable Energy, vol. 34, pp. 2765–2773, 2009.Google Scholar
  3. 3.
    P. M. Costa, M. A. Matos, and J. P. Lopes, “Regulation of microgeneration and microgrids,” Energy Policy, vol. 36, pp. 3893–3904, 2008.Google Scholar
  4. 4.
    P. Dondi, D. Bayoumi, C. Haederli, D. Julian, and M. Suter, “Network integration of distributed power generation,” Journal of Power Sources, vol. 106, pp. 1–9, 2002.Google Scholar
  5. 5.
    A. Soroudi, M. Ehsan, and H. Zareipour, “A practical eco-environmental distribution network planning model including fuel cells and non-renewable distributed energy resources,” Renewable Energy, vol. 36, pp. 179–188, 2011.Google Scholar
  6. 6.
    M. Motevasel, A. R. Seifi, and T. Niknam, “Multi-objective energy management of CHP (combined heat and power)-based micro-grid,” Energy, vol. 51, pp. 123–136, 2013.Google Scholar
  7. 7.
    A. Vasebi, M. Fesanghary, and S. Bathaee, “Combined heat and power economic dispatch by harmony search algorithm,” International Journal of Electrical Power & Energy Systems, vol. 29, pp. 713–719, 2007.Google Scholar
  8. 8.
    M. T. Hagh, S. Teimourzadeh, M. Alipour, and P. Aliasghary, “Improved group search optimization method for solving CHPED in large scale power systems,” Energy Conversion and Management, vol. 80, pp. 446–456, 2014.Google Scholar
  9. 9.
    M. Alipour, K. Zare, and B. Mohammadi-Ivatloo, “Short-term scheduling of combined heat and power generation units in the presence of demand response programs,” Energy, vol. 71, pp. 289–301, 2014.Google Scholar
  10. 10.
    A. K. Basu, A. Bhattacharya, S. Chowdhury, and S. Chowdhury, “Planned scheduling for economic power sharing in a CHP-based micro-grid,” Power Systems, IEEE Transactions on, vol. 27, pp. 30–38, 2012.Google Scholar
  11. 11.
    A. Chaouachi, R. M. Kamel, R. Andoulsi, and K. Nagasaka, “Multiobjective intelligent energy management for a microgrid,” Industrial Electronics, IEEE Transactions on, vol. 60, pp. 1688–1699, 2013.Google Scholar
  12. 12.
    C. Changsong, D. Shanxu, C. Tao, L. Bangyin, and Y. Jinjun, “Energy trading model for optimal microgrid scheduling based on genetic algorithm,” in Power Electronics and Motion Control Conference, 2009. IPEMC’09. IEEE 6th International, 2009, pp. 2136–2139.Google Scholar
  13. 13.
    A. Khodaei, “Microgrid optimal scheduling with multi-period islanding constraints,” Power Systems, IEEE Transactions on, vol. 29, pp. 1383–1392, 2014.Google Scholar
  14. 14.
    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,” International Journal of Electrical Power & Energy Systems, vol. 54, pp. 525–535, 2014.Google Scholar
  15. 15.
    M. Alipour, B. Mohammadi-Ivatloo, and K. Zare, “Stochastic risk-constrained short-term scheduling of industrial cogeneration systems in the presence of demand response programs,” Applied Energy, vol. 136, pp. 393–404, 2014.Google Scholar
  16. 16.
    M. Alipour, K. Zare, and B. Mohammadi-Ivatloo, “Optimal risk-constrained participation of industrial cogeneration systems in the day-ahead energy markets,” Renewable and Sustainable Energy Reviews, vol. 60, pp. 421–432, 2016.Google Scholar
  17. 17.
    M. Tasdighi, H. Ghasemi, and A. Rahimi-Kian, “Residential microgrid scheduling based on smart meters data and temperature dependent thermal load modeling,” Smart Grid, IEEE Transactions on, vol. 5, pp. 349–357, 2014.Google Scholar
  18. 18.
    G. M. Kopanos, M. C. Georgiadis, and E. N. Pistikopoulos, “Energy production planning of a network of micro combined heat and power generators,” Applied Energy, vol. 102, pp. 1522–1534, 2013.Google Scholar
  19. 19.
    M. Alipour, B. Mohammadi-Ivatloo, and K. Zare, “Stochastic Scheduling of Renewable and CHP-Based Microgrids,” Industrial Informatics, IEEE Transactions on, vol. 11, pp. 1049–1058, 2015.Google Scholar
  20. 20.
    W. Su, J. Wang, and J. Roh, “Stochastic energy scheduling in microgrids with intermittent renewable energy resources,” Smart Grid, IEEE Transactions on, vol. 5, pp. 1876–1883, 2014.Google Scholar
  21. 21.
    G. Piperagkas, A. Anastasiadis, and N. Hatziargyriou, “Stochastic PSO-based heat and power dispatch under environmental constraints incorporating CHP and wind power units,” Electric Power Systems Research, vol. 81, pp. 209–218, 2011.Google Scholar
  22. 22.
    J. Aghaei and M.-I. Alizadeh, “Multi-objective self-scheduling of CHP (combined heat and power)-based microgrids considering demand response programs and ESSs (energy storage systems),” Energy, vol. 55, pp. 1044–1054, 2013.Google Scholar
  23. 23.
    Z. W. Geem and Y.-H. Cho, “Handling non-convex heat-power feasible region in combined heat and power economic dispatch,” International Journal of Electrical Power & Energy Systems, vol. 34, pp. 171–173, 2012.Google Scholar
  24. 24.
    M. Moradi-Dalvand, B. Mohammadi-Ivatloo, and M. A. Fotouhi Ghazvini, “Short-Term Scheduling of Microgrid with Renewable sources and Combined Heat and Power,” in Smart microgrids, new advances, Challenges and Opportunities in the actual Power systems, ed.Google Scholar
  25. 25.
    A. K. Basu, S. Chowdhury, and S. Chowdhury, “Impact of strategic deployment of CHP-based DERs on microgrid reliability,” Power Delivery, IEEE Transactions on, vol. 25, pp. 1697–1705, 2010.Google Scholar
  26. 26.
    ”The gams software website,” Available:, 2012 [Online]
  27. 27.
    D. K. A. Brooke, and A. Meeraus. “Gams users guide”, Available: gams/GAMSUsersGuide.pdf [Online].

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of TabrizTabrizIran

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