Stochastic Short-Term Hydro-Thermal Scheduling Based on Mixed Integer Programming with Volatile Wind Power Generation

Document Type : Research paper

Authors

1 Department of Electrical Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran

2 Department of Electrical Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran

Abstract

This study addresses a stochastic structure for generation companies (GenCoʼs) that participate in hydro-thermal self-scheduling with a wind power plant on short-term scheduling for simultaneous reserve energy and energy market. In stochastic scheduling of HTSS with a wind power plant, in addition to various types of uncertainties such as energy price, spinning /non-spinning reserve prices, uncertainties of RESs, such as output power of the wind power plant are also taken into account. In the proposed framework, mixed-integer non-linear  programming of the HTSS problem is converted into a MIP. Since the objective of the study is to show how GenCosʼ aim to achieve maximum profit, mixed-integer programming is used here. Therefore, to formulate the MIP for the problem of HTSS with a wind power plant in the real-time modeling, some parameters like the impact of valve loading cost (VLC) that are accompanied by linear modeling, are considered. Furthermore, in thermal units, parameters such as prohibited operating zones (POZs) and different  uncertainties  like the energy  price and wind power are included  to formulate the problem more suitably. The point that is worth noting is the use of dynamic ramp rate (DRR). Also, the application of multi-functional curves (L) of hydro plants is considered  when studying  inter-unit scheduling. Finally, the required tests are conducted  on  a modified  IEEE 118-bus system to verify the accuracy and methodology of the proposed method.

Keywords

Main Subjects

References

[1]    M. Shahidehpour, H. Yamin and Z. Li, “Market operations in electric power systems, forecasting, scheduling, and risk management”, John Wiley & Sons Ltd-IEEE Press, New York, 2002.
[2]    A. Wood and B. Wollenberg, “Power generation operation and control”, John Wiley & Sons Ltd, New York, 2013.
[3]    M. Masouleh, et al., “Mixed-integer programming of stochastic hydro self-scheduling problem in joint energy and reserves markets,” Electr. Power Compon. Syst., vol. 44, pp. 752-762, 2016.
[4]    L. Lakshminarasimman and S. Subramanian, “Short-term scheduling of hydro-thermal power system with cascaded reservoirs by using modified differential evolution,” IEEE. Proc. Gener. Transm. Distrib., vol. 153,pp. 693-700, 2006.
[5]    A. Esmaeily et al., “Evaluating the effectiveness of mixedinteger linear programming for day-A head hydro-thermal selfscheduling considering price uncertainty and forced outage rate,” Energy, vol. 122, pp. 182-193, 2017.
[6]    S. Bisanovic, M. Hajro and M. Dlakic, “Hydro-thermal self-scheduling problem in a day-ahead electricity market ”, Electr. Power Syst. Res. vol. 78, pp.1579-1596, 2008.
[7]    M. Shahidehpour and M. Alomoush, “Restructured electrical power systems, Marcel Dekker”, New York, 2001
[8]    M. Giuntoli, “A novel mixed-integer linear algorithm to generate unit commitment and dispatching scenarios for reliability test grids”, Inter. Rev. Electr. Eng., vol. 6, pp. 1971-1982, 2011.
[9]    Q. Zeng, J. Wang and AL. Liu, “Stochastic optimization for unit commitment- A review”, IEEE Trans. Power Syst., vol. 30, 2014.
[10]    M. Gavrilas and V. Stahie, “Cascade hydro-power plants Optimization with honey bee mating optimization algorithm”, Inter. Rev. Electr. Eng., vol. 6, 2011.
[11]    A. Mezger and K. Almeida, “Short-term hydro-thermal scheduling with bilateral transactions via bundle Method ”, Electr. Power Energy Syst., vol. 29, pp. 387-396, 2007.
[12]    A. Conejo, J. Arroyo, J. Contreras and F. Villamor, “Self-schedulingof a hydro producer in A pool-based electricity market”, IEEE Trans. Power Syst., vol. 17, pp.1265-1272, 2002.
[13]    M. Karami, H. A. Shayanfar, J. Aghaei and A. Ahmadi, “Scenario-based security constrained hydro-therm coordination with volatile wind power generation”, Renewable Sustain. Energy Rev., vol. 28, pp.726-737,2013.
[14]    J. Aghaei, A. Ahmadi and H. A. Shayanfar and A. Rabiee, “A Mixed-integer programming of generalized hydro-Thermal self-scheduling of generating units”, Electr. Eng., vol. 95, no. 2, pp.109–125,2013.
[15]    A. Ahmadi, J. Aghaei, H. A. Shayanfar and A. Rabiee, “A Mixed-integer programming of multi-objective hydro-thermal self-scheduling”, Appl. Soft Comput. ,vol. 12, pp.2137-2146,2012.
[16]    UN, “World population prospects”, the 2008 revision highlights. New York ,United Nations. Department of Economic and Social Affairs. Population Division , 2009.
[17]    D. Connolly, H. Lund, B. Mathiesen and M. Leahy, “A review of computer tools for analyzing the integration of renewable energy into various energy systems”, Appl. Energy., vol. 87, pp.1059-1082, 2010.
[18]    A. Foley, P. Leahy, K. Li, E. McKeogh and A. Morrison, “Along term analysis of pumped hydro storage to firm wind power”, Appl Energy. vol. 137, pp. 638-648, 2015.
[19]    P. Ilak, I. Rajsl, S. Krajcar and M. Delimar, “The impact of a wind variable generation on the hydro generation water shadow price”, Appl. Energy., vol. 154, pp.197-208, 2015.
[20]    K. Wang, X. Luo, L. Wu and X. Liu, “Optimal coordination of wind-hydro-thermal based on water complementing wind”, Renew. Energy., vol. 60, pp.169-178, 2013.
[21]    E. Castronuovo and J. Lopes, “On the optimization of the daily operation of a wind-hydro power plant”, IEEE Trans. Power Syst., vol. 19 , pp.1599-1606, 2004.
[22]    Z. Jianzhong, et al., “Short-term hydro-thermal-wind complementary scheduling considering uncertainty ofwind power using an enhanced multi-objective bee colony optimization algorithm”, Energy Convers. Manage., vol. 123, pp.116-129, 2016.
[23]    H. Pousinho, V. Mendes and J. Catalão, “A risk-averse optimization model for trading wind energy in a market environment under uncertainty”, Energy, vol. 36, pp. 4935-4942, 2011.
[24]    J. Catalão, H. Pousinho and J. Contreras, “Optimal hydro scheduling and offering strategies considering price uncertainty and risk management”, Energy., vol. 37, pp. 237-244, 2012.
[25]    L. Wu, M. Shahidehpour and T. Li, “GENCO’s risk- based maintenance outage scheduling”, IEEE Trans. Power Syst., vol. 23, pp. 127-136, 2008.
[26]    L. Wu, M. Shahidehpour, Z. Li, “GENCOʼs risk-constrained Hydro-thermal scheduling”, IEEE Trans. Power Syst. vol. 23, pp.1847-1858, 2008.
[27]    C. Tseng and W. Zhu, “Optimal self-scheduling and bidding strategy of a thermal unit subject to ramp constraints and price uncertainty”, IET Gener. Transm. Distrib., vol. 4, pp. 125-137, 2010.
[28]    Swedish Energy Agency, “Energy in Sweden 2010, Facts and Figures”, 2010.
[29]    H. Moghimi, A. Ahmadi, A. Aghaei and M. Najafi, “Risk constrained self-scheduling of hydro-wind units for short term electricity markets considering intermittency and uncertainty”, Renewable. Sustain. Energy Rev., vol. 16, pp. 4734-4743, 2012.
[30]    G. Shrestha, S. Kai and L.Goel, “An efficient stochastic Self-scheduling technique for power producers in the deregulated power market”, Elect. Power Syst. Res., vol. 71, pp. 91-98, 2004.
[31]    M. Li, Y. Li and G. Huang, “An interval fuzzy two-stage stochastic programming model for planning carbon dioxid etrading under uncertainty”, Energy, vol. 36, pp. 5677-5689, 2011.
[32]    K. Meng, H. Wang, Z. Dong and W. KP, “Quantum inspired particle swarm optimization for valve point economic load dispatch”, IEEE Trans. Power Syst., vol. 25, pp. 215-222, 2010.
[33]    T. Li and M. Shahidehpour, “Dynamic ramping in unit commitment”, IEEE Trans. Power Syst., vol. 22, pp.1379-1381, 2007.
[34]    M. Karami, H.A. Shayanfar, J. Aghaei and A. Ahmadi, “Mixed-integer programming of security-constrained daily hydro-thermal generation scheduling”, Sci. Iran. Vol. 20, pp. 2036-2050, 2013.
[35]    A. Ahmadi, M. Charwand and J. Aghaei, “Risk-constrained optimal strategy for retailer forward contract portfolio”, Int. J. Elect. Power Energy Syst., vol. 53, pp. 704-713, 2013.
[36]    H. Wei, et al., “Short-term optimal operation of hydro-wind-solar hybrid system with Improved generative adversarial networks”, Appl. Energy, vol. 250, pp. 389-403, 2019.
[37]    G. Díaz, J. Coto and J. Aleixandre, “Optimal operation value of combined wind power and Energy storage in multi-stage electricity markets”, Appl. Energy, vol. 235, pp.1153-1168, 2019.
[38]    E. Akbari, R. Hooshmand, M. Gholipour and M. Parastegari, “Stochastic programming-based optimal bidding of compressed air energy storage with wind and thermal generation units in energy and reserve market”, Energy, vol. 171, pp. 535-546, 2019.
[39]    J. Xu, F. Wang, C. Lv, Q. Huang and H. Xie, “Economic-environmental equilibrium based optimal scheduling strategy towards wind-Solar-thermal power generation system under limitedresources”, Appl. Energy, vol. 231, pp. 355-371, 2018.
[40]    S. Zabetian and M. Oloomi, “How does large-scale wind power generation affect energy and reserve prices”, J. Oper. Autom. Power Eng., vol. 6, pp.169-182, 2018.
[41]    L.Wu, M. Shahidehpour and T. Li, “Stochastic security-constrained unit commitment”, IEEE Trans. Power Syst., vol. 22, pp. 800-811, 2007.
[42]    L.Wu, M. Shahidehpour and T. Li, “Cost of reliability Analysis based on stochastic unit commitment”, IEEE Trans. Power Syst., vol. 23, pp.1364-1374, 2008.
[43]    N.Amjady, J. Aghaei and H. A. Shayanfar, “Stochastic multi-objective market clearing of joint energy and reserves auctions ensuring power system security”, IEEE Trans. Power Syst., vol. 24, pp.1841-1854, 2009.
[44]    I. Damousis, A. Bakirtzis and P. Dokopolous, “A solution to the unit-commitment problem using integer coded genetic algorithm”, IEEE Trans. Power Syst., vol. 19, pp.198-205, 2003.
[45]    O. Nilsson and D.Sjelvgren, “Hydro unit start-up costs and their impact on the short-term scheduling strategies of swedish power producers”, IEEE Trans. Power Syst. vol.12, pp. 38-44, 1997.
[46]    H. Daneshi, A. Choobbari, M. Shahidehpour and Z. Li, “Mixed-integer programming method to solve security constrained unit commitment with restricted operating zone limits”, IEEE Int. Con. EIT., pp.187-192, 2008.
[47]    M. AlRashidi and M. El-Hawary, “Hybrid particle swarm optimization approach for solving the discrete OPF problemconsidering the valve loading effects”, IEEE Trans. Power Syst., vol. 22, pp. 2030-2038, 2007.
[48]    T. Li and M. Shahidehpour, “Price-based unit commitment: a case of lagrangian relaxation versus mixed-integer Programming”, IEEE Trans. Power Syst., vol. 20, pp. 2015-2025, 2005.
[49]    J. Arroyo and A. Conejo, “Optimal response of a thermal unit to an electricity spot market”, IEEE Trans. Power Syst., vol. 15, pp. 1098-1104, 2000.
[50]    Generalized Algebraic Modeling Systems (GAMS) , [Online] Available: http://www.gams.com
[51]    http : / / motor. ece.iit.edu / data / PBUC data . pdf. Also Market price is from http : / /motor . ece .iit.edu / data /PBUC data.pdf.
[52]    http://motor.ece.iit.edu/data /118bus_abreu. xls.
[53]    http://motor.ece.iit.edu/data/118_nonsmooth. xls.
[54]    B. Brown, R. Katz and A. Murph, “Timeseries models To simulateand forecast wind speed and wind power”, J. Appl. Meteorol., Vol. 23, pp.1184-1195, 1984.
Journal of Operation and Automation in Power Engineering
Volume 8, Issue 3
December 2020
Pages 195-208

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History

  • Receive Date: 23 April 2019
  • Revise Date: 19 July 2019
  • Accept Date: 14 September 2019

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