Antlion Optimization Algorithm for Optimal Self-Scheduling Unit ‎Commitment in Power System Under Uncertainties

Document Type : Research paper

Authors

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

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

Abstract

optimal and economic operation is one of the main topics in power systems. In this paper, a stochastic single objective framework for GenCoʼs optimal self-scheduling unit commitment under the uncertain condition and in the presence of SH units is proposed. In order to solve this problem, a new meta-heuristic optimization technique named antlion optimizer (ALO) has been used. Some of the capabilities of the ALO algorithm for solving the optimization problems included : (1) the exploration and utilization, (2) abiding convergence, (3) capable of maintaining population variety, (4) lack of regulation parameters, (5) solving problems with acceptable quality. To approximate the simulation conditions to the actual operating conditions, the uncertainties of the energy price, spinning and non-spinning reserve (operating services) prices, as well as the renewable energy resources uncertainty, are considered in the proposed model. The objective function of the problem is profit maximization and modeled as a mixed-integer programming (MIP) problem. The proposed model is implemented on an IEEE 118-bus test system and is solved in the form of six case studies. Finally, the simulation results substantiate the strength and accuracy of the proposed model.

Keywords

References

[1]    M. Shahidehpour et al., “Market operations in electric powersystems, forecasting, scheduling, and risk management”, John Wiley & Sons Ltd-IEEE Press, 2002.
[2]    A.j. Wood and B. Wollenberg, “Power generation operation and control”, John Wiley & Sons Ltd, 2013.
[3]    M. Sharafi 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., “Evaluatin the effectiveness of Mixed - Integer Linear programming for day-A head hydro-thermal self-scheduling 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. 1579-96, 2008.
[7]    A. Conejo et al., “Self-scheduling of a hydro producer in a pool-based electricity market”, IEEE Trans Power Syst. vol. 17, pp.1265-72, 2002.
[8]    M. Karami et al., “Scenario-basedsecurity constrained hydro- Therm coordination with volatile wind power Generation”, Renew. Sustain. Energy Rev ,vol. 28, pp. 726-737, 2013.
[9]    J. Aghaei et al., “A mixed-integer programming of generalized hydro - thermal self-scheduling of generating units”, Electr. Eng ,vol. 95, pp. 109-125, 2013.
[10]    A. Ahmadi et al., “A Mixed-integer programming of multi-objective Hydro - thermal self-scheduling”, Appl. Soft Comp. ,vol. 12, pp. 2137-46, 2012.
[11]    UN, World population prospects: the 2008 revision, highlights ”, New Yor : United Nations. Department of Economic and Social Affairs. Population Division. 2009.
[12]    D. Connolly et al., “A review of computer tools for analyzing the integration of renewable energy into various energy systems”, Appl.Energy, vol. 87, pp. 1059-82, 2010.
[13]    A. Foley et al., “A long-term analysis of pumped Hydrostorageto firm wind power”, Appl. Energy, vol. 137, pp. 638-648, 2015.
[14]    P. Ilak et al., “The impact of a wind variable generation on the Hydro generation water shadow price”, Appl. Energy, vol. 154, pp. 197-208, 2015.
[15]    K. Wang et al., “Optimal coordination of wind-hydro-thermal Based on water complementing wind”, Renew. Energy, vol. 60, pp.169-178, 2013.
[16]    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.
[17]    Z. Jianzhong et al., “Short-term hydro-thermal-wind complementary scheduling considering uncertainty of wind power using an enhanced multi-objective bee colony optimization algorithm”, Energy Conver. Manage., vol. 123, pp. 116-29, 2016.
[18]    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. 4935-42, 2011.
[19]    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.
[20]    L. Wu, M. Shahidehpour and T. Li, “GENCO’s risk-Based maintenance outage scheduling”, IEEE Trans. Power Syst, vol. 23, pp. 127-136, 2008.
[21]    L. Wu, M. Shahidehpour and Z. Li, “GENCO’s risk-constrained hydro-thermal scheduling”, IEEE Trans. Power Syst, vol. 23, pp.1847-58 , 2008.
[22]    Swedish Energy Agency, “Energy in Sweden 2010, Facts and Figures”, Swedish Energy Agency, 2010.
[23]    H. Moghimi et al., “Risk constrained self-scheduling of Hydro-wind units for short-term electricity markets Considering intermittency and uncertainty”, Renew. Sustain. Energy Rev, vol. 16, pp. 4734-43,2012.
[24]    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.
[25]    M. Li, Y. Li and G. Huang, “An interval Fuzzy two-stagesto chastic programming model for planning carbon dioxide trading under uncertainty”, Energy, vol. 36, pp. 5677-89 , 2011.
[26]    K. Meng et al., “Quantum inspired particle swarm optimization for valve point economic load dispatch”, IEEE Trans. Power Syst, vol. 25, pp. 215-22 , 2010.
[27]    T. Li and M. Shahidehpour, “Dynamic ramping in unit commitment”, IEEE Trans. Power Syst., vol. 22, pp. 1379-81, 2007.
[28]    M. Karami et al., “Mixed-integer programming of Security - constrained daily hydro - thermal generation scheduling”, Sci.Iran, vol. 20, pp.2036-50, 2013.
[29]    A. Ahmadi, M. Charw and J. Aghaei, “Risk-constrained optimal strategy for retailer forward contract portfolio”, Int. J. Elect. Power Energy Syst, vol. 53, pp. 704-13, 2013.
[30]    H. Wei et al., “Short-term optimal operation of hydro – wind–solar hybrid system with Improved generative adversarial networks” , Applied Energy, vol. 250 ,pp. 389-403, 2019.
[31]    G. Díaz, J. Coto and J. Aleixandre, “Optimal operation value of combined wind power and energy storage in multi-stage electricity markets”, Applied Energy, vol. 235,pp. 1153-68, 2019.
[32]    E. Akbari et al., “Stochastic programming based optimal bidding of compressed air energy storage with wind -thermal generation units in energy and reserve market”, Energy ,vol. 171, pp. 535-546, 2019.
[33]    J. Xu et al., “Economic - environmental equilibrium Based optimal scheduling strategy towards wind - solar - thermal power generation system under limited Resources”, Appl. Energy, vol. 231, pp.355-371, 2018.
[34]    S. Zabetian-Hosseini and M. Oloomi-Buygi, “How does large - scale wind power generation affect energy and reserve prices”, J. Oper. Autom. Power Eng., vol. 6, pp. 169-82, 2018.
[35]    S. Mirjalili, “The Antlion Optimizer”, Adv. Eng. Soft., vol. 83, pp. 80-98, 2015.
[36]    H. Dubey, M. Pandit and B. Panigrahi, “Hydro - thermal -wind scheduling employing novel Ant - lion optimization technique with composite ranking index”, Renew. Energy, vol. 99, pp. 18-34, 2016.
[37]    A. Wijesinghe and L. Lai, “Small hydro power plant analysis and development (Electric Utility Deregulation and Restructuring and Power Technologies IEEE)”, 4th Int. Conf., 2011.
[38]    M. Baneshi and F. Hadianfard, “Techno - economic feasibility of hybrid diesel / PV / wind / battery electricity generation systems for non- residential large electricity consumers under southern Iran climate conditions”, Energy Conv. Manage., vol. 127 , pp. 233-244, 2016.
[39]    F. Li and J. Qiu, “Multi-objective optimization for Integrated hydro-photovoltaic power system”, Appl. Energy, vol. 167, pp.377-84, 2016.
[40]    Z. Ding et al., “Performance analysis of a wind - solar Hybrid power generation system”, Energy Conv. Manage., vol. 181, pp. 223-34, 2019.
[41]    X. Wang et al., “Hydro - thermal - wind - PV Coordinated operation considering the comprehensive utilization of reservoirs”, Energy Conv. Manage., vol.198, 2019.
[42]    X. Wang et al., “Short–term hydro – thermal - wind- photovoltai complementary opertation of interconnected power systems”, Appl. Energy, vol. 229,pp. 945-62, 2018.
[43]    A. Zakaria et al., “Uncertainty models for stochastic optimizatio in renewable energy applications”, Renew. Energy Appl. , vol. 145, pp. 1543-71, 2020.
[44]    L.Wu, M. Shahidehpour and T. Li, “Stochastic Security - constrained unit commitment”, IEEE Trans. Power Syst., vol.22, pp. 800-811,2007.
[45]    L. Wu, M. Shahidehpour and T. Li, “Cost of reliability analysis based on stochastic unit commitment”, IEEE Trans. Power Syst. , vol. 23, pp.1364-74, 2008.
[46]    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 . on Power Syst., vol. 24, pp. 1841-54, 2009.
[47]    I. Damousis, A. Bakirtzis and P. Dokopolous, “Asolution to the unit-commitment problem using integer coded genetic algorithm”, IEEE Trans. Power Syst., vol.19, pp.198–205,2003.
[48]    O. Nilsson and D. Sjelvgren, “Hydro unit start-up costs and their impact on the shortterm scheduling strategies of swedish power producers”, IEEE Trans. Power Syst., vol. 12, pp. 38-44,1997.
[49]    H. Daneshi et al., “Mixed- integer programming method to solve constrained unit commitment with restricted operating zone limits”, IEEE.Int. Conon.  EIT, pp. 92-187, 2008.
[50]    M. AlRashidi and M. El-Hawary, “Hybrid particle swarm optimization approach for solving the discrete OPF problem considering the valve loading effects”, IEEE Trans. Power Syst. vol. 22, pp. 2030-38,2007.
[51]    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.2015-25,2005.
[52]    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.
[53]    Generalized Algebraic Modeling Systems (GAMS), [Online] Available : http://www.gams. com.
[54]    http: //motor.ece.iit. edu / data / PBUC data .pdf. Also market price is from http://motor.ece.iit.edu. /data/PBUC data.pdf.
[55]    http://motor.ece.iit.edu/data /118bus_abreu. xls.
[56]    http://motor.ece.iit.edu/data/118_nonsmooth. xls.
[57]    X. Yuan et al., “An extended NSGA-III for solution of multi-objective hydro-thermal-wind scheduling considering wind power cost”, Energy Conv. Manage, vol. 96, pp. 568-578, 2015.
[58]    P. Biswas, P. Suganthan and G. Amaratunga, “Optimal power flow solutions incorporating stochasticwind and solar power”, Energy Conv. Manage., vol.148, pp. 1194-1207, 2017.
[59]    M. Behnamfar, H. Barati and M. Karami, “Stochastic short - term hydro - thermal scheduling based on mixed integer programming with volatile wind power generation”, J.  Oper. Autom. Power Eng. , vol. 8, pp. 195-208, 2020.
[60]    X. Wang et al., “Improved multi - objective model and analysis of the coordinated operation of a hydro-wind-photovoltaic System”, Energy, 2017.
[61]    S. Mandal, B. Das and N. Hoque, “Optimum sizing of a stand-alone hybrid energy system for rural electrification in bangladesh”, J. Cleaner Prod., 2018.
[62]    Z. Movahediyan and A. Askarzadeh, “Multi-objective optimization framework of a Photovoltaic-diesel generator hybrid energy System considering operating reserve”, Sustain. Citiesand Soc., vol. 41, pp. 1-12, 2018.
[63]    E. Rakhshani, H. Mehrjerdi and A. Iqbal, “Hybrid Wind-Diesel- Battery System Planning Considering Multiple Different Wind Turbine Technologies Installation”, J. Cleaner Prod., 2019.
[64]    X. Shi et al., “Impacts of photovoltaic / wind turbine / Microgrid turbine and energy storage system for bidding model in power system”, J. Cleaner Prod., vol. 226, pp. 845-857, 2019.
[65]    O. Abedinia et al., “Optimal offering and Bidding Strategies of renewable energy based large consumer using a novel hybrid robust- stochastic approach”, J. Cleaner Prod., vol. 215, pp. 878-889, 2019.
[66]    L. Li et al., “Short -term wind power forecasting based on support vector machine with improved dragonfly algorithm”, J. Cleaner Prod., vol. 242, 2020.
[67]    H. Khaloie et al., “Co-optimized bidding strategy of an integrated wind-thermal-photovoltaic system in deregulated electricity market under uncertainties”, J. Cleaner Prod. , vol. 242, 2020.
[68]    A. Panda et al., “Hybrid power systems with emission Minimization : Multi-objective optimal operation”, J. Cleaner Prod., vol. 268 , 2020.
[69]    J. Lee, K. Aviso and R. Tan, “Multi-objective optimisation of hybrid power systems under uncertainties”, Energy, 2019.
[70]    Y. Yin, T. Liu and C. He, “Day-ahead stochastic coordinated scheduling for thermal-hydro-wind–pv Systems”, Energy, 2019.
[71]    A. Ioannou et al., “Multi-Stage stochastic optimization framework for power generation systems planning integrating hybrid uncertainty modelling”, Energy Eco., vol. 80, pp. 760-76,2019.
[72]    F. Zhu et al., “Short-term stochastic optimization of a hydro-wind-pv hybrid system under multiple uncertainies”, Energy Conv. Manage., 2020.
[73]    F. Alazemi and A. Hatata, “Ant-lion optimizer for optimum economic dispatch considering demand response as a visual power plant”, Electr. Power Compon. Syst., 2019.
[74]    F. Jabari et al., “Optimal short-term coordination of desalination, hydro and thermal units”, J. Oper. Autom. Power Eng., vol. 7, pp. 141-147,2019.
[75]    H. Siahkali, “Operation planning of wind farms with pumped storage plants based on interval type-2 fuzzy modeling of uncertainties”, J. Oper. Autom. Power Eng., vol. 8, pp.182 -194,2020.
Journal of Operation and Automation in Power Engineering
Volume 9, Issue 3
December 2021
Pages 226-241

Files

History

  • Receive Date: 26 October 2020
  • Revise Date: 16 January 2021
  • Accept Date: 09 February 2021

Share

How to cite

Statistics