برنامه ریزی مشارکت واحدهای نیروگاهی در شرایط عدم قطعیت و تغییر پذیری قیمت سوخت با اهداف صرفه جویی اقتصادی و کاهش آلایندگی
محورهای موضوعی : مهندسی برق قدرتمهیار عباسی 1 , جواد ابراهیمی 2 , سجاد باقری 3 , موید محسنی 4 , علیرضا نیکنام کومله 5 , محمود جورابیان 6
1 - گروه مهندسی برق، دانشکده مهندسی ، دانشگاه اراک، اراک، ایران
2 - پژوهشکده انرژی¬های تجدید پذیر، دانشگاه اراک، اراک، ایران
3 - دانشکده فنی و مهندسی، گروه مهندسی برق، دانشگاه آزاد اسلامی، واحد اراک، اراک، ایران
4 - شرکت سهامی برق منطقه ای خوزستان، اهواز، ایران
5 - گروه مهندسی برق، دانشگاه صنعتی امیر کبیر، تهران، ایران
6 - مهندسی برق، دانشکده مهندسی، دانشگاه شهید چمران اهواز، اهواز، ایران
کلید واژه: عدم قطعیت, بازار برق, مشارکت نیروگاهها , امنیت شبکه برق, تولید پراکنده,
چکیده مقاله :
ناترازی تولید و مصرف و نگرانی برای تأمین انرژی مورد نیاز در شبکه قدرت همواره یکی از مشکلات بهرهبرداران شبکه بوده است. همچنین تولید برق در سطح کلان همواره صنعتی پرهزینه و پرآلاینده بوده است. لذا همیشه مهندسان و شرکتهای تولید توان به دنبال راهی ارزان و پاک برای تولید توان بودهاند تا هم بتوانند بر ناترازی توان غلبه کنند و هم پایداری و سطح مجاز آلاینده در شبکه را تضمین کنند. معمولترین منابعی که در شبکه قدرت برای تولید توان استفاده میشوند واحدهای گازی و حرارتی هستند، این واحدها سریعتر وارد مدار شده و البته هزینه تولید بیشتری هم دارند. اینکه چه زمانی از واحدهای حرارتی و چه زمان از واحدهای گازی در شبکه استفاده شود مسئله پیچیدهای است، که به عوامل مختلفی چون زمان پیک، قیمت سوخت و شرایط شبکه گازرسانی بستگی دارد. هیچ کدام از این موارد در حالت واقعی خود دارای قطعیت نیستند. از این رو در این مقاله عدم قطعیت منابع گازی و تغییر¬پذیری قیمت¬گاز در مساله مشارکت واحدهای نیروگاهی در سیستم قدرت توسط نرمافزار گمز مدل¬سازی و تحلیل شده¬است. همچنین از روش حل برنامهریزی غیرخطی عدد صحیح مختلط برای حل مسئله استفاده شده است. نتایج نشان می¬دهد که در صورت وجود عدم قطعیت در منابع گازی، بیشتر تمایل به بهره¬گیری از سایر نیروگاه¬های شبکه است تا امنیت و پایداری شبکه حفظ گردد.
The mismatch between production and consumption and concern for providing the energy needed in the power grid has always been one of the problems of grid operators. Also, large-scale electricity production has always been a costly and polluting industry. Therefore, engineers and power generation companies have always been looking for a cheap and clean way to generate power so that they can overcome the power imbalance and ensure stability and the allowed level of pollutants in the network. The most common sources used in the power grid to generate power are gas and thermal units, these units enter the circuit faster and, of course, have a higher production cost. When to use thermal units and when to use gas units in the network is a complex issue, which depends on various factors such as peak time, fuel price and gas supply network conditions. None of these things are certain in their true state. Therefore, in this article, the uncertainty of gas sources and the variability of gas prices in the problem of the participation of power plant units in the power system have been modeled and analyzed by GAMS software. Also, the mixed integer non-linear programming solution method has been used to solve the problem. The results show that if there is uncertainty in the gas sources, there is a greater tendency to use other power plants in the network in order to maintain the security and stability of the network.
[1] C. He, X. Zhang, T. Liu, L. Wu, and M. Shahidehpour, “Coordination of Interdependent Electricity Grid and Natural Gas Network—a Review,” Current Sustainable/Renewable Energy Reports, vol. 5, no. 1. 2018. doi: 10.1007/s40518-018-0093-9.
[2] W. van Ackooij, I. Danti Lopez, A. Frangioni, F. Lacalandra, and M. Tahanan, “Large-scale unit commitment under uncertainty: an updated literature survey,” Ann Oper Res, vol. 271, no. 1, 2018, doi: 10.1007/s10479-018-3003-z.
[3] H. Ren, J. Ortega, and D. Watts Casimis, “Review of Operating Reserves and Day-Ahead Unit Commitment Considering Variable Renewable Energies: International Experience,” IEEE Latin America Transactions, vol. 15, no. 11, 2017, doi: 10.1109/TLA.2017.8070418.
[4] Q. P. Zheng, J. Wang, and A. L. Liu, “Stochastic Optimization for Unit Commitment - A Review,” IEEE Transactions on Power Systems, vol. 30, no. 4, 2015, doi: 10.1109/TPWRS.2014.2355204.
[5] A. Tuohy, P. Meibom, E. Denny, and M. O’Malley, “Unit commitment for systems with significant wind penetration,” IEEE Transactions on Power Systems, vol. 24, no. 2, 2009, doi: 10.1109/TPWRS.2009.2016470.
[6] S. Chen, Z. Wei, G. Sun, D. Wang, Y. Zhang, and Z. Ma, “Stochastic look-ahead dispatch for coupled electricity and natural-gas networks,” Electric Power Systems Research, vol. 164, 2018, doi: 10.1016/j.epsr.2018.07.038.
[7] C. He, L. Wu, T. Liu, and Z. Bie, “Robust Co-Optimization Planning of Interdependent Electricity and Natural Gas Systems with a Joint N-1 and Probabilistic Reliability Criterion,” IEEE Transactions on Power Systems, vol. 33, no. 2, 2018, doi: 10.1109/TPWRS.2017.2727859.
[8] J. Munoz, N. Jimenez-Redondo, J. Perez-Ruiz, and J. Barquin, “Natural gas network modeling for power systems reliability studies,” in 2003 IEEE Bologna PowerTech - Conference Proceedings, 2003. doi: 10.1109/PTC.2003.1304696.
[9] T. Li, M. Eremia, and M. Shahidehpour, “Interdependency of natural gas network and power system security,” IEEE Transactions on Power Systems, vol. 23, no. 4, 2008, doi: 10.1109/TPWRS.2008.2004739.
[10] A. Alabdulwahab, A. Abusorrah, X. Zhang, and M. Shahidehpour, “Coordination of Interdependent Natural Gas and Electricity Infrastructures for Firming the Variability of Wind Energy in Stochastic Day-Ahead Scheduling,” IEEE Trans Sustain Energy, vol. 6, no. 2, 2015, doi: 10.1109/TSTE.2015.2399855.
[11] A. Zlotnik, L. Roald, S. Backhaus, M. Chertkov, and G. Andersson, “Coordinated Scheduling for Interdependent Electric Power and Natural Gas Infrastructures,” IEEE Transactions on Power Systems, vol. 32, no. 1, 2017, doi: 10.1109/TPWRS.2016.2545522.
[12] C. Liu, C. Lee, and M. Shahidehpour, “Look ahead robust scheduling of wind-thermal system with considering natural gas congestion,” IEEE Transactions on Power Systems, vol. 30, no. 1, 2015, doi: 10.1109/TPWRS.2014.2326981.
[13] A. Alabdulwahab, A. Abusorrah, X. Zhang, and M. Shahidehpour, “Stochastic security-constrained scheduling of coordinated electricity and natural gas infrastructures,” IEEE Syst J, vol. 11, no. 3, 2017, doi: 10.1109/JSYST.2015.2423498.
[14] B. Zhao, A. J. Conejo, and R. Sioshansi, “Unit commitment under gas-supply uncertainty and gas-price variability,” IEEE Transactions on Power Systems, vol. 32, no. 3, 2017, doi: 10.1109/TPWRS.2016.2602659.
[15] P. Khademi Astaneh and H. Sheikh Shahrokh Dehkordi, “Integrated Optimal Active and Reactive Power Planning in Smart Microgrids with Possibility of One-Hour Islanding,” Technovations of Electrical Engineering in Green Energy System, vol. 2, no. 2, pp. 36–50, 2023.
[16] M. Emadi, H. R. Massrur, E. Rokrok, and A. Samanfar, “A Comprehensive Framework for Optimal Stochastic Operating of Energy Hubs Integrated with Responsive Cooling, Thermal and Electrical Loads, and Ice Storage System by an Improved Self-Adaptive Slime Mold Optimization Algorithm,” Technovations of Electrical Engineering in Green Energy System, vol. 2, no. 1, pp. 77–95, 2023, doi: 10.30486/teeges.2022.1969195.1043.
[17] J. Ebrahimi, M. Abedini, M. M. Rezaei, and M. Nasri, “Optimum design of a multi-form energy in the presence of electric vehicle charging station and renewable resources considering uncertainty,” Sustainable Energy, Grids and Networks, vol. 23, 2020, doi: 10.1016/j.segan.2020.100375.
[18] J. Ebrahimi and M. Abasi, “Design of a Power Management Strategy in Smart Distribution Networks with Wind Turbines and EV Charging Stations to Reduce Loss, Improve Voltage Profile, and Increase Hosting Capacity of the Network,” Journal of Green Energy Research and Innovation, vol. 1, no. 1, pp. 1–15, Mar. 2024, doi: 10.61186/jgeri.1.1.1.
[19] M. Abedini, R. Eskandari, J. Ebrahimi, M. H. Zeinali, and A. Alahyari, “Optimal Placement of Power Switches on Malayer Practical Feeder to Improve System Reliability Using Hybrid Particle Swarm Optimization with Sinusoidal and Cosine Acceleration Coefficients,” Computational Intelligence in Electrical Engineering, vol. 11, no. 2, pp. 73–86, 2020.
[20] S. Darvish Kermani, M. Fayazi, J. Barati, and M. Joorabian, “Percentage of Islanding and Peninsulating Detection in Large Microgrids with Renewable Energy Resources with Multiple Connection Points to Different Grids,” Journal of Green Energy Research and Innovation, vol. 1, no. 2, pp. 1–14, 2024.
[21] B. Arandian, “Utilizing Hybrid Sine Cosine Shuffled Frog Leaping Algorithm for Optimal Energy Management in the Residential building with Renewable Energy Resources and Corresponding Uncertainties,” Journal of Green Energy Research and Innovation, vol. 1, no. 1, pp. 66–79, 2024.
[22] A. A. Karimi Taleb, H. Makvandi, and A. Oraee, “The Impact of Wind Direction on Wind Farms’ Output Power and Income,” Journal of Green Energy Research and Innovation, vol. 1, no. 1, pp. 34–47, 2024.
[23] J. Ebrahimi, M. Abedini, and M. M. Rezaei, “Optimal scheduling of distributed generations in microgrids for reducing system peak load based on load shifting,” Sustainable Energy, Grids and Networks, vol. 23, 2020, doi: 10.1016/j.segan.2020.100368.
[24] J. Ebrahimi and M. Abedini, “A two-stage framework for demand-side management and energy savings of various buildings in multi smart grid using robust optimization algorithms,” Journal of Building Engineering, vol. 53, 2022, doi: 10.1016/j.jobe.2022.104486.