بهبود عملکرد شبکه توزیع با استراتژی هماهنگی بانک‌های خازنی قابل کلیدزنی و بازآرایی دینامیکی شبکه در حضور منابع تولید پراکنده
محورهای موضوعی : انرژی های تجدیدپذیر
رامین برجعلی نوه سی
1
,
داریوش نظرپور اکبری
2
,
رضا غنی زاده
3
,
پیام عالمی
4
1 - دانشکده فنی مهندسی- واحد ارومیه، دانشگاه آزاد اسلامی ، ارومیه، ایران
2 - دانشکده مهندسی برق- دانشگاه ارومیه، ارومیه، ایران
3 - دانشکده فنی مهندسی- واحد ارومیه، دانشگاه آزاد اسلامی ، ارومیه، ایران
4 - دانشکده فنی مهندسی- واحد ارومیه، دانشگاه آزاد اسلامی ، ارومیه، ایران
کلید واژه: منابع تولید پراکنده, خازن‌گذاری, بازآرایی دینامیک شبکه, خازن‌های قابل کلیدزنی, بارهای متغیر با زمان,
چکیده مقاله :
پنل های خورشیدی (PVs) و توربین های بادی (WTs) از مهمترین و پرکاربردترین منابع تولید پراکنده (DG) هستند. محدودیت‌های مکان احداث و مسائل زیست محیطی و اقتصادی، اتصال DG ها را به نقاط مورد نظر شبکه توزیع مشکل و در برخی موارد غیر ممکن کرده است. از این رو نصب DG ها در مکان‌های غیر بهینه، ممکن است باعث افزایش ولتاژ در نقاط اتصال مشترک (PCC) شود. در این مقاله، هماهنگی بانک‌های خازنی قابل کلیدزنی (SCB) و بازآرایی دینامیک شبکه بررسی شده و به منظور جلوگیری از تخطی حد بالا و پایین ولتاژ معرفی شده است. جهت دست یابی به مکان‌های بهینه SCB از الگوریتم بهینه‌سازی ازدحام ذرات (PSO) استفاده شده است. روش جدیدی در تعیین ظرفیت بهینه CBها (CSM) پیشنهاد شده که مقادیر بهینه توان راکتیو گره های مشخص شده توسط PSO را تعیین می کند. منحنی 24 ساعته به دست آمده از توان راکتیو پیشنهادی، نقش محوری را در طراحی SCB ایفا می‌کند. نتایج شبیه سازی به دست آمده بیانگر قابلیت و سودمندی رویکرد پیشنهادی در بهبود عملکرد شبکه توزیع است.
Wind turbines and photovoltaic arrays are the most prominent and widely used intermittent Distributed Generations (DGs). Due to the right-of-way, environmental, economical and other restrictions, the connection of this type of DGs to the preferred point of the distribution network is very difficult or in some cases impossible. Therefore, because of non-optimal locations, they may cause a voltage rise at the Points of Common Coupling (PCCs). In this paper, a coordinated design of Switchable Capacitor Banks (SCBs) with dynamic reconfiguration of the distribution network is proposed to avoid low and high voltage violations. For this purpose, distribution network reconfiguration is implemented to mitigate voltage rise at PCCs and Capacitor Banks (CBs) to solve the low voltage problem. A novel method is presented for determining the optimal size of CBs. The proposed Capacitor Sizing Method (CSM) effectively determines the optimal values of reactive power for the given nodes. The optimal locations of SCB are determined using particle swarm optimization algorithm. The 24–hour reactive power curve optimized by the proposed method plays a pivotal role in designing SCBs. The simulation results show that the implementation of the dynamic network reconfiguration and SCBs placement is required to maintain a standard voltage profile for better employment of DG embedded distribution networks.
[1] A. Azizivahed, S. Ghavidel, M.J. Ghadi, L. Li, J. Zhang, "New energy management approach in distribution systems considering energy storages", Proceeding of the IEEE/ICEMS, pp. 1-6, Sydney, NSW, Aug. 2017 (doi: 10.1109/ICEMS.2017.8056133).
[2] A.R. Jordehi, “Optimisation of electric distribution systems: A review”, Renewable and Sustainable Energy Reviews, vol. 51, pp. 1088-1100, Nov. 2015 (doi: 10.1016/j.rser.2015.07.004).
[3] S. Das, D. Das, A. Patra, “Operation of distribution network with optimal placement and sizing of dispatchable DGs and shunt capacitors”, Renewable and Sustainable Energy Reviews, vol. 113, Article Paper: 109219, Oct. 2019 (doi: 10.1016/j.rser.2019.06.026).
[4] F. Iqbal, M.T. Khan, A.S. Siddiqui, “Optimal placement of DG and DSTATCOM for loss reduction and voltage profile improvement”, Alexandria Engineering Journal, vol. 57, no. 2, pp. 755-785, June 2018 (doi: 10.1016/j.aej.2017.03.002).
[5] J.M. Home-Ortiz, O.D. Melgar-Dominguez, M. Pourakbari-Kasmaei, J.R.S. Mantovani, “A stochastic mixed-integer convex programming model for long-term distribution system expansion planning considering greenhouse gas emission mitigation” International Journal of Electrical Power and Energy Systems, vol. 100, pp. 86095, June 2019 (doi: 10.1016/j.ijepes.2018.12.042).
[6] C. Zhang, H. Chen, Z. Liang, M. Guo, D. Hua, and H. Ngan, “Reactive power optimization under interval uncertainty by the linear approximation method and its modified method”, IEEE Trans. on Smart Grid, vol. 9, pp. 4587-4600, Sept. 2018 (doi: 10.1109/TSG.2017.2664816)
[7] O.D. Montoya, W. Gil-González, L.F. Grisales-Noreña, “Relaxed convex model for optimal location and sizing of DGs in DC grids using sequential quadratic programming and random hyperplane approaches”, International Journal of Electrical Power and Energy Systems, vol. 115, Article Number: 105442, Feb. 2020 (doi: 10.1016/j.ijepes.2019.105442).
[8] Q. Zhao, S. Wang, K. Wang, B. Huang, “Multi-objective optimal allocation of distributed generations under uncertainty based on D-S evidence theory and affine arithmetic”, International Journal of Electrical Power and Energy Systems, vol. 112, pp. 70-82, Nov. 2019 (doi: 10.1016/j.ijepes.2019.04.044).
[9] A. Ameli, A. Ahmadifar, M.H. Shariatkhah, M. Vakilian, M.R. Haghifam, “A dynamic method for feeder reconfiguration and capacitor switching in smart distribution systems”, International Journal of Electrical Power and Energy Systems, vol. 85, pp. 200-211, Feb. 2017 (doi: 10.1016/j.ijepes.2016.09.008).
[10] V. Farahani, B.Vahidi, H.A. Abyaneh, “Reconfiguration and capacitor placement simultaneously for energy loss reduction based on an improved reconfiguration method”, IEEE Trans. on Power Systems,vol. 27, no. 2, pp. 587 – 595, May 2012 (doi: 10.1109/TPWRS.2011.2167688).
[11] H. B. Tolabi, A.L. Ara, R. Hosseini, “A new thief and police algorithm and its application in simultaneous reconfiguration with optimal allocation of capacitor and distributed generation units”, Energy, vol. 203, Article 117911, July 2020 (doi: 10.1016/j.energy.2020.117911).
[12] J.M. Home-Ortiz, R. Vargas, L.H. Macedo, R. Romero, “Joint reconfiguration of feeders and allocation of capacitor banks in radial distribution systems considering voltage-dependent models”, International Journal of Electrical Power and Energy Systems, vol. 107, pp. 298-310, May 2019 (doi: 10.1016/j.ijepes.2018.11.035).
[13] R.S. Rao, K. Ravindra, K. Satish, S.V.L. Narasimham, “Power loss minimization in distribution system using network reconfiguration in the presence of distributed generation”, IEEE Trans. on Power Systems, vol. 28, no. 1, pp. 317 – 325, Feb. 2013 (doi: 10.1109/TPWRS.2012.2197227).
[14] H. R. Esmaeilian, R. Fadaeinedjad, “Distribution system efficiency improvement using network reconfiguration and capacitor allocation”, International Journal of Electrical Power and Energy Systems, vol. 64, pp. 457–468, Jan. 2015 (doi: 10.1016/j.ijepes.2014.06.051) .
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[16] R. Enayatifar, M. Yousefi, A.H. Abdullah, A.N. Darus, “MOICA: A novel multi-objective approach based on imperialist competitive algorithm”, Applied Mathematics and Computation, vol. 219, no. 17, pp. 8829-8841, May 2013 (doi: 10.1016/j.amc.2013.03.099).
[17] A.R. Malekpour, T. Niknam, A. Pahwa, A.K. Fard, “Multi-objective stochastic distribution feeder reconfiguration in systems with wind power generators and fuel cells using the point estimate method”, IEEE Trans. on Power Systems, vol. 28, no. 2, pp. 1483-1492, May 2013 (doi: 10.1109/TPWRS.2012.2218261).
[18] E. Hooshmand, A. Rabiee, “Robust model for optimal allocation of renewable energy sources, energy storage systems and demand response in distribution systems via information gap decision theory”, IET Generation, Transmission and Distribution, vol. 13, no. 4, pp. 511-520, Feb. 2019 (doi: 10.1049/iet-gtd.2018.5671).
[19] A. Bayat, “Uniform voltage distribution based constructive algorithm for optimal reconfiguration of electric distribution networks”, Electric Power Systems Research, vol. 104, pp. 146-155, Nov. 2013 (doi: 10.1016/j.epsr.2013.06.010).
[20] A. Bayat, A. Bagheri, “Optimal active and reactive power allocation in distribution networks using a novel heuristic approach”, Applied Energy, vol.233-234, pp. 71-85, Jan. 2019 (doi: 10.1016/j.apenergy.2018.10.030).
[21] D. Shirmohammadi, H.W. Hong, A. Semlyen, G.X. Luo, “A compensation-based power flow method for weakly meshed distribution and transmission networks”, IEEE Trans. on Power Systems, vol. 3, no. 2, May 1988 (doi: 10.1109/59.192932).
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[23] A. Ehsan, Q. Yang, “State-of-the-art techniques for modelling of uncertainties in active distribution network planning: A review”, Applied Energy, vol. 239, pp. 1509-1523, April 2019 (doi: 10.1016/j.apenergy.2019.01.211)
[24] J. Kennedy, R. Eberhart, "Particle swarm optimization", Proceedings of the IEEE/ICNN, vol. 4, pp. 1942-1948, Perth, WA, Australia, Nov./Dec. 1995 (doi: 10.1109/ICNN.1995.488968).
[25] G. Shahgholian, M. Mahdavian, M. Noorani-Kalteh, M.R. Janghorbani, "Design of a new IPFC-based damping neurocontrol for enhancing stability of a power system using particle swarm optimization", International Journal of Smart Electrical Engineering, Vol. 3, No. 2, pp. 73-78, Spring 2014.
[26] A. M. Zin, A.K. Ferdavani, A.B. Khairuddin, M.M. Naeini, “Reconfiguration of radial electrical distribution network through minimum-current circular updating mechanism method”, IEEE Trans. on Power Systems, vol. 27, no, 2, pp. 968-974, May 2012 (doi: 10.1109/TPWRS.2011.2174258).
_||_[1] A. Azizivahed, S. Ghavidel, M.J. Ghadi, L. Li, J. Zhang, "New energy management approach in distribution systems considering energy storages", Proceeding of the IEEE/ICEMS, pp. 1-6, Sydney, NSW, Aug. 2017 (doi: 10.1109/ICEMS.2017.8056133).
[2] A.R. Jordehi, “Optimisation of electric distribution systems: A review”, Renewable and Sustainable Energy Reviews, vol. 51, pp. 1088-1100, Nov. 2015 (doi: 10.1016/j.rser.2015.07.004).
[3] S. Das, D. Das, A. Patra, “Operation of distribution network with optimal placement and sizing of dispatchable DGs and shunt capacitors”, Renewable and Sustainable Energy Reviews, vol. 113, Article Paper: 109219, Oct. 2019 (doi: 10.1016/j.rser.2019.06.026).
[4] F. Iqbal, M.T. Khan, A.S. Siddiqui, “Optimal placement of DG and DSTATCOM for loss reduction and voltage profile improvement”, Alexandria Engineering Journal, vol. 57, no. 2, pp. 755-785, June 2018 (doi: 10.1016/j.aej.2017.03.002).
[5] J.M. Home-Ortiz, O.D. Melgar-Dominguez, M. Pourakbari-Kasmaei, J.R.S. Mantovani, “A stochastic mixed-integer convex programming model for long-term distribution system expansion planning considering greenhouse gas emission mitigation” International Journal of Electrical Power and Energy Systems, vol. 100, pp. 86095, June 2019 (doi: 10.1016/j.ijepes.2018.12.042).
[6] C. Zhang, H. Chen, Z. Liang, M. Guo, D. Hua, and H. Ngan, “Reactive power optimization under interval uncertainty by the linear approximation method and its modified method”, IEEE Trans. on Smart Grid, vol. 9, pp. 4587-4600, Sept. 2018 (doi: 10.1109/TSG.2017.2664816)
[7] O.D. Montoya, W. Gil-González, L.F. Grisales-Noreña, “Relaxed convex model for optimal location and sizing of DGs in DC grids using sequential quadratic programming and random hyperplane approaches”, International Journal of Electrical Power and Energy Systems, vol. 115, Article Number: 105442, Feb. 2020 (doi: 10.1016/j.ijepes.2019.105442).
[8] Q. Zhao, S. Wang, K. Wang, B. Huang, “Multi-objective optimal allocation of distributed generations under uncertainty based on D-S evidence theory and affine arithmetic”, International Journal of Electrical Power and Energy Systems, vol. 112, pp. 70-82, Nov. 2019 (doi: 10.1016/j.ijepes.2019.04.044).
[9] A. Ameli, A. Ahmadifar, M.H. Shariatkhah, M. Vakilian, M.R. Haghifam, “A dynamic method for feeder reconfiguration and capacitor switching in smart distribution systems”, International Journal of Electrical Power and Energy Systems, vol. 85, pp. 200-211, Feb. 2017 (doi: 10.1016/j.ijepes.2016.09.008).
[10] V. Farahani, B.Vahidi, H.A. Abyaneh, “Reconfiguration and capacitor placement simultaneously for energy loss reduction based on an improved reconfiguration method”, IEEE Trans. on Power Systems,vol. 27, no. 2, pp. 587 – 595, May 2012 (doi: 10.1109/TPWRS.2011.2167688).
[11] H. B. Tolabi, A.L. Ara, R. Hosseini, “A new thief and police algorithm and its application in simultaneous reconfiguration with optimal allocation of capacitor and distributed generation units”, Energy, vol. 203, Article 117911, July 2020 (doi: 10.1016/j.energy.2020.117911).
[12] J.M. Home-Ortiz, R. Vargas, L.H. Macedo, R. Romero, “Joint reconfiguration of feeders and allocation of capacitor banks in radial distribution systems considering voltage-dependent models”, International Journal of Electrical Power and Energy Systems, vol. 107, pp. 298-310, May 2019 (doi: 10.1016/j.ijepes.2018.11.035).
[13] R.S. Rao, K. Ravindra, K. Satish, S.V.L. Narasimham, “Power loss minimization in distribution system using network reconfiguration in the presence of distributed generation”, IEEE Trans. on Power Systems, vol. 28, no. 1, pp. 317 – 325, Feb. 2013 (doi: 10.1109/TPWRS.2012.2197227).
[14] H. R. Esmaeilian, R. Fadaeinedjad, “Distribution system efficiency improvement using network reconfiguration and capacitor allocation”, International Journal of Electrical Power and Energy Systems, vol. 64, pp. 457–468, Jan. 2015 (doi: 10.1016/j.ijepes.2014.06.051) .
[15] A. Azizivahed, H. Narimani, M. Fathi, E. Naderi, H.R. Safarpour, M.R. Narimani, “Multi-objective dynamic distribution feeder reconfiguration in automated distribution systems”, Energy, vol. 147, pp. 896-914, March 2018 (doi: 10.1016/j.energy.2018.01.111).
[16] R. Enayatifar, M. Yousefi, A.H. Abdullah, A.N. Darus, “MOICA: A novel multi-objective approach based on imperialist competitive algorithm”, Applied Mathematics and Computation, vol. 219, no. 17, pp. 8829-8841, May 2013 (doi: 10.1016/j.amc.2013.03.099).
[17] A.R. Malekpour, T. Niknam, A. Pahwa, A.K. Fard, “Multi-objective stochastic distribution feeder reconfiguration in systems with wind power generators and fuel cells using the point estimate method”, IEEE Trans. on Power Systems, vol. 28, no. 2, pp. 1483-1492, May 2013 (doi: 10.1109/TPWRS.2012.2218261).
[18] E. Hooshmand, A. Rabiee, “Robust model for optimal allocation of renewable energy sources, energy storage systems and demand response in distribution systems via information gap decision theory”, IET Generation, Transmission and Distribution, vol. 13, no. 4, pp. 511-520, Feb. 2019 (doi: 10.1049/iet-gtd.2018.5671).
[19] A. Bayat, “Uniform voltage distribution based constructive algorithm for optimal reconfiguration of electric distribution networks”, Electric Power Systems Research, vol. 104, pp. 146-155, Nov. 2013 (doi: 10.1016/j.epsr.2013.06.010).
[20] A. Bayat, A. Bagheri, “Optimal active and reactive power allocation in distribution networks using a novel heuristic approach”, Applied Energy, vol.233-234, pp. 71-85, Jan. 2019 (doi: 10.1016/j.apenergy.2018.10.030).
[21] D. Shirmohammadi, H.W. Hong, A. Semlyen, G.X. Luo, “A compensation-based power flow method for weakly meshed distribution and transmission networks”, IEEE Trans. on Power Systems, vol. 3, no. 2, May 1988 (doi: 10.1109/59.192932).
[22] M.H.J. Bollen, Y. Yang, F. Hassan, "Integration of distributed generation in the power system - a power quality approach", Proceeding of the IEEE/ICHQP, pp. 1-8, Wollongong, NSW, Australia, Sept./Oct. 2008 (doi: 10.1109/ICHQP.2008.4668746).
[23] A. Ehsan, Q. Yang, “State-of-the-art techniques for modelling of uncertainties in active distribution network planning: A review”, Applied Energy, vol. 239, pp. 1509-1523, April 2019 (doi: 10.1016/j.apenergy.2019.01.211)
[24] J. Kennedy, R. Eberhart, "Particle swarm optimization", Proceedings of the IEEE/ICNN, vol. 4, pp. 1942-1948, Perth, WA, Australia, Nov./Dec. 1995 (doi: 10.1109/ICNN.1995.488968).
[25] G. Shahgholian, M. Mahdavian, M. Noorani-Kalteh, M.R. Janghorbani, "Design of a new IPFC-based damping neurocontrol for enhancing stability of a power system using particle swarm optimization", International Journal of Smart Electrical Engineering, Vol. 3, No. 2, pp. 73-78, Spring 2014.
[26] A. M. Zin, A.K. Ferdavani, A.B. Khairuddin, M.M. Naeini, “Reconfiguration of radial electrical distribution network through minimum-current circular updating mechanism method”, IEEE Trans. on Power Systems, vol. 27, no, 2, pp. 968-974, May 2012 (doi: 10.1109/TPWRS.2011.2174258).
