الگوی بهینه تعمیرات و نگهداری شبکه توزیع در حضور مقررات انگیزشی
محورهای موضوعی : انرژی های تجدیدپذیر
ایمان خنکدار طارسی
1
,
محمود فتوحی فیروزآباد
2
,
حسین محمدنژاد شورکایی
3
,
مهدی احسان
4
1 - دانشکده مکانیک، برق و کامپیوتر- واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
2 - دانشکده مهندسی برق- دانشگاه صنعتی شریف، تهران، ایران
3 - دانشکده مکانیک، برق و کامپیوتر- واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
4 - دانشکده مهندسی برق- دانشگاه صنعتی شریف، تهران، ایران
کلید واژه: قابلیت اطمینان, تعمیرات پیشگیرانه, جریمه و پاداش, مقررات انگیزشی,
چکیده مقاله :
به دلیل پیچیدگی شبکه‌های توزیع چگونگی انجام تعمیرات پیشگیرانه بسیار ضروری است. یکی از عوامل موثر بر بهبود عملکرد شرکت‌های توزیع وضع مقررات انگیزشی می‌باشد، که خود سبب پیچیده‌تر شدن برنامه‌ریزی تعمیرات می‌گردد. در این مقاله مسئله برنامه ریزی تعمیرات پیشگیرانه به منظور ارتقای قابلیت اطمینان در حضور عامل انگیزشی جریمه و پاداش هدف قرار داده شده است. از این رو تابع سود شرکت توزیع که شامل هزینه تعمیرات و جریمه و پاداش می‌باشد، بهینه سازی می‌گردد. در مدل انگیزشی برای سنجش عملکرد شرکت‌های توزیع، شاخصهای قابلیت اطمینان به تفکیک فیدرها مقایسه می‌گردند و در مقابل، برنامه تعمیرات نیز برای هر فیدر به‌صورت مستقل به دست می‌آید. با توجه به تفاوت علل خرابی در فیدرها ناشی از ویژگی‌ها‌ی ساختاری آن‌ها و شرایط آب و هوایی، کوچک شدن مقیاس مقایسه عملکرد از شرکتها به فیدرها، علاوه بر افزایش دقت اختصاص جریمه و پاداش سبب بیشینه‌شدن تاثیر تعمیرات و نگهداری بر سطح قابلیت اطمینان آن‌ها توام با بهینگی هزینه‌ها می‌گردد. به این منظور برای اطلاعات یک شبکه واقعی شامل ۱۹۴ فیدر پس از دسته بندی فیدرها و اعمال جریمه و پاداش، بهینه سازی سود حاصل از ارائه خدمات به روش BPSO انجام شده است. درنتیجه برنامه تعمیرات پیشگیرانه به تفکیک فیدرها برای سه دسته کلی خرابی پرتکرار یعنی خرابی پست، خرابی خطوط و برخورد شاخه درختان در یک دوره ۵ ساله به دست آمده است. نتایج بهینه سازی نشان می‌دهد که روش ارائه شده همزمان با بیشینه نمودن سود شرکت های توزیع، عملکرد آن‌ها از نظر قابلیت اطمینان را نیز ارتقا می‌دهد.
Due to the complexity of distribution networks, preventive maintenance is very important. Incentive regulation is also one of the factors influencing the performance of distribution companies, which in turn complicates maintenance planning. This paper addresses the issue of preventive maintenance planning to enhance reliability in the presence of reward and penalty as a motivational factor. Therefore, the profit function of the distribution company, which includes the cost of repairs and reward-penalty, is optimized. In the incentive model for measuring the performance, reliability indices are compared by feeders, and in contrast, the repair program is obtained for each feeder separately. Due to the different causes of feeder failure such as their structural properties and weather conditions, increasing the accuracy of performance comparison from companies to feeders, in addition to penalties and rewards assigning leads to maximise the impact of maintenance at the level of their reliability and is accompanied by cost savings. For this purpose, the information of a real network including 194 feeders is considered as primary data. After categorizing the feeders and calculating penalties and rewards, the profit from the provision of services are optimized by BPSO method. As a result, the preventive maintenance program is obtained separately for feeders for three general categories of frequent failures, which includes substation failure, line failure and tree branch collision in a period of 5 years. The optimization results show that the proposed method, while maximizing the profits of distribution companies, also improves their performance in terms of reliability.
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[2] S.M.M. Larimi, M.R. Haghifam, A. Moradkhani, “Risk-based reconfiguration of active electric distribution networks”, IET Generation, Transmission and Distribution, vol. 10, no. 4, pp. 1006-1015, March 2016 (doi: 10.1049/iet-gtd.2015.0777).
[3] A. Moradkhani, M.R. Haghifam, S.M. Abedi, “Risk-based maintenance scheduling in the presence of reward penalty scheme”, Electric Power Systems Research, vol. 121. pp. 126-133, April 2015 (doi: 10.1016/j.epsr.2014.12.006).
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_||_[1] R.E. Brown, Electric Power Distribution Reliability, Marcel Dekker Inc. New York, 2002 (ISBN: 9780849375675).
[2] S.M.M. Larimi, M.R. Haghifam, A. Moradkhani, “Risk-based reconfiguration of active electric distribution networks”, IET Generation, Transmission and Distribution, vol. 10, no. 4, pp. 1006-1015, March 2016 (doi: 10.1049/iet-gtd.2015.0777).
[3] A. Moradkhani, M.R. Haghifam, S.M. Abedi, “Risk-based maintenance scheduling in the presence of reward penalty scheme”, Electric Power Systems Research, vol. 121. pp. 126-133, April 2015 (doi: 10.1016/j.epsr.2014.12.006).
[4] M. Ghasemi, R. Dashti, “Designing a decision model to assess the reward and penalty scheme of electric distribution companies”, Energy, vol. 147, pp. 329-336, March 2018 (doi: 10.1016/j.energy.2018.01.021).
[5] H. Mohammadnezhad-Shourkaei, M. Fotuhi-Firuzabad, "Impact of penalty–reward mechanism on the performance of electric distribution systems and regulator budget", IET Generation, Transmission and Distribution, vol. 4, no. 7, pp. 770–779, July 2010 (doi:10.1049/iet-gtd.2009.0202).
[6] H. Mohammadnezhad-Shourkaei, A. Abiri-Jahromi, M. Fotuhi-Firuzabad, "Incorporating service quality regulation in distribution system maintenance strategy", IEEE Trans. on Power Delivery, vol. 26, no. 4, pp. 2495-2504, Oct. 2011 (doi: 10.1109/TPWRD.2011.2142200).
[7] H. Mohammadnezhad-Shourkaei, M. Fotuhi-Firuzabad, R. Billinton, "Integration of clustering analysis and reward/penalty mechanisms for regulating service reliability in distribution systems", IET Generation, Transmission and Distribution, vol. 5, no. 11, pp. 1192–1200, Nov. 2011 (doi:10.1049/iet-gtd.2010.0743).
[8] M. Ghasemi, & R. Dashti, “A risk-based model for performance-based regulation of electric distribution companies”, Utilities Policy, vol. 45(C), pp. 36-44, 2017 (doi: 10.1016/j.jup.2017.01.001).
[9] M. Jooshaki, A. Abbaspour, M. Fotuhi-Firuzabad, M. Moeini-Aghtaie, “Developing a combinatorial reward– penalty scheme to facilitate integration of distributed generations”, CIRED- Open Access Proceedings Journal, vol. 2017, no.1, pp. 2682–2686, June 2017 (doi: 10.1049/oap-cired.2017.0342).
[10] I. Khonakdar Tarsi, M. Fotuhi Firuzabad, M. Ehsan, H. Mohammadnezhad-Shourkaei, M. Jooshaki, "Reliability incentive regulation based on reward-penalty mechanism using distribution feeders clustering", International Trans. on Electrical Energy Systems, vol. 31, no. 8, Article Number: e12958, Aug. 2021 (doi: 10.1002/2050-7038.12958).
[11] S. Sumesh, V. Potdar, A. Krishna, “Cubic reward penalty structure for power distribution companies”, International Journal of System Assurance Engineering and Management, vol. 10, nov. 3, pp. 350-368, June 2019 (doi: 10.1007/s13198-019-00783-z).
[12] V. Mostaghim, M. R. Haghifam, M. Simab., “Regulation of electrical distribution companies via efficiency assessments and reward-penalty scheme”, Journal of Operation and Automation in Power Engineering, vol. 5, no. 1, pp. 19-30, June 2017 (doi: 10.22098/JOAPE.2017.546).
[13] M. Jooshaki, H. Farzin, A. Abbaspour, M. Fotuhi-Firuzabad, M. Lehtonen, "A risk-based framework to optimize distributed generation investment plans considering incentive reliability regulations”, Proceeding of the ICED/CIRED, pp. 982-986, Spain, June 2019 (doi: 10.34890/457).
[14] M. Jooshaki, H. Farzin, A. Abbaspour, M. Fotuhi-Firuzabad, M. Lehtonen, “MILP model of electricity distribution system expansion planning considering incentive reliability regulations”, IEEE Trans. on Power Systems, vol. 34, no. 6, pp. 4300-4316, Nov. 2019 (doi: 10.1109/TPWRS.2019.2914516).
[15] A. Bagheri, M.M. Ghasemi, “Investigation maintenance of distribution network based on health index formed on risk management (CBRM) along with a case study in Golestan power distribution company”, Proceeding of the PSC, Tehran, Iran, 2018 (in Persian).
[16] S. Moradi, V. Vahidinasab, M. Kia, P. Dehghanian, “A mathematical framework for reliability‐centered maintenance in microgrids”, International Trans. on Electrical Energy Systems, vol 29. no.1, Article Number: e2691, Aug. 2018 (doi: 10.1002/etep.2691).
[17] A. Alizadeh, M.R. Saghafi, J. Rezaei, A. Fereidunian, H. Lesani, "Performance-based regulation framework for demand-side management", Proceeding of the IEEE/EPDC, pp. 54-59, Khoramabad, Iran, June 2019 (doi: 10.1109/EPDC.2019.8903761).
[18] A.A. Lakvan, M.R. Haghifam, “A new method to plan preventive repairs in the electricity distribution network taking into account the value of existing equipment”, Proceeding of the PSC, Tehran, Iran, 2017 (in Persian).
[19] M. Joushaki, A. Abbaspour, M. Fotuhi Firuzabad, M.M. Aghtaei, M. Lehtonen, “Designing a new procedure for reward and penalty scheme in performance‐based regulation of electricity distribution companies”, International Trans. on Electrical Energy Systems, vol. 28, no.11, Article Number: e2628. Nov. 2018 (doi: 10.1002/etep.2628).
[20] A. Bagheri, H.M. Peyhani, M. Akbari, “Financial forecasting using anfis networks with quantum-behaved particle swarm optimization”, Expert Systems with Applications, vol. 41, no. 14, pp. 6235–6250, Oct. 2014 (doi: 10.1016/j.eswa.2014.04.003).
[21] S. Gholizadeh, R. Moghadas, “Performance-based optimum design of steel frames by an improved quantum particle swarm optimization”, Advances in Structural Engineering, vol. 17, no. 2, pp. 143-156, Nov. 2016 (doi: 10.1260/1369-4332.17.2.143).
[22] H. Zhao, A.P. Sinha, W. Ge, “Effects of feature construction on classification performance: An empirical study in bank failure prediction”, Expert Systems with Applications, vol. 36, no. 2, pp.2633–2644, March 2009 (doi:10.1016/j.eswa.2008.01.053).
[23] M. Zambrano-Bigiarini, M. Clerc, R. Rojas, "Standard particle swarm optimisation 2011 at CEC-2013: A baseline for future PSO improvements", Proceeding of the IEEE/CEC, pp. 2337-2344, Mexico, June 2013 (doi: 10.1109/CEC.2013.6557848).
[24] J. Kennedy, R.C. Eberhart, "A discrete binary version of the particle swarm algorithm", Proceeding of the IEEE/ICSMC, pp. 4104-4108, Orlando, FL, USA, Oct. 1997 (doi: 10.1109/ICSMC.1997.637339).
[25] J. Liu, Y. Mei, X. Li, "An analysis of the inertia weight parameter for binary particle swarm optimization", IEEE Trans. on Evolutionary Computation, vol. 20, no. 5, pp. 666-681, Oct. 2016 (doi: 10.1109/TEVC.2015.2503422).
[26] B. Xue, S. Nguyen, M. Zhang, “A new binary particle swarm optimisation algorithm for feature selection”, Proceeding of the ECAEC, pp. 501–513, Granada, Spain, April 2014 (doi: 10.1109/TEVC.2015.2503422).
[27] A.H. El-Maleh, A.T. Sheikh, S.M. Sait, “Binary particle swarm optimization (BPSO) based state assignment for area minimization of sequential circuits”, Applied Soft Computing, vol. 13, no. 12, pp. 4832–4840, Dec. 2013 (doi: 10.1016/j.asoc.2013.08.004).
[28] B.H. Nguyen, B. Xue, P. Andreae, M. Zhang, "A new binary particle swarm optimization approach: momentum and dynamic balance between exploration and exploitation", IEEE Trans. on Cybernetics, vol. 51, no. 2, pp. 589-603, Feb. 2021 (doi: 10.1109/TCYB.2019.2944141).
