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:: Volume 7, Issue 2 (3-2019) ::
ieijqp 2019, 7(2): 95-112 Back to browse issues page
Appropriate load modeling for evaluating the reliability of smart distribution systems in order to decrease the computing time concerning the accuracy of calculations
Ali Mohammad Hariri1 , Maryam Akhavan hejazi * 1, Hamed Hashemi-Dezaki1
1- University of Kashan
Abstract:   (4221 Views)
This paper presents a novel appropriate load modeling for decreasing the load levels and evaluating the reliability of smart distribution system including various distributed generations by using the analytical method. Because of several uncertainties in a smart distribution system, the computing time and complexity of calculations in evaluating the reliability indices are the major challenges. Hence by decreasing the computing time of reliability evaluation methods, it is possible to solve the large optimization problems according to the accelerated methods. During the decrement of computing time, it is necessary to achieve a reasonable accuracy. In the proposed method, three scenarios for reliability-based load modeling are presented. The time-based aggregation without any default, time-based aggregation according to several assumptions, and amplitude-based aggregation are the three presented scenarios. Providing the mathematical modeling for implementation of the discussed scenarios is one of the contributions of this article.
The proposed method has been applied to the IEEE 33-Bus Test Distribution System and an actual distribution system from Esfahan Province Electrical Distribution Company (EPEDC). The obtained results illustrated the effectiveness of the proposed method. The results show that, by choosing an appropriate load level reduction scenario (time-based or amplitude-based aggregation scenarios), the 90% of the required time for reliability calculations can be saved, while less than 1% error has occurred. Performing the sensitivity analyses in order to determine the more important parameters in the proposed load level reduction methodology is another advantage of this article. The sensitivity analyses results imply that due to increment in load variance of the distribution system in presence of other uncertain parts, the more comprehensive and smart studies are needed.
 
Keywords: reliability, load modeling, analytical method, distributed generations (DGs), smart electrical distribution systems, sensitivity analysis, increasing the computing speed
Full-Text [PDF 4411 kb]   (1069 Downloads)    
Type of Study: Research |
Received: 2018/08/19 | Accepted: 2019/01/1 | Published: 2019/02/27
References
1. [1] J. Feng, S. Member, B. Zeng, D. Zhao, and S. Member, “Evaluating Demand Response Impacts on Capacity Credit of Renewable Distributed Generation in Smart Distribution Systems,” IEEE Access, vol. 6, no.1, pp. 14307-14317, 2017.
2. [2] Ž. Popović, S. Knezević, B. Brbaklić, “Optimal reliability improvement strategy in radial distribution networks with island operation of distributed generation,” IET Generation, Transmission & Distribution, vol. 12, no. 1, pp. 78–87, 2018.
3. [3] H. Farzin, M. Fotuhi-firuzabad, and M. Moeini-aghtaie, “Role of Outage Management Strategy in Reliability Performance of Multi-Microgrid Distribution Systems,” IEEE Transactions on Power Systems, vol. 33, no. 3, pp. 2359-2369, 2018.
4. [4] H. Hashemi-dezaki, S. Mohammad, M. Agah, and H. Askarian-abyaneh, “Sensitivity analysis of smart grids reliability due to indirect cyber-power interdependencies under various DG technologies , DG penetrations , and operation times,” ENERGY Convers. Manag., vol. 108, no. 1, pp. 377–391, 2016.
5. [5] S. Wang, S. Member, Z. Li, and L. Wu, “New Metrics for Assessing the Reliability and Economics of Microgrids in Distribution System,” IEEE Transactions on Power Systems, vol. 28, no. 3, pp. 2852–2861, 2013.
6. [6] H. Hashemi-dezaki and H. Haeri-khiavi, “Impacts of direct cyber-power interdependencies on smart grid reliability under various penetration levels of microturbine / wind / solar distributed generations,” IET Generation, Transmission & Distribution, vol. 10, no. 4, pp. 928–937, 2016.
7. [7] A. Escalera, B. Hayes, and M. Prodanovic, “Analytical method to assess the impact of distributed generation and energy storage on reliability of supply,” CIRED International Conference and Exhibition on ElectricityDistribution, pp. 2092–2096, 2017.
8. [8]علی احمدیان، مهدی صدقی، مسعود علی اکبر گلکار، " بهره برداری بهینه از شبکه های توزیع فعال با قابلیت جزیره ای شدن در حضور منابع تولید توان بادی، ذخیره سازها و خودروهای برقی "، نشریه کیفیت و بهره وری صنعت برق ایران، 3 (6)، 20- 32، ۱۳۹۳.
9. [9]آقاابراهیمی محمدرضا، تورانی مهدی، " بهینه سازی حضور خودروهای الکتریکی در کنار واحدهای تولید توان به منظور بهبود قابلیت اطمینان ریزشبکه "، نشریه کیفیت و بهره وری صنعت برق ایران، 5 (9)، 90- 99، ۱۳۹5.
10. [10] M. Moeini-aghtaie, H. Farzin, and M. Fotuhi-firuzabad, “Generalized Analytical Approach to Assess Reliability of Renewable-Based Energy Hubs,” IEEE Transactions on Power Systems, vol. 32, no. 1, pp. 368–377, 2017.
11. [11] A. Bagchi, S. Member, L. Goel, P. Wang, and S. Member, “Generation Adequacy Evaluation Incorporating an Aggregated Probabilistic Model of Active Distribution Network Components and Features,” IEEE Transactions on Smart Grid, vol. 9, no. 4, pp. 2667 - 2680, 2018.
12. [12] S. Du, E. Zio, and R. Kang, “A New Analytical Approach for Interval Availability Analysis of Markov Repairable Systems,” IEEE Transactions on Reliability, vol. 67, no. 1, pp. 118-128, 2018.
13. [13] L. Wang, S. Member, S. Sharkh, S. Member, A. Chipperfield, and A. Cruden, “Dispatch of Vehicle-to-Grid Battery Storage Using an Analytic Hierarchy Process,” IEEE Transactions on Vehicular Technology, vol. 11, no. 1, pp. 776-785, 2016.
14. [14] X. Wang, S. Member, R. Karki, and S. Member, “Exploiting PHEV to Augment Power System Reliability,” IEEE Transactions on Smart Grid, vol. 8, no. 5, pp. 2100-2108, 2017.
15. [15] H. Hashemi-dezaki, M. Hamzeh, H. Askarian-abyaneh, and H. Haeri-khiavi, “Risk management of smart grids based on managed charging of PHEVs and vehicle-to-grid strategy using Monte Carlo simulation,” Energy Convers. Manag., vol. 100, no. 1, pp. 262–276, 2015.
16. [16] F. Sebastian, M. Holz, M. Frey, and F. Gauterin, “Stochastic Forecasting of Vehicle Dynamics Using Sequential Monte Carlo Simulation,” IEEE Transactions on Intelligent Vehicles, vol. 2, no. 2, pp. 111-122, 2017.
17. [17] M. Bashir, S. Member, and G. Branch, “Optimal Sizing of Hybrid Wind / Photovoltaic / Battery Considering the Uncertainty of Wind and Photovoltaic Power Using Monte Carlo,” 11th International Conference on Environment and Electrical Engineering, 2012.
18. [18] S. Shojaabadi, S. Abapour, M. Abapour, and A. Nahavandi, “Simultaneous planning of plug-in hybrid electric vehicle charging stations and wind power generation in distribution networks considering uncertainties,” Renew. Energy, vol. 99, no. 1, pp. 237–252, 2016.
19. [19] M. Moeini-aghtaie, H. Farzin, and M. Fotuhi-firuzabad, “Generalized Analytical Approach to Assess Reliability of Renewable-Based Energy Hubs,” IEEE Transactions on Power Systems, vol. 32, no. 1, pp. 368-377, 2017.
20. [20] C. Chen, S. Member, W. Wu, S. Member, and B. Zhang, “An Analytical Adequacy Evaluation Method for Distribution Networks Considering Protection Strategies and Distributed Generators,” IEEE Transactions on Power Delivery, vol. 30, no. 3, pp. 1392-1400, 2015.
21. [21] S. Sun, Q. Yang, and W. Yan, “A novel Markov - based temporal - SoC analysis for characterizing PEV charging demand,” IEEE Transactions on Industrial Informatics, vol. 14, no. 1, pp. 156-166, 2018.
22. [22] E. B. Iversen, J. K. Møller, J. M. Morales, and H. Madsen, “Inhomogeneous Markov Models for Describing Driving Patterns,” IEEE Transactions on Smart Grid, vol. 8, no. 2, pp. 581-588, 2017.
23. [23] A. Ito, A. Kawashima, and T. Suzuki, “Model Predictive Charging Control of In-Vehicle Batteries for Home Energy Management Based on Vehicle State Prediction,” IEEE Transactions on Control Systems Technology, vol. 26, no. 1, pp. 51-64, 2018.
24. [24] Y. M. Atwa, S. Member, and S. Member, “Supply Adequacy Assessment of Distribution System Including Wind-Based DG During Different Modes of Operation,” IEEE Transactions on Power Systems, vol. 25, no. 1, pp. 78-86, 2010.
25. [25] W. Xudong and Q. Ling, “Reliability evaluation for the distribution system with distributed generation,” IEEE Transactions on Power Systems, vol. 24, no. 2, pp. 572-581, 2009.
26. [26] Y. Xu and C. Singh, “Adequacy and Economy Analysis of Distribution Systems Integrated With Electric Energy Storage and Renewable Energy Resources ,” IEEE Transactions on Power Systems, vol. 27, no. 4, pp. 2332-2341, 2012.
27. [27] Q. Jia, “On State Aggregation to Approximate Complex Value Functions in Large-Scale Markov Decision Processes,” IEEE Transactions on Automatic Control, vol. 56, no. 2, pp. 333-344, 2011.
28. [28] S. Mhanna, S. Member, A. C. Chapman, and G. Verbiˇ, “A Fast Distributed Algorithm for Large-Scale Demand Response Aggregation,” IEEE Transactions on Smart Grid, vol. 7, no. 4, pp. 2094-2107, 2016.
29. [29] U. Chauhan and N. Delhi, “Reliability Analysis of Wind Turbine System Using Importance Measures,” Annual IEEE India Conference (INDICON), pp. 2–6, 2015.
30. [30] M. E. Honarmand, M. R. Haghifam, H. Doosti Barhagh, J. Talebi, “Maintenance priorities in distribution transformers based on importance and risk,” 22nd International Conference and Exhibition on Electricity Distribution (CIRED), no. 371, pp. 10–13, 2013.
31. [31] R. Al-Otaibi, N. Jin, T. Wilcox, and P. Flach, Feature Construction and calibration for clustering daily load curves from smart-meter data," IEEE Transactions on Industrial Informatics, vol. 12, no. 2, pp. 645-654, April 2016.
32. [32] Z. Jiang, R. Lin, F. Yang and B. Wu, "A Fused Load Curve Clustering Algorithm Based on Wavelet Transform," in IEEE Transactions on Industrial Informatics, vol. 14, no. 5, pp. 1856-1865, May 2018.
33. [33] Probability Methods Subcommittee, “IEEE Reliability Test Systems,” IEEE Transactions on Power Apparatus and Systems, vol. PAS-98, no. 6, pp. 2047–2054, 1979.
34. [34] M. Imaizumi et al., “Irradiation And Measurement Of Solar Cells At Low Intensity , Low Temperature ( Lilt) Conditions,” 33rd IEEE Photovoltaic Specialists Conference, pp. 3–7, 2008.
35. [35] J. A. Weicht, F. U. Hamelmann, and G. Behrens, “Changing in irradiation behavior and temperature-coefficient variation caused by light-induced degradation of a-Si / µc-Si solar cells,” IEEE 42nd Photovoltaic Specialist Conference (PVSC), 2015.
36. [36] J. J. John, M. C. Raval, A. Kottantharayil, and C. S. Solanki, “Novel PV Module Cleaning System using ambient moisture and self- cleaning coating,” IEEE 39th Photovoltaic Specialists Conference (PVSC), pp. 1481–1483, 2013.
37. [37] A. Syafiq, A. K. Pandey, N. A. Rahim, “Photovoltaic glass cleaning methods: An overview,” 4th IET Clean Energy and Technology Conference (CEAT), 2016.
38. [38] R. Karki, S. Member, and R. Billinton, “Reliability / Cost Implications of PV and Wind Energy Utilization in Small Isolated Power Systems,” IEEE Transactions on Energy Conversion, vol, 16, no. 4, 2001.
39. [39] M. A. Khallat and V. Tech, “A Probabilistic Approach To Photovoltaic Generator Performance Prediction,” IEEE Power Engineering Review, vol. PER-6, no. 9, pp. 34-40, 1986.
40. [40] H. Haddadian and R. Noroozian, “Multi-microgrids approach for design and operation of future distribution networks based on novel technical indices,” Appl. Energy, vol. 185, no. 1, pp. 1–14, 2016.
41. [41] A. A. Abdelsalam and E. F. El-saadany, “Probabilistic approach for optimal planning of distributed generators with controlling harmonic distortions,” IET Generation, Transmission & Distribution, vol. 7, no. 10, pp. 1105-1115, 2013 .
42. [42] V. Astapov, “The Applicability of Zero Inflated Beta Distributions for Stochastic Modeling of PV Plants Power Output,” 2018 19th Int. Sci. Conf. Electr. Power Eng., pp. 1–6, 2018.
43. [43] A. Sándor Kazsoki, B. Hartmann, “Mathematical approximation of the production of a photovoltaic system,” 6th International Youth Conference on Energy (IYCE), 2017.
44. [44] Li W. Risk assessment of power systems: models, methods, and applications. Hoboken, NJ (USA): John Wiley & Sons Inc; 2005.
45. [45] S. Ghasemi and J. Moshtagh, “Radial distribution systems reconfiguration considering power losses cost and damage cost due to power supply interruption of consumers,” International Journal on Electrical Engineering and Informatics, vol. 5, no. 3, pp. 297–315, 2013.
46. [46] A. K. Saonerkar and B. Y. Bagde, “Optimized DG Placement in Radial Distribution System with Reconfiguration and Capacitor Placement Using Genetic Algorithm,” IEEE International Conference on Advanced Communications, Control and Computing Technologies, no. 978, pp. 1077–1083, 2014.
47. [47] M. Al-muhaini, S. Member, G. T. Heydt, and L. Fellow, “A Novel Method for Evaluating Future Power Distribution System Reliability,” IEEE Transactions on Power Systems, vol. 28, no. 3, pp. 3018-3027, 2013.
48. [48] Y. Qi, F. Wang, and D. Wang, “Reliability Analysis of Distribution System with DG Considering Operation Homogeneity,” China International Conference on Electricity Distribution (CICED), pp. 10–13, 2016.
49. [49] H. H. Dezaki, H. A. Abyaneh, A. Agheli, and K. Mazlumi, “Optimized Switch Allocation To Improve The Restoration Energy In Distribution Systems,” Journal of Electrical Engineering, vol. 63, no. 1, pp. 47–52, 2012.
50. [50] http://www.mobinsb.com/News/Details/4/15682
51. [51] اداره کل هواشناسی استان اصفهان، " نمایه اقلیمی شهرستان کاشان"، تهیه شده در اداره تحقیقات هواشناسی کاربردی، شهریور 1394.
52. [52] http://re.jrc.ec.europa.eu/pvgis


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Hariri A M, Akhavan hejazi M, Hashemi-Dezaki H. Appropriate load modeling for evaluating the reliability of smart distribution systems in order to decrease the computing time concerning the accuracy of calculations. ieijqp 2019; 7 (2) :95-112
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Volume 7, Issue 2 (3-2019) Back to browse issues page
نشریه علمی- پژوهشی کیفیت و بهره وری صنعت برق ایران Iranian Electric Industry Journal of Quality and Productivity
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