Buy (120 RUB)
|Generate QR code
Open Access
Subscription or Fee Access
Aspects of renewable generation penetration in the electric power system
https://doi.org/10.15518/isjaee.2020.11.012
Abstract
The current upward trend in electricity demand determines the need to explore and apply alternative methods of generating electricity. At the same time, with an increase in the unit capacity and the share of renewable generation in the total installed capacity, studies aimed at a systematic study of the influence of the implemented facility on the parameters of the operating mode of the electric power system acquire relevance. A number of optimization tasks aimed at determining the optimal location and size of the generation units being implemented in terms of reducing power losses and maintaining an appropriate voltage level in the nodes of the power system can be noted here. Within the framework of this article, a variant of solving the indicated optimization problem for a typical 15-node IEEE scheme is presented by means of a software calculation using the bubble sorting method. On the way to achieve this goal, the following tasks were solved: an objective function was formed, which serves as an indicator of the optimality of the location and size of the generation units; limiting criteria are defined, such as voltage tolerance; the software implementation of the algorithm for calculating flows and power losses using the bubble sorting method has been carried out. The results of the work of the program code for two scenarios, in particular installation of one renewable generation unit with a different range of possible capacities, are presented and compared with the data obtained in the MATLAB / Simulink software.
About the Authors
Ya. Yu. Malkova
Tomsk Polytechnic University
Russian Federation
Russian Federation
Yana Yu. Malkova - Currently she is a Student of School of Energy & Power Engineering, Tomsk Polytechnic University.
Tomsk, Lenin Avenue, 30, 634050, tel.:+7(3822)60-63-33 (3454)
R. A. Ufa
Tomsk Polytechnic University
Russian Federation
Russian Federation
Ruslan A. Ufa - PhD., Currently he is an Associate professor of School of Energy & Power Engineering, Tomsk Polytechnic University.
Tomsk, Lenin Avenue, 30, 634050, tel.:+7(3822)60-63-33 (3454)
References
1. Global Energy and CO2 Status Report 2018 March 2019 International Energy Agency (IEA) [Электронный HTTPS://WWW.EENEWS.NET/ASSETS/2019/03/26/DOCUMENT_CW_01.PDF. - (Дата обращения: 25.10.2020).
2. Informatsionnyi obzor «Edinaya ehnergetiche-skaya sistema Rossii: promezhutochnye itogI» (operativnye dannye). Avgust 2020 goda. AO «SO EEHS» [Ehlektronnyi resurs]. - Rezhim dostupa: HTTP://WWW.SO-CDU.RU/FILEADMIN/FILES/COMPANY/REPORTS/UPS-REVIEW/2020/UPS_REVIEW_0820.PDF. - (Data obrash-cheniya: 30.10.2020).
3. Informatsionnyi obzor «Edinaya ehnergetiche-skaya sistema Rossii:promezhutochnye itogI» (opera-tivnye dannye). Avgust 2019 goda. AO «SO EEHS» [Ehlektronnyi resurs]. - Rezhim dostupa: HTTPS://SO-UPS.RU/FILEADMIN/FILES/COMPANY/REPORTS/UPS-REVIEW/2019/UPS_REVIEW_0819.PDF. - (Data obrashcheniya: 30.10.2020).
4. Bayindir, R. Effects of renewable energy sources on the power system / R. Bayindir, S. Demirbas, E. Irmak, U. Cetinkaya, A. Ova, and M. Yesil // 2016 IEEE International Power Electronics and Motion Control Conference (PEMC), Varna, Bulgaria. - 2016. - Pp. 388-393.
5. Liang, X. Emerging Power Quality Challenges Due to Integration of Renewable Energy Sources // IEEE Transactions on Industry Applications. - March-April 2017. - Vol. 53. - Iss. 2. - Pp. 855-866.
6. Verdejo, H. Impact of wind power generation on a large scale power system using stochastic linear stability / H. Verdejo, W. Escudero, W. Kliemann, A. Awerkin, C. Becker, and L. Vargas // Applied Mathematical Modelling. - September 2016. - Vol. 40. - Iss. 17-18. - Pp. 7977-7987.
7. Telu, N. Optimal Planning and Operation of Microgrid: A Comprehensive Review / N. Telu, R.G. Rao, and V.S. Vakula // 2018 International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (ICRIEECE), Bhubaneswar, India. - 2018. - Pp. 1167-1174.
8. Chen, S.X. Sizing of Energy Storage for Microgrids / S.X. Chen, H.B. Gooi, and M.Q. Wang // IEEE Transactions on Smart Grid. - March 2012. - Vol. 3. - Iss. 1. - Pp. 142-151.
9. Molina, M.G. Energy Storage and Power Electronics Technologies: A Strong Combination to Empower the Transformation to the Smart Grid // Proceedings of the IEEE. - November 2017. - Vol. 105. - Iss. 11. - Pp. 2191-2219.
10. Nadeem, F. Comparative Review of Energy Storage Systems, Their Roles, and Impact on Future Power Systems / F. Nadeem, S.M.S. Hussain, P.K. Tiwari, A.K. Goswami, and T.S. Ustun // IEEE Access. - December 2018. - Vol. 7. - Pp. 4555-4585.
11. Mirsaeidi, S. A Comprehensive Overview of Different Protection Schemes in Micro-Grids / S. Mirsaeidi, D.M. Said, M.W. Mustafa, M.H. Habibuddin, and M.R. Miveh // International Journal of Emerging Electric Power Systems. - July 2013. - Vol. 14. - Pp. 327-332.
12. Zhang, Ya. New Schemes of Feeder Protection for Distribution Networks Including Distributed Generation / Ya. Zhang, and F. Dai // Automation of Electric Power Systems. - 2009. - Vol. 33. - Iss. 12. - Pp. 7174.
13. Nazaripouya, H. Optimal sizing and placement of battery energy storage in distribution system based on solar size for voltage regulation / Nazaripouya, Yu. Wang, P. Chu, H.R. Pota, and R. Gadh // 2015 IEEE Power & Energy Society General Meeting, Denver, USA. - 2015. - Pp. 1-5.
14. Marra, F. A Decentralized Storage Strategy for Residential Feeders With Photovoltaics / F. Marra, G. Yang, C. Traeholt, J. Ostergaard, and E. Larsen // IEEE Transactions on Smart Grid. - March 2014. - Vol. 5. -Iss. 2. - Pp. 974-981.
15. Baran, M. Optimal sizing of capacitors placed on a radial distribution system / M. Baran, and F.F. Wu // IEEE Transactions on Power Delivery. - January 1989. -Vol. 4. - Iss. 1. - Pp. 735-743.
16. Zolotarev, A.A. Metody optimizatsii raspre-delitel'nykh protsessov/ A.A. Zolotarev. - Vologda: Izd-vo «Infra-InzheneriYA», 2014. - 160 s.
17. Colaco, M.J. A Survey of Basic Deterministic, Heuristic and Hybrid Methods for Single Objective Optimization and Response Surface Generation / M.J. Colaco, and G.S. Dulikravich // METTI - Thermal Measurements and Inverse Techniques, Rio de Janeiro, Brazil. - 2009. - Pp. 1-30.
18. Ventsov, N.N. Ehvolyutsionnyi podkhod k mode-lirovaniyu raspredelitel'nykh protsessov // Inzhenernyi vestnik Dona. - 2013. - № 4. - S. 1-9.
19. Dennis, J.E. Numerical Methods for Unconstrained Optimization and Nonlinear Equations / J.E. Dennis, and R.B. Schnabel. - Prentice Hall. - 1983. -375 p.
20. Goldberg, D.E. Genetic Algorithms in Search, Optimization, and Machine Learning / D.E. Goldberg. -Addison-Wesley Publishing Company. - 1989. - 412 p.
21. Kennedy J. Particle swarm optimization / J. Kennedy, and R. Eberhart // International Conference on Neural Networks (ICNN), Perth, Australia. - 1995. - Pp. 1942-1948.
22. Wang, C. Analytical approaches for optimal placement of distributed generation sources in power systems / C. Wang, and M.H. Nehrir // IEEE Transactions on Power Systems. - November 2004. - Vol. 19. -Iss. 4. - Pp. 2068-2076.
23. Singh, B. Impact assessment of DG in distribution systems from minimization of total real power loss viewpoint by using optimal power flow algorithms / B. Singh, and B.J. Gyanish // Energy Reports. - November 2018. - Vol. 4. - Pp. 407-417.
24. Chapman, A.C. Dynamic distributed energy resource allocation for load-side emergency reserve provision / A.C. Chapman, and G. Verbic // 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia), Melbourne, Australia. - 2016. - Pp. 1-6.
Review
For citations:
Malkova Ya.Yu., Ufa R.A. Aspects of renewable generation penetration in the electric power system. Alternative Energy and Ecology (ISJAEE). 2020;(31-33):113-122. (In Russ.) https://doi.org/10.15518/isjaee.2020.11.012
Views:
281
Email this article 