A Review of Mathematical Models of Energy Storage Systems for Electric Power Systems Simulation. Part II

Мұқаба

Дәйексөз келтіру

Толық мәтін

Ашық рұқсат Ашық рұқсат
Рұқсат жабық Рұқсат берілді
Рұқсат жабық Тек жазылушылар үшін

Аннотация

Currently the energy storage system (ESS) has become the development focus in the electric power systems (EPS) with the renewable energy power generation. At the same time, high penetration levels of ESS leads to a change the dynamic properties of the EPS. Accordingly, the analysis of the specifics of ESS operation becomes necessary for effective solution the problems of designing and operating EPS with ESS. Since mathematical simulation level is the main way to obtain the indicated information, the task of the adequacy of approaches and methods for modeling a processes in the ESS as part of the EPS becomes relevant. In the first part of the article, detailed mathematical models of the main elements of the ESS were considered. An analysis of mathematical models of ESS with different detailization level, depending on the type of energy storage device and a number of other factors, are presented within the framework of the second part of the article. The article also provides an overview of the approaches used to simplify the ESS models and their mathematical description. The areas of application of these models are considered. In addition, an analysis of the limitations and disadvantages associated with the simplification of models are presented. The article is an overview and can help in choosing an appropriate mathematical model of the ESS for solving a required designing and operating tasks.

Авторлар туралы

I. Razzhivin

National Research Tomsk Polytechnic University

Хат алмасуға жауапты Автор.
Email: lionrash@tpu.ru
Russia, Tomsk

A. Suvorov

National Research Tomsk Polytechnic University

Email: lionrash@tpu.ru
Russia, Tomsk

M. Andreev

National Research Tomsk Polytechnic University

Email: lionrash@tpu.ru
Russia, Tomsk

R. Ufa

National Research Tomsk Polytechnic University

Email: lionrash@tpu.ru
Russia, Tomsk

A. Askarov

National Research Tomsk Polytechnic University

Email: lionrash@tpu.ru
Russia, Tomsk

Әдебиет тізімі

  1. Tamilselvi S., Gunasundari S., Karuppiah N. A Review on Battery Modelling Techniques. Sustainability, 2021. 13. № 18: 10042. https://doi.org/10.3390/su131810042
  2. Hidalgo-Reyes J.I., Gómez-Aguilar J.F., Escobar-Jiménez R.F. Classical and fractional-order modeling of equivalent electrical circuits for supercapacitors and batteries, energy management strategies for hybrid systems and methods for the state of charge estimation: A state of the art review. Microelectronics Journal, 2019. V. 85. P. 109–128. https://doi.org/10.1016/j.mejo.2019.02.006.6
  3. Molina M.G. Dynamic Modelling and Control Design of Advanced Energy Storage for Power System Applications, In Dynamic Modelling, edited by Alisson Brito. London: IntechOpen, 2010. https://doi.org/10.5772/7092
  4. Jankovic Z., Novakovic B., Bhavaraju V., Nasiri A. Average modeling of a three-phase inverter for integration in a microgrid, IEEE Energy Conversion Congress and Exposition (ECCE), 2014. P. 793–799. https://doi.org/10.1109/ECCE.2014.6953477
  5. Rodriguez J.P. Dynamic Averaged Models of VSC-Based HVDC Systems for Electromagnetic Transient Programs. PhD Thesis. University of Montreal; 2013.
  6. Farrokhabadi M., König S., Cañizares C.A., Bhattacharya K. Battery Energy Storage System Models for Microgrid Stability Analysis and Dynamic Simulation, in IEEE Transactions on Power Systems, V. 33. № 2. P. 2301–2312. March 2018. https://doi.org/10.1109/TPWRS.2017.2740163
  7. Mousavi G.S.M., Nikdel M. Various battery models for various simulation studies and applications, Renewable and Sustainable Energy Reviews, 2014. V. 32. P. 477–485. https://doi.org/10.1016/j.rser.2014.01.048
  8. Kim Y.-H., Ha H.-D. Design of interface circuits with electrical battery models, in IEEE Transactions on Industrial Electronics, 1997. V. 44. № 1. P. 81–86. https://doi.org/10.1109/41.557502
  9. Dürr M., Cruden A., Gair S. Dynamic model of a lead acid battery for use in a domestic fuel cell system. Journal of Power Sources, 2006. V. 161. Iss. 2. P. 1400–1411. https://doi.org/10.1016/j.jpowsour.2005.12.075
  10. Cun J.P., Fiorina J.N., Fraisse M., Mabboux H. The experience of a UPS company in advanced battery monitoring, Proceedings of Intelec’96 – International Telecommunications Energy Conference, 1996. P. 646–653. https://doi.org/10.1109/INTLEC.1996.573404
  11. Pang S., Farrell J., Du J., Barth M. Battery state-of-charge estimation, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), 2001. V. 2. P. 1644–1649. https://doi.org/10.1109/ACC.2001.945964
  12. Chan H.L. A new battery model for use with battery energy storage systems and electric vehicles power systems, IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.00CH37077), 2000. V. 1. P. 470–475. https://doi.org/10.1109/PESW.2000.850009
  13. Daowd M., Omar N., Verbrugge B., Van den Bossche P., Van Mierlo J. Battery Models Parameter Estimation based on Matlab: Simulink.; 2013.
  14. Daowd M., Omar N., Verbrugge B. Battery models parameters estimation based on Matlab/ Simulink, the 25th world bat. hybrid and FC elec. Veh. Symp. & exh., 2010.
  15. Williamson S., Rimmalapudi S., Emadi A.C. Electrical modeling of renewable energy sources and energy storage devices. J Power Electron 2004. 4 (2).
  16. Zhan C.-J., Wu X.G., Kromlidis S. Two electrical models of the lead-acid battery used in a dynamic voltage restorer, IEE Proceedings – Generation, Transmission and Distribution, 2003. 150. (2). P. 175–182. https://doi.org/10.1049/ip-gtd:20030124
  17. Hegazy O., Barrero R., Mierlo J.V. An Advanced Power Electronics Interface for Electric Vehicles Applications, in IEEE Transactions on Power Electronics, 2013. V. 28. № 12. P. 5508–5521. https://doi.org/10.1109/TPEL.2013.2256469
  18. Naseri F., Karimi S., Farjah E., Schaltz E. Supercapacitor management system: A comprehensive review of modeling, estimation, balancing, and protection techniques, Renewable and Sustainable Energy Reviews, 2022. V. 155. https://doi.org/10.1016/j.rser.2021.111913
  19. Ban S., Zhang J., Zhang L. Charging and discharging electrochemical supercapacitors in the presence of both parallel leakage process and electrochemical decomposition of solvent, Electrochimica Acta, 2013. V. 90. P. 542–549. https://doi.org/10.1016/j.electacta.2012.12.056
  20. Naseri F., Farjah E., Ghanbari T. Online Parameter Estimation for Supercapacitor State-of-Energy and State-of-Health Determination in Vehicular Applications, in IEEE Transactions on Industrial Electronics, 2020. V. 67. № 9. P. 7963–7972. https://doi.org/10.1109/TIE.2019.2941151
  21. Cahela D.R., Tatarchuk B.J. Overview of electrochemical double layer capacitors, Proceedings of the IECON’97 23rd International Conference on Industrial Electronics, Control, and Instrumentation (Cat. No.97CH36066), 1997. V. 3. P. 1068–1073. https://doi.org/10.1109/IECON.1997.668430.
  22. Spyker R.L., Nelms R.M. Classical equivalent circuit parameters for a double-layer capacitor, in IEEE Transactions on Aerospace and Electronic Systems, 2000. V. 36. № 3. P. 829–836. https://doi.org/10.1109/7.869502
  23. Spyker R.L. Application of double-layer capacitors in power electronic systems. Ph.D. dissertation. Auburn University, 1997.
  24. Nelms R.M., Cahela D.R., Tatarchuk B.J. Modeling double-layer capacitor behavior using ladder circuits, in IEEE Transactions on Aerospace and Electronic Systems, 2003. V. 39. № 2. P. 430–438. https://doi.org/10.1109/TAES.2003.1207255
  25. Zubieta L., Bonert R. Characterization of double-layer capacitors for power electronics applications, in IEEE Transactions on Industry Applications, 2000. V. 36. № 1. P. 199–205. https://doi.org/10.1109/28.821816
  26. Funaki T., Hikihara T. Characterization and Modeling of the Voltage Dependency of Capacitance and Impedance Frequency Characteristics of Packed EDLCs, in IEEE Transactions on Power Electronics, 2008. V. 23. № 3. P. 1518–1525. https://doi.org/10.1109/TPEL.2008.921156
  27. Rafik F., Gualous H., Gallay R. Frequency, thermal and voltage supercapacitor characterization and modeling, Journal of Power Sources, 2007. V. 165. Iss. 2. P. 928–934. https://doi.org/10.1016/j.jpowsour.2006.12.021
  28. Zhang Y., Yang H. Modeling and characterization of supercapacitors for wireless sensor network applications, Journal of Power Sources, 2011. V. 196. Iss. 8. P. 4128–4135. https://doi.org/10.1016/j.jpowsour.2010.11.152
  29. Qu D., Shi H. Studies of activated carbons used in double-layer capacitors, Journal of Power Sources, 1998. V. 74. Iss. 1. P. 99–107. https://doi.org/10.1016/S0378-7753(98)00038-X
  30. Pean C., Rotenberg B., Simon P. Multi-scale modelling of supercapacitors: From molecular simulations to a transmission line model, Journal of Power Sources, 2016. V. 326. P. 680–685. https://doi.org/10.1016/j.jpowsour.2016.03.095
  31. Jiya I.N., Gurusinghe N., Gows R. Electrical Circuit Modelling of Double Layer Capacitors for Power Electronics and Energy Storage Applications: A Review. Electronics 2018. V. 7. № 11 P. 268. https://doi.org/10.3390/electronics7110268
  32. Saha P., Dey S., Khanra M. Modeling and State-of-Charge Estimation of Supercapacitor Considering Leakage Effect, in IEEE Transactions on Industrial Electronics, 2020. V. 67. № 1. P. 350–357. https://doi.org/10.1109/TIE.2019.2897506
  33. Alvarez V., Garcia A.F., Ramos-Paja C.A., Saavedra-Montes A.J., Arango E.I. Design of a low power system based on fuel cells. Revista EIA. 2012. V. 17. P. 85–103.
  34. Belhaj F.Z., El Fadil H., El Idrissi Z. New Equivalent Electrical Model of a Fuel Cell and Comparative Study of Several Existing Models with Experimental Data from the PEMFC Nexa 1200 W Micromachines 2021. V. 12. № 9. P. 1047. https://doi.org/10.3390/mi12091047
  35. Kundur P. Power System Stability and Control. McGraw-Hill Professional. 1994.
  36. Ise T., Murakami Y., Tsuji K. Simultaneous Active and Reactive Power Control of Superconducting Magnet Energy Storage Using GTO Converter. IEEE Trans. on PWRD 1986. V. 1. № 1. P. 143–150.
  37. Mosca C., Arrigo F., Mazza A. Mitigation of frequency stability issues in low inertia power systems using synchronous compensators and battery energy storage systems. IET Gener. Transm. Distrib., 2019. V. 13. P. 3951–3959. https://doi.org/10.1049/iet-gtd.2018.7008
  38. Akram U., Nadarajah M., Shah R., Milano F. A review on rapid responsive energy storage technologies for frequency regulation in modern power systems, Renewable and Sustainable Energy Reviews, 2020. V. 120. https://doi.org/10.1016/j.rser.2019.109626
  39. WECC battery storage dynamic modeling guideline, WECC Modeling and Validation Work Group. Salt Lake City. UT. USA. Rep., 2016. P. 1–38.
  40. WECC Battery Storage Guideline updates_ Bo 4-5-17 SLT 4-7-17 XX SC
  41. WECC Modeling and Validation Working Group, “WECC Type 4 Wind Turbine Generator Model – Phase II” January 23, 2013.
  42. WECC Modeling and Validation Working Group, “WECC Solar Plant Dynamic Modeling Guidelines” May 8, 2014.
  43. WECC Second Generation Wind Turbine Models, January 23, 2014.
  44. Pourbeik P., Sanchez-Gasca J.J., Senthil J., Weber J., Ellis A., Williams S., Seman S., Bolton K., Miller N., Nelson R.J., Nayebi K., Clark K., Tacke S. and Lu S. Value and Limitations of the Positive Sequence Generic Models of Renewable Energy Systems, WECC Modeling and Validation Working Group.
  45. Calero F., Cañizares C.A. and Bhattacharya K. Dynamic Modeling of Battery Energy Storage and Applications in Transmission Systems, in IEEE Transactions on Smart Grid, 2021. V. 12. № 1. P. 589–598. https://doi.org/10.1109/TSG.2020.3016298
  46. Ortega Á., Milano F. Generalized Model of VSC-Based Energy Storage Systems for Transient Stability Analysis, in IEEE Transactions on Power Systems, 2016. V. 31. № 5. P. 3369–3380. https://doi.org/10.1109/TPWRS.2015.2496217
  47. Choi J.-W., Sul S.-K. Inverter output voltage synthesis using novel dead time compensation, in IEEE Transactions on Power Electronics, 1996. V. 11. № 2. P. 221–227. https://doi.org/10.1109/63.486169
  48. Chiniforoosh S. et al. Definitions and Applications of Dynamic Average Models for Analysis of Power Systems, in IEEE Transactions on Power Delivery, 2010. V. 25. № 4. P. 2655–2669. https://doi.org/10.1109/TPWRD.2010.2043859
  49. Sanders S.R., Noworolski J.M., Liu X.Z., Verghese G.C. Generalized averaging method for power conversion circuits, in IEEE Transactions on Power Electronics, 1991. V. 6. № 2. P. 251–259. https://doi.org/10.1109/63.76811
  50. Sanders S.R., Verghese G.C. Synthesis of averaged circuit models for switched power converters, in IEEE Transactions on Circuits and Systems, 1991. V. 38. № 8. P. 905–915. https://doi.org/10.1109/31.85632
  51. Peralta J., Saad H., Dennetière S., Mahseredjian J. Dynamic performance of average-value models for multi-terminal VSC-HVDC systems, IEEE Power and Energy Society General Meeting, 2012. P. 1–8. https://doi.org/10.1109/PESGM.2012.6345610.
  52. Calero F., Cañizares C.A. and Bhattacharya K. Detailed and Average Battery Energy Storage Model Comparison, 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), 2019. P. 1–5. https://doi.org/10.1109/ISGTEurope.2019.8905772
  53. Rajashekara K. Propulsion System Strategies for Fuel Cell Vehicles, Tech. Rep., Energenix Ctr., Delphi Automotive Syst., 2000.
  54. Fuel Cell Control, Ltd., Tech. Rep., DC–DC Converter Module 2006 [Online]. Available: http://www.fuelcellcontrol.com/dcconverter.html [accessed 12 March 2022].
  55. Chen M., Rincon-Mora G.A. Accurate electrical battery model capable of predicting runtime and I–V performance, in IEEE Transactions on Energy Conversion, 2006. V. 21. № 2. P. 504–511. https://doi.org/10.1109/TEC.2006.874229
  56. Chen L., Liu Y., Arsoy A.B. Detailed modeling of superconducting magnetic energy storage (SMES) system, in IEEE Transactions on Power Delivery, 2006. V. 21. № 2. P. 699–710. https://doi.org/10.1109/TPWRD.2005.864075
  57. Andreev M. et al. A Hybrid Model of Type-4 Wind Turbine – Concept and Implementation for Power System Simulation. 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), 2020. P. 799–803. https://doi.org/10.1109/ISGT-Europe47291.2020.9248860
  58. Andreev M.V. et al. Hybrid Real-Time Simulator of Large-Scale Power Systems, in IEEE Transactions on Power Systems, March 2019. V. 34. № 2. P. 1404–1415. https://doi.org/10.1109/TPWRS.2018.2876668
  59. Friede W., Rael S., Davat B. Mathematical model and characterization of the transient behavior of a PEM fuel cell, in IEEE Transactions on Power Electronics, 2004. V. 19. № 5. P. 1234–1241. https://doi.org/10.1109/TPEL.2004.833449
  60. Li J., Cheng Y., Jia M. An electrochemical–thermal model based on dynamic responses for lithium iron phosphate battery, Journal of Power Sources, 2014. V. 255. P. 130–143. https://doi.org/10.1016/j.jpowsour.2014.01.007
  61. Freeborn T.J., Maundy B., Elwakil A.S. Fractional-order models of supercapacitors, batteries and fuel cells: a survey. Mater Renew Sustain Energy 2015. https://doi.org/10.1007/s40243-015-0052-y
  62. Ramadesigan V., Northrop P.W.C., De S., Santhanagopalan S., Braatz R.D., Subramanian V.R. Modeling and Simulation of Lithium-Ion Batteries from a Systems Engineering Perspective. J. Electrochem. Soc. 2012. 159. R31–R45. https://doi.org/10.1149/2.018203jes
  63. Huria T., Ceraolo M., Gazzarri J., Jackey R. High fidelity electrical model with thermal dependence for characterization and simulation of high power lithium battery cells, IEEE International Electric Vehicle Conference, 2012. P. 1–8. https://doi.org/10.1109/IEVC.2012.6183271
  64. Motapon S.N., Lupien-Bedard A., Dessaint L. A Generic Electrothermal Li-ion Battery Model for Rapid Evaluation of Cell Temperature Temporal Evolution, in IEEE Transactions on Industrial Electronics, 2017. V. 64. № 2. P. 998–1008. https://doi.org/10.1109/TIE.2016.2618363
  65. Li S., Ke B. Study of battery modeling using mathematical and circuit oriented approaches, IEEE Power and Energy Society General Meeting, 2011. P. 1–8. https://doi.org/10.1109/PES.2011.6039230

© Российская академия наук, 2023

Согласие на обработку персональных данных с помощью сервиса «Яндекс.Метрика»

1. Я (далее – «Пользователь» или «Субъект персональных данных»), осуществляя использование сайта https://journals.rcsi.science/ (далее – «Сайт»), подтверждая свою полную дееспособность даю согласие на обработку персональных данных с использованием средств автоматизации Оператору - федеральному государственному бюджетному учреждению «Российский центр научной информации» (РЦНИ), далее – «Оператор», расположенному по адресу: 119991, г. Москва, Ленинский просп., д.32А, со следующими условиями.

2. Категории обрабатываемых данных: файлы «cookies» (куки-файлы). Файлы «cookie» – это небольшой текстовый файл, который веб-сервер может хранить в браузере Пользователя. Данные файлы веб-сервер загружает на устройство Пользователя при посещении им Сайта. При каждом следующем посещении Пользователем Сайта «cookie» файлы отправляются на Сайт Оператора. Данные файлы позволяют Сайту распознавать устройство Пользователя. Содержимое такого файла может как относиться, так и не относиться к персональным данным, в зависимости от того, содержит ли такой файл персональные данные или содержит обезличенные технические данные.

3. Цель обработки персональных данных: анализ пользовательской активности с помощью сервиса «Яндекс.Метрика».

4. Категории субъектов персональных данных: все Пользователи Сайта, которые дали согласие на обработку файлов «cookie».

5. Способы обработки: сбор, запись, систематизация, накопление, хранение, уточнение (обновление, изменение), извлечение, использование, передача (доступ, предоставление), блокирование, удаление, уничтожение персональных данных.

6. Срок обработки и хранения: до получения от Субъекта персональных данных требования о прекращении обработки/отзыва согласия.

7. Способ отзыва: заявление об отзыве в письменном виде путём его направления на адрес электронной почты Оператора: info@rcsi.science или путем письменного обращения по юридическому адресу: 119991, г. Москва, Ленинский просп., д.32А

8. Субъект персональных данных вправе запретить своему оборудованию прием этих данных или ограничить прием этих данных. При отказе от получения таких данных или при ограничении приема данных некоторые функции Сайта могут работать некорректно. Субъект персональных данных обязуется сам настроить свое оборудование таким способом, чтобы оно обеспечивало адекватный его желаниям режим работы и уровень защиты данных файлов «cookie», Оператор не предоставляет технологических и правовых консультаций на темы подобного характера.

9. Порядок уничтожения персональных данных при достижении цели их обработки или при наступлении иных законных оснований определяется Оператором в соответствии с законодательством Российской Федерации.

10. Я согласен/согласна квалифицировать в качестве своей простой электронной подписи под настоящим Согласием и под Политикой обработки персональных данных выполнение мною следующего действия на сайте: https://journals.rcsi.science/ нажатие мною на интерфейсе с текстом: «Сайт использует сервис «Яндекс.Метрика» (который использует файлы «cookie») на элемент с текстом «Принять и продолжить».