Modeling the influence of electric load curve indicators on storage system capacity in hybrid power system
Sibirskie èlektronnye matematičeskie izvestiâ, Tome 21 (2024) no. 2, pp. 669-683 Cet article a éte moissonné depuis la source Math-Net.Ru

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In modern conditions, the key direction of ensuring the energy security of critical infrastructure facilities is associated with the use of local generation sources combined with battery energy storage systems into hybrid power systems. An urgent problem in the design of such systems is the determination of their optimal parameters, including the nominal capacity of storage system. Methods from guidance documents or scientific methods based on dynamic programming, genetic algorithms, and others can be used to solve these problems. There is no unified approach to calculating the energy capacity of storage systems. The paper deals with a hybrid power system based on a fuel cell. The purpose of the work and its scientific contribution is to study the influence of the type and characteristics of consumer electrical load curves on the nominal capacity of the storage system for a hybrid power system with a fuel cell operating in a constant power mode. An algorithm for determination the energy capacity of batteries based on consumer electrical load curves specified with a certain discretization has been developed. The criteria for choosing the nominal capacity of batteries are: maximum discharge current, peak coverage and the sum of load peaks coverage, charge level. The algorithm is implemented in MS Excel, and the collection and analysis of the obtained results is automated using Python. The dependences of the energy capacity of lithium iron phosphate batteries on the indicators of the electric load curve were obtained for a hybrid power system with a fuel cell.
Keywords: modeling, algorithm, energy storage system, battery, hybrid power system, load curve, energy capacity.
Mots-clés : fuel cell
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A. B. Loskutov; I. A. Lipuzhin; A. V. Shalukho. Modeling the influence of electric load curve indicators on storage system capacity in hybrid power system. Sibirskie èlektronnye matematičeskie izvestiâ, Tome 21 (2024) no. 2, pp. 669-683. http://geodesic.mathdoc.fr/item/SEMR_2024_21_2_a49/

[1] D.A. Gaskova, A.G. Massel, “The technology of cyber threat analysis and risk assessment of cybersecurity violation of critical infrastructure”, Voprosy kiberbezopasnosti, 30:2 (2019), 42–49 https://cyberrus.info/wp-content/uploads/2019/07/42-49-230-19_6.-Gaskova.pdf | DOI

[2] Q. Ouyang, F. Wang, J. Chen, X. Li, “Power management of PEM fuel cell hybrid systems”, Proceedings of the 33rd Chinese Control Conference (Nanjing, China), 2014, 7082–7087 | DOI

[3] A. Loskutov, A. Dar'enkov, I. Lipuzhin, A. Shalukho, R. Bedretdinov, V. Vanyaev, A. Shakhov, “Energy management system for hybrid PEMFC-battery power source for stationary consumers”, Int. J. Hydrogen Energ., 55 (2024), 1109–1121 | DOI

[4] M. Marracci, P. Bolognesi, A. Buffi, G. Caposciutti, B. Tellini, “Analysis of current ripple effect on lithium batteries”, Proceedings of the 2020 IEEE 20th Mediterranean Electrotechnical Conference (MELECON) (Palermo, Italy), 2020, 109–113 https://ieeexplore.ieee.org/document/9140598 | DOI

[5] Electric energy storage systems. Planning and performance assessment. Power intensive applications and renewable energy sources integration, GOST R 58092.3.2-2023 (IEC TS 62933-3-2:2023, NEQ), 2023 https://internet-law.ru/gosts/gost/79806/

[6] Electric energy storage (EES) systems. Planning and performance assessment. General requirements, GOST R 58092.3.1-2020 (IEC TS 62933-3-1:2018), 2020 https://internet-law.ru/gosts/gost/73859/

[7] H. Wang, T. Wang, X. Xie, Z. Ling, G. Gao, X. Dong, “Optimal capacity configuration of a hybrid energy storage system for an isolated microgrid using quantum-behaved particle swarm optimization”, Energies, 11:2 (2018), 454 | DOI | MR

[8] G.M. Karve, K.M. Kurundkar, G.A. Vaidya, “Implementation of analytical method and improved particle swarm optimization method for optimal sizing of a standalone PV/wind and battery energy storage hybrid system”, Proceedings of the 2019 IEEE 5th International Conference for Convergence in Technology (I2CT) (Bombay, India), 2019, 1–5 https://ieeexplore.ieee.org/document/9033540

[9] S.G. Obukhov, A. Ibrahim, “Optimization of equipment composition of hybrid energy systems with renewable energy sources”, Bulletin of the South Ural State University. Ser. Power Engineering, 20:2 (2020), 64–76 | DOI

[10] J. Fossati, A. Galarza, A. Martin-Villate, L. Fontan, “A method for optimal sizing energy storage systems for microgrids”, Renewable Energy, 77 (2015), 539–549 | DOI

[11] J. Dulout, B. Jammes, C. Alonso, A. Anvari-Moghaddam, A. Luna, J.M. Guerrero, “Optimal sizing of a lithium battery energy storage system for grid-connected photovoltaic systems”, Proceedings 2017 IEEE Second International Conference on DC Microgrids (ICDCM) (Nuremburg, Germany), 2017, 582–587 https://ieeexplore.ieee.org/document/8001106 | DOI

[12] R. Sioshansi, S.H. Madaeni, P. Denholm, “A dynamic programming approach to estimate the capacity value of energy storage”, IEEE Transactions on Power Systems, 29:1 (2014), 395–403 https://ieeexplore.ieee.org/document/6601729

[13] J. Rurgladdapan, K. Uthaichana, B. Kaewkham-ai, “Optimal Li-Ion battery sizing on PEMFC hybrid powertrain using dynamic programming”, Proceedings 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA) (Melbourne, VIC, Australia), 2013, 472–477 https://ieeexplore.ieee.org/document/6566415 | DOI

[14] M. Tyagunov, R. Sheverdiev, “Application of a digital twin for research and optimization of local hybrid power complexes with RES-based generation”, Energy Systems, 7:1 (2022), 60–71 https://j-es.ru/index.php/journal/article/view/2022-1-007 | DOI

[15] G.A. Prankevich, Development of a mathematical model and methodology for selecting parameters of an energy storage device as an element of the power system, Cand. of Tech. S. thesis, Novosibirsk State Technical University, Novosibirsk, 2021 https://www.nstu.ru/files/dissertations/avtoreferat_prankevich_ga_164086446525.pdf

[16] N.I. Smolentsev, Development of electrical energy storage devices using the superconductivity effect, control methods and methods for optimizing energy flows in power supply systems, Doctor of Tech. S. thesis, South Ural State University (national research university), Chelyabinsk, 2021 https://search.rsl.ru/ru/record/01010992524