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Optymalny i ekonomiczny projekt samodzielnego hybrydowego systemu energii odnawialnej zintegrowanego z magazynowaniem baterii przy użyciu algorytmu sztucznego pola elektrycznego
Języki publikacji
Abstrakty
In this paper, the optimal and economic design of a stand-alone hybrid photovoltaic-wind-battery storage (PV/WT/BA) system is performed using an artificial electric field algorithm (AEFA) to minimize the cost of energy generation (CEG) to supply an annual load during the 20 years of the useful life project. The AEFA algorithm is inspired by coulomb electrostatic force and has a high exploration power for optimal global achievement. In this study, the design problem is satisfied considering the probability of the energy not supplied (ENS) as a reliability constraint. The purpose of the design is to determine the number of photovoltaic panels, wind turbines, and batteries, taking into account the CEG and satisfaction of the ENS to provide annual load using the AEFA method. To verify the proposed AEFA, the results are compared with particle swarm optimization (PSO) and sine cosine algorithm (SCA) in view of CEG and ENS. The results show that the AEFA is superior to the PSO and SCA methods in finding the optimal solution with lower CEG and ENS (higher reliability).
W artykule przedstawiono optymalny i ekonomiczny projekt autonomicznego hybrydowego systemu magazynowania energii fotowoltaicznej z wiatrem (PV/WT/BA) z wykorzystaniem algorytmu sztucznego pola elektrycznego (AEFA) w celu zminimalizowania kosztów wytwarzania energii (CEG). dostarczać roczny ładunek w ciągu 20 lat projektu okresu użytkowania. Algorytm AEFA jest inspirowany siłą elektrostatyczną kulomba i ma wysoką moc eksploracyjną dla optymalnych globalnych osiągnięć. W niniejszym opracowaniu problem projektowy został rozwiązany, biorąc pod uwagę prawdopodobieństwo niedostarczenia energii (ENS) jako ograniczenie niezawodności. Celem projektu jest określenie ilości paneli fotowoltaicznych, turbin wiatrowych oraz baterii z uwzględnieniem CEG i satysfakcji ENS z zapewnienia rocznego obciążenia metodą AEFA. Aby zweryfikować proponowaną AEFA, wyniki porównuje się z optymalizacją roju cząstek (PSO) i algorytmem sinus cosinus (SCA) pod kątem CEG i ENS. Wyniki pokazują, że metoda AEFA przewyższa metody PSO i SCA w znalezieniu optymalnego rozwiązania o niższym CEG i ENS (wyższa niezawodność).
Wydawca
Czasopismo
Rocznik
Tom
Strony
313--319
Opis fizyczny
Bibliogr. 22 poz., rys., tab.
Twórcy
autor
- Department of Electrical Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran
autor
- Department of Physics, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran
Bibliografia
- [1] Jäger-Waldau , A., "Photovoltaics and renewable energies in Europe", Renewable and Sustainable Energy Reviews, 11(7), (2007), 1414-1437.
- [2] Nema, P., Nema, R.K., & Rangnekar, S., "A current and future state of art development of hybrid energy system using wind and PV-solar: A review". Renewable and Sustainable Energy Reviews, 13(8), (2009), 2096-2103.
- [3] Billinton, R., & Allan, R.N., "Reliability assessment of large electric power systems". Springer Science & Business Media, (2012).
- [4] Abdelhamid Kaabeche , Rachid Ibtiouen," Techno-economic optimization of hybrid photovoltaic/wind/diesel/battery generation in a stand-alone power system", Solar Energy 103 (2014), 171–182.
- [5] Hongxing Yang, Wei Zhou , Lin Lu , Zhaohong Fang ,” Optimal sizing method for stand-alone hybrid solar–wind system with LPSP technology by using genetic algorithm”, Solar Energy, 82 (2008), 354–367.
- [6] Katsigiannis, Y.A., Georgilakis, P.S., Karapidakis, E.S., “Multiobjective genetic algorithm solution to the optimumeconomic and environmental performance problem of small autonomous hybrid power systems with renewables”. Renew. Power Gen., IET 4 (5), (2010), 404–419.
- [7] Dufo, L.R., Bernal, A.J.L., “Multi-objective design of PV–wind– diesel–hydrogen–battery systems.” Renew. Energy 33 (12), (2008), 2559–2572.
- [8] Hadidian-Moghaddam, M.J., Arabi-Nowdeh, S., & Bigdeli, M. “Optimal sizing of a stand-alone hybrid photovoltaic/wind system using new grey wolf optimizer considering reliability.” Journal of Renewable and Sustainable Energy, 8(3), (2016), 035903.
- [9] Baghaee, H.R., Mirsalim, M., Gharehpetian, G.B., & Talebi, H. A. "Reliability/cost-based multi-objective Pareto optimal design of stand-alone wind/PV/FC generation microgrid system". Energy, 115, (2016), 1022-1041.
- [10] Gharavi, H., Ardehali, M.M., & Ghanbari- Tichi, S. "Imperial competitive algorithm optimization of fuzzy multi-objective design of a hybrid green power system with considerations for economics, reliability, and environmental emissions". Renewable Energy, 78, (2015), 427-437.
- [11] Bansal, A.K., Kumar, R., & Gupta, R.A. "Economic analysis and power management of a small autonomous hybrid power system (SAHPS) using biogeography based optimization (BBO) algorithm". IEEE Transactions on smart grid, 4(1), (2013), 638-648.
- [12] Jahannoosh , M., Nowdeh , S.A., Naderipour , A., Ka yab, H., Davoudkhani, I.F., & Klemeš, J.J. “New hybrid meta-heuristic algorithm for reliable and cost-effectivedesigning of photovoltaic/wind/fuel cell energy system considering load interruption probability.” Journal of Cleaner Production, 278, (2021), 123406.
- [13] Baghaee, H.R., Mirsalim, M., Gharehpetian, G.B., & Talebi, H.A. “ Reliability/cost-based multi-objective Pareto optimal design of stand-alone wind/PV/FC generation microgrid system.” Energy, 115, (2016), 1022-1041.
- [14] Maleki, A., Pourfayaz, F., & Rosen, M. A. “A novel framework for optimal design of hybrid renewable energy-based autonomous energy systems: a case study for Namin, Iran.” Energy, 98, (2016), 168-180.
- [15] Samy, M.M., Barakat, S., & Ramadan, H. S. “A flower pollination optimization algorithm for an off-grid PV-Fuel cell hybrid renewable system.” International journal of hydrogen energy, 44(4), (2019), 2141-2152.
- [16] Mohamed, M.A., Eltamaly, A.M., & Alolah, A. I. “Swarm intelligence-based optimization of grid-dependent hybridrenewable energy systems.” Renewable and Sustainable Energy Reviews, 77, (2017), 515-524.
- [17] Zhang, G., Shi, Y., Maleki, A., & Rosen, M.A. “Optimal location and size of a grid-independent solar/hydrogensystem for rural areas using an efficient heuristic approach.” Renewable Energy, (2020).
- [18] Yadav, A. “AEFA: Artificial electric field algorithm for global optimization.” Swarm and Evolutionary Computation, 48, (2019), 93-108.
- [19] Ahmadi, S., & Abdi, S. “Application of the Hybrid Big Bang–Big Crunch algorithm for optimal sizing of a stand-alone hybridPV/wind/battery system.” Solar Energy, 134, (2016), 366-374.
- [20] Mirjalili, S., Mirjalili, S.M., & Lewis, A. “Grey wolf optimizer.” Advances in engineering software, 69, (2014), 46-61.
- [21] Kaabeche, A., Diaf, S., & Ibtioue n, R. “Firefly-inspired algorithm for optimal sizing of renewable hybrid system considering reliability criteria.” Solar Energy, 155, (2017), 727-738.
- [22] Hosseinaliza deh, R., Shakouri, H., Amalnick , M.S., & Taghipour, P. “Economic sizing of a hybrid (PV–WT–FC) renewable energy system (HRES) for stand-alone usages by an optimization-simulation model: case study of Iran.” Renewable and Sustainable Energy Reviews, 54, (2016), 139-150
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
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