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• W4367301975 endingPage "137346" @default.
• W4367301975 startingPage "137346" @default.
• W4367301975 abstract "The high penetration rate of electric vehicles (EVs) will aggravate the uncertainty of both supply and demand sides of the power system, which will seriously affect the security of the power system. A microgrid (MG) system based on a hybrid energy storage system (HESS) with the real-time price (RTP) demand response and distribution network is proposed to deal with uncertainties. Through the guidance of RTP, the electricity consumption behavior of consumers and car owners is more adaptable to the output uncertainty of renewable energy source (RES) to ensure that the system has sufficient elastic capacity within the schedule cycle. The bi-level programming optimization model of HESS capacity is established, maximizing the MG's reliability and minimizing the comprehensive operating cost (COC). In addition, an EV's orderly charging/discharging strategy is formed, which effectively reduces operating costs and peak-to-valley load differences. The results show that EVs can effectively mitigate the peak-to-valley load difference by 20.5% under 100% participation in orderly charging/discharging. Under RTP-based demand response, MG can reduce the COC by 25.5%. In addition, the number of EVs participating in vehicle-to-grid also has different effects on scheduling. Based on the experimental data from Guizhou, results show that 200 electric private vehicles (EPVs) and ten electric buses (EBs) are the optimal number of vehicles to be solved; namely, the optimal capacity of the HESS is achieved." @default.
• W4367301975 created "2023-04-29" @default.
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• W4367301975 date "2023-07-01" @default.
• W4367301975 modified "2023-10-14" @default.
• W4367301975 title "Capacity optimization of hybrid energy storage system for microgrid based on electric vehicles’ orderly charging/discharging strategy" @default.
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• W4367301975 doi "https://doi.org/10.1016/j.jclepro.2023.137346" @default.
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