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[期刊论文]

Two-stage multi-strategy decision-making framework for capacity configuration optimization of grid-connected PV/battery/hydrogen integrated energy system

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author:

Lin, Liangguang (Lin, Liangguang.) [1] | Ou, Kai (Ou, Kai.) [2] (Scholars:欧凯) | Lin, Qiongbin (Lin, Qiongbin.) [3] (Scholars:林琼斌) | Unfold

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EI

Abstract:

The optimal capacity of energy storage facilities is a cornerstone for the investment and low-carbon operation of integrated energy systems (IESs). However, the intermittence of renewable energy and the different operating characteristics of facilities present challenges to IES configuration. Therefore, a two-stage decision-making framework is developed to optimize the capacity of facilities for six schemes comprised of battery energy storage systems and hydrogen energy storage systems. The objectives considered are to minimize the levelized cost of electricity (LCOE), power abandonment rate (PAR) and maximize self-sufficiency rate (SSR) simultaneously. In the first stage, each scheme is solved using NSGA-II. In the second stage, the weights of objective function are determined by entropy weight method, while the optimal individual is selected from the Pareto solutions by the technique for order preference by similarity to ideal solution approach. Life models of battery, fuel cell, and electrolyzer are introduced to quantify device replacement costs. Meanwhile, carbon trading mechanisms and time-of-use tariffs are considered to assess environmental and economic benefits. The results show that the hydrogen-electric coupling scheme demonstrated superior performance, with LCOE, SSR, and PAR of 0.6416 ¥/kWh, 48.9 %, and 1.96 %, respectively, and the hydrogen storage tank is closely related to LCOE and PAR. © 2024 Elsevier Ltd

Keyword:

Battery storage Carbon Decision making Entropy Fuel cells Genetic algorithms Hydrogen storage Investments

Community:

  • [ 1 ] [Lin, Liangguang]School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 2 ] [Ou, Kai]School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 3 ] [Lin, Qiongbin]School of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 4 ] [Xing, Jianwu]REFIRE Group, Shanghai; 201800, China
  • [ 5 ] [Wang, Ya-Xiong]School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China

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Source :

Journal of Energy Storage

Year: 2024

Volume: 97

8 . 9 0 0

JCR@2023

CAS Journal Grade:3

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 13

30 Days PV: 0

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