A Capacity Allocation Method for Advanced Adiabatic Compressed Air Energy Storage Systems Considering the Synergistic Complementarity of Multiple Heat Sources

被引:0
|
作者
Cui, Yang [1 ]
Yu, Yifan [1 ]
Fu, Xiaobiao [2 ]
Zhong, Wuzhi [3 ]
Zhao, Yuting [1 ]
机构
[1] Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Northeast Electric Power University, Ministry of Education Jilin, Jilin Province, 132012, China
[2] State Grid Jilin Electric Power Company, Jilin Province, Changchun,130012, China
[3] China Electric Power Research Institute, Haidian District, Beijing,100192, China
来源
基金
中国国家自然科学基金;
关键词
Air preheaters - Coal fired boilers - Coal storage - Collector efficiency - Compressed air - Compressed air energy storage - Compressibility of gases - Cost benefit analysis - Gas compressors - Linear programming - Nonlinear programming - Pressure vessel codes - Waste heat - Wind power;
D O I
10.13335/j.1000-3673.pst.2023.1683
中图分类号
学科分类号
摘要
Advanced adiabatic compressed air energy storage (AA-CAES) has natural cogeneration characteristics, which can effectively alleviate the problem of wind abandonment during the heating period. Suppose operational needs can be fully considered during the planning stage and energy storage capacity can be reasonably configured. In that case, it can maximize the clean replacement of coal-fired units while solving the problem of wind abandonment. This article proposes a capacity configuration model for AA-CAES systems with multiple heat sources that complement each other. Firstly, this model introduces an electric boiler to preheat the inlet air of the compressor at the energy input end to increase the compressor's gas transmission coefficient and the unit's heating capacity. Secondly, at the expansion heat source end, solar energy is collected through a reflective mirror field to improve the system's heat storage level And taking into account the actual operational efficiency constraints of each module of the energy storage system, to minimize the total operating cost, the optimal solution for energy storage capacity configuration is calculated. Once again, analyze the impact of factors such as the length of the heating period and environmental temperature on the investment cost recovery period and calculate the recovery period of the investment cost of this model under different circumstances to obtain the hard conditions for building this model to be profitable; Finally, based on typical daily loads and meteorological data during heating and non-heating periods in a certain region of Northeast China, a numerical analysis was completed in the IEEE-39 node system to verify the effectiveness of the proposed model. © 2024 Power System Technology Press. All rights reserved.
引用
收藏
页码:4195 / 4205
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