Statistical analysis for estimating the optimized battery capacity for roof-top PV energy system

被引：0

作者：

Zhang, Yuhang ^{[1
]}

Zhang, Yi ^{[1
]}

Zheng, Bo ^{[2
]}

Cui, Hongzhi ^{[3
]}

Qi, He ^{[4
]}

机构：

[1] Tsinghua Univ, Inst Future Human Habitats, Tsinghua Shenzhen Int Grad Sch, Shenzhen 518055, Peoples R China

[2] Tsinghua Univ, Inst Environm & Ecol, Tsinghua Shenzhen Int Grad Sch, Shenzhen Key Lab Ecol Remediat & Carbon Sequestrat, Shenzhen 518055, Peoples R China

[3] Shenzhen Univ, Coll Civil & Transportat Engn, State Key Lab Subtrop Bldg & Urban Sci, Shenzhen 518060, Guangdong, Peoples R China

[4] China Construct Sci & Technol Grp Cooperat, 2007 Pingshan Ave, Shenzhen 518118, Peoples R China

来源：

RENEWABLE ENERGY | 2025年 / 242卷

关键词：

Battery capacity estimation; Roof-top PV energy system; Allocation optimization; Statistical estimation regression; TECHNOECONOMIC ASSESSMENT; STORAGE;

D O I：

10.1016/j.renene.2025.122491

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Calculating optimized battery capacity for building rooftop photovoltaic (PV) consumption efficiently can benefit to decision making and policy implementation. Optimization for battery calculation requires speciality, but optimized battery capacities can be fitted based on similarities between buildings. This study aims to proposed a two-stage battery allocation model to efficiently estimate battery capacity for building PV consumption. An integer linear programming model has been constructed in the first stage to allocate battery for buildings. After optimizing part of buildings' battery capacities, quadratic polynomial regression analysis has been applied in the second stage to estimate optimized battery capacity using building information for the rest of buildings. Current scenario with electricity feed-in tariff, future scenario without feed-in tariff and baseline scenario have been considered for scenario analysis to compare method performance. 53,016 buildings in Pingshan district, Shenzhen, China have been used as a case study. For current and future scenarios, optimized battery capacity can be fitted by floor number and building area, the R2 score is higher than 0.94. Compared with current scenario, future scenario increases 5.81 % optimized battery capacity and 2.58 % annual cost. The proposed method can reduce nearly 100 % computation time with only 3.3 % accuracy loss compared with optimization method.

引用

页数：13

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