基于Trnsys能耗预测的村镇建筑不同供能方式碳-经济分析

doi:10.3969/j.issn.2097-0706.2023.08.008

摘要/Abstract

摘要：

在碳中和背景下，村镇生物质与太阳能资源具有广阔的应用前景。以河北迁安地区某农宅为基础，建立三维模型，采用Trnsys对农宅能耗进行预测，通过实测数据对模型进行验证。分析不同供能方式的碳排放和经济性，结果表明：该农宅以天然气为燃料的供暖方式具有较高的CO₂减排潜力，CO₂排放量为2 019.75 kg/年，以生物质+标准煤的供暖方式具有较好的经济性，花费1 034.42 元/年；当安装太阳能光伏发电系统时，采用生物质+空调的采暖方式具有较好的经济性，花费464.62 元/年，且CO₂排放量为3 060.56 kg/年，仅次于天然气。因此，合理利用天然气、生物质和太阳能成为减轻供暖经济负担，降低CO₂排放的有效方法。

关键词: 碳中和, 能耗预测, 生物质能, 太阳能光伏发电, CO₂排量

Abstract:

To achieve carbon neutrality，biomass and solar power are of promising prospects in rural areas. A 3D model based on rural housings in Qian'an city，Hebei province is constructed. Energy consumption of these buildings is predicted by Trnsys，and the model is validated by measured data. Analyzing the carbon emissions and economy of different energy supply methods for the buildings，it is found that the heating mode with natural gas as fuel can reduce the carbon emissions to 2 019.75 kg/a，and the heating mode integrating biomass and standard coal can bring the cost down to 1 034.42 yuan/a. If PV power generation system is installed，the heating mode integrating biomass and air conditioning will reduce the cost to 464.62 yuan/a，and the carbon emissions to 3 060.56 kg/a， which is only second to that emitted from the gas heater. Utilizing natural gas，biomass and solar energy reasonably is an effective technique for improving the economy of heating and reducing the CO₂ emission.

Key words: carbon neutrality, energy consumption prediction, biomass energy, PV power generation, CO₂ emission

中图分类号:

TK01

胡开永, 刘峰, 吴秀杰, 胡芸清, 郑怡, 田绅. 基于Trnsys能耗预测的村镇建筑不同供能方式碳-经济分析[J]. 综合智慧能源, 2023, 45(8): 64-71.

HU Kaiyong, LIU Feng, WU Xiujie, HU Yunqing, ZHENG Yi, TIAN Shen. Carbon-economy analysis on energy supply methods for rural buildings based on Trnsys energy consumption prediction[J]. Integrated Intelligent Energy, 2023, 45(8): 64-71.

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项目	红砖	水泥	瓷砖	屋瓦
导热系数/[W·(m·℃）^-1]	0.49	1.84	1.10	1.74
比热容/[kJ·(kg·K）^-1]	0.86	1.00	0.84	0.97
密度/（kg·m^-3）	1 560	1 400	2 090	2 020

农宅围护结构	水泥层	红砖	瓷砖	屋瓦
外墙	0.10	0.46	0.10	—
内墙	0.10	0.26	—	—
屋顶	0.26	—	—	—
夹层	0.36	—	—	—

农作物	K	M₁	M₂
玉米	0.90	0.548~0.600	0.49
水稻	0.78	0.645~1.103	0.50
小麦	0.73	0.467~0.488	0.34
棉花	3.53	0.138~0.142	0.49
大豆	0.75	0.195~0.225	0.15
油菜	1.29	0.195~0.206	0.25