基于储能的寒冷地区住宅多能源供能多目标优化研究

doi:10.3969/j.issn.2097-0706.2025.12.005

摘要/Abstract

摘要：

针对寒冷地区住宅建筑冬季供暖需求高、能源消耗大及碳排放压力重的现状，同时考虑电能与热能存储的耦合效应，构建了集成光伏发电、空气源热泵、燃气锅炉、储能电池与高温储热装置的综合能源系统多目标优化模型，对比了3种优化算法在5个异质性气候城市的性能。基于北京、郑州、银川、拉萨和喀什市的气象数据及在EnergyPlus中建立的建筑热模型，确定了基准能源需求。结合Matlab平台分别采用非支配排序遗传算法（NSGA-Ⅱ）、粒子群算法（PSO）和模拟退火算法（SA），进行碳排放最小化与经济成本最小化的多目标优化。结果表明：NSGA-Ⅱ在多数场景下能实现成本与碳排放的最佳权衡，综合性能最优；PSO在供暖期长的地区运行成本优化效果显著；SA则适用于寻求更低运行成本的场景，但通常伴随较高的初始投资。该优化模型能够兼顾寒冷地区住宅建筑的经济性与低碳性，可显著提升可再生能源利用率，并为寒冷气候条件下建筑综合能源系统的规划与运行提供参考。

关键词: 住宅建筑, 综合能源系统, 寒冷地区, 多目标优化, 储能

Abstract:

Aiming at the current situation of high heating demand， large energy consumption， and heavy carbon emission pressure for residential buildings in cold regions during winter， and considering the coupling effect of electrical and thermal energy storage， a multi-objective optimization model of an integrated energy system incorporating photovoltaic power generation， air-source heat pumps， gas boilers， energy storage batteries， and high-temperature thermal storage devices was constructed. The performance of three optimization algorithms was compared across five cities with heterogeneous climates. Based on meteorological data from Beijing， Zhengzhou， Yinchuan， Lhasa， and Kashgar， along with a building thermal model established in EnergyPlus， the baseline energy demand was determined. Utilizing the Matlab platform， the non-dominated sorting genetic algorithm Ⅱ（NSGA-Ⅱ）， particle swarm optimization（PSO）， and simulated annealing（SA） were employed to conduct multi-objective optimization for minimizing carbon emissions and economic cost. The results showed that the NSGA-Ⅱ achieved the best trade-off between cost and carbon emissions in most scenarios， demonstrating the optimal comprehensive performance. The PSO showed significant effectiveness in optimizing operating costs in regions with long heating periods. The SA was suitable for scenarios aiming for lower operating costs， but it was typically accompanied by higher initial investment. This optimization model can balance the economic and low-carbon performance of residential buildings in cold regions， significantly improve the utilization rate of renewable energy， and provide a reference for the planning and operation of building integrated energy systems under cold climate conditions.

Key words: residential buildings, integrated energy system, cold regions, multi-objective optimization, energy storage

中图分类号:

TK01⁺9：TU241

闫京, 李萌, 关宝良, 孟思宇, 樊颜搏, 王凤龙, 杨尚峰, 杨众杨, 熊亚选. 基于储能的寒冷地区住宅多能源供能多目标优化研究[J]. 综合智慧能源, 2025, 47(12): 46-56.

YAN Jing, LI Meng, GUAN Baoliang, MENG Siyu, FAN Yanbo, WANG Fenglong, YANG Shangfeng, YANG Zhongyang, XIONG Yaxuan. Multi-objective optimization of multi-energy supply for residential buildings in cold regions based on energy storage[J]. Integrated Intelligent Energy, 2025, 47(12): 46-56.

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围护结构	具体构造	传热系数/[W∙(m²·K)^-1]
屋面	水泥砂浆20 mm+加气混凝土120 mm+水泥砂浆20 mm	0.25
楼板	混凝土120 mm	1.45
底板	水泥砂浆20 mm+加气混凝土150 mm	1.45
外墙	水泥砂浆20 mm+空心砖200 mm+水泥砂浆20 mm	0.45
内墙	水泥砂浆20 mm+空心砖100 mm+水泥砂浆20 mm	1.45
外窗	3 mm双层透明玻璃	1.90

城市	典型日供热天然气消耗量/m³	典型日耗电量/(kW·h)	碳排放量/kg	能耗费用/元
北京	112	396	463.5	548
郑州	94	341	411.0	434
银川	118	346	477.0	420
拉萨	72	310	338.0	260
喀什	93	340	413.0	356

城市	典型日耗电量/(kW·h)	电力二氧化碳排放因子/[kg·(kW·h)^-1]	碳排放量/kg	电价/[元·(kW·h)^-1]	电费/元
北京	495	0.558 0	276	0.558 3	276
郑州	443	0.605 8	268	0.560 0	249
银川	411	0.642 3	264	0.498 6	205
拉萨	360	0.585 6	211	0.490 0	176
喀什	460	0.623 1	286	0.500 0	230

城市	算法	建设成本/万元	冬季		夏季
城市	算法	建设成本/万元	运行成本/元	碳排放/kg	运行成本/元	碳排放/kg
北京	原始状态	0	548	464	276	276
	NSGA-Ⅱ	20.027	118	119	77	109
	PSO	24.660	130	130	55	85
	SA	36.397	187	197	37	58
郑州	原始状态	0	434	411	249	268
	NSGA-Ⅱ	16.494	109	125	79	117
	PSO	18.615	129	172	68	110
	SA	31.258	160	202	63	102
银川	原始状态	0	420	477	205	264
	NSGA-Ⅱ	18.437	80	119	33	81
	PSO	28.171	104	167	19	49
	SA	24.625	127	214	23	58
拉萨	原始状态	0	260	338	176	211
	NSGA-Ⅱ	16.181	70	115	30	70
	PSO	20.075	138	227	29	69
	SA	30.503	91	176	45	105
喀什	原始状态	0	356	413	230	286
	NSGA-Ⅱ	13.200	104	154	82	163
	PSO	27.283	102	159	28	69
	SA	32.553	186	243	38	89