Multi-objective optimization of multi-energy supply for residential buildings in cold regions based on energy storage

doi:10.3969/j.issn.2097-0706.2025.12.005

Abstract

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

CLC Number:

TK01⁺9：TU241

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.

Figures/Tables 11

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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