Energy management strategy for smart commercial buildings considering electric vehicle charging patterns and building thermal inertia

doi:10.3969/j.issn.2097-0706.2025.07.002

Abstract

Abstract:

With the growth of the economic level， the proportion of electricity consumption in buildings continues to increase. Optimizing building energy management strategies can effectively enhance energy conservation and reduce emissions. In commercial building loads， air conditioning loads and electric vehicles loads account for a large proportion of energy consumption. Therefore， a smart commercial building energy management model considering building thermal inertia and incorporating electric vehicle integration was proposed， with the behavioral characteristics of electric vehicles modeled via Monte Carlo simulation. Based on the thermal capacitance-resistance model， a building thermal dynamic balance equation was established. Scenario-based stochastic programming was adopted to establish a smart commercial building energy management model， and Conditional Value at Risk （CVaR） was introduced to improve robustness. The results of case studies showed that considering building thermal inertia could reduce the operating costs of air conditioning loads by 9.7% and increase revenue by 1.4%. Although CVaR reduces the operational risks of buildings， it also lowers their comprehensive expected returns.

Key words: building thermal inertia, electric vehicle, stochastic programming, smart building, energy management, value at risk

CLC Number:

TK01⁺8：TM715

WU Guoliang, ZHENG Wen, HE Ling, WANG Lei, HUANG Yuan, LIU Junyong. Energy management strategy for smart commercial buildings considering electric vehicle charging patterns and building thermal inertia[J]. Integrated Intelligent Energy, 2025, 47(7): 12-22.

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备用发电机组			商业智能楼宇热惯性参数			储能设备		负荷		主网
参数	数值		参数	数值		参数	数值	参数	数值	参数	数值
固定花费/ [美元·(kW·h)^-1]	8	热容/(J·℃^-1)		9×10⁷	容量/(kW·h)		100	上爬坡速率/kW	40	最大交易功率/kW	400
上爬坡速率/kW	40	墙体热阻/(℃·W^-1)		4×10^-4	最小SOC/(kW·h)		10	下爬坡速率/kW	30
下爬坡速率/kW	40	窗户透射率		0.9	充、放电功率/kW		30
可变花费/ [美元·(kW·h)^-1]	35	窗户热阻/(℃·W^-1)		2×10^-4	充、放电效率		0.9
可变花费/ [美元·(kW·h)^-1]	35	窗户热阻/(℃·W^-1)		2×10^-4	初始SOC/(kW·h)		50

不确定性参数	场景编号	场景概率
室外温度	1	0.3
	2	0.3
	3	0.4
室外辐照度	1	0.5
	2	0.3
	3	0.2
日前市场电价	1	0.6
日前市场电价	2	0.4

场景	A1.B1.D1	A1.B1.D2	A1.B2.D1	A1.B2.D2	A1.B3.D1	A1.B3.D2	A2.B1.D1	A2.B1.D2	A2.B2.D1
收益	12 760.0	12 348.1	16 319.6	15 907.6	10 863.2	10 531.8	12 683.6	12 458.3	16 098.5
场景	A2.B2.D2	A2.B3.D1	A2.B3.D2	A3.B1.D1	A3.B1.D2	A3.B2.D1	A3.B2.D2	A3.B3.D1	A3.B3.D2
收益	16 159.4	10 596.4	11 984.8	11 864.5	11 358.7	15 987.6	15 394.8	9 876.1	9 548.6

情况	空调负荷运行费用	商业智能楼宇总收益
1	2 106.9	12 760.0
2	2 271.6	12 595.3