用户社会属性驱动的综合能源能量管理方法

doi:10.16182/j.issn1004731x.joss.26-0057

摘要/Abstract

摘要：

为探索能源服务商(energy service provider，ESP)与多用户互动的新机制，（提出了一种用户社会属性驱动的综合能源系统复杂建模与能量管理 方法。）构建包含ESP与用户集群的多主体交互框架；以最大化ESP运行效益与最小化用户用能成本为目标，基于强化学习框架建立主从博弈能量管理模型，（提出一种结合Q学习与二次规划的分布式协同求解算法。）仿真结果表明：相较于传统综合需求响应方法，该方法在考虑用户社会属性后，ESP运行效益提升10.79%，用户集群I和II的总运行成本分别降低6.98%和0.36%，系统碳排放降低5.05%，算法在收敛精度与计算效率之间实现了良好平衡。

关键词: 综合能源系统, 能源服务商, 用户, 能量管理, 社会属性

Abstract:

To explore a new interaction mechanism between an energy service provider (ESP) and multiple users, this paper proposes a complex modeling and energy management method for integrated energy systems driven by users' social attributes. A multi-agent interaction framework comprising an ESP and user clusters is established. To maximize the ESP's operational benefit and minimize users' energy costs, a leader-follower game-based energy management model is established within a reinforcement learning framework, and a distributed collaborative solution algorithm combining Q-learning and quadratic programming is proposed. Simulation results show that, compared with the traditional integrated demand response method, consideration of users' social attributes increases the ESP's operational benefit by 10.79%, reduces the total operating costs of user clusters I and II by 6.98% and 0.36%, respectively, and decreases system carbon emissions by 5.05%. The algorithm also achieves a good balance between convergence accuracy and computational efficiency.

Key words: integrated energy system, energy service provider, user, energy management, social attribute

中图分类号:

TP391

朱彦恺,黄玉晶,王庆华等 . 用户社会属性驱动的综合能源能量管理方法[J]. 系统仿真学报, 2026, 38(8): 2152-2166.

Zhu Yankai,Huang Yujing,Wang Qinghua,et al . Energy Management Method for Integrated Energy Driven by Users’ Social Attributes[J]. Journal of System Simulation, 2026, 38(8): 2152-2166.

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时段	售电价/(元/kW·h)
01:00-8:00	0.45
09:00-11:00; 16:00-17:00; 23:00-24:00	0.86
12:00-15:00; 20:00-22:00	1.35

参数	参数名称	用户1	用户2
价格敏感与偏好	电价敏感系数	0.8	0.6
	热价敏感系数	0.9	0.7
	热价容忍度	0.05	0.03
	心理热价参考阈值	0.4	0.4
社会网络影响	电能选择社会影响依赖度	0.3	0.3
	热能选择社会影响依赖度	0.3	0.3
	信息传播延迟系数	0.8	0.3
	异质性信任系数	0.7~0.9	0.4~0.6
	网络节点度均值	15	8
	社会影响权重矩阵	权重和为1
不确定性扰动	随机扰动方差	高峰段：0.05 平时段：0.03 低谷时段：0.01
负荷弹性与满意度	电负荷价格弹性系数	0.5	0.5
	热负荷价格弹性系数	0.4	0.4
	电能满意度损失系数	0.02	0.02
	热能满意度损失系数	0.03	0.03

设备	参数	ESP	用户1	用户2
CHP	机组容量/kW	1 500	—	—
	运维系数/(元/kW)	0.05	—	—
	热电比	1.35	—	—
HP	机组容量/kW	—	1 500	1 000
	运维系数/(元/kW)	—	0.026	0.026
	制热系数	—	0.90	0.86
GB	机组容量/kW	1 500	—	—
	运维系数/(元/kW)	0.15	—	—
	制热系数	0.85	—	—
PV	机组容量/kW	—	1 000	2 500
PV	运维系数/(元/kW)	—	0.025	0.025
ES	机组容量/kW	1 000	—	2 000
ES	运维系数/(元/kW)	0.02	—	0.02
HS	机组容量/kW	1 000	—	—
HS	运维系数/(元/kW)	0.02	—	—

性能指标	场景I	场景II
收敛迭代次数	35	48
总求解时间/s	24.15	27.68
最终系统Q值/元	48 229.79	47 913.14
相对理论最优误差/%	0.24	0.05

性能指标	本文方法	DRL方法
收敛迭代次数	48	152
总求解时间/s	27.68	68.42
最终系统Q值/元	47 913.14	48 120.56
相对理论最优误差/%	0.05	0.18