能源-交通融合下电-气-热多能系统协同优化调度方法

doi:10.16183/j.cnki.jsjtu.2024.114

摘要/Abstract

摘要：

“双碳”目标下,能源系统与交通系统的交互程度将不断加深,存在多元能源-交通系统的协同优化调度问题.为此,提出能源-交通融合下电-气-热多能系统协同优化调度方法.首先,采用融合基于密度的带噪声空间聚类(DBSCAN)算法的K-means聚类算法与Dijkstra算法,对待调度交通车辆进行聚类,并对道路网架结构及车辆运行与车到网技术参与的能量传递模式进行建模,交通对象为电动车、天然气车.然后,在此基础上以系统总成本最小与总用电负荷波动最小为目标构建双层优化调度模型.最后,算例分析验证了该模型降低系统成本、减小碳排放、提高风光消纳能力的有效性与多能系统调度的优越性.

关键词: 能源-交通融合系统, 双层优化, 电-气-热多能系统, 基于密度的带噪声空间聚类算法

Abstract:

Interaction between energy systems and transportation systems will continue to deepen under the context of the dual carbon goals, leading to a need for collaborative optimization scheduling of multiple energy-transportation systems. Therefore, a collaborative optimization scheduling method for electric-gas-thermal multi-energy systems with energy-transportation integration is proposed. First, traffic vehicles to be scheduled are clustered based on the K-means clustering algorithm fused with density-based spatial clustering of applications with noise (DBSCAN) algorithm and Dijkstra algorithm, and models are established for the road network structure and the energy transfer mode involving vehicle operation and vehicle to network technology. The traffic objects include electric vehicles and natural gas vehicles. Then, on this basis, a bi-level optimization scheduling model is developed with the objectives of minimizing the total system cost and minimizing the total power load fluctuation. Finally, numerical example analysis verifies the effectiveness of the proposed model in reducing system cost, reducing carbon emissions, improving wind-solar absorption capacity, and demonstrating the superiority of multi-energy system scheduling.

Key words: energy-transportation integrated system, bi-level optimization, electric-gas-thermal multi-energy system, density-based spatial clustering of applications with noise (DBSCAN) algorithm

中图分类号:

TM732

范宏, 魏心武, 贾庆山, 罗佳怡. 能源-交通融合下电-气-热多能系统协同优化调度方法[J]. 上海交通大学学报, 2026, 60(2): 211-223.

FAN Hong, WEI Xinwu, JIA Qingshan, LUO Jiayi. Collaborative Optimization Scheduling Method for Electric-Gas-Thermal Multi-Energy System Under Energy-Transportation Integration[J]. Journal of Shanghai Jiao Tong University, 2026, 60(2): 211-223.

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道路类型	小型电动汽车		天然气公交车
道路类型	最佳行驶速度/ (km·h^-1)	行驶输出功率/kW	最佳行驶速度/ (km·h^-1)	行驶输出功率/kW
高架线路	55	10	50	50
地面主路	40	7.5	35	40
地面辅路	25	5	25	30

类型	时段	购入单价/ [元·(kW·h)^-1]	售出单价/ [元·(kW·h)^-1]
电	9:00-13:00	0.977	0.780
	18:00—22:00	0.977	0.780
	6:00—9:00	0.817	0.680
	13:00—18:00	0.817	0.680
	22:00—24:00	0.817	0.680
	0:00—6:00	0.387	0.320
气	全时段	0.260
热	全时段	0.610

类型	最大功率/ MW	能量转换效率	运维单价/ (元· MW^-1)	最大爬坡功率/ kW	碳排放强度/ [t·(MW· h)^-1]
CHP	1.0	0.4	45	1000	0.85
GB	0.6	0.7	40	500	0.85
GB	0.4	0.7	40	500
P2G	0.4	0.5	65
风力机			30
光伏			25

类型	储能容量/ (kW·h)	最大输入和输出功率/kW	存储和释放电能效率	运维单价/ (元· MW^-1)	荷电状态
ESS	1000	200/200	0.95/0.95	0.6	1/0.1
电动汽车	100	60/30	0.95/0.95	0.6	1/0.1

聚类模型	WCSS	DBI
K-means	10129.42	6.09
改进后K-means	3692.85	5.18