Collaborative Optimization Scheduling Method for Electric-Gas-Thermal Multi-Energy System Under Energy-Transportation Integration

doi:10.16183/j.cnki.jsjtu.2024.114

Abstract

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

CLC Number:

TM732

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