Electric Cooling Demand Response and Multi-system Collaborative Pricing Optimization Method in Logistics Parks and Shared Energy Storage

doi:10.16183/j.cnki.jsjtu.2024.439

Abstract

Abstract:

With the rapid development of the logistics industry, the load demand in logistics parks equipped with electric heavy-duty trucks and cold chain facilities has increased sharply, leading to higher overall operating costs. Therefore, a shared energy storage system is introduced as a third-party energy transaction platform, and a scheduling optimization method for multi-integrated energy systems (IESs) based on a game-theoretic pricing incentive mechanism is proposed. For the complementary characteristics between the high load demand of logistics parks and the renewable energy generation features of conventional integrated energy systems, the interaction strategies of all participants are pursued while their respective optimal objectives are addressed. A load-shifting model is constructed by analyzing the load characteristics of electric heavy-duty trucks and cold chain facilities. A flexible charging and discharging capacity-sharing rental model for energy storage is adopted, and an interaction model between the logistics park system with shared energy storage and conventional integrated energy systems is developed. Then, a dynamic pricing analysis based on a Stackelberg game is established to explore energy transaction interactions among multiple systems. The results show that the multi-system interaction model can meet the energy scheduling demands of electricity and cooling loads in logistics parks but also reduce wind and solar energy curtailment, and ensure the lowest cost across multiple energy systems. Compared with traditional models, the total cost of system achieves a 5.9% reduction in overall system costs.

Key words: logistics park, integrated energy system (IES), shared energy storage, demand response, Stackelberg game

CLC Number:

TM732

XU Jimi, WU Yuhang, LI Canbing, JIANG Wenjie, HUANG Ziyu, CHENG Yu. Electric Cooling Demand Response and Multi-system Collaborative Pricing Optimization Method in Logistics Parks and Shared Energy Storage[J]. Journal of Shanghai Jiao Tong University, 2026, 60(4): 563-573.

Figures/Tables 6

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场景	系统	运行成本/元	SES 收益/元	需求响应补偿成本/元	交互成本/元
1	LIES	15 268.5	1 199.9	0	0
	CIES1	5 270.4	1 203.9	0	0
	CIES2	4 900.8	1 186.8	0	0
2	LIES	12 038.9	638.2	1 011.2	0
	CIES1	4 614.2	636.6	339.8	0
	CIES2	4 104.4	614.3	160.6	0
3	LIES	11 393.4	626.2	1 761.7	2 444.7
	CIES1	2 689.4	650.3	321.3	-998.7
	CIES2	2 650.5	634.8	295.5	-1 446.0
4	LIES	10 578.2	903.1	1 661.2	2 200.9
	CIES1	2 545.0	522.1	393.7	-1 040.3
	CIES2	2 616.8	672.5	374.3	-1 180.8

场景	系统	租赁成本/ 元	额定租赁容量/(kW·h)	可再生能源消纳率/%	储能利用率/%
1	LIES	2 926.0	4 391.0	100.0	94.9
	CIES1	2 758.6	3 936.0	87.2	94.9
	CIES2	3 519.1	6 000.0	93.5	94.9
2	LIES	1 452.7	1 971.0	100.0	95.0
	CIES1	1 532.1	2 186.0	82.7	95.0
	CIES2	2 522.0	4 873.0	92.8	95.0
3	LIES	2 636.4	5 145.0	100.0	95.0
	CIES1	1 562.4	2 230.0	98.1	95.0
	CIES2	2 256.2	4 113.0	98.6	95.0
4	LIES	2 619.8	4 366.0	100.0	122.4
	CIES1	1 595.4	2 658.0	100.0	95.0
	CIES2	2 000.1	3 333.0	100.0	95.0