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Research on the bi-layer low carbon optimization strategy of integrated energy system based on Stackelberg master slave game

2023-11-10

Research on the bi-layer low carbon optimization strategy of integrated energy system based on Stackelberg master slave game

基于Stackelberg主从博弈综合能源系统双层低碳优化策略研究

Lizhen Wu¹, Cuicui Wang¹, Wei Chen¹, Tingting Pei^1,2

1. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou,Gansu,730050, P. R. China

2. School of Electrical and Data Engineering, (University of Technology Sydney), NSW, Australia

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Research on the bi-layer low carbon optimization strategy of integrated energy system based on Stackelberg master slave game

Abstract

With increasing reforms related to integrated energy systems (IESs), each energy subsystem, as a participant based on bounded rationality, significantly influences the optimal scheduling of the entire IES through mutual learning and imitation. A reasonable multiagent joint operation strategy can help this system meet its low-carbon objectives. This paper proposes a bilayer low-carbon optimal operational strategy for an IES based on the Stackelberg master-slave game and multiagent joint operation. The studied IES includes cogeneration, power-to-gas, and carbon capture systems. Based on the Stackelberg master-slave game theory, sellers are used as leaders in the upper layer to set the prices of electricity and heat,while energy producers, energy storage providers, and load aggregators are used as followers in the lower layer to adjust the operational strategy of the system. An IES bilayer optimization model based on the Stackelberg master-slave game was developed. Finally, the Karush-Kuhn-Tucker (KKT) condition and linear relaxation technology are used to convert the bilayer game model to a single layer. CPLEX, which is a mathematical program solver, is used to solve the equilibrium problem and the carbon emission trading cost of the system when the benefits of each subject reach maximum and to analyze the impact of different carbon emission trading prices and growth rates on the operational strategy of the system. As an experimental demonstration, we simulated an IES coupled with an IEEE 39-node electrical grid system, a six-node heat network system,and a six-node gas network system. The simulation results confirm the effectiveness and feasibility of the proposed model.

Keywords

Integrated energy system; Stackelberg master-slave game; Power-to-gas system; Carbon capture systems

Fig. 1 Structural diagram of IES considering the dynamic characteristics of the heat network, which consists of wind turbine (WT), photovoltaic (PV), electrical storage (ES) systems, heat storage (HS) systems, gas turbines (GT), electrical load (EL), heat load (HL), and gas load (GL)

Fig. 2 Bilayer master–slave game structure diagram of IES based on CHP-CCS-P2G joint operation mode

Fig. 3 IES solution flowchart

Fig. 4 Power prediction curve

Fig. 5 Scenery output curves in three different scenarios

Fig. 6 Electricity price in the Stackelberg master-slave game

Fig. 7 Heat price in the Stackelberg master-slave game

Fig. 8 Return curve of each subject

Fig. 9 Electrical load optimization results

Fig. 10 Heat load optimization results

Fig. 11 Gas load optimization results

Fig. 12 Carbon emissions under different growth rates and carbon emission trading prices

本文引文信息

Wu LZ, Wang CC, Chen W, et al. (2023) Research on the bi- layer low carbon optimization strategy of integrated energy system based on Stackelberg master slave game, Global Energy Interconnection, 6(4): 389-402

吴丽珍，王翠翠，陈伟等 (2023) 基于Stackelberg主从博弈综合能源系统双层低碳优化策略研究. 全球能源互联网（英文）, 6(4): 389-402

Biographies

Lizhen Wu

received the M.S. degree in control theory and control engineering from the Lanzhou University of Technology, Gansu, China, in 2004, and a Ph.D. degree in control theory and control engineering from the Lanzhou University of Technology in 2017. She studied power systems and their

Cuicui Wang

was born in Tianshui, China in 1999. She received the B. Eng. degree in 2021. She is a master’s degree candidate at the Lanzhou University of Technology, Gansu, China, since 2021. Her interests include the optimization and scheduling of IES.

Wei Chen

received the M.S. degree in Power Systems and Automation from Xi’an Jiaotong University, Xi’an, China, in 2005, and the Ph.D. degree in Control Theory and Control Engineering from Lanzhou University of Technology in 2011. He is currently a professor and doctoral supervisor at the College of Electrical and Information Engineering,

Lanzhou University of Technology, where he teaches courses on power systems, automation, and control theory. His interests include smart grids, intelligent control theory and applications, artificial intelligence, power system stability analysis, and power quality- control technology.

Tingting Pei

received the Ph.D. degree in renewable energy and smart grid from Lanzhou University of Technology, Lanzhou, China, in 2020. She was a lecturer with the College of Electrical and Information Engineering, Lanzhou University of Technology since 2021. The author current research interests include fault diagnosis, reconfiguration, intelligent operation and maintenance of photovoltaic power generation system.

编辑：王彦博

审核：王伟

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