Two-level demand response scheduling optimization of park integrated energy system based on Stackelberg game

Tao Liang; Chao Meng

doi:10.1117/12.3007405

25 October 2023 Two-level demand response scheduling optimization of park integrated energy system based on Stackelberg game

Tao Liang, Chao Meng

Author Affiliations +

Proceedings Volume 12801, Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023); 1280140 (2023) https://doi.org/10.1117/12.3007405
Event: Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023), 2023, Dalian, China

Abstract

The introduction of demand response in the optimal scheduling of the park integrated energy system is of great significance for ensuring the economic, efficient and reliable operation of the system. In this paper, a two-level demand response scheduling optimization strategy based on Stackelberg game is proposed for the park integrated energy system. Firstly, according to the main components of the integrated energy system, the models of energy conversion devices and energy storage devices are built. Secondly, the Stackelberg game model with the integrated energy operator as the leader and the end user as the follower is established by taking the power demand response request of the power grid company as the input and considering the adjustable characteristics of the production load and temperature control load, and the adaptive particle swarm optimization and Gurobi solver are combined to solve the model. Finally, a case study is carried out and simulation results verify the feasibility and effectiveness of the proposed method.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tao Liang and Chao Meng "Two-level demand response scheduling optimization of park integrated energy system based on Stackelberg game", Proc. SPIE 12801, Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023), 1280140 (25 October 2023); https://doi.org/10.1117/12.3007405

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available