LAPSE:2023.34756
Published Article
LAPSE:2023.34756
Integrated Energy Microgrid Economic Dispatch Optimization Model Based on Information-Gap Decision Theory
Xiaowei Fan, Yongtao Chen, Ruimiao Wang, Jiaxin Luo, Jingang Wang, Decheng Cao
April 28, 2023
Abstract
To address the problems of “difficult to consume” renewable energy and the randomness of power output, we propose the CHP unit joint-operation model with power to gas (P2G) and carbon capture system (CCS) technologies and analyze the operation cost, carbon emission, and “electric-heat coupling” characteristics of this model. A dispatch optimization model is constructed based on the information-gap decision theory under the strategy to further consider the interval uncertainty of renewable energy unit output and load forecast. The optimized-dispatching model effectively solves the fate of renewable unit output and electric-thermal load and provides dispatching strategies for decision-makers to balance risk and capital management.
Keywords
information-gap decision theory, integrated energy, P2G
Suggested Citation
Fan X, Chen Y, Wang R, Luo J, Wang J, Cao D. Integrated Energy Microgrid Economic Dispatch Optimization Model Based on Information-Gap Decision Theory. (2023). LAPSE:2023.34756
Author Affiliations
Fan X: State Grid Chongqing Electric Power Company, Chongqing 400014, China
Chen Y: Electric Power Scientific Research Institute of State Grid Chongqing Electric Power Company, Chongqing 401121, China
Wang R: Electric Power Scientific Research Institute of State Grid Chongqing Electric Power Company, Chongqing 401121, China
Luo J: State Grid Wulong Power Supply Company, Chongqing 408506, China
Wang J: State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing 400044, China [ORCID]
Cao D: State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing 400044, China
Journal Name
Energies
Volume
16
Issue
8
First Page
3314
Year
2023
Publication Date
2023-04-07
ISSN
1996-1073
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Original Submission
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PII: en16083314, Publication Type: Journal Article
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LAPSE:2023.34756
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https://doi.org/10.3390/en16083314
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