Optimal Scheduling Considering Carbon Capture and Demand Response under Uncertain Output Scenarios for Wind Energy

Hongbin Sun (), Hongyu Zou, Jingya Wen, Wende Ke and Lei Kou ()
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Hongbin Sun: School of Electrical Engineering, Changchun Institute of Technology, Changchun 130012, China
Hongyu Zou: School of Electrical Engineering, Changchun Institute of Technology, Changchun 130012, China
Jingya Wen: Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266075, China
Wende Ke: Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Lei Kou: Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266075, China

Sustainability, 2024, vol. 16, issue 3, 1-21

Abstract: In light of the uncertainties associated with renewable energy sources like wind and photovoltaics, this study aims to progressively increase their proportion in the energy mix. This is achieved by integrating carbon capture devices into traditional thermal power plants and enhancing demand-side management measures, thereby advancing low-carbon objectives in the energy and electricity sectors. Initially, the research proposes utilizing the K-means clustering algorithm to consolidate and forecast the fluctuating outputs of renewable energies such as wind and photovoltaics. Further, it entails a comprehensive analysis of low-carbon resources on both the supply and demand sides of the electricity system. This includes installing carbon storage and power-to-gas facilities in carbon capture plants to create a versatile operating model that can be synchronized with wind power systems. Additionally, the limitations of carbon capture plants are addressed by mobilizing demand-side response resources and enhancing the system’s low-carbon performance through the coordinated optimization of supply and demand resources. Ultimately, this study develops an integrated energy system model for low-carbon optimal operation, aimed at minimizing equipment investment, carbon emission costs, and operational and maintenance expenses. This model focuses on optimizing the load and supply distribution plans of the electrical system and addressing issues of load shedding and the curtailment of wind and solar power. Validation through three typical scenarios demonstrates that the proposed scheduling method effectively utilizes adjustable resources in the power system to achieve the goal of low-carbon economic dispatch.

Keywords: carbon capture power plant; K-means clustering algorithm; demand-side response; integrated energy system; low-carbon economy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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