Optimal Capacity Configuration of a Low-Carbon Energy System Considering Carbon Capture Technology and Hydrogen-Diversified Utilization under Multiple Operational Scenarios

环境科学 工艺工程 电力转天然气 温室气体 风力发电 热电联产 计算机科学 发电 功率(物理) 工程类 电气工程 生态学 化学 物理 电解 电极 物理化学 量子力学 电解质 生物
作者
Zhaopeng Liu,Haiyun Wang,Yunguang Wang,Lei Wu
出处
期刊:IEEE Access [Institute of Electrical and Electronics Engineers]
卷期号:12: 85-112
标识
DOI:10.1109/access.2023.3347490
摘要

To enhance the utilization of concentrated solar power (CSP), and reduce the wind abandonment rate of wind power (WP) and the carbon emissions from the traditional coal-fired unit (CFU), this study introduces oxygen-enriched combustion capture technology to retrofit the conventional CFU, and configures CSP with heat recovery unit (HRU) for participation in heating, along with coupling methanation reactor (MR), high-temperature solid oxide electrolysis cell (SOEC), hydrogen-blended gas cogeneration system, and energy storage units to create a low-carbon energy system (LCES) that includes electricity-heat-gas integration and hydrogen diversified utilization. The system’s optimal capacity configuration methodology is also established. Firstly, considering the uncertainties of WP and direct normal irradiance (DNI), as well as their temporal correlations with electrical load (EL), a multi-operational scenarios extraction model is established based on a multi-stage data processing algorithm. Secondly, building upon the probability-based multi-operational scenarios, the conditional value at risk (CVaR) theory is employed to quantify the risks arising from uncertainties. With the objective of minimizing total cost, an LCES optimal capacity configuration model is formulated. Finally, through case study validation, the results indicate that the system, under the scenario of meeting the load demand, can reduce annual carbon emissions to 249,573.31 t, decrease the wind abandonment rate to 1.32%, enhance the CSP utilization rate to over 84%, and provide the basis of quantification for decision-makers with varying risk preferences when addressing capacity configuration issues.

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