电力系统保护与控制2025，Vol.53Issue(13)：11-22,12.DOI:10.19783/j.cnki.pspc.241282
基于OCC-IEGC模型的矿山综合能源系统运行效益评价
Operation benefit evaluation of mine integrated energy systems based on OCC-IEGC model
摘要
Abstract
The mine integrated energy system(MIES)is an important approach for supporting the green development of coal mines.Conducting a reasonable and effective evaluation of its operation benefits is an necessary prerequisite for promoting its development.However,evaluating the operation benefits of MIES requires not only consideration of the strong coupling between energy and coal flows,but also addressing the influence of operational uncertainties on the accuracy of the evaluation.To address these challenges,a MIES operation benefit evaluation framework is proposed based on the optimal clustering coefficient-based improved extension grey cloud(OCC-IEGC)model.First,considering the ecological characteristics of MIES,an evaluation index system is established based on the driving-pressure-state-impact-response(DPSIR)model,and the cloud-weighted selection method is applied to obtain an optimal rational combination weights.Second,the MIES operation benefit evaluation model based on the extensive grey cloud theory is constructed to mitigate the influence of system operational uncertainty,subjectivity and ambiguity during the evaluation process,while the optimal clustering coefficients are used to further improve the reliability of the evaluation results.Finally,the effectiveness of the proposed index system and evaluation model is verified through case studies.关键词
矿山综合能源系统/运行效益评价/改进可拓灰云模型/最优聚类系数/云雾化权重筛选Key words
mine integrated energy system/operation benefit evaluation/improved extensive grey cloud model/optimal clustering coefficients/cloudy weight screening引用本文复制引用
王雨晴,闫朝臣,王昭贞,郭浩楠,王俐英,曾鸣..基于OCC-IEGC模型的矿山综合能源系统运行效益评价[J].电力系统保护与控制,2025,53(13):11-22,12.基金项目
This work is supported by the National Natural Science Foundation of China(No.62133015). 国家自然科学基金项目资助(62133015) （No.62133015）
河北省自然科学基金项目资助(G2022502004) （G2022502004）
中央高校基金项目资助(2023MS156) （2023MS156）
