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农业综合能源系统多场景置信间隙决策低碳调度OA北大核心CSTPCD

Low-Carbon Scheduling for Multi-Scenario Confidence Gap Decision Making for Integrated Energy Systems in Agriculture

中文摘要英文摘要

针对现代农业园区风-光-垃圾-沼-储联合调度的碳排放问题,构建了基于多场景置信间隙决策理论的现代农业综合能源系统低碳调度优化模型.首先,使用改进的Shapley值法,对碳排放额度进行分摊,得到考虑风险因素的各主体阶梯碳交易价格模型.其次,在各主体阶梯碳交易价格模型基础上,考虑到源荷不确定性,基于多场景置信间隙决策理论构建了现代农业园区运行成本最小和二氧化碳排放量最低的多目标优化调度模型.最后,以我国东北地区某一农业园区数据为例,对所建模型进行验证.算例结果表明,改进的Shapley值使阶梯碳交易价格更贴合实际,同时,园区运行成本降低37.25%,碳排放量减少59.84%,最终实现了多能协调优化,提高了园区低碳环保性以及抵抗风险的能力.

A low-carbon dispatch optimization model of modern agricultural integrated energy systems based on the multi-scenario confidence gap decision theory is constructed to address the carbon emission problem of wind-solar-waste-biogas storage joint dispatch in modern agricultural parks.First,the improved Shapley value method is used to allocate carbon emission allowances,and a laddered carbon trading price model is obtained for each entity considering risk factors.Second,based on the laddered carbon trading price model of each entity and considering the uncertainty of the source load,a multi-objective optimal scheduling model with the lowest operating cost and carbon dioxide emissions of the modern agricultural park is constructed based on the multi-scenario confidence gap decision theory.Finally,the model is verified using data from an agricultural park in Northeast China.The results show that an improved Shapley value renders the laddered carbon trading price more realistic.Modern agricultural parks have experienced a 37.25%reduction in operating costs and a 59.84%reduction in carbon emissions.Furthermore,the park achieved multi-energy coordinated optimization.Consequently,the low-carbon environmental protection and risk resistance capabilities of modern agricultural parks have improved.

高建伟;张艺;高芳杰;刘嘉燕

华北电力大学经济与管理学院,北京市 102206||新能源电力与低碳发展研究北京市重点实验室(华北电力大学),北京市 102206

动力与电气工程

现代农业园区阶梯碳交易改进Shapley值多场景置信间隙决策风-光-垃圾-沼-储

modern agricultural parkladdered carbon tradingimproved Shapley valuemulti-scenario confidence gap decisionwind-solar-waste-biogas-storage

《电力建设》 2024 (005)

105-117 / 13

This work is supported by National Natural Science Foundation of China(No.72071076). 国家自然科学基金项目(72071076)

10.12204/j.issn.1000-7229.2024.05.011

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