学习效应视角下能源优化配置双层规划模型研究

Under the background of the reform of energy supply side, the energy optimal allocation is an import way to realize the modern energy transformation, reflecting the idea of "the quality of development, adjust the stock and make optimal increment". This topic has been received the widespread concern of the whole society and academic communities. Previous energy optimal allocation studies were usually based on the analysis of the historical trajectory, which rarely does not consider the effects of energy technology and environmental learning. Based on the theory of energy economics, systematical science, sustainable management and optimization approach, we take into account the mutual effects of the learning curve and the environmental learning curve under learning by doing. Investigating the master and slave hierarchical relationship in the optimal allocation of energy resources, a multi-objective bi-level programming model of energy optimal allocation is constructed from a novel perspective. The main contents in our research are as follows: (1)a multi-stage multi-level learning curve estimation of renewable energy technology; (2)analyzing the goals and constraints of upper and lower layers, we constructed an energy optimal allocation model based on multi-objective bi-level programming; (3)according to the characteristics of the model, a new multi-population and multi-level intelligent optimization algorithm is designed to solve the model; (4)examining the path and mode of China's energy optimal allocation, and give the corresponding policy enlightenment. The project has important theoretical and practical significance, enrich the existing energy allocation theory. Research results will provide a reference for the government to make appreciate energy policy.

能源供给侧改革的背景下，能源优化配置是实现现代能源转型，体现“发展质量，调整存量和做优增量”的重要途径，得到了全社会和学术界广泛关注。以往的能源优化配置研究多基于历史轨迹的分析，较少考虑能源技术和环境的学习效应，影响研究结论的有效性和可靠性。本项目以系统科学、能源经济学、可持续管理和优化理论为依托，基于学习效应视角，综合考虑能源技术学习和环境学习的双重作用。分析能源优化配置中主从递阶层次关系，构建能源优化配置的多目标双层规划模型。具体研究内容包括：(1)可再生能源技术的多阶段多层级学习曲线估计；(2)分析上层和下层各自目标和约束，构建能源优化配置双层规划模型；(3)根据模型特点，设计多种群多层次的新型智能优化算法用于模型求解；(4)分析中国能源优化配置路径，通过情景设置给出政策启示。本项目具有重要的理论和现实意义，丰富现有能源配置理论，研究成果将为政府制定合理的能源政策提供决策参考。

本课题的主要成果包括：对碳市场复杂系统建模进行了较为深入的探索，研究了碳市场和碳密集型行业的关联机制；根据分解集成框架和碳市场价格的变化规律，提出了多个基于局部回归的碳市场价格预测模型。考虑电网的网络结构特征，构建了一个新颖的合作博弈模型应用于碳市场配额分配；构建了一个集成式的投入产出分析框架，分析国内中间品投入的减排责任。为能源配置政策的制定提供科学依据，运用计量方法研究了自然地理、社会教育、科技设备、资源配置、经济投资、进出口贸易等因素对环境质量和碳排放的影响机制。通过节能政策的政府干预的“临床”计量和批判性话语分析，探讨我国能源配置转型过程中政策引导偏差、认知赤字和市场失灵问题；针对能源配置优化模型，提出了几种智能优化算法；构建了双碳目标下，我国长期能源优化配置和碳减排的双层路径优化模型。依托本课题，发表了SSCI/SCI检索的学术论文26篇和中文期刊2篇，其中项目负责人以第一作者或通讯作者发表JCR Q1学术论文24篇，培养硕士研究生4名。

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