基于分段分式的动态需求建模及多层全局优化的阶梯电价研究

项目名称： 基于分段分式的动态需求建模及多层全局优化的阶梯电价研究

项目编号： No.71301133

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 管理科学

项目作者： 傅馨

作者单位： 厦门大学

项目金额： 20.5万元

中文摘要： 实行阶梯电价已成为缓解我国能源供应紧缺、资源交叉补贴不合理等矛盾的重要举措。作为较独特的非线性定价，阶梯定价下的消费者行为经常难以甄别、存在价格变量难以确定等特征，这使得其设计机制的理论和实证研究都变得十分复杂。目前基于直观静态的设计方法在实行过程中已凸显出一些问题。为此，本课题将从以下三个模块对阶梯电价的设计进行系统科学的研究：1）提出一个全新的分段分式模型来刻画阶梯电价下的动态用电需求；并结合多层聚类分析，挖掘和解析不同特征的用电行为模式；2）通过改进多层优化算法，构建阶梯电价设计参数的全局最优模型；3）将多个理论研究成果集成到一个决策支持系统上，为阶梯电价设计和实施的不同任务提供系列支持。本课题将在理论层面上加深对能源消费行为与阶梯定价的内在平衡机制的科学认识，从实践层面为阶梯电价更好地发挥价格杠杆的作用、促进节能减排提供决策支持，还进一步可扩展到水、气、油等其他资源产品的价格改革。

中文关键词： 递增阶梯电价；聚类分析；分类负载预测；自组织模糊神经网络；智能电表

英文摘要： Increasing block electricity pricing (BEP) is the key strategy devised to resolve the current electricity shortage, and other complex social and environmental problems brought by single electricity tariff system, such as unfair cross subsidies, social inequalities, energy scarcity, environment deterioration, etc. Due to BEP's unique non-linear pricing characteristics, it is difficult to identify the number of pricing variables and explore the patterns of household electricity consumption in the design. To tackle with these problems, this research aims to systematically explore the following three directions in BEP: 1) develop a novel piecewise and multi-stage dynamic demand model that is built upon multi-layer clustering analysis and customer behaviour analysis; 2) apply and improve the multi-level global optimisation model to minimise the electricity usage, whilst taking a wide variety of constraints into consideration; 3) build a comprehensive decision support system to offer assistance to BEP at its various design and implementation stages. From the theoretical point of view, this research would deepen and widen our theoretical insight in both BEP and its influence to electricity consumption behavior. From the practical side, this work would make the BEP more effective so as to achieve the goal of better ener

英文关键词： Increasing-block Tariff (IBT)；Clustering analysis；Cluster-based load forecasting；Self-organising fuzzy neural network；Smart meters