基于层次分析法的配电网大数据价值精细化评估技术

Optimization of Big Data Transactions of Renewable Energy Based on Analytic Hierarchy Process in Distribution Network

介绍了可在生能源大数据发展的现状与当下B2B大数据交易面临的问题,提出了一种基于层次分析法的可再生能源大数据B2B交易方案优化方法,并利用该方法在交易双方协商的方案中搜索最优解。分析了影响大数据交易双方收益的因素,利用层次分析法对买卖双方在大数据交易中获得的收益进行系统化的评估;将大数据交易的问题转化为在保证交易相对公平的情况下,买卖双方收益最大的多目标优化问题,采用向量评估遗传算法寻找出方案中的非劣解,供买卖双方决策者参考。通过算例证明了该方法的有效性,同时对比了在数据属性取值范围不同的情况下,模型产生的最优协商方案的精确度。

A brief description of the production of renewable energy big data and the problems in current B2B big data transactions is given in this paper. A method of selecting B2B big data transaction scheme of renewable energy based on analytic hierarchy process(AHP)is proposed, which can search out the Pareto solution in the schemes of mutual negotiation. Firstly, the influencing factors of the profit of big data transaction are analyzed comprehensively, and the Analytic Hierarchy Process is used to make a systematic evaluation of the profits that buyers and sellers obtain in the transaction. Secondly, the big data transaction problem is converted into a multi- objective optimization issue which is relatively fair for buyers and sellers. Finally, a non- inferior solution in all proposals is found out by using Vector Evaluation Genetic Algorithm. The accuracy of the optimal negotiation scheme generated by the model is compared in the case of different range of data attributes.