能源互联网下基于储能调度及多源供能的数据中心能效优化

Energy Efficiency Optimization Based on Storage Scheduling and Multi-Source Power Supplying of Data Center in Energy Internet

近年来,数据中心能效优化问题得到业界的普遍关注.同时,能源互联网的发展为数据中心能效优化问题提供了新的研究思路.能源互联网中的用户,尤其是大型的工业用户,通常具备一定的储能能力和一定的智能化能源管理能力.随着清洁能源的大规模部署以及售电公司的快速发展,数据中心等大型能耗用户也随之获得购电的选择权,可以根据电价、清洁程度等因素,从不同的能源供应商购买能源,从而降低能源成本,提高能效.研究表明在污染指数及实时电价的调节下,用户更趋向于在用电低谷期买入更多廉价且清洁的能源.因此,一方面综合考虑污染指数函数与实时电价,构建多源购电成本模型;另一方面综合考虑储能的操作成本及潜在收益成本,构建储能充放电成本模型,简称储能成本模型.以此为基础,建立了有储能系统下的数据中心多源能源选择模型,并与无储能时序调度策略时的系统性能做了对比.仿真结果表明:提出的模型可以通过对储能时序及多源能源选择的综合优化,一定程度上降低数据中心的日能源成本,同时提高清洁能源利用率.

Recently,the optimization problem of energy efficiency for data centers has been paid widespread attention.In this paper,we investigate this problem in a new idea under the background of energy Internet,where subscribers are equipped with storage and smart energy management devices,especially for industry subscribers.In addition,there are large scale of clean energy generation and electricity-sale companies$which means that industry subscribers can purchase electricity from multi-source suppliers to cut down their energy cost and improve their energy efficiency based on real-time price,pollution index,etc.It is assumed that data centers always attempt to choose cheaper and cleaner energy in each hour and buy more electricity in valley hours with lower price compared to peak hours to reduce the energy cost.Thus both of pollution function and real-time price are adopted to formulate the multi-source energy-purchasing cost.And both of the operation cost and potential cost are adopted to model the charging and discharging cost of storage devices,or storage cost for short.Based on this,the energy cost model with storage is formulated and is compared with the one without storage.Then we give the related algorithm to solve these problems and give the analysis of performance.Simulation results confirm that the proposed model can greatly reduce the daily cost of electricity and encourage the utilization of resources by choosing optimal strategies of energy source selecting and daily storage scheduling.