考虑隐私保护的含互联网数据中心的综合能源系统时空联合规划  

Spatio-Temporal Joint Planning for Integrated Energy Systems with Internet Data Center Considering Privacy Preservation

作　　者：吴成邦 程志江 陆海峰[1,3] 杨涵棣 WU Chengbang;CHENG Zhijiang;LU Haifeng;YANG Handi(Engineering Research Center of Education Ministry for Renewable Energy Power Generation and Grid Technology,Xinjiang University,Urumqi 830047,China;School of Future Technology,Xinjiang University,Urumqi 830047,China;School of Electrical Engneering,Xinjiang University,Urumqi 830047,China)

机构地区：[1]新疆大学可再生能源发电与并网技术教育部工程研究中心,乌鲁木齐市830047 [2]新疆大学未来科学与技术学院,乌鲁木齐市830047 [3]新疆大学电气工程学院,乌鲁木齐市830047

出　　处：《电力建设》2025年第4期84-98,共15页Electric Power Construction

基　　金：新疆维吾尔自治区重大科技专项(2022A1001-3)。

摘　　要：【目的】随着云计算、“互联网+”的高速发展,互联网数据中心(internet data center,IDC)作为云计算底层核心的基础设施,正处于高速扩张阶段。然而,由于数据中心和综合能源系统(integrated energy systems,IES)均拥有底层用户的信息,数据泄露可能会导致多种风险。为此,在设计IDC与IES的协同优化方案时,必须得充分考虑两者的隐私保护。【方法】首先,分析数据中心灵活调节特性,采用图论及M/M/1排队论构建数据中心灵活需求响应模型,建立了IDC及IES各自的规划模型及运行模型。根据IDC及IES运行模型的KKT(Karush-Kuhn-Tucher)条件将运行模型转化为规划模型的附加约束,并采用大M法进行线性化处理。随后,考虑IDC及IES间的隐私保护,完善一种适用于混合整数线性规划(mixed-integer linear programming,MILP)子问题的增强型Benders分解算法并设计分布式求解框架对时空联合规划模型进行求解。【结果】研究结果表明,在所采用的算例场景下,引入所采用的IES及IDC需求响应模型后,系统的年化总成本降低了26.79%,增强型Benders分解算法在分布式求解速度上较交叉方向乘子法(alternating direction method of multipliers,ADMM)快1.11倍。【结论】分析了IDC与IES的灵活调节手段,构建了一种切实可行、兼顾隐私保护的含IDC的IES分布式优化方案。该研究可以为类似的多利益主体协同优化场景提供相应的方案及方法参考。[Objective]With the rapid development of cloud computing and"Internet+",internet data centers(IDCs),as the core infrastructure underlying cloud computing,are in a rapid expansion phase.However,because both IDC and integrated energy systems(IES)possess underlying user information,data leakage may lead to various risks.Therefore,when designing collaborative optimization solutions for IDCs and IES,it is essential to consider the privacy preservation of both systems.[Methods]First,the flexible regulation characteristics of data centers were analyzed,and a flexible demand response model for data centers based on the graph theory with M/M/1 queuing theory was constructed.Then,a spatial and temporal joint planning model for an IES incorporating IDCs was established.Based on the Karush-Kuhn-Tucker(KKT)conditions of the IDC and IES operational models,the operational models were transformed into additional constraints for the planning model,which were linearized using the big-M method.Considering the privacy preservation requirements between the IDC and IES,an enhanced Benders decomposition algorithm for mixed-integer linear programming subproblems was improved,and a distributed solution framework was designed to solve the spatio-temporal joint planning model.[Results]The results show that under the example scenarios adopted in this study,after the implementation of the IES and IDC demand response models established in this study,the annualized total cost of the system decreased by 26.79%.The enhanced Benders decomposition algorithm shows that its distributed solution speed is 1.11 times faster than the alternating direction method of multipliers.[Conclusions]This study analyzed the flexible regulation methods of IDC and IES,and constructed a feasible distributed optimization scheme for IES containing IDC that considers privacy preservation.The study provides corresponding solutions and methodological references for similar multistakeholder collaborative optimization scenarios.

关 键 词：互联网数据中心(IDC) 综合能源系统(IES) 需求响应 分层优化 Benders分解算法 

分 类 号：TM73[电气工程—电力系统及自动化]