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

[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.