[Objective] As the energy consumption of data centers continues to grow and renewable electricity rapidly penetrates the market， the synergy between computing power， electricity， and heat can break the siloed development of these sectors， thereby promoting the high-quality and sustainable development of data centers and novel energy systems. However， the construction of a coordinated system and the formulation of standards for computing power， electricity， and heat lack regulation and guidance， which hinders the enhancement of energy utilization efficiency and coordinated optimization levels in data centers. [Methods] This study analyzes the current research and development status of computing power-electricity-heat collaborative systems in data centers. It summarizes the deficiencies of existing terminologies and provides key terminology examples related to the synergy of computing power， electricity， and heat from aspects such as general terms， integrated optimization， and evaluation terminology. [Results] The results indicate that the coordination among computing power， electricity， and heat in data centers is still in its preliminary exploratory stage， and normative work on cross-industry collaborative terminologies should be strengthened. [Conclusions] This study establishes a standard system for the coordination of computing power， electricity， and heat， which primarily includes basic， business， and service standards， with the aim of effectively guiding the deep collaboration between data centers， power grids， and district heating networks， promoting the diversified and intensive utilization of energy， and offering references and suggestions for advancing the standardization efforts in this field.

[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.

Exploiting inherent load flexibility within data centers is key to achieving synergy between computing power and electricity. This study focuses on the idle flexibility of integrated backup and energy storage batteries on the side of high-voltage direct current （HVDC） communication power supplies in data centers. An application scenario for this idle flexibility， which enhances the supported workload of cabinets to achieve premium applications， is proposed. Meanwhile， it simultaneously provides load flexibility to the grid to obtain compensation from electricity ancillary service markets and reduce electricity costs. Specifically， this study presents the principle for this application scenario and discusses the value assessment under different scenarios， providing a theoretical foundation for determining the idle flexibility of HVDC-side integrated energy storage and backup batteries. Finally， prospects for exploring the synergy between computing power and electricity are discussed.

The era of intelligence has driven computing power resources to become highly flexible and adjustable. They have also made the bidirectional collaborative optimization of computing and electricity into a new method of economic optimization in comprehensive energy systems. The explosive growth in computational demand has led to a shortage of computational resources. It also brings the challenges of high energy consumption and carbon emissions for data centers， where the annual electricity consumption can reach billions of kilowatt-hours. When the cost of computing resources is high and the stability of power grid operations is affected， there is an urgent need to explore bidirectional collaborative technologies between computing and power nodes with adjustable resources to reduce the energy cost and enhance the stability and economic efficiency of power grid operation. This study constructs a bidirectional collaborative scheduling architecture for adjustable resources in computing and power nodes， and quantitatively models the diverse adjustable resources within them. Considering the matching and real-time adjustment characteristics between computing tasks and resources， a dual-layer two-stage collaborative optimization scheduling model is proposed by scheduling computing tasks under the computing node and adjustable loads under the power node. Through numerical examples， it was verified that the bidirectional collaborative optimization of adjustable resources for computing and power nodes is feasible and effective. Under the setting of an overall adjustable resource of approximately 2300 MW for power nodes， the cost reduction provided by computing nodes of 50 MW can account for 4.71% of the power node operation， while reducing its daily operating costs by approximately 0.70%.

The computing power integrated energy system （CPIES）， as a new type of energy system framework， realizes the intelligent management of energy production， transmission， storage， consumption， and information processing by integrating computing and energy resources with the acceleration of digital transformation and the popularity of renewable energy. First， the system architecture and key components of the CPIES， development status and trend， and the interaction mechanism between computing power and energy are outlined. Second， an in-depth analysis of the basis for the integration of information and communication technology （ICT）， artificial intelligence （AI）， big data analysis， and the internet of things （IoT） with the CPIES is presented to promote the intelligence and efficiency of energy systems. Furthermore， the application of information and energy technologies in CPIES is described in detail， demonstrating their potential to enhance energy utilization efficiency and other aspects. Finally， advanced international CPIES projects are listed， the challenges faced by current projects are identified， and countermeasures are suggested. The research results not only help to promote the theoretical development and practical application of CPIES but also provide new ideas and solutions for the digital transformation and sustainable development of the energy industry.