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卷 50 ， 期 17 ， 2026
- 摘要：The prevalence of artificial intelligence (AI) has led to a sharp increase in energy consumption and carbon emissions in intelligent computing centers. High volatility on both the resource and load sides makes the exploration of endogenous load flexibility the key to computing power-electric power coordination. First, addressing the unique characteristics of AI computing tasks and hardware infrastructure, this paper analyzes the three major challenges faced by the research on load flexibility of intelligent computing centers: multi-dimensional heterogeneity, fine-grained coupling, and multi-stakeholder collaboration. Then, based on the three core sub-problems of load modeling, flexibility exploitation, and flexibility utilization, this paper reviews the current status of domestic and international research. Finally, the paper prospects future research trends, highlighting the need to advance task-level load modeling adapted to multi-dimensional heterogeneity, conquer dynamic flexibility characterization technologies that perceive fine-grained coupling, and design flexibility expansion application strategies to incentivize multi-stakeholder collaboration.关键词：computing power-electric power coordination;artificial intelligence (AI);intelligent computing center;load flexibility;renewable energy;task scheduling79|16|0更新时间：2026-09-01
- 摘要：The rapid proliferation of artificial intelligence has significantly increased global computing power demands, drawing widespread attention to the coordination of data centers and power systems. To address this, this paper conducts a systematic study on computing power-electric power coordination. First, it summarizes fundamental models of data centers in computing resource allocation, energy consumption management, and thermal management, and synthesizes modeling that characterizes the coupling between data centers and various energy networks, which lays a foundation for the research on computing power-electric power coordination. It then focuses on research advances in long-term expansion planning and short-term optimal operation for computing power-electric power coordination in three representative scenarios, such as data center microgrids. Furthermore, the paper provides a comprehensive overview of relevant market mechanisms from five perspectives, including the electricity and flexibility markets. It also summarizes resilience enhancement measures, mathematical models, and solution algorithms for computing power-electric power coordination at both transmission and distribution network levels. Subsequently, to further promote the research in this area, the paper surveys and compiles key characteristics of mainstream open data center datasets, including temporal granularity, coverage period, features, and applicable scenarios. Finally, it outlines key research directions for future computing power-electric power coordination from both micro-level aspects, such as coordinated hardware deployment in AI data centers and AI-driven load demand response, and macro-level aspects, such as social and economic benefits.关键词：computing power-electric power coordination;artificial intelligence (AI);data center;coupling;resilience;load demand response35|7|0更新时间：2026-09-01
- 摘要：The rapid development of artificial intelligence has led to a surge in computing power demand and data center loads, making the efficient coordination between computing power and renewable energy an inevitable trend. However, this faces core difficulties such as temporal mismatch between the electricity consumption characteristics of computing power demand and the output characteristics of renewable energy, increased risks in data center power supply stability, and the lack of electricity market mechanisms tailored to load characteristics of computing power. Energy storage technology can improve the spatiotemporal characteristics in the coordination of computing power and renewable energy, enhance the power supply reliability and load flexibility of data centers, and provide new opportunities for data centers to participate in electricity markets. Scientific and efficient integration mechanisms between data centers and energy storage represent a critical research topic for the computing power-electric power coordination. From the perspective of computing power-electric power coordination, this paper comprehensively summarizes the state of the art in integration mechanisms between data centers and energy storage. Firstly, focusing on the scenario of configuring in-site energy storage for data centers (in-site energy storage configuration), it outlines ideas and schemes for rationally configuring and effectively regulating energy storage in data centers from planning and operating perspectives. Secondly, the scenario of sharing off-site public energy storage for data centers (off-site shared energy storage) is explored, and transaction mechanisms and mathematical methods for typical modes such as shared energy storage and leased energy storage are discussed. Finally, feasible solutions and research pathways to share private energy storage for data centers (shared private energy storage) are discussed from three aspects of development pattern, model construction and transaction architecture. By analyzing the current research situation, a new business model of integrating data centers and energy storage is explored, which can provide reference for solving the problem of green energy consumption in data centers and promoting efficient computing power-electric power coordination.关键词：computing power-electric power coordination;data center;renewable energy;electricity market;energy storage;energy consumption27|2|0更新时间：2026-09-01
- 摘要：As a new flexibility resource participating in demand response, data centers can effectively promote the accommodation of wind and photovoltaic power. However, this may lead to conflicts of interest between data centers and tenants, hinder the flexible scheduling of computing-power loads, and reduce the optimization effect of computing power-electric power integration. To address this, this paper designs a reward-penalty mechanism and proposes a mixed game based computing power-electric power integrated dispatch method. Firstly, the energy consumption characteristics of the data center are modeled to enable batch-processing data loads to participate in demand response. Penalty costs are considered for the impact of maximum latency on service quality. Secondly, a reward mechanism is designed and rewards are redistributed based on Shapley values. Meanwhile, a penalty mechanism is established. Then, the reward and penalty are balanced according to marginal contributions to achieve a reasonable allocation of benefits and risks. After that, a two-stage computing power-electric power integrated dispatch model is constructed. In the first stage, the microgrid operators aim at minimizing the operating cost. In the second stage, the tenant aims to maximize the net income. The relationship among tenants constitutes a cooperative game, while the relationship between phases forms a Stackelberg game, collectively forming a mixed game framework. Finally, the effectiveness of the proposed method is validated through cases.关键词：data center;demand response;reward mechanism;mixed game;computing power-electric power integration15|4|0更新时间：2026-09-01
- 摘要：Data centers can adjust their load through computing task scheduling, which will become an important flexible demand-side regulation resource in new power systems. However, data centers and power systems have different operation objectives and cannot fully share information. An effective bidirectional coordination method for computing power and electric power has not yet been established. This paper proposes a bidirectional coordinated optimization method for computing power and electric power driven by computing task scheduling from a cooperative game perspective. First, a bidirectional coordinated optimization framework for data center operators and power system operators is proposed based on the Nash bargaining theory, to coordinate utility improvements for both parties. Next, detailed computing task scheduling models for interactive workloads and batch jobs are established, and a computing task scheduling decision-making method based on rolling horizon optimization is proposed. Subsequently, a distributed online solution method for collaborative decision-making of computing power and electric power is proposed. It balances global optimality and real-time solution efficiency while preserving the confidentiality of internal operation parameters and information of both parties. Case study results verify the effectiveness of the proposed method.关键词：data center;computing power-electric power coordination;demand-side response;cooperative game;rolling horizon optimization;computing task scheduling16|2|0更新时间：2026-09-01
- 摘要：Computing power-electric power coupling network faces challenges such as model heterogeneity, computational complexity, and market incentive mechanisms. To address these issues, a graph modeling method for the computing power network is first proposed to express the computing power constraints and the mapping relationship of computing power-electric power regulation capabilities. Based on this, a unified graph model representation for both computing power and electric power networks is proposed, from which a scalable distributed coordinated optimization method and analytical solutions for non-energy-storage devices are derived. This method supports network splitting at the computing power-electric power interface to achieve privacy-preserving computation. Based on this coordinated optimization capability and the Nash bargaining method, an incentive mechanism for the spatial migration of computing power is proposed, which functions independently of nodal electricity prices. Simulation results demonstrate that the proposed distributed optimization method exhibits linear complexity. Furthermore, the proposed computing power-electric power coordinated incentive mechanism can fairly allocate the cooperative surplus generated by spatio-temporal migration, achieving a win-win outcome for both the power grid and data centers. It also effectively enhances the renewable energy accommodation rate while reducing system operation costs and carbon emissions.关键词：data center;computing power-electric power coupling network;graph model;coordinated optimization;alternating direction method of multipliers (ADMM);privacy-preserving computation;Nash bargaining17|1|0更新时间：2026-09-01
- 摘要：Currently,leveraging the transferable features of computation tasks in data centers to enhance quality of service and reduce operating costs has become a significant development direction in data center operations. However, as major energy consumers, the operation modes of data centers significantly impact the safe operation of distribution networks. Therefore, focusing on the coordinated scheduling of data center clusters and distribution networks, the computation task transfer is integrated with power system operation to construct a hierarchical reinforcement learning model. The model adopts an adaptive adjustment mechanism for computing resources and establishes a hierarchical coordinated framework that incorporates economic dispatch on the electric-power side and task transfer on the computing-power side. On this basis, a hierarchical multi-agent reinforcement learning algorithm based on abstract instructions is designed to generate power exchange instructions between the distribution network and data centers, task transfer commands, and computing resource scheduling strategies. Simulation results demonstrate that the proposed method can effectively reduce system economic costs, enhance renewable energy accommodation capability, significantly decrease execution violations of computation tasks, and improve overall quality of service.关键词：computing power-electric power coordination;scheduling;reinforcement learning;data center;computing resource;distribution network;task transfer;quality of service12|1|0更新时间：2026-09-01
- 摘要：To improve the participation capability and scheduling efficiency of multiple geographically dispersed data centers in electricity markets, this paper addresses the issues of excessively high dimensionality of action spaces and slow training convergence faced by end-to-end deep reinforcement learning (DRL) when simultaneously considering multi-data-center coordination, quality-of-service constraints, and photovoltaic-energy storage coordination. A data center aggregator (DCA) model is constructed, and a two-layer decision coordinated scheduling framework oriented to computing power-electric power coupling is proposed. By integrating multiple small-scale data centers managed by a single operator, the DCA model optimizes resource management and workload scheduling, and strengthens their competitiveness in dynamic electricity markets. The proposed model adopts a two-layer solution architecture combining DRL and mixed-integer linear programming (MILP). The upper-layer DRL agent handles far-sighted actions that affect future energy states, while the lower-layer MILP solver optimizes myopic actions related to immediate costs, realizing coordinated optimization of long-term strategies and real-time decisions. Simulation results demonstrate that compared with full-action-space DRL, the total cost of the proposed model is reduced by approximately 42.55%, and the training time is shortened to the minute level. In contrast to linear optimization methods, the proposed approach further decreases electricity purchase costs and total comprehensive costs in diverse workload scenarios. The simulation results also verify the impacts of various factors including workload types, energy storage capacity and quality-of-service requirements on scheduling, which reflects the robustness and universality of the proposed method.关键词：data center;aggregator;deep reinforcement learning (DRL);mixed-integer linear programming (MILP);scheduling;computing power-electric power coordination;electricity market9|3|0更新时间：2026-09-01
- 摘要：With the rapid development of artificial intelligence technologies and the increasing demand for computing power, the energy consumption of data centers has surged dramatically, and high operating costs and emissions have become one of the bottlenecks restricting their development. Therefore, a coordinated optimization method for computing power and electric power in data centers considering sequential flexibility of computing power tasks is proposed. Firstly, by analyzing the dependency characteristics among computing power tasks, a decomposition model for computing power task sequences based on directed acyclic graph is proposed to precisely quantify the deferrable time for various computing power tasks. Secondly, by integrating Transformer and Kolmogorov-Arnold network, a computing power task-energy consumption mapping model is built to accurately characterize the nonlinear relationship between computing power task and electricity/cooling energy consumption under varying indoor and outdoor temperatures. Finally, with the objective of minimizing operating costs, a coordinated optimization model for computing power and electric power in data centers is constructed. Validation results of a publicly available dataset indicate that the proposed method achieves flexible time-shifting capabilities for computing power tasks suitable for practical scenarios, and can effectively reduce the total operating costs of the system.关键词：data center;integrated energy system;computing power;dependency;energy consumption;coordinated optimization;data-driven7|10|0更新时间：2026-09-01
- 摘要：The continuous growth of energy consumption in data centers poses severe challenges to power systems. In particular, the real-time response requirements for large language model inference tasks generate significant electricity peak loads. Idle computing resources feature a large scale and spatio-temporal regulation flexibility, serving as high-potential resources for realizing computing power-electric power coordination. To fully exploit the flexibility regulation capability of idle computing power, this paper proposes a computing power-electric power coordination method based on idle computing power scheduling to achieve high stability and low cost for computing tasks. Firstly, the nonlinear “performance-power consumption” characteristics of a single graphics processing unit are analyzed. Secondly, a temporal-regional scheduling strategy oriented to idle computing power is constructed. Finally, engineering practical verification is conducted on a real idle computing power scheduling platform. The experimental results show that the temporal regulation reduces the total power consumption of the computing power system by more than 17%, the regional scheduling cuts computing costs by nearly 18%, and improves the regional green power utilization rate by approximately 20%. The proposed method achieves remarkable performance and possesses broad promotion potential in reducing computing energy consumption and boosting renewable energy utilization, which provides a feasible solution for the coordinated low-carbon development of new power systems and computing power networks.关键词：computing power-electric power coordination;artificial intelligence (AI);data center;computing power;scheduling;flexibility regulation7|2|0更新时间：2026-09-01
- 摘要：Aiming at the multi-source uncertainties and topology constraints of computing power network in the coordinated operation of energy-intensive data centers (DCs) and high proportion of renewable energy, this paper proposes an optimization method for coordinated operation of computing power network-electric power network under multi-source uncertainty risk constraints. Firstly, a refined DC energy consumption model is established, and a scheduling model that characterizes the spatio-temporal flexibility of task loads is constructed based on load heterogeneity and computing power network topology. Secondly, a bi-level optimization framework for bidirectional computing power-electric power coordination is built: the upper-level aims to maximize the profit of DC operators, and uses the spatio-temporal migration of loads to respond to electricity price signals; the lower-level aims to minimize the comprehensive cost of the power grid (including carbon cost), and coordinates source-load resources based on DC optimal power flow. On this basis, distributionally robust chance constraints are introduced to deal with the multi-source uncertainties of the load on the computing power side and the electric power side, and linearization techniques and data-driven node carbon emission constraints are adopted to ensure the convexity and solvability of the model. Case studies show that the proposed model can effectively guide DCs to utilize spatio-temporal flexibility to avoid regions and time periods with high electricity prices and high carbon emissions. While alleviating power grid congestion and promoting renewable energy accommodation, it achieves the comprehensive optimum of DC profit improvement and low-carbon economic operation of the system.关键词：data center;computing power-electric power coordination;flexibility;uncertainty;optimal power flow;distributionally robust chance constraint;computing power network;carbon emission9|2|0更新时间：2026-09-01
Computing Power-Electric Power Coordination
- 摘要：With the continuous expansion of grid-connected scale of renewable energy, the insufficient frequency regulation capability of traditional power grids has become increasingly prominent. Relying on its flexible response characteristics, the new energy storage has become a critical support for secondary frequency regulation. Nevertheless, the single-type energy storage has limitations in automatic generation control (AGC) frequency regulation capability. Moreover, the state coupling characteristics of energy storage stations complicate the evaluation of the technical and economic impacts of current frequency regulation decisions on subsequent operation. Therefore, this paper proposes an online frequency regulation optimization strategy for energy storage clusters considering the degradation cost and state of charge (SOC) balancing. In the upper-layer model, energy storage stations are clustered based on the affinity propagation algorithm to obtain energy storage clusters with distributed characteristics. Subsequently, an online AGC frequency regulation response model for energy storage clusters is established using the Lyapunov optimization method, without requiring explicit energy constraints of energy storage devices or grid load forecasting information. The lower-layer model constructs an online power allocation strategy considering both the degradation cost and SOC balancing characteristics of energy storage stations via the capacity-aware water-filling algorithm. Case studies verify the effectiveness of the proposed model. This two-layer model can accurately track frequency regulation signals without forecasting data, effectively improve the frequency regulation revenue of energy storage clusters, and expand the adjustable range of aggregated power. The proposed strategy not only balances robustness and unpredictability during frequency regulation, but also significantly boosts frequency regulation efficiency through energy storage cluster partitioning and rational power allocation.关键词：degradation cost;state of charge (SOC);energy storage;automatic generation control (AGC);frequency regulation;robustness;unpredictability2|1|0更新时间：2026-09-01
- 摘要：Regarding the multiple uncertainties on both sides of the source and load in the operation of virtual power plants (VPPs),a two-stage robust optimal scheduling method is proposed with vehicle energy storage system (VESS) coordinated with the vehicle-to-grid (V2G) technology against source-load uncertainty. Firstly, by taking into account the spatio-temporal characteristics of electric vehicle (EV) travel, state of charge, and time-of-use electricity price incentives, a VESS schedulable capacity forecasting model that integrates subjective and objective responses is constructed by quantifying the spatio-temporal differences and economic drivers of users’ charging and discharging behaviors. Secondly, a two-stage robust optimal framework with min-max-min structure is established, and robust adjustment parameters are introduced to achieve a flexible balance between conservatism and economy in scheduling schemes. Strong duality theory and column and constraint generation algorithm are adopted to achieve alternating iterative solution of master-subproblems. The simulation results show that the proposed model can significantly suppress the increase in system operation costs in extreme uncertainty scenarios, synchronously improve system economy and EV user benefits, effectively coordinate and optimize economy and robustness, and provide theoretical support for multiple resource collaborative scheduling of VPPs and V2G mechanism design in high uncertainty environments.关键词：virtual power plant (VPP);vehicle-to-grid (V2G);vehicle energy storage system (VESS);robust optimal scheduling;uncertainty;economic incentive mechanism2|1|0更新时间：2026-09-01
- 摘要：To address the growing disparity in the development scale of urban electric vehicle charging facilities, this paper proposes a differentiated charging facility planning model based on simulated trip chains. Taking Beijing municipality as an example, multi-source data such as vehicle operation, built environment, and charging facilities are integrated to extract user characteristics from time, space, and energy dimensions. And user groups with differentiated characteristics are constructed based on the two-step clustering model of Gaussian mixture clustering and spectral clustering. The potential charging demand distribution network is constructed from a large-scale trip chain and divided into 13 sub-areas with various characteristics by using the community detection algorithm. These sub-areas show differences in charging infrastructure scale, time-of-use pricing, land-use composition, and charging demand hotspot distribution. In view of these differences, the planning model adopts independent construction constraints for various sub-areas and takes the attribute influence factors derived from geographically weighted regression as the weights of the objective function to realize differentiation considerations. The optimized scheme reduces electricity consumption by 3.35% in the areas with oversaturated charging demand, significantly reducing carbon emissions, and the utilization rate of newly constructed charging facilities during peak hours is better than that of old facilities.关键词：electric vehicle (EV);charging facility planning;user characteristics;differentiation;trip chain;clustering4|1|0更新时间：2026-09-01
- 摘要：To address the problems of the coordinated clearing mechanism for electric vehicle aggregators (EVAs) participating in the electricity and auxiliary markets, this paper proposes a coordinated optimal clearing strategy for the electricity and auxiliary markets, in which EVAs participate in the electric energy market as the main market and the auxiliary service market as the reserve market. First, based on the output characteristics, load elasticity, and user response characteristics of electric vehicle clusters, a reserve capacity model for electric vehicle clusters is constructed to quantify their reserve potential. Secondly, an electric energy-reserve market coordinated optimal clearing model is established. On the basis of security constrained unit commitment and security constrained economic dispatch, nodal electricity prices are determined, and the efficient allocation of cross-market resources is realized through price conduction mechanisms and market coupling rules. Finally, case studies are conducted based on the IEEE 33-bus system. The results show that the proposed strategy can effectively improve the profits of EVAs and optimize the distribution of social welfare by coordinating the charging and discharging behaviors of electric vehicles and the dispatching of reserve capacity.关键词：electric vehicle (EV);reserve capacity;electric energy market;auxiliary service market;reserve market;electric vehicle aggregator (EVA)1|2|0更新时间：2026-09-01
- 摘要：With the high-proportion integration of renewable energy into the grid and the deepening of electricity market reform, the unit commitment (UC) problems face the dual challenges of refined time resolution and enhanced solution timeliness. To address the issue of solution efficiency for UC problems, a physical information inspired graph neural network model is proposed to predict the values of some integer variables, thereby reducing the search space dimensionality and the number of iterations for integer programming solvers. This method employs graph neural networks to encode both the spatio-temporal and spatial features of the power system separately and dynamically fuses these spatio-temporal dual-modal features through the cross-attention mechanism, enabling the efficient modeling of UC problems and overcoming the efficiency issues of bipartite graph-based methods. Simulation validation results demonstrate that the forward computation time of this method is reduced by an average of 96.75% compared to classical graph network methods, and the total solution time is reduced by an average of 71.6% compared to the commercial solver Gurobi, providing a novel approach for the rapid generation of scheduling plans for high-dimensional complex power systems.关键词：unit commitment (UC);spatio-temporal feature;spatial feature;machine learning enhanced optimization;physical information graph;graph neural network;cross-attention3|2|0更新时间：2026-09-01
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Risk Assessment and Cause Tracing of New Distribution System Based on Prior Feature Fitting Learning
摘要：New distribution systems with high penetration of distributed resources are prone to security issues such as reverse heavy overload and voltage limit violations. Existing risk assessment methods suffer from strong dependence on operational data features, poor cross-scenario generalization capability, and insufficient interpretability. To address these problems, this paper proposes a risk assessment and cause tracing method for new distribution systems based on prior feature fitting learning, which enables cross-scenario risk assessment with zero training samples. Firstly, considering the security risks caused by the massive integration of distributed resources, a multi-dimensional risk assessment indicator system is constructed. Secondly, a prior task-driven risk feature fitting learning model is proposed. The risk assessment problem is formulated as a Bayesian conditional inference problem constrained by prior tasks, and a bidirectional attention mechanism is adopted to characterize the interactive relationships among features and statistical dependencies among samples. Then, prior synthetic data that characterize the dependency relationships of risk features are generated based on the structural causal model, enabling the model to learn the distribution law of features from in-context samples and perform conditional inference. Finally, the synthetic minority oversampling technique is introduced to optimize the class imbalance, and the Shapley additive explanation method is integrated to quantify feature contributions, so as to realize risk feature analysis and cause tracing. Experimental results demonstrate that the proposed method possesses excellent generalization capability and robustness under variable topological structures, changing in-context set scales, and noise interference conditions.关键词：new distribution system;risk assessment;prior feature fitting learning;synthetic minority oversampling;in-context sample;attention;cause tracing3|1|0更新时间：2026-09-01 - 摘要：AC/DC power distribution systems, serving as a transitional form from AC systems to DC systems, combine the advantages of both AC and DC systems, constituting a vital component in achieving the goals of carbon emission peak and carbon neutrality. However, grid voltage sag (GVS) represents a typical large-disturbance scenario, in which the stable operation mechanism of AC/DC power distribution systems under GVS cannot be analyzed using small-signal methods. To address this, this paper employs the mixed potential function (MPF) theory to investigate the stability criterion for AC/DC power distribution systems under GVS and verifies key parameters affecting system stability under GVS through the form of stability domains. Firstly, the MPF for the AC/DC power distribution systems is established. Based on this, a large-signal stability criterion (LSSC) for AC/DC power distribution systems under GVS is proposed. Subsequently, based on LSSC, a stability region analysis method under voltage sag conditions is introduced. This method is used to study the influence law of system parameter variations under voltage sags on the stability region and the system dynamic performance. Finally, the theoretical analysis is validated through software simulation and hardware-in-the-loop experiments关键词：AC/DC power distribution system;large-signal stability;stability region;voltage sag;mixed potential function (MPF);hardware-in-the-loop4|1|0更新时间：2026-09-01
- 摘要：With the large-scale integration of power electronic devices into power systems, the increase in harmonic disturbance sources has led to continuously escalating harmonic pollution. Accurately evaluating the harmonic state of the power grid is of great guiding significance for power quality management. However, as the scale of power systems expands, harmonic disturbances exhibit pronounced coupling characteristics. The harmonic state estimation for the entire grid requires substantial computational resources. To enhance the accuracy of harmonic evaluation while reducing computational resource consumption, this paper proposes a partitioning harmonic evaluation method considering multi-frequency coupling influences. Firstly,in view of the strong coupling characteristics of harmonic, a coupling harmonic state estimation method is proposed for the refined evaluation of the harmonic state in the focus region. Secondly, an observable path identification algorithm based on measurement allocation is proposed to find all observable paths containing a specified focus bus. Finally, by selecting the focus region based on the observable paths, a coupling harmonic contribution evaluation method for non-focus regions is further proposed. This method clarifies the cross-frequency harmonic contribution from non-focus regions to the focus bus, constructing a global harmonic evaluation scheme for power systems that balances computational efficiency and evaluation completeness. Results of simulation verification conducted on a modified IEEE 33-bus system demonstrate that, compared with conventional algorithms, the proposed method effectively enhances the computational accuracy and efficiency of harmonic state estimation. Simultaneously, the proposed method precisely decouples the cross-frequency harmonic contributions from non-focus regions to the focus bus.关键词：power quality;state estimation;observable path;harmonic evaluation;multi-frequency coupling;measurement allocation1|1|0更新时间：2026-09-01
Basic Research
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