128 results about "Load scheduling" patented technology

The invention relates to the technical field of distributed computing, and discloses a heavy truck charging load scheduling-oriented distributed energy intelligent matching method, which comprises the following steps of: establishing a charging service computing power mapping table at a scheduling node; maintaining a shadow counter in a local memory, extracting a pre-estimated computing power consumption value according to an event type and accumulating the pre-estimated computing power consumption value to the shadow counter, and executing linear numerical deduction on the shadow counter according to a reference logic subtraction rate so as to simulate a scheduling data throughput evolution process; adjusting a linear deduction rate parameter according to the deviation between the state feedback data and the numerical value of the shadow counter; and distributing the charging matching task to a charging station edge computing node of which the shadow counter value does not exceed a preset logic saturation threshold value. According to the invention, through an open-loop estimation and closed-loop calibration mechanism of a local logic state, an instantaneous congestion risk caused by physical feedback lag is eliminated; and logic state consistency and self-adaptive distribution of the whole network computing power resources are realized.

The invention relates to a fault network card isolation method and device applied to a GPU cluster, electronic equipment, a storage medium and a computer program product, and the method comprises the steps: determining whether a fault network card exists on any GPU node in the GPU cluster or not according to the state information of each network card in the GPU node; when it is determined that fault network cards exist on the GPU node and the number of the fault network cards is smaller than or equal to a preset threshold value, an NCCL topological file of the GPU node is updated, and the NCCL topological file of the GPU node is used for indicating the topological relation between available GPU devices and available network cards included in the GPU node; and according to the NCCL topology file of the GPU node, isolating a fault network card in the GPU node when carrying out load scheduling on the GPU node. According to the embodiment of the invention, the fault isolation granularity can be effectively refined to a single network card in the GPU cluster.

The invention provides a micro-grid management method and system based on source grid load storage, and relates to the technical field of micro-grids, and the method comprises the steps: S1, obtaining renewable energy power generation data in a micro-grid, the state of charge (SOC) of an energy storage device, a load demand, and power grid operation state information; and S2, performing real-time prediction on the load demand by using an intelligent algorithm and generating a load scheduling plan. According to the micro-grid management method and system based on source grid load storage, load prediction is carried out by introducing an intelligent algorithm, power scheduling between renewable energy sources and an energy storage device is optimized by a dynamic coordination mechanism, and resources are allocated by a multi-objective optimization algorithm, so that efficient management of the micro-grid is successfully realized. By acquiring various data in the micro-grid in real time and combining with a dynamic adjustment scheduling strategy, the method can effectively deal with the uncertainty of load fluctuation and renewable energy output, optimize the operation efficiency of the power grid, and ensure the stability and economy of the system.

The invention provides a multi-element load joint prediction method and system based on sequence decomposition and feature screening, and the method comprises the steps: collecting multi-element load data and related influence factor data of an integrated energy system, and carrying out the preprocessing, and obtaining the processed multi-element load data and related influence factor data; performing sequence decomposition on the processed multivariate load data, and fusing decomposed component sequences to obtain a multivariate fusion sequence; performing feature screening on the processed related influence factor data to obtain an influence factor feature set; and inputting the multivariate fusion sequence and the influence factor feature set into an improved multi-task learning model for joint training and prediction, and outputting a joint prediction result of the multivariate load. According to the multi-element load joint prediction method based on multi-source data decomposition and multi-task coupling learning, the model precision, generalization ability and operation efficiency can be effectively improved, and the multi-element load joint prediction method is suitable for cooling, heating and power load scheduling and energy optimization management of a comprehensive energy system.

The invention provides a flexible distributed energy network multi-agent collaborative optimization method based on MARL, and the method comprises the steps: building a distributed energy network multi-agent interaction framework which comprises a local energy transaction model for distinguishing an intelligent soft switching path, a network cost model based on a power transmission distribution factor, and a power distribution network operator and user income model; a physical system operation constraint model is established and covers intelligent soft switch operation characteristics, network topology radial constraint, energy storage charging and discharging dynamic states, controllable distributed power supply and photovoltaic output limitation, translational load scheduling and DistFlow power flow constraint. And finally, constructing a collaborative optimization mechanism based on a multi-agent near-end strategy optimization algorithm, modeling the system into a partially observable Markov decision process, adopting a centralized training distributed execution framework, and guaranteeing transaction security and data privacy through block chain evidence storage, RSA encryption and an intelligent contract. The system economy and the operation flexibility are improved.

The invention discloses a source network load storage oriented multi-resource hierarchical regulation and control method and system, and relates to the technical field of source network load storage hierarchical regulation and control. The multi-resource hierarchical regulation and control method oriented to the source network load storage comprises the following steps of source-network transmission matching analysis, network-load scheduling analysis and load-storage collaborative analysis. According to the invention, in the source network transmission stage, transmission matching analysis is carried out according to source network double-side data to determine whether transmission matching optimization regulation is carried out, then in the power distribution stage, load scheduling analysis is carried out to determine whether load scheduling optimization regulation is carried out, and finally in the load-storage linkage stage, load scheduling optimization regulation is carried out. Load-storage collaborative analysis is carried out according to the load-storage bilateral data to judge whether load-storage collaborative optimization regulation is carried out or not, so that timely and efficient source-network-load-storage multi-resource layered regulation is realized; the problem that in the prior art, when renewable energy sources are accessed, corresponding multi-resource layered regulation and control are not timely, and consequently, the'source-network-load-storage 'cooperation stability is not high is solved.

The invention relates to a power equipment load scheduling optimization method and device. The method comprises the following steps: carrying out load operation data acquisition through monitoring units deployed in power equipment to obtain a distributed load operation data set, and obtaining strategy game relationship network data between the equipment based on the distributed load operation data set; obtaining initial load adjustment intention intensity data of each power device according to the policy game relationship network data between the devices; sending the initial load adjustment intention intensity data and the global collaborative scheduling optimization target to a distributed negotiation chain to obtain final load adjustment strategy data after negotiation consensus; and obtaining an optimized distributed load scheduling model according to the final load adjustment strategy data after negotiation of the consensus, distributing the optimized distributed load scheduling model to each power device, and executing decentralized power device load scheduling optimization. By adopting the method, the load scheduling of the power equipment can be optimized.

The invention relates to a micro-grid optimization scheduling method with electric vehicle and multi-source uncertainty. The micro-grid optimization scheduling method comprises the following steps: S1, constructing a multi-target optimization scheduling model of a micro-grid system layer; s2, performing robust modeling on multi-source uncertainty in the multi-target optimization scheduling model of the micro-grid system layer based on a multi-target confidence gap decision theory, and establishing a robust optimization model of the micro-grid system layer; s3, constructing an optimal scheduling model of an electric vehicle user layer based on a foreground theory, and taking electric vehicle user comprehensive foreground value maximization as a target function; and S4, constructing a double-layer segment model of day-ahead scheduling, carrying out cooperative solution on an upper layer and a lower layer by adopting a double-layer optimization algorithm, and outputting a day-ahead scheduling plan of each unit in the micro-grid and the electric vehicle. According to the method, the comprehensive operation cost, the load fluctuation level, the electric vehicle owner utility and the source load uncertainty of the micro-grid are integrated, and the internal load stability and the energy utilization rate of the micro-grid are improved by excavating the scheduling potential of the source load side.

The invention belongs to the technical field of charging pile load scheduling, and provides a power distribution network measurement data fusion driven ultrafast charging load flexible regulation and control method and system, and the method comprises the steps: firstly processing multi-source data through a multi-source measurement data fusion module by employing a self-adaptive weighted fusion and Kalman filtering algorithm, and forming a high-fidelity data pedestal; the load is predicted through a charging load accurate prediction module in combination with wavelet transform, an LSTM-GNN model and a variational mode decomposition algorithm; then, a dynamic regulation and control strategy generation module generates a regulation and control strategy through marginal cost pricing and deep reinforcement learning according to prediction; the source-load collaborative optimization module integrates related systems and optimizes scheduling by means of a particle swarm optimization algorithm; the real-time monitoring and feedback module monitors and corrects the strategy in real time; and the security protection module ensures data and system security. During use, the above module processes are operated in sequence, and flexible regulation and control of the ultrafast charging load are realized.

This invention provides a load balancing method and system for cryptographic devices based on shared locks. When receiving a request from a business system, the method determines the request source based on request parameters and calls the corresponding resource occupancy ratio and group identifier. Based on the group identifier, it searches the corresponding shared lock pool. Based on the number of locks in the shared lock pool, it calculates the maximum number of idle locks that the corresponding business system can use. It then searches the shared lock pool for idle locks and finds the corresponding Socket connection object through the association relationship of the successfully found locks. Finally, it uses this Socket connection object to call the cryptographic device for cryptographic operations. This method can solve the scheduling problem of multiple cryptographic devices and ensure balanced scheduling across multiple cryptographic machines.

The invention discloses a new energy charging pile operation data analysis processing method, and relates to the technical field of new energy charging pile data analysis, and the method comprises the following steps: S1, collecting multi-source operation data such as charging power, current and voltage of a charging pile, and storing the multi-source operation data according to a timestamp; s2, preprocessing data, removing abnormal values, complementing missing values and standardizing; s3, extracting instantaneous operation, time dimension and equipment state core features; s4, user charging behaviors are clustered and analyzed to generate a classification label library; s5, analyzing an operation load situation and identifying a load fluctuation factor; s6, evaluating the health state of the equipment, grading and performing early warning; s7, generating optimization strategies such as load scheduling; and S8, collecting new data to iteratively optimize model parameters and algorithm logic. According to the method, multi-source data fusion and accurate analysis are realized, the fault identification accuracy and the load balance degree are improved, and a landing optimization strategy is generated; different scenes are adapted through iterative optimization, and scientific support is provided for fine management of the charging pile.

The application provides a network load balancing algorithm based on a Xinyuan platform and a Xinyuan terminal, and relates to the technical field of Xinyuan, which comprises the following steps: collecting real-time traffic data from multiple ports; performing normalization, outlier elimination and protocol field standardization preprocessing on the collected data; performing multi-dimensional protocol fingerprint feature extraction on the traffic of each port based on a protocol analysis algorithm; modeling the historical and real-time traffic of each port using a time series neural network, and outputting traffic prediction values and abnormal traffic probability evaluation in a future short period; dynamically generating a multi-port distribution weight factor based on the protocol characteristics and traffic change trend of each port; inputting the prediction results and real-time load state into a reinforcement learning scheduling agent; and immediately performing feedforward load distribution adjustment when abnormal traffic trend is detected. The method can improve the data processing efficiency, realize the intelligence of load scheduling, ensure the resource allocation priority of the ports of the Xinyuan terminal, and guarantee service continuity.

The invention discloses a load optimization scheduling method, system and device for a virtual power plant and a medium, and the method comprises the steps: obtaining the target output data of a power grid system and the target electricity price data of the virtual power plant; a double-layer model is constructed, the double-layer model comprises a lower-layer uncertainty prediction model and an upper-layer multi-load optimization scheduling model, and the lower-layer uncertainty prediction model is used for predicting probability distribution of electricity price and renewable energy source output of the virtual power plant in a future time period; the upper-layer multi-load optimal scheduling model is used for generating a multi-load optimal scheduling strategy of the virtual power plant; inputting the target electricity price data and the target output data into the double-layer model to obtain a multi-load optimization scheduling strategy output by the double-layer model; and performing multi-load scheduling control on the virtual power plant according to the multi-load optimal scheduling strategy. The method can effectively improve the virtual power plant load optimization scheduling effect. The invention relates to the technical field of power dispatching.

The invention provides a hybrid cloud architecture-based power business load balancing scheduling method and system, and the method comprises the steps: classifying power business operation data collected in real time based on the importance of power businesses, and obtaining a target power business category; determining a current load state of the target power business category by combining resource state information of the private cloud resource pool and the public cloud resource pool based on the target power business category and the corresponding power business operation data; and determining a load scheduling strategy based on the current load state for the target power business category. According to the method, the problems that in an existing method, the resource capacity of a local physical server cluster is fixed, sudden peak loads are difficult to deal with, and service response delay and data processing interruption are likely to be caused are effectively solved, and meanwhile stable and efficient operation of a power system is guaranteed.

The invention discloses a chip low-power-consumption intelligent control method and system based on a proportion threshold value, and relates to the technical field of chip power consumption control. A proportion threshold reference model of each module of the chip is established, a proportion threshold sensing unit is designed to collect chip operation data in real time and identify proportion stress points influencing chip power consumption, and on this basis, chip power supply, clock and load scheduling are dynamically optimized through an intelligent control unit, so that the chip always works in an optimal proportion threshold interval. According to the method, the overall power consumption of the chip can be greatly reduced, the energy efficiency ratio is improved, the core task performance is guaranteed, the chip operation stability is improved, and the method is suitable for power consumption optimization of hardware such as various AI chips, processors and SoC.

The invention discloses a node management method in a distributed management system, relates to the technical field of distributed systems, and aims to solve the problems of load scheduling rigidness, low fault positioning efficiency and poor cross-node collaboration consistency in existing node management. The method comprises a node state sensing module, a dynamic load scheduling module, an intelligent fault processing module and a consistency maintenance module, accurate distribution of node loads is realized through an improved weighted adaptive load algorithm, a dynamic dependency graph is constructed in combination with a global TraceID penetration technology and a graph convolutional network model, and rapid positioning and self-healing of fault root causes are realized. A lease mechanism is innovatively introduced to optimize node state consensus, and reliability and high efficiency of cross-node data synchronization are guaranteed through a hierarchical consistency protocol. The method breaks through the limitation of traditional static scheduling and manual troubleshooting, improves the adaptive capacity, fault tolerance performance and operation and maintenance efficiency of the system, is suitable for a large-scale complex distributed cluster scene, and has wide engineering application value.

The invention discloses a multi-channel fusion task load scheduling method and device, electronic equipment and a storage medium, and belongs to the technical field of computers.The method comprises the steps that the task priority level of a current communication task is calculated according to task data of the current communication task, calculating a channel scene score of each communication channel according to the channel data of each communication channel; obtaining a channel switching condition of each communication channel in a historical task scheduling process and a corresponding historical performance index, and then calculating a channel comprehensive score of each communication channel relative to the current communication task according to the task priority level, the channel scene score, the channel switching condition and the historical performance index; and the communication channel with the highest channel comprehensive score is selected as the target communication channel of the current communication task to perform task scheduling of the current communication task, so that the problem of low communication efficiency or waste of channel resources in the prior art can be solved.

The application discloses a transformer area load peak shaving and valley filling cooperative regulation method based on an intelligent fusion terminal, and relates to the technical field of transformer area load regulation.The application obtains a load prediction curve and a controllable load curve of a target transformer area through historical load data prediction, identifies a peak-valley section in the load prediction curve in combination with a target optimization range, performs peak shaving on a peak section in combination with the controllable load curve, generates a load scheduling strategy for the purpose of valley filling on a valley section, and executes the load scheduling strategy by using the deployed intelligent fusion terminal, so as to realize the peak shaving and valley filling of the load of the target transformer area.The application can accurately predict by using a hyperbolic curve, avoid traditional load blind regulation, ensure the accuracy of the regulation direction by dynamic peak-valley identification, cooperatively reduce transformer overload and voltage drop by shifting and reducing strategies, improve the stability of a power distribution network, reduce the waste of high-peak light abandonment and low-valley resource idling, and significantly improve energy utilization efficiency.

A control method and a control device of an energy storage air conditioning system are disclosed. The control method comprises: in response to receiving a scheduling instruction from a load scheduling platform, determining a target time-sharing load of the energy storage air conditioning system in a preset time period; based on the target time-sharing load, determining a load distribution strategy of the energy storage air conditioning system; and according to the load distribution strategy, performing time-sharing load distribution on multiple air conditioning units of the energy storage air conditioning system, so that a sum of energy efficiency ratios of the multiple air conditioning units meets a first preset requirement. The control method can make the energy storage air conditioning system meet scheduling requirements, user requirements and economic requirements in an electricity market environment.

The invention discloses a flexible load scheduling method and device, electronic equipment and a storage medium, and belongs to the technical field of power distribution scheduling, and the method comprises the steps: carrying out the clustering of load data of flexible loads, and obtaining a plurality of clustering clusters of the flexible loads with similar available time, power demands and working time; according to the load data and the clustering cluster, constructing a modulation aggregation model corresponding to a movable load and a delayable load; taking the minimum peak-valley difference stabilizing cost and the minimum load scheduling cost of the power system as a target, and constructing a target function of corresponding flexible load scheduling; and solving the target function to obtain the scheduling amount of the movable load and the delayable load at each moment when the peak-valley difference stabilizing cost and the load scheduling cost of the power system are minimum, and carrying out load scheduling on the movable load and the delayable load. The problem that a reasonable flexible load scheduling mode is lacked in the prior art can be solved.

The invention provides a heat pump distributed heat supply system debugging evaluation method and system, and the method comprises the steps: determining a pipe network structure for a heat supply pipe network, and determining each monitoring node for the pipe network through the pipe network structure; preprocessing the original data of each monitoring node to extract a heat exchange active area; the heat exchange active area is used for monitoring the original temperature in the node original data to construct a temperature field distribution diagram; calculating the theoretical performance coefficient of the heat pump based on the power data of each heat pump unit, and evaluating an abnormal unit; determining a key and significant abnormal region by using the temperature field distribution diagram and the abnormal unit, evaluating an optimal transmission path for the key and significant abnormal region, and constructing a load scheduling instruction; and the heat pump distributed heat supply system is used for issuing a load scheduling instruction to perform fluid scheduling of the heat supply pipe network. The flow velocity of different pipe sections can be adjusted according to the constructed and readjusted load scheduling instruction, so that heat can be better transferred to weak pipe sections.

The application discloses a kind of flexible load collaborative scheduling method and system based on multi-agent reinforcement learning, belong to power system dispatching and demand side management technical field, aim at solving the dimension disaster of existing flexible load scheduling, poor real-time performance, global optimality is insufficient, the problem that limit risk is difficult to control.The method constructs global twin state perception system and three-layer multi-agent system, generates scheduling decision after simulation deduction limit check difference disposal, executes closed-loop correction digital model.The application effectively improves the real-time performance, global optimality and power grid operation safety of flexible load scheduling, realizes multi-objective adaptive balanced scheduling.

The invention provides a solid-state battery and a production and preparation method of the solid-state battery, and belongs to the technical field of load scheduling, and the method specifically comprises the following steps: determining parameter adjustment conditions of production equipment between different preparation processes based on deviation conditions of process control parameters and process control parameters of other preparation processes, and when it is determined that the process adjustment difficulty of the current production order can meet the requirement based on the reference adjustment condition, determining the adjustment mode of the preparation process of the current production order according to the matching condition of the process control parameters of the positive electrode material of the current production batch and the original production order in combination with the matched preparation processes of different original production orders. The positive electrode material based on the adjustment mode of the preparation process is used for producing and preparing the solid-state battery, so that the reliability and the stability of the preparation and treatment are improved.

The present application relates to the technical field of data processing, and more particularly to a time series prediction method for cloud server cluster load scheduling and electronic equipment, comprising: inputting a historical time series into a constructed PAPT model, the model adopting an encoder-decoder architecture, in the encoder, the historical time series is decomposed into a trend item and a periodic item; adaptive polynomial basis trend fitting is used for the trend item to capture trend characteristics, and after being combined with the periodic item, an attention layer integrated with periodic attention position coding is input, the periodic attention position coding drives a rotation angle through multiple periods, and the rotation angle is applied to each position of Q / K in attention, so that the attention calculation is integrated with the position dependence of multiple periods; in the decoder, the output of the attention layer integrated with periodic attention position coding is used for cross-attention calculation together with the output of the encoder, and finally the prediction result is output. Compared with the prior art, the present application has the advantages of realizing accurate prediction of general time series and the like.

The invention discloses a flexible load optimal scheduling method and device based on chaotic mapping, terminal equipment and a storage medium, and belongs to the field of flexible load scheduling. The method comprises the following steps: clustering a plurality of flexible loads according to user behavior data and a power curve to generate a plurality of load clusters; the method comprises the steps that a constraint function set is established, a load curve and a load adjusting range of the cluster center of each load cluster are determined, a plurality of initial scheduling schemes are generated through chaotic mapping according to the load curve and the load adjusting range of each cluster center, and iterative optimization is carried out on the initial scheduling schemes according to the target function under the constraint of the constraint function set to obtain the optimal scheduling scheme. Generating a target scheduling scheme; and scheduling the flexible load according to the target scheduling scheme. Therefore, the method solves the problem that the existing method does not consider the space-time association rule and the dynamic adjustment capability of the load and is difficult to realize the efficient calling of the flexible resources.

The invention relates to a user-side load-oriented optimal scheduling method, which belongs to the technical field of energy scheduling and intelligent power grids, and comprises the following steps of: constructing a household micro-grid model containing electric equipment and HVAC, establishing an HVAC thermodynamic model based on building heat balance, describing room temperature change in combination with meteorological and building parameters, and establishing an HVAC thermodynamic model based on the HVAC thermodynamic model. Classification modeling of the rigid load and the flexible load is completed; a multi-objective optimization framework with thermal comfort, power consumption behavior deviation and economy as objectives is constructed, human body thermal perception is quantified by adopting a PMV index based on a Fanger model, and dynamic coupling of scheduling and comfort is realized; hVAC scheduling is modeled as MDP, and double TD3 is introduced to realize continuous power and mode control; an integer programming model is constructed for a flexible uninterrupted load, and a starting time slot is optimized through a rolling time domain strategy, so that overall load coordinated scheduling is realized. According to the invention, the resource utilization efficiency and the load scheduling flexibility of the household micro-grid are obviously improved.

The invention discloses a data center economic optimization scheduling method based on multi-agent deep reinforcement learning. According to the method, a model covering three levels of a CPU, a cabinet and a data center is established, and an electricity-heat-calculation coupling relation among an IT system, a cooling system and an energy supply system is described; an economic optimization problem is converted into a Markov decision process, an operation target is mapped into a cooperative scheduling task among multiple agents, and an observation space, an action space and a reward function of three kinds of agents including load scheduling, temperature adjustment and battery energy management are constructed. And a multi-agent optimization operation framework is constructed, and a multi-agent cooperation mechanism based on an Actor-Critic framework is constructed, so that real-time optimization scheduling of the data center is realized. According to the method, typical day data in July in summer is selected for experimental verification, and the effectiveness and superiority of the method are fully verified through comparative analysis with MAPPO, PPO and MPC algorithms.

The invention provides a building temperature control load scheduling method and system based on joint modeling, and relates to the technical field of building load simulation and scheduling, and the method comprises the steps: employing numerical simulation software to simulate the microclimate condition of a building group, and calculating the solar radiation heat gain and surface temperature of the outer surface of a building envelope; a Markov chain is adopted to simulate personnel random activities, and personnel preference operation temperature and a comfortable temperature range are obtained; an equivalent heat circuit method is adopted to construct a building five-thermal-resistance one-thermal-capacity model, and the operation temperature of a building unit is solved; the flexibility of the temperature control load of the building unit is equivalently modeled as a virtual energy storage system, and a flexibility quantitative model and operation constraints of the temperature control load of the building group are obtained through aggregation calculation; intelligent building operation optimization models in a typical price type demand response (PDR) scene and an incentive type demand response (IDR) scene are established respectively; and solving the intelligent building operation optimization model to obtain a scheduling result of the building temperature control load.

The invention relates to an electric power system monitoring technology, in particular to an electric power monitoring method of a distribution box, and solves the problems of unstable data transmission of edge nodes and disconnection between load scheduling and equipment health in a distributed distribution box through collaborative operation of a plurality of technical links. According to the method, the health state evaluation model is constructed by fusing the load data and the circuit impedance data, and a dynamic priority scheduling and feedback regulation mechanism is realized in combination with the transmission quality and the load demand, so that the technical problems of unstable data transmission of edge nodes and disjunction between load scheduling and the equipment health state are effectively solved; the method has the advantages that the reliability and safety of power monitoring of the distribution box are improved, dynamic power dispatching and transmission parameter feedback adjustment based on the circuit health state are realized, the overload risk is effectively prevented, and the service life of equipment is prolonged.

The invention relates to an adjustable capacity optimization method and system for a data center participating in a peak shaving auxiliary service market, and the method comprises the steps: obtaining a data center computing power task plan, power load prediction data, power grid peak shaving auxiliary service demand information and external environment information, and carrying out the preprocessing; according to the preprocessed data, constructing a computing power-electric power cooperative regulation and control cost model; with maximization of computing power user satisfaction and minimization of data center operation cost as targets, according to the computing power-electric power cooperative regulation and control cost model, constructing a double-target optimization model of the adjustable capacity of the data center participating in the peak shaving auxiliary service market; solving the dual-objective optimization model by adopting a multi-objective particle swarm optimization algorithm to obtain an optimal load scheduling scheme; and obtaining the optimal adjustable capacity according to the optimal load scheduling scheme. According to the invention, the efficiency and economy of the data center participating in the peak regulation auxiliary service can be improved.