71 results about "Dynamic demand" patented technology

The invention discloses a virtual power plant green energy consumption cooperation method and system fusing digital twinning and reinforcement learning, and the method comprises the steps: building a green energy power plant digital twinning model through a simulation tool, collecting data in real time, and carrying out the normalization and abnormal value cleaning; inputting the data into the digital twinborn model, mapping the operation state of a physical system, and rehearsing the influence of different energy scheduling strategies on the green energy consumption rate and the power grid frequency in a virtual environment; optimizing and updating the energy scheduling strategy based on a near-end strategy optimization PPO algorithm; the optimized and updated energy scheduling strategy is fed back to a physical system to be executed, the strategy execution effect is monitored in real time, the digital twin model parameters are updated, and closed-loop control is formed; the method can solve the problem that the intermittency of renewable energy sources is not matched with the dynamic demand of the load.

The invention relates to the technical field of hydropower station optimization scheduling, in particular to a hydropower station multi-target scheduling decision-making method and system, and the method comprises the steps: obtaining the multi-source heterogeneous data of a target cascade hydropower station, and constructing and dynamically updating a scheduling knowledge graph fusing the cascade hydraulic coupling and collaborative operation association relationship; identifying a reference scheduling time period and a non-reference scheduling time period and establishing a differential output constraint; performing feature compression on the scheduling knowledge graph, extracting a key feature sub-graph influencing a scheduling decision, and predicting a state evolution path of related scheduling elements of the target cascade hydropower station in a future scheduling time domain; constructing and solving a multi-target dynamic decision model, and generating a candidate scheduling scheme set; and performing cross-scale conflict detection based on the candidate scheduling scheme set, performing hierarchical re-optimization on the candidate scheduling scheme set according to a detection result, and outputting and executing a scheduling decision result. The objective of the invention is to adapt to the dynamic demand of the power market for cascade hydropower station scheduling and realize rapid and accurate collaborative scheduling decision.

The invention discloses a flexible dynamic distribution method and system for a power module based on a charging pile, and relates to the field of power distribution, and the method comprises the steps: collecting dynamic demand parameters of a charging terminal in real time, and calculating a target power demand in combination with a battery safety constraint; obtaining the total number of available power modules and rated power, and generating an effective module set; calculating the minimum module demand number according to the total demand and the module average power; if the modules are sufficient, an efficiency priority strategy is adopted, and distribution is optimized through a greedy algorithm; if not, high-power terminals are preferentially distributed, and then residual power is distributed in a balanced mode; through real-time data, distribution is dynamically adjusted when a threshold value is exceeded; starting dormancy for the module with the load rate lower than 30%, migrating the terminal to other modules, and awakening when the load rises to 40%, so as to improve the efficiency and the resource utilization rate. The method has the advantages that through real-time monitoring and dynamic distribution of the power modules, the charging efficiency and safety are optimized, and the resource utilization rate and the system reliability of the charging pile are remarkably improved.

The invention relates to the technical field of virtual power plant resource configuration, and discloses a virtual power plant resource dynamic coordination configuration method and system, and the method comprises the following steps: collecting the operation data of various resources in a virtual power plant and the external power grid environment data in real time, and carrying out the dynamic coordination configuration of the resources based on the operation data; extracting operation characteristic parameters reflecting response speed differences of various resources; clustering analysis is carried out on the multiple resources to form resource clusters with different response speed levels, and the collaborative deployment demand intensity of the power grid in the current time period is evaluated; predicting a power grid load balance state in a future target time period, and determining the callable response capability of each resource cluster in the target time period; and carrying out multi-target optimization calculation meeting the stability constraint of the power grid, and generating a dynamic coordination configuration scheme of various resources in the target time period. According to the method, through the synergistic effect of multi-level technical characteristics, the matching problem between resource characteristic differences and dynamic requirements is fundamentally solved.

The invention relates to the technical field of ocean information processing, in particular to an ocean supercomputing environment-oriented heterogeneous computing power resource dynamic allocation method, which comprises the following steps of S1, monitoring dynamic demand characteristics of an ocean data processing task in real time, including a task calculation amount and a task real-time level; s2, dynamically calibrating task priorities; s3, screening out target resources which simultaneously meet the task calculation amount requirement and have response speeds matched with the real-time level; s4, allocating the task to a target resource; s5, if the load rate of the target resource exceeds a preset threshold value, triggering resource redistribution; and S6, outputting a final resource allocation scheme, and recording the completion time of the corresponding task and the utilization rate of each computing power resource. According to the invention, through the task priority-driven dynamic scheduling and real-time load-aware redistribution mechanism, the timeliness guarantee of key tasks and the efficient utilization of heterogeneous resources in the ocean supercomputing environment are realized.

The invention discloses a multi-target inventory optimization method based on dynamic demand prediction and an intelligent decision-making system, and the method comprises the steps: firstly collecting a cache library inventory state of a cache library system, including considering production order data and AGV transportation capability data, and carrying out the standardization processing of the cache library inventory state; inputting the data into an LSTM neural network model of a dynamic demand prediction module to predict raw and auxiliary material demands in a future time period; the replenishment amount and the replenishment period are solved based on an improved multi-objective optimization algorithm NSGA-II, and the optimization objectives are the lowest total cost, the lowest stockout rate and the lowest inventory fluctuation; issuing a replenishment instruction to the cache library system through the production management system, and scheduling the AGV to execute a transportation task; and the inventory state is monitored in real time in the production process, strategy adjustment or model retraining is triggered according to the fluctuation coefficient, and finally a zero-stockout, low-fluctuation and optimal-cost replenishment strategy is output, so that the method is suitable for a high-dynamic manufacturing environment.

The invention discloses a power grid project supply chain demand prediction and optimization system based on big data analysis. The system comprises a data acquisition module, a data preprocessing module, a dynamic demand prediction module, a supply chain optimization decision module, a real-time cooperative control module and a risk early warning and self-healing module. The data acquisition module is connected with an internet of things sensor, an engineering management system API and an external data interface. According to the invention, a full-chain intelligent system of data acquisition, demand prediction, dynamic optimization and risk management and control is constructed, so that the power grid project supply chain management efficiency is remarkably improved. The system fuses multi-source heterogeneous data, and adopts a machine learning model to adaptively correct prediction deviation, so that the material demand prediction precision is improved; and a multi-objective optimization decision is driven based on a dynamic prediction result, a global optimal scheme of purchase, storage and logistics is generated, redundant inventory is effectively reduced, and a material allocation period is shortened.

The invention relates to a tail end constant-pressure water supply strategy adjusting method and system based on AI, and belongs to the technical field of secondary water supply.The constant-pressure water supply strategy adjusting method comprises the steps that the flow of a header pipe and real-time health state data of each running pump are periodically collected; according to the header pipe flow, the dynamic demand lift is calculated, and all feasible pump set combination strategies are generated in combination with the dynamic demand lift and the real-time health state data; the score of each candidate strategy is calculated, the candidate strategy with the highest score is used as a pump set scheduling strategy, the pump with the most matched performance is selected from candidate pump sets corresponding to the pump set scheduling strategy to serve as a frequency conversion main guide pump, and the other pumps serve as frequency conversion auxiliary pumps; according to the set minimum service pressure and the dynamic demand lift, the target pressure of a pump outlet is calculated; and according to the target pressure and the actually-collected pump outlet pressure, the rotating speed of the variable-frequency main guide pump is adjusted. The method has the beneficial effects that the power of the pump set is matched with the actual requirement, so that the operation cost is reduced.

The invention discloses an ultra-low energy consumption building power utilization intelligent regulation and control method and system based on photovoltaic power generation, and the method comprises the steps: collecting photovoltaic voltage and current fluctuation and equipment power data in real time, and extracting supply instability features; classifying the dynamic demands by using a clustering algorithm according to the features, solving diffusion coefficients of each group of demands, and distinguishing high and low demands; extracting path candidates from high demands, and iteratively optimizing to obtain a complete electric energy distribution scheme; after the simulated energy utilization rate meets the ultra-low energy consumption requirement, the scheme is deployed as a timing scheduling execution plan; power deviation is continuously monitored during operation, if demand changes exist, diffusion coefficients are updated, demand groups are rearranged, an electric energy utilization report containing efficiency statistics is generated, and closed-loop control is achieved. Through dynamic optimization and real-time regulation and control, the electric energy distribution efficiency and the system stability are remarkably improved, and the requirements of ultra-low energy consumption buildings are met.

The invention belongs to the technical field of power distribution network power supply capacity potential tapping, and provides a power distribution network power supply capacity potential tapping method for multi-type load recombination, which comprises the following steps of: identifying a high-risk area in a power distribution network power supply area by performing power utilization time concentration analysis on the power distribution network power supply area; the method comprises the following steps: acquiring a flexible load and a rigid load of a high-risk area in a historical period, marking loads of different historical periods in the historical period, performing traversal analysis on a marking result, extracting all to-be-analyzed period groups in the historical period, and performing adjustability analysis on the flexible load of the historical periods in the to-be-analyzed period groups to obtain the flexible load of the high-risk area in the historical period. Outputting an adjustable high time period group in the to-be-analyzed time period group; according to the method, the multi-period collaborative optimization model is constructed, the optimal transfer scheme is solved in combination with the mixed integer linear programming and the branch and bound method, the residual capacity in the low-load period is fully utilized, and collaborative optimization of the dynamic demand response and the power supply capacity is achieved.

The invention relates to the technical field of kitchen appliance control, and discloses a kitchen appliance integrated control system based on dynamic power distribution, which comprises a power monitoring module, a load analysis module, a dynamic distribution module, a safety protection module, a user interaction module and a network integration module. The current, voltage and instantaneous power consumption value of each device are collected in real time through a power monitoring module, and whether a safe power interval is exceeded is judged; analyzing a total load state and a dynamic demand trend based on a load analysis module, and predicting a potential overload risk; the dynamic allocation module dynamically adjusts the power allocation proportion of key and non-key equipment in combination with a user priority rule; the safety protection module monitors in real time and automatically executes graded protection measures; and the user interaction module provides a graphical interface to support priority configuration and power upper limit setting so as to realize strategy closed-loop optimization.

The application relates to the technical field of order allocation, in particular to a dynamic distribution order allocation optimization method considering the supply-demand state of a transport capacity system.S1, a dynamic pickup and delivery problem model is constructed, the dynamic pickup and delivery problem system state and the action set are determined, and the dynamic pickup and delivery problem model is constructed;S2, a rolling time sequence mechanism is adopted to describe the dynamic nature of the urban dynamic pickup and delivery problem; and S3, a mixed allocation strategy is adopted to allocate the dynamic distribution order.The dynamic distribution order allocation optimization method considering the supply-demand state of the transport capacity system optimizes the design of the order allocation, efficiently allocates a scheme and a solution algorithm, realizes the matching of efficient dynamic time requirements and distribution resources, reduces the distribution cost and improves the distribution punctuality rate; meanwhile, the efficient order allocation strategy and the distribution optimization search operator based on the transport capacity supply-demand are designed, the response speed of the distribution system to the dynamic time requirements is improved, the distribution service quality of the distribution enterprise to the market and the customer requirements can be improved, and the distribution cost is optimized.

The invention discloses an engineering machinery lease management method and system based on dynamic demand matching, and relates to the technical field of engineering machinery lease management, and the method comprises the following steps: recording a construction site of a leasee, and enabling the leasee to upload the use state and demand state of engineering machinery in real time through a lease APP; performing dynamic demand matching on the engineering machinery based on the use states and demand states uploaded at different construction sites; based on a dynamic demand matching result and a construction site, analyzing an optimal matching result under the condition of considering the transportation cost; the engineering machinery is transported based on the optimal matching result and the construction site; the method is used for solving the problems of low utilization rate and high transportation cost of the engineering machinery due to the fact that the transportation and the use of the engineering machinery cannot be dynamically balanced based on the requirements of different leasers in the existing engineering machinery leasing management technology.

The invention relates to the technical field of energy monitoring, in particular to a smart energy monitoring system based on the Internet of Things technology, which comprises an Internet of Things energy monitoring center, a state module, a performance analysis module, a dynamic demand module, a period monitoring module and a rear-end feedback module. According to the method, analysis is carried out from the perspective of multiple data sources of the energy equipment, auxiliary support is carried out in combination with historical multi-source data, whether the state and energy consumption performance of the energy equipment are normal or not is judged, the state and energy consumption performance conditions of the energy equipment can be visually known, targeted management is carried out on the energy equipment based on feedback information, and the energy equipment management efficiency is improved. And meanwhile, along with energy utilization supervision and period dynamic adjustment analysis, analysis feedback is performed from the aspects of energy utilization supervision and prediction short-term stable performance, so that the energy effective utilization rate of the energy equipment is improved, and meanwhile, dynamic adjustment of the monitoring period of the energy equipment is realized. And thus, the whole energy monitoring cost is reduced.

The invention discloses an energy storage power station output power control method and device based on the dynamic demand of an energy storage power station, a storage medium, electronic equipment and a computer program product. The control method comprises the following steps: acquiring energy storage meter power data, load meter power data and allowable output power data of an energy storage power station in real time; determining maximum demand data of the energy storage power station according to the energy storage meter power data and the load meter power data; determining the dynamic demand of the energy storage power station according to the preset fixed demand data, the preset initial power data, and the maximum pure load power data and the maximum demand data of the current period; and determining output power data of the energy storage power station according to the preset fixed demand data, the dynamic demand, the load table power data and the allowable output power data, so as to control the output power of the energy storage power station according to the output power data.

The application discloses a method and system for material demand prediction and distribution path planning for a factory building machine, and relates to the technical field of demand prediction and path planning.The method comprises the following steps: obtaining design parameters of a target factory building; obtaining coordinates of the factory building machine in real time and calculating construction progress; running an adaptive speed prediction model to output future advance speed prediction; obtaining a future construction progress curve based on the predicted speed integral, combining with dynamic calculation of replenishment demand nodes to form a dynamic demand node set by mapping the predicted demand time and the factory building machine position; taking the dynamic demand node set as input, establishing a multi-objective vehicle path planning model, and solving an optimal distribution scheme; and finally issuing the scheme and periodically rolling and re-planning.The application realizes dynamic and accurate cooperation of material distribution and factory building machine construction, effectively improving the construction efficiency and industrialization level of the factory building machine.

The invention relates to the technical field of rail transit vehicle dynamics and intelligent suspension control, in particular to a high-speed train rotating arm node reverse dynamics design method based on dynamic requirements, which comprises the following steps: S1, constructing a mapping relation between vehicle dynamics performance and node rigidity, the method comprises the steps of S1, establishing a mapping relation between node rigidity and a magneto-sensitive elastomer material and structure, S3, establishing a magneto-sensitive elastomer node database, S4, carrying out reverse design based on a dynamic demand matrix, and S5, carrying out semi-active rigidity regulation and hardware-in-the-loop verification based on a controllable magnetic field. According to the invention, material and structure design parameters of the damping element can be deduced reversely, a multi-dimensional parameter database can be established, computable performance reverse indexing can be realized, a simulation-test-database self-learning mechanism is formed by combining a mechanical model and a test, and a closed-loop verification platform is constructed between a whole vehicle simulation model and a rack system. And engineering landing of the design method is realized.

Growing revenue and market to meet dynamic demand mix while managing complexity are major challenges to manufacturers. Building a right product mix that effectively manages supply and production cost without losing demand is therefore very imperative for offering to customers. Conventionally available tools and approaches only address particular parts of the whole problem in disconnected fashion, and these take too long to run. Embodiments of the present disclosure provide systems and methods for optimizing product revenue by setting up problems, model revenue drivers, configuring relevant scenarios including decision inputs like relevant data, constraints, or business rules to establish a total target revenue and analyze resulting outputs with iterative improvements towards the target value for product models.

This invention discloses a demand response scheduling method and system that considers the fairness of nodal carbon potential location, belonging to the field of low-carbon optimization scheduling technology. The demand response scheduling method considering the fairness of nodal carbon potential location includes: establishing a comprehensive energy system equipment model and a distribution network carbon emission flow model; establishing a hybrid index model considering the fairness of nodal carbon potential location; establishing a load-side dynamic demand response model based on the hybrid index model to generate carbon price signals; constructing a two-stage low-carbon optimization scheduling model considering the uncertainty of renewable energy output, with system operating costs, nodal carbon potential location fairness index, and carbon emission costs as optimization objectives. The optimal scheduling scheme is obtained through iterative solutions by determining unit output plans in the pre-scheduling stage and adjusting load distribution in the subsequent scheduling stage. This invention reduces system operating costs, achieves refined allocation and fair control of carbon emission responsibility in the comprehensive energy system, and improves the overall low-carbon operation level and the degree of reasonable carbon responsibility sharing of the system.

The invention discloses a regulation and control method for a fixed rail transfer type port power conversion system, and relates to the technical field of port equipment energy management. The system comprises a distributed intelligent control system, a multi-sensor fusion monitoring and feedback mechanism, an intelligent algorithm scheduling module, a classified battery replacement strategy oriented to different equipment and operation modes, a dynamic demand response mechanism and a battery full life cycle management method. Through distributed node cooperative control, real-time data fusion and intelligent path planning, high-efficiency, stable and adaptive scheduling of a battery replacement process is realized. The method can significantly improve the port battery replacement efficiency, optimize the battery resource configuration, enhance the system reliability, and reduce the operation cost, and is suitable for rapid battery replacement and energy management scenes of various port electric devices.

The invention relates to a coal mine unloading station driving route generation method and system, and relates to the technical field of automatic driving. According to the scheme, the method can be executed or configured on unloading equipment, path coordinate points and corresponding first heading such as an area entrance point, an area exit point, a backing point and an unloading point are determined in a driving area of a coal mine unloading station, and an initial driving route is generated based on the path coordinate points, the first heading and motion parameters of the unloading equipment; in the process of executing coal mine unloading operation along the initial driving route, the initial driving route is adjusted in response to the setting request of the passing points in the driving area and the second course of the passing points, so that the target driving route is obtained to continue operation. The scheme adapts to actual requirements of unloading equipment, and an initial driving route is determined according to the coordinate point and the course; and in the operation process, the route is adjusted according to the setting based on the passing points and the course, so that the method adapts to the dynamic demand change of equipment, operation and working conditions, and has better flexibility and accuracy.

The invention relates to the technical field of information recommendation, in particular to a ticket information recommendation system and method, and the system comprises a data fusion construction module which collects and standardizes multi-source heterogeneous data of an industrial environment; constructing an industrial entity relationship network based on the multi-source heterogeneous data; the dynamic demand response module is associated with the data fusion construction module and is used for monitoring a trigger event of the multi-source heterogeneous data in real time and positioning associated industrial scene elements; in response to the trigger event, matching ticket objects associated with the industrial scene elements in the industrial entity relationship network; and applying the industrial scene operation parameters as constraint conditions, and generating a ticket business recommendation scheme and a decision-making basis which meet the constraint conditions. According to the method, through graph structure model construction, real-time event response, constraint condition verification, decision basis interpretation and a feedback learning mechanism, accurate recommendation of ticket business objects in an industrial scene is realized, and the blank of the prior art in the field of industrial internet intelligent recommendation is filled.

The present invention discloses a comprehensive energy demand response scheduling method and platform for smart grids, which relates to the field of energy management technology. The method includes: predicting user load demand through a future time zone window, and establishing a comprehensive energy demand scheduling space based on multi-energy collaborative scheduling; forming a better scheduling space by modeling K energy supply units and combining the power grid energy scheduling evaluation channel for initial optimization and variation expansion optimization; introducing an energy scheduling optimization operator, deeply optimizing the scheduling space, obtaining a comprehensive energy demand scheduling strategy, and performing dynamic response scheduling. The present invention solves the technical problems in the prior art of lacking efficient collaborative scheduling of multiple energy types and accurate adaptation capabilities to dynamic user load demands, resulting in low energy utilization efficiency and insufficient scheduling flexibility, and achieves the technical effect of improving energy utilization efficiency and scheduling flexibility through multi-energy collaborative optimization and dynamic response scheduling.

Systems and methods for dynamic demand sensing in a supply chain in which constantly-updated data is used to select a machine learning model or retrain a pre-selected machine learning model, for forecasting sales of a product at a specific location. The updated data includes product information and geographic information. Also disclosed are systems and methods relating to demand forecasting and readjusting forecasts based on forecast error.

The present application relates to the technical field of low-voltage droop power adjustment at the end of rural power grid, and discloses a self-adaptive droop power control system for treating low-voltage at the end of rural power grid. The self-adaptive droop power control system for treating low-voltage at the end of rural power grid collects voltage data and current data at the end of rural power grid, pre-processes the two kinds of data, calculates an adjustment factor according to the two deviation values, calculates the actual power in a collection cycle according to the actual voltage measurement value and the actual current measurement value after the adjustment factor is generated, combines the actual power in the collection cycle and the adjustment factor to calculate a final adjustment power value, and adjusts the droop power of low-voltage at the end of rural power grid according to the final adjustment power value, so as to realize real-time dynamic adjustment of the voltage at the end of rural power grid, avoid static voltage adjustment mode, correspond to load changes in real time, meet dynamic demand, be suitable for use in various regions, and improve power quality stability.

The invention discloses an intelligent electric power engineering design method. The method comprises the following steps: firstly, constructing a standardized database according to collected target data; generating a design target and a constraint condition according to the obtained user demand data; constructing an electric power engineering design model according to the standardized database, the design target and the constraint condition; and training the electric power engineering design model, and after an initial design scheme is directly generated according to the trained electric power engineering design model in the later stage, performing multi-objective optimization on the initial design scheme by adopting a genetic algorithm and fuzzy logic to obtain an optimal design scheme. According to the method, the dynamic requirement can be met through the constructed electric power engineering design model, the rapid construction requirement can be met, after the initial design scheme is obtained, multi-target optimization is conducted on the initial design scheme, the optimal design scheme is obtained, the planning accuracy of the electric power engineering design scheme can be ensured, and resources are saved. In addition, the invention further provides a corresponding device and system and a readable storage medium, and the effects are as above.

The present application relates to the technical field of power system planning, and particularly relates to a regional low-carbon power grid multi-scenario dynamic expansion deduction method and system, comprising: modeling steel-electricity coupling demand according to steel electric arc furnace data and regional steel production capacity distribution characteristics to obtain regional power demand fluctuation distribution; identifying power grid stability critical point according to the regional power demand fluctuation distribution; obtaining expansion demand ranking table according to power grid equipment capacity data corresponding to the power grid stability critical point; generating phased power grid capacity expansion path graph according to the expansion demand ranking table, simulating peak load distribution, and obtaining carbon emission intensity index; and generating a low-carbon dynamic expansion planning scheme of steel-electricity coupling according to the carbon emission intensity index and regional energy structure characteristic data. The present application realizes the collaborative optimization of decarburization of the steel industry and expansion of the power system by establishing a dynamic demand model of steel-electricity coupling and based on the identification of the power grid stability critical point and the low-carbon oriented expansion planning.

The invention belongs to the technical field of charging infrastructure planning, and discloses a charging station dynamic adaptive planning method based on model deduction, which comprises the following steps: dividing a target city into a plurality of planning areas, collecting multi-source heterogeneous data of each planning area, and constructing a data block containing time-space coupling information; establishing a simulation environment of the multi-agent game based on the data blocks; a reinforcement learning model comprising a spatial-temporal feature extraction module and a strategy evaluation module is constructed, an output game equilibrium result is used for training, and the long-term value of the station building action of each planning area is evaluated; deploying the trained reinforcement learning model in a virtual environment which can be dynamically updated, performing multi-step strategy deduction in combination with an optimization algorithm, and selecting an optimal construction scheme of the charging station; the problems that in the prior art, charging infrastructure layout and dynamic demand matching is insufficient, a multi-body game decision mechanism is lacked, and long-term adaptability optimization capacity is lacked are solved.

The application discloses a method and system for vehicle path optimization in a single-vehicle channel under dynamic demand, acquires a path point request and adds the path point request to a request queue; a vehicle path optimization model is constructed, and according to a current position of the vehicle and the dynamic request queue, an optimal order of the request queue is dynamically calculated within a certain time limit after the request is sent, a next request is dynamically selected to minimize total travel time, and the next path point is determined; a priority queue is divided from the request queue according to a time when the request is sent, for the priority queue, a current target, a next target and a second next target are determined in sequence; when the vehicle reaches the next target, the next target is updated to the current target, and the second next target is updated to the next target, and the process is sequentially and circularly promoted to form a processing order until the requests in the priority queue are all responded.

The invention discloses a dynamic demand response method based on VPP participating in auxiliary service in a market environment, and relates to the technical field of virtual power plants. Comprising the following steps: 1, modeling a dynamic demand response problem of a virtual power plant participating in auxiliary service, and defining variables and parameters; 2, modeling is carried out on the uncertainty; 3, constructing a double-layer optimization model between the VPP income and the user electricity purchase cost; step 4, formulating constraint conditions, including constraints in the aspects of power market rules, coordinated operation requirements of VPP internal distributed energy and power purchase demands of users; 5, solving a double-layer optimization problem by using an optimization algorithm; and step 6, designing a dynamic demand response strategy, so that the VPP can perform flexible adjustment according to the market change, the user demand and the system condition real-time information. According to the method, the problem of dynamic demand response when the virtual power plant VPP participates in the auxiliary service in the market environment is solved.