1188 results about "Power market" patented technology

The invention relates to the technical field of power plant data processing, in particular to a virtual power plant load prediction and dynamic adjustment optimization system and method, and the system comprises a data collection module, a preprocessing module, a prediction module, an adjustment module and a verification module. The data acquisition module acquires real-time operation data and power market signals of distributed energy nodes; the preprocessing module performs standardization processing on the data through a quantum space-time alignment and anomaly reconstruction technology, and extracts strong correlation vectors of meteorological features and loads; the prediction module adopts an adaptive noise complete set empirical mode decomposition algorithm to separate a trend term, a periodic term and a residual component of a load sequence, and the adjustment module constructs a multi-target optimization model. Efficient aggregation of distributed resources, high-precision load prediction in a meteorological sudden change scene and cooperation of multi-market dynamic scheduling strategies are realized; and the clean energy consumption capability and the virtual power plant market response efficiency are improved.

The invention relates to the technical field of virtual power plants, in particular to a multi-target scheduling optimization method and system for a virtual power plant to participate in a power market, and the method comprises the steps: carrying out the dynamic integration of distributed energy, an adjustable load and an energy storage unit in the virtual power plant, and constructing a resource model; establishing a distributed resource identity authentication and data storage mechanism; constructing a multi-objective optimization function, comprehensively considering a plurality of objectives such as resource utilization rate, carbon emission and load balance, and designing a dynamic weight adjustment mechanism; calculating a multi-objective optimization result, determining the optimal output and operation state of virtual power plant resources, and generating a virtual power plant optimization scheduling strategy; and cooperative optimization of internal resource scheduling and external power grid scheduling of the virtual power plant is realized by combining a game theory and a swarm intelligence algorithm. According to the method, the utilization efficiency of distributed resources can be improved, source-network load-storage coordination and new energy consumption are promoted, and the flexibility and comprehensive benefits of a power system are enhanced.

The invention provides a power system optimization control method and device based on power market transaction. The method comprises the following steps: acquiring real-time electricity price fluctuation data, load demand prediction data and dynamic topology connection data between power grid nodes; generating a simplified network structure adaptive to a transaction scene based on the data, and adjusting an equivalent impedance parameter of a key transmission path in real time; a node voltage amplitude and a phase angle are cooperatively regulated and controlled through a multi-stage regulation mechanism of the solid-state transformer, and a power distribution scheme is formed; monitoring operation data of the solid-state transformer in real time by utilizing an edge computing node, and generating a local control instruction by combining the current equivalent impedance parameter; and carrying out space-time mapping matching on the local instruction and the electricity price fluctuation data, generating a combined control strategy through collaborative priority ranking, and dynamically adjusting a power distribution scheme. According to the technical scheme provided by the invention, collaborative optimization of economical operation and physical transmission performance of the power system is realized.

The invention provides a semantic enhancement and dynamic completion method and system for a power market data graph. The method comprises the following steps: acquiring target entity information and external data of a power market data graph by responding to a data query instruction; converting external data into structured semantic tags, then traversing the topological structure, establishing a mapping relationship between the tags and entity attributes, and combining query instruction influence logic to calculate topological integrity indexes and mark missing entities or correlate to form a missing tag set; establishing attribute mapping based on a target entity and external data, generating a semantic vector fusing cross-domain features, and projecting the semantic vector to a topological space according to a missing mark set to form cross-domain feature topological nodes and connections; embedding a missing position to reconstruct a topological structure, and checking logic connectivity; the dynamic conduction chain is analyzed, and a feedback interface containing the semantic relation and the completion structure is generated to respond to query. According to the method, conduction logic closed-loop verification of the missing elements in the power market data graph is realized.

The invention discloses a cross-platform power transaction data interaction optimization method, and particularly relates to the technical field of power transaction data digital twinning. Simulation transaction data of market participants is generated based on an antagonistic neural network model; identifying a causal relationship among market variables from the simulation transaction data of the digital twin through a causal discovery algorithm, and constructing a causal graph; modeling spatial dependence and causal conduction paths among market participants by using a graph neural network model, and performing anti-factual reasoning in a single external impact scene in a digital twinborn body by intervening key variables in a causal graph; simulating dynamic game behaviors of market participants in an extreme weather parameter distribution scene; the power market failure assessment is carried out by combining the causal conduction path to obtain the power market failure assessment index, and early warning is carried out based on the power market failure assessment index, so that the adaptability and stability of the power market in extreme weather scenes are improved.

The invention discloses an operation collaborative optimization method for an optical storage direct current flexible interaction system. Comprising the steps of collecting operation data such as photovoltaic output, an energy storage state, household load power and direct current bus transmission power, fusing power market price information, and constructing a multi-dimensional time series data set; then, predicting an adjustable load capacity interval of the system based on a coupled physical constraint neural network model embedded with DC bus power balance, voltage constraint and equipment operation limitation; further constructing a state-action space, solving a Pareto frontier by adopting a multi-objective optimization algorithm, and generating a light storage and home load collaborative scheduling strategy set; then combining the real-time operation state and the prediction deviation information, applying a voltage-power droop control mechanism to carry out strategy decoupling, and generating an energy storage power correction amount and a flexible load priority control instruction; and finally, a control instruction is issued to the optical storage direct flexible system, so that collaborative optimization operation with consideration of economical efficiency, safety and comfort of the system is realized.

The invention relates to the technical field of photovoltaic power generation power prediction, in particular to a photovoltaic power generation power high-precision prediction method and system. According to the technical scheme, the method comprises the following steps: data acquisition and preprocessing: historical photovoltaic power generation power data are widely collected, the time span covers at least one year in the past so as to obtain power generation power change information under different seasons, weather conditions and time periods, and meanwhile, meteorological data corresponding to the power generation power change information are collected; and cleaning the collected data, and removing missing values through data integrity check. The power generation efficiency and economy are improved, the operation and maintenance plan can be optimized, and the energy storage utilization efficiency is improved; the power grid stability and reliability are enhanced, scheduling is assisted, resource configuration is optimized, power market operation is greatly improved, support is provided for transaction decision making, renewable energy consumption is promoted, the system adaptability and the intelligent level are improved, real-time tracking and updating can be achieved, and intelligent management of a power station is assisted.

Disclosed is a power grid transmission and distribution cooperative dispatching method and system in a power market environment, relating to the technical filed of power market. The method includes: power grid transmission and distribution data are collected, and economic dispatching modeling is carried out on a hybrid system containing hydro-thermal power; linearization processing is carried out on nonlinear terms in the model; and accelerated solving is carried out on the model by adopting Benders decomposition, so that power gird transmission and distribution cooperative dispatching is optimized. The integration capacity of the power system containing hydro-thermal power to the renewable energy is enhanced, and the utilization efficiency of resources is improved, and the solving process is simplified in the present invention.

The invention discloses a power market large language model evaluation method and device based on dynamic scene perception, and the method comprises the steps: building a large model evaluation system covering the three dimensions of understanding, generation and safety according to a power market application scene; the method comprises the following steps: constructing a test question bank, evaluating a power market large language model according to various evaluation methods, endowing three dimensions with different weights based on a large model evaluation system in the evaluation process, constructing a test question-scene label fine tuning data set, and performing scene classification fine tuning on the model by using a fine tuning method. Evaluating scene classification performance of the fine-tuned model, and distributing corresponding initial evaluation dimension weights for different scenes; according to the requirements and differences of different scenes, the initial evaluation dimension weight is adjusted, the scene is judged to be matched with the corresponding evaluation dimension weight according to the input content, weighted summation is carried out from the three dimensions of understanding, generation and safety, and an evaluation result is obtained. According to the method, accurate evaluation of the performance and reliability of the large language model in the power field can be realized.

The invention discloses a multi-type virtual power plant participation market regulation capacity optimization distribution method and system, and the method comprises the steps: constructing a power supply type, load type and energy storage type virtual power plant participation market operation characteristic model, and then combining a value-at-risk assessment CvaR model to obtain a value-at-risk assessment model; a refined virtual power plant capacity distribution model is established by taking maximum pumped storage income and minimum risk when the virtual power plant participates in capacity distribution of main and auxiliary markets as targets, constraint conditions are set for modeling, and finally, the virtual power plant is combined with an electric energy market demand, a peak regulation market demand and a frequency modulation market demand for joint optimization. And continuously detecting and optimizing through the virtual power plant capacity distribution model. According to the method, on one hand, execution of a power generation plan is guaranteed along with wind and light output, on the other hand, a reasonable capacity distribution mechanism in a multi-element market is designed according to different mechanisms of the current power market, services such as peak regulation, frequency modulation and standby are provided, and the optimal configuration problem of a virtual power plant in the power market is solved.

The invention belongs to the technical field of power system planning and power market mechanisms, and relates to a network construction type energy storage auxiliary service market method combining electric energy and capacity markets. Comprising the steps of establishing a transfer factor matrix PTDF, establishing a network construction type energy storage upper-layer investment model, establishing a lower-layer joint clearing model, establishing a lower-layer capacity market clearing model, converting a double-layer model into a single-layer model based on a duality theory, processing a nonlinear term and converting the nonlinear term into a mixed integer linear programming problem, and solving based on a gurobi commercial solver. Carrying out economic evaluation on the network construction type energy storage investment planning; according to the invention, by considering that the network-building type energy storage participates in the electric energy, frequency modulation auxiliary service and capacity market at the same time, various values of the network-building type energy storage as flexible and power grid supporting resources are further mined, and the problem that the investment cost of the network-building type energy storage is difficult to recover is effectively solved.

The uncertainty of availability and risk associated with the cost of power procurement hinders the consumer from using new sources of supply / storage devices or taking part in the competitive power markets or procuring power from green energy resources. Present disclosure provides a method and a system for optimal scheduling of power sources for meeting electricity demand of enterprise. In particular, the system performs portfolio optimization formulation for cost, risk and carbon emission minimization for the enterprise. The portfolio optimization formulation simulates market scenario and guides in providing effective power purchase strategy for enterprise along with real-time adjustments. In particular, the level of risk to be considered along with cost of power procurement and carbon footprint determine portfolio allocation for each time block in portfolio optimization formulation. Thus, enterprise may choose most appropriate terms of contract, best installation size of renewable resources or batteries, and supply sources.

The invention discloses a virtual power plant optimal scheduling method based on artificial intelligence, and further reduces the power operation cost through an integrated intelligent management and scheduling strategy. The system is composed of a power generation end, an energy storage end, a power utilization end, a system management platform, a power grid, a power market and other key components. The core lies in that a depth deterministic strategy gradient (DDPG) algorithm is utilized to construct a simulation environment model, and a reward function is designed to evaluate the economic benefit of the strategy. By initializing Actor and Critic neural networks, the system can generate and evaluate a scheduling strategy, and continuously iteratively updates the network through an experience playback mechanism to optimize economic benefits. The invention provides an innovative virtual power plant optimal scheduling method based on artificial intelligence, and maximization of the benefits of the virtual power plant is realized by reducing the operation cost.

The invention discloses a power market user load prediction method, system and device and a medium, and relates to the technical field of power market user load prediction, and the method comprises the steps: collecting and preprocessing power market user load data, and dividing the preprocessed data into a training set and an evaluation set; and constructing a user load prediction model based on LSTM and dual Q network optimization, and inputting the training set to carry out single-step prediction. And evaluating the power market user load prediction model based on the evaluation set, and after the evaluation is passed, inputting future prediction data to the user load prediction model to obtain a predicted power market user load. According to the method, the external variables influencing the power load can be more comprehensively captured, so that the accuracy and the reliability of the prediction model are improved. And an LSTM network is combined with a double Q network model, so that the recognition capability of the model on the long-term trend is enhanced, and the accuracy of a prediction result and the generalization capability of the model are improved.

The invention discloses an active learning-based electric power knowledge graph construction method and system, and relates to the technical field of electric power market transaction data analysis, and the method comprises the steps: collecting data, cleaning the collected data, generating word prediction task data of an electric power transaction scene, and training an electric power field word embedding model; generating an entity relationship training sample, inputting a word embedding model, performing entity relationship label prediction through an entity relationship joint extraction model, extracting a high-quality sample based on active learning to perform part-of-speech tagging, and training the model based on a tagged sample; and applying the trained entity relationship joint extraction model to power transaction data to construct a knowledge graph, performing manual correction and entity unification, and storing the knowledge graph into a Neo4j database. According to the method, active learning and deep learning are combined, so that the sample labeling cost is effectively reduced, and meanwhile, the model performance is ensured.

The invention belongs to the technical field of virtual power plant resource aggregation, and particularly relates to a virtual power plant resource aggregation model considering multiple time scales and cross nodes. Comprising the following steps: S1, establishing a net load settlement model; s2, establishing an aggregator operation strategy model; s3, establishing a market subject operation strategy behavior model; s4, establishing a lower-layer clearing model, and determining network constraints; in order to solve the problem that a current market mechanism is still based on a traditional metering settlement mode and does not actively adapt to the requirements of an emerging market subject, the invention proposes an aggregation and operation strategy of an aggregator in consideration of a net load metering settlement method, and constructs a power market settlement mode based on the net load. Comparing and analyzing the operation strategy and the operation effect of the virtual power plant under the net load settlement mode and the incentive mechanism; and comparing and analyzing the influence of the quotation strategies of other market subjects under the finite rationality on the operation strategy of the virtual power plant after the market is further developed.

The invention provides a multi-data center computing power-electric power cooperative scheduling and demand response declaration capacity optimization method, which comprises the following steps of: firstly, acquiring multi-dimensional historical time sequence data of a data center; a machine learning algorithm is used to predict the workload of each data center in each time period of a future single day in a future scheduling period and the power market price of the place where each data center is located, and then a space-time coupling-oriented multi-data center computing power-power cooperation model considering uncertainty is constructed; the collaborative model comprises a joint optimization framework of computing power scheduling and power scheduling, and takes actual profit maximization as a target, then the established collaborative model is converted into a mixed integer linear programming problem and solved, and the optimal declaration capacity of each data center participating in demand response is obtained; and each data center carries out scheduling according to the task load of space migration and time migration, the charging and discharging power of an energy storage system and the power generation amount of renewable energy sources, so that overall optimization of demand response declaration capacity of the multiple data centers is realized.

The invention discloses an enterprise electricity consumption analysis and prediction method based on data mining, and relates to the technical field of DC power supply monitoring, historical electricity consumption data is acquired, and electricity consumption characteristic analysis and mining are performed from multiple directions according to the historical electricity consumption data to obtain electricity consumption rule information and seasonal change characteristics; analyzing the scale and speed increase of the agent electricity purchasing users and the annual change trend of the agent electricity purchasing quantity to obtain development and operation information of agent electricity purchasing; based on historical electric quantity data, constructing exponential smoothing, ARIMA, SARIMA and LSTM neural network multiple time sequence prediction models, and performing comparison to select an optimal model; through accurate power consumption prediction, the enterprise is helped to optimize power resource configuration, plan power purchase work in advance and reduce the power cost of the enterprise, and especially under the condition that the power market fluctuates greatly, the enterprise can better understand the market dynamic condition and formulate a corresponding power purchase strategy by predicting the scale and speed increase of the proxy power purchase user.

The invention belongs to the technical field of novel electric power system operation control and trust management, and particularly discloses a virtual power plant full-process trusted aggregation method and system based on a hierarchical trust chain. Differentiated hierarchical trust chain models of a data acquisition link, a scheduling control link and a market transaction link are constructed respectively; through real-time credibility evaluation and adaptive weight adjustment, credibility calculation of multi-agent collaborative decision is realized. According to the invention, a trust network formed by a plurality of efficient, safe and transparent virtual power plant trust chains is constructed, solid technical support is provided for fair competition, intelligent scheduling and reliable operation of a power market, and efficient interaction and stable operation of a power system are ensured.

The invention relates to an electricity-hydrogen comprehensive energy system planning method considering multi-time scale uncertainty. The method comprises the following steps: establishing an electricity-hydrogen comprehensive energy system model; based on the electricity-hydrogen comprehensive energy system model, considering the uncertainty of wind and light output and load requirements, and constructing a planning mathematical model of the electricity-hydrogen comprehensive energy system by using a multi-time scale uncertainty modeling method fusing opportunity constraint and stochastic optimization; based on the electricity-hydrogen comprehensive energy system model, establishing an electricity market model as a lower-layer optimization model of a planning mathematical model; and solving the planning mathematical model by adopting a two-stage optimization method to obtain a planning scheme. Compared with the prior art, a multi-time-scale planning mathematical model of the electricity-hydrogen comprehensive energy system is established, and planning of the electricity-hydrogen comprehensive energy system is achieved.

The invention discloses a control system and method based on charging pile V2G, and relates to the technical field of charging pile V2G control, and the method comprises the following steps: firstly, a sensor device obtains the operation state parameters of a power grid in real time, accurately monitors the actual working condition of the power grid through the real-time collection of the operation parameter data, and provides basic data support for the subsequent analysis; based on deep learning and intelligent feedback control, the self-adaptive adjustment of the output voltage and the phase angle of the electric vehicle is realized, the reactive power is accurately compensated, the power grid voltage fluctuation is reduced, and the overall stability is improved. According to the scheme, the response speed of the EV inverter is increased, millisecond-level power grid adaptation is achieved, the V2G cooperative regulation and control capacity is enhanced, dependence on traditional reactive compensation equipment is reduced, and the new energy consumption efficiency is improved. By optimizing a power regulation and control mechanism, deep fusion of the EV as distributed energy storage is promoted, development of smart energy management is promoted, and the economic value and feasibility of the V2G in the power market are improved.

The invention discloses an electric power system dispatching optimization method and system based on battery loss, and the method comprises the steps: carrying out the modeling of the relation among the charging and discharging power of a battery, the temperature of the battery and the capacity loss of the battery according to the operation data of an energy storage unit of an electric power system, and constructing a battery capacity loss constraint condition; based on a battery capacity loss constraint condition, constructing a power transaction optimization model by taking the lowest power transaction cost and the lowest battery operation and maintenance cost as targets; and performing iterative solution on the power transaction optimization model to obtain a battery optimization use strategy, and scheduling operation of the energy storage unit. According to the method, the model constraint condition is constructed according to the influence of the charging and discharging power of the battery and the generated thermal stress on the service life of the battery, and a more accurate and comprehensive battery optimization use strategy for maximizing the power market income is solved; the strategy is used to extend the battery life to maximize the power sold by the energy storage operator in the power market so as to improve economic benefits, and at the same time, the reliability and stability of the power system are improved.

The invention belongs to the technical field of intelligent energy and power system automatic control, and particularly relates to a cross-level collaborative regulation and control system integrating meteorological prediction, power market and equipment state, which comprises a central collaborative controller, a data acquisition unit, a communication network and an execution terminal, the data acquisition unit is used for acquiring meteorological data, power load data, electric energy consumption cost data and equipment operation state data in real time; the execution terminal at least comprises a photovoltaic inverter, an energy storage converter and a charging pile controller; the central cooperative controller is configured to execute three cooperative optimization closed loops, namely, a data fusion and prediction closed loop, a multi-target dynamic optimization closed loop and a multi-time scale control closed loop. According to the system and the method provided by the invention, the problems of real-time performance and accuracy of multi-source heterogeneous data fusion are solved; the balance optimization bottleneck of multiple targets such as economy, stability and environmental protection in the dynamic process is broken through; full-time-scale seamless cooperative control from second-level emergency response to hour-level economic dispatching is realized; and the self-adaptive capability and the overall performance of the system in different application scenes are improved.

The invention belongs to the technical field of new energy prediction, and discloses a power market new energy joint prediction method and system based on SA-GAN, and the method comprises the steps: obtaining the historical data of a power market; respectively adding the attention mechanism into a generator network and a discriminator network in the generative adversarial network, and establishing an attention-generative adversarial prediction model; inputting historical data of the electricity market into the attention-generative adversarial prediction model for training, and continuously optimizing parameters of the attention-generative adversarial prediction model through adversarial learning of a generator and a discriminator; and performing joint prediction by using the trained and optimized attention-generative adversarial prediction model to obtain new energy future power generation data. According to the method, the limitation of a traditional method on complex and nonlinear data processing is effectively solved, joint prediction of multivariable data is realized, interaction among variables is fully considered, and a more comprehensive and accurate prediction result is generated.

The invention discloses a virtual power plant resource regulation and control method and system considering resource uncertainty, and relates to the technical field of power system demand response, and the method comprises the steps: obtaining virtual power plant aggregation resource data, inputting the virtual power plant aggregation resource data into a pre-established virtual power plant aggregation model, and obtaining a virtual power plant aggregation model; outputting to obtain an adjustable resource mathematical model of the virtual power plant aggregation, wherein the adjustable resource mathematical model of the virtual power plant aggregation comprises an energy storage model, an air conditioner model and an electric vehicle model; generating a scene set based on the uncertainty of the virtual power plant aggregation resource data, reducing the scene set to obtain a reduced scene set, and performing conditional value-at-risk calculation to obtain conditional value-at-risk; and based on an adjustable resource mathematical model of virtual power plant aggregation, in combination with the reduced scene set and the conditional value-at-risk, generating an operation optimization model of the virtual power plant participating in the day-ahead power market considering resource uncertainty, and performing virtual power plant resource regulation and control.

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.

Provided is a day-ahead electricity price prediction method based on a parallel multi-dimensional attention mechanism, which method belongs to the technical field of day-ahead electricity price prediction in a power market. The maximum mutual information coefficient is used to select variables having a certain relevance with an electricity price, so as to assist in predicting future electricity price data; next, a decomposition algorithm is used to decompose an original electricity price signal; and then, electricity price sub-components obtained by means of decomposition and the electricity-price-related variables are inputted into an MBI-IPMDA-PBISA deep learning model, so as to predict a future electricity price. In this way, an MBI-IPMDA-PBISA deep learning model can fully learn a complex association relationship between electricity price sub-components and electricity-price-related variables and a future electricity price, the problem of error superimposition caused by separately predicting the electricity price sub-components and then superimposing same to obtain the future electricity price is also avoided, and the problem of the accuracy of existing attention mechanisms during prediction being low is solved, thereby improving the prediction accuracy.

The invention relates to an electricity market information extracting and pushing method and system based on multi-modal semantic fusion. The method comprises the steps that an electricity market field pre-training corpus is constructed; adopting a staged pre-training strategy; performing multi-modal coding on the input power market information data; a text confidence score and an image confidence score are generated, a cross-modal attention incidence matrix is established, and image-text fine-grained interaction features are captured and fused to generate unified semantic representation; a user model containing long-term stable preferences and short-term dynamic interests is established based on unified semantic representation generated through fusion, a sequence decreasing exploration strategy is adopted, high-correlation intelligence is pushed through known preferences, new domain content is randomly inserted to mine potential interests, and the weight of pushed content is adjusted in real time according to user feedback. The problems of heterogeneous sources, diversified formats, different standards, asymmetric information and the like of power market information data can be effectively solved, and accurate and real-time power market information pushing service is provided for users.

The invention provides a power market flexibility resource configuration method and device, and the method comprises the steps: obtaining the real-time measurement, weather forecast and equipment state data of a power grid node, and extracting the spatial-temporal characteristics of a power grid topology; calculating new energy output and equipment availability rate based on meteorological and equipment data; generating a dynamic adjustment margin matrix in combination with the spatial-temporal characteristics, the new energy output and the equipment availability rate; predicting a quantile map of the short-term flexibility demand; global optimization is decomposed into regional sub-problems and solved, power adjusting instructions of the power generation side and the load side are generated, and resource dynamic configuration is achieved. The method improves the evaluation precision and response speed, and guarantees the stability of the power grid.

The invention provides a virtual power plant scheduling method based on risk avoidance and demand response. The method comprises the following steps: S1, respectively constructing a power market price model, a power grid operation model, a power demand model of a virtual power plant and an available resource model; s2, considering market fluctuation and equipment fault risk factors, and establishing a risk avoidance model of the virtual power plant; s3, based on the risk avoidance model, designing an optimization scheduling strategy for minimizing the risk avoidance index; s4, considering interaction between the virtual power plant and the power market and the power grid, and designing an interaction response mechanism; and S5, performing virtual power plant scheduling simulation evaluation by integrating the models, the scheduling strategies, the constraint conditions and the interactive response mechanism, and adjusting model parameters and strategies according to an evaluation result to optimize performance. According to the scheme, the technical problems in virtual power plant scheduling are solved more comprehensively and flexibly, and compared with a traditional method, the method has obvious superiority in performance.