405 results about "Air conditioning load" patented technology

The invention discloses an intelligent linkage control management system based on air conditioner application, and relates to the technical field of intelligent control and energy management, and the system comprises a load prediction module which obtains an air conditioner load demand prediction value through an intelligent load prediction model based on preprocessed environment energy consumption data; the optimization scheduling module is used for optimizing the current air conditioner operation mode through the building energy management system based on the air conditioner load demand prediction value, and obtaining an optimized air conditioner operation scheduling instruction; the intelligent adjusting module is used for intelligently adjusting the operation parameters of the air conditioner system based on the optimized air conditioner operation scheduling instruction, adaptively adjusting the energy efficiency of the air conditioner and obtaining operation feedback data; and the fault management module is used for identifying potential fault risks of the air conditioner and formulating fault risk prevention measures through a time sequence analysis method based on the operation feedback data. According to the invention, through innovation of an intelligent load prediction and fault prevention mechanism, the energy utilization efficiency and the system stability are effectively improved.

The invention discloses an intelligent regulation and control method and system for a photovoltaic energy storage air conditioner. The method comprises the steps that photovoltaic related data, energy storage related data and air conditioner operation related data are collected; inputting the photovoltaic related data into the photovoltaic power generation prediction model to obtain a photovoltaic power generation prediction result; inputting the air conditioner operation related data into the air conditioner load prediction model to obtain an air conditioner load prediction result; inputting the photovoltaic generating capacity prediction result, the air conditioning load prediction result, the energy storage related data and the photovoltaic related data into an energy storage optimization scheduling model to obtain an energy storage charging and discharging decision; constructing a power supply strategy core rule base based on the energy storage related data, the electricity price peak time period and output results of the photovoltaic power generation prediction model and the air conditioner load prediction model; and optimizing an output result of the energy storage optimization scheduling model based on the power supply strategy core rule base. The method has the outstanding advantages of high reliability, high photovoltaic utilization rate, low dependence degree on a power grid and the like.

An air conditioner load monitoring and anomaly detection method and system. The method comprises: acquiring user power consumption data and meteorological data, the user power consumption data and the meteorological data comprising the total power of an electricity meter, the power of an air conditioner, and the outdoor temperature, preprocessing the data, and computing electrical characteristics of the user power consumption data; on the basis of the electrical characteristics, learning by using a two-tower neural network so as to obtain operating rules of the air conditioner, and extracting an air conditioner operation curve; on the basis of the air conditioner operation curve, computing electrical characteristics and energy consumption characteristics of the air conditioner, and performing cluster analysis by using a K-means clustering algorithm; and, on the basis of clustering results, comparing the clustering results with clustering results of collected electrical data of the air conditioner under normal and abnormal operating conditions to determine whether the air conditioner is in an abnormal state, so as to implement detection of anomalies in the operation of the air conditioner, thereby improving the precision of load decomposition of the air conditioner, reducing application costs, providing a decision-making reference for equipment maintenance personnel, and prolonging the service life of the equipment.

The invention discloses an ice storage air-conditioning system optimization control method based on a dynamic programming algorithm, which comprises the following steps: acquiring operation data of an ice storage air-conditioning system in real time, and constructing a directed graph model, directed graph nodes and interconnection weights among the nodes; transferring and converting a cold storage and release operation mode of the ice storage air-conditioning system in a preset optimization time domain into a shortest path for solving the directed graph model; based on a directed graph model, solving a shortest path by using a dynamic programming algorithm; and on the basis of the shortest path, an ice storage air conditioning system control strategy is generated and issued to a system execution layer, and the control strategy comprises but is not limited to a switching decision of a cold storage and release operation mode in each time period of the optimization time domain and an equipment start-stop state. The cold storage and release operation mode is controlled through the dynamic planning method, efficient matching of cold storage air conditioner loads is achieved, the system energy efficiency is improved, the operation cost is reduced, and operation and maintenance operation is simplified.

The invention relates to the technical field of intelligent control, and particularly discloses a linkage control method for energy-saving ventilation equipment, which comprises the following steps of: acquiring indoor and outdoor temperature, indoor carbon dioxide concentration and personnel occupancy state data in real time; according to the method, deep collaborative analysis is carried out on the indoor personnel occupancy state and the deviation between the carbon dioxide concentration and a target set value, so that joint characterization of the current indoor fresh air volume regulation and control requirement is achieved, control parameters of a fresh air volume PI D controller are dynamically optimized accordingly, and a basic fresh air volume regulation instruction is calculated. On the basis, thermal disturbance introduced by fresh air is further quantified based on the outdoor and indoor temperature difference, and the thermal disturbance serves as a feedforward signal to be compensated to an air conditioner load adjusting loop, so that dynamic adjustment of the air conditioner load is achieved. In this way, cooperative control over ventilation and air conditioning systems can be achieved, the fresh air-air conditioning equipment energy consumption coupling conflict in traditional control is restrained, and therefore the building energy consumption is reduced while the indoor environment quality is guaranteed.

The invention relates to the technical field of air conditioner load prediction, and discloses an air conditioner load prediction and energy saving method based on machine learning, and the method comprises the following steps: collecting and preprocessing the historical load, multi-dimensional meteorological data and equipment operation parameters of a building air conditioner; historical loads and meteorological data are input into a GBDT model, meteorological feature importance is calculated, and key features are screened; taking the key meteorological characteristics and the historical load as input, and performing load prediction by using an LSTM model; based on a particle swarm optimization algorithm, operating parameters of the air conditioning system are dynamically optimized; and updating the GBDT and LSTM model when the prediction error exceeds a threshold value or the period arrives. According to the method, key meteorological characteristics are screened through GBDT to improve the load prediction precision, adaptive modeling of different climate areas is achieved in combination with LSTM, the air conditioner operation parameters are dynamically adjusted through the particle swarm optimization algorithm, the comprehensive energy consumption of the air conditioner system is effectively reduced, and the overall energy-saving efficiency and the intelligent level of the air conditioner system are improved.

The invention relates to the technical field of intelligent building energy management, and particularly discloses an air conditioner load cluster optimization regulation and control method and system based on building thermal inertia modeling, and the method comprises the steps: collecting building structure parameters, building material thermophysical parameters, indoor and outdoor temperature and humidity historical data, air conditioner operation data and real-time electricity price data of a building; building a building thermal inertia model based on the collected data; inputting the collected air conditioner operation data, indoor and outdoor temperature and humidity historical data into a building thermal inertia model, and predicting air conditioner loads under different working conditions; constructing an optimized objective function, and solving the optimized objective function by adopting a genetic algorithm to obtain an optimal air conditioner load regulation and control strategy; and according to the optimal air conditioner load regulation and control strategy, the air conditioner load cluster is regulated and controlled in real time. The energy utilization efficiency can be effectively improved, the operation cost is reduced, and intelligent and refined regulation and control of the air conditioner load cluster are achieved.

The invention discloses a water-cooling central air conditioner load management and control method based on artificial intelligence, and relates to the technical field of load management and control. Load prediction is carried out by combining a long and short-term memory time sequence model and a physical constraint neural network in a water chilling unit load prediction link and utilizing historical data; errors are corrected through an energy balance equation of the water chilling unit, and the prediction accuracy is improved; multi-objective optimization control is adopted, COP maximization of a water chilling unit, energy consumption minimization of a cooling tower fan and cooling water supply temperature stability serve as optimization objectives, environment temperature and humidity prediction is combined, and a cooling water supply temperature set point and a cooling tower fan start-stop strategy are dynamically calculated; based on a cooling water supply temperature set point and a fan start-stop strategy, a water chilling unit, a cooling tower fan and a water pump are dynamically adjusted, and the high-frequency operation phenomenon is reduced through fan start-stop optimization.

The invention discloses an air conditioner load response optimization method based on multi-dimensional data fusion, relates to the technical field of air conditioner control, and solves the problem that the number of personnel may suddenly increase or suddenly decrease in an air conditioner refrigeration area. The load capacity predicted only from the air conditioning equipment is difficult to adapt to the complex scene; the accuracy of predicting the load capacity in the future time interval is reduced, and the comfort level of the user is correspondingly reduced. Comprising the following steps: 1, acquiring environment data and image data of each monitoring area; 2, importing the image data into a personnel distribution intelligent model for analysis to obtain a personnel distribution diagram; performing analysis based on the personnel distribution map to obtain a personnel density value used for representing the personnel density of the shopping mall; performing analysis based on the personnel density value and the environment data to obtain a load demand quantity; 3, performing dynamic adjustment based on the load demand quantity to obtain a corresponding optimization adjustment report; and the energy-saving effect of the air conditioner load and the comfort degree of a user are improved.

The invention discloses a single air conditioner load regulation potential evaluation method based on transfer learning and multi-task learning, and relates to the technical field of air conditioner management.The method comprises the steps that a regional association model is built based on air conditioner operation data in a general scene, and the regional association model is analyzed to obtain a cooperative regulation relation between air conditioner equipment; constructing an air conditioner load regulation potential evaluation model by using a deep residual neural network; a multi-task learning framework is formulated according to the cooperative adjustment relation between the air conditioning equipment, and the air conditioning load adjustment potential evaluation model in the general scene is optimized by using the multi-task learning framework; and adjusting the weight of the optimized air conditioner load regulation potential evaluation model in combination with a transfer learning technology to obtain an air conditioner load regulation potential evaluation model in a specific scene. According to the method, the adaptability and generalization ability of the air conditioner load regulation potential evaluation model are remarkably improved by constructing the regional correlation model and introducing self-supervised learning and adversarial training.

The invention belongs to the technical field of intelligent control of air conditioning systems, and discloses a split type air conditioner load management system and method. The system comprises a central controller, a health management module, an adaptive integrated control module AICA and an intelligent scene recognition module. The central controller integrates various sensor information such as temperature and humidity, air quality and user physiological data, data processing and intelligent decision making are carried out through an AICA module, and dynamic optimization management of air conditioner loads is achieved. The health management module automatically adjusts the air conditioner operation mode according to the body temperature, heart rate and other physiological indexes of the user, and a comfortable environment is provided. And the intelligent scene recognition module is used for pre-judging user requirements and adjusting the operation of the air conditioner by learning a user behavior mode. The energy efficiency and the intelligent level of air conditioner operation are improved, the method is suitable for air conditioner load management of various buildings, energy can be effectively saved, and the comfort level of users is improved.

The invention provides a cross-regional air conditioner regulation and control strategy modeling method and a cross-regional air conditioner regulation and control method and system, and is applied to the technical field of intelligent regulation and control of air conditioners. The method comprises the steps that clustering processing is conducted on air conditioner load data of users in all regions, and a clustering result is obtained; constructing a regional cold supply system simulation model corresponding to the clustering cluster based on the users in the same clustering cluster in the clustering result; the operation safety of an actual regional cooling system is considered, modeling is carried out on the regulation and control process of the simulation model based on the Markov decision process, and an optimization strategy model is constructed by taking expected return maximization of a value function corresponding to an operation strategy of the simulation model as a target; and training an optimization strategy model based on simulation operation data of the simulation model. According to the method, the problems that in the process that the DCS participates in power grid load regulation and control, a trial-and-error training-based model is prone to system damage in practical application, and single-model regulation and control cannot guarantee the load regulation and control effect of a whole power grid are solved.

The invention relates to the technical field of air conditioner control, and discloses an air conditioner load data control method and system.The method comprises the steps that firstly, meteorological parameters and load parameters of an air conditioner are collected, and data cleaning, timestamp definition and standardization processing are conducted; then, defining a feature vector according to the processed data, generating a feature data set, and constructing a load prediction model by using a neural network algorithm; and pre-training the model by using the source domain data set through a transfer learning strategy, and enhancing the target domain data to generate a new sample. Next, parameters of the pre-training model are migrated to the new task model, and partial network layer parameters are frozen to form an initialized reinforcement learning model; and finally, training unfrozen layer parameters by using the target domain data set, and finely adjusting the model to realize real-time and accurate optimal setting of the air conditioner temperature, thereby achieving the purpose of load optimization control. Through combination of transfer learning and reinforcement learning, the generalization ability, robustness and adaptability of the model are effectively improved.

The invention relates to the technical field of data processing, in particular to a computer-aided air conditioner load prediction model simulation construction method, which comprises the following steps of: acquiring data of each environmental parameter in a building at all moments in each period in real time to form a monitoring sequence; the method comprises the following steps: performing analogue simulation on energy consumption of an air conditioner to obtain load data of an air conditioner load at all moments in each period, and forming a load sequence; acquiring a lagging time length; extracting a time delay subsequence of each environmental parameter in each period; the trend synchronization degree between each environment parameter and the air conditioner load in each period is determined; determining a coupling coefficient of any two environmental parameters in each period; determining a reference evaluation value between the current period and each of the rest periods; obtaining each reference period; and constructing a load prediction model. According to the method, prediction deviation caused by neglecting interaction influence and lag influence among various environmental parameters can be avoided, and the prediction accuracy of the air conditioner load is improved.

The invention relates to a multivariable multi-step air conditioner load prediction model based on time sequence convolution and a double attention mechanism, and belongs to the technical field of air conditioner load prediction. The model adopts a parallel encoding structure, in an encoder, a time sequence convolution module is responsible for modeling local dependence and long-term trend in a time dimension, and a double attention mechanism module is used for modeling a dynamic dependence structure among multiple variables and a coupling relation between the variables and a target load from a variable dimension. The two structures are respectively subjected to feature extraction from a time dimension and a variable dimension, and are complementary to each other. In a decoder, a decoding module with a memory ability and a teacher mandatory strategy is designed, and continuous prediction from a historical multivariable sequence to a future target load is realized.

The invention discloses a control method and device of a photovoltaic air conditioning system and the photovoltaic air conditioning system. The technical problem that an existing photovoltaic air conditioning system is unstable in operation is solved. The control method comprises the following steps: when the photovoltaic air-conditioning system is in an off-grid state and the real-time output power of the photovoltaic module is smaller than the real-time required power of the air-conditioning load, executing the following control process: acquiring the difference between the real-time required power of the air-conditioning load and the real-time output power of the photovoltaic module as a power deviation; processing the power deviation to obtain a rotating speed for compensation; obtaining a difference between a given rotating speed and the compensation rotating speed as a target rotating speed; the rotating speed for compensation is processed, and the rotating speed adjusting step length is obtained; acquiring the real-time rotating speed of the air conditioner compressor, and comparing the real-time rotating speed with the target rotating speed; according to the comparison result, the real-time rotating speed, the rotating speed adjusting step length and the target rotating speed are combined to control the air conditioner compressor to operate.

The invention discloses a method and device for determining regulation and control parameters of an air conditioner load polymer and electronic equipment. The method comprises the steps that operation state parameters of the air conditioner and environment condition parameters of the environment where the air conditioner is located are obtained; based on the operation state parameters and the environment condition parameters, a load equivalent thermal parameter model of the air conditioner is constructed; based on a preset comfort temperature range and the load equivalent thermal parameter model, a load polymer model of the air conditioner is constructed, and the preset comfort temperature range is used for representing a preset comfort temperature range of a user; and regulation and control parameters of the air conditioner are determined based on the load polymer model, and the regulation and control parameters are used for representing the regulation and control potential of the load polymer of the air conditioner. According to the method and the device, the technical problems of relatively low accuracy and efficiency of determining the regulation and control parameters of the air conditioner load polymer in related technologies are solved.

The invention discloses an air conditioner load aggregator demand response optimization method and device based on user credit, and the method comprises the steps: building a user credit evaluation model according to a user subscription performance index, carrying out the evaluation processing of the response behavior performance of a user, and obtaining a user credit evaluation index; based on the thermal resistance-thermal capacity network model, constructing a building room temperature thermal dynamic model; describing the building room temperature thermal dynamic process through the building room temperature thermal dynamic model; based on the user credit evaluation indexes, constructing a load aggregator demand response double-layer optimization model; solving the optimization model by setting a solving strategy to obtain an optimal scheduling strategy of the load aggregator; constructing a credit updating model; and after the power grid demand response, updating the user credit evaluation index through the credit updating model according to the user response performance index, and obtaining the updated credit evaluation index of the user. According to the invention, the demand response effect can be improved and fine scheduling can be realized.

The invention relates to the technical field of air conditioner dispatching, in particular to a mass air conditioner multi-target day-ahead optimal dispatching method and system considering carbon emission flow. The method comprises the steps that a multi-target optimization model of air conditioner load day-ahead scheduling is constructed with the minimum temperature comfort deviation and the minimum comprehensive cost as targets; wherein the optimal solution of the multi-objective optimization model comprises the minimum temperature comfort deviation and the minimum comprehensive cost, and the comprehensive cost comprises the electricity cost and the carbon emission cost; determining a constraint condition in the multi-objective optimization model, and converting the constraint condition into a penalty function; the constraint conditions comprise a temperature constraint, a power constraint, a load balance constraint, a switch state constraint, a carbon emission constraint and an electric power cost constraint; solving an optimal solution of the multi-objective optimization model by adopting a parrot optimization algorithm, and controlling the operation of each air conditioner according to the optimal solution of the multi-objective optimization model; according to the method, dual-objective rapid optimization scheduling of the power consumption cost and the carbon emission cost of the mass air conditioners is realized.

The invention belongs to the technical field of power cost measurement and calculation, and particularly relates to a multi-type adjustment resource response cost measurement and calculation and economy analysis method. Comprising the following steps: S1, carrying out multi-type adjustment resource response cost calculation; s2, establishing a multi-type adjustment resource economy optimization configuration model considering the whole life cycle cost; s3, economic analysis of adjustment resource response in different scenes is carried out; according to the method, for typical adjustable electrical loads including an electric vehicle load, an air conditioner load and an electrolytic aluminum load, response characteristics of multiple types of adjustment resources are analyzed, and a response cost measuring and calculating method is provided; analyzing cost influence factors in the whole life cycle of the typical adjustable electrical load, designing an adjustment resource response model considering the whole life cycle cost, and proposing an economic optimization configuration model; and multi-scene economy analysis is carried out for typical scenes, supply-preserving scenes and consumption-preserving scenes of wind and light loads, and response cost and economic benefits of multi-type adjustment resources in multiple scenes are measured and calculated.

The invention provides an air conditioner load real-time monitoring and intelligent regulation and control system which comprises a water chilling unit located in a control room, fan coils located in all areas of the room, a temperature sensor, a people flow detector, a power detector and an illumination intensity detector and further comprises computing equipment, and a prediction model is operated in the computing equipment. The calculation module is used for calculating first correlation among the temperature, the heat production data and the valve opening in the time dimension; extracting data in the time dimension according to the first correlation, and calculating the first correlation among the temperature, the heat production data and the valve opening in the space dimension; the extraction module extracts data in a spatial dimension according to the first correlation, and is used for performing feature extraction on the extracted data to obtain feature data; and the regression module is used for predicting the feature data to obtain the valve opening at the next moment. The data is analyzed from the dimensions of time and space, prediction analysis is carried out by considering various data, and the prediction accuracy is improved.

The invention provides a demand side resource mutual aid potential quantitative evaluation method considering space-time collaboration, relates to the technical field of power resource management, and aims to optimize a collaborative mutual aid potential quantitative evaluation method of demand side resources, comprising: determining a plurality of evaluation dimensions including space-time collaboration degree, peak clipping potential and economic applicability; obtaining a space-time collaboration degree; obtaining peak clipping potential; economic applicability is obtained; and obtaining demand side resource mutual aid potential quantitative evaluation by endowing different weights to different index parameters. The method has the advantages that the charging and discharging strategy is reasonably formulated, the adjustable potential of electric vehicle and air conditioner loads is stimulated, the load pressure of a power grid is relieved, and a scientific and accurate quantitative basis is provided.

The invention discloses a virtual power plant energy management method and system based on an air conditioning load, and relates to the field of energy management, and the management method comprises the steps: obtaining power consumption data and integrated operation information data in a virtual power plant management range, carrying out the statistical division of the operation information data according to an operation rule and a potential distribution model, and carrying out the statistical division of the operation information data; generating a batch monitoring management set; constructing a virtual power plant electric energy transaction framework based on the batch monitoring management set, analyzing an electric power balance relationship between an electric quantity supply and demand difference and a virtual power plant, and outputting an energy scheduling result in a virtual power plant management range; and the air conditioner load data serve as demand response resources, the energy dispatching result is optimized, an air conditioner control issuing instruction is generated, the air conditioner control issuing instruction is recorded and decomposed, and the control state of the air conditioner is output. Through optimal scheduling of the air conditioner load, the demand of the air conditioner load can be reduced in the peak period of power demand, so that the load pressure of the system is effectively reduced, and excessive energy consumption is avoided.

The invention discloses a power distribution room air-conditioning load adjusting method based on model prediction, which designs a power distribution station thermodynamic model from several important aspects of space structure, time structure and power grid load demand, and fully considers the influence of the space size and structure of a power distribution station on heat energy transmission in the power distribution station. Meanwhile, the vehicle problem that the power distribution station absorbs the heat energy based on the time period is considered, the influence of the power grid load on the heat loss of the power distribution equipment is also considered, and the influence on the heat energy in the power distribution station is comprehensively and effectively supervised; meanwhile, along with intelligent development of a power grid, power distribution equipment in the power distribution station is gradually increased, the design of the scheme can also consider the situation that the equipment in the power distribution station is increased, space structure characteristics of the power distribution station are adjusted according to the increased power distribution equipment, and body surface temperature monitoring of the power distribution equipment is increased.

Disclosed in the present invention are a photovoltaic air conditioning system, and a control method and control apparatus therefor. The control method comprises: obtaining air conditioner load prediction result data on the basis of known air conditioner load data and an established air conditioner load prediction model; obtaining photovoltaic power generation prediction result data on the basis of known photovoltaic data and an established photovoltaic power generation prediction model; obtaining power grid load prediction result data on the basis of power grid data and an established power grid load prediction model; determining a first energy storage battery charging and discharging strategy on the basis of the air conditioner load prediction result data, the photovoltaic power generation prediction result data and the power grid load prediction result data; and determining a second energy storage battery charging and discharging strategy on the basis of the first energy storage battery charging and discharging strategy and a state-of-charge threshold adjustment strategy, and controlling the charging and discharging process of an energy storage battery assembly on the basis of the second energy storage battery charging and discharging strategy. The present invention can improve the efficient operation performance of a photovoltaic air conditioning system.

The invention discloses a data center dynamic air-conditioning load rapid calculation method, which comprises the following steps of: 1, summarizing data center cold load influence factors as an input condition of a calculation model; 2, constructing a standardized data center reference model based on building energy consumption simulation software; 3, based on the building geometrical characteristics and thermal performance of the target data center, correcting the enclosing structure load of the reference model by adopting an area correction method, and constructing an enclosing structure load rapid calculation model; 4, automatically resetting an internal heat source load calculation result, and constructing an internal heat source load rapid calculation model; 5, correcting the fresh air load of the reference model by adopting a volume correction method based on the geometric characteristics of the target data center building, and constructing a fresh air load rapid calculation model; and step 6, forming a target data center dynamic air-conditioning load rapid calculation method, and obtaining a hourly cooling load prediction result required for covering the whole year or a design stage.

The invention provides an energy-storage-free dynamic power supply method and system for a photovoltaic air conditioner. The method belongs to the technical field of building energy conservation and photovoltaic air conditioner control and comprises the steps that a photovoltaic output prediction value and an air conditioner load prediction value are obtained; time-of-use electricity price data serves as input, a linear programming model with the operation cost minimization as the target is constructed, and power is dynamically supplied to the air conditioner according to the power supply strategy and the time-of-use electricity price response strategy based on the linear programming model; the system can dynamically supply power to the air conditioner through photovoltaic in a non-energy-storage scene, so that the operation cost is minimized, the heat energy storage potential of a building envelope structure is developed, load cross-time transfer is achieved through pre-cooling / pre-heating, and the operation cost is further reduced.

The invention discloses a subway station air conditioning load adjusting system and method, and the system comprises a load simulation module which is used for simulating the time sequence change of cold loads and heat loads of different types of stations under different meteorological conditions and operation conditions, and generating a system training data set; the passenger travel behavior modeling module is used for establishing a time-period passenger distribution model by using subway historical passenger flow data and urban public transport survey data; outputting passenger flow densities of different areas by using a time period passenger flow distribution model; the machine learning prediction model is used for extracting multi-dimensional features as input features; a system training data set is utilized to learn the mapping relation between the input features and the target cold / heat load, and a load preset value is output; the intelligent load adjusting control module dynamically adjusts the output capacity of the air conditioning system through the building automation system according to the load preset value, and closed-loop control is achieved. According to the invention, TRNSYS simulation, behavior modeling and intelligent control are integrated, and accurate load prediction and energy-saving regulation and control are realized.

The invention is suitable for the technical field of power plant optimization scheduling, and provides a flexible virtual power plant optimization scheduling method based on deep reinforcement learning, comprising the following steps: establishing a flexible resource model including a storage battery energy storage equipment model, an air conditioning load model and a demand response load model; flexible virtual power plant optimization scheduling is carried out based on deep reinforcement learning, and the method specifically comprises the steps that importance sampling and an experience pool are set through a near-end strategy optimization algorithm, so that an agent is updated multiple times after interacting with the environment once, and the efficiency of the algorithm is improved; and constructing a deep reinforcement learning optimization model based on the state space, the action space and the reward function. According to the method, a neural network structure and a deep reinforcement learning algorithm are effectively combined to cope with a complex and high-dimensional state space. And a supervised learning pre-training method is used to train the initial strategy network in advance, and the optimization result of the strategy network learning model driving method is promoted.

The invention discloses a virtual power plant combined dispatching method, device and equipment and a medium, and relates to the technical field of virtual power plant combined dispatching, the combined dispatching method comprises the steps that a combined dispatching model of a virtual power plant is constructed, and the combined dispatching model is used for taking the minimum dispatching cost as an optimization target; and scheduling the cooperative operation process among the photovoltaic power generation equipment, the energy storage equipment, the electric vehicle charging station, the electric vehicle battery swap station, the air conditioning load and the power grid under the constraint of the constraint condition. The constraint conditions comprise an electric power balance constraint, an energy storage equipment constraint, an electric vehicle charging station constraint, an electric vehicle battery swap station constraint, an air conditioner load constraint and a power grid interaction power constraint, and then solving the combined scheduling model to obtain a combined scheduling scheme of the virtual power plant. The operation risk of the virtual power plant can be reduced, and the operation efficiency, stability, flexibility and reliability of the virtual power plant can be improved.