User side resource optimization scheduling method considering virtual energy storage and related device

Abstract

The invention provides a user side resource optimization scheduling method considering virtual energy storage and a related device, and the method comprises the steps: obtaining output data of a target region power distribution network in a preset time period, obtaining a first electricity price, determining a power utilization state data set and an operation state data set of a user side resource according to the first electricity price, determining a virtual energy storage model according to the operation state data set, determining a power output model according to the power utilization state data set and the first electricity price, and determining a first output model according to the new energy output data; determining a second output model according to the output data of the energy storage system, constructing a user-side resource collaborative scheduling model based on the virtual energy storage model, the power output model, the first output model and the second output model, and solving the model through a preset particle swarm algorithm to obtain a target calculation result, the target calculation result is used for configuring a user side resource collaborative scheduling model. By performing the method for appropriate optimization scheduling, the loss of power can be reduced.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a user-side resource optimization scheduling method and related devices considering virtual energy storage. Background Art

[0002] In the power system, the optimization dispatch model plays an important role. It can flexibly dispatch the energy system, energy storage system, virtual energy storage system and other resources in the power system. However, the existing optimization dispatch model is insufficient in exploring the flexible dispatch potential of various energy resources on the user side. The flexibility resource model shows a diversified trend, but a unified modeling method has not yet been formed, which limits the effective integration and dispatch of these resources in the power system and makes the user-side flexibility resources fail to be fully utilized. In addition, the existing life loss model of the energy storage system cannot scientifically and reasonably estimate the loss cost, which affects the economic benefit analysis and optimal dispatch of the energy storage system in the power system.

[0003] Therefore, in the power system, how to reduce power loss through appropriate optimization scheduling is an urgent problem to be solved. Summary of the invention

[0004] The embodiments of the present application provide a user-side resource optimization scheduling method and related devices taking into account virtual energy storage, which reduces the problem of power loss in the power system through appropriate optimization scheduling strategies.

[0005] In a first aspect, an embodiment of the present application provides a user-side resource optimization scheduling method considering virtual energy storage, which is applied to a server, and the method includes: Obtaining output data of the distribution network in the target area within a preset time period; the output data includes: new energy output data and energy storage system output data; Acquire a first electricity price, and determine a power usage status data set and an operation status data set of user-side resources according to the first electricity price; Determine a virtual energy storage model based on a preset first constraint condition and the operating status data set; Determine a distributed power output model based on a preset second constraint condition, the power consumption status data set and the first electricity price to obtain a power output model; Determining a first output model according to a preset third constraint condition and the new energy output data; Determining a second output model according to a preset fourth constraint condition and the energy storage system output data; Building a user-side resource collaborative scheduling model based on the virtual energy storage model, the power output model, the first output model and the second output model; The user-side resource collaborative scheduling model is solved by a preset particle swarm algorithm to obtain a target calculation result, and the target calculation result is used to configure the user-side resource collaborative scheduling model.

[0006] In a second aspect, an embodiment of the present application provides a user-side resource optimization scheduling device considering virtual energy storage, which is applied to a server, and the device includes: An acquisition unit is used to acquire output data of the distribution network in the target area within a preset time period; the output data includes: new energy output data and energy storage system output data; acquire a first electricity price, and determine a power consumption status data set and an operation status data set of user-side resources according to the first electricity price; A determination unit, configured to determine a virtual energy storage model based on a preset first constraint condition and the operating status data set; determine a distributed power supply output model based on a preset second constraint condition, the power consumption status data set and the first electricity price to obtain a power supply output model; determine a first output model based on a preset third constraint condition and the new energy output data; determine a second output model based on a preset fourth constraint condition and the energy storage system output data; A control unit, configured to construct a user-side resource collaborative scheduling model based on the virtual energy storage model, the power output model, the first output model, and the second output model; The computing unit is used to solve the user-side resource collaborative scheduling model by using a preset particle swarm algorithm to obtain a target computing result, and the target computing result is used to configure the user-side resource collaborative scheduling model.

[0007] In a third aspect, an embodiment of the present application provides a server, comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps of any method in the first aspect of the embodiment of the present application.

[0008] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps described in any method of the first aspect of the embodiment of the present application.

[0009] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in any method of the first aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0010] The present application describes a user-side resource optimization scheduling method considering virtual energy storage, which is applied to a server, by obtaining output data of a target area distribution network within a preset time period, wherein the output data includes: new energy output data and energy storage system output data; obtaining a first electricity price, and determining a power consumption status data set and an operation status data set of user-side resources according to the first electricity price; then, determining a virtual energy storage model based on a preset first constraint condition and the operation status data set; determining a distributed power supply output model based on a preset second constraint condition, the power consumption status data set and the first electricity price to obtain a power supply output model; determining a first output model according to a preset third constraint condition and the new energy output data; determining a second output model according to a preset fourth constraint condition and the energy storage system output data; then, constructing a user-side resource collaborative scheduling model based on the virtual energy storage model, the power supply output model, the first output model and the second output model; finally, solving the user-side resource collaborative scheduling model by a preset particle swarm algorithm to obtain a target calculation result, and the target calculation result is used to configure the user-side resource collaborative scheduling model. By implementing the embodiments of the present application, various energy resources on the user side are fully exploited and these resources are effectively integrated and scheduled, so that the resources on the user side can be fully utilized, thereby reducing the problem of power loss. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0012] Figure 1 This is a system architecture diagram of a user-side resource optimization scheduling method considering virtual energy storage provided in an embodiment of the present application; Figure 2 It is a structural diagram of a server provided in an embodiment of the present application; Figure 3 It is a flow chart of a method for optimizing and scheduling user-side resources considering virtual energy storage provided in an embodiment of the present application; Figure 4 It is a flow chart of another method for optimizing and scheduling user-side resources considering virtual energy storage provided in an embodiment of the present application; Figure 5 It is a flow chart of constructing a user-side resource optimization scheduling model considering virtual energy storage provided by an embodiment of the present application; Figure 6It is a flowchart of an execution method of a user-side resource optimization scheduling method considering virtual energy storage provided in an embodiment of the present application; Figure 7 It is a timing diagram of a user-side resource optimization scheduling method considering virtual energy storage provided in an embodiment of the present application; Figure 8 It is a block diagram of the functional units of a user-side resource optimization scheduling device considering virtual energy storage provided in an embodiment of the present application. DETAILED DESCRIPTION

[0013] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0014] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0015] In the embodiments of the present application, "at least one item" or similar expressions refer to any combination of these items, including any combination of single items or plural items, and refer to one or more, and multiple refers to two or more. For example, at least one item of a, b, or c can represent the following seven situations: a, b, c, a and b, a and c, b and c, a, b, and c. Among them, each of a, b, and c can be an element or a set containing one or more elements.

[0016] The "connection" that appears in the embodiments of the present application refers to various connection methods such as direct connection or indirect connection to achieve communication between devices, and the embodiments of the present application do not impose any limitations on this.

[0017] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0018] The following is an explanation of the relevant terms involved in this application, as follows: User-side resources: User-side resources refer to power generation, power consumption and energy storage equipment installed on the power consumption side, owned by the user and capable of interacting with the distribution network. It can participate in the optimal dispatch or market response of the power system through technical means, thereby improving energy utilization efficiency, reducing electricity costs and assisting the operation of the power grid.

[0019] Virtual energy storage: Virtual energy storage refers to the use of the regulation capabilities of independent control areas such as self-owned enterprises, DC supporting power sources, and user-side temperature control loads within the power grid without adding new energy storage facilities, thereby simulating the charging and discharging behavior of physical energy storage and providing a low-cost, highly flexible regulation method for the power system.

[0020] The existing optimization and scheduling models are insufficient in exploring the flexible scheduling potential of various energy resources on the user side. The flexibility resource models show a trend of diversification, but a unified modeling method has not yet been formed, which limits the effective integration and scheduling of these resources in the power system and makes the user-side resources unable to be fully utilized. Therefore, in the power system, there is a problem of poor optimization and scheduling strategies for user-side resources, which in turn affects power loss.

[0021] To solve the above problems, an embodiment of the present application provides a user-side resource optimization scheduling method and related devices considering virtual energy storage, which are applied to a server, by obtaining the output data of the target area distribution network within a preset time period; the output data includes: new energy output data and energy storage system output data; obtaining a first electricity price, and determining the power consumption status data set and the operation status data set of the user-side resources according to the first electricity price; determining a virtual energy storage model based on a preset first constraint condition and the operation status data set; determining a distributed power supply output model based on a preset second constraint condition, the power consumption status data set and the first electricity price to obtain a power supply output model; determining a first output model according to a preset third constraint condition and the new energy output data; determining a second output model according to a preset fourth constraint condition and the energy storage system output data; constructing a user-side resource collaborative scheduling model based on the virtual energy storage model, the power supply output model, the first output model and the second output model; solving the user-side resource collaborative scheduling model through a preset particle swarm algorithm to obtain a target calculation result, and the target calculation result is used to configure the user-side resource collaborative scheduling model. In this way, various energy resources on the user side can be fully tapped and these resources can be effectively integrated and scheduled, so that the resources on the user side can be fully utilized, thereby reducing electricity loss.

[0022] Combine the following Figure 1 The system architecture of a user-side resource optimization scheduling method considering virtual energy storage in an embodiment of the present application is described. Figure 1 1 is a system architecture diagram of a user-side resource optimization scheduling method considering virtual energy storage provided in an embodiment of the present application. The user-side resource optimization scheduling system 100 considering virtual energy storage includes a user-side resource collaborative control platform 110 and a target distribution network 120. Among them, the user-side resource collaborative control platform 110 includes: distributed power source 111, virtual energy storage 112, energy storage system 113, and new energy system 114.

[0023] Among them, the target distribution network 120 can refer to a distribution network in a region. The distribution network in the region can be distributed in rural areas or in small and medium-sized cities. There is no limitation here. The target distribution network 120 is used as a regional power hub to establish two-way power interaction and information transmission with the user-side resource collaborative control platform 110. On the one hand, the target distribution network 120 provides the user-side resource collaborative control platform 110 with key information such as real-time electricity prices and grid load status, and guides user-side resources to participate in grid peak regulation and new energy consumption; on the other hand, it receives electricity transmitted by the user-side resource collaborative control platform 110 to alleviate regional power supply pressure. In actual operation, the target distribution network 120 monitors the balance of power supply and demand, and guides the distributed power source 111 and the energy storage system 113 to generate or discharge power when there is a power shortage through the electricity price incentive mechanism. When there is a surplus of new energy, it cooperates with user-side resources to realize power storage and consumption, thereby improving the stability and economy of the power grid.

[0024] Among them, the user-side resource collaborative control platform 110 integrates and manages distributed power sources 111, virtual energy storage 112, energy storage system 113, and new energy system 114, and realizes resource collaborative control by constructing a unified optimization scheduling model. Among them, the distributed power source 111 can be a small thermal power generation, or a micro gas turbine power generation, etc. They all have independent power generation capabilities and are not specifically limited here. The user-side resource collaborative control platform 110 fits its power generation efficiency and output through a mathematical model, and dynamically adjusts the power generation power according to the electricity price signal and power demand of the target distribution network 120, while meeting the power demand, reducing the power generation cost and pollution emissions. Virtual energy storage 112 aggregates temperature control loads (such as air conditioners and water heaters) to form equivalent energy storage, uses the thermal inertia of the equipment, adjusts the power consumption without affecting the user's comfort, calculates the adjustable capacity and time period according to its thermal dynamic model, and incorporates it into the optimized scheduling to achieve low-cost flexible power regulation, assist the power grid peak regulation and new energy consumption. The energy storage system 113, as a physical energy storage unit, has charging and discharging constraints and a life loss model. The user-side resource collaborative control platform 110 monitors its charge state in real time, and formulates charging and discharging strategies according to electricity price fluctuations and new energy output forecasts, such as valley charging and peak discharging, to achieve electricity price arbitrage and smooth out new energy output fluctuations. The new energy system 114 includes: separation power generation, hydropower generation, or photovoltaic power generation, which is not limited here. Taking photovoltaics as an example, its output is intermittent. The user-side resource collaborative control platform 110 predicts the power generation curve through the prediction model, combines the power grid's absorption capacity, and coordinates virtual energy storage 112 and energy storage system 113 to formulate an absorption strategy. When there is excess power generation, virtual energy storage is used to increase load absorption first, and the remaining power is stored by the energy storage system to reduce the abandonment rate.

[0025] In one possible embodiment, when the user-side resource optimization and scheduling system 100 considering virtual energy storage is running, the user-side resource collaborative control platform 110 communicates bidirectionally with the target distribution network 120, and the user-side resource collaborative control platform 110 collects real-time resource data (such as distributed power efficiency, virtual energy storage temperature, battery health status of the energy storage system, and new energy output prediction), constructs a mathematical model, and establishes a multi-objective optimization function, and uses an improved particle swarm algorithm to iteratively search for the best, and generates an optimal scheduling strategy. Taking the rural distribution network as an example, there is an excess of photovoltaic power at noon in summer, and the target distribution network 120 sends a consumption instruction. The platform calls the virtual energy storage 112 to adjust the power consumption of the temperature control equipment, controls the energy storage system 113 to charge, and the remaining power starts the distributed power supply 111 to operate at low power. At night, there is no photovoltaic output, and the platform dispatches the energy storage system 113 to discharge according to the electricity price, thereby reducing the user's electricity purchase cost.

[0026] It can be seen that through the system architecture of the above-mentioned user-side resource optimization scheduling method considering virtual energy storage, deep coordination and refined regulation of user-side resources can be achieved, the level of new energy consumption can be improved, the operation stability of the distribution network can be enhanced, and then the loss of electricity can be reduced.

[0027] Combine the following Figure 2 The server in the embodiment of the present application is described. Figure 2 is a schematic diagram of the structure of a server provided in an embodiment of the present application, such as Figure 2 As shown, the server 200 includes one or more processors 210, a memory 220, a communication interface 230, and one or more programs 221. The processor 210 is communicatively connected with the memory 220 and the communication interface 230 via an internal communication bus.

[0028] Among them, the processor 210 can be a central processing unit (CPU), a general processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logic blocks, units and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of DSP and microprocessors, and the like. The communication unit can be a communication interface, a transceiver, a transceiver circuit, and the like, and the storage unit can be a memory.

[0029] The memory 220 may be a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memories. The nonvolatile memory may be a read-only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM).

[0030] The one or more programs 221 are stored in the memory 220 and are configured to be executed by the processor 210. The one or more programs 221 include instructions for executing any step in the following data processing method embodiment.

[0031] It is understandable that the server 200 may include more or fewer structural elements than those in the above structural block diagram, for example, including a power module, a physical button, a Wi-Fi module, a speaker, a Bluetooth module, a sensor, a display module, etc., which are not limited here. It is understandable that the server 200 may be equipped with Figure 1 The system architecture of a user-side resource optimization scheduling method considering virtual energy storage.

[0032] After understanding the software and hardware architecture of this application, Figure 3 A user-side resource optimization scheduling method considering virtual energy storage in an embodiment of the present application is described. Figure 3 : is a flow chart of a user-side resource optimization scheduling method considering virtual energy storage provided by an embodiment of the present application, which specifically includes the following steps: Step S310, obtaining output data of the distribution network in the target area within a preset time period; the output data includes: new energy output data and energy storage system output data.

[0033] Among them, the new energy output data is the power generation and related operating status data of the new energy power generation equipment in the target area distribution network within the preset time period. It can be photovoltaic output, wind power output, hydropower output, etc., which are not limited here. Taking distributed photovoltaic as an example, its data covers core parameters such as real-time power generation and cumulative power generation of photovoltaic power stations, as well as meteorological data such as light intensity and temperature that affect power generation efficiency. These data are collected in real time by power monitoring devices and meteorological sensors to form a time series data set, which accurately reflects the dynamic characteristics of new energy power generation.

[0034] Among them, the energy storage system output data is a collection of operating data such as the charge and discharge power, state of charge (SOC) and other operating data of the energy storage equipment in the target area distribution network within a preset time period. As a key unit for regulating power supply and demand, the output data of the energy storage system includes information such as the size and time distribution of the charge and discharge power, and real-time changes in SOC. It is collected through the built-in monitoring device of the energy storage system to reflect the operating status and regulation capability of the equipment.

[0035] Specifically, the output data is obtained through the intelligent monitoring system of the distribution network in the target area. The intelligent monitoring system of the distribution network in the target area integrates a variety of collection terminals. For new energy equipment, power sensors and meteorological monitoring equipment are deployed to collect power generation power, light intensity, etc. For energy storage systems, the charging and discharging power and remaining power are obtained through power monitoring modules and SOC calculation units. It should be noted that the selection of the preset time period needs to be combined with the operating characteristics of the distribution network and the scheduling requirements. If the proportion of new energy in the target area is high, the daily time period can be selected to analyze intraday fluctuations. If you focus on long-term strategies, you can select monthly or quarterly time periods to study seasonal patterns. In addition, the acquisition of output data is the basis of multi-objective optimization scheduling. Integrating the intermittent nature of new energy and the regulation capabilities of energy storage can build a more realistic mathematical model.

[0036] Step S320: acquiring a first electricity price, and determining a power usage status data set and an operation status data set of user-side resources according to the first electricity price.

[0037] Among them, the first electricity price is the real-time electricity price or time-of-use electricity price signal of the power market the next day released by the target distribution network, which is the core economic parameter used to solve the optimal scheduling of user-side resources. The determination of the first electricity price is usually affected by many factors, including power generation costs, supply and demand in the power market, policy regulation, etc. In different time periods, the first electricity price may change, forming different electricity price patterns such as peak and valley electricity prices. The electricity price is dynamically adjusted based on factors such as the real-time power supply and demand relationship of the distribution network, power generation costs, and fluctuations in the output of new energy. For example, the electricity price increases during peak hours and decreases during valley hours, forming a price signal to encourage user-side resources to participate in grid regulation. The power consumption status data set is a collection of power consumption behavior data of user-side resources, including key information such as user power consumption, power consumption time period, and start and stop status of power equipment, which is not limited here. The operating status data set is a collection of operating parameters of various types of equipment (such as distributed power sources, virtual energy storage, and energy storage systems) in user-side resources under the influence of electricity prices. For distributed power sources, it includes power generation, efficiency, and start / stop status; for energy storage systems, it involves charging and discharging power, SOC, number of cycles, etc.; for virtual energy storage, it includes temperature changes of temperature control equipment, power adjustment amount, and time period distribution. These data are collected by built-in sensors and monitoring modules of the equipment, reflecting the operating characteristics of the equipment under the guidance of electricity prices, and are used to build equipment operation models and support parameter input of multi-objective optimization scheduling algorithms.

[0038] Specifically, after obtaining the first electricity price, the data collection terminal is integrated through the user-side resource collaborative control platform. According to the power consumption status, the smart meter is used to monitor the user's total power consumption and the power consumption data of each device in real time. Combined with the division of electricity price periods (such as peak, flat, and valley periods), the power consumption behavior under different electricity price intervals is marked. According to the operating status, the distributed power supply monitoring system is used to obtain the operating parameters of the power generation equipment, and the energy storage management system is used to collect energy storage charging and discharging data. The temperature and power adjustment information of the temperature control equipment is collected by relying on the virtual energy storage aggregation platform. Then, the user-side resource collaborative scheduling platform cleans and extracts features from the original data. For example, it extracts features such as the peak power consumption ratio and load fluctuation rate from the power consumption data, and extracts parameters such as the adjustable capacity and response speed of the equipment from the operating data. Finally, a structured power consumption status data set and an operating status data set are formed, laying a data foundation for the subsequent optimization and scheduling model construction.

[0039] With the above Figure 3 For details on the embodiments shown in the drawings, please refer to Figure 4 , Figure 4 1 is a flow chart of another method for optimizing resource scheduling on the user side considering virtual energy storage provided by an embodiment of the present application, which is applied to a server. The method specifically includes the following steps: S410, obtaining historical electricity consumption data and historical electricity prices of the target area distribution network; S420, training a preset machine learning model according to the historical electricity consumption data and the historical electricity price to obtain a model of the relationship between electricity consumption and electricity price; S430, inputting the first electricity price into the electricity consumption and electricity price relationship model to obtain first electricity consumption data; S440, acquiring electric devices in the user-side resources to obtain a first device set; S450, determining the power consumption distribution of the first device set according to the first power consumption data, to obtain a first power consumption distribution set; S460: extracting the power consumption corresponding to the first device set from the first power consumption data to obtain a first power consumption set; S470, determining the power usage status data set according to the first power usage distribution set and the first power usage set; S480, determining the operating parameters of each device in the first device set to obtain a first operating parameter set; S490. Determine the operating status data set corresponding to the first device set according to the first power consumption set and the first operating parameter set.

[0040] Among them, the historical electricity consumption data includes the long-term accumulated electricity power, electricity consumption period, equipment start-stop frequency and other information on the user side of the target area distribution network, which describes the user's electricity consumption behavior pattern. The historical electricity price includes time-of-use electricity price, real-time electricity price and other sequence data, which reflects the law of electricity price fluctuation. The preset machine learning model can be a random forest model, a gradient boosting decision tree (GBDT), a long short-term memory network (LSTM), or a fusion model of an integrated learning method and LSTM, which is not limited here. Taking the machine learning model of GBDT and LSTM fusion as an example, GBDT can capture the nonlinear mapping of electricity price and electricity consumption data, and LSTM can process time-series dependent features. The model input includes historical electricity price, electricity power, user type and other features, and the output is predicted electricity consumption data. During the training process, the parameters are optimized through the mean square error loss function to improve the prediction accuracy.

[0041] Specifically, historical electricity consumption data is obtained through a cloud server, and outliers are removed after data cleaning (such as the power mutation point is corrected by the sliding average method, and the missing value is filled by the missing value method). The historical electricity price is obtained from the power grid trading platform and stored by timestamp alignment. Then, the time domain features (such as the maximum daily power consumption and the weekly power peak-to-valley difference) are extracted from the historical power consumption data, and the statistical features are extracted from the historical electricity price. The input feature matrix includes time period, electricity price, historical power consumption in the same period, etc. Then, the real-time first electricity price is input into the model. The model predicts the power consumption in each time period based on the historical learning model and generates the first power consumption data. Then, the power consumption equipment is obtained through the user-side resource platform to form a first equipment set, and based on the first power consumption data, the power consumption proportion of the equipment in different time periods is analyzed to determine the power consumption distribution set, the power consumption of the equipment is analyzed from the first power consumption data, the power consumption distribution and power consumption are integrated, and the power consumption status data set is constructed, including fields such as equipment identification, time period, power consumption, and power consumption. For the first equipment set, the operating parameters are obtained, such as the temperature adjustment range of the temperature control equipment, the speed and load rate of the motor, etc., to form the first operating parameter set. Finally, the operating status data set is determined by combining power consumption with operating parameters, such as equipment operating time, number of starts and stops, load factor changes, etc., to comprehensively characterize the equipment operating status.

[0042] Step S330: determining a virtual energy storage model based on a preset first constraint condition and the operating status data set.

[0043] Among them, the preset first constraint is a collection of a series of constraints that the virtual energy storage system must follow during operation. These conditions are set based on physical laws, equipment characteristics and actual application requirements, and are intended to ensure that the virtual energy storage system operates safely, stably and efficiently. From a physical perspective, it includes the power limits of the equipment, such as the maximum and minimum power ranges of temperature-controlled loads (such as air conditioners, water heaters, etc.), to prevent damage to the equipment due to overload operation, as well as time constraints and constraints on interaction with the power grid. The operating status data set is a detailed description of the operating conditions of various types of equipment in user-side resources, which reflects the actual operating status and performance of the equipment at different times. For the temperature-controlled load equipment involved in virtual energy storage, the operating status data set includes the real-time power, temperature changes, start-stop status, etc. of the equipment, which are not limited here. Through in-depth mining of the operating status data set, we can understand the operating laws, energy consumption characteristics and responses to different external conditions of the equipment, providing a data basis for accurately building a virtual energy storage model.

[0044] In a possible embodiment, determining the virtual energy storage model based on the preset first constraint condition and the operating status data set specifically includes the following steps: 331. Acquire temperature control devices in the target area distribution network to obtain k temperature control devices; k is a natural number greater than 1; 332. Virtualize each of the k temperature control devices into a virtual energy storage device to obtain k virtual energy storage devices; 333. Obtain device parameters of each of the k virtual energy storage devices to obtain k device parameters; 334. Determine k virtual energy storage parameters and k load temperatures according to the operating status data set and the k device parameters; 335. Determine the virtual energy storage model based on the first constraint condition, the k virtual energy storage parameters and the k load temperatures; The step of determining k virtual energy storage parameters and k load temperatures according to the operating status data set and the k device parameters includes: 3341. Obtain a target virtual energy storage device and its corresponding target device parameters; the target virtual energy storage device is any one of the k virtual energy storage devices; 3342. Determine the load temperature corresponding to the target virtual energy storage device according to the operating status data set to obtain the load temperature of the target virtual energy storage device; 3343. Obtain the actual ambient temperature corresponding to the target virtual energy storage device; 3344. Determine a heat loss value corresponding to the target virtual energy storage device according to the target device parameter to obtain a first heat loss value; 3345. Determine the difference between the load temperature of the target virtual energy storage device and the actual ambient temperature to obtain a first temperature difference; 3346. Determine a heat compensation value of the target virtual energy storage device according to the first temperature difference based on a preset mapping relationship between the temperature difference and the heat compensation, to obtain a first heat compensation value; 3347. Determine a target heat loss value according to the first heat compensation value and the first heat loss value; 3348. Determine a virtual energy storage parameter of the target virtual energy storage device based on the target heat loss value and the load temperature of the target virtual energy storage device.

[0045] Specifically, when determining the virtual energy storage model, the operating status data set needs to be preprocessed first. Since the collected data may have problems such as noise and missing values, which will affect the accuracy of the model, it is necessary to perform operations such as denoising and filling missing values. For example, for abnormal fluctuations in power data, a filtering algorithm can be used for smoothing; for missing values ​​in temperature data, interpolation can be used to fill them. Next, according to the constraints, the preprocessed operating status data is constrained. Then, the basic structure of the virtual energy storage model is constructed based on the screened data. The modeling methods of equivalent circuit models and thermodynamic models can be used, and they can be selected according to the characteristics and requirements of the virtual energy storage system. Taking the temperature control load as an example, the thermodynamic model can well describe the relationship between the temperature change of the equipment and the energy storage. When determining the model parameters, an optimization algorithm can be used for solving. For example, the least squares method, genetic algorithm, etc. are used, which are not limited here, so that the error between the output of the model and the actual operating status data is minimized.

[0046] For a clearer explanation, the above can be described by the following optimization model. Among the user-side resources, temperature control loads such as water heaters and air conditioners can be regarded as virtual energy storage. The virtual energy storage model of aggregated temperature control loads is as follows:

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054] ,

[0055] in, for t The state of charge of the virtual energy storage in the time period, for t -1 period of virtual energy storage charge state, is the heat capacity of the kth temperature control load (virtual energy storage), For the k The ambient temperature of the temperature-controlled load, is the temperature of the temperature control load during period t, is the number of temperature control loads (i.e. k in the above), For the k The set temperature of a temperature-controlled load, For the k The adjustable range of temperature control load, is the virtual energy storage state parameter, It is obtained by least squares fitting. for t The charging and discharging power of the time period, are the maximum and minimum values ​​of the state of charge, and To allow the maximum and minimum values ​​of charge and discharge power, To aggregate the power consumption of the temperature control load, is the heat loss power of the aggregate temperature control load, For the k The thermal resistance of a temperature-controlled load.

[0056] Among them, the heat loss power of the aggregate temperature control load The calculation formula is:

[0057]

[0058] in, For the k The thermal resistance of the temperature-controlled load, For the k The temperature control load is t Set temperature for the time period, For the k The temperature control load is t The ambient temperature during the period, For the k The thermal resistance of the temperature-controlled load, For the k The switching status of a temperature-controlled load.

[0059] Step S340: determining a distributed power output model based on a preset second constraint condition, the power usage status data set and the first electricity price to obtain a power output model.

[0060] Among them, the preset second constraint condition is a comprehensive calculation of various constraints that the distributed power source must follow during actual operation. These conditions are set based on multiple factors such as the safety, stability and economy of the power system. It includes the physical characteristics constraints of the distributed power source itself, such as the maximum and minimum output limits of the distributed power source, which are determined by the design parameters of the power supply equipment. At the same time, it also includes the power supply ramp rate limit, that is, the maximum change in the power supply output per unit time, which is to ensure the stability of the power system and avoid voltage fluctuations caused by sharp changes in output. From an economic point of view, the second constraint condition also includes cost-effectiveness, such as the start-up and shutdown costs of the power source, maintenance costs, etc. These factors will affect the optimal output decision of the power source.

[0061] Among them, the power consumption status data set is a detailed description of the power consumption of power users in a certain period of time, reflecting the power demand and power consumption mode on the user side. The data includes the user's real-time power consumption, power consumption time, power consumption equipment type and other information. Through the analysis of the power consumption status data set, we can understand the user's power consumption habits and demand change patterns, such as the peak and trough periods of power consumption of some users in a specific time period, and the power consumption characteristics of different types of power consumption equipment. This information is crucial to determine the output model of distributed power sources, because the output of distributed power sources needs to match the power demand of users to achieve a balance between supply and demand in the power system.

[0062] In a possible embodiment, determining the output model of the distributed power source according to the power consumption status data set and the first electricity price based on the preset second constraint condition to obtain the power output model specifically includes the following steps: 341. Obtaining a load demand of a target power consumption period from the power consumption status data set; the time length of the target power consumption period is a preset value; the preset time period includes the target power consumption period; 342. Determine the output demand of the distributed power source according to the load demand to obtain the power source output demand; 343. Determine a mapping relationship between output and efficiency based on the power output requirement to obtain a first mapping relationship; 344. Obtain the operating cost of the distributed power source and the electricity price corresponding to the target electricity consumption period to obtain a first operating cost and a target electricity price; 345. Determine the difference between the target electricity price and the first electricity price to obtain a first electricity price difference; 346. Determine an economic operation range of the distributed power source according to the first electricity price difference and the first operation cost to obtain a first economic operation range; 347. Determine the output model of the distributed power source based on the second constraint condition, the first economic operation range and the first mapping relationship to obtain the power source output model.

[0063] Among them, the power consumption status data set records the power consumption information of users in different time periods. By screening and extracting it, the load demand of the target power consumption period can be accurately obtained. The time length of the preset value can be flexibly set according to actual needs. For example, the common target power consumption period is 1 hour, 2 hours, etc. The preset time period usually covers multiple target power consumption periods. It can be a longer time span such as one day or one week, which helps to analyze the power consumption law from a more macro perspective. The load demand reflects the user's use of electricity in the target power consumption period. Distributed power sources need to provide corresponding electricity to meet this demand, which requires consideration of factors such as the loss during power transmission and the conversion efficiency of the distributed power source itself. At the same time, it is also necessary to consider the intermittent and volatile nature of different types of distributed power sources (such as solar energy, wind energy, etc.) and make reasonable adjustments and predictions on the output demand. In addition, the efficiency of distributed power sources is not fixed, it will change with the change of output. Within a certain output range, the power supply efficiency will increase with the increase of output, but when the output exceeds a certain threshold, the efficiency may decrease. The operating cost of the distributed power source and the electricity price corresponding to the target power consumption period are obtained to obtain the first operating cost and the target electricity price.

[0064] Among them, the operating costs of distributed power sources include equipment depreciation, maintenance costs, fuel costs, etc., and the first operating cost can be determined by cost analysis. The target electricity price refers to the electricity sales price during the target electricity consumption period, which can be obtained from the price data of the electricity market. The size of the price difference will affect the economic operation decision of the distributed power source. If the difference is positive and large, it means that generating electricity and selling it online during the target electricity consumption period may obtain more benefits; if the difference is negative, the economic efficiency of power generation needs to be carefully considered. The economic operation range refers to the range within which the distributed power source can operate to maximize economic benefits. This range can be determined by establishing an economic model. The preset second constraints include the physical limitations of the distributed power source (such as maximum and minimum output limits, ramp rate limits, etc.) and the safe and stable operation requirements of the power system. When determining the output model, it is necessary to combine the first economic operation range and the first mapping relationship under the premise of satisfying the second constraint condition to solve an optimal output plan. The solution can adopt a linear programming method or a nonlinear programming method, which is not limited here. The second constraint condition is taken as the constraint condition, and the maximization of economic benefits is taken as the objective function. At the same time, the mapping relationship between output and efficiency is considered to solve the optimal output curve of the distributed power source in the target power consumption period. The output curve is the power output model.

[0065] For a clearer explanation, the following is an example of a micro-turbines (MT) distributed power source. As a common distributed power source on the user side, the output and efficiency of a micro-turbines can be fitted into a cubic function relationship, and its mathematical model is:

[0066] in, For MT t The efficiency of the time period, For MT t The active output of the time period, Indicates the rated active output of MT. Indicates that the exhaust gas of MT is t The amount of calories in the time period, represents the heat dissipation rate of MT, represents the fuel cost of MT, represents the unit price of natural gas, T is the number of total scheduling periods, L is the minimum calorific value of natural gas, L =9.7kWh / m 3 , The time interval between using fuel for MT. In addition, , , , are the efficiency coefficients of MT respectively.

[0067] Step S350: determining a first output model according to a preset third constraint condition and the new energy output data.

[0068] Among them, the preset third constraint condition is a restriction condition set for the characteristics of renewable energy power generation and the acceptance capacity of the power grid, aiming to ensure the safe and stable operation of the power system. Among them, the renewable energy output data includes: output volatility constraint, which is used to limit the maximum change range of renewable energy output in unit time to avoid grid frequency fluctuation; prediction error constraint, which is used to determine the uncertainty of renewable energy output prediction, set the output confidence interval, and reserve spare capacity; grid access constraint, which is used to ensure that the renewable energy output does not exceed the maximum carrying capacity of the distribution network to avoid line overload or voltage over-limit. According to the renewable energy output data and the third constraint condition, the first output model (i.e., renewable energy output model) is determined.

[0069] The third constraint can be expressed by the following formula:

[0070] in, Indicates the actual output of distributed photovoltaics, Indicates the maximum value of the predicted photovoltaic output. It is the preset value according to the new energy system.

[0071] Step S360: determining a second output model according to a preset fourth constraint condition and the energy storage system output data.

[0072] Among them, the preset fourth constraint is a constraint set for the physical characteristics of the energy storage system and the economic operation requirements, including: charge and discharge power constraint, the energy storage system charge and discharge power must not exceed the rated value; state of charge (SOC) constraint, SOC must be kept within a safe range; life loss constraint: based on the cycle life model of the Peukert equation, limit the charge and discharge depth and the number of daily cycles to reduce loss costs. Among them, the energy storage system output data is a set of operating parameters of the energy storage equipment in the target area distribution network, including: charge and discharge power, SOC real-time value, cumulative number of cycles, temperature (affecting battery efficiency and life).

[0073] Among them, firstly, data preprocessing and feature extraction are used to filter the historical energy storage output data, remove abnormal values ​​(such as instantaneous power mutations), and use linear interpolation to fill in missing data. Then, a cycle life model based on the Peukert equation is constructed for the number of daily cycles, average DOD, SOC fluctuation rate, etc. Finally, the second output model (the output model of the energy storage system) is established based on the output data of the energy storage system and Peukert.

[0074] Specifically, assuming that each energy storage system in the user-side resources is mainly composed of batteries, the model of building the energy storage system according to the constraints can be expressed as:

[0075]

[0076]

[0077]

[0078] in, express t The state of charge of the energy storage system during the time period, express t -1 The state of charge of the energy storage system during the period, Indicates the time interval, represents the discharge efficiency of the energy storage system, represents the charging power of the energy storage system, represents the discharge power of the energy storage system, Represents the minimum value of the energy storage system capacity, Indicates the maximum capacity of the energy storage system, Represents the charge and discharge coefficient, Indicates the maximum value of the energy storage system charging power, Indicates the maximum discharge power of the energy storage system.

[0079] Among them, based on the battery life model of the Peukert equation, the impact of the number of cycles and charge and discharge depth of the energy storage battery on the battery life and loss cost are as follows:

[0080]

[0081]

[0082] in, Indicates the depth of charge and discharge. j Indicates the number of daily charge and discharge cycles. j Charge and discharge times, Represents the battery life factor, is a fixed value, Indicates the number of daily charge and discharge cycles, Indicates the battery life reduction ratio, Indicates the initial battery life. represents the loss cost coefficient, Represents investment cost.

[0083] Step S370: construct a user-side resource collaborative scheduling model based on the virtual energy storage model, the power output model, the first output model and the second output model.

[0084] Among them, the virtual energy storage model virtualizes the temperature control equipment in the target area distribution network into energy storage equipment, and determines the charging and discharging characteristics and capabilities of the virtual energy storage equipment by analyzing its operating status and parameters. It takes into account factors such as the load temperature, heat loss, and ambient temperature of the temperature control equipment, as well as the relationship between these factors and the virtual energy storage parameters (such as state of charge, charging and discharging power), and can simulate the functions of traditional energy storage equipment to a certain extent, providing additional flexibility and adjustability for the scheduling of user-side resources. The power output model is an output scheme of distributed power sources determined based on the preset second constraint, the power consumption status data set, and the first electricity price. It comprehensively considers the physical characteristics of distributed power sources (such as maximum and minimum output limits, ramp rate limits), the power demand of users, and the changes in electricity prices in the power market, aiming to achieve the maximization of economic benefits while meeting user needs. The model can dynamically adjust the output of distributed power sources according to different power consumption periods and electricity prices to adapt to the supply and demand balance of the power system. The first output model is the optimal output curve of new energy power generation determined based on the preset third constraint and new energy output data. Since new energy (such as solar energy and wind energy) has the characteristics of intermittency and volatility, the model takes into account the volatility constraints of new energy output, prediction error constraints, and grid access constraints. Through the optimization algorithm, the optimal output of new energy power generation equipment at different time periods is solved under these constraints to improve the absorption capacity of new energy and reduce the phenomenon of wind and light abandonment. The second output model is the operation strategy of the energy storage system determined based on the preset fourth constraint and the output data of the energy storage system. It takes into account the charging and discharging power constraints, state of charge constraints, and life loss constraints of the energy storage system. With the goal of minimizing the operating cost (including the cost of purchasing electricity and the cost of life loss), the charging and discharging power of the energy storage system at different time periods is determined through the optimization algorithm, thereby realizing the safe and economical operation of the energy storage system and providing support for the peak load regulation of the power grid and the absorption rate of new energy.

[0085] In a possible embodiment, constructing a user-side resource collaborative scheduling model based on the virtual energy storage model, the power output model, the first output model, and the second output model specifically includes the following steps: 371. Preprocess the first electricity price, the electricity usage status data set, and the operation status data set to obtain a target electricity data set; 372. Determine a first resource scheduling model based on a preset model coupling method, the virtual energy storage model, and the power output model; 373. Optimize parameters of the first resource scheduling model according to the target power data set to obtain a second resource scheduling model; 374. Determine a target resource scheduling model according to the second resource scheduling model, the first output model, and the second output model; 375. Determine the user-side resource collaborative scheduling model based on the first constraint condition, the second constraint condition, the third constraint condition, the fourth constraint condition and the target resource scheduling model.

[0086] Among them, for abnormal power values ​​in the power status data set, statistical analysis methods (such as triple sigma method, standard deviation-based method) are used to identify and eliminate them, and the erroneous operating parameters in the operating status data set are also processed in a similar manner. At the same time, interpolation methods (such as linear interpolation, spline interpolation) or statistical prediction methods based on historical data can be used to fill in the missing values ​​in the data set. Then, the first electricity price, the power status data set and the operating status data are standardized. The standardization method can be Z-score standardization, that is, the data is converted into a distribution with a mean of 0 and a standard deviation of 1. Then, key features are extracted from the power status data set, such as peak power consumption hours, valley power consumption hours, average power consumption, etc., and features such as equipment operating efficiency and failure frequency are extracted from the operating status data set.

[0087] Among them, the preset model coupling method is a strategy that organically combines different models, aiming to give full play to the advantages of each model and realize the coordinated scheduling of resources. The preset model coupling method can be an information interaction method. For example, the power output model transmits the real-time output information of the distributed power source to the virtual energy storage model, and the virtual energy storage model adjusts its own charging and discharging strategy according to the information to match the output of the distributed power source. It can also be a target coordination method to unify the goals of the two models to achieve the optimal overall resource scheduling. For example, with the common goal of maximizing the economic benefits and stability of the power system, the output of the virtual energy storage equipment and the distributed power source is reasonably allocated on the premise of meeting the electricity demand of users. No limitation is made here.

[0088] Among them, the target power data set contains the preprocessed first electricity price, power consumption status data set and operation status data, which reflect the actual operation of the power system and the market environment. Although the first resource scheduling model has integrated the virtual energy storage model and the power output model, the parameters therein may not be optimal and need to be adjusted and optimized according to the actual data. The second resource scheduling model has been parameter optimized and can better coordinate the resource scheduling of virtual energy storage equipment and distributed power sources. Specifically, the second resource scheduling model, the first output model and the second output model are integrated to form a unified resource scheduling framework. In this framework, the various models collaborate with each other to jointly complete the resource scheduling tasks of the power system.

[0089] In a possible embodiment, determining the target resource scheduling model according to the second resource scheduling model, the first output model, and the second output model specifically includes the following steps: 3741. Extract a constraint interval from the first output model to obtain a first constraint interval; 3742. Extract a constraint interval from the second output model to obtain a second constraint interval; 3743. Determine a constraint interval of the second resource scheduling model to obtain a third constraint interval; 3744. Determine a target constraint interval according to the first constraint interval, the second constraint interval, and the third constraint interval; 3745. Embed the first output model and the second output model into the second resource scheduling model to obtain a target embedded model; 3746. Determine the target resource scheduling model based on the target constraint interval and the target embedding model based on a preset dynamic programming algorithm.

[0090] Among them, the first output model is the optimal output curve of new energy power generation determined according to the preset third constraint condition and the new energy output data, and the first constraint interval is the feasible range of new energy output constituted by these restrictions. For example, due to the uncertainty of light intensity and wind force, the output of photovoltaic and wind power generation will have a certain fluctuation range in different periods, and the power grid also has an upper limit requirement for the power access of new energy, which will form constraints on the output of new energy. Extract the constraint interval from the second output model to obtain the second constraint interval. Among them, the second output model is the operation strategy of the energy storage system determined based on the preset fourth constraint condition and the output data of the energy storage system. The fourth constraint condition mainly involves the charging and discharging power limit, SOC limit and life loss of the energy storage system. The second constraint interval is the feasible operating range of the energy storage system under these constraints. Determine the constraint interval of the second resource scheduling model to obtain the third constraint interval. Among them, the second resource scheduling model is a resource scheduling model that coordinates virtual energy storage equipment and distributed power sources after parameter optimization. The constraints of this model mainly come from the relevant limitations of the virtual energy storage model and the power output model, such as the charging and discharging power and energy limits of the virtual energy storage equipment, the maximum and minimum output limits of the distributed power supply, and the ramp rate limits. The third constraint interval is the feasible scheduling range of the second resource scheduling model under these constraints. Then, the feasible operating range is obtained by comprehensively considering all the constraints of new energy power generation, energy storage system, virtual energy storage equipment, and distributed power scheduling.

[0091] Specifically, with the goal of minimizing abandoned light and electricity costs, a user-side flexible resource collaborative optimization scheduling model is constructed according to the first, second, third, and fourth constraint intervals. The model can be expressed as:

[0092]

[0093]

[0094]

[0095] in, Represents the electricity cost, represents the cost of abandoned light, Represents the electricity cost, represents the user side comfort loss cost, represents the fuel cost of the micro steam turbine, represents the cost of the energy storage system, represents the photovoltaic cost coefficient, represents the user side electricity cost coefficient, represents the virtual energy storage cost coefficient, t Indicates t Time period, T Indicates the preset time period. Indicates the actual output of distributed photovoltaics, Indicates the maximum value of the photovoltaic predicted output, represents the charging power of the energy storage system, express t The load of the period, Right now t The power demand load on the user side during the period, express t The actual load of the time period, Right now t The actual load after demand response regulation during a period.

[0096] For easier understanding, see Figure 5 , Figure 5This is a flow chart of the construction of a user-side resource optimization scheduling model considering virtual energy storage provided by an embodiment of the present application. It can be seen that the power output model, virtual energy storage model, new energy output model and energy storage system output model are all used as basic modules, and the parameters and constraints are input into the basic resource scheduling model. The basic resource scheduling model is used to further support the construction of the user-side resource collaborative scheduling model. The power output model defines the output strategy of the distributed power source based on the power consumption status data set, the first electricity price and the preset constraints, reflecting its operating characteristics under electricity price fluctuations and load demand; the virtual energy storage model virtualizes the temperature control equipment, combines the operating status data and constraints, and describes the charging and discharging characteristics of the virtual energy storage; the new energy output model optimizes the output curve of new energy power generation based on the new energy output data and the third constraint condition to cope with its intermittency and volatility; the energy storage system output model clarifies the charging and discharging strategy of energy storage based on the energy storage system output data and the fourth constraint condition to ensure safe and economical operation. The user-side resource collaborative scheduling model is built based on the basic resource scheduling model, comprehensively considering the constraints of each model and realizing global collaborative scheduling through optimization algorithms. The model not only integrates a single resource scheduling strategy, but also optimizes the configuration from the system level.

[0097] Step S380, solving the user-side resource collaborative scheduling model by using a preset particle swarm algorithm to obtain a target calculation result, and the target calculation result is used to configure the user-side resource collaborative scheduling model.

[0098] Among them, the preset particle swarm algorithm is an intelligent optimization algorithm based on the reverse learning and iterative selection mechanism, which aims to solve the problem that the traditional particle swarm algorithm is prone to fall into local optimality and slow convergence. The algorithm enhances the global search ability and local fine search ability of the algorithm by introducing the reverse solution generation strategy and the population diversity maintenance mechanism, and is suitable for the high-dimensional nonlinear optimization problem of the user-side resource collaborative scheduling model. Among them, the user-side resource collaborative scheduling model is a multi-objective optimization model, which includes the minimization of abandoned light cost, electricity cost, and user comfort loss. At the same time, it is subject to complex constraints such as virtual energy storage temperature constraints, energy storage life loss constraints, and distributed power supply climbing rate constraints. The particle swarm algorithm searches for the optimal scheduling strategy in the feasible solution space by discretizing continuous decision variables into particle positions.

[0099] In a possible embodiment, solving the user-side resource collaborative scheduling model by using a preset particle swarm algorithm to obtain a target calculation result specifically includes the following steps: 381. Obtaining the new energy utilization rate corresponding to the new energy output data; 382. Obtain the electricity cost of the target area distribution network according to the first electricity price to obtain a first electricity cost; 383. Construct an optimization function according to the new energy utilization rate and the first electricity cost to obtain a target optimization function; 384. Determine model parameters corresponding to the user-side resource collaborative scheduling model to obtain n model parameters; n is an integer greater than 1; 385. Generate a first particle group according to the n model parameters, and initialize the initial position and initial velocity of each particle in the first particle group; the first particle group includes n particles; each particle corresponds to a model parameter; 386. Determine the fitness of each particle in the first particle group according to the target optimization function to obtain n particle fitnesses; 387. Determine the weight corresponding to each particle in the n particle fitnesses to obtain n weights; 388. Adjust the positions of the n particles and the speeds of the n particles according to the n weights to obtain n particle target positions and n particle target speeds; 389. Solve the n particle target positions and the n particle target velocities to obtain a second particle group; the second particle group includes: positions of m particles and velocities of m particles; m is a natural number greater than 1, and m is less than or equal to n; 3810. Determine the fitness of each particle in the particle swarm according to the target optimization function and the second particle swarm to obtain m particle fitnesses; 3811. Determine the optimal solution of the user-side resource collaborative scheduling model based on the particle swarm algorithm and the fitness of the m particles to obtain the target calculation result.

[0100] Among them, the new energy output data represents the actual power generation of new energy power generation equipment such as photovoltaics and wind power at different times. The new energy utilization rate represents the degree to which new energy is actually effectively utilized. It is affected by many factors, including the power grid's absorption capacity, the volatility of new energy power generation, and the user's electricity demand, etc., which are not limited here. The calculation of the new energy utilization rate is the ratio between the actual amount of electricity used by new energy and the total amount of electricity used by new energy. Among them, the actual amount of electricity used by new energy can be obtained by counting the amount of electricity delivered to the power grid by new energy power generation equipment and effectively used by users. The total amount of electricity generated by new energy is the amount of electricity that new energy power generation equipment can theoretically generate in the corresponding time period. The power value can be obtained by integrating the power value in the new energy output data and the time interval. It can be obtained through empirical formulas, statistical models, or machine learning models, which are not limited here. The user-side resource collaborative scheduling model includes multiple factors that affect resource scheduling. The parameters corresponding to these factors constitute the model parameters. The model parameters may include the output coefficient of distributed power sources, the charging and discharging power of the energy storage system, the adjustment parameters of the virtual energy storage device, etc. By analyzing and sorting out the user-side resource collaborative scheduling model, these key model parameters are determined and their number is recorded as n.

[0101] Among them, the electricity cost of the target area distribution network is closely related to the user's electricity power and the first electricity price. When calculating the first electricity cost, it is necessary to multiply the electricity power of the target area in different time periods by the corresponding first electricity price, and then sum up all time periods. The calculation formula can be expressed as:

[0102] in, represents the first electricity cost, is the first electricity price, For the t The power consumption during the period, T is the total number of time periods, t For each period.

[0103] Specifically, the user-side flexible resource collaborative optimization model is solved by using an improved particle swarm algorithm. By introducing reverse learning and iterative selection operators into the traditional particle swarm algorithm, the initial value of the population and the comparison value of the iterative results are first recorded. In the suppression stage, particles with low similarity to the current particles and high fitness are retained to ensure that after the fitness of the particles with high fitness is compared with the initial particles, the particles in the optimal position within the set threshold suppression range can enter the next iteration. First, the speed and position of the particles are initialized, and in the process of particle swarm optimization, the collaborative optimization scheduling strategy of user-side flexible resources is generated according to the particle position of each optimization. Then, the particle fitness F (i.e., the objective function) under this optimization is calculated, the speed and position of the particle swarm are updated according to the speed and position update formula, and the particle swarm is reversely learned. Assuming that there is a feasible solution x in the search space, its reverse solution The calculation formula is:

[0104]

[0105] in, For the i The particle in m The iterative optimization j The reverse solution of the dimensional position, For the i The particle in m The first iteration j The location of the dimension, For the i The particle in m When searching for the best result, j The historical minimum value of dimension, For the i The particle in m The first time to find the best j The historical maximum value of dimension.

[0106] Next, find the adaptive weight that changes with the particle fitness F w , its calculation formula is:

[0107] in, f is the current fitness, is the average fitness, is the minimum fitness, is the maximum fitness, is the adaptive weight w The minimum value of is the adaptive weight w The maximum value of .

[0108] After each iteration, the particles are arranged in descending order according to their adaptability. The first particle is designated as the first initial particle and added to the memory group as a memory particle. The following operations are performed from the second particle to the last particle: if the distance from the current particle to the initial particle is greater than all particles in the memory group, the particle is designated as the new network center and added to the memory group as a memory particle in the same way. If the distance from the current particle to the designated initial particle is less than all particles in the memory group, the particle is eliminated. i and particles u The distance between Calculated as follows:

[0109] in, For the i Particle j The location of the dimension, For particles u No. j The location of the dimension, n is the dimension, For the j The maximum value of the dimension, For the j Minimum value of dimension.

[0110] Finally, within the range of iteration times, multiple optimization searches are performed to obtain the individual optimal value of the particle and the group optimal value. The particle position corresponding to the final group optimal value is the optimal solution of the collaborative optimization scheduling strategy of the user-side flexibility resources, and it is used as the target calculation result. This result can be used to configure the user-side resource collaborative scheduling model to achieve increased utilization of new energy and reduced electricity costs.

[0111] For easier understanding, see Figure 6 , Figure 6 This is a flow chart of the execution of a user-side resource optimization scheduling method considering virtual energy storage provided in an embodiment of the present application. It can be seen that by systematically integrating key information such as new energy output, distributed power output, virtual energy storage, energy storage system and electricity price, efficient scheduling of user-side resources can be achieved, thereby improving the new energy consumption rate and electricity economy, and promoting the economic and stable operation of the power system.

[0112] First, the output data of new energy sources such as wind power and photovoltaics connected to the power grid, as well as the real-time electricity price of the next day's power market, are obtained. The output data of new energy sources accurately reflects the power generation capacity of wind power, photovoltaics and other equipment. Its volatility and intermittent characteristics have a significant impact on the stable operation of the power grid. As a price signal of the supply and demand relationship in the power market, the real-time electricity price can effectively guide the user-side resources to adjust the operation strategy. Then, based on the real-time electricity price, the user-side resource collaborative control platform predicts the power consumption status and operation status information of the user-side resources, and builds a user-side resource operation model. The user-side resources cover multiple entities such as distributed power sources, virtual energy storage, and energy storage systems. The power consumption status involves key information such as user power consumption and time distribution, and the operation status includes core content such as equipment parameters and efficiency. Through in-depth data analysis, the platform predicts the response mode of resources in different electricity price periods. For example, in high electricity price periods, virtual energy storage adjusts loads and energy storage systems discharge to reduce electricity costs.

[0113] Then, with the core goal of minimizing the cost of abandoned solar power and electricity consumption, a user-side resource collaborative optimization scheduling model is constructed. The cost of abandoned solar power reflects the loss of new energy that has not been consumed, and the cost of electricity consumption is directly related to the electricity price and the user's electricity consumption strategy. The model organically integrates the virtual energy storage model, power output model, new energy output model, and energy storage system output model, fully considering the constraints of each model (such as power limit, state of charge constraint, etc.) to form a multi-objective optimization system.

[0114] Finally, the model is solved based on the improved particle swarm algorithm, and the optimal strategy for the coordinated optimization scheduling of user-side resources is finally obtained. The improved particle swarm algorithm innovatively introduces reverse learning, adaptive weights and other mechanisms to address the defects of traditional algorithms, significantly improving the algorithm's global search and local development capabilities. The algorithm maps the scheduling strategy to the particle position, and iteratively updates the particle position and speed to efficiently search for the optimal solution under constraints. During the solution process, the fitness function accurately measures the abandoned light and electricity costs, and the algorithm continuously optimizes the particle state, and finally determines the optimal scheduling plan for each resource at different time periods.

[0115] For easier understanding, see Figure 7 , Figure 7 is a timing diagram of a user-side resource optimization scheduling method considering virtual energy storage provided by an embodiment of the present application. It can be seen that: Figure 7The interactive logic of the target area distribution network, user-side platform, optimization model and improved particle swarm algorithm is clearly presented, and the whole process from electricity price release to optimal dispatch strategy generation is fully presented. First, the target area distribution network, as the information source of the power market, releases the next day's electricity price to the user-side platform. After receiving the electricity price, the user-side platform immediately predicts the user-side resource status, covering the power consumption and operation status of distributed power sources, virtual energy storage, energy storage systems, etc. By analyzing historical data and real-time monitoring information, the user-side platform constructs a resource model to predict the operating characteristics such as the temperature control regulation capability of virtual energy storage and the charging and discharging efficiency of the energy storage system, laying a data foundation for subsequent optimization. Then, the user-side platform inputs the resource model into the optimization model. The optimization model takes the minimization of abandoned light and electricity cost as the core goal, integrates the virtual energy storage model, power output model, new energy output model, etc., to form a collaborative optimization system to ensure that the dispatch strategy meets the actual operation requirements. At the same time, the optimization model inputs the optimization target to the improved particle swarm algorithm to carry out the solution process. Then, the improved particle swarm algorithm is used as a solution tool to initialize the particle swarm, set the initial position and speed of the particles, and each particle corresponds to the parameter combination of the dispatch strategy. During the iterative solution, the algorithm evaluates the particle swarm according to the objective function of the optimization model, updates the particle state through improvement mechanisms such as reverse learning and adaptive weights, and balances global exploration and local development. The result of each iteration is returned to the optimization model, which is screened and adjusted by the objective function until the convergence condition is met. Finally, the improved particle swarm algorithm feeds back the optimal solution to the optimization model to generate the optimal scheduling strategy. After the user-side platform receives the strategy, it verifies the feasibility in combination with the distribution network operation requirements in the target area.

[0116] It can be seen that, through a user-side resource optimization scheduling method considering virtual energy storage, the output data of the target area distribution network within a preset time period is obtained; the output data includes: new energy output data and energy storage system output data; a first electricity price is obtained, and the power consumption status data set and the operation status data set of the user-side resources are determined according to the first electricity price; a virtual energy storage model is determined based on a preset first constraint condition and the operation status data set; a distributed power supply output model is determined based on a preset second constraint condition, the power consumption status data set and the first electricity price to obtain a power supply output model; a first output model is determined according to a preset third constraint condition and the new energy output data; a second output model is determined according to a preset fourth constraint condition and the energy storage system output data; a user-side resource collaborative scheduling model is constructed based on the virtual energy storage model, the power supply output model, the first output model and the second output model; the user-side resource collaborative scheduling model is solved by a preset particle swarm algorithm to obtain a target calculation result, and the target calculation result is used to configure the user-side resource collaborative scheduling model. In this way, various energy resources on the user side can be fully tapped and these resources can be effectively integrated and scheduled, so that the resources on the user side can be fully utilized, thereby reducing electricity loss.

[0117] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the execution process on the method side. It is understandable that, in order to realize the above functions, the server includes a hardware structure and / or software module corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

[0118] The embodiment of the present application can divide the server into functional units according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical functional division. There may be other division methods in actual implementation.

[0119] In the case of dividing each functional module into corresponding functional modules, Figure 8 1 is a functional unit composition block diagram of a user-side resource optimization scheduling device considering virtual energy storage provided in an embodiment of the present application. The user-side resource optimization scheduling device considering virtual energy storage 800 is applied to a server, and the device 800 includes: The acquisition unit 810 is used to acquire the output data of the distribution network in the target area within a preset time period; the output data includes: new energy output data and energy storage system output data; acquire a first electricity price, and determine the power consumption status data set and the operation status data set of the user-side resources according to the first electricity price; The determination unit 820 is used to determine the virtual energy storage model based on the preset first constraint condition and the operating status data set; determine the distributed power output model based on the preset second constraint condition, the power consumption status data set and the first electricity price to obtain the power output model; determine the first output model according to the preset third constraint condition and the new energy output data; determine the second output model according to the preset fourth constraint condition and the energy storage system output data; A control unit 830, configured to construct a user-side resource collaborative scheduling model based on the virtual energy storage model, the power output model, the first output model, and the second output model; The calculation unit 840 is used to solve the user-side resource collaborative scheduling model through a preset particle swarm algorithm to obtain a target calculation result, and the target calculation result is used to configure the user-side resource collaborative scheduling model.

[0120] In a possible embodiment, the acquisition unit 810, in determining the power usage status data set and the operation status data set of the user-side resource according to the first electricity price, is specifically used to: Obtaining historical electricity consumption data and historical electricity prices of the target area distribution network; Training a preset machine learning model according to the historical electricity consumption data and the historical electricity prices to obtain a model of the relationship between electricity consumption and electricity prices; Inputting the first electricity price into the electricity consumption and electricity price relationship model to obtain first electricity consumption data; Acquire the electrical devices in the user-side resources to obtain a first device set; Determine the power consumption distribution of the first device set according to the first power consumption data to obtain a first power consumption distribution set; Extracting the power consumption corresponding to the first device set from the first power consumption data to obtain a first power consumption set; Determine the power usage status data set according to the first power usage distribution set and the first power usage set; Determine an operating parameter of each device in the first device set to obtain a first operating parameter set; The operating status data set corresponding to the first device set is determined according to the first power consumption set and the first operating parameter set.

[0121] In a possible embodiment, the determining unit 820, in determining the virtual energy storage model based on the preset first constraint condition and the operating status data set, is specifically configured to: Acquire temperature control devices in the target area distribution network to obtain k temperature control devices; k is a natural number greater than 1; Virtualizing each of the k temperature control devices into a virtual energy storage device to obtain k virtual energy storage devices; Obtaining device parameters of each of the k virtual energy storage devices to obtain k device parameters; Determining k virtual energy storage parameters and k load temperatures according to the operating status data set and the k device parameters; Determining the virtual energy storage model based on the first constraint condition, the k virtual energy storage parameters and the k load temperatures; The step of determining k virtual energy storage parameters and k load temperatures according to the operating status data set and the k device parameters includes: Obtaining a target virtual energy storage device and its corresponding target device parameters; the target virtual energy storage device is any one of the k virtual energy storage devices; Determine the load temperature corresponding to the target virtual energy storage device according to the operating status data set to obtain the load temperature of the target virtual energy storage device; Obtaining the actual ambient temperature corresponding to the target virtual energy storage device; Determine the heat loss value corresponding to the target virtual energy storage device according to the target device parameter to obtain a first heat loss value; Determine a difference between a load temperature of the target virtual energy storage device and the actual ambient temperature to obtain a first temperature difference; Determine the heat compensation value of the target virtual energy storage device according to the first temperature difference based on a preset mapping relationship between the temperature difference and the heat compensation, to obtain a first heat compensation value; determining a target heat loss value according to the first heat compensation value and the first heat loss value; A virtual energy storage parameter of the target virtual energy storage device is determined based on the target heat loss value and the load temperature of the target virtual energy storage device.

[0122] In a possible embodiment, the determining unit 820 determines the output model of the distributed power source according to the power consumption status data set and the first electricity price based on the preset second constraint condition to obtain the power output model, specifically for: Obtaining the load demand of the target power consumption period from the power consumption status data set; the time length of the target power consumption period is a preset value; the preset time period includes the target power consumption period; Determine the output demand of the distributed power source according to the load demand to obtain the power output demand; Determine a mapping relationship between output and efficiency based on the power output requirement to obtain a first mapping relationship; Acquire the operating cost of the distributed power source and the electricity price corresponding to the target electricity consumption period to obtain a first operating cost and a target electricity price; Determine the difference between the target electricity price and the first electricity price to obtain a first electricity price difference; Determine the economic operation range of the distributed power source according to the first electricity price difference and the first operation cost to obtain a first economic operation range; An output model of the distributed power source is determined based on the second constraint condition, the first economic operation range, and the first mapping relationship to obtain the power source output model.

[0123] In a possible embodiment, the control unit 830, in terms of building a user-side resource collaborative scheduling model based on the virtual energy storage model, the power output model, the first output model, and the second output model, is specifically configured to: Preprocessing the first electricity price, the electricity usage status data set, and the operation status data set to obtain a target electricity data set; Determine a first resource scheduling model based on a preset model coupling method, the virtual energy storage model and the power output model; Optimizing the parameters of the first resource scheduling model according to the target power data set to obtain a second resource scheduling model; Determine a target resource scheduling model according to the second resource scheduling model, the first output model, and the second output model; The user-side resource collaborative scheduling model is determined according to the first constraint condition, the second constraint condition, the third constraint condition, the fourth constraint condition and the target resource scheduling model.

[0124] In a possible embodiment, the control unit 830, in determining the target resource scheduling model according to the second resource scheduling model, the first output model, and the second output model, is specifically configured to: Extracting a constraint interval from the first output model to obtain a first constraint interval; Extracting a constraint interval from the second output model to obtain a second constraint interval; Determine the constraint interval of the second resource scheduling model to obtain a third constraint interval; Determine a target constraint interval according to the first constraint interval, the second constraint interval, and the third constraint interval; Embedding the first output model and the second output model into the second resource scheduling model to obtain a target embedding model; The target resource scheduling model is determined based on a preset dynamic programming algorithm according to the target constraint interval and the target embedding model.

[0125] In a possible embodiment, the calculation unit 840 solves the user-side resource collaborative scheduling model by using a preset particle swarm algorithm to obtain a target calculation result, and is specifically used to: Obtaining a new energy utilization rate corresponding to the new energy output data; Acquire the electricity cost of the target area distribution network according to the first electricity price to obtain a first electricity cost; Constructing an optimization function according to the new energy utilization rate and the first electricity cost to obtain a target optimization function; Determine the model parameters corresponding to the user-side resource collaborative scheduling model to obtain n model parameters, where n is an integer greater than 1; Generate a first particle group according to the n model parameters, and initialize the initial position and initial velocity of each particle in the first particle group; the first particle group includes n particles; each particle corresponds to a model parameter; Determine the fitness of each particle in the first particle group according to the target optimization function to obtain n particle fitnesses; Determine the weight corresponding to each particle in the n particle fitnesses to obtain n weights; Adjusting the positions of the n particles and the speeds of the n particles according to the n weights to obtain n particle target positions and n particle target speeds; Solving the n particle target positions and the n particle target velocities to obtain a second particle group; the second particle group includes: positions of m particles and velocities of m particles; m is a natural number greater than 1, and m is less than or equal to n; Determine the fitness of each particle in the particle swarm according to the target optimization function and the second particle swarm to obtain m particle fitnesses; Based on the particle swarm algorithm and the fitness of the m particles, an optimal solution of the user-side resource collaborative scheduling model is determined to obtain the target calculation result.

[0126] It can be seen that the embodiment of the present application describes a user-side resource optimization scheduling device that takes virtual energy storage into consideration. The scheduling optimization is performed by comprehensively considering the virtual energy storage, energy storage system, power output and new energy output of the user-side resources to construct a user-side resource collaborative optimization scheduling model, and the improved particle swarm algorithm is used to solve it. Finally, a user-side resource optimization scheduling method is obtained, thereby reducing power loss in the power system.

[0127] An embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method recorded in the above method embodiments, and the above computer includes a server.

[0128] The present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes a server.

[0129] It should be noted that, for the above-mentioned various embodiments, for the sake of simple description, they are all expressed as a series of action combinations. Those skilled in the art should be aware that the present application is not limited by the described order of actions, because some steps in the embodiments of the present application can be performed in other orders or simultaneously. In addition, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions, steps, modules or units involved are not necessarily required by the embodiments of the present application.

[0130] In the above embodiments, the embodiments of the present application have different focuses on the description of each embodiment. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0131] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

[0132] The steps of the method or algorithm described in the embodiments of the present application can be implemented in hardware or by executing software instructions by a processor. The software instructions can be composed of corresponding software modules, and the software modules can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (electrically EPROM, EEPROM), registers, hard disks, mobile hard disks, read-only compact disks (CD-ROMs) or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in a terminal device or a management device. Of course, the processor and the storage medium can also exist in a terminal device or a management device as discrete components.

[0133] Those skilled in the art should be aware that in one or more of the above examples, the functions described in the embodiments of the present application can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The various modules / units contained in the various devices and products described in the above embodiments may be software modules / units, hardware modules / units, or partly software modules / units and partly hardware modules / units. For example, for various devices and products applied to or integrated in a chip, the various modules / units contained therein may all be implemented in the form of hardware such as circuits, or at least some of the modules / units may be implemented in the form of software programs, which run on a processor integrated inside the chip, and the remaining (if any) modules / units may be implemented in the form of hardware such as circuits; for various devices and products applied to or integrated in a chip module, the various modules / units contained therein may all be implemented in the form of hardware such as circuits, and different modules / units may be located in the same component (such as a chip, circuit module, etc.) or different components of the chip module, or at least some of the modules / units may be It is implemented in the form of a software program that runs on a processor integrated inside the chip module, and the remaining (if any) modules / units can be implemented in hardware such as circuits; for various devices and products applied to or integrated in the terminal equipment, the various modules / units contained therein can be implemented in hardware such as circuits, and different modules / units can be located in the same component (for example, chip, circuit module, etc.) or different components in the terminal equipment, or at least some of the modules / units can be implemented in the form of a software program that runs on a processor integrated inside the terminal equipment, and the remaining (if any) modules / units can be implemented in hardware such as circuits.

[0134] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the embodiments of the present application. It should be understood that the above description is only the specific implementation method of the embodiments of the present application and is not intended to limit the protection scope of the embodiments of the present application. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solutions of the embodiments of the present application should be included in the protection scope of the embodiments of the present application.

Claims

1. A user-side resource optimization scheduling method considering virtual energy storage, characterized in that: Applied to a server, the method comprises: Obtaining output data of the distribution network in the target area within a preset time period; the output data includes: new energy output data and energy storage system output data; Acquire a first electricity price, and determine a power usage status data set and an operation status data set of user-side resources according to the first electricity price; Determine a virtual energy storage model based on a preset first constraint condition and the operating status data set; Determine a distributed power output model based on a preset second constraint condition, the power consumption status data set and the first electricity price to obtain a power output model; Determining a first output model according to a preset third constraint condition and the new energy output data; Determining a second output model according to a preset fourth constraint condition and the energy storage system output data; Building a user-side resource collaborative scheduling model based on the virtual energy storage model, the power output model, the first output model and the second output model; The user-side resource collaborative scheduling model is solved by a preset particle swarm algorithm to obtain a target calculation result, and the target calculation result is used to configure the user-side resource collaborative scheduling model.

2. The method according to claim 1, characterized in that The step of determining a power consumption status data set and an operation status data set of user-side resources according to the first electricity price includes: Obtaining historical electricity consumption data and historical electricity prices of the target area distribution network; Training a preset machine learning model according to the historical electricity consumption data and the historical electricity prices to obtain a model of the relationship between electricity consumption and electricity prices; Inputting the first electricity price into the electricity consumption and electricity price relationship model to obtain first electricity consumption data; Acquire the electrical devices in the user-side resources to obtain a first device set; Determine the power consumption distribution of the first device set according to the first power consumption data to obtain a first power consumption distribution set; Extracting the power consumption corresponding to the first device set from the first power consumption data to obtain a first power consumption set; Determine the power usage status data set according to the first power usage distribution set and the first power usage set; Determine an operating parameter of each device in the first device set to obtain a first operating parameter set; The operating status data set corresponding to the first device set is determined according to the first power consumption set and the first operating parameter set.

3. The method according to claim 1, characterized in that The determining of the virtual energy storage model based on the preset first constraint condition and the operating status data set includes: Acquire temperature control devices in the target area distribution network to obtain k temperature control devices; k is a natural number greater than 1; Virtualizing each of the k temperature control devices into a virtual energy storage device to obtain k virtual energy storage devices; Obtaining device parameters of each of the k virtual energy storage devices to obtain k device parameters; Determining k virtual energy storage parameters and k load temperatures according to the operating status data set and the k device parameters; Determining the virtual energy storage model based on the first constraint condition, the k virtual energy storage parameters and the k load temperatures; The step of determining k virtual energy storage parameters and k load temperatures according to the operating status data set and the k device parameters includes: Obtaining a target virtual energy storage device and its corresponding target device parameters; the target virtual energy storage device is any one of the k virtual energy storage devices; Determine the load temperature corresponding to the target virtual energy storage device according to the operating status data set to obtain the load temperature of the target virtual energy storage device; Obtaining the actual ambient temperature corresponding to the target virtual energy storage device; Determine the heat loss value corresponding to the target virtual energy storage device according to the target device parameter to obtain a first heat loss value; Determine a difference between a load temperature of the target virtual energy storage device and the actual ambient temperature to obtain a first temperature difference; Determine the heat compensation value of the target virtual energy storage device according to the first temperature difference based on a preset mapping relationship between the temperature difference and the heat compensation, to obtain a first heat compensation value; determining a target heat loss value according to the first heat compensation value and the first heat loss value; A virtual energy storage parameter of the target virtual energy storage device is determined based on the target heat loss value and the load temperature of the target virtual energy storage device.

4. The method according to claim 1, characterized in that The step of determining a distributed power output model based on a preset second constraint condition, the power consumption status data set and the first electricity price to obtain a power output model includes: Obtaining the load demand of the target power consumption period from the power consumption status data set; the time length of the target power consumption period is a preset value; the preset time period includes the target power consumption period; Determine the output demand of the distributed power source according to the load demand to obtain the power output demand; Determine a mapping relationship between output and efficiency based on the power output requirement to obtain a first mapping relationship; Acquire the operating cost of the distributed power source and the electricity price corresponding to the target electricity consumption period to obtain a first operating cost and a target electricity price; Determine the difference between the target electricity price and the first electricity price to obtain a first electricity price difference; Determine the economic operation range of the distributed power source according to the first electricity price difference and the first operation cost to obtain a first economic operation range; An output model of the distributed power source is determined based on the second constraint condition, the first economic operation range, and the first mapping relationship to obtain the power source output model.

5. The method according to any one of claims 1 to 4, characterized in that: The constructing of a user-side resource collaborative scheduling model based on the virtual energy storage model, the power output model, the first output model, and the second output model includes: Preprocessing the first electricity price, the electricity usage status data set, and the operation status data set to obtain a target electricity data set; Determine a first resource scheduling model based on a preset model coupling method, the virtual energy storage model and the power output model; Optimizing the parameters of the first resource scheduling model according to the target power data set to obtain a second resource scheduling model; Determine a target resource scheduling model according to the second resource scheduling model, the first output model, and the second output model; The user-side resource collaborative scheduling model is determined according to the first constraint condition, the second constraint condition, the third constraint condition, the fourth constraint condition and the target resource scheduling model.

6. The method according to claim 5, characterized in that The determining the target resource scheduling model according to the second resource scheduling model, the first output model, and the second output model includes: Extracting a constraint interval from the first output model to obtain a first constraint interval; Extracting a constraint interval from the second output model to obtain a second constraint interval; Determine the constraint interval of the second resource scheduling model to obtain a third constraint interval; Determine a target constraint interval according to the first constraint interval, the second constraint interval, and the third constraint interval; Embedding the first output model and the second output model into the second resource scheduling model to obtain a target embedding model; The target resource scheduling model is determined based on a preset dynamic programming algorithm according to the target constraint interval and the target embedding model.

7. The method according to claim 1, characterized in that The user-side resource collaborative scheduling model is solved by using a preset particle swarm algorithm to obtain a target calculation result, including: Obtaining a new energy utilization rate corresponding to the new energy output data; Acquire the electricity cost of the target area distribution network according to the first electricity price to obtain a first electricity cost; Constructing an optimization function according to the new energy utilization rate and the first electricity cost to obtain a target optimization function; Determine the model parameters corresponding to the user-side resource collaborative scheduling model to obtain n model parameters, where n is an integer greater than 1; Generate a first particle group according to the n model parameters, and initialize the initial position and initial velocity of each particle in the first particle group; the first particle group includes n particles; each particle corresponds to a model parameter; Determine the fitness of each particle in the first particle group according to the target optimization function to obtain n particle fitnesses; Determine the weight corresponding to each particle in the n particle fitnesses to obtain n weights; Adjusting the positions of the n particles and the speeds of the n particles according to the n weights to obtain n particle target positions and n particle target speeds; Solving the n particle target positions and the n particle target velocities to obtain a second particle group; the second particle group includes: positions of m particles and velocities of m particles; m is a natural number greater than 1, and m is less than or equal to n; Determine the fitness of each particle in the particle swarm according to the target optimization function and the second particle swarm to obtain m particle fitnesses; Based on the particle swarm algorithm and the fitness of the m particles, an optimal solution of the user-side resource collaborative scheduling model is determined to obtain the target calculation result.

8. A user-side resource optimization scheduling device considering virtual energy storage, characterized in that: Applied to a server, the device comprises: An acquisition unit is used to acquire output data of the distribution network in the target area within a preset time period; the output data includes: new energy output data and energy storage system output data; acquire a first electricity price, and determine a power consumption status data set and an operation status data set of user-side resources according to the first electricity price; A determination unit, configured to determine a virtual energy storage model based on a preset first constraint condition and the operating status data set; determine a distributed power supply output model based on a preset second constraint condition, the power consumption status data set and the first electricity price to obtain a power supply output model; determine a first output model based on a preset third constraint condition and the new energy output data; determine a second output model based on a preset fourth constraint condition and the energy storage system output data; A control unit, configured to construct a user-side resource collaborative scheduling model based on the virtual energy storage model, the power output model, the first output model, and the second output model; The computing unit is used to solve the user-side resource collaborative scheduling model by using a preset particle swarm algorithm to obtain a target computing result, and the target computing result is used to configure the user-side resource collaborative scheduling model.

9. A server, characterized in that: include: A processor, a memory, a communication interface, and one or more programs; The one or more programs are stored in the memory and configured to be executed by the processor, the programs comprising instructions for executing the steps in the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 7.

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