Virtual power plant multi-flexible resource coordinated control method, device, equipment and storage medium

Abstract

The present application discloses a method, device, equipment and storage medium for coordinated regulation of multiple flexible resources in a virtual power plant, and relates to the field of smart grid technology. The method for coordinated regulation of multiple flexible resources in a virtual power plant includes: obtaining an energy balance objective function, an energy storage state objective function and a user demand guarantee objective function; constructing an initial resource regulation model according to these objective functions; receiving a total regulation target sent by the virtual power plant, and setting weights for the initial resource regulation model according to the total regulation target to obtain a coordinated regulation model for multiple flexible resources; obtaining regulation instructions for each flexible resource through the coordinated regulation model for multiple flexible resources, and sending them to each flexible resource, so that the flexible resource performs power regulation according to the regulation instructions, and completes the coordinated regulation of the flexible resources. The present application realizes efficient coordinated regulation of multiple flexible resources, which can optimize the economic operation of the system and ensure the production and living needs of users.

Description

Technical Field

[0001] The present application relates to the field of smart grid technology, and in particular to methods, devices, equipment and storage media for coordinated regulation of multiple flexible resources in virtual power plants. Background Art

[0002] With the transformation of the global energy structure and the development of intelligent technology, the power system is undergoing a process of transformation from traditional centralized to distributed. As one of the key technologies for the transformation of the power system, the core of the virtual power plant is to integrate decentralized energy resources through information technology to achieve optimal allocation and efficient utilization of resources. This transformation is of great significance for improving energy efficiency, reducing costs, and enhancing the flexibility and reliability of the power grid. Especially in the face of the volatility and uncertainty of renewable energy, the virtual power plant can balance supply and demand by flexibly dispatching various resources to ensure the stable operation of the power system.

[0003] In the construction and operation of virtual power plants, the current common practice is to manage and dispatch resources such as distributed power sources, energy storage equipment and adjustable loads separately. These resources are usually controlled by different operators and lack a unified coordination mechanism. For example, distributed power sources may operate independently of the power grid, energy storage equipment may only be used for local peak-to-valley shaving, and the dispatch of adjustable loads depends on market signals or user responses. This decentralized management model leads to a lack of effective coordination between resources, making it difficult to achieve overall optimization, thus affecting the operating efficiency and economic benefits of virtual power plants.

[0004] The existing virtual power plant operation model faces several major challenges. First, due to the lack of coordinated regulation across resource types, the complementarity and synergy between resources have not been fully utilized, resulting in low energy efficiency. Second, the existing scheduling methods are often unable to flexibly respond to rapid changes in grid operation, such as demand fluctuations or unstable output of renewable energy, which limits the ability of virtual power plants to ensure the stability of power supply. In addition, the user-side participation is limited, and there is a lack of effective incentive mechanisms to mobilize the potential of user-side resources, such as demand response. These problems limit virtual power plants in realizing their potential to optimize power resource allocation and improve system operation efficiency. Therefore, how to achieve efficient coordinated regulation of multiple flexible resources in virtual power plants to optimize the economic operation of the system and ensure the basic production and living needs of users has become an urgent problem to be solved.

[0005] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0006] The purpose of this application is to provide a method, device, equipment and storage medium for coordinated regulation of multiple flexible resources in a virtual power plant, aiming to solve the technical problem of how to achieve efficient coordinated regulation of multiple flexible resources in a virtual power plant to optimize the economic operation of the system and ensure the basic production and living needs of users.

[0007] To achieve the above objectives, the present application proposes a method for coordinated regulation of multiple flexible resources of a virtual power plant, the method comprising: Obtain energy balance objective function, energy storage state objective function and user demand guarantee objective function; Constructing an initial resource regulation model according to the energy balance objective function, the energy storage state objective function and the user demand guarantee objective function; Receiving the overall control target sent by the virtual power plant, and setting weights for the initial resource control model according to the overall control target to obtain a multivariate flexible resource collaborative control model; Obtaining control instructions for each flexible resource through the multi-flexible resource collaborative control model; The control instruction is sent to each of the flexible resources, so that the flexible resources perform power control according to the control instruction, thereby completing the coordinated control of the flexible resources.

[0008] In one embodiment, the step of obtaining the control instructions of each flexible resource through the multi-element flexible resource collaborative control model includes: Obtain energy balance constraints, energy storage state constraints, adjustable load constraints, and power system loss constraints; Constructing a logarithmic barrier function according to the multivariate flexible resource coordinated regulation model and the energy balance constraint, the energy storage state constraint, the adjustable load constraint and the power system loss constraint, wherein the logarithmic barrier function includes a barrier parameter; Calculating the gradient of the multivariate flexible resource collaborative regulation model and the logarithmic barrier function to obtain a gradient vector; Calculating the second-order derivative matrix of the multivariate flexible resource collaborative regulation model; The obstacle parameters are adjusted according to the gradient vector and the second-order derivative matrix until the value of the logarithmic obstacle function is less than or equal to a preset penalty threshold, and control instructions for each flexible resource are obtained, wherein the control instructions include power output instructions for distributed power sources, charging power instructions and discharging power instructions for energy storage systems, and regulating power instructions for adjustable loads.

[0009] In one embodiment, the step of obtaining energy balance constraints, energy storage state constraints, adjustable load constraints, and power system loss constraints includes: Obtain distributed voltage generation power, energy storage system charging power, energy storage system discharging power, user load demand and transmission loss; Determine an energy balance constraint condition according to the distributed voltage generation power, the energy storage system charging power, the energy storage system discharging power and the user load demand; Determine energy storage state constraint conditions according to the energy storage system charging power and the energy storage system discharging power; Determining an adjustable load constraint condition according to the user load demand; A power system loss constraint condition is determined according to the transmission loss.

[0010] In one embodiment, the step of obtaining the energy balance objective function includes: Obtain the power generation of distributed generation, the discharge of energy storage system, the adjustable load of virtual power plant, residential power demand, commercial power demand, industrial power demand and power system loss; Obtaining the total power generation of the virtual power plant according to the power generation of the distributed power source, the discharge of the energy storage system and the adjustable load; Obtaining the total demand of the virtual power plant according to the residential electricity demand, the commercial electricity demand, the industrial electricity demand and the power system loss; An energy balance objective function is established according to the total power generation and the total demand.

[0011] In one embodiment, the step of obtaining the energy storage state objective function includes: Acquire the capacity of the energy storage system, historical charging data, historical discharging data, first current data and first voltage data at the input end of the energy storage system, and second current data and second voltage data at the output end of the energy storage system; Calculate the charging power of the energy storage system according to the first current data and the first voltage data; Calculate the energy storage system discharge power according to the second current data and the second voltage data; Determine the energy storage system constraint condition according to the energy storage system charging power, the energy storage system discharging power and the energy storage system capacity; Simulating the historical charging data and the historical discharging data to determine an energy storage state weight coefficient; The energy storage state objective function is established according to the energy storage system charging power, the energy storage system discharging power, the energy storage system constraint conditions and the energy storage state weight coefficient.

[0012] In one embodiment, the step of obtaining the user demand assurance objective function includes: Obtain user category priority, total power generation of virtual power plants, user historical power consumption data, non-critical electrical equipment information, and user comfort requirements; Performing statistical analysis, load curve analysis, and seasonal analysis on the user's historical electricity consumption data to identify the user's electricity consumption pattern; Obtaining the user's basic load demand according to the user's historical power consumption data and the user's power consumption pattern; Determine the power consumption data of non-critical electrical equipment according to the user's historical power consumption data and the non-critical electrical equipment information; Obtaining the user's adjustable load demand according to the user's comfort requirements and the power consumption data of the non-critical electrical equipment; Simulating the user's historical electricity consumption data according to the user category priority to determine the user demand guarantee weight coefficient; The user demand guarantee objective function is established according to the user's basic load demand, the user's adjustable load demand, the total power generation of the virtual power plant and the user demand guarantee weight coefficient.

[0013] In one embodiment, the step of receiving the overall control target sent by the virtual power plant and setting weights for the initial resource control model according to the overall control target to obtain the multivariate flexible resource collaborative control model includes: Receive the total control target sent by the virtual power plant; Analyze the overall control target to obtain an energy balance control target, an energy storage state control target, and a user demand guarantee control target; Determine a balance weight, an energy storage state weight, and a user demand weight according to the importance levels of the energy balance control target, the energy storage state control target, and the user demand guarantee control target; The weights of the initial resource regulation model are set according to the balance weight, the energy storage state weight and the user demand weight to obtain a multivariate flexible resource collaborative regulation model.

[0014] In one embodiment, after the step of sending the control instruction to each of the flexible resources so that the flexible resources perform power control according to the control instruction and completing the step of collaboratively controlling the flexible resources, the method further includes: Monitoring the input power and output power of the flexible resource to obtain monitoring results; The weight of the multi-flexible resource collaborative regulation model is adjusted according to the monitoring result and the overall regulation target, and the step of obtaining the regulation instructions of each flexible resource through the multi-flexible resource collaborative regulation model is returned.

[0015] In addition, to achieve the above-mentioned purpose, the present application also proposes a virtual power plant multi-flexible resource coordinated control device, the device comprising: A data acquisition module is used to obtain an energy balance objective function, an energy storage state objective function, and a user demand guarantee objective function; A model building module, used to build an initial resource regulation model according to the energy balance objective function, the energy storage state objective function and the user demand guarantee objective function; A weight setting module, used for receiving the overall control target sent by the virtual power plant, and setting weights for the initial resource control model according to the overall control target to obtain a multivariate flexible resource collaborative control model; A control instruction module, used to obtain control instructions for each flexible resource through the multi-flexible resource collaborative control model; The sending module is used to send the control instruction to each of the flexible resources, so that the flexible resources perform power control according to the control instruction, thereby completing the coordinated control of the flexible resources.

[0016] In addition, to achieve the above-mentioned purpose, the present application also proposes a device for coordinated regulation of multiple flexible resources in a virtual power plant, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the method for coordinated regulation of multiple flexible resources in a virtual power plant as described above.

[0017] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the method for coordinated control of multiple flexible resources of a virtual power plant as described above are implemented.

[0018] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the method for coordinated control of multiple flexible resources of a virtual power plant as described above.

[0019] One or more technical solutions proposed in this application have at least the following technical effects: Obtain the energy balance objective function, the energy storage state objective function and the user demand guarantee objective function; construct an initial resource control model based on the energy balance objective function, the energy storage state objective function and the user demand guarantee objective function; receive the total control target sent by the virtual power plant, and set the weight for the initial resource control model according to the total control target to obtain a multivariate flexible resource collaborative control model; obtain the control instructions of each flexible resource through the multivariate flexible resource collaborative control model; send the control instructions to each flexible resource so that the flexible resource performs power control according to the control instructions to complete the collaborative control of the flexible resources. First, obtain the energy balance objective function, the energy storage state objective function and the user demand guarantee objective function, which respectively ensure the balance of power supply and demand, the management of energy storage equipment status and the satisfaction of user electricity demand, and provide a basis for formulating a comprehensive control strategy. Then, construct an initial resource control model based on these objective functions, simulate and predict the effects of different control strategies, and provide a scientific basis for evaluation and optimization. Then, the total control target of the virtual power plant is received, and weights are set for each objective function in the model accordingly, forming a multi-flexible resource coordinated control model, so that the model can be flexibly adjusted according to actual conditions and better adapt to the dynamic changes and operational needs of the power grid. Through this model, specific control instructions for each flexible resource are obtained, which guide the resources on how to adjust power output or consumption to ensure the scientificity and effectiveness of the control strategy. Finally, the control instructions are sent to each flexible resource to guide them to make actual power adjustments to achieve optimal resource allocation and coordinated control. This process realizes the efficient coordinated control of multiple flexible resources in the virtual power plant, which can optimize the economic operation of the system and guarantee the basic production and living needs of users. It not only improves energy utilization efficiency, reduces operating costs, and enhances the stability and reliability of the power grid, but also reduces carbon emissions and promotes environmental sustainability by optimizing the use of renewable energy. At the same time, it gives priority to meeting the basic electricity needs of users, improves user satisfaction and quality of life, and enables virtual power plants to flexibly adapt to market and environmental changes and maximize economic and social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0022] Figure 1A flow chart of the first embodiment of the method for coordinated control of multiple flexible resources of a virtual power plant of the present application; Figure 2 A schematic diagram of the smart energy terminal system architecture provided in Example 1 of the method for coordinated control of multiple flexible resources of a virtual power plant of this application; Figure 3 A schematic diagram of the connection relationship between the smart energy terminal, the virtual power grid and the flexible resources provided in Example 1 of the method for coordinated control of multiple flexible resources of a virtual power plant of the present application; Figure 4 A flow chart of the second embodiment of the method for coordinated control of multiple flexible resources of a virtual power plant provided in this application; Figure 5 This is a schematic diagram of the module structure of a multi-flexible resource coordinated control device of a virtual power plant according to an embodiment of the present application; Figure 6 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the method for coordinated control of multiple flexible resources of a virtual power plant in an embodiment of the present application.

[0023] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0024] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0025] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0026] With the transformation of energy structure and the development of intelligent technology, the power system is changing from centralized to distributed. Virtual power plants, as a key technology, integrate decentralized energy resources through information technology, optimize configuration and efficient utilization to improve energy efficiency, reduce costs, and enhance grid flexibility and reliability. However, in the current operation of virtual power plants, resources such as distributed power sources, energy storage equipment and adjustable loads are independently managed by different operators, lacking unified coordination, resulting in insufficient resource coordination and difficulty in overall optimization, affecting operational efficiency and economic benefits. The main challenges faced by the existing model include insufficient coordination between resources, poor scheduling flexibility and low user participation, which limits the potential of virtual power plants in optimizing resource allocation and improving system operating efficiency.

[0027] The main solution of the embodiment of the present application is: first, obtain the objective functions of energy balance, energy storage status and user demand guarantee to ensure the balance of power supply and demand, energy storage equipment management and user demand satisfaction, and lay the foundation for formulating control strategies. Then, build an initial resource control model based on these objective functions to simulate the effects of different strategies and provide a basis for evaluation and optimization. Then, receive the overall control target of the virtual power plant, set weights for each objective function, and form a multi-element flexible resource collaborative control model to adapt to dynamic changes in the power grid. Generate specific control instructions through the model to guide each resource to adjust power output or consumption to ensure the effectiveness of the strategy. Finally, send the control instructions to each resource to achieve optimal configuration and collaborative control.

[0028] It should be noted that the execution subject of the embodiment of the present application may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a smart energy terminal system, etc. The following takes the smart energy terminal system as an example to illustrate this embodiment and the following embodiments.

[0029] Based on this, the embodiment of the present application provides a method for coordinated control of multiple flexible resources of a virtual power plant, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the method for coordinated control of multiple flexible resources of a virtual power plant in the present application.

[0030] In this embodiment, the virtual power plant multi-flexible resource coordinated control method includes steps S10 to S50: Step S10, obtaining an energy balance objective function, an energy storage state objective function, and a user demand guarantee objective function; It should be noted that the energy balance objective function refers to the objective function that ensures that the energy supply and demand within the power system are balanced. In the scenario of a virtual power plant, this means that by optimizing the scheduling of various energy resources (such as distributed generation, energy storage systems and adjustable loads), the power supply is matched with the demand to avoid oversupply or undersupply. This objective function aims to minimize the energy imbalance of the power system and improve the operating efficiency and economy of the system. The energy storage state objective function refers to the objective function that optimizes the charging and discharging state of the energy storage device. This function focuses on how to reasonably arrange the working state of the energy storage device while meeting the energy balance requirements, including the time and intensity of charging and discharging, so as to maintain the health of the energy storage device and extend its service life. It also considers the cost-effectiveness of the energy storage device. Specifically, the energy storage state objective function aims to maximize the efficiency of the energy storage system while ensuring that it can effectively respond to emergencies in the power system, such as fluctuations in renewable energy output. The user demand guarantee objective function refers to the objective function that ensures that the user's power demand is effectively met. This objective function emphasizes the quality and reliability of power supply from the user's perspective, including but not limited to power continuity and voltage quality. In the application of virtual power plants, the user demand guarantee objective function aims to ensure that users can obtain the required electricity services under any circumstances by flexibly scheduling various resources, while minimizing the impact on users, such as avoiding unplanned power outages. In addition, this objective function may also involve the rational use of user-side resources, such as encouraging users to participate in demand response programs to further optimize the operation of the power system.

[0031] It can be understood that, firstly, the energy balance objective function is to collect and analyze the real-time output data and predicted power demand of all energy resources in the virtual power plant, and establish a mathematical model to optimize resource allocation. This is done to ensure that the total power generation of the virtual power plant can match the total load demand at any time, avoid excess or insufficient power, improve the stability and reliability of the power grid, and reduce energy waste caused by supply and demand mismatch; secondly, the energy storage state objective function is to monitor the real-time SOC of the energy storage equipment, and formulate a charging and discharging strategy in combination with its charging and discharging efficiency, cost and safety restrictions. The purpose of this is to extend the service life of the energy storage equipment and reduce operating costs while meeting the needs of the power grid, thereby improving energy utilization efficiency and economic benefits; finally, the user demand guarantee objective function is to analyze the user's electricity usage habits and priorities, and formulate a scheduling strategy that can both meet user needs and improve energy efficiency. This is done to achieve the optimal configuration of energy resources while ensuring the normal power consumption of users, improve user satisfaction and the economic benefits of the virtual power plant, and reduce the impact on the environment. Through these three steps, the virtual power plant can achieve efficient coordinated regulation of multiple flexible resources, optimize the economic operation of the system and ensure the basic production and living needs of users.

[0032] As an example, the steps of obtaining an energy balance objective function include: obtaining the power generation of distributed power sources, the discharge of an energy storage system, the adjustable load of a virtual power plant, residential electricity demand, commercial electricity demand, industrial electricity demand, and power system losses; obtaining the total power generation of the virtual power plant based on the power generation of the distributed power sources, the discharge of the energy storage system, and the adjustable load; obtaining the total demand of the virtual power plant based on the residential electricity demand, the commercial electricity demand, the industrial electricity demand, and the power system losses; and establishing an energy balance objective function based on the total power generation and the total demand.

[0033] Distributed generation refers to the amount of electricity generated by small-scale power generation facilities scattered across the grid, such as rooftop solar panels, small wind farms, or micro hydropower stations. These power sources are usually small in scale, but can be flexibly deployed near the power demand, reducing the loss of long-distance power transmission. The discharge of energy storage systems refers to the amount of electricity released by energy storage systems (such as batteries) within a certain period of time. Energy storage systems can release energy during peak power demand or store energy during low power demand to balance the supply and demand of the grid. Virtual power plant adjustable loads involve loads that can flexibly adjust power consumption patterns. For example, certain industrial production processes can be operated during off-peak hours, or smart home systems can automatically adjust the operation of household appliances based on electricity price signals. Residential electricity demand refers to the electricity consumption of ordinary households in daily life, including lighting, household appliances, heating and cooling, etc., and the demand varies according to the season, weather, and the behavior patterns of residents. Commercial electricity demand involves the electricity consumption of commercial facilities such as shops, office buildings, and hotels, which usually includes lighting, air conditioning, electronic equipment, and commercial machinery. Industrial electricity demand involves electricity consumption in industrial fields such as manufacturing and processing industries, which usually includes production machinery, processing equipment and factory operations. The power system loss refers to the process of transmitting electricity from power plants to end users. Due to factors such as resistance and transformer efficiency, there will always be a part of the power loss. This part of the loss needs to be calculated to ensure the balance of supply and demand in the power grid. Virtual power plants are a new way of managing power resources. Through advanced information and communication technologies and software platforms, they integrate decentralized power generation resources, energy storage equipment and adjustable loads to form a coordinated power supply network. Virtual power plants can improve the flexibility and reliability of the power grid while reducing operating costs. The total power generation refers to the sum of the power generated by all power generation resources of the virtual power plant in a specific time period. This total amount needs to match the total demand of the power grid to ensure the stability of power supply. The total demand refers to all power demands that the virtual power plant needs to meet, including the power demand of residential, commercial and industrial users, as well as the losses in the power transmission process. This total amount is the basis for the virtual power plant to formulate power generation and dispatching plans.

[0034] Let P g is the total power generation, P d is the total power demand, P loss is the power system loss, the energy balance objective function can be expressed as:

[0035] The goal of this function is to ensure that the power supply is equal to the demand, while taking into account the power system losses, trying to minimize the square of the difference between the power supply and the demand plus the system losses to achieve a balance between supply and demand.

[0036] First, the system needs to collect specific data from each component of the virtual power plant, including the real-time power generation of distributed power sources, which is achieved by monitoring the output of equipment such as solar panels and wind turbines in order to understand the current power supply capacity of renewable energy; at the same time, the system also needs to obtain the discharge of the energy storage system, which is completed by monitoring the charging and discharging status of the battery energy storage system to evaluate the contribution and regulation capacity of the energy storage equipment to the power grid; in addition, it is necessary to collect data on the adjustable load of the virtual power plant, which involves negotiating with industrial and commercial users to understand the amount of electricity load they can adjust, in order to reduce electricity consumption during peak demand or increase electricity consumption during low demand to balance the load on the power grid. Secondly, the system adds the collected distributed power generation, energy storage system discharge and adjustable load data to calculate the total power generation of the virtual power plant. This step is to obtain a total power supply capacity indicator, which reflects the total amount of electricity that the virtual power plant can provide to the power grid during a specific period of time, which is crucial to ensuring the adequacy and stability of power supply. Then, the system calculates the total demand and adds the residential, commercial and industrial electricity demand to the power system loss. This step involves forecasting the electricity demand of different user groups and estimating power losses in order to obtain a total power demand indicator, which indicates the total amount of electricity that the virtual power plant needs to meet, which is critical to ensuring the match between power supply and demand and the economic operation of the power grid. Finally, the system establishes an energy balance objective function based on the calculated total power generation and total demand. The goal of this function is to minimize the difference between total power generation and total demand, and to achieve a balance between power supply and demand by optimizing resource allocation and scheduling strategies. Doing so can improve the operating efficiency of the power grid, reduce energy waste and power grid instability caused by supply and demand mismatch, and ultimately achieve a more economical and environmentally friendly power system operation.

[0037] Please refer to Figure 2 , Figure 2A schematic diagram of the smart energy terminal system architecture provided for the first embodiment of the method for collaborative control of multiple flexible resources in a virtual power plant of this application shows the integration and operation of the smart energy terminal in the virtual power plant platform, and how it is coordinated with multiple flexible resources. In this architecture, the virtual power plant platform is responsible for the formulation of the overall control target. As a key node connecting the virtual power plant platform with the underlying resources, the smart energy terminal receives the overall control target from the virtual power plant platform and generates control instructions for each flexible resource according to the overall control target. These instructions are then decomposed and communicated to the connected distributed power sources, energy storage systems, and adjustable loads to achieve precise power control. The smart energy terminal ensures that the output of distributed power sources such as solar energy and wind energy matches the grid demand through real-time monitoring and data analysis, while optimizing the charging and discharging plan of the energy storage system to improve energy utilization efficiency and grid stability. In addition, the smart energy terminal is also responsible for adjusting adjustable loads, such as non-critical equipment in industrial production processes or air conditioning systems in commercial buildings, to respond to the real-time needs of the grid and achieve demand-side management. Through this structure, the virtual power plant can achieve efficient coordinated regulation of diverse and flexible resources, which not only improves the economic operation efficiency of the power system, but also enhances its adaptability to fluctuations in renewable energy, ensures the basic production and living needs of users, and achieves stability and reliability of power supply.

[0038] Please refer to Figure 3 , Figure 3A schematic diagram of the connection relationship between the smart energy terminal, the virtual power grid and the flexible resources provided in the first embodiment of the method for collaborative control of multiple flexible resources of the virtual power plant of this application shows the location of the smart energy terminal in the virtual power plant system and its connection relationship with the power grid operator, the power trading platform, and various flexible resources. The virtual power plant platform is located in the cloud and is responsible for receiving and processing data from the power grid operator and the power trading platform as the brain of the entire system, while formulating the overall control target. The smart energy terminal is integrated in the cluster, and the control instructions of each flexible resource are obtained according to the overall control target. These clusters are components of the virtual power plant, and each cluster may be connected to different flexible resources, such as distributed power generation equipment (such as solar panels and wind turbines), energy storage systems (such as battery energy storage), controllable loads (such as industrial equipment and commercial air conditioning systems) and electric vehicles. The solid line in the figure represents the energy flow, showing the transmission path of electricity in the power grid, from distributed power generation equipment to the power grid, or from the power grid to energy storage equipment and electric vehicles. The dotted line represents the information flow, indicating the transmission of data and control instructions between the virtual power plant platform, the smart energy terminal and each cluster. The smart energy terminal is connected to the virtual power plant platform through the communication network, receives the overall control target issued by the platform, obtains the control instructions for each flexible resource, and then sends these instructions to the flexible resources. The flexible resources perform power control according to the instructions to ensure energy balance, energy storage status and user demand guarantee. In addition, the smart energy terminal is also responsible for monitoring the real-time status of each flexible resource in the cluster and feeding this information back to the virtual power plant platform so that the platform can adjust the control target based on real-time data. This architecture enables the virtual power plant to achieve efficient and coordinated control of multiple flexible resources, improve the stability and economy of the power system, meet the electricity needs of users, and achieve reliability of power supply and environmental sustainability. Through this integration and coordinated control, the virtual power plant can respond more flexibly to changes in the power grid, improve energy utilization efficiency, reduce operating costs, and enhance its adaptability to fluctuations in renewable energy.

[0039] As an example, the step of obtaining the energy storage state objective function includes: obtaining the capacity of the energy storage system, historical charging data, historical discharging data, first current data and first voltage data at the input end of the energy storage system, and second current data and second voltage data at the output end of the energy storage system; calculating the charging power of the energy storage system according to the first current data and the first voltage data; calculating the discharging power of the energy storage system according to the second current data and the second voltage data; determining the energy storage system constraints according to the energy storage system charging power, the energy storage system discharging power and the energy storage system capacity; simulating the historical charging data and the historical discharging data to determine the energy storage state weight coefficient; establishing the energy storage state objective function according to the energy storage system charging power, the energy storage system discharging power, the energy storage system constraints and the energy storage state weight coefficient.

[0040] The capacity of an energy storage system refers to the maximum amount of electrical energy that an energy storage device can store, usually in kilowatt-hours (kWh), which is a key parameter for measuring the scale and storage capacity of an energy storage system. Historical charging data refers to the record of the charging process of the energy storage system over the past period of time, including the time, amount and conditions of each charge, etc. These data are helpful for analyzing the charging behavior and efficiency of the energy storage system. Historical discharge data refers to the record of the discharge process of the energy storage system over the past period of time, including the time, amount and conditions of each discharge, etc. These data are helpful for analyzing the discharge behavior and efficiency of the energy storage system. The input end of the energy storage system refers to the interface through which the energy storage system receives electrical energy, and the electrical energy is stored in the energy storage device through this interface. The first current data refers to the current reading at the input end of the energy storage system during the charging process, which is one of the important parameters for calculating the charging power. The first voltage data refers to the voltage reading at the input end of the energy storage system during the charging process, and is used together with the first current data to calculate the charging power. The output end of the energy storage system refers to the interface through which the energy storage system releases electrical energy, and the electrical energy is released from the energy storage device through this interface. The second current data refers to the current reading at the output end of the energy storage system during the discharge process, which is one of the important parameters for calculating the discharge power. The second voltage data refers to the voltage reading at the output end of the energy storage system during the discharge process, which is used together with the second current data to calculate the discharge power. The charging power of the energy storage system refers to the rate at which the energy storage system stores electrical energy per unit time, which is calculated by the first current data and the first voltage data, and is usually measured in kilowatts (kW). The discharge power of the energy storage system refers to the rate at which the energy storage system releases electrical energy per unit time, which is calculated by the second current data and the second voltage data, and is usually measured in kilowatts (kW). The constraints of the energy storage system refer to the restrictions that the energy storage system needs to comply with during operation, including but not limited to maximum / minimum charging / discharging power, maximum / minimum SOC, temperature range, etc. These conditions ensure that the energy storage system operates safely and efficiently. Simulation refers to the use of computer models to simulate the historical charging and discharging process of the energy storage system. In this way, the behavior of the energy storage system under different conditions in the future can be predicted, providing a basis for optimizing the control strategy. The energy storage state weight coefficient refers to the coefficient determined during the simulation based on historical charging and discharging data. It is used to adjust the relative importance of the charging and discharging states in the objective function to reflect different operating objectives and constraints.

[0041] Let SOC be the state of charge of the energy storage device, SOC min and SOC max are the minimum and maximum charging states respectively, and the energy storage state objective function can be expressed as:

[0042] Among them, SOC optis the target state of charge of the energy storage device. The function goal is to keep the state of charge (SOC) of the energy storage device within a safe and effective range, trying to adjust the state of charge of the energy storage device to the optimal state to maintain the best performance and life of the energy storage device.

[0043] First, the capacity of the energy storage system is obtained by directly reading the technical specification manual and monitoring equipment of the energy storage system. This is to understand the maximum storage capacity of the energy storage system. At the same time, historical charging data and historical discharging data are collected, including the time, power and conditions of each charging and discharging. This is done to analyze the usage mode and performance changes of the energy storage system in order to predict future charging and discharging behaviors. Secondly, the current and voltage monitoring equipment at the input end of the energy storage system is used to obtain the first current data and the first voltage data in real time, and then the charging power of the energy storage system is calculated according to the formula P=IV (power=current×voltage); similarly, for the output end, the second current data and the second voltage data are obtained, and the discharge power is calculated. The purpose of this is to accurately grasp the energy flow of the energy storage system under different states and provide accurate data support for subsequent optimization. Then, based on the calculated charging and discharging power and the maximum capacity of the energy storage system, the constraints that the energy storage system must comply with during the charging and discharging process are determined, such as the maximum charging and discharging current, voltage range and SOC limit. This is done to ensure that the energy storage system will not be damaged due to exceeding the safe operating range, while ensuring the stable operation of the system. Finally, the historical charging and discharging data are simulated through simulation software to determine the energy storage state weight coefficient, which reflects the relative importance of the charging and discharging states when optimizing the objective function. This can help us adjust the optimization strategy according to the actual operation situation, so that the energy storage system can extend its service life while meeting the needs of the power grid. Finally, combining all the above data and parameters, an energy storage state objective function is established. This function will guide the daily operation of the energy storage system, so that it can maintain high efficiency and long life while meeting the grid regulation needs, thereby improving the economic benefits and reliability of the entire power system.

[0044] As an example, the steps of obtaining the user demand guarantee objective function include: obtaining user category priority, total power generation of a virtual power plant, user historical power consumption data, non-critical electrical equipment information, and user comfort requirements; performing statistical analysis, load curve analysis, and seasonal analysis on the user's historical power consumption data to identify the user's power consumption pattern; obtaining the user's basic load demand based on the user's historical power consumption data and the user's power consumption pattern; determining the non-critical electrical equipment power consumption data based on the user's historical power consumption data and the non-critical electrical equipment information; obtaining the user's adjustable load demand based on the user's comfort requirements and the non-critical electrical equipment power consumption data; simulating the user's historical power consumption data based on the user category priority to determine the user demand guarantee weight coefficient; and establishing the user demand guarantee objective function based on the user's basic load demand, the user's adjustable load demand, the total power generation of the virtual power plant, and the user demand guarantee weight coefficient.

[0045] User category priority refers to the order in which the electricity demand of different user categories (residential, commercial, and industrial) is met first when the power supply is tight or load management is required. Here, residential demand is given the highest priority, followed by commercial demand, and finally industrial demand. This is done to prioritize the basic living electricity consumption of residents under limited resources. The total power generation of a virtual power plant refers to the sum of the power generated by all power generation resources of a virtual power plant in a specific time period, including the power generation of distributed power sources, the discharge of energy storage systems, and the power generation of adjustable loads. This total amount is the amount of power that a virtual power plant can supply, which is crucial to ensuring the balance of power supply and demand. User historical power consumption data refers to the user's power consumption record in the past period of time, including information such as power consumption, power consumption time, and power consumption equipment. These data help analyze the user's power consumption habits and patterns. Non-critical electrical equipment information refers to the information of electrical equipment that can be temporarily shut down or reduced when the power supply is tight, such as non-essential equipment such as air conditioners and water heaters. Understanding these equipment helps adjust the power load without affecting the basic needs of users. User comfort requirements refer to the user's comfort requirements for the indoor environment (such as temperature, humidity, etc.), which usually affects the use of non-critical electrical equipment. Understanding these requirements helps to reasonably adjust the power load while ensuring user comfort. Statistical analysis refers to the use of mathematical methods to analyze the user's historical power consumption data to identify the regularity and trend in the power consumption data, providing a basis for subsequent load forecasting and power consumption pattern identification. Load curve analysis refers to the analysis of the curve of the user's power load changing over time. Through this analysis, peak and trough periods of power consumption, as well as fluctuations in power load, can be identified. Seasonal analysis refers to the analysis of the characteristics of user power consumption patterns changing with the seasons, because users' power demand and power consumption habits may vary in different seasons. User power consumption patterns refer to the rules of users' power consumption habits and behaviors in a certain period of time, including power consumption time, power consumption equipment and power consumption. Identifying power consumption patterns helps to better predict and meet users' power demand. User basic load demand refers to the minimum power demand required by users to ensure basic life or production activities. This is the power demand that needs to be prioritized in any case. The electricity consumption data of non-critical electrical equipment refers to the electricity consumption data of electrical equipment that can be temporarily reduced or shut down when the power supply is tight. Understanding this data helps to adjust the power load without affecting the basic needs of users. User adjustable load demand refers to the electricity demand that users can adjust according to their own comfort requirements and the use of non-critical electrical equipment after meeting their basic electricity needs. This helps to balance the supply and demand of electricity through demand-side management when the power supply is tight. The user demand guarantee weight coefficient refers to the weight assigned to different user demands based on user category priority and power consumption mode when establishing the user demand guarantee objective function. This coefficient helps to reasonably allocate power resources when power resources are limited, and give priority to the needs of high-priority users.

[0046] Let P res is the remaining power demand of the adjustable load, P crit Power demand for critical loads.

[0047] The user demand assurance objective function can be expressed as:

[0048] The goal of this function is to give priority to meeting users' electricity demands, especially critical demands, and try to minimize the square of the difference between the remaining power demand of adjustable loads and the power demand of critical loads to ensure that critical user demands are met first.

[0049] First, by setting user category priorities, that is, residential demand takes precedence over commercial demand, and commercial demand takes precedence over industrial demand, this is done to allocate electricity according to importance when power resources are tight, ensuring that residents' basic electricity consumption is not affected while maintaining the continuity of commercial and industrial activities. Secondly, collect the total power generation data of the virtual power plant, which includes the power generation data of all distributed power sources, energy storage systems and adjustable loads. Specifically, it is necessary to collect real-time power generation data from each distributed power source (such as wind power generation, solar photovoltaic, etc.), which is usually achieved through sensors and meters installed on power generation equipment, which can provide accurate power and energy readings. At the same time, the charging and discharging status of the energy storage system also needs to be tracked through similar monitoring equipment to obtain its discharge data. For adjustable loads, such as industrial or commercial loads in demand response projects, it is necessary to coordinate with users to obtain their adjustable load through smart meters or users' own energy management systems. These data are transmitted to the central control system of the virtual power plant through the communication network, and the system summarizes and analyzes them to determine the total power generation. The purpose is to accurately understand the current and predict future power supply capacity so as to match it with power demand and ensure the stability and reliability of power supply. Then, the user's historical electricity consumption data is deeply analyzed, including statistical analysis, load curve analysis and seasonal analysis, which includes the following steps: (1) Statistical analysis: By collecting the user's electricity consumption data in a certain period, statistical methods are used to identify the average level, peak and valley values ​​of electricity consumption, as well as the growth or decline trend of electricity consumption. (2) Load curve analysis: Analyze the pattern of user electricity consumption changes over time, draw daily load curves and weekly load curves to identify peak and valley periods of electricity consumption, as well as any periodic electricity consumption patterns. (3) Seasonal analysis: Considering the impact of seasonal changes on electricity consumption, analyze electricity consumption data in different seasons to identify seasonal electricity consumption patterns and demand changes. (4) Electricity consumption pattern identification: Combine the results of statistical analysis, load curve analysis and seasonal analysis to identify the user's electricity consumption pattern, such as which time periods are peak electricity consumption periods and which electrical equipment is frequently used in a specific time period. (5) Determination of user basic load demand: Based on the analysis results, determine the user's basic electricity demand in different time periods, that is, the minimum electricity consumption required by the user to ensure basic living or production activities. (6) Determination of electricity consumption data of non-critical electrical equipment: Analyze the electricity consumption of non-critical electrical equipment in the user's historical electricity consumption data, and determine the electrical equipment and its electricity consumption that can be reduced or suspended when the power supply is tight. (7) Calculation of user adjustable load demand: Combine the user's comfort requirements and the electricity consumption data of non-critical electrical equipment to calculate the user's adjustable electricity load under different circumstances. This helps to balance the supply and demand of electricity through demand-side management without affecting the user's basic needs.These analyses help us identify the user's electricity consumption patterns and habits, such as which periods of time have higher electricity consumption and which electrical equipment consumes more electricity. This can more accurately predict future electricity demand and provide a basis for power dispatching. Then, based on the user's historical electricity consumption data and electricity consumption patterns, the user's basic load demand is determined, that is, the minimum electricity consumption that the user needs to guarantee under any circumstances. This is done to give priority to meeting these key needs during power dispatching and ensure the user's basic quality of life and production activities. Then, combined with the user's historical electricity consumption data and non-critical electrical equipment information, the electricity consumption data of non-critical electrical equipment is determined. This can balance supply and demand by adjusting the electricity consumption of these non-critical equipment when the power supply is tight, reducing the impact on critical electricity demand. Then, based on the user's comfort requirements and the electricity consumption data of non-critical electrical equipment, the user's adjustable load demand is calculated. This can flexibly adjust the electricity load through demand response and other measures without sacrificing user comfort, thereby improving the regulation capacity of the power system. Finally, the historical electricity consumption data of users is simulated according to the priority of user categories to determine the weight coefficient of user demand protection. This coefficient reflects the relative importance of different user categories in the allocation of power resources. This can ensure that power resources are reasonably allocated when power resources are limited, and the needs of high-priority users are prioritized. Finally, the user demand protection objective function is established by integrating the basic load demand of users, the adjustable load demand of users, the total power generation of virtual power plants, and the weight coefficient of user demand protection. This function aims to optimize the allocation of power resources, ensure that while meeting the basic power demand of users, the power supply and demand are balanced by adjusting the adjustable load, and improve the economy and reliability of the power system.

[0050] Step S20, constructing an initial resource regulation model according to the energy balance objective function, the energy storage state objective function and the user demand guarantee objective function; It should be noted that the initial resource regulation model refers to a mathematical model constructed in order to achieve multiple goals in the operation and management of the virtual power plant. This model comprehensively considers the three objective functions of energy balance, energy storage status and user demand guarantee, and aims to achieve the following goals by optimizing resource allocation and scheduling strategies: (1) Energy balance goal: Ensure the balance between the total power generation and total demand of the virtual power plant, that is, the power supply must meet the user's power demand, while taking into account the loss of the power system to maintain the stability and reliability of the power grid. (2) Energy storage status goal: Manage the charging and discharging state of the energy storage system to ensure that the state (such as SOC) of the energy storage equipment remains within a safe and effective range while meeting the grid regulation needs, so as to extend the service life of the energy storage equipment and improve its economic benefits. (3) User demand guarantee goal: In the allocation of power resources, give priority to the power demand of users, especially the power priority of different categories of users such as residential, commercial and industrial users, and adjust the power consumption of non-critical electrical equipment while meeting the basic power demand to achieve demand-side management. The initial resource regulation model integrates these objective functions to form a multi-objective optimization problem. The solution of the model needs to find an optimal balance between these objectives to achieve overall optimization.

[0051] It can be understood that, first of all, the specific expressions and constraints of the energy balance objective function, the energy storage state objective function and the user demand guarantee objective function are integrated into a multi-objective programming model in order to achieve a balance between power supply and demand, optimize the operating state of the energy storage equipment and meet the user's electricity demand, so as to achieve comprehensive optimization of the power system. By defining decision variables, such as power generation plan, energy storage charging and discharging strategy, load adjustment, etc., and setting goals and constraints for each objective function, such as power generation capacity limit, energy storage capacity limit, and user electricity demand limit. Then, use the optimization algorithm to solve this multi-objective programming model, select a suitable algorithm, such as linear programming, mixed integer programming or heuristic algorithm, etc., find a set of decision variable values, and optimize each objective function as much as possible while satisfying all constraints. The purpose is to find a feasible solution in actual operation, which can simultaneously meet the stability of power supply, the rational use of energy storage equipment and the priority of user electricity demand. Finally, based on the results obtained by the optimization algorithm, a set of resource control strategies is formulated, including specific power generation plans, charging and discharging schedules for energy storage equipment, and load adjustment plans. This can improve the efficiency of energy storage equipment while ensuring the stable operation of the power system, and give priority to meeting users' electricity needs, especially the basic electricity needs of residential users, to achieve efficient allocation and scheduling of power resources, and improve the economy, reliability, and environmental friendliness of the entire power system.

[0052] Step S30, receiving the overall control target sent by the virtual power plant, and setting weights for the initial resource control model according to the overall control target to obtain a multivariate flexible resource collaborative control model; It should be noted that the total control target refers to the overall control index set by the virtual power plant in order to achieve its operating goals. These indicators may include the balance of supply and demand of the power system, cost minimization, carbon emission reduction, and system reliability improvement. For example, a specific total control target may be to reduce the operating cost of the virtual power plant by 5% while ensuring the reliability of power supply, or to reduce carbon emissions by 10% within a specific time period. Weight refers to the quantitative index of the relative importance of each objective function in the multi-element flexible resource coordinated control model. The weight is used to adjust the influence of different objectives in the optimization process to reflect the priority of different objectives. For example, if system reliability is considered to be more important than cost, then the objective function related to system reliability may be given a higher weight. The multi-element flexible resource coordinated control model refers to a comprehensive model formed by assigning weights to different objective functions on the basis of the initial resource control model. This model can simultaneously consider multiple objectives, such as energy balance, energy storage status, and user demand guarantee, and optimize the scheduling and allocation of resources according to the set weights. For example, if user demand assurance is given a higher weight, the model will prioritize ensuring that the user's electricity needs are met, even if this may slightly increase operating costs or affect the achievement of other goals. This model achieves the optimal balance between multiple goals of the virtual power plant by adjusting the regulation strategies of different resources.

[0053] The multi-flexible resource collaborative regulation model can be expressed as:

[0054] Among them, ω1, ω2, ω3 are weight coefficients used to balance the relative importance between different objective functions.

[0055] It can be understood that, first, the overall control objectives are received from the virtual power plant control system through the real-time communication network. These objectives may be comprehensively formulated based on factors such as the operation status of the power grid, market demand and environmental policies, for example, ensuring that there is sufficient power supply to meet demand during peak hours while controlling operating costs within a certain budget. This is done to ensure that the operation of the virtual power plant meets external requirements and internal strategies to achieve a balance between economic benefits and social responsibilities. Secondly, according to the received overall control objectives, the weights of each objective function in the initial resource control model are set. This step involves assigning different weights to objective functions such as energy balance, energy storage status and user demand guarantee according to their contribution to the overall control objectives. For example, if the overall control objective emphasizes user demand guarantee, then the objective function related to user electricity demand will be given a higher weight. The purpose of this is to reflect the relative importance of different objectives in the model and ensure that the resource control strategy can give priority to the most critical objectives. Finally, through weight adjustment, a multivariate flexible resource collaborative control model is obtained, which integrates various objective functions and performs optimization calculations according to the set weights. This enables the model to dynamically adjust resource scheduling strategies based on the overall control objectives of the virtual power plant, achieving the best balance between meeting multiple objectives such as power supply, cost control, environmental standards, and user needs. Such a control model helps improve the operational efficiency of virtual power plants, reduce costs, and ensure the reliability of power supply and environmental sustainability.

[0056] As an example, the steps of receiving the overall control target sent by the virtual power plant, and setting weights for the initial resource control model according to the overall control target to obtain the multi-element flexible resource collaborative control model include: receiving the overall control target sent by the virtual power plant; parsing the overall control target to obtain an energy balance control target, an energy storage status control target and a user demand guarantee control target; determining the balance weight, energy storage status weight and user demand weight according to the importance levels of the energy balance control target, the energy storage status control target and the user demand guarantee control target; setting weights for the initial resource control model according to the balance weight, the energy storage status weight and the user demand weight to obtain the multi-element flexible resource collaborative control model.

[0057] The energy balance control target refers to the goal of ensuring a balance between the total amount of power generation and the amount of power demand in the operation of the virtual power plant. Specifically, this goal involves monitoring and adjusting the power generation to match the real-time power demand of users while taking into account the losses of the power system. This is done to maintain the stability of the power grid and prevent power quality problems caused by insufficient or excessive power supply. It may include adjusting the output of power generation resources, such as increasing the output of wind or solar power generation, or reducing power generation during certain periods to ensure a balance between supply and demand. The energy storage state control target refers to managing the state of the energy storage system to ensure that the charge and discharge state of the energy storage equipment remains within a safe and effective range while meeting the grid regulation needs. This includes monitoring the SOC of the energy storage equipment and reasonably arranging the charge and discharge plan according to the grid demand and the health of the energy storage equipment. The purpose of this is to extend the service life of the energy storage equipment, improve its economic benefits, and ensure that the necessary regulation capabilities can be provided when needed. The user demand guarantee control target refers to giving priority to the user's electricity demand in the allocation of power resources, especially the electricity priority of different categories of users such as residential, commercial and industrial users. This involves determining the user's basic electricity demand and adjustable load demand based on the user's historical electricity consumption data, electricity consumption mode and comfort requirements. The purpose of this is to balance the power supply and demand through demand-side management while meeting the user's basic electricity demand and improve the flexibility of the power system. The balance weight refers to the weight corresponding to the relative importance of the energy balance control target in the multi-flexible resource coordinated control model. This weight reflects the importance of the goal of maintaining the balance of power supply and demand compared with other goals in the model optimization process. For example, if grid stability is the most important consideration, the balance weight may be set higher. The energy storage state weight refers to the weight corresponding to the relative importance of the energy storage state control target in the multi-flexible resource coordinated control model. This weight determines the priority of the energy storage equipment's operating state and health state goals compared with other goals in the model optimization process. The user demand weight refers to the weight corresponding to the relative importance of the user demand guarantee control target in the multi-flexible resource coordinated control model. This weight reflects the importance of the goal of meeting user electricity demand compared with other goals in the model optimization process. For example, if user satisfaction is the most important consideration, then the user demand weight may be set higher. By setting weights for these goals, the multi-flexible resource coordinated control model (weighted objective function) can comprehensively consider multiple goals and balance the conflicts between different goals according to these weights during the optimization process to achieve the overall operation goal of the virtual power plant.

[0058] First, by connecting with the control system interface of the virtual power plant, the total control target sent by it is received. These targets may be formulated based on the current state of the power grid and expected changes, including ensuring the stability of power supply, optimizing costs, reducing environmental impact, etc. This is done to ensure that the resource control model is consistent with the overall operation goals of the virtual power plant, thereby improving the operating efficiency and effectiveness of the entire system. Secondly, the received total control target is analyzed in detail and divided into three specific control targets: energy balance control target, that is, to ensure the real-time balance between power generation and power consumption; energy storage state control target, that is, to ensure that the energy storage equipment is kept in the best state during the charging and discharging process; user demand guarantee control target, that is, to ensure that the user's power demand is met first. This is done to decompose the complex total target into operational sub-targets, which is convenient for subsequent model construction and resource scheduling. Then, according to the importance of these sub-targets to achieve the total control target, the corresponding weights are determined, such as balance weight, energy storage state weight and user demand weight. These weights reflect the relative priority of each target in resource control. For example, if user satisfaction is the most important, then the user demand weight will be set higher. This is done to reasonably allocate resources in the model to ensure that the most important target is given priority while meeting all targets. Finally, these weights are applied to the initial resource regulation model, and the objective function in the model is adjusted to form a weighted multivariate flexible resource collaborative regulation model. This model will automatically adjust resource allocation according to the weights to achieve stability, cost-effectiveness and environmental friendliness of power supply, while giving priority to meeting users' electricity needs. This enables the virtual power plant to respond to grid changes more flexibly and efficiently, achieve optimal allocation and scheduling of resources, and improve the economy and reliability of the entire power system.

[0059] Step S40, obtaining control instructions for each flexible resource through the multi-flexible resource collaborative control model; It should be noted that flexible resources in the context of virtual power plants refer to various energy resources that can be adjusted or dispatched, and these resources can be flexibly adjusted according to the needs and operational objectives of the power grid. Specifically, flexible resources include but are not limited to: (1) Distributed power sources: such as renewable energy generation equipment such as solar photovoltaics and wind power generation, which can generate electricity according to weather and light conditions. (2) Energy storage systems: including equipment such as battery energy storage, which can store energy when power demand is low and release energy when demand is high. (3) Adjustable loads: refers to equipment or systems that can adjust power consumption according to power grid demand, such as some machinery in industrial production processes, air conditioning systems in commercial buildings, and household appliances in demand response projects. Control instructions refer to instructions calculated by the virtual power plant based on the multi-flexible resource coordinated control model. These instructions tell each flexible resource how to adjust its operating status to meet the real-time needs of the power grid and the preset operational objectives. Specifically, control instructions include: (1) Generation adjustment: adjusting the power generation output of distributed power sources, such as increasing or decreasing the speed of wind turbines, or adjusting the tilt angle of solar photovoltaic panels to maximize power generation. (2) Energy storage management: Scheduling the charging and discharging plan of the energy storage system, deciding when to charge and discharge, and the charging and discharging rate. (3) Load scheduling: Adjusting the power consumption of adjustable loads, such as reducing the power consumption of certain non-critical equipment during peak power demand periods, or increasing power consumption during low power demand periods.

[0060] It can be understood that the control instructions of each flexible resource are obtained through the multi-flexible resource coordinated control model. The specific approach is to combine the objective functions such as energy balance, energy storage status and user demand guarantee in the model with their respective weights to construct a weighted objective function, which comprehensively reflects the relative importance of all objectives. Then, the optimization algorithm, such as linear programming, dynamic programming or heuristic algorithm, is used to solve the minimum value problem of this weighted objective function. During the solution process, the algorithm will evaluate different resource scheduling combination schemes and calculate the contribution of each scheme to the weighted objective function, that is, the total cost or total benefit of each scheme. The algorithm will iteratively adjust the scheme and try different resource scheduling combinations until a scheme is found that satisfies all constraints (such as power supply capacity restrictions, energy storage capacity restrictions, user power demand restrictions, etc.) and minimizes the weighted objective function. This scheme is the desired solution, which provides a set of control instructions that clearly indicate how each flexible resource should adjust its operating status to achieve the optimization goal. These control instructions are then sent to each flexible resource to guide them to perform specific operations, such as adjusting power generation, changing charging and discharging plans, or adjusting power load, in order to achieve the optimal operation of the entire power system. This can minimize operating costs and improve energy efficiency while meeting the balance of power supply and demand, ensuring user power needs, and maintaining the health of energy storage equipment.

[0061] Step S50: sending the control instruction to each of the flexible resources, so that the flexible resources perform power control according to the control instruction, thereby completing the coordinated control of the flexible resources.

[0062] It should be noted that power regulation refers to the process of adjusting the output power or power consumption of each flexible resource. Specifically, power regulation involves the following aspects: (1) Output power adjustment: For power generation resources (such as distributed power sources such as solar photovoltaic and wind power generation), power regulation means changing the operating state of the power generation equipment according to the regulation instructions, such as adjusting the blade angle of the wind turbine or the tilt angle of the solar photovoltaic panel to increase or decrease the power generation. (2) Load control: For adjustable loads (such as non-critical electrical equipment in industry, commerce or residence), power regulation includes adjusting the power consumption of these equipment according to the regulation instructions, which may be achieved by directly controlling the equipment switch or adjusting the operating parameters of the equipment (such as temperature setting, operating speed, etc.). (3) Energy storage system management: For energy storage systems (such as battery energy storage), power regulation involves deciding when to charge and discharge, and at what rate to charge and discharge, according to the regulation instructions, in order to optimize the use of energy storage equipment and support the supply and demand balance of the power grid. (4) Real-time response: Power regulation also requires flexible resources to be able to respond to regulation instructions in real time, which means that the system can quickly identify changes in the state of the power grid and adjust power output or consumption according to the new instructions to maintain the stability and reliability of the power grid.

[0063] It can be understood that, firstly, the control instructions calculated by the multi-element flexible resource coordinated control model are transmitted to each flexible resource unit through the pre-established communication network. These instructions include specific power adjustment values ​​and time arrangements, such as telling the solar photovoltaic system to increase the output power during the peak sunshine period, or instructing the energy storage system to charge when the power demand is low. This is done to ensure that each resource unit can accurately adjust the power according to the real-time needs of the power grid and the preset operation goals. Secondly, after receiving the control instructions, each flexible resource unit performs the corresponding power control operations, such as adjusting the operating parameters of the power generation equipment, controlling the charging and discharging process of the energy storage system, or changing the power consumption mode of the adjustable load through the intelligent control system. These operations are to respond to the changes in the supply and demand of the power grid, optimize the efficiency of resource use, and reduce unnecessary energy waste. Finally, by completing the coordinated control of flexible resources in this way, the supply and demand balance of the power grid can be achieved, the stability and reliability of the power grid can be improved, the operating costs can be reduced, and the adaptability to the fluctuations of renewable energy can be enhanced. This enables the virtual power plant to respond to market and environmental changes more flexibly, improve energy utilization efficiency, and ensure that the power demand of users is met, so as to maximize economic and social benefits.

[0064] As an example, the control instruction is sent to each of the flexible resources so that the flexible resources perform power control according to the control instruction. After completing the step of collaborative control of the flexible resources, it also includes: monitoring the input power and output power of the flexible resources to obtain monitoring results; adjusting the weights of the multi-flexible resource collaborative control model according to the monitoring results and the overall control target, and returning to the step of obtaining the control instructions for each flexible resource through the multi-flexible resource collaborative control model.

[0065] Input power refers to the amount of electricity received by flexible resources. For power generation resources such as solar photovoltaic or wind power generation, input power can refer to the power converted from solar or wind energy into electrical energy. For energy storage systems, input power refers to the amount of electricity delivered by the grid to the energy storage device during charging. For adjustable loads, input power refers to the amount of electricity supplied by the grid to the load. Output power refers to the amount of electricity output by flexible resources to the grid or users. For power generation resources, output power refers to the amount of electricity actually supplied to the grid. For energy storage systems, output power refers to the amount of electricity supplied by the energy storage device to the grid during discharge. For adjustable loads, output power is usually not applicable because they mainly consume electrical energy, but in situations such as demand response, the reduction of electricity consumption by adjustable loads can also be regarded as a "negative output". Monitoring results refer to the actual data on the input power and output power of flexible resources collected in real time by monitoring equipment. These data reflect the actual operating status of flexible resources, including but not limited to key information such as power size, power change trend, and equipment operating efficiency.

[0066] First, the input power and output power of each flexible resource are monitored in real time through monitoring devices installed on each flexible resource, such as smart meters or sensors. These devices can accurately record and transmit power data to obtain monitoring results. This is done to ensure that the control instructions are correctly executed and the actual operating status of the resources can be understood in real time. The monitoring results include real-time data on the power output and input of each resource, which are essential for evaluating the operating efficiency and control effect of the resources. Then, using these monitoring results and combining them with the overall control objectives of the virtual power plant, the weights in the multi-flexible resource coordinated control model are adjusted. The weight adjustment is to reflect the latest grid operation status and market changes. For example, if the output power of a resource is lower than expected, it may be necessary to increase the weight of the resource in the model to encourage the model to pay more attention to the scheduling of this resource in future regulation. This helps the model to more accurately predict and respond to changes in grid demand and optimize resource scheduling strategies. Finally, after adjusting the weights, the new control instructions are calculated again through the multi-flexible resource coordinated control model to form a closed-loop control. This cyclic process ensures that the virtual power plant can continuously optimize its resource scheduling, adapt to the dynamic changes of the grid, improve the operating efficiency and reliability of the grid, while reducing costs and maximizing economic and social benefits. In this way, virtual power plants can achieve more flexible and precise resource management, respond to the real-time needs of the power grid, and ensure the stability and economy of power supply.

[0067] This embodiment provides a method for collaborative regulation of multiple flexible resources in a virtual power plant, obtaining an energy balance objective function, an energy storage state objective function, and a user demand guarantee objective function; constructing an initial resource regulation model based on the energy balance objective function, the energy storage state objective function, and the user demand guarantee objective function; receiving the total regulation target sent by the virtual power plant, and setting weights for the initial resource regulation model based on the total regulation target to obtain a multi-flexible resource collaborative regulation model; obtaining the regulation instructions of each flexible resource through the multi-flexible resource collaborative regulation model; sending the regulation instructions to each of the flexible resources so that the flexible resources perform power regulation according to the regulation instructions, and completing the collaborative regulation of the flexible resources. First, the energy balance objective function, the energy storage state objective function, and the user demand guarantee objective function are obtained. These functions respectively ensure the balance of power supply and demand, the management of energy storage equipment status, and the satisfaction of user electricity demand, and provide a basis for formulating a comprehensive regulation strategy. Then, an initial resource regulation model is constructed based on these objective functions to simulate and predict the effects of different regulation strategies, providing a scientific basis for evaluation and optimization. Then, the total control target of the virtual power plant is received, and weights are set for each objective function in the model accordingly, forming a multi-flexible resource coordinated control model, so that the model can be flexibly adjusted according to actual conditions and better adapt to the dynamic changes and operational needs of the power grid. Through this model, specific control instructions for each flexible resource are obtained, which guide the resources on how to adjust power output or consumption to ensure the scientificity and effectiveness of the control strategy. Finally, the control instructions are sent to each flexible resource to guide them to make actual power adjustments to achieve optimal resource allocation and coordinated control. This process realizes the efficient coordinated control of multiple flexible resources in the virtual power plant, which can optimize the economic operation of the system and guarantee the basic production and living needs of users. It not only improves energy utilization efficiency, reduces operating costs, and enhances the stability and reliability of the power grid, but also reduces carbon emissions and promotes environmental sustainability by optimizing the use of renewable energy. At the same time, it gives priority to meeting the basic electricity needs of users, improves user satisfaction and quality of life, and enables virtual power plants to flexibly adapt to market and environmental changes and maximize economic and social benefits.

[0068] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can refer to the above introduction, and will not be repeated later. Figure 4 , Figure 4 This is a flow chart of the second embodiment of the method for coordinated regulation of multiple flexible resources of a virtual power plant of the present application. Step S40 of the method for coordinated regulation of multiple flexible resources of a virtual power plant includes steps S41 to S45: Step S41, obtaining energy balance constraints, energy storage state constraints, adjustable load constraints, and power system loss constraints; It should be noted that the energy balance constraint refers to the constraint that ensures the balance between electricity supply and demand. Specifically, it requires that at any given point in time, the total power generation of the power system plus the discharge of the energy storage system must meet the total power demand, while taking into account the losses of the power system. This constraint ensures the balance of supply and demand of the power system and prevents power quality problems caused by insufficient or excessive power supply. The energy storage state constraint involves the operating state of the energy storage device (such as a battery), including its state of charge (SOC), maximum and minimum charge and discharge rates, and the number of charge and discharge cycles. These constraints ensure that the energy storage device operates in a safe and effective state, avoiding overcharging or discharging, thereby extending the service life of the device and maintaining its performance. The adjustable load constraint refers to the operating restrictions of the adjustable load (such as the industrial or commercial load in the demand response project). The adjustable load can reduce power consumption when the grid demand is high, or increase power consumption when the demand is low. These constraints include the maximum and minimum adjustment range of the adjustable load, the adjustment rate, and the adjustment time window, etc., to ensure that the adjustment of the adjustable load does not affect the normal operation of the user. Power system loss constraints involve power losses during transmission and distribution. Power system losses include line losses, transformer losses, etc. These constraints take into account energy losses during power transmission and ensure that these losses are taken into account during power system design and operation to optimize power transmission efficiency and reduce unnecessary energy waste.

[0069] It can be understood that, first, energy balance constraints are obtained, which requires ensuring the balance between power supply and demand by real-time monitoring of the total power generation and power demand of the power grid, as well as considering the losses in the transmission process. This is done to maintain the stability of the power grid, prevent power outages caused by insufficient power supply or waste of resources caused by oversupply, and ensure that the power grid can meet the power needs of users at any time while avoiding unnecessary energy losses. Secondly, energy storage state constraints are collected, which involves monitoring the current state of energy storage devices, such as the charge level (SOC) of the battery, and their charge and discharge rate limits. This is done to protect energy storage devices from damage, extend their service life, and ensure that they can provide the required power support when needed. The use of energy storage devices can be optimized, the efficiency of energy storage and release can be improved, and flexible adjustment capabilities can be provided for the power grid. Then, adjustable load constraints are determined, which requires analyzing and setting the power adjustment range and rate of adjustable load devices in industrial, commercial or residential users. This is done to balance the power grid load through demand-side management without affecting the normal activities of users. The flexibility and reliability of the power grid can be improved, and non-critical loads can be adjusted to cope with fluctuations in supply and demand of the power grid. Finally, identify the power system loss constraints, which requires evaluating and calculating the energy loss during power transmission and distribution. This is done to reduce the loss during transmission and improve the overall energy efficiency. Energy loss can be reduced by optimizing the power grid's transmission lines and equipment, thereby reducing operating costs and improving the economic benefits of energy. By integrating these constraints, a comprehensive power system control model can be constructed to provide a scientific basis for the efficient and safe dispatch of power resources.

[0070] As an example, the steps of obtaining energy balance constraints, energy storage state constraints, adjustable load constraints and power system loss constraints include: obtaining distributed voltage generation power, energy storage system charging power, energy storage system discharging power, user load demand and transmission loss; determining the energy balance constraint according to the distributed voltage generation power, the energy storage system charging power, the energy storage system discharging power and the user load demand; determining the energy storage state constraint according to the energy storage system charging power and the energy storage system discharging power; determining the adjustable load constraint according to the user load demand; and determining the power system loss constraint according to the transmission loss.

[0071] Distributed voltage generation power refers to the amount of electricity generated by distributed generation resources (such as solar photovoltaic panels, wind turbines, etc.) at a specific moment. These generation resources are usually connected directly to the distribution network rather than through centralized power stations. Obtaining distributed voltage generation power involves monitoring and recording the real-time output of these resources in order to understand their contribution to the grid. This is done to ensure that the supply and demand of the grid are balanced and that renewable energy is fully utilized. Energy storage system charging power refers to the amount of electricity absorbed by the energy storage system (such as the battery energy storage system) during the charging process. Obtaining the energy storage system charging power requires monitoring the current and voltage of the energy storage device during charging and calculating the product to obtain the power value. This parameter is critical for managing the charging process of energy storage devices to ensure that they operate in a safe and efficient state while optimizing the energy storage capacity of the grid. Energy storage system discharge power refers to the amount of electricity supplied to the grid by the energy storage system during the discharge process. Similar to charging power, discharge power is also calculated by monitoring the current and voltage of the energy storage device during discharge. This parameter is critical for dispatching energy storage resources to meet grid demand, provide peak load support and frequency regulation services. User load demand refers to the amount of electricity demanded by users (including residential, commercial and industrial users) at a specific moment. Obtaining user load demand involves monitoring and predicting the user's electricity consumption pattern, including basic demand and adjustable load. Understanding user load demand is crucial to ensuring the reliability of power supply and meeting user electricity demand. Transmission loss refers to the amount of electricity lost due to resistance, transformer efficiency and other factors during the transmission of electricity from the power station to the user. Obtaining transmission loss involves evaluating and calculating the energy loss of the power grid during the transmission process. Reducing transmission loss is very important for improving energy efficiency and reducing operating costs. Constraints include: (1) Energy balance constraint: g1(x) = P g -P d -P loss ≤ 0 (2) Energy storage state constraint: g2(x)=SOC−SOC max ≤ 0 and g3(x)=-(SOC-SOC min ) ≤ 0 (3) Adjustable load constraint: g4(x)=P res −P crit ≥ 0 (4) Other possible technical and operational constraints, such as power system loss constraints.

[0072] First, the distributed voltage generation power, that is, the amount of electricity generated by these devices at a specific moment, is obtained in real time through sensors and metering devices installed at various distributed power supply points, such as solar photovoltaic panels and wind turbines. Then, the monitoring equipment of the energy storage system is used to obtain its charging power and discharging power, which reflect the flow of electric energy during the charging and discharging process of the energy storage device. At the same time, the user load demand is collected through smart meters and load management systems to understand the user's power consumption in different time periods. In addition, the transmission loss is calculated through the grid operation data, which involves the energy loss caused by factors such as line resistance during the transmission of electricity. With this data, the energy balance constraints can be determined to ensure the balance between the generation power, the charging and discharging power of the energy storage system and the user load demand, and meet the immediate power supply needs of the grid. Next, based on the charging and discharging power data of the energy storage system, the energy storage state constraints are determined to manage the charging state and health state of the energy storage device to prevent overcharging or over-discharging. Then, based on the user load demand data, the adjustable load constraints are determined to identify the flexibility to reduce power consumption when the grid demand is high or increase power consumption when the demand is low. Finally, based on the transmission loss data, the power system loss constraints are determined to optimize power transmission efficiency and reduce unnecessary energy losses. This whole process helps to build a power system control model that comprehensively considers supply and demand balance, energy storage equipment status, adjustable load potential and power loss, providing a scientific basis for achieving efficient and safe dispatch of power resources.

[0073] Step S42, constructing a logarithmic barrier function according to the multivariate flexible resource coordinated regulation model and the energy balance constraint, the energy storage state constraint, the adjustable load constraint and the power system loss constraint, wherein the logarithmic barrier function includes a barrier parameter; It should be noted that the logarithmic barrier function is a method of "softening" the constraints. It punishes the behavior of violating the constraints by adding a term in the objective function that increases rapidly as the degree of constraint violation increases. This function usually contains a logarithmic term. When the constraints are satisfied (i.e., in the feasible region), the value of the logarithmic term is close to zero; when the constraints are violated (i.e., outside the feasible region), the value of the logarithmic term increases rapidly, resulting in a sharp increase in the value of the entire objective function. In this way, when the optimization algorithm is looking for the minimum value of the objective function, it will naturally avoid solutions that violate the constraints, thereby ensuring that the solution found satisfies all constraints. The barrier parameter is a positive constant that appears in the logarithmic barrier function. It controls the intensity of the penalty for violating the constraints. The smaller the barrier parameter, the greater the penalty for violating the constraints, which means that the degree of constraint satisfaction is higher in the optimization process. During the iteration of the optimization algorithm, the barrier parameter usually decreases gradually to gradually guide the solution to the boundary of the feasible region. When the barrier parameter approaches zero, the solution found by the algorithm will become closer and closer to the solution of the original constrained optimization problem. In this embodiment, the logarithmic barrier function can be expressed as:

[0074] Where B(x,μ) is the logarithmic barrier function, x is the decision variable vector, μ is the barrier parameter, f(x) is the objective function of the multivariate flexible resource coordinated regulation model, expressed as a weighted combination of the objective functions of minimizing energy balance, energy storage status, and user demand guarantee, g i (x) is the inequality constraint function, expressed as a function of the decision variable x, and for all i, it needs to satisfy g i (x)≤0,s j (x) is an equality constraint function, expressed as a function of the decision variable x, which needs to satisfy s for all j. j (x)=0, ln represents the natural logarithmic function.

[0075] It can be understood that, first of all, in the multivariate flexible resource coordinated regulation model, it is necessary to integrate the constraints such as energy balance, energy storage state, adjustable load and power system loss into a mathematical expression. This is done to consider all these constraints at the same time in a unified framework to ensure the stable operation of the power system and the efficient use of resources. The specific operation is to define a logarithmic barrier function to transform these constraints into part of the objective function, in which the logarithmic term will quickly increase the value of the objective function when the constraint is violated, thereby avoiding these solutions. Secondly, the barrier parameter is introduced, which is a positive constant used to control the intensity of the penalty for violating the constraint. During the iteration of the optimization algorithm, the barrier parameter is gradually reduced, so that the logarithmic term in the logarithmic barrier function gradually increases the penalty for violating the constraint. The purpose of this is to gradually guide the optimization algorithm to find solutions closer to the boundary of the actual feasible region, that is, those solutions that more strictly meet all the constraints. Finally, as the barrier parameter approaches zero, the solution found by the optimization algorithm will become closer and closer to the solution of the original constrained optimization problem, that is, the optimal value of the objective function is achieved under the premise of satisfying all the constraints. Doing so can simultaneously consider the optimization of the objective function and the satisfaction of the constraints in the solution process, thereby finding a resource scheduling solution that is both economical and efficient and meets all operating constraints, thereby improving the operating efficiency and reliability of the power system.

[0076] Step S43, calculating the gradient of the multivariate flexible resource collaborative control model and the logarithmic barrier function to obtain a gradient vector; It should be noted that the gradient refers to the derivative of a multivariate function at a certain point. It is a vector that points to the direction in which the function grows fastest. For a given function, the gradient is the set of all its first-order partial derivatives. In the multivariate flexible resource coordinated regulation model, the gradient can help us understand how small changes in different decision variables (such as power generation power, energy storage charging and discharging strategies, etc.) affect the performance of the entire system. The gradient vector is a specific manifestation of the gradient. It is a vector containing all first-order partial derivatives. For a function with multiple variables, each component in the gradient vector is the partial derivative of the function with respect to one of the variables. In optimization problems, the gradient vector indicates how to adjust the variables to reduce or increase the function value as quickly as possible, which is very useful when looking for the minimum or maximum value of the function. The gradient vector ∇B(x,μ) can be expressed as:

[0077] Among them, ∇f(x) is the gradient vector of the objective function f(x), which is the vector of the first-order partial derivatives of f(x) with respect to each decision variable x, and ∇(-g i (x)) is the inequality constraint function -g i The gradient vector of (x), which is -g i(x) is the vector of first-order partial derivatives of each decision variable x, ∇s j (x) is the equality constraint function s j The gradient vector of (x), which is s j (x) is the vector of first-order partial derivatives of each decision variable x, μ is the barrier parameter, a positive constant that controls the intensity of the penalty for violating the constraint, and g i (x) is the inequality constraint function, which needs to satisfy g i (x)≤0,s j (x) is an equality constraint function that needs to satisfy s j (x) = 0. The gradient vector ∇B(x, μ) provides the direction in which the logarithmic barrier function B(x, μ) grows fastest at the current decision point x. In the optimization algorithm, this gradient vector is used to guide the search direction, that is, by adjusting the decision variable x to iteratively update in the opposite direction of ∇B(x, μ) to minimize the logarithmic barrier function while satisfying all constraints.

[0078] It can be understood that, first of all, it is necessary to differentiate the objective function and the logarithmic barrier function in the multivariate flexible resource coordinated regulation model to calculate the partial derivatives of each decision variable (such as power generation, energy storage charging and discharging strategy, etc.). This process involves mathematically derivatizing the function to obtain the instantaneous rate of change of each variable. These partial derivatives show how a small adjustment of each variable affects the value of the entire objective function at the current decision point. This is done to determine how each variable should be adjusted during the optimization process in order to most effectively improve the performance of the objective function. Secondly, all calculated partial derivative arrays are synthesized into a vector, namely the gradient vector, which contains the partial derivative information of all decision variables. It indicates the steepest rise or fall direction of the objective function at the current decision point. The purpose of constructing the gradient vector is to use it in the optimization algorithm, because the gradient vector provides a direct way to guide how to adjust the variables so that in each iteration, we move in the direction of reducing the value of the objective function (for the minimization problem). Finally, the gradient vector is used to update the value of the decision variable. In optimization algorithms such as gradient descent, the variables are usually adjusted in the opposite direction of the gradient vector, because this can reduce the value of the objective function. Through this iterative process, the minimum value of the objective function is gradually approached while ensuring that all constraints are met. This can find a resource scheduling solution that achieves the optimal objective function (such as cost or efficiency) while satisfying all power system operation constraints, thereby achieving efficient and economical operation of the power system.

[0079] Step S44, calculating the second-order derivative matrix of the multivariate flexible resource collaborative regulation model; It should be noted that the second-order derivative matrix, also known as the Hessian Matrix, is a square matrix consisting of the second-order partial derivatives of a multivariate function. For a function with multiple variables, the Hessian Matrix provides information about how these variables affect the curvature of the function. Specifically, each element in the Hessian Matrix is ​​the second-order partial derivative of the function with respect to a pair of variables, that is, the rate of change of one variable with respect to the rate of change of another variable. The Hessian Matrix provides information about the curvature of the function, which is crucial for determining the direction of the steepest descent. For the second-order derivative matrix of the multivariate flexible resource collaborative regulation model, it is a square matrix consisting of all second-order partial derivatives of the objective function f(x). The Hessian matrix H for a function f(x) with n variables can be expressed as:

[0080] in, Represents the function f on the variable x i and x j The second-order partial derivatives of is the function f on the variable x i The second-order partial derivatives of , the off-diagonal elements is the function f on the variable x i and x j The mixed second-order partial derivatives of .

[0081] It is understandable that the objective function of the model needs to be determined first, which is usually a complex nonlinear function of multiple decision variables (for example, the power output of different resources). Then, the second-order partial derivatives of each variable in the objective function with respect to all other variables are calculated. These second-order partial derivatives measure the change in the curvature of the objective function when one variable is changed. Specifically, it is necessary to calculate the second-order derivative of each variable with respect to itself (that is, the coefficient of the square term of the variable), as well as the mixed second-order derivatives between each pair of variables (that is, the coefficient of the cross term), and fill these second-order partial derivatives into a square matrix in a certain order to obtain the Hessian matrix. This matrix not only contains all the information about the local curvature of the function, but also indicates the concavity and convexity of the function at a specific point, which is crucial for determining the local minimum or maximum point of the function. In the optimization algorithm, the Hessian matrix can be used to guide the search direction of second-order methods such as Newton's method, improve the efficiency and accuracy of finding the optimal solution, especially in the power system control model, which helps to more accurately adjust the generation, storage and load resources to maximize cost-effectiveness or meet other operational goals.

[0082] Step S45, adjusting the obstacle parameters according to the gradient vector and the second-order derivative matrix until the value of the logarithmic obstacle function is less than or equal to a preset penalty threshold, and obtaining control instructions for each flexible resource, the control instructions including the power output instructions of the distributed power source, the charging power instructions and the discharging power instructions of the energy storage system, and the regulating power instructions of the adjustable load.

[0083] It should be noted that the preset penalty threshold refers to a numerical standard set during the optimization process to determine when to stop adjusting the barrier parameters. This threshold represents the upper limit of the logarithmic barrier function value. When the value of the logarithmic barrier function is less than or equal to this threshold, it means that the solution is close enough to the feasible region and further adjustment of the penalty parameters can be stopped. For example, if the preset penalty threshold is set to 0.01, then when the value of the logarithmic barrier function drops to 0.01 or lower, the algorithm will stop adjusting the barrier parameters. Control instructions refer to instructions calculated based on the optimization model, which are used to guide each flexible resource on how to adjust its operating state to meet the control needs of the power grid. These instructions ensure that the power grid operates in the optimal state while meeting all constraints. The power output instruction of a distributed power source refers to a specific instruction that tells a distributed power source (such as a solar photovoltaic panel or a wind turbine) how much electricity it should generate in a specific time period. For example, if the power output instruction of a photovoltaic panel is 100 kilowatts, then the working state of the photovoltaic panel needs to be adjusted to generate 100 kilowatts of electricity. The charging power instruction of the energy storage system is an instruction to instruct the energy storage system (such as a battery) at what power it should charge in a specific time period. For example, if the charging power instruction is 50 kilowatts, the energy storage system needs to adjust its charging process to ensure that it is charged at this power. The discharge power instruction refers to an instruction to instruct the energy storage system at what power it should discharge in a specific time period. For example, if the discharge power instruction is 75 kilowatts, the energy storage system needs to adjust its discharge process to ensure that it discharges to the grid at this power. The adjustable power instruction of the adjustable load refers to an instruction to tell the adjustable load (such as an industrial production line or a commercial air conditioning system) how much power consumption should be adjusted in a specific time period. For example, if the adjustable power instruction is to reduce 20 kilowatts, the relevant load needs to reduce its power consumption to respond to the grid's regulation needs.

[0084] It can be understood that, first, the gradient vector and the second-order derivative matrix are used to evaluate the optimization state of the current solution. The gradient vector indicates the rate of change of the objective function in the direction of different decision variables, and the Hessian matrix provides information about the curvature of the objective function. This information is used to determine whether the current solution is close to the optimal and guide how to adjust the barrier parameters to improve the quality of the solution. The barrier parameters are adjusted to control the penalty for violating the constraints in the logarithmic barrier function, which is a key step in the optimization process because it directly affects the degree of satisfaction of the constraints. Secondly, the barrier parameters are adjusted according to the information of the gradient vector and the Hessian matrix. If the value of the logarithmic barrier function is higher than the preset penalty threshold, it means that the current solution has not yet satisfied the constraints well, so it is necessary to increase the penalty for violating the constraints, that is, to reduce the value of the barrier parameter. Doing so can guide the optimization algorithm to more actively find a solution that satisfies all constraints. With the adjustment of the barrier parameters, the optimization process is continuously iterated until the value of the logarithmic barrier function drops below the preset penalty threshold, indicating that an optimal solution under the constraints has been found. Finally, once the value of the logarithmic barrier function reaches a satisfactory level, the optimization algorithm will output the final control instructions, which include the power output instructions of distributed power sources, guiding the power generation equipment to adjust the power generation according to the grid demand; the charging and discharging power instructions of the energy storage system, ensuring that the energy storage equipment is charged and discharged at the best time to support the supply and demand balance of the grid; and the adjustable load adjustment power instructions, allowing certain non-critical power consumption to be reduced or postponed during peak grid load to relieve grid pressure. The effect of these control instructions is to improve the flexibility and reliability of the power system, optimize resource allocation, reduce operating costs, and enhance the adaptability to fluctuations in renewable energy, ultimately achieving stability and economy of power supply.

[0085] This embodiment obtains energy balance constraints, energy storage state constraints, adjustable load constraints, and power system loss constraints; constructs a logarithmic barrier function based on the multivariate flexible resource collaborative control model and the energy balance constraints, the energy storage state constraints, the adjustable load constraints, and the power system loss constraints, and the logarithmic barrier function includes a barrier parameter; calculates the gradient of the multivariate flexible resource collaborative control model and the logarithmic barrier function to obtain a gradient vector; calculates the second-order derivative matrix of the multivariate flexible resource collaborative control model; adjusts the barrier parameter according to the gradient vector and the second-order derivative matrix until the value of the logarithmic barrier function is less than or equal to a preset penalty threshold, and obtains the control instructions of each flexible resource, the control instructions include the power output instruction of the distributed power source, the charging power instruction and the discharging power instruction of the energy storage system, and the adjustable power instruction of the adjustable load. First, by collecting the constraints of energy balance, energy storage state, adjustable load, and power system loss, the boundary is set for the safe and effective operation of the power system, ensuring that resource scheduling is performed under the premise of meeting all operating requirements. Next, these constraints and the multivariate flexible resource collaborative control model are used to construct a logarithmic barrier function containing barrier parameters, which transforms the constrained optimization problem into an unconstrained optimization problem and simplifies the solution of the problem. At the same time, the barrier parameters control the penalty for violating the constraints. Then, the gradient vector and second-order derivative matrix of the model are calculated. These calculations are crucial for determining the size and direction of the optimization step and help to efficiently navigate to the optimal solution of the objective function. Subsequently, the barrier parameters are adjusted according to the gradient vector and the second-order derivative matrix until the value of the logarithmic barrier function is lower than the preset penalty threshold, ensuring that the solution is getting closer and closer to the feasible area while satisfying all constraints. By gradually reducing the barrier parameters, the solution can be finely adjusted to make it more accurately meet the actual operation requirements. Finally, when the value of the logarithmic barrier function reaches a satisfactory level, the system will output control instructions, including the power output of distributed power sources, the charging and discharging power of the energy storage system, and the adjustment power of the adjustable load, directly guiding the operation of each part of the power system to ensure the stability and economy of power supply. This process optimizes resource scheduling, improves energy utilization efficiency, reduces operating costs, enhances the power system's adaptability to fluctuations in renewable energy and changes in demand, ensures that key user needs are met, and improves user satisfaction.

[0086] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the method for coordinated regulation of multiple flexible resources of a virtual power plant of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0087] This application also provides a virtual power plant multi-flexible resource coordinated control device, please refer to Figure 5The virtual power plant multi-flexible resource coordinated control device includes: A data acquisition module 10 is used to acquire an energy balance objective function, an energy storage state objective function, and a user demand guarantee objective function; A model building module 20, configured to build an initial resource regulation model according to the energy balance objective function, the energy storage state objective function and the user demand guarantee objective function; The weight setting module 30 is used to receive the overall control target sent by the virtual power plant, and set the weight for the initial resource control model according to the overall control target to obtain a multivariate flexible resource collaborative control model; A control instruction module 40, used to obtain control instructions for each flexible resource through the multi-flexible resource collaborative control model; The sending module 50 is used to send the control instruction to each of the flexible resources, so that the flexible resources perform power control according to the control instruction, thereby completing the coordinated control of the flexible resources.

[0088] In one embodiment, the control instruction module 40 is also used to obtain energy balance constraints, energy storage state constraints, adjustable load constraints and power system loss constraints; construct a logarithmic barrier function based on the multi-element flexible resource collaborative control model and the energy balance constraints, the energy storage state constraints, the adjustable load constraints and the power system loss constraints, and the logarithmic barrier function includes barrier parameters; calculate the gradient of the multi-element flexible resource collaborative control model and the logarithmic barrier function to obtain a gradient vector; calculate the second-order derivative matrix of the multi-element flexible resource collaborative control model; adjust the barrier parameters according to the gradient vector and the second-order derivative matrix until the value of the logarithmic barrier function is less than or equal to a preset penalty threshold, and obtain control instructions for each flexible resource, and the control instructions include power output instructions for distributed power sources, charging power instructions and discharging power instructions for energy storage systems, and adjustable power instructions for adjustable loads.

[0089] In one embodiment, the control instruction module 40 is also used to obtain distributed voltage generation power, energy storage system charging power, energy storage system discharging power, user load demand and transmission loss; determine energy balance constraints according to the distributed voltage generation power, the energy storage system charging power, the energy storage system discharging power and the user load demand; determine energy storage state constraints according to the energy storage system charging power and the energy storage system discharging power; determine adjustable load constraints according to the user load demand; and determine power system loss constraints according to the transmission loss.

[0090] In one embodiment, the data acquisition module 10 is also used to obtain the power generation of distributed power sources, the discharge of the energy storage system, the adjustable load of the virtual power plant, residential electricity demand, commercial electricity demand, industrial electricity demand and power system loss; the total power generation of the virtual power plant is obtained according to the power generation of the distributed power sources, the discharge of the energy storage system and the adjustable load; the total demand of the virtual power plant is obtained according to the residential electricity demand, the commercial electricity demand, the industrial electricity demand and the power system loss; and an energy balance objective function is established according to the total power generation and the total demand.

[0091] In one embodiment, the data acquisition module 10 is further used to acquire the capacity of the energy storage system, historical charging data, historical discharging data, first current data and first voltage data at the input end of the energy storage system, and second current data and second voltage data at the output end of the energy storage system; calculate the charging power of the energy storage system according to the first current data and the first voltage data; calculate the discharging power of the energy storage system according to the second current data and the second voltage data; determine the constraints of the energy storage system according to the charging power of the energy storage system, the discharging power of the energy storage system and the capacity of the energy storage system; simulate the historical charging data and the historical discharging data to determine the energy storage state weight coefficient; establish the energy storage state objective function according to the charging power of the energy storage system, the discharging power of the energy storage system, the constraints of the energy storage system and the energy storage state weight coefficient.

[0092] In one embodiment, the data acquisition module 10 is also used to obtain user category priority, total power generation of the virtual power plant, user historical power consumption data, non-critical electrical equipment information and user comfort requirements; perform statistical analysis, load curve analysis and seasonal analysis on the user's historical power consumption data to identify the user's power consumption pattern; obtain the user's basic load demand based on the user's historical power consumption data and the user's power consumption pattern; determine the non-critical electrical equipment power consumption data based on the user's historical power consumption data and the non-critical electrical equipment information; obtain the user's adjustable load demand based on the user's comfort requirements and the non-critical electrical equipment power consumption data; simulate the user's historical power consumption data according to the user category priority to determine the user demand guarantee weight coefficient; establish the user demand guarantee objective function based on the user's basic load demand, the user's adjustable load demand, the total power generation of the virtual power plant and the user demand guarantee weight coefficient.

[0093] In one embodiment, the weight setting module 30 is also used to receive the overall control target sent by the virtual power plant; analyze the overall control target to obtain the energy balance control target, the energy storage state control target and the user demand guarantee control target; determine the balance weight, the energy storage state weight and the user demand weight according to the importance level of the energy balance control target, the energy storage state control target and the user demand guarantee control target; set weights for the initial resource control model according to the balance weight, the energy storage state weight and the user demand weight to obtain a multi-flexible resource collaborative control model.

[0094] In one embodiment, the sending module 50 is also used to monitor the input power and output power of the flexible resources to obtain monitoring results; adjust the weight of the multi-flexible resource collaborative control model according to the monitoring results and the overall control target, and return to the step of obtaining the control instructions of each flexible resource through the multi-flexible resource collaborative control model.

[0095] The virtual power plant multi-flexible resource coordinated control device provided by the present application adopts the virtual power plant multi-flexible resource coordinated control method in the above-mentioned embodiment, which can solve the technical problem of how to achieve efficient coordinated control of multi-flexible resources in a virtual power plant to optimize the economic operation of the system and ensure the basic production and living needs of users. Compared with the prior art, the beneficial effects of the virtual power plant multi-flexible resource coordinated control device provided by the present application are the same as the beneficial effects of the virtual power plant multi-flexible resource coordinated control method provided by the above-mentioned embodiment, and the other technical features in the virtual power plant multi-flexible resource coordinated control device are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0096] The present application provides a device for coordinated regulation of multiple flexible resources of a virtual power plant, the device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for coordinated regulation of multiple flexible resources of a virtual power plant in the first embodiment above.

[0097] Reference below Figure 6, which shows a schematic diagram of the structure of a virtual power plant multi-flexible resource coordinated control device suitable for implementing the embodiment of the present application. The virtual power plant multi-flexible resource coordinated control device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The virtual power plant multi-flexible resource coordinated control device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0098] like Figure 6 As shown, the virtual power plant multi-flexible resource coordinated control device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 to the random access memory (RAM: Random Access Memory) 1004. Various programs and data required for the operation of the virtual power plant multi-flexible resource coordinated control device are also stored in RAM1004. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. The input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the virtual power plant multi-flexible resource coordinated control device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a virtual power plant multi-flexible resource coordinated control device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0099] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0100] The virtual power plant multi-flexible resource coordinated control device provided by the present application adopts the virtual power plant multi-flexible resource coordinated control method in the above embodiment, which can solve the technical problem of how to achieve efficient coordinated control of multi-flexible resources in the virtual power plant to optimize the economic operation of the system and ensure the basic production and living needs of users. Compared with the prior art, the beneficial effects of the virtual power plant multi-flexible resource coordinated control device provided by the present application are the same as the beneficial effects of the virtual power plant multi-flexible resource coordinated control method provided by the above embodiment, and the other technical features in the virtual power plant multi-flexible resource coordinated control device are the same as the features disclosed in the previous embodiment method, which will not be repeated here.

[0101] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0102] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0103] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the method for coordinated regulation of multiple flexible resources of a virtual power plant in the above-mentioned embodiment.

[0104] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0105] The above-mentioned computer-readable storage medium may be included in the virtual power plant's multi-flexible resource coordinated control device; or it may exist independently without being assembled into the virtual power plant's multi-flexible resource coordinated control device.

[0106] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the virtual power plant multi-flexible resource coordinated control device, the virtual power plant multi-flexible resource coordinated control device: obtains the energy balance objective function, the energy storage state objective function and the user demand guarantee objective function; constructs an initial resource control model according to the energy balance objective function, the energy storage state objective function and the user demand guarantee objective function; receives the overall control target sent by the virtual power plant, and sets weights for the initial resource control model according to the overall control target to obtain a multi-flexible resource coordinated control model; obtains control instructions for each flexible resource through the multi-flexible resource coordinated control model; sends the control instructions to each of the flexible resources, so that the flexible resources perform power control according to the control instructions, and completes the coordinated control of the flexible resources.

[0107] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0108] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0109] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0110] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned method for coordinated regulation of multiple flexible resources in a virtual power plant, and can solve the technical problem of how to achieve efficient coordinated regulation of multiple flexible resources in a virtual power plant to optimize the economic operation of the system and ensure the basic production and living needs of users. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the method for coordinated regulation of multiple flexible resources in a virtual power plant provided in the above-mentioned embodiment, and will not be repeated here.

[0111] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for coordinated regulation of multiple flexible resources of a virtual power plant.

[0112] The computer program product provided by this application can solve the technical problem of how to achieve efficient coordinated regulation of multiple flexible resources in a virtual power plant to optimize the economic operation of the system and ensure the basic production and living needs of users. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the method for coordinated regulation of multiple flexible resources in a virtual power plant provided by the above embodiment, and will not be repeated here.

[0113] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for coordinated control of multiple flexible resources of a virtual power plant, characterized in that: The method comprises: Obtain energy balance objective function, energy storage state objective function and user demand guarantee objective function; Constructing an initial resource regulation model according to the energy balance objective function, the energy storage state objective function and the user demand guarantee objective function; Receiving the overall control target sent by the virtual power plant, and setting weights for the initial resource control model according to the overall control target to obtain a multivariate flexible resource collaborative control model; Obtaining control instructions for each flexible resource through the multi-flexible resource collaborative control model; The control instruction is sent to each of the flexible resources, so that the flexible resources perform power control according to the control instruction, thereby completing the coordinated control of the flexible resources.

2. The method according to claim 1, characterized in that The step of obtaining the control instructions of each flexible resource through the multi-element flexible resource collaborative control model includes: Obtain energy balance constraints, energy storage state constraints, adjustable load constraints, and power system loss constraints; Constructing a logarithmic barrier function according to the multivariate flexible resource coordinated regulation model and the energy balance constraint, the energy storage state constraint, the adjustable load constraint and the power system loss constraint, wherein the logarithmic barrier function includes a barrier parameter; Calculating the gradient of the multivariate flexible resource collaborative regulation model and the logarithmic barrier function to obtain a gradient vector; Calculating the second-order derivative matrix of the multivariate flexible resource collaborative regulation model; The obstacle parameters are adjusted according to the gradient vector and the second-order derivative matrix until the value of the logarithmic obstacle function is less than or equal to a preset penalty threshold, and control instructions for each flexible resource are obtained, wherein the control instructions include power output instructions for distributed power sources, charging power instructions and discharging power instructions for energy storage systems, and regulating power instructions for adjustable loads.

3. The method according to claim 2, characterized in that The steps of obtaining energy balance constraints, energy storage state constraints, adjustable load constraints and power system loss constraints include: Obtain distributed voltage generation power, energy storage system charging power, energy storage system discharging power, user load demand and transmission loss; Determine an energy balance constraint condition according to the distributed voltage generation power, the energy storage system charging power, the energy storage system discharging power and the user load demand; Determine energy storage state constraint conditions according to the energy storage system charging power and the energy storage system discharging power; Determining an adjustable load constraint condition according to the user load demand; A power system loss constraint condition is determined according to the transmission loss.

4. The method according to claim 1, characterized in that The step of obtaining the energy balance objective function comprises: Obtain the power generation of distributed generation, the discharge of energy storage system, the adjustable load of virtual power plant, residential power demand, commercial power demand, industrial power demand and power system loss; Obtaining the total power generation of the virtual power plant according to the power generation of the distributed power source, the discharge of the energy storage system and the adjustable load; Obtaining the total demand of the virtual power plant according to the residential electricity demand, the commercial electricity demand, the industrial electricity demand and the power system loss; An energy balance objective function is established according to the total power generation and the total demand.

5. The method according to claim 1, characterized in that The step of obtaining the energy storage state objective function comprises: Acquire the capacity of the energy storage system, historical charging data, historical discharging data, first current data and first voltage data at the input end of the energy storage system, and second current data and second voltage data at the output end of the energy storage system; Calculate the charging power of the energy storage system according to the first current data and the first voltage data; Calculate the energy storage system discharge power according to the second current data and the second voltage data; Determine the energy storage system constraint condition according to the energy storage system charging power, the energy storage system discharging power and the energy storage system capacity; Simulating the historical charging data and the historical discharging data to determine an energy storage state weight coefficient; The energy storage state objective function is established according to the energy storage system charging power, the energy storage system discharging power, the energy storage system constraint conditions and the energy storage state weight coefficient.

6. The method according to claim 1, characterized in that The step of obtaining the user demand guarantee objective function comprises: Obtain user category priority, total power generation of virtual power plants, user historical power consumption data, non-critical electrical equipment information, and user comfort requirements; Performing statistical analysis, load curve analysis, and seasonal analysis on the user's historical electricity consumption data to identify the user's electricity consumption pattern; Obtaining the user's basic load demand according to the user's historical power consumption data and the user's power consumption pattern; Determine the power consumption data of non-critical electrical equipment according to the user's historical power consumption data and the non-critical electrical equipment information; Obtaining the user's adjustable load demand according to the user's comfort requirements and the power consumption data of the non-critical electrical equipment; Simulating the user's historical electricity consumption data according to the user category priority to determine the user demand guarantee weight coefficient; The user demand guarantee objective function is established according to the user's basic load demand, the user's adjustable load demand, the total power generation of the virtual power plant and the user demand guarantee weight coefficient.

7. The method according to claim 1, characterized in that The step of receiving the overall control target sent by the virtual power plant and setting weights for the initial resource control model according to the overall control target to obtain the multivariate flexible resource collaborative control model includes: Receive the total control target sent by the virtual power plant; Analyze the overall control target to obtain an energy balance control target, an energy storage state control target, and a user demand guarantee control target; Determine a balance weight, an energy storage state weight, and a user demand weight according to the importance levels of the energy balance control target, the energy storage state control target, and the user demand guarantee control target; The weights of the initial resource regulation model are set according to the balance weight, the energy storage state weight and the user demand weight to obtain a multivariate flexible resource collaborative regulation model.

8. The method according to any one of claims 1 to 7, characterized in that After the step of sending the control instruction to each of the flexible resources so that the flexible resources perform power control according to the control instruction and completing the step of coordinated control of the flexible resources, the method further includes: Monitoring the input power and output power of the flexible resource to obtain monitoring results; The weight of the multi-flexible resource collaborative regulation model is adjusted according to the monitoring result and the overall regulation target, and the step of obtaining the regulation instructions of each flexible resource through the multi-flexible resource collaborative regulation model is returned.

9. A virtual power plant multi-flexible resource coordinated control device, characterized in that: The device comprises: A data acquisition module is used to obtain an energy balance objective function, an energy storage state objective function, and a user demand guarantee objective function; A model building module, used to build an initial resource regulation model according to the energy balance objective function, the energy storage state objective function and the user demand guarantee objective function; A weight setting module, used for receiving the overall control target sent by the virtual power plant, and setting weights for the initial resource control model according to the overall control target to obtain a multivariate flexible resource collaborative control model; A control instruction module, used to obtain control instructions for each flexible resource through the multi-flexible resource collaborative control model; The sending module is used to send the control instruction to each of the flexible resources, so that the flexible resources perform power control according to the control instruction, thereby completing the coordinated control of the flexible resources.

10. A virtual power plant multi-flexible resource coordinated control device, characterized in that: The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for coordinated regulation of multiple flexible resources of a virtual power plant as described in any one of claims 1 to 8.

11. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for coordinated regulation of multiple flexible resources of a virtual power plant as described in any one of claims 1 to 8 are implemented.

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