Virtual power plant scheduling method and device considering user side response priority

Abstract

The invention discloses a virtual power plant scheduling method and device considering a user side response priority. The method comprises the following steps: constructing a schedulable response model of a load of a user side; obtaining a performance index influencing the schedulable response performance, calculating a comprehensive performance index of each user according to the performance index, and determining the priority of the load of the user side according to the comprehensive performance index; and constructing a priority objective function according to the schedulable response model and the priority of the load, constructing a constraint condition, solving the priority objective function and the constraint condition to obtain a load scheduling optimal solution, and performing power plant scheduling according to the load scheduling optimal solution. According to the invention, the technical problems that the participation of distributed clean energy in coincidence scheduling is mainly considered, the participation of scheduling of a user side is not fully considered, the power consumption type of the user side is singly considered, and the response priority of load scheduling of the user side is ignored in the related technology are solved.

Description

Technical Field

[0001] The present invention relates to the field of smart grid technology, and in particular to a virtual power plant scheduling method, device, computer-readable storage medium and electronic device that take user-side response priority into consideration. Background Art

[0002] Virtual power plant (VPP) is one of the important technologies for realizing smart distribution network. It can coordinate the contradiction between smart grid and distributed energy, and fully tap the value and benefits that distributed energy brings to the grid and users. In the virtual power plant, the clean energy, controlled load and energy storage system installed in the distribution network are combined as a special power plant to participate in the grid operation. Each part is connected to the energy management system. The control center uses the energy management system to coordinate the machine-end flow, receiving-end load and energy storage system through the two-way information transmission of the smart grid, so as to achieve the purpose of reducing power generation losses, reducing greenhouse gas emissions, optimizing resource utilization, reducing peak load of the grid and improving power supply reliability.

[0003] The existing virtual power plants have taken into account the impact of new energy access on grid dispatching, fully realizing the participation of photovoltaic and wind power in peak shaving and valley filling, and promoting the balance of grid supply and demand. However, the dispatching of grid terminals is not yet mature, and the potential of user-side resources has not been deeply tapped. In addition, the existing virtual power plant distribution technology mainly considers the participation of distributed clean energy such as wind power and photovoltaics in dispatching, but its disadvantage is that it does not fully consider the participation of the user side in dispatching, considers the single type of electricity consumption on the user side, and ignores the response priority of the user side load dispatching.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present invention provide a virtual power plant scheduling method, device, computer-readable storage medium and electronic device that take into account the response priority of the user side, so as to at least solve the technical problems in the related technology that the participation of distributed clean energy in scheduling is mainly considered, the user side participation in scheduling is not sufficiently considered, the user side electricity consumption type is considered single, and the response priority of the user side load scheduling is ignored.

[0006] According to one aspect of an embodiment of the present invention, a virtual power plant scheduling method that takes into account user-side response priorities is provided, including: constructing a dispatchable response model for user-side loads, wherein the dispatchable response model is a model that characterizes the costs incurred by the user-side loads during the scheduling process; obtaining performance indicators that affect dispatchable response performance, calculating a comprehensive performance indicator for each user based on the performance indicator, and determining the priority of the user-side load based on the comprehensive performance indicator; constructing a priority objective function based on the dispatchable response model and the priority of the load, and constructing constraints, solving the priority objective function and the constraints to obtain an optimal solution for load scheduling, and scheduling the power plant based on the optimal solution for load scheduling.

[0007] Optionally, constructing a dispatchable response model of the load on the user side includes: constructing a dispatchable response model of the load that can be reduced on the user side as Among them, C R,i represents the total dispatch cost of the i-th load that can be reduced, k R,i represents the total cost unit price of dispatching the i-th load that can be reduced, represents the subsidy unit price for dispatching the i-th load that can be reduced, P R,i represents the actual dispatch amount of the i-th load that can be reduced, represents the original load value of the i-th load that can be reduced, β1 represents the scheduling maintenance cost coefficient of the load that can be reduced, and λ R represents the load reduction rate, ΔT represents the duration of the scheduling of the load that can be reduced within the scheduling period; the dispatchable response model of the transferable load on the user side is constructed as Among them, C T,j represents the total dispatch cost of the jth transferable load, k T,j represents the total cost unit price of the jth transferable load, represents the subsidy unit price of the jth transferable load, P T,j represents the transfer power of the jth transferable load, β2 represents the dispatch maintenance cost coefficient of the transferable load, S j Represents the scheduling status of the jth transferable load.

[0008] Optionally, calculating the comprehensive performance index of each user according to the performance index includes: using the formula Calculate the weight of each performance indicator, where H j represents the entropy value of the jth performance indicator, H k represents the entropy value of the kth performance indicator, x represents the number of performance indicators; through the formula Calculate the comprehensive performance index of each user, where z ijrepresents the jth performance indicator of the ith user, and I represents the comprehensive performance indicator.

[0009] Optionally, constructing a priority objective function according to the dispatchable response model and the priority of the load includes: constructing a priority objective function as Among them, F represents the daily profit value of the virtual power plant, C nev represents the benefit of completing the grid dispatching instruction load, C V represents the dispatching cost of user-side load, C DE represents the dispatching cost of distributed energy, C XB,i represents the dispatching cost of the shiftable load, k fin The revenue from completing the dispatching command load consumes the unit price of wind and solar resources, k lose represents the unit price of electricity purchased from the power grid consuming wind and solar resources, k WT Indicates the completion of wind power, k PV represents the unit price of photovoltaic electricity, k ws P represents the unit price of electricity purchased by the overall load energy storage system consuming wind and solar resources. fin Indicates the total power value of wind and solar resources consumed by the load to complete the dispatch instruction, P lose Indicates the total power value of wind and solar resources consumed to complete power purchase by the power grid, P WT Indicates the total power value of wind power system consuming wind and solar resources, P PV Indicates the total power value of photovoltaic power consumption of wind and solar resources, P ws Indicates the total power value of wind and solar resources consumed by the dispatching user load system, P V Represents the total power value of the overall load energy storage system, P R Indicates the total power value that can reduce the load, P T Indicates the total power value of the transferable load, C T,i represents the dispatching cost of the transferable load, C R,i Indicates the dispatch cost that can reduce load.

[0010] Optionally, constructing a constraint condition includes: constructing a power supply side constraint condition as Among them, P WT Indicates the total power value of wind power system consuming wind and solar resources, P PV Indicates the total power value of photovoltaic power consumption of wind and solar resources. Indicates the maximum output power of the fan unit. represents the maximum output power of the photovoltaic unit; the load reduction constraint condition is constructed as Among them, P R Indicates the total power value that can reduce the load, represents the maximum response amount of load reduction; the load transfer constraint condition is constructed as Among them, PT represents the total power value of the transferable load, Indicates the maximum response amount of the transferable load.

[0011] Optionally, solving the priority objective function and the constraint condition to obtain the optimal solution for load scheduling includes: using a seagull optimization algorithm based on a t-distribution perturbation strategy to solve the priority objective function and the constraint condition to obtain the optimal solution for load scheduling, wherein the probability density function of the t-distribution perturbation strategy is x represents the random variable and n represents the degrees of freedom.

[0012] Optionally, a Seagull optimization algorithm based on a t-distribution perturbation strategy is used to solve the priority objective function and the constraints to obtain the optimal solution for load scheduling, including: generating a random number for each algorithm individual of the Seagull optimization algorithm, wherein the random number is greater than or equal to 0 and less than or equal to 1; obtaining a mutation probability, and when the random number is less than the mutation probability, executing the Seagull optimization algorithm based on a t-distribution perturbation strategy to obtain the optimal solution for load scheduling.

[0013] According to another aspect of an embodiment of the present invention, a virtual power plant dispatching device that takes into account the user-side response priority is also provided, including: a construction unit, used to construct a dispatchable response model of the user-side load, wherein the dispatchable response model is a model that characterizes the costs generated by the user-side load during the dispatching process; a determination unit, used to obtain performance indicators that affect the dispatchable response performance, calculate the comprehensive performance indicators of each user based on the performance indicators, and determine the priority of the user-side load based on the comprehensive performance indicators; a dispatching unit, used to construct a priority objective function based on the dispatchable response model and the priority of the load, and construct constraints, solve the priority objective function and the constraints to obtain the optimal solution for load dispatching, and dispatch the power plant based on the optimal solution for load dispatching.

[0014] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is also provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned virtual power plant scheduling methods considering user-side response priority.

[0015] According to another aspect of an embodiment of the present invention, an electronic device is also provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing any one of the above-mentioned virtual power plant scheduling methods considering user-side response priorities.

[0016] In an embodiment of the present invention, a dispatchable response model of the load on the user side is constructed, wherein the dispatchable response model is a model that characterizes the cost generated by the load on the user side during the dispatching process; the performance index that affects the dispatchable response performance is obtained, the comprehensive performance index of each user is calculated according to the performance index, and the priority of the load on the user side is determined according to the comprehensive performance index; the priority objective function is constructed according to the dispatchable response model and the priority of the load, and the constraint condition is constructed, the priority objective function and the constraint condition are solved, the optimal solution for load dispatching is obtained, and the power plant is dispatched according to the optimal solution for load dispatching. Through the technical scheme provided by the present invention, the purpose of involving the user side load in the regulation of the virtual power plant through intensive management is achieved, and the priority evaluation of various resources is performed by using the improved entropy weight method, and the distributed energy and user side resources are aggregated and dispatched at the same time, thereby achieving the technical effect of fully considering the response priority of different types of user loads, improving the flexibility and reliability of the power system, and thus solving the technical problems that the participation of distributed clean energy in the related technology is mainly considered in accordance with the dispatching, the user side participation in the dispatching is not sufficiently considered, the user side electricity type is considered single, and the response priority of the user side load dispatching is ignored. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0018] Figure 1 It is a hardware structure block diagram of a mobile terminal of a virtual power plant scheduling method considering user-side response priority according to an embodiment of the present invention;

[0019] Figure 2 is a flowchart of a virtual power plant scheduling method considering user-side response priority according to an embodiment of the present invention;

[0020] Figure 3 is a flowchart of an optional virtual power plant scheduling method considering user-side response priority according to an embodiment of the present invention;

[0021] Figure 4 is a flow chart of model solving according to an embodiment of the present invention;

[0022] Figure 5 is a schematic diagram of a virtual power plant scheduling device considering user-side response priority according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] As introduced in the background technology, the related technologies mainly consider the participation of distributed clean energy in scheduling, but do not fully consider the user-side participation in scheduling, consider the user-side electricity consumption type in a single way, and ignore the response priority of user-side load scheduling. In view of the above defects, a virtual power plant scheduling method, device, computer-readable storage medium and electronic device that consider the user-side response priority are provided in the embodiments of the present invention.

[0026] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0027] The method embodiments provided in the embodiments of the present invention can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 1 is a hardware structure block diagram of a mobile terminal of a virtual power plant scheduling method considering user-side response priority according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0028] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the virtual power plant scheduling method considering the user-side response priority in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific network example may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0029] According to an embodiment of the present invention, a method embodiment of a virtual power plant scheduling method taking into account user-side response priorities is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0030] Figure 2 is a flow chart of a virtual power plant scheduling method considering user-side response priority according to an embodiment of the present invention. Figure 2 As shown, the method comprises the following steps:

[0031] Step S202: construct a dispatchable response model of the user-side load, wherein the dispatchable response model is a model that characterizes the cost generated by the user-side load during the dispatching process.

[0032] In this embodiment, a dispatchable response model of the user-side load can be constructed. Through this model, the user can reasonably arrange the use of the load according to the energy prices in different time periods and their own energy demand conditions, helping the user side to better manage and control the load during the energy scheduling process.

[0033] Figure 3 is a flowchart of an optional virtual power plant scheduling method considering user-side response priority according to an embodiment of the present invention. Figure 3 As shown, firstly, a dispatchable response model of user load is constructed, and mathematical modeling of curtailable load and transferable load is performed.

[0034] Step S204, obtaining performance indicators that affect the schedulable response performance, calculating a comprehensive performance indicator for each user based on the performance indicators, and determining the priority of the load on the user side based on the comprehensive performance indicators.

[0035] Optionally, the above performance indicators may include but are not limited to: delay, throughput, packet loss rate, etc.

[0036] In this embodiment, the performance indicators that affect the schedulable response performance are obtained, and the priority of each user-side load can be calculated according to the performance indicators, so as to better allocate resources and optimize the scheduling strategy.

[0037] Step S206, constructing a priority objective function according to the dispatchable response model and the priority of the load, and constructing constraints, solving the priority objective function and constraints, obtaining the optimal solution for load scheduling, and dispatching the power plant according to the optimal solution for load scheduling.

[0038] In this embodiment, a priority objective function and constraints can be constructed based on the dispatchable response model and the priority of the load, so as to achieve an optimal solution to the load scheduling problem, and perform power plant scheduling based on the optimal solution to load scheduling. By optimizing power plant scheduling, it is possible to achieve a reasonable distribution of grid load and improve grid operation efficiency and economy.

[0039] like Figure 3 As shown in the figure, after the priority evaluation of the curtailable load and the transferable load is carried out, the objective function considering the user load response priority is constructed, the constraints on the power supply side, the curtailable load and the transferable load are constructed, and the swarm intelligent optimization algorithm is improved to solve the model.

[0040] As can be seen from the above, in an embodiment of the present invention, a dispatchable response model of the load on the user side is constructed, wherein the dispatchable response model is a model that characterizes the costs incurred by the load on the user side during the dispatching process; performance indicators that affect the dispatchable response performance are obtained, the comprehensive performance indicators of each user are calculated based on the performance indicators, and the priority of the load on the user side is determined based on the comprehensive performance indicators; a priority objective function is constructed based on the dispatchable response model and the priority of the load, and constraints are constructed, the priority objective function and constraints are solved to obtain the optimal solution for load dispatching, and the power plant is dispatched based on the optimal solution for load dispatching, thereby achieving the purpose of involving the user-side load in the regulation of the virtual power plant through intensive management, and using the improved entropy weight method to evaluate the priority of various resources, and aggregating and dispatching distributed energy and user-side resources at the same time, thereby achieving the technical effect of fully considering the response priority of different types of user loads and improving the flexibility and reliability of the power system.

[0041] The technical solution provided by the embodiment of the present invention solves the technical problems that the related technologies mainly consider the participation of distributed clean energy in scheduling, insufficiently consider the participation of the user side in scheduling, single-mindedly consider the electricity consumption type on the user side, and ignore the response priority of the user side load scheduling.

[0042] According to the above embodiment of the present invention, constructing a dispatchable response model of the load on the user side includes: constructing a dispatchable response model of the load that can be reduced on the user side as follows: Among them, C R,i represents the total dispatch cost of the i-th load that can be reduced, k R,i represents the total cost unit price of dispatching the i-th load that can be reduced, represents the subsidy unit price for dispatching the i-th load that can be reduced, P R,i represents the actual dispatch amount of the i-th load that can be reduced, represents the original load value of the i-th load that can be reduced, β1 represents the scheduling maintenance cost coefficient of the load that can be reduced, and λ R represents the load reduction rate, ΔT represents the time for load reduction during the scheduling period; the dispatchable response model of the transferable load on the user side is constructed as Among them, C T,j represents the total dispatch cost of the jth transferable load, k T,j represents the total cost unit price of the jth transferable load, represents the subsidy unit price of the jth transferable load, P T,j represents the transfer power of the jth transferable load, β2 represents the dispatch maintenance cost coefficient of the transferable load, S j Represents the dispatching status of the jth transferable load.

[0043] It should be noted that VPP (i.e. virtual power plant) adopts a centralized control mode, and controls internal resources through an intelligent communication system to complete the dispatch instruction load. After meeting the demand, it implements the policy of grid-connected surplus electricity. The unfinished electricity must be purchased from the power grid to meet the power balance. Wind power and photovoltaic power fully participate in VPP regulation, and the movable resources on the user side are used as a storable spare capacity resource that can be flexibly managed. Peak loads are reduced or moved to normal periods. As a flexible and controllable dispatch resource for coordinating wind and solar power, its basic function is equivalent to energy storage. VPP realizes load transfer and enhances the synergy value among multiple energy sources through coordinated control of the power supply side and the user side, so as to meet the flexible and changeable grid dispatch needs in the future.

[0044] In addition, it should be noted that the above S j The dispatch state of the transferable load represented by is a 0-1 variable, where 0 is undispatched and 1 is dispatched; the dispatchable response model of the above user load consists of two aspects. The first is to use the part of the power that can be reduced on the user side as the discharge of the overall energy storage power generation system; the second is to make flexible adjustments to the usage time of the user-side load, transfer the load demand during the peak period to the off-peak period, and effectively alleviate the imbalance between electricity supply and demand.

[0045] In this embodiment, a dispatchable response model for load reduction and a dispatchable response model for load transfer on the user side within the scheduling time period can be constructed, which can help users manage their own electricity consumption behavior more flexibly. By setting different user-side response priorities, loads can be scheduled according to user needs and preferences to achieve maximum energy saving and reduce electricity costs.

[0046] According to the above embodiment of the present invention, the comprehensive performance index of each user is calculated according to the performance index, including: Calculate the weight of each performance indicator, where H j represents the entropy value of the jth performance indicator, H k represents the entropy value of the kth performance indicator, x represents the number of performance indicators; through the formula Calculate the comprehensive performance index of each user, where z ij represents the jth performance indicator of the ith user, and I represents the comprehensive performance indicator.

[0047] In this embodiment, a formula can be used to calculate the weight of each performance indicator and the comprehensive performance indicator of each user. By setting weights for different performance indicators, the importance of user needs can be better reflected, so that the scheduling results are more in line with the actual needs of users. By calculating the comprehensive performance indicator of each user, it is helpful to determine which users have higher priorities, so that the needs of these users are given priority during scheduling, thereby achieving effective utilization of energy resources.

[0048] It should be noted that since VPP fully mobilizes the enthusiasm of user-side loads to participate in scheduling, the corresponding performance levels of various types of users participating in scheduling are different. If the user-side load resources are introduced into the power management system according to a unified standard, the operating cost of virtual power plant scheduling will increase, and the reliability of scheduling cannot be guaranteed. Therefore, it is necessary to consider the user's response scheduling performance, and set the priority of load orderly participation in scheduling by evaluating its response performance. The three factors that affect the response performance of the reducible load are: response speed, response amount and response time. Among them, the response speed indicates the time from the user receiving the response instruction to the start of the response, the response amount indicates the load reduction rate, and the response time indicates the maximum time of the actual participation in the response. The three factors that affect the transferable load are: response amount, response time and response power (load operating power). Since it is necessary to comprehensively evaluate the responding users based on various indicators, the traditional entropy weight method determines the weight of each indicator, and finally obtains the comprehensive evaluation value of the user to be evaluated.

[0049] This embodiment is described by taking an example of n responding users and 3 indicators (ie, m=3), and the indicator matrix is: In the formula, z ij represents the jth (j=1, 2, 3) index of the i-th (i=1, ..., n) user, and the entropy value of the j-th index is: In the formula, F ij Represents the index after standardization. According to the entropy weight of each index, the entropy weight of the jth index is: In the formula, ω j ∈[0,1], since the entropy weight distribution result of the traditional entropy weight method is unreasonable, the improved entropy weight method is adopted. According to the above formula ω m , further, the user's load reduction response index is calculated as: The user's transferable load response index is calculated as:

[0050] According to the above embodiment of the present invention, a priority objective function is constructed according to the dispatchable response model and the priority of the load, including: constructing the priority objective function as follows: Among them, F represents the daily profit value of the virtual power plant, C nev represents the benefit of completing the grid dispatching instruction load, C V represents the dispatching cost of user-side load, C DE represents the dispatching cost of distributed energy, C XB,i represents the dispatching cost of the shiftable load, k fin The revenue from completing the dispatching command load consumes the unit price of wind and solar resources, k lose represents the unit price of electricity purchased from the power grid consuming wind and solar resources, k WTIndicates the completion of wind power, k PV represents the unit price of photovoltaic electricity, k ws P represents the unit price of electricity purchased by the overall load energy storage system consuming wind and solar resources. fin Indicates the total power value of wind and solar resources consumed by the load to complete the dispatch instruction, P lose Indicates the total power value of wind and solar resources consumed to complete power purchase by the power grid, P WT Indicates the total power value of wind power system consuming wind and solar resources, P PV Indicates the total power value of photovoltaic power consumption of wind and solar resources, P ws Indicates the total power value of wind and solar resources consumed by the dispatching user load system, P V Represents the total power value of the overall load energy storage system, P R Indicates the total power value that can reduce the load, P T Indicates the total power value of the transferable load, C T,i represents the dispatching cost of the transferable load, C R,i Indicates the dispatch cost that can reduce load.

[0051] In this embodiment, since the goal of the virtual power plant operator is to maximize its revenue, a priority objective function can be constructed to determine the execution order of tasks according to the load and priority of the tasks during the scheduling process, thereby achieving more efficient resource utilization and faster task completion.

[0052] According to the above embodiment of the present invention, constructing the constraint condition includes: constructing the power supply side constraint condition as Among them, P WT Indicates the total power value of wind power system consuming wind and solar resources, P PV Indicates the total power value of photovoltaic power consumption of wind and solar resources. Indicates the maximum output power of the fan unit. represents the maximum output power of the photovoltaic unit; the load reduction constraint condition is constructed as Among them, P R Indicates the total power value that can reduce the load, represents the maximum response amount of load reduction; the load transfer constraint condition is constructed as Among them, P T represents the total power value of the transferable load, Indicates the maximum response amount of the transferable load.

[0053] In this embodiment, power supply side constraints can be constructed. The formulation and compliance of power supply side constraints can ensure that the design and implementation of the power supply system comply with specifications and standards, improve the performance and reliability of the system, and reduce energy waste.

[0054] According to the above embodiment of the present invention, the priority objective function and the constraint conditions are solved to obtain the optimal solution for load scheduling, including: using the seagull optimization algorithm based on the t-distribution perturbation strategy to solve the priority objective function and the constraint conditions to obtain the optimal solution for load scheduling, wherein the probability density function of the t-distribution perturbation strategy is x represents the random variable and n represents the degrees of freedom.

[0055] In this embodiment, the Seagull optimization algorithm based on the t-distribution perturbation strategy is used to solve the priority objective function and constraints, which can find the global optimal solution or a solution close to the optimal solution in complex load scheduling problems, improve the efficiency of load scheduling, flexibly handle objective functions of different priorities, adjust the weight of the objective function according to actual conditions, achieve a balance between different objectives, and ensure that the production scheduling plan meets the actual operation requirements of the system.

[0056] It should be noted that the above-mentioned seagull optimization algorithm (SOA) is a fast optimization solution algorithm inspired by the behavior of seagull populations. Seagulls are gregarious seabirds with a wide range of habitats. When the seasons change, they search for food in different regions. When the seasons change, seagulls will hunt through their migration and attack behaviors. However, the position update idea of ​​SOA will inevitably fall into the problem of local optimal solutions, which will lead to its slow convergence speed. In the optimization iteration process, the algorithm is difficult to balance between global search and local search. Therefore, the improved SOA is adopted to perturb the t-distribution. In the process of integrating the dynamic strategy into the position update, i.e. the optimal solution of the target, in the early stage of the algorithm iteration, the t-distribution perturbation is similar to the Cauchy distribution, at this time, the perturbation to the seagull population is stronger, which is helpful to develop new space and improve the global search ability; in the later stage of the algorithm iteration, the t-distribution perturbation is similar to the Gaussian distribution, at this time, the perturbation ability is weakened, which is helpful to search in the surrounding space and improve the local search ability; in the middle stage of the algorithm iteration, the t-distribution perturbation combines the advantages of the Cauchy distribution and the Gaussian distribution to achieve the purpose of balancing the global search ability and the local search ability.

[0057] In the above embodiment of the present invention, the Seagull optimization algorithm based on the t-distribution perturbation strategy is used to solve the priority objective function and the constraints to obtain the optimal solution for load scheduling, including: generating a random number for each algorithm individual of the Seagull optimization algorithm, where the random number is greater than or equal to 0 and less than or equal to 1; obtaining the mutation probability, and when the random number is less than the mutation probability, executing the Seagull optimization algorithm based on the t-distribution perturbation strategy to obtain the optimal solution for load scheduling.

[0058] In this embodiment, a random number is generated for each individual algorithm of the Seagull optimization algorithm. The random number is used to determine whether to perform a mutation operation and obtain the mutation probability. When the random number is less than the mutation probability, the Seagull optimization algorithm based on the t-distribution perturbation strategy is executed. Through the mutation operation, the algorithm can be helped to jump out of the local optimal solution and improve the algorithm's exploration ability, thereby obtaining a better load scheduling optimal solution.

[0059] Figure 4 is a flow chart of model solving according to an embodiment of the present invention, such as Figure 4 As shown, the probability of t-distribution perturbation is set to L = 0.5, and a random number [0,1] is generated for each seagull individual. The random number is less than the mutation probability of the seagull individual. The execution t-distribution perturbation, each iteration will have a corresponding optimal solution, and judge whether it meets the accuracy requirements, and jump out of the objective function value of the iteration. The specific steps are as follows: Start, t = 1, obtain the wind and solar output, the overall load energy storage system state and dispatchable capacity and the grid dispatch instruction load P G , judge P PV +P WT >P G In the case of Y (yes), charge and update the state quantity and dispatchable capacity of the overall load energy storage system; judge P PV +P WT >P G , in the case of N (no), judge P PV +P WT +P V >P G In the case of N, the power grid purchases electricity. In the case of Y (yes), the fitness function is set and the population position is initialized. The individual positions of the population are searched and updated. F is determined. i k+1 >F k+1 In the case of N (no), perform t-distribution disturbance. In the case of Y (yes), determine whether the accuracy requirements are met. In the case of N, k=k+1, continue to search and update the individual positions of the population; in the case of Y, calculate the dispatch priority of the user-side load and evenly distribute the actual response amount, update the overall load energy storage system state quantity and dispatchable capacity, and determine whether t>24. In the case of Y, output the objective function value. In the case of N, t=t+1, continue to execute the above steps until it is determined that t>24 is Y, output the objective function value and end.

[0060] Through the technical solution provided by the above-mentioned embodiments of the present invention, user-side resources and distributed power sources are aggregated at the distribution terminal to participate in the regulation of virtual power plants. Taking into account that user-side loads have different response performances, a terminal virtual power plant scheduling model considering priority is proposed, and the dynamic response priority is used to set the scheduling priority order of user loads, thereby improving the scheduling elasticity and overall economic benefits of virtual power plants. It can correctly guide the response standards of community users and improve the reliability of user participation in scheduling responses. Distributed energy and user-side resources are aggregated and scheduled simultaneously, and the response priorities of different types of user loads are fully considered, which improves the enthusiasm of users to participate in power scheduling, enhances the standardization of the response of power grid terminal users, and promotes the balance of power grid supply and demand.

[0061] From the above, it can be seen that in an embodiment of the present invention, a virtual power plant dispatching system is proposed that takes into account the participation of renewable resources such as wind power and photovoltaics and adjustable resources on the user side. The dispatching system takes into account the uneven performance levels of user-side loads when participating in dispatching, and performs priority evaluation on them. The orderly participation in dispatching greatly improves the power quality and the enthusiasm of user participation.

[0062] That is, the technical solution provided by the embodiment of the present invention comprehensively considers various factors, and through intensive management, the user-side load is involved in the regulation of the virtual power plant, and the passive user load of the distribution network is effectively transformed into a controllable resource that actively participates in the regulation of electric power energy, which is conducive to the reasonable regulation of user-side resources, and uses the improved entropy weight method to evaluate the priority of various resources, effectively integrating resources while ensuring user electricity consumption, and improving the flexible access and management capabilities of the virtual power plant.

[0063] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0064] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0065] According to an embodiment of the present invention, a virtual power plant scheduling device considering user-side response priority for implementing the above-mentioned virtual power plant scheduling method considering user-side response priority is also provided. Figure 5 is a schematic diagram of a virtual power plant dispatching device considering user-side response priority according to an embodiment of the present invention, such as Figure 5 As shown, the device includes: a construction unit 501, a determination unit 503 and a scheduling unit 505. The virtual power plant scheduling device considering the user side response priority is described below.

[0066] The construction unit 501 is used to construct a dispatchable response model of the user-side load, wherein the dispatchable response model is a model that characterizes the cost generated by the user-side load during the dispatching process.

[0067] The determination unit 503 is used to obtain performance indicators that affect the schedulable response performance, calculate the comprehensive performance indicators of each user according to the performance indicators, and determine the priority of the load on the user side according to the comprehensive performance indicators.

[0068] The dispatching unit 505 is used to construct a priority objective function according to the dispatchable response model and the priority of the load, and to construct constraint conditions, solve the priority objective function and constraint conditions to obtain the optimal solution for load dispatching, and dispatch the power plant according to the optimal solution for load dispatching.

[0069] It should be noted here that the above-mentioned construction unit 501, determination unit 503 and scheduling unit 505 correspond to steps S202 to S206 in the above-mentioned embodiment, and the three modules are the same as the instances and application scenarios implemented by the corresponding steps, but are not limited to the contents disclosed in the above-mentioned embodiment.

[0070] As can be seen from the above, in the scheme recorded in the above-mentioned embodiment of the present invention, first, a construction unit can be used to construct a dispatchable response model of the load on the user side, wherein the dispatchable response model is a model that characterizes the costs incurred by the load on the user side during the dispatching process; then, a determination unit can be used to obtain performance indicators that affect the dispatchable response performance, and the comprehensive performance indicators of each user are calculated based on the performance indicators, and the priority of the load on the user side is determined based on the comprehensive performance indicators; finally, a dispatching unit can be used to construct a priority objective function based on the dispatchable response model and the priority of the load, and to construct constraints, solve the priority objective function and the constraints, obtain the optimal solution for load dispatching, and dispatch the power plant based on the optimal solution for load dispatching, so as to achieve the purpose of involving the user side load in the regulation of the virtual power plant through intensive management, and using the improved entropy weight method to evaluate the priority of various resources, and to simultaneously aggregate and dispatch distributed energy and user side resources, thereby achieving the technical effect of fully considering the response priority of different types of user loads and improving the flexibility and reliability of the power system.

[0071] The technical solution provided by the embodiment of the present invention solves the technical problems that the related technologies mainly consider the participation of distributed clean energy in scheduling, insufficiently consider the participation of the user side in scheduling, single-mindedly consider the electricity consumption type on the user side, and ignore the response priority of the user side load scheduling.

[0072] In an optional embodiment, the construction unit includes: a first construction subunit, which is used to construct a dispatchable response model of the user-side load that can be reduced: Among them, C R,i represents the total dispatch cost of the i-th load that can be reduced, k R,i represents the total cost unit price of dispatching the i-th load that can be reduced, represents the subsidy unit price for dispatching the i-th load that can be reduced, P R,i represents the actual dispatch amount of the i-th load that can be reduced, represents the original load value of the i-th load that can be reduced, β1 represents the scheduling maintenance cost coefficient of the load that can be reduced, and λ R represents the load reduction rate, ΔT represents the duration of load reduction execution scheduling within the scheduling period; the second construction subunit is used to construct the dispatchable response model of the transferable load on the user side: Among them, C T,j represents the total dispatch cost of the jth transferable load, k T,j represents the total cost unit price of the jth transferable load, represents the subsidy unit price of the jth transferable load, P T,j represents the transfer power of the jth transferable load, β2 represents the dispatch maintenance cost coefficient of the transferable load, S jrepresents the dispatching status of the jth transferable load.

[0073] In an optional embodiment, the determination unit includes: a first calculation subunit, which is used to calculate the value of Calculate the weight of each performance indicator, where H j represents the entropy value of the jth performance indicator, H k represents the entropy value of the kth performance indicator, and x represents the number of the performance indicators; the second calculation subunit is used to calculate the entropy value of the kth performance indicator through the formula Calculate the comprehensive performance index of each user, where z ij represents the jth performance indicator of the ith user, and I represents the comprehensive performance indicator.

[0074] In an optional embodiment, the scheduling unit includes: a third construction subunit, which is used to construct a priority objective function: Among them, F represents the daily profit value of the virtual power plant, C nev represents the benefit of completing the grid dispatching instruction load, C V represents the dispatching cost of user-side load, C DE represents the dispatching cost of distributed energy, C XB,i represents the dispatching cost of the shiftable load, k fin The revenue from completing the dispatching command load consumes the unit price of wind and solar resources, k lose represents the unit price of electricity purchased from the power grid consuming wind and solar resources, k WT Indicates the completion of wind power, k PV represents the unit price of photovoltaic electricity, k ws P represents the unit price of electricity purchased by the overall load energy storage system consuming wind and solar resources. fin Indicates the total power value of wind and solar resources consumed by the load to complete the dispatch instruction, P lose Indicates the total power value of wind and solar resources consumed to complete power purchase by the power grid, P WT Indicates the total power value of wind power system consuming wind and solar resources, P PV Indicates the total power value of photovoltaic power consumption of wind and solar resources, P ws Indicates the total power value of wind and solar resources consumed by the dispatching user load system, P V Represents the total power value of the overall load energy storage system, P R Indicates the total power value that can reduce the load, P T Represents the total power value of the transferable load, C T,i represents the dispatching cost of the transferable load, C R,i Indicates the dispatch cost that can reduce load.

[0075] In an optional embodiment, the scheduling unit includes: a fourth construction subunit, which is used to construct a power supply side constraint condition as follows: Among them, P WT Indicates the total power value of wind power system consuming wind and solar resources, P PV Indicates the total power value of photovoltaic power consumption of wind and solar resources. Indicates the maximum output power of the fan unit. Represents the maximum output power of the photovoltaic unit; the fifth construction subunit is used to construct the load reduction constraint condition: Among them, P R Indicates the total power value that can reduce the load, Represents the maximum response amount of the load that can be reduced; the sixth construction subunit is used to construct the transferable load constraint condition: Among them, P T represents the total power value of the transferable load, Indicates the maximum response amount of the transferable load.

[0076] In an optional embodiment, the dispatching unit includes: a solving subunit, which is used to solve the priority objective function and the constraint conditions by using the seagull optimization algorithm based on the t-distribution perturbation strategy to obtain the optimal solution for load dispatching, wherein the probability density function of the t-distribution perturbation strategy is x represents the random variable and n represents the degrees of freedom.

[0077] In an optional embodiment, the solving subunit includes: a generating subunit, used to generate a random number for each algorithm individual of the Seagull optimization algorithm, wherein the random number is greater than or equal to 0 and less than or equal to 1; a processing subunit, used to obtain the mutation probability, and when the random number is less than the mutation probability, execute the Seagull optimization algorithm based on the t-distribution perturbation strategy to obtain the optimal solution for load scheduling.

[0078] According to another aspect of an embodiment of the present invention, an electronic device is also provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing any of the above-mentioned virtual power plant scheduling methods that consider user-side response priorities.

[0079] According to another aspect of an embodiment of the present invention, a computer program product is also provided, comprising computer instructions, which, when executed by a processor, execute any one of the above-mentioned virtual power plant scheduling methods that consider user-side response priorities.

[0080] According to another aspect of an embodiment of the present invention, a processor is further provided, and the processor is used to run a program, wherein when the program is running, any one of the above-mentioned virtual power plant scheduling methods considering user-side response priority is executed.

[0081] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned virtual power plant scheduling methods considering user-side response priority.

[0082] Optionally, in this embodiment, the computer-readable storage medium may be located in any one of the computer terminals in a computer terminal group in a computer network, or in any one of the communication devices in a communication device group.

[0083] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: constructing a dispatchable response model of the load on the user side, wherein the dispatchable response model is a model that characterizes the costs incurred by the load on the user side during the dispatching process; obtaining performance indicators that affect the dispatchable response performance, calculating the comprehensive performance indicators of each user based on the performance indicators, and determining the priority of the load on the user side based on the comprehensive performance indicators; constructing a priority objective function based on the dispatchable response model and the priority of the load, and constructing constraints, solving the priority objective function and the constraints to obtain the optimal solution for load dispatching, and dispatching the power plant based on the optimal solution for load dispatching.

[0084] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: constructing a dispatchable response model of the user-side load that can be reduced as follows: Among them, C R,i represents the total dispatch cost of the i-th load that can be reduced, k R,i represents the total cost unit price of dispatching the i-th load that can be reduced, represents the subsidy unit price for dispatching the i-th load that can be reduced, P R,i represents the actual dispatch amount of the i-th load that can be reduced, represents the original load value of the i-th load that can be reduced, β1 represents the scheduling maintenance cost coefficient of the load that can be reduced, and λ R represents the load reduction rate, ΔT represents the time for load reduction during the scheduling period; the dispatchable response model of the transferable load on the user side is constructed as Among them, C T,j represents the total dispatch cost of the jth transferable load, k T,j represents the total cost unit price of the jth transferable load, represents the subsidy unit price of the jth transferable load, P T,j represents the transfer power of the jth transferable load, β2 represents the dispatch maintenance cost coefficient of the transferable load, S j Represents the dispatching status of the jth transferable load.

[0085] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for performing the following steps: Calculate the weight of each performance indicator, where H j represents the entropy value of the jth performance indicator, H k represents the entropy value of the kth performance indicator, x represents the number of performance indicators; through the formula Calculate the comprehensive performance index of each user, where z ij represents the jth performance indicator of the ith user, and I represents the comprehensive performance indicator.

[0086] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: constructing a priority objective function as Among them, F represents the daily profit value of the virtual power plant, C nev represents the benefit of completing the grid dispatching instruction load, C V represents the dispatching cost of user-side load, C DE represents the dispatching cost of distributed energy, C XB,i represents the dispatching cost of the shiftable load, k fin The revenue from completing the dispatching command load consumes the unit price of wind and solar resources, k lose represents the unit price of electricity purchased from the power grid consuming wind and solar resources, k WT Indicates the completion of wind power, k PV represents the unit price of photovoltaic electricity, k ws P represents the unit price of electricity purchased by the overall load energy storage system consuming wind and solar resources. fin Indicates the total power value of wind and solar resources consumed by the load to complete the dispatch instruction, P lose Indicates the total power value of wind and solar resources consumed to complete power purchase by the power grid, P WT Indicates the total power value of wind power system consuming wind and solar resources, P PV Indicates the total power value of photovoltaic power consumption of wind and solar resources, P ws Indicates the total power value of wind and solar resources consumed by the dispatching user load system, P V Represents the total power value of the overall load energy storage system, P R Indicates the total power value that can reduce the load, P T Indicates the total power value of the transferable load, C T,i represents the dispatching cost of the transferable load, C R,i Indicates the dispatch cost that can reduce load.

[0087] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: constructing a power supply side constraint condition as Among them, P WT Indicates the total power value of wind power system consuming wind and solar resources, P PV Indicates the total power value of photovoltaic power consumption of wind and solar resources. Indicates the maximum output power of the fan unit. represents the maximum output power of the photovoltaic unit; the load reduction constraint condition is constructed as Among them, P R Indicates the total power value that can reduce the load, represents the maximum response amount of load reduction; the load transfer constraint condition is constructed as Among them, P T represents the total power value of the transferable load, Indicates the maximum response amount of the transferable load.

[0088] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: using the Seagull optimization algorithm based on the t-distribution perturbation strategy to solve the priority objective function and the constraint conditions to obtain the optimal solution for load scheduling, wherein the probability density function of the t-distribution perturbation strategy is x represents the random variable and n represents the degrees of freedom.

[0089] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: generating a random number for each algorithm individual of the Seagull optimization algorithm, where the random number is greater than or equal to 0 and less than or equal to 1; obtaining the mutation probability, and when the random number is less than the mutation probability, executing the Seagull optimization algorithm based on the t-distribution perturbation strategy to obtain the optimal solution for load scheduling.

[0090] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0091] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0092] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0093] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0094] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0095] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0096] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A virtual power plant scheduling method considering user-side response priority, characterized in that: include: Constructing a dispatchable response model of the user-side load, wherein the dispatchable response model is a model that characterizes the cost generated by the user-side load during the dispatching process; Acquire performance indicators that affect dispatchable response performance, calculate comprehensive performance indicators for each user based on the performance indicators, and determine the priority of user-side loads based on the comprehensive performance indicators; A priority objective function is constructed according to the dispatchable response model and the priority of the load, and constraints are constructed. The priority objective function and the constraints are solved to obtain an optimal solution for load scheduling, and the power plant is dispatched according to the optimal solution for load scheduling.

2. The virtual power plant scheduling method according to claim 1, characterized in that: Construct a dispatchable response model for user-side loads, including: The dispatchable response model of the load reduction on the user side is constructed as Among them, C R,i represents the total dispatch cost of the i-th load that can be reduced, k R,i represents the total cost unit price of dispatching the i-th load that can be reduced, represents the subsidy unit price for dispatching the i-th load that can be reduced, P R,i represents the actual dispatch amount of the i-th load that can be reduced, represents the original load value of the i-th load that can be reduced, β1 represents the scheduling maintenance cost coefficient of the load that can be reduced, and λ R represents the load reduction rate, ΔT represents the duration of the scheduling of the load reduction within the scheduling period; The dispatchable response model of the transferable load on the user side is constructed as Among them, C T,j represents the total dispatch cost of the jth transferable load, k T,j represents the total cost unit price of the jth transferable load, represents the subsidy unit price of the jth transferable load, P T,j represents the transfer power of the jth transferable load, β2 represents the dispatch maintenance cost coefficient of the transferable load, S j Represents the scheduling status of the jth transferable load.

3. The virtual power plant scheduling method according to claim 1, characterized in that: The comprehensive performance index of each user is calculated according to the performance index, including: By formula Calculate the weight of each performance indicator, where H j represents the entropy value of the jth performance indicator, H k represents the entropy value of the kth performance indicator, and x represents the number of the performance indicators; By formula Calculate the comprehensive performance index of each user, where z ij represents the jth performance indicator of the ith user, and I represents the comprehensive performance indicator.

4. The virtual power plant scheduling method according to claim 1, characterized in that: Constructing a priority objective function according to the dispatchable response model and the priority of the load, including: The priority objective function is constructed as Among them, F represents the daily profit value of the virtual power plant, C nev represents the benefit of completing the grid dispatching instruction load, C V represents the dispatching cost of user-side load, C DE represents the dispatching cost of distributed energy, C XB,i represents the dispatching cost of the shiftable load, k fin The revenue from completing the dispatching command load consumes the unit price of wind and solar resources, k lose represents the unit price of electricity purchased from the power grid consuming wind and solar resources, k WT Indicates the completion of wind power, k PV represents the unit price of photovoltaic electricity, k ws P represents the unit price of electricity purchased by the overall load energy storage system consuming wind and solar resources. fin Indicates the total power value of wind and solar resources consumed by the load to complete the dispatch instruction, P lose Indicates the total power value of wind and solar resources consumed to complete power purchase by the power grid, P WT Indicates the total power value of wind power system consuming wind and solar resources, P PV Indicates the total power value of photovoltaic power consumption of wind and solar resources, P ws Indicates the total power value of wind and solar resources consumed by the dispatching user load system, P V Represents the total power value of the overall load energy storage system, P R Indicates the total power value that can reduce the load, P T Represents the total power value of the transferable load, C T,i represents the dispatching cost of the transferable load, C R,i Indicates the dispatch cost that can reduce load.

5. The virtual power plant scheduling method according to claim 1, characterized in that: Build constraints, including: The power supply side constraints are constructed as follows: Among them, P WT Indicates the total power value of wind power system consuming wind and solar resources, P PV Indicates the total power value of photovoltaic power consumption of wind and solar resources. Indicates the maximum output power of the fan unit. Indicates the maximum output power of the photovoltaic unit; The load reduction constraint condition is constructed as Among them, P R Indicates the total power value that can reduce the load, Indicates the maximum response amount that can reduce load; The transferable load constraint condition is constructed as Among them, P T represents the total power value of the transferable load, Indicates the maximum response amount of the transferable load.

6. The virtual power plant scheduling method according to claim 1, characterized in that: Solving the priority objective function and the constraint conditions to obtain the optimal solution for load scheduling includes: The priority objective function and the constraint conditions are solved by the Seagull optimization algorithm based on the t-distribution perturbation strategy to obtain the optimal solution for load scheduling, where the probability density function of the t-distribution perturbation strategy is: x represents the random variable and n represents the degrees of freedom.

7. The virtual power plant scheduling method according to claim 6, characterized in that: The priority objective function and the constraint conditions are solved by using the Seagull optimization algorithm based on the t-distribution perturbation strategy to obtain the optimal solution for load scheduling, including: Generate a random number for each algorithm individual of the Seagull optimization algorithm, wherein the random number is greater than or equal to 0 and less than or equal to 1; Obtain a mutation probability, and when the random number is less than the mutation probability, execute the Seagull optimization algorithm based on the t-distribution perturbation strategy to obtain the optimal solution for load scheduling.

8. A virtual power plant dispatching device considering user-side response priority, characterized in that: include: A construction unit, configured to construct a dispatchable response model of the user-side load, wherein the dispatchable response model is a model that characterizes the cost generated by the user-side load during the dispatching process; A determination unit, configured to obtain a performance indicator that affects the schedulable response performance, calculate a comprehensive performance indicator for each user according to the performance indicator, and determine a priority of a load on the user side according to the comprehensive performance indicator; A dispatching unit is used to construct a priority objective function according to the dispatchable response model and the priority of the load, and to construct constraint conditions, to solve the priority objective function and the constraint conditions to obtain an optimal solution for load dispatching, and to dispatch the power plant according to the optimal solution for load dispatching.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the virtual power plant scheduling method considering the user-side response priority as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing a virtual power plant dispatching method considering user-side response priorities as described in any one of claims 1 to 7.

Images