Virtual power plant operation optimization method and device based on multivariate load, equipment, medium and product

Abstract

The application discloses a virtual power plant operation optimization method and device based on multi-element load, equipment, medium and product, relates to the field of virtual power plant operation optimization, and comprises the following steps: acquiring optimization required parameters of a virtual power plant; calculating an operation cost estimation value of the virtual power plant according to the optimization required parameters; judging whether the operation cost estimation value is greater than or equal to a set value; if not, performing multiple iteration optimizations on multi-element load output in the virtual power plant by using an optimization factor to obtain optimal operation results of the virtual power plant; the optimal operation results comprise electric vehicle charging power, temperature control load power, distributed power storage system discharging power, distributed heat storage system heat releasing power and combined heat and power unit power corresponding to the case that the operation cost estimation value is greater than or equal to the set value, and the application can quickly and accurately realize virtual power plant operation optimization.

Description

Technical Field

[0001] The present application relates to the field of virtual power plant operation optimization, and in particular to a virtual power plant operation optimization method, device, equipment, medium and product based on multiple loads. Background Art

[0002] The increase in renewable energy grid-connected capacity has squeezed the grid-connected capacity of traditional thermal power units, reducing the grid's power regulation capabilities. The uncertainty of renewable energy output will further complicate grid power balance regulation. Virtual power plants, an emerging technology that integrates load-side adjustable resources, can aggregate the adjustable potential of multiple loads, providing controllable and flexible resources for grid power regulation. Furthermore, virtual power plant technology can aggregate distributed energy storage systems, cogeneration units, heat pumps, and other energy supply and conversion equipment to achieve broad, multi-load aggregation. This can further expand the power regulation range of virtual power plants and enable controllable and flexible resources to participate in grid power regulation in a timely and effective manner. This plays a significant role in improving the operational stability of grids with a high proportion of renewable energy.

[0003] Virtual power plants aggregate massive controllable and flexible resources, which can be called multi-loads, such as electric vehicles, temperature-controlled loads, industrial loads, commercial loads, distributed energy storage, and cogeneration units. This causes the variable dimension in the virtual power plant operation optimization model to grow exponentially, and the related virtual power plant operation optimization algorithm has low solution efficiency. Moreover, during the algorithm migration process, it is difficult to ensure the global optimality of the virtual power plant operation optimization results in different application scenarios, resulting in poor coordination of multi-loads. Summary of the Invention

[0004] The purpose of this application is to provide a virtual power plant operation optimization method, device, equipment, medium and product based on multiple loads, which can quickly and accurately achieve virtual power plant operation optimization.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a virtual power plant operation optimization method based on multiple loads, including:

[0007] Obtaining parameters required for optimization of a virtual power plant; the virtual power plant includes: multiple loads; the multiple loads include: electric vehicles, temperature control loads, distributed energy storage systems, and cogeneration units;

[0008] Calculating an estimated value of the operating cost of the virtual power plant based on the parameters required for the optimization;

[0009] determining whether the estimated operating cost is greater than or equal to a set value;

[0010] If not, the optimization factor is used to perform multiple iterative optimizations on the output of multiple loads in the virtual power plant to obtain the optimal operating result of the virtual power plant; the optimal operating result includes: the corresponding electric vehicle charging power, temperature control load power, distributed power storage system discharge power, distributed heat storage system heat release power and cogeneration unit power generation power when the estimated operating cost is greater than or equal to the set value.

[0011] In a second aspect, the present application provides a virtual power plant operation optimization device based on multiple loads, comprising:

[0012] A parameter acquisition module is used to obtain parameters required for optimization of a virtual power plant; the virtual power plant includes: multiple loads; the multiple loads include: electric vehicles, temperature control loads, distributed energy systems and cogeneration units;

[0013] An operating cost estimation value calculation module, configured to calculate an operating cost estimation value of the virtual power plant based on the parameters required for the optimization;

[0014] A state determination module, configured to determine whether the estimated operating cost is greater than or equal to a set value;

[0015] a parameter optimization module for iteratively optimizing the output of multiple loads in the virtual power plant using optimization factors for multiple times if the estimated operating cost is less than a set value, so as to obtain the optimal operating result of the virtual power plant; the optimal operating result includes: the corresponding electric vehicle charging power, temperature-controlled load power, distributed power storage system discharge power, distributed heat storage system heat release power, and cogeneration unit power generation power when the estimated operating cost is greater than or equal to the set value.

[0016] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement any one of the above-mentioned methods for optimizing virtual power plant operations based on multiple loads.

[0017] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned methods for optimizing the operation of a virtual power plant based on multiple loads.

[0018] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned virtual power plant operation optimization methods based on multiple loads.

[0019] According to the specific embodiments provided in this application, this application has the following technical effects:

[0020] The present application provides a method, device, equipment, medium and product for optimizing the operation of a virtual power plant based on multiple loads. It takes into account the massive multiple loads aggregated by the virtual power plant, optimizes the output of multiple loads in the virtual power plant by calculating the operating cost evaluation value of the virtual power plant, improves the solution efficiency of the optimization algorithm, and the solution algorithm has good migration capability, thereby realizing the optimization of the operating cost of the virtual power plant. The present application can quickly and accurately realize the optimization of the operation of the virtual power plant and improve the economic efficiency of the operation of the virtual power plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 This is an application environment diagram of a virtual power plant operation optimization method based on multiple loads in one embodiment of the present application;

[0023] Figure 2 A flowchart of a virtual power plant operation optimization method based on multiple loads provided in one embodiment of the present application;

[0024] Figure 3 A schematic diagram of the overall design concept of a virtual power plant operation optimization method based on multiple loads provided in one embodiment of the present application;

[0025] Figure 4 A schematic diagram of functional modules of a virtual power plant operation optimization device based on multiple loads provided in another embodiment of the present application;

[0026] Figure 5 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0028] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0029] The virtual power plant operation optimization method based on multiple loads provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store data that server 104 needs to process. The data storage system can be provided separately, integrated with server 104, or located in the cloud or on another server. Terminal 102 can send the parameters required for optimization to server 104. After receiving the parameters, server 104 calculates an estimated operating cost of the virtual power plant based on the parameters required for optimization. It then determines whether the estimated operating cost is greater than or equal to a set value. If the estimated operating cost is less than the set value, it uses optimization factors to perform multiple iterative optimizations on the multi-load output of the virtual power plant to obtain the optimal operating result of the virtual power plant. Server 104 can provide feedback on the optimal operating result to terminal 102. In addition, in some embodiments, the multi-load virtual power plant operation optimization method can also be implemented independently by server 104 or terminal 102. For example, terminal 102 can directly process the parameters required for optimization, or server 104 can obtain the parameters required for optimization from the data storage system and process them.

[0030] Terminal 102 may include, but is not limited to, various desktop computers, laptops, smartphones, tablet computers, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart air conditioners, and smart car devices. Portable wearable devices may include smart watches, smart bracelets, and head-mounted devices. Server 104 may be implemented as a standalone server or a server cluster consisting of multiple servers, or may be a cloud server.

[0031] In an exemplary embodiment, Figure 2 As shown, a virtual power plant operation optimization method based on multiple loads is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, or a terminal and a server. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used for explanation, including the following steps 201 to 204.

[0032] in:

[0033] Step 201: Obtain parameters required for optimization of the virtual power plant.

[0034] The virtual power plant includes multiple loads, including electric vehicles, temperature-controlled loads, distributed energy storage systems, and cogeneration units. The distributed energy storage system includes a distributed electricity storage system and a distributed heat storage system.

[0035] Step 202: Calculate an estimated operating cost of the virtual power plant based on the parameters required for the optimization.

[0036] Step 203: Determine whether the estimated operating cost is greater than or equal to a set value.

[0037] Step 204: If the estimated operating cost is less than the set value, the optimization factor is used to perform multiple iterative optimizations on the multi-load output in the virtual power plant to obtain the optimal operating result of the virtual power plant.

[0038] Among them, the optimal operation result includes: the corresponding electric vehicle charging power, temperature control load power, distributed power storage system discharge power, distributed heat storage system heat release power and cogeneration unit power generation power when the estimated operating cost is greater than or equal to the set value.

[0039] The implementation of the above steps 201 to 204 takes into account the massive multi-load aggregation of the virtual power plant. Under the premise that the variable dimension in the virtual power plant operation optimization model increases exponentially, the multi-load output in the virtual power plant is optimized by calculating the virtual power plant operation cost evaluation value, thereby improving the solution efficiency of the optimization algorithm, and the solution algorithm has good migration capability, thereby realizing the optimization of the virtual power plant operation cost. This embodiment can quickly and accurately realize the virtual power plant operation optimization and improve the economic efficiency of the virtual power plant operation.

[0040] In another exemplary embodiment of the present application, in step 201, the parameters required for optimization are parameters required for optimizing the operating cost of the virtual power plant. The parameters required for optimization include: the virtual power plant electricity purchase price Pr(t), the ambient temperature Tem(t), the electric vehicle charging power Pev(t), the number of electric vehicles N ev , power consumption of temperature control load Ptcl(t), number of temperature control loads N tcl , the discharge power of the distributed power storage system Pes(t), the electric energy stored in the distributed power storage system Ses(t), the number of distributed power storage systems N es , heat release power Phs(t) of distributed heat storage system, heat energy Shs(t) stored in distributed heat storage system, number of distributed heat storage systems N hs , the power generation capacity Pchp(t) of the cogeneration unit and the number of cogeneration units N chp .

[0041] In another exemplary embodiment of the present application, step 202 specifically includes:

[0042] (1) The parameters required for optimization are normalized to obtain the normalized values ​​of the parameters required for optimization. The normalized calculation formula is:

[0043]

[0044] Among them, Pr * (t) is the per-unit value of the electricity purchase price of the virtual power plant, Pr N The benchmark value of the electricity purchase price of the virtual power plant; Tem * (t) is the per-unit value of the ambient temperature, Tem N is the reference value of ambient temperature; Pev m * (t) is the per-unit value of the charging power of the mth electric vehicle, Pev N The benchmark value for electric vehicle charging power; Ptcl n * (t) is the per-unit value of the power consumption of the nth temperature control load, Ptcl N Pes is the reference value of the power consumption of the temperature control load; i * (t) is the per-unit value of the discharge power of the i-th distributed power storage system, Pes N The benchmark value of the discharge power of the distributed power storage system; Ses i * (t) is the per-unit value of the electric energy stored in the i-th distributed power storage system, Ses N Phs is the base value of the electric energy stored in the distributed power storage system; j * (t) is the per-unit value of the heat release power of the jth distributed heat storage system, Phs N Shs is the benchmark value of heat release power of distributed thermal storage system; j * (t) is the per-unit value of the thermal energy stored in the jth distributed thermal storage system, Shs N Pchp is the benchmark value of thermal energy stored in the distributed thermal storage system; l * (t) is the per-unit value of the power generation of the first cogeneration unit, Pchp N It is the benchmark value of the power generation capacity of the cogeneration unit.

[0045] (2) Calculating the estimated operating cost of the virtual power plant based on the per-unit values ​​of the parameters required for optimization. The calculation formula for the estimated operating cost is:

[0046]

[0047] Among them, φ vpp is the estimated value of operating cost; N es is the number of distributed power storage systems; Pr * (t) is the per-unit value of the electricity purchase price of the virtual power plant; Pes i* (t) is the per-unit value of the discharge power of the i-th distributed power storage system; Ses i * (t) is the per-unit value of the electric energy stored in the i-th distributed power storage system; N hs is the number of distributed heat storage systems; Tem * (t) is the per-unit value of ambient temperature; Phs j * (t) is the per-unit value of the heat release power of the j-th distributed heat storage system; Shs j * (t) is the per-unit value of the thermal energy stored in the jth distributed thermal storage system; Nchp is the number of cogeneration units; Pchp l * (t) is the per-unit value of the power generation of the first cogeneration unit; Ntcl is the number of temperature control loads; N ev is the number of electric vehicles; Pev m * (t) is the per-unit value of the charging power of the mth electric vehicle; Ptcl n * (t) is the per-unit value of the electric power of the nth temperature-controlled load; t represents time.

[0048] In another exemplary embodiment of the present application, in step 203, the set value can be 0. Step 203 is to judge the operating state of the virtual power plant. vpp ≥0, there is no need to optimize the output of multiple loads in the virtual power plant. vpp <0, it is necessary to optimize the output of multiple loads in the virtual power plant.

[0049] In another exemplary embodiment of the present application, step 204 specifically includes:

[0050] (1) Determine the optimization factor under the current number of iterations; the calculation formula of the optimization factor is:

[0051] θ b =θ1+(b-1)H;

[0052] Among them, θ b represents the optimization factor under the iteration number b; θ1 is the initial value of the optimization factor; H is a constant coefficient.

[0053] (2) According to the optimization factor at the current number of iterations, the optimization formula is used to optimize the multi-load output of the virtual power plant at the current number of iterations to obtain the operating results at the current number of iterations; the optimization formula is:

[0054]

[0055] in, is the per-unit value of the charging power of the mth electric vehicle after optimization; is the per-unit value of the power consumption of the nth temperature control load after optimization; is the per-unit value of the discharge power of the i-th distributed power storage system after optimization; is the per-unit value of the heat release power of the jth distributed heat storage system after optimization; Pev is the per-unit value of the power generation of the first cogeneration unit after optimization; m * (t) is the per-unit value of the charging power of the mth electric vehicle; Pr * (t) is the per-unit value of the electricity purchase price of the virtual power plant; Ptcl n * (t) is the per-unit value of the electric power of the nth temperature control load; Pes i * (t) is the per-unit value of the discharge power of the i-th distributed power storage system; Phs j * (t) is the per-unit value of the heat release power of the j-th distributed heat storage system; Tem * (t) is the per-unit value of ambient temperature; Pchp l * (t) is the per-unit value of the power generated by the first cogeneration unit; e is a natural constant; and t represents time.

[0056] (3) If the estimated operating cost corresponding to the operating result under the current number of iterations is greater than or equal to the set value, the operating result under the current number of iterations is determined as the optimal operating result of the virtual power plant; otherwise, the next iteration is performed.

[0057] This embodiment uses the optimization factor θ b , the electric vehicle charging power, temperature control load power, distributed power storage system discharge power, distributed heat storage system heat release power, and cogeneration unit power generation power in the virtual power plant are optimized. Specifically, by optimizing the factor θ b The virtual power plant parameters are optimized and iterated until the virtual power plant operation cost evaluation value φ vpp ≥0.

[0058] In another exemplary embodiment of the present application, after step 204, the virtual power plant operation optimization method based on multiple loads further includes:

[0059] (1) Calculate the virtual power plant operating cost index before and after virtual power plant optimization. The calculation formula of the virtual power plant operating cost index κ before optimization is:

[0060]

[0061] Among them, β ev is the electric vehicle charging cost coefficient; β tcl is the electricity cost coefficient of the temperature control load; β es is the cost coefficient of distributed power storage and discharge; β hs is the cost coefficient of distributed heat storage and release; β chp is the power generation cost coefficient of the cogeneration unit.

[0062] The calculation formula of the optimized virtual power plant operating cost index κ′ is:

[0063]

[0064] (2) Calculate the virtual power plant operating cost optimization ratio based on the virtual power plant operating cost index before optimization and the virtual power plant operating cost index after optimization. The calculation formula of the virtual power plant operating cost optimization ratio γ is:

[0065]

[0066] See also Figure 3 The overall design idea of ​​the above embodiment is as follows: collect the parameters required for virtual power plant operation cost optimization; normalize the parameters required for virtual power plant operation cost optimization; calculate the virtual power plant operation cost evaluation value φ vpp ; Using optimization factor θ b Optimize the virtual power plant parameters; calculate the virtual power plant operating cost index κ before the virtual power plant parameters are optimized; calculate the virtual power plant operating cost index κ′ after the virtual power plant parameters are optimized; and calculate the virtual power plant operating cost optimization ratio γ.

[0067] Taking a virtual power plant in Northeast China as an example, the above-mentioned virtual power plant operation optimization method based on multiple loads is further explained in detail.

[0068] In this virtual power plant, the electric vehicle charging power rating is 30kW (a total of 60 vehicles), the temperature control load power rating is 4kW (a total of 100), the distributed power storage system discharge power rating is 250kW (a total of 3), the distributed heat storage system heat release power rating is 200kW (a total of 4), the cogeneration unit power generation rating is 300kW (a total of 1 unit), and the virtual power plant electricity purchase price and ambient temperature are shown in Table 1.

[0069] Table 1 Virtual power plant electricity purchase price and ambient temperature

[0070]

[0071]

[0072] Collect the parameters required for virtual power plant operation cost optimization at time t, including: virtual power plant electricity purchase price Pr(t), ambient temperature Tem(t), electric vehicle charging power Pev m (t), m={1,2,…,N ev}、Number of electric vehicles N ev =60, Temperature control load power Ptcl n (t), n={1,2,…,N tcl}、Number of temperature control loads tcl =100, Distributed power storage system discharge power Pes i (t), i={1,2,…,N es}、Electric energy stored in distributed power storage system Ses i (t), the number of distributed electricity storage systems Nes = 3, the heat release power of the distributed heat storage system Phs j (t), j={1,2,…,N hs Thermal energy Shs stored in distributed thermal storage system j (t), the number of distributed heat storage systems N hs =4. Power generation power of cogeneration unit Pchp l (t), l={1,2,…,N chp}、Number of cogeneration units N chp =1.

[0073] The benchmark value of the virtual power plant's electricity purchase price Pr N Take 1 yuan / kWh, the reference value of ambient temperature Tem N Take -10℃, the benchmark value of electric vehicle charging power Pev N Take 30kW, the reference value of the temperature control load power Ptcl N Take 4kW as the benchmark value of the discharge power of the distributed power storage system Pes N Take 250kW, the baseline value of the electric energy stored in the distributed power storage system Ses N Take 1, the benchmark value Phs of the heat release power of the distributed heat storage system N Take 200kW as the benchmark value of thermal energy stored in the distributed thermal storage system Shs N Take 1, the base value of the power generation power of the cogeneration unit Pchp N Take 300kW.

[0074] The parameters required for optimizing the operating costs of the virtual power plant are normalized. The specific normalization calculation formula is not repeated here.

[0075] The operating cost evaluation value is calculated using the operating cost evaluation value formula. The specific calculation formula is not repeated here. The obtained operating cost evaluation value φvpp =-0.214.

[0076] Due to φ vpp <0, so it is necessary to optimize the multi-load output in the virtual power plant. Using the optimization factor θ b , optimize the electric vehicle charging power, temperature control load power, distributed power storage system discharge power, distributed heat storage system heat release power, and cogeneration unit power generation power in the virtual power plant, where θ b =θ1+(b-1)H, θ1=0.5, H=0.01, the specific optimization formula will not be repeated here.

[0077] Iteratively calculate the virtual power plant operation cost evaluation value and the multi-load output optimization value in the virtual power plant until φ vpp ≥0.

[0078] The virtual power plant operating cost index κ before optimizing the virtual power plant parameters is calculated to be 9.882, and the specific calculation formula will not be repeated here.

[0079] The virtual power plant operating cost index κ′ after optimizing the virtual power plant parameters is calculated to be 8.463. The specific calculation formula will not be repeated here.

[0080] Calculate the virtual power plant operating cost optimization ratio

[0081] The results show that the method of this embodiment can improve the economic efficiency of virtual power plant operation.

[0082] The above embodiment realizes the optimization of the operating cost of the virtual power plant based on the multi-load aggregation response. Specifically, by calculating the operating cost evaluation value of the virtual power plant, the multi-load output in the virtual power plant is optimized, thereby realizing fast and accurate virtual power plant operation optimization and improving the operating economy of the virtual power plant.

[0083] Based on the same inventive concept, the present application also provides a multi-load-based virtual power plant operation optimization device for implementing the aforementioned multi-load-based virtual power plant operation optimization method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the multi-load-based virtual power plant operation optimization device provided below can be found in the above-mentioned limitations of the multi-load-based virtual power plant operation optimization method, and will not be repeated here.

[0084] In an exemplary embodiment, Figure 4 As shown, a virtual power plant operation optimization device based on multiple loads is provided, comprising:

[0085] The parameter acquisition module 401 is used to obtain the parameters required for optimization of the virtual power plant; the virtual power plant includes: multiple loads; the multiple loads include: electric vehicles, temperature control loads, distributed energy systems and cogeneration units.

[0086] The estimated operating cost calculation module 402 is used to calculate the estimated operating cost of the virtual power plant based on the parameters required for the optimization.

[0087] The state judgment module 403 is used to judge whether the estimated value of the operating cost is greater than or equal to a set value.

[0088] The parameter optimization module 404 is used to use the optimization factor to perform multiple iterative optimizations on the output of multiple loads in the virtual power plant if the estimated operating cost is less than a set value, so as to obtain the optimal operating result of the virtual power plant; the optimal operating result includes: the corresponding electric vehicle charging power, temperature control load power, distributed power storage system discharge power, distributed heat storage system heat release power and cogeneration unit power generation power when the estimated operating cost is greater than or equal to the set value.

[0089] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store parameters required for optimization. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a virtual power plant operation optimization method based on multiple loads is implemented.

[0090] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0091] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0092] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0093] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0094] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0095] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, and the like.

[0096] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0097] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A virtual power plant operation optimization method based on multiple loads, characterized in that: The virtual power plant operation optimization method based on multiple loads includes: Obtaining parameters required for optimization of a virtual power plant; the virtual power plant includes: multiple loads; the multiple loads include: electric vehicles, temperature control loads, distributed energy storage systems, and cogeneration units; Calculating an estimated operating cost of the virtual power plant based on the parameters required for optimization, specifically comprising: normalizing the parameters required for optimization to obtain per-unit values ​​of the parameters required for optimization; and calculating an estimated operating cost of the virtual power plant based on the per-unit values ​​of the parameters required for optimization; determining whether the estimated operating cost is greater than or equal to a set value; If not, the optimization factor is used to perform multiple iterative optimization on the multiple load outputs in the virtual power plant to obtain the optimal operation result of the virtual power plant; the optimal operation result includes: the corresponding electric vehicle charging power, temperature control load power, distributed power storage system discharge power, distributed heat storage system heat release power and cogeneration unit power generation power when the estimated operating cost is greater than or equal to the set value; the optimization factor is used to perform multiple iterative optimization on the multiple load outputs in the virtual power plant to obtain the optimal operation result of the virtual power plant, specifically including: determining the optimization factor under the current number of iterations; the calculation formula of the optimization factor is: i b =θ1+(b-1)H; Among them, θ b represents the optimization factor under the iteration number b; θ1 is the initial value of the optimization factor; H is a constant coefficient; According to the optimization factor at the current number of iterations, the optimization formula is used to optimize the multi-load output in the virtual power plant at the current number of iterations to obtain the operation result at the current number of iterations; the optimization formula is: in, is the per-unit value of the charging power of the mth electric vehicle after optimization; is the per-unit value of the power consumption of the nth temperature control load after optimization; is the per-unit value of the discharge power of the i-th distributed power storage system after optimization; is the per-unit value of the heat release power of the jth distributed heat storage system after optimization; Pev is the per-unit value of the power generation of the first cogeneration unit after optimization; m * (t) is the per-unit value of the charging power of the mth electric vehicle; Pr * (t) is the per-unit value of the electricity purchase price of the virtual power plant; Ptcl n * (t) is the per-unit value of the electric power of the nth temperature control load; Pes i * (t) is the per-unit value of the discharge power of the i-th distributed power storage system; Phs j * (t) is the per-unit value of the heat release power of the j-th distributed heat storage system; Tem * (t) is the per-unit value of ambient temperature; Pchp l * (t) is the per-unit value of the power generated by the first cogeneration unit; e is a natural constant; t represents time; If the estimated operating cost corresponding to the operating result under the current number of iterations is greater than or equal to the set value, the operating result under the current number of iterations is determined as the optimal operating result of the virtual power plant; otherwise, the next iteration is performed.

2. The virtual power plant operation optimization method based on multiple loads according to claim 1 is characterized in that: The parameters required for optimization include: the electricity purchase price of the virtual power plant, ambient temperature, electric vehicle charging power, the number of electric vehicles, the power consumption of temperature-controlled loads, the number of temperature-controlled loads, the discharge power of the distributed power storage system, the electric energy stored in the distributed power storage system, the number of distributed power storage systems, the heat release power of the distributed heat storage system, the heat energy stored in the distributed heat storage system, the number of distributed heat storage systems, the power generation power of the cogeneration unit and the number of cogeneration units.

3. The virtual power plant operation optimization method based on multiple loads according to claim 1 is characterized in that: The operating cost estimate is calculated as follows: Among them, φ vpp is the estimated value of operating cost; N es is the number of distributed power storage systems; Pr * (t) is the per-unit value of the electricity purchase price of the virtual power plant; Pes i * (t) is the per-unit value of the discharge power of the i-th distributed power storage system; Ses i * (t) is the per-unit value of the electric energy stored in the i-th distributed power storage system; N hs is the number of distributed thermal storage systems; Tem * (t) is the per-unit value of ambient temperature; Phs j * (t) is the per-unit value of the heat release power of the j-th distributed heat storage system; Shs j * (t) is the per-unit value of the thermal energy stored in the jth distributed thermal storage system; N chp is the number of cogeneration units; Pchp l * (t) is the per-unit value of the power generation of the first cogeneration unit; N tcl is the number of temperature control loads; N ev is the number of electric vehicles; Pev m * (t) is the per-unit value of the charging power of the mth electric vehicle; Ptcl n * (t) is the per-unit value of the electric power of the nth temperature-controlled load; t represents time.

4. The virtual power plant operation optimization method based on multiple loads according to claim 1 is characterized in that: After performing multiple iterations of optimization on the output of multiple loads in the virtual power plant using the optimization factors to obtain the optimal operation result of the virtual power plant, the virtual power plant operation optimization method based on multiple loads further includes: Calculate the virtual power plant operating cost index before and after virtual power plant optimization; The virtual power plant operating cost optimization ratio is calculated based on the virtual power plant operating cost indicators before optimization and the virtual power plant operating cost indicators after optimization.

5. A virtual power plant operation optimization device based on multiple loads, characterized in that: The virtual power plant operation optimization device based on multiple loads includes: A parameter acquisition module is used to obtain parameters required for optimization of a virtual power plant; the virtual power plant includes: multiple loads; the multiple loads include: electric vehicles, temperature control loads, distributed energy systems and cogeneration units; an operating cost estimation calculation module, configured to calculate an operating cost estimation of the virtual power plant based on the parameters required for optimization, specifically comprising: normalizing the parameters required for optimization to obtain per-unit values ​​of the parameters required for optimization; and calculating an operating cost estimation of the virtual power plant based on the per-unit values ​​of the parameters required for optimization; A state determination module, configured to determine whether the estimated operating cost is greater than or equal to a set value; A parameter optimization module is configured to, if the estimated operating cost is less than a set value, perform multiple iterative optimizations on the output of multiple loads in the virtual power plant using an optimization factor to obtain an optimal operating result of the virtual power plant; the optimal operating result includes: the corresponding electric vehicle charging power, temperature control load power, distributed power storage system discharge power, distributed heat storage system heat release power, and cogeneration unit power generation power when the estimated operating cost is greater than or equal to the set value; perform multiple iterative optimizations on the output of multiple loads in the virtual power plant using the optimization factor to obtain the optimal operating result of the virtual power plant, specifically including: determining the optimization factor under the current number of iterations; the calculation formula of the optimization factor is: i b =θ1+(b-1)H; Among them, θ b represents the optimization factor under the iteration number b; θ1 is the initial value of the optimization factor; H is a constant coefficient; According to the optimization factor at the current number of iterations, the optimization formula is used to optimize the multi-load output in the virtual power plant at the current number of iterations to obtain the operation result at the current number of iterations; the optimization formula is: in, is the per-unit value of the charging power of the mth electric vehicle after optimization; is the per-unit value of the power consumption of the nth temperature control load after optimization; is the per-unit value of the discharge power of the i-th distributed power storage system after optimization; is the per-unit value of the heat release power of the jth distributed heat storage system after optimization; Pev is the per-unit value of the power generation of the first cogeneration unit after optimization; m * (t) is the per-unit value of the charging power of the mth electric vehicle; Pr * (t) is the per-unit value of the electricity purchase price of the virtual power plant; Ptcl n * (t) is the per-unit value of the electric power of the nth temperature control load; Pes i * (t) is the per-unit value of the discharge power of the i-th distributed power storage system; Phs j * (t) is the per-unit value of the heat release power of the j-th distributed heat storage system; Tem * (t) is the per-unit value of ambient temperature; Pchp l * (t) is the per-unit value of the power generated by the first cogeneration unit; e is a natural constant; t represents time; If the estimated operating cost corresponding to the operating result under the current number of iterations is greater than or equal to the set value, the operating result under the current number of iterations is determined as the optimal operating result of the virtual power plant; otherwise, the next iteration is performed.

6. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the virtual power plant operation optimization method based on multiple loads as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the virtual power plant operation optimization method based on multiple loads according to any one of claims 1 to 4 is implemented.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the virtual power plant operation optimization method based on multiple loads according to any one of claims 1 to 4 is implemented.

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