Virtual power plant operation optimization method and device based on multi-element load, equipment, medium and product

Abstract

The invention discloses a virtual power plant operation optimization method and device based on multiple loads, equipment, a medium and a product, and relates to the field of virtual power plant operation optimization, and the method comprises the steps: obtaining parameters required for optimization of a virtual power plant; calculating an operation cost estimation value of the virtual power plant according to the parameters required by optimization; judging whether the operation cost estimation value is greater than or equal to a set value; if not, the optimization factor is adopted to carry out multiple times of iterative optimization on the multi-element load output in the virtual power plant, and an optimal operation result of the virtual power plant is obtained; the optimal operation result comprises the corresponding electric vehicle charging power when the operation cost estimation value is larger than or equal to the set value, the temperature control load electricity utilization power, the distributed electricity storage system discharging power, the distributed heat storage system heat release power and the cogeneration unit generating power. The virtual power plant operation optimization method can quickly and accurately achieve 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 the capacity of new energy grid-connected units has compressed the capacity of traditional thermal power units, reducing the power regulation capacity of the power grid. The uncertainty of new energy output will further increase the difficulty of power grid power balance regulation. As an emerging technology that integrates adjustable resources on the load side, virtual power plants can aggregate the adjustable potential of multiple loads and provide controllable flexibility resources for power grid power regulation. At the same time, virtual power plant technology can aggregate distributed energy storage systems, cogeneration units, heat pumps and other energy supply and conversion equipment to achieve generalized multiple load aggregation, which can further expand the power regulation range of virtual power plants and enable controllable flexibility resources to participate in power grid power regulation in a timely and effective manner, which plays an important role in improving the operational stability of power grids with a high proportion of new energy.

[0003] Virtual power plants aggregate massive controllable and flexible resources, which can be called multiple 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. In the process of algorithm migration, the virtual power plant operation optimization results in different application scenarios are difficult to guarantee the global optimality, resulting in poor coordination of multiple 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 factors are 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, including:

[0012] A parameter acquisition module, used to acquire the 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 operation cost estimation value calculation module, used for calculating the operation cost estimation value of the virtual power plant according to the parameters required for the optimization;

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

[0015] A parameter optimization module is used to use optimization factors to perform multiple iterative optimizations on the output of multiple loads in the virtual power plant to obtain the optimal operation result of the virtual power plant if the estimated operating cost is less than a set value; 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.

[0016] In a third aspect, the present application provides 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 any of the above-mentioned methods for optimizing the operation of a virtual power plant 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, taking into account the massive multiple loads aggregated in the virtual power plant, and optimizing the output of multiple loads in the virtual power plant by calculating the operating cost assessment value of the virtual power plant, thereby improving the solution efficiency of the optimization algorithm, and the solution algorithm has good migration capabilities, thereby achieving optimization of the operating costs of the virtual power plant. The present application can quickly and accurately achieve 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 drawings required for use in the embodiments will be briefly introduced below. 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 paying 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 schematic diagram of a flow chart 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 the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0028] In order to make the above-mentioned objects, 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. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the parameters required for optimization to the server 104. After the server 104 receives the parameters required for optimization, for the parameters required for optimization, the server 10 calculates the estimated operating cost of the virtual power plant according to the parameters required for optimization; determines whether the estimated operating cost is greater than or equal to the set value; if the estimated operating cost is less than the set value, the optimization factor is used to perform multiple iterations of optimization on the multi-load output in the virtual power plant to obtain the optimal operating result of the virtual power plant. The server 104 can feedback the obtained optimal operating result to the terminal 102. In addition, in some embodiments, the virtual power plant operation optimization method based on multi-load can also be implemented separately by the server 104 or the terminal 102, such as the terminal 102 can directly process the parameters required for optimization, or the server 104 can obtain the parameters required for optimization from the data storage system and process the parameters required for optimization.

[0030] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent 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, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used for explanation, and the steps include the following steps 201 to 204.

[0032] in:

[0033] Step 201, obtaining the parameters required for optimization of the virtual power plant.

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

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

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

[0037] Step 204: If the estimated operating cost is less than a set value, the optimization factor is used to perform multiple iterations of optimization 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 steps 201 to 204 above takes into account the massive multi-loads aggregated in the virtual power plant. Under the premise that the variable dimension in the virtual power plant operation optimization model grows exponentially, the multi-load output in the virtual power plant is optimized by calculating the evaluation value of the virtual power plant operation cost, thereby improving the solving efficiency of the optimization algorithm, and the solving algorithm has good migration capability, thereby realizing the optimization of the operating cost of the virtual power plant. This embodiment can quickly and accurately realize the optimization of the virtual power plant operation and improve the economic efficiency of the virtual power plant operation.

[0040] In another exemplary embodiment of the present application, in step 201, the optimization parameters are the parameters required for optimizing the operating cost of the virtual power plant. The optimization parameters 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, thermal energy stored in distributed heat storage system Shs(t), number of distributed heat storage systems N hs , the power generation capacity Pchp(t) of the CHP unit and the number of CHP 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 power purchase price of the virtual power plant, Pr N Tem is the benchmark value of the electricity purchase price of the virtual power plant; * (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 charging power of electric vehicles; 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 base 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 thermal 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 power of the first cogeneration unit, Pchp N It is the base value of the power generation capacity of the cogeneration unit.

[0045] (2) Calculating the estimated operating cost of the virtual power plant according to the per-unit values ​​of the parameters required for optimization. The calculation formula of 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 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 jth 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 consumed by the nth temperature control load; t represents time.

[0048] In another exemplary embodiment of the present application, in step 203, the set value may 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 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:

[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 power consumption 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 jth 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 generation of the first cogeneration unit; e is a natural constant; t represents time.

[0056] (3) If the estimated operating cost corresponding to the operating result at the current number of iterations is greater than or equal to the set value, the operating result at 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 charging power of electric vehicles, the power consumption of temperature control loads, the discharge power of distributed power storage systems, the heat release power of distributed heat storage systems, and the power generation power of cogeneration units 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 heat release; β chp is the power generation cost coefficient of the cogeneration unit.

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

[0063]

[0064] (2) According to the virtual power plant operating cost index before optimization and the virtual power plant operating cost index after optimization, the virtual power plant operating cost optimization ratio γ is calculated. 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 optimizing the operating cost of the virtual power plant; standardize the parameters required for optimizing the operating cost of the virtual power plant; calculate the operating cost evaluation value φ of the virtual power plant vpp ; Using the 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; 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 rated power of electric vehicle charging is 30kW (a total of 60 vehicles), the rated power of temperature control load is 4kW (a total of 100), the rated power of distributed power storage system discharge is 250kW (a total of 3), the rated power of distributed heat storage system heat release is 200kW (a total of 4), the rated power of cogeneration unit generation is 300kW (a total of 1), and the power purchase price and ambient temperature of the virtual power plant 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 optimizing the operation cost of the virtual power plant at time t, including: the 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 ev =60, power consumption of temperature control load 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 stored in distributed thermal storage system Shs j (t), the number of distributed heat storage systems N hs =4. Power generation capacity of cogeneration unit Pchp l (t), l={1,2,…,N chp}、Number of cogeneration units chp =1.

[0073] The base value of the virtual power plant electricity purchase price Pr N Take 1 yuan / kWh, the reference value of ambient temperature Tem N Take -10℃, the reference 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 is Ses N Take 1, the base 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 virtual power plants are normalized, and the specific normalization calculation formula is not repeated here.

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

[0076] Due to φ vpp <0, so it is necessary to optimize the output of multiple loads in the virtual power plant. Using the optimization factor θ b , the charging power of electric vehicles, the power consumption of temperature control loads, the discharge power of distributed power storage systems, the heat release power of distributed heat storage systems, and the power generation power of cogeneration units in the virtual power plant are optimized, 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, and the specific calculation formula will not be repeated here.

[0080] Calculate the cost optimization ratio of virtual power plant operation

[0081] The results show that the method of this embodiment can improve the operating economy of the virtual power plant.

[0082] The above-mentioned embodiment realizes the optimization of the operating cost of the virtual power plant based on the aggregate response of multiple loads. Specifically, by calculating the operating cost evaluation value of the virtual power plant, the output of multiple loads 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 embodiment of the present application also provides a virtual power plant operation optimization device based on multiple loads for implementing the virtual power plant operation optimization method based on multiple loads involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the virtual power plant operation optimization device based on multiple loads provided below can refer to the limitations of the virtual power plant operation optimization method based on multiple loads above, 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 the 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 operation cost estimation value calculation module 402 is used to calculate the operation cost estimation value of the virtual power plant according to 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 optimization on the output of multiple loads in the virtual power plant to obtain the optimal operation result of the virtual power plant if the estimated operating cost is less than the set value; 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.

[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, referred to as I / O) and a communication interface. The processor, the memory and the 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 those shown in the figure, or combine certain components, or have a different arrangement of components.

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

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

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

[0094] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed 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 the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can 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 can 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 database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, etc., but is not limited thereto.

[0096] The technical features of the above embodiments may be arbitrarily combined. 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 article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will 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 value of the operating cost of the virtual power plant based on the parameters required for the optimization; determining whether the estimated operating cost is greater than or equal to a set value; If not, the optimization factors are 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.

2. The virtual power plant operation optimization method based on multiple loads according to claim 1 is characterized in that: The parameters required for the 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 distributed power storage systems, the electric energy stored in distributed power storage systems, the number of distributed power storage systems, the heat release power of distributed heat storage systems, the heat energy stored in distributed heat storage systems, the number of distributed heat storage systems, the power generation power of cogeneration units 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: Calculating an estimated value of the operating cost of the virtual power plant according to the parameters required for the optimization, specifically including: Normalizing the parameters required for optimization to obtain normalized values ​​of the parameters required for optimization; An estimated value of the operating cost of the virtual power plant is calculated based on the per-unit values ​​of the parameters required for optimization.

4. The virtual power plant operation optimization method based on multiple loads according to claim 3 is characterized in that: The calculation formula for the estimated operating cost is: 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 jth 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 consumed by the nth temperature control load; t represents time.

5. The virtual power plant operation optimization method based on multiple loads according to claim 3 is characterized in that: The optimization factors are used to perform multiple iterations of optimization on the output of multiple loads in the virtual power plant to obtain the optimal operation result of the virtual power plant, specifically including: Determine 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 power consumption 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 jth 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 at the current number of iterations is greater than or equal to the set value, the operating result at the current number of iterations is determined as the optimal operating result of the virtual power plant, otherwise, the next iteration is performed.

6. The virtual power plant operation optimization method based on multiple loads according to claim 1 is characterized in that: After the optimization factors are used to perform multiple iterations of optimization on the output of multiple loads in the virtual power plant 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 operating cost index of the virtual power plant 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.

7. 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 acquire the 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 operation cost estimation value calculation module, used for calculating the operation cost estimation value of the virtual power plant according to the parameters required for the optimization; A state determination module, used to determine whether the estimated value of the operating cost is greater than or equal to a set value; A parameter optimization module is used to use optimization factors to perform multiple iterative optimizations on the output of multiple loads in the virtual power plant to obtain the optimal operation result of the virtual power plant if the estimated operating cost is less than a set value; 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.

8. 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 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the virtual power plant operation optimization method based on multiple loads as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, it implements the virtual power plant operation optimization method based on multiple loads as described in any one of claims 1-6.

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