Multi-energy park load boundary determination method, device, terminal equipment and storage medium

Abstract

The present invention discloses a method, device, terminal equipment and storage medium for determining the load boundary of a multi-energy park, the method comprising: obtaining the load data of the multi-energy park; generating an uncertain fluctuation vector set; optimizing the constraint conditions according to the load data and a preset upper boundary coefficient, solving the uncertain fluctuation upper boundary coefficient of each load value of the load data; generating the uncertain fluctuation lower boundary coefficient according to the uncertain fluctuation upper boundary coefficient; extracting the uncertain fluctuation upper boundary and lower boundary of each load value from the uncertain fluctuation vector according to the uncertain fluctuation upper boundary coefficient and the lower boundary coefficient; determining the upper boundary and lower boundary of the load of the multi-energy park according to the uncertain fluctuation upper boundary and the lower boundary of each load value. The implementation of the present invention can determine the load boundary of the multi-energy park when there are uncertain factors under the premise of ensuring the safe operation of the multi-energy park.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation analysis, and in particular to a method, device, terminal equipment and storage medium for determining load boundaries of a multi-energy park. Background Art

[0002] Traditional fossil energy is non-renewable and highly polluting. Nowadays, despite the depletion of fossil energy and the increasing severity of environmental pollution, the traditional energy supply model has been greatly challenged, and the comprehensive utilization efficiency of energy needs to be improved urgently. This is an important research issue that needs to be solved urgently worldwide, and it is also an important means to solve the contradiction between the growth of energy demand and energy shortage and environmental pressure. Against this background, the concept of multi-energy complementary park came into being, which realizes the interconnection of various energy systems including electricity / gas / heat / cold, and improves the comprehensive utilization efficiency of energy. At the same time, the multi-energy park system has the characteristics of intelligent equipment, multi-energy coordination, decentralized supply and demand, information symmetry, flat system, and open transactions, which can coordinate and meet different types of energy needs in the park.

[0003] During the operation of a multi-energy park, there may be various uncertainties such as emergencies. For example, the sudden onset of extremely high temperatures may increase the power consumption of air conditioners, thereby increasing the power load, which means that the predicted data of the power load will be biased. These uncertainties make the optimal operation strategy of the multi-energy park face many difficulties. In order to make the results of the operation problems safer and more reliable, it is necessary to take the existing uncertainties into consideration when predicting the power load and redefine the boundaries of the power load. Therefore, how to determine the power load boundaries of the multi-energy park when there are uncertain factors while ensuring the safe operation of the multi-energy park is an urgent problem to be solved. Summary of the invention

[0004] The embodiments of the present invention provide a method, apparatus, terminal device and storage medium for determining the load boundary of a multi-energy park, which can determine the electric load boundary of the multi-energy park when there are uncertain factors while ensuring the safe operation of the multi-energy park.

[0005] An embodiment of the present invention provides a method for determining a load boundary of a multi-energy park, comprising:

[0006] Obtaining a number of load values ​​within a preset time period from the load data of the multi-energy park; generating an uncertain fluctuation vector set; wherein the uncertain fluctuation vector set includes an uncertain fluctuation vector corresponding to each load value; each uncertain fluctuation vector includes a number of elements;

[0007] According to the preset upper boundary coefficient, the constraint condition is optimized to determine the upper boundary coefficient of uncertain fluctuation corresponding to each load value;

[0008] According to the upper boundary coefficient of uncertain fluctuation corresponding to each load data, the lower boundary coefficient of uncertain fluctuation corresponding to each load value is generated;

[0009] According to each uncertain fluctuation lower boundary coefficient, an element is extracted from the corresponding uncertain fluctuation vector as the uncertain fluctuation lower boundary of the corresponding load value; according to each uncertain fluctuation upper boundary coefficient, an element is extracted from the corresponding uncertain fluctuation vector as the uncertain fluctuation upper boundary of the corresponding load value;

[0010] The upper load boundary of the multi-energy park is determined based on the upper uncertain fluctuation boundary of each load value; and the lower load boundary of the multi-energy park is determined based on the lower uncertain fluctuation boundary of each load value.

[0011] Furthermore, the uncertain wave vector set is: U = {U0, U1, U2, ... U i …,U I}, i∈[0,I];

[0012] in,

[0013] U is the uncertain fluctuation vector set; I is the preset time period; U i is the uncertain fluctuation vector corresponding to the load value of the load data at time i; U i Each element in is a value randomly selected from a normal distribution with a mean of 1; K i is the total number of elements selected from a normal distribution with a mean of 1; To select K i After sorting the values ​​from small to large, the Kth n elements.

[0014] Furthermore, the upper boundary coefficient optimization constraint condition includes:

[0015]

[0016]

[0017] 1≤si≤Ki

[0018] Ki-si+1 <si

[0019]

[0020] Among them, s i is the upper boundary coefficient of uncertain fluctuation corresponding to the load value at time i; N is a natural number set; j is an index value; α represents the significance level of hypothesis test; ∈ represents the risk level of the uncertain volatility vector set; is the uncertain fluctuation vector U i K i -s i +1 element; is the uncertain wave vector U i Middle i elements; p 0,i is the load value of the load data at time i; P Gr,i is the power transmitted from the power grid to the multi-energy park at time i; P g,i is the total power generation of all power generation equipment in the multi-energy park at time i; P shed,i is the load shedding at time i.

[0021] Furthermore, the generating of the uncertain fluctuation lower boundary coefficient corresponding to each load value according to the uncertain fluctuation upper boundary coefficient corresponding to each load data includes:

[0022] The lower boundary coefficient of uncertain fluctuation corresponding to each load value is generated according to the following formula:

[0023] M i =K i -s i +1;

[0024] Among them, M i is the lower boundary coefficient of uncertain fluctuation corresponding to the load value at time i.

[0025] Further, according to an uncertain fluctuation lower boundary coefficient, an element is extracted from the corresponding uncertain fluctuation vector as the uncertain fluctuation lower boundary of the corresponding load value; according to an uncertain fluctuation upper boundary coefficient, an element is extracted from the corresponding uncertain fluctuation vector as the uncertain fluctuation upper boundary of the corresponding load value, including:

[0026] According to s i From U i Extract the sth i Data As the upper boundary of the uncertain fluctuation corresponding to the load value at time i;

[0027] According to M i From U i Extract the Mth i Data As the lower boundary of the uncertain fluctuation corresponding to the load value at time i.

[0028] Further, the upper load boundary of the multi-energy park is determined according to the upper uncertain fluctuation boundary of each load value; and the lower load boundary of the multi-energy park is determined according to the lower uncertain fluctuation boundary of each load value, including:

[0029] According to the upper bound of uncertain fluctuation of each load value, the upper bound vector U of uncertain fluctuation of load data is determined. up ;in, Indicates the upper boundary of uncertain fluctuation corresponding to the load value at time I of load data;

[0030] According to the lower bound of uncertain fluctuation of each load value, determine the lower bound vector U of uncertain fluctuation of load data dn ;in, Indicates the lower boundary of the uncertain fluctuation corresponding to the load value at the moment of load data I;

[0031] The upper and lower load boundaries of the multi-energy park are calculated using the following formula:

[0032] p up =U up T ×p0

[0033] p dn =U dn T ×p0

[0034] p0=[p 0,1 ,p 0,2 ,…,p 0,i ,…,p 0,I ]

[0035] Among them, p up is the upper limit of the load of the multi-energy park, U up T is the upper boundary vector U of the uncertain fluctuation of load data up The transposed matrix of dn is the load lower boundary of the multi-energy park, U dn T is the upper boundary vector U of the uncertain fluctuation of load data dn The transposed matrix; p0 is the load data of the multi-energy park; p 0,I Represents the load value at load data I.

[0036] Based on the above method embodiment, the present invention provides a corresponding device embodiment;

[0037] An embodiment of the present invention provides a multi-energy park load boundary determination device, comprising: a load acquisition module, an uncertain fluctuation vector set generation module, an upper boundary coefficient determination module, a lower boundary coefficient determination module, an uncertain fluctuation boundary determination module, and a load boundary calculation module;

[0038] The predicted load acquisition module is used to obtain a number of load values ​​within a preset time period from the load data of the multi-energy park;

[0039] The uncertain fluctuation vector set generation module is used to generate an uncertain fluctuation vector set; wherein the uncertain fluctuation vector set includes an uncertain fluctuation vector corresponding to each load value; each uncertain fluctuation vector includes a number of elements;

[0040] The upper boundary coefficient determination module is used to determine the uncertain fluctuation upper boundary coefficient corresponding to each load value according to the preset upper boundary coefficient optimization constraint condition;

[0041] The lower boundary coefficient determination module is used to generate the uncertain fluctuation lower boundary coefficient corresponding to each load value according to the uncertain fluctuation upper boundary coefficient corresponding to each load data;

[0042] The uncertain fluctuation boundary determination module is used to extract an element from the corresponding uncertain fluctuation vector according to each uncertain fluctuation lower boundary coefficient as the uncertain fluctuation lower boundary of the corresponding load value; and to extract an element from the corresponding uncertain fluctuation vector according to each uncertain fluctuation upper boundary coefficient as the uncertain fluctuation upper boundary of the corresponding load value;

[0043] The load boundary calculation module is used to determine the upper load boundary of the multi-energy park according to the uncertain fluctuation upper boundary of each load value; and to determine the lower load boundary of the multi-energy park according to the uncertain fluctuation lower boundary of each load value.

[0044] Based on the above method embodiment, the present invention provides a terminal device embodiment;

[0045] An embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements any one of the multi-energy park load boundary determination methods described in the present invention.

[0046] Based on the above method embodiment, the present invention provides a storage medium embodiment;

[0047] An embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute any one of the multi-energy park load boundary determination methods described in the present invention.

[0048] The following beneficial effects are achieved by implementing the embodiments of the present invention:

[0049] An embodiment of the present invention provides a method, apparatus, terminal device and storage medium for determining the load boundary of a multi-energy park. The method introduces an uncertain fluctuation vector set, and based on the upper boundary coefficient, optimizes the constraint conditions, extracts the uncertain vector upper boundary and the uncertain vector lower boundary corresponding to each load value from each uncertain vector of the uncertain fluctuation vector set; finally, based on the uncertain fluctuation upper boundary and the uncertain fluctuation vector lower boundary of each load value, the load upper boundary and the lower boundary of the multi-energy park are calculated when uncertainty exists. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of a method for determining load boundaries of a multi-energy park provided in one embodiment of the present invention.

[0051] Figure 2 It is a structural schematic diagram of a multi-energy park load boundary determination device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] like Figure 1 As shown, an embodiment of the present invention provides a method for determining a load boundary of a multi-energy park, which at least includes the following steps:

[0054] Step S101: obtaining a number of load values ​​within a preset time period from the load data of the multi-energy park.

[0055] Step S102: Generate an uncertain fluctuation vector set; wherein the uncertain fluctuation vector set includes an uncertain fluctuation vector corresponding to each load value; and each uncertain fluctuation vector includes a number of elements.

[0056] Step S103: Optimizing the constraint conditions according to the preset upper boundary coefficient, and determining the uncertain fluctuation upper boundary coefficient corresponding to each load value.

[0057] Step S104: Generate the uncertain fluctuation lower boundary coefficient corresponding to each load value according to the uncertain fluctuation upper boundary coefficient corresponding to each load data.

[0058] Step S105: According to each uncertain fluctuation lower boundary coefficient, an element is extracted from the corresponding uncertain fluctuation vector as the uncertain fluctuation lower boundary of the corresponding load value; according to each uncertain fluctuation upper boundary coefficient, an element is extracted from the corresponding uncertain fluctuation vector as the uncertain fluctuation upper boundary of the corresponding load value.

[0059] Step S106: Determine the upper load boundary of the multi-energy park according to the upper uncertain fluctuation boundary of each load value; determine the lower load boundary of the multi-energy park according to the lower uncertain fluctuation boundary of each load value.

[0060] For step S101, schematically, firstly, based on the planning and energy supply information of the multi-energy park, the load value of the park's electric load scene data at each moment is obtained through the relevant dispatching department and the measuring device of the corresponding equipment: p0=[p 0,1 ,p 0,2 ,…,p 0,i ,…,p 0,I ], as several load values ​​of the present invention. Indicatively, p0 can be the predicted data of the electric load (MW) of all equipment in the multi-energy park every hour within 24 hours. 0,t represents the electric load value (MW) of the multi-energy park at the tth hour, that is, p0=[p 0,1 p 0,2 … p 0,24 ] T , p0 is a 24-dimensional vector with 24 rows and 1 column.

[0061] For step S102, in a preferred embodiment, the uncertain fluctuation vector set is: U = {U0, U1, U2, ... U i …,U I}, i∈[0,I];

[0062] in,

[0063] U is the uncertain fluctuation vector set; I is the preset time period; U i is the uncertain fluctuation vector corresponding to the load value of the load data at time i; U i Each element in is a value randomly selected from a normal distribution with a mean of 1; K i is the total number of elements selected from a normal distribution with a mean of 1; To select K i After sorting the values ​​from small to large, the Kth n elements.

[0064] For each load value, randomly select K values ​​u from a normal distribution with mean 1 (1) ,u(2) ,...,u (K) . Sort these K values ​​from small to large, and we get Thus, the uncertain fluctuation vector corresponding to each load value is obtained. represents the Kth element in the uncertain fluctuation vector.

[0065] For step S103, in a preferred embodiment, the upper boundary coefficient optimization constraint condition includes:

[0066]

[0067]

[0068] 1≤si≤Ki

[0069] Ki-si+1 <si

[0070]

[0071] Among them, s i is the upper boundary coefficient of the uncertain fluctuation corresponding to the load value at time i; N is a natural number set; j is an index value; α represents the significance level of the hypothesis test; ∈ represents the risk level of the uncertain fluctuation vector set; is the uncertain fluctuation vector U i K i -s i +1 element; is the uncertain wave vector U i Middle i elements; p 0,i is the load value of the load data at time i; P Gr,i is the power transmitted from the power grid to the multi-energy park at time i; P g,i is the total power generation of all power generation equipment in the multi-energy park at time i; P shed,i is the load shedding at time i.

[0072] According to the above upper boundary coefficient optimization constraint conditions, calculate the s that meets the conditions i The uncertain fluctuation upper boundary coefficient corresponding to the load value at time i is obtained by using this method. The uncertain fluctuation upper boundary coefficient corresponding to the load value at all times is calculated.

[0073] Regarding step S104, the step of generating the uncertain fluctuation lower boundary coefficient corresponding to each load value according to the uncertain fluctuation upper boundary coefficient corresponding to each load data includes:

[0074] The lower boundary coefficient of uncertain fluctuation corresponding to each load value is generated according to the following formula:

[0075] Mi =K i -s i +1;

[0076] Among them, M i is the lower boundary coefficient of uncertain fluctuation corresponding to the load value at time i.

[0077] For step S105, in a preferred embodiment, extracting an element from the corresponding uncertain fluctuation vector according to an uncertain fluctuation lower boundary coefficient as the uncertain fluctuation lower boundary of the corresponding load value; extracting an element from the corresponding uncertain fluctuation vector according to an uncertain fluctuation upper boundary coefficient as the uncertain fluctuation upper boundary of the corresponding load value includes:

[0078] According to s i From U i Extract the sth i Data As the upper boundary of the uncertain fluctuation corresponding to the load value at time i;

[0079] According to M i From U i Extract the Mth i Data As the lower boundary of the uncertain fluctuation corresponding to the load value at time i.

[0080] For step S106, in a preferred embodiment, determining the load upper boundary of the multi-energy park according to the upper boundary of the uncertain fluctuation of each load value; determining the load lower boundary of the multi-energy park according to the lower boundary of the uncertain fluctuation of each load value includes:

[0081] According to the upper bound of uncertain fluctuation of each load value, the upper bound vector U of uncertain fluctuation of load data is determined. up ;in, Indicates the upper boundary of uncertain fluctuation corresponding to the load value at time I of load data;

[0082] According to the lower bound of uncertain fluctuation of each load value, determine the lower bound vector U of uncertain fluctuation of load data dn ;in, Indicates the lower boundary of the uncertain fluctuation corresponding to the load value at the moment of load data I;

[0083] The upper and lower load boundaries of the multi-energy park are calculated using the following formula:

[0084] p up =U up T ×p0

[0085] pdn =U dn T ×p0

[0086] p0=[p 0,1 ,p 0,2 ,…,p 0,i ,…,p 0,I ]

[0087] Among them, p up is the upper limit of the load of the multi-energy park, U up T is the upper boundary vector U of the uncertain fluctuation of load data up The transposed matrix of dn is the load lower boundary of the multi-energy park, U dn T is the upper boundary vector U of the uncertain fluctuation of load data dn The transposed matrix; p0 is the load data of the multi-energy park; p 0,I Represents the load value at load data I.

[0088] Specifically, the uncertain fluctuation lower boundary and the uncertain fluctuation upper boundary corresponding to each load value are obtained from step S105, and then the uncertain fluctuation upper boundary vector U of the load data is constructed. up and the uncertain fluctuation lower boundary vector U dn Finally, the load upper boundary p of the multi-energy park is generated by combining the load data of the multi-energy park up and the lower boundary p dn .

[0089] like Figure 2 As shown, based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0090] An embodiment of the present invention provides a multi-energy park load boundary determination device, comprising: a load acquisition module, an uncertain fluctuation vector set generation module, an upper boundary coefficient determination module, a lower boundary coefficient determination module, an uncertain fluctuation boundary determination module, and a load boundary calculation module;

[0091] The load acquisition module is used to obtain a number of load values ​​within a preset time period from the load data of the multi-energy park;

[0092] The uncertain fluctuation vector set generation module is used to generate an uncertain fluctuation vector set; wherein the uncertain fluctuation vector set includes an uncertain fluctuation vector corresponding to each load value; each uncertain fluctuation vector includes a number of elements;

[0093] The upper boundary coefficient determination module is used to determine the uncertain fluctuation upper boundary coefficient corresponding to each load value according to the preset upper boundary coefficient optimization constraint condition;

[0094] The lower boundary coefficient determination module is used to generate the uncertain fluctuation lower boundary coefficient corresponding to each load value according to the uncertain fluctuation upper boundary coefficient corresponding to each load data;

[0095] The uncertain fluctuation boundary determination module is used to extract an element from the corresponding uncertain fluctuation vector according to each uncertain fluctuation lower boundary coefficient as the uncertain fluctuation lower boundary of the corresponding load value; and to extract an element from the corresponding uncertain fluctuation vector according to each uncertain fluctuation upper boundary coefficient as the uncertain fluctuation upper boundary of the corresponding load value;

[0096] The load boundary calculation module is used to determine the upper load boundary of the multi-energy park according to the uncertain fluctuation upper boundary of each load value; and to determine the lower load boundary of the multi-energy park according to the uncertain fluctuation lower boundary of each load value.

[0097] Based on the above method embodiment, the present invention provides a terminal device embodiment;

[0098] An embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements any one of the multi-energy park load boundary determination methods described in the present invention.

[0099] Based on the above method embodiment, the present invention provides a storage medium embodiment;

[0100] An embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute any one of the multi-energy park load boundary determination methods described in the present invention.

[0101] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0102] Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0103] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0104] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0105] The memory can be used to store the computer program, and the processor realizes various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0106] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, the steps of each method embodiment described above can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0107] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for determining load boundaries of a multi-energy park, characterized in that: include: Obtaining a number of load values ​​within a preset time period from the load data of the multi-energy park; Generate an uncertain fluctuation vector set; wherein the uncertain fluctuation vector set includes an uncertain fluctuation vector corresponding to each load value; each uncertain fluctuation vector includes a number of elements; the uncertain fluctuation vector set is: ; in, is the uncertain fluctuation vector set; is a preset time period; For load data The uncertain fluctuation vector corresponding to the load value at the moment; Each element in is a value randomly selected from a normal distribution with a mean of 1; is the total number of elements selected from a normal distribution with a mean of 1; To select After sorting the values ​​from small to large, the first elements; According to the preset upper boundary coefficient optimization constraint condition, the uncertain fluctuation upper boundary coefficient corresponding to each load value is determined; wherein the upper boundary coefficient optimization constraint condition includes: ； in, for The upper boundary coefficient of uncertain fluctuation corresponding to the load value at the moment; is the set of natural numbers; is the index value; Indicates the significance level of the hypothesis test; represents the risk level of the uncertain volatility vector set; is the uncertain fluctuation vector Middle elements; is the uncertain fluctuation vector Middle elements; for Load value of load data at each moment; for The power transmitted by the power grid to the multi-energy park at any given moment; for The total power generation of all power generation equipment in the multi-energy park at any given moment; for Load shedding at all times; The lower boundary coefficient of uncertain fluctuation corresponding to each load value is generated according to the following formula: ; in, for The lower boundary coefficient of uncertain fluctuation corresponding to the load value at the moment; according to from Extract the Data , as The upper boundary of uncertain fluctuation corresponding to the load value at the moment; according to from Extract the Data , as The lower boundary of the uncertain fluctuation corresponding to the load value at the moment; Determine the upper boundary vector of the uncertain fluctuation of the load data according to the upper boundary of the uncertain fluctuation of each load value ;in, ; Indicates load data The upper boundary of uncertain fluctuation corresponding to the load value at the moment; Determine the lower boundary vector of the uncertain fluctuation of the load data according to the lower boundary of the uncertain fluctuation of each load value ;in, ; Indicates load data The lower boundary of the uncertain fluctuation corresponding to the load value at the moment; The upper and lower load boundaries of the multi-energy park are calculated using the following formula: in, is the upper boundary of the load of the multi-energy park, is the upper boundary vector of the uncertain fluctuation of load data The transposed matrix of is the lower load boundary of the multi-energy park, is the upper boundary vector of the uncertain fluctuation of load data The transposed matrix of It is the load data of the multi-energy park; Indicates load data The load value at the moment.

2. A multi-energy park load boundary determination device, characterized in that: include: A load acquisition module, an uncertain fluctuation vector set generation module, an upper boundary coefficient determination module, a lower boundary coefficient determination module, an uncertain fluctuation boundary determination module, and a load boundary calculation module; The load acquisition module is used to obtain a number of load values ​​within a preset time period from the load data of the multi-energy park; The uncertain fluctuation vector set generation module is used to generate an uncertain fluctuation vector set; wherein the uncertain fluctuation vector set includes an uncertain fluctuation vector corresponding to each load value; each uncertain fluctuation vector includes a number of elements; the uncertain fluctuation vector set is: ; in, is the uncertain fluctuation vector set; is a preset time period; For load data The uncertain fluctuation vector corresponding to the load value at the moment; Each element in is a value randomly selected from a normal distribution with a mean of 1; is the total number of elements selected from a normal distribution with a mean of 1; To select After sorting the values ​​from small to large, the first elements; The upper boundary coefficient determination module is used to determine the uncertain fluctuation upper boundary coefficient corresponding to each load value according to the preset upper boundary coefficient optimization constraint condition; wherein the upper boundary coefficient optimization constraint condition includes: ； in, for The upper boundary coefficient of uncertain fluctuation corresponding to the load value at the moment; is the set of natural numbers; is the index value; Indicates the significance level of the hypothesis test; represents the risk level of the uncertain volatility vector set; is the uncertain fluctuation vector Middle elements; is the uncertain fluctuation vector Middle elements; for Load value of load data at each moment; for The power transmitted by the power grid to the multi-energy park at any given moment; for The total power generation of all power generation equipment in the multi-energy park at any given moment; for Load shedding at all times; The lower boundary coefficient determination module is used to generate the uncertain fluctuation lower boundary coefficient corresponding to each load value according to the following formula: ; in, for The lower boundary coefficient of uncertain fluctuation corresponding to the load value at the moment; The uncertain fluctuation boundary determination module is used to determine the from Extract the Data , as The upper boundary of uncertain fluctuation corresponding to the load value at the moment; according to from Extract the Data , as The lower boundary of the uncertain fluctuation corresponding to the load value at the moment; The load boundary calculation module is used to determine the uncertain fluctuation upper boundary vector of the load data according to the uncertain fluctuation upper boundary of each load value. ;in, ; Indicates load data The upper boundary of uncertain fluctuation corresponding to the load value at the moment; Determine the lower boundary vector of the uncertain fluctuation of the load data according to the lower boundary of the uncertain fluctuation of each load value ;in, ; Indicates load data The lower boundary of the uncertain fluctuation corresponding to the load value at the moment; The upper and lower load boundaries of the multi-energy park are calculated using the following formula: in, is the upper boundary of the load of the multi-energy park, is the upper boundary vector of the uncertain fluctuation of load data The transposed matrix of is the lower load boundary of the multi-energy park, is the upper boundary vector of the uncertain fluctuation of load data The transposed matrix of It is the load data of the multi-energy park; Indicates load data The load value at the moment.

3. A terminal device, characterized in that: The method comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the method for determining the load boundary of a multi-energy park as claimed in claim 1 when executing the computer program.

4. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the multi-energy park load boundary determination method as described in claim 1.

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