Power resource allocation method and device based on governance device leasing and related system

Abstract

The invention provides an electric power resource allocation method and device based on governance device lease and a related system, applied to a server, the method comprises the following steps: obtaining lease information and a first technical parameter of a governance device in a target power distribution network, the target power distribution network comprising the governance device and a sensitive device, the treatment device is applied to the sensitive equipment; determining the fault rate of the sensitive equipment according to a fault rate evaluation algorithm and the first technical parameter, determining configuration information of the sensitive equipment applied to the treatment device according to the target fault rate, obtaining first configuration information, determining a lease optimization calculation model according to the lease information and the first configuration information, and solving the lease optimization calculation model to obtain a lease optimization result; the target calculation result comprises second configuration information of the governance device, and lease power operation of the governance device in the target power distribution network is achieved according to the second configuration information. The flexibility of power resource configuration of management device leasing can be improved.

Description

Technical Field

[0001] This application relates to the technical field of power quality governance, and particularly to a power resource allocation method, device, and related system based on the lease of governance devices. Background Art

[0002] With the acceleration of industrial transformation and upgrading, many industrial production processes increasingly rely on high-precision power electronic devices. These devices are very sensitive to the quality of electric energy, which has significantly increased the frequency and impact degree of voltage sag events. Voltage sag is a short-term voltage drop phenomenon, which usually causes equipment shutdown, reduced production efficiency, and product loss, thus bringing huge economic losses to users. Power companies and sensitive users have both paid great attention to this problem. The usual solutions mainly rely on users to purchase governance devices, such as dynamic voltage restorers, uninterruptible power supplies, etc.

[0003] However, the high cost of these devices poses a relatively large financial pressure on small and medium-sized enterprises, which is not conducive to the popularization of high-quality power among such users. In addition, there is no market-oriented investment and revenue mechanism for voltage sag governance in the market, resulting in uneven interests among all parties and a lack of effective cooperation, which in turn affects the cooperation among the power grid, equipment manufacturers, and users. With the deepening of power market reform, the application of governance devices based on the lease model has gradually received attention to reduce the financial burden of enterprises and promote the popularization of high-quality power solutions.

[0004] Therefore, how to improve the flexibility of power resource allocation for the lease of governance devices urgently needs to be solved. Summary of the Invention

[0005] The embodiments of this application provide a power resource allocation method, device, and related system based on the lease of governance devices, which realizes the improvement of the flexibility of power resource allocation for the lease of governance devices.

[0006] In a first aspect, the embodiments of this application provide a power resource allocation method based on the lease of governance devices. The method includes:

[0007] Obtain the lease information and first technical parameters of the governance device in the target distribution network; the target distribution network includes the governance device and sensitive equipment; the governance device is applied to the sensitive equipment;

[0008] Determine the failure rate of the sensitive equipment according to a preset evaluation algorithm for the influence of the governance device on the failure rate of the sensitive equipment and the first technical parameters to obtain the target failure rate;

[0009] Determine the configuration information of the sensitive equipment applied to the governance device according to the target failure rate to obtain the first configuration information;

[0010] Determine a lease optimization calculation model according to a preset first constraint condition, the lease information, and the first configuration information; the lease optimization calculation model is used to determine the configuration information of the governance device;

[0011] Solve the lease optimization calculation model to obtain a target calculation result, where the target calculation result includes the second configuration information of the governance device, so as to realize the leased power operation of the governance device in the target distribution network according to the second configuration information.

[0012] In a second aspect, an embodiment of the present application provides a power resource configuration device based on the lease of a governance device, which is applied to a server. The device includes:

[0013] An acquisition module, configured to acquire lease information and first technical parameters of a governance device in a target distribution network; the target distribution network includes the governance device and sensitive equipment; the governance device is applied to the sensitive equipment;

[0014] A calculation module, configured to determine the failure rate of the sensitive equipment according to a preset evaluation algorithm for the influence of the governance device on the failure rate of the sensitive equipment and the first technical parameters, so as to obtain a target failure rate;

[0015] A determination module, configured to determine the configuration information of the sensitive equipment applied to the governance device according to the target failure rate, so as to obtain first configuration information;

[0016] The determination module is further configured to determine a lease optimization calculation model according to a preset first constraint condition, the lease information, and the first configuration information; the lease optimization calculation model is used to determine the configuration information of the governance device;

[0017] A control module, configured to solve the lease optimization calculation model to obtain a target calculation result, where the target calculation result includes the second configuration information of the governance device, so as to realize the leased power operation of the governance device in the target distribution network according to the second configuration information.

[0018] In a third aspect, an embodiment of the present application provides a server, including a processor, a memory, a communication interface, and one or more programs. Among them, the above one or more programs are stored in the above memory and are configured to be executed by the above processor. The above programs include instructions for executing the steps in any method in the first aspect of the embodiment of the present application.

[0019] In a fourth aspect, an embodiment of the present application provides a power resource configuration system based on the lease of a governance device. Among them, the power resource configuration system based on the lease of a governance device executes some or all of the steps described in any method in the first aspect of the embodiment of the present application.

[0020] Fifth aspect, an embodiment of the present application provides a computer program product. The computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in any method of the first aspect of the embodiments of the present application. The computer program product may be a software installation package.

[0021] By implementing the embodiments of the present application, the following beneficial effects are achieved:

[0022] A power resource allocation method based on the lease of governance devices described in the present application is applied to a server. By obtaining the lease information and first technical parameters of the governance devices in the target distribution network, the target distribution network includes the governance devices and sensitive devices, and the governance devices are applied to the sensitive devices. Then, according to a preset evaluation algorithm for the influence of governance devices on the failure rate of sensitive devices and the first technical parameters, the failure rate of the sensitive devices is determined to obtain the target failure rate. According to the target failure rate, the configuration information of the sensitive devices applied to the governance devices is determined to obtain the first configuration information. Then, according to a preset first constraint condition, the lease information and the first configuration information, a lease optimization calculation model is determined; the lease optimization calculation model is used to determine the configuration information of the governance devices. Finally, the lease optimization calculation model is solved to obtain a target calculation result, and the target calculation result includes the second configuration information of the governance devices, so as to realize the leased power operation of the governance devices in the target distribution network according to the second configuration information. In this way, by constructing a multi-party cooperation framework involving power grid companies, equipment manufacturers, and sensitive users, and by leasing governance devices, the financial pressure on small and medium-sized enterprises is alleviated, enabling them to obtain the necessary power quality guarantee, ensuring the efficient use and reasonable configuration of governance devices, maximizing the benefits of all stakeholders, promoting effective cooperation between power grid companies and sensitive users, and thus improving the flexibility of power resource allocation for the lease of governance devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0024] Figure 1 is an architecture diagram of a power resource allocation system based on the lease of governance devices provided by an embodiment of the present application;

[0025] Figure 2 is a schematic structural diagram of a server provided by an embodiment of the present application;

[0026] Figure 3 It is a schematic flowchart of a power resource allocation method based on the lease of governance devices provided by an embodiment of the present application;

[0027] Figure 4 It is a tolerance curve graph of a scenario where a governance device provides high-quality power to sensitive devices provided by an embodiment of the present application;

[0028] Figure 5 It is a tolerance curve graph of a governance device affecting sensitive devices provided by an embodiment of the present application;

[0029] Figure 6 It is a tolerance curve graph of a scenario where a governance device does not provide high-quality power to sensitive devices provided by an embodiment of the present application;

[0030] Figure 7 It is a characteristic distribution graph of voltage sag events provided by an embodiment of the present application;

[0031] Figure 8 It is a schematic diagram of the functional composition of the optimization configuration of a governance device provided by an embodiment of the present application;

[0032] Figure 9 It is a comparison graph of calculating the fault probability using traditional methods and the A-ME algorithm provided by an embodiment of the present application;

[0033] Figure 10 It is a block diagram of the functional module composition of a power resource allocation device based on the lease of governance devices provided by an embodiment of the present application. Detailed implementation manners

[0034] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0035] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0036] It should be understood that the term "and / or" in this text is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. Additionally, the character " / " in this text indicates that the associated objects before and after are in an "or" relationship. The "multiple" mentioned in the embodiments of this application refers to two or more.

[0037] The "at least one (piece)" or its similar expression in the embodiments of this application refers to any combination of these items, including any combination of single items (pieces) or plural items (pieces), which means one or more, and multiple refers to two or more. For example, at least one (piece) of a, b, or c can represent the following seven situations: a, b, c, a and b, a and c, b and c, a, b, and c. Among them, each of a, b, and c can be an element or a set containing one or more elements.

[0038] The "connection" that appears in the embodiments of this application refers to various connection methods such as direct connection or indirect connection to achieve communication between devices, and this application does not make any limitations on this.

[0039] Referring to "embodiment" in this text means that the specific features, structures, or characteristics described in combination with the embodiment can be included in at least one embodiment of this application. The appearance of this phrase in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0040] First, the relevant terms involved in this application are explained as follows:

[0041] Voltage sag: A voltage sag is a short-term voltage drop phenomenon, which means that the effective value of the supply voltage suddenly drops within a short period of time, usually dropping to 10% - 90% of the rated voltage, and the duration is 0.5 cycles. The voltage sag phenomenon can cause equipment shutdown, reduced production efficiency, and product loss, thus bringing huge economic losses to users.

[0042] Treatment device: It refers to an electrical device that can detect voltage sag events and improve or stabilize the voltage through corresponding technical means to enable the affected equipment to operate normally.

[0043] At present, due to the insufficient funds of small and medium-sized enterprises to bear the purchase cost of the governance device, and the lack of an effective market-oriented investment and revenue mechanism in the existing governance solutions, the responsibilities and rights between the power grid company and the users are unclear, which affects the willingness to cooperate and restricts the promotion and application of high-quality electricity. In addition, most traditional high-quality electricity solutions are static governance and cannot flexibly meet the real-time demand of voltage, resulting in insufficient flexibility in the allocation of leased power resources of the governance device.

[0044] To solve the problem of insufficient flexibility in the allocation of leased power resources of the governance device, the embodiment of the present application provides a method, device and related system for allocating power resources based on the lease of the governance device, which is applied to a server. By obtaining the lease information and the first technical parameters of the governance device in the target distribution network, the target distribution network includes the governance device and sensitive equipment, and the governance device is applied to the sensitive equipment. Then, according to the preset evaluation algorithm for the failure rate of sensitive equipment affected by the governance device and the first technical parameters, the failure rate of the sensitive equipment is determined to obtain the target failure rate. According to the target failure rate, the configuration information of the sensitive equipment applied to the governance device is determined to obtain the first configuration information. Then, according to the preset first constraint condition, the lease information and the first configuration information, a lease optimization calculation model is determined; the lease optimization calculation model is used to determine the configuration information of the governance device. Finally, the lease optimization calculation model is solved to obtain the target calculation result, and the target calculation result includes the second configuration information of the governance device, so as to realize the leased power operation of the governance device in the target distribution network according to the second configuration information. The flexibility of the allocation of leased power resources of the governance device can be improved.

[0045] The following combines Figure 1 to illustrate the system architecture of a method for allocating power resources based on the lease of a governance device in an embodiment of the present application. Figure 1 It is an architecture diagram of a power resource allocation system based on the lease of a governance device provided by an embodiment of the present application. The power resource allocation system 100 based on the lease of the governance device includes: a governance device manufacturer 110, a power grid company 120, and a sensitive user 130.

[0046] Among them, the governance device manufacturer 110 is used to produce, sell or lease governance devices. Relying on its own technology and production capacity, the governance device manufacturer 110 researches, develops and manufactures governance devices, which can be Uninterruptible Power Supply (UPS), Dynamic Voltage Restorer (DVR), Active Power Filter (APF), Passive Power Filter (PPF), or Voltage Sag Protection (VSP) system based on DC power supply technology. There is no limitation here to meet the market demand for power quality governance. The governance device manufacturer 110 can provide corresponding products according to the requirements of the power grid company to purchase governance devices as needed, expand its own business scope, and at the same time promote the development and innovation of governance device technology. In addition, the governance device manufacturer 110 can customize the production of governance devices according to the specific needs of different sensitive users to further improve the adaptability and effectiveness of the devices.

[0047] Among them, the power grid company 120 is used to purchase governance devices from the governance device manufacturer 110 as needed and lease the equipment to the sensitive user 130. The power grid company 120 plays an intermediate hub role in the whole system. On the one hand, according to market research and the demand situation of sensitive users, it purchases appropriate governance devices from the governance device manufacturer; on the other hand, it leases the purchased governance devices to sensitive users and charges rent. In this process, the power grid company can optimize the allocation of governance devices and improve the allocation efficiency of power resources by virtue of its own resource allocation ability.

[0048] Among them, the sensitive user 130 is used to provide its own power demand information to the power grid company 120 and pay rent to lease the equipment. Due to the sensitivity of its production equipment to power quality, such as high-precision power electronic equipment, industrial automation production lines, etc., the sensitive user is vulnerable to the impact when power quality problems such as voltage sags occur. By leasing governance devices, the sensitive user can ensure the stable operation of its own production without a large amount of capital investment in purchasing equipment and reduce production losses caused by power quality problems.

[0049] In a possible embodiment, the governance device manufacturer 110 cooperates with a large sensitive user 130 who is engaged in the manufacture of high-precision electronic chips and has extremely high requirements for power quality. Traditional standard governance devices are difficult to meet its needs. Relying on its own technical advantages, the governance device manufacturer 110 conducts in-depth research on the production process and equipment characteristics of this user, and customizes and develops a set of high-performance DVR devices. This device has unique advantages in aspects such as compensation voltage accuracy and response speed, precisely adapts to the production line of this sensitive user, effectively reduces the interference of voltage sags on the chip manufacturing process, and ensures the product yield. At the same time, after conducting market research in the region, the power grid company 120 finds that many small and medium-sized sensitive users 130 have demands for power quality governance but have limited funds. The power grid company 120 purchases a batch of different types of low-voltage governance devices from the governance device manufacturer 110 according to demand, such as UPS or small VSP systems, etc., which are not limited here. Then, the power grid company 120 reasonably allocates the leased equipment according to the electricity consumption characteristics and demands of each sensitive user, and formulates a flexible rent payment plan. For example, for sensitive users with seasonal production, the rent is adjusted according to the production cycle to relieve their financial pressure. A small precision instrument processing enterprise among the sensitive users 130 provides the power grid company 120 with detailed power demand information such as its own electricity load and production shifts. By leasing UPS equipment, the precision instruments of this enterprise can still operate stably when the mains power fluctuates, avoiding losses caused by equipment downtime and product damage. This enterprise also negotiates with the power grid company to determine a reasonable lease term and rent payment method according to its own business conditions, achieving the guarantee of stable production at a lower cost.

[0050] It can be seen that through the above architecture of a power resource allocation system based on the lease of governance devices, various resources can be effectively integrated, providing a feasible and efficient solution for solving the power quality problems of small and medium-sized enterprises, thereby improving the flexibility of power resource allocation for the lease of governance devices.

[0051] The following combines Figure 2 to illustrate the server in the embodiments of the present application. Figure 2 is a schematic structural diagram of a server provided by an embodiment of the present application. As Figure 2 shown, the server 200 includes one or more processors 210, a memory 220, a communication interface 230, and one or more programs 221. The processor 210 is communicatively connected to the memory 220 and the communication interface 230 through an internal communication bus.

[0052] Among them, the processor 210 may be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, units, and circuits described in connection with the disclosure of this application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and so on. The communication unit may be a communication interface, a transceiver, a transceiver circuit, etc., and the storage unit may be a memory.

[0053] Among them, the memory 220 may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable ROM (PROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of random access memory (RAM) are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0054] Among them, the one or more programs 221 are stored in the above-mentioned memory 220 and are configured to be executed by the above-mentioned processor 210. The one or more programs 221 include instructions for executing any step in the following embodiments of a data processing method.

[0055] It can be understood that the server 200 may include more or fewer structural elements than those in the above structural block diagram. For example, it includes a power module, physical buttons, a Wi-Fi module, a speaker, a Bluetooth module, sensors, a display module, etc., which are not limited herein. It can be understood that the server 200 can carry, for example, Figure 1 a power resource allocation system based on the lease of governance devices as described above.

[0056] After understanding the software and hardware architecture of the present application, the following combines Figure 3 to describe a power consumption resource allocation method based on the lease of governance devices in an embodiment of the present application. Figure 3 FIG. is a schematic flowchart of a power consumption resource allocation method based on the lease of governance devices provided by an embodiment of the present application, which specifically includes the following steps:

[0057] Step S310, obtaining the lease information and first technical parameters of the governance device in the target distribution network; the target distribution network includes the governance device and sensitive equipment; the governance device is applied to the sensitive equipment.

[0058] Among them, the lease information includes the type of governance device (such as UPS, DVR, VSP, etc.), quantity, lease term, rent payment method, maintenance responsibility division, renewal, recovery terms, etc., which mainly reflects the transfer relationship and responsibility allocation of the governance device in the distribution network. The first technical parameters include the action time (the time from detecting the voltage sag to the device being put into operation), the compensation time (the duration for which the device continuously provides high-quality power), and the compensation voltage (the maximum compensation voltage amplitude that the device can output), and these parameters directly determine the protection ability of the governance device for sensitive equipment.

[0059] Among them, the governance device manufacturer provides the basic device parameters and lease quotes, the power grid company provides the inventory and scheduling information of available devices in the regional distribution network, and sensitive users submit personalized lease demands (such as lease term, expected compensation accuracy) through historical power consumption data and production requirements. For example, in the indirect lease scheme, the power grid company, as an intermediate hub, needs to synchronize the device specification list of the manufacturer and the user's power consumption sensitivity level to form a comprehensive information database including device models, rated capacities, and lease unit prices; in the direct lease scheme, the manufacturer and the user clearly define the lease term and maintenance responsibility through a contract, and the relevant information is synchronized to the distribution network management platform in real time through a blockchain or an electronic contract system.

[0060] Specifically, first, the intelligent monitoring terminals deployed in the distribution network collect the physical state data of the governance devices (such as operation duration, number of faults) in real time and upload them to the cloud database through the Internet of Things platform; second, the power grid company or the third-party service platform synchronize the device technical manuals and lease agreement templates of the manufacturers through the interface to form standardized device files; finally, sensitive users enter their personalized requirements such as the voltage tolerance curve and production shift of their sensitive devices through the client interface, and perform a preliminary match with the technical parameters of the governance devices to generate a pre-screened lease plan. For example, a precision electronics processing factory submits the requirement of "compensation voltage ≥ 0.6 pu, compensation time ≥ 100 ms" through the client, and the system automatically matches the eligible DVR device and its lease quotation.

[0061] It should be noted that problems such as inconsistent data formats and multi-source data conflicts may be faced during the data acquisition process. For example, the definition of "compensation time" may vary among the governance devices of different manufacturers (some manufacturers calculate it based on the full cycle from device startup to shutdown, and some manufacturers deduct the fault response time). At this time, data normalization processing needs to be carried out through industry standard protocols. For the rent structure in the lease information (such as the ratio of fixed rent to floating rent), the system supports automatically parsing the contract text through smart contracts, extracting key terms and converting them into structured data. If data is missing (such as a certain batch of devices does not provide the action time parameter), the redundant verification mechanism is started: first, call the historical parameters of the same model device as the default value, or trigger the manual review process to require the manufacturer to supplement and upload.

[0062] Step S320, determine the failure rate of the sensitive device according to the preset evaluation algorithm for the impact of the governance device on the failure rate of the sensitive device and the first technical parameters, and obtain the target failure rate.

[0063] Among them, the first technical parameters include: action time t act , compensation time T ME and compensation voltage U ME . The action time t act refers to the time from when the governance device detects a voltage sag to when the governance device is put into operation, which is determined by the detection circuit and control logic inside the governance device; the compensation time T ME refers to the time from when the governance device is put into operation to when the governance device ends its work, which is the working time that the governance device can carry out governance, that is, the time that can provide high-quality power supply for sensitive users, and this is related to the capacity and performance of the energy storage components of the governance device; the compensation voltage U ME is the voltage amplitude output by the governance device during the compensation process, which directly affects the protection effect on sensitive devices.

[0064] Among them, the preset governance device impact sensitive equipment failure rate evaluation algorithm can be an algorithm for evaluating the tripping probability of sensitive equipment based on the impact of the governance device (algorithm based on governance equipment, A-GE), which is based on the Voltage Tolerance Curve (VTC) and combines the action time t act , compensation time T ME and compensation voltage U ME to quantitatively analyze the suppression effect of the governance device on the failure probability of sensitive equipment. This algorithm is to correct the tolerance threshold of sensitive equipment through the technical parameters of the governance device, and then calculate the failure probability under voltage sag events. Among them, the interaction between the first technical parameter and the tolerance characteristics of sensitive equipment is the key input of the algorithm. The tolerance parameters of sensitive equipment include the upper and lower limits of the tolerance voltage U max.new , U min.new and the upper and lower limits of the tolerance time T max , T min , which respectively represent the minimum voltage amplitude and the longest duration that the equipment can maintain normal operation during voltage sag.

[0065] In a possible embodiment, the first technical parameter includes: action time, compensation time, and compensation voltage; determining the failure rate of the sensitive equipment according to the preset governance device impact sensitive equipment failure rate evaluation algorithm and the first technical parameter to obtain the target failure rate specifically includes the following steps:

[0066] 321. Obtain the tolerance parameters of the sensitive equipment; the tolerance parameters include: withstand voltage and tolerance time;

[0067] 322. Determine the target tolerance voltage according to the compensation voltage and the withstand voltage;

[0068] 323. Determine the target tolerance time according to the action time, the compensation time, and the tolerance time;

[0069] 324. Conduct a failure assessment on the sensitive equipment according to the governance device impact sensitive equipment failure rate evaluation algorithm, the target tolerance time, and the target tolerance voltage to obtain the target failure rate.

[0070] Among the tolerance parameters of sensitive equipment, the withstand voltage refers to the lowest voltage value that the sensitive equipment can withstand. When the voltage is lower than the lowest voltage value, the equipment may malfunction; the tolerance time refers to the longest time that the sensitive equipment can operate normally under abnormal voltage conditions. These parameters are usually obtained through the technical manuals provided by equipment manufacturers or can also be obtained through actual tests on the equipment, which is not limited here.

[0071] Among them, the determination of the target withstand voltage is based on the interaction between the compensation voltage and the withstand voltage. When the governance device is put into operation, the output compensation voltage can change the voltage actually borne by the sensitive equipment. By calculating the compensation voltage and the withstand voltage (such as adding the compensation voltage and the withstand voltage), the target withstand voltage is obtained, which more accurately reflects the actual voltage withstand capacity of the sensitive equipment under the action of the governance device. The determination of the target withstand time is relatively complex and requires comprehensive consideration of the action time, compensation time, and withstand time. If the action time is short, the governance device can respond quickly, which can extend the effective withstand time of the sensitive equipment to a certain extent; the longer the compensation time, the longer the sensitive equipment can be protected. According to different time relationships, a specific algorithm (such as a piecewise function) is used to determine the target withstand time.

[0072] Specifically, on the basis of comprehensively considering the influence of the above three factors in the governance device, the upper and lower limits of the voltage and time withstand values of the new sensitive equipment are obtained. Then, combined with the new withstand values, the following new results are obtained according to the A-GE algorithm:

[0073]

[0074] Among them, T max.new represents the new maximum withstand time; T min.new represents the new minimum withstand time; T 0.new represents the new average withstand time; T0 represents the original average withstand time; T min represents the minimum withstand time; T max represents the maximum withstand time; U max.new represents the new maximum withstand voltage; U min.new represents the new minimum withstand voltage; U 0.new represents the new average withstand voltage; U0 represents the original average withstand voltage; U ME represents the compensation voltage of the governance device; σ 1.new represents the new time standard deviation parameter, σ 1.new reflects the degree of dispersion of the new withstand time distribution to evaluate the withstand stability of the sensitive equipment in terms of time; σ1 represents the original time standard deviation parameter; σ 2.new represents the new withstand voltage standard deviation parameter, σ 2.new reflects the degree of dispersion of the new withstand voltage distribution to evaluate the withstand stability of the sensitive equipment in terms of voltage; σ2 represents the original voltage standard deviation parameter.

[0075] In a possible embodiment, the determination of the target withstand time according to the action time, the compensation time, and the withstand time specifically includes the following steps:

[0076] 3231. Obtain the minimum tolerance maintenance time and the maximum tolerance maintenance time when the treatment device stops running according to the tolerance time.

[0077] 3232. When the action time is less than or equal to the minimum tolerance maintenance time, determine the first tolerance curve according to the minimum tolerance maintenance time, the maximum tolerance maintenance time, and the compensation time.

[0078] 3233. When the action time is between the minimum tolerance maintenance time and the maximum tolerance maintenance time, determine the second tolerance curve according to the minimum tolerance maintenance time, the maximum tolerance maintenance time, and the compensation time.

[0079] 3234. When the action time is greater than or equal to the maximum tolerance maintenance time, determine the third tolerance curve according to the minimum tolerance maintenance time and the maximum tolerance maintenance time.

[0080] 3235. Determine the target tolerance time according to the first tolerance curve, the second tolerance curve, the third tolerance curve, and the action time.

[0081] Among them, T min The minimum tolerance maintenance time and T max The maximum tolerance maintenance time are the inherent tolerance time parameters of the equipment without the treatment device, which respectively represent the shortest and longest times that the equipment can maintain normal operation during voltage sags. The parameters are obtained by the equipment manufacturer through laboratory tests and are usually determined according to the type and model of the treatment device. The tolerance maintenance time when the treatment device stops running reflects the basic tolerance ability of the equipment itself.

[0082] Specifically, the three technical parameters of the treatment device affect the voltage tolerance ability of sensitive equipment from two aspects: time and voltage. From the perspective of improving the tolerance voltage ability, the treatment device can provide a compensation voltage to the sensitive equipment after detecting a voltage sag, which means that the sensitive equipment can tolerate a lower voltage. Therefore, both the upper and lower limits of the voltage tolerated by the sensitive equipment become lower. The upper and lower limits of the voltage tolerated by the sensitive equipment equipped with the treatment device are U min.new and U max.new .

[0083] U max.new =U max -U ME

[0084] U min.new =U min -U ME

[0085] Among them, U max.new represents the new maximum tolerance voltage; U min.newDenote the new minimum withstand voltage as U ME Denote the compensation voltage of the governance device as U max , U min respectively represent the original maximum withstand voltage and minimum withstand voltage of the sensitive device before the governance device takes effect.

[0086] For example, assume that U min and U max are 0.5 pu and 0.75 pu respectively, while U ME is 0.3 pu, and U min.new , U max.new are 0.2 pu and 0.45 pu respectively.

[0087] From the perspective of increasing the withstand time, the relationship between t act and T min , T max will lead to differences in the withstand time of the sensitive device. Specifically, it can be divided into the following three types for discussion, and the maximum and minimum withstand times under different types can be obtained. In the first type, when t act is less than or equal to T min , the sensitive device is not affected during the action time. When the governance device is put into operation, it can continuously provide voltage support of the governance device to the sensitive device for a time of T ME ; when the governance device stops working, the self-withstand ability of the sensitive device can still maintain a duration of at least T min and at most T max . Therefore, the withstand time of the sensitive device is increased, and T min.new and T max.new are respectively the sum of three periods of time, as expressed by the following formula:

[0088] T max.new = T max + T ME + t act

[0089] T min.new = T min + T ME + t act

[0090] Among them, T max.new represents the new maximum withstand time; T min.new represents the new minimum withstand time.

[0091] For easy understanding, please refer to Figure 4 , Figure 4 which is the withstand curve diagram of a governance device provided by an embodiment of the present application for providing high-quality power to a sensitive device. Figure 4For the first type, the influence of three technical parameters of the governance device on the tolerance curve of sensitive devices can be seen. For example, assume that t act is 10 ms, T ME is 200 ms, and T min and T max are 45 ms and 150 ms respectively, then T min.new and T max.new are 255 ms and 360 ms respectively.

[0092] In the second type, when t act is between T min and T max , T max.new is still the sum of three time periods, while T min remains unchanged. Since t act is longer than T min , this means that before the governance device starts working, the sensitive device can only rely on its own tolerance ability, so the minimum tolerance time is T min . Therefore, the maximum and minimum values of the new tolerance time of the sensitive device can be expressed by the following formula:

[0093] T max.new = T max + T ME + t act

[0094] T min.new = T min

[0095] For easy understanding, please refer to Figure 5 , Figure 5 which is a tolerance curve diagram of the influence of a governance device on sensitive devices provided by an embodiment of the present application. Figure 5 For the second type, the influence of three technical parameters of the governance device on the tolerance curve of sensitive devices can be seen. For example, assume that t act is 50 ms, T ME is 200 ms, and T min and T max are 45 ms and 150 ms respectively, then T min.new and T max.new are 45 ms and 400 ms respectively.

[0096] In the third type, when t act is greater than or equal to T max , this means that the action time of the governance device is too long, and during this period, the sensitive device does not receive high-quality power, and the tolerance time of the sensitive device is not increased. Therefore, in this case, T min.new and T max.new can be calculated according to the following formula:

[0097] T max.new = T max

[0098] T min.new = T min

[0099] For ease of understanding, please refer to Figure 6 , Figure 6 which is a tolerance curve graph of a scenario where the governance device provided by an embodiment of the present application fails to supply high-quality power to sensitive devices. Figure 6 Corresponding to the third type, it can be seen the influence of three technical parameters of the governance device on the tolerance curve of sensitive devices. For example, assume t act is 50 ms, T ME is 200 ms, and T min and T max are 45 ms and 150 ms respectively, then T min.new and T max.new are 45 ms and 150 ms respectively.

[0100] Among them, through the above three types of piecewise function models, the determination of the target tolerance time accurately reflects the coupling relationship between the response speed of the governance device and the tolerance characteristics of the device. In practical applications, this process needs to combine the real-time monitored t act and T min , T max , and through real-time operations of the edge computing node or the cloud server, provide a dynamically updated time tolerance boundary for failure rate assessment.

[0101] Step S330, determine the configuration information of the sensitive device applied to the governance device according to the target failure rate, and obtain the first configuration information.

[0102] Among them, the target failure rate is the probability value of the sensitive device failing obtained through a preset algorithm after comprehensively considering various parameters of the governance device and the tolerance characteristics of the sensitive device. It reflects the degree of failure risk faced by the sensitive device under the current operating state of the governance device. The first configuration information is a series of detailed parameters about the governance device, including the type, quantity, installation location, and operating parameter settings of the governance device, which are not limited here.

[0103] Among them, the process of determining the first configuration information involves many factors. On the one hand, different types of sensitive equipment have different tolerance to power quality problems such as voltage sags and harmonics. Therefore, it is necessary to select the appropriate type of control device based on the target failure rate and the characteristics of the sensitive equipment. For example, for electronic equipment that is extremely sensitive to voltage sags, a DVR may need to be configured; for equipment with harmonic interference, an APF may be more suitable; on the other hand, the number and installation location of the control devices are also crucial. Insufficient quantity may not effectively reduce the failure rate, while too many will increase costs; improper installation location may lead to poor control effect. In addition, the operating parameter settings of the control device, such as compensation voltage, compensation time, etc., also need to be precisely adjusted according to the target failure rate and the needs of sensitive equipment.

[0104] In a possible embodiment, determining the configuration information of the sensitive device applied to the management device according to the target failure rate to obtain the first configuration information specifically includes the following steps:

[0105] 331. Obtain historical power operating costs to obtain a first operating cost; the historical power operating costs are the costs of the sensitive equipment before the treatment device is applied;

[0106] 332. Determine a first management device identifier corresponding to the target failure rate according to a preset mapping relationship between sensitive device failure rates and management device identifiers;

[0107] 333. Obtain the governance device corresponding to the first governance device identifier to obtain the governance device to be configured;

[0108] 334. Determine first management device configuration information of the management device to be configured;

[0109] 335. Determine a first operating cost based on the first governance device configuration information;

[0110] 336. Determine an optimization model for the configuration of the treatment device based on the first operating cost to obtain an optimal configuration model for the target treatment device;

[0111] 337. Solve the optimal configuration model of the target governance device according to the preset second constraint condition to obtain a first solution set; the first solution set is used to configure the optimal configuration model of the target governance device;

[0112] 338. Determine the configuration information corresponding to the governance device according to the first solution set to obtain the first configuration information.

[0113] Among them, the historical power operation cost is the first operation cost, and the cost data can be obtained from the historical financial records, equipment maintenance logs, and production loss statistical reports of the power system. The first operation cost includes various expenses generated during the operation of the power system when the sensitive equipment does not apply the governance device, such as electricity bills, equipment repair and replacement costs, production losses caused by equipment failures, etc. The mapping relationship between the preset failure rate of sensitive equipment and the governance device identifier is established through a large amount of experimental data, actual operation case analysis, and theoretical research. It can also be a mapping relationship obtained through machine learning methods for prediction, which is not limited here.

[0114] Among them, the first governance device configuration information includes the specific parameters of the governance device to be configured, such as model, action time, compensation ability, etc. These parameters determine the performance and applicable scenarios of the governance device. The optimal configuration model of the target governance device is a mathematical model with the goal of minimizing the first operation cost. It considers the relationship between the configuration scheme of the governance device and the reduction of the failure rate of sensitive equipment, as well as various constraints.

[0115] Specifically, first collect and organize the historical power operation cost data from the relevant databases and records of the power system. After data cleaning and analysis, obtain the accurate first operation cost. Then, according to the preset mapping relationship table, match the target failure rate with the failure rate interval in the table to determine the corresponding first governance device identifier. Then, according to this identifier, search and obtain the corresponding governance device to be configured from the equipment list. For the governance device to be configured, consult its product manual, technical manual and other materials to determine its first governance device configuration information. After that, use the cost estimation model, combined with the first governance device configuration information, to calculate the first operation cost including the full life cycle cost of the governance device. With the goal of minimizing the first operation cost, combined with the relationship between the configuration of the governance device and the failure rate, construct the optimal configuration model of the target governance device. Convert the preset second constraint condition into a mathematical expression and substitute it into the model. Use optimization algorithms such as genetic algorithms and particle swarm algorithms to solve the model to obtain the first solution set. Finally, select the solutions that meet the actual requirements and conditions from the first solution set to determine the specific configuration information such as the type, quantity, installation location, and operation parameters of the governance device, and form the first configuration information.

[0116] In a possible embodiment, determining the first operation cost according to the first governance device configuration information specifically includes the following steps:

[0117] 3351. Determine n governance devices according to the first governance device configuration information; n is an integer greater than 1;

[0118] 3352. Obtain the working parameters of each of the n governance devices to obtain n working parameters;

[0119] 3353. Obtain n operating costs based on the operating costs corresponding to each of the n operating parameters.

[0120] 3354. Determine the first operating cost based on the n operating costs.

[0121] Among them, the first governance device configuration information includes the type of governance device (such as DVR, UPS, VSP, etc.), specifications (rated capacity, voltage level), and the deployed quantity. For example, the configuration plan of a certain sensitive user may include 2 DVRs and 1 UPS. At this time, n = 3. The selection of the governance device is subject to "technical - economic" dual constraints. It is necessary to meet the tolerance parameters of sensitive equipment (such as the compensation voltage needs to cover the lowest operating voltage of the equipment) and also comply with the voltage level and capacity requirements of the distribution network. For example, when configuring a governance device for an ASD module cluster, it is necessary to determine the number of DVRs and the single - machine capacity according to the total module capacity (such as 200 kVA) and the voltage sag frequency.

[0122] Specifically, before applying the leasing operation model, the economic loss of the sensitive user is the loss caused by industrial process tripping due to voltage sags. After applying the leasing operation model, for the optimization of the governance device, the economic loss of the sensitive user is considered as the cost of the governance device and the economic loss caused by the failure of sensitive equipment under voltage sags that still have an impact after installing the governance device. Therefore, the objective function is as follows:

[0123] maxC income = max(C noDVR -(C DVR + C loss ))

[0124] Among them, C noDVR is the economic loss caused by voltage sags before applying the leasing operation model, C loss is the economic loss caused by voltage sags of the sensitive user after applying the leasing operation model, C DVR is the cost of the governance device DVR, and maxC income is the maximized benefit.

[0125] It should be noted that the rent is the actual cost of the sensitive user, rather than C DVR , but C DVR is used in the objective function for two reasons. The first reason is that C DVR is proportional to the rent; the second reason is that during the stage of configuring the governance device, the rent is an unknown quantity.

[0126] Among them, the cost calculation of the governance device DVR is as follows. C DVR has five components, as follows:

[0127] C DVR = C S + C ES + C act + C install + C year = C cap + C year

[0128] C S = c S × S DVR

[0129]

[0130] c s = f(S DVR )

[0131] Wherein, C s is the capacity cost of the DVR, and C s is related to the capacity S DVR of the DVR; C ES is the cost of the energy storage system in the DVR; C act is the cost related to the operation of the DVR; C install is the installation cost of the DVR; C year is the annual operation and maintenance cost of the DVR; C cap is the initial investment cost of the DVR; c s is the unit capacity cost of the DVR; S DVR is the rated capacity of the DVR, and S DVR is determined by the capacity of the sensitive equipment governed by the governing device and the voltage required by the sensitive equipment; U DVR is the compensation voltage of the DVR, and U DVR is an optimization variable; U n is the rated voltage; N is the number of sensitive devices; f represents a functional relationship.

[0132] According to market research, the functional relationship is as follows:

[0133] C ES = c ES × T DVR × S DVR

[0134] Wherein, C ES represents the energy storage cost of the DVR, and C ES is related to the duration for which the DVR can continuously compensate; c ES is the unit energy cost; T DVR is the compensation time of the DVR; S DVR is the rated capacity of the DVR.

[0135] For the cost of the operation time, the annual operation and maintenance cost of the DVR can be obtained from the following formulas, which are as follows:

[0136]

[0137] c year =P maintain ×C cap

[0138] where C act represents the cost of the operation time. C act is related to the switch cost. The faster the switch speed and the larger the rated current of the switch, the higher the cost. C year is the total operation and maintenance cost of the DVR during the lease period; c year represents the annual operation and maintenance cost; C cap is the initial investment cost of the DVR; y represents the number of time periods; i represents the discount rate; P maintain represents the operation and maintenance coefficient, and P maintain =10%.

[0139] where C Lo is the general term for economic losses. When sensitive users configure governance devices without applying the lease operation model, C Lo is C noDVR ; when sensitive users adopt the lease operation model and configure governance devices, C Lo is C loss . The difference lies in the failure rate of sensitive devices. The former is the failure rate when the governance device is not installed, and the latter is the failure rate considering the installation effect of the governance device, which can be expressed by the following formula:

[0140]

[0141] where C Lo represents the general term for economic losses; L(n) represents the economic loss caused by voltage sags of the nth sensitive device within one year; ptripn(r) is the failure rate of the nth sensitive device caused by the rth voltage sag within one year; R is the total number of voltage sags within one year; l(n) represents the average economic loss caused by failures of the nth sensitive device due to voltage sags, and l(n) is determined by statistical data or expert experience.

[0142] Then, generally speaking, the higher the cost of the governance device, the better the governance effect, but this does not mean that the benefits of sensitive users are greater. In addition to the technical parameters of the governance device, the benefits of sensitive users also depend on the corresponding amplitude, duration characteristics and occurrence frequency of voltage sags, the tolerance characteristics of sensitive equipment and the losses caused by sensitive equipment. The optimal governance device does not refer to the governance device with the best technical parameters for sensitive users. This paper proposes an index to characterize the optimal governance device, that is, the cost performance ratio of the governance device, as shown in the following formula:

[0143]

[0144] Among them, maxCP represents the maximized cost-benefit ratio; C DVR represents the total cost of the DVR; C income represents the benefits after applying the governance device.

[0145] Among them, technical parameters are the key to the configuration of the governance device. Technical parameters are related to costs and governance effects. Therefore, it is necessary to appropriately constrain the technical parameters to obtain the optimal governance device. First, consider the compensation voltage (that is, consider the measured voltage sag data). The compensation voltage U DVR plus the remaining voltage is the supply voltage for sensitive equipment. The supply voltage should not be higher than the rated voltage to avoid overvoltage phenomena. Second, consider the compensation voltage (that is, consider the tolerance characteristics of sensitive equipment). The voltage provided by the governance device (compensation voltage plus remaining voltage) should be higher than that of sensitive equipment U max . The worst case of voltage sag is that the voltage drops to zero, but the remaining voltage of most voltage sags is higher than 0.7 pu. Due to economic reasons, it is not necessary to consider the worst case. Finally, consider the compensation time. The compensation time T DVR should be greater than 0 and less than the possible duration of the voltage sag. The duration of the voltage sag depends on the setting value of the protection relay. The setting value of the transmission network is less than several hundred milliseconds, and the setting value of the distribution network is less than 1-2 seconds. Select 2 seconds as the upper limit of the compensation time. The constraints of the above technical parameters can be expressed by the following formula:

[0146] U DVR ≤max{U n -U}

[0147] U DVR ≤U max

[0148] 0<T DVR <2000ms

[0149] Among them, U is the remaining voltage of the measured voltage sag; U n represents the rated voltage of sensitive equipment; U DVR represents the compensation voltage of the governance device DVR; U maxDenote the original maximum tolerance voltage of the sensitive device before the action of the governance device; T DVR Denote the compensation time of the DVR.

[0150] Through the specific elaboration of the above-mentioned step S330, the calculation of the first operating cost realizes the layer-by-layer aggregation from single-device parameters to system-level costs, providing a quantitative basis for the subsequent optimization model. In summary, through standardized parameter acquisition and cost modeling, this embodiment transforms the technical characteristics of the governance device into quantifiable economic indicators, ensuring a balance between technical feasibility and economic rationality of the configuration plan and providing data support for the optimal allocation of power resources.

[0151] Step S340, determine a lease optimization calculation model according to the preset first constraint condition, the lease information, and the first configuration information; the lease optimization calculation model is used to determine the configuration information of the governance device.

[0152] Among them, the core of the lessor's income constraint is that the lessor needs to ensure that the income during the lease period is not lower than a reasonable rate of return on the initial investment (such as the agreed profit rate δ≥10%), that is, the total rent income needs to cover the procurement cost, operation and maintenance cost, and expected profit of the governance device. The core of the lessee's cost constraint is that the sum of the rent expenditure and the fault loss of the sensitive user shall not be higher than the total economic loss when the lease mode is not adopted. The lease information includes: lease mode (direct plan / indirect plan), lease term (Y years), rent payment method (equal annuity / floating rent), and equipment maintenance responsibility division, which are the input variables of the model.

[0153] At the same time, according to the lease term Y and the rent payment method, generate a rent cash flow sequence. Then the model solving framework uses a multi-objective optimization algorithm (such as NSGA-II) to handle the dual-objective conflict and balance the lessor's income and the lessee's cost through the Pareto optimal solution set. For example, when a precision manufacturing enterprise leases 2 DVRs, the model needs to optimize between the rent pricing and the enterprise income, and finally output a configuration plan that takes into account the interests of both parties. It should be noted that the model supports dynamic parameter input, such as adjusting the energy consumption cost according to the real-time electricity price or updating the operation and maintenance cost according to the equipment aging degree to ensure the adaptability of the lease plan.

[0154] In a possible embodiment, the determining the lease optimization calculation model according to the preset first constraint condition, the lease information, and the target configuration information specifically includes the following steps:

[0155] 341. Determine the lessor, lessee, and lease type in the lease information;

[0156] 342. Obtain the transaction profit rate of the lessor according to the lease type to obtain the first profit rate;

[0157] 343. Obtain the rent collected by the lessor within the preset lease period to get the first rent;

[0158] 344. Obtain the cost corresponding to the lease type according to the target configuration information to get the first lessor cost;

[0159] 345. Determine the first revenue of the lessor according to the first profit rate, the first lessor cost and the first rent;

[0160] 346. Obtain the economic loss of the lessee when applying the lease type to get the first economic loss;

[0161] 347. Determine the second revenue of the lessee according to the first economic loss and the first rent;

[0162] 348. Construct the lease optimization calculation model based on the first constraint condition, the first revenue, the second revenue and the target configuration information.

[0163] Among them, the lessor is the owner of the governance device, which is the governance device manufacturer (such as MEM) in the direct lease plan and the power grid company in the indirect lease plan; the lessee is the sensitive user (SU), that is, small and medium-sized enterprises with high requirements for power quality; the lease types are divided into direct lease (from manufacturer to user) and indirect lease (from manufacturer to power grid company and then to user), and the core difference between the two is whether to introduce the power grid company as an intermediate link.

[0164] Among them, the first profit rate reflects the premium level of the lessor in the lease transaction. In the direct lease plan, as the lessor, the manufacturer's profit rate includes the difference between the equipment production cost and the rent, which is usually determined by market competition or the cost-plus method; in the indirect lease plan, as the lessor, the power grid company needs to pay the supply profit of the manufacturer (α = 10%), that is, the first profit rate is the procurement premium rate negotiated between the manufacturer and the power grid company. For example, in the indirect plan, the manufacturer sells the DVR to the power grid company for 1 million yuan, and the two parties agree that α = 10%, then the procurement cost of the power grid company is 1.1 million yuan, and the first profit rate corresponds to 10%. The first profit rate is automatically retrieved from the preset parameter library triggered by the lease type to ensure matching with the transaction plan. The first rent is the fee paid by the lessee according to the contract, which is divided into two categories: fixed rent and floating rent. The first lessor cost covers the full-cycle costs incurred by the lessor to provide the lease service. The first revenue is the net present value revenue of the lessor. The model takes the maximization of the lessor's revenue and the maximization of the lessee's revenue as dual objectives, embeds constraints and solves them through a multi-objective genetic algorithm (such as NSGA-II) to generate the Pareto optimal solution set.

[0165] Specifically, after determining the leasing mode, the embodiment of the present application provides a rental optimization calculation model that takes into account the maximum benefits of both sensitive users (lessees) and lessors, that is, the leasing optimization calculation model. First, considering the maximization of the lessor's income, in order to maximize the lessor's income, the objective function is established as follows: the income is equal to the rent received during the lease period minus the initial investment cost. Second, the maximization of the sensitive user's income needs to be considered, which can be expressed by the following formula:

[0166]

[0167] Among them, B is the lessor's income, and maxB is the maximized income of the lessor; R k is the rent received by the lessor in the k-th lease period; i k is the interest rate in the k-th lease period; C DVR_rental is the total cost of the lessor in the lease operation mode; n is the total number of lease cycles; B cos is the net income of the sensitive user; C noDVR is the economic loss caused by voltage sags before applying the lease operation mode; C loss is the economic loss caused by voltage sags of the sensitive user after applying the lease operation mode.

[0168] Then, considering the income of the sensitive user and the income constraint of the lessor, if C noDVR is small, the sensitive user does not need to adopt the lease operation mode; the sensitive user will only adopt the lease operation mode when it can ensure income. Therefore, the income constraint of the sensitive user and the rent charged by the lessor should ensure that the lessor can earn at least δ% profit, which is expressed as follows:

[0169] B cos > 0

[0170]

[0171] Among them, δ% represents the expected profit rate; C DVR_rental represents the total cost of DVR leasing.

[0172] Step S350, solve the leasing optimization calculation model to obtain the target calculation result, where the target calculation result includes the second configuration information of the governance device, so as to realize the leased power operation of the governance device in the target distribution network according to the second configuration information.

[0173] Among them, the target calculation result is the multi-objective optimal solution obtained by solving the lease optimization calculation model, including the final configuration parameters of the treatment device (such as type, quantity, technical parameters) and the lease strategy (such as rent plan, lease term). The second configuration information is a feasible solution formed by comprehensively considering the lessor's income, the lessee's cost, and technical constraints, ensuring that the treatment device can not only meet the power quality requirements of sensitive equipment but also achieve a balance of the interests of all parties.

[0174] Among them, the solution process of the lease optimization calculation model follows the multi-objective optimization theory and uses evolutionary algorithms such as the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to handle the coupling relationship between the objective function and the constraints. This model takes the maximization of the lessor's income and the minimization of the lessee's cost as the dual objectives, and at the same time embeds technical feasibility constraints (such as compensation voltage threshold, compensation time upper limit) and economic rationality constraints (such as rent return rate, user income guarantee) to form a typical Pareto optimization problem.

[0175] In a possible embodiment, the solving of the lease optimization calculation model to obtain the target calculation result specifically includes the following steps:

[0176] 351. Determine the objective function and constraints of the lease optimization calculation model to obtain p objective functions and k constraints; both p and k are integers greater than 1, and p is less than k;

[0177] 352. Solve according to the preset multi-objective genetic algorithm based on the p objective functions and the k constraints to obtain a second solution set;

[0178] 353. Generate m particles according to the second solution set, and initialize the initial position and initial velocity of each particle in the m particles; each particle corresponds to each solution in the second solution set;

[0179] 354. Perform iterative optimization on the m particles, and stop the iteration when the m particles meet the preset iteration conditions to obtain the target calculation result.

[0180] Among them, the objective function of the lease optimization calculation model usually consists of multiple interrelated and possibly conflicting indicators, such as maximizing the lessor's income and minimizing the lessee's cost. The constraint conditions cover various restrictions in aspects such as technology and economy, such as the technical performance requirements of the governance device, the lessor's minimum profit margin requirement, the lessee's maximum cost tolerance, etc. Determining p objective functions and k constraint conditions is the basis for solving the model, and they jointly define the solution space and objective direction of the problem. Among them, the preset multi-objective genetic algorithm is an effective method for solving multi-objective optimization problems. It simulates the biological evolution process and searches for the optimal solution in the solution space through operations such as selection, crossover, and mutation. In this embodiment, this algorithm is used to solve p objective functions and k constraint conditions, and the obtained second solution set contains a series of possible feasible solutions, which satisfy the objective functions and constraint conditions to varying degrees.

[0181] Specifically, first, a certain number of individuals are randomly generated. Each individual represents a possible solution, and the encoding of the solution includes the configuration information of the governance device (type, quantity, parameters, etc.) and the lease strategy (rental, lease term, etc.). Then, according to p objective functions and k constraint conditions, the fitness value of each individual is calculated. For individuals that violate the constraint conditions, a lower fitness value is given. Then, through non-dominated sorting and crowding degree calculation, excellent individuals are selected to enter the next generation. Among them, the crossover and mutation operations are to perform crossover and mutation operations on the selected individuals to generate new individuals until the preset termination conditions are met (such as the number of iterations reaches the upper limit), and the second solution set is obtained. Finally, m particles are generated according to the second solution set, and the initial position and initial velocity of each particle in the m particles are initialized. Each particle corresponds to each solution in the second solution set. m solutions are selected from the second solution set, and each solution corresponds to a particle. The position of the particle represents a possible configuration plan, and its dimension is the same as that of the solution. After updating the position of the particle, it is necessary to check whether the new position satisfies k constraint conditions. If not, the position is corrected to make it satisfy the constraint conditions. The preset iteration conditions can be that the number of iterations reaches the upper limit, the change in the objective function value is less than a certain threshold, etc. When the iteration conditions are met, the iteration stops. At this time, the solution corresponding to the optimal position among all particles is the target calculation result, which includes the second configuration information of the governance device and can be used to realize the lease power operation of the governance device in the target distribution network.

[0182] For the sake of easy understanding, the following takes a grain and oil production enterprise in the southwest as an example for illustration. Please refer to Figure 7 , Figure 7 which is a characteristic distribution diagram of voltage sag events provided by the embodiment of the present application. Figure 7Shows the voltage sag events suffered by users during the period from 2016 to 2020. For the grain and oil industry, taking soybean oil production as an example to introduce the processing process. Its production process mainly includes pretreatment, leaching, miscella treatment, and crude oil refining. Pretreatment includes multiple steps such as cleaning, crushing, softening, and extrusion expansion of soybean oil. In the cleaning of soybean oil, process equipment such as vibrating screens used for screening oil contains adjustable speed drive (ASD) modules, and these modules are very sensitive to voltage sags. The crusher circuit used in the crushing process is powered by an AC contactor (ACC) and is prone to tripping when disturbed by voltage sags. In the leaching step, a leaching device is used to achieve sufficient contact between the solvent and the raw material, and its control loop contains a programmable logic controller (PLC) P module. There are also production equipment with sensitive devices in other production links. The upper computer (Personal Computer, PC), as an important device such as a central control terminal, is also very sensitive to voltage sags. To simplify the problem, sensitive devices with similar functions and types in the process are summarized, and the quantity, capacity, average loss, and VTC characteristic parameters of each type of sensitive device are shown in the following table.

[0183]

[0184] As determined by mutual negotiation between the two parties, the lease term is 5 years, with each year as a lease period. The governance device selects DVR, and its energy storage element selects a supercapacitor with a moderate price. The supercapacitor has a high utilization rate in actual production, and its unit cost is 4900 yuan / kWh. The installation cost of DVR is 30000 yuan per set. This user prefers t act is 10ms, C act is 3000 yuan. Assuming that the profit margin α in the indirect solution is 10%. Based on the basic information obtained above, the optimal configuration of the governance device and the optimal rent calculation can be completed. Using the above basic information, through the optimization solution process, the compromise solution of the optimal solution set is used as the optimal configuration for sensitive users. The configuration results include the technical parameters and the number of governance devices, C income and cost performance.

[0185] Please refer to Figure 8 , Figure 8It is a schematic diagram of the functional composition of the optimization configuration of a governance device provided by an embodiment of the present application. It can be seen that this sensitive user optimizes the configuration of two DVRs, which are named DVR1 and DVR2 respectively. DVR1 provides governance for ACC and PC, and DVR2 provides governance for ASD and PLC. TDVR1 is 1104ms, UDVR is 0.54pu, TDVR2 is 138ms, and UDVR2 is 0.66pu. The best technical parameters and cost details of DVR1 and DVR2 are shown in the following table:

[0186]

[0187] According to the recorded data of the past five years, C noDVR The calculation result is 14.304 million yuan, and the maximum benefit of the sensitive user is 13.146 million yuan. In this case, the best configuration of the governance device has a high cost performance of 13.59. This optimization configuration model helps sensitive users find the best governance device to avoid voltage sags while maintaining economy.

[0188] In addition, in order to calculate the failure probability of sensitive devices, the tolerance information of sensitive devices is first required. The information of the 4 types of sensitive devices contained in this user can be obtained from the above table. The failure rates of various sensitive devices are calculated using the traditional failure rate algorithm and A-ME. The difference in the failure probability between the case where the governance device is installed using the lease operation model and the case where the governance device is not installed is obvious. Six voltage sags in 2019 are used to illustrate the difference in the failure probability calculation of 4 types of sensitive devices by the two algorithms.

[0189] Please refer to Figure 9 , Figure 9 It is a comparison chart of calculating the failure probability using the traditional method and the A-ME algorithm provided by an embodiment of the present application. It can be seen the positive effect of the governance device on sensitive devices. In 2019, the failure probability of sensitive devices calculated by A-ME is significantly lower than that calculated by the traditional algorithm without the governance device. Specifically, according to the voltage sag records of 5 years and the optimization configuration information, the sum of the failure times of ASD, ACC, PLC, and PC without the governance device installed is 26.62, 20.14, 24.22, and 23.16 respectively. While the sum of the failure times of ASD, ACC, PLC, and PC calculated by A-ME considering the influence of the governance device is 0.051, 0.26, 0.79, and 0.25 respectively. The failure times of the four sensitive devices have been greatly reduced, reaching more than 95%. Especially, the failure times of ASD have decreased by 99%. Thus, it can be obtained that the proposed A-ME algorithm can evaluate the failure probability of sensitive devices before installation, which is difficult to achieve by the traditional algorithm, and can well reflect the governance effect of the governance device.

[0190] After obtaining the optimal configuration of the treatment device, the calculation of rent is crucial. There are different rent calculation results for the two transaction schemes of the leasing operation model. The rent calculation model is optimized through a multi-objective optimization algorithm. Assume that the expected profit margin δ of the lessor is 10%. Assume that the interest rates for each period within the five-year lease term are 6.22%, 6.65%, 5.75%, 5%, and 4.75% respectively. Then the optimal compromise results of the direct scheme and the indirect scheme are shown in the following table. It can be seen from the following table that both the direct scheme and the indirect scheme can guarantee the benefits of two stakeholders, including sensitive users and the lessor. And the income of sensitive users under the direct scheme is higher than that under the indirect model. However, in the case where the direct scheme is not available for selection, the indirect scheme is also a good choice, which also ensures that sensitive users and the lessor can obtain benefits. Obviously, in the past five years, the annual rent of the direct scheme is lower than that of the indirect scheme. This is because the leasing cost of the indirect method is higher than that of the direct scheme. From the above, it can be known that both schemes of the leasing operation model can relieve the financial pressure of small and medium-sized sensitive users, which enables sensitive users to use appropriate treatment devices by paying less fees annually.

[0191]

[0192] It can be seen that through the above method for allocating power resources based on the lease of treatment devices, by obtaining the lease information and the first technical parameters of the treatment devices in the target distribution network, the target distribution network includes the treatment devices and sensitive devices, and the treatment devices are applied to the sensitive devices. Then, according to the preset evaluation algorithm for the impact of treatment devices on the failure rate of sensitive devices and the first technical parameters, the failure rate of the sensitive devices is determined to obtain the target failure rate. According to the target failure rate, the configuration information of the sensitive devices applied to the treatment devices is determined to obtain the first configuration information. Then, according to the preset first constraint condition, the lease information, and the first configuration information, a lease optimization calculation model is determined; the lease optimization calculation model is used to determine the configuration information of the treatment devices. Finally, the lease optimization calculation model is solved to obtain the target calculation result, and the target calculation result includes the second configuration information of the treatment devices, so as to realize the leased power operation of the treatment devices in the target distribution network according to the second configuration information. It can improve the flexibility of power resource allocation for the lease of treatment devices.

[0193] The above mainly introduces the solution of the embodiment of the present application from the perspective of the execution process on the method side. It can be understood that in order for the server to implement the above functions, it includes the corresponding hardware structure and / or software module for executing each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments provided in this article, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the way of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0194] The embodiments of the present application can divide the functional units of the electronic device according to the above method examples. For example, each functional unit can be divided corresponding to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. It should be noted that the division of units in the embodiments of the present application is illustrative, only a logical function division, and there may be other division methods in actual implementation.

[0195] In the case of dividing each functional module corresponding to each function, Figure 10 is a block diagram of the functional modules of a power resource allocation device based on the lease of a governance device provided by an embodiment of the present application. The power resource allocation device 1000 based on the lease of the governance device includes:

[0196] An acquisition module 1010, configured to acquire lease information and first technical parameters of a governance device in a target distribution network; the target distribution network includes the governance device and sensitive equipment; the governance device is applied to the sensitive equipment;

[0197] A calculation module 1020, configured to determine the failure rate of the sensitive equipment according to a preset evaluation algorithm for the influence of the governance device on the failure rate of the sensitive equipment and the first technical parameters, and obtain a target failure rate;

[0198] A determination module 1030, configured to determine configuration information of the sensitive equipment applied to the governance device according to the target failure rate, and obtain first configuration information.

[0199] The determination module 1030 is further configured to determine a lease optimization calculation model according to a preset first constraint condition, the lease information, and the first configuration information; the lease optimization calculation model is used to determine the configuration information of the governance device;

[0200] A control module 1040 is configured to solve the lease optimization calculation model to obtain a target calculation result, where the target calculation result includes second configuration information of the governance device, so as to implement the leased power operation of the governance device in the target distribution network according to the second configuration information.

[0201] In a possible embodiment, the first technical parameters include: action time, compensation time, and compensation voltage; when determining the failure rate of the sensitive device according to the preset evaluation algorithm for the failure rate of the sensitive device affected by the governance device and the first technical parameters, the calculation module 1020 is specifically configured to:

[0202] Obtain the tolerance parameters of the sensitive device; the tolerance parameters include: withstand voltage and withstand time;

[0203] Determine a target withstand voltage according to the compensation voltage and the withstand voltage;

[0204] Determine a target withstand time according to the action time, the compensation time, and the withstand time;

[0205] Perform a failure assessment on the sensitive device according to the evaluation algorithm for the failure rate of the sensitive device affected by the governance device, the target withstand time, and the target withstand voltage to obtain the target failure rate.

[0206] In a possible embodiment, when determining the target withstand time according to the action time, the compensation time, and the withstand time, the calculation module 1020 is specifically configured to:

[0207] Obtain the minimum withstand maintenance time and the maximum withstand maintenance time when the governance device stops running according to the withstand time;

[0208] When the action time is less than or equal to the minimum withstand maintenance time, determine a first withstand curve according to the minimum withstand maintenance time, the maximum withstand maintenance time, and the compensation time;

[0209] When the action time is between the minimum withstand maintenance time and the maximum withstand maintenance time, determine a second withstand curve according to the minimum withstand maintenance time, the maximum withstand maintenance time, and the compensation time;

[0210] When the action time is greater than or equal to the maximum withstand maintenance time, determine a third withstand curve according to the minimum withstand maintenance time and the maximum withstand maintenance time;

[0211] Determine the target withstand time according to the first withstand curve, the second withstand curve, the third withstand curve, and the action time.

[0212] In a possible embodiment, the determining module 1030 is specifically configured to obtain the first configuration information by determining the configuration information of the sensitive device applied to the governance device according to the target failure rate, specifically including:

[0213] Obtain the historical power operation cost to get the first operation cost; the historical power operation cost is the cost before the sensitive device applies the governance device;

[0214] Determine the first governance device identifier corresponding to the target failure rate according to the mapping relationship between the preset sensitive device failure rate and the governance device identifier;

[0215] Obtain the governance device corresponding to the first governance device identifier to get the governance device to be configured;

[0216] Determine the first governance device configuration information of the governance device to be configured;

[0217] Determine the first operation cost according to the first governance device configuration information;

[0218] Determine the optimization model of the governance device configuration according to the first operation cost to obtain the optimal configuration model of the target governance device;

[0219] Solve the optimal configuration model of the target governance device according to the preset second constraint condition to obtain the first solution set; the first solution set is used to configure the optimal configuration model of the target governance device;

[0220] Determine the configuration information corresponding to the governance device according to the first solution set to obtain the first configuration information.

[0221] In a possible embodiment, the determining module 1030 is specifically configured to determine the first operation cost according to the first governance device configuration information, specifically including:

[0222] Determine n governance devices according to the first governance device configuration information; n is an integer greater than 1;

[0223] Obtain the working parameters of each governance device among the n governance devices to get n working parameters;

[0224] Obtain n operation costs according to the operation cost corresponding to each working parameter among the n working parameters;

[0225] Determine the first operation cost according to the n operation costs.

[0226] In a possible embodiment, the determining module 1030 is specifically configured to determine the lease optimization calculation model according to the preset first constraint condition, the lease information, and the target configuration information, specifically including:

[0227] Determine the lessor, lessee, and lease type in the lease information;

[0228] Obtain the transaction profit margin of the lessor according to the lease type to obtain the first profit margin;

[0229] Obtain the rent received by the lessor within the preset lease period to obtain the first rent;

[0230] Obtain the cost corresponding to the lease type according to the target configuration information to obtain the first lessor cost;

[0231] Determine the first income of the lessor according to the first profit margin, the first lessor cost, and the first rent;

[0232] Obtain the economic loss of the lessee when applying the lease type to obtain the first economic loss;

[0233] Determine the second income of the lessee according to the first economic loss and the first rent;

[0234] Construct the lease optimization calculation model based on the first constraint condition, the first income, the second income, and the target configuration information.

[0235] In a possible embodiment, the control module 1040 is specifically configured to solve the lease optimization calculation model to obtain the target calculation result:

[0236] Determine the objective function and constraint conditions of the lease optimization calculation model to obtain p objective functions and k constraint conditions; both p and k are integers greater than 1, and p is less than k;

[0237] Solve according to the preset multi-objective genetic algorithm based on the p objective functions and the k constraint conditions to obtain a second solution set;

[0238] Generate m particles according to the second solution set and initialize the initial positions and initial velocities of each of the m particles; each particle corresponds to each solution in the second solution set;

[0239] Iteratively optimize the m particles, and stop the iteration when the m particles meet the preset iteration conditions to obtain the target calculation result.

[0240] It should be noted that the specific functional implementation method of a power resource allocation device 1000 based on the lease of a governance device is referred to the above Figure 3Description of a power resource allocation method based on the lease of governance devices. For example, the acquisition module 1010 is used to implement the relevant content of executing S310, which will not be elaborated here. Each unit or module in a power resource allocation device 1000 based on the lease of governance devices can be separately or all combined into one or several other units or modules to form, or some of the units or modules can be further split into multiple smaller units or modules in terms of function to form, which can achieve the same operations without affecting the realization of the technical effects of the embodiments of the present invention. The above units or modules are divided based on logical functions. In practical applications, the function of one unit (or module) is realized by multiple units (or modules), or the functions of multiple units (or modules) are realized by one unit (or module).

[0241] It can be seen that a power resource allocation device described in the embodiments of the present application based on the lease of governance devices can, by leasing governance devices, relieve the financial pressure on small and medium-sized enterprises, enabling them to obtain necessary power quality guarantees, and has developed an evaluation algorithm for the failure rate of sensitive devices affected by governance devices, established an optimization configuration model for governance devices, considered the constraints of economy and technical conditions, ensured the efficient use and reasonable configuration of governance devices, and thus improved the flexibility of power resource allocation based on the lease of governance devices.

[0242] The embodiments of the present application also provide a power resource allocation system based on the lease of governance devices. Among them, the power resource allocation system based on the lease of governance devices executes some or all of the steps of any method recorded in the above method embodiments, and the above computer includes a server.

[0243] The embodiments of the present application also provide a computer program product. The above computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the above computer program can be operated to cause a computer to execute some or all of the steps of any method recorded in the above method embodiments. The computer program product can be a software installation package, and the above computer includes a server.

[0244] It should be noted that for the above various embodiments, for the sake of simple description, they are all expressed as a series of action combinations. Those skilled in the art should know that the present application is not limited by the described action sequence because some steps in the embodiments of the present application can be performed in other sequences or simultaneously. In addition, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions, steps, modules or units involved are not necessarily essential to the embodiments of the present application.

[0245] In the above embodiments, the embodiments of the present application focus on different aspects in the description of each embodiment. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0246] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by hardware instructed by a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The aforementioned storage medium includes: ROM or random access memory RAM, magnetic disk, or optical disk, etc., various media that can store program codes.

[0247] The steps of the methods or algorithms described in the embodiments of the present application can be implemented in a hardware manner or by a processor executing software instructions. The software instructions can be composed of corresponding software modules. The software modules can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (EEPROM), registers, hard disk, removable hard disk, CD-ROM, or any other form of storage medium well-known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in a terminal device or a management device. Of course, the processor and the storage medium can also exist as discrete components in the terminal device or the management device.

[0248] Each device and product described in the above embodiments, and each module / unit included therein, may be a software module / unit, a hardware module / unit, or may be partly a software module / unit and partly a hardware module / unit. For example, for each device and product applied to or integrated into a chip, each module / unit included therein may be implemented in a hardware manner such as a circuit. Alternatively, at least some of the modules / units may be implemented in the form of a software program that runs on a processor integrated inside the chip, and the remaining (if any) part of the modules / units may be implemented in a hardware manner such as a circuit. For each device and product applied to or integrated into a chip module, each module / unit included therein may be implemented in a hardware manner such as a circuit. Different modules / units may be located in the same component (such as a chip, a circuit module, etc.) or different components of the chip module. Alternatively, at least some of the modules / units may be implemented in the form of a software program that runs on a processor integrated inside the chip module, and the remaining (if any) part of the modules / units may be implemented in a hardware manner such as a circuit. For each device and product applied to or integrated into a terminal device, each module / unit included therein may be implemented in a hardware manner such as a circuit. Different modules / units may be located in the same component (such as a chip, a circuit module, etc.) or different components within the terminal device. Alternatively, at least some of the modules / units may be implemented in the form of a software program that runs on a processor integrated inside the terminal device, and the remaining (if any) part of the modules / units may be implemented in a hardware manner such as a circuit.

[0249] The specific embodiments described above further elaborate on the objectives, technical solutions, and beneficial effects of the embodiments of the present application. It should be understood that the above description is only the specific embodiments of the embodiments of the present application and is not used to limit the protection scope of the embodiments of the present application. Any modifications, equivalent replacements, improvements, etc. made based on the technical solutions of the embodiments of the present application shall be included within the protection scope of the embodiments of the present application.

Claims

1. A method for power resource allocation based on the lease of governance devices, characterized in that Applied to a server, the method includes: Obtaining the lease information and first technical parameters of a governance device in a target distribution network; the target distribution network includes the governance device and sensitive equipment; the governance device is applied to the sensitive equipment; Determining the failure rate of the sensitive equipment according to a preset evaluation algorithm for the impact of the governance device on the failure rate of the sensitive equipment and the first technical parameters to obtain a target failure rate; Determining the configuration information of the sensitive equipment applied to the governance device according to the target failure rate to obtain first configuration information; Determining a lease optimization calculation model according to a preset first constraint condition, the lease information and the first configuration information; the lease optimization calculation model is used to determine the configuration information of the governance device; Solving the lease optimization calculation model to obtain a target calculation result, where the target calculation result includes the second configuration information of the governance device, so as to realize the lease power operation of the governance device in the target distribution network according to the second configuration information.

2. The method according to claim 1, wherein The first technical parameters include: action time, compensation time, and compensation voltage; determining the failure rate of the sensitive equipment according to a preset evaluation algorithm for the impact of the governance device on the failure rate of the sensitive equipment and the first technical parameters to obtain a target failure rate includes: Obtaining the tolerance parameters of the sensitive equipment; the tolerance parameters include: withstand voltage and withstand time; Determining a target withstand voltage according to the compensation voltage and the withstand voltage; Determining a target withstand time according to the action time, the compensation time, and the withstand time; Performing a failure assessment on the sensitive equipment according to the evaluation algorithm for the impact of the governance device on the failure rate of the sensitive equipment, the target withstand time, and the target withstand voltage to obtain the target failure rate.

3. The method according to claim 2, wherein Determining the target withstand time according to the action time, the compensation time, and the withstand time includes: Obtaining the minimum withstand maintenance time and the maximum withstand maintenance time when the governance device stops operating according to the withstand time; When the action time is less than or equal to the minimum withstand maintenance time, determining a first withstand curve according to the minimum withstand maintenance time, the maximum withstand maintenance time, and the compensation time; When the action time is between the minimum withstand maintenance time and the maximum withstand maintenance time, determining a second withstand curve according to the minimum withstand maintenance time, the maximum withstand maintenance time, and the compensation time; When the action time is greater than or equal to the maximum withstand maintenance time, determining a third withstand curve according to the minimum withstand maintenance time and the maximum withstand maintenance time; Determining the target withstand time according to the first withstand curve, the second withstand curve, the third withstand curve, and the action time.

4. The method according to any one of claims 1-3, characterized in that, Determining the configuration information of the sensitive equipment applied to the governance device according to the target failure rate to obtain first configuration information includes: Obtaining the historical power operation cost to obtain a first operation cost; the historical power operation cost is the cost of the sensitive equipment before applying the governance device; Determining a first governance device identifier corresponding to the target failure rate according to a preset mapping relationship between the failure rate of the sensitive equipment and the governance device identifier; Obtain the governance device corresponding to the first governance device identifier to obtain the governance device to be configured; Determine the first governance device configuration information of the governance device to be configured; Determine the first operation cost according to the first governance device configuration information; Determine an optimization model for the governance device configuration according to the first operation cost to obtain an optimal configuration model for the target governance device; Solve the optimal configuration model of the target governance device according to the preset second constraint condition to obtain a first solution set; the first solution set is used to configure the optimal configuration model of the target governance device; Determine the configuration information corresponding to the governance device according to the first solution set to obtain the first configuration information.

5. The method according to claim 4, wherein The determining the first operation cost according to the first governance device configuration information includes: Determine n governance devices according to the first governance device configuration information; n is an integer greater than 1; Obtain the working parameters of each governance device among the n governance devices to obtain n working parameters; Obtain n operation costs according to the operation cost corresponding to each working parameter among the n working parameters; Determine the first operation cost according to the n operation costs.

6. The method according to any one of claims 1 to 3, characterized in that, The determining the lease optimization calculation model according to the preset first constraint condition, the lease information and the target configuration information includes: Determine the lessor, lessee and lease type in the lease information; Obtain the transaction profit margin of the lessor according to the lease type to obtain the first profit margin; Obtain the rent collected by the lessor during the preset lease period to obtain the first rent; Obtain the cost corresponding to the lease type according to the target configuration information to obtain the first lessor cost; Determine the first income of the lessor according to the first profit margin, the first lessor cost and the first rent; Obtain the economic loss of the lessee when applying the lease type to obtain the first economic loss; Determine the second income of the lessee according to the first economic loss and the first rent; Construct the lease optimization calculation model based on the first constraint condition, the first income, the second income and the target configuration information.

7. The method according to any one of claims 1 to 3, characterized in that, The solving the lease optimization calculation model to obtain a target calculation result includes: Determine the objective function and constraint conditions of the lease optimization calculation model to obtain p objective functions and k constraint conditions; p and k are both integers greater than 1, and p is less than k; Solve according to the preset multi-objective genetic algorithm according to the p objective functions and the k constraint conditions to obtain a second solution set; Generate m particles according to the second solution set and initialize the initial position and initial velocity of each particle among the m particles; each particle corresponds to each solution in the second solution set; Perform iterative optimization on the m particles, and stop the iteration when the m particles meet the preset iteration condition to obtain the target calculation result.

8. A power resource allocation device based on the lease of governance devices, characterized in that, Applied to a server, the device includes: An acquisition module, configured to acquire lease information and first technical parameters of governance devices in a target distribution network; the target distribution network includes the governance devices and sensitive devices; the governance devices are applied to the sensitive devices; A calculation module, configured to determine the failure rate of the sensitive device according to a preset failure rate evaluation algorithm of the governance device affecting the sensitive device and the first technical parameter, so as to obtain a target failure rate; A determination module, configured to determine the configuration information of the sensitive device applied to the governance device according to the target failure rate, so as to obtain first configuration information; The determination module is further configured to determine a lease optimization calculation model according to a preset first constraint condition, the lease information and the first configuration information; the lease optimization calculation model is used to determine the configuration information of the governance device; A control module, configured to solve the lease optimization calculation model to obtain a target calculation result, where the target calculation result includes second configuration information of the governance device, so as to implement the leased power operation of the governance device in the target distribution network according to the second configuration information.

9. A server, characterized in that, Including: A processor, a memory, a communication interface, and one or more programs; The one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for executing the steps in the method according to any one of claims 1-7.

10. A power resource allocation system based on the lease of governance devices, characterized in that, The power resource configuration system based on the lease of the governance device is used to execute the method according to any one of claims 1-7.

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