Capacity adequacy-based power supply full life cycle cost calculation method

Abstract

The invention relates to the technical field of power supply full-life-cycle cost calculation, in particular to a power supply full-life-cycle cost calculation method based on capacity adequacy, and the calculation method comprises the steps: building a cost optimization model based on the investment cost; cost prediction of the power supply life cycle is completed based on the distributed power supply area node data; determining the cost of the full life cycle of the power supply based on the capacity adequacy; according to the invention, the cost prediction of the power supply life cycle is carried out based on the distributed power supply area node data, and the real-time monitoring and prediction of the power supply operation state are realized; by introducing a calculation formula of a whole life cycle cost minimum value, calculation of an optimal planning scheme of a distributed power supply planning model is realized; the cost prediction of the power supply life cycle is carried out based on the distributed power supply area node data, and the real-time monitoring and prediction of the power supply operation state are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power supply full life cycle cost calculation, in particular to a power supply full life cycle cost calculation method based on capacity adequacy. BACKGROUND

[0002] Guaranteeing the capacity adequacy of the power system is crucial for the safe and stable operation of the power grid. The higher the adequacy of the power grid, the more stable the operation of the power grid. With the construction of new power systems, the number of distributed power sources and energy storage devices such as new energy storage devices in the power grid is gradually increasing, and the investment in the construction of the power grid is also increasing. Relatively, the cost and benefit should be considered. The cost calculation of the full life cycle of the power supply is evaluated from the aspects of power generation, power grid, power consumption and other economic benefits. The investment, operation and maintenance costs and the service life of the power grid operation performance, as well as the power demand are analyzed from two aspects to analyze the influence of the investment and construction of the power grid on the power grid enterprise. Therefore, the economic analysis of the full life cycle of the power supply in the process of the investment and construction of the power grid is very important, and the calculation result directly affects the rationality of the investment scale and the implementability of the power supply project. The current power supply cost calculation method includes benefit evaluation of the power supply, benefit calculation of the power supply and calculation of the flat rate cost, but the problem is that the benefit evaluation of the power supply is only calculated from the financial aspect, and the overall analysis of the economy of the full life cycle of the power supply is lacking. The benefit calculation of the power supply does not consider the land cost and the capital cost, and the loss during the operation of the equipment and the recovery cost after the expiration of the service life of the equipment. In the process of calculating the cost and benefit of the power supply, there are limitations. SUMMARY

[0003] In view of the above problems, the present application is proposed.

[0004] Therefore, the present application provides a power supply full life cycle cost calculation method based on capacity adequacy, which realizes real-time monitoring and prediction of the power supply operation state by predicting the cost of the power supply life cycle based on distributed power supply area node data. The optimal planning scheme of the distributed power supply planning model is calculated by introducing the calculation formula of the minimum value of the full life cycle cost.

[0005] To solve the above technical problems, the present application provides the following technical scheme: a power supply full life cycle cost calculation method based on capacity adequacy, comprising the following steps,

[0006] Constructing a cost optimization model based on investment cost;

[0007] Completing the cost prediction of the power supply life cycle based on distributed power supply area node data;

[0008] Determining the cost of the full life cycle of the power supply based on capacity adequacy.

[0009] As a preferred embodiment of the power supply full life cycle cost calculation method based on capacity adequacy described in this invention, the cost optimization model based on investment cost is constructed by calculating the initial investment cost based on the power storage system cost, power conversion and control equipment cost, construction cost and installation and commissioning cost, and calculating the full life cycle investment cost of the power supply based on the initial investment cost, financial expenses, operation and maintenance costs and recovery residual value, calculating the initial revenue of the power supply full life cycle, and obtaining the comprehensive revenue by combining the full life cycle investment cost;

[0010] The cost optimization model calculates the total power consumption over its entire lifecycle using the power supply's cycle life, depth of discharge, energy conversion efficiency, and capacity retention rate. Based on the calculated lifecycle cost and total power consumption, the model then calculates the power supply's total lifecycle cost. The total power consumption over its entire lifecycle is calculated by collecting data on the power supply's cycle life, depth of discharge, energy conversion efficiency, and capacity retention rate. The specific calculation formula is as follows:

[0011]

[0012] Where N represents the cycle life of the power supply, D represents the depth of discharge of the power supply, with a value ranging from (0,1), and E represents the proportion of capacity occupied by discharge. ct E represents the power supply capacity in year t. c0 E represents the rated capacity of the power supply. f R represents the power supply's energy conversion efficiency, with a value ranging from (0,1). R represents the power supply's capacity retention rate, also ranging from (0,1), indicating the degree to which the power supply's capacity remains unchanged over time. E O It represents the total energy processed throughout the entire lifecycle of the power system.

[0013] As a preferred embodiment of the power supply lifecycle cost calculation method based on capacity adequacy described in this invention, the specific formula for calculating the lifecycle cost is as follows:

[0014]

[0015] Among them, R S R represents the total revenue over the power supply's lifetime. t C represents the revenue generated by the power system in year t. t IC0 represents the operating cost required for the power system to operate in year t, DC represents the residual value of the power system, and N represents the operating life of the power system.

[0016] The initial investment cost of the power system is calculated based on the cost of the power storage system, the cost of power conversion and control equipment, the construction cost, and the installation and commissioning cost. The specific calculation formula is as follows:

[0017] IC0 = E sys +E tra +E con +E ins

[0018] Among them, E sys E represents the cost of a power storage system. tra E represents the cost of power conversion. con E indicates the construction cost. ins This indicates the cost of installation and commissioning.

[0019] As a preferred embodiment of the power supply lifecycle cost calculation method based on capacity adequacy described in this invention, the power supply lifecycle cost prediction based on distributed power area node data is achieved by collecting distributed power area node data, processing the collected data, mining the overlapping areas of distributed power sources based on the data processing results, and then calculating the optimal planning scheme of the planning model including distributed power sources with the goal of minimizing the lifecycle cost based on the power distribution areas with overlapping parts, thereby completing the power supply lifecycle cost prediction.

[0020] The specific formula for calculating the minimum total lifecycle cost is as follows:

[0021]

[0022] Among them, L S IC0 represents the minimum total lifecycle cost, DC represents the initial investment cost of the power system, OMC represents the residual value of the power system, and E represents the maintenance cost of the power system. O It represents the total energy processed throughout the entire lifecycle of the power system.

[0023] As a preferred embodiment of the power supply lifecycle cost calculation method based on capacity adequacy described in this invention, the data processing of the collected data involves substituting the collected distributed power generation area node data into the distributed power generation area node power grid, and predicting and filling in any missing data in the distributed power generation area node data to ensure the completeness and accuracy of the data; the distributed power generation area node data includes distributed power generation area node voltage data, user active load data, reactive load data, and three-phase average voltage change data.

[0024] The prediction and filling of missing data in the distributed power generation area node data is achieved by substituting the distributed power generation area node data into the distributed power generation area node power grid and using a linear regression algorithm to complete the data prediction and filling. The specific implementation formula is as follows:

[0025]

[0026]

[0027] in, This represents the predicted target variable value, where X represents the input distributed power source node data, and β0 and β1 represent the intercept and slope in the linear regression algorithm, respectively. X represents the mean of the input data and the mean of the predicted data, respectively. i Y i Let represent the i-th input data and the predicted data, respectively, and n represent the total amount of input data;

[0028] The process of mining overlapping areas of distributed power sources based on data processing results involves sorting the nodes in the distributed power source area and mining the overlapping areas of distributed power sources by identifying the nodes in the power grid overlapping the distributed power source area. The specific implementation formula is as follows:

[0029]

[0030] Where {Q} represents the power distribution area, k represents the regional node coefficient, {n} represents the weight value of the non-overlapping part of the distributed power source, and A ij Let represent the weight of the overlapping portion of node i and node j, ki represent the node in the direction of distributed power region i, kj represent the node in the direction of distributed power region j, and (ci,cj) represent the nodes of all distributed power regions.

[0031] As a preferred embodiment of the power supply lifecycle cost calculation method based on capacity adequacy described in this invention, the power supply lifecycle cost prediction involves: acquiring data of distributed power supply area nodes, preprocessing the acquired data, mining power supply distribution areas based on the preprocessed data, the power supply distribution areas including active load areas and reactive load areas, clustering the active load areas, and the reactive load area clustering results being the same as the corresponding active load areas. Then, the derivative functions of each order of the load areas are incorporated into the distribution area clustering algorithm. Finally, the load areas and derivatives of each quarter are clustered separately, and the distributed power supply area node voltage and branch current are calculated for the predicted load areas. The active power and reactive power of each distributed power supply area node and the load rate are calculated to obtain the actual power supply lifecycle cost value.

[0032] The active power load area is clustered by using the k-means clustering algorithm to perform cluster analysis on the active power load area, as specifically implemented as follows:

[0033] Randomly initialize k cluster centers, assign each data point to the cluster corresponding to the nearest cluster center, recalculate the coordinates of each cluster center, and obtain the mean of the corresponding data points, until the cluster centers no longer change;

[0034] The nearest cluster center is determined by calculating the distance from the data point to the cluster center using the Euclidean aggregation formula. The specific calculation formula is as follows:

[0035]

[0036] Where, x i Let c represent the i-th data point. j Let x represent the j-th cluster center. ik c jk They represent data points x respectively i and distance from center c j The coordinates in the k-th dimension, where n represents the number of dimensions of the data point;

[0037] The voltage calculation of the distributed power generation area nodes is based on the power injected into the nodes, while the current calculation is obtained by combining Ohm's law with the calculated voltage. The specific calculation formula is as follows:

[0038]

[0039]

[0040] Among them, V i P represents the voltage at node i that has been calculated. i Q i V represents the active power and reactive power of node i, respectively. i0 V i1 Let Z represent the node voltages at the start and end points of the branch, respectively, and let I represent the branch impedance. i This represents the calculated node current.

[0041] As a preferred embodiment of the power supply lifecycle cost calculation method based on capacity adequacy described in this invention, the capacity adequacy is calculated based on voltage sequence and power factor sequence, including static reactive power compensation adequacy and dynamic reactive power compensation adequacy. The voltage sequence is composed of collected voltage data, and the power factor sequence is composed of collected power factor data. The static reactive power compensation adequacy is determined based on the voltage sequence and power factor sequence, and the dynamic reactive power compensation adequacy is determined by the static reactive power compensation adequacy, the degree of voltage fluctuation exceeding the limit, and the degree of power factor fluctuation exceeding the limit.

[0042] The voltage fluctuation exceeding the limit is calculated based on the voltage sequence length, and the specific calculation formula is as follows:

[0043]

[0044] Among them, Ul Indicates the degree of voltage fluctuation exceeding the limit, where n represents the length of the voltage sequence, and U di The elements in the voltage limit characterization array represent the voltage limits.

[0045] The degree of power factor fluctuation exceeding the limit is calculated based on the power factor sequence, and the specific calculation formula is as follows:

[0046]

[0047] in, Let n represent the elements in the power factor sequence, and n represent the length of the power factor sequence. The elements in the power factor exceeding the limit characterization array represent the power factor exceeding the limit.

[0048] Another objective of this invention is to provide a power supply lifecycle cost calculation system based on capacity adequacy. This system can predict the lifecycle cost of power supplies by using distributed power supply node data, enabling real-time monitoring and prediction of power supply operating status. By introducing a formula for minimizing the lifecycle cost, it calculates the optimal planning scheme for distributed power supply planning models. By collecting and processing distributed power supply node data and identifying overlapping areas, this invention provides a scientific basis for power system planning and layout, aiming to minimize the lifecycle cost, thereby improving power supply utilization efficiency and economic benefits.

[0049] As a preferred embodiment of the power supply lifecycle cost calculation system based on capacity adequacy described in this invention, it includes: a cost optimization model construction module, a power supply lifecycle cost prediction module, and a power supply lifecycle cost determination module; the cost optimization model construction module constructs a cost optimization model based on investment costs; the power supply lifecycle cost prediction module completes power supply lifecycle cost prediction based on distributed power supply node data; and the power supply lifecycle cost determination module determines the power supply lifecycle cost based on capacity adequacy.

[0050] A computer device includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program to implement a method for calculating the cost of a power supply over its entire lifecycle based on capacity adequacy.

[0051] A computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of a method for calculating the cost of a power supply over its entire lifecycle based on capacity adequacy.

[0052] The beneficial effects of this invention are as follows: By predicting the cost of power supply lifecycle based on distributed power source node data, this invention achieves real-time monitoring and prediction of power supply operating status; by introducing a formula for calculating the minimum lifecycle cost, it realizes the calculation of the optimal planning scheme for the distributed power supply planning model; by collecting and processing distributed power source node data and mining overlapping areas of distributed power sources, this invention can provide a scientific basis for the planning and layout of power systems with the goal of minimizing the lifecycle cost, thereby improving the utilization efficiency and economic benefits of power sources. Attached Figure Description

[0053] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0054] Figure 1 This is a schematic diagram of the overall method steps of the present invention, which is a method for calculating the cost of a power supply throughout its entire life cycle based on capacity adequacy.

[0055] Figure 2 This is a schematic diagram of the overall structure of a power supply lifecycle cost calculation system based on capacity adequacy, according to the present invention. Detailed Implementation

[0056] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0057] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0058] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0059] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0060] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0061] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0062] Example 1

[0063] Reference Figure 1 This is the first embodiment of the present invention, which provides a method for calculating the cost of a power supply throughout its entire lifecycle based on capacity adequacy, including the following steps:

[0064] S1: Construct a cost optimization model based on investment costs

[0065] Specifically, the cost optimization model based on investment cost calculates the initial investment cost based on the cost of the power storage system, the cost of power conversion and control equipment, the construction cost and the installation and commissioning cost. Based on the initial investment cost, financial expenses, operation and maintenance costs and residual value, the total life cycle investment cost of the power supply is calculated, the initial life cycle revenue of the power supply is calculated, and the comprehensive revenue is obtained by combining the total life cycle investment cost.

[0066] Furthermore, the cost optimization model calculates the total power consumption over its entire lifecycle using the power supply's cycle life, depth of discharge, energy conversion efficiency, and capacity retention rate. Based on the calculated lifecycle cost and total lifecycle power consumption, the model then calculates the power supply's lifecycle cost. The total lifecycle power consumption is calculated by collecting data on the power supply's cycle life, depth of discharge, energy conversion efficiency, and capacity retention rate. The specific calculation formula is as follows:

[0067]

[0068] Where N represents the cycle life of the power supply, D represents the depth of discharge of the power supply, with a value ranging from (0,1), and E represents the proportion of capacity occupied by discharge. ct E represents the power supply capacity in year t. c0 E represents the rated capacity of the power supply. f R represents the power supply's energy conversion efficiency, with a value ranging from (0,1). R represents the power supply's capacity retention rate, also ranging from (0,1), indicating the degree to which the power supply's capacity remains unchanged over time. E O This represents the total energy processed throughout the entire lifecycle of the power system.

[0069] The specific formula for calculating the total lifecycle cost is as follows:

[0070]

[0071] Among them, R S R represents the total revenue over the power supply's lifetime. t C represents the revenue generated by the power system in year t. t IC0 represents the operating cost required for the power system to operate in year t, DC represents the residual value of the power system, and N represents the operating life of the power system.

[0072] It should be noted that the initial investment cost of the power system is calculated based on the cost of the power storage system, the cost of power conversion and control equipment, the construction cost, and the installation and commissioning cost. The specific calculation formula is as follows:

[0073] IC0 = E sys +E tra +E con +E ins

[0074] Among them, E sys E represents the cost of a power storage system. tra E represents the cost of power conversion. con E indicates the construction cost. ins This indicates the cost of installation and commissioning.

[0075] S2: Based on distributed power area node data, complete the cost prediction of the power life cycle.

[0076] Specifically, the cost prediction of the power supply lifecycle based on distributed power area node data is achieved by collecting distributed power area node data, processing the collected data, mining the overlapping areas of distributed power sources based on the data processing results, and then calculating the optimal planning scheme of the planning model including distributed power sources with the goal of minimizing the total lifecycle cost based on the power distribution areas with overlapping parts, thereby completing the cost prediction of the power supply lifecycle.

[0077] Furthermore, the specific formula for calculating the minimum total lifecycle cost is as follows:

[0078]

[0079] Among them, L S IC0 represents the minimum total lifecycle cost, DC represents the initial investment cost of the power system, OMC represents the residual value of the power system, and E represents the maintenance cost of the power system. O It represents the total energy processed throughout the entire lifecycle of the power system.

[0080] It should be noted that the data processing of the collected data involves substituting the collected distributed power generation area node data into the distributed power generation area node power grid, and predicting and filling in any missing data in the distributed power generation area node data to ensure the integrity and accuracy of the data; the distributed power generation area node data includes distributed power generation area node voltage data, user active load data, reactive load data, and three-phase average voltage change data.

[0081] Furthermore, the prediction and filling of missing data in the distributed power generation area node data is achieved by substituting the distributed power generation area node data into the distributed power generation area node power grid and using a linear regression algorithm to complete the data prediction and filling. The specific implementation formula is as follows:

[0082]

[0083]

[0084] in, This represents the predicted target variable value, where X represents the input distributed power source node data, and β0 and β1 represent the intercept and slope in the linear regression algorithm, respectively. X represents the mean of the input data and the mean of the predicted data, respectively. i Y i Let represent the i-th input data and the predicted data, respectively, and n represent the total amount of input data.

[0085] It should be noted that the process of mining overlapping areas of distributed power sources based on data processing results involves sorting the nodes of the distributed power source areas and mining the overlapping areas of distributed power sources by identifying the nodes in the power grid-covered overlapping areas. The specific implementation formula is as follows:

[0086]

[0087] Where {Q} represents the power distribution area, k represents the regional node coefficient, {n} represents the weight value of the non-overlapping part of the distributed power source, and A ij Let represent the weight of the overlapping portion of node i and node j, ki represent the node in the direction of distributed power region i, kj represent the node in the direction of distributed power region j, and (ci,cj) represent the nodes of all distributed power regions.

[0088] Furthermore, the cost prediction of the power supply lifecycle involves acquiring data on distributed power generation area nodes, preprocessing the acquired data, and mining the power distribution area based on the preprocessed data. The power distribution area includes active load areas and reactive load areas. The active load areas are clustered, and the clustering results of the reactive load areas are the same as those of the corresponding active load areas. Then, the derivative functions of each order of the load area are incorporated into the distribution area clustering algorithm. Finally, the load areas and derivatives of each quarter are clustered separately. The voltage of the distributed power generation area nodes and the branch current of the predicted load areas are calculated, and the active power, reactive power and load rate of each distributed power generation area node are calculated to obtain the actual power supply lifecycle cost value.

[0089] It should be noted that the active load area is clustered by using the k-means clustering algorithm to perform cluster analysis on the active load area, specifically as follows:

[0090] Randomly initialize k cluster centers, assign each data point to the cluster corresponding to the nearest cluster center, recalculate the coordinates of each cluster center, and obtain the mean of the corresponding data points, until the cluster centers no longer change;

[0091] The nearest cluster center is determined by calculating the distance from the data point to the cluster center using the Euclidean aggregation formula. The specific calculation formula is as follows:

[0092]

[0093] Where, x i Let c represent the i-th data point. j Let x represent the j-th cluster center. ik c jk They represent data points x respectively i and distance from center c jThe coordinates in the k-th dimension, where n represents the number of dimensions of the data point.

[0094] The voltage calculation of the distributed power generation area nodes is based on the power injected into the nodes, while the current calculation is obtained by combining Ohm's law with the calculated voltage. The specific calculation formula is as follows:

[0095]

[0096]

[0097] Among them, V i P represents the voltage at node i that has been calculated. i Q i V represents the active power and reactive power of node i, respectively. i0 V i1 Let Z represent the node voltages at the start and end points of the branch, respectively, and let I represent the branch impedance. i This represents the calculated node current.

[0098] S3: Determine the cost of the power supply throughout its entire lifecycle based on capacity adequacy.

[0099] Specifically, the capacity adequacy is calculated based on the voltage sequence and power factor sequence, including static reactive power compensation adequacy and dynamic reactive power compensation adequacy. The voltage sequence is composed of collected voltage data, and the power factor sequence is composed of collected power factor data. The static reactive power compensation adequacy is determined based on the voltage sequence and power factor sequence, and the dynamic reactive power compensation adequacy is determined by the static reactive power compensation adequacy, the degree of voltage fluctuation exceeding the limit, and the degree of power factor fluctuation exceeding the limit.

[0100] Furthermore, the voltage fluctuation exceeding the limit is calculated based on the voltage sequence length, and the specific calculation formula is as follows:

[0101]

[0102] Among them, U l Indicates the degree of voltage fluctuation exceeding the limit, where n represents the length of the voltage sequence, and U di The elements in the voltage limit characterization array represent the voltage limits.

[0103] The degree of power factor fluctuation exceeding the limit is calculated based on the power factor sequence, and the specific calculation formula is as follows:

[0104]

[0105] in, Let n represent the elements in the power factor sequence, and n represent the length of the power factor sequence. The elements in the power factor exceeding the limit characterization array represent the power factor exceeding the limit.

[0106] Furthermore, the static reactive power compensation margin is based on the voltage sequence and power factor sequence, and is comprehensively represented according to the degree of voltage fluctuation exceeding the limit and the degree of power factor fluctuation exceeding the limit of the system, as detailed below:

[0107]

[0108] The dynamic reactive power compensation margin is comprehensively represented by the static reactive power compensation margin, the degree of voltage fluctuation exceeding the limit, and the degree of power factor fluctuation exceeding the limit, as detailed below:

[0109]

[0110] Among them, U l Indicates the degree to which voltage fluctuations exceed limits. It indicates the degree of power factor fluctuation exceeding the limit. SVC indicates the adequacy of static reactive power compensation, and DVC indicates the adequacy of dynamic reactive power compensation.

[0111] It should be noted that the static reactive power compensation margin is dynamically affected by the degree of voltage fluctuation exceeding the limit and the degree of power factor fluctuation exceeding the limit, and the dynamic reactive power compensation margin is dynamically affected by the static reactive power compensation margin, specifically as follows:

[0112] The degree of voltage fluctuation exceeding the limit and the degree of power factor fluctuation exceeding the limit are divided into three levels: A, B, and C, according to the degree of exceeding the limit. At the same time, the adequacy of static reactive power compensation and the adequacy of dynamic reactive power compensation are divided into three corresponding levels: a, b, and c, according to the degree of adequacy.

[0113] When both the voltage fluctuation limit and the power factor fluctuation limit are at level A, the static reactive power compensation margin of the power system is level C, and it has level A reactive power compensation capability.

[0114] When the voltage fluctuation exceeds the limit at level B and the power factor fluctuation exceeds the limit at level C, the static reactive power compensation margin of the power system is level b, and the dynamic reactive power compensation margin needs to be increased to improve the system's compensation capability.

[0115] When the voltage fluctuation exceeds the limit at level C and the power factor fluctuation exceeds the limit at level A, the static reactive power compensation margin of the power system is level a, and the dynamic reactive power compensation margin needs to be increased to improve the power system's compensation capability.

[0116] Furthermore, the total cost of a power supply over its entire lifecycle is affected by both the adequacy of static reactive power compensation and the adequacy of dynamic reactive power compensation, specifically as follows:

[0117] When the static reactive power compensation margin is at the highest level, level C, the reactive power compensation capacity of the power system is sufficient. By reducing the investment and operating costs of reactive power compensation equipment, the cost of the power supply's entire life cycle can be reduced.

[0118] When the dynamic power compensation margin is at the highest level, level C, the power system has sufficient reactive power compensation and voltage stability capabilities. This reduces losses and costs caused by voltage fluctuations and power factor, thereby lowering the cost of the power supply throughout its entire life cycle.

[0119] Example 2

[0120] Reference Figure 2 The second embodiment of the present invention provides a power supply lifecycle cost calculation system based on capacity adequacy, including a cost optimization model construction module, a power supply lifecycle cost prediction module, and a power supply lifecycle cost determination module.

[0121] Specifically, the cost optimization model construction module constructs a cost optimization model based on investment costs; the power supply lifecycle cost prediction module completes power supply lifecycle cost prediction based on distributed power supply node data; and the power supply full lifecycle cost determination module determines the power supply full lifecycle cost based on capacity adequacy.

[0122] Furthermore, the cost optimization model construction module is the cornerstone of the entire system. Based on investment costs, this module constructs a cost optimization model, calculating the initial investment cost through detailed analysis of key factors such as the cost of the power storage system, the cost of power conversion and control equipment, construction costs, and installation and commissioning costs. Building upon this, and considering factors such as financial expenses, operation and maintenance costs, and residual value, the module further calculates the total lifecycle investment cost and initial revenue of the power supply. The output of the cost optimization model construction module is the comprehensive revenue over the entire lifecycle of the power supply, providing crucial data support for subsequent modules.

[0123] The power supply lifecycle cost prediction module, relying on the output of the cost optimization model construction module, uses distributed power source area node data for cost prediction. The module first collects distributed power source area node data, including voltage data, active load data, reactive load data, and three-phase average voltage variation data. Then, it uses a linear regression algorithm to predict and fill in missing data, ensuring data completeness and accuracy. Based on this, the module further mines overlapping areas of distributed power sources to reveal the inherent patterns in power distribution areas. Finally, based on the mining results, it calculates the optimal planning scheme for a planning model including distributed power sources, aiming to minimize the total lifecycle cost, thereby achieving accurate prediction of power supply lifecycle costs.

[0124] The power supply lifecycle cost determination module is the core of the entire system. Based on capacity adequacy and combining the outputs of the previous two modules, this module determines the power supply's lifecycle cost. In this process, the module fully considers key factors such as cycle life, depth of discharge, energy conversion efficiency, and capacity retention. By accurately calculating the total processed power and lifecycle cost over the entire lifecycle, it arrives at the final value of the power supply's lifecycle cost. This result not only helps enterprises scientifically evaluate power supply investment decisions but also provides strong support for optimized power supply configuration and operation and maintenance management.

[0125] It should be noted that the system achieves comprehensive and accurate calculation of the entire life cycle cost of power sources through the close collaboration of its three modules. From the construction of the cost optimization model to the prediction of the life cycle cost of power sources, and finally to the determination of the total life cycle cost, every step reflects the scientific nature and practicality of the system. The application of this system will help improve the efficiency and effectiveness of power management and promote the sustainable development of the energy industry.

[0126] Furthermore, if the aforementioned function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0127] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0128] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0129] Furthermore, in order to provide a concise description of exemplary embodiments, not all features of actual embodiments (i.e., those features that are not relevant to the currently considered best mode for carrying out the invention, or those features that are not relevant to implementing the invention) may be omitted.

[0130] It should be understood that numerous specific implementation decisions can be made during the development of any practical implementation, such as in any engineering or design project. Such development efforts may be complex and time-consuming, but for those skilled in the art who benefit from this disclosure, the development effort will be a routine work of design, manufacturing, and production without requiring much experimentation.

[0131] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for calculating the cost of a power supply's full life cycle based on capacity adequacy, characterized by: The method comprises the following steps, constructing a cost optimization model based on investment cost; predicting the cost of the life cycle of the power supply based on distributed power supply area node data; determining the cost of the life cycle of the power supply based on capacity adequacy.

2. A method for calculating the cost of a power supply throughout its life cycle based on capacity adequacy as claimed in claim 1, wherein: The constructing of the cost optimization model based on investment cost is to calculate the initial investment cost according to the cost of the power storage system, the cost of power conversion and control equipment, the construction cost and the installation and debugging cost, and to calculate the total life cycle input cost of the power supply, the initial income of the life cycle of the power supply, and the comprehensive income combined with the total life cycle input cost according to the initial investment cost, the financial cost, the operation and maintenance cost and the recovered residual value; The cost optimization model is to calculate the total life cycle total processing power by using the cycle life, the depth of discharge, the energy conversion efficiency and the capacity retention rate of the power supply, and to calculate the total life cycle cost of the power supply according to the calculated total life cycle cost and the total life cycle total processing power, wherein the total life cycle total processing power is calculated by collecting the cycle life, the depth of discharge, the energy conversion efficiency and the capacity retention rate of the power supply, and the specific calculation formula is as follows: wherein N represents the cycle life of the power source, D represents the depth of discharge of the power source, and takes a value in the range (0, 1), E ct represents the capacity of the power source in the tth year, E c0 represents the rated capacity of the power source, E f represents the energy conversion efficiency of the power source, and takes a value in the range (0, 1), R represents the capacity retention rate of the power source, and takes a value in the range (0, 1), E O represents the total processing energy of the power source system for the entire life cycle.

3. A method for calculating the cost of a power supply throughout its life cycle based on capacity adequacy as claimed in claim 2, wherein: The specific calculation formula of the total life cycle cost is as follows: wherein R S represents the total revenue over the life of the power source, R t represents the revenue generated by the power source system in the tthyear of operation, C t represents the operating cost required by the power source system in the tthyear of operation, IC0represents the initial investment cost of the power source system, DCrepresents the residual value recovered by the power source system, and N represents the life of the power source system in years. The initial investment cost of the power supply system is calculated according to the cost of the power storage system, the cost of power conversion and control equipment, the construction cost and the installation and debugging cost, and the specific calculation formula is as follows: IC0 = E sys + E tra + E con + E ins wherein E sys represents the cost of the power storage system, E tra represents the cost of power conversion, E con represents the cost of construction, E ins represents the cost of installation and commissioning.

4. A method for calculating the cost of a power supply throughout its life cycle based on capacity adequacy as claimed in claim 3, wherein: The prediction of the cost of the life cycle of the power supply based on distributed power supply area node data is to collect distributed power supply area node data, process the collected data, mine the overlapping area of the distributed power supply according to the data processing result, calculate the optimal planning scheme of the planning model containing the distributed power supply with the minimum total life cycle cost as the target according to the power supply distribution area with the overlapping part, and then complete the prediction of the cost of the life cycle of the power supply; The specific calculation formula of the minimum total life cycle cost is as follows: wherein L S represents the minimum value of the total life cycle cost, IC0represents the initial investment cost of the power supply system, DCrepresents the residual value of the power supply system, OMCrepresents the maintenance cost of the power supply system, E O represents the total processing energy of the power supply system.

5. A method for calculating the cost of a power supply throughout its life cycle based on capacity adequacy as claimed in claim 4, wherein: The data processing of the collected data is to substitute the collected distributed power supply area node data into the distributed power supply area node grid, and to predict and fill in the missing data of the distributed power supply area node data to ensure the integrity and accuracy of the data; the distributed power supply area node data includes distributed power supply area node voltage data, user active load data, reactive load data and three-phase average voltage change data; The prediction and filling of the missing data of the distributed power supply area node data is to substitute the distributed power supply area node data into the distributed power supply area node grid, and to complete the data prediction and filling by using the linear regression algorithm, and the specific implementation formula is as follows: wherein, represents the predicted target variable value, X represents the input distributed power zone node data, β0, β1respectively represent the intercept and slope in the linear regression algorithm, respectively represent the mean of the input data and the predicted data, X i , Y i respectively represent the i-th input data and the predicted data, and n represents the total amount of input data; The mining of the overlapping area of the distributed power supply according to the data processing result is to sort the distributed power supply area nodes, and to complete the mining of the overlapping area of the distributed power supply by the grid coverage of the overlapping distributed power supply area nodes, and the specific implementation formula is as follows: where {Q} represents the power distribution area, k represents the area node coefficient, {n} represents the weight value of the non-overlapping part of the distributed power supply, A ij represents the weight of the overlapping part of node i and node j, ki represents the node in the direction of distributed power supply area i, kj represents the node in the direction of distributed power supply area j, and (ci, cj) represents the node of all distributed power supply areas.

6. A method for calculating the cost of a power supply throughout its life cycle based on capacity adequacy as claimed in claim 5, wherein: The power life cycle cost prediction is to obtain data of distributed power area nodes, preprocess the obtained data, mine the power distribution area based on the preprocessed data, the power distribution area includes active load area and reactive load area, cluster the active load area, the reactive area clustering result is the same as the corresponding active load area, then the derivative function of each load area is included in the distribution area clustering algorithm, and finally each quarterly load area and derivative are clustered respectively, the distributed power area node voltage calculation and branch current calculation are performed on the predicted load area, the active power and reactive power of each distributed power area node are calculated, and the load rate is calculated, and the actual power full life cycle cost value is obtained; The active load area clustering is clustering analysis of the active load area by using a k-means clustering algorithm, and the specific implementation is as follows: Randomly initialize k cluster centers, assign each data point to the cluster corresponding to the nearest cluster center, and recalculate the coordinates of each cluster center to obtain the mean value of the corresponding data points, until the cluster center no longer changes; The nearest cluster center is calculated by using the Euclidean aggregation formula to calculate the distance from the data point to the cluster center, and the nearest cluster center is determined, and the specific calculation formula is as follows: where x i represents the i-th data point, c j represents the j-th cluster center, x ik , c jk respectively represent the coordinates of the data point x i and the distance center c j in the k-th dimension, and n represents the number of dimensions of the data point; The distributed power area node voltage calculation is calculated according to the power injected into the node, and the current calculation is obtained by using Ohm's law and the calculated voltage, and the specific calculation formula is as follows: where V i represents the voltage of the node i under calculation, P i and Q i represent the active power and the reactive power of the node i, respectively, V i0 and V i1 represent the node voltage at the start and end of the branch, respectively, Z represents the branch impedance, and I i represents the node current under calculation.

7. A method for calculating the cost of a power supply throughout its life cycle based on capacity adequacy as claimed in claim 6, wherein: The capacity adequacy is calculated according to the voltage sequence and the power factor sequence, including static reactive power compensation adequacy and dynamic reactive power compensation adequacy, the voltage sequence is composed of collected voltage data, the power factor sequence is composed of collected power factor data, the static reactive power compensation adequacy is determined based on the voltage sequence and the power factor sequence, and the dynamic reactive power compensation adequacy is determined by the static reactive power compensation adequacy, the voltage fluctuation overrun degree and the power factor fluctuation overrun degree; The voltage fluctuation overrun degree is calculated based on the length of the voltage sequence, and the specific calculation formula is as follows: where U l represents the voltage fluctuation out-of-limit degree, n represents the voltage sequence length, U di represents the element in the voltage out-of-limit representation array; The power factor fluctuation overrun degree is calculated based on the power factor sequence, and the specific calculation formula is as follows: wherein, represents an element in the power factor sequence, n represents the power factor sequence length, represents an element in the power factor out-of-limit representation array.

8. A system employing the method of claim 1 to 7 for calculating the cost of the full life cycle of a power supply based on capacity sufficiency, characterized in that, It includes a cost optimization model construction module, a power life cycle cost prediction module and a power full life cycle cost determination module; The cost optimization model construction module constructs a cost optimization model based on investment cost; The power life cycle cost prediction module completes power life cycle cost prediction based on distributed power area node data; The power full life cycle cost determination module determines the power full life cycle cost based on the capacity adequacy. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to realize the steps of the method of any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the method of any one of claims 1 to 7.

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