Adaptability evaluation method of power grid planning scheme, computing device and storage medium

Abstract

The application discloses a kind of adaptability evaluation method of power grid planning scheme, computing device and storage medium, it is related to computer field.The adaptability evaluation method of power grid planning scheme of the application, comprising: first, obtaining each evaluation index of each power grid planning scheme, and constructing evaluation index matrix and calculating the combination weight of each evaluation index, the combination weight is based on subjective weight and objective weight calculation.Then, according to the combination weight of each evaluation index, the weighting processing is carried out to evaluation index matrix, and the decision matrix is obtained.Next, according to the decision matrix, determine the positive and negative optimal solution, and respectively obtain the distance measure of each power grid planning scheme and positive optimal solution and negative optimal solution.Finally, according to the distance measure obtained, the adaptability of each power grid planning scheme is evaluated.The application can improve the accuracy of evaluation result and evaluation efficiency.

Description

Technical Field

[0001] This invention relates to the field of computers, and more particularly to an adaptive evaluation method, computing device, and storage medium for power grid planning schemes. Background Technology

[0002] The proportion of new energy sources in the power grid is constantly increasing. However, the output of new energy sources is characterized by intermittency, volatility, and randomness. For example, the daily fluctuation range of wind power output can reach up to 80%, with peak output occurring around dawn and reaching its lowest point in the afternoon, exhibiting a more pronounced "reverse load" characteristic. The daily fluctuation range of photovoltaic power can reach up to 100%, with distinct peak-valley characteristics, reaching its peak at noon and showing a uniform downward trend around noon, with zero output at night. Therefore, with the high proportion of new energy sources connected to the grid, power grid planning is facing more and more problems, such as increased random fluctuations in power flow and an increase in planning objectives.

[0003] Therefore, there is an urgent need for an adaptability evaluation method for power grid planning schemes to improve the adaptability of the planned schemes, so as to lay the foundation for the economic, reliable and safe operation of new power systems. Summary of the Invention

[0004] Therefore, the present invention provides an adaptive evaluation method, computing device and storage medium for power grid planning schemes, in an attempt to solve or at least alleviate the problems mentioned above.

[0005] According to one aspect of the present invention, an adaptability evaluation method for power grid planning schemes is provided, comprising: obtaining evaluation indicators for each power grid planning scheme, constructing an evaluation indicator matrix, and calculating the combined weights of each evaluation indicator, wherein the combined weights are calculated based on subjective weights and objective weights; assigning weights to the evaluation indicator matrix according to the combined weights of each evaluation indicator to obtain a decision matrix; determining the positive and negative optimal solutions according to the decision matrix; obtaining the distance measures between each power grid planning scheme and the positive and negative optimal solutions, respectively; and evaluating the adaptability of each power grid planning scheme according to the obtained distance measures.

[0006] Optionally, in the adaptive evaluation method for the power grid planning scheme according to the present invention, calculating the combined weight of each evaluation index includes: obtaining the subjective weight of each evaluation index using the improved analytic hierarchy process (AHP) and obtaining the objective weight of each evaluation index using the index correlation method; constructing a combined weight acquisition model with the objective of minimizing the sum of squares of the first deviation and the second deviation, where the first deviation is the deviation between the combined weight and the subjective weight, and the second deviation is the deviation between the combined weight and the objective weight; inputting the obtained subjective weight and objective weight into the model, solving the model with the objective of minimizing the sum of squares of the first deviation and the second deviation, and outputting the combined weight of each evaluation index.

[0007] Optionally, in the adaptive evaluation method for the power grid planning scheme according to the present invention, the subjective weights of each evaluation index are obtained using an improved analytic hierarchy process, including: constructing a judgment matrix based on the importance of each evaluation index, wherein a ij Let a represent the element in the i-th row and j-th column of the matrix. ij A value of 1 indicates that evaluation indicator i is more important than evaluation indicator j, a ij Setting it to 0 indicates that evaluation index i and evaluation index j are equally important, a ij A value of -1 indicates that evaluation indicator i is less important than evaluation indicator j. The values ​​of i and j are 1, 2, 3, ..., n, where n represents the total number of evaluation indicators. Based on the judgment matrix, the optimal transfer matrix is ​​obtained, where d... ij d represents the element in the i-th row and j-th column of the optimal transfer matrix. ij = a ik Let a represent the element in the i-th row and k-th column of the matrix. jk Let a represent the element in the j-th row and k-th column of the matrix. kj Let represent the element in the k-th row and j-th column of the judgment matrix; based on the obtained optimal transfer matrix, obtain the consistency matrix of the judgment matrix, where This represents the element in the i-th row and j-th column of the consistency matrix. Obtain the eigenvector corresponding to the largest eigenvalue of the consistency matrix, and use the obtained eigenvector as the subjective weight of each evaluation index.

[0008] Optionally, in the adaptive evaluation method for the power grid planning scheme according to the present invention, obtaining the eigenvector corresponding to the largest eigenvalue of the consistency matrix includes: obtaining the eigenvector corresponding to the largest eigenvalue of the consistency matrix using the square root method.

[0009] Optionally, in the adaptive evaluation method for the power grid planning scheme according to the present invention, the eigenvector corresponding to the largest eigenvalue of the consistency matrix is ​​obtained using the root method, including: obtaining the nth root of the product of the elements in each row of the consistency matrix, and taking each of them as a row element to obtain a column vector; normalizing the column vector to obtain the eigenvector; wherein, the column vector is normalized using the following formula:

[0010]

[0011] In the formula, W i This represents the i-th element in the eigenvector. This represents the nth root of the product of the elements in the i-th row of the consistency matrix. This represents the nth root of the product of the elements in the j-th column of the consistency matrix.

[0012] Optionally, in the adaptive evaluation method for the power grid planning scheme according to the present invention, the objective weights of each evaluation index are obtained using the index correlation method, including: performing homogenization processing on the evaluation indexes in the evaluation index matrix to obtain a homogenized evaluation index matrix; obtaining the standard deviation of each evaluation index and the correlation coefficient between the evaluation indexes based on the homogenized evaluation index matrix; obtaining the information content contained in each evaluation index using the index correlation method based on the obtained standard deviation and correlation coefficient; and obtaining the objective weights of each evaluation index based on the information content contained in each evaluation index.

[0013] Optionally, in the adaptive evaluation method for the power grid planning scheme according to the present invention, the evaluation indicators in the evaluation indicator matrix are subjected to a homogenization process, including: converting negative indicators in the evaluation indicator matrix into positive indicators; wherein, the negative indicators are converted into positive indicators by the following formula:

[0014]

[0015] In the formula, x′ il This represents the evaluation index in the i-th row and l-th column of the evaluation index matrix for homogenization, x il This represents the evaluation index in the i-th row and l-th column of the evaluation index matrix, where max|x i | represents the maximum evaluation index in the i-th row of the evaluation index matrix, p represents the coordination coefficient, l takes the values ​​1, 2, 3, ..., m, and m represents the number of power grid planning schemes.

[0016] Optionally, in the adaptive evaluation method for the power grid planning scheme according to the present invention, obtaining the standard deviation of each evaluation index and the correlation coefficient between evaluation indices based on the homogenized evaluation index matrix includes: dimensionless transformation of the homogenized evaluation index matrix to obtain a normalized evaluation index matrix, wherein x″ il This represents the element in the i-th row and l-th column of the normalized evaluation index matrix. Based on the standardized evaluation index matrix, the standard deviation of each evaluation index and the correlation coefficient between the evaluation indices are obtained.

[0017] Optionally, in the adaptive evaluation method for the power grid planning scheme according to the present invention, obtaining the standard deviation of each evaluation index and the correlation coefficient between the evaluation indices based on the standardized evaluation index matrix includes:

[0018] The standard deviation of each evaluation index is obtained using the following formula:

[0019]

[0020] The correlation coefficient between the evaluation indicators is obtained using the following formula:

[0021] p it =cov(x″i ,x″ t ) / (s i ,s t )

[0022] In the formula, s i The standard deviation of evaluation index i is represented. p represents the mean of the evaluation indicators in the i-th row of the normalized evaluation indicator matrix. it Cov(x″) represents the correlation coefficient between evaluation index i and evaluation index t. i ,x″ t ) represents the covariance between the i-th row and the t-th row in the normalized evaluation index matrix, s t It represents the standard deviation of the evaluation index t.

[0023] Optionally, in the adaptive evaluation method for the power grid planning scheme according to the present invention, in the step of obtaining the information content contained in each evaluation index using the index correlation method, the information content contained in each evaluation index is obtained by the following formula:

[0024]

[0025] In the formula, G i This indicates the amount of information contained in evaluation index i.

[0026] Optionally, in the adaptive evaluation method for the power grid planning scheme according to the present invention, in the step of obtaining the objective weights of each evaluation index, the objective weights of each evaluation index are obtained by the following formula:

[0027]

[0028] In the formula, V i G represents the objective weight of evaluation index i. t This indicates the amount of information contained in the evaluation index t.

[0029] Optionally, in the adaptive evaluation method for the power grid planning scheme according to the present invention, the weighting process for the evaluation index matrix includes: standardizing the evaluation index matrix to obtain a standardized evaluation index matrix, wherein v ul This represents the evaluation index in the i-th row and l-th column of the standardized evaluation index matrix. By assigning weights to the standardized evaluation index matrix, a decision matrix is ​​obtained, where b il b represents the element in the i-th row and l-th column of the decision matrix. il =v il ×u i u i This represents the combined weight of evaluation index i.

[0030] Optionally, in the adaptive evaluation method for the power grid planning scheme according to the present invention, determining the positive and negative optimal solutions based on the decision matrix includes:

[0031] The optimal solution is determined by the following formula:

[0032] S + =[b1 + b2 + ,…b n + ] = [max(b 11 ,b 12 ,…b 1m ),max(b 21, b 22 ,…b 2m ),…max(b n1 ,b n2 ,…b nm )]

[0033] The negative optimal solution is determined by the following formula:

[0034] S - =[b1 - b2 - ,…bn - ] = [min(b 11, b 12, …b 1m ),min(b 21 ,b 22, …b 2m ),…min(b n1, b n2, …b nm )]

[0035] In the formula, S + b represents the positive optimal solution. n + S represents the positive optimal solution for the evaluation index n. - b represents the negative optimal solution. n - This represents the negative optimal solution for the evaluation index n.

[0036] Optionally, in the adaptive evaluation method for power grid planning schemes according to the present invention, the distance measures between each power grid planning scheme and the positive optimal solution and the negative optimal solution are obtained respectively, including:

[0037] The distance measure between each power grid planning scheme and the forward optimal solution is obtained using the following formula:

[0038]

[0039] The distance measure between each power grid planning scheme and the negative optimal solution is obtained by the following formula:

[0040]

[0041] In the formula, D l + D represents the distance measure between power grid planning scheme l and the forward optimal solution. l - This represents the distance measure between power grid planning scheme l and the negative optimal solution.

[0042] Optionally, in the method for evaluating the adaptability of power grid planning schemes according to the present invention, the adaptability of each power grid planning scheme is evaluated based on the obtained distance measure, including: obtaining the closeness between each power grid planning scheme and the optimal solution scheme based on the obtained distance measure; evaluating the adaptability of each power grid planning scheme based on the obtained closeness, wherein the scheme with a higher closeness to the optimal solution scheme is better.

[0043] Optionally, in the adaptability evaluation method for power grid planning schemes according to the present invention, in the step of obtaining the closeness between each power grid planning scheme and the optimal solution scheme, the closeness between each power grid planning scheme and the optimal solution scheme is obtained by the following formula:

[0044]

[0045] In the formula, C l This indicates the closeness between power grid planning scheme l and the optimal solution scheme.

[0046] Optionally, in the adaptability evaluation method of the power grid planning scheme according to the present invention, the evaluation indicators include: economic adaptability indicators, energy structure adaptability indicators, power grid structure adaptability indicators, reliability adaptability indicators, and environmental adaptability indicators. The economic adaptability indicators include the power production elasticity coefficient, power balance coefficient, net present value, internal rate of return, power grid construction life cycle cost, power grid construction investment payback period, power generation increment per unit of new asset, and load increment per unit of new asset. The energy structure adaptability indicators include the capacity to accept new energy sources, the proportion of clean energy, and the proportion of new energy sources consumed across provinces and regions. The power grid structure adaptability indicators include the line-to-machine ratio, capacity-to-load growth ratio, capacity-to-load ratio, transformer ratio, power supply capacity ratio between different voltage levels, average power outage ratio, network loss rate, and the peak-to-valley difference that the power grid can withstand. The reliability adaptability indicators include the expected value of insufficient power, the expected value of insufficient electricity, the probability of load shedding, the average duration of load shedding, the degree of power grid frequency deviation, and the degree of power grid voltage deviation. The environmental adaptability indicators include carbon dioxide emission reduction, sulfur dioxide emission reduction, and nitrogen oxide emission reduction.

[0047] According to another aspect of the present invention, a computing device is provided, comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, the program instructions including instructions for performing an adaptive evaluation method for a power grid planning scheme according to the present invention.

[0048] According to another aspect of the present invention, a readable storage medium storing program instructions is provided, which, when read and executed by a computing device, causes the computing device to perform an adaptive evaluation method for a power grid planning scheme according to the present invention.

[0049] In summary, this invention employs a combined weighting method, which fully considers both subjective decision-making and objective information of the evaluation indicators. This effectively avoids the problem of inaccurate evaluation results caused by using only subjective or objective weighting.

[0050] Furthermore, this invention constructs a consistency matrix for the judgment matrix based on a three-level evaluation scaling method for element importance. This eliminates the need for consistency checks, thereby greatly simplifying the calculation process and improving the evaluation efficiency of power grid planning schemes.

[0051] In addition, this invention analyzes the impact of the proportion of new energy sources on power grid planning and establishes an evaluation system for the adaptability of power grid planning schemes from five dimensions: economic adaptability, energy structure adaptability, power grid structure adaptability, reliability adaptability, and environmental adaptability, thereby improving the accuracy of the evaluation results. Attached Figure Description

[0052] To achieve the foregoing and related objectives, certain illustrative aspects are described herein in conjunction with the following description and accompanying drawings. These aspects indicate various ways in which the principles disclosed herein may be practiced, and all aspects and their equivalents are intended to fall within the scope of the claimed subject matter. The foregoing and other objectives, features, and advantages of this disclosure will become more apparent from the following detailed description, taken in conjunction with the accompanying drawings. Throughout this disclosure, the same reference numerals generally refer to the same parts or elements.

[0053] Figure 1 A schematic diagram of an adaptability evaluation method for a power grid planning scheme according to an embodiment of the present invention is shown;

[0054] Figure 2 A structural block diagram of a computing device 200 according to an embodiment of the present invention is shown;

[0055] Figure 3 A flowchart of an adaptive evaluation method 300 for a power grid planning scheme according to an embodiment of the present invention is shown. Detailed Implementation

[0056] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0057] Currently, with the high proportion of renewable energy connected to the grid, power grid planning faces challenges such as increased random fluctuations in power flow and a greater number of planning objectives. Therefore, there is an urgent need for an adaptability evaluation method for power grid planning schemes to improve their adaptability. However, subjective evaluation of power grid planning schemes alone ignores objective information about the indicators, while objective evaluation alone overlooks the role of subjective decision-making in practical problems, such as expert opinions and other subjective factors.

[0058] Based on this, this invention fully considers the roles of subjective and objective factors in the indicators and proposes an adaptive evaluation method for power grid planning schemes. Specifically, an improved AHP combined with CRITIC method is used to calculate subjective and objective weights and obtain combined weights. Then, the TOPSIS method is used to evaluate and analyze the planning scheme, such as... Figure 1 The adaptive evaluation method for the power grid planning scheme of the present invention can be executed in a computing device.

[0059] Figure 2 A block diagram of the physical components (i.e., hardware) of a computing device 200 is shown. In a basic configuration, the computing device 200 includes at least one processing unit 202 and a system memory 204. According to one aspect, depending on the configuration and type of the computing device, the processing unit 202 may be implemented as a processor. The system memory 204 includes, but is not limited to, volatile memory (e.g., random access memory), non-volatile memory (e.g., read-only memory), flash memory, or any combination of such memories. According to one aspect, the system memory 204 includes an operating system 205 and a program module 206, the program module 206 including a scheme evaluation module 220 configured to execute the adaptive evaluation method 300 for the power grid planning scheme of the present invention.

[0060] According to one aspect, operating system 205 is, for example, suitable for controlling the operation of computing device 200. Furthermore, examples are practiced in conjunction with graphics libraries, other operating systems, or any other applications, and are not limited to any particular application or system. Figure 2The basic configuration is illustrated by the components within the dashed lines 208. According to one aspect, the computing device 200 has additional features or functions. For example, according to one aspect, the computing device 200 includes additional data storage devices (removable and / or non-removable), such as disks, optical discs, or magnetic tapes. This additional storage... Figure 2 The image is shown by removable storage 209 and non-removable storage 210.

[0061] As stated above, according to one aspect, a program module is stored in system memory 204. According to one aspect, the program module may include one or more applications. The present invention does not limit the type of application; for example, applications may include: email and contact applications, word processing applications, spreadsheet applications, database applications, slideshow applications, drawing or computer-aided applications, web browser applications, etc.

[0062] According to one aspect, examples can be practiced on circuits including discrete electronic components, packaged or integrated electronic chips containing logic gates, circuits utilizing microprocessors, or on a single chip containing electronic components or a microprocessor. For example, it can be practiced via wherein... Figure 2 Each or many of the components shown can be implemented as an example by integrating a System-on-a-Chip (SOC) on a single integrated circuit. According to one aspect, such an SOC device may include one or more processing units, graphics units, communication units, system virtualization units, and various application functions, all integrated (or “burned in”) as a single integrated circuit onto a chip substrate. When operating via the SOC, the functions described herein can be operated via dedicated logic integrated on a single integrated circuit (chip) with other components of the computing device 200. Embodiments of the invention can also be implemented using other techniques capable of performing logical operations (e.g., AND, OR, and NOT), including but not limited to mechanical, optical, fluid, and quantum technologies. Additionally, embodiments of the invention can be implemented within a general-purpose computer or in any other circuit or system.

[0063] According to one aspect, computing device 200 may also have one or more input devices 212, such as a keyboard, mouse, pen, voice input device, touch input device, etc. It may also include output devices 214, such as a display, speaker, printer, etc. The foregoing devices are examples and other devices may also be used. Computing device 200 may include one or more communication connections 216 that allow communication with other computing devices 218. Examples of suitable communication connections 216 include, but are not limited to: RF transmitter, receiver and / or transceiver circuitry; Universal Serial Bus (USB), parallel and / or serial ports.

[0064] As used herein, the term computer-readable medium includes computer storage medium. Computer storage medium can include volatile and non-volatile, removable and non-removable media implemented using any method or technology for storing information (e.g., computer-readable instructions, data structures, or program modules). System memory 204, removable storage 209, and non-removable storage 210 are examples of computer storage media (i.e., memory storage). Computer storage media can include random access memory (RAM), read-only memory (ROM), electrically erasable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital universal disc (DVD) or other optical storage, magnetic tape, magnetic tape, disk storage or other magnetic storage devices, or any other article of manufacture that can be used to store information and is accessible by computer device 200. According to one aspect, any such computer storage medium can be part of computing device 200. Computer storage media does not include carrier waves or other transmitted data signals.

[0065] According to one aspect, a communication medium is implemented by computer-readable instructions, data structures, program modules, or other data in a modulated data signal (e.g., a carrier wave or other transmission mechanism), and includes any information transmission medium. According to one aspect, the term "modulated data signal" describes a signal having one or more sets of characteristics or altered in a manner that encodes information in the signal. By way of example and not limitation, a communication medium includes wired media such as wired networks or direct wired connections, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.

[0066] Figure 3 An adaptive evaluation method for a power grid planning scheme according to an embodiment of the present invention is shown. Method 300 is adapted to be used in a computing device (e.g., Figure 2 Executed in the computing device 200 shown. Figure 3 As shown, this method starts at 310 from 300.

[0067] In section 310, the evaluation indicators for each power grid planning scheme are obtained, and an evaluation indicator matrix is ​​constructed, as well as the combined weights of each evaluation indicator are calculated.

[0068] The high proportion of renewable energy power fluctuations transforms the original unilaterally random demand system into a bilaterally random system, leading to problems such as decreased rotational inertia and insufficient voltage support capacity. Therefore, to maintain grid security and stability, increased investment in reliability is necessary during the planning stage. Secondly, the power structure after high-proportion renewable energy grid integration shifts from a grid dominated by conventional power sources and unidirectional power supply to one with a high proportion of power electronics and bidirectional power supply. With the development of large-scale wind and solar power bases, inter-regional power transmission will continue to increase. Grid planning should consider increasing the development of ultra-high voltage (UHV) and all levels of the grid to enhance the capacity for high-proportion renewable energy transmission and absorption, as well as the capacity for multiple DC transmission lines. Furthermore, the large-scale development of distributed renewable energy places higher demands on the access capacity of the distribution network. Grid planning should consider flexible mutual support and coordinated operation between the main grid and the distribution network to achieve coordinated development of large-scale renewable energy and the grid. Finally, grid planning objectives should balance safety and environmental friendliness. Therefore, based on existing reliability and economic considerations, grid planning objectives should further incorporate environmental requirements, shifting the planning and design goals from a single focus on "safety and supply" to a balance between "safety and environmental friendliness."

[0069] Based on this, according to an embodiment of the present invention, considering the impact of high-proportion renewable energy access on power grid planning, an adaptability evaluation index system for power grid planning schemes can be established using economic adaptability, energy structure adaptability, power grid structure adaptability, reliability adaptability, and environmental adaptability as primary indicators. That is, the evaluation indicators include economic adaptability indicators, energy structure adaptability indicators, power grid structure adaptability indicators, reliability adaptability indicators, and environmental adaptability indicators.

[0070] The economic adaptability indicators may include the power production elasticity coefficient, power balance coefficient, net present value, internal rate of return, total life-cycle cost of power grid construction, power grid construction investment payback period, power generation increment per unit of new asset, and load increment per unit of new asset. Energy structure adaptability indicators may include the capacity to accept new energy sources, the proportion of clean energy, and the proportion of new energy sources consumed across provinces and regions. Power grid structure adaptability indicators may include the line-to-machine ratio, capacity-to-load growth ratio, capacity-to-load ratio, transformer ratio, power supply capacity allocation between different voltage levels, average power outage ratio, grid loss rate, and the peak-to-valley difference that the power grid can withstand. Reliability adaptability indicators may include the expected value of insufficient power supply, the expected value of insufficient electricity generation, the probability of load shedding, the average duration of load shedding, the degree of grid frequency deviation, and the degree of grid voltage deviation. Environmental adaptability indicators may include carbon dioxide emission reduction, sulfur dioxide emission reduction, and nitrogen oxide emission reduction. Table 1 below shows the adaptability evaluation index system of a power grid planning scheme according to an embodiment of the present invention.

[0071]

[0072]

[0073] Table 1

[0074] The following sections will explain each evaluation indicator and how to obtain them.

[0075] Economic adaptability indicators:

[0076] (1) The electricity production elasticity coefficient is the ratio of the average growth rate of electricity generation to the average growth rate of GDP. Specifically:

[0077]

[0078] In the formula, P represents the elasticity coefficient of electricity production, E represents the average annual growth rate of electricity consumption, and G represents the average annual growth rate of GDP.

[0079] (2) The power balance coefficient is the ratio between the power supply and the power demand, representing the degree of adaptability between power supply and demand, i.e., whether the power grid experiences power shortages or power surpluses. Specifically:

[0080]

[0081] In the formula, λ represents the power balance coefficient, O represents the power supply, which can be expressed using transformer capacity or power generation, and S represents the power demand.

[0082] (3) Net present value (NPV) refers to the present value of the net cash flows in each year of the power grid project's lifespan, discounted at a certain discount rate. Specifically:

[0083] NPV = ∑(CI-CO)(1+g) -h

[0084] In the formula, NPV represents net present value, CI represents cash inflow, CO represents cash outflow, g represents the benchmark rate of return, and h represents the number of periods.

[0085] (4) Internal Rate of Return (IRR) refers to the discount rate at which the net present value (NPV) of a power grid project is zero within the observation period. It represents the desired rate of return for an investment, and a higher IRR is generally better. Typically, a project is considered feasible when the IRR is greater than or equal to the benchmark rate of return. The sum of the discounted present values ​​of the cash flows of an investment project in each year is the project's NPV. The discount rate at which the NPV is zero is the project's IRR. Specifically:

[0086]

[0087] In the formula, IRR represents the internal rate of return, CI represents cash inflow, CO represents cash outflow, and (CI-CO) h Let r represent the net cash flow of the project in period h, and r represent the project's calculation period.

[0088] (5) The total life-cycle cost of power grid construction refers to the total expenses incurred during the entire period from the design to the decommissioning of the power grid, including power grid construction investment costs, power grid operation costs, power outage loss costs, equipment scrapping costs, etc. Specifically:

[0089] LCC = DI + DO + CF + CD

[0090] In the formula, LCC represents the total life cycle cost of power grid construction, DI represents the investment cost of power grid construction, DO represents the operating cost of power grid, CF represents the cost of power outage losses, and CD represents the cost of equipment scrapping.

[0091] (6) The power grid construction investment payback period refers to the time required for the net income of a power grid project to offset the entire investment, reflecting the project's profitability. Specifically:

[0092]

[0093] In the formula, Q′ represents the payback period for power grid construction investment, and N p This indicates the year in which the cumulative net cash flow is positive, and |ANCF| indicates the year in which the cumulative net cash flow is positive (N). p -1) The absolute value of net cash flow corresponding to year N, |NCF| represents the Nth year. p The absolute value of net cash flow corresponding to the year.

[0094] (7) The increase in electricity generation per unit of newly added assets refers to the increase in electricity supply that can be brought about by a new unit of investment in a power grid project. It is the ratio of the increase in electricity supply to the increase in investment. Specifically:

[0095]

[0096] In the formula, M represents the increase in electricity consumption per unit of newly added assets, L represents the increase in electricity supply, and N represents the increase in investment.

[0097] (8) The load increment per unit of newly added assets refers to the increase in load that can be generated by a unit of newly added investment in the power grid from the initial stage to the completion of the planning and execution stage. It is the ratio of the load increment to the investment increment. Specifically:

[0098]

[0099] In the formula, J represents the load increment per unit of newly added assets, G represents the load increment, and R′ represents the investment increment.

[0100] Energy structure adaptability indicators:

[0101] (1) New energy acceptance capacity refers to the installed capacity of new energy sources that the power grid is allowed to accept. Specifically, it refers to the maximum installed capacity of new energy sources that the power grid can accept under the conditions that all power sources, including new energy sources, maintain power balance with the load, all operating power sources participate in peak shaving reasonably, and the power grid operates safely and reliably, and transformers and lines are not overloaded. (That is, the maximum installed capacity of new energy sources that the power grid can accept at this time is the new energy acceptance capacity of the power grid.) This indicator can reflect the adaptability of the planned power grid to the access of new energy sources. Among them, the new energy acceptance capacity of the power grid is determined by the adaptability of the power grid structure and the peak shaving capacity of the power grid. According to an embodiment of the present invention, the operating conditions, peak shaving characteristics and capabilities of all generating units in the entire network can be collected first, the minimum start-up mode and minimum output of typical days can be found, the maximum peak shaving amount can be calculated, and then the boundary conditions such as dispatch requirements and peak shaving conditions are considered. Under the conditions of ensuring the safety constraints of the power grid and the line not being overloaded, the peak shaving margin can be determined, and the minimum start-up mode and minimum output can be checked to meet the requirements. Finally, based on the peak shaving margin, the anti-peak shaving effect and simultaneity rate of new energy sources are considered to calculate the capacity of the power grid to accept new energy sources. Specifically:

[0102]

[0103] In the formula, maxC energy Indicating the capacity to accept new energy sources, C wind.e C solar.f The installed capacities of the e-th wind farm and the f-th photovoltaic power station are N, respectively. wind N solar These represent the total number of wind farms and photovoltaic power stations in the power distribution network, respectively.

[0104] (2) The proportion of clean energy refers to the ratio of the installed capacity of clean energy sources such as solar and wind power to the total installed capacity. It mainly measures the grid connection of clean energy capacity. Specifically:

[0105]

[0106] In the formula, F represents the proportion of clean energy, C represents the installed capacity of clean energy such as solar and wind power, and Z represents the total installed capacity.

[0107] (3) The proportion of new energy consumption across provinces and regions refers to the grid's ability to optimize the allocation of new energy, specifically:

[0108]

[0109] In the formula, P′ represents the proportion of new energy consumption across provinces and regions, Y represents the capacity of the receiving-end power grid to consume new energy, and Y 总 This indicates the total installed capacity of new energy sources.

[0110] Power grid structure adaptability indicators:

[0111] (1) Line-to-machine ratio refers to the ratio of the length of a transmission line at a certain voltage level to the installed capacity connected to that voltage level. Specifically:

[0112]

[0113] In the formula, G′ represents the line-to-machine ratio, H′ represents the transmission line length at a certain voltage level, and L′ represents the installed capacity connected to that voltage level.

[0114] (2) Capacity-to-load growth ratio refers to the ratio of the growth rate of transformer capacity at a certain voltage level to the growth rate of load during the power grid development period. It reflects the relative speed of the average annual growth rate of transformer capacity and the average annual growth rate of load. This indicator can reflect the coordination between transformer capacity and load growth to a certain extent. Specifically:

[0115]

[0116] In the formula, q represents the capacity-load growth rate ratio, s′ represents the average annual growth rate of the main transformer capacity at a certain voltage level, and D′ represents the average annual growth rate of the maximum load under unified dispatch.

[0117] (3) Capacity-to-load ratio refers to the ratio of the total substation capacity to the corresponding total load in a certain area and at a certain voltage level. Specifically:

[0118]

[0119] In the formula, R represents the load capacity ratio, K1 represents the load simultaneity rate, K2 represents the average power factor of the substation, which is generally required to reach 0.95 or above on the transformer side in power grid planning, K3 represents the safe operation rate of the transformer, which is generally taken as 80% to 85%, and K4 represents the load development reserve coefficient, whose value is directly related to the load growth rate.

[0120] (4) Transformer-to-capacity ratio refers to the ratio of the capacity of a public step-down transformer at a certain voltage level to the installed capacity of the centrally dispatched power supply. Specifically:

[0121]

[0122] In the formula, U represents the transformer-to-station ratio, I represents the capacity of a common step-down transformer at a certain voltage level, and E′ represents the installed capacity of the centrally dispatched power supply.

[0123] (5) The power supply capacity ratio of each voltage level refers to the degree of adaptability of the power supply capacity ratio of each voltage level, which can reflect the degree of coordination and adaptability of the transformer capacity of each voltage level. Specifically:

[0124]

[0125] In the formula, R 下 R represents the load capacity ratio of the next voltage level.上 S represents the load capacity ratio of the next higher voltage level. w The capacity of the main transformer in the substation of the previous voltage level is represented by m′, and the number of main transformers in the substation of that voltage level is represented by S. c n' represents the main transformer capacity of the substation at the next voltage level, and n′ represents the number of main transformers at that level. By comparing the calculated capacity-to-load ratio with the recommended capacity-to-load ratio value in the power guidelines, the degree of coordination and adaptation of the power supply capacity between the upper and lower voltage levels can be analyzed.

[0126] (6) Average power outage ratio refers to the ratio of the average local load that cannot be transmitted after a fault at a substation of a certain voltage level during the statistical period to the total load carried by the substation's lines. Specifically:

[0127]

[0128] In the formula, Indicates the average power loss ratio. d represents the average load that cannot be transmitted after a fault in a substation of a certain voltage level during the statistical period. z This indicates the total load carried by the substation at this voltage level. A high average power outage ratio indicates that the substation at this voltage level has weak interconnection with the upstream and downstream, poor power transfer capacity, and poor adaptability between the two.

[0129] (7) Network loss rate refers to the ratio of network loss to power supply, specifically:

[0130]

[0131] In the formula, P″ represents the network loss rate, and Q... loss Q represents the power loss in the network, and Q represents the power supply.

[0132] (8) The peak-to-valley difference that the power grid can withstand refers to the difference between the maximum load and the minimum load that the power grid can withstand, specifically:

[0133] K = k max dk min d

[0134] In the formula, K represents the peak-to-valley difference that the power grid can withstand, k max k represents the maximum load multiple of the power grid, that is, the ratio of the maximum power supply load to the actual power supply load. min This represents the minimum load multiple of the power grid, that is, the ratio of the minimum power supply load to the actual power supply load, where d represents the actual power supply load.

[0135] Reliability adaptability indicators:

[0136] (1) Expected power shortage refers to the expected power reduction caused by insufficient generation capacity and grid constraints within a known time period. In other words, it is the average duration of load outages due to insufficient power supply during the study period. Specifically:

[0137] EDNS = ∑ y∈M A y P y

[0138] In the formula, EDNS represents the expected power deficit, usually expressed in MW, and A y P represents the load power reduction under state y. y Let represent the probability that the system is in state y, and M represent the set of system states with load shedding.

[0139] (2) Expected power shortage value refers to the expected value of power reduction caused by insufficient power generation capacity and grid constraints within a known time period. In other words, it is the average value of electrical energy lost due to power outages caused by insufficient power supply during the study period. Specifically:

[0140] EENS = EDNS × T

[0141] In the formula, EENS represents the expected value of insufficient power, usually expressed in MW·h / a, and T represents the duration of the known time period.

[0142] (3) Load shedding probability refers to the probability that the system will take load shedding measures during operation. Specifically:

[0143]

[0144] In the formula, P PLC t represents the probability of load shedding. y T represents the duration of system state y. 总 Indicates the total mode time.

[0145] (4) Average load shedding duration refers to the average outage duration of each load affected by the outage in the power grid within a specified time period. Specifically:

[0146]

[0147] In the formula, ADLC represents the average duration of load shedding, and F y This represents the frequency of state y.

[0148] (5) The degree of power grid frequency deviation refers to the ratio of the cumulative operating time during which the actual operating frequency value of the power grid exceeds the specified allowable frequency deviation to the total monitoring time. Specifically:

[0149]

[0150] In the formula, Δf represents the degree of grid frequency deviation, and t f This represents the cumulative operating time (t) during which the actual operating frequency value of the power grid exceeds the specified allowable deviation. 总 This indicates the total monitoring time.

[0151] (6) The degree of voltage deviation in the power grid refers to the ratio of the cumulative operating time during which the actual voltage value exceeds the specified allowable voltage deviation to the total monitoring time. Specifically:

[0152]

[0153] In the formula, ΔU represents the degree of grid voltage deviation, and t u This represents the cumulative operating time (t) during which the actual voltage value of the power grid exceeds the specified allowable voltage deviation. 总 This indicates the total monitoring time.

[0154] Environmental adaptability indicators:

[0155] (1) Carbon dioxide emission reduction refers to the reduction in CO2 emissions through the use of renewable energy sources such as wind power, solar power, hydropower, geothermal power, and hydrogen power. Specifically:

[0156] C′ e =δ e ×W′ g

[0157] In the formula, C′ e δ represents the amount of carbon dioxide emission reduction. e W′ represents the carbon emission coefficient of electricity production. g This indicates the annual electricity generation from renewable energy sources.

[0158] (2) Sulfur dioxide emission reduction refers to the total amount of sulfur dioxide emissions controlled in the power industry, specifically:

[0159] E 电 =α×W′ g

[0160] In the formula, E 电 This represents the reduction in sulfur dioxide emissions, and α represents the sulfur emission coefficient from electricity production.

[0161] (3) Nitrogen oxide emission reduction refers to the total amount of nitrogen oxide emissions controlled in the power industry, specifically:

[0162]

[0163] In the formula, ε represents the reduction in nitrogen oxide emissions, and ε represents the nitrogen oxide emission coefficient from power generation.

[0164] After determining and obtaining each evaluation index, the combined weight of each evaluation index is calculated. According to one embodiment of the present invention, the combined weight can be calculated based on objective weights and subjective weights. The methods for obtaining subjective weights and objective weights are explained below.

[0165] Regarding subjective weights, if the traditional Analytic Hierarchy Process (AHP) is used for calculation, decision-makers may have difficulty distinguishing the differences between indicators, leading to errors. Furthermore, during weight calculation, decisions may be impossible to make due to inconsistencies in the judgment matrix. Therefore, this invention proposes an improved AHP method to obtain the subjective weights of each evaluation indicator.

[0166] First, a three-level evaluation scale based on element importance—important, equally important, and unimportant—is used to construct a judgment matrix. This approach simplifies the process by comparing the importance of elements, rather than their relative importance, making the judgment matrix more intuitive and facilitating the calculation of the consistency matrix. Furthermore, the degree of importance among the various indicators is easier to determine and differentiate. Second, a consistency matrix is ​​constructed from the judgment matrix, allowing the matrix to naturally satisfy consistency requirements. This eliminates the need for consistency checks (human perception is subjective, so traditional analytic hierarchy process methods require consistency checks on expert-provided judgment matrices, and adjustments are made mathematically if inconsistencies arise, which is quite complex), thus simplifying the calculation process.

[0167] Next, we will explain how to obtain the subjective weights of each evaluation indicator using the improved analytic hierarchy process.

[0168] First, a judgment matrix is ​​constructed based on the importance of each evaluation indicator. The importance of each evaluation indicator can be determined by experts. According to one embodiment of the present invention, an n-order judgment matrix can be constructed based on the importance of each evaluation indicator, where n is the total number of evaluation indicators. That is, when the number of evaluation indicators is n, an n-order judgment matrix is ​​constructed. In some embodiments, the n-order judgment matrix can be represented as A = (a ij ) n×n , where a ij This represents the element in the i-th row and j-th column of the judgment matrix A, which is the value obtained by comparing the importance of evaluation index i and evaluation index j. Specifically, a ij A value of 1 indicates that evaluation indicator i is more important than evaluation indicator j, a ij Setting it to 0 indicates that evaluation index i and evaluation index j are equally important, a ijA value of -1 indicates that evaluation indicator i is less important than evaluation indicator j. The values ​​of i and j are 1, 2, 3, ..., n, i.e., i = 1, 2, 3, ..., n, j = 1, 2, 3, ..., n. Additionally, it should be noted that when i = j, a... ij This represents a comparison of evaluation index i (or j) itself. Each evaluation index is equally important relative to itself. Therefore, when i = j, a ij Set it to 0. An example of the judgment matrix A is given below.

[0169]

[0170] Then, based on the constructed judgment matrix, the optimal transfer matrix is ​​obtained. The meaning of each element in the judgment matrix can be understood as follows: There is a ij =-a ji Therefore, the judgment matrix is ​​an antisymmetric matrix. Thus, according to an embodiment of the present invention, the optimal transfer matrix of the judgment matrix A can be obtained by the following formula, where, for ease of description, the optimal transfer matrix of the judgment matrix A can be expressed as D = (d ij ) n×n .

[0171]

[0172] In the formula, d ij Let a represent the element in the i-th row and j-th column of the optimal transfer matrix D. ik Let a represent the element in the i-th row and k-th column of matrix A. jk Let a represent the element in the j-th row and k-th column of matrix A. kj This indicates the element in the k-th row and j-th column of matrix A.

[0173] The following is an example of the optimal transfer matrix D for judgment matrix A.

[0174]

[0175] Next, based on the obtained optimal transfer matrix, the consistency matrix of the judgment matrix is ​​obtained. The consistency matrix of judgment matrix A can be represented as follows: Represents the consistency matrix A * The element in the i-th row and j-th column of the matrix. According to an embodiment of the present invention, the consistency matrix A of the judgment matrix A can be obtained by the following formula. * .

[0176]

[0177] The following gives the consistency matrix A of the judgment matrix A. * An example.

[0178]

[0179] Finally, the eigenvector corresponding to the largest eigenvalue of the consistency matrix is ​​obtained, and this eigenvector is used as the subjective weight of each evaluation index. The eigenvector corresponding to the largest eigenvalue of the consistency matrix can be obtained using the square root method, as detailed below.

[0180] First, obtain the nth root of the product of the elements in each row of the consistency matrix, and treat each of these as a row element to obtain a column vector. The nth root of the product of the elements in the i-th row of the consistency matrix can be represented as... but Additionally, in some embodiments, a column vector consisting of the nth root of the product of each row's elements can be represented as a single row element. but in, This represents the nth root of the product of the elements in the first row of the consistency matrix (or the column vector). The first row of elements), This represents the nth root of the product of the elements in the second row of the consistency matrix. This represents the nth root of the product of the elements in the nth row of the consistency matrix.

[0181] Then, the obtained column vectors are normalized to obtain the eigenvector corresponding to the largest eigenvalue of the consistency matrix. The eigenvector corresponding to the largest eigenvalue of the consistency matrix can be represented as W. Furthermore, in some embodiments, the column vectors can be normalized using the following formula. To formalize the process:

[0182]

[0183] In the formula, W i This represents the i-th element in the eigenvector. This represents the nth root of the product of the elements in the i-th row of the consistency matrix (i.e., the element in the i-th row of the column vector). This represents the nth root of the product of the elements in the j-th column of the consistency matrix.

[0184] The following is an example of the eigenvector W corresponding to the largest eigenvalue of the consistency matrix.

[0185] W = [W1, W2, ..., W n ] T

[0186] In the formula, W1 represents the first row element of the feature vector W, W2 represents the second row element of the feature vector W, and W... n This represents the element in the nth row of the eigenvector W.

[0187] After obtaining the eigenvector W corresponding to the largest eigenvalue of the consistency matrix, each element in the eigenvector W is used as the subjective weight of each evaluation index. Specifically, W... i This represents the subjective weight of evaluation indicator i. For example, W1 and W2 above represent the subjective weights of evaluation indicator 1 and evaluation indicator 2, respectively.

[0188] The above describes the method for obtaining subjective weights. The method for obtaining objective weights will now be explained. According to one embodiment of the present invention, objective weights can be obtained using the Criteria Importance Through Intercriteria Correlation (CRITIC) method. This method considers not only the amount of information contained in the evaluation indicators but also the contrast between different schemes and the conflict between various evaluation indicators.

[0189] In obtaining objective weights using the index correlation method, an evaluation index matrix needs to be constructed first. According to one embodiment of the present invention, when constructing the evaluation index matrix, each evaluation index of each power grid planning scheme can be treated as a column vector. In some embodiments, the evaluation index matrix can be represented as X. Thus, when there are m power grid planning schemes, and each scheme has n evaluation indicators, the evaluation index matrix X can be represented as...

[0190]

[0191] In the formula, x il Let i represent the evaluation index of power grid planning scheme l, where i takes the values ​​1, 2, ..., n, and l takes the values ​​1, 2, ..., m.

[0192] Next, we will explain the process of obtaining the objective weights of each evaluation indicator using the indicator correlation method.

[0193] First, the evaluation indicators in the evaluation indicator matrix are homogenized to obtain a homogenized evaluation indicator matrix. Here, we first explain the concepts of positive and negative indicators. Positive indicators are those whose larger values ​​indicate better performance, while negative indicators are those whose smaller values ​​indicate better performance. In power grid adaptability evaluations, both positive and negative indicators are often included simultaneously; however, the coexistence of both types of indicators increases the computational burden of the indicator system. Therefore, we can first homogenize the evaluation indicators in the evaluation indicator matrix. According to one embodiment of the present invention, negative indicators in the evaluation indicator matrix can be converted into positive indicators. Further, the negative indicators in the evaluation indicator matrix can be converted into positive indicators using the following formula:

[0194]

[0195] In the formula, x il Let x' represent the evaluation index in the i-th row and l-th column of the evaluation index matrix. il This represents the evaluation index in the i-th row and l-th column of the evaluation index matrix for homogenization (i.e., the negative index x in the i-th row and l-th column of the evaluation index matrix X). il The positive index x′ converted il ), max | x i | represents the largest evaluation index in the i-th row of the evaluation index matrix (i.e., the evaluation index with the largest value in the i-th row of the evaluation index matrix X), p represents the coordination coefficient, which is generally 0.1, and l takes values ​​of 1, 2, 3, ..., m, where m represents the number of power grid planning schemes. In some embodiments, the aligned evaluation index matrix (or the positive evaluation index matrix) can be represented as X′.

[0196] Then, based on the homogenized evaluation index matrix, the standard deviation of each evaluation index and the correlation coefficient between the evaluation indices are obtained. In the index correlation method, the standard deviation of the index represents the magnitude of the difference in the value of the same index in different schemes, and the quantitative expression representing the conflict, constructed based on the correlation coefficient between the indices, reflects the conflict between the indices.

[0197] According to one embodiment of the present invention, the standard deviation and correlation coefficient of each evaluation index can be obtained from the homogenized evaluation index matrix in the following manner. Specifically, the homogenized evaluation index matrix is ​​dimensionless to obtain a normalized evaluation index matrix. The normalized evaluation index matrix can be represented as X″. Further, in some embodiments, the normalized evaluation index matrix X″ can be obtained by dimensionless transformation of the homogenized evaluation index matrix X′ using the following formula.

[0198]

[0199] In the formula, x′ il X″ represents the element in the i-th row and l-th column of the evaluation index matrix X′ for homogenization. il This represents the element in the i-th row and l-th column of the normalized evaluation index matrix X″ (i.e., the evaluation index x′ in the i-th row and l-th column of the homogenized evaluation index matrix X′). il The evaluation index x″ obtained after dimensionless transformation il ).

[0200] After obtaining the standardized evaluation index matrix, the standard deviation of each evaluation index and the correlation coefficient between the evaluation indices are obtained based on the standardized evaluation index matrix, as follows.

[0201] The standard deviation of each evaluation index is obtained using the following formula:

[0202]

[0203] The correlation coefficient between the evaluation indicators is obtained using the following formula:

[0204] p it =cov(x″ i ,x″ t ) / (s i ,s t )

[0205] In the formula, s i The standard deviation of evaluation index i is represented. Let p represent the mean of the evaluation indicators in the i-th row of the normalized evaluation indicator matrix X″ (i.e., the mean of the i-th evaluation indicator). it Cov(x″) represents the correlation coefficient between evaluation index i and evaluation index t. i ,x″ t ) represents the covariance between the i-th row and the t-th row in the normalized evaluation index matrix, s t It represents the standard deviation of the evaluation index t.

[0206] Next, based on the obtained standard deviation and correlation coefficient, the information content of each evaluation index is obtained using the index correlation method. In some embodiments, the information content of evaluation index i can be represented as G. i Furthermore, the amount of information contained in each evaluation indicator can be obtained using the following formula:

[0207]

[0208] in, This is a quantitative indicator of the conflict between the i-th evaluation indicator (i.e., evaluation indicator i) and other evaluation indicators. Additionally, G... i The larger the value, the more information the i-th evaluation indicator contains, and the more important the evaluation indicator becomes; correspondingly, its weight should also be greater.

[0209] Finally, based on the amount of information contained in each evaluation indicator, the objective weight of each evaluation indicator is obtained. In some embodiments, the objective weight of evaluation indicator i can be represented as V. i Furthermore, the objective weights of each evaluation indicator can be obtained using the following formula:

[0210]

[0211] In the formula, G t This indicates the amount of information contained in the evaluation index t.

[0212] At this point, the subjective and objective weights of each evaluation indicator are obtained. Subsequently, the combined weights are calculated based on the obtained subjective and objective weights.

[0213] Specifically, firstly, a combined weight acquisition model is constructed with the objective of minimizing the sum of the squares of the first deviation and the second deviation. Here, the first deviation is the deviation between the combined weights and the subjective weights, and the second deviation is the deviation between the combined weights and the objective weights. That is, the combined weight acquisition model is established with the objective of minimizing the sum of the squares of the deviations between the combined weights and the subjective weights, and between the combined weights and the objective weights. According to one embodiment of the present invention, the combined weight acquisition model may include an objective function and constraints, specifically:

[0214] The objective function is:

[0215]

[0216] The constraints are:

[0217]

[0218] u i ≥0

[0219] Where z represents the sum of the squares of the first deviation and the second deviation, u i This represents the combined weight of evaluation index i (i.e., the weight resulting from the combination of subjective and objective weighting methods).

[0220] Then, the acquired subjective and objective weights are input into the constructed model. With the objective of minimizing the sum of the squares of the first and second deviations, the model is solved, and the combined weights of each evaluation index are output. Specifically, by solving the constructed model, the combined weights of evaluation index i can be obtained as follows:

[0221] u i =βW i +(1-β)V i

[0222] Where β is the proportion of subjective preference coefficient weight to the combined weight, and (1-β) is the proportion of objective preference coefficient weight to the combined weight.

[0223] Thus, the evaluation index matrix and the combined weights of each evaluation index are obtained. Next, based on the constructed evaluation index matrix and the obtained combined weights of each evaluation index, each power grid planning scheme is evaluated. Specifically, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) can be used to evaluate the power grid planning schemes: constructing positive and negative ideal solutions to the multi-objective decision problem, and then evaluating the degree of closeness of different schemes to the positive and negative ideal solutions by calculating the Euclidean distance between each feasible scheme and the positive and negative ideal solutions, thereby evaluating each scheme. In some embodiments, this can be achieved through steps 320 to 350 below.

[0224] In step 320, the evaluation index matrix is ​​weighted according to the combined weights of each evaluation index to obtain the decision matrix. In some embodiments, the evaluation index matrix can be weighted in the following way.

[0225] First, the evaluation index matrix is ​​standardized to obtain a standardized evaluation index matrix. This standardized evaluation index matrix can be represented as V = (v... il ) n×m v il Let X represent the evaluation index in the i-th row and l-th column of the standardized evaluation index matrix V. Further, in some embodiments, the evaluation index matrix X can be standardized using the following formula:

[0226]

[0227] Then, the obtained standardized evaluation index matrix is ​​weighted to obtain the decision matrix. The decision matrix can be represented as B = (b il ) n×m b il This represents the element in the i-th row and l-th column of the decision matrix B. Further, in some embodiments, the standardized evaluation index matrix V can be weighted using the following formula:

[0228] b il =v il ×u i

[0229] That is, the decision matrix B = (b il ) n×m =(u il ×u i ) n×m .

[0230] Then, proceeding to step 330, the optimal solutions for both positive and negative directions are determined based on the decision matrix. This can be represented by S.+ S represents the forward optimal solution. - This represents the negative optimal solution. Further, in some embodiments, the positive and negative optimal solutions can be determined using the following formula. Specifically:

[0231] The optimal solution is determined by the following formula:

[0232] S + =[b1 + b2 + ,…b n + ] = [max(b 11 ,b 12 ,…b 1m ),max(b 21 ,b 22 ,…b 2m ),…max(b n1 ,b n2 ,…b nm )]

[0233] The negative optimal solution is determined by the following formula:

[0234] S - =[b1 - b2 - ,…bn - ] = [min(b 11 ,b 12 ,…b 1m ),min(b 21 ,b 22 ,…b 2m ),…min(b n1 ,b n2 ,…b nm )]

[0235] Among them, b n + b represents the positive optimal solution of the evaluation index n. n - This represents the negative optimal solution for the evaluation index n.

[0236] Subsequently, step 340 is used to obtain the distance measures between each power grid planning scheme and the positive and negative optimal solutions. Among these, D can be used... l + Represents the power grid planning scheme l and the forward optimal solution S + The distance measure is D. l - Representing the power grid planning scheme l and the negative optimal solution S -The distance measure. Further, in some embodiments, the distance measure between each power grid planning scheme and the positive and negative optimal solutions can be obtained by calculating the Euclidean distance, as detailed below.

[0237] The distance measure between each power grid planning scheme and the forward optimal solution is obtained using the following formula:

[0238]

[0239] The distance measure between each power grid planning scheme and the negative optimal solution is obtained by the following formula:

[0240]

[0241] Among them, D l - The larger the value of D, the further the power grid planning scheme l is from the worst solution, and the better the power grid planning scheme l is. l + The smaller the value, the closer the power grid planning scheme l is to the optimal solution, and the better the power grid planning scheme l is.

[0242] Subsequently, at step 350, the adaptability of each power grid planning scheme is evaluated based on the obtained distance metric. According to one embodiment of the present invention, evaluating the adaptability of each power grid planning scheme based on the obtained distance metric may specifically include the following steps.

[0243] First, based on the obtained distance metric, the closeness between each power grid planning scheme and the optimal solution is determined. This can be represented by C. l This represents the closeness between the power grid planning scheme 1 and the optimal solution. Further, in some embodiments, the closeness between each power grid planning scheme and the optimal solution can be obtained using the following formula:

[0244]

[0245] Then, based on the obtained proximity scores, the adaptability of each power grid planning scheme is evaluated, with the scheme having a higher proximity to the optimal solution being superior. That is, C l The larger the value of C, the better the power grid planning scheme. Therefore, among various power grid planning schemes, C... l The scheme with the highest value is the optimal scheme. Specifically, in some embodiments, after obtaining the closeness between each power grid planning scheme and the optimal solution, the closeness scores can be sorted in descending order to demonstrate the merits of each power grid planning scheme.

[0246] In summary, this invention employs a combined weighting method, which fully considers both subjective decision-making and objective information of the evaluation indicators. This effectively avoids the problem of inaccurate evaluation results caused by using only subjective or objective weighting.

[0247] Furthermore, this invention constructs a consistency matrix for the judgment matrix based on a three-level evaluation scaling method for element importance. This eliminates the need for consistency checks, thereby greatly simplifying the calculation process and improving the evaluation efficiency of power grid planning schemes.

[0248] In addition, this invention analyzes the impact of the proportion of new energy sources on power grid planning and establishes an evaluation system for the adaptability of power grid planning schemes from five dimensions: economic adaptability, energy structure adaptability, power grid structure adaptability, reliability adaptability, and environmental adaptability, thereby improving the accuracy of the evaluation results.

[0249] The various techniques described herein can be implemented in combination with hardware or software, or a combination thereof. Thus, the methods and apparatus of the present invention, or certain aspects or portions thereof, can take the form of program code (i.e., instructions) embedded in a tangible medium, such as a removable hard disk, USB flash drive, floppy disk, CD-ROM, or any other machine-readable storage medium, wherein when the program is loaded into and executed by a machine such as a computer, the machine becomes an apparatus for practicing the present invention.

[0250] When the program code is executed on a programmable computer, the computing device generally includes a processor, a processor-readable storage medium (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device. The memory is configured to store program code; the processor is configured to execute the adaptability evaluation method of the power grid planning scheme of the present invention according to instructions in the program code stored in the memory.

[0251] By way of example, and not limitation, readable media include readable storage media and communication media. Readable storage media stores information such as computer-readable instructions, data structures, program modules, or other data. Communication media generally embodies computer-readable instructions, data structures, program modules, or other data in the form of modulated data signals such as carrier waves or other transmission mechanisms, and includes any information delivery medium. Any combination of the above is also included within the scope of readable media.

[0252] In the specification provided herein, the algorithms and displays are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used with the examples of this invention. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0253] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0254] It should be understood that, in order to simplify this disclosure and aid in understanding one or more aspects of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the invention. However, this method of disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim.

[0255] Those skilled in the art will understand that modules, units, or components of the devices disclosed in the examples herein can be arranged in the devices described in this embodiment, or alternatively, can be located in one or more devices different from the devices in this example. The modules in the foregoing examples can be combined into a single module or, in addition, can be divided into multiple sub-modules.

[0256] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0257] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the invention and form different embodiments.

[0258] Furthermore, some of the embodiments described herein are methods or combinations of method elements that can be implemented by a processor of a computer system or by other means of performing the functions. Therefore, a processor having the necessary instructions for implementing the methods or method elements forms means for implementing the methods or method elements. Furthermore, the elements described herein in the apparatus embodiments are examples of means for implementing the functions performed by elements for the purposes of carrying out the invention.

[0259] As used herein, unless otherwise specified, the use of ordinal numbers such as “first,” “second,” “third,” etc., to describe ordinary objects merely indicates different instances of similar objects and is not intended to imply that the objects being described must have a given order in time, space, ordering, or any other manner.

[0260] Although the invention has been described with reference to a limited number of embodiments, those skilled in the art will understand from the foregoing description that other embodiments are conceivable within the scope of the invention described herein. Furthermore, it should be noted that the language used in this specification has been chosen primarily for readability and instructional purposes, and not for the purpose of interpreting or limiting the subject matter of the invention. Therefore, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. The disclosure of the invention is illustrative and not restrictive, and the scope of the invention is defined by the appended claims.

Claims

1. An adaptability evaluation method for power grid planning schemes, comprising: The evaluation indicators for each power grid planning scheme are obtained, and an evaluation indicator matrix is ​​constructed and the combined weight of each evaluation indicator is calculated. The combined weight is calculated based on subjective weight and objective weight. The evaluation indicators include economic adaptability indicators, energy structure adaptability indicators, power grid structure adaptability indicators, reliability adaptability indicators and environmental adaptability indicators. Based on the combined weights of each evaluation indicator, the evaluation indicator matrix is ​​weighted to obtain the decision matrix; Based on the decision matrix, determine the positive and negative optimal solutions; Obtain the distance measure between each of the power grid planning schemes and the positive and negative optimal solutions respectively; The adaptability of each power grid planning scheme is evaluated based on the obtained distance measure, including: obtaining the closeness between each power grid planning scheme and the optimal solution based on the obtained distance measure; evaluating the adaptability of each power grid planning scheme based on the obtained closeness, wherein the scheme with the higher closeness to the optimal solution is better. in: The determination of positive and negative optimal solutions based on the decision matrix includes: The optimal solution is determined by the following formula: ， The negative optimal solution is determined by the following formula: ， In the formula, This represents the positive optimal solution. This represents the positive optimal solution for the evaluation index n. This represents the negative optimal solution. This represents the negative optimal solution for the evaluation index n; Obtain the distance measures between each of the power grid planning schemes and the positive and negative optimal solutions, including: The distance measure between each of the power grid planning schemes and the forward optimal solution is obtained by the following formula: ， The distance measure between each of the power grid planning schemes and the negative optimal solution is obtained by the following formula: ， In the formula, Indicates power grid planning scheme l Distance measure to the positive optimal solution Indicates power grid planning scheme l Distance measure to the negative optimal solution This represents the element in the i-th row and l-th column of the decision matrix; In the step of obtaining the closeness between each power grid planning scheme and the optimal solution, the closeness between each power grid planning scheme and the optimal solution is obtained by the following formula: ， In the formula, Indicates power grid planning scheme l The degree of closeness to the optimal solution.

2. The method as described in claim 1, wherein, The calculation of the combined weights of each evaluation indicator includes: The subjective weights of each evaluation indicator are obtained by using the improved analytic hierarchy process, and the objective weights of each evaluation indicator are obtained by using the indicator correlation method. A combined weight acquisition model is constructed with the objective of minimizing the sum of the squares of the first deviation and the second deviation. The first deviation is the deviation between the combined weight and the subjective weight, and the second deviation is the deviation between the combined weight and the objective weight. The obtained subjective and objective weights are input into the model. The model is solved with the objective of minimizing the sum of the squares of the first and second deviations, and the combined weights of the evaluation indicators are output.

3. The method as described in claim 1 or 2, wherein, The subjective weights of each evaluation index are obtained using an improved analytic hierarchy process, including: Based on the importance of each evaluation indicator, a judgment matrix is ​​constructed, where... This indicates the element in the i-th row and j-th column of the judgment matrix. A value of 1 indicates that evaluation indicator i is more important than evaluation indicator j. Setting it to 0 indicates that evaluation index i and evaluation index j are equally important. -1 indicates that evaluation indicator i is not as important as evaluation indicator j. The values ​​of i and j are 1, 2, 3, ..., n, where n represents the total number of evaluation indicators. Based on the judgment matrix, obtain the optimal transfer matrix of the judgment matrix, where, This represents the element in the i-th row and j-th column of the optimal transfer matrix. , This indicates the element in the i-th row and k-th column of the matrix. This indicates the element in the j-th row and k-th column of the matrix. This indicates the element in the k-th row and j-th column of the judgment matrix; Based on the obtained optimal transfer matrix, the consistency matrix of the judgment matrix is ​​obtained, wherein, This represents the element in the i-th row and j-th column of the consistency matrix. ; Obtain the eigenvector corresponding to the largest eigenvalue of the consistency matrix, and use the obtained eigenvector as the subjective weight of each evaluation index; The judgment matrix is ​​as follows: 。 4. The method of claim 3, wherein, The step of obtaining the eigenvector corresponding to the largest eigenvalue of the consistency matrix includes: The eigenvector corresponding to the largest eigenvalue of the consistency matrix is ​​obtained by using the square root method.

5. The method of claim 4, wherein, The step of obtaining the eigenvector corresponding to the largest eigenvalue of the consistency matrix using the square root method includes: Obtain the nth root of the product of the elements in each row of the consistency matrix, and use each of these roots as a row element to obtain a column vector; The column vectors are normalized to obtain the feature vectors; The column vector is normalized using the following formula: ， In the formula, This represents the i-th element in the eigenvector. This represents the nth root of the product of the elements in the i-th row of the consistency matrix. This represents the nth root of the product of the elements in the j-th column of the consistency matrix.

6. The method of claim 2, wherein, The objective weights of each evaluation indicator are obtained using the correlation method, including: The evaluation indicators in the evaluation indicator matrix are homogenized to obtain a homogenized evaluation indicator matrix. Based on the homogenized evaluation index matrix, the standard deviation of each evaluation index and the correlation coefficient between the evaluation indices are obtained. Based on the obtained standard deviation and correlation coefficient, the information content of each evaluation index is obtained by using the index correlation method; Based on the amount of information contained in each evaluation indicator, obtain the objective weight of each evaluation indicator.

7. The method of claim 6, wherein, The process of normalizing the evaluation indicators in the evaluation indicator matrix includes: Convert the negative indicators in the evaluation indicator matrix into positive indicators; The negative indicator is converted into a positive indicator using the following formula: ， In the formula, The evaluation index matrix representing the homogenization is located in the i-th row. l The evaluation indicators of the series The i-th row of the evaluation index matrix represents the index of the index that is... l The evaluation indicators of the series This represents the maximum evaluation index in the i-th row of the evaluation index matrix. Indicates the coordination coefficient. l The value of is 1, 2, 3, ..., m, where m represents the number of power grid planning schemes.

8. The method of claim 7, wherein, The step of obtaining the standard deviation of each evaluation indicator and the correlation coefficient between evaluation indicators based on the homogenized evaluation indicator matrix includes: The dimensionless transformation of the homogenized evaluation index matrix yields a normalized evaluation index matrix, where... The i-th row of the normalized evaluation index matrix represents the index of ... l Column elements, ; Based on the standardized evaluation index matrix, the standard deviation of each evaluation index and the correlation coefficient between the evaluation indices are obtained.

9. The method of claim 8, wherein, The step of obtaining the standard deviation of each evaluation indicator and the correlation coefficient between evaluation indicators based on the normalized evaluation indicator matrix includes: The standard deviation of each evaluation index is obtained using the following formula: ， The correlation coefficient between the evaluation indicators is obtained using the following formula: ， In the formula, The standard deviation of evaluation index i is represented. Let represent the mean of the evaluation indicators in the i-th row of the normalized evaluation indicator matrix. This represents the correlation coefficient between evaluation index i and evaluation index t. This represents the covariance between the i-th row and the t-th row in the normalized evaluation index matrix. It represents the standard deviation of the evaluation index t.

10. The method of claim 9, wherein, In the step of obtaining the information content of each evaluation indicator using the indicator correlation method, the information content of each evaluation indicator is obtained by the following formula: ， In the formula, This indicates the amount of information contained in evaluation index i.

11. The method of claim 10, wherein, In the step of obtaining the objective weights of each evaluation indicator, the objective weights of each evaluation indicator are obtained using the following formula: ， In the formula, This represents the objective weight of evaluation index i. This indicates the amount of information contained in the evaluation index t.

12. The method of claim 7, wherein, The evaluation index matrix is ​​weighted, including: The evaluation index matrix is ​​standardized to obtain a standardized evaluation index matrix, wherein, Represents the i-th row and i-th index in the standardized evaluation index matrix. l The evaluation indicators of the series ; By assigning weights to the standardized evaluation index matrix, a decision matrix is ​​obtained, wherein... This represents the element in the i-th row and l-th column of the decision matrix. , This represents the combined weight of evaluation index i.

13. The method as claimed in claim 1 or 2, wherein, The economic adaptability indicators include the power production elasticity coefficient, power balance coefficient, net present value, internal rate of return, total life-cycle cost of power grid construction, investment payback period for power grid construction, power generation increment per unit of new asset, and load increment per unit of new asset. The energy structure adaptability indicators include the capacity to accept new energy sources, the proportion of clean energy, and the proportion of new energy sources consumed across provinces and regions. The power grid structure adaptability indicators include the line-to-machine ratio, capacity-to-load growth ratio, capacity-to-load ratio, transformer ratio, power supply capacity ratio between different voltage levels, average power outage ratio, grid loss rate, and the peak-to-valley difference that the power grid can withstand. The reliability adaptability indicators include the expected value of insufficient power supply, the expected value of insufficient electricity supply, the probability of load shedding, the average duration of load shedding, the degree of power grid frequency deviation, and the degree of power grid voltage deviation. The environmental adaptability indicators include carbon dioxide emission reduction, sulfur dioxide emission reduction, and nitrogen oxide emission reduction.

14. A computing device, comprising: At least one processor; as well as A memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, the program instructions including instructions for performing the method as described in any one of claims 1-13.

15. A readable storage medium storing program instructions that, when read and executed by a computing device, cause the computing device to perform the method as described in any one of claims 1-13.

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