Power grid construction project investment decision-making method and device and terminal equipment

Abstract

The invention relates to a decision-making method and device for power grid construction project investment and terminal equipment. The method comprises the steps of obtaining the total investment amount, the total number of construction projects and project construction parameters of each construction project; performing model construction according to the total number of the construction projects and the project construction parameters of each construction project to obtain a power grid optimization combination decision model with a multi-dimensional objective function and a multi-dimensional constraint condition; and screening all the construction projects by adopting an improved particle swarm optimization algorithm according to the total investment in the power grid optimization combination decision model to obtain an investment decision result. According to the decision-making method for the power grid construction project investment, an optimal investment decision-making result and expected income data are output on the basis of the total investment amount and the total construction project amount by constructing a power grid optimization combination decision-making model with a multi-dimensional objective function and a multi-dimensional constraint condition and combining with an improved particle swarm optimization algorithm; and obtaining an investment decision result of the optimal construction project combination.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a decision-making method, apparatus, and terminal equipment for investment in power grid construction projects. Background Art

[0002] Currently, there are still some obvious problems in existing investment decision-making research, among which the most core and typical ones are the simplification of objective functions and constraints. Specifically, in existing technologies, only one project benefit indicator is generally used as the objective function for decision-making, and the consideration of constraints is relatively one-sided, making it difficult to accurately solve the multi-criteria decision-making problem of power grid construction project portfolio investment. Technical methods and data models for using corporate investment capacity as a constraint for investment decisions are also very scarce; in other words, the objective functions and constraints studied are not comprehensive and systematic, lacking constraints on investment capacity, and the resulting investment plans are not reasonable and scientific enough, and still need further research and improvement. Summary of the Invention

[0003] The present application provides a decision-making method, device and terminal equipment for investment in power grid construction projects, which are used to solve the technical problem that the output decision results of existing investment decision-making models are not reasonable and scientific due to incomplete objective functions and constraints.

[0004] In order to achieve the above objectives, this application provides the following technical solutions:

[0005] On the one hand, a decision-making method for investment in a power grid construction project is provided, comprising the following steps:

[0006] Obtain the total investment amount, the total number of construction projects, and the project construction parameters of each construction project;

[0007] Constructing a model based on the total number of construction projects and the project construction parameters of each construction project to obtain a power grid optimization combination decision model with multi-dimensional objective functions and multi-dimensional constraint conditions;

[0008] In the power grid optimization combination decision model, all the construction projects are screened according to the total investment amount using an improved particle swarm optimization algorithm to obtain an investment decision result;

[0009] The improved particle swarm optimization algorithm includes an adaptive inertia weight based on average particle size and a linear adjustment learning factor.

[0010] Preferably, the model is constructed based on the total number of construction projects and the project construction parameters of each construction project to obtain a power grid optimization combination decision model with multi-dimensional objective functions and multi-dimensional constraints, including:

[0011] Constructing a multidimensional objective function based on the total number of construction projects and the project construction parameters of each construction project based on economic benefits, safety benefits, energy-saving benefits and construction costs;

[0012] According to the total number of construction projects and the project construction parameters of each construction project, multi-dimensional constraint conditions are constructed based on investment capacity constraints, power demand constraints, and investment project relationship constraints.

[0013] Preferably, the expression of the multidimensional objective function is:

[0014]

[0015] Where F(x) is the maximum benefit function of investment, E(x) is the economic benefit function of investment, S(x) is the safety benefit function of investment, D(x) is the energy-saving benefit function of investment, and Q(x) is the investment capacity penalty function.

[0016] Preferably, the economic benefit function is:

[0017]

[0018] The safety benefit function is:

[0019]

[0020] The energy saving benefit function is:

[0021]

[0022] The investment capacity penalty function is:

[0023]

[0024] Where N is the total number of construction projects, T is the life cycle of the construction project, S it is the sales revenue of the i-th construction project in year t, C it is the operating cost of the i-th construction project in year t, a i is the investment variable of the i-th construction project, Q i is the construction cost of the i-th construction project, c1 is the influence coefficient of the remaining capacity of the power grid in the construction project, c2 is the influence coefficient of insufficient capacity in the construction project, L is the average load of the power grid, b i is the newly added capacity of the i-th construction project, b is the capacity of the original power grid in the construction project, W h-i is the electricity consumption of the i-th construction project, Δη loss-i is the network loss rate of the i-th construction project, P f is the rated power of the fth clean energy power generation, T fis the maximum utilization hours type coefficient of the fth type of clean energy power generation, l is the total number of clean energy power generation, e i is the emission reduction benefit per kilowatt-hour of the i-th construction project, m1 is the coal price per ton of standard coal, m2, m3, m4 and m5 are the environmental treatment costs per ton of carbon dioxide, sulfur dioxide, nitrogen oxides and dust, respectively, λ1 is the weight of standard coal saved per kilowatt-hour, λ2, λ3, λ4 and λ5 are the emissions of carbon dioxide, sulfur dioxide, nitrogen oxides and dust.

[0025] Preferably, the multidimensional constraint condition includes an investment capacity constraint condition, a power demand constraint condition, and an investment project relationship constraint condition. The expression of the investment capacity constraint condition is:

[0026]

[0027] The expression of the power demand constraint condition is:

[0028]

[0029] The expression of the investment project relationship constraint condition is:

[0030]

[0031] Where N is the total number of construction projects, Q 0i is the investment amount of the i-th construction project, Q is the total investment, a i is the investment variable of the i-th construction project, b i is the newly added capacity of the i-th construction project, B is the power demand of the grid, a i+1 is the investment variable of the i+1th construction project, and N0 is the total investment variable value of the construction project.

[0032] In yet another aspect, a decision-making device for power grid construction project investment is provided, comprising a data acquisition module, a model building module, and a decision output module;

[0033] The data acquisition module is used to obtain the total investment, the total number of construction projects and the project construction parameters of each construction project;

[0034] The model building module is used to build a model based on the total number of construction projects and the project construction parameters of each construction project to obtain a power grid optimization combination decision model with multi-dimensional objective functions and multi-dimensional constraint conditions;

[0035] The decision output module is used to screen all the construction projects according to the total investment amount using an improved particle swarm optimization algorithm in the power grid optimization combination decision model to obtain an investment decision result;

[0036] The improved particle swarm optimization algorithm includes an adaptive inertia weight based on average particle size and a linear adjustment learning factor.

[0037] Preferably, the model construction module includes an objective function construction submodule and a constraint condition construction submodule;

[0038] The objective function construction submodule is used to construct a multidimensional objective function based on the total number of construction projects and the project construction parameters of each construction project based on economic benefits, safety benefits, energy-saving benefits and construction costs;

[0039] The constraint condition construction submodule is used to construct multi-dimensional constraint conditions based on the total number of construction projects and the project construction parameters of each construction project based on investment capacity constraints, power demand constraints, and investment project relationship constraints.

[0040] Preferably, the expression of the multidimensional objective function is:

[0041]

[0042] Where F(x) is the maximum benefit function of investment, E(x) is the economic benefit function of investment, S(x) is the safety benefit function of investment, D(x) is the energy-saving benefit function of investment, and Q(x) is the investment capacity penalty function.

[0043] Preferably, the economic benefit function is:

[0044]

[0045] The safety benefit function is:

[0046]

[0047] The energy saving benefit function is:

[0048]

[0049] The investment capacity penalty function is:

[0050]

[0051] The multi-dimensional constraints include investment capacity constraints, power demand constraints, and investment project relationship constraints. The expression of the investment capacity constraints is:

[0052]

[0053] The expression of the power demand constraint condition is:

[0054]

[0055] The expression of the investment project relationship constraint condition is:

[0056]

[0057] Where Q 0i is the investment amount of the i-th construction project, Q is the total investment, N0 is the total investment variable value of the construction project, B is the power demand of the power grid, a i+1 is the investment variable of the i+1th construction project, N is the total number of construction projects, T is the life cycle of the construction project, S it is the sales revenue of the i-th construction project in year t, C it is the operating cost of the i-th construction project in year t, a i is the investment variable of the i-th construction project, Q i is the construction cost of the i-th construction project, c1 is the influence coefficient of the remaining capacity of the power grid in the construction project, c2 is the influence coefficient of insufficient capacity in the construction project, L is the average load of the power grid, b i is the newly added capacity of the i-th construction project, b is the capacity of the original power grid in the construction project, W h-i is the electricity consumption of the i-th construction project, Δη loss-i is the network loss rate of the i-th construction project, P f is the rated power of the fth clean energy power generation, T f is the maximum utilization hours type coefficient of the fth type of clean energy power generation, l is the total number of clean energy power generation, e i is the emission reduction benefit per kilowatt-hour of the i-th construction project, m1 is the coal price per ton of standard coal, m2, m3, m4 and m5 are the environmental treatment costs per ton of carbon dioxide, sulfur dioxide, nitrogen oxides and dust, respectively, λ1 is the weight of standard coal saved per kilowatt-hour, λ2, λ3, λ4 and λ5 are the emissions of carbon dioxide, sulfur dioxide, nitrogen oxides and dust.

[0058] In another aspect, a terminal device is provided, comprising a processor and a memory;

[0059] The memory is used to store program code and transmit the program code to the processor;

[0060] The processor is used to execute the above-mentioned decision-making method for power grid construction project investment according to the instructions in the program code.

[0061] The decision-making method, device and terminal equipment for the power grid construction project investment include obtaining the total investment, the total number of construction projects and the project construction parameters of each construction project; constructing a model based on the total number of construction projects and the project construction parameters of each construction project to obtain a power grid optimization combination decision-making model with multi-dimensional objective functions and multi-dimensional constraints; using an improved particle swarm optimization algorithm in the power grid optimization combination decision-making model to screen all construction projects based on the total investment to obtain an investment decision result; wherein the improved particle swarm optimization algorithm includes an adaptive inertia weight based on average granularity and a linear adjustment learning factor.

[0062] It can be seen from the above technical solutions that the present application has the following advantages: the decision-making method for investment in power grid construction projects constructs a power grid optimization combination decision model with multi-dimensional objective functions and multi-dimensional constraints, and combines the improved particle swarm optimization algorithm to output the optimal investment decision results and expected return data based on the total investment amount and the total number of construction projects, thereby obtaining the investment decision results of the optimal construction project combination; it solves the technical problem that the existing investment decision-making model is not comprehensive enough in terms of objective functions and constraints, resulting in unreasonable and scientific output decision results.

[0063] The decision-making device for power grid construction project investment constructs a power grid optimization combination decision model with multi-dimensional objective functions and multi-dimensional constraints, and combines the improved particle swarm optimization algorithm to output the optimal investment decision results and expected return data based on the total investment amount and the total number of construction projects, thereby obtaining the investment decision results of the optimal construction project combination; it solves the technical problem that the existing investment decision-making model is not comprehensive enough in terms of objective functions and constraints, resulting in unreasonable and scientific output decision results. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0065] Figure 1 A flowchart of the steps of the decision-making method for power grid construction project investment according to an embodiment of the present application;

[0066] Figure 2 A line graph for screening a power grid optimization combination decision model in the decision-making method for power grid construction project investment described in an embodiment of the present application;

[0067] Figure 3A schematic diagram of a framework of a decision-making device for power grid construction project investment according to an embodiment of the present application;

[0068] Figure 4 This is a schematic diagram of the terminal device described in an embodiment of the present application. DETAILED DESCRIPTION

[0069] In order to make the purpose, features, and advantages of the invention of this application more obvious and easy to understand, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described below are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0070] In the description of the embodiments of the present application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0071] In the embodiments of the present application, unless otherwise expressly specified or limited, the terms "installed," "connected," "connected," "fixed," etc. should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections, electrical connections; direct connections, or indirect connections through an intermediate medium; internal connections between two components, or interactions between two components. Those skilled in the art can understand the specific meanings of the above terms in the embodiments of the present application based on specific circumstances.

[0072] Investment decisions are crucial for the rational allocation of corporate funds. Correct and rational investment decisions are crucial for improving economic efficiency and ensuring faster capital turnover. Power grid investment decisions are based on phased construction projects, taking into account grid planning, investment capacity, project importance, and construction schedule requirements. Therefore, decision-making on power grid investment projects is particularly crucial.

[0073] Currently, research on investment decisions for power grid construction projects is still in a continuous development stage. Traditional methods for solving the problem of grid investment decision-making target planning usually include discrete approximation iteration and backward induction. Among them, the technical route of the backward induction method to solve the grid investment decision-making model is generally through empirical simulation research, which provides the optimal combination strategy, investment scale and investment timing of power grid enterprises from the micro level of project investment decision-making. Existing intelligent optimization algorithms such as genetic algorithms and ant colony algorithms are also widely used to solve optimization problems. For example, genetic algorithms are used to find the best matching combination to achieve the optimal goal of the overall investment plan, or to consider factors such as investment amount and construction period from the perspective of overall project optimization, and establish a cost minimization model for the overall project decision-making plan.

[0074] In existing technologies, methods such as technology maturity theory, Markowitz model, and the eigenvalue method of expert group decision-making have also been applied to related research and to the optimization of power grid investment decisions. However, these existing technologies still have some obvious problems in investment decision-making research, among which the most core and typical ones are the simplification of objective functions and constraints. These technical deficiencies are the key points that restrict the optimization of investment decisions for power grid construction projects. Specifically, only one project benefit indicator is used as the objective function for decision-making, and the consideration of constraints is relatively one-sided, making it difficult to accurately solve the multi-criteria decision-making problem of portfolio investment in power grid construction projects. Therefore, in existing technologies, the objective functions and constraints studied are not comprehensive and systematic enough, there is a lack of constraints on investment capacity, and the resulting investment plans are not reasonable and scientific enough, and further research and improvement are still needed.

[0075] The embodiments of the present application provide a method, apparatus, and terminal device for making decisions about investments in power grid construction projects, which solve the technical problem that existing investment decision-making models are not comprehensive enough in terms of objective functions and constraints, resulting in unreasonable and unscientific output decision results. In this embodiment, the method, apparatus, and terminal device for making decisions about investments in power grid construction projects are described using power grid construction projects as a case study. The purpose of the method, apparatus, and terminal device for making decisions about investments in power grid construction projects is to select the optimal combination of investments from all power grid construction projects under limited capital constraints (investment capacity, such as total investment amount) to meet the power demand of the regional power grid. In current power grid construction, multiple construction projects are usually invested and constructed simultaneously. On the premise of meeting power demand, constraints such as capital are introduced to optimize the combination of construction projects that require investment and obtain investment decision results.

[0076] Example 1:

[0077] Figure 1 This is a flowchart of the steps of the decision-making method for investment in power grid construction projects described in an embodiment of the present application.

[0078] like Figure 1As shown, the embodiment of the present application provides a decision-making method for investment in a power grid construction project, comprising the following steps:

[0079] S1. Obtain the total investment amount, the total number of construction projects, and the project construction parameters of each construction project.

[0080] It should be noted that in step S1, the data required for decision-making on power grid construction project investment is obtained, and the data includes the total investment Q, the total number of construction projects N, and the project construction parameters of each construction project. In this embodiment, the project construction parameters of each construction project include the life cycle T of the construction project, the sales revenue S of the construction project, and the project life cycle T. it , Operation cost of construction project C it , investment variable a of construction project i 、Construction cost Q of the construction project i , the influence coefficient of the remaining capacity of the power grid in the construction project c1, the influence coefficient of insufficient capacity in the construction project c2, the average load of the power grid L, the new capacity of the construction project b i , the capacity of the original power grid in the construction project b, the power consumption of the construction project W h-i , network loss rate Δη of construction projects loss-i , Rated power P of clean energy power generation f , Maximum utilization hours type coefficient T of clean energy power generation f , the total amount of clean energy power generation l, the emission reduction benefit per kilowatt-hour of construction project emissions e i , coal price per ton of standard coal m1, environmental treatment cost per ton of carbon dioxide m2, environmental treatment cost per ton of sulfur dioxide m3, environmental treatment cost per ton of nitrogen oxides m4, environmental treatment cost per ton of dust m5, λ1 is the weight of standard coal saved per kilowatt-hour, carbon dioxide emissions λ2, sulfur dioxide emissions λ3, nitrogen oxide emissions λ4, dust emissions λ5, investment amount of the construction project Q 0i 、New production capacity of construction projectsb i , the power demand B of the power grid and the total investment variable value N0 of the construction project, etc. Among them, when the value of the investment variable is 1, it means that the construction project can be invested; when the value of the investment variable is 0, it means that the construction project cannot be invested.

[0081] S2. A model is constructed based on the total number of construction projects and the project construction parameters of each construction project to obtain a power grid optimization combination decision model with multi-dimensional objective functions and multi-dimensional constraints.

[0082] It should be noted that in step S2, a power grid optimization combination decision model is constructed based on the total number of construction projects and the project construction parameters of each construction project obtained in step S1. In this embodiment, the power grid optimization combination decision model includes a multidimensional objective function and multidimensional constraints. Among them, the construction of the power grid optimization combination decision model not only includes a data module of a multidimensional objective function that is compatible with many factors of power grid project investment, but also embeds and constructs various data processes that contain independence, association, dependence, and repulsion for global optimization based on the data module of the multidimensional objective function, and is also compatible with the data module of the combined multidimensional constraints that are subsequently expanded.

[0083] S3. In the power grid optimization combination decision model, an improved particle swarm optimization algorithm is used to screen all construction projects according to the total investment amount to obtain the investment decision result.

[0084] It should be noted that in step S3, based on the total investment Q, an improved particle swarm optimization algorithm is used in the power grid optimization portfolio decision model to optimize and screen all construction projects to obtain investment decision results and expected investment returns. In this embodiment, the existing particle swarm optimization algorithm is introduced into the power grid optimization portfolio decision model and adaptively improved so that the power grid optimization portfolio decision model outputs the optimal investment decision results and expected investment returns.

[0085] In the embodiments of the present application, the grid construction project investment decision-making method constructs a grid optimization portfolio decision-making model with the goal of maximizing investment utility, taking into account the constraints of power demand and investment capacity. Based on the shortcomings of existing particle swarm optimization algorithms, such as slow convergence speed and long computation time, this grid optimization portfolio decision-making model utilizes an improved particle swarm optimization algorithm to output better investment decision results and expected return data, thereby increasing the convergence speed of the optimization screening and enabling the grid optimization portfolio decision-making model to output investment decision results for the optimal construction project portfolio.

[0086] In an embodiment of the present application, the improved particle swarm optimization algorithm includes an adaptive inertia weight based on average particle size and a linear adjustment learning factor.

[0087] It should be noted that the existing particle swarm optimization algorithm is an intelligent evolutionary algorithm that simulates the behavior of biological groups such as flocks. Through collective collaboration among birds, the entire group can reach an optimal value. The existing particle swarm optimization algorithm iteratively updates the position and velocity of particles based on their velocity and displacement. This particle swarm optimization algorithm has excellent search performance, including clear concepts, simple formulas, few parameters, easy programming, no special requirements for initial values, efficient hidden parallelism, and suitability for handling non-differentiable and non-convex functions.

[0088] In the embodiment of the present application, the main parameters of the existing particle swarm optimization algorithm include inertia weight w, learning factors (such as c1 and c2), maximum velocity vmax, population size N, and maximum number of iterations Itera. The improved particle swarm optimization algorithm is obtained based on the inertia weight and learning factor.

[0089] It should be noted that the inertia weight w significantly influences the convergence performance of the particle swarm optimization algorithm. A large inertia weight w results in stronger global optimization and exploration capabilities, while a low inertia weight results in stronger local optimization and exploration capabilities. The learning factors c1 and c2 reflect the strength of the particle's own thinking and the information exchange between particles. The maximum speed vmax balances global and local search capabilities, effectively controlling the search range and setting a value to predict the optimization problem in advance. The population size N (also representing the total number of grid construction projects) represents the number of particles searching simultaneously and determines the minimum number of updates required per iteration. The maximum number of iterations Itera determines the computational effort, which indirectly affects convergence accuracy. A low number of iterations will not allow the population sufficient time to find the global optimum.

[0090] In the embodiment of the present application, the improvement of the inertia weight of the improved particle swarm optimization algorithm is achieved by an adaptive inertia weight adjustment strategy based on the average particle size. The adaptive inertia weight adjustment strategy includes: adaptive adjustment of the average particle distance and the inertia weight; the average particle distance is used to reflect the degree of dispersion between particles in the swarm. The calculation formula of the "average particle distance" of the k-th iteration population is as follows:

[0091]

[0092] Where L0 is the diagonal length of the search space; N is the total number of construction projects; D is the dimension of the solution space; x ij k is the coordinate value of the i-th particle relative to the j-th dimension in the k-th iteration; x j k is the average value of the kth iteration relative to the jth dimension coordinate value; x j,max and x j，min are the upper and lower limits of the jth dimension of the feasible domain space respectively. The expression for adaptive adjustment of inertia weight is:

[0093]

[0094] Where Dis(k) is the average particle distance; w(k) is the inertia weight at the kth iteration. In this embodiment, upper and lower limits can be set to prevent the inertia weight from exceeding the boundaries. When the average particle distance is large, the inertia weight is large, achieving a global search. When the average particle distance is small, the inertia weight is small, achieving a refined regional search.

[0095] In an embodiment of the present application, the improvement of the inertia weight of the improved particle swarm optimization algorithm is achieved based on the improvement of the learning factor. The expression of the linear adjustment learning factor is:

[0096]

[0097] Where c 1，max 、c 1，min 、c 2，max 、c 2，min are the upper and lower bounds of c1 and c2, respectively. At the beginning of the iteration, c1 is large and c2 is small. Iterative updates of particles in the population are primarily based on their own experience. Subsequently, c1 decreases and c2 increases. Cooperation between particles is strengthened, leading the population toward the global optimum.

[0098] Figure 2 A line chart for screening the power grid optimization combination decision model in the decision-making method for power grid construction project investment described in an embodiment of the present application.

[0099] In an embodiment of the present application, N=7 power grid construction projects to be invested in are used as a case study to analyze the practicality of the decision-making method for the investment in the power grid construction project. The relevant data of N=7 power grid construction projects to be invested in are shown in Table 1. Assume that the expected life of each power grid construction project is T=30 years, and the annual net income and operating costs of each construction project will never change in the next 30 years. The parameter values ​​of the c1 and c2 learning factors are 0.1 and 0.2 respectively, and the total investment Q is 180 million yuan. The sum of the capacities of the selected projects is the new capacity of the power grid system. The seven selected construction projects are optimized and analyzed by the constructed power grid optimization combination decision model, and the improved adaptive particle swarm optimization algorithm is used to solve the power grid optimization combination decision model. In the power grid optimization combination decision model, the parameter settings include: population size N is 50; the maximum number of iterations k is 150 times; the maximum speed vmax is 10; the learning factor c 1，max 、c 1，min 、c 2，max 、c 2，min The optimization process of the improved adaptive particle swarm optimization algorithm is as follows: Figure 2 As shown in Table 2, the power grid optimization combination decision model is based on the economic, social and reliability of the power grid construction project. The investment capacity constraint is converted into an objective function to establish an optimization investment model. Then, the improved particle swarm optimization algorithm is used to optimize the power grid optimization combination decision model. The power grid optimization combination decision model outputs the final optimization result, which includes the investment decision result and expected return data, as shown in Table 2.

[0100] Table 1 shows the relevant data of each construction project

[0101]

[0102] Table 2 shows the main indicators of the regional 220 kV power grid construction scale

[0103]

[0104] As shown in Table 2, the power grid optimization combination decision model based on the improved particle swarm optimization algorithm outputs the final investment decision results of the power grid construction projects, which are project names B, D, E, and f, and the expected profit data is 56.8 million yuan.

[0105] The present application provides a decision-making method for power grid construction project investment, comprising obtaining a total investment amount, a total number of construction projects, and project construction parameters for each construction project; constructing a model based on the total number of construction projects and the project construction parameters for each construction project to obtain a power grid optimization combination decision-making model with a multi-dimensional objective function and multi-dimensional constraints; using an improved particle swarm optimization algorithm in the power grid optimization combination decision-making model to screen all construction projects based on the total investment amount to obtain an investment decision result; wherein the improved particle swarm optimization algorithm includes an adaptive inertia weight based on average granularity and a linear adjustment learning factor. The decision-making method for power grid construction project investment outputs the optimal investment decision result and expected return data based on the total investment amount and the total number of construction projects by constructing a power grid optimization combination decision-making model with a multi-dimensional objective function and multi-dimensional constraints, and outputs the optimal investment decision result and expected return data based on the improved particle swarm optimization algorithm, thereby obtaining the investment decision result of the optimal construction project combination; and solves the technical problem that the output decision result of the existing investment decision-making model is not reasonable and scientific due to the incompleteness of the objective function and constraint conditions.

[0106] In one embodiment of the present application, a model is constructed based on the total number of construction projects and the project construction parameters of each construction project to obtain a power grid optimization combination decision model with multi-dimensional objective functions and multi-dimensional constraints, including:

[0107] Constructing a multidimensional objective function based on the total number of construction projects and the construction parameters of each construction project based on economic benefits, safety benefits, energy-saving benefits and construction costs;

[0108] According to the total number of construction projects and the project construction parameters of each construction project, multidimensional constraints are constructed based on investment capacity constraints, power demand constraints, and investment project relationship constraints.

[0109] It should be noted that the expression of the multidimensional objective function is:

[0110]

[0111] Where F(x) is the maximum return function of investment, E(x) is the economic benefit function of investment, S(x) is the safety benefit function of investment, D(x) is the energy-saving benefit function of investment, and Q(x) is the investment capacity penalty function. The multidimensional objective function comprehensively considers the economic benefit, safety benefit, and energy-saving benefit of power grid optimization investment.

[0112] In the embodiment of the present application, the economic benefit function is:

[0113]

[0114] The safety benefit function is:

[0115]

[0116] The energy-saving benefit function is:

[0117]

[0118] The investment capacity penalty function is:

[0119]

[0120] Where N is the total number of construction projects, T is the life cycle of the construction project, S it is the sales revenue of the i-th construction project in year t, C it is the operating cost of the i-th construction project in year t, a i is the investment variable of the i-th construction project, Q i is the construction cost of the i-th construction project, c1 is the influence coefficient of the remaining capacity of the power grid in the construction project, c2 is the influence coefficient of insufficient capacity in the construction project, L is the average load of the power grid, b i is the newly added capacity of the i-th construction project, b is the capacity of the original power grid in the construction project, W h-i is the electricity consumption of the i-th construction project, Δη loss-i is the network loss rate of the i-th construction project, P f is the rated power of the fth clean energy power generation, T f is the maximum utilization hours type coefficient of the fth type of clean energy power generation, l is the total number of clean energy power generation, e i is the emission reduction benefit per kilowatt-hour of the i-th construction project, m1 is the coal price per ton of standard coal, m2, m3, m4 and m5 are the environmental treatment costs per ton of carbon dioxide, sulfur dioxide, nitrogen oxides and dust, respectively, λ1 is the weight of standard coal saved per kilowatt-hour, λ2, λ3, λ4 and λ5 are the emissions of carbon dioxide, sulfur dioxide, nitrogen oxides and dust.

[0121] It should be noted that the economic benefit can be obtained by calculating the investment cost, annual income, investment fee and investment fee through the economic benefit function. The safety benefit function uses the capacity load ratio to measure the reliability investment of the construction project. When the capacity load ratio is too high, more power equipment will be idle, resulting in lower investment efficiency. On the contrary, if it is too low, it will suppress power consumption and is not conducive to economic development. Therefore, under the premise of meeting demand and reliability, the value of the capacity load ratio should be gradually reduced and controlled within a certain range. The energy-saving benefit is the benefit obtained by calculating the power generated by distributed power sources and the network loss of the construction project through the energy-saving benefit function. The construction cost is the total investment of all construction projects calculated through the investment capacity penalty function.

[0122] In one embodiment of the present application, the multidimensional constraint condition includes an investment capacity constraint condition, a power demand constraint condition, and an investment project relationship constraint condition. The expression of the investment capacity constraint condition is:

[0123]

[0124] The expression of power demand constraint is:

[0125]

[0126] The expression of the investment project relationship constraint is:

[0127]

[0128] Where N is the total number of construction projects, Q 0i is the investment amount of the i-th construction project, Q is the total investment, a i is the investment variable of the i-th construction project, b i is the newly added capacity of the i-th construction project, B is the power demand of the grid, a i+1 is the investment variable of the i+1th construction project, and N0 is the total investment variable value of the construction project.

[0129] It should be noted that the investment capacity constraint indicates that the total investment amount after optimization should not exceed the investment capacity of the power grid company. The power demand constraint indicates that, subject to investment capacity constraints, the newly added capacity of the power grid must meet social electricity demand. The investment project relationship constraint can be implemented by establishing multiple relationships between N construction projects.

[0130] In the embodiment of the present application, if all construction projects are independent of each other, the investment project relationship constraint condition is:

[0131]

[0132] If all construction projects are mutually exclusive, the investment project relationship constraints are:

[0133]

[0134] If the construction projects are interdependent, it can be understood that only if construction project i is selected can construction project i+1 be selected; otherwise, if construction project i is not selected, construction project i+1 may not be selected. Then the investment project relationship constraint condition is: ;

[0135] If the construction projects are closely related, it can be understood that the two construction projects must be selected at the same time or at different times. Then the investment project relationship constraint is: .

[0136] Example 2:

[0137] Figure 3 A schematic diagram of the framework of the decision-making device for power grid construction project investment described in an embodiment of the present application.

[0138] like Figure 3 As shown, the embodiment of the present application provides a decision-making device for power grid construction project investment, including a data acquisition module 10, a model building module 20 and a decision output module 30;

[0139] The data acquisition module 10 is used to obtain the total investment, the total number of construction projects and the project construction parameters of each construction project;

[0140] A model building module 20 is used to build a model based on the total number of construction projects and the project construction parameters of each construction project to obtain a power grid optimization combination decision model with multi-dimensional objective functions and multi-dimensional constraints;

[0141] The decision output module 30 is used to screen all construction projects according to the total investment amount using the improved particle swarm optimization algorithm in the power grid optimization combination decision model to obtain the investment decision result;

[0142] Among them, the improved particle swarm optimization algorithm includes adaptive inertia weight based on average particle size and linear adjustment learning factor.

[0143] It should be noted that the contents of the modules in the decision-making device for power grid construction project investment in Example 2 correspond to the contents of the steps in the decision-making method for power grid construction project investment. The contents of the steps in the decision-making method for power grid construction project investment have been described in Example 1, and the contents of the modules in the decision-making device for power grid construction project investment will not be described in detail in this example. The decision-making device for power grid construction project investment constructs a power grid optimization combination decision model with multi-dimensional objective functions and multi-dimensional constraints, and outputs the optimal investment decision results and expected return data based on the total investment amount and the total number of construction projects in combination with an improved particle swarm optimization algorithm, thereby obtaining the investment decision results of the optimal construction project combination; it solves the technical problem that the existing investment decision-making model is not comprehensive enough in terms of objective functions and constraints, resulting in unreasonable and scientific output decision results.

[0144] In the embodiment of the present application, the model construction module 20 includes an objective function construction submodule and a constraint condition construction submodule;

[0145] An objective function construction submodule is used to construct a multidimensional objective function based on economic benefits, safety benefits, energy-saving benefits and construction costs according to the total number of construction projects and the project construction parameters of each construction project;

[0146] The constraint condition construction submodule is used to construct multi-dimensional constraint conditions based on the total number of construction projects and the project construction parameters of each construction project based on investment capacity constraints, power demand constraints, and investment project relationship constraints.

[0147] In the embodiment of the present application, the expression of the multidimensional objective function is:

[0148]

[0149] Where F(x) is the maximum benefit function of investment, E(x) is the economic benefit function of investment, S(x) is the safety benefit function of investment, D(x) is the energy-saving benefit function of investment, and Q(x) is the investment capacity penalty function.

[0150] In the embodiment of the present application, the economic benefit function is:

[0151]

[0152] The safety benefit function is:

[0153]

[0154] The energy-saving benefit function is:

[0155]

[0156] The investment capacity penalty function is:

[0157]

[0158] The multidimensional constraints include investment capacity constraints, power demand constraints, and investment project relationship constraints. The expression of investment capacity constraints is:

[0159]

[0160] The expression of power demand constraint is:

[0161]

[0162] The expression of the investment project relationship constraint is:

[0163]

[0164] Where Q 0i is the investment amount of the i-th construction project, Q is the total investment, N0 is the total investment variable value of the construction project, B is the power demand of the power grid, a i+1 is the investment variable of the i+1th construction project, N is the total number of construction projects, T is the life cycle of the construction project, S it is the sales revenue of the i-th construction project in year t, C it is the operating cost of the i-th construction project in year t, a i is the investment variable of the i-th construction project, Q i is the construction cost of the i-th construction project, c1 is the influence coefficient of the remaining capacity of the power grid in the construction project, c2 is the influence coefficient of insufficient capacity in the construction project, L is the average load of the power grid, b i is the newly added capacity of the i-th construction project, b is the capacity of the original power grid in the construction project, W h-i is the electricity consumption of the i-th construction project, Δη loss-i is the network loss rate of the i-th construction project, P f is the rated power of the fth clean energy power generation, T f is the maximum utilization hours type coefficient of the fth type of clean energy power generation, l is the total number of clean energy power generation, e i is the emission reduction benefit per kilowatt-hour of the i-th construction project, m1 is the coal price per ton of standard coal, m2, m3, m4 and m5 are the environmental treatment costs per ton of carbon dioxide, sulfur dioxide, nitrogen oxides and dust, respectively, λ1 is the weight of standard coal saved per kilowatt-hour, λ2, λ3, λ4 and λ5 are the emissions of carbon dioxide, sulfur dioxide, nitrogen oxides and dust.

[0165] Example 3:

[0166] Figure 4This is a schematic diagram of the terminal device described in an embodiment of the present application.

[0167] like Figure 4 As shown, an embodiment of the present application provides a terminal device, including a processor and a memory;

[0168] A memory, configured to store program codes and transmit the program codes to a processor;

[0169] The processor is configured to execute the above-mentioned decision-making method for power grid construction project investment according to instructions in the program code.

[0170] It should be noted that the processor is configured to execute the steps of the aforementioned embodiment of a method for making decisions about power grid construction project investment according to the instructions in the program code. Alternatively, the processor implements the functions of the modules / units in the aforementioned system / device embodiments when executing the computer program.

[0171] For example, a computer program may be divided into one or more modules / units, one or more of which are stored in a memory and executed by a processor to complete the present application. One or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in a terminal device.

[0172] Terminal devices can be computing devices such as desktop computers, laptops, PDAs, and cloud servers. Terminal devices may include, but are not limited to, processors and memory. Those skilled in the art will appreciate that this does not constitute a limitation on terminal devices and may include more or fewer components than shown, or a combination of certain components, or different components. For example, terminal devices may also include input / output devices, network access devices, buses, and the like.

[0173] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0174] Memory can be an internal storage unit of a terminal device, such as a hard drive or memory. It can also be an external storage device, such as a plug-in hard drive, a Smart Memory Card (SMC), a Secure Digital (SD) card, or a flash memory card. Furthermore, memory can include both internal and external storage units. Memory is used to store computer programs and other programs and data required by the terminal device. Memory can also be used to temporarily store data that has been output or is about to be output.

[0175] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0176] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0177] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0178] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0179] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0180] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A decision-making method for power grid construction project investment, characterized in that: The following steps are involved: Obtain the total investment amount, the total number of construction projects, and the project construction parameters of each construction project; Constructing a model according to the total number of construction projects and the project construction parameters of each construction project to obtain a power grid optimization combination decision model with multi-dimensional objective functions and multi-dimensional constraint conditions; In the power grid optimization combination decision model, all the construction projects are screened according to the total investment amount using an improved particle swarm optimization algorithm to obtain an investment decision result; The improved particle swarm optimization algorithm includes an adaptive inertia weight based on average particle size and a linear adjustment learning factor.

2. The decision-making method for power grid construction project investment according to claim 1, characterized in that: The model is constructed according to the total number of construction projects and the project construction parameters of each construction project to obtain a power grid optimization combination decision model with multi-dimensional objective functions and multi-dimensional constraint conditions, including: Constructing a multidimensional objective function based on the total number of construction projects and the project construction parameters of each construction project based on economic benefits, safety benefits, energy-saving benefits and construction costs; According to the total number of construction projects and the project construction parameters of each construction project, multi-dimensional constraint conditions are constructed based on investment capacity constraints, power demand constraints, and investment project relationship constraints.

3. The decision-making method for power grid construction project investment according to claim 2, characterized in that: The expression of the multidimensional objective function is: Where F(x) is the maximum benefit function of investment, E(x) is the economic benefit function of investment, S(x) is the safety benefit function of investment, D(x) is the energy-saving benefit function of investment, and Q(x) is the investment capacity penalty function.

4. The decision-making method for power grid construction project investment according to claim 3 is characterized in that: The economic benefit function is: The safety benefit function is: The energy saving benefit function is: The investment capacity penalty function is: Where N is the total number of construction projects, T is the life cycle of the construction project, S it is the sales revenue of the i-th construction project in year t, C it is the operating cost of the i-th construction project in year t, a i is the investment variable of the i-th construction project, Q i is the construction cost of the i-th construction project, c1 is the influence coefficient of the remaining capacity of the power grid in the construction project, c2 is the influence coefficient of insufficient capacity in the construction project, L is the average load of the power grid, b i is the newly added capacity of the i-th construction project, b is the capacity of the original power grid in the construction project, W h-i is the electricity consumption of the i-th construction project, Δη loss-i is the network loss rate of the i-th construction project, P f is the rated power of the fth clean energy power generation, T f is the maximum utilization hours type coefficient of the fth clean energy power generation, l is the total number of clean energy power generation, e i is the emission reduction benefit per kilowatt-hour of the ith construction project, m1 is the coal price per ton of standard coal, m2, m3, m4 and m5 are the environmental treatment costs per ton of carbon dioxide, sulfur dioxide, nitrogen oxides and dust, respectively, λ1 is the weight of standard coal saved per kilowatt-hour, λ2, λ3, λ4 and λ5 are the emissions of carbon dioxide, sulfur dioxide, nitrogen oxides and dust.

5. The decision-making method for power grid construction project investment according to claim 2, characterized in that: The multidimensional constraints include investment capacity constraints, power demand constraints and investment project relationship constraints. The expression of the investment capacity constraints is: The expression of the power demand constraint condition is: The expression of the investment project relationship constraint condition is: Where N is the total number of construction projects, Q 0i is the investment amount of the i-th construction project, Q is the total investment, a i is the investment variable of the i-th construction project, b i is the newly added capacity of the i-th construction project, B is the power demand of the power grid, a i+1 is the investment variable of the i+1th construction project, and N0 is the total investment variable value of the construction project.

6. A decision-making device for power grid construction project investment, characterized in that: It includes data acquisition module, model building module and decision output module; The data acquisition module is used to obtain the total investment, the total number of construction projects and the project construction parameters of each construction project; The model building module is used to build a model according to the total number of construction projects and the project construction parameters of each construction project to obtain a power grid optimization combination decision model with multi-dimensional objective functions and multi-dimensional constraint conditions; The decision output module is used to screen all the construction projects according to the total investment amount using an improved particle swarm optimization algorithm in the power grid optimization combination decision model to obtain an investment decision result; The improved particle swarm optimization algorithm includes an adaptive inertia weight based on average particle size and a linear adjustment learning factor.

7. The decision-making device for power grid construction project investment according to claim 6, characterized in that: The model construction module includes an objective function construction submodule and a constraint condition construction submodule; The objective function construction submodule is used to construct a multidimensional objective function based on economic benefits, safety benefits, energy-saving benefits and construction costs according to the total number of construction projects and the project construction parameters of each construction project; The constraint condition construction submodule is used to construct multi-dimensional constraint conditions based on the total number of construction projects and the project construction parameters of each construction project based on investment capacity constraints, power demand constraints, and investment project relationship constraints.

8. The decision-making device for power grid construction project investment according to claim 7, characterized in that: The expression of the multidimensional objective function is: Where F(x) is the maximum benefit function of investment, E(x) is the economic benefit function of investment, S(x) is the safety benefit function of investment, D(x) is the energy-saving benefit function of investment, and Q(x) is the investment capacity penalty function.

9. The decision-making device for power grid construction project investment according to claim 7, characterized in that: The economic benefit function is: The safety benefit function is: The energy saving benefit function is: The investment capacity penalty function is: The multidimensional constraints include investment capacity constraints, power demand constraints and investment project relationship constraints. The expression of the investment capacity constraints is: The expression of the power demand constraint condition is: The expression of the investment project relationship constraint condition is: In the formula, Q 0i is the investment amount of the i-th construction project, Q is the total investment, N0 is the total investment variable value of the construction project, B is the power demand of the power grid, a i+1 is the investment variable of the i+1th construction project, N is the total number of construction projects, T is the life cycle of the construction project, S it is the sales revenue of the i-th construction project in year t, C it is the operating cost of the i-th construction project in year t, a i is the investment variable of the i-th construction project, Q i is the construction cost of the i-th construction project, c1 is the influence coefficient of the remaining capacity of the power grid in the construction project, c2 is the influence coefficient of insufficient capacity in the construction project, L is the average load of the power grid, b i is the newly added capacity of the i-th construction project, b is the capacity of the original power grid in the construction project, W h-i is the electricity consumption of the i-th construction project, Δη loss-i is the network loss rate of the i-th construction project, P f is the rated power of the fth clean energy power generation, T f is the maximum utilization hours type coefficient of the fth clean energy power generation, l is the total number of clean energy power generation, e i is the emission reduction benefit per kilowatt-hour of the ith construction project, m1 is the coal price per ton of standard coal, m2, m3, m4 and m5 are the environmental treatment costs per ton of carbon dioxide, sulfur dioxide, nitrogen oxides and dust, respectively, λ1 is the weight of standard coal saved per kilowatt-hour, λ2, λ3, λ4 and λ5 are the emissions of carbon dioxide, sulfur dioxide, nitrogen oxides and dust.

10. A terminal device, characterized in that: including a processor and a memory; The memory is used to store program codes and transmit the program codes to the processor; The processor is used to execute the decision-making method for investment in a power grid construction project as described in any one of claims 1 to 5 according to the instructions in the program code.

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