Power grid project investment dynamic regulation and control method and platform under multiple interference factors

Abstract

The invention discloses a power grid project investment dynamic regulation and control method under multiple interference factors. The method comprises the following steps: S1, constructing an internal and external interference factor set of power grid investment benefits; the internal and external interference factor set is composed of a plurality of interference factor indexes; s2, building a power grid investment benefit model considering multiple interference factors; and S3, obtaining an optimal investment strategy based on the power grid investment benefit model established in the step S2, and inversely calculating the investment amount in the basic parameters of the power grid project according to the expected benefit of the power grid project input by an investment decision maker, so as to realize the dynamic regulation and control of the power grid project investment under multiple interference factors. Compared with the prior art, the method fully considers internal and external disturbance factors of power grid investment, and provides a dynamic regulation and control basis for project investment. According to the platform, the power grid project investment dynamic regulation and control method under multiple interference factors is applied, and the platform is suitable for popularization and application.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid investment control, and in particular relates to a method and platform for dynamic control of power grid project investment under multiple interference factors. Background Art

[0002] At present, the development and scale of distributed energy and grid connection have increased significantly, the grid structure and operation characteristics have become increasingly complex, and the interference factors of grid investment have become increasingly diversified. The evaluation of grid benefits is no longer limited to operational benefits, but also needs to take into account social and economic benefits. Grid project investment occupies a very important position in the power industry. It is not only closely related to people's daily activities, but also has a great relationship with the stable development of the national economy. With the continuous advancement of a series of national policies such as power system reform and central enterprise investment supervision, it is urgent for grid companies to improve the level of grid investment decision-making and improve grid investment benefits.

[0003] The existing technology does not have a grid investment control method that is targeted at new and complex power systems and can take into account internal and external disturbance factors of grid investment, and cannot quickly and effectively guide grid investment decisions.

[0004] Therefore, it is necessary to provide a new method and platform for dynamic regulation of power grid project investment under multiple interference factors to solve the above technical problems. Summary of the invention

[0005] The technical problem to be solved by the present invention is that in view of the deficiencies in the above-mentioned prior art, a method and platform for dynamic regulation of power grid project investment under multiple interference factors is disclosed to solve the technical problem that the prior art lacks a power grid investment regulation method for new complex power systems and can take into account internal and external disturbance factors of power grid investment, and cannot provide rapid and effective guidance for power grid investment decisions.

[0006] The technical solution adopted by the present invention to solve its technical problems is: a method for dynamic regulation of power grid project investment under multiple interference factors, comprising the following steps: S1, constructing a set of internal and external interference factors of power grid investment benefits based on the natural, economic and social environment, supply and demand environment and the current status of power grid development in the area where the power grid is located; the set of internal and external interference factors is composed of multiple interference factor indicators. S2, based on the set of internal and external interference factors established in step S1, constructing a power grid investment benefit model considering multiple interference factors. S3, based on the power grid investment benefit model constructed in step S2, obtaining the optimal investment strategy, and according to the expected benefits of the power grid project input by the investment decision maker, reversely calculating the investment amount in the basic parameters of the power grid project, to achieve dynamic regulation of power grid project investment under multiple interference factors.

[0007] Preferably, in step S1, the set of internal and external interference factors includes external factors and internal factors; the external factors include: natural environment indicators, economic and social environment indicators and new power system policy indicators; the internal factors include: power supply reliability indicators, power supply quality indicators, power supply capacity indicators, grid structure indicators, safety and reliability indicators, operation efficiency indicators and disaster prevention and mitigation level indicators.

[0008] Preferably, the natural environment indicators include regional terrain features and unfavorable geological conditions; the social environment indicators include: regional GDP growth rate, per capita disposable income, social public opinion environment and load electricity consumption growth rate; the new power system policy indicators include: clean energy installed capacity ratio, clean energy investment and clean energy power generation contribution.

[0009] Preferably, the power supply reliability indicators include the power supply reliability rate and the average power outage time for users; the power supply quality indicator is the comprehensive voltage qualification rate; the power supply capacity indicators include the transformer capacity load ratio and the average distribution transformer capacity per household; the grid structure indicators include the N-1 pass rate and the line connection rate; the safety and reliability indicators include unplanned outage time and planned outage time; the operating efficiency indicators include the maximum load rate of equipment with the same voltage level in the region and the average load rate of equipment with the same voltage level in the region; the disaster prevention and mitigation level indicators include: the proportion of old equipment, the proportion of heavily overloaded equipment, the proportion of single-power substations and the proportion of single radial lines.

[0010] Preferably, the method of building a power grid investment benefit model considering multiple interference factors based on the set of internal and external interference factors established in step S1 in step S2 is as follows:

[0011] S21, calculate the similarity between the interference factor indicators, conduct a preliminary correlation analysis on the power grid project investment benefit indicators, and use the K-means clustering method to classify and analyze the interference factor indicators; establish the correlation and mapping relationship between the interference factor indicators and the power grid project benefits.

[0012] S22, screening effective influencing factors from the interference factor indicators by improving the clustering algorithm.

[0013] S23, using the path analysis method and the Delphi method, from the influencing factors, the paths and impact benefits between the key interference factor indicators and the investment benefits are explored, and the direct effect, indirect effect and comprehensive effect values ​​between the independent variables and other independent variables and between the dependent variables are obtained, forming the power grid investment benefit model under multiple interference factors that comprehensively considers the interference factors of various dimensions.

[0014] Preferably, in step S21, the power grid project investment benefit indicators include: operating benefit indicators, financial benefit indicators and social benefit indicators; the operating benefit indicators include: project maximum load rate, project average load rate and unit investment increase in electricity sales; the financial benefit indicators include: internal rate of return, financial net present value, financial payback period, debt repayment reserve ratio and interest reserve ratio; the social benefit indicators include: driving local employment, GDP support capacity, policy satisfaction, service vehicle and passenger volume and transmission of new energy grid-connected electricity.

[0015] Preferably, in step S22, the implementation process of the improved clustering algorithm is as follows: for a clustered data set D=(D1, D2, ..., D n ), where D i =(D i，1 ,D i，2 ,...,D i，m );

[0016] First, based on the centrality of each object in the clustered set, the first cluster center is selected; then, based on this, the number of clusters and the initial cluster center are determined based on the Canopy algorithm; finally, the initial clustering result is substituted into the median-based K-means algorithm to perform clustering operations and obtain the final clustering result.

[0017] Preferably, in step S3, in order to obtain the optimal investment strategy, the following steps are also included: constructing a multi-attribute evaluation index system for investment projects, considering the effects of interference factors of various types of projects, and based on the fuzzy prospect gray target theory, conducting comprehensive benefit measurement of the evaluation projects from three aspects: power grid operation benefits, economic benefits, and social benefits.

[0018] The present invention also provides a dynamic control platform for power grid project investment under multiple interference factors such as the above-mentioned dynamic control method for power grid project investment under multiple interference factors. The dynamic control platform for power grid project investment under multiple interference factors takes interference factor indicators, basic parameters for evaluating power grid projects, and expected benefits of decision makers as inputs, and takes the proposed project benefit parameters and power grid investment control values ​​as outputs.

[0019] Preferably, the dynamic control platform for power grid project investment under multiple interference factors uses the cuckoo algorithm to optimize the matching of the prediction results of the neural network algorithm; the dynamic control platform for power grid project investment under multiple interference factors also uses the GA-BP algorithm to establish a sample learning library with given inputs and outputs as learning objects.

[0020] Compared with the prior art, the method for dynamic control of power grid project investment under multiple interference factors of the present invention fully considers the internal and external disturbance factors of power grid investment, and establishes a technical research model for dynamic control of power grid investment under multiple interference factors. Based on the interference factor index and the evaluation of the basic parameters of the power grid project, the investment benefits of the power grid project are calculated, and the project scores are optimized; based on the expected benefits of the decision maker, the reference investment amount of the power grid project is reversed to provide a dynamic control basis for the investment of a single project. According to the calculated benefits of a single project, based on the investment portfolio optimization model, a project portfolio investment optimization strategy is proposed to provide a dynamic control basis for the investment of the project group. The dynamic platform for power grid project investment under multiple interference factors of the present invention applies the method for dynamic control of power grid project investment under multiple interference factors, which can be directly copied, promoted and applied in the investment decision-making of power grid infrastructure projects of power grid companies across the country according to local conditions, with obvious economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of a set of internal and external interference factors in the present invention;

[0022] Figure 2 This is a schematic diagram of path analysis in the present invention;

[0023] Figure 3 The present invention is: a schematic diagram of a power grid investment benefit model;

[0024] Figure 4 This is a schematic diagram of the improved clustering algorithm flow in the present invention;

[0025] Figure 5 In the present invention: a schematic diagram of calculating the comprehensive benefits of the evaluation project and obtaining the optimal investment strategy;

[0026] Figure 6 This is a schematic diagram of the GA-BP neural network algorithm flow in the present invention;

[0027] Figure 7 This is an interface diagram of a power grid investment control platform that takes multiple interference factors into consideration in the present invention. DETAILED DESCRIPTION

[0028] The present invention is described in detail below in conjunction with the various embodiments shown in the accompanying drawings, but it should be noted that these embodiments are not limitations of the present invention, and any equivalent transformations or substitutions in functions, methods, or structures made by ordinary technicians in the field based on these embodiments are all within the scope of protection of the present invention.

[0029] The present invention is described in detail below in conjunction with the various embodiments shown in the accompanying drawings, but it should be noted that these embodiments are not limitations of the present invention, and any equivalent transformations or substitutions in functions, methods, or structures made by ordinary technicians in the field based on these embodiments are all within the scope of protection of the present invention.

[0030] The following is combined with Figure 1-7 The method and platform for dynamic control of power grid project investment under multiple interference factors of the present invention are further explained.

[0031] The present invention discloses a method for dynamically regulating power grid project investment under multiple interference factors, comprising the following steps:

[0032] S1, based on the natural, economic, social, supply and demand environment and the current status of power grid development in the region where the power grid is located, a set of internal and external interference factors of power grid investment benefits is constructed; the set of internal and external interference factors is composed of multiple interference factor indicators.

[0033] S2, based on the set of internal and external interference factors established in step S1, building a power grid investment benefit model taking into account multiple interference factors.

[0034] S3, based on the power grid investment benefit model constructed in step S2, obtain the optimal investment strategy, and reversely calculate the investment amount in the basic parameters of the power grid project according to the expected benefits of the power grid project input by the investment decision maker, so as to realize the dynamic regulation of power grid project investment under multiple interference factors (such as Figure 5 shown).

[0035] like Figure 1 According to a specific implementation of the present invention, in step S1, the set of internal and external interference factors includes external factors and internal factors; the external factors include: natural environment indicators, economic and social environment indicators and new power system policy indicators; the internal factors include: power supply reliability indicators, power supply quality indicators, power supply capacity indicators, grid structure indicators, safety and reliability indicators, operation efficiency indicators and disaster prevention and mitigation level indicators.

[0036] According to a specific implementation method of the present invention, the natural environment indicators include regional terrain characteristics and unfavorable geological conditions; the social environment indicators include: regional GDP growth rate, per capita disposable income, social public opinion environment and load electricity consumption growth rate; the new power system policy indicators include: clean energy installed capacity ratio, clean energy investment and clean energy power generation contribution.

[0037] According to a specific implementation manner of the present invention, the power supply reliability index includes the power supply reliability rate and the average power outage time of users; the power supply quality index is the comprehensive voltage qualification rate; the power supply capacity index includes the transformer load ratio and the average distribution transformer capacity per household; the grid structure index includes the N-1 pass rate and the line connection rate; the safety reliability index includes the unplanned outage time and the planned outage time; the operating efficiency index includes the maximum load rate of equipment with the same voltage level in the region and the average load rate of equipment with the same voltage level in the region; the disaster prevention and mitigation level indicators include: the proportion of old equipment, the proportion of heavily overloaded equipment, the proportion of single-power substations and the proportion of single radial lines.

[0038] According to a specific embodiment of the present invention, the method of building a power grid investment benefit model considering multiple interference factors based on the set of internal and external interference factors established in step S1 in step S2 is as follows:

[0039] S21, calculate the similarity between the interference factor indicators, conduct a preliminary correlation analysis on the power grid project investment benefit indicators, and use the K-means clustering method to classify and analyze the interference factor indicators; establish the correlation and mapping relationship between the interference factor indicators and the power grid project benefits (such as Figure 3 );

[0040] More specifically, the potential changes, strengths, and persistence between multiple independent variables and the dependent variable were analyzed by adopting the path analysis method.

[0041] Step 1) Calculate the correlation coefficient between variables using the Pearson correlation coefficient formula;

[0042] Step 2) Calculate the path coefficient group by formula (4);

[0043] Step 3) Make a path diagram, such as Figure 2 As shown, the relationship between the independent variables X1-Xn and Y is established.

[0044] Step 4) Calculate the direct and indirect effects of each cause on the result and analyze them.

[0045] Step 4) Calculate the direct and indirect effects of each cause on the result and analyze them.

[0046] Q 1Y +r 12 Q 2Y +r 13 Q 3Y +...+r 1m Q mY =r ny (4)

[0047] Q y,1 is the direct effect of the independent variable x1 on y, r12 P y2 is the indirect effect of x1 on y through x2, and so on.

[0048] By calculating the magnitude of the direct effect coefficient and the indirect effect coefficient, a mapping relationship is established.

[0049] S22, screening effective influencing factors from the interference factor indicators by improving the clustering algorithm.

[0050] Combine the following Figure 4 The process of improving the clustering algorithm is explained in more detail.

[0051] Step 1) Preprocessing. Normalize the features of each dimension in each clustered object in the clustered data set D, as shown in formula (1), and obtain the normalized data set Y = (Y1, Y2, ..., Y n )(where Y i =(y i1 ,y i2 ,...,y in )).

[0052]

[0053] In the formula, max(D j )、min(D j ) represent the maximum and minimum values ​​of the j-th feature of the clustered object set.

[0054] Step 2) Initialization. First, calculate the centrality value of each clustered object, and select the object with the smallest centrality value as the first cluster center C1; then, use the Canopy algorithm, take the average distance Ave(Y) as the threshold, determine the remaining cluster centers, and obtain the initial number of cluster centers L and the matrix of the initial cluster center set C=(C1,C2,...,C L ) T . Set the number of algorithm iterations I.

[0055] Step 3) Similarity measurement: The clustered objects are similarly calculated based on the Euclidean distance with respect to each cluster center, and the objects are classified into the most similar category according to the similarity value (the smaller the similarity value, the more similar).

[0056] Step 4) Cluster center update. This paper is different from the traditional method of updating the cluster center by the category average value. It selects the median value of each dimension of each clustered object in each category as the new generation of cluster center: arrange the values ​​of each dimension, take the median of each dimension as the update value of the new cluster center. When the algorithm meets any of the following conditions, it will jump out of the iteration program and output the final clustering result: a) The clustering dispersion runs to convergence, that is, |J I+1-J I |<e, as shown in formula (2); b) the cluster center no longer changes with the increase of the number of iterations. Otherwise, jump to step 3) until the algorithm meets the condition to jump out of the iteration or runs to the maximum number of iterations I.

[0057]

[0058] Where C L (I) is the cluster center of the Lth class after the algorithm performs the Ith iteration, Y n Indicates the load curve belonging to the L category.

[0059] S23, using the path analysis method and the Delphi method, from the influencing factors, the paths and impact benefits between the key interference factor indicators and the investment benefits are explored, and the direct effect, indirect effect and comprehensive effect values ​​between the independent variables and other independent variables and between the dependent variables are obtained, forming the power grid investment benefit model under multiple interference factors that comprehensively considers the interference factors of various dimensions.

[0060] According to a specific implementation manner of the present invention, in step S21, the power grid project investment benefit indicators include: operating benefit indicators, financial benefit indicators and social benefit indicators; the operating benefit indicators include: project maximum load rate, project average load rate and unit investment increase in electricity sales; the financial benefit indicators include: internal rate of return, financial net present value, financial payback period, debt repayment reserve ratio and interest reserve ratio; the social benefit indicators include: driving local employment, GDP support capacity, policy satisfaction, service trips and passenger volume and transmission of new energy grid-connected electricity.

[0061] According to a specific embodiment of the present invention, in step S22, the implementation process of the improved clustering algorithm is as follows: for a clustered data set D=(D1, D2, ..., D n ), where D i =(D i，1 ,D i，2 ,...,D i，m ). First, based on the centrality of each object in the clustered set, the first cluster center is selected; then, based on this, the number of clusters and the initial cluster center are determined based on the Canopy algorithm; finally, the initial clustering result is substituted into the median-based K-means algorithm to perform clustering operations and obtain the final clustering result.

[0062] According to a specific implementation of the present invention, in step S3, in order to obtain the optimal investment strategy, the following steps are also included: constructing a multi-attribute evaluation index system for investment projects, considering the role of interference factors of various types of projects, and based on the fuzzy prospect gray target theory, conducting comprehensive benefit measurement of the evaluation projects from three aspects: grid operation benefits, economic benefits, and social benefits.

[0063] like Figure 7 The present invention also discloses a dynamic control platform for power grid project investment under multiple interference factors, which applies the dynamic control method for power grid project investment under multiple interference factors as described in the above implementation mode. The dynamic control platform for power grid project investment under multiple interference factors takes interference factor indicators, basic parameters for evaluating power grid projects, and expected benefits of decision makers as inputs, and takes the proposed project benefit parameters and power grid investment control values ​​as outputs.

[0064] According to a specific implementation of the present invention, the dynamic control platform for power grid project investment under multiple interference factors uses the cuckoo algorithm to optimize the matching of the prediction results of the neural network algorithm; the dynamic control platform for power grid project investment under multiple interference factors also uses the GA-BP algorithm to establish a sample learning library with given inputs and outputs as learning objects.

[0065] Among them, the GA-BP neural network algorithm flow chart is as follows Figure 6 shown.

[0066] The specific process of the cuckoo algorithm is as follows.

[0067] First, you need to set the following three ideal states:

[0068] (1) A cuckoo lays only one egg at a time and chooses a random nest to hatch it.

[0069] (2) Among a group of randomly selected bird nests, the best one will be preserved for the next generation.

[0070] (3) The number of available bird nests n is fixed, and the probability that the owner of a bird nest can find an alien bird egg is Pa∈[0, 1]. Then, based on these three ideal states, the path and position update formula for the cuckoo to find a nest is as follows:

[0071] x(t+1)i=x(t)i+α*L(λ), i=1, 2,…,n.(3)

[0072] where x(t)i represents the position of the ith bird nest in the tth generation, * is point-to-point multiplication, α represents the step size control, L(λ) is the Levy random search path, and (1pa, then x(t+1)i is randomly changed, otherwise it remains unchanged.

[0073] Finally, keep a group of bird nest positions y(t+1)i with better test values, and still record y(t+1)i as x(t+1)i.

[0074] It should be noted that the method based on the present invention has been applied in typical pilot projects of Heilongjiang Power Grid. With the help of the post-project evaluation work of the State Grid, the optimal investment strategy of the completed power grid project under multiple interference factors was retrospectively calculated for the benefits, and the deviation between the benefit calculation value and the actual value was within 5%. By using the reverse calculation of the project investment amount, the comprehensive project benefits were improved to varying degrees compared with the actual value, providing a reference for the dynamic regulation of investment in Heilongjiang Power Grid infrastructure projects.

[0075] In summary, the dynamic control method of power grid project investment under multiple interference factors of the present invention takes into account the internal and external interference factors under the background of the new power system, and quantifies the influence of various interference factors of different types of projects in different regions on the investment of power grid projects according to the expected goals of project construction. The economic benefits (indicators such as internal rate of return), operating benefits (indicators such as maximum load rate and average load rate), and social benefits (disaster prevention and mitigation effects, facing extreme weather, policy satisfaction, etc.) of power grid projects are fully calculated, and a strong reference basis is provided for project investment demonstration. The method of the present invention has an important reference and enlightenment role in future engineering construction. The relevant theoretical research results can enhance the control of investment decision makers on the influencing factors of the construction process of power grid infrastructure projects, strengthen the expected investment risk management capabilities, and realize the intelligent evaluation of power grid infrastructure project decision-making beforehand, dynamic control during project construction, and full coverage management process of project completion after the completion of the project, and effectively improve the level of precise investment management. Using the method of the present invention, the investment benefits of power grid projects can be improved, ineffective investment and inefficient investment can be avoided, and energy consumption changes and technological changes can be effectively supported, and the transformation and upgrading of my country's energy system can be promoted, which has a broad market prospect.

[0076] In the power grid project investment dynamic control platform under multiple interference factors of the present invention, the mapping and correlation of power grid project benefits based on historical interference factors are realized, and the benefits of power grid projects currently under construction or to be constructed in the future are predicted. The promotion of the power grid project investment dynamic control platform under multiple interference factors of the present invention can bring significant direct benefits to the State Grid Corporation.

[0077] The series of detailed descriptions listed above are only specific descriptions of feasible implementation methods of the present invention. They are not intended to limit the scope of protection of the present invention. Any equivalent implementation methods or changes that do not deviate from the technical spirit of the present invention should be included in the scope of protection of the present invention.

[0078] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A method for dynamic control of power grid project investment under multiple interference factors, characterized in that: The steps include: S1, based on the natural, economic, social, supply and demand environment and the current status of the power grid development in the region where the power grid is located, construct a set of internal and external interference factors of the power grid investment benefits; the set of internal and external interference factors is composed of multiple interference factor indicators; S2, based on the set of internal and external interference factors established in step S1, building a power grid investment benefit model considering multiple interference factors; S3, based on the power grid investment benefit model constructed in step S2, the optimal investment strategy is obtained, and the investment amount in the basic parameters of the power grid project is reversely calculated according to the expected benefits of the power grid project input by the investment decision maker, so as to realize dynamic regulation of power grid project investment under multiple interference factors.

2. The method for dynamic control of power grid project investment under multiple interference factors according to claim 1 is characterized in that: In step S1, the set of internal and external interference factors includes external factors and internal factors; the external factors include: natural environment indicators, economic and social environment indicators and new power system policy indicators; the internal factors include: power supply reliability indicators, power supply quality indicators, power supply capacity indicators, grid structure indicators, safety and reliability indicators, operation efficiency indicators and disaster prevention and mitigation level indicators.

3. The method for dynamic control of power grid project investment under multiple interference factors according to claim 2 is characterized in that: The natural environment indicators include regional topographical features and unfavorable geological conditions; The social environment indicators include: regional GDP growth rate, per capita disposable income, social public opinion environment and load electricity consumption growth rate; the new power system policy indicators include: proportion of clean energy installed capacity, clean energy investment and contribution of clean energy power generation.

4. The method for dynamic control of power grid project investment under multiple interference factors according to claim 3 is characterized in that: The power supply reliability index includes the power supply reliability rate and the average power outage time for users; the power supply quality index is the comprehensive voltage qualification rate; the power supply capacity index includes the transformer capacity load ratio and the average distribution transformer capacity per household; the grid structure index includes the N-1 pass rate and the line connection rate; the safety reliability index includes the unplanned outage time and the planned outage time; The operating efficiency indicators include the maximum load rate of equipment with the same voltage level in the region and the average load rate of equipment with the same voltage level in the region; the disaster prevention and mitigation level indicators include: the proportion of old equipment, the proportion of heavily overloaded equipment, the proportion of single-power substations and the proportion of single radial lines.

5. The method for dynamic control of power grid project investment under multiple interference factors according to any one of claims 1 to 4, characterized in that: The method of step S2 for building a power grid investment benefit model considering multiple interference factors based on the set of internal and external interference factors established in step S1 is as follows: S21, calculating the similarity between the interference factor indicators, performing a preliminary correlation analysis on the power grid project investment benefit indicators, and using the K-means clustering method to perform classification analysis on the interference factor indicators; establishing the correlation and mapping relationship between the interference factor indicators and the power grid project benefits; S22, screening effective influencing factors from the interference factor indicators by improving the clustering algorithm; S23, using the path analysis method and the Delphi method, from the influencing factors, the paths and impact benefits between the key interference factor indicators and the investment benefits are explored, and the direct effect, indirect effect and comprehensive effect values ​​between the independent variables and other independent variables and between the dependent variables are obtained, forming the power grid investment benefit model under multiple interference factors that comprehensively considers the interference factors of various dimensions.

6. The method for dynamic control of power grid project investment under multiple interference factors according to claim 5 is characterized in that: In step S21, the investment benefit indicators of the power grid project include: operating benefit indicators, financial benefit indicators and social benefit indicators; the operating benefit indicators include: project maximum load rate, project average load rate and unit investment increase in electricity sales; the financial benefit indicators include: internal rate of return, financial net present value, financial payback period, debt repayment reserve ratio and interest reserve ratio; the social benefit indicators include: driving local employment, GDP support capacity, policy satisfaction, service trips and passenger volume and transmission of new energy grid-connected electricity.

7. The method for dynamic control of power grid project investment under multiple interference factors according to claim 6 is characterized in that: In step S22, the implementation process of the improved clustering algorithm is as follows: for a clustered data set D=(D1, D2, ..., D n ), where D i =(D i，1 ,D i，2 ,...,D i，m ); First, based on the centrality of each object in the clustered set, the first cluster center is selected; then, based on this, the number of clusters and the initial cluster center are determined based on the Canopy algorithm; finally, the initial clustering result is substituted into the median-based K-means algorithm to perform clustering operations and obtain the final clustering result.

8. The method for dynamic control of power grid project investment under multiple interference factors according to claim 7 is characterized in that: In step S3, in order to obtain the optimal investment strategy, the following steps are also included: constructing a multi-attribute evaluation index system for investment projects, considering the role of interference factors of various types of projects, and based on the fuzzy prospect gray target theory, conducting comprehensive benefit calculations on the evaluation projects from three aspects: power grid operation benefits, economic benefits, and social benefits.

9. A power grid project investment dynamic control platform under multiple interference factors using the power grid project investment dynamic control method under multiple interference factors as described in any one of claims 1 to 9, characterized in that: The power grid project investment dynamic control platform under multiple interference factors takes interference factor indicators, basic parameters for evaluating power grid projects, and expected benefits of decision makers as inputs, and takes proposed project benefit parameters and power grid investment control values ​​as outputs.

10. The power grid project investment dynamic control platform under multiple interference factors according to claim 9, characterized in that: The dynamic control platform for power grid project investment under multiple interference factors uses the cuckoo algorithm to optimize the matching of the prediction results of the neural network algorithm; the dynamic control platform for power grid project investment under multiple interference factors also uses the GA-BP algorithm to establish a sample learning library with given inputs and outputs as learning objects.

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