An intelligent terminal primary-secondary collaborative investment configuration method, system, device and medium

Abstract

The application discloses a kind of intelligent terminal primary-secondary collaborative investment configuration method, system, equipment and medium, method includes: obtaining multiple types distributed resource data and power grid operation data;Based on the multiple types distributed resource data and power grid operation data, investment necessity evaluation model is constructed, and the investment necessity evaluation model is judged;According to the judgment result, intelligent terminal configuration optimization model is constructed, and configuration scheme is obtained;Based on the configuration scheme, multiple types resource aggregation and control model is established, and the adjustable potential of resource cluster is obtained by excavation;According to the adjustable potential of the resource cluster, primary-secondary collaborative operation optimization model is constructed, and the operation strategy of resource cluster is optimized, and the optimized operation scheme is obtained;The optimized operation scheme is optimized using multi-criteria decision method, and the optimal configuration and operation scheme are obtained, with significant engineering application prospect.

Description

Technical Field

[0001] This invention relates to the field of smart distribution network planning and operation technology, and in particular to a method, system, equipment and medium for primary and secondary collaborative investment configuration of smart terminals. Background Technology

[0002] With the widespread integration of distributed photovoltaic, energy storage, electric vehicles, and other resources, the operational complexity of power distribution networks has increased significantly. As key devices for resource aggregation and coordinated control, the configuration strategies of smart terminals directly impact the system's economy and reliability. Existing configuration methods often focus on single resources or single objectives, lacking comprehensive optimization across the entire process from investment necessity assessment and resource aggregation discovery to primary and secondary coordinated operation, making it difficult to adapt to the needs of multi-type resource clusters participating in network construction.

[0003] To this end, this invention proposes a smart terminal primary and secondary collaborative investment allocation strategy that supports the aggregation of multiple types of source loads. Through multi-step modeling and optimization, it realizes a data-driven scientific investment allocation strategy for smart terminal primary and secondary collaborative operations. Summary of the Invention

[0004] In view of the above-mentioned existing problems, the present invention provides a method, system, device and medium for primary and secondary collaborative investment configuration of smart terminals, so as to solve the problem that the configuration methods mostly focus on a single resource or a single goal, lack the whole process optimization from investment necessity judgment, resource aggregation mining to primary and secondary collaborative operation, and are difficult to adapt to the needs of multi-type resource clusters participating in network construction.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for configuring primary and secondary collaborative investments in smart terminals, comprising: Acquire various types of distributed resource data and power grid operation data; An investment necessity assessment model is constructed based on the aforementioned multi-type distributed resource data and power grid operation data, and the investment necessity assessment model is then evaluated. Based on the judgment results, a smart terminal configuration optimization model is constructed to obtain a configuration scheme; Based on the configuration scheme, a multi-type resource aggregation and control model is established to explore the adjustable potential of resource clusters. Based on the adjustable potential of the resource cluster, a primary and secondary collaborative operation optimization model is constructed to optimize the operation strategy of the resource cluster and obtain the optimized operation scheme. The optimized operation scheme is further refined using a multi-criteria decision-making method to obtain the optimal configuration and operation scheme.

[0006] As a preferred embodiment of the intelligent terminal primary and secondary collaborative investment allocation method described in this invention, the construction of the investment necessity assessment model includes: Calculate total investment revenue and annualized operating benefits based on multi-type distributed resource data and power grid operation data; Calculate the net present value based on total investment income and annualized operating efficiency; Based on the annualized operating efficiency and annualized operating input, the annualized net benefit is obtained. The net benefits of each year from the first year of operation to the kth year are accumulated, and the minimum year in which the cumulative net benefit is greater than or equal to the initial total investment is obtained, thus obtaining the investment payback period. An investment necessity assessment model is derived based on net present value and payback period.

[0007] The beneficial effects of this preferred technical solution are that it accurately assesses the necessity of investment, avoids ineffective investment, and ensures that decision-making is scientific and reasonable.

[0008] As a preferred embodiment of the intelligent terminal primary and secondary collaborative investment allocation method described in this invention, the determination of the investment necessity assessment model includes: Set a first threshold; If the net present value is greater than zero and the investment payback period is less than the first threshold, it is determined that it is worthwhile to configure a smart terminal; otherwise, it is determined that configuration is not recommended.

[0009] As a preferred embodiment of the intelligent terminal primary and secondary collaborative investment configuration method described in this invention, the intelligent terminal configuration optimization model includes: Based on the judgment results, a set of optional nodes for intelligent terminal configuration is obtained, and flag variables for configuring each type of intelligent terminal on each node of the distribution network are defined. If the node has the corresponding smart terminal installed, the flag variable is the first value; if the node does not have the corresponding smart terminal installed, the flag variable is the second value. The first objective function is constructed with the goal of minimizing configuration revenue and installation difficulty; To maximize resource coverage, a second objective function is constructed. The constraints must include at least one type of terminal per node, and the number of terminals configured for each type must be less than or equal to the maximum configurable number.

[0010] The beneficial effect of this preferred technical solution is that it optimizes the terminal layout and configuration, balancing economic investment and resource coverage efficiency.

[0011] As a preferred embodiment of the intelligent terminal primary and secondary collaborative investment allocation method described in this invention, the establishment of a multi-type resource aggregation and control model includes: Based on the configuration scheme, the feature vector of the distributed resources of each node is obtained; Distributed resources are classified using clustering algorithms based on feature vectors to obtain resource clusters of different categories; The resources of each type of resource cluster are equivalently aggregated to form a virtual power plant model, and the aggregated power parameters of the virtual power plant are calculated to obtain a multi-type resource aggregation and control model.

[0012] The beneficial effect of this preferred technical solution is that it enhances the control and coordination capabilities of virtual power plants by aggregating distributed resource clusters.

[0013] As a preferred embodiment of the intelligent terminal primary and secondary collaborative investment configuration method described in this invention, the construction of the primary and secondary collaborative operation optimization model includes: objective function and constraints; To minimize the running revenue, a third objective function is constructed; To maximize the network support capability, a fourth objective function is constructed; The constraints must include at least power balance constraints, node voltage constraints, and line current constraints. The power balance constraint is that at any given time, the sum of the power purchased by the grid and the active power of all virtual power plants equals the sum of the load power and the grid loss power at the corresponding time. The node voltage constraint is that the voltage amplitude of any node at any time is between the lower voltage limit and the upper voltage limit; The line current constraint is that the current amplitude of any line at any time is less than or equal to the maximum allowable current of the corresponding line.

[0014] As a preferred embodiment of the intelligent terminal primary and secondary collaborative investment configuration method described in this invention, the optimization of the optimized operation scheme includes: An evaluation matrix is ​​constructed for the optimized operation plan; The objective weights of the evaluation indicators in each evaluation matrix are calculated using the entropy weight method. Based on objective weights, a fuzzy comprehensive evaluation is performed on each optimized operating scheme to obtain a comprehensive evaluation vector; The comprehensive evaluation vector is converted into a scalar score, and the optimal configuration and operation scheme are selected based on the scalar score.

[0015] Secondly, the present invention provides a smart terminal primary and secondary collaborative investment allocation system, comprising: The data acquisition module is used to acquire various types of distributed resource data and power grid operation data. The module for constructing and judging the investment necessity assessment model is used to construct an investment necessity assessment model based on the multi-type distributed resource data and power grid operation data, and to judge the investment necessity assessment model. The intelligent terminal configuration optimization model construction module is used to construct an intelligent terminal configuration optimization model based on the judgment results and obtain a configuration scheme. A multi-type resource aggregation and regulation model is constructed to establish a multi-type resource aggregation and regulation model based on the configuration scheme, and to explore the adjustable potential of the resource cluster. The primary and secondary collaborative operation optimization model construction module is used to construct a primary and secondary collaborative operation optimization model based on the adjustable potential of the resource cluster, optimize the operation strategy of the resource cluster, and obtain an optimized operation scheme. The scheme optimization module is used to optimize the optimized operation scheme using a multi-criteria decision-making method to obtain the optimal configuration and operation scheme.

[0016] Thirdly, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the aforementioned intelligent terminal primary and secondary collaborative investment configuration method.

[0017] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the aforementioned method for configuring primary and secondary collaborative investments in a smart terminal.

[0018] Compared with existing technologies, the beneficial effects of this invention are as follows: Starting with an investment economics assessment, this invention first determines the necessity of configuration, avoiding resource waste; then, it comprehensively considers terminal configuration revenue, the aggregation potential of multiple resource types, and the collaborative operation benefits of primary and secondary systems, achieving full-chain optimization from planning to operation. Compared with traditional configuration methods that focus on a single link or a single type of resource, this invention achieves overall optimal decision-making under investment and operational constraints, resulting in a more scientific and economical solution. This invention incorporates multi-dimensional innovations in the modeling and decision-making stages, improving the accuracy and practicality of the method. In the modeling stage, a series of mathematical models are constructed, particularly through virtual power plant equivalence and clustering models to deeply explore the network construction potential of distributed resource clusters; in the decision-making stage, a method combining entropy weighting and fuzzy comprehensive evaluation is adopted to perform objective weight analysis and refined ranking from the Pareto optimal solution set, achieving an organic combination of quantitative optimization results and qualitative engineering experience. This invention can flexibly adapt to the needs of different resource compositions and network structures, exhibiting stronger adaptability and decision reliability compared to traditional experience-based decision-making or fixed-weight evaluation methods; the strategy proposed in this invention is based on engineering practice and possesses good operability and promotional value. The entire methodology is clear and the model is complete. Through the formalization and indexation of the entire process, the reproducibility and calculability of the technical solution are ensured. Key parameters can be flexibly adjusted according to the specific project's investment and financing policies, technical standards, and operating procedures, and are easily integrated into existing distribution network planning and operation decision support platforms. This invention provides a systematic solution for the deployment and coordinated control of intelligent terminals in distribution networks with a high proportion of distributed energy resources. It helps improve the system's aggregation and utilization of diverse resources, operational economy, and proactive support capabilities, and has significant engineering application prospects. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the overall process logic of a smart terminal primary and secondary collaborative investment configuration method provided in one embodiment of the present invention. Detailed Implementation

[0021] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0022] Example 1, referring to Figure 1 As an embodiment of the present invention, a method for configuring primary and secondary collaborative investments in smart terminals is provided, comprising: S100: Acquire various types of distributed resource data and power grid operation data; In this embodiment of the invention, the acquisition of various types of distributed resource data and power grid operation data includes the capacity, power curves, and adjustable range of distributed photovoltaic, energy storage, electric vehicles, flexible loads, and other resources; power grid topology, line parameters, and transformer parameters; historical and predicted load data and electricity price information; and the type, function, and revenue parameters of smart terminals.

[0023] S200: Construct an investment necessity assessment model based on multi-type distributed resource data and power grid operation data, and make a judgment on the investment necessity assessment model; S300: Based on the judgment results, construct a smart terminal configuration optimization model to obtain a configuration scheme; S400: Based on the configuration scheme, a multi-type resource aggregation and control model is established to explore the adjustable potential of resource clusters; S500: Based on the adjustable potential of the resource cluster, a primary and secondary collaborative operation optimization model is constructed to optimize the operation strategy of the resource cluster and obtain the optimized operation scheme. S600: Utilizes a multi-criteria decision-making method to select the optimal operating scheme and obtain the optimal configuration and operating scheme.

[0024] In one optional embodiment, the multi-criteria decision-making method can be the analytic hierarchy process (AHP), which establishes a hierarchical structure for scheme evaluation, determines the target layer, criterion layer, and scheme layer; compares each evaluation index pairwise to construct a judgment matrix; calculates the weight of each index and performs a consistency test; comprehensively scores each optimized scheme based on the index weights; and selects the scheme with the highest comprehensive score as the optimal configuration and operation scheme. In another optional embodiment, the multi-criteria decision-making method can also be the superior-inferior solution distance method, which constructs an evaluation index matrix for each candidate solution; standardizes the evaluation index; determines the optimal solution and the worst solution; calculates the distance from each candidate solution to the optimal solution and the worst solution; calculates the comprehensive closeness of each solution, and selects the solution with the largest closeness as the optimal solution. In this embodiment of the invention, the multi-criteria decision-making method includes a multi-attribute decision-making method; Specifically, a matrix of candidate solutions and evaluation indicators is constructed; the objective weights of each indicator are calculated using the entropy weight method; the solutions are scored using fuzzy comprehensive evaluation; and the solution with the highest comprehensive score is selected as the optimal solution.

[0025] It should be noted that this invention forms a complete technical closed loop through data collection, necessity assessment of investment, terminal optimization configuration, resource aggregation and control, primary and secondary collaborative operation, and multi-criteria decision optimization. First, the necessity of investment is determined from the source to avoid ineffective investment; then, the terminal layout is optimized and distributed resources are aggregated to improve the cluster's adjustability; at the same time, primary and secondary collaborative optimization is achieved to balance system benefits and operational safety, and finally, the optimal solution that is economical, efficient, and feasible is selected, which significantly improves the resource utilization level and control stability of the distribution network.

[0026] In this embodiment of the invention, step S200 includes the following sub-steps A1-A4; In A1: Calculate the total investment revenue and annualized operating benefits based on multi-type distributed resource data and power grid operation data; In A2: Calculate the net present value based on total investment income and annualized operating efficiency; In A3: Based on the annualized operating efficiency and annualized operating input, the annualized net benefit is obtained. The net benefits of each year from the first year of operation to the kth year are accumulated, so that the cumulative net benefit meets the minimum year in which the first year is greater than or equal to the initial total investment, and the investment payback period is obtained. In A4: The investment necessity assessment model is obtained based on net present value and investment payback period.

[0027] In one optional embodiment, the payback period can be calculated using the static payback period method, which involves calculating the initial total investment and the average annual net benefit; dividing the initial total investment by the average annual net benefit; and obtaining the static payback period. In another optional embodiment, the payback period can also be calculated using the dynamic payback period method, which involves discounting the net benefits of each year to their present value using a preset discount rate; accumulating the discounted net benefits year by year; and finding the year in which the cumulative present value first covers the initial investment, which is the dynamic payback period. In this embodiment of the invention, the investment payback period is calculated using the dynamic cumulative net benefit method; Specifically, based on the obtained revenue data, the total investment income is calculated as follows: in, Total investment income, Revenue from the purchase of smart terminal equipment For installation and commissioning revenue, Revenue from communication and secondary line construction; Based on the obtained benefit-related data, the annualized operating benefit is calculated as follows: in, For the total annual benefits, For energy-saving benefits, In order to participate in the benefits of power grid ancillary services, For other auxiliary benefits.

[0028] Based on total investment income and annualized operating efficiency, the net present value is calculated as follows: in, Net present value, For the evaluation period, This is the discount rate, which can range from 6% to 10%. For the first Annual operation and maintenance revenue.

[0029] The payback period is calculated as follows: in, This is the investment recovery period.

[0030] It should be noted that the present invention uses the dynamic cumulative net benefit method to calculate the net present value and investment payback period, accurately assesses the necessity of investment, avoids ineffective investment from the source, and improves the economy and decision reliability of smart terminal configuration.

[0031] In this embodiment of the invention, after completing steps A1-A4, step S200 also includes steps A5-A6; In A5: Set the first threshold; In A6: If the net present value is greater than zero and the investment payback period is less than the first threshold, it is determined that it is worthwhile to configure a smart terminal; otherwise, it is determined that configuration is not recommended.

[0032] In this embodiment of the invention, the first threshold can be 5 to 8 years; If the net present value (NPV) is greater than 0 and the payback period (PP) is less than the first threshold, then it is considered a worthwhile investment, i.e., to configure smart terminals.

[0033] In this embodiment of the invention, step S300 includes the following sub-steps B1-B5; In B1: Based on the judgment result, obtain the set of optional nodes for the configuration of intelligent terminals, and define the flag variables for configuring each type of intelligent terminal on each node of the distribution network; In B2: If the node has the corresponding smart terminal installed, the flag variable is the first value; if the node does not have the corresponding smart terminal installed, the flag variable is the second value. In B3: Construct the first objective function with the minimum overall allocation revenue; In B4: To maximize resource coverage, a second objective function is constructed; In B5: Set constraints that include at least one type of terminal per node, and the number of terminals configured for each type is less than or equal to the maximum configurable number limit.

[0034] In one optional embodiment, the flag variable for configuring each type of smart terminal at each node of the distribution network can be defined by the continuous capacity ratio method. For each distribution network node, a terminal configuration capacity ratio variable is defined. The variable takes a continuous value between 0 and 1, which represents the terminal configuration intensity of the node. When the ratio is greater than a set threshold, it is determined that the terminal of that type is configured. In another optional embodiment, the flag variable for configuring each type of smart terminal at each node of the distribution network can also be defined as a type number mapping method, assigning a unique type number to each type of smart terminal; defining a terminal type selection variable for each node, with the value being the corresponding number; a fixed value for the variable represents no configuration, and other numbers represent the corresponding terminal type; In this embodiment of the invention, the flag variables for configuring various types of smart terminals at each node of the distribution network are defined as follows: in, For a set of nodes, This is a set of terminal types.

[0035] The primary objective function, which minimizes configuration revenue and installation difficulty, is expressed as: in, for Terminal-like revenue, For nodes The installation difficulty level The installation difficulty weighting coefficient can range from 0.2 to 0.4.

[0036] The second objective function, which takes maximizing resource coverage, is expressed as: in, for The coverage coefficient of resources for terminal-like devices. For nodes Resource capacity.

[0037] The constraint condition includes that each node can be configured with at most one type of terminal, represented as follows: The maximum number of terminals configured for each type is expressed as follows: in, for The maximum number of terminal types that can be configured.

[0038] It should be noted that this invention achieves the minimum investment in terminal configuration and the maximum resource coverage through dual-objective optimization and constraint control, while taking into account installation difficulty. Under constraints, it obtains the optimal layout and improves the rationality of distribution network terminal configuration and engineering practicality.

[0039] In this embodiment of the invention, step S400 includes the following sub-steps C1-C3; In C1: Based on the configuration scheme, the feature vector of the distributed resources of each node is obtained; In C2: Distributed resources are classified using clustering algorithms based on feature vectors to obtain resource clusters of different categories; In C3: the resources of each type of resource cluster are equivalently aggregated to form a virtual power plant model, and the aggregated power parameters of the virtual power plant are calculated to obtain a multi-type resource aggregation and control model.

[0040] In one optional embodiment, the clustering algorithm can be a hierarchical clustering method, which treats each distributed resource as an initial independent class; calculates the distance between classes based on the similarity of feature vectors; merges classes step by step according to the principle of closest distance; and stops at a preset number of clusters or a distance threshold to obtain the resource classification result. In another alternative embodiment, the clustering algorithm can also be density clustering, which takes the resource feature vector as input, sets the neighborhood radius and the minimum number of points; starts from any unvisited resource, searches for samples with high density to form clusters; marks low-density samples as discrete points, and finally outputs multiple resource clusters; In this embodiment of the invention, the clustering algorithm includes the K-means clustering method; Specifically, the K-means clustering method is used to classify resources as follows: in, The number of clusters, For the first Class resource collection, For resources The feature vectors include power curves, adjustable range, response time, etc. For the first Cluster centers of classes.

[0041] Each type of resource is aggregated equivalently to form a virtual power plant model. The active power of the virtual power plant is represented as: in, For the first A virtual power plant at any time active power, For resources At any moment The active power.

[0042] The reactive power of a virtual power plant is represented as follows: in, For the first A virtual power plant at any time reactive power, For resources At any moment The reactive power.

[0043] The adjustable power range of a virtual power plant must meet the following requirements: in, For virtual power plants at any time Adjustable power These represent the minimum and maximum adjustable power, respectively.

[0044] It should be noted that this invention relies on resource feature vectors to complete clustering and partitioning, performs equivalent aggregation of active and reactive power for resources of the same type, limits the adjustable power range of virtual power plants, and improves the control accuracy of distributed resource clusters and the coordination capability of distribution network operation.

[0045] In this embodiment of the invention, step S500 includes the following sub-steps D1-D6; In D1: Construct a third objective function to minimize the running revenue; In D2: To maximize the network support capability, a fourth objective function is constructed; In D3: the constraints must include at least power balance constraints, node voltage constraints, and line current constraints; In D4: The power balance constraint is that at any given time, the sum of the power purchased by the grid and the active power of all virtual power plants equals the sum of the load power and the grid loss power at the corresponding time. In D5: The node voltage constraint is that the voltage amplitude of any node at any time is between the lower voltage limit and the upper voltage limit; In D6: The line current constraint is that the current amplitude of any line at any time is less than or equal to the maximum allowable current of the corresponding line.

[0046] In this embodiment of the invention, minimizing operating revenue is taken as the third objective function, expressed as: in, For a moment Electricity purchase revenue, For a moment Network loss revenue, For a moment Equipment depreciation revenue, The scheduling period is [number].

[0047] The objective function, which aims to maximize the network support capacity, is expressed as: in, This refers to the voltage support capability indicator. As an indicator of frequency support capability, These are weighting coefficients, which can take values ​​of 0.6 and 0.4 respectively.

[0048] The power balance constraint that must be satisfied during operation is expressed as follows: in, Power purchased for the power grid For load power, This represents the power loss due to network loss.

[0049] The node voltage constraint is expressed as: in, For nodes At any moment voltage, , These are the lower and upper voltage limits, respectively, and can be 0.93 pu and 1.07 pu.

[0050] The line current constraint is expressed as: in, For the line At any moment The current, This is the maximum allowable current for the line.

[0051] It should be noted that the present invention constructs a multi-objective optimization model that balances minimum operational input with optimal network support capability. Through various safety constraints, it strictly controls voltage and line power flow to ensure economic operation and frequency and voltage stability of the distribution network.

[0052] In this embodiment of the invention, step S600 includes the following sub-steps E1-E4; In E1: Construct an evaluation matrix for the optimized operating plan; In E2: The objective weights of the evaluation indicators in each evaluation matrix are calculated using the entropy weight method; In E3: Fuzzy comprehensive evaluation is performed on each optimized operating scheme based on objective weights to obtain a comprehensive evaluation vector; In E4: The comprehensive evaluation vector is converted into a scalar score, and the optimal configuration and operation scheme are selected based on the scalar score.

[0053] In this embodiment of the invention, the evaluation matrix for constructing all candidate optimized running schemes is represented as follows: in, For the first The first scheme is in the The value under each indicator For the total number of schemes, This represents the total number of evaluation indicators.

[0054] The objective weights of each indicator are calculated using the entropy weight method. The calculation of the... The entropy value of each indicator is expressed as: in, For the first The entropy value of each indicator, This is the standardized ratio.

[0055] The weights calculated based on entropy are expressed as follows: in, For the first The weight of each indicator.

[0056] The fuzzy comprehensive evaluation of each scheme is expressed as follows: in, For the first The comprehensive evaluation vector of each scheme, For fuzzy evaluation matrix, For fuzzy synthesis operators, a weighted average type operator is used.

[0057] The comprehensive evaluation vector is transformed into a scalar score representation as follows: in, For the first The overall score of each option For the first The rating scale is calculated as follows: Excellent = 1, Good = 0.8, Average = 0.6, Poor = 0.4.

[0058] Choosing the solution with the highest score as the optimal solution is expressed as follows: in, The optimal solution number is assigned.

[0059] It should be noted that this invention constructs a scheme evaluation matrix, combines objective weighting using the entropy weight method with fuzzy comprehensive evaluation, avoids subjective human interference, accurately quantifies the advantages and disadvantages of each operating scheme, and quickly selects the optimal coordinated operation scheme for the distribution network.

[0060] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0061] Example 2: The above is an illustrative scheme of a primary and secondary collaborative investment configuration method for intelligent terminals according to this embodiment. It should be noted that the technical solution of this primary and secondary collaborative investment configuration system for intelligent terminals and the technical solution of the aforementioned primary and secondary collaborative investment configuration method for intelligent terminals belong to the same concept. Details not described in detail in the technical solution of the primary and secondary collaborative investment configuration system for intelligent terminals in this embodiment can be found in the description of the technical solution of the aforementioned primary and secondary collaborative investment configuration method for intelligent terminals.

[0062] This embodiment of a smart terminal primary and secondary collaborative investment allocation system includes: The data acquisition module is used to acquire various types of distributed resource data and power grid operation data. The module for constructing and judging the investment necessity assessment model is used to construct an investment necessity assessment model based on the multi-type distributed resource data and power grid operation data, and to judge the investment necessity assessment model. The intelligent terminal configuration optimization model construction module is used to construct an intelligent terminal configuration optimization model based on the judgment results and obtain a configuration scheme. A multi-type resource aggregation and regulation model is constructed to establish a multi-type resource aggregation and regulation model based on the configuration scheme, and to explore the adjustable potential of the resource cluster. The primary and secondary collaborative operation optimization model construction module is used to construct a primary and secondary collaborative operation optimization model based on the adjustable potential of the resource cluster, optimize the operation strategy of the resource cluster, and obtain an optimized operation scheme. The scheme optimization module is used to optimize the optimized operation scheme using a multi-criteria decision-making method to obtain the optimal configuration and operation scheme.

[0063] This embodiment also provides a computer device applicable to a method for configuring primary and secondary collaborative investments in a smart terminal, including: The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement a method for configuring primary and secondary collaborative investments in a smart terminal, as described in the above embodiments.

[0064] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a method for configuring primary and secondary collaborative investments in a smart terminal as proposed in the above embodiments.

[0065] The storage medium proposed in this embodiment and the method for implementing a primary and secondary collaborative investment configuration of a smart terminal proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0066] Based on the above description of the implementation methods, those skilled in the art will clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computing device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

Claims

1. A method for primary and secondary collaborative investment allocation in intelligent terminals, characterized in that, include: Acquire various types of distributed resource data and power grid operation data; An investment necessity assessment model is constructed based on the aforementioned multi-type distributed resource data and power grid operation data, and the investment necessity assessment model is then evaluated. Based on the judgment results, a smart terminal configuration optimization model is constructed to obtain a configuration scheme; Based on the configuration scheme, a multi-type resource aggregation and control model is established to explore the adjustable potential of resource clusters. Based on the adjustable potential of the resource cluster, a primary and secondary collaborative operation optimization model is constructed to optimize the operation strategy of the resource cluster and obtain the optimized operation scheme. The optimized operation scheme is further refined using a multi-criteria decision-making method to obtain the optimal configuration and operation scheme.

2. The method for configuring primary and secondary collaborative investments in intelligent terminals as described in claim 1, characterized in that, The construction of an investment necessity assessment model includes: Calculate total investment revenue and annualized operating benefits based on multi-type distributed resource data and power grid operation data; Calculate the net present value based on total investment income and annualized operating efficiency; Based on the annualized operating efficiency and annualized operating input, the annualized net benefit is obtained. The net benefits of each year from the first year of operation to the kth year are accumulated, and the minimum year in which the cumulative net benefit is greater than or equal to the initial total investment is obtained, thus obtaining the investment payback period. An investment necessity assessment model is derived based on net present value and payback period.

3. The method for primary and secondary collaborative investment allocation of intelligent terminals as described in claim 2, characterized in that, The assessment of the necessity of the investment includes: Set a first threshold; If the net present value is greater than zero and the investment payback period is less than the first threshold, it is determined that it is worthwhile to configure a smart terminal; otherwise, it is determined that configuration is not recommended.

4. The method for primary and secondary collaborative investment allocation of intelligent terminals as described in claim 3, characterized in that, The construction of a smart terminal configuration optimization model includes: Based on the judgment results, a set of optional nodes for intelligent terminal configuration is obtained, and flag variables for configuring each type of intelligent terminal on each node of the distribution network are defined. If the node has the corresponding smart terminal installed, the flag variable is the first value; if the node does not have the corresponding smart terminal installed, the flag variable is the second value. The first objective function is constructed with the goal of minimizing configuration revenue and installation difficulty; To maximize resource coverage, a second objective function is constructed. The constraints must include at least one type of terminal per node, and the number of terminals configured for each type must be less than or equal to the maximum configurable number.

5. The method for primary and secondary collaborative investment allocation of intelligent terminals as described in claim 4, characterized in that, Establishing a multi-type resource aggregation and regulation model includes: Based on the configuration scheme, the feature vector of the distributed resources of each node is obtained; Distributed resources are classified using clustering algorithms based on feature vectors to obtain resource clusters of different categories; The resources of each type of resource cluster are equivalently aggregated to form a virtual power plant model, and the aggregated power parameters of the virtual power plant are calculated to obtain a multi-type resource aggregation and control model.

6. The method for configuring primary and secondary collaborative investments in intelligent terminals as described in claim 5, characterized in that, The construction of a primary and secondary collaborative optimization model includes: objective function and constraints; To minimize the running revenue, a third objective function is constructed; To maximize the network support capability, a fourth objective function is constructed; The constraints must include at least power balance constraints, node voltage constraints, and line current constraints. The power balance constraint is that at any given time, the sum of the power purchased by the grid and the active power of all virtual power plants equals the sum of the load power and the grid loss power at the corresponding time. The node voltage constraint is that the voltage amplitude of any node at any time is between the lower voltage limit and the upper voltage limit; The line current constraint is that the current amplitude of any line at any time is less than or equal to the maximum allowable current of the corresponding line.

7. A method for configuring primary and secondary collaborative investments in intelligent terminals as described in claim 1 or 6, characterized in that, The optimization of the operating scheme includes: An evaluation matrix is ​​constructed for the optimized operation plan; The objective weights of the evaluation indicators in each evaluation matrix are calculated using the entropy weight method. Based on objective weights, a fuzzy comprehensive evaluation is performed on each optimized operating scheme to obtain a comprehensive evaluation vector; The comprehensive evaluation vector is converted into a scalar score, and the optimal configuration and operation scheme are selected based on the scalar score.

8. A smart terminal primary and secondary collaborative investment configuration system, employing the smart terminal primary and secondary collaborative investment configuration method as described in any one of claims 1-7, characterized in that, include: The data acquisition module is used to acquire various types of distributed resource data and power grid operation data. The module for constructing and judging the investment necessity assessment model is used to construct an investment necessity assessment model based on the multi-type distributed resource data and power grid operation data, and to judge the investment necessity assessment model. The intelligent terminal configuration optimization model construction module is used to construct an intelligent terminal configuration optimization model based on the judgment results and obtain a configuration scheme. A multi-type resource aggregation and regulation model is constructed to establish a multi-type resource aggregation and regulation model based on the configuration scheme, and to explore the adjustable potential of the resource cluster. The primary and secondary collaborative operation optimization model construction module is used to construct a primary and secondary collaborative operation optimization model based on the adjustable potential of the resource cluster, optimize the operation strategy of the resource cluster, and obtain an optimized operation scheme. The scheme optimization module is used to optimize the optimized operation scheme using a multi-criteria decision-making method to obtain the optimal configuration and operation scheme.

9. A computer device, characterized in that, include: A memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the intelligent terminal primary and secondary collaborative investment configuration method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the steps of a smart terminal primary and secondary collaborative investment configuration method according to any one of claims 1 to 7.

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