Power grid equipment investment comprehensive benefit evaluation and optimization method, system and medium

Abstract

The invention provides a power grid equipment investment comprehensive benefit evaluation and optimization method and system and a medium, and belongs to the technical field of power grid benefit evaluation and optimization. According to the method, the comprehensiveness of evaluation indexes is ensured by constructing a multi-dimensional index system, covering functions and economy and environment multiple dimensions, and meanwhile, the evaluation efficiency is improved by constructing a function contribution degree quantitative model, an investment and income allocation model, a function and cost coupling benefit model and a region and equipment collaborative optimization model. A coupling model between equipment operation characteristics and investment cost is established, an area and equipment collaborative optimization mechanism is introduced, quantitative evaluation, dynamic comparison and optimal configuration of power grid equipment investment benefits are realized, and optimization of decision demands is realized while comprehensiveness and stability of power grid equipment investment comprehensive benefit evaluation are improved.

Description

Technical Field

[0001] This invention belongs to the field of power grid benefit assessment and optimization technology, specifically relating to a method for comprehensive benefit assessment and optimization of power grid equipment investment. Background Technology

[0002] With the high proportion of renewable energy connected to the grid and the expansion of inter-provincial and inter-regional power transmission, power grid equipment investment has become multi-type and multi-level. Scientific comprehensive investment benefit assessment and optimization have become the core support for the planning of new power systems, directly affecting system security, economy and the capacity for renewable energy absorption.

[0003] Currently, when conducting comprehensive benefit assessments of power grid equipment investment, on the one hand, static economic indicators such as unit capacity investment cost and net present value are relied upon; on the other hand, power system simulation models are used to measure the contribution of equipment operation, which can reflect some of the system's impact. However, traditional assessment methods are relatively one-sided and lack stability, and their optimization methods are insufficient, making it difficult to meet the needs of refined decision-making in the power system. Summary of the Invention

[0004] The technical problem to be solved by this invention is how to optimize decision-making needs while improving the comprehensiveness and stability of the overall benefit assessment of power grid equipment investment. In view of the shortcomings of the prior art, this invention provides a method for the comprehensive benefit assessment and optimization of power grid equipment investment.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a method for comprehensive benefit assessment and optimization of power grid equipment investment, comprising: S1. Based on the historical operating data of the power grid equipment, filter the data parameters that characterize the functional, economic and environmental characteristics of the power grid equipment, obtain the functional, economic and environmental indicators of the power grid equipment, and construct a multi-dimensional indicator system. S2. Based on the multi-dimensional indicator system, a combination of the analytic hierarchy process and the entropy weight method is used for weighting, combined with the fuzzy comprehensive evaluation model, and a marginal contribution correction factor is introduced to construct a functional contribution quantification model to obtain the comprehensive functional value of the power grid equipment. S3. Divide the total investment into fixed costs and amortizable costs. Based on the comprehensive functional value, calculate the amortizable cost of a single power grid device. Allocate the total revenue of the power system to a single power grid device according to the functional contribution and economic return ratio. Establish the revenue and investment mapping relationship of the equipment layer and regional layer of the power system through a hierarchical matrix and construct an investment and revenue amortization model. S4. Integrate functional weights and economic weights, define a coupling benefit index, quantify the coupling benefit value of the power grid equipment based on the comprehensive functional value, and conduct benefit sensitivity analysis through partial derivative analysis to clarify the impact of weight adjustment on each type of power grid equipment. Construct a functional and cost coupling benefit model to calculate the comprehensive benefit index of the power system. S5. At the regional level, with the goal of maximizing the overall benefits of the region, an investment objective function and investment constraints are set. At the equipment level, with the goal of minimizing the risk of individual equipment, an operational objective function and operational constraints are set. A regional and equipment collaborative optimization model is constructed through the cooperation of the regional level and the equipment level. S6. Set the initial investment ratio, import the operation data of the power grid equipment, and based on the functional contribution quantification model, the investment and benefit sharing model, the function and cost coupling benefit model, and the regional and equipment collaborative optimization model, iterate alternately through the regional layer and the equipment layer until the rate of change of investment ratio and the rate of change of benefit are less than the convergence threshold, so as to output the optimal investment allocation, equipment operation plan and comprehensive benefit evaluation results.

[0006] Compared to existing technologies, the beneficial effects of the comprehensive benefit assessment and optimization method for power grid equipment investment of the present invention include: First, based on historical operating data of power grid equipment, data parameters characterizing the functional, economic, and environmental characteristics of the power grid equipment are screened to obtain functional, economic, and environmental dimension indicators of the power grid equipment, and a multi-dimensional indicator system is constructed. This provides comprehensive indicator data for subsequent assessment and optimization processes, effectively solving the problem of single assessment indicators and improving the comprehensiveness of the comprehensive benefit assessment of power grid equipment investment. For example, in the functional dimension, core indicators that reflect the impact of power grid equipment on the flexibility, power quality, and safe operation of the power system can be selected. For example, peak-shaving capacity, ramp-up response speed, and flexibility reserves are selected to ensure that the constructed model can accurately represent the dynamic operating characteristics of power grid equipment. In the economic dimension, typical indicators reflecting the full life-cycle costs and benefits of power grid equipment, such as unit investment cost, operation and maintenance cost, and unit rate of return, are selected to demonstrate the economic feasibility of investment decisions. In the environmental dimension, indicators such as emission reduction contribution, improvement in curtailment rate, and energy efficiency improvement are selected to measure the role of power grid construction in promoting carbon emission reduction and clean energy consumption. Secondly, based on a multi-dimensional indicator system, a combination of the analytic hierarchy process (AHP) and entropy weighting method is used for weighting, combined with a fuzzy comprehensive evaluation model, and a marginal contribution correction factor is introduced to construct a functional contribution metric. The model is used to obtain the comprehensive functional value of power grid equipment. Based on a multi-dimensional indicator system, in the process of quantifying the functional contribution of power grid equipment, it combines expert experience and data characteristics through weighted balancing, while incorporating marginal correction factors to quantify dynamic functional value. This addresses the problems in existing technologies where the functional contribution of power grid equipment is difficult to quantify, and where subjective or simplistic weighting leads to biased quantification results, neglecting the dynamic marginal contribution of power grid equipment. This effectively improves the accuracy of functional assessment, providing a core basis for subsequent cost allocation and revenue distribution, and enhancing the stability of the comprehensive benefit assessment of power grid equipment investment. Furthermore, the total investment is divided into fixed costs and amortizable costs, based on the comprehensive functional value... This method calculates the allocated cost of individual power grid equipment and distributes the overall revenue of the power system to individual equipment according to their functional contribution and economic return ratio. This achieves fair cost allocation based on functional contribution, effectively solving the problem of lack of functional orientation in the allocation of total investment and revenue, and providing parameters for subsequent coupling benefit calculations. Then, a hierarchical matrix is ​​used to establish the revenue and investment mapping relationship between the equipment layer and the regional layer of the power system, and an investment and revenue allocation model is constructed. This hierarchical matrix establishes a mapping between power grid equipment, regional revenue, and investment, avoiding local optima and achieving global optima in allocation. This improves the accuracy of subsequent coupling benefit calculations and ensures the stability of the comprehensive benefit assessment of power grid equipment investment.Then, by integrating functional and economic weights, a coupling benefit index is defined. Based on the comprehensive functional value, the coupling benefit value of power grid equipment is quantified, thus avoiding the problem of separating functional value from economic cost evaluation, effectively reflecting comprehensive benefits, and providing core parameters for subsequent collaborative optimization. Simultaneously, benefit sensitivity analysis is conducted through partial derivative analysis to clarify the impact of weight adjustments on various types of power grid equipment, thereby defining the optimization direction of weights and supporting differentiated decision-making. Subsequently, the comprehensive benefit index of the power system is calculated through the constructed functional and cost coupling benefit model to ensure the global optimum of the comprehensive benefit index. Next, at the regional level, the goal is to maximize regional comprehensive benefits, setting investment objective functions and investment constraints. At the equipment level, the goal is to minimize the risk of individual equipment, setting operational objective functions and operational constraints. Through the cooperation of the regional and equipment levels, a regional and equipment collaborative optimization model is constructed, forming a two-layer collaborative optimization model that maximizes regional benefits and minimizes power grid equipment risk, taking into account both global budget and local operational feasibility. This process provides objective functions and constraints for subsequent iterative optimization, clarifying the optimization boundaries. Finally, an initial investment ratio is set, and operational data of the power grid equipment is imported. Based on a functional contribution measurement model, an investment and revenue sharing model, a function and cost coupling benefit model, and a regional and equipment collaborative optimization model, it iterates alternately at the regional and equipment levels until the rate of change in the investment ratio and the rate of change in the benefit are less than the convergence threshold. This outputs the optimal investment allocation, equipment operation plan, and comprehensive benefit evaluation results. In this way, after multiple models are built, an initial investment ratio is preset. Combined with the operational data of the power grid equipment, an initial evaluation and optimization plan is generated through the alternating operation of multiple models. Then, through alternating iterations at the regional and equipment levels, the data parameters are adjusted during the optimization process, ensuring that the rate of change in the investment ratio and the rate of change in the benefit are ultimately less than the preset convergence threshold. This guarantees the stability of the optimization results, yields the optimal plan, and outputs the optimal investment allocation, equipment operation plan, and comprehensive benefit evaluation results, thus optimizing the decision-making requirements.

[0007] Optionally, in S1, the functional dimension indicators include peak-shaving capacity, ramp-up response speed, and flexibility reserve; the economic dimension indicators include unit investment cost, operation and maintenance cost, and unit rate of return; and the environmental dimension indicators include emission reduction contribution, improvement in curtailment rate, and energy efficiency improvement. After screening the data parameters characterizing the functional, economic, and environmental characteristics of the power grid equipment, the method of extreme values ​​is used to normalize the data parameters.

[0008] Optionally, the weighting method in S2 based on the multi-dimensional indicator system, using a combination of the analytic hierarchy process (AHP) and the entropy weighting method, includes: S21. Based on the multi-dimensional indicator system, construct the judgment matrix of the functional dimension indicators. Where m is the number of the functional dimension indicators, the This represents the importance comparison value between the j-th functional indicator and the k-th functional indicator; S22. Calculate the maximum eigenvalue of A. and the corresponding feature vector ,in, ; S23, Based on the following formula, the above Perform normalization to obtain the subjective weight vector. : , Among them, the The subjective weight of the j-th functional dimension indicator, the The component of the j-th functional dimension index in the feature vector; S24. Based on the aforementioned multi-dimensional indicator system, calculate the information entropy of the functional dimension indicators using the following formula: , in, The The information entropy of the j-th functional dimension indicator, the The functional dimension indicators after normalization processing, the To find the minimum value, take ; S25. Calculate the objective weights of the functional dimension indicators based on the following formula: , in, Where n is the number of the power grid equipment, The normalized data percentage of the i-th power grid device on the j-th functional dimension indicator; S26. Calculate the combined weights based on the following formula: , Among them, the The combined weight of the j-th functional dimension indicator, the This is the balance coefficient, with a value ranging from 0 to 1.

[0009] Optionally, the step S2, which combines a fuzzy comprehensive evaluation model and introduces a marginal contribution correction factor to construct a functional contribution quantification model to obtain the comprehensive functional value of the power grid equipment, includes: S27. Based on the fuzzy comprehensive evaluation model, calculate the initial comprehensive functional value of the power grid equipment using the following formula: , Among them, the This represents the initial integrated functional value of the i-th power grid device. S28. Introduce the marginal contribution correction factor, and based on the following formula, correct the initial comprehensive function value to construct the function contribution quantification model: , in, The The marginal contribution correction factor for the i-th power grid device, the The flexibility index for the operation of all grid equipment in the power system. To determine the flexibility index of the power system after removing the i-th grid device, the The comprehensive functional value is the corrected value for the i-th power grid device. This is the sensitivity coefficient, with a value ranging from 0 to 1.

[0010] Optionally, the step in S3 of calculating the allocated cost of a single grid device based on the comprehensive functional value and allocating the overall revenue of the power system to the single grid device according to the proportion of functional contribution and economic return includes: S31. Calculate the allocated cost of the individual power grid equipment based on the following formula: , in, The For the allocated cost of the i-th power grid device, the The basic allocated cost of the i-th power grid device calculated according to the postage stamp method, the The total amortizable cost of the power system, the For the rated capacity of the i-th power grid device, the The fusion coefficient between traditional apportionment and functional apportionment, with a value range of 0-1, is described below. The comprehensive functional value of the i-th power grid device is given, where n is the number of power grid devices. S32. Calculate the total revenue allocated to a single grid device based on the following formula: , in, The For the total revenue allocated to the i-th grid device, the The weights for functional and economic benefits range from 0 to 1. For the functional benefits of the i-th power grid device, the For the total functional benefits of the power system, the The direct economic benefit of the i-th power grid device is given.

[0011] Optionally, S4 includes: S41. Integrating the functional weights and the economic weights, define the coupling benefit index, and calculate the coupling benefit value of a single power grid device using the following formula: , Among them, the Let i be the coupling benefit value of the i-th power grid device, and the... The functional and economic weighting coefficients range from 0 to 1. The comprehensive functional value of the i-th power grid device is... The reference functional value of the power system or benchmark equipment is taken as the average value of the comprehensive functional values ​​of the power grid equipment within the power system or the comprehensive functional value of the benchmark power grid equipment. For the total revenue allocated to the i-th grid device, the This refers to the allocated cost of the i-th power grid device. S42. Calculate the partial derivative of the coupling benefit value with the functional and economic weighting coefficients using the following formula, and perform a benefit sensitivity analysis: , Among them, the Let be the sensitivity of the coupling benefit value of the i-th power grid device to the functional and economic weighting coefficients. , improve the To enhance the ,like , reduce the To enhance the ; S43. Construct the aforementioned function-cost coupling benefit model, and calculate the comprehensive benefit index of the power system based on the following formula: , in, The The comprehensive benefit index is the... Let n be the investment weight of the i-th power grid device, and n be the number of power grid devices.

[0012] Optionally, the step S5, which sets an investment objective function and investment constraints at the regional level with the goal of maximizing the overall regional benefits, and at the equipment level with the goal of minimizing the risk of individual equipment, includes: S51. With the goal of maximizing the overall regional benefits at the regional level, the following investment objective function is set: , Wherein, Z is the number of regions, and the The benefit weight of region z is obtained by considering the region's electricity load or the proportion of new energy sources. The region z contains the set of power grid devices. The investment weight of the i-th power grid equipment, the The coupling benefit value of the i-th power grid device; S52. Set the following investment constraints: , , , Among them, the The total investment budget for the power system is... For the allocated cost of the i-th power grid device, the The minimum investment quota for region z, the The maximum investment quota for region z, where n is the number of power grid devices; S53. At the device layer, with the goal of minimizing the risk of individual devices, the following objective function is set: , Among them, the For the risk value of the i-th power grid device, the For risk weighting coefficients, the For the total revenue allocated to the i-th grid device, the The reference functional value of the power system or benchmark equipment is taken as the average value of the comprehensive functional values ​​of the power grid equipment within the power system or the comprehensive functional value of the benchmark power grid equipment. This is the comprehensive functional value of the i-th power grid device; S54. Set the following runtime constraints: , , , Among them, the Let i be the power generation capacity of the i-th grid device in time period t. Let i be the power purchase capacity of the i-th grid device in time period t. Let i be the power consumption of the i-th power grid device in time period t. Let i be the power output of the i-th power grid device in time period t. The maximum allowable power ramp rate for the i-th power grid device is... Let i be the power generation capacity of the i-th grid device in time period t-1. The minimum power generation capacity of the i-th power grid device is... This represents the peak-shaving capacity that the i-th power grid device needs to provide during the dispatching cycle.

[0013] Optionally, the step in S6, which involves alternating between the region layer and the equipment layer until the rate of change in investment ratio and the rate of change in benefit are less than a convergence threshold, includes: S61. Calculate the rate of change of the investment ratio based on the following formula: , Among them, the Let i be the investment weight of the i-th power grid device in the t-th iteration. The investment weight of the i-th power grid device in the (t-1)-th iteration is... For the minimum value, take the value of The number of power grid devices is n. S62. Calculate the rate of change of the aforementioned benefits based on the following formula: , Among them, the Let be the comprehensive benefit index of the power system in the t-th iteration. This represents the comprehensive benefit index of the power system in the (t-1)th iteration. S63, when and When the iteration stops, the following occurs: and stated All of these are the convergence thresholds, ranging from 0.01 to 0.05.

[0014] Secondly, the present invention provides a system for evaluating and optimizing the comprehensive benefits of power grid equipment investment, including a memory and a processor; the memory is used to store a computer program; when the program is executed by the processor, the method for evaluating and optimizing the comprehensive benefits of power grid equipment investment as described above is implemented.

[0015] Compared with the prior art, the beneficial effects of the power grid equipment investment comprehensive benefit assessment and optimization system of the present invention are the same as those of the power grid equipment investment comprehensive benefit assessment and optimization method described above, and will not be repeated here.

[0016] Thirdly, the present invention provides a computer storage medium storing a computer program thereon, characterized in that, when the computer program is executed by a processor, it implements the method for comprehensive benefit assessment and optimization of power grid equipment investment as described above.

[0017] Compared to existing technologies, the beneficial effects of the computer storage medium of the present invention are the same as those of the comprehensive benefit evaluation and optimization method for power grid equipment investment described above, and will not be repeated here. Attached Figure Description

[0018] The present invention will now be described in further detail with reference to the accompanying drawings.

[0019] Figure 1 : A flowchart illustrating the method for comprehensive benefit assessment and optimization of power grid equipment investment in this embodiment of the invention; Figure 2 The following is an optimization flowchart of the method for evaluating and optimizing the comprehensive benefits of power grid equipment investment in this embodiment of the invention. Detailed Implementation

[0020] To better understand the present invention, the following embodiments further illustrate the content of the invention, but the scope of protection of the present invention is not limited to the following embodiments. Numerous specific details are set forth in the following description to provide a more thorough understanding of the invention. However, it will be apparent to those skilled in the art that the present invention can be practiced without one or more of these details.

[0021] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to"; the term "based on" means "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; and the term "optionally" means "optional embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first," "second," etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0022] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0023] In a first aspect, an embodiment of the present invention provides a method for evaluating and optimizing the comprehensive benefits of power grid equipment investment, comprising: S1, based on historical operating data of power grid equipment, screening data parameters characterizing the functional, economic, and environmental characteristics of power grid equipment, obtaining functional, economic, and environmental dimension indicators of power grid equipment, and constructing a multi-dimensional indicator system; S2, based on the multi-dimensional indicator system, using a combination of the analytic hierarchy process (AHP) and entropy weighting method for weighting, combined with a fuzzy comprehensive evaluation model, and introducing a marginal contribution correction factor, constructing a functional contribution quantification model to obtain the comprehensive functional value of power grid equipment; S3, dividing the total investment into fixed costs and amortizable costs, calculating the amortized cost of a single power grid device based on the comprehensive functional value, allocating the overall revenue of the power system to a single power grid device according to the proportion of functional contribution and economic return, and establishing a revenue-investment mapping relationship between the equipment layer and the regional layer of the power system through a hierarchical matrix, constructing an investment-revenue allocation model; S4, integrating functional weights. In addition to economic weights, a coupling benefit index is defined. The coupling benefit value of power grid equipment is quantified based on the comprehensive functional value. A benefit sensitivity analysis is conducted through partial derivative analysis to clarify the impact of weight adjustments on various types of power grid equipment. A function-cost coupling benefit model is constructed to calculate the comprehensive benefit index of the power system. S5. At the regional level, with the goal of maximizing the comprehensive regional benefit, an investment objective function and investment constraints are set. At the equipment level, with the goal of minimizing the risk of individual equipment, an operation objective function and operation constraints are set. A regional and equipment collaborative optimization model is constructed through the cooperation of the regional and equipment levels. S6. An initial investment ratio is set, and the operation data of power grid equipment is imported. Based on the functional contribution quantification model, investment and benefit allocation model, function-cost coupling benefit model, and regional and equipment collaborative optimization model, the regional and equipment levels are iterated alternately until the rate of change of investment ratio and the rate of change of benefit are less than the convergence threshold. The optimal investment allocation, equipment operation plan, and comprehensive benefit evaluation results are output.

[0024] In this embodiment, firstly, as... Figure 1As shown in S1, based on historical operating data of power grid equipment, data parameters characterizing the functional, economic, and environmental characteristics of the equipment are selected to obtain functional, economic, and environmental indicators, thus constructing a multi-dimensional indicator system. This provides comprehensive indicator data for subsequent evaluation and optimization, effectively solving the problem of single evaluation indicators and improving the comprehensiveness of the overall benefit assessment of power grid equipment investment. For example, in the functional dimension, core indicators reflecting the impact of power grid equipment on power system flexibility, power quality, and safe operation can be selected, such as peak-shaving capacity, ramp-up response speed, and flexibility reserves, to ensure that the constructed model can truly characterize the dynamic operating characteristics of power grid equipment. In the economic dimension, typical indicators reflecting the full life-cycle costs and benefits of power grid equipment, such as unit investment cost, operation and maintenance cost, and unit rate of return, are selected to demonstrate the economic feasibility of investment decisions. In the environmental dimension, indicators such as emission reduction contribution, improvement in curtailment rate, and energy efficiency improvement are selected to measure the promoting effect of power grid construction on carbon emission reduction and clean energy consumption. Secondly, such as Figure 1 As shown in S2, based on a multi-dimensional indicator system, a combination of the analytic hierarchy process (AHP) and entropy weighting method is used for weighting, combined with a fuzzy comprehensive evaluation model, and a marginal contribution correction factor is introduced to construct a functional contribution quantification model to obtain the comprehensive functional value of power grid equipment. Thus, based on the multi-dimensional indicator system, in the process of quantifying the functional contribution of power grid equipment, the combination of weighting can balance expert experience and data characteristics, while simultaneously quantifying the dynamic functional value using a marginal correction factor. This solves the problems of difficulty in quantifying the functional contribution of power grid equipment in existing technologies, and the bias in quantification results caused by subjective or simplistic weighting, neglecting the dynamic marginal contribution of power grid equipment. This effectively improves the accuracy of functional assessment, provides a core basis for subsequent cost allocation and revenue distribution, and enhances the stability of the comprehensive benefit assessment of power grid equipment investment. Furthermore, as... Figure 1 As shown in S3, the total investment is divided into fixed costs and amortizable costs. Based on the comprehensive functional value, the amortized cost of individual grid equipment is calculated. The overall revenue of the power system is allocated to individual grid equipment according to the proportion of functional contribution and economic return, thereby achieving fair cost allocation based on functional contribution. This effectively solves the problem of lack of functional orientation in the allocation of total investment and revenue, and provides parameters for subsequent coupling benefit calculations. Then, a hierarchical matrix is ​​used to establish the revenue and investment mapping relationship between the equipment layer and the regional layer of the power system, constructing an investment and revenue allocation model. This hierarchical matrix establishes a mapping between grid equipment, regional revenue, and investment, avoiding local optima and achieving global optima in allocation, improving the accuracy of subsequent coupling benefit calculations, and ensuring the stability of the comprehensive benefit assessment of grid equipment investment. Then, as... Figure 1As shown in S4, a coupled benefit index is defined by integrating functional and economic weights. Based on the comprehensive functional value, the coupled benefit value of power grid equipment is quantified, thus avoiding the problem of separating functional value from economic cost evaluation. This effectively reflects the comprehensive benefit and provides core parameters for subsequent collaborative optimization. Simultaneously, benefit sensitivity analysis is conducted through partial derivative analysis to clarify the impact of weight adjustments on various types of power grid equipment, thereby defining the optimization direction of the weights and supporting differentiated decision-making. Then, the comprehensive benefit index of the power system is calculated through the constructed functional and cost coupled benefit model, ensuring the global optimum of the comprehensive benefit index. Next, as... Figure 1 As shown in S5, at the regional level, the goal is to maximize the overall regional benefits, setting an investment objective function and investment constraints. At the equipment level, the goal is to minimize the risk of individual equipment, setting an operational objective function and operational constraints. By coordinating the regional and equipment levels, a regional and equipment collaborative optimization model is constructed, thus forming a two-layer collaborative optimization model that maximizes regional benefits and minimizes grid equipment risks. This model considers both global budget and local operational feasibility, while providing objective functions and constraints for subsequent alternating iterative optimization and clarifying the optimization boundary. Finally, as... Figure 1 As shown in S6, an initial investment ratio is set, and the operating data of the power grid equipment is imported. Based on the functional contribution measurement model, the investment and benefit allocation model, the function and cost coupling benefit model, and the regional and equipment collaborative optimization model, the system iterates alternately at the regional and equipment levels until the rate of change of the investment ratio and the rate of change of the benefit are less than the convergence threshold. This outputs the optimal investment allocation, equipment operation plan, and comprehensive benefit evaluation results. In this way, after multiple models are built, the initial investment ratio is preset first. Combined with the operating data of the power grid equipment, the initial evaluation and optimization plan is generated through the alternating operation of multiple models. Then, through the alternating iteration of the regional and equipment levels, the data parameters are adjusted during the optimization process so that the rate of change of the investment ratio and the rate of change of the benefit are ultimately less than the preset convergence threshold. This ensures the stability of the optimization results, obtains the optimal plan, and outputs the optimal investment allocation, equipment operation plan, and comprehensive benefit evaluation results, thereby optimizing the decision-making requirements.

[0025] Optionally, in S1, the functional dimension indicators include peak-shaving capacity, ramp-up response speed, and flexibility reserve; the economic dimension indicators include unit investment cost, operation and maintenance cost, and unit rate of return; and the environmental dimension indicators include emission reduction contribution, improvement in curtailment rate, and energy efficiency improvement. After screening the data parameters characterizing the functional, economic, and environmental characteristics of power grid equipment, the data parameters are also normalized using the extreme value method.

[0026] Specifically, the formula for calculating the peak-shaving capacity index is as follows: ,in, To enhance the peak-shaving capacity of power grid equipment, This is the minimum output of the power grid equipment. The rated capacity of the power grid equipment; the formula for calculating the ramp response speed index is: ,in, For the ramp response speed of power grid equipment, This refers to the change in power of power grid equipment per unit time. The time interval corresponding to the power change; the formula for calculating the flexibility reserve index is: ,in, To provide flexibility reserves for power grid equipment, This refers to the upward capability of power grid equipment. For the downlink capability of power grid equipment, The formula for calculating the unit investment cost index is: (This is the formula for calculating the unit investment cost index, which is the load demand index.) ,in, The unit investment cost of power grid equipment, The total investment cost of power grid equipment, The rated capacity of the equipment; the formula for calculating the operation and maintenance cost index is: ,in, The average annual operation and maintenance cost of power grid equipment, Let be the equipment's operation and maintenance cost in year k; the formula for calculating the unit rate of return index is: ,in, The unit rate of return for power grid equipment, For the annual net revenue of power grid equipment, The total investment cost of power grid equipment; the formula for calculating the emission reduction contribution index is: , Contribution to annual emission reduction of power grid equipment The average annual power generation of the power grid equipment. The equivalent utilization hours of power grid equipment, The emission reduction factor per unit of electricity is 0.45~0.8 kgCO2 / kWh. The formula for calculating the improvement index of power curtailment rate is as follows: , To improve the curtailment rate of power grid equipment, This serves as the baseline for the amount of power system curtailment before the adoption of new grid equipment. The energy efficiency improvement index is calculated using the following formula to determine the baseline amount of power curtailment in the power system after the adoption of new grid equipment: , To improve the energy efficiency of power grid equipment, The baseline energy conversion efficiency of the power system before the adoption of new grid equipment. This represents the baseline energy conversion efficiency of the power system after the adoption of new grid equipment.

[0027] In this optional embodiment, the functional dimension indicators include peak-shaving capacity, ramp-up response speed, and flexibility reserve; the economic dimension indicators include unit investment cost, operation and maintenance cost, and unit rate of return; and the environmental dimension indicators include emission reduction contribution, improvement in curtailment rate, and energy efficiency improvement. This allows for subsequent evaluation from three dimensions: functional, economic, and environmental, effectively improving the comprehensiveness of the assessment. Furthermore, since different indicators have different dimensions, to unify the data scale and ensure comparability among the three types of indicators (functional, economic, and environmental), laying the foundation for subsequent weighting and functional quantification, after selecting the data parameters, the data parameters are normalized using the extreme value method. This transforms the original data to the [0,1] interval, eliminating dimensional differences. Specifically, for positive indicators where larger values ​​indicate better performance, the extreme value method is used to normalize the data parameters. Normalization was performed, where, The raw data for the i-th power grid device on the j-th indicator is derived from the historical operating data of the power grid device. Let j be the maximum value of all power grid equipment on the j-th index. The minimum value of all power grid equipment on the j-th index is obtained by statistically analyzing the extreme values ​​in the original dataset. The normalized index values, correspondingly, for negative indicators where smaller values ​​are considered better, are obtained through... Normalization is performed.

[0028] Optionally, the weighting method in S2 based on a multi-dimensional indicator system, using a combination of the analytic hierarchy process (AHP) and entropy weighting, includes: S21, constructing a judgment matrix for functional dimension indicators based on the multi-dimensional indicator system. Where m is the number of functional dimension indicators. S22: Calculate the importance comparison value between the j-th functional indicator and the k-th functional indicator; Calculate the maximum eigenvalue of A. and the corresponding feature vector ,in, S23, based on the following formula Perform normalization to obtain the subjective weight vector. : (1.1), in, The subjective weight of the j-th functional dimension indicator. S24. Based on the multi-dimensional indicator system, the information entropy of the functional dimension indicator is calculated using the following formula: (1.2), in, , Let the information entropy be the j-th functional dimension indicator. The functional dimension indicators after normalization, To find the minimum value, take S25. Calculate the objective weights of functional dimension indicators based on the following formula: (1.3), in, where n is the number of power grid devices. S26. Calculate the combined weights based on the following formula: (The formula is not provided in the original text.) (1.4) in, The combined weight of the j-th functional dimension indicator This is the balance coefficient, with a value ranging from 0 to 1.

[0029] In this optional embodiment, since single subjective weighting relies on expert experience and is easily affected by subjective bias; and single objective weighting ignores the differences in the importance of indicators and cannot reflect actual needs, in order to ensure the rationality and comprehensiveness of functional contribution quantification, balance expert experience and the objective characteristics of data, obtain more reasonable indicator weights, and improve the accuracy of subsequent functional quantification, a combination of analytic hierarchy process and entropy weighting is used for weighting to provide data for the calculation of the initial comprehensive functional value. Specifically, firstly, based on the multi-dimensional indicator system, a judgment matrix of functional dimension indicators is constructed, the maximum eigenvalue of A and the corresponding eigenvector are calculated, thereby obtaining the subjective weights of the functional dimension indicators, and normalizing them through equation (1.1) to obtain the subjective weight vectors. First, ensure that subjective weights can be directly used for weighted calculation. Then, based on the multi-dimensional indicator system, calculate the information entropy of the functional dimension indicators based on the dispersion of the indicator data according to Equation (1.2), which reflects the objective distinguishability of the indicators. Information entropy measures the uncertainty of the data. The higher the dispersion of the indicator data, the smaller the entropy value, indicating that the indicator can better distinguish the functional differences of different devices and should be given a higher objective weight. Therefore, calculate the objective weight of the functional dimension indicators based on Equation (1.3). Finally, integrate subjective weights and objective weights and calculate the combined weights through Equation (1.4). The proportion of subjective and objective weights can be adjusted by the balance coefficient α. For example, when α is 0.5, the two are balanced. If expert experience is valued, α is 0.6-0.7.

[0030] Optionally, in S2, by combining the fuzzy comprehensive evaluation model and introducing a marginal contribution correction factor, a functional contribution quantification model is constructed to obtain the comprehensive functional value of the power grid equipment, including: S27, combining the fuzzy comprehensive evaluation model, calculating the initial comprehensive functional value of the power grid equipment based on the following formula: (2.1), in, S28. Introduce a marginal contribution correction factor, and based on the following formula, correct the initial comprehensive function value to construct a function contribution quantification model: (2.2), in, , The marginal contribution correction factor for the i-th power grid device. It is a flexibility indicator for the operation of all grid equipment in a power system. To determine the flexibility index of the power system after removing the i-th grid device, This is the corrected comprehensive function value for the i-th power grid device. This is the sensitivity coefficient, with a value ranging from 0 to 1.

[0031] In this optional embodiment, firstly, after calculating the combined weights, the combined weights can be weighted and summed using Equation (2.1) in conjunction with the fuzzy comprehensive evaluation model to obtain the static initial comprehensive function value of the power grid equipment, reflecting the comprehensive performance of the power grid equipment in each functional indicator. Among them, the larger the combined weight of the indicator, the greater its influence on the initial comprehensive function value. However, the initial function value only reflects the weighted result of the static indicators and ignores the dynamic marginal contribution of the equipment to the overall system, such as the degree of decrease in system flexibility after removing a certain equipment. Therefore, after obtaining the initial comprehensive function value, a marginal contribution correction factor can be introduced based on Equation (2.2). First, the marginal contribution of the i-th power grid equipment to the overall flexibility of the power system is quantified by the marginal contribution correction factor, that is, the degree of influence of the existence or absence of the power grid equipment on the function of the power system. By comparing the system flexibility index of all power grid equipment in operation and the system flexibility index of removing the power grid equipment, the relative change rate is calculated to reflect the irreplaceability of the power grid equipment. Then, the static initial function value is combined with the dynamic marginal contribution to obtain a more comprehensive comprehensive function value, that is, the degree of influence of the marginal contribution is adjusted by the sensitivity coefficient β. For example, when β is 0.3-0.5, it reflects the dynamic contribution while avoiding excessive amplification.

[0032] Optionally, S3, based on the comprehensive functional value, calculates the allocated cost of a single grid device and allocates the overall revenue of the power system to the individual grid devices according to the proportion of functional contribution and economic return. This includes: S31, calculating the allocated cost of a single grid device based on the following formula: (3.1), in, , Let i be the allocated cost of the i-th power grid device. The basic allocated cost of the i-th power grid device calculated using the postage stamp method is... The total amortizable cost of the power system. Let i be the rated capacity of the i-th power grid device. This is the fusion coefficient between traditional allocation and functional allocation, with a value ranging from 0 to 1. S32. Calculate the total revenue allocated to a single grid device based on the following formula: (where n is the number of grid devices, and n is the comprehensive functional value of the i-th grid device) (3.2), in, , The total revenue allocated to the i-th grid device, The weighting of functional benefits and economic benefits, with a value ranging from 0 to 1. For the functional benefits of the i-th power grid device, For the total functional benefits of the power system, Let be the direct economic benefit of the i-th power grid device.

[0033] In this optional embodiment, since traditional cost allocation only considers capacity ratio and ignores the functional differences of power grid equipment, the allocated cost of a single power grid equipment can be calculated using equation (3.1) based on the calculated comprehensive functional value. This setting allows the basic allocated cost of power grid equipment calculated based on capacity ratio to reflect the rationality of traditional allocation logic. Furthermore, by integrating traditional capacity allocation and functional allocation, and adjusting their proportions using a fusion coefficient γ (0.4-0.6), the higher the functional value of the power grid equipment, the higher its allocated cost proportion, the greater its functional contribution, and the more reasonable its cost allocation. This results in a final allocated cost that balances capacity and function. However, due to the lack of functional orientation in revenue distribution, the benefits at the equipment level and the regional level become disconnected. Therefore... Based on equation (3.2), the total revenue of the power system is allocated to individual grid equipment according to the ratio of functional contribution and economic return. This setting allocates the total functional revenue of the system according to the proportion of equipment functional contribution, such as peak-shaving service revenue and new energy consumption gain. The higher the functional value of the equipment, the more functional revenue it will receive, which incentivizes the grid equipment to improve its functional contribution. On this basis, the functional revenue and direct economic revenue are integrated, and the ratio of the two is adjusted by weight δ. When δ is 0.5, the balance is achieved, and the total allocated revenue of the equipment is finally obtained. This reflects both functional contribution and economic return, meets the needs of comprehensive benefit assessment, and achieves the fairness of cost allocation and the rationality of revenue distribution. It also establishes a benefit transmission mechanism between the equipment layer and the regional layer, laying the foundation for global optimization.

[0034] Optionally, S4 includes: S41. Integrate functional weights and economic weights to define a coupling benefit index, and calculate the coupling benefit value of a single power grid device using the following formula: (4.1), in, Let i be the coupling benefit value of the i-th power grid device. This is the weighting coefficient for functionality and economy, with a value range of 0-1. Let i be the comprehensive functional value of the i-th power grid device. For reference functional values ​​of power systems or benchmark equipment, the average of the comprehensive functional values ​​of power grid equipment within the power system or the comprehensive functional value of benchmark power grid equipment is taken. The total revenue allocated to the i-th grid device, Let S1 be the allocated cost of the i-th power grid device; S42. Calculate the partial derivative of the coupling benefit value with the functional and economic weight coefficients using the following formula, and perform a benefit sensitivity analysis: (4.2), in, Let be the sensitivity of the coupling benefit value of the i-th power grid device to the functional and economic weighting coefficients. ,promote To improve ,like ,reduce To improve S43. Construct a function-cost coupled benefit model, and calculate the comprehensive benefit index of the power system based on the following formula: (4.3), in, , For comprehensive benefit index, Let be the investment weight of the i-th power grid device, and n be the number of power grid devices.

[0035] In this optional embodiment, since traditional assessments separate function and cost, failing to reflect comprehensive benefits, this embodiment calculates the coupling benefit value of a single power grid device using equation (4.1) based on achieving fairness in cost allocation and rationality in benefit distribution. This integrates functional value and economic benefits, quantifies the comprehensive coupling benefit of the power grid device, and achieves a unified assessment of function and cost. The importance of function and economy is adjusted by weight θ; a larger θ emphasizes system support value, while a smaller θ emphasizes investment returns, adapting to different assessment scenarios. Based on this, the partial derivative of the coupling benefit value with the functional and economic weight coefficients is calculated using equation (4.2), and a benefit sensitivity analysis is performed to analyze the sensitivity of the coupling benefit value to weight θ, thereby clarifying the weight... The adjustment direction supports differentiated decision-making. The influence of weights is judged by the sign of the partial derivative. A positive sign indicates that increasing θ can improve efficiency, which is applicable to flexible equipment. A negative sign indicates that decreasing θ can improve efficiency, which is applicable to economically oriented equipment. Finally, based on the quantification of the coupling benefits of equipment functions and costs and the clarification of the direction of weight optimization, a function-cost coupling benefit model is constructed. The comprehensive benefit index of the power system is calculated by formula (4.3). The overall comprehensive benefit of the power system is calculated by weighting, which reflects the impact of investment weights on the benefits of the power system and provides a basis for regional comparison and investment ranking. The weighted coupling benefit value is calculated according to the cost allocation ratio of grid equipment. The larger the investment in grid equipment, the greater the impact on the benefits of the power system, which is consistent with the investment decision-making logic.

[0036] Optionally, in S5, the investment objective function and investment constraints are set at the regional level with the goal of maximizing the overall regional benefits, and at the equipment level with the goal of minimizing the risk of individual equipment, the operation objective function and operation constraints are set, including: S51, at the regional level, with the goal of maximizing the overall regional benefits, the following investment objective function is set: (5.1), Where Z is the number of regions. The benefit weight for region z is obtained through the region's electricity load scale or the proportion of new energy sources. Let z be the set of power grid devices contained in region z. Let i be the investment weight of the i-th power grid equipment. Let the coupling benefit value of the i-th power grid device be denoted as S52. Set the following investment constraints: (5.2), (5.3), (5.4), in, For the total investment budget of the power system, Let i be the allocated cost of the i-th power grid device. The minimum investment quota for region z. Let n be the maximum investment quota for region z, and n be the number of power grid equipment; S53. At the equipment level, with the goal of minimizing the risk of individual equipment, set the following objective function: (5.5), in, Let i be the risk value of the i-th power grid device. For risk weighting coefficients, The total revenue allocated to the i-th grid device, For reference functional values ​​of power systems or benchmark equipment, the average of the comprehensive functional values ​​of power grid equipment within the power system or the comprehensive functional value of benchmark power grid equipment is taken. S54. Set the following operating constraints: (This is the comprehensive functional value of the i-th power grid device.) (5.6), (5.7), (5.8), in, Let be the power generation capacity of the i-th grid device in time period t. Let be the power purchased by the i-th grid device in time period t. Let be the power load of the i-th grid device in time period t. Let be the electricity sales power of the i-th grid device in time period t. Let i be the maximum allowable power ramp rate of the i-th power grid device. Let be the power generation of the i-th grid device in time period t-1. Let i be the minimum power generation capacity of the i-th grid device. This represents the peak-shaving capacity that the i-th power grid device needs to provide during the dispatching cycle.

[0037] In this optional embodiment, to address the conflict between regional investment and power grid equipment operation constraints, and the lack of a two-layer coordination mechanism, firstly, at the regional level, with the goal of maximizing the comprehensive benefits of the region, an investment objective function as shown in equation (5.1) is set. This objective function reflects the differences in benefit weights among different regions, achieving global optimization. Specifically, the importance of different regions is adjusted through regional benefit weights, and the coupling benefits of power grid equipment within the region are calculated weighted to achieve inter-regional coordination. Simultaneously, investment constraints as shown in equations (5.2), (5.3), and (5.4) are set. Equation (5.2) is an investment budget constraint, limiting the total investment to no more than the budget to ensure optimization feasibility; Equation (5.3) is a regional quota constraint, limiting the scope of regional investment to avoid excessive or insufficient investment; and Equation (5.4) is a non-negative investment ratio constraint, ensuring the rationality of investment weights and avoiding negative investment ratios. Based on this, at the equipment level, with the goal of minimizing the risk of individual equipment, an operational objective function is set as shown in Equation (5.5). This objective function aims to minimize the risk of power grid equipment, taking into account both economic risk (cost and benefit) and functional risk (reference functional value and actual functional value). The proportion of the two types of risk is adjusted by a risk weight μ, with μ set to 0.5 for balanced consideration, ensuring the economic efficiency and functionality of power grid equipment operation. Simultaneously, investment constraints are set as shown in Equations (5.6), (5.7), and (5.8). Equation (5.6) is a power balance constraint, ensuring that power generation + electricity purchase = load + electricity sales during power grid equipment operation, maintaining power balance. Equation (5.7) is a regional quota constraint, limiting the rate of power change of power grid equipment, avoiding frequency fluctuations, and ensuring system stability. Equation (5.8) is a non-negative investment ratio constraint, ensuring that power grid equipment has sufficient peak-shaving capacity to cope with load fluctuations and new energy fluctuations. This setup constructs a two-layer collaborative optimization framework, taking into account both regional global benefits and local equipment risks, ensuring the feasibility and optimality of the optimization results.

[0038] Optionally, the iterative process in S6, alternating between the regional layer and the equipment layer until the rate of change in investment ratio and the rate of change in benefit are less than the convergence threshold, includes: S61, calculating the rate of change in investment ratio based on the following formula: (6.1), in, Let i be the investment weight of the i-th power grid device in the t-th iteration. Let i be the investment weight of the i-th power grid device in the (t-1)-th iteration. For the minimum value, take the value of where n is the number of power grid devices; S62. Calculate the rate of change of benefits based on the following formula: (6.2), in, Let be the comprehensive benefit index of the power system in the t-th iteration. S63 represents the comprehensive benefit index of the power system in the (t-1)th iteration; and When the iteration stops, the iteration is terminated. and All are convergence thresholds, ranging from 0.01 to 0.05.

[0039] In this optional embodiment, due to the lack of optimization in the prior art or the lack of clear convergence judgment criteria in traditional iteration, the optimization results are unstable or the iteration efficiency is low. In order to achieve the optimization required for decision-making, the regional layer and equipment layer are alternately iterated until the rate of change of investment ratio and the rate of change of benefit are less than the convergence threshold. The iteration convergence threshold is clearly defined to ensure that the investment ratio and system benefit tend to be stable and output the optimal and reliable decision scheme. Specifically, firstly, the rate of change of investment ratio is calculated based on equation (6.1), and the maximum relative change rate of investment weight between round t and round t-1 is calculated to measure the stability of investment ratio. The maximum relative change rate reflects the adjustment range of investment ratio. The smaller the change rate, the more stable the investment allocation is. Next, the rate of change of benefit is calculated based on equation (6.2), and the relative change rate of system comprehensive benefit index between round t and round t-1 is calculated to measure the stability of power system benefit. The smaller the change rate, the more optimal the power system benefit is, avoiding over-iteration or under-iteration. Finally, when and When the iteration stops, the dual criteria for termination are clearly defined, ensuring that both the investment ratio and system efficiency tend to stabilize.

[0040] It should be noted that the collaborative optimization process can be divided into three steps: The first step is to set investment weights based on the total system investment budget and the initial technical parameters of each piece of equipment. The first step involves determining the objective functions, constraints, and sensitivity parameters for the regional and equipment layers. The second step is equipment layer optimization, where, through multiple iterations, the regional layer adjusts the current investment ratio. The problem is passed down to the equipment layer, where it independently solves the operation optimization problem of the power grid equipment under given investment constraints to determine the optimal operating power, revenue level, and overall benefits. The regional layer then determines the allocated cost of the power grid equipment based on the investment allocation results. This value is then passed as a constraint to the equipment layer. Simultaneously, the technical parameters and operating data of each power grid device are imported. The technical parameters include rated power, ramp rate, and minimum stable power, while the operating data includes load curves, power output boundaries, and environmental parameters. The equipment layer establishes a separate optimization sub-model for each device, with each sub-problem using the maximization of function-cost coupling benefits as the objective function, as shown in the equation. ,in, This is a vector representing the power grid equipment operation strategy, including decision variables such as output power, charging / discharging plan, and start / stop status. The solution is a function of the environment and power system. During the solution process, the equipment layer must simultaneously satisfy power balance constraints, power ramping constraints, and peak-shaving capacity constraints. A multi-stage iterative correction and dynamic penalty strategy is adopted for constraint handling and feasible region search. For power balance constraints, the node power balance matrix is ​​used... Linearization is performed to uniformly incorporate the power variables into the decision vector. For power parameters, auxiliary variables are introduced using a piecewise linearization approach to address power ramping constraints. and Transform absolute value constraints into , , This maintains the linear structure of the model. For peak-shaving capacity constraints, they are treated as cumulative reserve capacity constraints, transformed into linear inequality constraints, to ensure that the equipment has sufficient up-shaving capacity (peak-shaving capacity) throughout its entire lifecycle. Based on this, before solving, an initial feasible region set is established by reading equipment operating parameters, load curves, and system scheduling boundaries. ,in, and These are the equality and inequality constraint matrices, respectively. and For the corresponding boundary values, in the actual optimization process, some constraints may be temporarily not satisfied due to data fluctuations or scheduling errors. Therefore, a dynamic penalty mechanism is introduced, adding a constraint deviation term to the objective function. ,in, For power balance deviation, For power balance deviation, For the available peak-shaving capacity, , and The penalty coefficient is dynamically adjusted according to the degree of constraint violation. As the amount of constraint violation gradually decreases, the system automatically reduces the penalty intensity, causing the optimization direction to gradually return to the original objective function. This ensures that the search path smoothly converges to the optimal point within the feasible region. When the change in constraint residuals from two consecutive rounds of solving satisfies... and This means that the current operating point of the equipment is considered to be stable within the feasible region. The preset convergence threshold is used. At this point, the operating power curve, peak-shaving capability, and coupling benefit value of the output equipment are displayed, and the results are fed back to the upper layer for investment ratio adjustment. In the third step, the regional layer adjusts the investment weights based on the coupling benefit values ​​fed back from the equipment layer to maximize the overall benefit of the regional layer. A weight update strategy based on benefit ratio adjustment is adopted. To avoid iterative oscillations, a relaxation factor η∈(0,1] is introduced to smooth the updated investment ratio. The updated results are then normalized and projected to ensure they meet budget constraints and regional upper limit constraints. and After projection As the input vector for the next iteration, the regional layer and the equipment layer iterate alternately, achieving dynamic coordination between the upper and lower layers. After each adjustment of the investment ratio, the equipment layer re-solves the corresponding equipment optimization sub-problem and feeds back the new benefit value. This information exchange process continues until the rate of change of the investment ratio vector and the rate of change of benefit are both less than the convergence threshold, or the number of iterations reaches the upper limit. When the convergence condition is met, the investment weights obtained are the optimal investment allocation scheme for regional and equipment co-optimization. Ultimately, the power system can form a complete optimal configuration scheme including regional investment structure, equipment operation scheme, and comprehensive benefit evaluation results, providing a scientific basis for power grid planning and equipment investment decisions.

[0041] For example, such as Figure 2 As shown, in this embodiment, during collaborative optimization, firstly, referring to industry benchmarks such as the historical investment structure of the power grid and the proportion of regional electricity demand, the initial investment weight of each power grid device is determined, and the initial investment ratio is set as the initial input for alternating iterations, providing the initial boundary for the optimization of the device layer and the regional layer. Secondly, the device layer optimization is solved by using the pre-set operating objective function and operating constraints of the regional and device collaborative optimization model, thereby obtaining the optimal operating power, revenue level, and comprehensive benefits of each power grid device. Thirdly, based on the pre-set investment objective function and investment constraints of the regional and device collaborative optimization model, the investment amount of power grid device i is determined by cost allocation. At this time, the data parameters characterizing the functional characteristics of the power grid device are imported as technical parameters, and optimization is performed with the goal of maximizing the comprehensive benefits of the region. Next, it is verified whether the operating parameters of the above device layer meet the operating constraints of the device layer. If so, it indicates that the device layer meets the operational feasibility and can proceed to the subsequent regional optimization. If not, the process needs to return to the point before importing technical parameters to verify the accuracy and completeness of the selected power grid equipment parameter data. After correcting the parameters, the process re-enters the equipment layer optimization phase. Then, in the regional layer adjustment phase, operating parameters are obtained based on historical operating data of the power grid equipment, load curves are obtained based on load demand from functional dimension indicators, and scheduling boundaries are obtained based on investment constraints of the regional layer. Subsequently, an initial feasible domain set is established, forming a constraint boundary for collaborative optimization between the regional layer and the equipment layer, ensuring that subsequent iterations are conducted within the feasible range. A constraint deviation term is then added to the investment objective function. If the result deviates from the initial feasible domain set during the iteration process, the optimization direction is adjusted through a dynamic penalty mechanism to ensure that the final result converges to the optimal solution within the feasible domain, improving the reliability of the iteration and the feasibility of the result. Finally, the investment ratio is adjusted based on the benefit results fed back by the power grid equipment at the regional layer, and alternating iterations are performed. The optimal investment scheme is output when the convergence threshold is met.

[0042] Secondly, an embodiment of the present invention provides a system for evaluating and optimizing the comprehensive benefits of power grid equipment investment, including a memory and a processor; the memory is used to store computer programs; when the program is executed by the processor, the above-mentioned method for evaluating and optimizing the comprehensive benefits of power grid equipment investment is implemented.

[0043] The technical effects of the power grid equipment investment comprehensive benefit assessment and optimization system in this embodiment are similar to those of the power grid equipment investment comprehensive benefit assessment and optimization method described above, and will not be repeated here.

[0044] Thirdly, the present invention provides a computer storage medium storing a computer program thereon, wherein when the computer program is executed by a processor, the above-mentioned method for comprehensive evaluation and optimization of investment benefits of power grid equipment is implemented.

[0045] The technical effects of the computer storage medium in this embodiment are similar to those of the above-mentioned method for evaluating and optimizing the comprehensive benefits of power grid equipment investment, and will not be repeated here.

[0046] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for comprehensive benefit evaluation and optimization of power grid equipment investment, characterized in that, include: S1. Based on the historical operating data of the power grid equipment, filter the data parameters that characterize the functional, economic and environmental characteristics of the power grid equipment, obtain the functional, economic and environmental indicators of the power grid equipment, and construct a multi-dimensional indicator system. S2. Based on the multi-dimensional indicator system, a combination of the analytic hierarchy process and the entropy weight method is used for weighting, combined with the fuzzy comprehensive evaluation model, and a marginal contribution correction factor is introduced to construct a functional contribution quantification model to obtain the comprehensive functional value of the power grid equipment. S3. Divide the total investment into fixed costs and amortizable costs. Based on the comprehensive functional value, calculate the amortizable cost of a single power grid device. Allocate the total revenue of the power system to a single power grid device according to the functional contribution and economic return ratio. Establish the revenue and investment mapping relationship of the equipment layer and regional layer of the power system through a hierarchical matrix and construct an investment and revenue amortization model. S4. Integrate functional weights and economic weights, define a coupling benefit index, quantify the coupling benefit value of the power grid equipment based on the comprehensive functional value, and conduct benefit sensitivity analysis through partial derivative analysis to clarify the impact of weight adjustment on each type of power grid equipment. Construct a functional and cost coupling benefit model to calculate the comprehensive benefit index of the power system. S5. At the regional level, with the goal of maximizing the overall benefits of the region, an investment objective function and investment constraints are set. At the equipment level, with the goal of minimizing the risk of individual equipment, an operational objective function and operational constraints are set. A regional and equipment collaborative optimization model is constructed through the cooperation of the regional level and the equipment level. S6. Set the initial investment ratio, import the operation data of the power grid equipment, and based on the functional contribution quantification model, the investment and benefit sharing model, the function and cost coupling benefit model, and the regional and equipment collaborative optimization model, iterate alternately through the regional layer and the equipment layer until the rate of change of investment ratio and the rate of change of benefit are less than the convergence threshold, so as to output the optimal investment allocation, equipment operation plan and comprehensive benefit evaluation results.

2. The method for comprehensive benefit evaluation and optimization of power grid equipment investment as described in claim 1, characterized in that, In S1, the functional dimension indicators include peak shaving capacity, ramp-up response speed, and flexibility reserve; the economic dimension indicators include unit investment cost, operation and maintenance cost, and unit rate of return; and the environmental dimension indicators include emission reduction contribution, improvement in curtailment rate, and energy efficiency improvement. After screening the data parameters that characterize the functional, economic, and environmental properties of the power grid equipment, the method of extreme values ​​is used to normalize the data parameters.

3. The method for comprehensive benefit evaluation and optimization of power grid equipment investment as described in claim 1, characterized in that, The weighting method based on the multi-dimensional indicator system and the combined analytic hierarchy process and entropy weighting method in S2 includes: S21. Based on the multi-dimensional indicator system, construct the judgment matrix of the functional dimension indicators. Where m is the number of the functional dimension indicators, the This represents the importance comparison value between the j-th functional indicator and the k-th functional indicator; S22. Calculate the maximum eigenvalue of A. and the corresponding feature vector ,in, ; S23, Based on the following formula, the above Perform normalization to obtain the subjective weight vector. : ， Among them, the The subjective weight of the j-th functional dimension indicator, the The component of the j-th functional dimension index in the feature vector; S24. Based on the aforementioned multi-dimensional indicator system, calculate the information entropy of the functional dimension indicators using the following formula: ， in, The The information entropy of the j-th functional dimension indicator, the The functional dimension indicators after normalization processing, the To find the minimum value, take ; S25. Calculate the objective weights of the functional dimension indicators based on the following formula: ， in, Where n is the number of the power grid equipment, The normalized data percentage of the i-th power grid device on the j-th functional dimension indicator; S26. Calculate the combined weights based on the following formula: ， Among them, the The combined weight of the j-th functional dimension indicator, the This is the balance coefficient, with a value ranging from 0 to 1.

4. The method for comprehensive benefit evaluation and optimization of power grid equipment investment as described in claim 3, characterized in that, The S2 step, which combines a fuzzy comprehensive evaluation model and introduces a marginal contribution correction factor to construct a functional contribution quantification model to obtain the comprehensive functional value of the power grid equipment, includes: S27. Based on the fuzzy comprehensive evaluation model, calculate the initial comprehensive functional value of the power grid equipment using the following formula: ， Among them, the This represents the initial integrated functional value of the i-th power grid device. S28. Introduce the marginal contribution correction factor, and based on the following formula, correct the initial comprehensive function value to construct the function contribution quantification model: ， in, The The marginal contribution correction factor for the i-th power grid device, the The flexibility index for the operation of all grid equipment in the power system. To determine the flexibility index of the power system after removing the i-th grid device, the The comprehensive functional value is the corrected value for the i-th power grid device. This is the sensitivity coefficient, with a value ranging from 0 to 1.

5. The method for comprehensive benefit evaluation and optimization of power grid equipment investment as described in any one of claims 1 to 4, characterized in that, The step in S3, which involves calculating the allocated cost of a single grid device based on the comprehensive functional value and distributing the overall revenue of the power system to the single grid device according to the functional contribution and economic return ratio, includes: S31. Calculate the allocated cost of the individual power grid equipment based on the following formula: ， in, The For the allocated cost of the i-th power grid device, the The basic allocated cost of the i-th power grid device calculated according to the postage stamp method, the The total amortizable cost of the power system, the For the rated capacity of the i-th power grid device, the The fusion coefficient between traditional apportionment and functional apportionment, with a value range of 0-1, is described below. The comprehensive functional value of the i-th power grid device is given, where n is the number of power grid devices. S32. Calculate the total revenue allocated to a single grid device based on the following formula: ， in, The For the total revenue allocated to the i-th grid device, the The weights for functional and economic benefits range from 0 to 1. For the functional benefits of the i-th power grid device, the For the total functional benefits of the power system, the The direct economic benefit of the i-th power grid device is given.

6. The method for comprehensive benefit evaluation and optimization of power grid equipment investment as described in any one of claims 1 to 4, characterized in that, S4 includes: S41. Integrating the functional weights and the economic weights, define the coupling benefit index, and calculate the coupling benefit value of a single power grid device using the following formula: ， Among them, the Let i be the coupling benefit value of the i-th power grid device, and the... The functional and economic weighting coefficients range from 0 to 1. The comprehensive functional value of the i-th power grid device is... The reference functional value of the power system or benchmark equipment is taken as the average value of the comprehensive functional values ​​of the power grid equipment within the power system or the comprehensive functional value of the benchmark power grid equipment. For the total revenue allocated to the i-th grid device, the This refers to the allocated cost of the i-th power grid device. S42. Calculate the partial derivative of the coupling benefit value with the functional and economic weighting coefficients using the following formula, and perform a benefit sensitivity analysis: ， Among them, the Let be the sensitivity of the coupling benefit value of the i-th power grid device to the functional and economic weighting coefficients. , improve the To enhance the ,like , reduce the To enhance the ; S43. Construct the aforementioned function-cost coupling benefit model, and calculate the comprehensive benefit index of the power system based on the following formula: ， in, The The comprehensive benefit index is the... Let n be the investment weight of the i-th power grid device, and n be the number of power grid devices.

7. The method for comprehensive benefit evaluation and optimization of power grid equipment investment as described in any one of claims 1 to 4, characterized in that, The step S5, which sets an investment objective function and investment constraints at the regional level with the goal of maximizing the overall regional benefits, and at the equipment level with the goal of minimizing the risk of individual equipment, includes the following: S51. With the goal of maximizing the overall regional benefits at the regional level, the following investment objective function is set: ， Wherein, Z is the number of regions, and the The benefit weight of region z is obtained by considering the region's electricity load or the proportion of new energy sources. The region z contains the set of power grid devices. The investment weight of the i-th power grid equipment, the The coupling benefit value of the i-th power grid device; S52. Set the following investment constraints: ， ， ， Among them, the The total investment budget for the power system is... For the allocated cost of the i-th power grid device, the The minimum investment quota for region z, the The maximum investment quota for region z, where n is the number of power grid devices; S53. At the device layer, with the goal of minimizing the risk of individual devices, the following objective function is set: ， Among them, the For the risk value of the i-th power grid device, the For risk weighting coefficients, the For the total revenue allocated to the i-th grid device, the The reference functional value of the power system or benchmark equipment is taken as the average value of the comprehensive functional values ​​of the power grid equipment within the power system or the comprehensive functional value of the benchmark power grid equipment. This is the comprehensive functional value of the i-th power grid device; S54. Set the following runtime constraints: ， ， ， Among them, the Let i be the power generation capacity of the i-th grid device in time period t. Let i be the power purchase capacity of the i-th grid device in time period t. Let i be the power consumption of the i-th power grid device in time period t. Let i be the power output of the i-th power grid device in time period t. The maximum allowable power ramp rate for the i-th power grid device is... Let i be the power generation capacity of the i-th grid device in time period t-1. The minimum power generation capacity of the i-th power grid device is... This represents the peak-shaving capacity that the i-th power grid device needs to provide during the dispatching cycle.

8. The method for comprehensive benefit evaluation and optimization of power grid equipment investment as described in any one of claims 1 to 4, characterized in that, The step in S6, which involves alternating between the region layer and the equipment layer until the rate of change in investment ratio and the rate of change in benefit are less than the convergence threshold, includes: S61. Calculate the rate of change of the investment ratio based on the following formula: ， Among them, the Let i be the investment weight of the i-th power grid device in the t-th iteration. The investment weight of the i-th power grid device in the (t-1)-th iteration is... For the minimum value, take the value of The number of power grid devices is n. S62. Calculate the rate of change of the aforementioned benefits based on the following formula: ， Among them, the Let be the comprehensive benefit index of the power system in the t-th iteration. This represents the comprehensive benefit index of the power system in the (t-1)th iteration. S63, when and When the iteration stops, the following occurs: and stated All of these are the convergence thresholds, ranging from 0.01 to 0.

05.

9. A comprehensive benefit evaluation and optimization system for power grid equipment investment, characterized in that, It includes a memory and a processor; the memory is used to store a computer program; when the program is executed by the processor, it implements the method for comprehensive evaluation and optimization of power grid equipment investment benefits as described in any one of claims 1 to 8.

10. A computer storage medium, characterized in that, It stores a computer program, characterized in that, when the computer program is executed by a processor, it implements the method for comprehensive benefit evaluation and optimization of power grid equipment investment as described in any one of claims 1 to 8.

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