A power transmission and distribution price dynamic simulation and linkage decision method

Abstract

This invention discloses a dynamic simulation and linkage decision-making method for transmission and distribution prices. First, it acquires data on new entities' access, voltage levels, pricing elements, and operational indicators. Based on this data, it performs feature quantification on a massive number of entities using algorithms such as sliding window, LSTM, and KDE. Then, it uses XGBoost in conjunction with a power flow model and a three-dimensional transmission model of power supply range, load share, and asset correlation for hybrid simulation. Subsequently, it utilizes sliding window monitoring and GNN to dynamically update the bidirectional linkage relationship between transmission and distribution prices and operational indicators, achieving four-level indicator penetration analysis from headquarters to province to city to county. Finally, it further constructs a multi-objective optimization model combining linear programming and LightGBM to output the optimal linkage decision-making scheme, thereby solving technical problems such as inaccurate price simulation, coarse transmission, missing indicator linkage, and resource allocation disconnection in the context of massive entity access.

Description

Technical Field

[0001] This invention relates to the technical fields of power system transmission and distribution price simulation, massive new entity access and operation decision optimization, and particularly to a method for dynamic simulation and linkage decision-making of transmission and distribution prices. Background Technology

[0002] With the continuous advancement of new power system construction, the scale of integration of new entities such as distributed energy resources, energy storage devices, and electric vehicle charging facilities into the power system continues to expand. The power grid operation mode is gradually shifting from the traditional one-way power supply model to a complex operation mode involving multiple entities and bidirectional interaction. Against this backdrop, transmission and distribution tariffs, as an important pricing mechanism reflecting the costs, benefits, and resource allocation efficiency of the power grid, are crucial. Their calculation results not only affect the cost recovery and reasonable profits of power grid companies but also directly influence distribution network investment decisions, user electricity consumption behavior, and the enthusiasm of new entities to participate.

[0003] Existing transmission and distribution pricing technologies primarily rely on assets, operating costs, and pricing parameters at different voltage levels, employing static cost allocation or fixed-ratio distribution methods. These methods typically assume a relatively stable power grid structure and load distribution, making it difficult to dynamically reflect the impact of new entities joining the grid on power flow distribution, asset utilization, and cost structure. Furthermore, current technologies often rely on ex-post analysis or one-way correlations with operational and business indicators, lacking systematic linkage modeling methods. This makes it difficult to characterize the feedback effects of price adjustments on distribution network upgrade investment decisions, the effectiveness of power grid business expansion, and user-side electricity consumption behavior.

[0004] In practical applications, with the increasing refinement of power grid management levels and the growing demands for sophisticated operations, traditional methods for calculating and deciding on transmission and distribution prices, which rely on experience-based judgment or single-objective analysis, are no longer sufficient to meet the needs of multi-objective collaborative optimization. Especially in scenarios with a large influx of new entities and diversified operational objectives, existing technologies struggle to comprehensively analyze the complex relationships between transmission and distribution prices, operational indicators, and business metrics, leading to a disconnect between pricing results and actual operational decisions. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a dynamic simulation and linkage decision-making method for transmission and distribution prices, which can be adapted to a large number of new types of entities accessing the market, and improve the precision of transmission and distribution price calculation and the reliability of decision results.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A dynamic simulation and linkage decision-making method for transmission and distribution prices includes the following steps: S1. Acquire new entity access and operation data, voltage level data, pricing element data and business indicator data related to transmission and distribution price calculation; standardize the acquired data; quantify the characteristics of the new entity; and generate new entity quantitative parameters. S2. Based on the quantitative parameters, the voltage level data, the pricing element data, and the business indicator data, a basic dataset is generated by correlation and integration. Based on the business indicator data, a linkage threshold and a transmission coefficient are fitted to construct a two-way linkage model between transmission and distribution prices and business indicators, as well as between transmission and distribution prices and business indicators. S3. Input the basic dataset into the preset simulation model and output the simulation results of transmission and distribution price; S4. Based on the simulation results of the transmission and distribution price and the two-way linkage model, generate the index linkage analysis results; S5. Based on the simulation results of the transmission and distribution price and the results of the index linkage analysis, with the improvement of operating indicators and business indicators as the dual decision-making objectives, a multi-objective optimization model is constructed, and the optimal linkage decision scheme is output using the multi-objective optimization model.

[0007] The beneficial effects of this invention are as follows: A dynamic simulation and linkage decision-making method for transmission and distribution prices, by acquiring data on new entity access and operation, voltage level data, pricing element data, and business operation indicator data, and introducing dynamic simulation of transmission and distribution prices, indicator linkage analysis, and multi-objective optimization decision-making into the same process, enables transmission and distribution price calculation to no longer be limited to static costs or single pricing parameters, but to comprehensively reflect the mutual influence between new entity access, changes in grid structure, and business operation objectives. By constructing a basic dataset and introducing a two-way linkage model, the synchronous analysis of the impact of transmission and distribution price changes on business and operational indicators is achieved. While ensuring the rationality of price calculation, it provides quantifiable and verifiable technical support for business decision-making, thereby improving the precision of transmission and distribution price calculation and the reliability of decision results. Attached Figure Description

[0008] Figure 1 This is a flowchart of a dynamic simulation and linkage decision-making method for transmission and distribution prices according to an embodiment of the present invention. Detailed Implementation

[0009] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0010] In existing technologies, transmission and distribution price calculation and decision analysis are mainly applied to scenarios such as power grid planning, cost supervision, and operation management. These calculations typically rely on asset data, operating cost data, and pricing parameters at different voltage levels. Current methods often employ static cost allocation or empirical proportional allocation to calculate transmission and distribution prices at different voltage levels, making it difficult to fully reflect the dynamic impact of large-scale integration of new entities (such as distributed energy resources, energy storage, and electric vehicle charging facilities) on the power grid's operational structure and cost composition. Furthermore, the price calculation results in existing technologies often exhibit a unidirectional or weak correlation with operational and business indicators, making it difficult to characterize the feedback effect of price adjustments on distribution network investment, user electricity consumption behavior, and business performance. In terms of application environment, with the advancement of new power system construction, power grid operation exhibits characteristics of multi-entity participation, multi-level management, and strong interaction. Traditional transmission and distribution price calculation methods based on static assumptions and single analytical dimensions are no longer sufficient to meet the needs of refined pricing and scientific decision-making.

[0011] To at least solve the above problems, please refer to Figure 1 This invention provides a method for dynamic simulation and linkage decision-making of transmission and distribution prices, including the following steps: S1. Acquire new entity access and operation data, voltage level data, pricing element data and business indicator data related to transmission and distribution price calculation, standardize the acquired data, quantify the characteristics of the new entity, and generate new entity quantitative parameters. S2. Based on the quantitative parameters, the voltage level data, the pricing element data, and the business indicator data, a basic dataset is generated by correlation and integration. Based on the business indicator data, a linkage threshold and a transmission coefficient are fitted to construct a two-way linkage model between transmission and distribution prices and business indicators, as well as between transmission and distribution prices and business indicators. S3. Input the basic dataset into the preset simulation model and output the simulation results of transmission and distribution price; S4. Based on the simulation results of the transmission and distribution price and the two-way linkage model, generate the index linkage analysis results; S5. Based on the simulation results of the transmission and distribution price and the results of the index linkage analysis, with the improvement of operating indicators and business indicators as the dual decision-making objectives, a multi-objective optimization model is constructed, and the optimal linkage decision scheme is output using the multi-objective optimization model.

[0012] As described above, the beneficial effects of this invention are as follows: By acquiring data on new entity access and operation, voltage level data, pricing element data, and business operation indicators, and by introducing dynamic simulation of transmission and distribution prices, indicator linkage analysis, and multi-objective optimization decision-making within the same methodology, transmission and distribution price calculation is no longer limited to static costs or single pricing parameters, but can comprehensively reflect the interrelationships between new entity access, changes in grid structure, and business operation objectives. By constructing a basic dataset and introducing a two-way linkage model, the impact of transmission and distribution price changes on operational and business indicators can be analyzed synchronously. While ensuring the rationality of price calculation, this provides quantifiable and verifiable technical support for business decision-making, thereby improving the precision of transmission and distribution price calculation and the reliability of decision-making results.

[0013] The "new entity access and operation data" refers to a dataset reflecting the structural characteristics and operational status of new power entities after their connection to the power grid. This dataset is used to characterize the impact of these new entities on grid operation and transmission and distribution pricing. In this invention, new entities include, but are not limited to, distributed power sources, energy storage devices, electric vehicle charging facilities, and virtual power plants. The new entity access and operation data includes at least information such as the access capacity, access location, operating sequence, power variation characteristics, and regulation capabilities of the new entity. This data reflects the dynamic impact of the new entity's access on grid load distribution, power flow structure, and asset utilization. By extracting and quantifying this data, the operational characteristics of the new entity can be incorporated as calculable parameters into subsequent transmission and distribution pricing simulation and linkage analysis processes.

[0014] Voltage-level segmented data refers to a dataset formed by dividing the power grid assets, loads, and operational characteristics according to different voltage levels. This data describes the cost structure and operational characteristics of each voltage level within the power grid structure. In this invention, the voltage-level segmented data includes at least information such as the power supply range, load scale, asset scale, asset utilization, and operating costs corresponding to each voltage level. This information supports the calculation of permissible revenue and cost transmission analysis for each voltage level. By introducing voltage-level segmented data, transmission and distribution price calculations can distinguish the differences between different voltage levels, avoiding the use of uniform proportions or static assumptions for cost allocation, thereby improving the precision of voltage-level segmented price calculations.

[0015] Pricing element data refers to the set of basic parameter data used for calculating transmission and distribution prices and permitted revenue, reflecting the costs, benefits, and regulatory requirements of the power grid. In this invention, the pricing element data includes at least information such as fixed asset depreciation parameters, operation and maintenance cost parameters, financial cost parameters, permitted rate of return, and asset utilization rate, used to construct a calculation model of "Permitted Revenue = Permitted Cost + Reasonable Profit". By parametrically configuring and performing sensitivity analysis on the pricing element data, the impact of changes in different pricing parameters on the transmission and distribution price calculation results can be assessed, providing boundary conditions and constraints for electricity price simulation and multi-objective decision-making.

[0016] Operational business indicator data refers to a set of data reflecting the operating status and business performance of a power grid company, used to support the linkage analysis between transmission and distribution price adjustments and business decisions. In this invention, the operational business indicator data includes at least information such as electricity sales volume, power supply reliability level, distribution network investment scale, changes in operating costs, and business development indicators, used to characterize the impact of electricity price changes on operating performance and business behavior. By introducing operational business indicator data into a two-way linkage model, dynamic analysis of the impact of transmission and distribution price changes on operational and business indicators is achieved, and the analysis results are fed back to the decision-making model, thereby improving the synergy between transmission and distribution price adjustments and business management.

[0017] In some embodiments, steps S1 and S2 include: Extract the access and operation data of the new entity, quantify the characteristics of the new entity through sliding window, LSTM, and KDE algorithms to obtain quantification parameters, and integrate the quantification parameters with the voltage level data, the pricing element data, and the business operation indicator data as the initial parameters of the XGBoost model, build a simulation model, and use the simulation model to generate a basic dataset. Obtain the power supply transmission coefficient, fit the linkage threshold and transmission coefficient based on historical data, and construct a two-way linkage model between transmission and distribution prices and operating and business indicators.

[0018] As described above, by introducing quantitative parameter extraction, permitted revenue calculation, and the construction of a two-way linkage model, this invention integrates the access characteristics of new entities, voltage level differences, and operational indicators into a unified modeling framework. On the one hand, by quantifying the operational characteristics of new entities and using them as initial parameters for the simulation model, subsequent transmission and distribution price simulations can accurately reflect the impact of new entities on grid operation and cost structure. On the other hand, by calculating permitted revenue at different voltage levels, the cost basis for each voltage level is clarified. Simultaneously, by introducing a power supply transmission coefficient to construct a two-way linkage model, a clear technical correlation is formed between electricity price adjustments and operational indicators, thereby improving the scientific rigor of linkage analysis and decision-making outputs from the outset.

[0019] In some implementations, step S3 specifically includes: S31. Input the basic dataset into the preset simulation model and output the simulation results of transmission and distribution price; S32. Based on the voltage level data, calculate the basic value of the allowable income for each voltage level according to the preset allowable income calculation rules, and output the allowable income calculation results for each voltage level. Based on the permitted revenue calculation results, a three-dimensional transmission model of power supply range, load ratio and asset correlation is constructed. The three-dimensional transmission model is used to perform trial calculations of electricity prices at all voltage levels, and the trial calculation results of transmission and distribution prices corresponding to each sub-voltage level are output. Through a system dynamics model, the chain effect of electricity price adjustment on power grid investment and user electricity consumption behavior is simulated.

[0020] S33. Based on the pricing element data, call the preset simulation model to generate sensitivity analysis results.

[0021] As described above, this invention simultaneously performs electricity price simulation, voltage level trial calculation, and sensitivity analysis of pricing factors during the transmission and distribution price simulation process. Based on a basic dataset, it outputs transmission and distribution price simulation results, enabling dynamic response capabilities in price calculation. By constructing a three-dimensional transmission model of power supply range, load share, and asset correlation, it achieves full voltage level electricity price trial calculation, avoiding the problem of coarse voltage level cost allocation in traditional methods. Simultaneously, sensitivity analysis identifies the degree of impact of changes in pricing factors on electricity prices, providing boundary conditions for subsequent decision-making, thereby comprehensively improving the accuracy and applicability of transmission and distribution price simulation.

[0022] In some implementations, the preset simulation model is a hybrid simulation model combining XGBoost and power system power flow calculation, and step S31 specifically includes: The preset simulation model is initially calibrated using historical new subject pricing data; Using a grid search method, the new main characteristic weights are dynamically allocated with the goal of minimizing the error between the simulated electricity price and the actual assessed price, and the preset simulation model is calibrated a second time. The basic dataset is input into the preset simulation model after secondary calibration to obtain the output simulation results of transmission and distribution prices.

[0023] As described above, by limiting the preset simulation model to a hybrid simulation model combining XGBoost and power system flow calculation, and introducing a multi-stage calibration mechanism, this invention effectively solves the problem that a single model cannot simultaneously accommodate the complex characteristics of new entities and the physical constraints of the power grid. Preliminary calibration is performed using historical pricing data, followed by a secondary calibration using a grid search method. This significantly reduces the deviation between simulated and actual electricity prices while maintaining the simulation model's generalization ability, thereby improving the reliability of transmission and distribution price simulation results and providing a stable and reliable data foundation for subsequent indicator linkage analysis and decision-making.

[0024] In some implementations, step S4 specifically includes: S41. Input the simulation results of the transmission and distribution price into the two-way linkage model, execute the two-way simulation of the preset forward link and the preset backward link, and output the index linkage analysis results. S42. Obtain the four-level execution criteria of "headquarters-province-city-county", and perform a step-by-step penetrating analysis on the execution criteria, outputting multi-level penetrating analysis results, input-output analysis reports, and quantitative results of the impact of new entities on indicators.

[0025] As described above, by introducing a two-way linkage model and performing multi-level penetration analysis, this invention can systematically analyze the impact path of transmission and distribution price changes on operational and business indicators. The two-way simulation of the forward and backward links makes the causal relationships between indicators clearer, while the multi-level penetration analysis can refine the analysis results from the overall level to different regions or management levels, thereby avoiding the bias caused by evaluating indicators at only a single level and providing technical support for refined management and regionally differentiated decision-making.

[0026] In some implementations, step S41 specifically includes: The simulation results of the transmission and distribution price are input into the bidirectional linkage model to perform bidirectional simulation of the preset forward link and the preset backward link; The preset forward link includes at least the associated electricity price adjustment link, cost change link, operating indicator fluctuation link and business indicator optimization link; The preset backward link includes at least the related business indicator improvement link, the operational indicator improvement link, the cost saving link, and the electricity price adjustment space calculation link; The correlation between each link of the preset forward link and the preset backward link is monitored by the sliding window method. When the correlation of any link is detected to change beyond a preset threshold, the GNN model is called to replace the correlation that exceeds the preset threshold. Based on the results of the bidirectional simulation, the results of the index linkage analysis are output.

[0027] As described above, the introduction of the sliding window method and GNN model into the two-way linkage analysis enables dynamic monitoring and updating of the correlation between indicators. When the relationship between indicators changes significantly, the linkage structure can be automatically adjusted to avoid distortion of analysis results due to the solidification of indicator relationships. This enhances the adaptability of the two-way linkage model to complex scenarios such as the access of new entities and electricity price fluctuations, and improves the real-time performance and accuracy of indicator linkage analysis.

[0028] In some implementations, step S5 specifically includes: S51. Based on the results of the linkage analysis of the aforementioned indicators, set decision objectives and constraints. The decision objectives include at least the improvement of operational indicators and the improvement of business indicators. The constraints include at least resource budget constraints and electricity price adjustment constraints. S52. Under the decision-making objectives and constraints, construct a multi-objective optimization model, taking the simulation results of the transmission and distribution price and the results of the index linkage analysis as inputs, and output multiple sets of candidate linkage decision schemes. S53. Optimal linkage decision-making scheme is determined by evaluating the target achievement, feasibility, and cost-effectiveness in three dimensions, and the optimal linkage decision-making scheme is input into the preset simulation model to output the linkage decision-making result.

[0029] As described above, by introducing a multi-objective optimization mechanism, this invention uses the simulation results of transmission and distribution prices and the results of index linkage analysis as unified inputs. Under the constraints of resource budgeting and electricity price adjustments, it simultaneously considers the improvement goals of operational and business indicators. This approach avoids the biases caused by single-objective optimization or experience-based judgment in traditional decision-making, ensuring that the decision-making process has clear technical logic and verifiable optimization results, thereby improving the rationality and feasibility of the linkage decision-making scheme.

[0030] In some implementations, step S52 specifically includes: A linear programming layer is set up to handle the linear constraints in the decision objective and constraints, and a LightGBM layer is set up to learn the nonlinear relationships in the decision objective and constraints. A multi-objective optimization model with the objective function MaxF = 0.6 × comprehensive improvement of business indicators + 0.4 × improvement of operating efficiency is constructed using the linear programming layer and the LightGBM layer. The simulation results of the transmission and distribution price and the results of the indicator linkage analysis are used as inputs to output multiple sets of candidate linkage decision schemes.

[0031] As described above, by implementing the multi-objective optimization model as a combination of a linear programming layer and a LightGBM layer, this invention can handle linear constraints and nonlinear relationships separately. While ensuring controllable decision constraints, it accurately characterizes the nonlinear influence between complex indicators. This structure achieves a balance between computational efficiency and decision accuracy in multi-objective optimization results, improves the quality of candidate linkage decision schemes, and provides a more reliable technical basis for final scheme selection.

[0032] Please refer to Figure 1 Embodiment 1 of the present invention is as follows: This embodiment provides a dynamic simulation and linkage decision-making method for transmission and distribution prices, applicable to scenarios involving transmission and distribution price calculation, linkage analysis of business indicators, and auxiliary decision-making under the background of large-scale access by new entities. This method achieves closed-loop linkage between transmission and distribution price simulation results and business decisions by uniformly modeling and collaboratively analyzing the operating characteristics of new entities, the voltage level structure of the power grid, pricing elements, and business indicators.

[0033] Including the following steps: S1. Acquire massive amounts of new entity access and operation data, voltage level data, pricing element data, and business indicator data related to transmission and distribution price calculation. Standardize the acquired data and quantify the characteristics of the new entities to generate quantitative parameters for the new entities. Specifically, the new entity access and operation data reflects the access scale and operational status of new entities such as distributed photovoltaics, energy storage, electric vehicle charging facilities, and virtual power plants; the voltage level data characterizes the asset structure, load distribution, and operational characteristics at different voltage levels; the pricing element data reflects the cost and revenue parameters required for permitted revenue calculation; and the business indicator data characterizes operational and business-related indicators such as electricity sales, asset profitability, and power supply reliability. It is necessary to extract the quantitative parameters from the new entity access and operation data.

[0034] S2. Based on the quantitative parameters, voltage level data, pricing element data, and business indicator data, a basic dataset is generated by correlation and integration. Based on the business indicator data, a linkage threshold and transmission coefficient are fitted to construct a two-way linkage model between transmission and distribution prices and business indicators.

[0035] Specifically, step S2 further includes: S21. The quantified parameters are correlated and integrated with voltage level data, pricing element data, and operational business indicator data as initial parameters for the XGBoost model (max_depth=8, learning_rate=0.05, n_estimators=100). The nodal power balance equations for power system flow calculation (`ΣP_in=ΣP_out+P_loss`) are defined, and a simulation model consisting of an input layer, a feature layer, a simulation layer, and an output layer is constructed. The simulation model is then used to generate a basic dataset. Through this method, the operational characteristics of the new entity are explicitly introduced into the simulation model input, enabling the basic dataset to realistically reflect the impact of the new entity's access on grid operation and cost structure.

[0036] S22. Based on the data of different voltage levels, calculate the basic value of the allowable revenue for each voltage level according to the preset allowable revenue calculation rules, and output the allowable revenue calculation results of different voltage levels, thereby clarifying the cost and revenue boundaries corresponding to different voltage levels.

[0037] Specifically, a formula for calculating permitted revenue by voltage level is constructed based on the principle of "permitted revenue = permitted cost + reasonable profit". The permitted cost includes fixed asset depreciation, operation and maintenance costs, and financial expenses.

[0038] In this embodiment, the depreciation of fixed assets is calculated as follows: Fixed asset depreciation = compliant assets × depreciation rate The depreciation period is set according to the voltage level: 25 years for 500kV voltage level, 20 years for 220kV voltage level, and 15 years for 110kV and below voltage level; the depreciation rate is determined based on the depreciation period of the corresponding voltage level.

[0039] The reasonable profit is calculated as follows: Reasonable return = Effective assets × Permissible rate of return The permitted rate of return is set at 6.5%; the effective assets are determined by both compliant assets and asset utilization rate, and are calculated as follows: Effective assets = Compliant assets × Asset utilization rate Asset utilization rate is calculated as the ratio of actual operating time to designed operating time.

[0040] After calculating the above parameters, the fixed asset depreciation, operation and maintenance costs, financial expenses, and reasonable profits corresponding to each voltage level are substituted into the permitted revenue calculation formula to calculate the basic value of permitted revenue for each sub-voltage level, and the permitted revenue calculation results for each voltage level are output. Through this method, a unified and comparable calculation of permitted revenue for different voltage levels is achieved, providing a reliable cost and revenue basis for subsequent electricity price transmission and simulation analysis.

[0041] S23. Obtain the power supply transmission coefficient, fit the linkage threshold and transmission coefficient based on historical data, and construct a two-way linkage model between transmission and distribution price and operating and business indicators, so that a clear technical correlation is formed between changes in transmission and distribution price and operating and business indicators.

[0042] Specifically, the standardized operational and business indicator data are first integrated, and based on a pre-built relational database, initial relational rules for "electricity price adjustment - cost change - operational indicators - business indicators" are established to describe the transmission path of electricity price changes at the cost, operational, and business levels.

[0043] In this embodiment, historical operating data from the past three years for a preset area are further introduced to perform fitting analysis on the initial association rules in order to determine the linkage threshold and transmission coefficient of the indicators. For example, by fitting historical data, a linkage threshold relationship is obtained in which electricity sales increase by 3%–5% when the electricity price decreases by 5%; and a transmission coefficient relationship is obtained in which power supply reliability increases by 0.8%–1.2% when the investment in distribution network transformation increases by 10%.

[0044] Based on the aforementioned linkage threshold and transmission coefficient, the initial association rules are parameterized to construct a two-way linkage model framework. This framework enables electricity price changes to be transmitted forward to costs, operational indicators, and business indicators, and also to provide feedback backward on the impact of changes in business indicators on the electricity price adjustment space. Finally, the basic model of indicator linkage and its corresponding association parameters are output for subsequent indicator linkage analysis and linkage decision calculation.

[0045] S3. Input the basic dataset into the preset simulation model and output the simulation results of transmission and distribution prices.

[0046] Specifically, step S3 includes: S31. Input the basic dataset into the preset simulation model and output the simulation results of transmission and distribution price, so that the calculation of transmission and distribution price has dynamic response capability.

[0047] S32. Based on the permitted revenue calculation results, a three-dimensional transmission model of power supply range, load ratio and asset correlation is constructed, and the three-dimensional transmission model is used to perform full voltage level electricity price trial calculation, and output the transmission and distribution price trial calculation results corresponding to each sub-voltage level, thereby avoiding the problem of the crudeness of the traditional voltage level cost allocation method.

[0048] Among them, the power supply range ratio is used to reflect the proportion of each voltage level in the spatial power supply range. It is calculated as follows: Power supply range ratio = Power supply area corresponding to the voltage level / Total power supply area. Load percentage, used to reflect the relative contribution of each voltage level to the system load, is calculated as follows: Load percentage = Annual maximum load of this voltage level / Total annual maximum load of all voltage levels; Asset correlation is used to reflect the asset sharing relationship between low voltage level and high voltage level. It is calculated as follows: Asset correlation = Value of assets shared by the low voltage level and high voltage level / Total asset value corresponding to the high voltage level.

[0049] In this embodiment, based on the actual power supply operation, different weights are assigned to the above three-dimensional indicators, and the calculation formula for the electricity price transmission sharing ratio is as follows: Conductive load sharing ratio = Power supply range percentage × 0.4 + Load percentage × 0.4 + Asset correlation degree × 0.2 After completing the calculation of the conduction allocation ratio, follow the procedure below to complete the trial calculation of transmission and distribution prices by voltage level: First, the permitted revenue corresponding to the high voltage level is used as the starting point for transmission; Secondly, according to the aforementioned conduction sharing ratio, the permitted revenue of the high voltage level is allocated to each low voltage level; Then, the allocated permitted revenue is added to the permitted cost of the corresponding low voltage level itself; Finally, the transmission and distribution price calculations for all voltage levels from 500kV to 10kV were completed, and the calculation results for the transmission and distribution prices for each sub-voltage level were output.

[0050] In some implementations, to further analyze the systemic impact of electricity price adjustments, a system dynamics model is introduced based on the trial calculation results of the transmission and distribution prices at different voltage levels. This model is used to simulate and analyze the cascading effects of electricity price adjustments on grid investment decisions and user electricity consumption behavior. These cascading effects include at least the impact on the willingness to invest in distribution network upgrades and the peak-shifting response behavior of user-side charging facilities.

[0051] This embodiment achieves refined cost transmission across all voltage levels from 500kV to 10kV, significantly improving the accuracy of voltage level-specific electricity price calculations. Compared to traditional cost allocation methods based on static proportions, the voltage level cost transmission accuracy in this embodiment can be controlled within 0.1%, avoiding the 10%–18% deviation problem present in traditional methods. Simultaneously, the three-dimensional transmission model can dynamically reflect the impact of electricity price adjustments on the willingness to upgrade the distribution network and user-side electricity consumption behavior, providing closed-loop technical support for tiered pricing and coordinated decision-making.

[0052] S33. Based on the pricing factor data, call the preset simulation model to generate sensitivity analysis results, which are used to identify the degree of impact of changes in pricing factors on the simulation results of transmission and distribution prices.

[0053] In this embodiment, the preset simulation model is a hybrid simulation model combining XGBoost and power system power flow calculation. Specifically, in step S31, the preset simulation model is first initially calibrated using historical new main body pricing data. Then, a grid search method is used to perform a second calibration of the preset simulation model with the goal of minimizing the error between the simulated electricity price and the actual price. Finally, the basic dataset is input into the second-calibrated preset simulation model, and the transmission and distribution price simulation results are output. Through multi-stage calibration, the simulation model significantly reduces simulation bias while maintaining its generalization ability.

[0054] S4. Based on the simulation results of the transmission and distribution price and the two-way linkage model, generate the index linkage analysis results.

[0055] Specifically, step S4 includes: S41. Input the transmission and distribution price simulation results into the two-way linkage model, execute the preset forward link and preset backward link two-way simulation, and output the index linkage analysis results. Among them, the forward link includes at least the links of electricity price adjustment, cost change, fluctuation of operating indicators, and optimization of business indicators; the backward link includes at least the links of business indicator improvement, improvement of operating indicators, cost saving, and calculation of electricity price adjustment space.

[0056] S42. Obtain the four-level execution criteria of "headquarters-province-city-county" and perform a step-by-step penetrating analysis on the execution criteria. Output the multi-level penetrating analysis results, input-output analysis report and quantitative results of the impact of new entities on indicators, thereby refining the indicator linkage analysis results to different regions or management levels.

[0057] S41 further includes: monitoring the correlation between each link in the forward and backward links using the sliding window method; when the correlation of a certain link is detected to exceed a preset threshold, calling the GNN model to replace the correlation that exceeds the threshold, so as to realize the dynamic update of the correlation of indicators.

[0058] S5. Based on the simulation results of the transmission and distribution price and the results of the index linkage analysis, with the improvement of operating indicators and business indicators as the dual decision-making objectives, construct a multi-objective optimization model and output the optimal linkage decision-making scheme.

[0059] Specifically, step S5 includes: S51. Based on the results of the indicator linkage analysis, set decision objectives and constraints. The decision objectives include at least the improvement of operating indicators and business indicators, and the constraints include at least resource budget constraints and electricity price adjustment constraints.

[0060] S52. Under the stated decision objectives and constraints, construct a multi-objective optimization model, and output multiple sets of candidate linkage decision schemes by taking the simulation results of transmission and distribution prices and the results of index linkage analysis as inputs.

[0061] S53. Optimal linkage decision-making scheme is determined by evaluating the target achievement, feasibility, and cost-effectiveness in three dimensions. The optimal linkage decision-making scheme is then input into the preset simulation model for verification, and the final linkage decision-making result is output.

[0062] Step S52 specifically includes: setting up a linear programming layer to handle linear constraints in the decision objectives and constraints, and setting up a LightGBM layer to learn the nonlinear relationships in the decision objectives and constraints (parameter settings: number of decision trees 100, maximum depth 8, learning rate 0.05). By combining the linear programming layer and the LightGBM layer, a multi-objective optimization model with the objective function MaxF = 0.6 × comprehensive improvement of business indicators + 0.4 × improvement of operating efficiency is constructed, thereby accurately characterizing the nonlinear effects between complex indicators while ensuring that the constraints are controllable.

[0063] This invention first introduces a novel subject characteristic quantification mechanism to transform the abstract characteristics of the new subject, such as intermittency, adjustability, and operational uncertainty, into high-precision quantitative parameters, which serve as the basic input for the simulation model. This fundamentally solves the problem of the lack of accurate input in traditional transmission and distribution price simulation models, providing a reliable data foundation for subsequent simulation, transmission, and decision-making processes, and enabling the entire technology chain to have calculable and verifiable input conditions.

[0064] Secondly, the synergistic application of a hybrid simulation architecture and a three-dimensional transmission model forms a dual-core technical support system of simulation and transmission. On the one hand, the hybrid simulation architecture, while meeting the physical constraints of the power system, can accurately characterize the impact of new entities' access on transmission and distribution prices. On the other hand, by constructing a three-dimensional transmission model of "power supply range—load share—asset correlation," the cost transmission logic across all voltage levels from 500kV to 10kV is clarified. Compared with the traditional allocation method based on static proportions, this invention significantly reduces pricing errors from over 10% to within 2%, improves cost transmission accuracy by an order of magnitude, and significantly enhances the accuracy of voltage level-specific electricity price calculations.

[0065] Furthermore, by constructing a two-way linkage model between transmission and distribution prices and operational and business indicators, the correlation between electricity prices and indicators is established, enabling dynamic analysis and feedback on the impact of electricity price changes on operational and business indicators. This two-way linkage mechanism overcomes the problems of static and fixed linkage relationships and single analytical dimensions in traditional technologies, allowing the simulation results of transmission and distribution prices to accurately reflect business performance at multiple levels and dimensions, significantly enhancing the guiding value of electricity price calculation results in business management.

[0066] Furthermore, by introducing a multi-objective optimization model, the simulation results of transmission and distribution prices, the results of indicator linkage analysis, and resource allocation decisions are organically combined. Under the premise of meeting the constraints of electricity price regulation and resource budget, the synergistic improvement of operational and business indicators is achieved. Through a closed-loop verification mechanism, it is ensured that the decision-making scheme not only meets the requirements of electricity price regulation but also maximizes business effectiveness. Compared with traditional experience-based or single-objective optimization methods, this achieves an improvement of approximately 15%–20% in business indicators and a cost saving of approximately 8%–12%, significantly improving resource allocation efficiency.

[0067] In summary, this invention not only solves individual technical problems such as modeling of new entity characteristics, simulation of transmission and distribution prices, cost transmission, indicator linkage, and decision optimization, but also achieves comprehensive technical progress at the system level in terms of precise pricing, dynamic linkage, and intelligent decision-making. For the first time, it has constructed a closed-loop technical system for the entire chain of electricity price simulation, indicator linkage, and resource allocation in a scenario with massive new entity access, providing key technical support for two-way interaction and refined pricing in new power systems.

[0068] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A dynamic simulation and linkage decision-making method for transmission and distribution prices, characterized in that, Including the following steps: S1. Acquire new entity access and operation data, voltage level data, pricing element data and business indicator data related to transmission and distribution price calculation, standardize the acquired data, quantify the characteristics of the new entity, and generate new entity quantitative parameters. S2. Based on the quantitative parameters, the voltage level data, the pricing element data, and the business indicator data, a basic dataset is generated by correlation and integration. Based on the business indicator data, a linkage threshold and a transmission coefficient are fitted to construct a two-way linkage model between transmission and distribution prices and business indicators, as well as between transmission and distribution prices and business indicators. S3. Input the basic dataset into the preset simulation model and output the simulation results of transmission and distribution price; S4. Based on the simulation results of the transmission and distribution price and the two-way linkage model, generate the index linkage analysis results; S5. Based on the simulation results of the transmission and distribution price and the results of the index linkage analysis, with the improvement of operating indicators and business indicators as the dual decision-making objectives, a multi-objective optimization model is constructed, and the optimal linkage decision scheme is output using the multi-objective optimization model.

2. The method for dynamic simulation and linkage decision-making of transmission and distribution prices according to claim 1, characterized in that, Step S3 further includes: Based on the voltage level data, the basic value of the permissible income for each voltage level is calculated according to the preset permissible income calculation rules, and the permissible income calculation results for each voltage level are output. Based on the permitted revenue calculation results, a three-dimensional transmission model of power supply range, load ratio and asset correlation is constructed. The three-dimensional transmission model is used to perform trial calculations of electricity prices at all voltage levels, and the trial calculation results of transmission and distribution prices corresponding to each sub-voltage level are output. Based on the pricing element data, the preset simulation model is invoked to generate sensitivity analysis results.

3. The method for dynamic simulation and linkage decision-making of transmission and distribution prices according to claim 2, characterized in that, The preset simulation model is a hybrid simulation model of XGBoost and power system power flow calculation. Step S31 specifically includes: The preset simulation model is initially calibrated using historical new subject pricing data; Using a grid search method, the new main characteristic weights are dynamically allocated with the goal of minimizing the error between the simulated electricity price and the actual assessed price, and the preset simulation model is calibrated a second time. The basic dataset is input into the preset simulation model after secondary calibration to obtain the output simulation results of transmission and distribution prices.

4. The method for dynamic simulation and linkage decision-making of transmission and distribution prices according to claim 2, characterized in that, Step S4 specifically includes: S41. Input the simulation results of the transmission and distribution price into the two-way linkage model, execute the two-way simulation of the preset forward link and the preset backward link, and output the index linkage analysis results. S42. Obtain the four-level execution criteria of "headquarters-province-city-county", and perform a step-by-step penetrating analysis on the execution criteria, outputting multi-level penetrating analysis results, input-output analysis reports, and quantitative results of the impact of new entities on indicators.

5. The method for dynamic simulation and linkage decision-making of transmission and distribution prices according to claim 4, characterized in that, Step S41 specifically includes: The simulation results of the transmission and distribution price are input into the bidirectional linkage model to perform bidirectional simulation of the preset forward link and the preset backward link; The preset forward link includes at least the associated electricity price adjustment link, cost change link, operating indicator fluctuation link and business indicator optimization link; The preset backward link includes at least the related business indicator improvement link, the operational indicator improvement link, the cost saving link, and the electricity price adjustment space calculation link; The correlation between each link of the preset forward link and the preset backward link is monitored by the sliding window method. When the correlation of any link is detected to change beyond a preset threshold, the GNN model is called to replace the correlation that exceeds the preset threshold. Based on the results of the bidirectional simulation, the results of the index linkage analysis are output.

6. The method for dynamic simulation and linkage decision-making of transmission and distribution prices according to claim 4, characterized in that, Step S5 specifically includes: S51. Based on the results of the linkage analysis of the aforementioned indicators, set decision objectives and constraints. The decision objectives include at least the improvement of operational indicators and the improvement of business indicators. The constraints include at least resource budget constraints and electricity price adjustment constraints. S52. Under the decision-making objectives and constraints, construct a multi-objective optimization model, taking the simulation results of the transmission and distribution price and the results of the index linkage analysis as inputs, and output multiple sets of candidate linkage decision schemes. S53. Optimal linkage decision-making scheme is determined by evaluating the target achievement, feasibility, and cost-effectiveness in three dimensions, and the optimal linkage decision-making scheme is input into the preset simulation model to output the linkage decision-making result.

7. The method for dynamic simulation and linkage decision-making of transmission and distribution prices according to claim 4, characterized in that, Step S52 specifically includes: A linear programming layer is set up to handle the linear constraints in the decision objective and constraints, and a LightGBM layer is set up to learn the nonlinear relationships in the decision objective and constraints. A multi-objective optimization model with the objective function MaxF = 0.6 × comprehensive improvement of business indicators + 0.4 × improvement of operating efficiency is constructed using the linear programming layer and the LightGBM layer. The simulation results of the transmission and distribution price and the results of the indicator linkage analysis are used as inputs to output multiple sets of candidate linkage decision schemes.

8. The method for dynamic simulation and linkage decision-making of transmission and distribution prices according to claim 1, characterized in that, The characteristics of the novel entity are quantified to generate quantitative parameters for the novel entity. Based on the quantitative parameters, the voltage level data, the pricing element data, and the business operation indicator data, a basic dataset is generated through correlation and integration, specifically including: Extract the access and operation data of the new entity, quantify the characteristics of the new entity to obtain quantification parameters, and integrate the quantification parameters with the voltage level data, the pricing element data and the business operation indicator data as the initial parameters of the XGBoost model to build a preset simulation model and use the preset simulation model to generate a basic dataset.

9. The method for dynamic simulation and linkage decision-making of transmission and distribution prices according to claim 1, characterized in that, In step S2, the step of constructing a two-way linkage model between transmission and distribution prices and operational indicators, and between transmission and distribution prices and operational indicators, based on the operational business indicator data, specifically includes: Obtain the power supply transmission coefficient, fit the linkage threshold and transmission coefficient based on historical data, and construct a two-way linkage model between transmission and distribution prices and operating and business indicators.

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