Power transmission and distribution price structure adjustment method and device, storage medium and electronic equipment

Abstract

The invention discloses a power transmission and distribution price structure adjustment method and device, a storage medium and electronic equipment. The method comprises the following steps: for a target power grid comprising new energy power, obtaining a plurality of historical index values of a target index in the target power grid, the target index being an index having an influence on a power transmission and distribution price composition structure in the target power grid, and the power transmission and distribution price composition structure changing along with the power generation amount change of the new energy power in the target power grid; generating a plurality of predicted index values of the target index in the future time period based on the plurality of historical index values; performing time sequence simulation processing according to a predetermined power scene, and determining an index evaluation threshold value in a future time period; based on the plurality of prediction index values and the index evaluation threshold, determining an electricity price evaluation result in a future time period; and determining a power transmission and distribution price adjustment strategy of the target power grid in the future time period according to an electricity price evaluation result. The technical problem that the dynamic electricity price evaluation result of the power transmission and distribution price is not ideal in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of power systems, and in particular, to a method, device, storage medium, and electronic device for adjusting the transmission and distribution price structure. Background Technique

[0002] With the advancement of the construction of the new power system, the transmission and distribution price structure and the price approval model have been gradually optimized. However, with the large-scale development of new energy, the current transmission and distribution price structure still needs to be further improved. To meet the requirements of the transformation of the transmission and distribution price structure, the adaptive evaluation of the transmission and distribution price at different stages of the new power system has become an important topic in the power industry. The related technologies mainly adopt the method of cost analysis, that is, the transmission and distribution price is formulated based on the power generation cost, the grid maintenance cost, and the market supply and demand situation. However, with the continuous increase in the new energy penetration rate, the current electricity price system faces new challenges. The new energy power generation cost is low, but the volatility is high, resulting in large fluctuations in the electricity market price. The existing electricity price evaluation methods are difficult to adapt to the volatility of the transmission and distribution price, resulting in an inability to accurately evaluate whether the current transmission and distribution price is suitable for the construction of the new power system, and there is a problem that the dynamic electricity price evaluation result of the transmission and distribution price is not ideal.

[0003] In response to the above problems, no effective solution has been proposed yet. Summary of the Invention

[0004] The embodiments of the present application provide a method, device, storage medium, and electronic device for adjusting the transmission and distribution price structure, so as to at least solve the technical problem that the dynamic electricity price evaluation result of the transmission and distribution price in the related technology is not ideal.

[0005] According to an aspect of the embodiments of the present application, a method for adjusting the transmission and distribution price structure is provided, including: for a target power grid including new energy power, obtaining multiple historical index values of a target index in the target power grid, where the target index is an index that affects the composition structure of the transmission and distribution price in the target power grid, and the composition structure of the transmission and distribution price changes with the power generation amount of the new energy power in the target power grid; generating multiple predicted index values of the target index in a future time period based on the multiple historical index values; performing time series simulation processing according to a predetermined power scenario to determine an index evaluation threshold in the future time period; determining an electricity price evaluation result in the future time period based on the multiple predicted index values and the index evaluation threshold; and determining a transmission and distribution price adjustment strategy for the target power grid in the future time period according to the electricity price evaluation result.

[0006] Optionally, there is a time series relationship among the multiple historical index values. Generating multiple predicted index values of the target index in a future time period based on the multiple historical index values includes: determining a target parameter reflecting the change trend of the target index based on the multiple historical index values; and using the target parameter to perform data fitting based on the multiple historical index values to determine the multiple predicted index values.

[0007] Optionally, the index evaluation threshold includes multiple segment thresholds. Based on multiple predicted index values and the index evaluation threshold, determining the electricity price evaluation result for a future time period includes: segmenting the multiple predicted index values according to the multiple segment thresholds to obtain segment score values corresponding to the multiple predicted index values respectively; and determining the electricity price evaluation result of the target index based on the segment score values corresponding to the multiple predicted index values respectively.

[0008] Optionally, there are multiple target indexes. Based on multiple predicted index values and the index evaluation threshold, determining the electricity price evaluation result for a future time period includes: determining the relative weight corresponding to the future time period based on the multiple predicted index values respectively corresponding to each target index; in the case where there are multiple future time periods, obtaining the fitness corresponding to each of the multiple future time periods based on the relative weights respectively corresponding to the multiple future time periods, where the fitness represents the similarity between the composition structure of the transmission and distribution electricity price corresponding to the future time period and a predetermined plan, and the predetermined plan is determined based on the dispatching scheme of new energy power in the target power grid; and determining the electricity price evaluation result based on the fitness respectively corresponding to the multiple future time periods and the index evaluation threshold.

[0009] Optionally, obtaining the fitness corresponding to each of the multiple future time periods based on the relative weights respectively corresponding to the multiple future time periods includes: for a single time period among the multiple future time periods, determining the group utility value corresponding to the single time period based on the relative weight corresponding to the single time period, the multiple predicted index values corresponding to each target index, and the predetermined positive and negative ideal solutions of the index, where the group utility value is used to represent the deviation degree of all index values in the single time period from the positive and negative ideal solutions of the index; determining the individual regret value corresponding to the single time period based on the relative weight corresponding to the single time period, the multiple predicted index values corresponding to each target index, and the predetermined positive and negative ideal solutions of the index, where the individual regret value represents the deviation degree of a single index value in the single time period from the positive and negative ideal solutions of the index; determining the compromise decision value of the single time period according to the group utility value and the individual regret value corresponding to the single time period; and determining the fitness of the single time period based on the compromise decision value of the single time period.

[0010] Optionally, determining the relative weight corresponding to the future time period based on the multiple predicted index values respectively corresponding to each target index includes: performing normalization processing on the multiple predicted index values respectively corresponding to each target index to obtain the comparison elements of each target index in the future time period; obtaining the comparison sequence corresponding to the future time period based on the comparison elements respectively corresponding to the multiple target indexes in the future time period; and determining the relative weight corresponding to the future time period based on the comparison sequence corresponding to the future time period, the predetermined positive ideal solution of the index, and the predetermined negative ideal solution of the index.

[0011] Optionally, the target indicator is at least one of the following: the proportion of new energy installed capacity, the power carbon emission intensity, the forced outage rate of transmission and transformation equipment, the average power supply reliability rate, the annual electricity sales volume, the proportion of energy storage capacity, the power supply line loss rate, and the source-load matching confidence level.

[0012] According to another aspect of the embodiments of the present application, there is provided a device for adjusting the transmission and distribution price structure, including: a historical indicator value acquisition module, configured to acquire multiple historical indicator values of a target indicator in a target power grid including new energy power, where the target indicator is an indicator that affects the composition structure of the transmission and distribution price in the target power grid, and the composition structure of the transmission and distribution price changes with the power generation amount of new energy power in the target power grid; a predicted indicator value acquisition module, configured to generate multiple predicted indicator values of the target indicator in a future time period based on the multiple historical indicator values; an evaluation threshold determination module, configured to perform time series simulation processing according to a predetermined power scenario to determine an indicator evaluation threshold in the future time period; an evaluation result determination module, configured to determine a price evaluation result of the future time period based on the multiple predicted indicator values and the indicator evaluation threshold; and an adjustment strategy determination module, configured to determine a transmission and distribution price adjustment strategy for the target power grid in the future time period according to the price evaluation result.

[0013] According to another aspect of the embodiments of the present application, there is provided a non-volatile storage medium storing multiple instructions adapted to be loaded and executed by a processor to perform the transmission and distribution price structure adjustment method according to any one of the above.

[0014] According to another aspect of the embodiments of the present application, there is provided an electronic device including: one or more processors and a memory, where the memory is used to store one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the transmission and distribution price structure adjustment method according to any one of the above.

[0015] In an embodiment of the present application, for a target power grid including new energy power, a plurality of historical index values of a target index in the target power grid are obtained, where the target index is an index that affects the composition structure of the transmission and distribution electricity price in the target power grid, and the composition structure of the transmission and distribution electricity price changes with the power generation amount of the new energy power in the target power grid; based on the plurality of historical index values, a plurality of predicted index values of the target index in a future time period are generated; time series simulation processing is performed according to a predetermined power scenario to determine an index evaluation threshold in the future time period; based on the plurality of predicted index values and the index evaluation threshold, a power price evaluation result in the future time period is determined; according to the power price evaluation result, a transmission and distribution electricity price adjustment strategy for the target power grid in the future time period is determined. The purpose of determining the weight of the target index and using the time series production simulation method to determine the power price evaluation result is achieved, the technical effect of improving the accuracy of the dynamic power price evaluation result of the transmission and distribution electricity price is realized, and furthermore, the technical problem of the unsatisfactory dynamic power price evaluation result of the transmission and distribution electricity price in the related art is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments and descriptions thereof of the present application are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings:

[0017] Figure 1 is a flowchart of an optional method for adjusting the transmission and distribution electricity price structure provided by an embodiment of the present application;

[0018] Figure 2 is a schematic diagram of an optional method for adjusting the transmission and distribution electricity price structure provided by an embodiment of the present application;

[0019] Figure 3 is a schematic diagram of an optional device for adjusting the transmission and distribution electricity price structure provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] In order to enable those skilled in the art to better understand the solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0021] It should be noted that the terms "first", "second", etc. in the description, claims and the above-mentioned drawings of this application are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0022] For ease of description, some of the nouns or terms related to the embodiments of this application are described below:

[0023] VIKOR (VIseKriterijumska Optimizacija I Komoromiracija, multi-criteria compromise solution ranking method) is a multi-criteria decision-making method used to determine the optimal decision-making solution for all decision-making criteria among a set of decision-making solutions (or projects, products, etc.). This method can simplify the multi-criteria decision-making problem into a single-criteria decision-making problem, which is convenient for decision-makers to understand and select.

[0024] Chronological production simulation is a technology used for power system analysis. By simulating the actual or hypothetical operating state of the power system and considering time series characteristics such as seasonality, daily variations, and long-term trends, it can predict system performance and evaluate the impact of different scenarios or scheduling strategies.

[0025] Grey relational analysis is a statistical method used to evaluate the degree of association between different sequences and is used to process data with incomplete information or grey systems. This method is particularly suitable for the analysis of time series data, can reveal the degree of association between different factors, and does not require assuming a causal relationship or statistical independence between sequences. It is widely used in decision support and trend analysis in fields such as power systems and environmental assessment.

[0026] Grey GM model (Grey Prediction Model) is a mathematical model for prediction in the case of incomplete information or insufficient data. It is suitable for processing data sequences with "grey" characteristics, that is, data sequences with a small amount of data, large fluctuations in the sequence, or a certain degree of uncertainty. This method performs cumulative generation processing on the original data sequence to transform it into a data sequence with relatively stable characteristics, then constructs a differential equation based on this sequence, and predicts future data by solving this equation.

[0027] The Proximate Mean Generation Sequence is an important concept in grey system theory for data smoothing and predictive analysis. Since the original data sequence may contain random fluctuations or noise, which can interfere with the accurate analysis and prediction of data trends, the Proximate Mean Generation Sequence converts the original sequence into a smoother and more trend - obvious sequence through a specific data - processing method, providing a more reliable data basis for subsequent grey prediction models.

[0028] According to an embodiment of the present application, an embodiment of a method for adjusting the transmission and distribution price structure is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer - executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0029] Figure 1 It is a flowchart of the method for adjusting the transmission and distribution price structure according to an embodiment of the present application. As Figure 1 shown, the method includes the following steps:

[0030] Step S102, for a target power grid including new - energy power, obtain multiple historical index values of a target index in the target power grid, where the target index is an index that affects the composition structure of the transmission and distribution price in the target power grid, and the composition structure of the transmission and distribution price changes with the power generation of new - energy power in the target power grid;

[0031] It can be understood that since the change in the power generation of new - energy power will cause the corresponding change in the composition structure of the transmission and distribution price of the target power grid, therefore, for a specific target power grid containing new - energy power, it is necessary to determine the target index of the target power grid according to the degree of influence on the composition structure of the transmission and distribution price in the target power grid. According to the above - mentioned target index, obtain multiple historical index values of the target index. By obtaining and analyzing the historical index values of the target index, the trend of the price structure changing with the new - energy power generation can be identified, which helps to predict the future change direction of the electricity price and provides a data basis for the dynamic adjustment of the price structure.

[0032] In an alternative embodiment, the target index is at least one of the following: the proportion of new - energy installed capacity, the power carbon - emission intensity, the forced outage rate of transmission and transformation equipment, the average power - supply reliability rate, the annual electricity sales volume, the proportion of energy - storage capacity, the power - supply line loss rate, and the source - load matching confidence.

[0033] It can be understood that according to the degree of influence on the composition structure of the transmission and distribution price in the target power grid, the target indicators of the target power grid are determined. The target indicators can be one or more of the new energy installation ratio, power carbon emission intensity, forced outage rate of transmission and transformation equipment, average power supply reliability rate, annual electricity sales volume, energy storage capacity ratio, power supply line loss rate, source-load matching confidence level, power grid investment, and digital development index score. By considering multiple target indicators that affect the composition structure of the transmission and distribution price, a more comprehensive evaluation perspective is provided for the adaptability evaluation of the transmission and distribution price, avoiding the one-sidedness of single-index evaluation, and ensuring that the electricity price strategy can comprehensively support the green, low-carbon, safe and controllable development of the power system.

[0034] Optionally, the proportion of new energy installed capacity refers to the proportion of the installed capacity of new energy power generation equipment (such as solar energy, wind energy, hydropower, biomass energy, etc.) in a region to the total installed capacity, which is an important indicator to measure the degree of green transformation of the power system and the development level of renewable energy. The carbon emission intensity of electricity refers to the amount of carbon dioxide emissions generated during the production or consumption of unit electricity, which is an indicator to evaluate the contribution degree of the power industry to climate change and reflects the cleanliness and energy efficiency level of electricity production. The forced outage rate of transmission and transformation equipment (abbreviated as FOR or FORR) is an indicator in evaluating the operation reliability of the power system. It reflects the frequency of unplanned outages of transmission and transformation equipment during operation, that is, the situation where the equipment has to suddenly stop running due to faults or abnormalities. The average service availability index (ASAI) refers to the percentage of the effective power supply time of the power system to users within a certain period (such as one year), which is an indicator to measure the continuity and reliability of power grid power supply. The higher the average service availability index, the fewer the number and duration of power supply interruptions, and the better the quality of power supply services. The annual electricity sales volume refers to the total electricity volume sold by the power company to end users (including residential, commercial and industrial users) within one year, which is an indicator to measure the operating performance of the power company, the trend of power demand, and the supply and demand situation of the power market. The power supply line loss rate, also known as the line loss rate, reflects the degree of energy loss during the power transmission and distribution process. Grid investment refers to the cost expenditure for the construction and upgrading of the power transmission and distribution network, which is a key factor to ensure the stable operation of the power system, meet the growing power demand, and promote the energy transformation. The digital development index score is an indicator to measure the progress and maturity of a region or industry in digital transformation, reflecting the comprehensive effect of the digital process on improving productivity, promoting innovation, enhancing connectivity and inclusiveness, etc. The source-load matching configuration reliability reflects the matching degree between the power source (energy production) and the load (energy demand) and the reliability of system operation. A high source-load matching configuration reliability indicates that the power system can effectively balance power supply and demand during planning and operation, ensuring the stability and efficiency of the power system.

[0035] Step S104, based on multiple historical indicator values, generate multiple predicted indicator values of the target indicator in the future time period;

[0036] It can be understood that based on multiple historical index values of the target indicators of the target power grid, multiple predicted index values of the target indicators of the target power grid in a future time period are determined. By predicting future index values, the adaptability of transmission and distribution prices at different development stages of the new power system can be dynamically evaluated, providing forward-looking analysis for the adjustment of the electricity price structure. The prediction results not only contribute to the adjustment of the electricity price structure, but also provide data support for power grid planning, formulation of new energy generation strategies, etc., promoting the intelligent and green transformation of the power system.

[0037] In an alternative embodiment, there is a time series relationship among multiple historical index values. Based on the multiple historical index values, generating multiple predicted index values of the target index in a future time period includes: determining a target parameter reflecting the change trend of the target index based on the multiple historical index values; using the target parameter to perform data fitting based on the multiple historical index values to determine multiple predicted index values.

[0038] It can be understood that by processing and performing time series analysis on historical index values with a time series relationship, a target parameter development coefficient and a grey action quantity are obtained. Among them, the development coefficient is used to evaluate the growth rate and development trend of the target index in a future time period; the grey action quantity is used to evaluate the influence degree of the target index on the prediction result of the target index. Based on the target parameter development coefficient and the grey action quantity, data fitting is performed on the multiple historical index values, and using a prediction model, multiple predicted index values are obtained. By analyzing the time series relationship of historical index values and determining key parameters reflecting trend changes, the accuracy of index prediction results can be significantly improved, providing a more reliable data basis for the optimization of transmission and distribution prices and power grid strategies.

[0039] Optionally, the above prediction model can use a grey GM prediction model to determine the predicted index values. Grey GM prediction is based on grey system theory. On the basis that the data information of the target index is relatively incomplete and it is difficult to model using traditional methods, by processing multiple historical index values of the target index, the change of the predicted node of the target index data in a future time period of the new power system can be predicted.

[0040] Optionally, the grey GM prediction steps are as follows:

[0041] For the time series of target index j Construct an adjacent mean generation sequence The formula is as follows:

[0042]

[0043] where p represents the number of nodes of known data points, represents the index value of target index j at the pth node, Denote the index value of the target index j of the t-th node after processing by the adjacent mean production sequence, where t represents the t-th known data point node and k represents the k-th known data point node.

[0044] The target parameters a and u of the model are obtained by solving the differential equation through regression analysis or the least squares method. The formulas are as follows:

[0045]

[0046] Among them, a represents the development coefficient and u represents the grey action quantity.

[0047] Calculate the predicted value based on the historical index values and predict the values for the next m periods. The prediction formula is as follows:

[0048]

[0049] Among them, m represents that there are m future time periods, that is, the number of prediction nodes corresponding to each of the multiple future time periods for prediction.

[0050] Step S106: Perform time-series simulation processing according to the predetermined power scenario to determine the index evaluation threshold in the future time period.

[0051] It can be understood that based on the long-term plan of the new power system and the analysis of the development trend of new energy, the power scenario of the target power grid is preset, and the set power scenario is simulated using the time-series simulation method. According to the time-series simulation results, the evaluation threshold of each target index in the future time period is determined. Through time-series simulation and index prediction, it provides data support and decision-making basis for the adaptability evaluation and optimization strategy of the transmission and distribution price, and is a key step to realize the dynamic adjustment of the price and the efficient operation of the power system.

[0052] Step S108: Determine the electricity price evaluation result in the future time period based on multiple predicted index values and the index evaluation threshold.

[0053] It can be understood that based on multiple predicted index values of the target index and the corresponding multiple predicted index values, the adaptability of the transmission and distribution price is quantitatively evaluated using a scoring model to obtain the electricity price evaluation result in the future time period. By transforming the qualitative adaptability analysis into quantitative scoring, it helps to clearly understand the performance of the power grid in different indexes, facilitates horizontal and vertical comparisons, identifies key problems. At the same time, the evaluation result can guide the adjustment of the electricity price structure, ensure the stability and reliability of power supply, and achieve strategic optimization.

[0054] In an alternative embodiment, the index evaluation threshold includes a plurality of segmented thresholds. Based on the plurality of predicted index values and the index evaluation threshold, the electricity price evaluation result for the future time period is determined, including: segmenting the plurality of predicted index values according to the plurality of segmented thresholds to obtain segmented score values corresponding to the plurality of predicted index values respectively; and determining the electricity price evaluation result of the target index based on the segmented score values corresponding to the plurality of predicted index values respectively.

[0055] It can be understood that according to the predetermined power scenario, a plurality of segmented thresholds for each index are determined. According to the predicted index values and the set segmented thresholds, the predicted values of each index are mapped to the corresponding score intervals, and the segmented score values corresponding to the plurality of predicted index values of each index in the future time period are obtained by using the scoring function of the scoring model. According to the electricity price evaluation results of the target index corresponding to the plurality of predicted index values of each index in the future time period, and the relative weights corresponding to the plurality of target index values in the future time period, the comprehensive electricity price evaluation result for the future time period is obtained by using the weighted summation method. By setting a plurality of segmented thresholds, refined processing of the electricity price adaptability evaluation can be realized, accurately reflecting the contribution or influence of each index on the electricity price structure, improving the accuracy and credibility of the evaluation. At the same time, the comprehensive electricity price evaluation result provides strong decision-making support for power strategy makers, helping them find a balance in multi-objective decision-making, ensuring that the electricity price strategy supports the development of green energy while maintaining the stability and efficiency of the power market.

[0056] Optionally, the weighted scoring method can be used to evaluate the systematic comprehensive effectiveness. First, the segmented linear scoring method is adopted, and 3 segments can be set to convert the index values of the future evaluation year into percentage-based score values. Denote the index value as x and the score value as y. For an index where the larger the better, the functional relationship is as follows:

[0057]

[0058] For an index where the smaller the better, the functional relationship is as follows:

[0059]

[0060] where k 0 , k 1 , k 2 respectively represent the slopes of each segment; y p represents the segmented score value of the index; [x p,1 , x p,2 represents the target year index compliance interval set for assessment; [y p,1 , y p,2Indicates the scoring range corresponding to the compliance range.

[0061] Based on the scoring results (i.e., the segmented scoring values), the electricity price evaluation results of the target indicators can be obtained. Based on the electricity price evaluation results corresponding to multiple target indicators in the future time period and the relative weights corresponding to multiple target indicators in the future time period, the comprehensive electricity price evaluation result of the transmission and distribution electricity price adapting to the new power system in the future evaluation year (i.e., the future time period) is obtained by using the weighted summation method.

[0062] In an alternative embodiment, there are multiple target indicators. Based on multiple predicted indicator values and indicator evaluation thresholds, the electricity price evaluation results in the future time period are determined, including: determining the relative weights corresponding to the future time period based on the multiple predicted indicator values corresponding to each target indicator; in the case of multiple future time periods, obtaining the fitness corresponding to each of the multiple future time periods based on the relative weights corresponding to the multiple future time periods, where the fitness represents the similarity between the composition structure of the transmission and distribution electricity price in the corresponding future time period and the predetermined plan, and the predetermined plan is determined based on the scheduling plan of new energy power in the target power grid; determining the electricity price evaluation results based on the fitness corresponding to each of the multiple future time periods and the indicator evaluation thresholds.

[0063] It can be understood that based on the multiple predicted indicator values corresponding to each target indicator, the relative weights corresponding to each target indicator in the future time period are obtained; if there are multiple future time periods, that is, if there are multiple prediction nodes, the fitness corresponding to each of the multiple future time periods is obtained based on the relative weights corresponding to the multiple future time periods. Among them, the fitness represents the similarity between the composition structure of the transmission and distribution electricity price in the corresponding future time period and the predetermined plan, and the predetermined plan is determined based on the scheduling plan of new energy power in the target power grid. The electricity price evaluation results corresponding to each future time period are determined based on the fitness corresponding to each of the multiple future time periods and the indicator evaluation thresholds. By calculating the fitness of each target indicator in different future time periods, the refined evaluation of the adaptability of the transmission and distribution electricity price can be realized, avoiding the limitations of the traditional single-time-point evaluation, providing more comprehensive and accurate information for electricity price decision-making. At the same time, based on the calculation results of the fitness, it can be identified which time periods the transmission and distribution electricity price structure needs to be adjusted, as well as the specific direction and degree of the adjustment, which helps to formulate dynamic and timely electricity price strategies to cope with the uncertainty of power grid operation.

[0064] In an alternative embodiment, obtaining the fitness values corresponding to multiple future time periods based on the relative weights corresponding to the multiple future time periods respectively includes: for a single time period among the multiple future time periods, based on the relative weight corresponding to the single time period, the multiple predicted index values corresponding to each target index, and the predetermined positive and negative ideal solutions of the index, determining the group utility value corresponding to the single time period, where the group utility value is used to represent the degree of deviation of all index values in the single time period from the positive and negative ideal solutions of the index; based on the relative weight corresponding to the single time period, the multiple predicted index values corresponding to each target index, and the predetermined positive and negative ideal solutions of the index, determining the individual regret value corresponding to the single time period, where the individual regret value represents the degree of deviation of a single index value in the single time period from the positive and negative ideal solutions of the index; determining the compromise decision value of the single time period according to the group utility value and the individual regret value corresponding to the single time period; and determining the fitness of the single time period based on the compromise decision value of the single time period.

[0065] It can be understood that for a single time period among the multiple future time periods, that is, a certain prediction node among the multiple future prediction nodes, according to the relative weight corresponding to the prediction node, the multiple predicted index values corresponding to each target index, and the predetermined positive and negative ideal solutions of the index, the group utility value corresponding to the time period is determined. Among them, the group utility value is used to represent the degree of deviation of all index values in the single time period from the positive and negative ideal solutions of the index, reflecting the adaptability of the transmission and distribution electricity price in the single time period. According to the relative weight corresponding to the prediction node, the multiple predicted index values corresponding to each target index, and the predetermined positive and negative ideal solutions of the index, the individual regret value corresponding to the time period is determined. Among them, the individual regret value represents the degree of deviation of a single index value in the single time period from the positive and negative ideal solutions of the index, reflecting the performance shortcoming of the transmission and distribution electricity price in different time periods and different target indexes. According to the group utility value and the individual regret value corresponding to each target index in the single time period, the compromise decision value corresponding to each target index in the single time period is obtained. Among them, the compromise decision value reflects the compromise position of the transmission and distribution electricity price considering the group interests and individual regrets. Based on the compromise decision value corresponding to each target index in the single time period, the fitness value corresponding to each target index in the single time period is determined. By calculating the group utility value and the individual regret value, the performance of the transmission and distribution electricity price in each future time period can be evaluated in detail, identifying which indexes need to be focused on and in which aspects the electricity price mechanism needs to be adjusted. At the same time, the determination of the fitness value takes into account the change of the index importance in different time periods, enabling the electricity price evaluation to dynamically reflect the development needs of the new power system, helping the power industry to adapt to changes, and improving the overall adaptability and flexibility of the system.

[0066] Optionally, the VIKOR method can be used to solve the fitness of the target indicators in the future time period. VIKOR (Multi-criteria compromise solution ranking method) is a method for multi-attribute decision-making, which is applicable to evaluations where preferences are difficult to determine, and there are conflicts and incommensurabilities among evaluation criteria. In the evaluation of the adaptability of transmission and distribution prices, since the preferences in different dimensions have different emphases in different periods of the new power system, it is difficult to completely determine the preference weights according to a single standard. Therefore, the VIKOR method can be used to better evaluate the phased adaptability of transmission and distribution prices.

[0067] Since different data have different dimensions and large differences in value ranges, first, the data of multiple predicted indicator values of the target indicators should be cleaned and normalized to ensure their accuracy and consistency, providing a reliable basis for subsequent analysis and prediction. The average value of the sequence is used as the scaling factor data for preprocessing, and finally, the standardized matrix Z is obtained.

[0068] First, determine the positive and negative ideal solutions That is, the set of the maximum and minimum values corresponding to each indicator The formula for is:

[0069]

[0070] Among them, i represents the i-th time period, j represents the j-th indicator, j = 1, 2,..., n; z ij Represents the element in the standardized matrix Z; Represents the maximum value of the -th indicator; Represents the minimum value of the -th indicator.

[0071] According to the relative weight corresponding to the prediction node, the multiple predicted indicator values corresponding to each target indicator, and the predetermined positive and negative ideal solutions of the indicators, determine the group utility value S corresponding to this time period i , and its formula is:

[0072]

[0073] Among them, W i Represents the relative weight of the i-th time period.

[0074] According to the relative weight corresponding to the prediction node, the multiple predicted indicator values corresponding to each target indicator, and the predetermined positive and negative ideal solutions of the indicators, determine the individual regret value R corresponding to this time period i , and its formula is:

[0075]

[0076] According to the group utility value and individual regret value corresponding to each target indicator in a single time period, obtain the compromise decision value Q corresponding to each target indicator in a single time period i, and its formula is:

[0077]

[0078] Among them, ε represents the decision coefficient, and ε ∈ [0, 1].

[0079] Sort the compromise decision value Q i in ascending order. If the following evaluation criteria are met, the adaptability for multiple future times can be sorted according to the value of Q i . The evaluation criteria are:

[0080] (1) Acceptable advantage threshold condition, that is Among them, Q 1 and Q 2 represent the optimal transmission and distribution price plan and the sub-optimal transmission and distribution price plan respectively, and m represents a total of m future time periods.

[0081] (2) Acceptable decision reliability condition. When sorting according to the value of Q i , the S i or R i value of the optimal transmission and distribution price plan should also be sorted optimally. When sorting according to the Q value, the S i or R i value of the optimal plan should also be sorted optimally. That is, when the Q i value is the smallest (the plan is the best), the corresponding group utility S i should be max{S 1 ……S m}, and the individual regret Ri should be min{R 1 ……R m}.

[0082] Take the optimal plan in the i-th time period as the best fitness plan, and use the Euclidean distance to determine the fitness level of the remaining time periods (that is, the time periods other than the time period corresponding to minQ i ). The fitness calculation formula is as follows:

[0083]

[0084] Among them, α i represents the fitness in the i-th time period.

[0085] In an alternative embodiment, determining the relative weight corresponding to a future time period based on multiple predicted index values corresponding to each target index includes: performing a normalization process on the multiple predicted index values corresponding to each target index to obtain a comparison element of each target index in the future time period; obtaining a comparison sequence corresponding to the future time period based on the comparison elements respectively corresponding to the multiple target indexes in the future time period; and determining the relative weight corresponding to the future time period based on the comparison sequence corresponding to the future time period, a predetermined positive ideal solution of the index, and a predetermined negative ideal solution of the index.

[0086] It can be understood that in order to eliminate the influence of dimension and ensure the comparability of all indexes during evaluation, it is necessary to perform a normalization process on the multiple predicted index values of each target index. Based on the normalization result of the multiple predicted index values corresponding to each target index, a comparison element of each target index in the future time period is obtained. According to the comparison elements respectively corresponding to the multiple target indexes in the future time period, a comparison sequence corresponding to the future time period is constructed. The positive ideal solution of the index and the negative ideal solution of the index are set as reference points. Among them, the positive ideal solution refers to the situation where the maximum value is obtained among all target indexes, while the negative ideal solution refers to the situation where the minimum value is obtained among all target indexes. According to the comparison sequence, positive ideal solution, and negative ideal solution corresponding to each index in the future time period, the relative weight corresponding to each index in the future time period is obtained. The normalization process ensures that index values with different dimensions and different ranges can be compared and weighted calculated under the same evaluation framework, enhances the comparability of data and the accuracy of analysis. Obtaining the relative weight corresponding to the target index in the future time period based on the normalized data provides an important basis for the evaluation and structural adjustment of transmission and distribution prices, helps to identify and prioritize key indexes affecting adaptability, and ensures the close fit between the transmission and distribution price structure and the development goals of the new power system.

[0087] Optionally, when solving the relative weight corresponding to the target index in the future time period, due to the large difference in value ranges caused by different data dimensions, the data should first be cleaned and normalized to ensure its accuracy and consistency, providing a reliable basis for subsequent analysis and prediction. The average value of the sequence is used as the scaling factor data for preprocessing, and finally the standardized matrix Z is obtained. The optimal value of each positive index and the minimum value of each negative index are taken as the reference sequence, and the processed reference sequence is denoted as Y 0 , the comparison sequence is Y i . The normalization process is as follows:

[0088]

[0089] where i represents the i-th time period, k represents the k-th index, k = 1, 2, 3, …, n, and y i (k) represents the element of the comparison sequence before normalization, and yi The element after the normalization process of the comparison sequence is denoted as (k)'.

[0090] Calculate the correlation coefficient matrix ξ. According to the comparison sequence Y i and the reference sequence Y 0 calculate the correlation coefficient ξ of each corresponding element respectively i , and the calculation formula is as follows:

[0091]

[0092] where ρ represents the discrimination coefficient, and ρ ∈ [0, 1].

[0093] Calculate the grey correlation γ i , usually using the mean method. The formula for γ i is as follows:

[0094]

[0095] According to the comparison sequence, the positive ideal solution, and the negative ideal solution corresponding to each index in the future time period, obtain the relative weight corresponding to each index in the future time period. The relative weight W of the target index in the i-th time period i The formula is:

[0096]

[0097] where m represents a total of m future time periods.

[0098] Step S110, according to the electricity price evaluation result, determine the transmission and distribution price adjustment strategy of the target power grid in the future time period.

[0099] It can be understood that according to the electricity price evaluation result of the transmission and distribution price, determine the key points that need to be adjusted in the electricity price structure, and formulate the transmission and distribution price adjustment strategy of the target power grid in the future time period. The detailed evaluation result and adjustment strategy provide data support and decision-making basis for power strategy makers, which helps to formulate a more scientific and reasonable electricity price strategy, thereby optimizing the allocation of power resources, improving the economy and operation efficiency of the power system, and promoting the sustainable development of the power industry.

[0100] Optionally, the transmission and distribution price adjustment strategy includes differential pricing, dynamic adjustment mechanism, incentive measures, etc. Differential pricing means implementing differential pricing strategies for different types of electricity users according to the proportion of new energy and carbon emission intensity to promote energy conservation, emission reduction, and the use of green energy. The dynamic adjustment mechanism means creating a dynamic electricity price adjustment mechanism so that the electricity price can be adjusted according to factors such as real-time power supply and demand, new energy power generation, and grid operation conditions to achieve flexible adaptation of the electricity price. Incentive measures are to design incentive strategies, such as giving price discounts to users using new energy and providing subsidies for grid investment and digital transformation projects, to promote the access of new energy and the intelligent transformation of the grid.

[0101] Through the above step S102, for the target power grid including new energy power, obtain multiple historical index values of the target index in the target power grid, where the target index is an index that affects the composition structure of the transmission and distribution price in the target power grid, and the composition structure of the transmission and distribution price changes with the power generation of new energy power in the target power grid; step S104, based on the multiple historical index values, generate multiple predicted index values of the target index in the future time period; step S106, perform time-series simulation processing according to a predetermined power scenario to determine the index evaluation threshold in the future time period; step S108, based on the multiple predicted index values and the index evaluation threshold, determine the electricity price evaluation result in the future time period; step S110, according to the electricity price evaluation result, determine the transmission and distribution price adjustment strategy of the target power grid in the future time period. It can achieve the purpose of determining the electricity price evaluation result by determining the weight of the target index and using the time-series production simulation method, and achieve the technical effect of improving the accuracy of the dynamic electricity price evaluation result of the transmission and distribution price, thereby solving the technical problem of the unsatisfactory dynamic electricity price evaluation result of the transmission and distribution price in the related technology.

[0102] Based on the above embodiments and alternative embodiments, the present application proposes an alternative implementation manner. By using the embodiments of the present application, the accuracy of the dynamic electricity price evaluation result of the transmission and distribution price can be improved. Figure 2 It is a schematic diagram of an alternative method for adjusting the transmission and distribution price structure provided according to an embodiment of the present application, as Figure 2As shown in the figure, the method for adjusting the transmission and distribution price structure includes: an index weight determination module based on grey relational degree, which is used to construct an objective index system based on the characteristics of the new power system and introduce grey relational degree to determine the objective weight (i.e., relative weight); a transmission and distribution price adaptability evaluation model based on multi-attribute decision-making, which calculates the compromise decision value of the transmission and distribution price in the evaluation year (the evaluation year corresponding to the future time period) by considering the preference emphasis of different attributes, and measures the fitness level of each year using the Euclidean distance; the determination of the phased evaluation standard and the phased index calculation based on grey GM prediction, that is, according to the time-series production simulation method, determine the adaptability evaluation index of the key year (the key future time period, that is, several future time periods selected from multiple future time periods according to a certain standard), use the grey GM prediction method to calculate the change of the evaluation index data in different stages, and use the piecewise linear scoring function to evaluate the score of the future evaluation year (i.e., the piecewise score value), and according to the score and relative weight of the evaluation year, obtain the comprehensive electricity price evaluation result of the evaluation year; the electricity price structure adjustment and system optimization module, which is used to dynamically adjust the electricity price structure and grid investment based on the phased evaluation standard to achieve the optimization of the transmission and distribution price.

[0103] The embodiment of the present application provides a transmission and distribution price adaptability evaluation method and a phased prediction method based on multi-attribute decision-making, aiming to realize the evaluation of whether the transmission and distribution price in the current cycle adapts to the construction of the new power system through technologies such as metering statistics and big data analysis, and measure and characterize each development stage of the transmission and distribution price adapting to the new power system through grade description. First, through the target index system construction and data acquisition module, a measurable and normalized data system that fits the evaluation target is formed as the input of the system, and grey relational analysis is used to determine the objective weight of the target index. Second, based on the objective weight, use VIKOR (Multi-Criteria Optimization and Compromise Solution Ranking) that considers preference emphasis to determine the optimal adaptation year, and determine the adaptability level of the transmission and distribution price of other evaluation years according to the optimal adaptation year, considering the determination of the phased rolling fitness. Third, use the time-series production simulation method to determine the standard evaluation interval and multiple piecewise thresholds of each index, and based on the grey prediction method, measure the situation of the index data at the key nodes of the new power system (i.e., the key prediction nodes corresponding to the key future time periods), and obtain the scoring standard through the piecewise linear scoring function. Finally, the optimization module dynamically adjusts the electricity price structure based on the predicted data to adapt to the development of the new power system and ensure the realization of the transmission and distribution price target. The embodiment of the present application has good scalability and can adapt to the dynamic requirements of transmission and distribution price evaluation under different nodes.

[0104] Based on the goals of transmission and distribution tariffs and the development characteristics of the new power system, and considering the availability of data and the coupling between factors, evaluation indicators are selected from five dimensions: green and low-carbon, safe and controllable, flexible and efficient, intelligent and friendly, and open and interactive. For the evaluation indicators, an objective weight determination method using grey relational analysis is considered. The greater the degree of correlation, the greater the weight. The more common entropy weight method and standard deviation method can more accurately reflect the adaptable weight information in practical applications.

[0105] When solving the relative weights of the target indicators corresponding to future time periods, since different data have different dimensions and large differences in value ranges, the data should first be cleaned and normalized to ensure its accuracy and consistency, providing a reliable basis for subsequent analysis and prediction. The average value of the sequence is used as the scaling factor data for preprocessing, and finally the standardized matrix Z is obtained. The optimal value of each positive indicator and the minimum value of each negative indicator are taken as the reference sequence, and the processed reference sequence is denoted as Y 0 , and the comparison sequence is Y i . The normalization method is as follows:

[0106]

[0107] where i represents the i-th time period, k represents the k-th indicator, k = 1, 2, 3,..., n, and y i (k) represents the element of the comparison sequence before normalization, and y i (k)' represents the element of the comparison sequence after normalization.

[0108] Calculate the correlation coefficient matrix ξ. According to the comparison sequence Y i and the reference sequence Y 0 , calculate the correlation coefficient ξ i of each corresponding element respectively. The calculation formula is as follows:

[0109]

[0110] where ρ represents the discrimination coefficient, ρ ∈ [0, 1].

[0111] Calculate the grey correlation γ i . Usually, the mean method is adopted. The formula for γ i is:

[0112]

[0113] According to the comparison sequence, positive ideal solution and negative ideal solution corresponding to each indicator in the future time period, the relative weight corresponding to each indicator in the future time period is obtained. The relative weight W i of the target indicator in the i-th time period is calculated by the formula:

[0114]

[0115] Among them, m represents that there are m future time periods in total.

[0116] The VIKOR method is used to solve the fitness of the target indicators in the future time periods. VIKOR (Multi-Criteria Compromise Solution Ranking Method) is a method for multi-attribute decision-making, which is applicable to evaluations where preferences are difficult to decide, and there are conflicts and incommensurabilities among evaluation criteria. In the evaluation of the adaptability of transmission and distribution prices, since the preferences in different dimensions have different emphases in different periods of the new power system, it is difficult to determine the preference weights completely according to a single standard. Therefore, the VIKOR method can better evaluate the stage adaptability degree of transmission and distribution prices.

[0117] The dimensionless evaluation matrix Z and the calculated relative weights are used as the input set of the evaluation model. First, the positive and negative ideal solutions are determined That is, the set of the maximum and minimum values corresponding to each index, The formula for is:

[0118]

[0119] Among them, i represents the i-th time period, j represents the j-th index, j = 1, 2,..., n; z ij represents the element in the standardized matrix Z; represents the maximum value of the -th index; represents the minimum value of the -th index.

[0120] According to the relative weight corresponding to this prediction node, the multiple prediction index values corresponding to each target index, and the predetermined positive and negative ideal solutions of the index, the group utility value S corresponding to this time period is determined i , and its formula is:

[0121]

[0122] Among them, W i represents the relative weight of the i-th time period.

[0123] According to the relative weight corresponding to this prediction node, the multiple prediction index values corresponding to each target index, and the predetermined positive and negative ideal solutions of the index, the individual regret value R corresponding to this time period is determined i , and its formula is:

[0124]

[0125] According to the group utility value and the individual regret value corresponding to each target index in a single time period, the compromise decision value Q corresponding to each target index in a single time period is obtained i , and its formula is:

[0126]

[0127] Among them, ε represents the decision coefficient, and ε ∈ [0, 1].

[0128] Sort the compromise decision value Q i in ascending order. If the following evaluation criteria are met, then according to Q i values, the adaptability for multiple future times can be sorted. The evaluation criteria are as follows:

[0129] (1) Acceptable advantage threshold condition: Among them, Q 1 , Q 2 represent the optimal transmission and distribution price plan and the sub-optimal transmission and distribution price plan respectively, and m represents that there are m future time periods.

[0130] (2) Acceptable decision reliability condition: When sorting according to Q i values, the S i or R i values of the optimal transmission and distribution price plan should also be sorted optimally. When sorting according to Q values, the S i or R i values should also be sorted optimally. That is, when Q i values are the smallest (the plan is the best), the corresponding group utility S i should be max{S 1 ……S m}, and the individual regret Ri should be min{R 1 ……R m}.

[0131] Take the optimal plan in the i-th time period as the best fitness plan, and use the Euclidean distance to determine the fitness levels of the remaining time periods. The fitness calculation formula is as follows:

[0132]

[0133] Among them, α i represents the fitness in the i-th time period.

[0134] Based on the prediction results of the target indicator data, the evaluation criteria for the adaptability of future transmission and distribution prices are further determined. Based on the time-series operation simulation (i.e., the time-series simulation method), the evaluation criteria analysis for different stages is constructed. Considering the new power system in aspects such as energy and power transformation and the construction of transmission and distribution networks, time-series production simulation is used. By setting pre-constructed targets and typical scenarios, the time-series power and electricity balance, line transmission power, carbon emissions, fuel consumption, etc. are obtained. Time-series production simulation is carried out for the prediction nodes corresponding to multiple future times respectively, and the optimal determination is carried out for each prediction node, and it is used as the evaluation criteria. The evaluation criteria can provide guidance for the improvement direction of future transmission and distribution prices.

[0135] The grey GM prediction model is used to determine the prediction index value. Grey GM prediction is based on grey system theory. On the basis that the information of the target indicator data is relatively incomplete and it is difficult to model with traditional methods, by processing multiple historical indicator values of the target indicator, the change of the target indicator data at the prediction nodes in the future time period of the new power system can be predicted.

[0136] Optionally, the grey GM prediction steps are as follows:

[0137] For the time series of target indicator j Construct the adjacent mean generation sequence The formula is as follows:

[0138]

[0139] where p represents the number of nodes of known data points, represents the indicator value of target indicator j at the pth node, represents the indicator value of target indicator j at the tth node after being processed by the adjacent mean generation sequence, t represents the tth known data point node, and k represents the kth known data point node.

[0140] Solve the differential equation through regression analysis or the least squares method to obtain the target parameters a and u of the model. The formula is as follows:

[0141]

[0142] where a represents the development coefficient and u represents the grey action quantity.

[0143] Calculate the predicted value according to the historical indicator values and predict the values in the next m periods The prediction formula is as follows:

[0144]

[0145] where m represents a total of m future time periods, that is, the number of prediction nodes corresponding to the multiple future time periods to be predicted.

[0146] The weighted scoring method is used to evaluate the systematic comprehensive effectiveness. First, the piecewise linear scoring method is adopted. Three sections can be set to convert the index values in the future evaluation year into percentage scoring values. Denote the index value as x and the scoring value as y. For the index that the larger the better, the functional relationship is as follows:

[0147]

[0148] For the index that the smaller the better, the functional relationship is as follows:

[0149]

[0150] Among them, k 0 , k 1 , k 2 respectively represent the slopes of each section; y p represents the piecewise scoring value of the index; [x p,1 , x p,2 represents the target year index compliance interval set for assessment; [y p,1 , y p,2 represents the scoring interval corresponding to the compliance interval.

[0151] According to the scoring results (i.e., the piecewise scoring values), the electricity price evaluation results of the target index can be obtained. Based on the electricity price evaluation results corresponding to multiple target indexes in the future time period and the relative weights corresponding to multiple target indexes in the future time period, the comprehensive electricity price evaluation result of the transmission and distribution electricity price adapting to the new power system in the future evaluation year (i.e., the future time period) is obtained by using the weighted summation method.

[0152] The optimization module of the system dynamically adjusts the electricity price structure and the dispatching plan of new energy power generation based on the phased evaluation criteria, and determines the adjustment strategy of the transmission and distribution electricity price of the target power grid in the future time period. Through real-time optimization, the stability of the power system and the realization of the transmission and distribution electricity price target are ensured. At the same time, the system can encourage more new energy power generation to be connected to the grid by optimizing the electricity price structure, promote the effective utilization of new energy, and help achieve the clean energy target.

[0153] The above optional implementation methods achieve at least the following effects: By selecting target indicators related to the transmission and distribution price structure and solving the relative weights of the target indicators in the future time period, it helps to identify and prioritize the key indicators affecting adaptability, and improve the accuracy of the dynamic evaluation results of the transmission and distribution price; Using the relative weights of the target indicators in the future time period to determine the fitness of the target indicators in the future time period enables the electricity price evaluation to dynamically reflect the development needs of the new power system, and improve the overall adaptability and flexibility of the system; Adopting the time-series production simulation method to determine the evaluation thresholds of each target indicator in the future time period, and based on the evaluation thresholds and relative weights, obtaining the comprehensive electricity price evaluation results in the future time period, realizing the refined processing of the electricity price adaptability evaluation, and improving the accuracy and credibility of the evaluation.

[0154] The transmission and distribution price structure adjustment method can be applied to a software application. The software application obtains the predicted index values of the target indicators of a target power grid in a certain region in the next 10 years through multiple historical index values of the target indicators of the target power grid in a certain region. By normalizing the above predicted index values, a dimensionless standardized matrix Z of the predicted index values is obtained. The specific data in matrix Z is shown in Table 1. The following data is only for illustration and is not specifically limited.

[0155] The specific data in Table 1 represents

[0156]

[0157] Based on the data in Table 1, calculate the group utility value, individual regret value and compromise decision value of each evaluation year (i.e., multiple future time periods with one year as the step length). The calculation results are shown in Table 2.

[0158] Table 2 Group utility, individual regret and decision-making indicators for each evaluation year

[0159]

[0160]

[0161] Use the Euclidean distance to determine the fitness levels of the remaining years (i.e., the evaluation years other than the evaluation year corresponding to minQ i as shown in Table 3.

[0162] Table 3 Fitness levels of each evaluation year

[0163] Assessment year 1 2 3 4 5 6 7 8 9 10 Adaptability 0.015 0.008 0.249 0.307 0.467 0.624 0.914 1.000 0.829 0.995

[0164] After time-series simulation, the evaluation standard index intervals of the key years are obtained. The index data of key year 1 and key year 2 in this region are obtained by using the grey GM prediction model. The data is shown in Table 4.

[0165] Table 4 Index data for Key Year 1 and Key Year 2

[0166]

[0167] The index data of the evaluation year of this region obtained by prediction is compared and evaluated with the standard indexes of Key Year 1 and Key Year 2 in the time-series simulation plan. After substituting the staged linear scoring and weighting, the comprehensive electricity price evaluation results of Key Year 1 and Key Year 2 are obtained, and the results are shown in Table 5.

[0168] Table 5 Comprehensive electricity price evaluation results of Key Year 1 and Key Year 2

[0169] Year Comprehensive electricity price assessment result Key year 1 79.5 Key year 2 90.5

[0170] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0171] In this embodiment, a device for adjusting the transmission and distribution electricity price structure is further provided. This device is used to implement the above-mentioned embodiments and preferred implementation manners, and those that have been described will not be repeated. As used below, the terms "module" and "device" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0172] According to an embodiment of the present application, an embodiment of a device for implementing the method for adjusting the transmission and distribution electricity price structure is also provided. Figure 3 is a schematic diagram of a device for adjusting the transmission and distribution electricity price structure according to an embodiment of the present application, as Figure 3 shown, the above-mentioned device for adjusting the transmission and distribution electricity price structure includes a historical index value acquisition module 302, a predicted index value acquisition module 304, an evaluation threshold determination module 306, an evaluation result determination module 308, and an adjustment strategy determination module 310. The device will be described below.

[0173] The historical index value acquisition module 302 is used to acquire multiple historical index values of a target index for a target power grid including new energy power. Among them, the target index is an index that affects the composition structure of the transmission and distribution electricity price in the target power grid, and the composition structure of the transmission and distribution electricity price changes with the power generation of new energy power in the target power grid;

[0174] The predicted index value acquisition module 304 is connected to the historical index value acquisition module 302 and is used to generate multiple predicted index values of the target index in a future time period based on the multiple historical index values;

[0175] An evaluation threshold determination module 306, connected to the prediction index value acquisition module 304, is configured to perform sequential simulation processing according to a predetermined power scenario to determine an index evaluation threshold in a future time period;

[0176] An evaluation result determination module 308, connected to the evaluation threshold determination module 306, is configured to determine a future-time electricity price evaluation result based on multiple prediction index values and the index evaluation threshold;

[0177] An adjustment strategy determination module 310, connected to the evaluation result determination module 308, is configured to determine a transmission and distribution price adjustment strategy for a target power grid in a future time period according to the electricity price evaluation result.

[0178] In a transmission and distribution price structure adjustment device provided by an embodiment of the present application, by setting a historical index value acquisition module 302, which is configured to acquire multiple historical index values of a target index in a target power grid for a target power grid including new energy power, where the target index is an index that affects the composition structure of the transmission and distribution price in the target power grid, and the composition structure of the transmission and distribution price changes with the power generation of new energy power in the target power grid; a prediction index value acquisition module 304, connected to the historical index value acquisition module 302, is configured to generate multiple prediction index values of the target index in a future time period based on the multiple historical index values; an evaluation threshold determination module 306, connected to the prediction index value acquisition module 304, is configured to perform sequential simulation processing according to a predetermined power scenario to determine an index evaluation threshold in a future time period; an evaluation result determination module 308, connected to the evaluation threshold determination module 306, is configured to determine a future-time electricity price evaluation result based on the multiple prediction index values and the index evaluation threshold; an adjustment strategy determination module 310, connected to the evaluation result determination module 308, is configured to determine a transmission and distribution price adjustment strategy for the target power grid in a future time period according to the electricity price evaluation result. The purpose of determining the weight of the target index and using the sequential production simulation method to determine the electricity price evaluation result is achieved, the technical effect of improving the accuracy of the dynamic electricity price evaluation result of the transmission and distribution price is realized, and further the technical problem of the unsatisfactory dynamic electricity price evaluation result of the transmission and distribution price in the related art is solved.

[0179] It should be noted that the above-mentioned various modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following manner: the above-mentioned various modules can be located in the same processor; or, the above-mentioned various modules are located in different processors in any combination.

[0180] It should be noted that the above historical index value acquisition module 302, prediction index value acquisition module 304, evaluation threshold determination module 306, evaluation result determination module 308, and adjustment strategy determination module 310 correspond to steps S102 to S110 in the embodiment. The examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can run in a computer terminal.

[0181] It should be noted that the optional or preferred implementation manners of this embodiment can be referred to the relevant descriptions in the embodiment, and will not be elaborated here.

[0182] The above transmission and distribution price structure adjustment device may further include a processor and a memory. The historical index value acquisition module 302, prediction index value acquisition module 304, evaluation threshold determination module 306, evaluation result determination module 308, adjustment strategy determination module 310, etc. are all stored in the memory as program units, and the corresponding functions are implemented by the processor executing the above program units stored in the memory.

[0183] The processor includes a kernel, and the kernel retrieves the corresponding program units from the memory. One or more kernels can be set. The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of, for example, read-only memory (ROM) or flash memory (flash RAM), and the memory includes at least one storage chip.

[0184] The embodiment of the present application provides a non-volatile storage medium, on which a program is stored, and when the program is executed by a processor, it implements the transmission and distribution price structure adjustment method.

[0185] The embodiment of the present application provides an electronic device, which includes a processor, a memory, and a program stored on the memory and executable on the processor. When the processor executes the program, the following steps are implemented: for a target power grid including new energy power, obtain multiple historical index values of a target index in the target power grid, where the target index is an index that affects the composition structure of the transmission and distribution price in the target power grid, and the composition structure of the transmission and distribution price changes with the power generation of new energy power in the target power grid; based on the multiple historical index values, generate multiple predicted index values of the target index in a future time period; perform time series simulation processing according to a predetermined power scenario to determine the index evaluation threshold in the future time period; based on the multiple predicted index values and the index evaluation threshold, determine the electricity price evaluation result in the future time period; according to the electricity price evaluation result, determine the transmission and distribution price adjustment strategy of the target power grid in the future time period. The device herein can be a server, a PC, etc.

[0186] The present application also provides a computer program product which, when executed on a data processing device, is adapted to execute a program initialized with the following method steps: for a target power grid including new energy power, obtain a plurality of historical index values of a target index in the target power grid, where the target index is an index that affects the composition structure of the transmission and distribution price in the target power grid, and the composition structure of the transmission and distribution price changes with the power generation amount of new energy power in the target power grid; based on the plurality of historical index values, generate a plurality of predicted index values of the target index in a future time period; perform a time series simulation process according to a predetermined power scenario to determine an index evaluation threshold in the future time period; based on the plurality of predicted index values and the index evaluation threshold, determine a price evaluation result in the future time period; and according to the price evaluation result, determine an adjustment strategy for the transmission and distribution price of the target power grid in the future time period.

[0187] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0188] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0189] These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing devices to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device realizes the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0190] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide for implementing the steps in a process Figure 1 one process or multiple processes and / or blocks Figure 1 steps of the functions specified in one block or multiple blocks.

[0191] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0192] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0193] Computer-readable media includes permanent and non-permanent, removable and non-removable media and can store information by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0194] It should also be noted that the term "comprises", "comprising" or any other variation thereof is intended to cover a non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0195] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0196] The above are only the embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for adjusting the transmission and distribution price structure, characterized in that: include: For a target power grid including new energy power, a plurality of historical index values ​​of a target index in the target power grid are obtained, wherein the target index is an index that affects a transmission and distribution price composition structure in the target power grid, and the transmission and distribution price composition structure changes with a change in the power generation amount of the new energy power in the target power grid; Based on the multiple historical indicator values, generating multiple predicted indicator values ​​of the target indicator in a future time period; Perform time series simulation processing according to the predetermined power scenario to determine the indicator evaluation threshold in the future time period; Determining an electricity price evaluation result for the future time period based on the multiple prediction index values ​​and the index evaluation threshold; According to the electricity price evaluation result, a transmission and distribution price adjustment strategy of the target power grid in the future time period is determined.

2. The method according to claim 1, characterized in that The multiple historical indicator values ​​have a time series relationship, and generating multiple predicted indicator values ​​of the target indicator in a future time period based on the multiple historical indicator values ​​includes: Based on the multiple historical indicator values, determining a target parameter reflecting a change trend of the target indicator; The target parameters are used to perform data fitting based on the multiple historical indicator values ​​to determine the multiple predicted indicator values.

3. The method according to claim 1, characterized in that The index evaluation threshold includes a plurality of segmented thresholds, and determining the electricity price evaluation result for the future time period based on the plurality of predicted index values ​​and the index evaluation threshold includes: According to the multiple segmentation thresholds, the multiple prediction index values ​​are segmented to obtain segmentation scoring values ​​corresponding to the multiple prediction index values ​​respectively; Based on the segmented scoring values ​​respectively corresponding to the multiple prediction index values, the electricity price evaluation result of the target index is determined.

4. The method according to claim 1, characterized in that: There are multiple target indicators, and determining the electricity price evaluation result for the future time period based on the multiple prediction indicator values ​​and the indicator evaluation threshold includes: Determining a relative weight corresponding to the future time period based on a plurality of prediction indicator values ​​corresponding to each target indicator; In the case where there are multiple future time periods, based on the relative weights corresponding to the multiple future time periods, respectively, the fitness corresponding to the multiple future time periods is obtained, wherein the fitness represents the similarity between the transmission and distribution price composition structure corresponding to the future time period and the predetermined plan, and the predetermined plan is determined based on the scheduling plan of the new energy power in the target power grid; The electricity price evaluation result is determined based on the fitness corresponding to the multiple future time periods respectively and the indicator evaluation threshold.

5. The method according to claim 4, characterized in that The acquiring the fitness corresponding to the multiple future time periods respectively based on the relative weights corresponding to the multiple future time periods respectively includes: For a single time period among the multiple future time periods, based on the relative weight corresponding to the single time period, the multiple predicted indicator values ​​corresponding to each target indicator, and the predetermined positive and negative ideal solutions of the indicator, determine the group utility value corresponding to the single time period, wherein the group utility value is used to indicate the degree of deviation of all indicator values ​​in the single time period from the positive and negative ideal solutions of the indicator; Based on the relative weight corresponding to the single time period, the multiple predicted indicator values ​​corresponding to each target indicator, and the predetermined positive and negative ideal solutions of the indicator, determine the individual regret value corresponding to the single time period, wherein the individual regret value indicates the degree of deviation of a single indicator value in the single time period from the positive and negative ideal solutions of the indicator; Determining a compromise decision value for the single time period according to the group utility value and the individual regret value corresponding to the single time period; The fitness of the single time period is determined based on the compromise decision value of the single time period.

6. The method according to claim 4, characterized in that The determining, based on the multiple prediction indicator values ​​corresponding to each target indicator, the relative weight corresponding to the future time period includes: Performing normalization processing based on multiple prediction indicator values ​​corresponding to each target indicator to obtain a comparison element for each target indicator in the future time period; Based on the comparison elements respectively corresponding to the plurality of target indicators in the future time period, obtaining a comparison sequence corresponding to the future time period; Based on the comparison sequence corresponding to the future time period, the predetermined positive ideal solution of the indicator, and the predetermined negative ideal solution of the indicator, the relative weight corresponding to the future time period is determined.

7. The method according to any one of claims 1 to 6, characterized in that: The target indicators are at least one of the following: proportion of new energy installed capacity, electricity carbon emission intensity, forced outage rate of power transmission and transformation equipment, average power supply reliability, annual electricity sales, proportion of energy storage capacity, power supply line loss rate, and source-load matching confidence.

8. A transmission and distribution price structure adjustment device, characterized in that: include: A historical indicator value acquisition module, for acquiring, for a target power grid including new energy power, a plurality of historical indicator values ​​of a target indicator in the target power grid, wherein the target indicator is an indicator that affects the composition structure of the transmission and distribution price in the target power grid, and the composition structure of the transmission and distribution price changes with the change in the power generation amount of the new energy power in the target power grid; A prediction index value acquisition module, used to generate a plurality of prediction index values ​​of the target index in a future time period based on the plurality of historical index values; An evaluation threshold determination module, used to perform time series simulation processing according to a predetermined power scenario to determine the indicator evaluation threshold in the future time period; An evaluation result determination module, configured to determine an electricity price evaluation result for the future time period based on the multiple prediction index values ​​and the index evaluation threshold; The adjustment strategy determination module is used to determine the transmission and distribution price adjustment strategy of the target power grid in the future time period according to the electricity price evaluation result.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method for adjusting the transmission and distribution price structure as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: One or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method for adjusting the transmission and distribution price structure as described in any one of claims 1 to 7.

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