Market scale influence factor analysis method for energy digital economic platform

Abstract

The invention provides a market scale influence factor analysis method for an energy digital economy platform, and belongs to the field of big data analysis. The method comprises the following steps: acquiring a plurality of energy digital economic platform index sets and a plurality of new energy market scale index sets; according to the plurality of energy digital economic platform index sets and the plurality of new energy market scale index sets, determining an initial influence index for constructing the energy digital economic platform; calculating a marginal influence degree and a balance influence degree of each initial influence index; determining a construction index according to the marginal influence degree and the balance influence degree of each initial influence index; wherein the construction index is used for constructing an energy digital economic platform with the new energy operation data; the energy digital economic platform is used for generating a power utilization strategy. The method can promote the development of new energy on the basis of promoting the optimal configuration of resources and improving the energy utilization efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and in particular to a method for analyzing factors affecting the market size of an energy digital economy platform. Background Art

[0002] With the rapid development of my country's social economy, the energy industry has also ushered in a new stage of rapid development and is in urgent need of transformation and upgrading. Energy digital economy, as a product of the deep integration of digital technology, energy economy and digital economy, will greatly promote the innovation of the traditional energy industry and become a key direction for the future revolution of the power industry. Energy digital economy platform, as the core of the development of energy digital economy, will show great development potential in the process of upgrading the power grid to energy Internet. It can not only effectively promote the high-quality construction of new power systems, but also support the digital transformation of the power industry chain, and help build a new intelligent dispatching and operation system that adapts to the development of new energy.

[0003] However, as an emerging scheduling platform and industrial form, the effective judgment of the factors affecting the market size of the energy digital economy platform has become a key issue that needs to be solved in its development. Although there are some methods for analyzing the factors affecting the market size, these traditional methods are not specifically targeted at the energy digital economy platform and cannot fully meet its development needs. Therefore, in this context, it has become a necessary condition for the development of the energy digital economy platform to build a set of systematic and comprehensive analysis methods to accurately evaluate the factors affecting the market size and market potential of the energy digital economy platform, and then promote the optimal allocation of resources and the improvement of energy utilization efficiency. Summary of the invention

[0004] The embodiment of the present invention provides a method for analyzing factors affecting the market size of an energy digital economy platform to solve the problem that the current energy digital economy platform cannot effectively promote the optimal allocation of new energy resources and cannot improve their utilization rate.

[0005] In a first aspect, an embodiment of the present invention provides an analysis of factors affecting the market size of an energy digital economy platform, including:

[0006] Obtain multiple energy digital economy platform indicator sets and multiple new energy market size indicator sets;

[0007] Determine the initial impact indicators for building an energy digital economy platform based on multiple energy digital economy platform indicator sets and multiple new energy market size indicator sets;

[0008] Calculate the marginal impact and balanced impact of each initial impact indicator;

[0009] The construction indicators are determined based on the marginal impact and balanced impact of each initial impact indicator; the construction indicators are used to construct an energy digital economy platform with new energy operation data; and the energy digital economy platform is used to generate electricity consumption strategies.

[0010] In a possible implementation, the marginal influence degree and the balanced influence degree of each initial influence indicator are calculated, including:

[0011] Quantify the impact of the energy digital economy platform on the new energy market and obtain the total impact;

[0012] For any initial impact indicator, the marginal impact of the initial impact indicator shall be determined based on the total impact and the total size of the new energy market without the initial impact indicator; the balanced impact of the initial impact indicator shall be determined based on the total impact and the proportion of the initial impact indicator in the total impact share.

[0013] In a possible implementation, the total influence is calculated by the following formula:

[0014] G=XN+XQ+YN+DS+ZL+TZ

[0015] Among them, G is the total impact; XN is the market size of virtual power plants; XQ is the market size of demand response; YN is the market size of energy optimization; DS is the size of the electricity market; ZL is the growth rate of the new energy market; TZ is the investment amount in the new energy market;

[0016] For any initial impact indicator, the marginal impact, total impact and the total market size after the initial impact indicator is missing satisfy the following relationship:

[0017]

[0018] Among them, G wb is the marginal influence; is the total market size after the initial impact indicator w is missing;

[0019] For any initial impact indicator, the balanced impact, total impact and the proportion of the initial impact indicator in the total impact share satisfy the following relationship:

[0020]

[0021] Among them, G wp To balance the impact; is the proportion of the initial impact indicator in the total impact share.

[0022] In a possible implementation, the construction indicators are determined according to the marginal influence and the balanced influence of each initial influence indicator, including:

[0023] According to the marginal influence and the balanced influence, the comprehensive influence of each initial influence indicator is calculated; wherein the comprehensive influence of each initial influence indicator is used to represent the average influence of its marginal influence and the balanced influence;

[0024] The initial impact index with a comprehensive impact greater than the preset threshold is used as the construction index.

[0025] In one possible implementation, the initial impact indicators for building an energy digital economy platform are determined based on multiple energy digital economy platform indicator sets and multiple new energy market size indicator sets, including:

[0026] Determine the intersection of indicators in multiple energy digital economy platform indicator sets and multiple new energy market size indicator sets;

[0027] Count the number of occurrences of each indicator in the intersection of indicators;

[0028] Arrange each indicator in descending order according to the number of occurrences, starting from the first indicator after sorting, and select a preset number of indicators as the initial impact indicators for building the energy digital economy platform;

[0029] Alternatively, each indicator is arranged in ascending order according to the number of occurrences, and starting from the last indicator after sorting, a preset number of indicators are selected as initial impact indicators for building the energy digital economy platform.

[0030] In one possible implementation, the energy digital economy platform is constructed in the following way:

[0031] Determine the weight coefficient of each construction indicator;

[0032] An energy digital economy platform is constructed based on each construction indicator and its corresponding weight coefficient, as well as new energy operation data.

[0033] In a possible implementation, the weight coefficient of each construction indicator is determined, including:

[0034] Determine multiple subjective weights and multiple objective weights for each construction indicator;

[0035] The weight coefficient of each construction indicator is determined according to the multiple subjective weights and the multiple objective weights corresponding to each construction indicator.

[0036] In a possible implementation, the weight coefficient of each construction indicator is determined according to multiple subjective weights and multiple objective weights corresponding to each construction indicator, including:

[0037] According to the multiple subjective weights and multiple objective weights corresponding to each construction indicator, a comprehensive weighted optimization model corresponding to each construction indicator is constructed;

[0038] The optimization algorithm is used to solve the comprehensive weighted optimization model to obtain the optimal weight corresponding to each construction indicator;

[0039] The optimal weight corresponding to each construction indicator is used as the weight coefficient of each construction indicator.

[0040] In a possible implementation, a comprehensive weighted optimization model corresponding to each construction indicator is constructed according to multiple subjective weights and multiple objective weights corresponding to each construction indicator, including:

[0041] For any build indicator, perform the following steps:

[0042] According to the moment estimation theory, the subjective weighted expected value and objective weighted expected value of the constructed indicator are calculated;

[0043] Calculate the subjective weight relative importance coefficient and the objective weight relative importance coefficient of the construction indicator;

[0044] A comprehensive weighted optimization model is constructed based on the expected value of subjective weight, the expected value of objective weight, the relative importance coefficient of subjective weight and the relative importance coefficient of objective weight.

[0045] In a second aspect, an embodiment of the present invention provides an energy digital economy platform construction device for promoting the development of new energy, including:

[0046] An acquisition module, used to acquire multiple energy digital economy platform indicator sets, multiple new energy market size indicator sets, and multiple new energy operation indicator sets;

[0047] A screening module, for determining initial impact indicators for building an energy digital economy platform based on multiple energy digital economy platform indicator sets, multiple new energy market size indicator sets, and multiple new energy operation indicator sets;

[0048] A calculation module, used to calculate the marginal influence and balanced influence of each initial influence indicator;

[0049] The screening module is also used to determine the construction indicators based on the marginal impact and balanced impact of each initial impact indicator; among which, the construction indicators are used to construct an energy digital economy platform with new energy operation data; the energy digital economy platform is used to generate electricity consumption strategies.

[0050] The embodiment of the present invention provides a method and device for constructing an energy digital economic platform for promoting the development of new energy. By comprehensively considering the energy digital economic platform indicators and the new energy market scale indicators, multiple initial influencing indicators for constructing the energy digital economic platform are determined. On the basis of reducing the amount of calculation, an accurate range can be provided for determining the indicators required for constructing the energy digital economic platform. Afterwards, considering the influencing factors on the market scale, not only the influence of the factors on the market scale should be considered, but also the influence of the interaction between the indicators on the output results should be excluded. The embodiment of the present invention analyzes the marginal influence and balanced influence of each initial influencing indicator. On the basis of ensuring objectivity and fairness, there is no mutual influence between the selected influencing factors, and the market scale influencing factors of the energy digital economic platform are determined, and then the energy digital economic platform is constructed according to the selected construction indicators and new energy operation data. The constructed energy digital economic platform can formulate electricity consumption strategies based on the consideration of market scale and new energy operation conditions, and can promote the development of new energy on the basis of promoting the optimal allocation of resources and improving energy utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0052] Figure 1 It is a flow chart for implementing a method for analyzing factors affecting the market scale of an energy digital economy platform provided by an embodiment of the present invention;

[0053] Figure 2 It is a flowchart for implementing a method for analyzing and constructing market scale influencing factors of an energy digital economy platform provided by another embodiment of the present invention;

[0054] Figure 3 It is a structural schematic diagram of a device for analyzing factors affecting the market size of an energy digital economy platform provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0056] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0057] Figure 1 1 is a flowchart of an implementation method of analyzing factors affecting the market size of an energy digital economy platform provided by an embodiment of the present invention. Figure 1 As shown, the method may include:

[0058] Step 110: Obtain multiple energy digital economy platform indicator sets and multiple new energy market size indicator sets.

[0059] In this embodiment, there are certain differences in the indicators included in multiple energy digital economy platform indicator sets and multiple new energy market scale indicator sets. The optional energy digital economy platform indicator set may include energy efficiency, renewable energy proportion, energy trading volume, market scale indicator value, new energy market growth rate, market penetration, investment amount, etc.; the new energy market scale indicator set may include new energy power generation, new energy consumption, new energy industry consumption, power investment, market-based trading electricity, etc., which are not limited here.

[0060] Step 120: Determine initial impact indicators for building an energy digital economy platform based on multiple energy digital economy platform indicator sets and multiple new energy market size indicator sets.

[0061] Optionally, in step 120, determining the initial impact indicators for building the energy digital economy platform according to multiple energy digital economy platform indicator sets and multiple new energy market size indicator sets may include:

[0062] Determine the intersection of indicators in multiple energy digital economy platform indicator sets and multiple new energy market size indicator sets.

[0063] Count the number of occurrences of each indicator in the intersection of indicators.

[0064] Arrange each indicator in descending order according to the number of occurrences, starting from the first indicator after sorting, and select a preset number of indicators as the initial impact indicators for building the energy digital economy platform.

[0065] Alternatively, each indicator is arranged in ascending order according to the number of occurrences, and starting from the last indicator after sorting, a preset number of indicators are selected as initial impact indicators for building the energy digital economy platform.

[0066] In this embodiment, in the process of determining the initial impact indicators, the overlapping parts of the indicator sets can be extracted based on multiple energy digital economy platform indicator sets and multiple new energy market size indicator sets to obtain the indicator intersection.

[0067] Among them, the intersection of indicators can be expressed as:

[0068] C=A∩B

[0069] Among them, C is the intersection of indicators, that is, the overlapping part of the indicator set, A is the energy digital economy platform indicator set, and B is the new energy market size indicator set.

[0070] The number of occurrences of each indicator can be expressed as:

[0071] X iz =(x i1 +x i2 +x i3 +…+x iz )

[0072] X iy =(x i1 +x i2 +x i3 +…+x iy )

[0073] X i =X iz +X iy

[0074] Among them, X iz is the number of times index i appears in the z index set, X iy is the number of times indicator i appears in the y indicator set, X i is the number of overlaps of index i.

[0075] In this embodiment, the preset number may be 50, that is, the first 50 most suitable indicators may be extracted as initial impact indicators. Taking the preset number of 50 as an example, the initial impact indicator extraction may be expressed as:

[0076] X i ∈S

[0077] S * =sort(S,descending)

[0078]

[0079] Where S is a set of real numbers greater than fifty, S * is the set of S sorted from largest to smallest, is a set representing the first fifty elements after sorting.

[0080] It can be seen that in this embodiment, by extracting the overlapping parts of each indicator set to obtain the initial impact indicator, a reasonable range can be provided for the subsequent extraction of the construction indicator, and the amount of calculation can be reduced.

[0081] Step 130: Calculate the marginal influence and balanced influence of each initial influence indicator.

[0082] In this embodiment, the marginal influence degree and the balanced influence degree of each initial influence indicator can be calculated in the following way:

[0083] Quantify the impact scale of the energy digital economy platform on the new energy market and obtain the total impact.

[0084] The total impact is calculated by the following formula:

[0085] G=XN+XQ+YN+DS+ZL+TZ

[0086] In the formula, G is the total impact; XN is the market size of virtual power plants; XQ is the market size of demand response; YN is the market size of energy optimization; DS is the size of the electricity market; ZL is the growth rate of the new energy market; TZ is the investment amount in the new energy market.

[0087] For any initial impact indicator, the marginal impact of the initial impact indicator shall be determined based on the total impact and the total size of the new energy market without the initial impact indicator; the balanced impact of the initial impact indicator shall be determined based on the total impact and the proportion of the initial impact indicator in the total impact share.

[0088] Among them, for any initial impact indicator, the marginal impact can be calculated by the following formula:

[0089]

[0090] In the formula, G wb is the marginal influence; It is the total market size after the initial impact indicator w is missing.

[0091] For any initial impact indicator, the balanced impact can be calculated by the following formula:

[0092]

[0093] In the formula, G wp To balance the impact; is the proportion of the initial impact indicator in the total impact share.

[0094] Step 140: Determine the construction index according to the marginal influence and balanced influence of each initial influence index; wherein the construction index is used to construct an energy digital economy platform with new energy operation data; and the energy digital economy platform is used to generate electricity consumption strategies.

[0095] In this embodiment, in step 140, determining the construction index according to the marginal influence degree and the balanced influence degree of each initial influence index may include:

[0096] According to the marginal influence and the balanced influence, the comprehensive influence of each initial influence indicator is calculated; wherein the comprehensive influence of each initial influence indicator is used to characterize the average influence of its marginal influence and the balanced influence.

[0097] The initial impact index with a comprehensive impact greater than the preset threshold is used as the construction index.

[0098] In this embodiment, the comprehensive influence can be calculated by the following formula:

[0099]

[0100] After calculating the comprehensive impact corresponding to each initial impact indicator, the initial impact indicator with a large impact can be selected as the construction indicator.

[0101] Among them, in this embodiment, the construction indicators may include gross domestic product, total energy consumption, total installed capacity of renewable energy, total energy production, total investment in energy cloud platforms, the size of the digital market in China's energy and power sector, the size of China's energy cloud industry market, the size of China's power spot market users, and the new energy consumption rate.

[0102] Optionally, the energy digital economy platform can be constructed in the following ways:

[0103] Step 141: Determine the weight coefficient of each construction indicator.

[0104] Step 142: Construct an energy digital economy platform based on each construction indicator and its corresponding weight coefficient, as well as new energy operation data.

[0105] In this embodiment, determining the weight coefficient of each construction indicator is a key step in building a new energy digital economy platform, which involves quantitative analysis and evaluation of the new energy market scale indicator set. The new energy operation data reflects the operating status of new energy equipment in the power system at the current stage, as well as the power transmission and power consumption in the power system. The new energy operation data is integrated, and after integration, each construction indicator and its corresponding weight coefficient are used as fixed parameters of the energy digital economy platform, and the new energy operation data is used as the energy digital economy platform control parameter to obtain the energy digital economy platform.

[0106] Through the constructed energy digital economy platform, based on market size, user demand, meteorological data and power generation data in energy operation data, market size and user demand are used as optimization targets, so that the generated electricity consumption strategy can meet the market size and user demand in each time period; the power generation of new energy and the operation of energy equipment are used as constraints to formulate peak and valley time-sharing electricity consumption strategies that meet the market size and user demand.

[0107] In an optional embodiment, determining the weight coefficient of each construction indicator in step 141 may include:

[0108] Multiple subjective weights and multiple objective weights are determined for each construction indicator.

[0109] The weight coefficient of each construction indicator is determined according to the multiple subjective weights and the multiple objective weights corresponding to each construction indicator.

[0110] In this embodiment, subjective weighting can be performed based on the analytic hierarchy process and the improved Delphi method to obtain multiple subjective weights of each construction indicator.

[0111] In this embodiment, the analytic hierarchy process is used to rank the degree of influence of indicators at the subjective level, and the improved Delphi method is used to calculate the weight offset after weighting, thereby enhancing the accuracy of weighting. Specifically:

[0112] The hierarchical structure is determined according to the obtained construction indicators, and m experts are organized to fill in the indicator judgment matrix. The importance is judged according to the 1-9 ratio scaling method. The scoring criteria are shown in Table 1:

[0113] Table 1 1-9 Ratio Scale Evaluation Criteria

[0114] Scale meaning 1 Indicates that two factors are equally important. 3 Indicates that one factor is slightly more important than the other factor. 5 Indicates that one factor is more important than the other factor. 7 Indicates that one factor is more important than the other factor. 9 Indicates that one factor is extremely more important than the other factor. 2,4,6,8 The median of the two adjacent judgments above

[0115] According to the content in Table 1, for any construction indicator, when its score is an odd number, the larger the score, the more important the construction indicator. When its score is an even number, it means that the importance of the construction indicator is between the two adjacent odd numbers corresponding to the even number, and similarly, the larger the value of the even number, the more important the construction indicator.

[0116] The discriminant matrix of each constructed indicator is normalized, wherein the eigenvector corresponding to the matrix, i.e., the weight vector, can be used to obtain the weights corresponding to the indicators at each level and determine the ranking weight of the relative importance of the factors at the same level to a factor at the previous level.

[0117] Among them, normalization can be performed by the following formula:

[0118]

[0119] In the formula, C uv is the element in the uth row and vth column of the judgment matrix; n is the number of indicators; w u is the weight of the u-th indicator.

[0120] In order to avoid contradictory evaluation results in the judgment matrix, it is necessary to perform a consistency test on the obtained judgment matrix. Accordingly, the random consistency ratio can be obtained based on the judgment matrix. When the random consistency ratio meets the preset conditions, it can be considered that the current judgment matrix meets the conditions. If it does not meet the preset conditions, it is considered that the current judgment matrix does not meet the conditions and needs to be readjusted and corrected.

[0121] In this embodiment, the random consistency ratio and whether the random consistency ratio meets the preset condition can be determined by the following formula:

[0122]

[0123] Among them, CR is the random consistency ratio; CI is the consistency test coefficient; RI is the average random consistency index.

[0124] In this embodiment, the magnitude of the RI value is only related to the order of the judgment matrix, and its corresponding value can be obtained by looking up a table, as shown in Table 2:

[0125] Table 2 Random consistency index value comparison table

[0126] n 1 2 3 4 5 6 7 8 9 10 11 RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 1.51

[0127] In Table 2, the first row is the order of the judgment matrix, and the second row is the corresponding RI value.

[0128] In this embodiment, generally speaking, the smaller the value of the random consistency ratio CR is, the better the consistency of the judgment matrix is. Here, it is set to be less than a preset random consistency ratio threshold as a preset condition. In this embodiment, the random consistency ratio threshold is set to 0.1. In actual applications, it can be set as needed.

[0129] After that, the hierarchical total ranking and consistency test are carried out. The hierarchical total ranking is to calculate the relative importance of all factors at a certain level to the highest level (total goal). This process is carried out from the highest level to the lowest level. When the hierarchical total ranking meets its consistency test, the step of subjective level analysis using the hierarchical analysis method is completed.

[0130] In this embodiment, after using the hierarchical analysis method at the subjective level, the hierarchical single-rank weight matrix W is obtained. ij ) m×n , where n is the number of indicators; m is the number of experts; and w ijrepresents the hierarchical single ranking weight of the i-th expert on the j-th indicator, satisfying

[0131] Based on the obtained content, the modified Delphi method is used to calculate the subjective weight:

[0132] First, calculate the average weight of the indicator:

[0133]

[0134] In the formula, It represents the average weight of the jth indicator evaluated by m experts.

[0135] Next, calculate the weight offset:

[0136]

[0137] Then, determine the new weights:

[0138]

[0139] In the formula, p ij is the coefficient of deviation; w' j The unnormalized weight of the j-th metric.

[0140] Finally, weight normalization is performed:

[0141]

[0142] The calculated subjective weights corresponding to each construction indicator are fed back to the experts. If the experts agree, the subjective weights obtained by the subjective weighting method are output; if the experts disagree, the hierarchical analysis method is returned to re-score the factors affecting market size until the experts agree.

[0143] In this embodiment, considering that only using subjective weighting method to measure the impact of market size indicators on the platform lacks certain scientificity, therefore, objective weighting can be performed based on improved entropy weight method and mean square error method to obtain objective weights of each constructed indicator.

[0144] The following is an explanation of the objective weights of each construction indicator calculated using the improved entropy weight method:

[0145] In this embodiment, considering that each construction indicator involves both quantitative indicators and qualitative indicators, the traditional entropy weight method is used to calculate the weight of the quantitative indicators; for the qualitative indicators, the concept of membership is introduced, and some actual discussions of the indicators in newspapers, books, papers, etc. are collected to make the objective indicators subjective. Among them, the importance is judged according to the 1-10 ratio scaling method and the membership index, and the scoring criteria are shown in Table 3.

[0146] Table 3 1-10 ratio scaling method and membership index evaluation criteria

[0147]

[0148] In this embodiment, contrary to Table 1, the larger the scale value is, the less important the construction indicator is. However, for the membership index, the larger the value is, the more important the construction indicator is.

[0149] Assume Z ij is the contribution of the jth sub-indicator in the i-th construction indicator; n is the number of construction indicators. Accordingly, the information entropy output by the j-th sub-indicator can be expressed as:

[0150]

[0151] Among them, when Z ij =0, it is specified that Z ij LqCy ij =0.

[0152] The weight vector in constructing the indicator can be expressed as:

[0153] w=(w1,w2,…,w n )

[0154]

[0155] The following is an explanation of the mean square error method for calculating the objective weights of each construction indicator:

[0156] The mean value of the jth sub-indicator in the i-th constructed indicator can be expressed as:

[0157]

[0158] The mean square error of the jth sub-indicator in the i-th constructed indicator can be expressed as:

[0159]

[0160] Correspondingly, the weight vector in the constructed indicator can be expressed as:

[0161] w=(w1,w2,…,w n )

[0162]

[0163] In this embodiment, the new energy operation data may include new energy power prediction data, such as meteorological platform data such as wind speed, wind direction, temperature, and second-level data of the station SCADA system, such as wind speed, wind direction, power, unit operation status, blade angle, yaw angle and fault information, etc.; new energy station operation data: such as generator sets, AGC (automatic generation control), AVC (automatic voltage control) and other second-level operation data; meteorological data; power grid control center data, such as hierarchical operation data including new energy power prediction, smart grid control system, single machine information, etc.; new energy station network-related performance data, such as multi-source second-level data including new energy station AGC operation data, AVC operation data, SVG (static VAR generator) operation data, SCADA operation data, etc.; new energy consumption analysis data, etc.

[0164] According to the determined construction indicators, the corresponding weight coefficients of the construction indicators and the new energy operation data, an energy digital economy platform is constructed. The energy digital economy platform can detect and analyze the operation data of new energy in real time, and formulate a reasonable electricity use strategy based on the energy market.

[0165] In an optional embodiment, determining the weight coefficient of each construction indicator according to multiple subjective weights and multiple objective weights corresponding to each construction indicator may include:

[0166] According to the multiple subjective weights and multiple objective weights corresponding to each construction indicator, a comprehensive weighted optimization model corresponding to each construction indicator is constructed.

[0167] The optimization algorithm is used to solve the comprehensive weighted optimization model to obtain the optimal weight corresponding to each construction indicator.

[0168] The optimal weight corresponding to each construction indicator is used as the weight coefficient of each construction indicator.

[0169] In this embodiment, for any construction indicator, a plurality of subjective weights and a plurality of objective weights obtained therefrom can be combined to obtain a comprehensive weighted optimization model. In the comprehensive weighted optimization model, the goal is to minimize the deviation between the subjective weight and the objective weight, and an optimization algorithm is used to determine the optimal combination of the subjective weight and the objective weight, and to determine the ratio therebetween, so as to determine the optimal weight corresponding to each construction indicator. The optimal weight corresponding to each construction indicator obtained is determined as the weight coefficient of each indicator.

[0170] In an optional embodiment, constructing a comprehensive weighted optimization model corresponding to each construction indicator according to multiple subjective weights and multiple objective weights corresponding to each construction indicator may include:

[0171] For any build indicator, perform the following steps:

[0172] According to the moment estimation theory, the subjective weighted expected value and objective weighted expected value of the constructed indicator are calculated.

[0173] Calculate the subjective weight relative importance coefficient and the objective weight relative importance coefficient of the constructed indicator.

[0174] A comprehensive weighted optimization model is constructed based on the expected value of subjective weight, the expected value of objective weight, the relative importance coefficient of subjective weight and the relative importance coefficient of objective weight.

[0175] In this embodiment, it is assumed that w hj is the weight vector of the jth indicator of the hth subjective weighting method; n is the number of evaluation indicators; w zj is the weight vector of the jth indicator of the zth objective weighting method; d is the number of subjective weighting methods, and q is the number of objective weighting methods.

[0176] Through the moment estimation theory, the subjective weighted expected value and objective weighted expected value of each construction indicator are calculated.

[0177] Among them, the subjective weighted expected value and objective weighted expected value of each construction indicator can be expressed as:

[0178]

[0179] Among them, E(w hj ) is the subjective weighted expected value; E(w zj ) is the objective weighted expected value.

[0180] In each construction indicator, the relative importance coefficient of subjective weight and the relative importance coefficient of objective weight of each sub-indicator are expressed as follows:

[0181]

[0182]

[0183] Among them, α j is the relative importance coefficient of subjective weight; β j is the relative importance coefficient of objective weight.

[0184] Correspondingly, for any construction indicator, the relative importance coefficient of subjective weight and the relative importance coefficient of objective weight are:

[0185]

[0186] The comprehensive weighted optimization model can be expressed as:

[0187]

[0188] Among them, w j is the optimal weight of the jth sub-index to be solved; E(w hj ) and E(w zj ) is the expected value of the known subjective and objective weights; α and β are the relative importance coefficients of the known subjective and objective weights.

[0189] The following is an explanation of the solution process of the comprehensive weighted optimization model:

[0190] Step 1: Set the current weight w of the jth sub-indicator j Set it as the unknown number of the optimization function, and take the subjective and objective weighted expected value E(w hj )、E(w zj ) and the subjective and objective relative importance coefficients α and β are substituted into the optimization function.

[0191] Step 2: Set the population size N and the initial value of the learning factor c 1,max , c 1,min , c 2,max , c 2,min , the maximum number of iterations I, initial velocity v, index number D and initial inertia weight and other parameters are set.

[0192] In this embodiment, based on the comprehensive weighted optimization model, the algorithm parameters are set as follows: the maximum number of iterations I is 200; the learning factor c 1,max , c 1,min , c 2,max , c 2,min , respectively c1, c2 upper and lower limits, take 2.75, 1.25, 2.25, 1.05; inertia weight initial value α max =0.8, final value of inertia weight α min =0.3; population size n=50.

[0193] Step 3: Calculate the fitness of each group of current weights and find the initial global optimal particle gbest and the individual optimal particle pbest.

[0194] Step 4: Update the adaptive inertia weight;

[0195] In order to overcome the problem of falling into local optimality that the particle swarm algorithm is prone to, the inertia weight of the particle swarm algorithm is improved: the fitness of the i-th particle is f i ; The fitness value of the global extremum is f m ; The average fitness of the extreme value of the particle swarm is According to f i and f avg The group is divided into two subgroups, and different adaptive operations are performed on each subgroup. The adjustment of the inertia weight α is as follows:

[0196] If f i Below f avg ,but

[0197]

[0198] If f i Higher than f avg ,but

[0199] α=α max

[0200] In the formula, f i Below f avg The particle is close to the global optimal solution, so it is given a smaller inertia weight α to enhance its local optimization exploration ability; i Higher than f avg Since the particles are not close to the global optimal solution, they need better global search capabilities to avoid falling into the local optimum.

[0201] Step 5: Update the learning factors c1, c2:

[0202] The linear adjustment learning factor strategy calculation formula is as follows:

[0203]

[0204] In the formula, c 1,max , c 1,min , c 2,max , c 2,min are c1, c2 upper and lower limits respectively, k is the current iteration number, I tera is the maximum number of iterations. In the initial iteration, c1 is large and c2 is small, and the iterative update of particles in the population is mainly based on the experience of the particles themselves. Then c1 becomes smaller and c2 becomes larger, and the cooperation between particles is strengthened, so that the population flies to the global optimum and avoids falling into the local optimum problem.

[0205] Step 6: Update the particle's velocity v and position x. The iterative optimization formula of the adaptive particle swarm algorithm is as follows:

[0206]

[0207] in, represents the position and velocity of the jth particle at the kth iteration; represents the individual extreme value of the jth particle in the kth iteration; represents the global extremum of the kth iteration.

[0208] Step 7: Calculate the fitness of the newly generated position and update the global optimal particle gbest and the individual optimal particle pbest;

[0209] Step 8: Repeat steps 3 to 7 until the maximum number of iterations I is reached, the algorithm ends, and the optimal weight is output.

[0210] Figure 2 is a flowchart of an implementation method for analyzing market scale influencing factors of an energy digital economy platform provided by another embodiment of the present invention, such as Figure 2 As shown, the method includes:

[0211] Step 1: Obtain multiple energy digital economy platform indicator sets and multiple new energy market size indicator sets, and based on data overlap technology, determine the overlapping indicators among the multiple energy digital economy platform indicator sets and multiple new energy market size indicator sets as the initial impact indicators.

[0212] Step 2: For the multiple initial impact indicators obtained, consider the marginal impact and balanced impact of each initial impact indicator, conduct a secondary screening of the initial impact indicators, and obtain the construction indicators for constructing the energy digital economy platform.

[0213] Step 2: For each construction indicator obtained, subjective weighting is performed based on the hierarchical analysis method and the improved Delphi method to obtain the subjective weight of each construction indicator.

[0214] Step 3: For each construction indicator obtained, objective re-weighting is performed based on the improved entropy weight method and mean square error method to obtain the objective weight of each construction indicator.

[0215] Step 4: For the objective weight and subjective weight of each construction indicator, combined weighting is performed based on the moment estimation theory to comprehensively weight the optimization model.

[0216] Step 5: Based on the adaptive particle swarm algorithm, the comprehensive weighted optimization model is solved to determine the weight coefficient of each construction indicator. The energy digital economy platform is constructed based on the construction indicators, the weight coefficients of each construction indicator and the new energy operation data.

[0217] In summary, the embodiment of the present invention determines multiple initial influencing indicators for constructing an energy digital economy platform by comprehensively considering the energy digital economy platform indicators and the new energy market scale indicators. It can provide an accurate range for determining the indicators required for constructing an energy digital economy platform on the basis of reducing the amount of calculation. Afterwards, considering the influencing factors on the market scale, it is necessary not only to consider the impact of the factors on the market scale, but also to exclude the impact of the interaction between the indicators on the output results. The embodiment of the present invention analyzes the marginal influence and balanced influence of each initial influencing indicator. There is no mutual influence between the selected influencing factors. On the basis of ensuring objectivity and fairness, the market scale influencing factors of the energy digital economy platform are determined, and then the energy digital economy platform is constructed according to the selected construction indicators and new energy operation data. The constructed energy digital economy platform can formulate electricity consumption strategies based on the consideration of market scale and new energy operation conditions, and can promote the development of new energy on the basis of promoting the optimal allocation of resources and improving energy utilization efficiency.

[0218] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0219] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0220] Figure 3 The structure diagram of the device for analyzing market scale influencing factors of the energy digital economy platform provided by the embodiment of the present invention is shown. For the convenience of explanation, only the part related to the embodiment of the present invention is shown, which is described in detail as follows:

[0221] like Figure 3 As shown, the energy digital economy platform construction device 3 for promoting the development of new energy includes:

[0222] An acquisition module 31, used to acquire multiple energy digital economy platform indicator sets, multiple new energy market scale indicator sets and multiple new energy operation indicator sets;

[0223] A screening module 32, for determining initial impact indicators for building an energy digital economy platform based on a plurality of energy digital economy platform indicator sets, a plurality of new energy market size indicator sets, and a plurality of new energy operation indicator sets;

[0224] A calculation module 33 is used to calculate the marginal influence degree and the balanced influence degree of each initial influence indicator;

[0225] The screening module 32 is also used to determine the construction indicators according to the marginal influence and balance influence of each initial influencing indicator; wherein the construction indicators are used to construct an energy digital economy platform with new energy operation data; and the energy digital economy platform is used to generate electricity consumption strategies.

[0226] In a possible implementation, the calculation module 33 is specifically configured to:

[0227] Quantify the impact of the energy digital economy platform on the new energy market and obtain the total impact;

[0228] For any initial impact indicator, the marginal impact of the initial impact indicator shall be determined based on the total impact and the total size of the new energy market without the initial impact indicator; the balanced impact of the initial impact indicator shall be determined based on the total impact and the proportion of the initial impact indicator in the total impact share.

[0229] In a possible implementation, the total influence is calculated by the following formula:

[0230] G=XN+XQ+YN+DS+ZL+TZ

[0231] Among them, G is the total impact; XN is the market size of virtual power plants; XQ is the market size of demand response; YN is the market size of energy optimization; DS is the size of the electricity market; ZL is the growth rate of the new energy market; TZ is the investment amount in the new energy market;

[0232] For any initial impact indicator, the marginal impact, total impact and the total market size after the initial impact indicator is missing satisfy the following relationship:

[0233]

[0234] Among them, G wb is the marginal influence; is the total market size after the initial impact indicator w is missing;

[0235] For any initial impact indicator, the balanced impact, total impact and the proportion of the initial impact indicator in the total impact share satisfy the following relationship:

[0236]

[0237] Among them, G wp To balance the impact; is the proportion of the initial impact indicator in the total impact share.

[0238] In a possible implementation, the screening module 32 is specifically configured to:

[0239] According to the marginal influence and the balanced influence, the comprehensive influence of each initial influence indicator is calculated; wherein the comprehensive influence of each initial influence indicator is used to represent the average influence of its marginal influence and the balanced influence;

[0240] The initial impact index with a comprehensive impact greater than the preset threshold is used as the construction index.

[0241] In a possible implementation, the screening module 32 is specifically configured to:

[0242] Determine the intersection of indicators in multiple energy digital economy platform indicator sets and multiple new energy market size indicator sets;

[0243] Count the number of occurrences of each indicator in the intersection of indicators;

[0244] Arrange each indicator in descending order according to the number of occurrences, starting from the first indicator after sorting, and select a preset number of indicators as the initial impact indicators for building the energy digital economy platform;

[0245] Alternatively, each indicator is arranged in ascending order according to the number of occurrences, and starting from the last indicator after sorting, a preset number of indicators are selected as initial impact indicators for building the energy digital economy platform.

[0246] In a possible implementation, the device further includes a construction module 34;

[0247] The building block 34 is specifically used for:

[0248] Determine the weight coefficient of each construction indicator;

[0249] A source digital economy platform is constructed based on each construction indicator and its corresponding weight coefficient, as well as new energy operation data.

[0250] In a possible implementation, the construction module 34 is specifically configured to:

[0251] Determine multiple subjective weights and multiple objective weights for each construction indicator;

[0252] The weight coefficient of each construction indicator is determined according to the multiple subjective weights and the multiple objective weights corresponding to each construction indicator.

[0253] In a possible implementation, the construction module 34 is specifically configured to:

[0254] According to the multiple subjective weights and multiple objective weights corresponding to each construction indicator, a comprehensive weighted optimization model corresponding to each construction indicator is constructed;

[0255] The optimization algorithm is used to solve the comprehensive weighted optimization model to obtain the optimal weight corresponding to each construction indicator;

[0256] The optimal weight corresponding to each construction indicator is used as the weight coefficient of each construction indicator.

[0257] In a possible implementation, the construction module 34 is specifically configured to:

[0258] For any build indicator, perform the following steps:

[0259] According to the moment estimation theory, the subjective weighted expected value and objective weighted expected value of the constructed indicator are calculated;

[0260] Calculate the subjective weight relative importance coefficient and the objective weight relative importance coefficient of the construction indicator;

[0261] A comprehensive weighted optimization model is constructed based on the expected value of subjective weight, the expected value of objective weight, the relative importance coefficient of subjective weight and the relative importance coefficient of objective weight.

[0262] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0263] Those of ordinary skill in the art will appreciate that the templates, units, and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0264] If the module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned embodiments of the market scale influencing factor analysis method of the energy digital economy platform. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium.

[0265] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for analyzing factors affecting the market size of an energy digital economy platform, characterized in that: include: Obtain multiple energy digital economy platform indicator sets and multiple new energy market size indicator sets; Determining initial impact indicators for constructing an energy digital economy platform based on the multiple energy digital economy platform indicator sets and the multiple new energy market size indicator sets; Calculate the marginal impact and balanced impact of each initial impact indicator; According to the marginal influence and balanced influence of each initial influencing indicator, the construction indicator is determined; wherein the construction indicator is used to construct an energy digital economy platform with new energy operation data; the energy digital economy platform is used to generate electricity consumption strategies.

2. The method for analyzing factors affecting the market size of an energy digital economy platform according to claim 1 is characterized in that: The calculation of the marginal influence degree and the balanced influence degree of each initial influence indicator includes: Quantify the impact of the energy digital economy platform on the new energy market and obtain the total impact; For any initial impact indicator, the marginal impact of the initial impact indicator is determined based on the total impact and the total size of the new energy market without the initial impact indicator; the balanced impact of the initial impact indicator is determined based on the total impact and the proportion of the initial impact indicator in the total impact share.

3. The method for analyzing factors affecting the market size of an energy digital economy platform according to claim 2 is characterized in that: The total impact is calculated by the following formula: G=XN+XQ+YN+DS+ZL+TZ Among them, G is the total impact; XN is the market size of virtual power plants; XQ is the market size of demand response; YN is the market size of energy optimization; DS is the size of the electricity market; ZL is the growth rate of the new energy market; TZ is the investment amount in the new energy market; For any initial impact indicator, the marginal impact, the total impact and the total market size after the initial impact indicator is missing satisfy the following relationship: Among them, G wb is the marginal impact; is the total market size after the initial impact indicator w is missing; For any initial impact indicator, the balanced impact, the total impact and the proportion of the initial impact indicator in the total impact share satisfy the following relationship: Among them, G wp is the balance influence; is the proportion of the initial impact indicator in the total impact share.

4. The method for analyzing factors affecting the market size of an energy digital economy platform according to claim 1 is characterized in that: Determining the construction index according to the marginal influence degree and the balanced influence degree of each initial influence index includes: According to the marginal influence and the balanced influence, the comprehensive influence of each initial influence indicator is calculated; wherein the comprehensive influence of each initial influence indicator is used to represent the average influence of its marginal influence and balanced influence; The initial impact index with a comprehensive impact greater than the preset threshold is used as the construction index.

5. The method for analyzing factors affecting the market size of an energy digital economy platform according to claim 1 is characterized in that: The determining of initial impact indicators for constructing an energy digital economy platform according to the multiple energy digital economy platform indicator sets and the multiple new energy market size indicator sets includes: Determine the intersection of indicators in the multiple energy digital economy platform indicator sets and the multiple new energy market scale indicator sets; Count the number of occurrences of each indicator in the intersection of the indicators; Arrange each indicator in descending order according to the number of occurrences, starting from the first indicator after sorting, and select a preset number of indicators as the initial impact indicators for building the energy digital economy platform; Alternatively, each indicator is arranged in ascending order according to the number of occurrences, and starting from the last indicator after sorting, a preset number of indicators are selected as initial impact indicators for building the energy digital economy platform.

6. The method for analyzing factors affecting the market size of an energy digital economy platform according to claim 1 is characterized in that: The energy digital economy platform is constructed in the following ways: Determine the weight coefficient of each construction indicator; An energy digital economy platform is constructed based on each construction indicator and its corresponding weight coefficient, as well as new energy operation data.

7. The method for analyzing factors affecting the market size of an energy digital economy platform according to claim 6 is characterized in that: The step of determining the weight coefficient of each construction indicator includes: Determine multiple subjective weights and multiple objective weights for each construction indicator; The weight coefficient of each construction indicator is determined according to the multiple subjective weights and the multiple objective weights corresponding to each construction indicator.

8. The method for analyzing factors affecting the market size of an energy digital economy platform according to claim 7 is characterized in that: Determining the weight coefficient of each construction indicator according to the multiple subjective weights and multiple objective weights corresponding to each construction indicator includes: According to the multiple subjective weights and multiple objective weights corresponding to each construction indicator, a comprehensive weighted optimization model corresponding to each construction indicator is constructed; Using an optimization algorithm to solve the comprehensive weighted optimization model to obtain the optimal weight corresponding to each construction indicator; The optimal weight corresponding to each construction indicator is used as the weight coefficient of each construction indicator.

9. The method for analyzing factors affecting the market size of an energy digital economy platform according to claim 8 is characterized in that: The method of constructing a comprehensive weighted optimization model corresponding to each construction indicator according to multiple subjective weights and multiple objective weights corresponding to each construction indicator includes: For any build indicator, perform the following steps: According to the moment estimation theory, the subjective weighted expected value and objective weighted expected value of the constructed indicator are calculated; Calculate the subjective weight relative importance coefficient and the objective weight relative importance coefficient of the construction indicator; A comprehensive weighted optimization model is constructed based on the subjective weight expected value, the objective weight expected value, the subjective weight relative importance coefficient and the objective weight relative importance coefficient.

10. A device for analyzing factors affecting the market size of an energy digital economy platform, characterized in that: include: An acquisition module, used to acquire multiple energy digital economy platform indicator sets, multiple new energy market size indicator sets, and multiple new energy operation indicator sets; A screening module, used to determine the initial impact indicators for constructing the energy digital economy platform according to the multiple energy digital economy platform indicator sets, the multiple new energy market size indicator sets and the multiple new energy operation indicator sets; A calculation module, used to calculate the marginal influence and balanced influence of each initial influence indicator; The screening module is also used to determine the construction indicators based on the marginal influence and balanced influence of each initial influencing indicator; wherein the construction indicators are used to construct an energy digital economy platform with new energy operation data; and the energy digital economy platform is used to generate electricity consumption strategies.

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