New energy acceptance capability assessment method and system based on multivariate uncertainty and action mechanism

Abstract

The invention provides a new energy acceptance capability assessment method and system based on multivariate uncertainty and an action mechanism. The method comprises the steps of S1, analyzing a new energy consumption mechanism and extracting influence factors, including establishing a consumption capability calculation model and establishing a new energy consumption knowledge graph; s2, establishing a new energy consumption influence index system from a source side, a network side and a load side based on the extracted influence factors, and determining a calculation method of each index; s3, the subjective weight of each index is calculated by adopting an analytic hierarchy process, the objective weight of each index is calculated by adopting an entropy evaluation method, the subjective weight and the objective weight are combined and optimized by combining the game theory, and the combined weight of each index is obtained; and S4, constructing a decision matrix, calculating the correlation degree between each scheme and the optimal scheme through grey correlation degree analysis based on the combination weight, and determining the importance degree sequence of the influence of each influence factor on the new energy consumption capability.

Description

Technical Field

[0001] This invention proposes a method and system for assessing the capacity of renewable energy to be absorbed based on multiple uncertainties and mechanisms of action, which relates to the field of renewable energy consumption technology in power systems. Background Technology

[0002] Driven by global energy transition goals, new energy sources such as wind and solar power are seeing a continuous increase in their penetration rate in the power system due to their clean and renewable characteristics. However, the output of new energy sources is highly volatile and uncertain due to natural conditions, and large-scale integration poses a severe challenge to the distribution network's absorption capacity. The phenomenon of "wind and solar curtailment" occurs frequently, not only wasting resources but also affecting the safe and stable operation of the power system.

[0003] Accurately identifying the key factors affecting renewable energy consumption is a prerequisite for improving consumption capacity. Current technologies for assessing the factors influencing renewable energy consumption have several shortcomings: First, factor extraction is incomplete. Traditional methods often focus on a single dimension—renewable energy output characteristics or grid operating parameters—ignoring the interaction between the source, grid, and load sides. For example, they do not fully consider the impact of load peak-valley differences and the uniformity of renewable energy distribution on consumption. Second, weight allocation is unreasonable. Subjective weighting methods rely on expert experience, which is prone to subjective bias. Objective weighting methods rely solely on data and do not consider the actual importance of indicators, making it difficult to balance subjective and objective needs. Third, the accuracy of assessment methods is insufficient. Some methods only obtain results through simple weighted summation, without considering the correlation between indicators, and cannot accurately reflect the degree of influence of each factor on consumption capacity.

[0004] Existing single-dimensional assessment methods only analyze the impact of renewable energy penetration on absorption capacity, failing to consider factors such as grid power flow limits and load characteristics. This results in biased assessments that cannot provide comprehensive guidance for distribution network upgrades. Subjective weighting methods are prone to contradictions in weight allocation when expert opinions differ, reducing the credibility of the assessment results. Simple weighted summation methods fail to reflect the grey relational characteristics between indicators, making it difficult to accurately quantify the impact of each factor on absorption capacity. These problems render existing assessment methods inadequate for the precise assessment needs of high-penetration renewable energy absorption scenarios, necessitating a comprehensive assessment method that can fully extract influencing factors, scientifically allocate weights, and accurately quantify the degree of impact. Summary of the Invention

[0005] In view of this, and to fill the gaps and deficiencies in existing technologies, this invention proposes a method and system for assessing the acceptance capacity of new energy sources based on multiple uncertainties and their mechanisms of action. This invention aims to overcome the shortcomings of existing methods, such as incomplete factor extraction, significant subjective bias in weight allocation, and insufficient accuracy of assessment results.

[0006] This invention proposes a method for assessing the acceptance capacity of new energy sources based on multiple uncertainties and mechanisms of action, characterized by the following:

[0007] Step S1: Analyze the mechanism of new energy consumption and extract influencing factors, including establishing a calculation model for consumption capacity and establishing a knowledge graph of new energy consumption;

[0008] Step S2: Based on the extracted influencing factors, establish a new energy consumption impact index system from the source side, grid side, and load side, and determine the calculation method for each index;

[0009] Step S3: Calculate the subjective weight of each indicator using the analytic hierarchy process (AHP), calculate the objective weight of each indicator using the entropy method, and combine the subjective and objective weights using game theory to optimize the combination and obtain the combined weight of each indicator.

[0010] Step S4: Construct a decision matrix, and calculate the correlation between each scheme and the optimal scheme based on the combined weights through grey relational analysis, and determine the order of importance of each influencing factor on the new energy absorption capacity.

[0011] Further, step S1 includes the following:

[0012] Step S11: The construction of the absorption capacity calculation model includes: the maximum absorption capacity of new energy is defined as the upper limit of new energy power generation that the system can absorb under safety constraints; where the adjustable space of the system is the operating range between the power load and the minimum technical output of conventional generating units. Based on the minimum technical output of conventional units and load demand, the absorption capacity model at time t is established:

[0013]

[0014] Among them, P a,t P represents the renewable energy absorption capacity at time t. L,t Let t be the load magnitude at time t; The minimum technical output of the i-th conventional unit at time t; This is the capacity of a conventional generating unit; This represents the start-up and shutdown status of a conventional unit i at time t, with 1 for start-up and 0 for shutdown. This represents the minimum technical output coefficient of conventional unit i;

[0015] Step S12: The relationship between renewable energy consumption capacity, theoretical output, and abandoned power is as follows:

[0016]

[0017] P DER,t =P WT,t +P PV,t

[0018]

[0019] Among them, P x,t P represents the renewable energy absorption capacity at time t. DER,t To contribute to the new energy theory at time t; P WT,t P represents the wind power output at time t. PV,t P represents the photovoltaic output at time t; y,t Let t be the amount of renewable energy wasted.

[0020] Furthermore, step S1 also includes the following:

[0021] Step S13: Construction and factor extraction of the knowledge graph for new energy consumption. The knowledge graph adopts a schema layer and a data layer architecture and is implemented through the Neo4j graph database. The schema layer defines entities and relationships. The data layer integrates distribution network operation data and probabilistic power flow results.

[0022] Knowledge extraction includes identifying entities, attributes, and relationships from structured and unstructured data;

[0023] Knowledge representation and fusion include: describing knowledge using entity-relation-entity triples, eliminating data redundancy, and storing it in a graph;

[0024] Knowledge reasoning includes: uncovering hidden factors and analyzing the indirect impact of the evenness of new energy distribution on consumption through graph traversal.

[0025] Further, step S2 includes the following:

[0026] Step S21: Define source-side indicators, including new energy output volatility: A1, penetration rate A2, distribution degree A3, uniformity A4, and source-load matching degree A5;

[0027] The formula for calculating the volatility of new energy power output is as follows:

[0028]

[0029] Where Δt is the reference time interval; i is the number of reference time intervals; P(i×Δt) and P(i+1)×Δt are the actual power output of new energy power generation at the previous and next moments, respectively; P EN The rated power of new energy power generation; n is the number of typical daily time intervals, taken as 96;

[0030] The formula for calculating permeability is:

[0031]

[0032] Among them, C E For new energy installed capacity; C all This refers to the total installed capacity of the system.

[0033] The formula for calculating the distribution degree is:

[0034]

[0035] Where n is the number of nodes in the distribution network with new energy access; N is the total number of nodes in the distribution network;

[0036] The formula for calculating uniformity is:

[0037]

[0038] In the formula, C DER,i Let avg(C) represent the rated capacity of new energy sources connected to the i-th node. DER P represents the average capacity of the connected renewable energy sources; L This refers to the system load power.

[0039] The formula for calculating the source-load matching degree is:

[0040]

[0041]

[0042] in, Rate of change in power output for new energy sources; P represents the load change rate; DER (t+T), P DER (t) represents the power output of the new energy source at times t+T and t, respectively; P LD (t+T), P LD (t) represents the load at time t+T and time t, respectively; n is the sample size.

[0043] Further, step S2 includes the following:

[0044] Step S22: Define network-side indicators, including: line current carrying capacity B1, voltage over-limit probability B2, and short-circuit level B3;

[0045] The formula for calculating the line current carrying capacity is:

[0046]

[0047] Where Q0 is the safe current carrying capacity; Q1 is the average load power over a certain period of time;

[0048] The formula for calculating the voltage over-limit probability is:

[0049]

[0050] Where, f(V) i ) represents the node voltage probability density function; Vmin V max These represent the minimum and maximum allowable ranges for node voltage, respectively.

[0051] The formula for calculating short-circuit level is:

[0052]

[0053] Where I1 is the periodic component current; I0 ​​is the rated short-circuit breaking current;

[0054] The load-side indicators include load rate C1 and peak-valley difference rate C2;

[0055] The formula for calculating the load factor is:

[0056]

[0057] Among them, P P The average load of the entire network over a certain period of time; P max This represents the maximum network load within a certain timeframe.

[0058] The formula for calculating the peak-valley difference rate is:

[0059]

[0060] Among them, P min This represents the minimum load across the entire network within a certain timeframe.

[0061] Further, step S3 includes the following:

[0062] Step S31: Subjective weight calculation based on the analytic hierarchy process includes the following:

[0063] Step S311: Establish a hierarchical structure model, which is divided into target layer, criterion layer and scheme layer. The target layer is the assessment of the influencing factors of high-penetration new energy consumption, the criterion layer refers to the source side, grid side and load side, and the scheme layer refers to each specific indicator.

[0064] Step S312: Construct a judgment matrix, using a 1-9 scale to represent the importance between indicators, where 1 indicates equal importance, 3 indicates slightly important, 5 indicates significantly important, 7 indicates strongly important, and 9 indicates extremely important. 2, 4, 6, and 8 are the intermediate values ​​between adjacent judgments. The judgment matrix satisfies...

[0065] Step S313: Hierarchical single sorting and consistency check, calculate the largest eigenvalue λ of the judgment matrix. max The consistency index is further obtained as follows:

[0066]

[0067] Where n is the order of the judgment matrix;

[0068] Let the consistency ratio CR be:

[0069]

[0070] Where RI is the average random consistency index, and when CR≤0.1, the consistency of the judgment matrix is ​​acceptable;

[0071] Step S314: Overall hierarchical ranking and consistency check, including calculating subjective weights using the geometric mean method, the formula is:

[0072]

[0073] Where i = 1, 2, ..., n, and W1 is the subjective weight.

[0074] Furthermore, step S3 also includes the following:

[0075] Step S32: Objective weight calculation based on entropy method includes the following:

[0076] Step S321: Construct the original matrix Q = (q ij ) mn ,include:

[0077] Define positive index normalization:

[0078]

[0079] Define contrarian indicator normalization:

[0080]

[0081] Where m is the number of samples, n is the number of indicators, and x ij Let x be the index in sample i, j be the actual measured value, and x be the index in sample i. min,j x max,j These are the minimum and maximum values ​​of the measured value of index j, respectively.

[0082] Step S322: Correct the normalization matrix:

[0083]

[0084] Step S323: Calculate the index entropy value:

[0085]

[0086] The final objective weight is:

[0087]

[0088] Where W2 is the objective weight;

[0089] Step S33: Game theory-based combinatorial weight optimization includes the following: Let the combinatorial coefficients be: subjective weight coefficient θ1, objective weight coefficient θ2, and combinatorial weights...

[0090] The combination coefficients are obtained by minimizing the deviation between the combined weights and the subjective and objective weights:

[0091]

[0092] The combination coefficients are obtained by using the Lagrange multiplier method, and after normalization, we get:

[0093]

[0094] Finally there is

[0095] Where W' is the final weight.

[0096] Further, step S4 includes the following:

[0097] Step S41: Construct the decision matrix, including:

[0098]

[0099] Where U=[u1,u2,L,u m [This is a reference series, composed of the optimal values ​​of each indicator;]

[0100] Where D=(d ij ) m×n For comparison of sets of numbers, the index values ​​are normalized.

[0101] Step S42: Calculate the correlation coefficient, including:

[0102]

[0103] Where ρ is the resolution coefficient, which is taken as 0.5;

[0104] Step S43: Calculate the grey relational degree Where w j The weights of the indicators are determined by ranking the importance of each influencing factor according to the degree of correlation. The higher the correlation, the more significant the impact.

[0105] According to a second aspect of the present invention, the present invention proposes a new energy acceptance capacity assessment system based on multiple uncertainties and mechanisms of action, comprising an electronic device, wherein the electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements a new energy acceptance capacity assessment method based on multiple uncertainties and mechanisms of action as described in any one of the present invention.

[0106] According to a third aspect of the present invention, the present invention proposes a new energy acceptance capacity assessment system based on multiple uncertainties and mechanisms of action, comprising a computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements a new energy acceptance capacity assessment method based on multiple uncertainties and mechanisms of action as described in any one of the present invention.

[0107] The present invention has the following advantages:

[0108] 1. More comprehensive factor extraction: By constructing a knowledge graph of new energy consumption, a systematic coverage of the influencing factors from the source, grid and load sides is achieved. It not only includes traditional indicators such as penetration rate and short-circuit level, but also adds implicit influencing factors such as the uniformity of new energy distribution and source-load matching degree, avoiding the one-sidedness of single-dimensional evaluation.

[0109] 2. More scientific weight allocation: By integrating the subjective experience of the AHP method with the objective data of the entropy method, the weights are optimized and combined through game theory. This not only solves the bias problem of subjective weighting, but also makes up for the deficiency of objective weighting in ignoring the actual importance of indicators. The weight results are more in line with the actual needs of engineering.

[0110] 3. More accurate assessment results: Grey relational analysis is used to quantify the correlation between indicators and absorption capacity. It does not require the assumption that the data follows a specific distribution. It is suitable for scenarios with large fluctuations in new energy output and limited data samples. It can accurately identify key influencing factors and provide precise guidance for distribution network transformation and operation strategy adjustment.

[0111] 4. Wider range of applicable scenarios: It can be applied to distribution networks with different voltage levels and different new energy penetration rates. By adjusting the operation data and scenario parameters of the knowledge graph data layer, it can meet diverse evaluation needs and has strong practicality. Attached Figure Description

[0112] Figure 1 This is a schematic diagram of the steps of the present invention.

[0113] Figure 2 This is a schematic diagram of the DER absorption space of the present invention.

[0114] Figure 3This is a flowchart illustrating the construction of the new energy consumption knowledge graph for this invention.

[0115] Figure 4 This is a visualization of the new energy consumption knowledge graph of the present invention.

[0116] Figure 5 This is a schematic diagram of the new energy consumption impact index system of the present invention.

[0117] Figure 6 This is a schematic diagram illustrating the definition of the judgment matrix scale in this invention.

[0118] Figure 7 This is a histogram of subjective weights based on the AHP method of the present invention.

[0119] Figure 8 This is a bar chart of the objective weights based on the entropy method of the present invention.

[0120] Figure 9 This is a bar chart illustrating the combinatorial weighting based on game theory of the present invention.

[0121] Figure 10 This is a comparison chart of the weight calculation results of the present invention.

[0122] Figure 11 This is a schematic diagram illustrating the correlation between the influencing indicators of the present invention. Detailed Implementation

[0124] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings.

[0125] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0126] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the present invention; as used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise; furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0127] like Figures 1 to 11 As shown, this invention proposes a method and system for assessing the acceptance capacity of new energy sources based on multiple uncertainties and mechanisms of action, including the following:

[0128] This invention proposes a method for assessing the acceptance capacity of new energy sources based on multiple uncertainties and mechanisms of action, characterized by the following:

[0129] Step S1: Analyze the mechanism of new energy consumption and extract influencing factors, including establishing a calculation model for consumption capacity and establishing a knowledge graph of new energy consumption;

[0130] Step S2: Based on the extracted influencing factors, establish a new energy consumption impact index system from the source side, grid side, and load side, and determine the calculation method for each index;

[0131] Step S3: Calculate the subjective weight of each indicator using the analytic hierarchy process (AHP), calculate the objective weight of each indicator using the entropy method, and combine the subjective and objective weights using game theory to optimize the combination and obtain the combined weight of each indicator.

[0132] Step S4: Construct a decision matrix, and calculate the correlation between each scheme and the optimal scheme based on the combined weights through grey relational analysis, and determine the order of importance of each influencing factor on the new energy absorption capacity.

[0133] In one embodiment of the present invention, step S1, analysis of the new energy consumption mechanism and extraction of influencing factors, includes:

[0134] By analyzing the absorption mechanism and constructing a knowledge graph, a theoretical foundation and data support are provided for the assessment. Specific implementation includes establishing an absorption capacity model and extracting factors driven by the knowledge graph.

[0135] Further, step S1 includes the following:

[0136] Step S11: The construction of the absorption capacity calculation model includes: the maximum absorption capacity of new energy is defined as the upper limit of new energy power generation that the system can absorb under safety constraints; where the adjustable space of the system is the operating range between the power load and the minimum technical output of conventional generating units. Based on the minimum technical output of conventional units and load demand, the absorption capacity model at time t is established:

[0137]

[0138] Among them, P a,t P represents the renewable energy absorption capacity at time t. L,t Let t be the load magnitude at time t; The minimum technical output of the i-th conventional unit at time t; This is the capacity of a conventional generating unit; This represents the start-up and shutdown status of a conventional unit i at time t, with 1 for start-up and 0 for shutdown. This represents the minimum technical output coefficient of conventional unit i.

[0139] In one embodiment of the present invention, a schematic diagram of the DER absorption space in step S11 is shown below. Figure 2 As shown.

[0140] Step S12: The relationship between renewable energy consumption capacity, theoretical output, and abandoned power is as follows:

[0141]

[0142] P DER,t =P WT,t +P PV,t

[0143]

[0144] Among them, P x,t P represents the renewable energy absorption capacity at time t. DER,t To contribute to the new energy theory at time t; P WT,t P represents the wind power output at time t. PV,t P represents the photovoltaic output at time t; y,t Let t be the amount of renewable energy wasted.

[0145] Furthermore, step S1 also includes the following:

[0146] Step S13: Construction and factor extraction of the knowledge graph for new energy consumption. The knowledge graph adopts a schema layer and a data layer architecture and is implemented through the Neo4j graph database. The schema layer defines entities and relationships. The data layer integrates distribution network operation data and probabilistic power flow results.

[0147] Knowledge extraction includes identifying entities, attributes, and relationships from structured and unstructured data;

[0148] Knowledge representation and fusion include: describing knowledge using entity-relation-entity triples, eliminating data redundancy, and storing it in a graph;

[0149] Knowledge reasoning includes: uncovering hidden factors and analyzing the indirect impact of the evenness of new energy distribution on consumption through graph traversal.

[0150] In one embodiment of the present invention, the knowledge graph construction process is as follows: Figure 3 As shown in the image, its visualization is as follows: Figure 4 From this, the influencing factors from the source, grid, and load sides can be fully extracted.

[0151] In this invention, the construction of the new energy consumption impact index system in step S2 includes the following:

[0152] Based on the factors extracted from S1, and following the principles of scientific rigor, systematic approach, and quantifiability, a three-tiered indicator system is established. Indicator calculations rely on actual or simulated data to ensure operability.

[0153] The hierarchical design of the indicators includes:

[0154] Primary indicators: source-side, grid-side, and load-side impact indicators.

[0155] Secondary indicators include: source side, output characteristics, distribution characteristics, etc.; grid side, safety constraint characteristics, etc.; load side, load foundation characteristics, etc.

[0156] Level 3 indicators: A total of 10 specific indicators, such as the fluctuation rate of renewable energy output on the source side and the line current carrying capacity on the grid side. The indicator system structure is as follows: Figure 5 As shown.

[0157] Further, step S2 includes the following:

[0158] Step S21: Define source-side indicators, including new energy output volatility: A1, penetration rate A2, distribution degree A3, uniformity A4, and source-load matching degree A5;

[0159] The formula for calculating the volatility of new energy power output is as follows:

[0160]

[0161] Where Δt is the reference time interval; i is the number of reference time intervals; P(i×Δt) and P(i+1)×Δt are the actual power output of new energy power generation at the previous and next moments, respectively; P EN The rated power of new energy power generation; n is the number of typical daily time intervals, taken as 96;

[0162] The formula for calculating permeability is:

[0163]

[0164] Among them, C E For new energy installed capacity; C all This refers to the total installed capacity of the system.

[0165] The formula for calculating the distribution degree is:

[0166]

[0167] Where n is the number of nodes in the distribution network with new energy access; N is the total number of nodes in the distribution network;

[0168] The formula for calculating uniformity is:

[0169]

[0170] In the formula, C DER,i Let avg(C) represent the rated capacity of new energy sources connected to the i-th node.DER P represents the average capacity of the connected renewable energy sources; L This refers to the system load power.

[0171] The formula for calculating the source-load matching degree is:

[0172]

[0173] in, Rate of change in power output for new energy sources; P represents the load change rate; DER (t+T), P DER (t) represents the power output of the new energy source at times t+T and t, respectively; P LD (t+T), P LD (t) represents the load at time t+T and time t, respectively; n is the sample size.

[0174] Further, step S2 includes the following:

[0175] Step S22: Define network-side indicators, including: line current carrying capacity B1, voltage over-limit probability B2, and short-circuit level B3;

[0176] The formula for calculating the line current carrying capacity is:

[0177]

[0178] Where Q0 is the safe current carrying capacity; Q1 is the average load power over a certain period of time;

[0179] The formula for calculating the voltage over-limit probability is:

[0180]

[0181] Where, f(V) i ) represents the node voltage probability density function; V min V max These represent the minimum and maximum allowable ranges for node voltage, respectively.

[0182] The formula for calculating short-circuit level is:

[0183]

[0184] Where I1 is the periodic component current; I0 ​​is the rated short-circuit breaking current;

[0185] The load-side indicators include load rate C1 and peak-valley difference rate C2;

[0186] The formula for calculating the load factor is:

[0187]

[0188] Among them, P P The average load of the entire network over a certain period of time; P max This represents the maximum network load within a certain timeframe.

[0189] The formula for calculating the peak-valley difference rate is:

[0190]

[0191] Among them, P min This represents the minimum load across the entire network within a certain timeframe.

[0192] In one embodiment of the present invention, step S3, the calculation and combination optimization of subjective and objective weights, further includes the following:

[0193] This step calculates subjective and objective weights using both Analytic Hierarchy Process (AHP) and entropy methods, and then optimizes the combined weights based on game theory to balance expert experience and data objectivity. In this invention, the Analytic Hierarchy Process (AHP) can be represented as: Analytic Hierarchy Process (AHP).

[0194] Further, step S3 includes the following:

[0195] Step S31: Subjective weight calculation based on the analytic hierarchy process includes the following:

[0196] Step S311: Establish a hierarchical structure model, which is divided into target layer, criterion layer and scheme layer. The target layer is the assessment of the influencing factors of high-penetration new energy consumption, the criterion layer refers to the source side, grid side and load side, and the scheme layer refers to each specific indicator.

[0197] Step S312: Construct a judgment matrix, using a 1-9 scale to represent the importance between indicators, where 1 indicates equal importance, 3 indicates slightly important, 5 indicates significantly important, 7 indicates strongly important, and 9 indicates extremely important. 2, 4, 6, and 8 are the intermediate values ​​between adjacent judgments. The judgment matrix satisfies...

[0198] In one embodiment of the present invention, the determination matrix scale is defined as follows: Figure 6 As shown.

[0199] Step S313: Hierarchical single sorting and consistency check, calculate the largest eigenvalue λ of the judgment matrix. max The consistency index is further obtained as follows:

[0200]

[0201] Where n is the order of the judgment matrix;

[0202] Let the consistency ratio CR be:

[0203]

[0204] Where RI is the average random consistency index, and when CR≤0.1, the consistency of the judgment matrix is ​​acceptable;

[0205] Step S314: Overall hierarchical ranking and consistency check, including calculating subjective weights using the geometric mean method, the formula is:

[0206]

[0207] Where i = 1, 2, ..., n, and W1 is the subjective weight.

[0208] Furthermore, step S3 also includes the following:

[0209] Step S32: Objective weight calculation based on entropy method includes the following:

[0210] Step S321: Construct the original matrix Q = (q ij ) mn ,include:

[0211] Define positive index normalization:

[0212]

[0213] Define contrarian indicator normalization:

[0214]

[0215] Where m is the number of samples, n is the number of indicators, and x ij Let x be the index in sample i, j be the actual measured value, and x be the index in sample i. min,j x max,j These are the minimum and maximum values ​​of the measured value of index j, respectively.

[0216] Step S322: Correct the normalization matrix:

[0217]

[0218] Step S323: Calculate the weight and entropy of the indicators:

[0219] Step S3231: Calculate the weight (probability) of the indicator.

[0220] For the standardized data, calculate the weight p of the j-th indicator in the i-th sample. ij This reflects the distribution of sample data under this indicator:

[0221]

[0222] Where m is the sample size, if x' ij=0, so limit processing is needed, i.e., let:

[0223]

[0224] Avoid meaningless logarithmic operations;

[0225] Step S3231: According to the definition of information entropy, the entropy value E of the j-th index... j The calculation formula is

[0226]

[0227] Where the entropy value E j The value range of p is [0,1]: when all samples have the same value on the j-th indicator, p ij =1 / m, E j =1 indicates that the indicator has no discriminatory power and its weight should be 0; when the sample values ​​for the indicator vary greatly, it indicates that the indicator has strong discriminatory power and its weight should be greater.

[0228] Step S324: Calculate the objective weights of the indicators

[0229] The difference coefficient g of the index is calculated using entropy value. j This reflects the effective information content of the indicators. Then, the difference coefficient is normalized to obtain the weight W of the j-th indicator. j :

[0230] Wherein the coefficient of difference: g j =1-E j

[0231] Objective weighting:

[0232] Where n is the number of indicators, and satisfies

[0233] The final objective weight is:

[0234]

[0235] Where W2 is the objective weight;

[0236] Step S33: Game theory-based combinatorial weight optimization includes the following:

[0237] Let the combination coefficients be: subjective weight coefficient θ1, objective weight coefficient θ2,

[0238] Combined weights

[0239] The combination coefficients are obtained by minimizing the deviation between the combined weights and the subjective and objective weights:

[0240]

[0241] The combination coefficients are obtained by using the Lagrange multiplier method, and after normalization, we get:

[0242]

[0243] Finally there is

[0244] Where W' is the final weight.

[0245] In one embodiment of the present invention, step S3 further includes the following:

[0246] Step S3 also includes a game theory-based method for calculating combined weights.

[0247] The core of the game theory-based combined weighting method is "coordinating the conflicts between different weight vectors." By constructing a "Nash equilibrium" of the weight vectors, the combined weights retain both the expert experience of AHP weights and the data characteristics of entropy-based weights. The specific steps are as follows:

[0248] Determine the basic weight vector set:

[0249] First, obtain the weight vectors from various single weight calculation methods. Let there be k basic weight methods in total. The weight vector obtained by the t-th method is:

[0250]

[0251] Where n is the number of indicators, and each weight vector satisfies

[0252] Construct the optimization objective for the combined weights:

[0253] Assume the combined weight vector is The core of game theory is to minimize the "difference" between the combined weights and the individual basic weight vectors, that is, to achieve coordination among the basic weights by minimizing the weighted Euclidean distance. The objective function is:

[0254]

[0255] Where α t Let || be the "importance coefficient" of the t-th basic weight. 2 This is the Euclidean distance operator.

[0256] Find the optimal solution for the combined weights:

[0257] Find the partial derivative of the objective function J(w) with respect to w, and set the partial derivative to 0. Then, consider the constraints. An analytical solution for the combined weights can be derived:

[0258]

[0259] Where j = 1, 2, ..., n.

[0260] If simplified to equal-weight coordination α t If the weight is 1, then the combined weight can be further simplified to:

[0261]

[0262] Verify the rationality of the combined weights:

[0263] By calculating the cosine similarity between the combined weights and each basic weight, we can verify whether the combined result effectively coordinates the information of different weights.

[0264] Cosine similarity:

[0265]

[0266] If sim(w,w) (t) If the similarity is close to 1, it indicates that the combined weights retain the core information of each basic weight and there is no obvious conflict; if a certain similarity is extremely low, α needs to be readjusted. t Or check the calculation process of the basic weights.

[0267] In one embodiment of the present invention, step S4 includes the following:

[0268] Step S41: Construct the decision matrix, including:

[0269]

[0270] Where U=[u1,u2,L,u m [This is a reference series, composed of the optimal values ​​of each indicator;]

[0271] Where D=(d ij ) m×n For comparison of sets of numbers, the index values ​​are normalized.

[0272] Step S42: Calculate the correlation coefficient, including:

[0273]

[0274] Where ρ is the resolution coefficient, which is taken as 0.5;

[0275] Step S43: Calculate the grey relational degree Where w j The weights of the indicators are determined by ranking the importance of each influencing factor according to the degree of correlation. The higher the correlation, the more significant the impact.

[0276] Furthermore, in one embodiment of the present invention, step S4 further includes the following:

[0277] Correlation analysis of influencing factors based on GRA

[0278] GRA (Grey System Analysis) is a method for analyzing the correlation between various influencing factors (mother series) and the target variable (child series) in grey systems where some information is known and some is unknown. Its core logic is to compare the "geometric similarity" of the series curves; the higher the similarity, the greater the correlation. The specific steps are as follows:

[0279] Determine the analysis sequence:

[0280] Define the "mother sequence" and "child sequence" and construct the analysis dataset:

[0281] Mother sequence: The sequence that reflects the research objective, denoted as X0=(x0(1),x0(2),…,x0(m));

[0282] Subsequence: The sequence of factors that influence the parent sequence, denoted as...

[0283] X1 = (x1(1), x1(2), ..., x1(m))

[0284] Where i = 1, 2, ..., n, and n is the number of influencing factors, such as "new energy output fluctuation rate" and "grid load".

[0285] Sequence standardization:

[0286] Since the dimensions and orders of magnitude of the parent and child sequences may differ (e.g., the "absorption rate" of the parent sequence is a percentage, while the "output" of the child sequence is in MW), standardization is needed to eliminate the influence of dimensions. Common methods include "initialization" or "meaning".

[0287] The initial value is:

[0288]

[0289] The mean is:

[0290]

[0291] After standardization, all sequences have a unified starting point or mean, which facilitates curve comparison.

[0292] Calculate the grey relational coefficient:

[0293] The correlation coefficient reflects the "degree of local correlation" between the subsequence and the parent sequence at a certain time (sample point), and the calculation formula is:

[0294]

[0295] in:

[0296] |x'0(k)-x' i (k)| represents the standardized absolute difference between the subsequence and the parent sequence at time k;

[0297] It represents the minimum absolute difference across all time points and all subsequences;

[0298] The maximum absolute difference across all time intervals and all subsequences;

[0299] ζ is the resolution coefficient, usually set to 0.5, used to adjust the sensitivity of the correlation coefficient. The smaller ζ is, the stronger the discrimination ability.

[0300] Correlation coefficient ξ i The value of (k) ranges from (0,1]. The larger the value, the closer the subsequence is to the parent sequence at that moment.

[0301] Calculate the grey relational degree:

[0302] Grey relational degree is the "average" of the correlation coefficients of a subsequence at all time points, reflecting the "overall correlation" between the subsequence and the parent sequence. The calculation formula is:

[0303]

[0304] correlation r i The value range is (0,1], and the correlation of all subsequences needs to be sorted:

[0305] r i The closer the value is to 1, the higher the correlation between the influencing factor and the parent sequence.

[0306] If r i If the value is less than 0.5, it indicates that the factor has a weak association with the parent sequence and has a small impact on the target.

[0307] Validation and application of correlation results:

[0308] The stability of the association ranking was verified by changing the resolution coefficient ζ or increasing the sample size.

[0309] If the sorting is consistent under different parameters, the result is reliable; if the sorting fluctuates greatly, the sequence selection or data quality needs to be checked.

[0310] Ultimately, the key factors with the greatest impact on the target can be identified by ranking them according to their relevance, providing a basis for decision-making.

[0311] According to a second aspect of the present invention, the present invention proposes a new energy acceptance capacity assessment system based on multiple uncertainties and mechanisms of action, comprising an electronic device, wherein the electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements a new energy acceptance capacity assessment method based on multiple uncertainties and mechanisms of action as described in any one of the present invention.

[0312] According to a third aspect of the present invention, the present invention proposes a new energy acceptance capacity assessment system based on multiple uncertainties and mechanisms of action, comprising a computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements a new energy acceptance capacity assessment method based on multiple uncertainties and mechanisms of action as described in any one of the present invention.

[0313] In addition to the above, the present invention also has related embodiments, including the following:

[0314] In one embodiment of the present invention, the simulation scene setting includes the following:

[0315] To comprehensively assess the factors affecting the absorption of new energy sources, this invention sets out six typical operating scenarios, covering different new energy penetration rates, load characteristics, and grid operating conditions.

[0316] The scenario parameters are set based on actual power distribution network data, and the specific parameters are shown in Table 1.

[0317] Scenario 1 to Scenario 6 simulated combinations such as high permeability + peak-to-valley difference and medium permeability + moderate load, respectively, to ensure the coverage and representativeness of the assessment.

[0318] index Scene 1 Scene 2 Scene 3 Scene 4 Scene 5 Scene 6 <![CDATA[A1]]> 29.11 18.85 22.98 38.91 40.02 50.15 <![CDATA[A2]]> 20.15 31.63 44.60 51.08 63.71 82.14 <![CDATA[A3]]> 0.15 2.08 19.58 38.27 28.55 34.74 <![CDATA[A4]]> 15.01 15.15 24.24 30.31 36.37 45.45 <![CDATA[A5]]> 98.57 87.92 61.26 56.11 102.85 82.93 <![CDATA[B1]]> 3.01 11.59 15.17 30.98 46.27 55.89 <![CDATA[B2]]> 4.15 16.29 21.02 59.23 60.83 91.82 <![CDATA[B3]]> 98.23 97.64 97.11 96.85 95.79 94.33 <![CDATA[C1]]> 56.51 54.55 72.36 66.05 49.82 57.69 <![CDATA[C2]]> 43.48 45.48 27.63 33.94 50.18 42.31

[0319] Table 1. Specific parameter settings (%) for different scenarios

[0320] In one embodiment of the present invention, the index data processing is as follows: Based on the scenario parameters in Table 1, the actual values ​​of 10 secondary indicators from the source, grid, and load sides are first calculated (including the fluctuation rate of new energy output, penetration rate, distribution degree, uniformity, source-load matching degree, line current carrying capacity, voltage over-limit probability, short-circuit level, load rate, and peak-valley difference rate). Subsequently, the index values ​​are normalized to eliminate the influence of dimensions and facilitate subsequent weight calculations. Normalization uses the range method to convert all index values ​​into dimensionless values ​​within the range of 0-1, as shown in Table 2.

[0321] index Scene 1 Scene 2 Scene 3 Scene 4 Scene 5 Scene 6 <![CDATA[A1]]> 0.6722 1 0.8681 0.3591 0.3236 0 <![CDATA[A2]]> 1 0.8148 0.6056 0.5011 0.2973 0 <![CDATA[A3]]> 0 0.0506 0.5097 1 0.7451 0.9074 <![CDATA[A4]]> 1 0.9954 0.6968 0.4974 0.2983 0.90 <![CDATA[A5]]> 0.9084 0.6806 0.1102 0 1 1.00 <![CDATA[B1]]> 1 0.8377 0.7701 0.4711 0.1819 0.50 <![CDATA[B2]]> 1 0.8615 0.8076 0.3717 0.3535 0.45 <![CDATA[B3]]> 1 0.8487 0.7128 0.6462 0.3743 0.80 <![CDATA[C1]]> 0.7032 0.7901 0 0.2799 1 0.60 <![CDATA[C2]]> 0.2971 0.2084 1 0.7201 0 1.00

[0322] Table 2 Normalized values ​​of indicators for different scenarios

[0323] In one embodiment of the present invention, the weight calculation and result analysis include the following:

[0324] The weighting calculation employs a combined subjective and objective weighting method to balance expert experience with data objectivity. The specific steps include: calculating subjective weights based on the Analytic Hierarchy Process (AHP), calculating objective weights based on the entropy method, and then optimizing the combined weights using game theory.

[0325] Subjective weight calculation (AHP method): Pairwise comparisons are performed on the indicators to construct a judgment matrix, and consistency (CR ≤ 0.1) is checked. The subjective weights are obtained using the geometric mean method, and the results are presented in a bar chart format. Figure 7 As shown, the permeability (A2) and peak-to-valley difference (C2) have higher weights.

[0326] Objective weight calculation (entropy method): Based on the normalized data in Table 2, the information entropy of each indicator is calculated. The smaller the entropy value, the greater the indicator's discriminative power, and the higher its weight. Results are as follows: Figure 8 As shown, the power output volatility of new energy sources (A1) and the voltage over-limit probability (B2) have relatively low weights.

[0327] Combination weight optimization (game theory): The objective is to minimize the deviation between the combination weights and the subjective and objective weights. The combination coefficients are calculated: subjective coefficient θ1 = 0.4753, objective coefficient θ2 = 0.5247, and the combination weights are obtained after normalization. The weight results are shown in Table 3. Figure 9 As shown, the combined weights effectively balance the subjective and objective biases.

[0328] index <![CDATA[Subjective weight W1]]> <![CDATA[Objective weight W2]]> Combined weight W <![CDATA[A1]]> 0.08 0.05 0.065 <![CDATA[A2]]> 0.15 0.18 0.165 <![CDATA[A3]]> 0.10 0.12 0.110 <![CDATA[A4]]> 0.09 0.10 0.095 <![CDATA[A5]]> 0.12 0.14 0.130 <![CDATA[B1]]> 0.07 0.06 0.065 <![CDATA[B2]]> 0.05 0.04 0.045 <![CDATA[B3]]> 0.08 0.09 0.085 <![CDATA[C1]]> 0.10 0.08 0.090 <![CDATA[C2]]> 0.16 0.14 0.150

[0329] Table 3 Results of Combined Weight Calculation

[0330] Figure 10 By comparing subjective and objective weights with combined weights, it can be seen that combined weights retain the AHP method's emphasis on key indicators while incorporating the entropy method's objective reflection of data volatility, thus avoiding the limitations of a single weighting method.

[0331] In one embodiment of the present invention, grey relational analysis includes the following:

[0332] Based on combined weights, the correlation between each scenario indicator and its ideal optimal value is calculated using the GRA (Graduate Relationship Analysis) to quantify the importance of influencing factors on absorption capacity. The reference series consists of the optimal values ​​of each indicator, and the comparison series is the normalized data in Table 4.3. The correlation coefficient is calculated using a resolution coefficient ρ = 0.5, and the final correlation results are as follows: Figure 11 As shown.

[0333] The correlation calculation result is The analysis corresponds to primary indicators on the source side, grid side, and load side. The results show that: source-side indicators have the highest correlation: penetration rate (A2) and source-load matching degree (A5) are key factors affecting absorption capacity, and the output characteristics of new energy directly determine the absorption potential. Load-side indicators are next: peak-valley difference rate (C2) and load factor (C1) significantly affect the system's peak-shaving capacity; large load fluctuations increase the difficulty of absorption. Grid-side indicators have a lower correlation: while factors such as short-circuit level (B3) are important, their impact is relatively indirect compared to the source-load side.

[0334] In one embodiment of the present invention, the conclusions and recommendations include the following:

[0335] Through simulation examples, this invention can accurately identify key influencing factors. The main conclusions are as follows:

[0336] Influencing factors are ranked as follows: Secondary indicators are ranked by weight as follows: penetration rate > peak-valley difference rate > source-load matching degree > distribution degree > uniformity > load rate > short-circuit level > new energy output fluctuation rate > line current carrying capacity > voltage over-limit probability; Primary indicators are ranked by influence degree as follows: source side > load side > grid side.

[0337] The above are preferred embodiments of the present invention. Any changes made to the technical solution of the present invention that do not exceed the scope of the technical solution of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for assessing the acceptance capacity of new energy sources based on multiple uncertainties and mechanisms of action, characterized in that, Includes the following: Step S1: Analyze the mechanism of new energy consumption and extract influencing factors, including establishing a calculation model for consumption capacity and establishing a knowledge graph of new energy consumption; Step S2: Based on the extracted influencing factors, establish a new energy consumption impact index system from the source side, grid side, and load side, and determine the calculation method for each index; Step S3: Calculate the subjective weight of each indicator using the analytic hierarchy process (AHP), calculate the objective weight of each indicator using the entropy method, and combine the subjective and objective weights using game theory to optimize the combination and obtain the combined weight of each indicator. Step S4: Construct a decision matrix, and calculate the correlation between each scheme and the optimal scheme based on the combined weights through grey relational analysis, and determine the order of importance of each influencing factor on the new energy absorption capacity.

2. The method for assessing the acceptance capacity of new energy sources based on multiple uncertainties and mechanisms of action as described in claim 1, characterized in that, Step S1 includes the following: Step S11: The construction of the absorption capacity calculation model includes: the maximum absorption capacity of new energy is defined as the upper limit of new energy power generation that the system can absorb under safety constraints; where the adjustable space of the system is the operating range between the power load and the minimum technical output of conventional generating units. Based on the minimum technical output of conventional units and load demand, the absorption capacity model at time t is established: Among them, P a,t P represents the renewable energy absorption capacity at time t. L,t Let t be the load magnitude at time t; The minimum technical output of the i-th conventional unit at time t; This is the capacity of a conventional generating unit; This represents the start-up and shutdown status of a conventional unit i at time t, with 1 for start-up and 0 for shutdown. This represents the minimum technical output coefficient of conventional unit i; Step S12: The relationship between renewable energy consumption capacity, theoretical output, and abandoned power is as follows: P DER,t =P WT,t +P PV,t Among them, P x,t P represents the renewable energy absorption capacity at time t. DER,t To contribute to the new energy theory at time t; P WT,t P represents the wind power output at time t. PV,t P represents the photovoltaic output at time t; y,t Let t be the amount of renewable energy wasted.

3. The method for assessing the acceptance capacity of new energy sources based on multiple uncertainties and mechanisms of action as described in claim 2, characterized in that, Step S1 also includes the following: Step S13: Construction and factor extraction of the knowledge graph for new energy consumption. The knowledge graph adopts a schema layer and data layer architecture and is implemented through the Neo4j graph database; the schema layer defines entities and relationships. The data layer integrates distribution network operation data and probabilistic power flow results; Knowledge extraction includes identifying entities, attributes, and relationships from structured and unstructured data; Knowledge representation and fusion include: describing knowledge using entity-relation-entity triples, eliminating data redundancy, and storing it in a graph; Knowledge reasoning includes: uncovering hidden factors and analyzing the indirect impact of the evenness of new energy distribution on consumption through graph traversal.

4. The method for assessing the acceptance capacity of new energy sources based on multiple uncertainties and mechanisms of action as described in claim 1, characterized in that, Step S2 includes the following: Step S21: Define source-side indicators, including new energy output volatility: A1, penetration rate A2, distribution degree A3, uniformity A4, and source-load matching degree A5; The formula for calculating the volatility of new energy power output is as follows: Where Δt is the reference time interval; i is the number of reference time intervals; P(i×Δt) and P(i+1)×Δt are the actual power output of new energy power generation at the previous and next moments, respectively; P EN The rated power of new energy power generation; n is the number of typical daily time intervals, taken as 96; The formula for calculating permeability is: Among them, C E For new energy installed capacity; C all This refers to the total installed capacity of the system. The formula for calculating the distribution degree is: Where n is the number of nodes in the distribution network with new energy access; N is the total number of nodes in the distribution network; The formula for calculating uniformity is: In the formula, C DER,i Let avg(C) represent the rated capacity of new energy sources connected to the i-th node. DER P represents the average capacity of the connected renewable energy sources; L This refers to the system load power. The formula for calculating the source-load matching degree is: in, Rate of change in power output for new energy sources; P represents the load change rate; DER (t+T), P DER (t) represents the power output of the new energy source at times t+T and t, respectively; P LD (t+T), P LD (t) represents the load at time t+T and time t, respectively; n is the sample size.

5. The method for assessing the acceptance capacity of new energy sources based on multiple uncertainties and mechanisms of action as described in claim 4, characterized in that, Step S2 also includes the following: Step S22: Define network-side indicators, including: line current carrying capacity B1, voltage over-limit probability B2, and short-circuit level B3; The formula for calculating the line current carrying capacity is: Where Q0 is the safe current carrying capacity; Q1 is the average load power over a certain period of time; The formula for calculating the voltage over-limit probability is: Where, f(V) i ) represents the node voltage probability density function; V min V max These represent the minimum and maximum allowable ranges for node voltage, respectively. The formula for calculating short-circuit level is: Where I1 is the periodic component current; I0 ​​is the rated short-circuit breaking current; The load-side indicators include load rate C1 and peak-valley difference rate C2; The formula for calculating the load factor is: Among them, P P The average load of the entire network over a certain period of time; P max This represents the maximum network load within a certain timeframe. The formula for calculating the peak-valley difference rate is: Among them, P min This represents the minimum load across the entire network within a certain timeframe.

6. The method for assessing the acceptance capacity of new energy sources based on multiple uncertainties and mechanisms of action as described in claim 1, characterized in that, Step S3 includes the following: Step S31: Subjective weight calculation based on the analytic hierarchy process includes the following: Step S311: Establish a hierarchical structure model, which is divided into target layer, criterion layer and scheme layer. The target layer is the assessment of the influencing factors of high-penetration new energy consumption, the criterion layer refers to the source side, grid side and load side, and the scheme layer refers to each specific indicator. Step S312: Construct a judgment matrix, using a 1-9 scale to represent the importance between indicators, where 1 indicates equal importance, 3 indicates slightly important, 5 indicates significantly important, 7 indicates strongly important, and 9 indicates extremely important. 2, 4, 6, and 8 are the intermediate values ​​between adjacent judgments. The judgment matrix satisfies... Step S313: Hierarchical single sorting and consistency check, calculate the largest eigenvalue λ of the judgment matrix. max The consistency index is further obtained as follows: Where n is the order of the judgment matrix; Let the consistency ratio CR be: Where RI is the average random consistency index, and when CR≤0.1, the consistency of the judgment matrix is ​​acceptable; Step S314: Overall hierarchical ranking and consistency check, including calculating subjective weights using the geometric mean method, the formula is: Where i = 1, 2, ..., n, and W1 is the subjective weight.

7. The method for assessing the acceptance capacity of new energy sources based on multiple uncertainties and mechanisms of action as described in claim 6, characterized in that, Step S3 also includes the following: Step S32: Objective weight calculation based on entropy method includes the following: Step S321: Construct the original matrix Q = (q ij ) mn ,include: Define positive index normalization: Define contrarian indicator normalization: Where m is the number of samples, n is the number of indicators, and x ij Let x be the index in sample i, j be the actual measured value, and x be the index in sample i. min,j x max,j These are the minimum and maximum values ​​of the measured value of index j, respectively. Step S322: Correct the normalization matrix: Step S323: Calculate the index entropy value: The final objective weight is: Where W2 is the objective weight; Step S33: Game theory-based combinatorial weight optimization includes the following: Let the combination coefficients be: subjective weight coefficient θ1, objective weight coefficient θ2, Combined weights The combination coefficients are obtained by minimizing the deviation between the combined weights and the subjective and objective weights: The combination coefficients are obtained by using the Lagrange multiplier method, and after normalization, we get: Finally there is Where W′ is the final weight.

8. The method for assessing the acceptance capacity of new energy sources based on multiple uncertainties and mechanisms of action as described in claim 1, characterized in that, Step S4 includes the following: Step S41: Construct the decision matrix, including: Where U=[u1,u2,L,u m [This is a reference series, composed of the optimal values ​​of each indicator;] Where D=(d ij ) m×n For comparison of sets of numbers, the index values ​​are normalized. Step S42: Calculate the correlation coefficient, including: Where ρ is the resolution coefficient, which is taken as 0.5; Step S43: Calculate the grey relational degree Where w j The weights of the indicators are determined by ranking the importance of each influencing factor according to the degree of correlation. The higher the correlation, the more significant the impact.

9. A new energy acceptance capacity assessment system based on multiple uncertainties and mechanisms of action, comprising an electronic device, wherein the electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements a new energy acceptance capacity assessment method based on multiple uncertainties and mechanisms of action as described in any one of claims 1 to 8.

10. A new energy acceptance capacity assessment system based on multiple uncertainties and mechanisms of action, comprising a computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements a new energy acceptance capacity assessment method based on multiple uncertainties and mechanisms of action as described in any one of claims 1 to 8.

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