Evaluation method of compensation allocation scheme of hydropower station with maximum power generation benefit as target

Abstract

The application discloses a hydropower station compensation allocation scheme evaluation method with the maximum power generation benefit as a target, which comprises the following steps: establishing a hydropower station power generation optimization scheduling model, comparing the power generation of each hydropower station under different alliance modes; using different compensation allocation methods to perform compensation allocation of cascade hydropower stations; performing stability evaluation on the allocation results of different compensation allocation methods; establishing an index system, constructing an evaluation index value matrix, and formulating a compensation allocation scheme of cascade hydropower stations; and based on the regret theory, the optimal compensation allocation scheme is calculated. The application formulates multiple compensation allocation schemes for cascade hydropower stations, takes stability as one of the indexes, establishes an index system, performs comprehensive evaluation on the compensation allocation schemes, and selects the optimal compensation allocation scheme, which has stability and realizes the overall optimization of the cascade hydropower stations, thereby promoting the joint scheduling and mutual cooperation of the cascade hydropower stations.

Description

Technical Field

[0001] This invention belongs to the field of hydropower station dispatching, and specifically relates to an evaluation method for hydropower station compensation allocation scheme with the goal of maximizing power generation benefits. Background Technology

[0002] Under the large-scale development pattern of hydropower resources in my country, cascade hydropower station clusters in major river basins have taken initial shape. The focus of these clusters is no longer solely on maximizing the power generation of individual stations, but rather on the overall cascade as a whole, rationally utilizing limited hydropower resources to generate the greatest possible comprehensive benefits. However, in the development of cascade hydropower, upstream and downstream stations may belong to different owners. During joint operation, the upstream leading hydropower station and other stations with significant runoff regulation capabilities often need to sacrifice some of their own interests to improve the power generation efficiency of downstream stations, thereby optimizing the entire cascade. To promote joint operation and cooperation among cascade hydropower stations, the issue of sharing compensation benefits among multi-owner cascade hydropower stations arises.

[0003] Currently, the main methods for allocating compensation benefits for cascade hydropower stations include: treaties, agreements or rules, single-index methods, comprehensive index methods, proportional allocation methods, and Shapley value methods. Numerous case studies show that various allocation methods can be applied to the allocation of compensation benefits for cascade hydropower stations; however, selecting the optimal solution remains a pressing practical problem.

[0004] In view of this, this study investigates the evaluation method of compensation sharing schemes for hydropower stations, establishes an index system for the compensation benefit sharing schemes of the Sanliping, Siping, and Guoduwan cascade hydropower stations in the Nanhe River Basin of the middle reaches of the Han River, and conducts a comprehensive evaluation of various compensation benefit sharing schemes for cascade hydropower stations based on the subjective and objective comprehensive weighting method and regret theory. Summary of the Invention

[0005] The technical problem of this invention is that there are various existing methods for allocating compensation for cascade hydropower stations, and each of these methods has its own advantages and disadvantages in terms of compensation allocation schemes. Currently, there is a lack of evaluation methods for compensation allocation schemes for cascade hydropower stations. How to formulate a fair and reasonable compensation allocation scheme and select the optimal scheme from multiple schemes remains an urgent problem to be solved.

[0006] The purpose of this invention is to solve the above-mentioned problems and provide an evaluation method for hydropower station compensation allocation scheme with the goal of maximizing power generation benefits. This method utilizes different compensation allocation methods to allocate compensation among cascade hydropower stations and evaluates the stability of the compensation allocation results. By establishing an index system, a compensation allocation scheme for hydropower stations is formulated. Based on regret theory, the optimal compensation allocation scheme is selected, promoting the joint scheduling and mutual cooperation of cascade hydropower stations and enabling the overall cascade hydropower stations to achieve optimal performance.

[0007] The technical solution of this invention is a method for evaluating compensation allocation schemes for hydropower stations with the goal of maximizing power generation benefits, comprising the following steps:

[0008] Step 1: Establish a hydropower station power generation optimization scheduling model and compare the power generation of each hydropower station under different alliance methods;

[0009] Step 1.1: Establish power generation optimization scheduling models with the objective function of maximizing the power generation benefit of a single hydropower station or maximizing the power generation benefit under different alliance methods:

[0010] Step 1.2: Determine the constraint equations, which mainly include the water balance equation, the water storage capacity constraint of the hydropower station, the water level constraint of the reservoir, the power output constraint of the hydropower station, the discharge flow constraint of the reservoir, and the flow relationship between upstream and downstream hydropower stations.

[0011] Step 1.3: Use dynamic programming to perform power generation scheduling optimization calculations to obtain the power generation and power generation benefits of each hydropower station under different alliance modes;

[0012] Step 2: Use different compensation allocation methods to allocate compensation for cascade hydropower stations;

[0013] Step 3: Evaluate the stability of the compensation allocation results in Step 2;

[0014] Step 4: Establish an indicator system, construct an evaluation indicator value matrix, and formulate a compensation sharing scheme for cascade hydropower stations;

[0015] The indicator system includes the sharing ratio, cascaded increase in power generation, minimum output, and splitting tendency value;

[0016] Step 5: Based on regret theory, calculate the optimal compensation allocation scheme according to the evaluation index values ​​of each compensation allocation scheme in Step 4.

[0017] Preferably, in step 2, the different compensation allocation methods include the contribution method, the coefficient of variation method, the Shapley value method, and the coefficient of variation-Shapley value method.

[0018] Preferably, in step 3, the stability evaluation of the compensation allocation results in step 2 is performed using the splitting tendency method.

[0019] Furthermore, step 4 also includes calculating the weights of each indicator in the indicator system using expert scoring and entropy weighting methods.

[0020] Preferably, the indicator system in step 4 includes the sharing ratio, cascaded increase in power generation, minimum output, and splitting tendency value.

[0021] Furthermore, in step 1.1, the power generation optimization scheduling model is as follows:

[0022] 1) A single hydropower station generates the most electricity annually:

[0023]

[0024] 2) The annual power generation benefits are maximized under different alliance models:

[0025]

[0026] Where B represents the total power generation benefit of the alliance's hydropower stations during the scheduling period; K represents the total number of alliance hydropower stations; i represents the hydropower station number, indicating the i-th hydropower station in the cascade; t and T represent the time period number and total number of time periods during the scheduling period, respectively; A i Q is the overall output coefficient of hydropower station i; i，t H represents the power generation flow of hydropower station i during time period t. i，t Let p be the hydropower head of hydropower station i during time period t; Δt is the time period length; i The on-grid electricity price for hydropower station i.

[0027] Furthermore, step 5 includes the following sub-steps:

[0028] Step 5.1: Construct the evaluation index value matrix of the compensation sharing scheme and standardize it;

[0029] Step 5.2: For each indicator, take the optimal value of the indicator evaluation value in the standardized matrix and establish an ideal solution;

[0030] Step 5.3: Construct the regret value matrix;

[0031] Step 5.4: Construct the perceived utility matrix;

[0032] Step 5.5: Calculate the overall perceived utility value of each compensation sharing scheme by combining the weights of each indicator;

[0033] Step 5.6: Compare the overall perceived utility values ​​of each compensation sharing scheme and select the optimal compensation sharing scheme.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0035] 1) This invention formulates multiple compensation allocation schemes for cascade hydropower stations, takes the stability of the compensation allocation result as one of the indicators, establishes an indicator system for the compensation allocation scheme, and conducts a comprehensive evaluation of the compensation allocation scheme to facilitate the selection of the optimal compensation allocation scheme for cascade hydropower stations. The selected optimal compensation allocation scheme is not only stable, but also achieves the overall optimization of cascade hydropower stations, thus promoting the joint scheduling and mutual cooperation of cascade hydropower stations.

[0036] 2) The variation coefficient-Shapley value method proposed in this invention takes into account both the individual characteristics of the power station and its marginal contribution. While fully affirming the role and value of the leading power station in the alliance, it also promotes the active participation of downstream power stations at all levels in the cascade joint dispatch.

[0037] 3) This invention combines regret theory with utility, realistically reflecting the decision-making behavior of hydropower station owners, and increasing the accuracy and scientific nature of the optimal compensation allocation scheme. Attached Figure Description

[0038] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0039] Figure 1 This is a flowchart illustrating the evaluation method for compensation allocation schemes of hydropower stations aimed at maximizing power generation benefits.

[0040] Figure 2 This is a schematic diagram of a cascade hydropower station as an example.

[0041] Figure 3 This is a comparison diagram of different compensation allocation methods in the embodiments. Detailed Implementation

[0042] like Figure 1 As shown, the evaluation method for hydropower station compensation allocation schemes aimed at maximizing power generation benefits includes the following steps:

[0043] Step 1: Establish a hydropower station power generation optimization scheduling model and calculate the power generation benefits of each hydropower station under different alliance methods;

[0044] Step 1.1: Establish power generation optimization scheduling models with the objective function of maximizing the power generation benefit of a single hydropower station or maximizing the power generation benefit under different alliance methods:

[0045] ① A single hydropower station generates the most electricity annually:

[0046]

[0047] ② The annual power generation benefits are maximized under different alliance models:

[0048]

[0049] Where B represents the total power generation benefit of the alliance's hydropower stations during the scheduling period; K represents the total number of alliance hydropower stations; i represents the hydropower station number, indicating the i-th hydropower station in the cascade; t and T represent the time period number and total number of time periods during the scheduling period, respectively; A i Q is the overall output coefficient of hydropower station i; i，t H represents the power generation flow of hydropower station i during time period t. i，tLet p be the hydropower head of hydropower station i during time period t; Δt is the time period length; i The on-grid electricity price for hydropower station i.

[0050] Step 1.2: Determine the constraint equations, which mainly include the water balance equation, the water storage capacity constraint of the hydropower station, the water level constraint of the reservoir, the power output constraint of the hydropower station, the discharge flow constraint of the reservoir, and the flow relationship between upstream and downstream hydropower stations.

[0051] Step 1.3: Use dynamic programming to perform power generation scheduling optimization calculations to obtain the power generation and power generation benefits of each hydropower station under different alliance modes.

[0052] Step 2: The compensation benefits are allocated using the contribution method, coefficient of variation method, Shapley value method, and coefficient of variation-Shapley value method, respectively.

[0053] The contribution method refers to the principle that in the power generation revenue of cascade hydropower stations through joint optimal dispatch, the higher the efficiency of the power station in converting unit hydropower into electrical energy and then into power generation benefits, the greater its contribution to the power generation revenue of joint dispatch. The calculation formula is as follows:

[0054]

[0055] Where x i p represents the apportionment ratio of the i-th hydropower station; n represents the total number of cascade hydropower stations; p i k i Let k be the conversion coefficient for the potential energy of the i-th hydropower station to the power generation revenue; i The power generation efficiency coefficient represents the coefficient by which potential energy is converted into electrical energy; p i The grid connection price represents the coefficient that indicates the conversion of electrical energy into electricity generation efficiency.

[0056] The coefficient of variation method is an objective weighting method that assigns weights to individual characteristic parameters of a hydropower station based on the degree of variation of their observed values ​​across all evaluated individual characteristic parameters. Assuming there are n hydropower stations, each with m individual characteristic parameters, this forms a parameter value matrix X = (X... ij ) n×m The calculation process is as follows:

[0057] ① First, normalize the values ​​of each parameter, then standardize the parameter value matrix. To ensure that each parameter value is greater than 0 and within the range [0.6, 1], for positive parameters:

[0058]

[0059] Among them, Y ij Y is the standard value of characteristic parameter j of the i-th hydropower station. ij ∈[0.6, 1]; Xmax j X min j Let be the maximum and minimum eigenvalues ​​of the characteristic parameter j.

[0060] ② Calculate the mean and standard deviation of the j-th characteristic parameter value:

[0061]

[0062]

[0063] ③ Calculate the coefficient of variation of the j-th characteristic parameter value:

[0064]

[0065] ④ Calculate the weight value of the j-th feature parameter:

[0066]

[0067] ⑤ Calculate the weight of the j-th characteristic parameter value of the i-th hydropower station:

[0068]

[0069] ⑥ Calculate the comprehensive weight value of the i-th hydropower station:

[0070]

[0071] Among them, υ i is the individual characteristic weighting coefficient of the i-th hydropower station, i.e., the allocation ratio.

[0072] The Shapley method is a mathematical approach used to solve the problem of benefit allocation in multi-party cooperative strategies. This method allocates benefits according to the expected value of the marginal costs of the participants to ensure the effectiveness and fairness of the allocation.

[0073] The Shapley value is based on the following axioms: symmetry axiom, validity axiom, and additivity axiom. When these basic axioms are satisfied, the Shapley value for each hydroelectric power station has a unique solution.

[0074]

[0075] Where s is the number of hydroelectric power stations in the alliance; N is all possible alliance types; S is a sub-alliance of N; (s-1)! (ns)! / n! is the alliance probability of alliance S; ν(S)-ν(Si) is the marginal contribution of power station i to alliance S.

[0076] Combining the advantages of the coefficient of variation method and the Shapley value method, the coefficient of variation-Shapley value method is proposed. The individual characteristic weight coefficients υ of each hydropower station are obtained according to formulas (4) to (10). i The Shapley value σ of each hydropower station is obtained according to formula (11). i The formula for the coefficient of variation-Shapley value method is as follows:

[0077] θ i =υ i σ i (12)

[0078]

[0079] Where x i υ represents the cost-sharing ratio for the i-th hydropower station. i σ is the individual characteristic weighting coefficient for the i-th hydropower station; i Let be the Shapley value of the i-th hydroelectric power station.

[0080] Step 3: Use the Propensity to Disrupt (PTD) method to evaluate the stability of the allocation results of the four compensation allocation methods in Step 2;

[0081] The splitting tendency method is a commonly used approach to quantitatively evaluate the stability of solutions in cooperative games. It is the ratio of the loss suffered by the large coalition from losing member i to the loss suffered by member i from leaving the large coalition under a certain allocation outcome.

[0082]

[0083] Among them PTD i c represents the splitting tendency of member i under a certain allocation outcome. i V(i) represents the revenue allocated to member i in the large alliance; v(i) represents the revenue obtained by member i when performing single-database scheduling; V(N-{i}) represents the total revenue of the large alliance that loses member i.

[0084] Step 4: Establish an indicator system based on the sharing ratio, cascaded increase in power generation, minimum output, and PTD value; formulate four compensation sharing schemes; and construct an indicator evaluation value matrix.

[0085] Step 5: Evaluate the four allocation schemes formulated in Step 4 based on regret theory, and obtain the optimal allocation scheme.

[0086] Regret theory is a theory that, while abandoning the axiom of independence, considers both regret and elation—two distinct psychological behaviors—simultaneously in the decision-making process. Specifically, it manifests as a perceived utility function. This function mainly consists of two parts: first, the utility function of the current chosen option; and second, the regret-elation function of choosing this option. Let a and b represent the results of choosing option A and option B, respectively. Then, the perceived utility function of the decision-maker choosing option A is:

[0087] q(a,b)=v(a)+R(v(a)-v(b)) (15)

[0088] Functions v(a) and v(b) are the utility functions obtained by choosing option A and option B, respectively, while function R(v(a)-v(b)) is the regret-joy function. When the regret-joy function value is greater than 0, it means that you feel joy for choosing option A and not choosing option B; when the regret-joy function value is less than 0, it means that you feel regret for choosing option A and not choosing option B.

[0089] For multiple different options, the decision-maker's perceived utility function is as follows:

[0090] q i =v(a i )+R(v(a i )-v(a*)) (16)

[0091] Where a*=max{a i ,i=1,2,...,m};R(v(a i If )-v(a*))<0, that is, the regret-joy function value is negative, which indicates the degree of regret of the decision-maker.

[0092] The evaluation process for the cost-sharing scheme includes:

[0093] Suppose there are F compensation benefit sharing schemes forming a decision scheme set M = (M1, M2, ..., Mn) F The set of indicators consists of L decision indicators, I = (I1, I2, ..., I...). L ), where the tendency to split (PTD) is one of the indicators, and the indicator weights are W = (w1, w2, ..., w L ).

[0094] ① Constructing the evaluation index value matrix and its standardization process

[0095] The evaluation index value matrix of the evaluation values ​​of scheme set M to index set I is as follows:

[0096]

[0097] Because the various decision indicators differ in nature or magnitude, it is necessary to standardize the sample matrix first.

[0098] To ensure that each parameter value is greater than 0 and falls within the range of [0.6, 1], for benefit-type indicators:

[0099]

[0100] For cost-related indicators:

[0101]

[0102] Where, x ij Indicates that decision-makers are in I j Under the indicator, for scheme M i Evaluation value; x maxj Indicates that decision-makers are in I j Under the indicator, for scheme M i The largest value among the evaluation values; x minj Indicates that decision-makers are in I j Under the indicator, for scheme M i The smallest value among the evaluation values; y ij For I j Scheme M under the indicator i Standard value, y ij ∈[0.6, 1], (i=1, 2,…,F; j=1, 2,…,L).

[0103] ② Establish an ideal solution

[0104] Let x j + =max(x ij ), (i = 1, 2, ..., F; j = 1, 2, ..., L), are called

[0105] x j + =(x1) + x2 + , ..., x L + (20)

[0106] The ideal solution for decision-making;

[0107] ③ Construct the regret value matrix

[0108] Calculate different index values ​​x ij Functional effect value v ijFirst, we need to construct the utility function v(x). Given that decision-makers in real-world risk-based decision-making tend to avoid risk as much as possible, the utility function v(x) is a monotonically increasing concave function, satisfying v′(x) > 0 and v″(x) < 0. Using a power function to represent the utility function, the calculation formula is as follows:

[0109] v(x)=x α (twenty one)

[0110] Where 1 > α > 0, the smaller α is, the more risk-averse the decision-maker is.

[0111] ④ Construct a perceived utility matrix

[0112] To calculate the regret value for each option, a regret-pleasure function R(·) needs to be constructed. Since decision-makers are risk-averse in risky decisions, the regret-pleasure function R(·) is a monotonically increasing concave function, meaning it satisfies R′(·)>0 and R″(·)<0. The formula for calculating the decision-maker's regret-pleasure function value is as follows:

[0113]

[0114] Where ε is the decision-maker's regret aversion coefficient, ε > 0, and the larger ε is, the greater the degree of regret aversion; v ab This represents the difference in utility values ​​between two different options.

[0115] Then, the utility perception function matrix of the decision-maker is constructed using equations (8) and (9).

[0116] q ij =v ij +R ij (twenty three)

[0117] ⑤ Determine the combined subjective and objective weights using expert scoring and entropy weight methods:

[0118] The expert scoring method is a qualitative descriptive-quantitative approach. It first selects several evaluation indicators based on the specific requirements of the evaluation object, then develops evaluation criteria based on these indicators, and finally, engages several representative experts to assign evaluation scores to each indicator based on their experience and these criteria. These scores are then aggregated. The weight of indicator j calculated using the expert scoring method is represented by w. zj .

[0119] Entropy weighting is a type of comprehensive index method. Information is a measure of the degree of order within a system, while entropy is a measure of the degree of disorder within a system; they have opposite signs but equal absolute values. Entropy can be used to measure and evaluate the amount of information contained in index data, and at the same time, it can be used to determine the weight values ​​of each index.

[0120] The steps for calculating weights using the entropy weight method are as follows:

[0121] a. Calculate the information entropy of each indicator.

[0122]

[0123] Where e j U represents the information entropy of evaluation index j. ij This represents the proportion of the j-th evaluation index value of the i-th scheme. y ij Let j be the standard value of the evaluation index j for the i-th scheme;

[0124] b. Determine the weight of each indicator

[0125]

[0126] Where w kj g represents the weight of evaluation index j calculated by the entropy weight method. j G represents the difference coefficient of the j-th feature index. j =1-e j .

[0127] Overall weighting of subjective and objective factors:

[0128]

[0129] w j To evaluate the comprehensive subjective and objective weights of index j, the parameters are... Determined based on the decision-maker's preference information.

[0130] ⑥ Calculation scheme M i The formula for calculating the overall perceived utility value under different indicators is as follows:

[0131]

[0132] The implementation example selects the cascade hydropower stations—Sanliping Hydropower Station A, Sipin Hydropower Station B, and Guoduwan Hydropower Station C—on the main stream of the middle and lower reaches of the Nanhe River as the implementation object. The annual runoff process is calculated using the monthly runoff of the upper reaches of Sanliping during a 50% normal water year. The location relationship of the three power stations is as follows: Figure 2 As shown.

[0133] The implementation results are as follows:

[0134] (1) The dynamic programming method was used to solve the power generation optimization scheduling model in step 1, and the power generation and power generation benefits of the three hydropower stations of Sanliping, Siping and Guoduwan under different alliance modes were obtained, as shown in Table 1.

[0135] Table 1. Power Generation and Power Generation Benefits of Hydropower Stations under Different Alliance Models

[0136]

[0137] The combined cascade power generation of the Sanliping, Sipin, and Guoduwan hydropower stations increased by 0.738 billion kWh compared to the combined power generation of the three stations operating individually, resulting in an increase in total power generation benefits of 0.29 billion yuan. The compensation allocation ratio was calculated using the four compensation allocation methods described in step 2, and the results are as follows: Figure 3 As shown in Table 2.

[0138] Table 2 Compensation Sharing Ratio for Each Hydropower Station

[0139]

[0140] As can be seen from Table 2, the Sanliping Hydropower Station, as the leading power station, significantly improves the overall power generation efficiency of the cascade due to its compensation and regulation capabilities. It also obtains the highest compensation benefit when allocating the compensation benefits, which fully affirms the compensation and regulation role of the leading power station. While the Sipin and Guoduwan Hydropower Stations obtain higher compensation benefits than when each power station operates alone, the difference in their allocation is also significant. This shows the individual differences among secondary power stations and that choosing an effective allocation method is conducive to promoting the active participation of downstream power stations at all levels in joint dispatch.

[0141] (2) The splitting tendency value was calculated, and the results are shown in Table 3. The larger the PTD value of hydropower station i, the smaller the revenue it receives relative to its contribution to the coalition, and the more inclined it is to leave the coalition; conversely, the smaller the PTD value, the more inclined hydropower station i is to remain in the large coalition. If the PTD values ​​of all hydropower stations are relatively close and small, it indicates that the bargaining power of each power station under this sharing scheme is similar, and they all tend to remain in the large coalition, that is, this scheme is a more stable and acceptable sharing scheme.

[0142] Table 3. Split Tendency Values ​​of Hydropower Stations under Different Allocation Methods

[0143]

[0144] (3) Construct an evaluation index value matrix and select the sharing ratio, the increase in power generation of the cascade under different alliance methods compared with the individual dispatch, the minimum output, and the PTD value as evaluation indicators. The evaluation index values ​​of the four schemes are shown in Table 4.

[0145] Table 4 Evaluation Index Values

[0146]

[0147] (4) The subjective and objective comprehensive weights were determined by the expert scoring method and the entropy weight method. The results are shown in Table 5.

[0148] Table 5. Overall Weighting of Evaluation Indicators (Subjective and Objective)

[0149]

[0150] (5) Evaluation of the options is conducted using regret theory, where the regret value matrix is:

[0151]

[0152] The regret-joy function matrix is:

[0153]

[0154] The perceived utility matrix is ​​as follows:

[0155]

[0156] The overall perceived utility value of each scheme and the scheme evaluation results are shown in Table 6.

[0157] Table 6. Overall perceived utility value of each scheme

[0158]

[0159] Based on the above evaluation process, the following conclusions can be drawn:

[0160] ① The Sanliping Hydropower Station, as the leading power station, received the highest compensation. The compensation obtained using the coefficient of variation-Shapley value method showed varying degrees of increase compared to other methods. The Sipin and Guoduwan Hydropower Stations also achieved higher compensation benefits than when operating independently, with significant differences in their compensation allocation. This fully affirms the role and value of the leading power station in the alliance, ensuring greater fairness and promoting active participation of downstream power stations in cascade joint dispatch while also highlighting the individual differences among secondary power stations.

[0161] ② Through comparative analysis of PTD values, it was found that the coefficient of variation-Shapley value method is the most stable and widely accepted allocation method. Among them, the PTD values ​​of Sanliping and Sipin hydropower stations are the smallest, which fully affirms their compensation and regulation capabilities as upstream power stations. The PTD value of Guoduwan hydropower station is relatively large, mainly because the contribution rate of each hydropower station is fully considered in the allocation, and the upstream power stations with larger contribution rates and greater sacrifices are compensated more.

[0162] ③ Among the four evaluation schemes, the comprehensive perceived utility value of scheme M1 is 0.822, the comprehensive perceived utility value of scheme M2 is 0.832, the comprehensive perceived utility value of scheme M3 is 0.877, and the comprehensive perceived utility value of scheme M4 is 0.948. Scheme M4 > Scheme M3 > Scheme M2 > Scheme M1, where > indicates that it is better than, Scheme M4 is the best, and Scheme M1 is the worst.

Claims

1. An evaluation method for compensation allocation schemes of hydropower stations aimed at maximizing power generation benefits, characterized in that: The following steps are performed sequentially. Step 1: Establish a hydropower station power generation optimization scheduling model and compare the power generation of each hydropower station under different alliance methods; Step 1.1: Establish power generation optimization scheduling models with the objective function of maximizing the power generation benefit of a single hydropower station or maximizing the power generation benefit under different alliance methods: Step 1.2: Determine the constraint equations, including the water balance equation, the hydropower station's water storage constraint, the reservoir's water level constraint, the hydropower station's output constraint, the reservoir's outflow constraint, and the flow relationship between upstream and downstream hydropower stations; Step 1.3: Use dynamic programming to perform power generation scheduling optimization calculations to obtain the power generation and power generation benefits of each hydropower station under different alliance modes; Step 2: Use different compensation allocation methods to allocate compensation for cascade hydropower stations; Step 3: Evaluate the stability of the compensation allocation results in Step 2; Step 4: Establish an indicator system, construct an evaluation indicator value matrix, and formulate a compensation sharing scheme for cascade hydropower stations; The indicator system includes the sharing ratio, cascaded increase in power generation, minimum output, and splitting tendency value; Step 5: Based on regret theory, calculate the optimal compensation allocation scheme according to the evaluation index values ​​of each compensation allocation scheme in Step 4.

2. The evaluation method for hydropower station compensation sharing scheme according to claim 1, characterized in that, In step 1.1, the objective function is: 1) A single hydropower station generates the most electricity annually: ；（1） 2) The annual power generation benefits are maximized under different alliance models: ；（2） in The total power generation benefits of the alliance's hydropower stations during the scheduling period; This represents the total number of hydroelectric power stations in the alliance. The numbering of the hydropower station indicates the number of stations in the cascade hydropower series. One hydroelectric power station; , These are the time slot numbers and the total number of time slots during the scheduling period; For hydroelectric power station The overall output coefficient; For hydroelectric power station During the period The power generation flow rate; For hydroelectric power station During the period Hydropower head; For a long period of time; For hydroelectric power station The on-grid electricity price.

3. The evaluation method for hydropower station compensation sharing scheme according to claim 2, characterized in that, In step 2, the different compensation allocation methods include the contribution method, the coefficient of variation method, the Shapley value method, and the coefficient of variation-Shapley value method.

4. The evaluation method for hydropower station compensation sharing scheme according to claim 2, characterized in that, In step 3, the stability evaluation of the compensation allocation results in step 2 is performed using the splitting tendency method.

5. The evaluation method for hydropower station compensation sharing scheme according to claim 3, characterized in that, Step 4 also includes using expert scoring and entropy weighting to calculate the weights of each indicator in the indicator system.

6. The evaluation method for hydropower station compensation sharing scheme according to claim 5, characterized in that, Step 5 includes the following sub-steps: Step 5.1: Construct the evaluation index value matrix of the compensation sharing scheme and standardize it; Step 5.2: For each indicator, take the optimal value of the indicator evaluation value in the standardized matrix and establish an ideal solution; Step 5.3: Construct the regret value matrix; Step 5.4: Construct the perceived utility matrix; Step 5.5: Calculate the overall perceived utility value of each compensation sharing scheme by combining the weights of each indicator; Step 5.6: Compare the overall perceived utility values ​​of each compensation sharing scheme and select the optimal compensation sharing scheme.

7. The evaluation method for hydropower station compensation sharing schemes according to claim 6, characterized in that, The contribution method refers to the fact that in the power generation revenue of cascade hydropower stations through joint optimal scheduling, the higher the efficiency of the power station in converting unit water energy into electrical energy and then into power generation benefits, the greater its contribution to the power generation revenue of joint scheduling. The formula for calculating the contribution method is as follows: ；（3） in For the first The cost-sharing ratio for each hydropower station; This represents the total number of cascade hydropower stations. For the first The conversion coefficient of potential energy of a hydropower station into power generation revenue; The power generation efficiency coefficient represents the coefficient by which potential energy is converted into electrical energy. The feed-in tariff represents the coefficient that indicates the efficiency of converting electrical energy into power generation. The coefficient of variation method assigns weights to the individual characteristic parameters of a hydropower station based on the degree of variation of their observed values ​​across all evaluated individual characteristic parameters. It assumes that... n There are [number] hydroelectric power stations, each with [number] [units]. m Each individual feature parameter forms a parameter value matrix. The calculation process is as follows: ① First, normalize the values ​​of each parameter, then standardize the parameter value matrix. To ensure that each parameter value is greater than 0 and within the range [0.6, 1], for positive parameters: ；（4） in, For the first Characteristic parameters of a hydropower station j The standard value, ∈[0.6,1]; , These are the characteristic parameters. j The maximum and minimum eigenvalues; X ij Represents the parameter value matrix X The i Line number j Column elements; ② Calculate the first j The mean and standard deviation of the characteristic parameter values: ；（5） ；（6） Indicates the first j The average value of each feature parameter; D j Indicates the first j The mean squared error of each feature parameter value; ③ Calculate the first j Coefficient of variation of each characteristic parameter value: ；（7） ④ Calculate the first j Weight values ​​for each feature parameter: ；（8） ⑤ Calculate the first i The first hydropower station j The proportion of each feature parameter value: ；（9） ⑥ Calculate the first i Overall weighting value of each hydropower station: ；（10） in, For the first i The individual characteristic weighting coefficient of each hydropower station, i.e., the allocation ratio; The Shapley value method allocates benefits based on the expected marginal costs of participants to ensure the effectiveness and fairness of the allocation. When the symmetry, efficiency, and additivity axioms are satisfied, the Shapley value for each hydropower station has a unique solution. ；（11） in The number of hydroelectric power stations in the alliance; N For all possible alliance methods; S for A sub-alliance; For the alliance The probability of an alliance appearing; For power station Against the alliance The marginal contribution value; Combining the advantages of the coefficient of variation method and the Shapley value method, the coefficient of variation-Shapley value method is proposed. The individual characteristic weight coefficients of each hydropower station are obtained according to equations (4) to (10). The Shapley values ​​of each hydropower station are obtained according to formula (11). The formula for calculating the coefficient of variation-Shapley value is as follows: ；（12） ；（13） in For the first The cost-sharing ratio for each hydropower station; For the first Individual characteristic weighting coefficients for each hydropower station; For the first Shapley value for each hydroelectric power station.

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