Power transmission network planning scheme simulation evaluation method for renewable energy consumption and delivery

Abstract

The invention discloses a power transmission network planning scheme simulation evaluation method for renewable energy consumption and delivery. The method comprises the following steps: carrying out typical daily operation simulation considering multivariate parameters on a planning scheme of intra-region absorption and cross-region delivery; a data standardization method is provided, quantitative indexes are subjected to classification standardization, and qualitative indexes are subjected to fuzzy quantitative conversion; proposing an evaluation index system construction method which comprises the following steps: determining indexes of the power transmission network planning evaluation index system and specific index weights; the indexes of the power transmission network evaluation index system comprise evaluation contents in four aspects of technicality, economy, flexibility and safety and stability; and proposing a power transmission network planning evaluation method, performing evaluation by adopting a TOPSIS (approximate ideal solution sorting) method, and outputting a power transmission network planning evaluation result. According to the power transmission network planning scheme simulation evaluation method for renewable energy consumption and delivery provided by the invention, the power transmission network planning scheme can be reasonably evaluated under the general background of novel power system construction.

Description

TECHNICAL FIELD

[0001] The application relates to a simulation evaluation method of a power transmission network planning scheme, in particular to a simulation evaluation method of a power transmission network planning scheme for renewable energy consumption and external sending. BACKGROUND

[0002] The power transmission network planning scheme decision is made on the premise of meeting the power consumption end demand, considering when, where and what type of line and its loop number are built, and generally seeking the planning scheme with the least investment cost on the basis of meeting various technical indexes.

[0003] Literatures [1-3] apply the analytic hierarchy process (AHP) to the comprehensive evaluation, decompose the complex problem into sub-factors with hierarchical structure, and artificially assign weights to each layer factor. However, the subjectivity of the AHP weight assignment process is too strong, is easily affected by the preference of experts, and the judgment matrix of AHP may not pass the consistency test. Literature [4] applies the entropy weight method to the power transmission network scheme evaluation, but can only calculate the objective weight of each index, ignoring the role of subjective decision in the actual processing process. Therefore, it is necessary to propose a set of evaluation index weight determination method combining subjective and objective factors, and the reasonable processing of the consistency test in the analytic hierarchy process has research value.

[0004] To cope with global environmental deterioration and traditional energy depletion, renewable energy power generation technology has developed rapidly, and the grid-connected proportion has been continuously improved. China has proposed to increase the renewable energy power generation ratio to 60% by 2050, at which time wind power and photovoltaic power generation will become the main power source in the system. However, due to the temporal and spatial distribution characteristics of renewable energy output, the strong uncertainty caused by multi-time and space coupling, the problem of wind and light abandonment is increasingly prominent [5], and the safe and stable operation of the power grid is facing severe challenges. The accommodation problem of renewable energy needs to be solved. Flexibility as an index to evaluate the system's ability to withstand uncertainty is of great significance to improve the accommodation capacity of renewable energy and enhance the reliability of the power system after the grid connection of renewable energy. Therefore, in the future power transmission network planning and scheme selection, the accommodation and cross-regional sending of renewable energy, especially wind and light power, must be considered.

[0005] In the existing power transmission network planning evaluation research, the deterministic evaluation is often used. However, the actual evaluation result is often not deterministic.

[0006] Reference:

[0007] [1] Luo Zikun, Liu Xiaoxiao, Chen Xingying, et al. Substation energy efficiency evaluation index system and modeling method [J]. Electric Power Automation Equipment, 2017, 37(03): 132-138.

[0008] [2]Guo LY, Li KJ, Liang YL, et al. Condition assessment of high voltage circuit breaker based on grey fuzzy comprehensive evaluation [J]. Electric Power Automation Equipment, 2014, 34(11): 161-167.

[0009] [3] Luo Y, Li YL. Comprehensive decision of transmission network planning scheme based on entropy weight method and grey correlation analysis method [J]. Power System Technology, 2013, 37(01): 77-81.

[0010] [4] Zhao SQ, Li Y, Wang CL. Dynamic comprehensive evaluation of transmission network planning based on set pair analytic hierarchy process [J]. Journal of North China Electric Power University (Natural Science Edition), 2009, 36(05): 17-21.

[0011] [5] Cheng HZ, Li J, Wu YW, et al. Challenges and prospects of AC / DC transmission network planning considering high proportion of renewable energy [J]. Power System Automation, 2017, 41(09): 19-27. SUMMARY

[0012] In order to solve the problems in the background art, the application provides a simulation evaluation method for a transmission network planning scheme of renewable energy consumption and external sending.

[0013] The technical scheme adopted by the application is:

[0014] A simulation evaluation method for a transmission network planning scheme of renewable energy consumption and external sending, which comprises the following steps: firstly, performing operation simulation on the "in-district consumption and cross-district external sending" planning scheme; then, performing data standardization on the evaluation indexes, and then establishing a transmission network evaluation index system from the aspects of technology, economy, flexibility and safety and stability; finally, performing evaluation on the transmission network planning scheme by using the TOPSIS method.

[0015] In the above technical scheme, further, the operation simulation on the "in-district consumption and cross-district external sending" planning scheme is performed by using an electrical simulation model to perform typical day operation simulation by considering the line parameters, load parameters, generator parameters and external sending power parameters.

[0016] The electrical simulation model is specifically:

[0017] The objective function is to minimize the typical day operation cost of the system, and the constraint conditions are unit output constraint, climbing constraint, system power balance constraint, transmission line capacity constraint, load demand response constraint and external sending power constraint.

[0018] (1) Objective function:

[0019] min C sys = C ther + C gas + Cwind +C pho +C load +C out

[0020]

[0021]

[0022]

[0023]

[0024]

[0025]

[0026] where C ther is the typical daily operation cost of the power grid system, C gas is the typical daily operation cost of the gas turbine unit, C wind is the typical daily penalty amount of wind curtailment, C pho is the typical daily penalty amount of light curtailment, C load is the typical daily penalty amount of load curtailment, C out is the typical daily penalty amount of insufficient power export; α i , β i , θ i , σ i , ρ i , γ i , Ω wind , Ω pho , Ω load , Ω out are unit coefficients of the corresponding cost or penalty amount, is the actual generation output of the thermal power unit i at time t,

[0027] is the actual generation output of the gas turbine unit i at time t, are the available generation output and the actual generation output of the wind turbine unit i at time t, respectively, are the available generation output and the actual generation output of the photovoltaic unit i at time t, respectively, are the demand load and the actual load of node i at time t, respectively, are the power export demand and the actual power export of node i at time t, respectively.

[0028] (2) Constraint conditions

[0029] Unit output constraint:

[0030] ​

[0031]

[0032]

[0033]

[0034] In the formula, These represent the minimum output of thermal power unit i, gas power unit i, photovoltaic power unit i, and wind power unit i, respectively. Θ represents the maximum output of thermal power unit i, gas power unit i, photovoltaic power unit i, and wind power unit i, respectively; T is the simulation time scale, Θ ther Θ gas Θ pho Θ wind These are respectively a collection of thermal power units, a collection of gas power units, a collection of photovoltaic power units, and a collection of wind power units;

[0035] Unit ramp-up constraints:

[0036]

[0037]

[0038] In the formula, Δp thermax Δp gasmax These are the maximum ramp rates per unit time for the output of thermal power units and gas power units, respectively.

[0039] Power balance constraints:

[0040]

[0041] Power constraints on transmission lines:

[0042]

[0043] In the formula, Let be the actual power of line ij at time t. is the maximum transmission power of line ij;

[0044] Demand response limits:

[0045]

[0046] In the formula, and Let be the minimum and maximum loads of node i at time t, respectively;

[0047] External power supply:

[0048]

[0049] In the formula, and Let be the minimum and maximum power transmission requirements of node i at time t, respectively.

[0050] Furthermore, the data standardization for the evaluation indicators is specifically implemented using the following method:

[0051] Distinguish between qualitative and quantitative indicators, and standardize the data accordingly.

[0052] For quantitative indicators, cost-type and benefit-type indicators are distinguished and standardized. The standardized indicator values ​​range from 0 to 1. The specific method is as follows:

[0053] For efficiency-type indicators:

[0054]

[0055] In the formula: Let be the evaluation value of the i-th evaluation scheme for the k-th indicator; m is the number of schemes to be evaluated;

[0056] For cost-related indicators:

[0057]

[0058] For qualitative indicators, the standardization method is as follows:

[0059] Expectation, entropy, and hyperentropy are used to characterize the quantitative features of qualitative indicators; Expectation E x The point in the number space that best represents a qualitative concept, i.e., the most typical sample point for quantifying this concept; entropy E n Entropy reflects the uncertainty of qualitative concepts;

[0060] Five rating levels are defined as {Excellent, Good, Average, Poor, Very Poor}. The golden ratio method is used to obtain the numerical characteristics corresponding to the five rating levels. The numerical characteristics of the rating levels include the expected value E. x and entropy E n ;

[0061] The evaluation level of each qualitative indicator is determined by scoring from multiple experts. If h experts provide comments, the following formula is used to quantify the evaluations from those h experts:

[0062]

[0063] In the formula, E xi E represents the expected evaluation value of the planning scheme by expert i. ni Let be the evaluation entropy of expert i for the planning scheme, i = 1, 2, 3, ..., h.

[0064] Furthermore, the aforementioned evaluation index system for power transmission networks, established from four aspects—technical feasibility, economic efficiency, flexibility, and safety and stability—specifically includes:

[0065] The aforementioned power transmission network evaluation index system includes the following evaluation indicators: technical evaluation indicators include maximum line load rate, average line load rate, load reduction power, insufficient power transmitted across regions, photovoltaic absorption rate within the region, and wind power absorption rate within the region; economic evaluation indicators include construction investment costs and operating costs; flexibility evaluation indicators include minimum upward adjustment flexibility, average upward adjustment flexibility, minimum downward adjustment flexibility, average downward adjustment flexibility, and operational flexibility indicators; and safety and stability evaluation indicators include subjective evaluation indicators.

[0066] The overall weight of each evaluation indicator is determined by combining subjective and objective weights. The specific method is as follows:

[0067] First, the fuzzy hierarchical analysis method is used to determine the single-objective priority value of each planning scheme:

[0068] Definition 1: Let the fuzzy matrix B = (b ij ) m×n If there is b ij +b ji If = 1, then matrix B is called a fuzzy complementary matrix;

[0069] Definition 2: Fuzzy priority relation matrix B = (b ij ) m×n element b ij The advantages and disadvantages of reaction scheme i and scheme j;

[0070] Definition 3: Let there be a fuzzy complementary matrix B = (b ij ) m×n If for any k, b ij =b ik -b jk If the value is +0.5, then B is called a fuzzy consistency matrix;

[0071] The specific steps are as follows:

[0072] Construct the fuzzy priority relation matrix using the following formula:

[0073]

[0074] In the formula: r i k and Let be the membership values ​​of the i-th and j-th evaluation schemes with respect to the k-th indicator; This reflects the relative merits of the i-th and j-th solutions;

[0075] The fuzzy priority relation matrix B obtained from the quantitative and qualitative indicators is transformed into a fuzzy consistency matrix A; the following formula is used to transform the fuzzy priority relation matrix B into a fuzzy consistency matrix A:

[0076]

[0077] In the formula: r i and r j Let be the sums of the elements in the i-th and j-th rows of matrix B, respectively. m is the dimension of the matrix;

[0078] Single-objective sorting: using the square root method, utilizing... Calculate the dominance value of scheme i under a single objective. in

[0079] Next, the subjective weights of each evaluation indicator were determined using an improved analytic hierarchy process, and the objective weights of each evaluation indicator were determined using the entropy weight method.

[0080] The improved analytic hierarchy process is used to determine the subjective weights of each evaluation indicator, specifically as follows:

[0081] The judgment matrix is ​​determined by using the scaling method. The n indicators are sorted in a way that does not reduce their importance. The importance of two adjacent indicators is compared and recorded as one scale. The judgment matrix obtained in this way is consistent and does not need to be checked.

[0082] Judgment matrix R = [r ij The following conditions must be met: ①r ij >0; ②r ii =1; ③r ij =1 / r ji ;④r ij =r ik ·r kj ;where r ij Let be the scale value of the i-th indicator relative to the j-th indicator;

[0083] Let there be n indices x1, x2, ..., xn. n The indicators were subjectively ranked according to the principle of no decrease in importance, based on the meaning of the scale value and x. i With x i+1 The importance relationship is determined, the scale value is calculated, and the corresponding scale is denoted as t. i Finally, the scale values ​​t1, t2, ..., t between all adjacent indices are obtained. n-1 Based on the conditions that the judgment matrix must satisfy, the other elements in the judgment matrix are obtained, resulting in the final judgment matrix R:

[0084]

[0085] The subjective weights of each indicator are determined by the following formula:

[0086]

[0087] In the formula, α i Let be the weight value of the i-th indicator; This represents the product of all elements in the i-th row of the judgment matrix R; thus, the subjective weights of each evaluation indicator in the power transmission network planning can be quantitatively determined.

[0088] The method of using entropy weight to determine the objective weights of each evaluation index is as follows:

[0089] Let the evaluation matrix consisting of m evaluation schemes and n indicators be X = (x ij ) m×n For i = 1, 2, ..., m; for j = 1, 2, ..., n; the standardization method for the index is as follows:

[0090]

[0091] In the formula, P ij The data are standardized indicators; the entropy of each evaluation indicator is...

[0092]

[0093] When P ij When = 0, let P ij lnP ij =0;

[0094] Objective weights w of each indicator j for:

[0095]

[0096] Finally, subjective and objective weights are combined to obtain the comprehensive weight of each evaluation indicator in the evaluation indicator system.

[0097] Furthermore, the method of evaluating power grid planning schemes using the approximation ideal solution ranking method is as follows:

[0098] (1) Determine the weighted evaluation matrix Z

[0099] Based on the standard matrix X″ composed of the comprehensive weights of the indicators and the single-objective superiority values ​​of each scheme, the row vectors Z of the weighted evaluation matrix are calculated using the following formula. i :

[0100]

[0101] The final weighted evaluation matrix Z, ω is obtained. iThe comprehensive weight of the evaluation indicators;

[0102] (2) Calculate the relative distance

[0103] The positive ideal solution Z is formed by taking the maximum value of each index. + The minimum value of each index is taken to form the negative ideal solution Z. - ;

[0104] Distance from the evaluation scheme to the ideal solution Distance to the negative ideal solution The formulas are as follows:

[0105]

[0106]

[0107] In the formula, z ij Z is the row vector of the weighted evaluation matrix i The j-th element, The positive ideal solution Z is respectively + and negative ideal solution Z - The i-th element in;

[0108] (3) Calculate the relative proximity

[0109] Relative proximity is a physical quantity that comprehensively characterizes the distance relationship between the evaluation scheme and the positive and negative ideal solutions; the definition of relative proximity is shown in the following formula:

[0110]

[0111] According to C j The evaluation schemes are ranked according to the value of C. j The larger the value, the closer the solution is to the ideal solution, and the better the overall evaluation result.

[0112] The beneficial effects of this invention are:

[0113] 1) It provides a complete simulation and evaluation method for the planning scheme of renewable energy consumption and transmission, which is conducive to the reasonable evaluation of the planning scheme of the transmission network in the context of the construction of new power system.

[0114] 2) A method for simulating typical daily operation under a power transmission network planning scheme is provided.

[0115] 3) For the standardization of indicator data, distinguish between qualitative and quantitative indicators, and provide corresponding data standardization methods.

[0116] 4) The established evaluation index system uses a combination of subjective and objective weighting to determine the weight of each index, which is more conducive to truly reflecting the importance of the indicators.

[0117] 5) The single-objective superiority values ​​of each scheme in the evaluation index system are obtained by fuzzy hierarchical analysis based on fuzzy consistency matrix, and are therefore fuzzy.

[0118] 6) The comparison of power transmission network evaluation schemes adopts the approximation ideal solution ranking method, which can compare alternative schemes from multiple dimensions and provide a reference for power transmission network planning engineers. Attached Figure Description

[0119] Figure 1 This is a flowchart of the simulation evaluation method for the power transmission network planning scheme of the present invention. Detailed Implementation

[0120] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0121] like Figure 1 The figure shows a simulation evaluation method for power grid planning schemes for renewable energy consumption and transmission, according to the present invention. The steps of the method are as follows:

[0122] 1. Simulation of the operation of the planning scheme

[0123] The operation simulation of the "inter-regional consumption and inter-regional transmission" planning scheme is carried out. The specific method is as follows: considering line parameters, load parameters, generator set parameters, and external power transmission parameters, an electrical simulation model is used to simulate the operation of a typical day.

[0124] The electrical simulation model is specifically as follows:

[0125] The objective function is to minimize the typical daily operating cost of the system, and the constraints are unit output constraints, ramp-up constraints, system power balance constraints, transmission line capacity constraints, load demand response constraints, and external power transmission constraints.

[0126] (1) Objective function:

[0127] minC sys =C ther +C gas +C wind +C pho +C load +C out

[0128]

[0129]

[0130]

[0131]

[0132]

[0133]

[0134] In the formula, C ther For the typical daily operating cost of a power transmission network system, C gas For the operating cost of a typical Japanese gas generator set, C wind C represents the typical daily wind turbine curtailment penalty amount. pho C represents the typical daily curtailment penalty amount for a photovoltaic power generation unit. load C represents the penalty amount for typical daily load abandonment. out This represents the penalty amount for insufficient daily power delivery. α i β i θ i σ i ρ i γ i Ω wind Ω pho Ω load Ω out These are all unit coefficients corresponding to the cost or penalty amount. Let be the actual power generation output of thermal power unit i at time t. Let be the actual power output of gas turbine unit i at time t. Let be the power output and actual power output of wind turbine generator i at time t, respectively. Let be the power output and actual power output of photovoltaic generator unit i at time t, respectively. Let be the demand load and the actual load of node i at time t, respectively. These represent the power transmission demand and actual power transmitted from node i at time t, respectively.

[0135] (2) Constraints

[0136] Unit output constraints:

[0137]

[0138]

[0139]

[0140]

[0141] In the formula, These represent the minimum output of thermal power unit i, gas power unit i, photovoltaic power unit i, and wind power unit i, respectively. Θ represents the maximum output of thermal power unit i, gas power unit i, photovoltaic power unit i, and wind power unit i, respectively. T is the simulation time scale, Θ ther Θgas Θ pho Θ wind These are respectively a collection of thermal power units, a collection of gas power units, a collection of photovoltaic power units, and a collection of wind power units.

[0142] Unit ramp-up constraints:

[0143]

[0144]

[0145] In the formula, Δp thermax Δp gasmax These represent the maximum ramp rate per unit time for the output of thermal power units and gas power units, respectively.

[0146] Power balance constraints:

[0147]

[0148] Power constraints on transmission lines:

[0149]

[0150] In the formula, Let be the actual power of line ij at time t. represents the maximum transmission power of line ij.

[0151] Demand response limits:

[0152]

[0153] In the formula, and Let be the minimum and maximum loads at time t for node i, respectively.

[0154] External power supply:

[0155]

[0156] In the formula, and Let be the minimum and maximum power transmission requirements of node i at time t, respectively.

[0157] 2. Data standardization

[0158] For quantitative indicators, data standardization is used to distinguish between benefit-type indicators and cost-type indicators. The standardized indicator values ​​range from 0 to 1.

[0159] For efficiency-type indicators:

[0160]

[0161] In the formula: Let be the evaluation value of the i-th evaluation scheme for the k-th indicator; m is the number of schemes to be evaluated.

[0162] For cost-related indicators:

[0163]

[0164] For the standardization of qualitative indicators, the specific methods are as follows:

[0165] Expectation, entropy, and hyperentropy are used to characterize the quantitative features of qualitative indicators.

[0166] Expected E x (Expectation): The point in the number space that best represents a qualitative concept, that is, the most typical sample point for the quantification of this concept.

[0167] Entropy E n (Entropy): Entropy reflects the uncertainty of a qualitative concept.

[0168] Five rating levels are defined as {Excellent, Good, Average, Poor, Very Poor}. The golden ratio method is used to obtain the numerical characteristics corresponding to the five rating levels. The numerical characteristics of the rating levels include the expected value E. x and entropy E n (Details are shown in Table 1);

[0169] Table 1 Numerical Characteristics of Evaluation Levels

[0170] Evaluation Very good Good Fair Poor Very poor E x ]]> 1 0.69 0.5 0.31 0 E n ]]> 0.1 0.06 0.04 0.06 0.1

[0171] The evaluation levels for each qualitative indicator were determined using a multi-expert scoring method. If h experts provided comments, their evaluations can be quantified using the following formula:

[0172]

[0173] In the formula, E xi Let E be the expected value of expert i's evaluation of the planning scheme, i = 1, 2, 3, ..., h; ni Let be the evaluation entropy of expert i for the planning scheme, i = 1, 2, 3, ..., h.

[0174] 3. Construction of Evaluation Index System

[0175] The power transmission network evaluation index system is established from four aspects: technical, economic, flexible, and safety and stability. Specifically, the technical evaluation index includes the maximum line load rate, average line load rate, load reduction, insufficient cross-regional power transmission, regional photovoltaic absorption rate, and regional wind power absorption rate; the economic evaluation index includes construction investment costs and operating costs; the flexibility evaluation index includes minimum upward adjustment flexibility, average upward adjustment flexibility, minimum downward adjustment flexibility, average downward adjustment flexibility, and operational flexibility index; and the safety and stability evaluation index is a subjective evaluation index.

[0176] The overall weight of each evaluation indicator is determined by combining subjective and objective weights. The specific method is as follows:

[0177] First, the fuzzy hierarchical analysis (FAHP) method is used to determine the superiority values ​​of each scheme under a single objective:

[0178] Definition 1: Let the fuzzy matrix B = (b ij ) m×n If there is b ij +b ji If = 1, then matrix B is called a fuzzy complementary matrix.

[0179] Definition 2: Fuzzy priority relation matrix B = (b ij ) m×n element b ij The advantages and disadvantages of reaction scheme i and scheme j.

[0180] Definition 3: Let there be a fuzzy complementary matrix B = (b ij ) m×n If for any k, b ij =b ik -b jk If B is +0.5, then B is called a fuzzy consistency matrix.

[0181] The specific steps are as follows:

[0182] Construct the fuzzy priority relation matrix using the following formula:

[0183]

[0184] In the formula: r i k and Let be the membership values ​​of the i-th and j-th evaluation schemes with respect to the k-th indicator; This reflects the relative merits of the i-th and j-th solutions.

[0185] The fuzzy priority relationship matrix B of the obtained quantitative and qualitative indicators is transformed into a fuzzy consistency matrix A.

[0186] The fuzzy priority relation matrix B can be transformed into a fuzzy consistency matrix A using the following formula:

[0187]

[0188] In the formula: r i and r j Let be the sums of the elements in the i-th and j-th rows of matrix B, respectively. m is the dimension of the matrix.

[0189] Single-objective sorting: using the square root method, utilizing... Calculate the dominance value of scheme i under a single objective. in

[0190] Next, a combination of subjective and objective weighting methods was used to determine the weights of each indicator. Subjective weights were determined using a modified analytic hierarchy process (AHP), the advantage of which is that no consistency check is required. Objective weights were determined using the entropy weighting method.

[0191] When using the traditional analytic hierarchy process (AHP) for weight calculation, decisions often fail to be made due to the judgment matrix not satisfying consistency checks, leading to difficulties in the actual evaluation process. An improved AHP addresses this problem by using a scaling method to determine the judgment matrix. The n indicators are ranked according to their importance without decreasing, and the importance of adjacent pairs of indicators is compared and recorded as one scale. The resulting judgment matrix satisfies consistency and does not require further testing.

[0192] Judgment matrix R = [r ij The following conditions must be met: ①r ij >0; ②r ii =1; ③r ij =1 / r ji ;④r ij =r ik ·r kj Where r ij This represents the scale value of the i-th indicator relative to the j-th indicator. The meaning of the scale value is shown in Table 2.

[0193] Table 2 Meaning of Scale Values

[0194]

[0195] Let there be n indices x1, x2, ..., xn. n The indicators were subjectively ranked according to the principle of no decrease in importance, based on the meaning of the scale values ​​in Table 2 and x. i With x i+1 The importance relationship is determined, the scale value is calculated, and the corresponding scale is denoted as t. i Finally, the scale values ​​t1, t2, ..., t between all adjacent indices are obtained.n-1 Based on the conditions that the judgment matrix must satisfy, the other elements in the judgment matrix are obtained, resulting in the final judgment matrix R as shown below:

[0196]

[0197] The judgment matrix obtained in this way satisfies consistency and can be directly used for weight calculation without verification. The subjective weights of each indicator are determined by the following formula.

[0198]

[0199] In the formula, α i Let be the weight value of the i-th indicator; This represents the product of all elements in the i-th row of matrix R; thus, the subjective weights of each indicator in the power transmission network planning can be quantitatively determined.

[0200] Objective weighting employs the entropy weighting method. Based on the definition of information entropy, the entropy value can be used to determine the degree of dispersion of a given indicator. The smaller the information entropy value, the greater the dispersion of the indicator, and the greater its impact (i.e., weight) on the overall evaluation. If all values ​​of an indicator are equal, then that indicator has no effect on the overall evaluation.

[0201] Let the evaluation matrix consisting of m evaluation schemes and n indicators be X = (x ij ) m×n Let i = 1, 2, ..., m; j = 1, 2, ..., n. The standardization method for the indicators is as follows:

[0202]

[0203] In the formula P ij These are standardized indicator data. The entropy of each evaluation indicator is...

[0204]

[0205] In particular, when P ij When = 0, let P ij lnP ij =0.

[0206] Objective weights w of each indicator j for:

[0207]

[0208] w j It reflects the information content of the indicators. The larger the entropy weight, the greater the role of the indicator in comprehensive decision-making, and it intuitively and effectively reflects the degree of difference between indicators.

[0209] By combining the weights determined by the improved analytic hierarchy process and the entropy weight method, a comprehensive weight for the evaluation index system is obtained, forming a rating index system.

[0210] 4. Evaluation of power transmission network planning

[0211] TOPSIS uses relative proximity to characterize the distance between each evaluation scheme and the ideal solution. First, the ideal solution is determined, including positive and negative ideal solutions, representing the optimal and worst-case scenarios, respectively. Then, the distance between each evaluation scheme and the two ideal solutions is calculated. The closer the scheme is to the positive ideal solution or the further it is from the negative ideal solution, the better its overall performance. The power transmission network planning schemes are evaluated using the approximation-ideal-solution ranking method, with the following steps:

[0212] (1) Determine the weighted evaluation matrix Z.

[0213] Based on the standard matrix X″ composed of the comprehensive weights of the indicators and the single-objective superiority values ​​of each scheme, the row vectors Z of the weighted evaluation matrix are calculated by the following formula. i :

[0214]

[0215] The final weighted evaluation matrix Z, ω is obtained. i The comprehensive weight of the evaluation indicators.

[0216] (2) Calculate the relative distance.

[0217] Since all data has been positively evaluated, a larger value indicates a better result. Therefore, the maximum value of each indicator is taken to form the positive ideal solution Z. + The minimum value of each index is taken to form the negative ideal solution Z. - .

[0218] Distance from the evaluation scheme to the ideal solution Distance to the negative ideal solution The formulas are as follows:

[0219]

[0220]

[0221] In the formula, z ij Z is the row vector of the weighted evaluation matrix i The j-th element, The positive ideal solution Z is respectively + and negative ideal solution Z - The i-th element in.

[0222] (3) Calculate the relative proximity.

[0223] Relative proximity is a physical quantity that comprehensively characterizes the distance relationship between the evaluation scheme and the positive and negative ideal solutions. The definition of relative proximity in TOPSIS is shown in the following equation:

[0224]

[0225] According to C j The value of C allows for the ranking of evaluation schemes. j The larger the value, the closer the solution is to the ideal solution, and the better the overall evaluation result.

Claims

1. A simulation evaluation method for power grid planning schemes for renewable energy consumption and transmission, characterized in that, First, an operational simulation was conducted on the "inter-regional consumption and inter-regional transmission" planning scheme. Next, the data of the evaluation indicators were standardized. Then, an evaluation index system for the power transmission network was established from four aspects: technicality, economy, flexibility, safety and stability. Finally, the power transmission network planning scheme was evaluated using the approximation ideal solution ranking method.

2. The simulation evaluation method for power grid planning schemes for renewable energy consumption and transmission according to claim 1, characterized in that, The operation simulation of the "inter-regional consumption and inter-regional transmission" planning scheme is carried out by: considering line parameters, load parameters, generator set parameters, and transmission power parameters, and using an electrical simulation model to simulate the operation on a typical day. The electrical simulation model is specifically as follows: The objective function is to minimize the typical daily operating cost of the system, and the constraints are unit output constraints, ramp-up constraints, system power balance constraints, transmission line capacity constraints, load demand response constraints, and external power transmission constraints. (1) Objective function: minC sys =C ther +C gas +C wind +C pho +C load +C out In the formula, C ther For the typical daily operating cost of a power transmission network system, C gas For the operating cost of a typical Japanese gas generator set, C wind C represents the typical daily wind turbine curtailment penalty amount. pho C represents the typical daily curtailment penalty amount for a photovoltaic power generation unit. load C represents the penalty amount for typical daily load abandonment. out This is the penalty amount for insufficient daily power transmission; α i β i θ i σ i ρ i γ i Ω wind Ω pho Ω load Ω out These are all unit coefficients corresponding to the cost or penalty amount. Let be the actual power generation output of thermal power unit i at time t. Let be the actual power output of gas turbine unit i at time t. Let be the power output and actual power output of wind turbine generator i at time t, respectively. Let be the power output and actual power output of photovoltaic generator unit i at time t, respectively. Let be the demand load and the actual load of node i at time t, respectively. These represent the power transmission demand and actual power transmitted from node i at time t, respectively. (2) Constraints Unit output constraints: In the formula, These represent the minimum output of thermal power unit i, gas power unit i, photovoltaic power unit i, and wind power unit i, respectively. Θ represents the maximum output of thermal power unit i, gas power unit i, photovoltaic power unit i, and wind power unit i, respectively; T is the simulation time scale, Θ ther Θ gas Θ pho Θ wind These are respectively a collection of thermal power units, a collection of gas power units, a collection of photovoltaic power units, and a collection of wind power units; Unit ramp-up constraints: In the formula, Δp thermax Δp gasmax These are the maximum ramp rates per unit time for the output of thermal power units and gas power units, respectively. Power balance constraints: Power constraints on transmission lines: In the formula, Let be the actual power of line ij at time t. is the maximum transmission power of line ij; Demand response limits: In the formula, and Let be the minimum and maximum loads of node i at time t, respectively; External power supply: In the formula, and Let be the minimum and maximum power transmission requirements of node i at time t, respectively.

3. The simulation evaluation method for power grid planning schemes for renewable energy consumption and transmission according to claim 1, characterized in that, The specific method for standardizing the data for the evaluation indicators is as follows: Distinguish between qualitative and quantitative indicators, and standardize the data accordingly. For quantitative indicators, cost-type and benefit-type indicators are distinguished and standardized. The standardized indicator values ​​range from 0 to 1. The specific method is as follows: For efficiency-type indicators: In the formula: Let be the evaluation value of the i-th evaluation scheme for the k-th indicator; m is the number of schemes to be evaluated; For cost-related indicators: For qualitative indicators, the standardization method is as follows: Expectation, entropy, and hyperentropy are used to characterize the quantitative features of qualitative indicators; Expectation E x The point in the number space that best represents a qualitative concept, i.e., the most typical sample point for quantifying this concept; entropy E n Entropy reflects the uncertainty of qualitative concepts; Five rating levels are defined as {Excellent, Good, Average, Poor, Very Poor}. The golden ratio method is used to obtain the numerical characteristics corresponding to the five rating levels. The numerical characteristics of the rating levels include the expected value E. x and entropy E n ; The evaluation level of each qualitative indicator is determined by scoring from multiple experts. If h experts provide comments, the following formula is used to quantify the evaluations from those h experts: In the formula, E xi E represents the expected evaluation value of the planning scheme by expert i. ni Let be the evaluation entropy of expert i for the planning scheme, i = 1, 2, 3, ..., h.

4. The simulation evaluation method for power grid planning schemes for renewable energy consumption and transmission according to claim 1, characterized in that, The aforementioned evaluation index system for power transmission networks is established from four aspects: technicality, economy, flexibility, and safety and stability. Specifically: The aforementioned power transmission network evaluation index system includes the following evaluation indicators: technical evaluation indicators include maximum line load rate, average line load rate, load reduction power, insufficient power transmitted across regions, photovoltaic absorption rate within the region, and wind power absorption rate within the region; economic evaluation indicators include construction investment costs and operating costs; flexibility evaluation indicators include minimum upward adjustment flexibility, average upward adjustment flexibility, minimum downward adjustment flexibility, average downward adjustment flexibility, and operational flexibility indicators; and safety and stability evaluation indicators include subjective evaluation indicators. The overall weight of each evaluation indicator is determined by combining subjective and objective weights. The specific method is as follows: First, the fuzzy hierarchical analysis method is used to determine the single-objective priority value of each planning scheme: Definition 1: Let the fuzzy matrix B = (b ij ) m×n If there is b ij +b ji If = 1, then matrix B is called a fuzzy complementary matrix; Definition 2: Fuzzy priority relation matrix B = (b ij ) m×n element b ij The advantages and disadvantages of reaction scheme i and scheme j; Definition 3: Let there be a fuzzy complementary matrix B = (b ij ) m×n If for any k, b ij =b ik -b jk If the value is +0.5, then B is called a fuzzy consistency matrix; The specific steps are as follows: Construct the fuzzy priority relation matrix using the following formula: In the formula: and Let be the membership values ​​of the i-th and j-th evaluation schemes with respect to the k-th indicator; This reflects the relative merits of the i-th and j-th solutions; The fuzzy priority relation matrix B obtained from the quantitative and qualitative indicators is transformed into a fuzzy consistency matrix A; the following formula is used to transform the fuzzy priority relation matrix B into a fuzzy consistency matrix A: In the formula: r i and r j Let be the sums of the elements in the i-th and j-th rows of matrix B, respectively. m is the dimension of the matrix; Single-objective sorting: using the square root method, utilizing... Calculate the dominance value of scheme i under a single objective. in Next, the subjective weights of each evaluation indicator were determined using an improved analytic hierarchy process, and the objective weights of each evaluation indicator were determined using the entropy weight method. The improved analytic hierarchy process is used to determine the subjective weights of each evaluation indicator, specifically as follows: The judgment matrix is ​​determined by using the scaling method. The n indicators are sorted in a way that does not reduce their importance. The importance of two adjacent indicators is compared and recorded as one scale. The judgment matrix obtained in this way is consistent and does not need to be checked. Judgment matrix R = [r ij The following conditions must be met: ①r ij >0; ②r ii =1; ③r ij =1 / r ji ;④r ij =r ik ·r kj ;where r ij Let be the scale value of the i-th indicator relative to the j-th indicator; Let there be n indices x1, x2, ..., xn. n The indicators were subjectively ranked according to the principle of no decrease in importance, based on the meaning of the scale value and x. i With x i+1 The importance relationship is determined, the scale value is calculated, and the corresponding scale is denoted as t. i Finally, the scale values ​​t1, t2, ..., t between all adjacent indices are obtained. n-1 Based on the conditions that the judgment matrix must satisfy, the other elements in the judgment matrix are obtained, resulting in the final judgment matrix R: The subjective weights of each indicator are determined by the following formula: In the formula, α i Let be the weight value of the i-th indicator; This represents the product of all elements in the i-th row of the judgment matrix R; thus, the subjective weights of each evaluation indicator in the power transmission network planning can be quantitatively determined. The method of using entropy weight to determine the objective weights of each evaluation index is as follows: Let the evaluation matrix consisting of m evaluation schemes and n indicators be X = (x ij ) m×n For i = 1, 2, ..., m; for j = 1, 2, ..., n; the standardization method for the index is as follows: In the formula, P ij The data are standardized indicators; the entropy of each evaluation indicator is... When P ij When = 0, let P ij lnP ij =0; Objective weights w of each indicator j for: Finally, subjective and objective weights are combined to obtain the comprehensive weight of each evaluation indicator in the evaluation indicator system.

5. The simulation evaluation method for power grid planning schemes for renewable energy consumption and transmission according to claim 1, characterized in that, The method of using the approximation ideal solution ranking method to evaluate the power transmission network planning scheme is as follows: (1) Determine the weighted evaluation matrix Z Based on the standard matrix X″ composed of the comprehensive weights of the indicators and the single-objective superiority values ​​of each scheme, the row vectors Z of the weighted evaluation matrix are calculated using the following formula. i : Z i =X″ i ω i The final weighted evaluation matrix Z, ω is obtained. i The comprehensive weight of the evaluation indicators; (2) Calculate the relative distance The positive ideal solution Z is formed by taking the maximum value of each index. + The minimum value of each index is taken to form the negative ideal solution Z. - ; Distance from the evaluation scheme to the ideal solution Distance to the negative ideal solution The formulas are as follows: In the formula, z ij Z is the row vector of the weighted evaluation matrix i The j-th element, The positive ideal solution Z is respectively + and negative ideal solution Z - The i-th element in; (3) Calculate the relative proximity Relative proximity is a physical quantity that comprehensively characterizes the distance relationship between the evaluation scheme and the positive and negative ideal solutions; the definition of relative proximity is shown in the following formula: According to C j The evaluation schemes are ranked according to the value of C. j The larger the value, the closer the solution is to the ideal solution, and the better the overall evaluation result.

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