Method, device and equipment for evaluating stability of power distribution network containing high-proportion distributed power supply and medium

Abstract

The invention relates to the technical field of power system dispatching control, in particular to a stability evaluation method, device and equipment for a power distribution network containing high-proportion distributed power sources and a medium. Establishing a power distribution network dispatching control strategy evaluation index containing the high-proportion distributed power supply based on the dispatching control scene; based on the evaluation index and the scheduling control scene, establishing a power distribution network scheduling control strategy comprehensive evaluation model containing the high-proportion distributed power supply based on a DS evidence fusion theory, and discretizing the evaluation index; and determining a semantic mapping relationship by using an adaptive artificial bee colony algorithm, and outputting a mapping result. According to the method, through the new method combining the DS evidence fusion theory and the self-adaptive bee colony algorithm, the scheduling control strategy can be comprehensively evaluated from multiple dimensions, reasonable balance of different indexes is ensured, and a solid foundation is laid for efficient utilization of new energy and stable operation of a power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching and control, and in particular to a method, device, equipment and medium for evaluating the stability of a distribution network containing a high proportion of distributed power sources. Background Art

[0002] With the rapid development of renewable energy, especially wind and solar energy, the distribution network faces unprecedented challenges under the background of high proportion of distributed power access. The access of high proportion of new energy not only significantly improves the flexibility and renewability of the power system, but also puts higher requirements on the dispatching and control of the distribution network. Effective dispatching and control strategies are of great significance to ensure the safety, economy and environmental protection of power supply. Therefore, establishing a scientific and systematic operation scenario stability evaluation method can effectively improve the regulation level of the distribution network and promote the optimization and sustainable development of the energy structure.

[0003] However, the current operation scenario stability evaluation methods have certain limitations. Existing research focuses on a single evaluation indicator, such as voltage or current over-limit conditions, and lacks comprehensive consideration of multi-dimensional indicators. In addition, many methods lack sufficient adaptability and flexibility in the face of dynamically changing power grid environments, resulting in limited accuracy and reliability of evaluation results. These limitations make it difficult for existing methods to meet the complex and changing power market needs under the access of a high proportion of distributed power sources, and further improvement is urgently needed.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0005] The present invention provides a method, device, equipment and medium for evaluating the stability of a distribution network containing a high proportion of distributed power sources, thereby effectively solving the problems in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is: a method for evaluating the stability of a distribution network containing a high proportion of distributed power sources, comprising the following steps:

[0007] Construct a dispatching and control scenario for distributed resources in a distribution network, and establish dispatching and control strategy evaluation indicators for a distribution network with a high proportion of distributed power sources based on the dispatching and control scenario;

[0008] Based on the evaluation indicators and dispatching control scenarios, the evaluation indicators are discretized based on DS evidence fusion theory, and a comprehensive evaluation model for the dispatching control strategy of a distribution network containing a high proportion of distributed power sources is established based on the discretized evaluation indicators;

[0009] The adaptive artificial bee colony algorithm is used to determine the semantic mapping relationship and output the mapping result.

[0010] Furthermore, the distributed resources of the distribution network include new energy power generation systems, adjustable loads and energy storage devices.

[0011] Furthermore, the construction of the dispatching and control scenario of the distributed resources of the distribution network includes:

[0012] The sequence of dispatching control scenarios for renewable energy power generation systems:

[0013]

[0014] Where Y PV Control scenario sequences for the dispatch of new energy sources; They are the sequence quantities of the scheduling control scenarios of new energy at each time node;

[0015] The sequence of scheduling control scenarios for adjustable loads:

[0016]

[0017] Where Y LOAD is the control quantity sequence of the adjustable load; They are the sequence quantities of adjustable load dispatch control scenarios at each time node;

[0018] The scheduling control scenario sequence of the energy storage device:

[0019]

[0020] Where Y W Scheduling control scenarios sequences for energy storage devices; They are the sequence quantities of energy storage device scheduling control scenarios at each time node.

[0021] Furthermore, the evaluation indicators include:

[0022] Safety indicators:

[0023]

[0024] In the formula, Γ l is the safety index of the dispatching control strategy of the distribution network with a high proportion of distributed generation; P max is the power flow limit of the distribution network containing a high proportion of distributed generation; P Lmax is the actual maximum value of the power flow; V is the voltage of the distribution network containing a high proportion of distributed generation; V i,max is the actual maximum voltage of node i; V i,min is the actual minimum voltage of node i; G(·) distribution network operation uncertainty distribution function; γ2, γ2, γ3 are weight coefficients respectively;

[0025] Economic indicators:

[0026] Γ2=λ1C1+λ2C2+λ3C3+λ4C4;

[0027] Wherein, Γ2 is the economic indicator of the dispatching and control strategy of the distribution network with a high proportion of distributed generation; C1 is the dispatching and operating cost of new energy in the distribution network; C2 is the dispatching and operating cost of adjustable load in the distribution network; C3 is the dispatching and operating cost of energy storage in the distribution network; C4 is the dispatching and operating cost of traditional units in the distribution network; λ1, λ2, λ3, λ4 are weight coefficients respectively.

[0028] Furthermore, the DS evidence fusion theory includes the following steps:

[0029] Discretizing the evaluation index;

[0030] Calculating the Euclidean space distance of each discretized evaluation indicator;

[0031] Based on the Euclidean space distance of each discretized evaluation index, the similarity of stability evaluation index of distribution network operation scenarios containing a high proportion of distributed generation is calculated;

[0032] The support coefficient of the stability evaluation index of the distribution network operation scenario containing a high proportion of distributed power sources is calculated according to the similarity of the evaluation index, and the weight of the evaluation index is calculated.

[0033] Furthermore, the discretization of the evaluation index includes:

[0034] X u ={s1,s2,s3,s4};

[0035] In the formula, X u The set of evaluation indicators after discretization; s1, s2, s3, and s4 are the fuzzy values ​​of the evaluation indicators after discretization, corresponding to the fuzzy values ​​of excellent, good, medium, and poor evaluation levels respectively.

[0036] Furthermore, the calculating of the Euclidean space distance of each discretized evaluation index includes:

[0037]

[0038] In the formula, F ds (·,·) is the Euclidean space distance calculation function between the fuzzy values ​​of the stability evaluation index of the distribution network operation scenario with a high proportion of distributed generation, s i is the fuzzy value of each discretized evaluation index.

[0039] Furthermore, the similarity of stability evaluation indexes of distribution network operation scenarios containing a high proportion of distributed power sources is calculated based on the Euclidean space distance of each discretized evaluation index, including:

[0040]

[0041] In the formula, It is a similarity calculation function between the fuzzy values ​​of stability evaluation indicators of distribution network operation scenarios containing a high proportion of distributed generation.

[0042] Furthermore, the calculation of the support coefficient of the stability evaluation index of the distribution network operation scenario containing a high proportion of distributed power sources according to the similarity of the evaluation index includes:

[0043]

[0044] In the formula, is the support coefficient of the stability evaluation index i for the distribution network operation scenario with a high proportion of distributed generation; n is the number of evaluation indicators;

[0045] The calculating of the evaluation index weights includes:

[0046]

[0047] In the formula, ξ i is the weight calculation function.

[0048] Furthermore, the method of determining the semantic mapping relationship by using the adaptive artificial bee colony algorithm comprises the following steps:

[0049] Establish a semantic mapping relationship between the evaluation index value and the discretized evaluation index fuzzy value:

[0050]

[0051] Where S is the fuzzy value after mapping; η is the stability evaluation index value of the distribution network operation scenario with a high proportion of distributed generation; k1, k2, k3, k4 are the semantic thresholds of the mapping relationship to be adjusted respectively;

[0052] The adaptive artificial bee colony algorithm is used to optimize and adjust k1, k2, k3, and k4.

[0053] Furthermore, the optimization and setting of k1, k2, k3, and k4 by using the adaptive artificial bee colony algorithm comprises the following steps:

[0054] Take k1, k2, k3, and k4 as the foraging space vector parameters of the adaptive artificial bee colony algorithm;

[0055] The foraging fitness function of the adaptive artificial bee colony algorithm is designed to evaluate the effects of the current k1, k2, k3, and k4. The foraging fitness function is specifically as follows:

[0056] F e =E ac -E tr ;

[0057] In the formula, F e is the foraging fitness function; E ac , E tr They are the actual control effect of the dispatching control strategy of the distribution network containing a high proportion of distributed generation and the control evaluation results when the current k1, k2, k3, and k4 are taken;

[0058] Explore the neighborhood solutions of k1, k2, k3, and k4, specifically:

[0059]

[0060] In the formula, f i k+1 , f i k are the foraging positions of bee i in foraging rounds k+1 and k, respectively; are the foraging positions of bee j and the bee with the highest foraging fitness at foraging round k, respectively, and α is the adaptive coefficient;

[0061] If the foraging fitness of k1, k2, k3, k4 in the neighborhood solution is higher than the foraging fitness of the current k1, k2, k3, k4, then replace the current k1, k2, k3, k4 with k1, k2, k3, k4 in the neighborhood solution; after the foraging round reaches the maximum value, output the current k1, k2, k3, k4.

[0062] The present invention also includes a distribution network stability evaluation device containing a high proportion of distributed power sources, using the above method, the device comprising:

[0063] An evaluation index construction unit, used to construct a dispatching and control scenario of distributed resources in a distribution network, and to establish a dispatching and control strategy evaluation index for a distribution network containing a high proportion of distributed power sources based on the dispatching and control scenario;

[0064] A modeling unit, for discretizing the evaluation index based on the evaluation index and the dispatching control scenario and based on the DS evidence fusion theory, and establishing a comprehensive evaluation model for the dispatching control strategy of a distribution network containing a high proportion of distributed power sources based on the discretized evaluation index;

[0065] The mapping unit is used to determine the semantic mapping relationship by using an adaptive artificial bee colony algorithm and output the mapping result.

[0066] The present invention also includes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described above is implemented.

[0067] The present invention also includes a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0068] The beneficial effects of the present invention are as follows: comprehensively considering indicators such as the safety and economy of the distribution network, using the DS evidence fusion theory to achieve quantitative fusion of distribution network fuzzy indicators, and improving the distribution network stability evaluation effect considering uncertainty; at the same time, using the adaptive artificial bee colony algorithm to optimize the fuzzy boundaries of the distribution network stability evaluation model, further improving the accuracy of the distribution network stability evaluation model. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0070] Figure 1 is a flow chart of the method in Example 1;

[0071] Figure 2 It is a structural schematic diagram of the device in Example 1;

[0072] Figure 3 This is a schematic diagram of the evaluation results in Example 2;

[0073] Figure 4 It is the iterative convergence diagram of the artificial bee colony algorithm in Example 2;

[0074] Figure 5 It is a schematic diagram of the structure of the computer device of the present invention. DETAILED DESCRIPTION

[0075] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0076] Embodiment 1:

[0077] like Figure 1 As shown: A method for evaluating the stability of a distribution network containing a high proportion of distributed power sources comprises the following steps:

[0078] Construct the dispatching and control scenarios of distributed resources in the distribution network, and establish the evaluation indicators of the dispatching and control strategies of the distribution network with a high proportion of distributed power sources based on the dispatching and control scenarios;

[0079] Based on the evaluation indicators and dispatching control scenarios, the evaluation indicators are discretized based on DS evidence fusion theory, and a comprehensive evaluation model for the dispatching control strategy of a distribution network containing a high proportion of distributed power sources is established based on the discretized evaluation indicators;

[0080] The adaptive artificial bee colony algorithm is used to determine the semantic mapping relationship and output the mapping result.

[0081] By combining the DS evidence fusion theory and the new method of adaptive bee colony algorithm, this method can comprehensively evaluate the dispatching control strategy from multiple dimensions, ensure the reasonable balance of different indicators, and improve the adaptability and response speed of the system through intelligent optimization. This not only provides new ideas for improving the scientificity and effectiveness of the distribution network dispatching control strategy, but also lays a solid foundation for the efficient use of new energy and the stable operation of the power system.

[0082] Among them, the distributed resources of the distribution network include new energy power generation systems, adjustable loads and energy storage devices.

[0083] In this embodiment, a dispatching and control scenario of distributed resources in a distribution network is constructed, including:

[0084] The sequence of dispatching control scenarios for renewable energy power generation systems:

[0085]

[0086] Where Y PV A sequence of scenarios for dispatch control of new energy sources; They are the sequence quantities of the scheduling control scenarios of new energy at each time node;

[0087] The sequence of scheduling control scenarios for adjustable loads:

[0088]

[0089] Where Y LOAD is the control quantity sequence of the adjustable load; They are the sequence quantities of adjustable load dispatch control scenarios at each time node;

[0090] The scheduling control scenario sequence of the energy storage device:

[0091]

[0092] Where Y W Scheduling control scenarios sequences for energy storage devices; They are the sequence quantities of energy storage device scheduling control scenarios at each time node.

[0093] Evaluation metrics include:

[0094] Safety indicators:

[0095]

[0096] In the formula, Γ l is the safety index of the dispatching control strategy of the distribution network with a high proportion of distributed generation; P max is the power flow limit of the distribution network containing a high proportion of distributed generation; P Lmax is the actual maximum value of the power flow; V is the voltage of the distribution network containing a high proportion of distributed generation; V i,max is the actual maximum voltage of node i; V i,min is the actual minimum voltage of node i; G(·) distribution network operation uncertainty distribution function; γ2, γ2, γ3 are weight coefficients respectively;

[0097] Economic indicators:

[0098] Γ2=λ1C1+λ2C2+λ3C3+λ4C4;

[0099] Wherein, Γ2 is the economic indicator of the dispatching and control strategy of the distribution network with a high proportion of distributed generation; C1 is the dispatching and operating cost of new energy in the distribution network; C2 is the dispatching and operating cost of adjustable load in the distribution network; C3 is the dispatching and operating cost of energy storage in the distribution network; C4 is the dispatching and operating cost of traditional units in the distribution network; λ1, λ2, λ3, λ4 are weight coefficients respectively.

[0100] In this embodiment, the DS evidence fusion theory includes the following steps:

[0101] Discretize the evaluation indicators;

[0102] Calculate the Euclidean space distance of each discretized evaluation indicator;

[0103] Based on the Euclidean space distance of each discretized evaluation index, the similarity of stability evaluation index of distribution network operation scenarios containing a high proportion of distributed generation is calculated;

[0104] According to the similarity of evaluation indicators, the support coefficient of stability evaluation indicators of distribution network operation scenarios with a high proportion of distributed power sources is calculated, and the weight of evaluation indicators is calculated.

[0105] Discretize the evaluation indicators, including:

[0106] X u ={s1,s2,s3,s4};

[0107] Where, Xu The set of evaluation indicators after discretization; s1, s2, s3, and s4 are the fuzzy values ​​of the evaluation indicators after discretization, corresponding to the fuzzy values ​​of excellent, good, medium, and poor evaluation levels respectively.

[0108] Calculate the Euclidean space distance of each discretized evaluation indicator, including:

[0109]

[0110] In the formula, F ds (·,·) is the Euclidean space distance calculation function between the fuzzy values ​​of the stability evaluation index of the distribution network operation scenario with a high proportion of distributed generation, s i is the fuzzy value of each discretized evaluation index.

[0111] Based on the Euclidean space distance of each discretized evaluation index, the similarity of the stability evaluation index of the distribution network operation scenario containing a high proportion of distributed generation is calculated, including:

[0112]

[0113] In the formula, It is a similarity calculation function between the fuzzy values ​​of stability evaluation indicators of distribution network operation scenarios containing a high proportion of distributed generation.

[0114] The support coefficient of the stability evaluation index of the distribution network operation scenario with a high proportion of distributed power sources is calculated based on the similarity of the evaluation index, including:

[0115]

[0116] In the formula, is the support coefficient of the stability evaluation index i for the distribution network operation scenario with a high proportion of distributed generation; n is the number of evaluation indicators;

[0117] Calculate the evaluation index weights, including:

[0118]

[0119] In the formula, ξ i is the weight calculation function.

[0120] As a preferred embodiment of the above, the semantic mapping relationship is determined by using an adaptive artificial bee colony algorithm, including the following steps:

[0121] Establish a semantic mapping relationship between the evaluation index value and the fuzzy value of the discretized evaluation index:

[0122]

[0123] Where S is the fuzzy value after mapping; η is the stability evaluation index value of the distribution network operation scenario with a high proportion of distributed generation; k1, k2, k3, k4 are the semantic thresholds of the mapping relationship to be adjusted respectively;

[0124] The adaptive artificial bee colony algorithm is used to optimize and adjust k1, k2, k3, and k4.

[0125] The adaptive artificial bee colony algorithm is used to optimize k1, k2, k3, and k4, including the following steps:

[0126] Take k1, k2, k3, and k4 as the foraging space vector parameters of the adaptive artificial bee colony algorithm;

[0127] The foraging fitness function of the adaptive artificial bee colony algorithm is designed to evaluate the effects of the current k1, k2, k3, and k4. The foraging fitness function is specifically as follows:

[0128] F e =E ac -E tr ;

[0129] In the formula, F e is the foraging fitness function; E ac , E tr They are the actual control effect of the dispatching control strategy of the distribution network containing a high proportion of distributed generation and the control evaluation results when the current k1, k2, k3, and k4 are taken;

[0130] Explore the neighborhood solutions of k1, k2, k3, and k4, specifically:

[0131]

[0132] In the formula, f i k+1 , f i k are the foraging positions of bee i in foraging rounds k+1 and k, respectively; are the foraging positions of bee j and the bee with the highest foraging fitness at foraging round k, respectively, and α is the adaptive coefficient;

[0133] If the foraging fitness of k1, k2, k3, k4 in the neighborhood solution is higher than the foraging fitness of the current k1, k2, k3, k4, then replace the current k1, k2, k3, k4 with k1, k2, k3, k4 in the neighborhood solution; after the foraging round reaches the maximum value, output the current k1, k2, k3, k4.

[0134] like Figure 2 As shown, this embodiment also includes a distribution network stability evaluation device containing a high proportion of distributed power sources, using the above method, the device includes:

[0135] An evaluation index construction unit is used to construct a dispatching and control scenario for distributed resources in a distribution network, and to establish evaluation indicators for a dispatching and control strategy for a distribution network containing a high proportion of distributed power sources based on the dispatching and control scenario;

[0136] A modeling unit, for discretizing the evaluation index based on the evaluation index and the dispatching control scenario and based on the DS evidence fusion theory, and establishing a comprehensive evaluation model for the dispatching control strategy of a distribution network containing a high proportion of distributed power sources based on the discretized evaluation index;

[0137] The mapping unit is used to determine the semantic mapping relationship by using an adaptive artificial bee colony algorithm and output the mapping result.

[0138] Embodiment 2:

[0139] The present invention provides a method for constructing an operation scenario of a distribution network containing a high proportion of distributed power sources and evaluating its stability. The following technical solutions are used to achieve this:

[0140] Step 1: Establish evaluation indicators for the dispatching and control strategy of distribution networks with a high proportion of distributed generation.

[0141] Step 2: Based on the evaluation indicators of the dispatching and control strategy of the distribution network with a high proportion of distributed generation proposed in step 1, a comprehensive evaluation model of the dispatching and control strategy of the distribution network with a high proportion of distributed generation based on the DS evidence fusion theory is established.

[0142] Step 3: For the comprehensive evaluation model of the dispatching and control strategy of the distribution network with a high proportion of distributed generation designed in step 2, the adaptive artificial bee colony algorithm is used to optimize the semantic threshold in the evaluation model.

[0143] The dispatching and control strategy of the distribution network with a high proportion of distributed generation in step 1 is used to construct the dispatching and control scenario of the distributed resources of the distribution network. The distributed resources of the distribution network include new energy, adjustable loads, and energy storage devices.

[0144] The sequence of new energy dispatch control scenarios can be expressed as:

[0145]

[0146] Among them, Y PV Control scenario sequences for the dispatch of new energy sources; They are the sequence quantities of scheduling control scenarios of new energy at each time node.

[0147] The sequence of adjustable load dispatch control scenarios can be expressed as:

[0148]

[0149] Among them, YLOAD is the control quantity sequence of the adjustable load; They are the adjustable load dispatching control scenario sequence quantities at each time node.

[0150] The sequence of energy storage device dispatch control scenarios can be expressed as:

[0151]

[0152] Among them, Y W Scheduling control scenarios sequences for energy storage devices; They are the sequence quantities of energy storage device scheduling control scenarios at each time node.

[0153] Based on the dispatching and control scenario sequence of distributed resources in the distribution network, the topological structure of the distribution network with a high proportion of distributed power sources, and the operating status of the distribution network with a high proportion of distributed power sources, a simulation scenario of the dispatching and control strategy of the distribution network with a high proportion of distributed power sources is generated.

[0154] In the simulation scenario of the dispatching and control strategy of the distribution network with a high proportion of distributed generation, the evaluation index of the dispatching and control strategy of the distribution network with a high proportion of distributed generation is established. Among them, the evaluation index of the dispatching and control strategy of the distribution network with a high proportion of distributed generation includes safety index and economic index.

[0155] The safety index of the dispatching control strategy of the distribution network with a high proportion of distributed generation can be expressed as:

[0156]

[0157] Among them, Γ l is the safety index of the dispatching control strategy of the distribution network with a high proportion of distributed generation; P max is the power flow limit of the distribution network containing a high proportion of distributed generation; P Lmax is the actual maximum value of the power flow; V is the voltage of the distribution network containing a high proportion of distributed generation; V i,max is the actual maximum voltage of node i. V i,min is the actual minimum voltage of node i; G(·) distribution network operation uncertainty distribution function; γ2, γ2, γ3 are weight coefficients respectively.

[0158] The economic indicators of the dispatching control strategy of the distribution network with a high proportion of distributed generation can be expressed as:

[0159] Γ2=λ1C1+λ2C2+λ3C3+λ4C4;

[0160] Among them, Γ2 is the economic indicator of the distribution network dispatching and control strategy with a high proportion of distributed generation; C1 is the dispatching and operating cost of new energy in the distribution network; C2 is the dispatching and operating cost of adjustable load in the distribution network; C3 is the dispatching and operating cost of energy storage in the distribution network; C4 is the dispatching and operating cost of traditional units in the distribution network; λ1, λ2, λ3, and λ4 are weight coefficients respectively.

[0161] Step 2 The comprehensive evaluation model of the dispatching control strategy of the distribution network with a high proportion of distributed generation is realized by constructing the DS evidence fusion framework, including:

[0162] The stability evaluation index values ​​of the distribution network operation scenario with a high proportion of distributed generation in step 1 are discretized. The evaluation index set after discretization is:

[0163] X u ={s1,s2,s3,s4};

[0164] Among them, X u The set of evaluation indicators after discretization; s1, s2, s3, and s4 are the fuzzy values ​​of the evaluation indicators after discretization, corresponding to the fuzzy values ​​of excellent, good, medium, and poor evaluation levels respectively.

[0165] Based on the fuzzy value s of each discretized evaluation index i , calculate the Euclidean space distance of each discretized evaluation index. It can be expressed as:

[0166]

[0167] Among them, F ds (·,·) is the Euclidean space distance calculation function between the fuzzy values ​​of stability evaluation indicators of distribution network operation scenarios containing a high proportion of distributed generation.

[0168] Based on the Euclidean space distance of each discretized evaluation index, the similarity of the stability evaluation index of the distribution network operation scenario containing a high proportion of distributed power generation is calculated. It can be expressed as:

[0169]

[0170] in, It is a similarity calculation function between the fuzzy values ​​of stability evaluation indicators of distribution network operation scenarios containing a high proportion of distributed generation.

[0171] Based on the similarity between the fuzzy values ​​of the stability evaluation index of the distribution network operation scenario with a high proportion of distributed power generation, the support coefficient of the stability evaluation index of the distribution network operation scenario with a high proportion of distributed power generation is calculated. It can be expressed as:

[0172]

[0173] in, is the support coefficient of the stability evaluation index i for the distribution network operation scenario with a high proportion of distributed generation; n is the number of evaluation indicators.

[0174] Based on the support coefficient of the stability evaluation index of the distribution network operation scenario with a high proportion of distributed generation, the evaluation index weight is calculated. It can be expressed as:

[0175]

[0176] Among them, ξ i is the weight calculation function.

[0177] In step 3, the adaptive artificial bee colony algorithm is used to optimize the semantic threshold in the evaluation model, aiming to adjust the semantic mapping relationship between the stability evaluation index value of the distribution network operation scenario containing a high proportion of distributed power sources and the fuzzy value of the discretized evaluation index.

[0178] The semantic mapping relationship between the stability evaluation index value of the distribution network operation scenario with a high proportion of distributed generation and the fuzzy value of the evaluation index after discretization can be expressed as:

[0179]

[0180] Among them, S is the fuzzy value after mapping; η is the stability evaluation index value of the distribution network operation scenario with a high proportion of distributed generation; k1, k2, k3, and k4 are the semantic thresholds of the mapping relationship to be adjusted.

[0181] Adaptive artificial bee colony algorithm is used to optimize and adjust k1, k2, k3, and k4. The specific steps are:

[0182] S301: Use k1, k2, k3, and k4 as foraging space vector parameters of the adaptive artificial bee colony algorithm.

[0183] S302: Design an adaptive artificial bee colony algorithm foraging fitness function to evaluate the effects of current k1, k2, k3, and k4. The foraging fitness function is specifically:

[0184] F e =E ac -E tr ;

[0185] In the formula, F e is the foraging fitness function; E ac , E tr They are the actual control effect of the distribution network dispatching control strategy containing a high proportion of distributed generation and the control evaluation results when the current k1, k2, k3, and k4 values ​​are taken.

[0186] S303: Explore the neighborhood solutions of k1, k2, k3, and k4. Specifically:

[0187]

[0188] Among them, f i k+1 , f i k are the foraging positions of bee i in foraging rounds k+1 and k, respectively; are the foraging positions of bee j and the bee with the highest foraging fitness in foraging round k, respectively. α is the adaptive coefficient.

[0189] S304: If the foraging fitness of k1, k2, k3, k4 in the neighborhood solution is higher than the foraging fitness of the current k1, k2, k3, k4, then the current k1, k2, k3, k4 is replaced with the k1, k2, k3, k4 in the neighborhood solution. After the foraging round reaches the maximum value, the current k1, k2, k3, k4 is output.

[0190] The influence of distributed new energy fluctuations on the evaluation results is handled through semantic fuzzy mapping relationships, and the threshold of semantic fuzzy mapping relationships is optimized using an adaptive artificial bee colony algorithm, solving the problem of the proposed method relying on prior knowledge. Therefore, this embodiment has the advantages of efficient processing of uncertain data and intelligent optimization of models.

[0191] In order to verify the performance of the present invention, the IEEE 14-node standard system is selected as the application example system. The IEEE 14-node standard system includes 11 load nodes, 3 generator nodes, 20 transmission lines and 9 transformers. The application example includes 2 distributed renewable energy sources, 2 electric vehicle stations and 3 energy storages. The number of bees in the adaptive artificial bee colony algorithm is 200, and the maximum number of iterations is 200.

[0192] Figure 3 The stability evaluation results of distribution network operation scenarios under different scenarios. Among them, S1 is the evaluation result of a distribution network with 30% distributed power sources, but does not adopt multiple distributed resource scheduling and control strategies; S2 is the evaluation result of a distribution network with 30% distributed power sources, and adopts multiple distributed resource scheduling and control strategies; S3 is the stability evaluation result of a distribution network with 10% distributed power sources. In the figure, the horizontal axis is the timing simulation time, and the vertical axis is the evaluation result.

[0193] Depend on Figure 3It can be seen that for distribution networks with a high proportion of distributed power sources, when multiple decentralized resource dispatching and control are adopted, the overall dispatching and control effect of the distribution network can be effectively improved; if multiple decentralized resource dispatching and control are not adopted, the system will be difficult to achieve a good control effect due to the volatility of a high proportion of new energy. For distribution networks with a small proportion of distributed power sources, their dispatching and control effect is generally better than that of distribution networks with a high proportion of distributed power sources but without the use of multiple decentralized resource dispatching and control.

[0194] Figure 4 This is the iterative convergence diagram of the adaptive artificial bee colony algorithm. It can be seen that the algorithm can converge quickly within the early stage (50 times). Such a fast convergence feature enables the proposed method to quickly explore the semantic mapping relationship between the optimal stability evaluation index value of the distribution network operation scenario with a high proportion of distributed power sources and the fuzzy value of the discretized evaluation index, thereby improving the overall efficiency of the algorithm.

[0195] See also Figure 5 A computer device 400 provided in an embodiment of the present application includes: a processor 410 and a memory 420, wherein the memory 420 stores a computer program executable by the processor 410, and when the computer program is executed by the processor 410, the above method is executed.

[0196] The embodiment of the present application further provides a storage medium 430 on which a computer program is stored. When the computer program is run by the processor 410, the above method is executed.

[0197] Among them, the storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0198] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0199] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0200] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0201] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0202] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0203] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0204] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0205] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for evaluating the stability of a distribution network containing a high proportion of distributed power sources, characterized in that: The steps include: Construct a dispatching and control scenario for distributed resources in a distribution network, and establish dispatching and control strategy evaluation indicators for a distribution network with a high proportion of distributed power sources based on the dispatching and control scenario; The evaluation index is discretized based on DS evidence fusion theory, and a comprehensive evaluation model for the dispatching control strategy of a distribution network containing a high proportion of distributed power sources is established based on the discretized evaluation index; The adaptive artificial bee colony algorithm is used to determine the semantic mapping relationship and output the mapping result.

2. The method for evaluating the stability of a distribution network containing a high proportion of distributed power sources according to claim 1, characterized in that: The distributed resources of the distribution network include new energy power generation systems, adjustable loads and energy storage devices.

3. The method for evaluating the stability of a distribution network containing a high proportion of distributed power sources according to claim 2, characterized in that: The construction of the dispatching and control scenario of the distributed resources of the distribution network includes: The sequence of dispatching control scenarios for renewable energy power generation systems: Where Y PV A sequence of dispatching control scenarios for renewable energy power generation systems; They are the sequence quantities of dispatching control scenarios of the renewable energy power generation system at each time node; The sequence of scheduling control scenarios for adjustable loads: Where Y LOAD A sequence of dispatch control scenarios for adjustable loads; They are the scheduling control scenario sequence quantities of the adjustable load at each time node; The scheduling control scenario sequence of the energy storage device: Where Y W A sequence of scheduling control scenarios for energy storage devices; They are the scheduling control scenario sequence quantities of the energy storage device at each time node.

4. The method for evaluating the stability of a distribution network containing a high proportion of distributed power sources according to claim 1, characterized in that: The evaluation indicators include safety indicators and economic indicators; The safety indicators are: In the formula, Γ l is the safety index of the dispatching control strategy of the distribution network with a high proportion of distributed generation; P max is the power flow limit of the distribution network containing a high proportion of distributed generation; P Lmax is the actual maximum value of the power flow; V is the voltage of the distribution network containing a high proportion of distributed generation; V i,max is the actual maximum voltage of node i; V i,min is the actual minimum voltage of node i; G(·) distribution network operation uncertainty distribution function; γ2, γ3 are weight coefficients respectively; The economic indicators are: Γ2=λ1C1+λ2C2+λ3C3+λ4C4; Wherein, Γ2 is the economic indicator of the dispatching and control strategy of the distribution network with a high proportion of distributed generation; C1 is the dispatching and operating cost of new energy in the distribution network; C2 is the dispatching and operating cost of adjustable load in the distribution network; C3 is the dispatching and operating cost of energy storage in the distribution network; C4 is the dispatching and operating cost of traditional units in the distribution network; λ1, λ2, λ3, λ4 are weight coefficients respectively.

5. The method for evaluating the stability of a distribution network containing a high proportion of distributed power sources according to claim 1, characterized in that: The DS evidence fusion theory includes the following steps: Discretizing the evaluation index; Calculate the Euclidean space distance between each discretized evaluation index; The similarity of evaluation indicators of the stability of the distribution network containing a high proportion of distributed generation is calculated based on the Euclidean space distance between the discretized evaluation indicators; The support coefficient of the stability evaluation index of the distribution network operation scenario containing a high proportion of distributed power sources is calculated according to the similarity of the evaluation index, and the weight of the evaluation index is calculated.

6. The method for evaluating the stability of a distribution network containing a high proportion of distributed power sources according to claim 5, characterized in that: The step of discretizing the evaluation index comprises: X u ={x1,x2,x3,x4}; Where, X u is the set of evaluation indicators after discretization; x1, x2, x3, and x4 are the fuzzy values ​​of the evaluation indicators after discretization, corresponding to the fuzzy values ​​of excellent, good, medium, and poor evaluation levels, respectively.

7. The method for evaluating the stability of a distribution network containing a high proportion of distributed power sources according to claim 6, characterized in that: The Euclidean space distance between the discretized evaluation indicators is calculated as follows: In the formula, F ds (·,·) is the Euclidean space distance between the fuzzy values ​​of the stability evaluation index of the distribution network operation scenario with a high proportion of distributed generation, s i ,s j is the fuzzy value of the i-th and j-th evaluation indicators after discretization; Indicates i A feature space vector in feature space.

8. The method for evaluating the stability of a distribution network containing a high proportion of distributed power sources according to claim 7, characterized in that: The similarity of the evaluation indexes for calculating the stability of the distribution network operation scenario containing a high proportion of distributed power sources based on the Euclidean space distance between the various evaluation indexes after discretization is as follows: In the formula, It is the similarity between the fuzzy values ​​of the evaluation indicators for the stability of distribution network operation scenarios with a high proportion of distributed generation.

9. The method for evaluating the stability of a distribution network containing a high proportion of distributed power sources according to claim 8, characterized in that: The evaluation index support coefficient for calculating the stability of the distribution network operation scenario containing a high proportion of distributed power sources according to the evaluation index similarity is as follows: In the formula, is the support factor of the stability evaluation index i for the distribution network operation scenario with a high proportion of distributed generation; n is the number of evaluation indicators; The calculating of the evaluation index weights includes: In the formula, ξ i is the weight.

10. The method for evaluating the stability of a distribution network containing a high proportion of distributed power sources according to claim 1, characterized in that: The method of determining the semantic mapping relationship by using the adaptive artificial bee colony algorithm comprises the following steps: Establish a semantic mapping relationship between the evaluation index value and the discretized evaluation index fuzzy value: Where S is the fuzzy value after mapping; η is the stability evaluation index value of the distribution network operation scenario with a high proportion of distributed generation; k1, k2, k3, k4 are the semantic thresholds of the mapping relationship to be adjusted respectively; Adaptive artificial bee colony algorithm is used to optimize and adjust k1, k2, k3, and k4.

11. The method for evaluating the stability of a distribution network containing a high proportion of distributed power sources according to claim 10, characterized in that: The method of optimizing and setting k1, k2, k3, and k4 by using the adaptive artificial bee colony algorithm comprises the following steps: Take k1, k2, k3, and k4 as the foraging space vector parameters of the adaptive artificial bee colony algorithm; The foraging fitness function of the adaptive artificial bee colony algorithm is designed to evaluate the effects of the current k1, k2, k3, and k4. The foraging fitness function is specifically as follows: F e =And ac -AND tr ; In the formula, F e is the foraging fitness function; E ac , E tr They are the actual control effect of the dispatching control strategy of the distribution network containing a high proportion of distributed generation and the control evaluation results when the current k1, k2, k3, and k4 are taken; Explore the neighborhood solutions of k1, k2, k3, and k4, specifically: In the formula, f i k+1 , f i k are the foraging positions of bee i in foraging rounds k+1 and k, respectively; are the foraging positions of bee j and the bee with the highest foraging fitness at foraging round k, respectively, and α is the adaptive coefficient; If the foraging fitness of k1, k2, k3, k4 in the neighborhood solution is higher than the foraging fitness of the current k1, k2, k3, k4, then replace the current k1, k2, k3, k4 with k1, k2, k3, k4 in the neighborhood solution; after the foraging round reaches the maximum value, output the current k1, k2, k3, k4.

12. A distribution network stability evaluation device containing a high proportion of distributed power sources, characterized in that: Using the method according to any one of claims 1 to 11, the device comprises: An evaluation index construction unit, used to construct a dispatching and control scenario of distributed resources in a distribution network, and to establish a dispatching and control strategy evaluation index for a distribution network containing a high proportion of distributed power sources based on the dispatching and control scenario; A modeling unit, for discretizing the evaluation index based on the evaluation index and the dispatching control scenario and based on the DS evidence fusion theory, and establishing a comprehensive evaluation model for the dispatching control strategy of a distribution network containing a high proportion of distributed power sources based on the discretized evaluation index; The mapping unit is used to determine the semantic mapping relationship by using an adaptive artificial bee colony algorithm and output the mapping result.

13. A computer device comprising 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, the method according to any one of claims 1 to 11 is implemented.

14. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 11 is implemented.

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