Adaptability evaluation method for accessing optical storage system to railway traction network

Abstract

The invention discloses an adaptability evaluation method for accessing a light storage system to a railway traction network, and belongs to the technical field of electrified railway traction power supply system and new energy access, and the method comprises the steps: constructing a light storage topological structure adaptability evaluation index system adaptive to the power supply demand and operation characteristics of a railway scene, the system covers three dimensions of electric energy quality, reliability and economy; determining the comprehensive weight of each index in the suitability evaluation index system by integrating the judgment of multiple experts based on a Bayesian optimal worst method; and based on the comprehensive weight of each index, adopting a compromise solution to carry out quantitative evaluation and suitability sorting on the multiple optical storage access topological structures, and determining an optimal adaptation topology for a specific railway scene. According to the invention, the problem of lack of systematic and multi-dimensional suitability evaluation in the prior art is solved.

Description

Technical Field

[0001] This invention belongs to the field of electrified railway traction power supply system and new energy access technology, specifically involving a method for assessing the compatibility of a photovoltaic energy storage system with the railway traction network. Background Technology

[0002] Electrified railway traction power supply systems generally adopt single-phase AC power, which has inherent limitations such as a single energy source, susceptibility to power quality problems, and large load fluctuations. Driven by the "dual carbon" goal, integrating photovoltaic and energy storage systems into the traction power supply system has become an effective way to achieve railway decarbonization. Photovoltaic and energy storage systems can provide clean electricity, smooth load fluctuations, and recover braking energy, playing an important role in improving system energy efficiency.

[0003] However, traditional traction power supply system architectures do not fully consider the access requirements of distributed energy sources such as photovoltaics and energy storage in their design and operation. This makes it difficult for photovoltaic-storage systems to meet the diverse requirements of flexible and efficient renewable energy consumption in different railway scenarios when actually connected to the grid. Current research mainly focuses on two directions: one is to optimize power quality by making partial improvements to the existing traction power supply system, such as adding compensation devices; the other is to propose new power supply system standards through architectural innovation. Although these studies have improved the local performance of the system, they have common shortcomings: they fail to systematically consider the differentiated needs of different railway scenarios, the compatibility between various photovoltaic-storage access topologies and specific railway scenarios is still unclear, and there is a lack of targeted evaluation systems and methods, making it difficult to select the optimal access scheme according to the characteristics of the scenario in actual engineering. This lack of adaptability evaluation methods restricts the accurate deployment and efficient utilization of photovoltaic-storage systems in the railway field. Especially when facing the complex needs of different railway scenarios, the lack of effective evaluation methods leads to resource waste and poor system performance.

[0004] Therefore, there is an urgent need to establish a scientific and systematic adaptability assessment method that can quantitatively analyze the adaptability of different photovoltaic-storage topologies in various railway scenarios, and provide a basis for decision-making for the planning, design and transformation of photovoltaic-storage systems for access to railway traction networks. Summary of the Invention

[0005] To address the aforementioned shortcomings in existing technologies, this invention provides a method for assessing the compatibility of a photovoltaic energy storage system with railway traction network, which solves the problem of the lack of systematic and multi-dimensional compatibility assessment in existing technologies.

[0006] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: a method for assessing the compatibility of a photovoltaic energy storage system with a railway traction network, comprising the following steps: S1: Construct an evaluation index system for the adaptability of photovoltaic-storage topology to meet the power supply requirements and operating characteristics of railway scenarios. This system covers three dimensions: power quality, reliability, and economy. S2: Based on the Bayesian optimal and worst-case method, and by combining the judgments of multiple experts, the comprehensive weight of each indicator in the suitability evaluation index system is determined. S3: Based on the comprehensive weight of each indicator, a compromise solution is adopted to quantitatively evaluate and rank the adaptability of various optical-storage access topologies, and determine the optimal adaptable topology for a specific railway scenario.

[0007] Furthermore, the power quality includes power factor index, negative sequence current index, and harmonic current index.

[0008] Furthermore, step S2 includes the following sub-steps: S21: Define the set of indicators for assessing the adaptability of photovoltaic and energy storage systems; S22: The best and worst indicators are identified from the set of indicators by multiple experts, and the best comparison vector and the worst comparison vector are constructed. The optimal comparison vector and the worst comparison vector are represented as follows:

[0009]

[0010] in, To be the optimal comparison vector, This is the worst comparison vector. Indicates the first m The expert's first j The relative importance of each indicator compared to the optimal indicator Indicates the first m The expert's first j The relative importance of each indicator compared to the worst-case indicator; S23: For each expert, establish a nonlinear constrained optimization model with the goal of minimizing the absolute error, and solve for the individual weight vector of each expert; The nonlinear constraint optimization model is as follows:

[0011] in, For the first n The expert's first j The weight of each indicator The weight of the most important indicator The weight given to the least important indicator. This is the absolute error; S24: Based on the Bayesian model, and considering the needs of photovoltaic storage systems to access different railway scenarios, the individual weight vectors of all experts are integrated to obtain a global comprehensive weight vector. The comprehensive weight vector for:

[0012]

[0013] in, For the first j Individual weight vectors of each indicator, This is the consistency adjustment coefficient. This represents the consensus coefficient among experts.

[0014] Furthermore, step S3 includes the following sub-steps: S31: Construct the initial decision matrix, where the rows correspond to the optical-storage topology and the columns correspond to the evaluation indicators; S32: Normalize the initial decision matrix and multiply the normalized decision matrix by the comprehensive weight vector to obtain the weighted decision matrix; S33: Determine the ideal solution and the anti-ideal solution from the weighted decision matrix; The ideal solution With anti-ideal solution Represented as:

[0015]

[0016] in, For the first j The ideal solution for each indicator For the first j The anti-ideal solution of each indicator For the first i The topology in the first j Normalized values ​​for each indicator; S34: Calculate the utility of each topology relative to the ideal and antiideal solutions; Topology The weighted Euclidean distance to the ideal and antiideal solutions is:

[0017]

[0018] in, For topology The weighted Euclidean distance from the ideal solution. For topology The weighted Euclidean distance to the antiideal solution; S35: Combining the two utility degrees, calculate the comprehensive utility function value of various optical storage access topologies and rank them accordingly, with the highest value being the optimal adaptive topology; The comprehensive utility function value for:

[0019]

[0020]

[0021] in, The utility function value of the ideal solution. The utility function value is the value of the anti-ideal solution.

[0022] The beneficial effects of this invention are as follows: By constructing a multi-dimensional evaluation index system and combining Bayesian optimal and worst-case methods with compromise solutions, this invention achieves a scientific and quantitative evaluation of the optical-storage access topology under different railway scenarios. This method effectively solves the problem of the lack of adaptability evaluation in existing technologies, providing a theoretical basis and methodological support for the planning, transformation, and performance improvement of optical-storage systems in traction power supply systems. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating a method for assessing the compatibility of a photovoltaic energy storage system with a railway traction network.

[0024] Figure 2 This is a topology diagram of photovoltaic and energy storage access to the railway traction network.

[0025] Figure 3 To assess the reliability of the ranking of indicators for heavy-haul railways in Case Study 1.

[0026] Figure 4 To assess the reliability of the ranking of indicators for conventional railways in Case Study 2.

[0027] Figure 5 To assess the reliability of the ranking of high-speed railway indicators in Case Study 3. Detailed Implementation

[0028] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0029] like Figure 1 As shown, a method for assessing the compatibility of a photovoltaic energy storage system with railway traction network includes the following steps: S1: Construct an evaluation index system for the adaptability of photovoltaic-storage topology to meet the power supply requirements and operating characteristics of railway scenarios. This system covers three dimensions: power quality, reliability, and economy. The railway scenarios include heavy-haul railways, conventional railways, and high-speed railways; the analysis of operating characteristics and power supply requirements is based on the differentiated characteristics of various scenarios in terms of traction load, power demand, and power quality requirements; various photovoltaic-storage access topologies include: three-phase high-voltage access, three-phase medium-voltage access, single-phase access, two-phase access, in-phase combined access, in-phase through access, and DC access, such as... Figure 2 As shown.

[0030] The differentiated characteristics are specifically characterized by at least one of the following key operating parameters: train weight, running distance, average power supply, peak power supply, train starting current characteristics, and train climbing current characteristics.

[0031] This embodiment analyzes the power supply requirements and operating characteristics of three typical scenarios: heavy-haul railways, conventional railways, and high-speed railways. The key operating parameters for each scenario are shown in Table 1. Table 1 Key parameters for various railway scenarios

[0032] Power quality dimensions include power factor, negative sequence current, and harmonic current indicators; reliability dimensions include system power supply reliability indicators; and economic dimensions include comprehensive cost indicators. Each indicator undergoes specific quantification and normalization. 1) C 1. Power factor index: The closer its value is to 1, the higher the quantification score.

[0033] 2) C 2. Negative sequence current index: The smaller the ratio, the higher the quantification score.

[0034] 3) C 3. Harmonic current index: The smaller the value, the higher the quantification score.

[0035] 4) C 4. Power supply reliability index: The reliability value within the system's design life is normalized, with the maximum reliability value normalized to 1 and the minimum reliability value normalized to 0.

[0036] 5) C 5. Comprehensive cost index: linearly mapped with a minimum value of 1 and a maximum value of 0.

[0037] The normalized quantized values ​​of the performance indicators of each optical-storage topology were obtained through calculation, as shown in Table 2: Table 2 Normalized Quantitative Values ​​of Photovoltaic-Storage Topology Indicators

[0038] S2: Based on the Bayesian optimal and worst-case method, and by combining the judgments of multiple experts, the comprehensive weight of each indicator in the suitability evaluation index system is determined. S2 includes the following steps: S21: Define the set of indicators for assessing the compatibility of photovoltaic and energy storage systems, including... m Three different evaluation indicators, denoted as... ; S22: Identify the optimal indicator from the set of indicators through multiple experts. With worst indicators The optimal and worst comparison vectors are constructed, and the elements in these vectors are represented using a scale of 1 to 9. Relative importance; The optimal comparison vector and the worst comparison vector are represented as follows:

[0039]

[0040] in, To be the optimal comparison vector, This is the worst comparison vector. Indicates the first m The expert's first j The relative importance of each indicator compared to the optimal indicator Indicates the first m The expert's first j The relative importance of each indicator compared to the worst-case indicator; S23: For each expert, establish a nonlinear constrained optimization model with the goal of minimizing the absolute error, and solve for the individual weight vector of each expert; The nonlinear constraint optimization model is as follows:

[0041] in, For the first n The expert's first j The weight of each indicator The weight of the most important indicator The weight given to the least important indicator. This is the absolute error; S24: Based on the Bayesian model, and considering the needs of the photovoltaic storage system to access different railway scenarios, the individual weight vectors of all experts are integrated to obtain a global comprehensive weight vector, ensuring that the weight allocation can reflect the actual needs of each scenario. The comprehensive weight vector for:

[0042]

[0043] in, For the first j Individual weight vectors of each indicator, This is the consistency adjustment coefficient. This represents the consensus coefficient among experts.

[0044] S3: Based on the comprehensive weight of each indicator, a compromise solution is adopted to quantitatively evaluate and rank the adaptability of various optical-storage access topologies, and determine the optimal adaptable topology for a specific railway scenario.

[0045] S3 includes the following steps: S31: Construct the initial decision matrix Its row corresponds k Photovoltaic storage topology, corresponding to the column m Evaluation indicators Representing the i The topology in the first j The values ​​of each indicator; S32: Normalize the initial decision matrix, and multiply the normalized decision matrix by the comprehensive weight vector to obtain the weighted decision matrix. ; S33: Determine the ideal solution and the anti-ideal solution from the weighted decision matrix; The ideal solution With anti-ideal solution Represented as:

[0046]

[0047] in, For the first j The ideal solution for each indicator For the first j The anti-ideal solution of each indicator For the first i The topology in the first j Normalized values ​​for each indicator; S34: Calculate the utility of each topology relative to the ideal and antiideal solutions; Topology The weighted Euclidean distance to the ideal and antiideal solutions is:

[0048]

[0049] in, For topology The weighted Euclidean distance from the ideal solution. For topology The weighted Euclidean distance to the antiideal solution; S35: Combining the two utility values, calculate the comprehensive utility function value of various optical-storage access topologies and rank them accordingly. The one with the highest value is the optimal adaptation topology, ensuring that the evaluation results can accurately reflect the optimal adaptation topology in each railway scenario. The comprehensive utility function value for:

[0050]

[0051]

[0052] in, The utility function value of the ideal solution. The utility function value is the value of the anti-ideal solution.

[0053] In Implementation Case 1 of this invention, taking the Shenchi South to Xibaipo section of the Shuohuang Railway as an example, this line is approximately 300 kilometers long, with an annual transport volume exceeding 300 million tons and an average of over 40 pairs of trains running daily, representing a typical heavy-haul railway scenario. The BBWM method was used to determine the index weights, and five experts were invited to participate in the evaluation and scoring to obtain the optimal and worst comparison vectors: ,

[0054] The comprehensive weights are calculated by solving the hierarchical Bayesian model to obtain the index. C 1 to C The weighting results for 5 are: 0.1602, 0.3587, 0.1099, 0.2232, 0.1479, indicating that reliability is the most important indicator and cost is the least important, which is consistent with expectations. The Credal ranking of the indicators for heavy-haul railways is as follows: Figure 3 As shown, the Credal ranking values ​​among all indicators reached 0.8 or higher.

[0055] Based on this, and using Table 2 and the MARCOS compatibility evaluation method, the compatibility ranking from highest to lowest is shown in Table 3: three-phase high-voltage access, three-phase medium-voltage access, DC access, in-phase continuous access, in-phase combined access, two-phase access, and single-phase access. Heavy-haul railways, due to the large impact power during train startup and climbing, have extremely high requirements for the reliability and stability of the power supply system. Three-phase high-voltage access is suitable for long-distance, high-power power supply with low loss; medium-voltage access has low cost and low maintenance difficulty, making it suitable for medium-scale photovoltaic and energy storage systems. Two-phase and single-phase access have limited transmission capacity, making it difficult to meet high-power demands, resulting in low compatibility and risks of insufficient power supply and transportation interruptions.

[0056] Table 3. Quantification of Topology Access Methods for Heavy-Haul Railways

[0057] In the second implementation case of this invention, taking the Hujiaying-Dazhou section of the Xiangyu Railway as an example, its total length is approximately 255 kilometers, its operating speed is 160 km / h, and it operates more than 49 pairs of trains daily, representing a typical conventional-speed railway scenario. The BBWM method was used to determine the index weights, and five experts were invited to participate in the evaluation and scoring to obtain the optimal and worst comparison vectors: ,

[0058] The comprehensive weights are calculated by solving the hierarchical Bayesian model to obtain the index. C 1 to C The weighting results for 5 are: 0.1433, 0.0985, 0.1785, 0.2272, 0.3525, indicating that cost is the most important indicator and negative sequence current is the least important indicator, which is consistent with expectations. The Credal ranking of the indicators for conventional speed railways is as follows: Figure 4 As shown, the Credal ranking values ​​among all indicators reached 0.8 or higher.

[0059] Based on this, and using Table 2 and the MARCOS compatibility evaluation method, the compatibility ranking from highest to lowest is shown in Table 4: three-phase medium-voltage access, three-phase high-voltage access, two-phase access, single-phase access, in-phase combined access, in-phase continuous access, and DC access. Conventional railways have stable power supply demands, prioritizing continuous and stable power supply over rapid response. Three-phase medium-voltage access, due to its low cost and high flexibility, meets the needs of conventional railways; while high-voltage access is efficient, its high cost and maintenance difficulty limit its compatibility. In-phase and DC access have poor compatibility due to the difficulty of modification.

[0060] Table 4. Quantification of Topology Access Methods for Conventional Railways

[0061] In the third implementation case of this invention, taking the Bengbu South to Xuzhou East section of the Beijing-Shanghai Railway as an example, its total length is approximately 156 kilometers, its operating speed is 350 km / h, and it operates more than 150 pairs of trains daily, representing a typical high-speed railway scenario. The BBWM method is used to determine the index weights, and five experts are invited to participate in the evaluation and scoring to obtain the optimal and worst comparison vectors: ,

[0062] The comprehensive weights are calculated by solving the hierarchical Bayesian model to obtain the index. C 1 to C The weighting results for 5 are: 0.2762, 0.0796, 0.1263, 0.3249, 0.1930, indicating that reliability is the most important indicator and negative sequence current is the least important indicator, which is consistent with expectations. The Credal ranking of the indicators for high-speed railways is as follows: Figure 5 As shown, the Credal ranking values ​​among all indicators reached 0.8 or higher.

[0063] Based on this, and using Table 2 and the MARCOS compatibility evaluation method, the compatibility ranking from highest to lowest is shown in Table 5: three-phase high-voltage access, three-phase medium-voltage access, DC access, in-phase continuous access, in-phase combined access, single-phase access, and two-phase access. High-speed railways experience large and rapid load fluctuations, placing extremely high demands on power supply response and power quality. Three-phase high-voltage access offers high efficiency and excellent power quality, making it suitable for high-speed railways; medium-voltage access has low cost but slightly lower efficiency. Single-phase and two-phase accesses have small capacities, making it difficult to meet load demands, resulting in low compatibility and potential power shortages that affect train operation.

[0064] Table 5. Quantification of High-Speed ​​Railway Topology Access Method Adaptation

[0065] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of the invention.

Claims

1. A method for assessing the adaptability of a light storage system to access a railway traction network, characterized in that, The method comprises the following steps: S1: constructing an adaptability evaluation index system of photovoltaic storage topology structure suitable for power supply demand and operation characteristics of a railway scene, the system covering three dimensions of power quality, reliability and economy; S2: determining the comprehensive weight of each index in the adaptability evaluation index system based on the Bayesian optimal worst method and the judgment of multiple experts; S3: based on the comprehensive weight of each index, using a compromise solution method to quantitatively evaluate and adaptively sort multiple photovoltaic storage access topology structures, and determining the optimal adaptive topology for a specific railway scene.

2. The method for evaluating the adaptability of a light storage system accessing a railway traction network according to claim 1, characterized in that, The power quality includes power factor indicators, negative sequence current indicators and harmonic current indicators.

3. The method for evaluating the adaptability of a light storage system accessing a railway traction network according to claim 1, characterized in that, The S2 comprises the following steps: S21: setting an index set for adaptability evaluation of a photovoltaic storage system; S22: identifying optimal indicators and worst indicators from the index set by multiple experts, and constructing an optimal comparison vector and a worst comparison vector; The optimal comparison vector and the worst comparison vector are expressed as: wherein, is the best comparison vector, is the worst comparison vector, denotes the first m indicator of the expert in the j th position, denotes the importance of the first m indicator of the expert in the j th position compared to the best indicator; S23: for each expert, establishing a nonlinear constraint optimization model with the objective of minimizing absolute error, and solving to obtain an individual weight vector of each expert; The nonlinear constraint optimization model is: wherein, is the first n is the second j is the weight of the first indicator, is the weight of the most important indicator, is the weight of the least important indicator, is the absolute error; S24: based on the Bayesian model, integrating the individual weight vectors of all experts to obtain a global comprehensive weight vector for the demand of photovoltaic storage system access to different railway scenes; The combined weight vector is: wherein, is the individual weight vector for the j th index, is the consistency adjustment coefficient, is the consistency coefficient of the expert.

4. The method for evaluating the adaptability of a light storage system accessing a railway traction network according to claim 3, characterized in that, The S3 comprises the following steps: S31: constructing an initial decision matrix, the rows of which correspond to photovoltaic storage topology structures, and the columns of which correspond to evaluation indexes; S32: normalizing the initial decision matrix, multiplying the normalized decision matrix with the comprehensive weight vector, and obtaining a weighted decision matrix; S33: determining ideal solutions and anti-ideal solutions from the weighted decision matrix; the ideal solution with the anti-ideal solution is expressed as: wherein, is the ideal solution for the j thindex, is the anti-ideal solution for the j thindex, is the normalized value of the i thtopology on the j thindex. S34: calculating the utility degree of each topology structure relative to the ideal solution and the anti-ideal solution; Topology The weighted Euclidean distance from the ideal solution and the anti-ideal solution is: wherein, is a topological structure is a weighted Euclidean distance from an ideal solution, is a topological structure is a weighted Euclidean distance from an anti-ideal solution; S35: combining the two utility degrees, calculating the comprehensive utility function value of multiple photovoltaic storage access topology structures and ranking them in accordance with the value, and the highest one is the optimal adaptive topology. The overall utility function value is: wherein, the utility function value for the ideal solution, the utility function value for the anti-ideal solution.

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