Capacity assessment method, system and equipment for railway traction power supply system connected to photovoltaic power generation based on measured data

Abstract

The present invention belongs to the field of AC electrified railway traction power supply, and specifically relates to a capacity assessment method, system and equipment for accessing photovoltaic power supply systems for railway traction based on measured data. It aims to solve the problems of static assessment in the prior art that is not suitable for dynamic loads, time scale mismatch and single-objective optimization defects. The present invention includes: collecting voltage data and current data of the traction load through the voltage transformer and current transformer on the traction bus, calculating the load power of the two traction buses once at a set calculation frequency every interval, and smoothing the load power that is less than a preset threshold after eliminating it; calculating the maximum photovoltaic carrying capacity for the whole year based on the smoothed load power; constructing a multi-objective optimization model, optimizing the distribution ratio of photovoltaic capacity between the dual traction buses through particle swarm iterative search, and performing an evaluation. The present invention improves the accuracy of photovoltaic capacity assessment through dynamic filtering, and takes into account both economic efficiency and technical constraints through multi-objective optimization.

Description

Technical Field

[0001] The present invention belongs to the field of AC electrified railway traction power supply, and in particular relates to a method, system and device for evaluating the capacity of a railway traction power supply system connected to photovoltaic power generation based on measured data. Background Art

[0002] As a typical representative of high-power impact loads, the load characteristics of the electrified railway traction power supply system are significantly nonlinear and time-varying. The fluctuation amplitude of the traction load can reach several megawatts, and the change period covers dynamic characteristics ranging from minutes to hours. This is fundamentally different from the load characteristics of traditional power systems that are mainly based on stability. With the expansion of the application of new energy technologies in the field of rail transportation, the feasibility study of connecting photovoltaic power generation systems to the traction power supply network has become the focus of industry attention. However, the existing photovoltaic capacity assessment methods face the following technical bottlenecks when dealing with such special load scenarios:

[0003] Static capacity assessment has limited adaptability: Traditional assessment methods based on transformer rated capacity use a fixed proportionality factor to convert load demand. This approach fails to account for the periodic fluctuations in load rates (such as the difference between low loads in the early stages of operation and later expansion) and transient surges that occur during traction substation operation. Actual monitoring data indicates that the correlation between traction load peak power and transformer capacity is less than 30%, resulting in capacity estimation errors exceeding 200% in railway scenarios.

[0004] Mismatched timescales: The "load stability assumption" implicit in the annual electricity consumption assessment method fundamentally conflicts with the minute-by-minute fluctuations in traction load. For example, measured data from a heavy-haul railway showed that load fluctuations within 15-minute intervals could reach 5-8 times the average. Using a capacity solution based on annual electricity consumption would result in a mismatch between PV output and load demand over 89% of the time, creating the risk of power reverse flow.

[0005] Insufficient dynamic response capability: While capacity selection strategies based on hourly maximum power can mitigate instantaneous overload risks, they fail to establish a mechanism for dynamically matching minute-by-minute load fluctuations with PV output. Actual project cases have shown that systems using this approach experience PV output exceeding the real-time load during 70% of operating hours. This results in an average of 42% of daily PV power being fed back into the grid, significantly reducing the efficiency of self-consumption.

[0006] Limitations of a single optimization dimension: Traditional single-objective optimization models focus solely on economic efficiency or absorptive efficiency, failing to address multi-dimensional objectives such as system reverse power constraints and equipment lifespan loss. For example, while optimizing a single economic objective for a passenger-dedicated line reduced initial investment costs by 15%, it also increased the probability of traction transformer overload by 28%, exposing a significant imbalance between safety and economic efficiency.

[0007] Based on this, the present invention proposes a method, system and device for evaluating the capacity of a railway traction power supply system connected to photovoltaic power generation based on measured data. Summary of the Invention

[0008] In order to solve the above-mentioned problems in the prior art, namely, the problems that the prior art has static evaluation not adapting to dynamic loads, time scale mismatch and single-objective optimization defects, the present invention provides a capacity evaluation method, system and device for the connection of railway traction power supply system to photovoltaic power generation based on measured data.

[0009] In a first aspect of the present invention, a method for evaluating the capacity of a railway traction power supply system connected to photovoltaic power generation based on measured data is provided. The railway traction power supply system includes two traction buses, each of which includes a voltage transformer and a current transformer. The method includes:

[0010] The voltage and current data of the traction load are collected in real time through the voltage transformer and current transformer on the traction bus;

[0011] Based on the voltage data and the current data, the load power of the two traction buses is calculated once at a predetermined calculation frequency, and load power less than a preset threshold is eliminated and smoothed; the smoothing method includes using an adaptive dynamic window filter based on load change rate perception for smoothing;

[0012] Based on the smoothed load power, the maximum daily PV load capacity is determined using the sliding window mean and probability statistics methods. Combined with the normal distribution characteristics of the traction load data for the entire year, the maximum annual PV load capacity is calculated.

[0013] A multi-objective optimization model, encompassing PV consumption rate, reverse power transmission, and system cost, is constructed using power balance constraints, capacity limitation constraints, and voltage fluctuation constraints. The power balance and capacity limitation constraints are based on the maximum annual PV load capacity.

[0014] Through particle swarm iterative search, the allocation ratio of photovoltaic capacity between the two traction buses is dynamically optimized while satisfying power balance constraints, capacity limitations, and voltage fluctuation constraints. A photovoltaic capacity assessment value is generated based on the allocation ratio, which is used to guide the capacity configuration of the photovoltaic system in engineering design.

[0015] Furthermore, the load power that is less than a preset threshold is eliminated by:

[0016] The load power output that is less than 0 is set to 0.

[0017] Furthermore, the load power less than 0 is smoothed by removing it, and the method is as follows:

[0018] Calculate the standard deviation of the load change rate based on the load power difference between the current time t and the previous time t-1 and the mean of N historical data;

[0019] Calculating a normalized perception factor based on the standard deviation, and dynamically adjusting the filter window length in combination with the minimum window length and the maximum window length;

[0020] The weight of the data in the window is allocated in combination with the adjusted filter window length and the exponential decay weight function. The original load power data at each moment in the window is weighted according to the weight to calculate the smoothed load power.

[0021] Furthermore, the maximum photovoltaic load capacity for the whole year is calculated as follows:

[0022] Sort the load powers of the two traction buses after smoothing in ascending order. M The data is used as the maximum photovoltaic load capacity in a single day P max1 and P max2 ;in, P max1 is the maximum photovoltaic load capacity in a single day corresponding to the first traction bus, P max2 is the maximum daily photovoltaic load capacity corresponding to the second traction bus;

[0023] Select the daily load power of each of the 12 months of the year, calculate the corresponding daily maximum photovoltaic load capacity, arrange them in descending order, and calculate the probability value of each ranking position based on the normal distribution;

[0024] The maximum photovoltaic carrying capacity for the whole year is obtained by multiplying the sorted daily maximum photovoltaic carrying capacity with the corresponding probability value and summing them up.

[0025] Furthermore, maximize the real-time absorption rate of traction load by photovoltaic system , and its construction method is:

[0026] ;

[0027] in, is the load power corresponding to the first traction bus, is the load power corresponding to the second traction bus, is the photovoltaic power generation power corresponding to the first traction bus, is the photovoltaic power generation power corresponding to the second traction bus.

[0028] Furthermore, the reverse power transmission from the photovoltaic system to the grid is minimized. , and its construction method is:

[0029] ;

[0030] in, is the load power corresponding to the first traction bus at time t, is the load power corresponding to the second traction bus at time t, is the photovoltaic power generation power corresponding to the first traction bus at time t, is the photovoltaic power generation corresponding to the second traction bus at time t, and T is the time period.

[0031] Furthermore, the system cost is to minimize the investment and operation and maintenance costs of the photovoltaic system. , and its construction method is:

[0032] ;

[0033] in, is the preset initial investment cost coefficient, is the operation and maintenance cost coefficient.

[0034] Furthermore, the power balance constraint, capacity limitation constraint and voltage fluctuation constraint are specifically:

[0035] Power balance constraints: ;

[0036] Capacity limit constraints: ; ; ;

[0037] in, P max1 is the maximum photovoltaic load capacity in a single day corresponding to the first traction bus, P max2 is the maximum photovoltaic load capacity in a single day corresponding to the second traction bus, It is the maximum photovoltaic carrying capacity of the whole year;

[0038] Voltage fluctuation constraints: ;in, is the voltage fluctuation value of the traction bus, It is the maximum voltage fluctuation threshold allowed by the system.

[0039] Another aspect of the present invention provides a capacity assessment system for a railway traction power supply system connected to photovoltaic power generation based on measured data. A capacity assessment method for a railway traction power supply system connected to photovoltaic power generation based on measured data is provided. The system comprises:

[0040] A current and voltage data acquisition module is configured to collect voltage and current data of the traction load in real time through the voltage transformer and current transformer on the traction bus;

[0041] a smoothing processing module configured to calculate the load power of the two traction buses once per interval at a set calculation frequency based on the voltage data and the current data, and to perform smoothing processing on the load power excluding the load power less than a preset threshold; the smoothing processing method includes using an adaptive dynamic window filter based on load change rate perception for smoothing processing;

[0042] The annual maximum photovoltaic load capacity calculation module is configured to determine the daily maximum photovoltaic load capacity based on the smoothed load power through a sliding window mean and probability statistics method, and calculate the annual maximum photovoltaic load capacity by combining the normal distribution characteristics of the traction load data for 12 months of the year;

[0043] A multi-objective optimization model construction module is configured to construct a multi-objective optimization model that includes PV consumption rate, reverse power transmission power, and system cost based on power balance constraints, capacity limit constraints, and voltage fluctuation constraints. The power balance constraints and capacity limit constraints are constructed based on the maximum annual PV load capacity.

[0044] The evaluation module is configured to dynamically optimize the distribution ratio of photovoltaic capacity between the two traction buses through particle swarm iterative search, while satisfying power balance constraints, capacity limit constraints, and voltage fluctuation constraints; and generate a photovoltaic capacity evaluation value based on the distribution ratio. The evaluation value is used to guide the capacity configuration of the photovoltaic system in engineering design.

[0045] A third aspect of the present invention provides an electronic device, comprising:

[0046] at least one processor; and

[0047] a memory communicatively connected to at least one of the processors; wherein,

[0048] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the above-mentioned capacity assessment method for the connection of a railway traction power supply system to photovoltaic power generation based on measured data.

[0049] Beneficial effects of the present invention:

[0050] Because traction loads are highly volatile and impactful, traditional methods—such as those based on load transformer capacity, annual electricity consumption, and hourly peak power—are unsuitable for assessing the capacity of railway traction power systems connected to photovoltaic systems. This present invention uses real-time traction load measurements to obtain traction load data on a smaller timescale, providing a more accurate picture of traction load magnitude. Compared to traditional methods, the traction load magnitudes obtained using this method are closer to the actual value, enabling more precise assessments of photovoltaic access capacity.

[0051] The present invention uses an adaptive dynamic window filter based on load change rate perception to smooth the load power curve. By monitoring the change rate of load data in real time and dynamically adjusting the window length and filter weight of the sliding window, the following are achieved:

[0052] Fast response: When the load fluctuates violently (with a high rate of change), the window length is shortened to prioritize capturing sudden changes.

[0053] Stable filtering: When the load is stable (low rate of change), the window length is expanded to enhance the noise suppression capability;

[0054] Adaptive weighting: Dynamically adjusts the weight of data within a window based on the rate of change to improve the ability to retain key features.

[0055] Traction load is a typical fluctuating load, with significant variations between days. This method assumes that the traction load follows a normal distribution. The traction load data for a random day of each month is used to calculate the maximum PV load capacity for that day. The 12 maximum PV load capacities for each month are then calculated using the normal distribution N(6.5, 5) to determine the maximum PV load capacity that accounts for variations in traction load throughout the year. This method accounts for monthly traction load variations and assigns weights to the calculated results for each month using a normal distribution. This method is more scientific and reasonable than methods that only consider traction load on a single day or a short period of time.

[0056] The MOPOS algorithm is used to achieve multi-objective dynamic optimization of photovoltaic capacity, which improves absorption efficiency while reducing the risk of reverse power transmission. It is closer to the actual needs of the project than traditional single-objective optimization methods.

[0057] The introduction of the MOPOS algorithm enables the evaluation results to take into account both economic and technical constraints, providing more comprehensive decision support for photovoltaic system design, and breaking through the limitations of traditional methods that only focus on a single indicator. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Other features, objects and advantages of the present application will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0059] Figure 1 It is a flow chart of a method for evaluating the capacity of a railway traction power supply system connected to photovoltaic power generation based on measured data of the present invention;

[0060] Figure 2 This is a schematic diagram of the structure of the railway traction power supply system connected to photovoltaics;

[0061] Figure 3 This is a comparison chart before and after load power removal in the invented capacity assessment method for connecting the railway traction power supply system to photovoltaic power generation based on measured data; Figure 3 (a) is a schematic diagram before load power removal. Figure 3 (b) is a schematic diagram after the load power is removed;

[0062] Figure 4 This is a schematic diagram of load power smoothing in the invented capacity assessment method for connecting a railway traction power supply system to photovoltaics based on measured data. DETAILED DESCRIPTION

[0063] The present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the relevant invention are shown in the accompanying drawings.

[0064] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0065] A first embodiment of the present invention provides a method for evaluating the capacity of a railway traction power supply system connected to photovoltaic power generation based on measured data. The method includes:

[0066] Step S10, collecting voltage data and current data of the traction load in real time through the voltage transformer and current transformer on the traction bus;

[0067] Step S20, based on the voltage data and the current data, the load power of the two traction buses is calculated once at a predetermined calculation frequency interval, and load power less than a preset threshold is eliminated and smoothed. The smoothing method includes using an adaptive dynamic window filter based on load change rate perception for smoothing.

[0068] Step S30: Based on the smoothed load power, the maximum daily photovoltaic load capacity is determined by using a sliding window mean and probability statistics method. The maximum annual photovoltaic load capacity is calculated by combining the normal distribution characteristics of the traction load data for the 12 months of the year.

[0069] Step S40: constructing a multi-objective optimization model including the photovoltaic consumption rate, reverse power transmission power, and system cost based on the power balance constraint, capacity limit constraint, and voltage fluctuation constraint; wherein the power balance constraint and capacity limit constraint are constructed based on the maximum photovoltaic load capacity throughout the year;

[0070] Step S50, through particle swarm iterative search, dynamically optimize the distribution ratio of photovoltaic capacity between the two traction buses under the conditions of satisfying power balance constraints, capacity limit constraints and voltage fluctuation constraints; generate a photovoltaic capacity evaluation value based on the distribution ratio, and the evaluation value is used to guide the capacity configuration of the photovoltaic system in engineering design.

[0071] In order to more clearly illustrate the capacity evaluation method of the railway traction power supply system connected to photovoltaic power generation based on measured data of the present invention, the following is combined with Figure 1 Each step in the embodiment of the present invention is described in detail, including step S10 to step S50, and each step is described in detail as follows:

[0072] Step S10, collecting voltage data and current data of the traction load in real time through the voltage transformer and current transformer on the traction bus;

[0073] like Figure 2 As shown, the railway traction power supply system includes two traction buses, each of which includes a voltage transformer and a current transformer. In this embodiment, in order to ensure good sampling accuracy of voltage and current, the sampling rate is generally not less than 2kHz.

[0074] Step S20, based on the voltage data and the current data, the load power of the two traction buses is calculated once at a predetermined calculation frequency interval, and load power less than a preset threshold is eliminated and smoothed. The smoothing method includes using an adaptive dynamic window filter based on load change rate perception for smoothing.

[0075] In order to reflect the rapid change of load power, the load power is generally calculated once every 1 to 5 seconds, that is, the calculation frequency is 1-5 seconds. Those skilled in the art can select the specific time according to actual needs, and this embodiment does not make any specific limitations here.

[0076] The load power in this embodiment is calculated as follows:

[0077] ;

[0078] in, U 1 and U 2 are the voltage signals measured by the voltage transformers on the first traction bus and the second traction bus, IL1 and I L2 are the current signals of the first traction bus and the second traction bus measured by the current transformer, and are the load power factor angles of the first traction bus and the second traction bus, P L1 and P L2 are the load powers of the first traction bus and the second traction bus respectively.

[0079] like Figure 3 As shown, in this embodiment, the load power less than the preset threshold is eliminated by:

[0080] The load power output that is less than 0 is set to 0, specifically:

[0081] ;

[0082] in, and are the load powers after the first traction bus and the second traction bus are removed, k Indicates the data sequence, i.e. the first k A load power.

[0083] The load power less than 0 is smoothed by:

[0084] Step S21, calculating the standard deviation of the load change rate based on the load power difference between the current time t and the previous time t-1 and the mean of N historical data;

[0085] ;

[0086] The mean , which is calculated as follows:

[0087] ;

[0088] in, Indicates at a point in time i Compared with the previous time point i -1, the absolute value of the load power change, i is the index.

[0089] The standard deviation , which is calculated as follows:

[0090] ;

[0091] Step S22: Calculate the normalized perception factor based on the standard deviation, and dynamically adjust the filter window length based on the minimum window length and the maximum window length. : Dynamic window adaptive adjustment;

[0092] Among them, the normalized perception factor , which is calculated as follows:

[0093] ;

[0094] in, is the preset maximum standard deviation threshold, .

[0095] ;

[0096] in is the minimum window length, used to quickly track mutations, is the maximum window length, used for stationary filtering.

[0097] like Figure 4 As shown, in step S23, the weight of the data in the window is assigned in combination with the adjusted filter window length and the exponential decay weight function, and the original load power data at each moment in the window is weighted according to the weight to calculate the smoothed load power.

[0098] The weight of the data in the window is assigned by combining the adjusted filter window length with the exponential decay weight function, specifically:

[0099] Window weight , and its allocation method is:

[0100] Use exponential decay weights to emphasize the importance of recent data:

[0101] ;

[0102] Attenuation coefficient and Related: ;

[0103] β is the adjustment coefficient, and the weight decays faster when the change rate is high. is the preset base coefficient.

[0104] The original load power data at each moment in the window is weighted to calculate the smoothed load power , the method is:

[0105] .

[0106] Step S30: Based on the smoothed load power, the maximum daily photovoltaic load capacity is determined by using a sliding window mean and probability statistics method. The maximum annual photovoltaic load capacity is calculated by combining the normal distribution characteristics of the traction load data for the 12 months of the year.

[0107] In this embodiment, the maximum photovoltaic load capacity throughout the year is calculated as follows:

[0108] Sort the load powers of the two traction buses after smoothing in ascending order. M The data is used as the maximum photovoltaic load capacity in a single day P max1 and P max2 ;in, P max1 is the maximum photovoltaic load capacity in a single day corresponding to the first traction bus, P max2 is the maximum daily photovoltaic load capacity corresponding to the second traction bus;

[0109] Select the daily load power of each of the 12 months of the year, calculate the corresponding daily maximum photovoltaic load capacity, arrange them in descending order, and calculate the probability value of each ranking position based on the normal distribution;

[0110] The maximum photovoltaic carrying capacity for the whole year is obtained by multiplying the sorted daily maximum photovoltaic carrying capacity with the corresponding probability value and summing them up.

[0111] Specifically, the maximum PV load capacity is related to the PV consumption model, which is generally divided into two modes: full self-consumption and self-consumption with surplus power connected to the grid. In areas with high electricity loads and large ground grid capacity, the self-consumption with surplus power connected to the grid model is generally chosen. This means that if the PV power cannot be fully consumed by the local load, it is fed back to the grid and consumed by other loads supplied by the same grid. In areas with low electricity loads and small ground grid capacity, the full self-consumption model is generally chosen, meaning that all PV power is consumed by the local load.

[0112] Due to current electricity billing methods for electrified railways and power grids, the amount of electricity fed back to the grid is generally not used for bill settlement. Therefore, distributed photovoltaic systems for electrified railways should be designed to operate entirely on a self-sustaining basis. Furthermore, unlike traditional electrical loads, traction loads are typically subject to shock and fluctuation, with load power fluctuating significantly over short periods of time. The installed capacity of the traction transformer and its maximum load-hourly power output cannot accurately reflect the magnitude of the traction load. Therefore, the installed capacity, maximum load-hourly power output, or maximum power output of the traction transformer are generally not used as indicators of the maximum PV load capacity.

[0113] Because electricity charges settled between electrified railways and power grids typically include a basic electricity charge, which is calculated using the demand method. The demand method typically multiplies the maximum demand value by the unit price of the demand electricity charge. The maximum demand value is typically calculated using a demand meter to obtain the maximum 15-minute average power for each month. Therefore, the present invention uses the maximum load power with a 95% probability of maximum value after smoothing using a 15-minute sliding window mean filter as the maximum photovoltaic load capacity. The specific process is as follows:

[0114] 1) Define the maximum daily photovoltaic load capacity of the two traction buses as P max1 and P max2 .

[0115] 2) The load power of the two traction buses after the above smoothing process and Sort them from small to large to get two load power arrays, which are recorded as ArrayP1 and ArrayP2 respectively. The number of data points in the two arrays is N - N 0.

[0116] 3) Take M=0.95×( N - N 0), and M is an integer, then P max1 =ArrayP1(M),P max2 =ArrayP2(M). In the formula, ArrayP1(M) and ArrayP2(M) represent the value of the Mth element in the two arrays respectively. If the maximum photovoltaic carrying capacity is evaluated based on the single-day load data, the maximum photovoltaic carrying capacity of the two traction buses is ArrayP1(M) and ArrayP2(M), respectively. The maximum capacity of the railway traction power supply system connected to the photovoltaic power supply is S PV Not greater than the sum of the maximum photovoltaic load capacity of the two traction buses, that is S PV ≤ArrayP1(M)+ArrayP2(M).

[0117] In addition, due to factors such as transportation tasks and special natural conditions, the daily traction load is not completely consistent. In particular, the average daily traction load varies with the monthly changes. In order to consider the impact of traction load changes on different days on the analysis of the maximum photovoltaic carrying capacity, one day is randomly selected in each month to obtain traction load data for a total of 12 days in 12 months. The maximum capacity of the connected photovoltaic is calculated according to the above process, which is recorded as S PV ( k ), here k =1,2,3,...11,12, indicating January, February, March, ..., November, December.

[0118] The monthly traction load change is affected by many factors and is difficult to calculate accurately. For the convenience of analysis, it is assumed that the traction load change on different days satisfies the normal distribution law. The normal distribution function is generally N( μ , σ ２ ), that is, the proportion of traction loads with smaller and larger values ​​is relatively small, and the proportion of traction loads with values ​​in the middle range is relatively large. Therefore, the maximum capacity S connected to photovoltaic PV ( k ) also satisfies the normal distribution law.

[0119] Sort the maximum capacities of the 12 connected photovoltaics from small to large to obtain a new group of maximum capacities of connected photovoltaics, recorded as S PV_sort ( j ), j is the sequence number after sorting, i.e. 1, 2, 3, ..., 11, 12. Therefore, the maximum capacity of the connected photovoltaic after sorting is S PV_sort ( j ) also satisfies the normal distribution, and its normal distribution function N ( μ , σ ２ ), that is j The probability value of P ( j ) is as follows:

[0120] ;

[0121] in, μ for j The average value of μ =6.5. σ Its standard deviation, here we take σ ２ =5.

[0122] Finally, the maximum capacity of the photovoltaic connected after the above sorting is S PV_sort ( j ) and the corresponding normal distribution probability value P ( j ) are multiplied by the following formula to obtain the maximum capacity of photovoltaic access considering the traction load size for 12 months of the year: S PV12 , which is the maximum photovoltaic carrying capacity throughout the year.

[0123] .

[0124] Step S40: constructing a multi-objective optimization model including photovoltaic absorption rate, reverse power transmission power, and system cost based on power balance constraints, capacity limitation constraints, and voltage fluctuation constraints; wherein the power balance constraint and capacity limitation constraint are constructed based on the maximum photovoltaic carrying capacity;

[0125] To further enhance the scientific nature and dynamic adaptability of PV capacity assessment, this paper introduces a multi-objective particle swarm optimization (MOPOS) algorithm to optimize the allocation of PV capacity between dual traction buses while meeting the traction load fluctuation characteristics and grid constraints. The specific steps are as follows:

[0126] Maximize the real-time absorption rate of traction load by photovoltaic system , and its construction method is:

[0127] ;

[0128] in, is the load power corresponding to the first traction bus, is the load power corresponding to the second traction bus, is the photovoltaic power generation power corresponding to the first traction bus, is the photovoltaic power generation power corresponding to the second traction bus.

[0129] Minimize the reverse power sent from the photovoltaic system to the grid , and its construction method is:

[0130] ;

[0131] in, is the load power corresponding to the first traction bus at time t, is the load power corresponding to the second traction bus at time t, is the photovoltaic power generation power corresponding to the first traction bus at time t, is the photovoltaic power generation corresponding to the second traction bus at time t, and T is the time period.

[0132] The system cost is to minimize the investment and operation and maintenance costs of the photovoltaic system , and its construction method is:

[0133] ;

[0134] in, is the preset initial investment cost coefficient, is the operation and maintenance cost coefficient.

[0135] Power balance constraints, capacity limitation constraints, and voltage fluctuation constraints, specifically:

[0136] Power balance constraints: ;

[0137] Capacity limit constraints: ; ; ;

[0138] in, P max1 is the maximum photovoltaic load capacity in a single day corresponding to the first traction bus, P max2 is the maximum photovoltaic load capacity in a single day corresponding to the second traction bus, It is the maximum photovoltaic carrying capacity of the whole year;

[0139] Voltage fluctuation constraints: ;in, is the voltage fluctuation value of the traction bus, It is the maximum voltage fluctuation threshold allowed by the system.

[0140] In step S50, through particle swarm iterative search, the distribution ratio of photovoltaic capacity between the dual traction buses is dynamically optimized under the conditions of satisfying power balance constraints, capacity limit constraints, and voltage fluctuation constraints; a photovoltaic capacity evaluation value is generated based on the distribution ratio, and the evaluation value is used to guide the capacity configuration of the photovoltaic system in engineering design.

[0141] MOPOS algorithm implementation process

[0142] Step S51, initialize the particle swarm: randomly generate N particles, each particle represents a set of photovoltaic capacity allocation schemes P PV1 , P PV2 .

[0143] Step S52, calculating the fitness value: calculating the multi-objective fitness of each particle according to the above objective function and constraints.

[0144] Step S53, update individual extreme values ​​and global extreme values:

[0145] Individual extreme value ( pBest ): The particle’s own historical optimal solution.

[0146] Global extreme value ( gBest ): The set of non-dominated solutions of all particles in the population.

[0147] Step S54, update particle velocity and location :

[0148] ;

[0149] in, w is the inertia weight, c 1 , c 2 is the acceleration factor, r 1 , r 2 is a random number, a is the number of iterations, g is the index of the particle, h The index of the dimension.

[0150] Step S55, non-dominated sorting and crowding calculation: perform non-dominated sorting on the population and calculate the crowding of each solution to maintain diversity.

[0151] Step S56, judging the termination condition: if the maximum number of iterations is reached or the solution set converges, output the optimal solution set; otherwise, return to step S53.

[0152] Step S57: Optimal capacity solution decision

[0153] Through the fuzzy membership function, the optimal capacity allocation scheme that takes into account the consumption efficiency, return flow and cost is selected from the Pareto optimal solution set as the final evaluation result.

[0154] Although the various steps in the above embodiment are described in the above-mentioned order, those skilled in the art will understand that in order to achieve the effect of this embodiment, different steps do not have to be executed in such an order. They can be executed simultaneously (in parallel) or in a reverse order. These simple changes are within the scope of protection of the present invention.

[0155] A second embodiment of the present invention provides a capacity assessment system for a railway traction power supply system connected to photovoltaic power generation based on measured data, and a capacity assessment method for a railway traction power supply system connected to photovoltaic power generation based on measured data. The system includes:

[0156] A current and voltage data acquisition module is configured to collect voltage and current data of the traction load in real time through the voltage transformer and current transformer on the traction bus;

[0157] a smoothing processing module configured to calculate the load power of the two traction buses once per interval at a set calculation frequency based on the voltage data and the current data, and to perform smoothing processing on the load power excluding the load power less than a preset threshold; the smoothing processing method includes using an adaptive dynamic window filter based on load change rate perception for smoothing processing;

[0158] The annual maximum photovoltaic load capacity calculation module is configured to determine the daily maximum photovoltaic load capacity based on the smoothed load power through a sliding window mean and probability statistics method, and calculate the annual maximum photovoltaic load capacity by combining the normal distribution characteristics of the traction load data for 12 months of the year;

[0159] A multi-objective optimization model construction module is configured to construct a multi-objective optimization model that includes PV consumption rate, reverse power transmission power, and system cost based on power balance constraints, capacity limit constraints, and voltage fluctuation constraints. The power balance constraints and capacity limit constraints are constructed based on the maximum annual PV load capacity.

[0160] The evaluation module is configured to dynamically optimize the distribution ratio of photovoltaic capacity between the two traction buses through a particle swarm iterative search, while satisfying power balance constraints, capacity limit constraints, and voltage fluctuation constraints. Based on the distribution ratio, a photovoltaic capacity evaluation value is generated, which is used to guide the capacity configuration of the photovoltaic system during engineering design. Those skilled in the art will clearly understand that for ease and brevity of description, the specific operating process and related description of the system described above can be referred to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0161] It should be noted that the above embodiment provides a capacity assessment system for connecting a railway traction power supply system to photovoltaic power generation based on measured data. The system is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiments can be combined into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for the purpose of distinguishing the modules or steps and are not to be regarded as improper limitations on the present invention.

[0162] An electronic device according to a third embodiment of the present invention includes:

[0163] at least one processor; and

[0164] a memory communicatively connected to at least one of the processors; wherein,

[0165] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the above-mentioned capacity assessment method for the connection of a railway traction power supply system to photovoltaic power generation based on measured data.

[0166] A computer-readable storage medium according to a fourth embodiment of the present invention stores computer instructions, which are used to be executed by the computer to implement the above-mentioned method for evaluating the capacity of a railway traction power supply system connected to photovoltaic power generation based on measured data.

[0167] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes and related instructions of the storage device and processing device described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0168] Those skilled in the art should be able to appreciate that the modules and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two, and the programs corresponding to the software modules and method steps can be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art. In order to clearly illustrate the interchangeability of electronic hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0169] The terms "first", "second", etc. are used to distinguish similar objects, rather than to describe or indicate a particular order or sequence.

[0170] The term "comprise" or any other similar term is intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0171] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. A method for evaluating the capacity of a railway traction power supply system connected to photovoltaic power generation based on measured data, wherein the railway traction power supply system includes two traction buses, each of which includes a voltage transformer and a current transformer, and wherein: The method includes: The voltage and current data of the traction load are collected in real time through the voltage transformer and current transformer on the traction bus; Based on the voltage data and the current data, the load power of the two traction buses is calculated once at a predetermined calculation frequency, and load power less than a preset threshold is eliminated and smoothed; the smoothing method includes using an adaptive dynamic window filter based on load change rate perception for smoothing; Based on the smoothed load power, the maximum daily PV load capacity is determined using the sliding window mean and probability statistics methods. Combined with the normal distribution characteristics of the traction load data for the entire year, the maximum annual PV load capacity is calculated. A multi-objective optimization model, encompassing PV consumption rate, reverse power transmission, and system cost, is constructed using power balance constraints, capacity limitation constraints, and voltage fluctuation constraints. The power balance and capacity limitation constraints are based on the maximum annual PV load capacity. Through particle swarm iterative search, the allocation ratio of photovoltaic capacity between the two traction buses is dynamically optimized under the conditions of satisfying power balance constraints, capacity limit constraints, and voltage fluctuation constraints. Based on the allocation ratio, a photovoltaic capacity assessment value is generated, which is used to guide the capacity configuration of the photovoltaic system in engineering design.

2. The method for evaluating the capacity of a railway traction power supply system connected to photovoltaic power generation based on measured data according to claim 1, characterized in that: Eliminate load power that is less than a preset threshold by: The load power output that is less than 0 is set to 0.

3. The method for evaluating the capacity of a railway traction power supply system connected to photovoltaic power generation based on measured data according to claim 2, characterized in that: The load power less than 0 is smoothed by: Calculate the standard deviation of the load change rate based on the load power difference between the current time t and the previous time t-1 and the mean of N historical data; Calculating a normalized perception factor based on the standard deviation, and dynamically adjusting the filter window length in combination with the minimum window length and the maximum window length; The weight of the data in the window is allocated in combination with the adjusted filter window length and the exponential decay weight function. The original load power data at each moment in the window is weighted according to the weight to calculate the smoothed load power.

4. The method for evaluating the capacity of a railway traction power supply system connected to photovoltaic power generation based on measured data according to claim 1, characterized in that: The maximum photovoltaic load capacity for the whole year is calculated as follows: Sort the load powers of the two traction buses after smoothing in ascending order. M The data is used as the maximum photovoltaic load capacity in a single day P max1 and P max2 ; in, P max1 is the maximum photovoltaic load capacity in a single day corresponding to the first traction bus, P max2 is the maximum daily photovoltaic load capacity corresponding to the second traction bus; Select the daily load power of each of the 12 months of the year, calculate the corresponding daily maximum photovoltaic load capacity, arrange them in descending order, and calculate the probability value of each ranking position based on the normal distribution; The maximum photovoltaic carrying capacity for the whole year is obtained by multiplying the sorted daily maximum photovoltaic carrying capacity with the corresponding probability value and summing them up.

5. The method for evaluating the capacity of a railway traction power supply system connected to photovoltaic power generation based on measured data according to claim 1, characterized in that: Maximize the real-time absorption rate of traction load by photovoltaic system , and its construction method is: ； in, is the load power corresponding to the first traction bus, is the load power corresponding to the second traction bus, is the photovoltaic power generation power corresponding to the first traction bus, is the photovoltaic power generation power corresponding to the second traction bus.

6. The method for evaluating the capacity of a railway traction power supply system connected to photovoltaic power generation based on measured data according to claim 5, characterized in that: Minimize the reverse power sent from the photovoltaic system to the grid , and its construction method is: ； in, is the load power corresponding to the first traction bus at time t, is the load power corresponding to the second traction bus at time t, is the photovoltaic power generation power corresponding to the first traction bus at time t, is the photovoltaic power generation corresponding to the second traction bus at time t, and T is the time period.

7. The method for evaluating the capacity of a railway traction power supply system connected to photovoltaic power generation based on measured data according to claim 6, characterized in that: The system cost is to minimize the investment and operation and maintenance costs of the photovoltaic system , and its construction method is: ； in, is the preset initial investment cost coefficient, is the operation and maintenance cost coefficient.

8. The method for evaluating the capacity of a railway traction power supply system connected to photovoltaic power generation based on measured data according to claim 7, characterized in that: Power balance constraints, capacity limitation constraints, and voltage fluctuation constraints, specifically: Power balance constraints: ; Capacity limit constraints: ; ; ; in, P max1 is the maximum photovoltaic load capacity in a single day corresponding to the first traction bus, P max2 is the maximum photovoltaic load capacity in a single day corresponding to the second traction bus, It is the maximum photovoltaic carrying capacity of the whole year; Voltage fluctuation constraints: ;in, is the voltage fluctuation value of the traction bus, It is the maximum voltage fluctuation threshold allowed by the system.

9. A capacity assessment system for a railway traction power supply system connected to photovoltaic power generation based on measured data, based on a capacity assessment method for a railway traction power supply system connected to photovoltaic power generation based on measured data according to any one of claims 1 to 8, characterized in that: The system includes: A current and voltage data acquisition module is configured to collect voltage and current data of the traction load in real time through the voltage transformer and current transformer on the traction bus; a smoothing processing module configured to calculate the load power of the two traction buses once per interval at a set calculation frequency based on the voltage data and the current data, and to perform smoothing processing on the load power excluding the load power less than a preset threshold; the smoothing processing method includes using an adaptive dynamic window filter based on load change rate perception for smoothing processing; The annual maximum photovoltaic load capacity calculation module is configured to determine the daily maximum photovoltaic load capacity based on the smoothed load power through a sliding window mean and probability statistics method, and calculate the annual maximum photovoltaic load capacity by combining the normal distribution characteristics of the traction load data for 12 months of the year; A multi-objective optimization model construction module is configured to construct a multi-objective optimization model that includes PV consumption rate, reverse power transmission power, and system cost based on power balance constraints, capacity limit constraints, and voltage fluctuation constraints. The power balance constraints and capacity limit constraints are constructed based on the maximum annual PV load capacity. The evaluation module is configured to dynamically optimize the distribution ratio of photovoltaic capacity between the two traction buses through particle swarm iterative search, while satisfying power balance constraints, capacity limit constraints, and voltage fluctuation constraints; and generate a photovoltaic capacity evaluation value based on the distribution ratio. The evaluation value is used to guide the capacity configuration of the photovoltaic system in engineering design.

10. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to at least one of the processors; wherein, The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement a capacity assessment method for connecting a railway traction power supply system to photovoltaic power generation based on measured data as described in any one of claims 1 to 8.

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