A method for optimizing the configuration of photovoltaic and energy storage capacity in a traction power supply system based on source-load coordination

Abstract

This invention provides a method for optimizing the photovoltaic and energy storage capacity configuration of a traction power supply system based on source-load coordination. The method includes: constructing a photovoltaic and energy storage capacity optimization configuration model for the traction power supply system based on source-load coordination; setting a cost function for the model; setting constraints on the model; and solving the model using a heuristic algorithm based on the cost function and constraints to obtain the optimal train departure time and the optimal installed capacity of photovoltaic and energy storage. This invention effectively improves the photovoltaic and energy storage capacity of the traction power supply system by solving the source-load coordination problem caused by fluctuations in traction load and photovoltaic power generation, further enhancing economic efficiency. This method can provide a basis for the implementation and promotion of railway photovoltaic-energy storage traction power supply systems, improving the reliability of railway operation services.

Description

Technical Field

[0001] This invention relates to the fields of railway and new energy technology, and in particular to a method for optimizing the configuration of photovoltaic and energy storage capacity in a traction power supply system based on source-load coordination. Background Technology

[0002] The energy consumption and cost of railway train operation remain high, and its heavy reliance on the external power grid not only results in high carbon emissions but also poses a serious threat to the safety of socio-economic operations due to the lack of system resilience. Railway infrastructure and the space along its lines offer ample opportunities for the development and utilization of clean energy sources such as wind and solar power. Therefore, fully utilizing its own clean energy potential to build a highly resilient traction power supply system and promote the integration of energy and railways is imperative and has enormous development potential.

[0003] The integration of clean energy into traction power supply systems has become a research hotspot both domestically and internationally. Due to the greater infrastructure space required for wind power generation, existing research primarily focuses on the planning, design, structural optimization, and operation control of photovoltaic (PV) and energy storage systems within traction power supply systems. The planning and design of PV and energy storage in traction power supply systems mainly aims to improve the overall efficiency of traction power supply through capacity optimization of PV and energy storage. The planning and design process includes three parts: clean energy potential assessment, traction load assessment, and optimized configuration of PV and energy storage capacity.

[0004] The assessment of clean energy potential along railway lines involves calculating the area of ​​suitable energy-generating spaces and combining this with photovoltaic (PV) power generation calculation methods to determine the technological, economic, and environmental potential of PV power generation along these lines. The core of this assessment method lies in identifying suitable PV-generating spaces along railway lines. Due to the vast area of ​​railway space and the difficulty of measurement, the identification and calculation of suitable spaces can be achieved using remote sensing map data, through manual processing or deep learning technology. Currently, the main suitable energy-generating spaces along railway lines include the space on both sides of the railway, above the overhead contact lines, and on train roofs. Because of the limited space in PV sheds and on train roofs, PV systems are still mainly installed on both sides of the railway and on station roofs. Although some potential assessment methods consider the energy load demand of trains, they do not consider the energy flow mechanism of connecting to new traction power supply systems. They can only assess the benefits of PV and energy storage integration from a macro perspective, making the assessment results insufficient to support planning decisions.

[0005] Currently, existing traction load assessment methods consider factors such as train traction and auxiliary energy consumption, as well as power factor, and calculate the energy load of a train group based on train timetables and track conditions. For calculating the traction load of a single train, a section train motion model with energy-saving or time-saving objectives can be established to calculate the train traction load sequence. The traction load of the entire train group can be calculated by the spatiotemporal accumulation of the load of each individual train. Alternatively, traction load can be obtained through on-site electrical measurements. However, since the measurement data is the cumulative traction load of multiple trains, it is difficult to decouple the load from the train, thus failing to provide a data basis for dynamic load adjustment. Furthermore, this method is mainly applicable to existing lines and cannot be used for calculating the traction load of future newly built lines.

[0006] Currently, existing photovoltaic (PV) and energy storage (ESS) capacity optimization design methods are based on the source-load interaction energy flow mechanism. By solving the established PV-ESS capacity optimization model, the optimal design capacity for both PV and energy storage is derived. While there is considerable theoretical research and application of PV-ESS capacity optimization in urban integrated energy planning, railway PV-ESS microgrids differ from other PV-ESS energy systems primarily in that their traction load exhibits significant fluctuations and intermittent characteristics. This makes it difficult to effectively utilize PV power generation, which also exhibits fluctuations and periodicity, resulting in persistently high energy storage capacity. This hinders economic efficiency and subsequent system maintenance and management.

[0007] Therefore, existing planning methods based on a single energy source are no longer sufficient to further improve economic efficiency. It is necessary to further analyze the interaction mechanism between railway transportation sources and loads from the perspective of transportation energy integration, establish an improved capacity optimization configuration model, and achieve the optimal design of the system. Summary of the Invention

[0008] The embodiments of the present invention provide a method for optimizing the configuration of optical and energy storage capacity in a traction power supply system based on source-load coordination, so as to effectively improve the optical and energy storage capacity of the traction power supply system.

[0009] To achieve the above objectives, the present invention adopts the following technical solution.

[0010] A method for optimizing the configuration of photovoltaic and energy storage capacity in a traction power supply system based on source-load coordination includes:

[0011] Construct a photovoltaic-storage capacity optimization configuration model for a traction power supply system based on source-load coordination, and set the cost function of the photovoltaic-storage capacity optimization configuration model for the traction power supply system;

[0012] Set the constraints for the photovoltaic-storage capacity optimization configuration model of the traction power supply system;

[0013] Based on the cost function and constraints, a heuristic algorithm is used to solve the photovoltaic and energy storage capacity optimization configuration model of the traction power supply system, so as to obtain the optimal train departure time and the optimal installed capacity of photovoltaic and energy storage.

[0014] Preferably, the construction of the traction power supply system photovoltaic-storage capacity optimization configuration model based on source-load coordination, and the setting of the cost function of the traction power supply system photovoltaic-storage capacity optimization configuration model, includes:

[0015] The total cost I of setting up a photovoltaic-storage capacity optimization configuration model for a traction power supply system based on source-load coordination. t For the total life cycle cost of photovoltaics I v Energy storage lifecycle cost s and electricity purchase cost I pp The sum is:

[0016] I t =I v +I s +I p (1)

[0017] The total lifecycle cost of photovoltaics and energy storage v and I s The calculation formula is as follows:

[0018] I v =C p ×I1+C p ×I2×Y (2)

[0019] I s =(P s ×I3+Q s ×I4)+(Q s ×I5)×Y (3)

[0020] Among them, C p and Q s For the installed capacity of photovoltaics and energy storage, P s I1 and I2 are the construction and operation and maintenance costs per unit installed capacity of the photovoltaic system, I3 and I4 are the power cost and capacity cost of the energy storage system, respectively, I5 is the operation and maintenance cost per unit installed capacity of the energy storage system, and Y is the operating life of the photovoltaic-storage power generation system.

[0021] The formula for calculating the cost of electricity is:

[0022]

[0023] Among them I grid For grid electricity price, Δt represents the power transmitted to the power grid, and Δt represents the set time interval.

[0024] Preferably, the constraints for setting the optimal configuration model of the traction power supply system's photovoltaic-storage capacity include:

[0025] The constraints for setting the photovoltaic-storage capacity optimization configuration model of the traction power supply system include the operation constraints of the photovoltaic-storage traction power supply system and the interval train running time constraints.

[0026] (1) The train departure time constraint is:

[0027]

[0028] in This refers to the adjusted departure time of train k. Let ΔT be the original departure time of train k, and ΔT be the maximum deviation time.

[0029] The departure time interval between adjacent trains must not be less than the minimum departure time interval h. dd ,Right now:

[0030]

[0031] (2) Microgrid operation constraints

[0032] Railway traction substations, photovoltaic systems, and energy storage are interconnected to form a microgrid system. The operation of this microgrid system satisfies power balance constraints, namely:

[0033]

[0034] In the formula: These represent the grid power transmission capacity, photovoltaic power generation capacity, energy storage discharge capacity, and regenerative braking capacity at time t, respectively. as well as These are respectively the section traction load, energy storage charging power, and abandoned power.

[0035] The photovoltaic power generation simulation software PVsyst was used to obtain the daily power generation data per unit of photovoltaic installed capacity throughout the year. The average daily power generation per unit of photovoltaic installed capacity was calculated. Considering the degradation of photovoltaic power generation capacity, the average daily power generation per unit of photovoltaic installed capacity over the entire life cycle was calculated. The calculation formula is:

[0036]

[0037] Photovoltaic installed capacity C p Cannot exceed the maximum installed capacity C pmax C p ≤C pmax The maximum installed capacity is obtained by considering the energy space area A and the land area per unit photovoltaic installed capacity a, i.e., C. pmax=A / a, the charging and discharging operation constraints of battery energy storage are:

[0038]

[0039] In the formula: Let be the amount of electricity in the energy storage system at time t; and For the charging and discharging efficiency of the energy storage system; and Let t be the charging and discharging power of the energy storage system at time t.

[0040] Preferably, the step of using a heuristic algorithm based on the cost function and constraints to solve the photovoltaic and energy storage capacity optimization configuration model of the traction power supply system to obtain the optimal train departure time and the optimal installed capacity of photovoltaic and energy storage includes:

[0041] Step (1): Determine the maximum installed capacity of photovoltaic and energy storage, the technical parameters of charging and discharging efficiency, and the economic parameters of photovoltaic energy storage construction and operation and maintenance costs;

[0042] Step (2): Based on the original timetable, combined with the line data and train traction and braking curves, the traction load of each train in the section is calculated using the traction calculation method. The traction load time series of train k is p. k ;

[0043] Step (3): Set the iteration counter i = 0, set the maximum iteration number to M, and the number of trains in the same direction to K. For each train k going up or down, keep the departure time of other trains unchanged, and according to the train operation constraints, obtain the upper and lower limits for adjusting the departure time of train k. Specifically as follows:

[0044]

[0045] Step (4) according to Update the departure time of train K The traction load and regenerative braking power will accumulate the traction load. and regenerative braking power And substitute the relevant parameters into the following linear programming model to solve;

[0046] min I t

[0047]

[0048] If for each train, two consecutive updates cannot reduce the total lifecycle cost I... tIf the number of iterations is less or the maximum number of iterations is reached, the iteration stops, the optimal train departure time is output, and the obtained train departure time is substituted into equation (13) to obtain the installed capacity of photovoltaic and energy storage; otherwise, the processing of step (4) continues.

[0049] As can be seen from the technical solutions provided by the embodiments of the present invention above, the present invention effectively improves the photovoltaic-storage capacity of the traction power supply system by solving the source-load coordination problem caused by fluctuations in traction load and photovoltaic power generation, thereby further improving economic efficiency. The method of the present invention can provide a basis for the implementation and promotion of railway photovoltaic-storage traction power supply systems, improving the reliability of railway operation services.

[0050] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of the invention. Attached Figure Description

[0051] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 A schematic diagram illustrating the relationship between train operation and photovoltaic power generation, provided as an embodiment of the present invention;

[0053] Figure 2 A flowchart illustrating a method for optimizing the configuration of photovoltaic and energy storage capacity in a traction power supply system based on source-load coordination, provided in an embodiment of the present invention.

[0054] Figure 3 A train timetable for the Beijing West to Baoding East section provided as an embodiment of the present invention;

[0055] Figure 4 A time series diagram of traction load and photovoltaic power generation capacity provided for an embodiment of the present invention;

[0056] Figure 5 This is a schematic diagram of an iterative process of a heuristic algorithm provided in an embodiment of the present invention;

[0057] Figure 6 An optimized train timetable provided in an embodiment of the present invention;

[0058] Figure 7 A schematic diagram of the time-varying curves of multiple energy flows, including all-day grid power supply, photovoltaic power supply, and energy storage power supply, provided for an embodiment of the present invention;

[0059] Figure 8This is a schematic diagram illustrating the sensitivity analysis results of maximum deviation time and natural endowment provided in an embodiment of the present invention. Detailed Implementation

[0060] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0061] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or couplings. The term “and / or” as used herein includes any and all combinations of one or more of the associated listed items.

[0062] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as herein.

[0063] To facilitate understanding of the embodiments of the present invention, the following will provide further explanation and description with reference to the accompanying drawings and several specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.

[0064] When photovoltaic (PV) power is integrated into trains, both PV power generation and train traction load exhibit periodic fluctuations due to the simultaneous influence of solar trajectory and travel demand. By adjusting train departure times, the spatiotemporal patterns of traction load and PV power generation can be coupled, improving the economic efficiency of train operation. Figure 1 This invention provides a schematic diagram of the relationship between train operation and photovoltaic power generation, as shown below. Figure 1As shown, before the train departure times were adjusted, only 7 trains could utilize photovoltaic power generation; after the adjustment, the number of trains utilizing photovoltaic power generation increased to 9, significantly improving the utilization rate of photovoltaic power generation. However, adjusting train departure times must meet passenger travel needs and cannot deviate excessively from the original train timetable. Therefore, this invention will establish a capacity optimization configuration model based on source-load coordination to achieve fixed-capacity optimization of photovoltaic and energy storage.

[0065] This invention, based on the patterns of photovoltaic (PV) power generation along railway lines and considering the impact of train operation plans on the temporal distribution of traction load, aims to minimize the total lifecycle cost. By setting dual constraints on train operation and energy system operation, it establishes an optimization model for the PV-storage capacity configuration of the traction power supply system based on source-load coordination, and designs a heuristic algorithm to solve the model. To verify the effectiveness of the model algorithm, calculations and comparisons were conducted on the Beijing West-Baoding East section of the Beijing-Guangzhou Expressway. Analysis of five different optimization scenarios revealed that, compared to traditional PV and PV-storage integration scenarios, the source-load coordination scenario, due to the more consistent temporal distribution of traction load and PV power generation, reduces energy storage capacity, investment and construction costs, and electricity purchase costs, resulting in a 5.7% (0.5 billion yuan) and 5.4% (0.32 billion yuan) reduction in total lifecycle cost, respectively. Multi-energy flow analysis results show that PV-storage power generation can meet the traction load demand for most periods (8:00-24:00), avoiding train operation interruptions due to grid power supply failures and improving the reliability of railway operation services. Model sensitivity analysis results show that, with the increase in the flexibility of traction load adjustment and the abundance of solar energy resources, the total lifecycle cost gradually decreases. Even in areas with limited solar resources, the optimized configuration of photovoltaic and energy storage capacity in the traction power supply system still has significant economic benefits, proving that the scheme has a certain degree of regional applicability.

[0066] The flowchart of a method for optimizing the configuration of photovoltaic and energy storage capacity in a traction power supply system based on source-load coordination provided in this invention is as follows: Figure 2 As shown, the processing steps include the following:

[0067] Step S1: Construct a photovoltaic-storage capacity optimization configuration model for the traction power supply system based on source-load coordination, and set the cost function of the photovoltaic-storage capacity optimization configuration model for the traction power supply system.

[0068] This invention assumes an operating mode of "self-generation and self-consumption, with surplus electricity stored as energy." The objective of the traction power supply system photovoltaic and energy storage capacity optimization configuration model based on source-load coordination is to maximize economic benefits. Firstly, the construction and operation and maintenance costs of the photovoltaic and energy storage system increase due to the addition of photovoltaic and energy storage power generation equipment. Secondly, under photovoltaic grid connection conditions, train energy consumption includes both photovoltaic power generation and the main power grid, leading to a reduction in electricity purchase costs. In summary, the total cost I... t For the total life cycle cost of photovoltaics I v Energy storage lifecycle cost sand electricity purchase cost I pp The sum is:

[0069] I t =I v +I s +I p (1)

[0070] The total lifecycle cost of photovoltaics and energy storage v and I s The calculation formula is as follows:

[0071] I v =C p ×I1+C p ×I2×Y (2)

[0072] I s =(P s ×I3+Q s ×I4)+(Q s ×I5)×Y (3)

[0073] Among them, C p and Q s For the installed capacity of photovoltaics and energy storage, P s I represents the power of the energy storage system, I1 and I2 represent the construction and operation and maintenance costs per unit installed capacity of the photovoltaic system, I3 and I4 represent the power cost and capacity cost of the energy storage system, respectively, I5 represents the operation and maintenance cost per unit installed capacity of the energy storage system, and Y represents the operating life of the photovoltaic-energy storage power generation system.

[0074] The formula for calculating the cost of electricity is:

[0075]

[0076] Among them I grid For grid electricity price, Let Δt be the power transmitted by the power grid, and T be the time interval. For ease of solution, the time interval Δt in the model of this invention is set to 1 minute, T = 1440.

[0077] Step S2: Set the constraints for the traction power supply system's photovoltaic and energy storage capacity optimization configuration model.

[0078] Unlike traditional capacity configuration models, the constraints of the photovoltaic-storage capacity optimization configuration model for the traction power supply system in this invention include the operational constraints of the photovoltaic-storage traction power supply system and the running time constraints of the trains in the interval.

[0079] (1) Train running time constraints

[0080] Adjusting train departure times can alter the temporal distribution of traction loads. However, considering travel demand, train departure times cannot deviate excessively from the original timetable. Therefore, the train departure time constraints are as follows:

[0081]

[0082] in This refers to the adjusted departure time of train k. Let ΔT be the original departure time of train k, and ΔT be the maximum deviation time.

[0083] Assuming the train departure order remains unchanged, to avoid train operation conflicts, the departure time interval between adjacent trains cannot be less than the minimum departure time interval h. dd ,Right now:

[0084]

[0085] (2) Microgrid operation constraints

[0086] Railway traction substations, photovoltaic systems, and energy storage are interconnected to form a microgrid system. The operation of this system must meet power balance constraints, namely:

[0087]

[0088] In the formula: These represent the grid power transmission capacity, photovoltaic power generation capacity, energy storage discharge capacity, and regenerative braking capacity at time t, respectively. as well as These are the interval traction load, energy storage charging power, and curtailed power, respectively. To improve calculation efficiency, this invention averages the total lifecycle cost to a daily cost; therefore, it is necessary to calculate the average photovoltaic power generation over the entire lifecycle. First, the photovoltaic power generation simulation software PVsyst is used to obtain the power generation data per unit of photovoltaic installed capacity per day throughout the year; then, the average daily power generation per unit of photovoltaic installed capacity is calculated; finally, considering the degradation of photovoltaic power generation capacity, the average daily power generation per unit of photovoltaic installed capacity over the entire lifecycle is calculated. The calculation formula is:

[0089]

[0090] Photovoltaic installed capacity C p Cannot exceed the maximum installed capacity C pmax C p ≤C pmax The maximum installed capacity can be obtained by considering the available space area A and the land area per unit photovoltaic installed capacity a, i.e., C. pmax =A / a. The energy storage system type of this invention is battery energy storage, and the charging and discharging operation constraints of battery energy storage are:

[0091]

[0092] In the formula: Let be the amount of electricity in the energy storage system at time t; and For the charging and discharging efficiency of the energy storage system; and Let t be the charge / discharge power of the energy storage system at time t. To extend the lifespan of energy storage, this invention specifies the maximum value of the energy storage's state of charge (SOC). max and minimum SOC min .

[0093] Step 3: Based on the above cost function and constraints, a heuristic algorithm is used to solve the photovoltaic and energy storage capacity optimization configuration model of the traction power supply system to obtain the optimal train departure time and the optimal installed capacity of photovoltaic and energy storage.

[0094] Although the objective and constraints of the model in this invention are linear, the mapping relationship between train departure time and traction load distribution is difficult to represent using a linear model. Therefore, the model in this invention still belongs to the category of nonlinear programming. Based on the characteristics of the model, this invention proposes a heuristic algorithm for solving the problem. The solution steps are as follows:

[0095] (1) Determine the technical parameters such as the maximum installed capacity and charging / discharging efficiency of photovoltaic and energy storage, as well as the economic parameters such as the construction and operation and maintenance costs of photovoltaic and energy storage;

[0096] (2) Based on the original timetable, combined with the line data and train traction and braking curves, the traction load of each train in the section is calculated using the traction calculation method. The traction load time series of train k is p. k .

[0097] (3) Let the iteration counter i = 0, set the maximum iteration number to M, and the number of trains in the same direction to K. For each train k going up or down, keep the departure time of other trains unchanged, and according to the train operation constraints, derive the upper and lower limits for adjusting the departure time of train k. Specifically as follows:

[0098]

[0099] (4) According to Update the departure time of train K The traction load and regenerative braking power will accumulate the traction load. and regenerative braking power And substitute the relevant parameters into the following linear programming model to solve.

[0100] min I t

[0101]

[0102] (5) If for each train, if two consecutive updates cannot reduce the total lifecycle cost I t If the number of iterations is less than the maximum number of iterations, stop iterating; otherwise, continue to Step (4).

[0103] (6) Output the optimal train departure time, and substitute the obtained train departure time into equation (13) to obtain the installed capacity of photovoltaic and energy storage.

[0104] Example 1

[0105] To verify the effectiveness of the model, a case study was conducted on the section between Beijing West and Baoding East, which is approximately 178 km long. First, train timetables were obtained through the 12306 railway system. Figure 3 A train timetable for the Beijing West to Baoding East section provided as an embodiment of the present invention, such as... Figure 3 As shown, there are 41 trains going up and 45 trains going down throughout the day.

[0106] The initial settings for the model parameters are shown in Table 1.

[0107] Table 1 Model Parameter Settings

[0108]

[0109]

[0110] Considering the safety requirements of railway operation, this invention selects open space within 3 meters on both sides of the railway line for the construction of photovoltaic and energy storage systems. To prevent photovoltaic panels from being blown onto the track by strong winds and affecting train operation safety, the photovoltaic modules are laid flat on both sides of the track with an installation tilt angle of 0°. After removing tunnels, bridges, and large residential areas, approximately 80 kilometers of the entire line can be used for photovoltaic installation, with a usable space area of ​​about 240,000 square meters. Based on train number and model information, the traction load time series of this section is obtained through traction calculation and compared with the photovoltaic power generation capacity of this section. Figure 4 A time series diagram of traction load and photovoltaic power generation capacity is provided for an embodiment of the present invention, as shown below. Figure 4 As shown, the peak traction load period does not coincide with the peak photovoltaic power generation period. Therefore, the original train operation plan cannot fully utilize photovoltaic power generation, leaving room for optimization.

[0111] (1) Model Result Analysis

[0112] Using the algorithm proposed in this invention, the maximum number of iterations M = 10. An embodiment of this invention provides a heuristic algorithm iterative process as follows: Figure 5 As shown, the minimum total lifecycle cost I is obtained after 7 iterations.t =563 million yuan.

[0113] An optimized train timetable provided in this embodiment of the invention is as follows: Figure 6 As shown, the total absolute deviation from the original timetable is 342 minutes, with an average ΔT = 4 minutes per train. Analysis of the total deviations between the departure times of morning and afternoon trains and the original timetable reveals that the total deviations for morning northbound and southbound trains are 22 minutes and 65 minutes, respectively, while the total deviations for afternoon northbound and southbound trains are -47 minutes and -48 minutes, respectively. These results indicate that departing trains during peak photovoltaic power generation periods can better utilize photovoltaic power and reduce electricity purchase costs.

[0114] (2) Scenario Solution Comparison

[0115] To demonstrate the effectiveness of the model, this invention compares the economic benefits of five scenarios: no solar-storage integration, solar-storage integration, solar-storage integration with source-load coordination, solar-storage integration, and solar-storage integration with source-load coordination. Table 2 compares the results of different scenarios. Without solar-storage integration, the total lifecycle energy cost is 1.45 billion yuan, entirely from electricity purchase costs. With solar-storage integration, the total lifecycle cost is 880 million yuan, including 170 million yuan in investment costs, 58 million yuan in maintenance costs, and 652 million yuan in electricity purchase costs. With solar-storage integration and source-load coordination, the total lifecycle energy cost is 830 million yuan, including 170 million yuan in investment costs, 58 million yuan in maintenance costs, and 601 million yuan in electricity purchase costs. With solar-storage integration, the total lifecycle energy cost is 595 million yuan, including 472 million yuan in investment costs, 55 million yuan in maintenance costs, and 68 million yuan in electricity purchase costs. With solar-storage integration and source-load coordination, the total lifecycle energy cost is 563 million yuan, including 450 million yuan in investment costs, 56 million yuan in maintenance costs, and 57 million yuan in electricity purchase costs.

[0116] The results show that the integration of railways and new energy has brought significant economic benefits. Photovoltaic (PV) and solar-storage (PSS) integration schemes reduced costs by 39% and 59%, respectively. The addition of a source-load coordination mechanism further reduced costs in both scenarios by 5.7% and 5.4%. In terms of overall economic benefits, the scheme derived from this invention's model is optimal, with costs 60.8%, 35.5%, 31.6%, and 5.4% lower than other schemes, respectively. Compared to PSS integration, due to the consideration of source-load interaction, the photovoltaic installed capacity in this invention's model is slightly increased, but the reduced energy storage installed capacity and lower electricity purchase costs lead to a lower overall cost. Therefore, PSS integration combined with source-load coordination results in the lowest cost, demonstrating the superiority and effectiveness of this invention's model.

[0117] Table 2 Comparison results for different scenarios

[0118]

[0119] In scenarios where photovoltaic and energy storage are integrated, the energy sources for train operation become more diversified. Figure 7 This is a schematic diagram illustrating the time-varying curves of multiple energy flows, including all-day grid power supply, photovoltaic power supply, and energy storage power supply, provided as an embodiment of the present invention. Figure 7 It is known that after 8:00 AM, all the power for train operation will come from photovoltaic and energy storage generation, eliminating the need to purchase electricity from the main power grid. Photovoltaic and energy storage generation becomes the primary energy source for train operation, while the power grid becomes a secondary energy source. Under this "photovoltaic and energy storage as the primary source, with power purchase from the grid as a secondary source" model, even if the power grid experiences a power outage due to a fault, photovoltaic and energy storage generation can still maintain train operation, thus improving the reliability of railway operation services.

[0120] In the photovoltaic-storage capacity optimization configuration model of the traction power supply system, the maximum deviation time ΔT is a crucial factor affecting the spatiotemporal distribution of load. As the maximum deviation time ΔT increases, load adjustment becomes more flexible. On the other hand, my country's vast territory and varying natural solar irradiance across different regions result in different photovoltaic power generation per unit installed capacity. To assess the universality of this approach, it is necessary to analyze the economics under different solar irradiance conditions. Solar irradiance and power generation per unit installed capacity generally exhibit a linear relationship; higher solar irradiance leads to higher power generation per unit installed capacity. Therefore, the natural endowment sensitivity analysis uses the photovoltaic power generation per unit installed capacity in this invention as a benchmark to analyze the impact of the rate of change in power generation per unit installed capacity on the economics of the capacity configuration scheme. Figure 8 This diagram illustrates the sensitivity analysis results of maximum allowable deviation time and natural endowment provided in an embodiment of the present invention. As the maximum allowable deviation time increases and the abundance of natural endowment improves, the total life cycle cost gradually decreases. The maximum allowable deviation time is linearly negatively correlated with the total life cycle cost, while natural endowment is non-linearly negatively correlated with the total life cycle cost. Even in areas with low sunlight resources, significant economic benefits can still be generated, indicating that this scheme has a certain degree of regional applicability.

[0121] In summary, this invention establishes a capacity optimization model for a photovoltaic-storage traction power supply system based on source-load coordination, designs a heuristic algorithm for solving the model, and applies this model to the planning of photovoltaic-storage traction power supply systems in high-speed rail sections. By comparing the economic benefits of different scenarios, it is found that compared to traditional photovoltaic or photovoltaic-storage access schemes, the source-load coordination optimization mechanism can reduce costs by 5.7% and 5.4%, respectively, demonstrating the necessity of incorporating the source-load coordination optimization mechanism into the capacity optimization configuration model.

[0122] The present invention analyzes the multi-energy flow variation curves of train operation in the photovoltaic-storage access scenario and finds that photovoltaic-storage power generation can still meet the train traction load demand for most periods of time (8:00 am to 12:00 pm). As an emergency power source, photovoltaic-storage power generation can largely avoid train operation interruptions caused by grid failures and improve the reliability of railway operation services.

[0123] In this invention, as the maximum permissible deviation time increases, the flexibility of traction load adjustment improves, and the total lifecycle cost decreases linearly. Sensitivity analysis based on natural endowment reveals that even in areas with limited solar resources, photovoltaic-storage integration still offers significant economic benefits, indicating that the photovoltaic energy storage capacity configuration scheme for traction power supply systems based on source-load coordination has certain regional applicability and potential for implementation and promotion.

[0124] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of one embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing the present invention.

[0125] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0126] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for apparatus or system embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The apparatus and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0127] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimizing the configuration of photovoltaic and energy storage capacity in a traction power supply system based on source-load coordination, characterized in that, include: Construct a photovoltaic-storage capacity optimization configuration model for a traction power supply system based on source-load coordination, and set the cost function of the photovoltaic-storage capacity optimization configuration model for the traction power supply system; Set the constraints for the photovoltaic-storage capacity optimization configuration model of the traction power supply system; Based on the aforementioned cost function and constraints, a heuristic algorithm is used to solve the photovoltaic and energy storage capacity optimization configuration model of the traction power supply system, obtaining the optimal train departure time and the optimal installed capacity of photovoltaics and energy storage, including: Step (1): Determine the maximum installed capacity of photovoltaic and energy storage, the technical parameters of charging and discharging efficiency, and the economic parameters of photovoltaic energy storage construction and operation and maintenance costs; Step (2): Based on the original timetable, combined with the line data and train traction and braking curves, the traction load of each train in the section is calculated using the traction calculation method. The traction load time series of train k is p. k ; Step (3): Set the iteration counter i = 0, set the maximum iteration number to M, and the number of trains in the same direction to K. For each train k going up or down, keep the departure time of other trains unchanged, and according to the train operation constraints, obtain the upper and lower limits for adjusting the departure time of train k. Specifically as follows: Let h be the original departure time of train k, ΔT be the maximum deviation time, and h be the time of departure. dd The departure time interval between adjacent trains must not be less than the minimum departure time interval; Step (4) according to Update the departure time of train K The traction load and regenerative braking power will accumulate the traction load. and regenerative braking power And substitute the relevant parameters into the following linear programming model to solve; my you t In the formula: These represent the grid power transmission capacity, photovoltaic power generation capacity, energy storage discharge capacity, and regenerative braking capacity at time t, respectively. as well as These are respectively the section traction load, energy storage charging power, and abandoned power. Let be the amount of electricity in the energy storage system at time t; and For the charging and discharging efficiency of the energy storage system; and Let t be the charge / discharge power of the energy storage system, and SOC. min The minimum state of charge (SOC) of energy storage max This represents the maximum state of charge of the energy storage. If for each train, if two consecutive updates cannot reduce the total cost I... t If the number of iterations is less or the maximum number of iterations is reached, the iteration stops, the optimal train departure time is output, and the obtained train departure time is substituted into equation (13) to obtain the installed capacity of photovoltaic and energy storage; otherwise, the processing of step (4) continues.

2. The method according to claim 1, characterized in that, The construction of the photovoltaic-storage capacity optimization configuration model for the traction power supply system based on source-load coordination, and the setting of the cost function of the photovoltaic-storage capacity optimization configuration model for the traction power supply system, includes: The total cost I of setting up a photovoltaic-storage capacity optimization configuration model for a traction power supply system based on source-load coordination. t For the total life cycle cost of photovoltaics I v Energy storage lifecycle cost s and electricity purchase cost I p The sum is: I t =I v +I s +I p (1) The total lifecycle cost of photovoltaics and energy storage v and I s The calculation formula is as follows: I v =C p ×I1+C p ×I2×Y (2) I s =(P s ×I3+Q s ×I4)+(Q s ×I5)×Y (3) Among them, C p and Q s For the installed capacity of photovoltaics and energy storage, P s I1 and I2 are the construction and operation and maintenance costs per unit installed capacity of the photovoltaic system, I3 and I4 are the power cost and capacity cost of the energy storage system, respectively, I5 is the operation and maintenance cost per unit installed capacity of the energy storage system, and Y is the operating life of the photovoltaic-storage power generation system. The formula for calculating the cost of electricity is: Where I grid For grid electricity price, Δt represents the power transmitted to the power grid, and Δt represents the set time interval.

3. The method according to claim 2, characterized in that, The constraints for setting the optimal configuration model of the traction power supply system's photovoltaic and energy storage capacity include: The constraints for setting the photovoltaic-storage capacity optimization configuration model of the traction power supply system include the operation constraints of the photovoltaic-storage traction power supply system and the interval train running time constraints. (1) The train departure time constraint is: in This refers to the adjusted departure time of train k; (2) Microgrid operation constraints Railway traction substations, photovoltaic systems, and energy storage are interconnected to form a microgrid system. The operation of this microgrid system satisfies power balance constraints, namely: The photovoltaic power generation simulation software PVsyst was used to obtain the daily power generation data per unit of photovoltaic installed capacity throughout the year. The average daily power generation per unit of photovoltaic installed capacity was calculated. Considering the degradation of photovoltaic power generation capacity, the average daily power generation per unit of photovoltaic installed capacity over the entire life cycle was calculated. The calculation formula is: Photovoltaic installed capacity C p Cannot exceed the maximum installed capacity C pmax C p ≤C pma x, the maximum installed capacity, is obtained by considering the available space area A and the land area a per unit photovoltaic installed capacity, i.e., C. pmax =A / a, the charging and discharging operation constraints of battery energy storage are: SOC min The minimum state of charge (SOC) of energy storage max This represents the maximum value of the energy storage state of charge.

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