Traction power supply system optical storage capacity optimization configuration method based on source-load coordination

Abstract

The invention provides a source-load coordination-based optical storage capacity optimal configuration method for a traction power supply system. The method comprises the following steps: constructing a traction power supply system optical storage capacity optimization configuration model based on source-load coordination, and setting a cost function of the traction power supply system optical storage capacity optimization configuration model; setting constraint conditions of the optical storage capacity optimal configuration model of the traction power supply system; and based on the cost function and the constraint condition, a heuristic algorithm is adopted to solve the optical storage capacity optimal configuration model of the traction power supply system, and the optimal train departure time and the optimal photovoltaic and energy storage installed capacity are obtained. By solving the problem of source load coordination caused by traction load and photovoltaic power generation fluctuation, the optical storage capacity of the traction power supply system is effectively improved, and the economic benefit is further improved. According to the method, a basis can be provided for implementation and popularization of a railway optical storage traction power supply system, and the reliability of railway operation service is improved.

Description

Technical Field

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

[0002] The energy consumption and cost of railway train operation have always remained high, and due to its high dependence on the external power grid, not only has the carbon emissions remained high, but also the lack of system flexibility has formed a serious hidden danger to the safety of social and economic operations. Railway infrastructure and space along the line have sufficient energy space that can be used for the development and utilization of clean energy such as wind and light. Therefore, it is imperative and has huge room for development to make full use of its own clean energy potential, build a highly flexible traction power supply system, and promote the integration of energy and railways.

[0003] At present, the issue of clean energy access to traction power supply systems has become a research hotspot at home and abroad. Since wind power generation requires more infrastructure space, existing technical research mainly focuses on the planning and design of photovoltaic and energy storage in traction power supply systems, structural optimization and operation control. The planning and design of photovoltaic and energy storage in traction power supply systems mainly improves the comprehensive benefits of traction power supply through the optimization of photovoltaic and energy storage capacity. The steps of the system planning and design include three parts: clean energy potential assessment, traction load assessment and optimal configuration of photovoltaic and energy storage capacity.

[0004] The potential assessment of clean energy for railways is to calculate the area of ​​suitable energy spaces along the railways and combine it with the calculation method of photovoltaic power generation to obtain the technical potential, economic potential and environmental potential of photovoltaic power generation along the railways. The core of the potential assessment method is to identify the photovoltaic energy spaces along the railways. Since the measurement of railway space is difficult due to its wide range, the identification and calculation of suitable energy spaces can be realized based on remote sensing map data through manual processing or deep learning technology. At present, the main suitable energy spaces for railways include the spaces on both sides of the railways, above the contact network and on the top of the trains. Due to the limited space of photovoltaic sheds and train tops, photovoltaics are still mainly installed on both sides of the railways and on the top of the stations. Although some potential assessment methods take into account the energy load demand of trains, they do not take into account the energy flow mechanism of connecting to the new traction power supply system, and can only evaluate the benefits of photovoltaic storage access from a macro perspective, making it difficult for the assessment results to support planning decisions.

[0005] At present, the traction load assessment method in the prior art is to obtain the energy load of the train group through traction calculation by considering factors such as train traction and auxiliary energy consumption and power factor, according to the train timetable and line conditions. For the problem of calculating the traction load of a single vehicle, a section train motion model with energy saving or time saving as the goal can be established to calculate the train traction load sequence, and the traction load of the group of vehicles can be calculated by the time-space accumulation of the single vehicle load. In addition, the traction load can also be obtained through on-site electrical measurements, but because the measurement data is the cumulative traction load of multiple vehicles, it is difficult to achieve the decoupling of the load to the train, so it is impossible to provide a data basis for the dynamic adjustment of the load, and this method is mainly used for existing lines and cannot be applied to the traction load calculation of new lines in the future.

[0006] At present, the existing technology of photovoltaic storage capacity optimization design method is based on the source-load interactive energy flow mechanism, and the optimal design capacity of photovoltaic and energy storage is obtained by solving the photovoltaic storage capacity optimization model established. There have been many theoretical studies and applications on the optimal configuration of photovoltaic storage capacity in urban comprehensive energy planning, but the railway photovoltaic storage microgrid is different from other photovoltaic storage energy systems, mainly reflected in the obvious fluctuation and intermittent characteristics of traction load, which makes it difficult to effectively utilize photovoltaic power generation, which also has fluctuations and periodicity, resulting in high energy storage capacity, which is not conducive to economic improvement and later maintenance and management of the system.

[0007] Therefore, the planning method based on a single energy perspective in existing technologies can no longer further improve economic benefits. It is necessary to further analyze the source-load interaction mechanism of railway transportation 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 embodiment of the present invention provides a method for optimizing the configuration of the photovoltaic storage capacity of a traction power supply system based on source-load coordination, so as to effectively improve the photovoltaic storage capacity of the traction power supply system.

[0009] In order to achieve the above object, the present invention adopts the following technical scheme.

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

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

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

[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 to obtain the optimal train departure time and the optimal photovoltaic and energy storage installed capacity.

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

[0015] The total cost of setting the optimal configuration model of photovoltaic storage capacity for traction power supply system based on source-load coordination t The total life cycle cost of photovoltaic v 、Energy storage life cycle cost I s And the electricity purchase cost I pp The sum is:

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

[0017] The full life cycle cost of photovoltaic and energy storage I 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 is the installed capacity of photovoltaic and energy storage, P s is the power of the energy storage system, 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 power generation system;

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

[0022]

[0023] Among them I grid is the grid electricity price, is the power supplied to the grid, and Δt is the set time interval.

[0024] Preferably, the constraint conditions for setting the traction power supply system photovoltaic storage capacity optimization configuration model include:

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

[0026] (1) The train departure time constraints are:

[0027]

[0028] in is the adjusted departure time of train k, is the original departure time of train k, ΔT is the maximum deviation time;

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

[0030]

[0031] (2) Microgrid operation constraints

[0032] Railway traction substation, photovoltaic and energy storage are interconnected to form a microgrid system, and the operation of the microgrid system meets the power balance constraint, namely:

[0033]

[0034] Where: They are the grid transmission power, photovoltaic power generation power, energy storage discharge power and regenerative braking power at time t. as well as They are the interval traction load, energy storage charging power and abandoned power;

[0035] The photovoltaic power generation simulation software PVsyst is used to obtain the power generation data of each photovoltaic installed capacity per day throughout the year, and the average power generation per photovoltaic installed capacity per day is calculated. Taking into account the attenuation of photovoltaic power generation capacity, the average power generation per photovoltaic installed capacity per day over the entire life cycle is 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 the suitable energy space area A and the unit photovoltaic installed capacity area a, that is, C pmax=A / a, the charge and discharge operation constraints of battery energy storage are:

[0038]

[0039] Where: is the power of the energy storage system at time t; and is the charging and discharging efficiency of the energy storage system; and is the charging and discharging power of the energy storage system at time t.

[0040] Preferably, the method of using a heuristic algorithm based on the cost function and the constraint conditions 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 photovoltaic and energy storage installed capacity includes:

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

[0042] Step (2): According to the original timetable, combined with the line data and the train traction and braking curve, the traction load of each train in the interval 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, set the number of trains in the same direction to K, and for each up or down train k, keep the departure time of other trains unchanged. According to the train operation constraints, obtain the upper and lower limits of the departure time adjustment of train k. The details are as follows:

[0044]

[0045] Step (4) according to Update the departure time of train k The traction load and regenerative braking power will be the cumulative traction load and regenerative braking power And related parameters are brought into the following linear programming model for solution;

[0046]

[0047] If for each train, two consecutive updates cannot make the life cycle cost I t If it is smaller or reaches the maximum number of iterations, the iteration is stopped and the optimal train departure time is output. The obtained train departure time is substituted into formula (13) to obtain the installed capacity of photovoltaic and energy storage; otherwise, the processing of step (4) is continued.

[0048] It can be seen from the technical solutions provided by the above embodiments of the present invention that the present invention solves the source-load coordination problem caused by the fluctuation of traction load and photovoltaic power generation, effectively improves the photovoltaic storage capacity of the traction power supply system, and further improves the economic benefits. The method of the present invention can provide a basis for the implementation and promotion of the railway photovoltaic storage traction power supply system, and improve the reliability of railway operation services.

[0049] Additional aspects and advantages of the present invention will be given in part in the following description, which will become obvious from the following description, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0051] Figure 1 A schematic diagram of train operation and photovoltaic power generation provided by an embodiment of the present invention;

[0052] Figure 2 A processing flow chart of a method for optimizing the configuration of photovoltaic storage capacity of a traction power supply system based on source-load coordination provided by an embodiment of the present invention;

[0053] Figure 3 A train operation diagram between Beijing West and Baoding East provided in an embodiment of the present invention;

[0054] Figure 4 A schematic diagram of a time series of traction load and photovoltaic power generation capacity provided by an embodiment of the present invention;

[0055] Figure 5 A schematic diagram of an iterative process of a heuristic algorithm provided in an embodiment of the present invention;

[0056] Figure 6 An optimized train operation diagram provided by an embodiment of the present invention;

[0057] Figure 7 A schematic diagram of a curve showing changes in multiple energy flows such as grid power supply, photovoltaic power supply and energy storage power supply over time provided by an embodiment of the present invention;

[0058] Figure 8 A schematic diagram of sensitivity analysis results of maximum deviation time and natural endowment provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The 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 throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0060] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the 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 refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or coupling. The term "and / or" used herein includes any unit and all combinations of one or more associated listed items.

[0061] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless defined as herein.

[0062] To facilitate understanding of the embodiments of the present invention, several specific embodiments will be further explained below with reference to the accompanying drawings, and each embodiment does not constitute a limitation on the embodiments of the present invention.

[0063] When photovoltaic power is connected, photovoltaic power generation and train traction load have periodic fluctuation characteristics due to the influence of the sun's trajectory and travel demand. By adjusting the train departure time, the coupling of traction load and photovoltaic power generation can be achieved in time and space, improving the economic benefits of train operation. Figure 1 A schematic diagram of a train operation and photovoltaic power generation law provided by the present invention, such as Figure 1 As shown in the figure, before the train departure time is adjusted, only 7 trains can use photovoltaic power generation; after the adjustment, the number of trains that can use photovoltaic power generation increases to 9, and the utilization rate of photovoltaic power generation is significantly improved. However, the adjustment of train departure time must meet the travel needs of passengers and cannot deviate too much from the original train schedule. Therefore, the present invention will establish a capacity optimization configuration model based on source-load coordination to achieve fixed capacity optimization of photovoltaic and energy storage.

[0064] Based on the law of photovoltaic power generation along the railway, the present invention considers the influence of train operation plan on the time distribution of traction load, takes the minimization of the whole life cycle cost as the goal, sets the dual constraints of train operation and energy system operation, establishes the optimization configuration model of photovoltaic storage capacity of traction power supply system based on source-load coordination, and designs the heuristic algorithm solution model. In order to verify the effectiveness of the model algorithm, the Beijing West-Baoding East section of Beijing-Guangzhou Expressway was selected for measurement and comparison. By analyzing the optimization schemes of five different scenarios, it is found that: compared with the traditional photovoltaic and photovoltaic storage access scenarios, the traction load and photovoltaic power generation time distribution are more consistent in the source-load coordination scenario, the energy storage capacity, investment and construction and electricity purchase costs are reduced, which reduces the whole life cycle cost by 5.7% (0.5 billion yuan) and 5.4% (0.32 billion yuan) respectively. The results of multi-energy flow analysis show that photovoltaic storage power generation can meet the traction load demand in most periods (8 am-24 am), avoid train operation interruption due to power grid power failure, and improve the reliability of railway operation service. The results of model sensitivity analysis show that with the improvement of traction load adjustment flexibility and the richness of solar energy endowment, the whole life cycle cost gradually decreases. Even in areas with limited sunlight resources, the optimal configuration scheme of photovoltaic storage capacity in the traction power supply system still has significant economic benefits, proving that the scheme has a certain regional universality.

[0065] The processing flow chart of a method for optimizing the configuration of photovoltaic storage capacity of a traction power supply system based on source-load coordination provided by an embodiment of the present invention is as follows: Figure 2 As shown, the processing steps include the following:

[0066] Step S1: construct a photovoltaic-storage capacity optimization configuration model for a traction power supply system based on source-load coordination, and set a cost function for the photovoltaic-storage capacity optimization configuration model for a traction power supply system.

[0067] The present invention assumes that the operation mode is "self-generation for self-use, and surplus power for energy storage". The goal of setting the photovoltaic storage capacity optimization configuration model of the traction power supply system based on source-load coordination is to maximize economic benefits. First, due to the addition of photovoltaic and energy storage and other power generation equipment, the construction and operation and maintenance costs of the photovoltaic storage system lead to increased costs; secondly, under the photovoltaic access condition, the train energy consumption includes photovoltaic power generation and large power grid, resulting in a reduction in electricity purchase costs. In summary, the total cost I t The total life cycle cost of photovoltaic v 、Energy storage life cycle cost I s And the electricity purchase cost I pp The sum is:

[0068] I t =I v +I s +I p (1)

[0069] The full life cycle cost of photovoltaic and energy storage I v and I s The calculation formula is as follows:

[0070] I v =C p ×I1+C p ×I2×Y(2)

[0071] I s =(P s ×I3+Q s ×I4)+(Q s ×I5)×Y(3)

[0072] Among them, C p and Q s is the installed capacity of photovoltaic and energy storage, P s is the power of the energy storage system, 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 power generation system.

[0073] The formula for calculating the cost of purchasing electricity is:

[0074]

[0075] Among them I grid is the grid electricity price, is the power delivered by the power grid, and Δt is the time interval. For the convenience of solving, the time interval Δt in the model of the present invention is set to 1 minute, T=1440.

[0076] Step S2: setting the constraint conditions of the traction power supply system photovoltaic storage capacity optimization configuration model.

[0077] Different from the traditional capacity configuration model, the constraint conditions of the photovoltaic storage capacity optimization configuration model of the traction power supply system of the present invention include the operation constraints of the photovoltaic storage traction power supply system and the operation time constraints of the section trains.

[0078] (1) Train running time constraints

[0079] The time distribution of traction load can be changed by adjusting the train departure time. Considering the travel demand, the train departure time cannot deviate too much from the original train schedule. The train departure time constraint is:

[0080]

[0081] in is the adjusted departure time of train k, is the original departure time of train k, and ΔT is the maximum deviation time.

[0082] Assuming that the train departure order remains unchanged, in order to avoid train running conflicts, the departure time interval of adjacent trains cannot be less than the minimum departure time interval h dd ,Right now:

[0083]

[0084] (2) Microgrid operation constraints

[0085] Railway traction substation, photovoltaic and energy storage are interconnected to form a microgrid system. The operation of this system must meet the power balance constraints, namely:

[0086]

[0087] Where: They are the grid transmission power, photovoltaic power generation power, energy storage discharge power and regenerative braking power at time t. as well as They are the interval traction load, energy storage charging power and abandoned power respectively. In order to improve the calculation efficiency, the present invention averages the whole life cycle cost to the daily cost. Therefore, it is necessary to find out the average photovoltaic power generation power in the whole life cycle. First, the photovoltaic power generation simulation software PVsyst is used to obtain the power generation data of each unit photovoltaic installed capacity every day throughout the year; then, the average daily power generation of each unit photovoltaic installed capacity is calculated; finally, considering the attenuation of photovoltaic power generation capacity, the average daily power generation of each unit photovoltaic installed capacity in the whole life cycle is calculated. The calculation formula is:

[0088]

[0089] 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 the suitable energy space area A and the unit photovoltaic installed capacity area a, that is, C pmax =A / a. The energy storage system type of the present invention is battery energy storage, and the charging and discharging operation constraints of battery energy storage are:

[0090]

[0091] Where: is the power of the energy storage system at time t; and is the charging and discharging efficiency of the energy storage system; and is the charging and discharging power of the energy storage system at time t. In order to extend the service life of the energy storage, the present invention stipulates the maximum value SOC of the energy storage state of chargemax and minimum SOC min .

[0092] Step 3: Based on the above cost function and constraints, a heuristic algorithm is used to solve the optimal configuration model of photovoltaic and energy storage capacity of the traction power supply system to obtain the optimal train departure time and the optimal installed capacity of photovoltaic and energy storage.

[0093] Although the model objectives and constraints in the present invention are linear, the mapping relationship between the train departure time and the traction load distribution is difficult to express through a linear model. Therefore, the model of the present invention still belongs to a nonlinear programming model. In combination with the characteristics of the model, the present invention proposes a heuristic algorithm for solving the problem. The solution steps are as follows:

[0094] (1) Determine technical parameters such as the maximum installed capacity of photovoltaic and energy storage, charging and discharging efficiency, and economic parameters such as photovoltaic energy storage construction and operation and maintenance costs;

[0095] (2) According to the original timetable, combined with the line data and the train traction and braking curve, the traction calculation method is used to calculate the traction load of each train in the section. The traction load time series of train k is p k .

[0096] (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 up or down train k, keep the departure time of other trains unchanged. According to the train operation constraints, the upper and lower limits of the departure time adjustment of train k are obtained. The details are as follows:

[0097]

[0098] (4) Based on Update the departure time of train k The traction load and regenerative braking power will be the cumulative traction load and regenerative braking power And related parameters are brought into the following linear programming model for solution.

[0099]

[0100] (5) If for each train, two consecutive updates cannot make the life cycle cost I t If it is smaller or reaches the maximum number of iterations, stop the iteration; otherwise, continue with Step (4).

[0101] (6) Output the optimal train departure time, and substitute the obtained train departure time into formula (13) to obtain the installed capacity of photovoltaic and energy storage.

[0102] Embodiment 1

[0103] In order to verify the effectiveness of the model, the Beijing West to Baoding East section is selected for case study. The mileage of this section is about 178km. First, the train operation diagram is obtained through the railway 12306 system. Figure 3 A train operation diagram between Beijing West and Baoding East provided in an embodiment of the present invention, such as Figure 3 As shown, there are 41 up trains and 45 down trains throughout the day.

[0104] According to relevant literature research and the "China Photovoltaic Industry Development Roadmap", the initial settings of the model parameters are shown in Table 1.

[0105] Table 1 Model parameter settings

[0106]

[0107]

[0108] Taking into account the need for railway operation safety, the present invention selects open space within 3 meters on both sides of the line for the construction of photovoltaic and energy storage systems. In order to prevent photovoltaics from being blown onto the line by strong winds and affecting the safety of train operation, photovoltaic modules are laid flat on both sides of the line with an installation inclination of 0. After removing tunnels, bridges and large residential areas, about 80 kilometers of the entire line can be used to lay photovoltaics, and the suitable energy space area is about 240,000 square meters. According to the train number and model information, the traction load time series of the section is obtained through traction calculation and compared with the photovoltaic power generation capacity of the section. Figure 4 A schematic diagram of a time series of traction load and photovoltaic power generation capacity provided by an embodiment of the present invention, such as Figure 4 As shown in the figure, the peak period of traction load is inconsistent with the peak period of photovoltaic power generation. Therefore, the original train operation plan cannot make full use of photovoltaic power generation, and there is room for optimization.

[0109] (1) Model results analysis

[0110] The algorithm proposed in the present invention is used to solve the model. The maximum number of iterations of the algorithm is M = 10. The iterative process of a heuristic algorithm provided in the embodiment of the present invention is as follows: Figure 5 As shown in Figure 2, after 7 iterations, the minimum life cycle cost I is obtained. t =563 million yuan.

[0111] An optimized train operation diagram provided by an embodiment of the present invention is as follows: Figure 6As shown in the figure, the total absolute deviation from the original schedule is 342 minutes, with an average ΔT = 4 minutes per train. By analyzing the total deviations of the departure times of morning and afternoon trains from the original schedule, it is found that the total deviations of the morning up and down trains from the original schedule are 22 minutes and 65 minutes respectively, and the total deviations of the afternoon up and down trains from the original schedule are -47 minutes and -48 minutes respectively. This result shows that starting trains during the peak period of photovoltaic power generation can better utilize photovoltaic power generation and reduce the cost of purchasing electricity.

[0112] (2) Comparison of scenario solutions

[0113] In order to prove the effectiveness of the model, the present invention compares the economic benefits of five scenarios, namely: no PV-storage access, PV access, PV access + source-load coordination, PV-storage access, PV-storage access + source-load coordination, etc. Table 2 shows the comparison results of different scenario solutions. In the case of no PV storage access, the energy cost of the whole life cycle is 1.45 billion yuan, all of which comes from the cost of purchasing electricity; in the PV access scenario, the total cost of the whole life cycle is 880 million yuan, of which investment cost is 170 million yuan, maintenance cost is 58 million yuan, and electricity cost is 652 million yuan; in the PV access + source-load coordination scenario, the energy cost of the whole life cycle is 830 million yuan, of which investment cost is 170 million yuan, maintenance cost is 58 million yuan, and electricity cost is 601 million yuan; in the PV storage access scenario, the energy cost of the whole life cycle is 595 million yuan, of which investment cost is 472 million yuan, maintenance cost is 55 million yuan, and electricity cost is 68 million yuan; in the PV storage access + source-load coordination scenario, the energy cost of the whole life cycle is 563 million yuan, of which investment cost is 450 million yuan, maintenance cost is 56 million yuan, and electricity cost is 57 million yuan.

[0114] Judging from the results, the integration of railways and new energy has brought significant economic benefits. The photovoltaic access and photovoltaic storage access solutions reduced costs by 39% and 59% respectively; the addition of the source-load coordination mechanism further reduced the costs of the two scenarios by 5.7% and 5.4%. From the perspective of overall economic benefits, the solution obtained by the model of the present invention is the best, with costs 60.8%, 35.5%, 31.6% and 5.4% lower than those of other scenario solutions. Compared with photovoltaic storage access, due to the consideration of the source-load interaction effect, the photovoltaic installed capacity in the model of the present invention is slightly increased, but the reduction in energy storage installed capacity and the reduction in purchased electricity brightness lead to a reduction in overall costs. Therefore, the cost of photovoltaic storage access + source-load coordination is the lowest, which proves the superiority and effectiveness of the model of the present invention.

[0115] Table 2 Comparison results of different scenarios

[0116]

[0117] In the scenario of solar-storage access, the energy sources for train operation are diversified. Figure 7A schematic diagram of a curve showing the changes of multiple energy flows such as grid power supply, photovoltaic power supply and energy storage power supply over time provided by an embodiment of the present invention. Figure 7 It can be seen that after 8 am, the train's operating power will all come from photovoltaic power generation, and there is no need to purchase electricity from the large power grid. Photovoltaic power generation becomes the main energy source for train operation, and the power grid becomes the auxiliary energy source for train operation. Under the mode of "photovoltaic power generation as the main and power grid purchase as the auxiliary", even if the power grid is interrupted due to a fault, photovoltaic power generation can still maintain train operation, and the reliability of railway operation services is improved.

[0118] The maximum deviation time ΔT in the optimization configuration model of photovoltaic storage capacity of the traction power supply system is an important factor affecting the temporal and spatial distribution law of the load. With the increase of the maximum deviation time ΔT, the load adjustment is more flexible. On the other hand, my country has a vast territory, and the natural endowment of light intensity in different regions varies greatly, resulting in different photovoltaic power generation per installed capacity in different regions. In order to evaluate the universality of the problem, it is necessary to analyze the economy under different light intensity conditions. The light intensity and the power generation per unit installed capacity generally show a linear relationship. The greater the light intensity, the greater the power generation per unit installed capacity. Therefore, the natural endowment sensitivity analysis is based on the photovoltaic power generation per unit installed capacity in the present invention, and analyzes the impact of the power generation rate per unit installed capacity on the economy of the capacity configuration scheme. Figure 8 A schematic diagram of the sensitivity analysis results of the maximum deviation time and natural endowment provided for an embodiment of the present invention. With the increase of the maximum allowable deviation time and the improvement of the richness of natural endowment, the life cycle cost gradually decreases. The maximum allowable deviation time is linearly negatively correlated with the life cycle cost, and the natural endowment is nonlinearly negatively correlated with the life cycle cost. Even in some areas with low light resources, it can still generate huge economic benefits, indicating that the solution has a certain regional universality.

[0119] In summary, the embodiment of the present invention establishes a sizing optimization model for a photovoltaic-storage traction power supply system based on source-load coordination, designs a heuristic algorithm solution model, and applies the model to the planning of a photovoltaic-storage traction power supply system for high-speed rail sections. By comparing the economic benefits of different scenario solutions, it is found that compared with traditional photovoltaic or photovoltaic-storage access solutions, the source-load coordination optimization mechanism can reduce costs by 5.7% and 5.4%, respectively, proving the necessity of incorporating the source-load coordination optimization mechanism into the capacity optimization configuration model.

[0120] By analyzing the multi-energy flow change curve of train operation in the photovoltaic storage access scenario, the embodiment of the present invention finds that photovoltaic storage power generation can still meet the train traction load demand in most time periods (8 am to 24:00). Photovoltaic storage power generation as an emergency power supply can largely avoid train operation interruption caused by power grid failure, thereby improving the reliability of railway operation services.

[0121] In the embodiment of the present invention, as the maximum allowable deviation time increases, the flexibility of traction load adjustment is improved, and the life cycle cost decreases linearly. Through the natural endowment sensitivity analysis, it is found that even in some areas with insufficient light resources, photovoltaic storage access still has great economic benefits, indicating that the photovoltaic energy storage capacity configuration scheme of the traction power supply system based on source-load coordination has certain regional universality and potential for implementation and promotion.

[0122] Those skilled in the art can understand that the accompanying drawings are only schematic diagrams of an embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0123] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention or certain parts of the embodiments.

[0124] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0125] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for optimizing the configuration of photovoltaic storage capacity in a traction power supply system based on source-load coordination, characterized in that: include: Constructing a photovoltaic storage capacity optimization configuration model for a traction power supply system based on source-load coordination, and setting a cost function of the photovoltaic storage capacity optimization configuration model for the traction power supply system; Setting constraints of the traction power supply system photovoltaic storage capacity optimization configuration model; 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 to obtain the optimal train departure time and the optimal photovoltaic and energy storage installed capacity.

2. The method according to claim 1, characterized in that The construction of the photovoltaic storage capacity optimization configuration model of the traction power supply system based on source-load coordination and setting the cost function of the photovoltaic storage capacity optimization configuration model of the traction power supply system include: The total cost of setting the optimal configuration model of photovoltaic storage capacity for traction power supply system based on source-load coordination t The total life cycle cost of photovoltaic v 、Energy Storage Life Cycle Cost I s And the electricity purchase cost I pp The sum is: I t =I v +I s +I p (1) The full life cycle cost of photovoltaic and energy storage I 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 is the installed capacity of photovoltaic and energy storage, P s is the power of the energy storage system, 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 power generation system; The formula for calculating the cost of purchasing electricity is: Among them I grid is the grid electricity price, is the power supplied to the grid, and Δt is the set time interval.

3. The method according to claim 2, characterized in that The constraint conditions for setting the traction power supply system photovoltaic storage capacity optimization configuration model include: The constraint conditions for setting the photovoltaic storage capacity optimization configuration model of the traction power supply system include the photovoltaic storage traction power supply system operation constraints and the section train operation time constraints. (1) The train departure time constraints are: in is the adjusted departure time of train k, is the original departure time of train k, ΔT is the maximum deviation time; The departure time interval between adjacent trains cannot be less than the minimum departure time interval h dd ,Right now: (2) Microgrid operation constraints Railway traction substation, photovoltaic and energy storage are interconnected to form a microgrid system, and the operation of the microgrid system meets the power balance constraint, namely: Where: They are the grid transmission power, photovoltaic power generation power, energy storage discharge power and regenerative braking power at time t. as well as They are the interval traction load, energy storage charging power and abandoned power; The photovoltaic power generation simulation software PVsyst is used to obtain the power generation data of each photovoltaic installed capacity per day throughout the year, and the average power generation per photovoltaic installed capacity per day is calculated. Taking into account the attenuation of photovoltaic power generation capacity, the average power generation per photovoltaic installed capacity per day over the entire life cycle is calculated. The calculation formula is: Photovoltaic installed capacity C p Cannot exceed the maximum installed capacity C pmax , C p ≤C pmax The maximum installed capacity is obtained by the suitable energy space area A and the unit photovoltaic installed capacity area a, that is, C pmax =A / a, the charge and discharge operation constraints of battery energy storage are: Where: is the power of the energy storage system at time t; and is the charging and discharging efficiency of the energy storage system; and is the charging and discharging power of the energy storage system at time t.

4. The method according to claim 3, characterized in that The method of using a heuristic algorithm based on the cost function and the constraint conditions 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 photovoltaic and energy storage installed capacity includes: Step (1): Determine the maximum installed capacity of photovoltaic and energy storage, charging and discharging efficiency technical parameters, and the economic parameters of photovoltaic energy storage construction and operation and maintenance costs; Step (2): According to the original timetable, combined with the line data and the train traction and braking curve, the traction load of each train in the interval 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, set the number of trains in the same direction to K, and for each up or down train k, keep the departure time of other trains unchanged. According to the train operation constraints, obtain the upper and lower limits of the departure time adjustment of train k. The details are as follows: Step (4) according to Update the departure time of train k The traction load and regenerative braking power will be the cumulative traction load and regenerative braking power And related parameters are brought into the following linear programming model for solution; If for each train, two consecutive updates cannot make the life cycle cost I t If it is smaller or reaches the maximum number of iterations, the iteration is stopped and the optimal train departure time is output. The obtained train departure time is substituted into formula (13) to obtain the installed capacity of photovoltaic and energy storage; otherwise, the processing of step (4) is continued.

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