CN114977182A - Photovoltaic access-considered optimal power flow optimization method for flexible traction power supply system interval - Google Patents

Photovoltaic access-considered optimal power flow optimization method for flexible traction power supply system interval Download PDF

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CN114977182A
CN114977182A CN202210437650.7A CN202210437650A CN114977182A CN 114977182 A CN114977182 A CN 114977182A CN 202210437650 A CN202210437650 A CN 202210437650A CN 114977182 A CN114977182 A CN 114977182A
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赵鼎威
张翼扬
龚志恒
朱楚扬
刘湘
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Southwest Jiaotong University
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Abstract

The invention discloses a photovoltaic access-considered optimal power flow optimization method for a flexible traction power supply system interval, which specifically comprises the following steps: acquiring traction load data and illumination intensity data in the running process of a train; establishing an objective function of an optimization model according to the data and the electric charge parameters; establishing a constraint condition of an optimization model according to power capacity parameters of the hybrid energy storage device and the photovoltaic system and power balance of a converter, and linearizing the constraint condition of the optimization model; considering the random fluctuation of the photovoltaic energy and the train traction load, performing interval optimization, and establishing a mixed integer programming model; and solving the model to obtain the lowest daily running cost of the traction substation, the peak load shifting comparison of the train power and the charge state of the hybrid energy storage device, namely finishing the optimal power flow optimization of the flexible traction power supply system interval. The invention improves the utilization rate of photovoltaic energy and train regenerative braking energy, reduces the cost of electric railway charges, realizes peak load shifting and valley filling of traction load, and can effectively solve a series of cost problems.

Description

Photovoltaic access-considered optimal power flow optimization method for flexible traction power supply system interval
Technical Field
The invention belongs to the technical field of flexible traction power supply systems, and particularly relates to a photovoltaic access-considered flexible traction power supply system interval optimal power flow optimization method.
Background
The rapid construction of the electrified railway in China promotes the development of economy and society in China, but has higher requirements on a power supply system of the railway. In the traditional electrified railway, particularly a high-speed railway, the problem of electric energy quality mainly based on a negative sequence problem is increasingly serious, the increase of the speed and the vehicle load capacity of a locomotive is severely restricted by the existence of electric phase separation, the power supply efficiency and quality are reduced, and the reliability, the safety and the economical efficiency of railway operation are seriously influenced. The traction power supply system of the single-phase power frequency alternating current power supply system is limited by a self topological structure and traction load, and the following two problems generally exist in the operation process:
(1) negative sequence dominated power quality problems
The traction load has the characteristics of single-phase high power and asymmetry, and can generate negative sequence current in a three-phase power grid to cause a negative sequence problem, and the asymmetry problem caused by the traction load is more and more obvious along with the continuous increase of the traction power and the running density of a locomotive running on a high-speed and heavy-load railway. For example, the wind turbines of the inner Mongolia maglev wind farm are often shut down due to "current asymmetry" faults under the influence of negative sequence currents generated by the Shuhuang heavy-duty railway.
(2) Excessive phase separation problem
The locomotive can not continuously take current from a traction net when passing through the neutral section, only inertia is utilized to pass through, and the loss of the traction speed and the traction power of the locomotive is caused, for example, the single-pass running time of the Jinghushi high-speed rail is prolonged by about 20 minutes due to the existence of the electric neutral section. Meanwhile, the over-phase of the locomotive can generate over-voltage, over-current and other electrical transient processes, so that the risk of equipment burning loss and failure is increased, the malfunction of a protection device is easily caused, and the efficient and safe operation of a railway is directly influenced.
On the other hand, the research on new energy in China develops rapidly in recent years, and the national energy structure reformation trend is obvious. China has wide breadth of members, wide distribution of electrified railways, more geographical intersections of a railway network and a renewable new energy network and very high consumption potential. Taking the Sichuan-Tibet railway as an example, a large amount of renewable new energy sources are distributed along the electrified railway, and if the renewable new energy sources can be consumed nearby, the long-distance transmission cost of photovoltaic power generation can be reduced, and the operation cost of the electrified railway can be reduced.
Disclosure of Invention
Aiming at the problems, the invention provides a photovoltaic access-considered optimal power flow optimization method for a flexible traction power supply system interval.
The invention relates to a photovoltaic access-considered optimal power flow optimization method for a flexible traction power supply system interval, which comprises the following steps of:
step 1: and acquiring traction load data and illumination intensity data in the running process of the train.
And 2, step: and (3) establishing an objective function of an optimization model according to the electric charge parameters and the traction load data and the illumination intensity data in the train operation process obtained in the step (1).
And step 3: and (3) establishing a constraint condition of an optimization model based on traction load data and illumination intensity data in the running process of the train obtained in the step (1) according to power capacity parameters of the hybrid energy storage device and the photovoltaic system and power balance of a converter, and linearizing the constraint condition of the optimization model.
And 4, step 4: and (3) according to the objective function obtained in the step (2) and the constraint condition obtained in the step (3), considering the volatility of the photovoltaic energy and the random volatility of the train traction load, carrying out statistics on the illumination intensity data and the traction load data of the specified area to realize prediction error, carrying out interval optimization, and establishing a mixed integer programming model.
And 5: and (4) solving the model obtained in the step (4) to obtain the lowest daily running cost of the traction substation, the peak load shifting comparison of the train power and the charge state of the hybrid energy storage device, namely finishing the optimal power flow optimization of the flexible traction power supply system interval.
Further, the load process data of the traction substation in the step 1 is calculated by load process simulation software according to the high-speed railway line, the train and the schedule, such as ELBAS/WEBANET.
Further, the typical illumination intensity scene in step 1 is obtained by reducing the historical data of the illumination intensity scene based on a scene reduction method, such as a synchronous back-substitution elimination method.
Further, the objective function in step 2 is:
minf＝ECC+DC+PC (1)
Figure BDA0003613232850000021
Figure BDA0003613232850000022
Figure BDA0003613232850000023
DC＝max(P t dem )×c dem (5)
in the formula: f is total operating cost, ECC (energy conservation charge) is electricity consumption cost, PC (penalty charge) is feedback electricity cost, DC (demand charge) is demand electricity cost, P is total operating cost t grid The power purchased from the grid for co-directional traction power,
Figure BDA0003613232850000024
for the price of electricity charge purchased from the grid, P t fed The power fed back to the grid for the rail grid connection,
Figure BDA0003613232850000025
charge price, P, levied for feedback of electric energy t dem For the magnitude of the electric energy demand, c dem For the demand electricity price, Δ t and Nt represent the sampling interval and the number of samples, respectively. Due to different areas to the railwayThe collection standards of feedback electric energy charges of grid connection are different, so the charge price of the feedback electric energy also changes due to different standards of each region.
Further, the constraint conditions in step 3 include photovoltaic power constraint, power balance constraint, energy storage and charge and discharge constraint, same energy storage constraint at the beginning and end, and charge and discharge power constraint.
Photovoltaic power constraint:
Figure BDA0003613232850000031
Figure BDA0003613232850000032
0≤P t pv ≤S pv (8)
in the formula: p t pv For photovoltaic consumption, η pv For photovoltaic efficiency, A pv In order to obtain a photovoltaic active area,
Figure BDA0003613232850000033
in order to predict the photovoltaic intensity,
Figure BDA0003613232850000034
is the photovoltaic output upper bound value, S pv Is the photovoltaic converter capacity; drawing out eta pv 12% of A pv Is 10000m 2 ，S pv Is 1 MVA.
And power balance constraint:
Figure BDA0003613232850000035
in the formula: p t batdis For discharging power of the battery, P t ucdis For discharging power of capacitor, P t back Feeding back power, P, to the train t load For train tractive power, P t batch Charging power for batteries, P t ucch Power is charged to the capacitor.
And (3) charge and discharge restraint:
Figure BDA0003613232850000036
Figure BDA0003613232850000037
in the formula:
Figure BDA0003613232850000038
representing the energy stored by the battery and the super capacitor at the moment t;
Figure BDA0003613232850000039
and
Figure BDA00036132328500000310
respectively representing the charging and discharging efficiency of the battery and the super capacitor; epsilon b And ε c The self-discharge rates of the battery and the super capacitor are respectively.
And (4) reservoir restriction:
Figure BDA00036132328500000311
Figure BDA00036132328500000312
in the formula:
Figure BDA00036132328500000313
the upper and lower limit values of the state of charge of the battery,
Figure BDA00036132328500000314
is the upper and lower limit values of the charge state of the super capacitor,
Figure BDA00036132328500000315
representing the total rated stored energy of the battery and the super capacitor.
The energy storage at the beginning and the end has the same constraint:
Figure BDA00036132328500000316
Figure BDA00036132328500000317
in the formula:
Figure BDA0003613232850000041
indicating the initial state of charge values of the battery and the super capacitor,
Figure BDA0003613232850000042
and the energy stored by the battery and the super capacitor at the moment t is represented.
Charge and discharge power constraint:
Figure BDA0003613232850000043
Figure BDA0003613232850000044
Figure BDA0003613232850000045
Figure BDA0003613232850000046
Figure BDA0003613232850000047
Figure BDA0003613232850000048
in the formula:
Figure BDA0003613232850000049
and
Figure BDA00036132328500000410
is a one-bit binary number;
Figure BDA00036132328500000411
when the super capacitor is discharged, otherwise, the super capacitor is charged,
Figure BDA00036132328500000412
the battery is discharged, otherwise, the battery is charged, and the charging and discharging power of the energy storage element is also limited by the capacity of the converter;
Figure BDA00036132328500000413
rated power for discharging and charging the battery and the super capacitor, respectively.
Further, in the interval optimization in the step 4, aiming at the volatility of the photovoltaic energy and the random volatility of the train traction load, the illumination intensity data and the traction load data of a specified area need to be counted, a prediction error model is established, the ultra-short-term errors of the photovoltaic and the traction load in a future period are predicted, and the output plan of the hybrid energy storage system is modified under different photovoltaic and traction load conditions to obtain a system control strategy.
Photovoltaic interval optimization:
aiming at the uncertainty of photovoltaic, a Gaussian distribution model is adopted, and the photovoltaic mean value S is used t ＝ave(solar t N ) As expected values, the expected error value is 0 and the standard deviation is
Figure BDA00036132328500000414
Photovoltaic prediction error
Figure BDA00036132328500000415
Density of probability distributionThe function is:
Figure BDA00036132328500000416
Figure BDA00036132328500000417
wherein the solar t N The illumination intensity data in N days.
Optimizing a traction load interval:
when system load prediction is performed, normal distribution is often used to describe a load prediction error, and the prediction error is considered to have the following relationship with time t.
Load power prediction error:
Figure BDA0003613232850000051
regenerative braking power prediction error:
Figure BDA0003613232850000052
where ρ is L ，ρ B Are prediction error coefficients.
The beneficial technical effects of the invention are as follows:
the invention improves the utilization rate of photovoltaic energy and train regenerative braking energy, reduces the cost of electric railway charges, realizes peak load shifting and valley filling of traction load, and can effectively solve a series of cost problems.
Drawings
Fig. 1 is a schematic diagram of the novel flexible traction power supply system of the invention.
Fig. 2 is a graph of the peak clipping and valley filling effects of the traction load in the example.
Fig. 3 shows the optimization of the illumination intensity interval in the embodiment, and a Matlab solver is used to predict the photovoltaic output condition of a simulated area 24 hours a day.
FIG. 4 is a power prediction interval of the traction load obtained by optimizing the traction load interval in the embodiment.
Detailed Description
The invention is further described with reference to the following figures and specific examples.
The method takes a novel flexible traction power supply system as a research object, takes the lowest daily operation cost of a traction substation as a target, takes single day hour as a coordinate variable, takes energy flow, the operation power of a hybrid energy storage system and the maximum capacity of a photovoltaic substation as constraints, considers the uncertainty of photovoltaic and the fluctuation of traction load, performs interval optimization by using a hybrid integer programming model, establishes an interval optimal flow optimization method for the flexible traction power supply system considering photovoltaic access, analyzes the data of the past year in a certain area of China, and finally verifies the correctness and the effectiveness of the optimization model through example analysis.
The structure of a traction power supply system aimed at by the invention is shown in fig. 1, and the flexible traction power supply system interval optimal power flow optimization method considering photovoltaic access comprises the following steps:
step 1: traction load process simulation software, such as ELBAS/WEBANET software of SIGNAN company in Germany, is used for inputting parameters of a high-speed railway line, a train and a schedule, and load process data of a traction substation are obtained through simulation.
And inputting illumination intensity scene data, and performing scene reduction on the illumination intensity scene based on a synchronous back substitution elimination method to obtain four typical illumination intensity scenes.
Step 2: and (3) establishing an objective function of an optimization model according to the electric charge parameters and the traction load data and the illumination intensity data in the train operation process obtained in the step (1).
The objective function is:
minf＝ECC+DC+PC (1)
Figure BDA0003613232850000061
Figure BDA0003613232850000062
Figure BDA0003613232850000063
DC＝max(P t dem )×c dem (5)
in the formula: f is total operating cost, ECC (energy conservation charge) is electricity consumption cost, PC (penalty charge) is feedback electricity cost, DC (demand charge) is demand electricity cost, P is total operating cost t grid The power purchased from the grid for co-directional traction power,
Figure BDA0003613232850000064
for the price of electricity charge purchased from the grid, P t fed The power fed back to the grid for the rail grid connection,
Figure BDA0003613232850000065
charge price, P, assessed for feedback of electric energy t dem For the magnitude of the electric energy demand, c dem The price of the required electricity fee. Because different regions have different standards for collecting feedback electric energy charges for railway grid connection, the cost price of the feedback electric energy also changes due to different standards of each region.
And step 3: and (3) establishing a constraint condition of an optimization model based on traction load data and illumination intensity data in the running process of the train obtained in the step (1) according to power capacity parameters of the hybrid energy storage device and the photovoltaic system and power balance of a converter, and linearizing the constraint condition of the optimization model.
The constraint conditions comprise photovoltaic power constraint, power balance constraint, energy storage and charge-discharge constraint, initial and final energy storage identical constraint and charge-discharge power constraint.
Photovoltaic power constraint:
Figure BDA0003613232850000066
Figure BDA0003613232850000067
0≤P t pv ≤S pv (8)
in the formula: p t pv For photovoltaic consumption, η pv For photovoltaic efficiency, A pv In order to obtain a photovoltaic active area,
Figure BDA0003613232850000068
in order to predict the photovoltaic intensity,
Figure BDA0003613232850000069
is the photovoltaic output upper bound value, S pv Is the photovoltaic converter capacity; drawing out eta pv 12% of A pv Is 10000m 2 ，S pv Is 1 MVA.
And power balance constraint:
Figure BDA0003613232850000071
in the formula: p t batdis For battery discharge power, P t ucdis For discharging power of capacitor, P t back Feeding back power, P, to the train t load For train tractive power, P t batch Charging power for batteries, P t ucch Power is charged to the capacitor.
And (3) charge and discharge restraint:
Figure BDA0003613232850000072
Figure BDA0003613232850000073
in the formula:
Figure BDA0003613232850000074
representing the energy stored by the battery and the super capacitor at the moment t;
Figure BDA0003613232850000075
and
Figure BDA0003613232850000076
respectively representing the charging and discharging efficiency of the battery and the super capacitor; epsilon b And ε c The self-discharge rates of the battery and the super capacitor are respectively.
And (4) reservoir restriction:
Figure BDA0003613232850000077
Figure BDA0003613232850000078
in the formula:
Figure BDA0003613232850000079
the upper and lower limit values of the state of charge of the battery,
Figure BDA00036132328500000710
is the upper and lower limit values of the charge state of the super capacitor,
Figure BDA00036132328500000711
representing the total rated stored energy of the battery and the super capacitor.
The energy storage at the beginning and the end has the same constraint:
Figure BDA00036132328500000712
Figure BDA00036132328500000713
in the formula:
Figure BDA00036132328500000714
indicating the initial state of charge values of the battery and the super capacitor,
Figure BDA00036132328500000715
and the energy stored by the battery and the super capacitor at the moment t is represented.
Charge and discharge power constraint:
Figure BDA00036132328500000716
Figure BDA00036132328500000717
Figure BDA00036132328500000718
Figure BDA00036132328500000719
Figure BDA0003613232850000081
Figure BDA0003613232850000082
in the formula:
Figure BDA0003613232850000083
and
Figure BDA0003613232850000084
is a one-bit binary number;
Figure BDA0003613232850000085
when the super capacitor is discharged, otherwise, the super capacitor is charged,
Figure BDA0003613232850000086
the battery is discharged, otherwise, the battery is charged, and the charging and discharging power of the energy storage element is also limited by the capacity of the converter;
Figure BDA0003613232850000087
rated power for discharging and charging the battery and the super capacitor, respectively.
And 4, step 4: and (3) according to the objective function obtained in the step (2) and the constraint condition obtained in the step (3), considering the volatility of the photovoltaic energy and the random volatility of the train traction load, carrying out statistics on the illumination intensity data and the traction load data of the specified area to realize prediction error, carrying out interval optimization, and establishing a mixed integer programming Model (MILP).
The interval optimization is as follows:
photovoltaic interval optimization:
aiming at the uncertainty of the photovoltaic, a Gaussian distribution model is adopted, and the photovoltaic average value S is used t ＝ave(solar t N ) As expected values, the expected error value is 0 and the standard deviation is
Figure BDA0003613232850000088
Photovoltaic prediction error
Figure BDA0003613232850000089
The probability distribution density function is then:
Figure BDA00036132328500000810
Figure BDA00036132328500000811
wherein the solar t N The illumination intensity data in N days.
Optimizing a traction load interval:
when system load prediction is performed, normal distribution is often used to describe a load prediction error, and the prediction error is considered to have the following relationship with time t.
Load power prediction error:
Figure BDA00036132328500000812
regenerative braking power prediction error:
Figure BDA00036132328500000813
where ρ is L ，ρ B Are prediction error coefficients.
And 5: and (3) solving the mixed integer linear programming in the step (4) by utilizing optimization software, such as MATLAB R2018b, an integrated optimization tool box YALMIP and a solver Gurobi (version 9.1.2), so as to obtain the lowest daily running cost of the traction substation, the peak load shifting comparison of train power and the charge state of the hybrid energy storage device, and thus finishing the optimal power flow optimization of the flexible traction power supply system interval.
Examples
The topological structure of the electrified railway traction power supply system integrating the hybrid energy storage device and the photovoltaic is shown in figure 1, and the parameters and the electric energy cost are shown in tables 1 and 2:
TABLE 1 simulation model setup parameters
Figure BDA0003613232850000091
TABLE 2 Electricity charge parameter and charging mode
Figure BDA0003613232850000092
Figure BDA0003613232850000101
Other partial parameters, drawing eta pv 12% of A pv Is 10000m 2 ，S pv Is 1 MVA.
In order to analyze a traditional traction power supply system and a traction power supply system accessed with new energy, the situation that feedback electric energy charging is carried out by two charging modes of fixed electricity price and time-of-use electricity price in different regions is considered, and three feedback electric energy charging modes under the situation of the two charging modes are compared and analyzed. Table 3 shows the comparison results of the time-of-use electricity rates and the fixed electricity rates.
Case I: the flexible traction power supply system does not comprise an energy storage device and a photovoltaic device;
case II: the flexible traction power supply system comprises an energy storage device and a photovoltaic device;
TABLE 3 comparison of time of use price and fixed price
Figure BDA0003613232850000102
Figure BDA0003613232850000111
Analysis of peak clipping and valley filling effects:
because the energy density of the battery is high, the change speed of the charge state is low, and the power change is not obvious in a short time, the electric energy fed back by the train can be stored by the battery to a certain extent. The super capacitor has the characteristics of high charging speed, high power density and the like, so that the change speed of the charge state is high, and a good instantaneous charging and discharging task can be carried out while the cost is saved. Therefore, when the train obtains electric energy from the power grid and the train brakes to generate feedback electric energy, the battery plays a role in peak clipping, and the super capacitor plays a role in valley filling. In the simulation, the upper and lower limits of the working state of the battery and the capacitor are set to be 0.2 and 0.8, and the initial state is 0.5.
After the concrete model is established, the image drawing of the output power of the single-day power grid is carried out by introducing the traditional power supply mode and accessing the data of the hybrid energy storage and the photovoltaic, and the load peak can be obviously reduced after the hybrid energy storage and the light energy are accessed, as shown in fig. 2. The energy absorption of the regenerative braking is increased, which means that the model can realize the peak clipping and valley filling effects, and the effectiveness of the model after the new energy is accessed is verified.
And (3) interval optimization:
in the calculation, the hybrid energy storage and photovoltaic traction power supply system is used for solving the optimal energy management strategy under different conditions. On the basis, the fixed photovoltaic energy is optimized, the uncertain problem in the train running process is considered, interval optimization is carried out on the model, and calculation is carried out again.
Due to the volatility of photovoltaic energy and the uncertainty of a train traction power supply system, a mixed integer programming model is used for counting the illumination intensity data and traction load data of a specified area and bringing the data into the model, so that the electricity cost reduced by the traction power supply system with new energy accessed is solved.
Optimizing an illumination intensity interval:
because the photovoltaic volatility is large, average analysis on data of the whole year is not significant, so that after the illumination characteristics of all regions are considered and compared with the illumination intensity data of a certain region, the daily average value of the data of the region from the 160 th day to the 189 th day of the year is selected for analysis and calculation.
The N in the formulae (22) to (25) is taken as required by itself.
In the analysis, a mixed gaussian distribution solving mode is used, a Matlab solver is used for predicting the photovoltaic output situation of the simulation area 24 hours a day, and the upper bound and the lower bound of the photovoltaic interval are obtained, as shown in fig. 3.
Optimizing a traction load interval:
in consideration of the particularity of the traction load, a fixed error coefficient alpha is set, and a 90% interval confidence level is set by a Gaussian distribution method to obtain a power prediction interval of the traction load, as shown in FIG. 4. The error coefficient was taken to be 0.04.
For the prediction error coefficients ρ in equations (26) to (27) L ，ρ B 0.04 is taken.
Optimizing an electric energy cost interval:
in the process, the upper and lower bounds of the prediction interval of the photovoltaic output and the traction load are obtained, and at the moment, two uncertain factors in the train operation are converted into two deterministic factors. Therefore, the upper and lower extreme conditions in the train operation are selected for research and analysis, and are combined with the electric energy cost factors discussed above for analysis, so that the upper and lower bounds of the electric energy cost are obtained. Meanwhile, due to the difference of the two charging modes of the fixed electricity price and the time-of-use electricity price, the optimization research of the electric energy cost interval of the train is still divided into two parts of conditions for research, and the upper and lower bounds of the electric energy cost of the two different charging modes are respectively solved.
Two extreme cases are defined here: the lowest electricity price condition is the electricity price under the condition that all environment conditions during the running of the train are considered comprehensively as the optimal condition, namely the lower bound of the traction load factor of the train is taken, and the upper bound of the photovoltaic output condition is taken; the highest electricity price condition is the electricity price under the worst condition considering all environment conditions during the running of the train, namely the traction load factor of the train is taken as an upper bound, and the photovoltaic output condition is taken as a lower bound. Due to the fact that data of other possible factors are lacked during train operation, the other possible factors cannot be effectively substituted and analyzed, and therefore the analysis only aims at photovoltaic output conditions and train traction load factors. Table 4 shows comparison of the optimization effect of the electricity rate interval.
TABLE 4 comparison of optimization results between electricity fee intervals
Figure BDA0003613232850000121
The lowest electricity price: and considering the optimal condition of cost, the lower limit of traction load is taken, and the upper limit of photovoltaic load is taken.
The highest electricity price: considering the worst cost condition, the upper limit of the traction load is taken, and the lower limit of the photovoltaic load is taken.
The photovoltaic power generation system and the hybrid energy storage system are connected to a direct current link of a back-to-back converter of the traction power supply system, and meanwhile, a model is built according to the volatility of photovoltaic and traction load, interval optimization is carried out, so that the superiority of the system in the aspects of reducing electricity charge and improving electric energy quality compared with the traditional system is fully proved, and a series of economic problems can be effectively solved.

Claims (4)

1. A photovoltaic access-considered optimal power flow optimization method for a flexible traction power supply system interval is characterized by comprising the following steps:
step 1: acquiring traction load data and illumination intensity data in the running process of a train;
step 2: establishing an objective function of an optimization model according to the electric charge parameters and the traction load data and the illumination intensity data obtained in the step 1 in the running process of the train;
and step 3: according to power capacity parameters of the hybrid energy storage device and the photovoltaic system and power balance of a converter, establishing constraint conditions of an optimization model based on traction load data and illumination intensity data in the running process of the train obtained in the step 1, and linearizing the constraint conditions of the optimization model;
and 4, step 4: according to the objective function obtained in the step 2 and the constraint condition obtained in the step 3, the fluctuation of photovoltaic energy and the random fluctuation of train traction load are considered, prediction errors are achieved by counting illumination intensity data and traction load data of a specified area, interval optimization is carried out, and a mixed integer programming model is established;
and 5: and (4) solving the model obtained in the step (4) to obtain the lowest daily running cost of the traction substation, the peak load shifting comparison of the train power and the charge state of the hybrid energy storage device, namely finishing the optimal power flow optimization of the flexible traction power supply system interval.
2. The method for optimizing power flow between intervals of the flexible traction power supply system considering photovoltaic access according to claim 1, wherein the objective function in the step 2 is as follows:
min f＝ECC+DC+PC (1)
Figure FDA0003613232840000011
Figure FDA0003613232840000012
Figure FDA0003613232840000013
DC＝max(P t dem )×c dem (5)
in the formula: f is total operating cost, ECC is electricity charge cost, PC is feedback charge cost, DC is demand charge cost, P t grid The power purchased from the grid for co-directional traction power,
Figure FDA0003613232840000014
for the price of electricity charge purchased from the grid, P t fed The power fed back to the grid for the rail grid connection,
Figure FDA0003613232840000015
charge price, P, assessed for feedback of electric energy t dem For the magnitude of the electric energy demand, c dem To demand electricity price, Δ t and Nt represent the sampling interval and the number of samples, respectively.
3. The interval optimal power flow optimization method considering the photovoltaic accessed flexible traction power supply system is characterized in that the constraint conditions in the step 3 comprise photovoltaic power constraint, power balance constraint, energy storage and charge-discharge constraint, energy storage identical constraint from beginning to end and charge-discharge power constraint;
photovoltaic power constraint:
Figure FDA0003613232840000021
Figure FDA0003613232840000022
0≤P t pv ≤S pv (8)
in the formula: p t pv For photovoltaic consumption, η pv For photovoltaic efficiency, A pv The photovoltaic active area is the effective area of the photovoltaic,
Figure FDA0003613232840000023
in order to predict the photovoltaic intensity,
Figure FDA0003613232840000024
is the photovoltaic output upper bound value, S pv Is the photovoltaic converter capacity; drawing out eta pv 12% of A pv Is 10000m 2 ，S pv Is 1 MVA;
and power balance constraint:
Figure FDA0003613232840000025
in the formula: p t batdis For discharging power of the battery, P t ucdis For discharging power of capacitor, P t back Feeding back power, P, to the train t load For train tractive power, P t batch Charging power for batteries, P t ucch Charging power to the capacitor;
and (3) charge and discharge restraint:
Figure FDA0003613232840000026
Figure FDA0003613232840000027
in the formula:
Figure FDA0003613232840000028
representing the energy stored by the battery and the super capacitor at the moment t;
Figure FDA0003613232840000029
and
Figure FDA00036132328400000210
respectively representing the charging and discharging efficiency of the battery and the super capacitor; epsilon b And ε c The self-discharge rates of the battery and the super capacitor are respectively;
and (4) reservoir restriction:
Figure FDA00036132328400000211
Figure FDA00036132328400000212
in the formula:
Figure FDA00036132328400000213
the upper and lower limit values of the state of charge of the battery,
Figure FDA00036132328400000214
is the upper and lower limit values of the charge state of the super capacitor,
Figure FDA00036132328400000215
represents the total rated stored energy of the battery and the super capacitor;
the energy storage at the beginning and the end has the same constraint:
Figure FDA00036132328400000216
Figure FDA00036132328400000217
in the formula:
Figure FDA0003613232840000031
indicating the initial state of charge values of the battery and the super capacitor,
Figure FDA0003613232840000032
representing the energy stored by the battery and the super capacitor at the moment t;
charge and discharge power constraint:
Figure FDA0003613232840000033
Figure FDA0003613232840000034
Figure FDA0003613232840000035
Figure FDA0003613232840000036
Figure FDA0003613232840000037
Figure FDA0003613232840000038
in the formula:
Figure FDA0003613232840000039
and
Figure FDA00036132328400000310
is a one-bit binary number;
Figure FDA00036132328400000311
when the super capacitor is discharged, otherwise, the super capacitor is charged,
Figure FDA00036132328400000312
the battery is discharged, otherwise, the battery is charged, and the charging and discharging power of the energy storage element is also limited by the capacity of the converter;
Figure FDA00036132328400000313
rated power for discharging and charging the battery and the super capacitor, respectively.
4. The interval optimal power flow optimization method considering the photovoltaic access flexible traction power supply system according to claim 3, wherein in the interval optimization in the step 4, aiming at the volatility of photovoltaic energy and the random volatility of train traction load, the illumination intensity data and traction load data of a specified area need to be counted, a prediction error model is established, ultra-short-term errors of the photovoltaic and traction load in a future period are predicted, and under different photovoltaic and traction load conditions, the output plan of the hybrid energy storage system is modified to obtain a system control strategy;
photovoltaic interval optimization:
aiming at the uncertainty of photovoltaic, a Gaussian distribution model is adopted, and the photovoltaic mean value S is used t ＝ave(solar t N ) As expected values, the expected error value is 0 and the standard deviation is
Figure FDA00036132328400000314
Photovoltaic prediction error
Figure FDA00036132328400000315
The probability distribution density function is then:
Figure FDA00036132328400000316
Figure FDA00036132328400000317
wherein the solar t N The illumination intensity data in N days;
optimizing a traction load interval:
when system load prediction is performed, normal distribution is often used to describe a load prediction error, and the prediction error and time t are considered to have the following relationship:
load power prediction error:
Figure FDA0003613232840000041
regenerative braking power prediction error:
Figure FDA0003613232840000042
where ρ is L ，ρ B Are prediction error coefficients.
CN202210437650.7A 2022-04-25 2022-04-25 Photovoltaic access-considered optimal power flow optimization method for flexible traction power supply system interval Pending CN114977182A (en)

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CN116596195A (en) * 2023-07-17 2023-08-15 清华大学 Photovoltaic digestion benefit evaluation method for flexible direct-current traction power supply system
CN116596195B (en) * 2023-07-17 2023-09-19 清华大学 Photovoltaic digestion benefit evaluation method for flexible direct-current traction power supply system

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