Method and device for configuring hybrid energy storage capacity of traction substation and related medium

Abstract

The application discloses a traction substation hybrid energy storage capacity configuration method and device and related media, aiming at the scene that a traction power supply system accesses a wind power generation and photovoltaic power generation system and a hybrid energy storage system, power distribution is performed on a power difference between wind and light output and train load based on double-layer fuzzy control, optimal capacity configuration of each energy storage medium is determined, the utilization rate of wind power, photovoltaic power and regenerative braking energy is improved, and the electricity cost of an electrified railway is reduced, in the case that the uncertainty of'source' output and 'train' load is considered, input and output power of three kinds of energy storage modules is reasonably distributed, capacity configuration and power distribution of the traction substation hybrid energy storage are solved, the hybrid energy storage cost is minimized, and new energy consumption and compensation of train load fluctuation are maximized.

Description

Technical Field

[0001] This invention relates to the field of hybrid energy storage configuration in traction substations, and particularly to a method, apparatus, and related medium for configuring hybrid energy storage capacity in traction substations. Background Technology

[0002] Electrified railway traction power supply systems are characterized by large power fluctuations, wide distribution, and strong regularity. With the continuous expansion of my country's electrified railway network, enormous energy demands have arisen. Simultaneously, the rapid development of clean energy sources such as wind and solar power, as well as energy storage media, provides a solid foundation for constructing a new "source-grid-load-storage" transportation power supply system. Among these, energy storage plays a crucial role as an energy carrier in the entire transportation power supply system. It can both mitigate the intermittent and fluctuating power output of new energy sources, improving the absorption rate of renewable energy such as wind and solar power, and play a "peak shaving and valley filling" role during train operation, reducing the impact of traction load on the system.

[0003] Considering the uncertainties of clean energy sources like wind and solar power on the "source" side and the load of electrified railways on the "load" side, energy storage modules need to have high power density, energy density, a high number of charge-discharge cycles, and high charge-discharge efficiency. They also require a reasonable combination of energy storage media to adapt to different operating scenarios. Existing traction substations have not achieved a reasonable configuration of hybrid energy storage capacity, resulting in high costs for hybrid energy storage, large fluctuations in train load, and a lack of promotion of new energy consumption. Summary of the Invention

[0004] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention proposes a method, device, and related medium for configuring hybrid energy storage capacity in traction substations, which can reduce the cost of hybrid energy storage, compensate for small fluctuations in train load, and promote the consumption of new energy sources.

[0005] A method for configuring hybrid energy storage capacity in traction substations according to a first aspect of the present invention includes the following steps:

[0006] S100. Construct uncertainty models for wind power generation and photovoltaic power generation, and predict wind and solar power output curves using the uncertainty models;

[0007] S200: Obtain train operation data corresponding to the traction substation, predict the load curve of the traction substation based on the train operation data corresponding to the traction substation, calculate the deviation power curve between train load and wind and solar power output based on the load curve of the traction substation and the wind and solar power output curve, and perform power allocation for hybrid energy storage charging and discharging based on the deviation power curve of wind and solar power output.

[0008] S300: Construct a priority strategy for hybrid energy storage charging and discharging, and optimize the power allocation of hybrid energy storage charging and discharging based on two-layer fuzzy control;

[0009] S400, construct the objective function that minimizes the total cost of coordinated operation of traction substations;

[0010] S500: Solve the objective function to obtain the optimal capacity and power ratio of the hybrid energy storage configuration;

[0011] S600. Configure the capacity of hybrid energy storage in traction substations according to the optimal capacity and power ratio of hybrid energy storage configuration.

[0012] According to some embodiments of the present invention, the specific steps of step 100 are as follows:

[0013] S101. Construct the relationship between wind turbine output power and wind speed.

[0014]

[0015] Among them, P i (t) represents the output power of wind turbine i during time period t; This is the rated power of wind turbine i; v i (t) is the wind speed at the hub of wind turbine i at time t before day t; v in,i and v out,i These are the cut-in and cut-out wind speeds of wind turbine i; v N,i This is the rated wind speed of wind turbine unit i;

[0016] S102. Collect meteorological data of the area where the wind turbine is located, predict the wind speed curve based on the previous day's meteorological data, and obtain the predicted value of the wind turbine's output power based on the predicted wind speed curve and the relationship between the wind turbine's output power and wind speed.

[0017] S103. Construct the relationship between the output power of photovoltaic power generation equipment and the light intensity.

[0018]

[0019] Among them, P l (t) represents the output power of photovoltaic power generation equipment l during time period t. This is the rated power of the photovoltaic power generation equipment l;

[0020] S104. Collect meteorological data of the area where the photovoltaic power generation equipment is located, predict the light intensity curve based on the meteorological data, and obtain the predicted value of the output power of the photovoltaic power generation equipment by combining the predicted light intensity curve with the relationship between the output power of the photovoltaic power generation equipment and the light intensity.

[0021] S105. Based on the predicted output power of the wind turbine and the predicted output power of the photovoltaic power generation equipment, the predicted wind and solar power output curves are obtained.

[0022] According to some embodiments of the present invention, the specific steps of step S200 are as follows:

[0023] S201. Constructing the formula for calculating train load power

[0024]

[0025] P(t)=F(t)v(t)1000 / 3.6

[0026] Among them, P T It is the train load power; v(t) is the train speed; P aux (t) is the power consumed by the auxiliary winding; F(t) is the traction or braking force of the train at time t; η is the power transmission efficiency from the on-board transformer to the wheel.

[0027] S202. Based on train operation data and train load power, predict the load power of the traction substation. The formula for calculating the load power is as follows:

[0028]

[0029] Among them, P tr,d,t K is the actual load power of the traction substation at time t on day d; tr,d,t It is a random variable; ΔP tr,d,t It is the fluctuation value of the load power; It is the maximum fluctuation of load power; Γ tr It is an uncertain budget of load power fluctuations;

[0030] S203. Based on the predicted load power of the traction substation, the load curve of the predicted traction substation is obtained. The train load curve of the predicted traction substation is subtracted from the wind and solar power output curve to obtain the deviation power curve between the train load and the wind and solar power output.

[0031] S204. Power allocation for hybrid energy storage charging and discharging is performed based on the deviation power curve between train load and wind and solar power output.

[0032] According to some embodiments of the present invention, the hybrid energy storage in step S300 includes supercapacitor energy storage, battery energy storage and flywheel energy storage.

[0033] According to some embodiments of the present invention, the specific steps of optimizing the power allocation of hybrid energy storage charging and discharging based on two-layer fuzzy control in step S300 are as follows:

[0034] S301. Select a fuzzy membership function and construct an input-output fuzzy set based on the fuzzy membership function, including a first input fuzzy value and a first output fuzzy value, a second input fuzzy value and a second output fuzzy value;

[0035] S302. Construct a first-layer fuzzy control rule based on the first input fuzzy value and the first output fuzzy value, and construct a second-layer fuzzy control rule based on the second input fuzzy value and the second output fuzzy value.

[0036] S303. According to the first-level fuzzy control rules, the power of the high-power energy storage module and the battery energy storage module is initially allocated to determine the output power of the battery energy storage module and the high-power energy storage module. The high-power energy storage module includes a supercapacitor energy storage module and a flywheel energy storage module.

[0037] S304. Based on the second-level fuzzy control rules, the power allocated to the high-power energy storage module is re-allocated to determine the output power of the flywheel energy storage module and the supercapacitor energy storage module.

[0038] According to some embodiments of the present invention, the objective function in step S400 is

[0039]

[0040] Among them, C total This refers to the daily operating cost of the hybrid energy storage system in the traction substation; T plan It is the entire life cycle of the hybrid energy storage system; C fly C represents the energy storage cost of the flywheel. bat The energy storage cost of batteries; C sc The energy storage cost of supercapacitors; C sy Cost of supporting equipment for hybrid energy storage; C wh,total Maintenance costs for hybrid energy storage; C buy It is the cost of purchasing electricity for the traction substation.

[0041] According to some embodiments of the present invention, the constraints of the objective function include constraints on power balance, constraints on battery energy storage modules, supercapacitor energy storage modules and flywheel energy storage modules, and constraints on the charging and discharging states of the energy storage modules.

[0042] The constraint formula for power balance is:

[0043] P buy +P f +P c +P b +P new =P train +P loss

[0044] Among them, P buyPower is drawn from the power grid; P new It refers to the output power of wind and solar power; P train This refers to the power supply for train traction; Ploss is the system power loss.

[0045] The constraints for battery energy storage modules, supercapacitor energy storage modules, and flywheel energy storage modules are as follows:

[0046]

[0047] Where i=1 represents the battery energy storage module, i=2 represents the supercapacitor energy storage module, and i=3 represents the flywheel energy storage module; OC i (t) represents the state of charge (SOC) of each energy storage element at time t; i,min and SOC i,max These are the lower and upper limits of the state of charge of the energy storage element, respectively. and These represent the charging and discharging states of the corresponding energy storage modules; Let t be the power of the energy storage module at time t; and These are the rated charging and discharging power of the corresponding energy storage modules;

[0048] The constraint formula for the charging and discharging states of the energy storage module is as follows;

[0049]

[0050] According to a first aspect of the present invention, a hybrid energy storage capacity configuration device for traction substations includes:

[0051] The prediction unit is used to construct uncertainty models for wind power generation and photovoltaic power generation, and to predict the wind and solar power output curves through the uncertainty models.

[0052] The first calculation unit is used to acquire the train operation data corresponding to the traction substation, predict the train load curve of the traction substation during the day based on the train operation data corresponding to the traction substation, calculate the deviation power curve between train load and wind and solar power output based on the predicted train load curve and wind and solar power output curve, and perform power allocation for hybrid energy storage charging and discharging based on the deviation power curve between train load and wind and solar power output.

[0053] An optimization unit is used to establish a priority strategy for hybrid energy storage charging and discharging, and optimizes the power allocation of hybrid energy storage charging and discharging based on two-layer fuzzy control.

[0054] The objective function unit is used to construct the objective function that minimizes the total cost of coordinated operation of traction substations.

[0055] The second calculation unit is used to solve the objective function and obtain the optimal capacity and power ratio of the energy storage configuration.

[0056] The configuration unit is used to configure the capacity of hybrid energy storage in traction substations according to the optimal capacity and power ratio of the energy storage configuration.

[0057] According to a third aspect of the present invention, the electronic device includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for implementing connection communication between the processor and the memory, wherein the program, when executed by the processor, implements the steps of the method described above.

[0058] According to a fourth aspect of the present invention, the storage medium is a computer-readable storage medium for computer-readable storage, characterized in that the storage medium stores one or more programs, which can be executed by one or more processors to implement the steps of the above-described method.

[0059] The method, apparatus, and related medium for configuring hybrid energy storage capacity in traction substations according to embodiments of the present invention have at least the following beneficial effects:

[0060] This invention discloses a method for configuring hybrid energy storage capacity in traction substations considering uncertainties in the "source-vehicle" relationship. The method involves constructing uncertainty models for wind and photovoltaic power generation on the "source" side, predicting wind and solar power output curves using these models, acquiring train operation data corresponding to the traction substations, predicting the intraday train load curve of the traction substations based on this data, calculating the deviation power curve between train load and wind / solar output based on the predicted train load curve and the wind / solar output curves, and allocating power for hybrid energy storage charging and discharging based on this deviation power curve. Furthermore, it involves constructing a priority strategy for hybrid energy storage charging and discharging by combining the energy storage characteristics of supercapacitors, batteries, and flywheels on the "storage" side, optimizing the power allocation for hybrid energy storage charging and discharging based on two-layer fuzzy control, constructing an objective function to minimize the total cost of coordinated operation of the traction substations, solving the objective function to obtain the optimal capacity and power ratio for energy storage configuration, and configuring the hybrid energy storage capacity in the traction substations according to the optimal capacity and power ratio.

[0061] This invention improves the utilization rate of wind, photovoltaic and regenerative braking energy, reduces the electricity cost of electrified railways, minimizes the cost of hybrid energy storage, and maximizes the absorption of new energy sources and compensation for train load fluctuations.

[0062] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0063] The present invention will be further described below with reference to the accompanying drawings and embodiments, wherein:

[0064] Figure 1 This is a flowchart of the method for configuring hybrid energy storage capacity in traction substations according to an embodiment of the present invention. Detailed Implementation

[0065] Embodiments of the present invention are described in detail below. Examples of these embodiments 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.

[0066] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, etc., are based on the orientation or positional relationship shown in the drawings and are only for the convenience of describing this invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention.

[0067] In the description of this invention, "multiple" refers to two or more. The use of "first" and "second" is for distinguishing technical features only and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features or their sequential relationship.

[0068] In the description of this invention, unless otherwise explicitly defined, terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0069] Reference Figure 1 As shown, a method for configuring hybrid energy storage capacity in a traction substation includes the following steps:

[0070] S100. Construct uncertainty models for wind power generation and photovoltaic power generation, and predict wind and solar power output curves using uncertainty models;

[0071] It should be noted that both wind power and photovoltaic power generation belong to the "source" side. The uncertainty models for wind power generation and photovoltaic power generation include the relationship between wind turbine output power and wind speed, and the relationship between photovoltaic power generation equipment output power and solar irradiance. The specific steps of step 100 are as follows:

[0072] S101. Because there is a near piecewise linear relationship between wind turbine output and wind speed, a formula for the relationship between wind turbine output and wind speed is constructed.

[0073]

[0074] Among them, P i (t) represents the output power of wind turbine i during time period t; This is the rated power of wind turbine i; v i (t) is the wind speed at the hub of wind turbine i at time t before day t; v in, i and v ou t ,i These are the cut-in and cut-out wind speeds of wind turbine i; v N,i It is the rated wind speed of wind turbine unit i.

[0075] S102. Collect meteorological data of the area where the wind turbine is located, predict the intraday wind speed curve based on the previous day's meteorological data, and obtain the predicted value of the intraday output power of the wind turbine by combining the predicted intraday wind speed curve with the relationship between the wind turbine's output power and wind speed.

[0076] It should be understood that the meteorological data in step S102 includes wind speed, temperature, and humidity in the area where the wind turbine is located. Data is collected hourly, and then the daily wind speed curve v(t) is predicted based on the daytime wind speed and meteorological factor data to obtain the predicted daily output power of the wind turbine. According to the predicted value The actual daily output power P of the wind turbine can be calculated. wt,d,t P wt,d,t The expression is

[0077]

[0078] Among them, P wt,d,t K is the actual output power of the wind turbine at time t on day d; wt,d,t It is a random variable, taking values ​​[-1, 1]; ΔP wt,d,t It is the fluctuation value of the wind turbine's output power; It is the maximum fluctuation of the wind turbine's output power; Γ pv It is the uncertain budget for the output power fluctuation of wind turbine units.

[0079] S103. Construct the relationship between the output power of photovoltaic power generation equipment and the light intensity.

[0080]

[0081] Among them, P l (t) represents the output power of photovoltaic power generation equipment l during time period t. It is the rated power of photovoltaic power generation equipment l, I l (t) represents the light intensity of photovoltaic power generation device l during time period t. lmax This represents the maximum light intensity.

[0082] S104. Collect meteorological data of the area where the photovoltaic power generation equipment is located, predict the intraday light intensity curve based on the previous day's meteorological data, and obtain the predicted value of the intraday output power of the photovoltaic power generation equipment by combining the predicted intraday light intensity curve with the relationship between the output power of the photovoltaic power generation equipment and the light intensity.

[0083] It should be understood that the meteorological data in step S104 includes the solar irradiance, ambient temperature, and humidity of the area where the photovoltaic power generation equipment is located. Based on hourly meteorological data collection, the predicted daily solar irradiance curve I(t) is calculated using the daily solar irradiance and meteorological data to obtain the predicted daily output power of the photovoltaic power generation equipment. Actual value P pv,d,t It can be represented as

[0084]

[0085] Among them, P pv,d,t K is the actual output power of the photovoltaic unit at time t on day d; pv,d,t It is a random variable, taking values ​​[-1, 1]; ΔP pv,d,t It is the fluctuation value of the output power of the photovoltaic unit; It is the maximum fluctuation of the output power of the photovoltaic unit; Γ pv It is the uncertain budget for the output power fluctuation of photovoltaic units.

[0086] S105. Based on the predicted daily output power of the wind turbine and the predicted daily output power of the photovoltaic power generation equipment, the predicted wind and solar power output curves are obtained.

[0087] S200: Obtain train operation data corresponding to the traction substation, predict the load curve of the traction substation based on the train operation data corresponding to the traction substation, calculate the deviation power curve between train load and wind and solar power output based on the load curve of the traction substation and the wind and solar power output curve, and perform power allocation for hybrid energy storage charging and discharging based on the deviation power curve of wind and solar power output.

[0088] It should be noted that the train load curve of the traction substation belongs to the "load" side. The specific steps of step S200 in this embodiment of the invention are as follows:

[0089] S201. Constructing the formula for calculating train load power

[0090]

[0091] P(t)=F(t)v(t)1000 / 3.6

[0092] Among them, P T It is the train load power; v(t) is the train speed; P aux(t) is the power consumed by the auxiliary winding; F(t) is the traction or braking force of the train at time t; η is the power transmission efficiency from the on-board transformer to the wheel.

[0093] S202. Predict the intraday load power of the traction substation based on train operation data, including train timetables and day-ahead train speed data. Predict the load power of the traction substation based on the train timetables and day-ahead train speed data. Based on the predicted load power The actual value P of the load power can be calculated. tr,d,t P tr,d,t The calculation formula is:

[0094]

[0095] Among them, P tr,d,t K is the actual load power of the traction substation at time t on day d; tr,d,t It is a random variable; ΔP tr,d,t It is the fluctuation value of the load power; It is the maximum fluctuation of load power; Γ tr It is an uncertain budget of load power fluctuations;

[0096] S203. Based on the predicted train load power of the traction substation during the day, the train load curve of the predicted traction substation during the day is obtained. The train load curve of the predicted traction substation during the day is subtracted from the wind and solar power output curve to obtain the deviation power curve between train load and wind and solar power output.

[0097] Specifically, the power difference ΔP(t) is obtained by subtracting the load power of the traction substation from the output power of wind and solar power. Based on the power difference ΔP(t), the deviation power curve between the train load and the output power of wind and solar power is obtained.

[0098] S204. Based on the deviation power curve between train load and wind and solar power output, the power allocation for hybrid energy storage charging and discharging is carried out, that is, the power shared by each energy storage is determined based on the deviation power curve.

[0099] S300: Construct a priority strategy for hybrid energy storage charging and discharging, and optimize the power allocation of hybrid energy storage charging and discharging based on two-layer fuzzy control.

[0100] It should be noted that the hybrid energy storage in this embodiment of the invention includes supercapacitor energy storage, battery energy storage and flywheel energy storage, and other hybrid energy storage combinations can also be used.

[0101] Constructing a priority strategy for hybrid energy storage charging and discharging, specifically including power P based on supercapacitors, batteries, and flywheel energy storage. i , capacity Q iFeatures are used to construct a priority strategy for charging and discharging hybrid energy storage.

[0102] It should be noted that the two-layer fuzzy control method utilizes MATLAB's Fuzzy Logic Designer module to optimize the power allocation of hybrid energy storage. The specific steps of step S300 are as follows:

[0103] S301. Select a fuzzy membership function and construct an input-output fuzzy set based on the fuzzy membership function, including a first input fuzzy value and a first output fuzzy value, a second input fuzzy value and a second output fuzzy value;

[0104] Specifically, when the power difference ΔP > 0, the train is in traction mode. Three power thresholds are set, P... th1 The first discharge threshold is equal to the maximum output power P of the hybrid energy storage system. H,max ;P th2 The second discharge threshold is equal to the maximum output power P of the supercapacitor. sc,max ;P th3 The third discharge threshold is equal to the maximum output power P of the battery's energy storage. b,max Based on three power thresholds, ΔP(t) is divided into four operating conditions, expressed as follows:

[0105]

[0106] When the power difference ΔP < 0, the train is in braking condition. A power threshold P is set. re The value is equal to the maximum energy storage power of the supercapacitor. Dividing ΔP(t) into two working domains, we can express it as follows:

[0107]

[0108] Hybrid energy storage module output power P HESS The relationship between the power difference ΔP(T) at time T and the power difference ΔP(T) is as follows:

[0109] P HESS =ΔP(T)-P t

[0110] Among them, P t It is the power that the traction substation obtains from the power grid.

[0111] The output power P of the hybrid energy storage module HESS The operating conditions corresponding to ΔP(t) are divided into six working domains and projected onto [-1, 1]. Values ​​exceeding the upper or lower bound are taken as 1 or -1, respectively. The output power ambiguity P' of the hybrid energy storage module is... HESS The universe of discourse is set to [-1, 1], the fuzzy subset is represented as {NBNSXSSMB}, and a rectangular membership function is selected.

[0112]

[0113]

[0114] Among them, P th1 It is the set first discharge threshold; P th2 It is the set second discharge threshold, the value of which is equal to the maximum output power P of the high-power module. f,max ;P th3 It is the set third discharge threshold, the value of which is equal to the maximum output power P of the battery's energy storage. b,max .

[0115] S302. Construct a first-layer fuzzy control rule based on the first input fuzzy value and the first output fuzzy value, and construct a second-layer fuzzy control rule based on the second input fuzzy value and the second output fuzzy value.

[0116] S303. According to the first-level fuzzy control rules, the power of the high-power energy storage module and the battery energy storage module is initially allocated to determine the output power of the battery energy storage module and the high-power energy storage module. The high-power energy storage module includes a supercapacitor energy storage module and a flywheel energy storage module.

[0117] Specifically, the first output fuzzy quantity includes the output power P' of the hybrid energy storage module at time T. HESS High-power module State of Charge (SOC) fc and battery state of charge (SOC) bat The first output fuzzy value is the ratio K of the power allocated to the high-capacity module and the battery module. m,cp State of charge and K m,cp The membership functions are all triangular membership functions, and the three fuzzy subsets {SMB} are selected for description.

[0118] State of charge (SOC) of high-power energy storage modules fc Represented as

[0119]

[0120]

[0121] Among them, SOC sc It refers to the state of charge (SOC) of a supercapacitor. fly It is the state of charge of the flywheel energy storage; E f,N It is the rated capacity of flywheel energy storage; E sc,N This is the rated capacitance of the supercapacitor; k N It is the ratio of flywheel energy storage to the rated capacity of supercapacitors.

[0122] The specific rules for the first layer of fuzzy control are as follows:

[0123] a1). When the output power P of the hybrid energy storage module HESS It belongs to the XS subset, and the battery module has a state of charge (SOC). bat If the value is within the range of [0.2, 0.8], the battery is operating within a safe charge range, and only the battery cell outputs power.

[0124] a2). When the output power P of the hybrid energy storage module HESS Belonging to the S and M subsets, and the state of charge (SOC) of the high-power module fc If the value is [0.1, 0.9], the high-power module operates within a safe charge range, prioritizing power output from the high-power module, while the battery output supplements the power deficit.

[0125] a3). When the output power P of the hybrid energy storage module HESS If a module belongs to subset B and both the high-power module and the battery are within their operating state of charge, then each energy storage module will output power.

[0126] a4). When the hybrid energy storage module outputs any power, if the state of charge (SOC) of the high-power energy storage module is... fc Less than or equal to the lower limit SOC fc,min However, if the battery module operates within a safe charge range, only the battery module will output power.

[0127] a5). When the hybrid energy storage module outputs any power, if the battery's state of charge (SOC) is... bat Less than or equal to the lower limit SOC bat,min However, if the high-power energy storage module operates within a safe charge range, only the high-power module will output power.

[0128] a6). When the energy storage input power P of the hybrid energy storage module is... HESS If a module belongs to the NS subset and its state of charge is less than the upper limit, then the high-power module is set to charge first.

[0129] a7). When the energy storage input power P of the hybrid energy storage module HESS If it belongs to the NB subset and the state of charge of the energy storage module is within the operating range, then the battery and the large-capacity module absorb electrical energy at the maximum energy storage power.

[0130] The first-level fuzzy control output fuzzy quantity K m,cp Deblurring, converting to the actual quantity K cp This yields the power allocated to the high-power module and the battery module.

[0131]

[0132] Where ΔP(T) is the difference between the train load and the power of clean energy at time T; P hp This is the power allocated to the high-power module; P bat This is the power allocated to the battery module.

[0133] S304. Based on the second-level fuzzy control rules, allocate the power P to the high-power energy storage module. hp A secondary allocation is performed to determine the output power of the flywheel energy storage module and the supercapacitor energy storage module.

[0134] Specifically, the second input fuzzy quantity includes the power P' allocated to the high-power module. hp State of charge (SOC) of supercapacitors sc State of charge (SOC) of flywheel energy storage fly The second output fuzzy quantity is the ratio K of the power allocated to the supercapacitor and the flywheel energy storage. m,sf P' hp Triangular membership functions are used, and four fuzzy subsets {NSSMB} are selected for description. State of charge and K... m,sf The membership functions are all triangular membership functions, and the three fuzzy subsets {SMB} are selected for description.

[0135] The specific rules for the second-level fuzzy control are as follows:

[0136] b1) When the input power of the high-power energy storage module belongs to a subset of NS, the power is absorbed by the supercapacitor. The flywheel energy storage is set to absorb power only when the state of charge of the supercapacitor is greater than the upper limit.

[0137] b2). When the output power of the high-power module belongs to the S subset, the power is output by the flywheel energy storage. The supercapacitor output power is set only when the flywheel energy storage state of charge is less than the lower limit.

[0138] b3). When the output power of the high-power module belongs to the M subset, the power is output by the supercapacitor. The flywheel energy storage output power is set only when the state of charge of the supercapacitor is less than the lower limit.

[0139] b4). When the output power of the high-power module belongs to subset B, and the flywheel energy storage and the supercapacitor state of charge are both within the operating range, then both will output power.

[0140] The second-layer fuzzy control outputs the fuzzy quantity K. m,sf Deblurring, converting to the actual quantity K sf The power allocated by the supercapacitor and flywheel energy storage is obtained:

[0141]

[0142] Among them, P flyThis is the power allocated to the flywheel energy storage module; P sc This is the power allocated to the supercapacitor module.

[0143] S400, construct the objective function that minimizes the total cost of coordinated operation of traction substations.

[0144] It should be noted that the objective function in this embodiment of the invention is

[0145]

[0146] Among them, C total This refers to the daily operating cost of the hybrid energy storage system in the traction substation; T plan It is the entire life cycle of the hybrid energy storage system; C fly C represents the energy storage cost of the flywheel. bat The energy storage cost of batteries; C sc The energy storage cost of supercapacitors; C sy Cost of supporting equipment for hybrid energy storage; C wh,total Maintenance costs for hybrid energy storage; C buy It is the cost of purchasing electricity for the traction substation.

[0147] Specifically, the formula for calculating the energy storage cost of batteries, supercapacitors, and flywheels is as follows:

[0148]

[0149] In the above formula, when the subscript i is 1, 2, or 3, it represents the battery, supercapacitor, and flywheel energy storage module, respectively. These are the unit power cost and unit capacity cost of the corresponding energy storage modules; P i,N E i,N These are the rated power and capacity of the corresponding energy storage modules.

[0150] Specifically, the cost C of hybrid energy storage supporting equipment sy The calculation formula is:

[0151] C sy =K sy (P sc,N +P f,N +P b,N )

[0152] Among them, K sy It is the cost of the equipment per unit power of the system, P sc,N P represents the rated power and capacitance of a supercapacitor. f,N P represents the rated power and capacity of the flywheel. b,N This refers to the rated power and capacity of the battery.

[0153] Specifically, the maintenance cost C of hybrid energy storage wh,total The calculation formula is:

[0154] C wh,total =C p,bat E b,N +C p,sc E sc,N +C p,fly E f,N

[0155] Among them, C p,bat C p,sc and C p,fly These are the average annual maintenance costs per unit capacity of batteries, supercapacitors, and flywheel energy storage, respectively.

[0156] Specifically, the electricity purchase cost C of the traction substation buy The calculation formula is:

[0157]

[0158] Among them, P buy It is the power that the traction substation obtains from the power grid; C p This refers to the unit price of electricity.

[0159] It should be noted that the constraints of the objective function in step S400 include constraints on power balance, constraints on the battery energy storage module, the supercapacitor energy storage module, and the flywheel energy storage module, as well as constraints on the charging and discharging states of the energy storage modules, as detailed below:

[0160] The constraint formula for power balance is:

[0161] P buy +P f +P c +P b +P new =P train +P loss

[0162] Among them, P buy Power is drawn from the power grid; P new It refers to the output power of wind and solar power; P train This refers to the power supply for train traction; Ploss is the system power loss.

[0163] The constraints for battery energy storage modules, supercapacitor energy storage modules, and flywheel energy storage modules are as follows:

[0164]

[0165] Where i=1 represents the battery energy storage module, i=2 represents the supercapacitor energy storage module, and i=3 represents the flywheel energy storage module; OC i(t) represents the state of charge (SOC) of each energy storage element at time t; i,min and SOC i,max These are the lower and upper limits of the state of charge of the energy storage element, respectively; X i t and Y i t These represent the charging and discharging states of the corresponding energy storage modules; Let t be the power of the energy storage module at time t; and These are the rated charging and discharging power of the corresponding energy storage modules;

[0166] The constraint formula for the charging and discharging states of the energy storage module is as follows;

[0167]

[0168] S500: Solve the objective function to obtain the optimal capacity and power ratio of the hybrid energy storage configuration;

[0169] It should be noted that in step S500, the Cplex solver is used to optimize the objective function and obtain the optimal capacity and power ratio of the three energy storage configurations. In addition to the Cplex solver, other solvers can also be used for the calculation.

[0170] S600. Configure the capacity of hybrid energy storage in traction substations according to the optimal capacity and power ratio of hybrid energy storage configuration.

[0171] This invention also relates to a hybrid energy storage capacity configuration device for traction substations, comprising:

[0172] The prediction unit is used to construct uncertainty models for wind power generation and photovoltaic power generation, and to predict the wind and solar power output curves through the uncertainty models.

[0173] The first calculation unit is used to acquire the train operation data corresponding to the traction substation, predict the train load curve of the traction substation during the day based on the train operation data corresponding to the traction substation, calculate the deviation power curve between train load and wind and solar power output based on the predicted train load curve and wind and solar power output curve, and perform power allocation for hybrid energy storage charging and discharging based on the deviation power curve between train load and wind and solar power output.

[0174] An optimization unit is used to establish a priority strategy for hybrid energy storage charging and discharging, and optimizes the power allocation of hybrid energy storage charging and discharging based on two-layer fuzzy control.

[0175] The objective function unit is used to construct the objective function that minimizes the total cost of coordinated operation of traction substations.

[0176] The second calculation unit is used to solve the objective function and obtain the optimal capacity and power ratio of the energy storage configuration.

[0177] The configuration unit is used to configure the capacity of hybrid energy storage in traction substations according to the optimal capacity and power ratio of the energy storage configuration.

[0178] This invention also relates to an electronic device, which includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for enabling communication between the processor and the memory. When the program is executed by the processor, it implements the steps of the method described in the above embodiments.

[0179] This invention also relates to a storage medium, which is a computer-readable storage medium for computer-readable storage, characterized in that the storage medium stores one or more programs, which can be executed by one or more processors to implement the steps of the method described above.

[0180] This invention discloses a method for configuring hybrid energy storage capacity in traction substations considering uncertainties in the "source-vehicle" relationship. The method involves constructing uncertainty models for wind and photovoltaic power generation on the "source" side, predicting wind and solar power output curves using these models, acquiring train operation data corresponding to the traction substations, predicting the intraday train load curve of the traction substations based on this data, calculating the deviation power curve between train load and wind / solar output based on the predicted train load curve and the wind / solar output curves, and allocating power for hybrid energy storage charging and discharging based on this deviation power curve. Furthermore, it involves constructing a priority strategy for hybrid energy storage charging and discharging by combining the energy storage characteristics of supercapacitors, batteries, and flywheels on the "storage" side, optimizing the power allocation for hybrid energy storage charging and discharging based on two-layer fuzzy control, constructing an objective function to minimize the total cost of coordinated operation of the traction substations, solving the objective function to obtain the optimal capacity and power ratio for energy storage configuration, and configuring the hybrid energy storage capacity in the traction substations according to the optimal capacity and power ratio.

[0181] This invention addresses scenarios where traction power supply systems are integrated with wind power, photovoltaic power generation, and hybrid energy storage systems. Based on dual-layer fuzzy control, it allocates power based on the power difference between wind and solar power output and train load, determining the optimal capacity configuration of each energy storage medium. This improves the utilization rate of wind, photovoltaic, and regenerative braking energy, reduces the electricity cost of electrified railways, and rationally allocates the input and output power of the three energy storage modules while considering the uncertainties in power output and train load. It solves for the capacity configuration and power allocation of hybrid energy storage in traction substations, minimizing hybrid energy storage costs and maximizing the absorption of new energy sources and compensating for train load fluctuations.

[0182] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A method for configuring hybrid energy storage capacity in a traction substation, characterized in that, Includes the following steps: S100. Construct uncertainty models for wind power generation and photovoltaic power generation, and predict wind and solar power output curves using the uncertainty models; S200: Obtain train operation data corresponding to the traction substation, predict the load curve of the traction substation based on the train operation data corresponding to the traction substation, calculate the deviation power curve between train load and wind and solar power output based on the load curve of the traction substation and the wind and solar power output curve, and perform power allocation for hybrid energy storage charging and discharging based on the deviation power curve of wind and solar power output. S300: Construct a priority strategy for hybrid energy storage charging and discharging, and optimize the power allocation of hybrid energy storage charging and discharging based on two-layer fuzzy control; S400, construct the objective function that minimizes the total cost of coordinated operation of traction substations; S500: Solve the objective function to obtain the optimal capacity and power ratio of the hybrid energy storage configuration; S600. Configure the capacity of hybrid energy storage in traction substations according to the optimal capacity and power ratio of hybrid energy storage configuration; The specific steps of step 100 are as follows: S101. Construct the relationship between wind turbine output power and wind speed. ； Among them, P i (t) represents the output power of wind turbine i during time period t; This is the rated power of wind turbine i; v i (t) is the wind speed at the hub of wind turbine i at time t before day t; and These are the cut-in wind speed and cut-out wind speed of wind turbine i; This is the rated wind speed of wind turbine unit i; S102. Collect meteorological data of the area where the wind turbine is located, predict the wind speed curve based on the previous day's meteorological data, and obtain the predicted value of the wind turbine's output power based on the predicted wind speed curve and the relationship between the wind turbine's output power and wind speed. S103. Construct the relationship between the output power of photovoltaic power generation equipment and the light intensity. ； Among them, P l (t) represents the output power of photovoltaic power generation equipment l during time period t. This is the rated power of the photovoltaic power generation equipment l. The light intensity of photovoltaic power generation device l during time period t. Maximum light intensity; S104. Collect meteorological data of the area where the photovoltaic power generation equipment is located, predict the light intensity curve based on the meteorological data, and obtain the predicted value of the output power of the photovoltaic power generation equipment by combining the predicted light intensity curve with the relationship between the output power of the photovoltaic power generation equipment and the light intensity. S105. Based on the predicted output power of the wind turbine and the predicted output power of the photovoltaic power generation equipment, the predicted wind and solar power output curves are obtained. The specific steps of step S200 are as follows: S201. Constructing the formula for calculating train load power ； P(t)=F(t)v(t)1000 / 3.6; Among them, P T It is the train load power; v(t) is the train speed; P aux (t) is the power consumed by the auxiliary winding; F(t) is the traction or braking force of the train at time t; η is the power transmission efficiency from the on-board transformer to the wheel. S202. Based on train operation data and train load power, predict the load power of the traction substation. The formula for calculating the load power is as follows: ； Among them, P tr,d,t K is the actual load power of the traction substation at time t on day d; tr,d,t It is a random variable; ΔP tr,d,t It is the fluctuation value of the load power; It is the maximum fluctuation of load power; Γ tr It is an uncertain budget of load power fluctuations; S203. Based on the predicted load power of the traction substation, the load curve of the predicted traction substation is obtained. The train load curve of the predicted traction substation is subtracted from the wind and solar power output curve to obtain the deviation power curve between the train load and the wind and solar power output. S204. Power allocation for hybrid energy storage charging and discharging is performed based on the deviation power curve between train load and wind and solar power output. The hybrid energy storage in step S300 includes supercapacitor energy storage, battery energy storage, and flywheel energy storage. The specific steps in step S300, which optimize the power allocation of hybrid energy storage charging and discharging based on dual-layer fuzzy control, are as follows: S301. Select a fuzzy membership function and construct an input-output fuzzy set based on the fuzzy membership function, including a first input fuzzy value and a first output fuzzy value, a second input fuzzy value and a second output fuzzy value; S302. Construct a first-layer fuzzy control rule based on the first input fuzzy value and the first output fuzzy value, and construct a second-layer fuzzy control rule based on the second input fuzzy value and the second output fuzzy value. S303. According to the first-level fuzzy control rules, the power of the high-power energy storage module and the battery energy storage module is initially allocated to determine the output power of the battery energy storage module and the high-power energy storage module. The high-power energy storage module includes a supercapacitor energy storage module and a flywheel energy storage module. S304. Based on the second-level fuzzy control rules, the power allocated to the high-power energy storage module is re-allocated to determine the output power of the flywheel energy storage module and the supercapacitor energy storage module.

2. The method for configuring hybrid energy storage capacity in traction substations according to claim 1, characterized in that, The objective function in step S400 is: ； Among them, C total This refers to the daily operating cost of the hybrid energy storage system in the traction substation; T plan It is the entire life cycle of a hybrid energy storage system; The energy storage cost of the flywheel; The energy storage cost of batteries; The energy storage cost of supercapacitors; Cost of supporting equipment for hybrid energy storage; Maintenance costs for hybrid energy storage; C buy It is the cost of purchasing electricity for the traction substation.

3. The method for configuring hybrid energy storage capacity in traction substations according to claim 2, characterized in that, The constraints of the objective function include constraints on power balance, constraints on battery energy storage modules, supercapacitor energy storage modules and flywheel energy storage modules, and constraints on the charging and discharging states of the energy storage modules. The constraint formula for power balance is: ； Among them, P buy Power is drawn from the power grid; P new It refers to the output power of wind and solar power; P train This refers to the power supply for train traction; Ploss is the system power loss. The constraints for battery energy storage modules, supercapacitor energy storage modules, and flywheel energy storage modules are as follows: ； Where i=1 represents the battery energy storage module, i=2 represents the supercapacitor energy storage module, and i=3 represents the flywheel energy storage module; OC i (t) represents the state of charge (SOC) of each energy storage element at time t; i,min and SOC i,max These are the lower and upper limits of the state of charge of the energy storage element, respectively; X i t and Y i t These represent the charging and discharging states of the corresponding energy storage modules; P i t Let t be the power of the energy storage module at time t; and These are the rated charging and discharging power of the corresponding energy storage modules; The constraint formula for the charging and discharging states of the energy storage module is as follows; 。 4. A hybrid energy storage capacity configuration device for a traction substation for implementing the method according to any one of claims 1 to 3, characterized in that, include: The prediction unit is used to construct uncertainty models for wind power generation and photovoltaic power generation, and to predict the wind and solar power output curves through the uncertainty models. The first calculation unit is used to acquire the train operation data corresponding to the traction substation, predict the train load curve of the traction substation during the day based on the train operation data corresponding to the traction substation, calculate the deviation power curve between train load and wind and solar power output based on the predicted train load curve and wind and solar power output curve, and perform power allocation for hybrid energy storage charging and discharging based on the deviation power curve between train load and wind and solar power output. An optimization unit is used to establish a priority strategy for hybrid energy storage charging and discharging, and optimizes the power allocation of hybrid energy storage charging and discharging based on two-layer fuzzy control. The objective function unit is used to construct the objective function that minimizes the total cost of coordinated operation of traction substations. The second calculation unit is used to solve the objective function and obtain the optimal capacity and power ratio of the energy storage configuration. The configuration unit is used to configure the capacity of hybrid energy storage in traction substations according to the optimal capacity and power ratio of the energy storage configuration.

5. An electronic device, characterized in that, The electronic device includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for enabling communication between the processor and the memory, wherein the program, when executed by the processor, implements the steps of the method as described in any one of claims 1 to 3.

6. A storage medium, said storage medium being a computer-readable storage medium for computer-readable storage, characterized in that, The storage medium stores one or more programs, which can be executed by one or more processors to implement the steps of the method according to any one of claims 1 to 3.

Images