Traction power supply system hybrid energy storage capacity configuration method considering stepped carbon transaction

Abstract

The invention relates to the technical field of hybrid energy storage capacity configuration of a traction power supply system, in particular to a hybrid energy storage capacity configuration method of a traction power supply system considering stepped carbon transaction, which comprises the following steps: inputting system basic parameters; generating an initial population number of an upper layer model by using a Cat chaotic sequence, calculating the cost of the hybrid energy storage system, and transmitting a hybrid energy storage capacity configuration scheme to an operation layer; according to a hybrid energy storage capacity configuration scheme transmitted by the planning layer, in combination with an operation control strategy, calculating the carbon transaction cost, the external electricity purchase cost and the wind and light abandoning cost of the system, and returning the final daily operation cost to the upper layer optimization model; performing conversion according to the daily operation cost transmitted by the operation layer to obtain the annual operation cost of the system, and calculating the comprehensive cost of the system in the whole life cycle of hybrid energy storage by combining the cost of the hybrid energy storage system; and repeating the steps to reach the maximum number of iterations, comparing the system comprehensive cost corresponding to the scheme generated by each iteration, and finally outputting the optimal hybrid energy storage capacity configuration scheme.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hybrid energy storage capacity configuration of traction power supply system, in particular to a hybrid energy storage capacity configuration method of traction power supply system considering stepwise carbon trading. BACKGROUND

[0002] Around the low-carbon transformation of electrified railway, the current research mainly focuses on the energy management mode and capacity optimization configuration after the access of new energy and energy storage. The existing technology proposes a scheme of photovoltaic access to electrified railway, and analyzes the economic and environmental benefits of the system. The existing technology analyzes the change of system power supply capacity after the access of new energy to the Sichuan-Tibet Railway. The above research proves the feasibility of the access of new energy to the traction power supply system. The existing technology further analyzes the high energy-saving and economic efficiency of the access of new energy and hybrid energy storage to the electrified railway. The existing technology analyzes the topology structure and control strategy of the access of photovoltaic and energy storage to the traction power supply system. Based on the above background, the existing technology proposes a hybrid energy storage optimal capacity configuration scheme for the traction power supply system, which can reduce the system cost and achieve good energy-saving effect.

[0003] The above existing technologies propose a series of researches for the electrified railway containing new energy and energy storage system, but lack intuitive analysis of the change of system carbon emission after the access of new energy and hybrid energy storage. At present, in order to reduce regional carbon emission, the stepwise carbon trading model has been widely introduced into various comprehensive energy systems. The existing technology considers the stepwise carbon trading and new energy output uncertainty, proposes a scheme for energy storage capacity configuration and comprehensive energy system optimization operation, reasonably plans the energy storage capacity and reduces the regional carbon emission. The existing technology constructs a comprehensive energy system optimization scheduling model considering stepwise carbon trading and demand response, improves the system flexibility, and obtains certain benefits in the carbon trading market. The existing technology proposes a hybrid energy storage double-layer capacity configuration model considering stepwise carbon trading for the harbor comprehensive energy system, not only completes the efficient configuration of electric-thermal energy storage, but also proves the role of stepwise carbon trading in reducing port carbon emission and promoting new energy consumption. The above research proves that the introduction of stepwise carbon trading model for comprehensive energy system optimization scheduling and energy storage capacity configuration can balance the economy and low-carbon development of the system. SUMMARY

[0004] The present application aims to provide a hybrid energy storage capacity configuration method of traction power supply system considering stepwise carbon trading, which introduces stepwise carbon trading into the traction power supply system containing new energy generation and hybrid energy storage.

[0005] In order to achieve the above purpose, the technical scheme is as follows:

[0006] A traction power supply system hybrid energy storage capacity configuration method considering stepped carbon trading, which divides the capacity configuration process into a planning layer model and a running layer model, first obtains a hybrid energy storage capacity configuration scheme through the planning layer model, then calculates the running cost of the system through the running layer model, and returns the result to the upper layer, and iteratively determines the optimal energy storage configuration scheme; Specifically, the following steps are included:

[0007] Step one, input the system basic parameters, including different unit output, traction load power, stepped carbon trading model parameters and hybrid energy storage system parameters;

[0008] Step two, use Cat chaotic sequence to generate the initial population number of the upper layer model, calculate the cost of the hybrid energy storage system, and pass the hybrid energy storage capacity configuration scheme to the running layer;

[0009] Step three, according to the hybrid energy storage capacity configuration scheme passed by the planning layer, combined with the operation control strategy, calculate the carbon trading cost, external power purchase cost and wind and light abandonment cost of the system, and return the final daily operation cost to the upper optimization model;

[0010] Step four, according to the daily operation cost passed by the running layer, the annual operation cost of the system is obtained by conversion, combined with the cost of the hybrid energy storage system, the comprehensive cost of the system in the whole life cycle of the hybrid energy storage system is calculated;

[0011] Step five, repeat steps two to four until the maximum iteration number is reached, compare the system comprehensive cost corresponding to the scheme generated by each iteration, and finally output the optimal hybrid energy storage capacity configuration scheme.

[0012] Preferably, the planning layer model includes a planning layer objective function and a planning layer constraint condition.

[0013] Specifically, the planning layer objective function takes the comprehensive cost C SYS of the system in the planning period as the objective function, specifically as shown in formula (1):

[0014] min C SYS = C HESS +C OP (1)

[0015] In the formula: C OP is the annual operation cost of the system; C HESS is various fees generated by the hybrid energy storage system;

[0016] Various fees C HESS generated by the hybrid energy storage system include purchase, maintenance, replacement cost and residual value recovery benefit, the invention defaults that photovoltaic and wind turbine have been connected to the traction power supply system in advance, only the cost generated by the energy storage system is considered here, specifically as shown in formula (2):

[0017] C HESS =C BAT +C HES =C init +C om +C rep +C inv -C res (2)

[0018] In the formula: C BAT For battery costs, C HES For the cost of hydrogen energy storage, C init For energy storage purchase costs, C om For operation and maintenance costs, C rep For replacement cost, C inv For the cost of the matching converter, C res For residual value recovery benefits.

[0019] The energy storage purchase cost C init The calculation method is shown in equation (3):

[0020]

[0021] In the formula, and These are the initial purchase costs of the battery and the hydrogen energy storage system, respectively; c BATP With c BATE These are the unit power and capacity investment cost coefficients for the storage battery, respectively. and These refer to the power and capacity of the battery in the configuration scheme; c ELP c FCP and c STOE These are the unit power and capacity investment cost coefficients for electrolyzers, fuel cells, and hydrogen storage tanks, respectively. as well as These represent the power and capacity of the electrolyzer, fuel cell, and hydrogen storage tank in the configuration scheme, respectively; R CR This is the capital recovery coefficient;

[0022] The capital recovery coefficient R CR The calculation method is shown in equation (4):

[0023]

[0024] In the formula, r is the discount rate, which is taken as 0.09, and Y is the system planning period;

[0025] The energy storage maintenance cost C om The cost can be calculated based on the initial investment cost of energy storage, as shown in equation (5):

[0026]

[0027] In the formula, is the ratio of the operation and maintenance cost of the hybrid energy storage to the initial purchase cost, and is 0.02;

[0028] The energy storage replacement cost C rep is calculated according to formula (6):

[0029]

[0030] The equivalent cycle life A of the battery is calculated by using the rainflow counting and the equivalent life method, by converting the cycle times A of the battery in different operation states to the cycle times in the full operation state of the battery and summing up. ESS The replacement times N of the battery are finally calculated.

[0031] Since the life of the energy storage system is affected by the number of charging and discharging times, the replacement cost is considered. It is worth mentioning that since the life of the hydrogen energy storage system is generally 15-20 years, which is the same as the planning period time of the present application, the present application only considers the replacement cost of the battery.

[0032] The energy storage matching inverter cost C inv is calculated according to the configuration scheme of the hybrid energy storage, and is specifically shown in formula (7):

[0033]

[0034] In the formula, is the inverter investment coefficient of the unit power of the hybrid energy storage, and is 0.1;

[0035] The hybrid energy storage needs to be connected to the traction power supply system, and therefore the cost required for purchasing the inverter needs to be considered.

[0036] The energy storage residual value recovery benefit C res can be calculated according to the initial investment cost and the replacement cost of the battery, and is specifically shown in formula (8):

[0037]

[0038] In the formula, is the residual value recovery rate of the battery, and is 0.05;

[0039] The total operation cost C op of the system in the planning period can be calculated by weighting the single-day cost C op,d , and is specifically shown in formula (9):

[0040]

[0041] In the formula, N op is the total number of days of system operation per year.

[0042] Specifically, the planning layer constraint condition mainly includes the upper and lower limit constraints of the power of the hybrid energy storage, and is specifically shown in formula (10):

[0043]

[0044] In the formula: and are the maximum and minimum values of the battery power, respectively; and are the maximum and minimum values of the electrolytic tank and fuel cell power, respectively.

[0045] Preferably, the operation layer model includes an operation layer objective function and an operation layer constraint condition.

[0046] Specifically, the operation layer objective function takes the system daily operation cost as the objective function, and the calculation method is shown in formula (11), mainly including the carbon trading cost , the external power grid purchase cost C g , and the wind and light abandonment penalty cost C ab .

[0047]

[0048] The carbon trading cost is calculated by a traction power supply system carbon trading cost calculation model, specifically, according to the definition of the stepped carbon trading mechanism, a plurality of emission intervals are defined, and the stepped carbon trading cost calculation model is shown in formula (12):

[0049]

[0050] In the formula: represents the carbon trading cost, λ represents the carbon emission coefficient, ρ c represents the carbon trading base price, l is the carbon emission interval length, α is the carbon trading price growth rate, E ct represents the available carbon emission right transaction amount;

[0051] The calculation method of the available carbon emission right transaction amount E ct is shown in formula (13):

[0052] E ct = E c,a -E c (13)

[0053] In the formula: E c,a represents the actual carbon emission amount of the traction power supply system, and E c represents the free carbon emission quota of the traction power supply system;

[0054] The actual carbon emission E of the traction power supply system c,a The calculation method is shown in formula (14):

[0055]

[0056] In the formula, E buy,a is the actual carbon emission, λ2 is the actual carbon emission coefficient of the conventional unit, P T (t) is the traction load at t moment.

[0057] The free carbon emission quota E of the traction power supply system c The calculation method is shown in formula (15):

[0058]

[0059] In the formula, E buy represents the free carbon emission quota of the conventional unit, E re represents the free carbon emission quota of the new energy unit, λ1 represents the free carbon emission quota coefficient, P T (t), P PV (t) and P WIND (t) are the traction load, photovoltaic and wind turbine output at t moment, and T represents the scheduling period, which is 24 hours.

[0060] It is worth mentioning that, since the train generates regenerative braking energy when braking, the direction is opposite to the power direction of the external power grid to the traction power supply system, according to the policy of "not counting in the opposite direction", the present application only calculates the moment when the traction load power is positive when calculating the free carbon emission quota.

[0061] The purchase cost C of the external power grid of the system g The calculation method is shown in formula (16):

[0062]

[0063] In the formula, C grid is the electricity charge, specifically the cost generated by the actual consumption of the traction power supply system, with the unit of ￥ / kwh; p grid is the purchase price; C dem is the demand charge, specifically the maximum value of the average load of the traction power supply system at 15-minute or 30-minute scale, with the unit of ￥ / kW; p dem is the demand charge price; N day,op is the number of system operation days; P dem is the electricity demand of the traction power supply system, which is obtained by the slip method.

[0064] The penalty cost C of abandoned wind and light abThe calculation method (17) is as follows:

[0065] C ab = c ab P AB (17)

[0066] In the formula, c ab is a wind and light abandonment penalty coefficient.

[0067] Specifically, the operation layer constraint condition includes a power balance constraint, a new energy output constraint, a wind and light abandonment amount constraint, a storage energy state of charge constraint, a storage energy charging and discharging constraint, and an energy conservation constraint.

[0068] The power balance constraint is that at any time t, the system should satisfy the power balance, as shown in formula (18):

[0069]

[0070] In the formula, P T (t), P GRID (t), P PV (t), and P WIND (t) are the traction load, the external power grid, the photovoltaic output, and the wind turbine output at time t, respectively; P BAT (t), P FC (t), and P EL (t) are the battery output, the fuel cell output, and the electrolytic tank output at time t, respectively; and P AB (t) is the wind and light abandonment amount at time t.

[0071] The new energy output constraint is that the new energy output should satisfy the unit maximum output constraint, as shown in formula (19):

[0072]

[0073] In the formula, P max and P max are the predicted maximum outputs of the photovoltaic and wind power at time t, respectively.

[0074] The wind and light abandonment amount constraint is that the new energy wind and light abandonment amount constraint is as shown in formula (20):

[0075] P AB ≥ 0 (20)

[0076] The storage energy state of charge constraint and the storage energy charging and discharging constraint are to prolong the service life of the storage energy, as shown in formula (21):

[0077]

[0078] In the formula, SOC BAT and SOC STOThese are the states of charge of the battery and the hydrogen storage tank, respectively.

[0079] To improve the recycling efficiency of energy storage, the energy storage system should satisfy the energy conservation constraint within a scheduling cycle, that is, the remaining electricity at the end of each scheduling zone should be equal to the electricity at the beginning of the next scheduling period. According to equations (22) and (23), we can obtain:

[0080] E BAT (0)=E BAT (E) (22)

[0081] E STO (0)=E STO (E) (23)

[0082] In the formula, E BAT (0), E BAT (E) and E STO (0), E STO (E) represents the energy stored in the batteries and hydrogen storage tanks at the beginning and end of the system scheduling period, respectively.

[0083] This invention introduces tiered carbon trading into a traction power supply system that includes renewable energy generation and hybrid energy storage, and uses batteries and hydrogen fuel cells as energy storage media, which has the following beneficial effects:

[0084] 1) A hybrid energy storage capacity configuration model was established by introducing tiered carbon trading into the traction power supply system that includes renewable energy generation. The planning layer uses the minimum overall system cost as the objective function to determine the hybrid energy storage configuration scheme; the operation layer performs cost calculations including the carbon trading mechanism and returns the system operation cost to the planning layer. After iterative iteration, the final hybrid energy storage capacity configuration result is determined.

[0085] 2) A hybrid energy storage scheme combining battery and hydrogen energy storage is proposed. Compared with a single energy storage medium, the proposed hybrid energy storage scheme has significant advantages in reducing system operating costs and improving the capacity for renewable energy consumption.

[0086] 3) Based on the standard Seagull algorithm, an improved Seagull optimization algorithm based on Cat chaotic sequence and Levy flight strategy is proposed, which achieves efficient solution of the proposed model. Attached Figure Description

[0087] Figure 1 This is a flowchart of the model solution process for the present invention;

[0088] Figure 2 This is a schematic diagram of the model structure of the present invention;

[0089] Figure 3 This is a system framework diagram of the present invention;

[0090] Figure 4 This is a diagram illustrating the system operation control strategy of the present invention.

[0091] Figure 5 This is a schematic diagram of the traction load power of the present invention;

[0092] Figure 6 This is a schematic diagram of the new energy power of the present invention;

[0093] Figure 7 This is a schematic diagram of the hybrid energy storage response results of the present invention;

[0094] Figure 8 In the table, (a) shows the operating results of traction substation 1; (b) shows the operating results of traction substation 2.

[0095] Figure 9 This is the traction power curve of the present invention;

[0096] Figure 10 This describes the changes in carbon trading revenue and total cost according to the present invention;

[0097] Figure 11 The diagram illustrates the results of different algorithm models of this invention. Detailed Implementation

[0098] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0099] Example 1

[0100] A method for configuring hybrid energy storage capacity in a traction power supply system that takes into account tiered carbon trading, such as Figure 2 As shown, the capacity configuration process is divided into a planning layer model and an operation layer model. First, the hybrid energy storage capacity configuration scheme is obtained through the planning layer model. Then, the operating cost of the system is calculated through the operation layer model, and the result is returned to the upper layer. This iterative cycle determines the optimal energy storage configuration scheme. Figure 1 As shown, the specific steps include:

[0101] Step 1: Input the basic system parameters, including the output of different units, traction load power, tiered carbon trading model parameters, and hybrid energy storage system parameters;

[0102] Step 2: Use Cat chaotic sequences to generate the initial population size of the upper-level model, calculate the cost of the hybrid energy storage system, and pass the hybrid energy storage capacity configuration scheme to the operation layer;

[0103] Step 3: Based on the hybrid energy storage capacity configuration scheme transmitted from the planning layer, and combined with the operation control strategy, calculate the carbon trading cost, external power purchase cost, and wind and solar curtailment cost of the system, and return the final daily operating cost to the upper-level optimization model.

[0104] Step 4: Based on the daily operating cost transmitted from the operation layer, calculate the annual operating cost of the system. Combine this with the cost of the hybrid energy storage system to calculate the comprehensive cost of the hybrid energy storage system over its entire life cycle.

[0105] Step 5: Repeat steps 2 to 4 until the maximum number of iterations is reached. Compare the overall system cost corresponding to the solutions generated in each iteration, and finally output the optimal hybrid energy storage capacity configuration solution.

[0106] Preferably, the planning layer model includes a planning layer objective function and planning layer constraints.

[0107] Specifically, the objective function of the planning layer is the comprehensive cost C of the system during the planning period. SYS The minimum is the objective function, as shown in equation (1):

[0108] minC SYS =C HESS +C OP (1)

[0109] In the formula: C OP C represents the annual operating cost of the system. HESS Various costs incurred for hybrid energy storage systems;

[0110] Various costs incurred by hybrid energy storage systems C HESS Including purchase, maintenance, replacement costs and residual value recovery benefits, this invention assumes that the photovoltaic and wind turbine units have been pre-connected to the traction power supply system. Here, only the cost generated by the energy storage system is considered, as shown in equation (2):

[0111] C HESS =C BAT +C HES =C init +C om +C rep +C inv -C res (2)

[0112] In the formula: C BAT For battery costs, C HES For the cost of hydrogen energy storage, C init For energy storage purchase costs, C om For operation and maintenance costs, C rep For replacement cost, C invC res for the residual value recovery benefit.

[0113] The energy storage purchase cost C init is calculated as shown in equation (3):

[0114]

[0115] wherein, and c are the initial purchase costs of the battery and hydrogen energy storage system, respectively; c BATP and c BATE are the unit power and capacity investment cost coefficients of the battery, respectively; and c are the power and capacity of the battery in the configuration scheme, respectively; c ELP , c FCP , and c STOE are the unit power and capacity investment cost coefficients of the electrolyzer, fuel cell, and hydrogen storage tank, respectively; and c are the power and capacity of the electrolyzer, fuel cell, and hydrogen storage tank in the configuration scheme, respectively; R CR is the capital recovery factor;

[0116] The capital recovery factor R CR is calculated as shown in equation (4):

[0117]

[0118] wherein r is the discount rate, taken as 0.09, and Y is the system planning period;

[0119] The energy storage maintenance cost C om can be converted according to the initial investment cost of the energy storage, as shown in equation (5):

[0120]

[0121] wherein, is the ratio coefficient of the operating and maintenance cost and the initial purchase cost of the hybrid energy storage, taken as 0.02;

[0122] The energy storage replacement cost C rep is calculated as shown in equation (6):

[0123]

[0124] Using the rainflow counting and equivalent life method, the equivalent cycle life A ESSThe number of replacement of the battery is finally obtained as N;

[0125] Since the life of the energy storage system is affected by the number of charge and discharge, the replacement cost is considered. It is worth mentioning that since the life of the hydrogen energy storage system is generally 15-20 years, which is the same as the planning period time of the present application, the present application only considers the replacement cost of the battery;

[0126] The cost of the energy storage matching converter C inv According to the configuration scheme of the hybrid energy storage, the conversion is carried out, which is specifically shown in formula (7):

[0127]

[0128] In the formula, The converter investment coefficient of the unit power of the hybrid energy storage is 0.1;

[0129] The hybrid energy storage needs to be matched with the converter equipment when accessing the traction power supply system, so the cost required for purchasing the converter needs to be considered.

[0130] The energy storage residual value recovery benefit C res The initial investment cost and the replacement cost of the battery can be converted, which is specifically shown in formula (8):

[0131]

[0132] In the formula, The battery recovery residual value is 0.05;

[0133] The total operation cost of the system in the planning period C op The single-day cost C op,d The weighted calculation is obtained, which is specifically shown in formula (9):

[0134]

[0135] In the formula, N op The total number of system operation days per year.

[0136] Specifically, the planning layer constraint condition mainly includes the power upper and lower limit constraint of the hybrid energy storage, which is specifically shown in formula (10):

[0137]

[0138] In the formula: And The maximum and minimum values of the battery power are respectively; And The maximum and minimum values of the electrolytic tank and the fuel cell power are respectively.

[0139] Preferably, the operation layer model comprises an operation layer objective function and operation layer constraints.

[0140] Specifically, the operation layer objective function takes the system daily operation cost as the objective function, and the calculation method is shown in formula (11), mainly including carbon trading cost external grid power purchase cost C g and wind and light abandonment penalty cost C ab ;

[0141]

[0142] The carbon trading cost is calculated by the traction power supply system carbon trading cost calculation model, specifically, according to the definition of the stepped carbon trading mechanism, a plurality of emission intervals are specified, and the stepped carbon trading cost calculation model is shown in formula (12):

[0143]

[0144] In the formula: represents the carbon trading cost, λ represents the carbon emission coefficient, ρ c represents the carbon trading base price, l is the carbon emission interval length, α is the carbon trading price growth rate, E ct represents the available carbon emission right transaction amount;

[0145] The calculation method of the available carbon emission right transaction amount E ct is shown in formula (13):

[0146] E ct = E c,a -E c (13)

[0147] In the formula: E c,a represents the actual carbon emission of the traction power supply system, E c represents the free carbon emission quota of the traction power supply system;

[0148] The calculation method of the actual carbon emission E c,a of the traction power supply system is shown in formula (14):

[0149]

[0150] In the formula: E buy,a is the actual carbon emission, λ2 is the actual carbon emission coefficient of the conventional unit, P T (t) is the traction load at time t;

[0151] The calculation method of the free carbon emission quota E c of the traction power supply system is shown in formula (15):

[0152]

[0153] E buy represents the free carbon quota of conventional generating units, E re represents the free carbon quota of new energy generating units, λ1 represents a free carbon quota coefficient, P T (t), P PV (t) and P WIND (t) are respectively the traction load, photovoltaic and wind turbine output at time t, and T represents a dispatching period, which is 24 hours;

[0154] It is worth mentioning that, since the train generates regenerative braking energy when braking, the direction of the power flowing from the external power grid to the traction power supply system is opposite to the direction of the power supplied by the traction power supply system, and according to the policy of “not counting against the sending”, the present application only calculates the time when the traction load power is positive when calculating the free carbon quota.

[0155] The external power grid power purchase cost C g The calculation method is shown in formula (16);

[0156]

[0157] C grid is the electricity charge, specifically the cost generated by the actual consumption of the traction power supply system, and the unit is ￥ / kwh; p grid is the power purchase price; C dem is the demand charge, specifically the maximum value of the average load of the traction power supply system at a 15-minute or 30-minute scale, and the unit is ￥ / kW; p dem is the demand charge price; N day,op is the number of system operation days; P dem is the electricity demand of the traction power supply system, which is obtained by the slip method.

[0158] The abandoned wind and light penalty cost C ab The calculation method (17) is as follows:

[0159] C ab = c ab P AB (17)

[0160] In the formula: c ab is the abandoned wind and light penalty coefficient.

[0161] Specifically, the operation layer constraint condition includes a power balance constraint, a new energy output constraint, an abandoned wind and light amount constraint, a storage energy state of charge constraint, a storage energy charging and discharging constraint, and an energy conservation constraint.

[0162] The power balance constraint is that the system should satisfy the power balance at any time t, as shown in equation (18):

[0163]

[0164] In the formula, P T (t), P GRID (t), P PV (t) and P WIND (t) are the traction load, external power grid, photovoltaic and wind turbine output at time t, respectively; P BAT (t), P FC (t) and P EL (t) are the battery, fuel cell and electrolytic tank output at time t, respectively; P AB (t) is the wind and light abandoned amount at time t;

[0165] The new energy output constraint is that the new energy output should satisfy the unit maximum output constraint, as shown in equation (19):

[0166]

[0167] In the formula: P

[0168] The wind and light abandoned amount constraint is the new energy wind and light abandoned amount constraint, as shown in equation (20):

[0169] P AB ≥ 0 (20)

[0170] The energy storage state of charge constraint and the energy storage charge and discharge constraint are to prolong the service life of the energy storage, as shown in equation (21):

[0171]

[0172] In the formula, SOC BAT and SOC STO are the state of charge of the battery and the hydrogen storage tank, respectively;

[0173] In order to improve the recycling efficiency of the energy storage, the energy storage system should satisfy the energy conservation constraint in a scheduling period, that is, the remaining power at the end of each scheduling area should be equal to the power at the beginning of the next scheduling period, that is, the energy conservation constraint, which can be obtained according to equation (22) and equation (23):

[0174] E BAT (0) = E BAT (E) (22)

[0175] E STO (0) = E​STO (E) (23)

[0176] wherein E BAT (0), E BAT (E) is the initial and final period of the system scheduling battery and hydrogen storage tank storage energy, respectively. STO (0), E STO (E) is the initial and final period of the system scheduling battery and hydrogen storage tank storage energy, respectively.

[0177] System operation control strategy and solving method of embodiment 2

[0178] 1. System operation control strategy

[0179] During system operation, each link cooperates with each other, and a corresponding control strategy needs to be proposed for energy distribution. Therefore, the control strategy proposed by the present application comprehensively considers factors such as energy storage response speed, energy conversion efficiency and system economic cost, and is as shown in A-G in the figure. Figure 4 SYS P is the difference between the output of new energy and the demand of traction load; P CHMAX and P DHMAX are the maximum charging and discharging power of the battery; P ELMAX and P FCMAX are the maximum absorption and discharge power of the electrolytic cell and fuel cell, respectively.

[0180] A: After meeting the required power of the load, the remaining power of the new energy power generation system is all used for charging the battery.

[0181] B: After meeting the required power of the load and the maximum absorption power of the battery, the remaining power of the new energy power generation system is absorbed by the electrolytic cell, and the remaining energy is converted into hydrogen gas into the hydrogen storage tank through the process of electrolyzing water.

[0182] C: After meeting the required power of the load, the maximum absorption power of the battery and the maximum capacity of the hydrogen storage tank, the remaining power of the new energy power generation system is P AB , which participates in the calculation of the cost of abandoned wind and light.

[0183] D: After meeting the required power of the load and the remaining power of the new energy power generation system being completely absorbed by the hybrid energy storage system, if the battery and the hydrogen storage tank still do not reach the maximum absorption power or the maximum capacity, at this time if there is regenerative braking energy of the train, energy recovery is continued in the order of the battery-electrolytic cell until the maximum capacity of the hydrogen storage tank is reached.

[0184] E: When the output of the new energy power generation system is insufficient, the missing energy is all supplemented by the battery.

[0185] F: When the output of the new energy power generation system is insufficient, after the battery is completely discharged, the remaining energy is supplemented by the fuel cell.​

[0186] G: When the new energy power generation system output is insufficient, the battery and the fuel cell are completely discharged, and the energy is purchased from the external power grid, which is recorded as P GRID , and participates in the system electricity cost calculation.

[0187] 2. Solution method

[0188] The solution of the energy storage capacity configuration model belongs to a large-scale optimization problem, and it is difficult to balance the accuracy and speed of the solution. The present application further considers the carbon trading mechanism, traction power supply system electricity cost and other factors on the basis of the energy storage capacity configuration, so that the original problem becomes more complex. When solving large-scale optimization problems, the SOA algorithm has the advantages of flexible search mode, strong robustness, and rich diversity, which helps to improve the global exploration ability and search efficiency of the algorithm, and has high applicability to the energy storage capacity optimization configuration model proposed in the present application. Therefore, the present application proposes an SOA algorithm based on Cat chaotic mapping strategy and Levy flight strategy to solve the model.

[0189] 2.1 Seagull optimization algorithm

[0190] The seagull optimization algorithm (SOA) was proposed by Gaurav Dhiman in 2018. SOA simulates the migration (global search) and attack (local search) behavior of seagulls to establish the exploration and exploitation processes of the algorithm. SOA has the advantages of requiring fewer parameters and being easy to understand, but still has the disadvantages of slow convergence speed and being prone to local optimization. 23 Therefore, the present application introduces Cat chaotic mapping and Levy flight strategy to improve the standard SOA algorithm.

[0191] 2.2 Cat chaotic mapping strategy

[0192] The present application introduces Cat chaotic mapping strategy to generate more diverse initial population, improve the optimization ability of SOA, make the generated sequence have better ergodicity and uniformity, and the chaotic sequence interval is [0, 1], as shown in formula (24).

[0193]

[0194] In the formula, n is the iteration number, x n and y n , x n+1 and y n+1 are two-dimensional variable values generated in the nth and (n+1)th iterations, respectively.

[0195] 2.3 Levy flight strategy

[0196] The Levy flight strategy introduced in the present application is a non-Gaussian random process subject to Levy distribution, which can take into account the distances of small steps and large steps, and enhance the search range of the SOA algorithm in the early stage.

[0197] Example 3 analysis

[0198] The present application sets the system life to 20 years, uses two domestic traction stations and local meteorological measured data to analyze the proposed hybrid energy storage capacity configuration scheme, the sampling interval is 5 minutes, the dispatching period is 24 hours, and the traction load power is as shown in Figure 5 .

[0199] It can be seen that the traction load power presents great volatility, when the load curve is positive, it means that the train absorbs power from the outside, and when the load curve is negative, it means that the train generates regenerative braking energy. The maximum load demand of traction station 1 and traction station 2 is 20.46MW and 14.78MW respectively, and the peak value of regenerative braking energy is-7.86MW and-7.82MW respectively.

[0200] Affected by geographical environment and weather factors, the output of new energy has certain periodicity and volatility, the present application uses K-means clustering algorithm to reduce the original scene, and obtains the final new energy output scene as shown in Figure 6 . It can be seen that the wind power at traction station 1 and traction station 2 has little difference, and the photovoltaic power at traction station 2 is greater than that at traction station 1 during 8:00-20:00, therefore, traction station 2 has more abundant new energy resources.

[0201] The carbon emission of the traction power supply system mainly comes from the carbon emission caused by load power consumption, which is similar to the carbon emission characteristics of thermal power units. Therefore, in order to encourage the traction power supply system to reduce emissions, the present application refers to the carbon quota allocation benchmark and carbon trading parameters of thermal power units, takes the actual carbon emission quota of the traction power supply system as 1.08kg / kWh, the free carbon emission quota as 0.59kg / kWh, the carbon trading base price as 200yuan / t, the carbon emission interval length as 2t, and the carbon trading price growth rate as 0.25.

[0202] The electricity charge and the demand electricity charge are shown in Table 1:

[0203] Table 1 Electricity price information

[0204]

[0205] 1、System operation scheduling result

[0206] Considering that the peak of traction load and new energy output is generally between 08:00-20:00, the system change in this period is mainly analyzed, the time interval is 5 min, and the mixed energy storage response results of different tractions are as shown in Figure 7 .

[0207] From Figure 7 it can be seen that the charging and discharging actions of the mixed energy storage system of different tractions are relatively frequent, because the power of new energy and traction load has great volatility and randomness, and the mixed energy storage system needs to respond quickly and make adjustment. Overall, the mixed energy storage system can timely consume the surplus energy when the new energy output is greater than the traction load demand, and can also timely respond to the strong impact of the load when the traction load demand is greater than the new energy output, proving the effectiveness of the mixed energy storage system proposed in the application.

[0208] The system operation results are as shown in Figure 8 and Table 2, it can be seen that after the mixed energy storage system is configured, the power of each part of the two tractions can meet the power balance demand of the system, and the power change of traction 1 is taken as an example for analysis.

[0209] After the new energy power meets the traction load demand, the surplus energy of the system is first recovered by the battery, such as the period of 12:40-12:45 in the middle of the day and 16:40-16:45 in the afternoon, which is in the peak period of photovoltaic and wind power output, but still not in the peak period of total new energy output, so the battery can completely recover the surplus energy of the system, corresponding to Figure Three control strategy A. When the total new energy output is in the peak period, such as the period of 13:40-13:45 in the middle of the day, the surplus energy is recovered by the electrolytic tank together with the battery, corresponding to Figure Three control strategy B. In the period of 12:10-12:20, after reaching the maximum storage capacity of the hydrogen storage tank, the surplus energy of the new energy is considered as the amount of abandoned wind and light, corresponding to the control strategy C. In the period of 9:30-9:35 in the morning, the new energy power has been completely absorbed by the mixed energy storage system, but the mixed energy storage system still has surplus capacity, so the mixed energy storage system continues to absorb the regenerative braking energy, corresponding to the control strategy D. Since the regenerative braking energy does not directly participate in the cost calculation of the model proposed in the application, it is no longer shown in Figure 8 .

[0210] When the traction load demand is higher than the new energy output, such as 8:10-8:30 in the morning and 17:10-17:25 in the afternoon, at this time, the wind and light output does not reach the peak value, and cannot meet the traction load demand, the system missing energy is supplemented by the battery, corresponding to the control strategy E. At 18:25 in the evening, affected by the decline of photovoltaic output, the new energy output cannot support the traction load demand, but due to the fact that it is still in the peak period of wind power generation at this time, the load demand and the new energy output are small, the system energy difference is supplemented by the storage and fuel cell, and there is no need to purchase electricity from the external power grid, corresponding to the control strategy F. In the period of 9:45-9:55 and 18:15-18:20 in the morning, at this time, it is in the rising and falling period of photovoltaic power generation, the total new energy output level is low, in order to meet the demand of traction load, it is necessary to purchase electricity from the external power grid after the hybrid energy storage reaches the maximum, corresponding to the control strategy G. In combination with Table 2, the system can maintain power balance in each period, which proves the effectiveness of the control strategy proposed in the application.

[0211] In order to observe the synergistic effect of the hybrid energy storage system, the power changes of each energy storage device are observed in the new energy output peak period of traction station 1 (13:30-15:30) and the new energy output rising period of traction station 2 (9:30-11:30), respectively, and the results are shown in Table 2. Figure 9 As can be seen, the battery and hydrogen energy storage system can adjust the power in time according to the current load and new energy changes, and perform charging and discharging actions. From the power change of each part of the traction station 1, it can be seen that during the period of 13:30-15:30 in the afternoon, the new energy output level can meet the demand of traction load in most of the time, at this time, the battery is preferentially charged, and the remaining energy is absorbed by the electrolytic cell to produce hydrogen. When the new energy cannot meet the demand of traction load, the battery is preferentially discharged, and the missing energy is supplemented by the fuel cell. From the power change of each part of the traction station 2, it can be seen that during the period of 9:30-11:30 in the morning, the new energy power is not at the peak value due to the influence of weather factors, but because the battery capacity of the traction station 2 is large, the battery cooperates with the new energy to supply power to the traction load in most of the time. The battery and the hydrogen energy storage system cooperate with each other to complete the efficient consumption of new energy and the reasonable recovery of the remaining energy of the system, which proves the stability of the operation of the hybrid energy storage system.

[0212] Table 2 Power of each part of traction station 1

[0213]

[0214] Table 3 is a comparison of partial operation results of different traction stations. It can be seen that after the hybrid energy storage system is configured, different traction stations can obtain benefits from the carbon trading market. Compared with traction station 1, the benefit of traction station 2 on the carbon trading market increases by 13%, because the photovoltaic resources of traction station 2 are more abundant, resulting in that the overall new energy output of traction station 2 is greater than that of traction station 1. After meeting the basic demand of traction load, the remaining carbon quota can be sold on the carbon trading market, thereby obtaining higher carbon trading benefits. The wind curtailment and light curtailment rates of traction station 1 and traction station 2 are relatively small, and under the double driving of higher new energy resources and larger energy storage configuration, the external grid output ratio of traction station 2 decreases by 4% compared with traction station 1, which shows that the hybrid energy storage system can effectively promote new energy consumption and reduce the external power purchase of the system. Overall, the total cost of traction station 2 with more abundant new energy resources is lower, which reflects the feasibility of connecting new energy to the traction power supply system.

[0215] Table 3 is a comparison of partial operation results of different traction stations. It can be seen that after the hybrid energy storage system is configured, different traction stations can obtain benefits from the carbon trading market. Compared with traction station 1, the benefit of traction station 2 on the carbon trading market increases by 13%, because the photovoltaic resources of traction station 2 are more abundant, resulting in that the overall new energy output of traction station 2 is greater than that of traction station 1. After meeting the basic demand of traction load, the remaining carbon quota can be sold on the carbon trading market, thereby obtaining higher carbon trading benefits. The wind curtailment and light curtailment rates of traction station 1 and traction station 2 are relatively small, and under the double driving of higher new energy resources and larger energy storage configuration, the external grid output ratio of traction station 2 decreases by 4% compared with traction station 1, which shows that the hybrid energy storage system can effectively promote new energy consumption and reduce the external power purchase of the system. Overall, the total cost of traction station 2 with more abundant new energy resources is lower, which reflects the feasibility of connecting new energy to the traction power supply system.

[0216]

[0217] Table 4 is the energy storage capacity configuration result of different traction stations. From Table 4, it can be seen that due to the difference between new energy resources and traction load demand, the energy storage configuration results of the two traction stations are also different. The configuration results of the battery, electrolytic tank and fuel cell of traction station 1 are 4500kw, 1800kw and 1200kW respectively, and the configuration results of the hybrid energy storage of traction station 2 are 6300kW, 2400kW and 960kW respectively. In combination with the load demand and new energy resource level of the two traction stations, traction station 2 needs to recover more new energy power, so it needs to configure more battery and electrolytic tank units to achieve the effect of new energy consumption; and the fuel cell as the discharge unit in the hydrogen energy storage system, after most of the traction load demand is responded by new energy, the configuration result is lower than that of traction station 1. In summary, the hybrid energy storage configuration scheme proposed in the present application can achieve timely response to traction load and efficient consumption of new energy under different traction stations and wind and light resources, and can reasonably allocate the capacity of the hybrid energy storage system according to different load demand and new energy output level, proving the effectiveness of the model proposed in the present application.

[0218] Table 4 is a comparison of energy storage configuration results of different traction stations

[0219]

[0220] 2、Different scheme results comparison

[0221] In order to verify the effectiveness of the model proposed in the present application, taking traction station 1 as an example, five different schemes are set to compare and analyze, and the specific results are shown in Table 5.

[0222] Scheme one: only consider the ladder carbon trading mechanism

[0223] Scheme two: consider the ladder carbon trading mechanism and battery

[0224] Scheme three: consider the ladder carbon trading mechanism and hydrogen energy storage

[0225] Scheme four: only consider hybrid energy storage

[0226] Scheme five: consider the ladder carbon trading mechanism and hybrid energy storage

[0227] For the ladder carbon trading introduced in the application, by comparing the results of scheme four and scheme five, after introducing the ladder carbon trading, the total cost of the system is reduced by about 36%. The wind and light curtailment rate of the system is reduced by 3%, which shows that the ladder carbon trading can promote the consumption of new energy of the system, so as to obtain more free carbon emission shares, and sell more in the carbon trading market to obtain more income, thereby effectively reducing the system cost.

[0228] For the hybrid energy storage system scheme proposed in the application, by comparing the results of scheme five and scheme one, it can be seen that the external grid output ratio of the system is reduced by 26%, and the total cost of the system is reduced by 48%, which shows that the hybrid energy storage system can effectively store and utilize new energy, thereby reducing the output of thermal power units, improving the environmental friendliness of the traction power supply system, and reducing the cost of purchasing electricity from the external grid. By comparing the results of scheme five with scheme two and three, compared with the configuration results of single energy storage, the configuration results of each part of the energy storage in scheme five are reduced to different degrees, which shows that the hybrid energy storage scheme proposed in the application can effectively reasonably plan the hybrid energy storage system, thereby reducing the land area and economic cost of the energy storage system.

[0229] For the ladder carbon trading and hybrid energy storage system scheme proposed in the application, by comparing the results of scheme five with scheme one and four, the total cost of scheme one and scheme four is higher than that of scheme five which considers hybrid energy storage and ladder carbon trading, and the wind and light curtailment rate of scheme five is reduced by 11% and 3%, and the external grid output ratio is reduced by 26% and 10%. It can be seen that the ladder carbon trading model can promote the consumption of new energy of the system, and improve the economic benefit of the system; and the hybrid energy storage system can reasonably plan different energy storage capacities, reduce the cost of the energy storage system, and reduce the external grid purchase quantity. In summary, the electric-hydrogen hybrid energy storage configuration model considering the ladder carbon trading proposed in the application has good economic benefit and environmental friendliness.

[0230] Table 5 comparison of results of different schemes

[0231]

[0232] 3. Comparison of different carbon trading base price results

[0233] In order to further study the relationship between the carbon trading base price and the total cost of the system, the present application draws a curve graph showing the relationship between different carbon trading base prices and carbon trading income and the total cost of the system, as shown in Figure 10 .

[0234] As can be seen from Figure 10 , the carbon trading income of the system is positively correlated with the growth of the carbon trading base price, but the trend of change is opposite to the total cost. This is because in the stepped carbon trading model introduced in the present application, the carbon trading base price is equivalent to the weight coefficient of the model. With the increase of the carbon trading base price, in order to obtain more income in the carbon trading market, the system needs to continuously increase the consumption rate of new energy, so as to obtain higher free carbon emission quota. When the carbon trading base price exceeds 320 yuan / ton, the income trend of the system in the carbon trading market slows down, which is because the consumption of new energy by the system has approached saturation at this time, and it is impossible to obtain higher carbon emission quota, so as to make the carbon income cost of the system tend to be stable. With the increase of the proportion of new energy access, the traction power supply system needs to configure more energy storage units to recover the new energy, which leads to the increase of the cost of the hybrid energy storage system, thereby slowing down the downward trend of the total cost of the system.

[0235] In summary, in the scheme of the present application, the carbon trading cost is an important part of the system operation cost, and appropriately increasing the carbon trading base price can effectively reduce the total cost of the system and improve the economic benefit of the system.

[0236] 4. Comparison of different algorithm results

[0237] According to the solving method in embodiment 2, the initial population number is set to 50, the iteration number is set to 100 times, and the standard SOA and PSO algorithms are used for comparison, and the results are shown in Figure 11 .

[0238] It can be seen that the three optimization algorithms can meet the operation requirements of the system, but the total cost of the improved SOA algorithm is lower than that of the standard SOA algorithm and the PSO algorithm, which shows that the improved SOA algorithm is more beneficial to the economic operation of the system. From the solving speed, the improved SOA algorithm reaches convergence at the 32nd iteration, while the standard SOA algorithm and the PSO algorithm reach convergence after the 56th and 62nd iterations respectively, which shows that the algorithm proposed in the present application has a faster convergence speed, proving the effectiveness and superiority of using the improved SOA algorithm.

[0239] 5. Conclusion

[0240] The application proposes a traction power supply system electric-hydrogen hybrid energy storage capacity configuration scheme considering stepped carbon trading, and compares the changes of model results under different situations and different algorithms. The main conclusions are as follows:

[0241] 1) The hybrid energy storage capacity configuration scheme considering stepped carbon trading can effectively improve the economy and new energy consumption capacity of the system. Compared with the scheme considering only carbon trading or hybrid energy storage, the total cost is reduced by about 48% and 36%, and the wind and light curtailment rate is reduced by 11% and 3%.

[0242] 2) The proposed hybrid energy storage scheme and control strategy can reasonably plan the energy storage capacity and complete the efficient cooperation between the electric-hydrogen hybrid energy storage system. Compared with the single energy storage configuration scheme, the carbon income of the hybrid energy storage system scheme is increased by 55% and 66%, the external grid output ratio is reduced by 19% and 17%, the indirect carbon emission of the system is reduced, and the economic benefit and environmental friendliness of the system are improved.

[0243] 3) For the optimization model considering electric-hydrogen hybrid energy storage capacity configuration and system operation, the improved SOA algorithm proposed in the application has faster solving speed and accuracy compared with the standard SOA and PSO algorithms. The proposed algorithm can effectively determine the hybrid energy storage capacity and reduce the system cost.

[0244] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be regarded as exemplary and non-limiting, and the scope of the present application is defined by the appended claims rather than the above description, and all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present application. Any reference signs in the claims should not be regarded as limiting the claims involved.

Claims

1. A method for hybrid energy storage capacity configuration of traction power supply system considering step carbon trading, the capacity configuration process is divided into a planning layer model and a running layer model, the hybrid energy storage capacity configuration scheme is obtained through the planning layer model first, the running cost of the system is calculated through the running layer model, and the result is returned to the upper layer, and the optimal energy storage configuration scheme is determined through iterative loop. Specifically comprising the following steps: Step one, input system basic parameters, including different unit output, traction load power, step carbon trading model parameters and hybrid energy storage system parameters; Step two, use Cat chaos sequence to generate the initial population number of the upper model, calculate the cost of the hybrid energy storage system, and pass the hybrid energy storage capacity configuration scheme to the operation layer; Step three, according to the hybrid energy storage capacity configuration scheme passed by the planning layer, combined with the operation control strategy, calculate the carbon trading cost, external power purchase cost and wind and light abandoned cost of the system, and return the final daily operation cost to the upper optimization model; Step four, according to the daily operation cost passed by the operation layer, the annual operation cost of the system is obtained by conversion, and the comprehensive cost of the system in the whole life cycle of the hybrid energy storage system is calculated; Step five, repeat steps two to four until the maximum iteration number is reached, compare the system comprehensive cost corresponding to the scheme generated by each iteration, and finally output the optimal hybrid energy storage capacity configuration scheme.

2. The method of claim 1, wherein: The planning layer model includes a planning layer objective function and a planning layer constraint condition.

3. The method of claim 2, wherein: The planning layer objective function aims to minimize the total cost C of the system over the planning horizon SYS The objective function is to minimize, as shown in equation (1): minC SYS = C HESS + C OP (1) where: C HESS is the various costs incurred for the hybrid energy storage system; C OP is the annual operating cost of the system; Various costs C generated by the hybrid energy storage system HESS Including purchase, maintenance, replacement cost and residual value recovery benefit, the invention defaults that photovoltaic and wind turbine have been connected to the traction power supply system in advance, here only consider the cost generated by the energy storage system, as shown in equation (2): C HESS = C BAT + C HES = C init + C om + C rep + C inv - C res (2) In the formula: C BAT C is the battery cost HES C is the hydrogen storage energy cost init C is the storage energy purchase cost om C is the storage energy operation and maintenance cost rep C is the storage energy replacement cost inv C is the storage energy supporting converter cost res C is the storage energy residual value recycling benefit The energy storage acquisition cost C init The calculation method is shown as formula (3): wherein, are the initial purchase costs of the battery and hydrogen storage system, respectively; c are the initial purchase costs of the battery and hydrogen storage system, respectively; c BATP are the initial purchase costs of the battery and hydrogen storage system, respectively; c BATE are the unit power and capacity investment cost coefficients of the battery, respectively; are the initial purchase costs of the battery and hydrogen storage system, respectively; c are the initial purchase costs of the battery and hydrogen storage system, respectively; c ELP are the unit power and capacity investment cost coefficients of the battery, respectively; FCP are the unit power and capacity investment cost coefficients of the battery, respectively; STOE are the unit power and capacity investment cost coefficients of the battery, respectively; are the initial purchase costs of the battery and hydrogen storage system, respectively; c are the initial purchase costs of the battery and hydrogen storage system, respectively; c CR is the capital recovery factor; The funds recovery coefficient R CR The calculation method of R is shown as formula (4): In the formula, r is the discount rate, which is 0.09, and Y is the system planning period; The energy storage maintenance cost C om The initial investment cost of the energy storage can be converted according to the energy storage, as shown in formula (5): In the formula, is the ratio of the operating and maintenance cost of the hybrid energy storage to the initial purchase cost, and is taken as 0.02; The energy storage replacement cost C rep The calculation method of the energy storage replacement cost C is shown as formula (6): The equivalent cycle life A of the storage battery is calculated by using the rain flow counting and the equivalent life method, by converting the cycle times A of the storage battery in different operating states to the cycle times in the full operating state of the storage battery and summing up ESS , and finally the replacement times N of the storage battery are calculated. Since the life of the energy storage system is affected by the number of charging and discharging times, the replacement cost needs to be considered. It is worth mentioning that since the life of the hydrogen energy storage system is generally 15-20 years, which is the same as the planning period time proposed in the application, the application only considers the replacement cost of the battery; The cost C of the energy storage matching converter inv According to the configuration scheme of the hybrid energy storage, the conversion is performed, and specifically, as shown in equation (7): In the formula, is the investment coefficient of the hybrid energy storage unit power converter, and is taken as 0.1; The hybrid energy storage needs to be matched with a converter device when accessing the traction power supply system, so the cost of purchasing the converter needs to be considered. The energy storage residual value recovery benefit C res This can be discounted according to the initial investment cost of the battery and the replacement cost, specifically shown by equation (8): In the formula, The battery recovery residual value is 0.

05. The total operating cost C of the system over the planning period op The single-day cost C op,d The weighted calculation is obtained, as shown in equation (9): In the formula, N op is the total number of days the system is in operation per year.

4. The method of claim 2, wherein: The planning layer constraint condition mainly includes the power upper and lower limit constraint of the hybrid energy storage, which is specifically shown in formula (10): wherein: Pbatmax and Pbatmin are the maximum and minimum values of the battery power; Pbatmax and Pbatmin are the maximum and minimum values of the battery power; Pelecmax and Pelecmin are the maximum and minimum values of the electrolyser power; Pelecmax and Pelecmin are the maximum and minimum values of the electrolyser power.

5. The method of claim 1, wherein: The operation layer model includes an operation layer objective function and an operation layer constraint condition.

6. The method of claim 5, wherein: The operation layer objective function takes the system daily operation cost as the objective function, and the calculation method is shown in formula (11). The system daily operation cost mainly includes carbon trading cost The external grid power purchase cost C g And the wind and light abandonment penalty cost C ab ; 7. The method of claim 6, wherein: The carbon trading cost The carbon trading cost is calculated by the traction power supply system carbon trading cost calculation model. Specifically, according to the definition of the stepped carbon trading mechanism, a plurality of emission intervals are defined, and the stepped carbon trading cost calculation model is as shown in formula (12): In the formula: represents the carbon trading cost, λ represents the carbon emission coefficient, and ρ c represents the carbon trading base price, l is the length of the carbon emission interval, α is the carbon trading price growth rate, E ct represents the available carbon emission right transaction amount; Wherein, the available carbon emission rights trading amount E ct The calculation method is shown as formula (13): E ct = E c,a - E c (13) In the formula: E c,a represents the actual carbon emissions of the traction power supply system, E c represents the free carbon emission quota of the traction power supply system; The actual carbon emission E of the traction power supply system c,a The calculation method is shown as formula (14): In the formula, E buy,a is the actual carbon emission, λ2 is the actual carbon emission coefficient of the conventional unit, P T (t) is the traction load at time t; The traction power supply system free carbon emission quota E c The calculation method is shown as formula (15): In the formula: E buy represents the free carbon quota of conventional generating units, E re represents the free carbon quota of new energy generation, λ1 represents the free carbon quota coefficient, P T (t), P PV (t) and P WIND (t) are the traction load, photovoltaic and wind turbine output at time t, respectively, and T represents the dispatching period, which is 24 hours.

8. The method of claim 6, wherein: The system external grid electricity purchase cost C g The calculation method is shown in formula (16); In the formula: C grid is the electricity charge, specifically refers to the cost generated by the actual consumption of the traction power supply system, with the unit of ￥ / kwh; p grid is the electricity purchase price; C dem is the demand charge, specifically refers to the maximum value of the average load of the traction power supply system at the scale of 15 minutes or 30 minutes, with the unit of ￥ / kW; p dem is the demand charge price; N day,op is the number of system operation days; P dem is the electricity demand of the traction power supply system, which is obtained by the slip method.

9. The method of claim 6, wherein: The penalty cost C for abandoning wind and light ab The calculation method (17) is as follows: C ab = c ab P AB (17) In the formula: c ab is the penalty coefficient for abandoned wind and light.

10. The method of claim 5, wherein: The operation layer constraint condition includes power balance constraint, new energy output constraint, wind and light abandoned amount constraint, energy storage state of charge constraint, energy storage charging and discharging constraint and energy conservation constraint; The power balance constraint is that the system should satisfy the power balance at any time t, as shown in formula (18): wherein: P T (t), P GRID (t), P PV (t) and P WIND (t) are the traction load, the external power grid, the photovoltaic and the wind turbine output at time t, respectively; P BAT (t), P FC (t) and P EL (t) are the battery, the fuel cell and the electrolyzer output at time t, respectively; P AB (t) is the amount of abandoned wind and light at time t. The new energy output constraint is that the new energy output should satisfy the unit maximum output constraint, as shown in formula (19): In the formula: and respectively, are the predicted maximum output of photovoltaic and wind power at time t. The wind and light abandoned amount constraint is the new energy wind and light abandoned amount constraint, as shown in formula (20): P AB ≥0 (20) The energy storage state of charge constraint and the energy storage charging and discharging constraint are to prolong the service life of the energy storage, as shown in formula (21): In the formula, SOC BAT SOC STO SOC and SOC are the state of charge of the battery and the hydrogen storage tank, respectively. In order to improve the recycling efficiency of the energy storage, the energy storage system should satisfy the energy conservation constraint in a scheduling period, that is, the remaining power at the end of each scheduling area should be equal to the power at the beginning of the next scheduling period, that is, the energy conservation constraint, which can be obtained according to formula (22) and formula (23): E BAT (0) = E BAT (E) (22) E STO (0) = E STO (E) (23) In the formula, E BAT (0), E BAT (E) and E STO (0), E STO (E) represents the energy stored in the batteries and hydrogen storage tanks at the beginning and end of the system scheduling period, respectively.

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