Capacity planning method of co-phase traction power supply system based on life cycle cost

Abstract

The present invention discloses a capacity planning method for a co-phase traction power supply system based on the full life cycle cost, comprising the steps of: establishing an upper-level capacity planning model for a co-phase traction power supply system based on the full life cycle cost; establishing a lower-level optimization scheduling model with the goal of minimizing the daily tariff cost of the co-phase traction power supply system according to the capacity parameters optimized by the upper level, and solving to obtain the charging and discharging power of the hybrid energy storage device and the compensation power of the flow controller; finally, solving using the whale algorithm with an embedded GUROBI solver, and obtaining the system planning capacity of the co-phase traction power supply system when the cost is the lowest during the full life cycle. The present invention utilizes the charging and discharging of the hybrid energy storage device to improve the utilization rate of renewable energy and train regenerative braking energy, so as to achieve peak shaving and valley filling of the traction load, and combines the full life cycle cost of the system with optimized operation to realize the capacity planning of the co-phase traction power supply system containing photovoltaic and hybrid energy storage.

Description

Technical Field

[0001] The present invention belongs to the technical field of traction power supply systems, and in particular relates to a capacity planning method for a same-phase traction power supply system based on full life cycle cost. Background Art

[0002] With the rapid development of electrified railways in my country, by the end of 2021, the total operating mileage of railways in China will exceed 150,000 kilometers, of which the operating mileage of high-speed railways will exceed 40,000 kilometers. Renewable energy represented by photovoltaics has ushered in great development. In 2013, my country's photovoltaic power generation was 9.1 billion kWh. By 2020, my country's photovoltaic power generation was 260.5 billion kWh, a 28-fold increase from 2013. This indicates that in order to achieve the "dual carbon" goal, it is of great significance to implement energy conservation and emission reduction in my country's railways (especially high-speed railways).

[0003] The main problems of my country's traditional traction power supply system are as follows: (1) Train over-phase. When the train passes through the electrical phase, it will cause serious stalling of the train and be accompanied by electrical transient processes such as overvoltage and arcing. (2) Three-phase voltage imbalance, which is caused by the single-phase asymmetry of the traction load. (3) Low utilization rate of regenerative braking energy. The proposal of the same-phase traction power supply system can solve the above problems. At the same time, the DC link of the power flow controller can provide a flexible interface for hybrid energy storage devices and renewable energy systems.

[0004] However, the existing technology does not provide a method for planning the capacity of the same-phase traction power supply system, and is unable to simultaneously achieve the lowest investment cost and the best system operation, and is unable to consider the costs involved in the entire life cycle of the engineering project. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a capacity planning method for a co-phase traction power supply system based on the full life cycle cost. The utilization rate of renewable energy and train regenerative braking energy is improved by charging and discharging a hybrid energy storage device, so as to achieve peak shaving and valley filling of the traction load. Based on the life cycle cost theory, the system life cycle cost is combined with the system optimization operation to realize the capacity planning of the co-phase traction power supply system containing photovoltaic and hybrid energy storage.

[0006] To achieve the above object, the technical solution adopted by the present invention is: a capacity planning method for a co-phase traction power supply system based on the full life cycle cost, which divides the capacity optimization process into an upper capacity planning model and a lower system optimization scheduling model, including the steps of:

[0007] Step 1: Establish an upper-level capacity planning model for the co-phase traction power supply system based on the full life cycle cost, taking into account the life of the battery and the power flow controller, and taking the minimum full life cycle cost of the co-phase traction power supply system as the goal to obtain the capacity configuration parameters that meet the system operation constraints;

[0008] Step 2: Under the scenario where the traction load data and photovoltaic output are given, according to the power and capacity parameters of the hybrid energy storage device and the capacity parameters of the power flow controller obtained in step 1, a lower-level optimization scheduling model with the goal of minimizing the daily tariff cost of the same-phase traction power supply system is established, and the charging and discharging power of the hybrid energy storage device and the compensation power of the power flow controller are solved, and the results are passed to step 1;

[0009] Step 3: With the goal of minimizing the life cycle cost of the same-phase traction power supply system, the whale algorithm embedded in the CPLEX commercial solver is used to solve the two-layer capacity planning model of the same-phase traction power supply system, and the system planning capacity of the same-phase traction power supply system containing photovoltaics and hybrid energy storage is calculated when the cost is the lowest during the whole life cycle.

[0010] Furthermore, the objective function of the upper capacity planning model of the same-phase traction power supply system based on the full life cycle cost is established:

[0011] minC LCC =f(P br ,E br ,P ur ,E ur ,S PFC )=(C Inv CRF+C O&M +C Rep -C D&R ·SFF)+C e ;

[0012] in,

[0013] Where: C LCC is the life cycle cost, P br and P ur E are the rated power of the battery and supercapacitor respectively; br and E ur are the rated capacities of the battery and supercapacitor respectively; S PFC is the capacity of the power flow controller; CRF is the capital recovery factor; SFF is the debt repayment fund factor; C Inv is the investment cost, C O&M is the operation and maintenance cost, C Rep is the battery replacement cost, C D&R is the decommissioning recovery cost, C e is the total electricity cost of the same-phase traction substation; r is the interest rate, T proj The duration of the entire project life cycle.

[0014] Furthermore, the hybrid energy storage device includes the cost of batteries, supercapacitors, DC / DC converters and auxiliary equipment. The cost of DC / DC converters and auxiliary equipment is related to the rated power of the hybrid energy storage device. The investment cost of the power flow controller is determined by its installed capacity. Therefore, the investment cost C Inv for:

[0015] C Inv =C hi +C pi ;

[0016]

[0017]

[0018] Where: k be and k ue Represents the cost per unit rated capacity of battery and supercapacitor respectively; k bp and k up Represent the cost per unit rated power of battery and supercapacitor respectively; k bop is the unit cost of system auxiliary equipment; k p is the unit capacity cost of the power flow controller; C hi is the investment cost of hybrid energy storage; C pi The investment cost of the power flow controller.

[0019] Furthermore, the system operation and maintenance costs include the hybrid energy storage device, the power flow controller and the photovoltaic power generation. The daily operation and maintenance costs of the hybrid energy storage device include the planned maintenance costs and the unplanned maintenance costs. The operation and maintenance costs C O&M for:

[0020] C O&M =C hm +C pm +C PV ;

[0021] in:

[0022]

[0023] C ps =(P tot.T +P tot.D )×k c ×T p ;

[0024]

[0025] Where: k bo.f and k bo.v T is the planned and unplanned maintenance cost per unit power of the battery; bhP is the battery working time per day; tot.T and P tot.D are the total losses of the IGBT and Diode of the power flow controller; k c is the daily basic electricity price of the power flow controller; T p is the annual operation time of the power flow controller; κ1 is the planned maintenance cost conversion coefficient of the power flow controller; λ is the failure rate of the power flow controller; C lt Travel and labor costs for repairing the device; T MTTR is the maintenance time of the power flow controller; C hm is the battery operation and maintenance cost; C pi is the power flow controller loss cost; C pm is the operation and maintenance cost of the power flow controller.

[0026] Furthermore, the battery replacement cost C Rep for:

[0027]

[0028] in:

[0029] Where: k br Represents the replacement cost of battery per unit capacity; N R T is the number of battery replacements during the entire life cycle; b For battery life.

[0030] Furthermore, the system's decommissioning and recycling costs are reflected in the cost of the recyclable value of the remaining materials after the batteries that have not reached the end of their service life and the power flow controller are disassembled. The decommissioning and recycling costs C D&R for:

[0031]

[0032] Where: κ2 and κ3 are the retirement depreciation coefficients of the battery and the power flow controller respectively.

[0033] Furthermore, the objective function of the lower-level optimization dispatching model with the goal of minimizing the daily tariff cost of the same-phase traction power supply system is:

[0034] minC e =C ECC +C CD +C PC ;

[0035] in:

[0036]

[0037]

[0038]

[0039]

[0040]

[0041] Where: C e is the objective function, which represents the total electricity cost of the same-phase traction substation; C ECC is the electricity cost; C DC is the demand electricity cost; C PC For the cost of fines; is the unit price of electricity, is the active power input from the public grid to the same-phase traction power supply system; is the unit price of demand electricity, It is the average active power load passing through the traction transformer for 15 consecutive minutes in one month; The unit price of the fine is is the active power fed back to the public grid by the same-phase traction power supply system; is the unit operation and maintenance cost of photovoltaic power generation, is the active power of photovoltaic power generation; Δt is the unit time interval; N T is the total number of time intervals in a day;

[0042] Constraints include power balance, hybrid energy storage, photovoltaic, negative sequence and power flow controller constraints.

[0043] Furthermore, the constraints include power balance, hybrid energy storage device, photovoltaic, negative sequence and power flow controller constraints, and the power balance constraints are:

[0044]

[0045]

[0046]

[0047]

[0048]

[0049] Where: and They are the active power of phase α and phase β of the power flow controller and the reactive power of phase β; is the active power of the single-phase transformer; and are the discharge power of the battery and supercapacitor respectively; and are the charging power of the battery and supercapacitor respectively; and They are respectively the active power and reactive power during load traction; and They are respectively the active power and reactive power during load braking;

[0050] The hybrid energy storage device constraints:

[0051]

[0052]

[0053]

[0054]

[0055] In the formula: j represents a battery or supercapacitor; and The minimum, maximum and t=0 state of charge of the battery or supercapacitor respectively; and are the actual capacities of the battery or supercapacitor at time t, t=1 and t=T respectively; ε j , and are the self-discharge rate, charging and discharging efficiency of the battery or supercapacitor respectively; is a binary variable to prevent the coexistence of battery or supercapacitor charging and discharging; T bh is the daily operating time of the battery;

[0056] The photovoltaic output constraints are:

[0057]

[0058] Where: P t PV.max To provide photovoltaic active power to the same-phase traction power supply system;

[0059] The negative sequence constraint:

[0060]

[0061]

[0062]

[0063] By introducing non-negative variables and binary variables And linearization is performed based on the big M method:

[0064]

[0065]

[0066]

[0067] Where: a=e j120 ,u ε and u ε.max They are the three-phase voltage unbalance and its national standard limit; U S ,U T and U α are the grid rated voltage, the output voltage of the single-phase transformer on the traction side, and the input voltage of the α-phase converter on the grid side; S - and S d are the negative sequence power and the short-circuit capacity of the power system at the PCC point, respectively; is the negative sequence current; N1 and N2 are the transformation ratios of the single-phase traction transformer and the YNd11 connection matching transformer respectively; and are the voltage and current phase differences between the single-phase traction transformer and the α-phase converter respectively.

[0068] The power flow controller constraints:

[0069]

[0070]

[0071] Use multiple circumscribed squares to approximate the circle. Three squares are sufficient to meet the accuracy requirement. After linearization, the result is:

[0072]

[0073] Where: S α is the converter capacity of the α-side of the power flow controller, S β is the capacity of the β-side converter of the power flow controller.

[0074] Furthermore, the life evaluation of the power flow controller includes: first, calculating the IGBT module failure rate λ based on the physical failure mechanism of the power electronic device IGBT ; Second, the failure rate of other components λ other ;

[0075] Includes steps:

[0076] Calculate the IGBT module power loss based on the power flow controller compensation power obtained by optimizing the scheduling of the lower model;

[0077] Establish a thermal network model to calculate the junction temperature of IGBT and FWD;

[0078] Based on the rain flow counting algorithm, the junction temperature T of each thermal cycle is decomposed m and junction temperature fluctuation ΔT j ;

[0079] The life of the power flow controller is obtained based on the Coffin-Manson component failure life model and the linear fatigue damage model.

[0080] Furthermore, the battery life assessment process comprises the steps of:

[0081] The battery SOC curve obtained by optimizing the lower model is used as a known input;

[0082] The rain flow counting algorithm is used to extract a series of full cycles and half cycles from the battery SOC curve and calculate the discharge depth DOD of the corresponding cycle;

[0083] The battery life is evaluated based on the relationship curve between the remaining battery cycles and the depth of discharge.

[0084] The beneficial effects of adopting this technical solution are:

[0085] The present invention establishes a two-layer model of the full life cycle cost of a co-phase traction power supply system containing photovoltaics and hybrid energy storage. The upper model realizes system capacity planning with the goal of minimizing the comprehensive cost, and the lower model realizes system optimization scheduling with the goal of minimizing the daily tariff cost while meeting the national standard limit requirements for three-phase voltage imbalance.

[0086] The present invention takes into account the life of the power flow controller and the battery, combines the system life cycle cost with the system optimized operation, and thus prolongs the life of the power flow controller and the battery through the optimized scheduling of the same-phase traction power supply system.

[0087] The present invention adopts the whale algorithm with embedded CPLEX solver to solve the two-layer model of the co-phase traction power supply system, that is, it can obtain the planned capacity of the co-phase traction power supply system when the whole life cycle cost is the lowest, and verifies that the model has faster convergence speed and higher accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 Schematic diagram of the same-phase traction power supply system containing photovoltaic and hybrid energy storage in the present invention.

[0089] Figure 2 The present invention is a flow chart of capacity planning of a co-phase traction power supply system based on the whale algorithm with an embedded CPLEX solver.

[0090] Figure 3 It is a schematic diagram of realizing peak shaving and valley filling of traction load according to the present invention.

[0091] Figure 4This is a comparison chart of the convergence curves of the objective function of the model of the present invention solved by the whale algorithm and other intelligent algorithms. DETAILED DESCRIPTION

[0092] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further described below with reference to the accompanying drawings.

[0093] The present invention is directed to a co-phase traction power supply system topology such as Figure 1 As shown, Figure 2 A flowchart of the whale algorithm embedded in the CPLEX solver for solving the capacity planning model of the same-phase traction power supply system is shown. The present invention is a capacity planning method for the same-phase traction power supply system based on the life cycle cost theory, and the parameter value settings of its embodiment are shown in Table 1-3.

[0094] Table 1 Unit cost parameters

[0095]

[0096] Table 2 Electricity price parameters

[0097]

[0098] Table 3 Technical parameters of co-phase traction power supply system

[0099]

[0100] The present invention is a method for capacity planning of a co-phase traction power supply system based on the full life cycle cost theory, comprising the following steps:

[0101] Step 1: Establish an upper-level capacity planning model for the co-phase traction power supply system based on the life cycle cost, taking into account the life of the battery and the power flow controller, with the goal of minimizing the life cycle cost of the co-phase traction power supply system, and obtain the capacity configuration that meets the system operation constraints.

[0102] Upper model objective function: minC LCC =(C Inv CRF+C O&M +C Rep -C D&R ·SFF)+C e (1).

[0103]

[0104] Where: P br and P ur are the rated power of battery and supercapacitor, MW; E br and E ur are the rated capacity of battery and supercapacitor, MVA; SPFC is the capacity of the power flow controller, MVA; T proj is the life cycle of the project, year; CRF is the capital recovery factor; SFF is the debt repayment fund factor; r is the interest rate.

[0105] The cost of the same-phase traction power supply system during its entire life cycle is divided into investment cost, operation and maintenance cost, battery replacement cost and decommissioning recovery cost, and the calculation formula is as follows (3)~(12)+(18).

[0106] ① Investment cost C Inv

[0107] The hybrid energy storage device mainly includes batteries, supercapacitors, DC / DC converters and auxiliary equipment costs, such as insulation protection devices, monitoring systems, etc. The DC / DC converter and auxiliary equipment costs are related to the rated power of the hybrid energy storage device. The investment cost of the power flow controller is determined by its installed capacity.

[0108] C Inv =C hi +C pi (3)

[0109]

[0110] Where: k be and k ue Represents the cost of battery and supercapacitor per unit rated capacity, ￥ / kWh; k bp and k up Represents the cost of battery and supercapacitor per unit rated power, ￥ / kW; k bop is the unit cost of system auxiliary equipment, ￥ / kWh; k p is the unit capacity cost of the power flow controller, ￥ / kWh; C hi is the investment cost of hybrid energy storage; C pi The investment cost of the power flow controller.

[0111] ②Operation and maintenance cost C O&M

[0112] The system operation and maintenance costs include hybrid energy storage devices, power flow controllers, and photovoltaic power generation. The daily operation and maintenance costs of hybrid energy storage devices include planned maintenance costs and unplanned maintenance costs.

[0113] C O&M =C hm +C pm +C PV (6)

[0114]

[0115] C ps =(P tot.T +P tot.D )×k c ×T p (8)

[0116]

[0117] Where: k bo.f and k bo.v is the planned and unplanned maintenance cost per unit power of the battery, ￥ / kW; T bh is the battery working time per day, h; P tot.T and P tot.D are the total losses of the power flow controller IGBT and diode, W; k c is the basic electricity price for daily operation of the power flow controller, ￥ / kW; T p is the annual operation time of the power flow controller, h; κ1 is the planned maintenance cost reduction coefficient of the power flow controller; λ is the failure rate of the power flow controller, Fit, 1Fit defines the failure rate of the device in 10 9 A fault occurs within h; C lt Travel and labor costs for repairing devices, ￥ / kW / year; T MTTR is the maintenance time of the power flow controller, h; C hm is the battery operation and maintenance cost; C pi is the power flow controller loss cost; C pm is the operation and maintenance cost of the power flow controller.

[0118] ③Battery replacement cost C Rep

[0119]

[0120]

[0121] Where: k br Indicates the replacement cost of battery per unit capacity, ￥ / kWh; T b is the battery life, year; N R It is the number of times the battery is replaced during its entire life cycle.

[0122] ④ Decommissioning recovery cost C D&R

[0123] The system's decommissioning and recycling costs are reflected in the cost of recovering the value of batteries that have not reached the end of their service life and the remaining materials after the power flow controller is disassembled.

[0124]

[0125] Where: κ2 and κ3 are the retirement depreciation coefficients of the battery and the power flow controller respectively.

[0126] The life evaluation of the power flow controller consists of two parts. The first part is to calculate the IGBT module failure rate λ based on the physical failure mechanism of power electronic devices. IGBT ; Second, other components (such as control baseboard λ) can be directly obtained through the reliability manual D 、DC support capacitance λ C and series reactor λ L Failure rate λ other .

[0127] Specific steps: 1. Calculate the IGBT module power loss (P) based on the power flow controller compensation power obtained by optimizing the scheduling of the lower model. tot.T / P tot.D );2. Establish a thermal network model to calculate the junction temperature of IGBT and FWD (T j.T / T j.D ) ; 3. Based on the rain flow counting algorithm, the junction temperature T of each thermal cycle is decomposed m and junction temperature fluctuation ΔT j ; 4. The life of the power flow controller is obtained based on the Coffin-Manson component failure life model and the linear fatigue damage model.

[0128] T j.T =P tot.T Z js.T +(P tot.T +P tot.D )Z sa ;

[0129] T j.D =P tot.D Z js.D +(P tot.T +P tot.D )Z sa ;

[0130] T m =(T jmax +T jmin ) / 2,ΔT j =T jmax -T jmin ;

[0131] N f (ΔT j ,T m )=a·(ΔT j ) -n ·exp[E a / (k·T m )];

[0132]

[0133]

[0134] In the formula, Z js.T ,Z js.D and Z sa are the impedance between the cutoff layer of IGBT / FWD and the heat sink, and the impedance between the heat sink and the environment; P tot.T and P tot.D are the total losses of a single IGBT and FWD respectively; T jmax and T jmin The maximum and minimum junction temperatures for each thermal cycle; N f For T m and ΔT j The number of cycles to failure under load; T is the unit time under load thermal cycle; N is the corresponding T in T m and ΔT j The number of thermal cycles; a and n are adjustment parameters, generally a = 302,500, n = 5.039; E a is the activation energy constant, 9.891×10 -20 J; k is the Boltzmann constant, 1.38×10 -23 J / K; T MTTF The mean time between failures.

[0135] Battery life assessment process: 1. The battery SOC curve obtained by optimizing the lower model is used as a known input; 2. The rain flow counting algorithm is used to extract a series of full cycles and half cycles from the battery SOC curve, and the discharge depth DOD of the corresponding cycle is calculated; 3. The battery life is assessed based on the relationship curve between the remaining battery cycles and the discharge depth in the battery technical manual.

[0136] N c (DOD) = ae -bDOD +ce -dDOD ;

[0137]

[0138] Where N c is the number of remaining cycles of the battery; a, b, c, d are battery parameters; N is the total number of cycles of the battery in a day; T b is the battery life. Step 2: Under the scenario where the traction load data and photovoltaic output are given, according to the power and capacity parameters of the hybrid energy storage device and the capacity parameters of the power flow controller obtained in step 1, a lower-level optimization scheduling model with the goal of minimizing the daily tariff cost of the same-phase traction power supply system is established, and the charging and discharging power of the hybrid energy storage device and the compensation power of the power flow controller are solved, and the results are passed to step 1;

[0139] Lower model objective function: minC e =C ECC +C CD +C PC (13).

[0140] The electricity cost includes electricity charges, demand charges and penalty charges, and the calculation formulas are as follows (14) to (17).

[0141]

[0142]

[0143]

[0144]

[0145]

[0146] Where: C e is the objective function, which represents the total electricity cost of the same-phase traction substation; C ECC is the electricity cost; C DC is the demand electricity cost; C PC For the cost of fines; is the unit price of electricity, is the active power input from the public grid to the same-phase traction power supply system; is the unit price of demand electricity, It is the average active power load passing through the traction transformer for 15 consecutive minutes in one month; The unit price of the fine is is the active power fed back to the public grid by the same-phase traction power supply system; is the unit operation and maintenance cost of photovoltaic power generation, is the active power of photovoltaic power generation; Δt is the unit time interval; N T The total number of time intervals in a day.

[0147] By introducing an auxiliary variable Linearize formula (17):

[0148]

[0149]

[0150] The constraints include power balance, hybrid energy storage device, photovoltaic, negative sequence, and power flow controller constraints, as shown in formulas (21) to (39).

[0151] Power balance constraints:

[0152]

[0153]

[0154]

[0155]

[0156]

[0157] Where: and They are the active power of phase α and phase β of the power flow controller and the reactive power of phase β; is the active power of the single-phase transformer; and are the discharge power of the battery and supercapacitor respectively; and are the charging power of the battery and supercapacitor respectively; and They are respectively the active power and reactive power during load traction; and They are respectively the active power and reactive power during load braking;

[0158] Hybrid energy storage device constraints:

[0159]

[0160]

[0161]

[0162]

[0163] In the formula: j represents a battery or supercapacitor; and The minimum, maximum and t=0 state of charge of the battery or supercapacitor respectively; and are the actual capacities of the battery or supercapacitor at time t, t=1 and t=T respectively; ε j , and are the self-discharge rate, charging and discharging efficiency of the battery or supercapacitor respectively; is a binary variable to prevent the coexistence of battery or supercapacitor charging and discharging; T bh It is the daily operating time of the battery.

[0164] Photovoltaic output constraints:

[0165]

[0166] Where: It is the photovoltaic active power provided to the same-phase traction power supply system.

[0167] Negative order constraint:

[0168]

[0169]

[0170]

[0171] By introducing non-negative variables and binary variables And formula (34) is linearized based on the big M method.

[0172]

[0173]

[0174]

[0175] Where: a=e j120 ,u ε and u ε.max They are the three-phase voltage unbalance and its national standard limit; U S ,U T and U α are the grid rated voltage, the output voltage of the single-phase transformer on the traction side, and the input voltage of the α-phase converter on the grid side; S - and S d are the negative sequence power and the short-circuit capacity of the power system at the PCC point, respectively; is the negative sequence current; N1 and N2 are the transformation ratios of the single-phase traction transformer and the YNd11 connection matching transformer respectively; and are the voltage and current phase differences between the single-phase traction transformer and the α-phase converter respectively.

[0176] Power flow controller constraints:

[0177]

[0178]

[0179] This patent uses multiple circumscribed squares of a circle to achieve an approximate expression of the circle. Under the premise of ensuring accuracy, taking 3 squares can meet the accuracy requirement. Formula (39) is linearized to:

[0180]

[0181] Step 3: With the goal of minimizing the life cycle cost of the same-phase traction power supply system, the whale algorithm embedded in the CPLEX commercial solver is used to solve the objective function of formula (1) to obtain the system planning capacity of the same-phase traction power supply system containing photovoltaics and hybrid energy storage when the cost is lowest during the whole life cycle.

[0182] Combined with the two-layer capacity planning model of the same-phase traction power supply system, a whale algorithm program with an embedded CPLEX commercial solver is written. The parameters in the algorithm program are set as follows: the whale population size is 50, the number of iterations is 50, and the decision variable range is: P br ∈[1,4]MW,E br ∈[5,15]MWh,P ur ∈[10,20]MW,E ur ∈[0.1,0.5]MWh,S PFC ∈[5,12]MVA.

[0183] Case 1 (existing traditional traction power supply system), Case 2 (existing invention considering hybrid energy storage and photovoltaic access but not taking into account the optimization method of the system's full life cycle cost) and Case 3 (the method of the present invention) are compared; the traction load and photovoltaic data in the three models are consistent, and the traction load data is calculated by the load process simulation software (ELBAS / WEBANET software). The electricity cost calculation method of the traditional traction power supply system is consistent with the method of the present invention, and the electricity cost of the traditional traction power supply system is the comprehensive cost.

[0184] After simulation calculation, the results are shown in Table 4.

[0185] Table 4 Optimization results

[0186]

[0187] Note: R%=(C LCC_2 / 3 -C LCC_1 ) / C LCC_1

[0188] Table 4 shows the calculation results of a single traction substation under the two methods: According to Table 4, the energy management optimization method of the same-phase traction power supply system containing photovoltaics and hybrid energy storage (Case 2 and Case 3) has a daily tariff cost that is much lower than the traditional traction power supply system optimization method (Case 1). Case 3 takes into account the full life cycle cost of the system and optimizes the capacity of the power flow controller. The total cost is reduced by 22.43% compared to Case 1; while the total cost of Case 2 is only reduced by 8.02% compared to Case 1. This is because the full life cycle cost of the power flow controller considered in Case 3 is added, but the capacity of the power flow controller is not optimized, resulting in a significant increase in the total system cost. Compared with Case 2, the life of the power flow controller of the optimization method proposed in the present invention is greatly improved (from 6.92 years to 22.85 years). Figure 3 It can be seen that the present invention achieves peak shaving and valley filling of traction load. Figure 4 The present invention provides the convergence curve of the objective function of the model solved by the present invention using the whale algorithm and other intelligent algorithms.

[0189] The present invention takes the same-phase traction power supply system connected to photovoltaic and hybrid energy storage as the object. Based on the full life cycle cost theory in the upper model, the energy optimization management is used to solve the parameter configuration of the power flow controller and the hybrid energy storage device when the system has the lowest full life cycle cost. In the lower model, the peak load is shaving and valley filling is achieved to reduce the electricity cost of the railway operating department, while ensuring that the three-phase voltage imbalance reaches the national standard. Therefore, the capacity planning method of the same-phase traction power supply system of the present invention is closer to reality and can provide a basis for the access and engineering application of energy storage systems and renewable energy in future electrified railways.

[0190] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A capacity planning method for a co-phase traction power supply system based on the full life cycle cost, characterized in that: The capacity optimization process is divided into an upper-level capacity planning model and a lower-level system optimization scheduling model, including the following steps: Step 1: Establish an upper-level capacity planning model for the co-phase traction power supply system based on the full life cycle cost, taking into account the life of the battery and the power flow controller, and taking the minimum full life cycle cost of the co-phase traction power supply system as the goal to obtain the capacity configuration parameters that meet the system operation constraints; The objective function of the upper capacity planning model of the same-phase traction power supply system based on the full life cycle cost is established: minC LCC =f(P br ,E br ,P ur ,E ur ,S PFC )=(C Inv ·CRF+C O&M +C Rep -C D&R ·SFF)+C e ; in, Where: C LCC is the life cycle cost, P br and P ur E are the rated power of the battery and supercapacitor respectively; br and E ur are the rated capacities of the battery and supercapacitor respectively; S PFC is the capacity of the power flow controller; CRF is the capital recovery factor; SFF is the debt repayment fund factor; C Inv is the investment cost, C O&M is the operation and maintenance cost, C Rep is the battery replacement cost, C D&R is the decommissioning recovery cost, C e is the total electricity cost of the same-phase traction substation; r is the interest rate, T proj The duration of the project's entire life cycle; Step 2: Based on the measured data of traction load and the predicted data of photovoltaic power generation, according to the power and capacity parameters of the hybrid energy storage device and the capacity parameters of the power flow controller obtained in step 1, a lower-level optimization scheduling model with the goal of minimizing the daily tariff cost of the same-phase traction power supply system is established, and the charging and discharging power of the hybrid energy storage device and the compensation power of the power flow controller are solved, and the results are passed to step 1; The objective function of the lower-level optimization dispatch model with the goal of minimizing the daily tariff cost of the same-phase traction power supply system is: minC e =C ECC +C CD +C PC ; in: Where: C e is the objective function, which represents the total electricity cost of the same-phase traction substation; C ECC is the electricity cost; C DC is the demand electricity cost; C PC For the cost of fines; is the unit price of electricity, is the active power input from the public grid to the same-phase traction power supply system; is the unit price of demand electricity, It is the average active power load passing through the traction transformer for 15 consecutive minutes in one month; The unit price of the fine is is the active power fed back to the public grid by the same-phase traction power supply system; is the unit operation and maintenance cost of photovoltaic power generation, is the active power of photovoltaic power generation; Δt is the unit time interval; N T is the total number of time intervals in a day; Constraints include power balance, hybrid energy storage, photovoltaic, negative sequence and power flow controller constraints; Step 3: With the goal of minimizing the life cycle cost of the same-phase traction power supply system, the whale algorithm embedded in the CPLEX commercial solver is used to solve the two-layer capacity planning model of the same-phase traction power supply system, and the system planning capacity of the same-phase traction power supply system containing photovoltaics and hybrid energy storage is calculated when the cost is the lowest during the whole life cycle.

2. The method for capacity planning of a co-phase traction power supply system based on full life cycle cost according to claim 1 is characterized in that: The hybrid energy storage device includes the cost of batteries, supercapacitors, DC / DC converters and auxiliary equipment. The cost of DC / DC converters and auxiliary equipment is related to the rated power of the hybrid energy storage device. The investment cost of the power flow controller is determined by its installed capacity. Therefore, the investment cost C Inv for: C Inv =C hi +C pi ; Where: k be and k ue Represents the cost per unit rated capacity of battery and supercapacitor respectively; k bp and k up Represent the cost per unit rated power of battery and supercapacitor respectively; k bop is the unit cost of system auxiliary equipment; k p is the unit capacity cost of the power flow controller; C hi is the investment cost of hybrid energy storage; C pi The investment cost of the power flow controller.

3. The method for capacity planning of a co-phase traction power supply system based on full life cycle cost according to claim 1 is characterized in that: The system operation and maintenance costs include the hybrid energy storage device, the power flow controller and the photovoltaic power generation. The daily operation and maintenance costs of the hybrid energy storage device include the planned maintenance costs and the unplanned maintenance costs. The operation and maintenance costs C O&M for: C O&M =C hm +C pm +C PV ; in: C ps =(P tot.T +P tot.D )×k c ×T p ; Where: k bo.f and k bo.v T is the planned and unplanned maintenance cost per unit power of the battery; bh P is the battery working time per day; tot.T and P tot.D are the total losses of the IGBT and Diode of the power flow controller respectively; k c It is the basic electricity price for daily operation of the power flow controller; T p is the annual operation time of the power flow controller; κ1 is the planned maintenance cost conversion coefficient of the power flow controller; λ is the failure rate of the power flow controller; C lt Travel and labor costs for repairing the device; T MTTR is the maintenance time of the power flow controller; C hm is the battery operation and maintenance cost; C pi is the power flow controller loss cost; C pm is the operation and maintenance cost of the power flow controller.

4. The method for capacity planning of a co-phase traction power supply system based on full life cycle cost according to claim 1 is characterized in that: The battery replacement cost C Rep for: in: Where: k br Represents the replacement cost of battery per unit capacity; N R is the number of battery replacements during the entire life cycle; T b For battery life.

5. The method for capacity planning of a co-phase traction power supply system based on full life cycle cost according to claim 1 is characterized in that: The system's decommissioning and recycling costs are reflected in the cost of the recyclable value of the remaining materials after the batteries that have not reached the end of their service life and the power flow controller are disassembled. The decommissioning and recycling costs C D&R for: Where: κ2 and κ3 are the retirement depreciation coefficients of the battery and the power flow controller respectively.

6. The method for capacity planning of a co-phase traction power supply system based on full life cycle cost according to claim 1 is characterized in that: The constraints include power balance, hybrid energy storage device, photovoltaic, negative sequence and power flow controller constraints. The power balance constraints are: P t grid -P t fed =P t a +P t T ; P t a +P t PV +P t b,dis +P t u,dis =P t β +P t b,ch +P t u,ch ; P t β +P t T =P t TL -P t RBE ; Where: and They are the active power of phase α and phase β of the power flow controller and the reactive power of phase β; is the active power of the single-phase transformer; and are the discharge power of the battery and supercapacitor respectively; and are the charging power of the battery and supercapacitor respectively; and They are respectively the active power and reactive power during load traction; and They are respectively the active power and reactive power during load braking; The hybrid energy storage device constraints: Where: j represents a battery or supercapacitor; and The minimum, maximum and t=0 state of charge of the battery or supercapacitor respectively; and are the actual capacities of the battery or supercapacitor at time t, t=1 and t=T respectively; ε j , and are the self-discharge rate, charging and discharging efficiency of the battery or supercapacitor respectively; is a binary variable to prevent the coexistence of battery or supercapacitor charging and discharging; T bh is the daily operating time of the battery; The photovoltaic output constraints are: 0≤P t PV ≤P t PV.max ; Where: P t PV.max To provide photovoltaic active power to the same-phase traction power supply system; The negative sequence constraint: By introducing non-negative variables and binary variables And linearization is performed based on the big M method: Where: a=e j120 ,u ε and u ε.max They are the three-phase voltage unbalance and its national standard limit; U S ,U T and U a are the grid rated voltage, the output voltage of the single-phase transformer on the traction side, and the input voltage of the α-phase converter on the grid side; S - and S d are the negative sequence power and the short-circuit capacity of the power system at the PCC point, respectively; is the negative sequence current; N1 and N2 are the transformation ratios of the single-phase traction transformer and the YNd11 connection matching transformer respectively; and are the voltage and current phase differences between the single-phase traction transformer and the α-phase converter respectively; The power flow controller constraints: Use multiple circumscribed squares to approximate the circle. Three squares are sufficient to meet the accuracy requirement. After linearization, the result is: Where: S α is the converter capacity of the α-side of the power flow controller, S β is the capacity of the β-side converter of the power flow controller.

7. The method for capacity planning of a co-phase traction power supply system based on full life cycle cost according to claim 1 is characterized in that: The life evaluation of the power flow controller includes: first, calculating the IGBT module failure rate λ based on the physical failure mechanism of power electronic devices; IGBT ; Second, the failure rate of other components λ other ; Includes steps: Calculate the IGBT module power loss based on the power flow controller compensation power obtained by optimizing the scheduling of the lower model; Establish a thermal network model to calculate the junction temperature of IGBT and FWD; Based on the rain flow counting algorithm, the junction temperature T of each thermal cycle is decomposed m and junction temperature fluctuation ΔT j ; The life of the power flow controller is obtained based on the Coffin-Manson component failure life model and the linear fatigue damage model.

8. The method for capacity planning of a co-phase traction power supply system based on full life cycle cost according to claim 1 is characterized in that: The battery life assessment process comprises the following steps: The battery SOC curve obtained by optimizing the lower model is used as a known input; The rain flow counting algorithm is used to extract a series of full cycles and half cycles from the battery SOC curve and calculate the discharge depth DOD of the corresponding cycle; The battery life is evaluated based on the relationship curve between the remaining battery cycles and the depth of discharge.

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