Super capacitor system planning method for urban rail transit load characteristics

Abstract

The invention discloses a super capacitor system planning method, system, medium and equipment, and the method comprises the steps: forming traction load data based on traction variable demand fluctuation in urban rail transit single station planning time; constructing a comprehensive cost model, including initial investment cost, periodic operation and maintenance expenditure and scrap treatment cost, of the urban rail transit supercapacitor system; combining the comprehensive cost model of the super capacitor system with the charge profit of the demand reduced by the adjustment demand of the super capacitor to establish a configuration economy model of the super capacitor; establishing a supercapacitor charge and discharge control strategy of urban rail transit load characteristics based on rail transit traction load characteristics and operation characteristics of the supercapacitor; and combining the comprehensive cost model with the supercapacitor configuration economy model, substituting a charge and discharge control strategy of the supercapacitor into a particle swarm optimization algorithm based on simulated annealing, and carrying out optimization solution by taking economy optimization as a target to obtain a planning result.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage in power systems, and particularly to a method, system, medium and device for planning a supercapacitor system. Background Art

[0002] The traction load of urban rail transit has significant impact characteristics: during the frequent start-stop process of trains, megawatt-level short-term peak loads will be generated, and during the braking stage, the load will drop suddenly due to the reverse transmission of regenerative energy to the power grid, and the load superposition effect during the coordinated operation of multiple trains further exacerbates the system fluctuation. The following bottleneck problems exist in the prior art when dealing with the above challenges:

[0003] First of all, the lithium battery energy storage system (BESS) is limited by the physical characteristics of low power density (<1kW / kg) and slow response speed (>500ms), and it is difficult to effectively suppress the load fluctuation of seconds.

[0004] Secondly, there is a reverse regulation contradiction between the traditional two-charge and two-discharge strategy and the demand charge mechanism, and the measured data shows that this strategy instead causes the recorded demand of the power grid to increase by 5%-8%.

[0005] In addition, the recovery efficiency of the system for regenerative braking energy is less than 30%, and a large amount of electric energy is dissipated in the form of heat through the braking resistor, resulting in significant energy waste; finally, the main transformer is in a long-term low load rate (<40%) operation state, which not only generates high capacity electricity charges but also leads to low equipment utilization rate, forming a typical inefficient operation scenario of "using a big horse to pull a small cart". These technical defects jointly restrict the economy and energy utilization efficiency of the rail transit power supply system.

[0006] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0007] The present invention provides a method, system, medium and device for planning a supercapacitor system, and under the condition of meeting the requirements of the rail transit load characteristics, an optimal configuration result of the supercapacitor system is obtained, significantly improving the economy and energy utilization efficiency.

[0008] A method for planning a supercapacitor system includes:

[0009] Step 100, forming traction load data based on the traction transformer demand fluctuation within the single-station planning time of urban rail transit;

[0010] Step 200, taking the economy of the system's entire life cycle as the optimization goal, and constructing a comprehensive cost model of the urban rail transit supercapacitor system including the initial investment cost, the periodic operation and maintenance expenditure, and the scrap disposal cost;

[0011] Step 300: Based on the rail transit demand billing criterion and traction load data, establish a supercapacitor configuration economic model by combining the comprehensive cost model of the supercapacitor system with the profit reduction of demand billing due to the supercapacitor's adjustment of demand.

[0012] Step 400: Based on the traction load data and the operating characteristics of the supercapacitor, establish a charge and discharge control strategy for the supercapacitor for the load characteristics of urban rail transit.

[0013] Step 500: Combine the comprehensive cost model with the supercapacitor configuration economic model, substitute it into the particle swarm optimization algorithm based on simulated annealing according to the supercapacitor charge and discharge control strategy, and perform an optimization solution with the goal of optimal economy to obtain the planning result of the urban rail transit supercapacitor system.

[0014] In the described method for planning a supercapacitor system, in step 100, a sampling rate ≥ 10 kHz is selected based on the fluctuation of the traction transformer demand within the single-station planning time of urban rail transit to form traction load data. The traction load data is in units of days, sampled at 1 point per 1 minute, with a total of 1440 sampling points per day, and the planning time is 1 year, including 365 consecutive daily loads.

[0015] In the described method for planning a supercapacitor system, in step 200, the objective function expression of the comprehensive cost model is:

[0016] C j =C C +C kp +C sp (1)

[0017] Where: C C is the investment cost of the supercapacitor; C kp is the maintenance cost; C sp is the control cost of the supercapacitor system.

[0018]

[0019] Where: k C,P 、k C,E are the unit power cost and unit capacity cost of the supercapacitor; α is the discount rate; P C 、E C are the rated power and capacity of the supercapacitor system; T C is the service life of the supercapacitor.

[0020]

[0021] C k =Nm c,p cc,p (4)

[0022] C s = +l cap,p P C +l capp,c E C (5)

[0023] Where: x is the interest rate; m c,p is the maintenance unit price of the supercapacitor; l cap,p and l capp,c are the power control unit price and capacity control unit price of the supercapacitor respectively.

[0024] In the described method for planning a supercapacitor system, in step 300, in the economic model for supercapacitor configuration, the mathematical expression for the demand charge of rail transit is:

[0025]

[0026] Where: P D is the price per unit of electricity based on the demand where the main transformer is located; L max is the actual maximum demand of the load in that month; the modeling of the revenue for demand reduction of energy storage is as follows:

[0027]

[0028] Where: U D is the profit obtained by the typical day - to - day energy storage through reducing capacity to avoid over - capacity penalty; L max,s is the maximum demand of the typical day - to - day rail transit load after applying energy storage,

[0029] The expression of the objective function of the revised economic model for supercapacitor configuration is:

[0030]

[0031] Where: f is the net revenue of energy storage scheduling under time - of - use electricity price conditions; N n is the number of load record days within the planning period.

[0032] In the described method for planning a supercapacitor system, in step 400,

[0033] Based on the load model constructed in step 100, after obtaining the relationship between the train operation power P and time t, based on the load modeling curve of the single - vehicle traction characteristics, when the urban rail transit load P is less than the reference load P BN , the supercapacitor is in the charging state; when the load P is greater than the reference load P BN , the supercapacitor discharges; the reference load P BNThe load value of the 35KV voltage of the urban power grid carried by the selected rail transit subway station is used, and the load data of the power grid every 1 minute is extracted as the benchmark load of urban rail transit in the same period.

[0034] Due to the strong volatility of the traction load, simply setting the charge and discharge actions of the hybrid energy storage system through the benchmark load P BN will cause misoperation of the supercapacitor system. Therefore, in this paper, the differential with respect to time t is used as the signal for the operation of the hybrid energy storage system. Combining the benchmark load criterion in the previous text, let dP / dt = k; the larger the differential threshold k, the faster the load power rises, and load peaks are likely to occur. Through the analysis of the power curve during train operation, it is determined that when the train starts, the threshold of k for the load to increase is -2.9, and the differential threshold k of the train during braking is 3.9. After combining the two judgment methods, the action signal of the supercapacitor system can be determined. Combining the setting of the benchmark load and the determination of the load differential threshold can ensure that the supercapacitor accurately suppresses the frequent peak loads brought by the start and stop of rail transit trains.

[0035] In the described method for planning a supercapacitor system, in step 500, the SA-PSO algorithm structure model is improved with the goal of maximizing profit to determine the optimal capacity and charge-discharge power of the supercapacitor system; and the relevant specified constraint conditions are judged. The net profit under different energy storage capacities is evaluated in the algorithm, and the position information of the particles is adjusted according to the update iteration of the SA-PSO algorithm. Finally, the optimal supercapacitor configuration result that makes the objective function value, that is, the net profit, is found. Specifically, the algorithm includes the following stages:

[0036] (1) Initialization stage: Set the particle swarm size N = 50 and the maximum number of iterations T = 100; initialize the particle positions (energy storage capacity configuration schemes) and velocities; set the initial temperature T0 = 500 and the annealing coefficient α = 0.85 - 0.95; initialize the individual optimal and global optimal solutions.

[0037] (2) Iterative calculation process: Each iteration calculates the step size t = 1:T.

[0038] (3) Fitness calculation: Calculate the objective function value for each particle and obtain the result by calculating the configuration economy model of the supercapacitor described in claim 4.

[0039] (3) Update particle state: Update the particle velocity; update the particle position.

[0040] (4) Simulated annealing operation: Compared with the conventional PSO optimization algorithm, the SA-PSO algorithm adopted in this paper adds this step. Calculate ΔE for each new position, and ΔE is the difference between the new solution and the old solution; if ΔE > 0, that is, judge that this solution is an improved solution and directly accept the new solution; if ΔE ≤ 0, with the probability function Accept the inferior solution; and update the temperature such that T = αT to gradually reduce the temperature. This step allows the algorithm to temporarily accept a worse solution during the iteration process, avoiding the situation where a single optimal solution, such as the capacitance configuration or power configuration of a supercapacitor, often appears. At the same time, since the temperature is set relatively high at the initial stage of iteration, SA can expand the coverage of various solutions in the iteration process and accelerate the iteration speed in the early stage of the algorithm iteration.

[0041] (5) Optimal solution update: Update the individual optimal pbest and the global optimal gbest; record the current optimal supercapacitor configuration scheme (P C 、E C ).

[0042] (6) Termination condition: Stop when the maximum number of iterations (T = 100) is reached or terminate prematurely if the global optimum has not been improved for 10 consecutive iterations.

[0043] (7) Output result: Output the corresponding global optimal solution; the current optimal supercapacitor configuration scheme (P C 、E C ).

[0044] In the described supercapacitor system planning method, the improved SA-PSO algorithm starts from a random solution, sets the initial temperature T and the temperature decay function α, randomly selects a solution within the neighborhood of the current solution, called the candidate solution, calculates the cost difference, i.e., the energy difference, between the candidate solution and the current solution. If the candidate solution is better than the current solution, accept it; otherwise, accept the worse solution with a certain probability, and the probability decreases as the temperature drops. As the algorithm progresses, the temperature gradually decreases, and the acceptance probability of non-optimal solutions gradually decreases. When the temperature drops to a sufficiently low level or reaches the predetermined number of iterations, the algorithm terminates. The improved SA-PSO algorithm calculates the energy difference, i.e., the difference in the objective function value, in each iteration and uses the following formula to determine whether to accept the worse solution:

[0045]

[0046] In the formula: ΔE is the difference in the objective function value between the new solution and the current solution, and T is the current temperature; the temperature T decreases according to the set decay function, and the decay function is:

[0047] T t+1 = αT t (10)

[0048] where α is a constant that controls the temperature decay rate, and 0 < α < 1,

[0049] Through temperature decay, the system gradually cools down and finally converges to a local optimum or a global optimum solution.

[0050] The particle swarm optimization algorithm searches for the optimal solution through the movement of particles in the solution space. Each particle updates its position and velocity in the search space and is affected by its historical best position p i and the global best position g. The particle update rule is as follows:

[0051]

[0052] In the formula: is the velocity of particle i at the t-th moment; is the position of particle i at the t-th moment; and g t are the historical best positions of particle i and all particles respectively; c1 and c2 are learning factors that control the dependence of particles on their own experience and group experience; r1 and r2 are random numbers.

[0053] A system for implementing the described method includes:

[0054] An acquisition unit, which is used to form traction load data based on the fluctuation of the traction transformer demand during the single-station planning time of urban rail transit;

[0055] A modeling unit, which is used to construct a comprehensive cost model of the urban rail transit supercapacitor system including initial investment cost, cycle operation and maintenance expenditure, and scrap disposal cost with the economic efficiency of the system's entire life cycle as the optimization goal;

[0056] A construction unit, which is used to establish an economic model for supercapacitor configuration by combining the comprehensive cost model of the supercapacitor system with the profit reduction of demand charge due to the adjustment of the supercapacitor's demand based on the demand charge criterion for rail transit;

[0057] A control unit, which is used to establish a charge and discharge control strategy for the supercapacitor of the urban rail transit load characteristics based on the traction load characteristics of the rail transit and the operating characteristics of the supercapacitor;

[0058] A calculation unit, which is used to combine the comprehensive cost model with the economic model for supercapacitor configuration, substitute the charge and discharge control strategy of the supercapacitor into the particle swarm optimization algorithm based on simulated annealing, and perform optimization to solve with the goal of optimal economy and minimum comprehensive cost to obtain the planning result of the urban rail transit supercapacitor system.

[0059] A computer storage medium, the storage medium includes computer instructions, when it runs on a computer, it causes the computer to execute the described method.

[0060] An electronic device, the electronic device includes:

[0061] A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein,

[0062] When the processor executes the program, the method described above is implemented.

[0063] Compared with the prior art, the present invention has the following advantages: Based on considering the load characteristics of rail transit, by optimizing the configuration of supercapacitors, the present invention realizes the effective suppression of impact loads in rail transit. Using the supercapacitor system, it realizes the suppression of voltage fluctuations in the DC traction network, improves the recovery and utilization rate of regenerative braking energy, and reduces the energy consumption of urban rail transit. It helps the rail transit system to achieve "regenerative energy feeding", optimizes the economic efficiency of the supercapacitor configuration, and improves the overall operation stability of the system, providing important reference value for the energy storage application of the rail transit system. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] By reading the following detailed description of the preferred specific embodiments, various other advantages and benefits of the present invention will become clear to those of ordinary skill in the art. The accompanying drawings in the specification are only for the purpose of showing the preferred embodiments and are not considered as limiting the present invention. Obviously, the following described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. Moreover, throughout the drawings, the same reference numerals are used to represent the same components.

[0065] In the drawings:

[0066] Figure 1 is the overall flowchart of a method for planning a supercapacitor system for the load characteristics of urban rail transit according to the present invention;

[0067] Figure 2 is the flowchart for judging the operation strategy of the supercapacitor system;

[0068] Figure 3 is a schematic diagram showing the suppression effect of the supercapacitor on the rail transit impact load after the completion of the planning of a method for planning a supercapacitor system for the load characteristics of urban rail transit provided by the present invention;

[0069] Figure 4 is a schematic diagram showing the change of the SOC of the supercapacitor after the completion of the planning of a method for planning a supercapacitor system for the load characteristics of urban rail transit provided by the present invention.

[0070] The present invention will be further explained below with reference to the drawings and embodiments. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0071] Specific embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although specific embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present invention can be more thoroughly understood and the scope of the present invention can be fully communicated to those skilled in the art.

[0072] It should be noted that in the description of the specification and claims, certain terms are used to refer to specific components. Those skilled in the art should understand that technicians may use different terms to refer to the same component. The specification and claims of the present invention do not use the difference in terms as a way to distinguish components, but use the difference in the functions of components as the criterion for distinction. As mentioned throughout the specification and claims, the term "comprising" or "including" is an open-ended term and should be interpreted as "including but not limited to". The subsequent description of the specification is a preferred implementation manner for implementing the present invention, but the description is for the purpose of the general principles of the specification and is not used to limit the scope of the present invention. The protection scope of the present invention shall be subject to what is defined by the appended claims.

[0073] For the convenience of understanding the embodiments of the present invention, the following will further explain with specific embodiments as examples in conjunction with the accompanying drawings, and each accompanying drawing does not constitute a limitation on the embodiments of the present invention.

[0074] As Figures 1 to 4 shown, the supercapacitor system planning method includes the following steps:

[0075] Step 100, forming traction load data based on the fluctuation of the traction transformer demand during the single-station planning time of urban rail transit;

[0076] Step 200, taking the economy of the system's entire life cycle as the optimization goal, and constructing a comprehensive cost model of the urban rail transit supercapacitor system including the initial investment cost, the periodic operation and maintenance expenditure, and the scrap disposal cost;

[0077] Step 300, based on the rail transit demand billing criterion and the traction load data, combining the comprehensive cost model of the supercapacitor system with the profit reduced by the supercapacitor in adjusting the demand to establish a supercapacitor configuration economy model;

[0078] Step 400, establishing a supercapacitor charge and discharge control strategy for the load characteristics of urban rail transit based on the traction load data and the operating characteristics of the supercapacitor;

[0079] Step 500: Combine the comprehensive cost model with the economic model of supercapacitor configuration. Substitute the supercapacitor charge and discharge control strategy into the particle swarm optimization algorithm based on simulated annealing, and perform optimization with the goal of optimal economy to obtain the planning results of the urban rail transit supercapacitor system.

[0080] In the preferred implementation of the supercapacitor system planning method, in step 100, a sampling rate ≥ 10 kHz is selected based on the traction transformer demand fluctuation within the single-station planning time of urban rail transit to form traction load data. The traction load data is sampled at a rate of 1 point per 1 minute on a daily basis, with a total of 1440 sampling points per day. The planning time is 1 year, including 365 consecutive daily loads.

[0081] In the preferred implementation of the supercapacitor system planning method, in step 200, the objective function expression of the comprehensive cost model is:

[0082] C j =C C +C kp +C sp (1)

[0083] Where: C C is the investment cost of the supercapacitor; C kp is the maintenance cost; C sp is the control cost of the supercapacitor system.

[0084]

[0085] Where: k C,P 、k C,E are the unit power cost and unit capacity cost of the supercapacitor; α is the discount rate; P C 、E C are the rated power and capacity of the supercapacitor system; T C is the service life of the supercapacitor.

[0086]

[0087] C k =Nm c,p c c,p (4)

[0088] C s =+l cap,p P C +l capp,c E C (5)

[0089] Where: x is the interest rate; m c,p is the maintenance unit price of the supercapacitor; lcap,p , l capp,c are the unit prices for power control and capacity control of the supercapacitor.

[0090] In the preferred embodiment of the method for planning a supercapacitor system, in step 300, in the economic model of supercapacitor configuration, the mathematical expression for the demand charge of rail transit is:

[0091]

[0092] In the formula: P D is the price per unit of electricity based on the demand of the main transformer; L max is the actual maximum demand of the load in that month; the modeling of the revenue for demand reduction of energy storage is as follows:

[0093]

[0094] In the formula: U D is the profit obtained by the typical daily energy storage through capacity reduction to avoid overcapacity penalty; L max,s is the maximum demand of the typical daily rail transit load after applying energy storage,

[0095] The objective function expression of the scheduling model under the corrected time-of-use electricity price condition is:

[0096]

[0097] In the formula: f is the net revenue of energy storage scheduling under the time-of-use electricity price condition; N n is the number of load record days within the planning time.

[0098] In the preferred embodiment of the method for planning a supercapacitor system, in step 400,

[0099] After constructing a load model and obtaining the relationship between the train operation power P and time t, based on the load modeling curve of the single vehicle traction characteristics, when the urban rail transit load P is less than the reference load P BN , the supercapacitor is in the charging state; when the load P is greater than the reference load P BN , the supercapacitor discharges. The reference load P BN is the load value of the 35KV voltage of the urban power grid uploaded by the selected rail transit subway station, and the load data of the power grid every 1 minute is extracted as the reference load of the urban rail transit in the same period.

[0100] In the preferred implementation of the described method for planning a supercapacitor system, in step 500, the SA-PSO algorithm structure model is improved with the goal of maximizing profit to determine the optimal capacity and charge-discharge power of the supercapacitor system; and relevant specified constraints are judged, the net profit under different energy storage capacities is evaluated in the algorithm, and the position information of the particles is adjusted according to the update iteration of the SA-PSO algorithm. Finally, the optimal supercapacitor configuration result that makes the objective function value, that is, the net profit, is found. Specifically, the algorithm includes the following stages:

[0101] (1) Initialization stage: Set the particle swarm size N = 50 and the maximum number of iterations T = 100; initialize the particle positions (energy storage capacity configuration plans) and velocities; set the initial temperature T0 = 500 and the annealing coefficient α = 0.85 - 0.95; initialize the individual optimal and global optimal solutions.

[0102] (2) Iterative calculation process: Calculate the step size t = 1:T for each iteration.

[0103] (3) Fitness calculation: Calculate the objective function value for each particle and obtain the result by calculating the configuration economy model of the supercapacitor described in claim 4.

[0104] (4) Update particle state: Update the particle velocity; update the particle position.

[0105] (5) Simulated annealing operation: Compared with the conventional PSO optimization algorithm, the SA-PSO algorithm adopted in this paper adds this step. Calculate ΔE for each new position, where ΔE is the difference between the new solution and the old solution; if ΔE > 0, that is, judge that this solution is an improved solution and directly accept the new solution; if ΔE ≤ 0, accept the inferior solution with the probability function ; and update the temperature so that T = αT to gradually reduce the temperature. This step allows the algorithm to temporarily accept worse solutions during the iteration process, avoiding the situation where a single solution optimal situation such as the capacity configuration or power configuration of the supercapacitor often appears. At the same time, due to the relatively high temperature set in the initial stage of the iteration, SA can expand the coverage of various solutions in the iteration process and accelerate the iteration speed in the early stage of the algorithm iteration.

[0106] (6) Optimal solution update: Update the individual optimal pbest and the global optimal gbest; record the current optimal supercapacitor configuration plan (P C 、E C );

[0107] (7) Termination condition: Stop when the maximum number of iterations (T = 100) is reached or terminate prematurely if the global optimum has not improved for 10 consecutive iterations.

[0108] (8) Output result: Output the corresponding global optimal solution; the current optimal supercapacitor configuration plan (PC , E C )

[0109] In a preferred embodiment of the described method for supercapacitor system planning, the improved SA-PSO algorithm starts from a random solution, sets the initial temperature T and the temperature decay function α, randomly selects a solution within the neighborhood of the current solution, called the candidate solution, calculates the cost difference, i.e., the energy difference, between the candidate solution and the current solution. If the candidate solution is better than the current solution, it is accepted; otherwise, a worse solution is accepted with a certain probability, and the probability decreases as the temperature drops. As the algorithm progresses, the temperature gradually decreases, and the acceptance probability of non-optimal solutions gradually decreases. When the temperature drops to a sufficiently low level or reaches a predetermined number of iterations, the algorithm terminates. The improved SA-PSO algorithm calculates the energy difference, i.e., the difference in the objective function value, in each iteration and uses the following formula to determine whether to accept a worse solution:

[0110]

[0111] where: ΔE is the difference in the objective function value between the new solution and the current solution, and T is the current temperature; the temperature T decreases according to the set decay function, and the decay function is:

[0112] T t+1 = αT t (10)

[0113] where α is a constant that controls the temperature decay rate, and 0 < α < 1.

[0114] Through temperature decay, the system gradually cools down and finally converges to a local optimum or a global optimum solution.

[0115] The particle swarm algorithm searches for the optimal solution by the movement of particles in the solution space. Each particle updates its position and velocity in the search space, influenced by its historical best position p i and the global best position g. The particle update rule is:

[0116]

[0117] where: is the velocity of particle i at time t; is the position of particle i at time t; and g t are the historical best positions of particle i and all particles, respectively; c1 and c2 are learning factors that control the dependence of the particle on its own experience and the group experience; r1 and r2 are random numbers.

[0118] A system for implementing the described method includes:

[0119] A collection unit, which is used to form traction load data based on the fluctuation of the traction transformer demand within the planned time of a single urban rail transit station;

[0120] A modeling unit, which is used to construct a comprehensive cost model of the urban rail transit supercapacitor system including initial investment cost, cycle operation and maintenance expenditure, and scrap disposal cost with the economic efficiency of the system's entire life cycle as the optimization goal;

[0121] A construction unit, which is used to establish an economic model for supercapacitor configuration by combining the comprehensive cost model of the supercapacitor system with the profit reduction of demand charge due to the adjustment of the supercapacitor demand based on the demand charge criterion for rail transit;

[0122] A control unit, which is used to establish a charge and discharge control strategy for the supercapacitor of the urban rail transit load characteristics based on the traction load characteristics of rail transit and the operating characteristics of the supercapacitor;

[0123] A calculation unit, which is used to combine the comprehensive cost model with the economic model for supercapacitor configuration, substitute the charge and discharge control strategy of the supercapacitor into the particle swarm optimization algorithm based on simulated annealing, and perform optimization to solve with the goal of optimal economy and minimum comprehensive cost, so as to obtain the planning result of the urban rail transit supercapacitor system.

[0124] In one embodiment, Figure 1 This is the overall flowchart of a method for planning a supercapacitor system for urban rail transit load characteristics according to the present invention. The method includes,

[0125] Step 100: First, use the urban rail transit demand management platform to select and form traction load data based on the fluctuation of the traction transformer demand within the planned time of a single urban rail transit station.

[0126] Specifically, select and form traction load data (sampling rate ≥ 10 kHz) based on the fluctuation of the traction transformer demand within the planned time of a single urban rail transit station. The formed data is in units of days. In the daily rail transit traction load data collected, sample every 1 minute as 1 point. The total number of sampling points per day is 1440. The set planned time is 1 year, including 365 consecutive daily loads. Among them, missing or significantly distorted sampling data is ignored.

[0127] Step 200: With the economic efficiency of the system's entire life cycle as the optimization goal, construct a comprehensive cost model of the urban rail transit supercapacitor system including initial investment cost, cycle operation and maintenance expenditure, and scrap disposal cost.

[0128] Specifically, with the economic efficiency of the system's entire life cycle as the optimization goal, construct a comprehensive cost model of the urban rail transit supercapacitor system including initial investment cost, cycle operation and maintenance expenditure, and scrap disposal cost. It includes:

[0129] The objective function of the supercapacitor system planning and configuration is to minimize the comprehensive cost, and its expression is:

[0130] C j = C C + C kp + C sp (1)

[0131] In the formula: C C is the investment cost of the supercapacitor; C kp is the maintenance cost; C sp is the control cost of the supercapacitor system.

[0132] The investment costs of lithium batteries and supercapacitors are related to the unit power cost and the capacity cost, and their expressions are:

[0133]

[0134] In the formula: k C,P , k C,E are the unit power cost and the unit capacity cost of the supercapacitor; α is the discount rate; P C , E C are the rated power and the capacity of the supercapacitor system; T C is the service life of the supercapacitor.

[0135] The expressions for the maintenance cost and the control cost of the supercapacitor system are:

[0136]

[0137] C k = Nm c,p c c,p (4)

[0138] C s = + l cap,p P Cp + l capp,c C Cp (5)

[0139] In the formula: x is the interest rate; m c,p is the unit maintenance price of the supercapacitor; l cap,p , l capp,c are the unit power control price and the unit capacity control price of the supercapacitor.

[0140] Step 300, based on the current electricity market's two-part electricity price policy for large industrial and commercial loads and the rail transit demand pricing policy in the demand pricing policy, the supercapacitor system comprehensive cost model and the demand billing profit reduced by the supercapacitor demand adjustment are combined to establish a supercapacitor configuration economic model. This includes:

[0141] Specifically, a rail transit demand electricity fee pricing model is established, and its mathematical expression is:

[0142]

[0143] Where: P D The electricity price is calculated based on the demand of the main transformer; L max The actual maximum demand of the load in that month. The regulations on the pricing of demand electricity per kilowatt-hour vary from province to province, but the unit price is basically 1.6 times the capacity electricity fee. If the actual maximum demand exceeds 105% of the rated capacity of the main transformer, the excess will still be charged double.

[0144] The benefits of demand reduction for energy storage are modeled and combined with the operation and dispatch model of energy storage. The modeling of the benefits of demand reduction for energy storage is as follows:

[0145]

[0146] Where: U D The profit obtained by energy storage in a typical day by reducing capacity to avoid overcapacity penalties; L max,s is the maximum demand of rail transit load in a typical day after applying energy storage and:

[0147] Substituting the profitability of energy storage's control of demand into the energy storage planning and configuration model, the demand reduction benefits of energy storage can be quantified into economic benefits when the model is solved with the goal of maximizing benefits, thus achieving the rationalization of operation and scheduling. The objective function expression of the scheduling model under the modified time-of-use electricity price condition is:

[0148]

[0149] Where: f is the net benefit of energy storage dispatch under time-of-use electricity price conditions; N n The number of load record days within the planning time.

[0150] Step 400: establishing a supercapacitor charging and discharging control strategy for urban rail transit load characteristics based on rail transit traction load characteristics and supercapacitor operation characteristics.

[0151] Figure 2It is a flow chart for judging the operation strategy of the supercapacitor system. After constructing the load model and obtaining the relationship between the train operation power P and time t, it is also necessary to control the response of the supercapacitor. The braking energy of urban rail transit has the characteristics of high power and high energy, and the characteristics of the supercapacitor are similar to those of the braking energy. Based on the load modeling curve of the single-car traction characteristics, when the urban rail transit load P is less than the reference load P BN at this time, the supercapacitor is in the charging state; when the load P is greater than the reference load P BN at this time, due to the fast charge and discharge characteristics of the supercapacitor, the supercapacitor performs the discharge operation.

[0152] Due to the strong volatility of the traction load, simply setting the charge and discharge actions of the hybrid energy storage system through the reference load P BN will cause misoperation of the supercapacitor system. Therefore, this paper uses the differential with respect to time t as the signal for the operation of the hybrid energy storage system. Combining the reference load criterion in the previous text, let dP / dt = k; the larger the differential threshold k is, the faster the load power rises, and load spikes are likely to occur. Through the analysis of the power curve during train operation, it is determined that when the train starts, the threshold k for the load to increase is -2.9, and the differential threshold k for the train during braking is 3.9. After combining the two judgment methods, the operation signal of the supercapacitor system can be determined. Combining the setting of the reference load and the determination of the load differential threshold can ensure that the supercapacitor accurately suppresses the frequent peak loads brought by the start and stop of the rail transit train.

[0153] The reference load P BN is the load value of the 35KV voltage of the urban power grid uploaded by the selected rail transit subway station. This value is recorded by the grid machine. Although the load data acquisition frequency is 15 minutes, the grid system can still extract the load data per minute through interpolation. The load data of the grid per 1 minute is extracted as the reference load of the urban rail transit in the same period.

[0154] Step 500 combines the constructed comprehensive cost model of the urban rail transit supercapacitor system and the economic model of supercapacitor configuration. Based on the charge and discharge control strategy of the supercapacitor, the particle swarm optimization algorithm based on simulated annealing is substituted, and the optimization solution is carried out with the goal of the best economy and the minimum comprehensive cost to obtain the planning result of the urban rail transit supercapacitor system.

[0155] The improved SA-PSO algorithm structure model aims to maximize the profit, determine the optimal capacity and charge and discharge power of the supercapacitor system; and judge the relevant specified constraint conditions, evaluate the net profit under different energy storage capacities in the algorithm, and adjust the position information of the particles according to the update iteration of the SA-PSO algorithm, and finally find the optimal supercapacitor configuration result that makes the objective function value (net profit) optimal.

[0156] In step 300, in order to make the operation law of the supercapacitor conform to the actual physical meaning, constraints need to be added to the operation characteristics of the supercapacitor.

[0157] To prevent overcharging and over-discharging of the supercapacitor and the lithium battery, constraints are imposed on their state of charge (SOC). Hybrid supercapacitor system SOC constraint:

[0158] SOC min ≤SOC≤SOC max (9)

[0159]

[0160] In the formula: SOC min and SOC max are the upper and lower limits of the state of charge of the supercapacitor; η C,d is the charge and discharge efficiency of the lithium battery; E C is the capacity of the lithium battery; P C (t) > 0 represents discharge, and P C (t) < 0 represents charge.

[0161] The power constraint is expressed as:

[0162] -P Cn ≤P C (t)≤P Cn (12)

[0163] In the formula: -P Cn and P Cn are the rated charge and discharge powers of the supercapacitor respectively.

[0164] In step 500, the constructed comprehensive cost model of the urban rail transit supercapacitor system is combined with the supercapacitor configuration economy model. Based on the supercapacitor charge and discharge control strategy, the particle swarm optimization algorithm based on simulated annealing is substituted, and the optimization solution is carried out with the goal of optimal economy and minimum comprehensive cost to obtain the planning result of the urban rail transit supercapacitor system. Its content includes:

[0165] The algorithm starts from a random solution, sets the initial temperature T and the temperature decay function α. A solution (called the candidate solution) is randomly selected within the neighborhood of the current solution. Calculate the cost difference (energy difference) between the candidate solution and the current solution. If the candidate solution is better than the current solution, accept it; otherwise, accept the worse solution with a certain probability, and the probability decreases as the temperature drops. As the algorithm progresses, the temperature gradually decreases, and the acceptance probability of non-optimal solutions gradually decreases. When the temperature drops to a sufficiently low level or reaches the predetermined number of iterations, the algorithm terminates. SA calculates the energy difference (i.e., the difference in the objective function value) in each iteration and uses the following formula to decide whether to accept the worse solution:

[0166]

[0167] where: ΔE is the difference in the objective function values between the new solution and the current solution, and T is the current temperature; the temperature T decreases according to a set decay function, and the decay function is:

[0168] T t+1 = αT t (14)

[0169] where α is a constant that controls the temperature decay rate, and 0 < α < 1.

[0170] Through temperature decay, the system gradually "cools" and finally converges to a local or global optimal solution.

[0171] The particle swarm optimization (PSO) algorithm searches for the optimal solution through the movement of particles in the solution space. Each particle updates its position and velocity in the search space, influenced by its historical best position p i and the global best position g. The particle update rules are:

[0172]

[0173] where: is the velocity of particle i at time t; is the position of particle i at time t; and g t are the historical best positions of particle i and all particles respectively; c1 and c2 are learning factors that control the degree of dependence of the particle on its own experience and the group experience; r1 and r2 are random numbers to ensure the diversity of the search. Simulated annealing helps the algorithm maintain the global search ability during the scheduling process. Through the temperature control mechanism of simulated annealing, it is avoided that the solution in the process of the PSO loop calculation of the two-stage algorithm is the optimal solution of a single objective.

[0174] The solution of the described algorithm is obtained using the MATLAB cplex algorithm package. All numerical simulations are implemented on a 64-bit PC with a 1.70-GHz CPU and 16 GB of RAM, and the MATLAB algorithm package is used for calculation. The parameters of the energy storage device selected in this paper are shown in Table 2, and the battery degradation and leakage effects are ignored. The typical daily scheduling period is set to 15 minutes per month, and a total of 96 scheduling periods are set in a day.

[0175] The comprehensive technical and economic parameters selected for this case of supercapacitors are as follows: its operation and maintenance cost is 0.05 yuan / kWh. The investment cost includes two dimensions of capacity and power. The investment cost per unit capacity is as high as 20,000 yuan / kWh, and the investment cost per unit power is 3,000 yuan / kW. In terms of cost control, the power-related control cost is 60 yuan / kW, and the capacity-related control cost reaches 300 yuan / kWh. The device operation parameters show that its charge and discharge efficiency reaches 95%, and the state of charge (SOC) needs to be maintained in the working range of 5% to 95%. The designed service life is 10 years. The economic evaluation parameter adopts a discount rate of 7% (α = 0.07), and this parameter system covers the technical performance indicators and cost accounting elements within the entire life cycle of the device.

[0176] The core parameter configuration of the optimization algorithm of the present invention is as follows: The algorithm uses a population size of 50 particles, sets the maximum number of iterations to 100 times, and balances the global search and local development capabilities by dynamically adjusting the inertia weight (range 0.1 to 0.5). The individual learning factor and the group learning factor are set to 0.1 and 0.2 respectively, guiding the particles to move towards the individual historical optimum and the group optimum directions. To enhance the ability of the algorithm to jump out of the local optimum, a simulated annealing strategy is integrated. The initial temperature is set to 500, and the annealing speed is dynamically controlled between 0.85 and 0.95, and a refined search for solutions is achieved through progressive temperature reduction. This parameter system combines the advantages of swarm intelligence and annealing mechanism to form a complete hybrid optimization framework.

[0177] After configuration, the planning result of the supercapacitor system for this case is a rated power of 2.062 kW and a rated capacity of 0.03217 MWh. Under this configuration, the annual profit of the supercapacitor is 198,210.29 yuan.

[0178] Figure 3 and Figure 4 respectively show the schematic diagram of the suppression effect of the supercapacitor on the rail transit impact load and the schematic diagram of the change of the supercapacitor SOC after the completion of the planning of a supercapacitor system planning method for urban rail transit load characteristics provided by the present invention.

[0179] Verified by the measured data, this solution achieves: the maximum demand is reduced by 20.7%, and the recovery rate of regenerative energy is increased to 67.3%.

[0180] Although the embodiments of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the above specific embodiments and application fields. The above specific embodiments are merely illustrative and guiding, rather than restrictive. Those of ordinary skill in the art can also make many forms under the inspiration of this specification and without departing from the scope protected by the claims of the present invention, and these all belong to the scope of protection of the present invention.

Claims

1. A supercapacitor system planning method, characterized in that: The steps include: Step 100, forming traction load data based on the fluctuation of traction variable demand within the planning time of a single station of urban rail transit; Step 200, taking the economic efficiency of the entire life cycle of the system as the optimization goal, constructing a comprehensive cost model of the urban rail transit supercapacitor system including initial investment cost, periodic operation and maintenance expenditure and scrapping cost; Step 300, based on the rail transit demand charging criteria and traction load data, the comprehensive cost model of the supercapacitor system and the demand charging profit reduced by the supercapacitor demand adjustment are combined to establish a supercapacitor configuration economic model; Step 400, establishing a supercapacitor charging and discharging control strategy for urban rail transit load characteristics based on traction load data and supercapacitor operation characteristics; Step 500, combining the comprehensive cost model with the supercapacitor configuration economic model, substituting the supercapacitor charge and discharge control strategy into the particle swarm optimization algorithm based on simulated annealing, performing optimization solution with the optimal economic goal, and obtaining the urban rail transit supercapacitor system planning result.

2. A supercapacitor system planning method according to claim 1, characterized in that: Preferably, in step 100, the sampling rate ≥ 10kHz is selected based on the fluctuation of traction variable demand within the planning time of a single urban rail transit station to form traction load data. The traction load data is in units of days, with 1 minute as 1 point. The total number of sampling points per day is 1440. The planning time is 1 year, including 365 consecutive daily loads.

3. A supercapacitor system planning method according to claim 1, characterized in that: In step 200, the objective function expression of the comprehensive cost model is: C j =C C +C kp +C sp (1); Where: C C is the investment cost of supercapacitor; C kp is the maintenance cost; C sp To control the cost of supercapacitor system, Where: k C,P , k C,E is the unit power cost and unit capacity cost of the supercapacitor; α is the discount rate; P C 、E C is the rated power and capacity of the supercapacitor system; T C is the service life of the supercapacitor; C k =Nm c,p c c,p (4); C s =+l cap,p P C +l capp,c E C (5); Where: x is the interest rate; m c,p is the maintenance unit price of supercapacitor; cap,p , l capp,c It is the power control unit price and capacity control unit price of supercapacitor.

4. A supercapacitor system planning method according to claim 1, characterized in that: In step 300, in the supercapacitor configuration economic model, the mathematical expression of rail transit demand charging is: Where: P D The electricity price is calculated based on the demand of the main transformer; L max is the actual maximum demand of the load in that month; the modeling of the benefits of demand reduction for supercapacitors is as follows: Where: U D The profit obtained by energy storage in a typical day by reducing capacity to avoid overcapacity penalties; L max,s The maximum demand of rail transit load in a typical day after the application of energy storage, the objective function expression of the modified supercapacitor configuration economic model is: Where: f is the net benefit of energy storage dispatch under time-of-use electricity price conditions; N n The number of load record days within the planning time.

5. A supercapacitor system planning method according to claim 1, characterized in that: In step 400, Based on the traction load data, after obtaining the relationship between the train running power P and time t, the load modeling curve based on the traction characteristics of a single vehicle is constructed. When the urban rail transit load P is less than the benchmark load P BN When , the supercapacitor is in charging state; When the load P is greater than the reference load P BN When the supercapacitor is discharging, the reference load P BN The load value of the 35KV voltage of the urban power grid is uploaded to the selected rail transit subway station, and the load data of the power grid every 1 minute is extracted as the benchmark load of the urban rail transit in the same period. The differential with respect to time t is used as the action signal of the hybrid energy storage system. Combined with the previous reference load criterion, dP / dt=k is set. The larger the differential threshold k is, the faster the load power rises, and the more likely a load peak will occur. By analyzing the power curve of the train when it is running, it is determined that the threshold k of the load increase when the train starts is -2.9, and the differential threshold k of k when the train brakes is 3.

9. After combining the two judgment methods, the action signal of the supercapacitor system is determined. Combined with the setting of the reference load and the determination of the load differential threshold, it is ensured that the supercapacitor system can complete the smoothing of the frequent peak loads caused by the start and stop of rail transit trains.

6. A supercapacitor system planning method according to claim 1, characterized in that: Step 500 includes: (1) Initialization stage: set the particle swarm size N = 50 and the maximum number of iterations T = 100; initialize the particle position and velocity; set the initial temperature T0 = 500 and the annealing coefficient α = 0.85-0.95; initialize the individual optimal solution and the global optimal solution; (2) Iterative calculation process: each iterative calculation step length is t = 1:T; (3) Fitness calculation: Calculate the objective function value for each particle and calculate the configuration economic model of the supercapacitor to obtain the result; (4) Update particle status: update particle velocity; update particle position; (5) Simulated annealing operation: Calculate ΔE for each new position, where ΔE is the difference between the new solution and the old solution. If ΔE>0, the solution is considered to be an improved solution and the new solution is directly accepted. If ΔE≤0, the probability function is used to calculate the difference between the new solution and the old solution. Accept the inferior solution; and update the temperature so that T = αT to gradually reduce the temperature; (6) Optimal solution update: Update the individual optimal solution pbest and the global optimal solution gbest; record the current optimal supercapacitor configuration solution (P C 、E C ); (7) Termination condition: stop when the maximum number of iterations (T = 100) is reached or terminate early if the global optimum is not improved after 10 consecutive iterations; (8) Output result: Output the current optimal supercapacitor configuration scheme corresponding to the global optimal solution (P C 、E C ).

7. A supercapacitor system planning method according to claim 6, characterized in that: The improved SA-PSO algorithm starts with a random solution, sets the initial temperature T and the temperature attenuation function α, and randomly selects a solution in the neighborhood of the current solution, called a candidate solution. The cost difference between the candidate solution and the current solution, that is, the energy difference, is calculated. If the candidate solution is better than the current solution, it is accepted; otherwise, the inferior solution is accepted with a certain probability, and the probability decreases as the temperature decreases. As the algorithm proceeds, the temperature gradually decreases, gradually reducing the probability of accepting suboptimal solutions. When the temperature drops low enough or reaches the predetermined number of iterations, the algorithm terminates. The improved SA-PSO algorithm calculates the energy difference, that is, the objective function value difference, in each iteration, and uses the following formula to decide whether to accept the inferior solution: Where: ΔE is the difference between the objective function value of the new solution and the current solution, T is the current temperature; the temperature T decreases according to the set attenuation function, and the attenuation function is: Tt+1=αTt(10); Among them, α is a constant that controls the temperature decay rate, and 0<α<1, Through temperature decay, the system gradually cools down and eventually tends to a local optimal or global optimal solution. The particle swarm algorithm searches for the optimal solution through the movement of particles in the solution space. Each particle updates its position and speed in the search space, subject to its historical best position p. i and the global optimal position g, the particle update rule is: Where: is the velocity of particle i at time t; is the position of particle i at time t; and g t are the historical best positions of particle i and all particles respectively; c1 and c2 are learning factors that control the degree of dependence of particles on their own experience and group experience; r1 and r2 are random numbers.

8. A system for implementing the method according to any one of claims 1 to 7, characterized in that: It includes: A collection unit, which is used to form traction load data based on the fluctuation of traction demand within the planning time of a single station of urban rail transit; A modeling unit, which is used to build a comprehensive cost model of the urban rail transit supercapacitor system, including initial investment cost, periodic operation and maintenance expenditure, and scrapping cost, with the economy of the system throughout its life cycle as the optimization goal; A construction unit is used to establish a supercapacitor configuration economic model based on rail transit demand charging criteria and traction load data, combining a comprehensive cost model of the supercapacitor system with a demand charging profit reduced by adjusting demand for supercapacitors; A control unit, which is used to establish a supercapacitor charging and discharging control strategy for urban rail transit load characteristics based on traction load data and supercapacitor operating characteristics; A calculation unit is used to combine the comprehensive cost model with the supercapacitor configuration economic model, substitute the supercapacitor charge and discharge control strategy into the particle swarm optimization algorithm based on simulated annealing, perform optimization solution with the goal of optimal economy and minimum comprehensive cost, and obtain the urban rail transit supercapacitor system planning result.

9. A computer storage medium, characterized in that The storage medium includes computer instructions, which, when executed on a computer, enable the computer to execute the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The electronic device comprises: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

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