Power supply system capacity configuration optimization method, device, equipment, medium and program

Abstract

The invention relates to a power supply system capacity configuration optimization method and device, equipment, a medium and a program. The method comprises the following steps: calculating a profit value of a target power supply system according to a pre-generated constraint condition of a day-ahead energy management strategy and a pre-determined target function of the day-ahead energy management strategy; the current position of the artificial fish is determined according to the capacity parameter corresponding to the income value, foraging processing is conducted on the artificial fish according to the current position, and the next position of the artificial fish is obtained; clustering processing and rear-end collision processing are carried out on the artificial fish corresponding to the next position, and then the target food concentration of the artificial fish is generated; and updating the capacity parameter according to the target position of the artificial fish corresponding to the target food concentration until the updating frequency of the capacity parameter is equal to a preset frequency threshold value, and generating a target capacity configuration strategy of the target power supply system according to the updated capacity parameter. The capacity configuration optimization efficiency of the power supply system can be improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of power supply system optimization, and particularly to an optimization method, device, equipment, storage medium and computer program for the capacity configuration of a power supply system. Background Art

[0002] In a railway traction power supply system, traditional power supply methods face problems such as electrical phase separation, harmonic pollution, excessive negative sequence current, and failure to effectively utilize the regenerative braking energy of trains. To solve these problems, a "network-source-storage-vehicle" collaborative power supply system combining AC-DC-AC traction substation technology and microgrids along railway lines has been proposed. This system can not only eliminate power quality problems, but also promote the access and utilization of renewable energy, improve the recovery efficiency of regenerative braking energy, and increase the flexibility and controllability of the system.

[0003] To further optimize the energy management of the system, a reasonable capacity configuration method needs to be designed to achieve optimal energy flow control under multi-source and multi-load conditions; however, due to the non-linear characteristics of the energy management strategy model, traditional linear programming methods are no longer applicable, so new optimization algorithms need to be explored to determine the optimal capacity configuration. Summary of the Invention

[0004] The present invention discloses an optimization method, device, equipment, storage medium and computer program for the capacity configuration of a power supply system.

[0005] In a first aspect, the present invention discloses an optimization method for the capacity configuration of a power supply system, including:

[0006] S1. Generate the constraint conditions of the day-ahead energy management strategy according to the preset basic constraints and preset reliability constraints, and determine the objective function of the day-ahead energy management strategy;

[0007] S2. Calculate the revenue value of the target power supply system according to the constraint conditions and the objective function;

[0008] S3. Determine the current position of the artificial fish in the artificial fish swarm algorithm according to the capacity parameter corresponding to the revenue value, and perform foraging processing on the artificial fish according to the current position to obtain the next position of the artificial fish;

[0009] S4. Perform clustering processing and chasing processing on the artificial fish corresponding to the next position, and generate the target food concentration of the artificial fish according to the clustering position determined by the clustering processing and the chasing position determined by the chasing processing;

[0010] Update the capacity parameter according to the target position of the artificial fish corresponding to the target food concentration, and return to step S2. When the update times of the capacity parameter are equal to the preset number threshold, generate the target capacity configuration strategy of the target power supply system according to the updated capacity parameter.

[0011] In some embodiments, the constraint conditions for generating the day-ahead energy management strategy according to the preset basic constraints and preset reliability constraints include:

[0012] The preset basic constraints include: the power balance constraint of the target power supply system, the self-power constraint of the energy storage device in the target power supply system, the rated power constraint of the energy storage device, the power limit constraint of the target power supply system, and the 10kV distribution network load power constraint of the target power supply system;

[0013] The preset reliability constraint is:

[0014]

[0015] Among them, is the remaining energy of the energy storage device i in the current state, N is the total number of energy storage devices, is the minimum energy limit of the energy storage device i, represents the power at the t-th time step in the traction load power sequence, and τ represents the number of time steps required for the train to pass through the power supply section.

[0016] In some embodiments, the determination of the objective function of the day-ahead energy management strategy includes:

[0017] The objective function of the day-ahead energy management strategy is:

[0018] maxRtotal = 365·Lexpec(Corigin - Coptim) - Cinvest

[0019] Among them, maxRtotal is the revenue value of the flexible traction power supply system, Lexpec is the shortest expected life of each part of the flexible traction power supply system, Corigin is the daily operating cost of the system before implementing the day-ahead energy management strategy, Coptim is the daily operating cost of the system after implementing the day-ahead energy management strategy, and Cinvest is the investment cost of the flexible traction power supply system.

[0020] In some embodiments, the foraging process of the artificial fish according to the current position to obtain the next position of the artificial fish includes:

[0021] Generate a random position of the artificial fish according to the current position and a preset random position generation algorithm, where the preset random position generation algorithm is:

[0022]

[0023] wherein, X h is the random position of the artificial fish, is the current position of the artificial fish k, Visual is the visual range of the artificial fish, Rand(1) is a random number uniformly distributed between -1 and 1, and k is the identifier of the artificial fish;

[0024] Generate the next position of the artificial fish according to the random position and a preset next-position generation algorithm, wherein the preset next-position generation algorithm is:

[0025]

[0026] wherein, is the next position of the artificial fish k, X h is the random position of the artificial fish, is the current position of the artificial fish k, Rand(1) is a random number uniformly distributed between -1 and 1, is the Euclidean distance between the random position and the current position, k is the identifier of the artificial fish that needs to perform foraging processing, and Step represents the step size.

[0027] In some embodiments, performing flocking processing and following processing on the artificial fish corresponding to the next position includes:

[0028] Generate the center position of the artificial fish according to a preset center-position generation algorithm and the next position, wherein the preset center-position generation algorithm is:

[0029]

[0030] wherein, X C is the center position of the artificial fish, n f is the total number of artificial fish within the visual range of the current artificial fish, i is the identifier of the artificial fish that needs to perform flocking processing, and X i is the next position;

[0031] Determine the foraging food concentration at the next position and the center food concentration at the center position, and judge the flocking crowding degree of the artificial fish according to the foraging food concentration and the center food concentration;

[0032] Generate the flocking position of the artificial fish according to the judgment result of the flocking crowding degree judgment and a preset flocking-position generation algorithm.

[0033] In some embodiments, the flocking process and following process for the artificial fish corresponding to the next position include:

[0034] Determine the foraging food concentration corresponding to the next position, and generate the maximum food concentration position of the artificial fish according to the foraging food concentration and a preset maximum food concentration position generation algorithm, where the preset maximum food concentration position generation algorithm is:

[0035]

[0036] where X MAX is the maximum food concentration position of the artificial fish, Y1 is the food concentration of the first artificial fish within the field of view, and n f is the total number of artificial fish within the field of view of the current artificial fish, is a set containing the food concentration values of each artificial fish within the current field of view, is to select X that maximizes the function value from the given set, and X is the position of the artificial fish;

[0037] Judge the following fish school congestion degree of the artificial fish according to the maximum food concentration corresponding to the maximum food concentration position and the foraging food concentration;

[0038] Generate the following position of the artificial fish according to the judgment result of the following fish school congestion degree judgment and a preset following position update algorithm.

[0039] In a second aspect, the present disclosure provides an optimization device for power supply system capacity configuration, including:

[0040] A condition generation module, configured to generate constraint conditions for a day-ahead energy management strategy according to preset basic constraints and preset reliability constraints, and determine the objective function of the day-ahead energy management strategy;

[0041] A revenue value generation module, configured to calculate the revenue value of the target power supply system according to the constraint conditions and the objective function;

[0042] A foraging processing module, configured to determine the current position of the artificial fish in the artificial fish swarm algorithm according to the capacity parameter corresponding to the revenue value, perform foraging processing on the artificial fish according to the current position, and obtain the next position of the artificial fish;

[0043] A target food concentration generation module, configured to perform flocking processing and following processing on the artificial fish corresponding to the next position, and generate the target food concentration of the artificial fish according to the flocking position determined by the flocking processing and the following position determined by the following processing;

[0044] A configuration strategy generation module, configured to update the capacity parameter according to the target position of the artificial fish corresponding to the target food concentration, and when the update times of the capacity parameter is equal to a preset number threshold, generate a target capacity configuration strategy for the target power supply system according to the updated capacity parameter.

[0045] In a third aspect, the present disclosure provides a computer device, including a memory, a processor, and a computer program stored on the memory, and the processor executes the computer program to implement the steps of the method described in the above aspect.

[0046] In a fourth aspect, the present disclosure provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described in the above aspect are implemented.

[0047] In a fifth aspect, the present disclosure provides a computer program product, including a computer program / instructions, and when the computer program is executed by a processor, the steps of the method described in the above aspect are implemented.

[0048] Compared with the prior art, the above technical solution of the present invention has the following beneficial effects:

[0049] In the embodiment of the present invention, a mathematical model of a day-ahead energy management strategy is established based on basic constraints and reliability constraints to clarify the optimization objective. The established model is used to calculate the revenue value of the power supply system, and the initial capacity configuration is determined according to the revenue value and an initial solution is found in the artificial fish swarm algorithm. Then, by simulating the foraging behavior to explore better solutions; next, by performing clustering and chasing operations on the artificial fish at the new position to simulate the interaction between groups, further refining the optimization path and determining the new target state, adjusting the capacity parameter according to the target food concentration in the new state, and continuously iterating this process until the predetermined number of iterations is reached, so as to obtain a final capacity configuration strategy optimized through multiple rounds. This method can quickly converge to a better solution by simulating the optimization mechanism in nature, significantly improving the optimization efficiency of the power supply system capacity configuration. Therefore, the power supply system capacity configuration optimization method, device, equipment, and medium proposed by the present invention can solve the problem of low efficiency in optimizing the power supply system capacity configuration. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Hereinafter, the present disclosure will be described in more detail based on embodiments and with reference to the drawings:

[0051] Figure 1 Shows the working flow chart of the power supply system capacity configuration optimization method according to Embodiment 1 of the present invention;

[0052] Figure 2 Shows the functional module diagram of the power supply system capacity configuration optimization device according to Embodiment 2 of the present invention;

[0053] Figure 3 Shows the schematic diagram of the composition structure of the electronic device for implementing the power supply system capacity configuration optimization method in the third embodiment of the present invention. Detailed implementation manners

[0054] In order to enable those skilled in the art of the present technology to better understand the technical solutions of the present disclosure, and to fully understand and implement how the present disclosure uses technical means to solve technical problems and achieve the corresponding technical effects, the following will combine the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. The embodiments of the present disclosure and each feature in the embodiments can be combined with each other without conflict, and the formed technical solutions are all within the protection scope of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present disclosure.

[0055] It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0056] Example 1

[0057] Figure 1 Is the flowchart of a power supply system capacity configuration optimization method provided for the embodiments of the present disclosure. As Figure 1 shown, a power supply system capacity configuration optimization method includes:

[0058] S1. Generate the constraint conditions of the day-ahead energy management strategy according to the preset basic constraints and the preset reliability constraints, and determine the objective function of the day-ahead energy management strategy.

[0059] In the embodiment of the present invention, the generating the constraint conditions of the day-ahead energy management strategy according to the preset basic constraints and the preset reliability constraints includes:

[0060] The preset basic constraints include: the power balance constraint of the target power supply system, the self-power constraint of the energy storage device in the target power supply system, the rated power constraint of the energy storage device, the power limit constraint of the target power supply system, and the 10kV distribution network load power constraint of the target power supply system;

[0061] The preset reliability constraint is:

[0062]

[0063] Among them, is the remaining energy of the energy storage device i in the current state, N is the total number of energy storage devices, is the minimum energy limit of the energy storage device i, represents the power at the t-th time step in the traction load power sequence, and τ represents the number of time steps required for the train to pass through the power supply section.

[0064] Specifically, the target power supply system refers to a flexible traction power supply system.

[0065] Specifically, the power balance constraint of the target power supply system is as follows:

[0066]

[0067] Among them, represents the power generated by the photovoltaic panel, is the regenerative braking power of the traction load, is the load power of the 10kV distribution network, is the power consumed by the grid in the flexible traction power supply section, is the power returned to the grid in the flexible traction power supply section, is the charging power of the super capacitor on the AC-DC-AC traction substation side, is the discharging power of the super capacitor on the AC-DC-AC traction substation side, is the battery charging power on the AC-DC-AC traction substation side, is the battery discharging power on the AC-DC-AC traction substation side, is the power provided by the AC-DC-AC traction substation to the traction network, is the power returned from the traction network to the AC-DC-AC traction substation, is the charging power of the super capacitor on the microgrid side, is the discharging power of the super capacitor on the microgrid side, is the power provided by the microgrid to the traction network, is the power provided by the traction network to the microgrid, is the power provided by the microgrid to the 10kV distribution network, is the power provided by the 10kV distribution network to the 10kV distribution network load, and t is the time.

[0068] Among them, represents the power balance of the DC bus on the AC-DC-AC traction substation side, represents the power balance of the DC bus on the microgrid side, represents the power balance of the traction network, represents the power balance of the 10kV distribution network.

[0069] Specifically, the self - power constraint of the energy storage device and the rated power constraint of the energy storage device in the target power supply system are as follows:

[0070]

[0071] Among them, κ i,j is the self - discharge coefficient of the energy storage device j in system i, and are the charging and discharging efficiencies of the energy storage device j in system i respectively, and are the maximum and minimum values of the state of charge of the energy storage device j in system i, is the rated power of the energy storage device j in system i, i is the AC - DC - AC traction substation or the micro - grid along the railway line, and j is the battery or the super - capacitor.

[0072] Among them, means that the remaining energy of the energy storage device at the current moment is equal to the remaining energy at the previous moment plus (or minus) the energy charged (or discharged) at the previous moment. The initial energy of the energy storage device should be equal to the energy of the energy storage device at the end of a day (if the energies are not equal, this energy difference will affect the optimization result), and mean that the charging and discharging power of the energy storage device is limited by the rated power of the energy storage device and the current remaining energy.

[0073] Specifically, the power limit constraint of the target power supply system is as follows:

[0074]

[0075] Among them, is the capacity of the three - phase converter of the AC - DC - AC traction substation, is the capacity of the single - phase converter of the AC - DC - AC traction substation, S m,conv is the capacity of the converter between the micro - grid and the traction network (abbreviated as the single - phase converter on the micro - grid side), is the rated power of the energy storage device j in system i, i is the AC - DC - AC traction substation or the micro - grid along the railway line, and j is the battery or the super - capacitor.

[0076] Among them, and are the restrictions on the charging and discharging states of the energy storage device, used to ensure that the energy storage device is only in one of the charging or discharging states at a certain moment,

[0077] and It is a limit on power exchange, which ensures that the power exchanged between the AC-DC-AC traction substation and the 220 kV power grid, the power exchanged between the AC-DC-AC traction substation and the traction network, and the power exchanged between the microgrid and the traction network can only be in one of the power-taking and power-feeding states.

[0078] Specifically, the load power constraint of the 10 kV distribution network of the target power supply system is as follows:

[0079]

[0080] Among them, S m-load,conv is the capacity of the converter responsible for power transmission between the microgrid and the 10 kV distribution network (abbreviated as the three-phase converter on the microgrid side), is the short-circuit capacity of the 10 kV distribution network.

[0081] Among them, indicates that the power exchange between the microgrid and the 10 kV distribution network cannot exceed the upper limit of the converter capacity, indicates that the output power of the 10 kV distribution network cannot exceed its own short-circuit capacity.

[0082] Specifically, the generation reliability constraint is because the remaining energy of the energy storage device should meet the demand of the current interval trains to pass through the target power supply interval when the power grid is powered off, and its function is similar to an uninterruptible power supply.

[0083] In the embodiment of the present invention, the objective function for determining the day-ahead energy management strategy includes:

[0084] The objective function of the day-ahead energy management strategy is:

[0085] maxRtotal = 365·Lexpec(Corigin - Coptim) - Cinvest

[0086] Among them, maxRtotal is the revenue value of the flexible traction power supply system, Lexpec is the shortest expected life of each part of the flexible traction power supply system, Corigin refers to the daily operating cost of the system before implementing the day-ahead energy management strategy, Coptim is the daily operating cost of the system after implementing the day-ahead energy management strategy, and Cinvest is the investment cost of the flexible traction power supply system.

[0087] Specifically, the investment cost of the flexible traction power supply system is mainly composed of four parts: isolation transformer, converter, photovoltaic panel, and energy storage device.

[0088] Furthermore, the investment cost can be calculated by the following algorithm:

[0089]

[0090] Among them, λ Tran , λ con , λ PV , λ uc and λ bat respectively represent the unit investment costs of the isolation transformer, the current converter, the photovoltaic panel, the super capacitor, and the battery, and C Tran , C Con , C PV and C ESD respectively represent the investment costs of the isolation transformer, the current converter, the photovoltaic panel, and the energy storage device.

[0091] S2. Calculate the revenue value of the target power supply system according to the constraint conditions and the objective function.

[0092] In the embodiment of the present invention, calculating the revenue value of the target power supply system according to the constraint conditions and the objective function includes: inputting the pre-acquired scenario data into the constraint conditions and the objective function to calculate the revenue value of the target power supply system, where the pre-acquired scenario data includes: traction load power, photovoltaic power, and 10 kV load power.

[0093] S3. Determine the current position of the artificial fish in the artificial fish swarm algorithm according to the capacity parameter corresponding to the revenue value, and perform foraging processing on the artificial fish according to the current position to obtain the next position of the artificial fish.

[0094] In the embodiment of the present invention, determining the current position of the artificial fish in the artificial fish swarm algorithm according to the capacity parameter corresponding to the revenue value includes: representing the position of the artificial fish by using the following variables:

[0095]

[0096] Among them, P m,conv , P m-load,conv , are each capacity parameter, and X is the position of the artificial fish.

[0097] In the embodiment of the present invention, performing foraging processing on the artificial fish according to the current position to obtain the next position of the artificial fish includes:

[0098] Generating a random position of the artificial fish according to the current position and a preset random position generation algorithm, where the preset random position generation algorithm is:

[0099]

[0100] Among them, X h is the random position of the artificial fish, is the current position of the artificial fish k, Visual is the visual field range of the artificial fish, Rand(1) is a random number uniformly distributed between -1 and 1, and k is the identifier of the artificial fish;

[0101] Generate the next position of the artificial fish according to the random position and a preset next-position generation algorithm, where the preset next-position generation algorithm is:

[0102]

[0103] where, is the next position of the artificial fish k, X h is the random position of the artificial fish, is the current position of the artificial fish k, Rand(1) is a random number uniformly distributed between -1 and 1, is the Euclidean distance between the random position and the current position, k is the identifier of the artificial fish that needs to perform foraging processing, and Step represents the step size.

[0104] Specifically, when the artificial fish performs foraging behavior, it continuously discovers the food concentration at other positions within its visual field. If the food concentration at the found position is higher than the food concentration at the current position, it will move a random distance in that direction; otherwise, it will continue to find the food concentration at another random position within the visual field and repeat the above steps until the maximum number of attempts is reached.

[0105] Furthermore, if the maximum number of attempts is reached and the artificial fish still cannot find a position with a food concentration higher than the current position, it will perform the same random behavior as the same.

[0106] S4. Perform schooling processing and following processing on the artificial fish corresponding to the next position, and generate the target food concentration of the artificial fish according to the schooling position determined by the schooling processing and the following position determined by the following processing.

[0107] In the embodiment of the present invention, the performing schooling processing and following processing on the artificial fish corresponding to the next position includes:

[0108] Generate the center position of the artificial fish according to a preset center-position generation algorithm and the next position, where the preset center-position generation algorithm is:

[0109]

[0110] where, X C is the center position of the artificial fish, n fis the total number of artificial fish within the visual range of the current artificial fish, i is the identifier of the artificial fish that needs to perform flocking processing, X i is the next position;

[0111] Determine the foraging food concentration at the next position and the central food concentration at the central position, and judge the flocking fish swarm density of the artificial fish according to the foraging food concentration and the central food concentration;

[0112] Generate the flocking position of the artificial fish according to the judgment result of the flocking fish swarm density judgment and the preset flocking position generation algorithm.

[0113] Specifically, judging the flocking fish swarm density of the artificial fish according to the foraging food concentration and the central food concentration means using the following formula to judge the flocking fish swarm density:

[0114]

[0115] where Y C is the food concentration at the central position, n f is the total number of artificial fish within the visual range of the current artificial fish, δ is the density factor, Y k is the food concentration at the current position.

[0116] Further, the foraging food concentration refers to the food concentration at the current position, and the central food concentration refers to the food concentration at the central position.

[0117] Specifically, the preset flocking position generation algorithm is:

[0118]

[0119] where is the next position of artificial fish k, is the current position of the artificial fish k, X C is the central position of the artificial fish k, Rand(1) is a random number uniformly distributed between -1 and 1, is the Euclidean distance between the central position and the current position, k is the identifier of the artificial fish that needs to perform flocking processing, and Step represents the step size.

[0120] Specifically, when an artificial fish performs flocking behavior, it first uses to calculate the central position of the fish swarm within the visual range of the current fish, and then executes If the condition of is satisfied, then execute Otherwise, execute foraging behavior.

[0121] In the embodiment of the present invention, the clustering process and following process for the artificial fish corresponding to the next position include:

[0122] Determine the foraging food concentration corresponding to the next position, and generate the maximum food concentration position of the artificial fish according to the foraging food concentration and a preset maximum food concentration position generation algorithm, where the preset maximum food concentration position generation algorithm is:

[0123]

[0124] where X MAX is the maximum food concentration position of the artificial fish, Y1 is the food concentration of the first artificial fish within the visual field, and n f is the total number of artificial fish within the visual field of the current artificial fish. is a set containing the food concentration values of each artificial fish within the current visual field. is to select X that maximizes the function value from the given set, where X is the position of the artificial fish.

[0125] Judge the following fish school congestion degree of the artificial fish according to the maximum food concentration corresponding to the maximum food concentration position and the foraging food concentration.

[0126] Generate the following position of the artificial fish according to the judgment result of the following fish school congestion degree judgment and a preset following position update algorithm.

[0127] Specifically, judging the clustering fish school congestion degree of the artificial fish according to the foraging food concentration and the central food concentration means using the following formula to judge the clustering fish school congestion degree:

[0128]

[0129] where Y MAX is the maximum food concentration corresponding to the maximum food concentration position, n f is the total number of artificial fish within the visual field of the current artificial fish, δ is the congestion factor, and Y k is the foraging food concentration.

[0130] Furthermore, the foraging food concentration refers to the food concentration at the current position, and the maximum food concentration refers to the maximum food concentration corresponding to the maximum food concentration position.

[0131] Specifically, the preset clustering position generation algorithm is:

[0132]

[0133] where is the next position of artificial fish k. is the current position of the artificial fish k, X MAX is the position of the maximum food concentration of the artificial fish, and Rand(1) is a random number uniformly distributed between -1 and 1. is the Euclidean distance between the position of the maximum food concentration and the current position, k is the identifier of the artificial fish that needs to perform the following pursuit process, and Step represents the step size.

[0134] Specifically, first, the algorithm determines the positions of each fish within the current fish's field of vision and the food concentration at their positions. Then, it uses to find the maximum food concentration and the position where this food concentration is located. If the maximum food concentration satisfies then execute Otherwise, execute the foraging behavior.

[0135] In the embodiment of the present invention, generating the target food concentration of the artificial fish according to the aggregation position determined by the aggregation process and the following pursuit position determined by the following pursuit process includes: determining the food concentration of the artificial fish according to the aggregation position determined by the aggregation process; determining the food concentration of the artificial fish according to the following pursuit position determined by the following pursuit process; comparing the two food concentrations and selecting the maximum food concentration as the target food concentration.

[0136] S5. Update the capacity parameter according to the target position of the artificial fish corresponding to the target food concentration, and return to step S2. Until the update times of the capacity parameter are equal to the preset number threshold, generate the target capacity configuration strategy of the target power supply system according to the updated capacity parameter.

[0137] In the embodiment of the present invention, updating the capacity parameter according to the target position of the artificial fish corresponding to the target food concentration means that in each iteration of the artificial fish swarm algorithm, the target position of the artificial fish determined according to the target food concentration represents a potentially better solution. Therefore, after finding the new target position, the capacity parameter needs to be updated according to this new position. This update process can be regarded as a learning process, in which the system gradually adjusts its parameters to find the optimal configuration.

[0138] Specifically, the specific method of updating the capacity parameter will depend on the specific optimization objectives and constraints. For example, if the goal is to reduce the operating cost, then the new capacity parameters are those that can better meet the energy demand while reducing the overall cost.

[0139] In an embodiment of the present invention, generating the target capacity configuration strategy of the target power supply system based on the updated capacity parameters means that once the artificial fish swarm algorithm has undergone a predetermined number of iterations (i.e., the number of updates has reached the preset threshold), the capacity parameters at this time should have approached or reached the optimal solution. Based on these updated capacity parameters, the target capacity configuration strategy of the target power supply system can be generated.

[0140] Generally speaking, based on the capacity parameters of the last iteration, determine the optimal capacity of each power generation unit and how they cooperate to meet the load demand. Based on the optimal capacity configuration, formulate a detailed power generation plan, including when to start or stop the power generation units and their output levels, to ensure that the generated strategy not only meets the load demand but also conforms to the cost - effectiveness and reliability standards. The ultimate goal is to ensure that the power supply system can operate efficiently and reliably, and while meeting all technical constraints, achieve economic optimization.

[0141] Embodiment 2

[0142] As Figure 2 shown, this embodiment also provides a functional module diagram of a power supply system capacity configuration optimization device.

[0143] The power supply system capacity configuration optimization device 100 described in this embodiment can be installed in an electronic device. According to the implemented functions, the power supply system capacity configuration optimization device 100 can include a condition generation module 101, a revenue value generation module 102, a foraging processing module 103, a target food concentration generation module 104, and a configuration strategy generation module 105. The modules in the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by the processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0144] In this embodiment, the functions of each module / unit are as follows:

[0145] The condition generation module 101 is used to generate the constraint conditions of the day - ahead energy management strategy according to the preset basic constraints and preset reliability constraints, and determine the objective function of the day - ahead energy management strategy.

[0146] The revenue value generation module 102 is used to calculate the revenue value of the target power supply system according to the constraint conditions and the objective function.

[0147] The foraging processing module 103 is used to determine the current position of the artificial fish in the artificial fish swarm algorithm according to the capacity parameters corresponding to the revenue value, and perform foraging processing on the artificial fish according to the current position to obtain the next position of the artificial fish.

[0148] The target food concentration generation module 104 is configured to perform clustering processing and following processing on the artificial fish corresponding to the next position, and generate the target food concentration of the artificial fish according to the clustering position determined by the clustering processing and the following position determined by the following processing;

[0149] The configuration strategy generation module 105 is configured to update the capacity parameter according to the target position of the artificial fish corresponding to the target food concentration, and when the update times of the capacity parameter is equal to a preset number threshold, generate a target capacity configuration strategy for the target power supply system according to the updated capacity parameter.

[0150] Specifically, each module in the power supply system capacity configuration optimization device 100 in the embodiments of the present invention adopts the same technical means as the power supply system capacity configuration optimization method described in Embodiment 1 and Embodiment 2 when in use, and can produce the same technical effects, which will not be elaborated here.

[0151] Embodiment 3

[0152] As Figure 3 shown, this embodiment further provides a computer electronic device, which may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may further include a computer program stored in the memory 11 and executable on the processor 10, such as a power supply system capacity configuration optimization program.

[0153] Among them, the processor 10 may be composed of integrated circuits in some embodiments. For example, it may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits with the same or different functions, including a combination of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control core (Control Unit) of the electronic device, connects various components of the entire electronic device through various interfaces and lines, and executes various functions of the electronic device and processes data by running or executing programs or modules stored in the memory 11 (such as executing a power supply system capacity configuration optimization program, etc.) and calling data stored in the memory 11.

[0154] The memory 11 includes at least one type of medium, which includes flash memory, external hard drive, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, magnetic disk, optical disc, etc. In some embodiments, the memory 11 may be an internal storage unit of an electronic device, such as the external hard drive of the electronic device. In some other embodiments, the memory 11 may also be an external storage device of the electronic device, such as a plug-in external hard drive, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. equipped on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 can be used not only to store application software installed in the electronic device and various types of data, such as the code of the power supply system capacity configuration optimization program, etc., but also to temporarily store data that has been output or will be output.

[0155] The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable connection communication between the memory 11 and at least one processor 10, etc.

[0156] The communication interface 13 is used for communication between the above-mentioned electronic device and other electronic devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), and is generally used to establish a communication connection between this electronic device and other electronic devices. The user interface may be a display, an input unit (such as a keyboard), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, and is used to display information processed in the electronic device and to display a visual user interface.

[0157] Only the electronic device with components is shown in the figure. Those skilled in the art can understand that the structure shown in the figure does not constitute a limitation on the electronic device, and it may include fewer or more components than those shown, or combine some components, or have different component arrangements.

[0158] For example, although not shown, the electronic device may further include a power source (such as a battery) for supplying power to each component. Preferably, the power source can be logically connected to the at least one processor 10 through a power management device, so as to implement functions such as charge management, discharge management, and power consumption management through the power management device. The power source may also include any components such as one or more DC or AC power sources, a recharge device, a power failure detection circuit, a power converter or inverter, and a power status indicator. The electronic device may also include various sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be elaborated here.

[0159] It should be understood that the above embodiments are only for illustration purposes and are not limited by this structure in the scope of the patent application.

[0160] The power supply system capacity configuration optimization program stored in the memory 11 of the electronic device is a combination of multiple instructions. When running in the processor 10, it can implement:

[0161] S1. Generate the constraint conditions of the day-ahead energy management strategy according to the preset basic constraints and preset reliability constraints, and determine the objective function of the day-ahead energy management strategy;

[0162] S2. Calculate the revenue value of the target power supply system according to the constraint conditions and the objective function;

[0163] S3. Determine the current position of the artificial fish in the artificial fish swarm algorithm according to the capacity parameter corresponding to the revenue value, and perform foraging processing on the artificial fish according to the current position to obtain the next position of the artificial fish;

[0164] S4. Perform schooling processing and following processing on the artificial fish corresponding to the next position, and generate the target food concentration of the artificial fish according to the schooling position determined by the schooling processing and the following position determined by the following processing;

[0165] S5. Update the capacity parameter according to the target position of the artificial fish corresponding to the target food concentration, and return to step S2. Until the update times of the capacity parameter are equal to the preset number threshold, generate the target capacity configuration strategy of the target power supply system according to the updated capacity parameter.

[0166] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to the description of the relevant steps in the corresponding embodiments of the attached drawings, which will not be elaborated here.

[0167] Furthermore, if the modules / units integrated in the electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a medium. The medium can be volatile or non-volatile. For example, the medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory).

[0168] Embodiment 4

[0169] This embodiment provides a medium storing a computer program, and when the computer program is executed by a processor, it implements the steps of the power supply system capacity configuration optimization method as described above.

[0170] These program codes can also be loaded onto a computer or other programmable data processing devices, so that a series of operation steps are executed on the computer or other programmable devices to generate computer-implemented processing, and thus the instructions executed on the computer or other programmable devices provide steps for implementing the functions specified in one process or multiple processes. Figure 1 steps of the functions specified in one or more processes.

[0171] The medium includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of the medium can include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information accessible by a computing device.

[0172] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. When the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0173] It should be understood that such terms can be interchanged under appropriate circumstances so that the embodiments of the present application described herein can be implemented, for example, in an order other than those illustrated or described herein.

[0174] In several embodiments provided by the present invention, it should be understood that the disclosed electronic devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation.

[0175] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0176] In addition, in each embodiment of the present invention, the functional modules can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a hardware plus software functional module.

[0177] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0178] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is a theory, method, technology, and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0179] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An optimization method for power supply system capacity configuration, characterized in that Including: S1. Generate the constraint conditions of the day-ahead energy management strategy according to the preset basic constraints and the preset reliability constraints, and determine the objective function of the day-ahead energy management strategy; S2. Calculate the revenue value of the target power supply system according to the constraint conditions and the objective function; S3. Determine the current position of the artificial fish in the artificial fish swarm algorithm according to the capacity parameter corresponding to the revenue value, and perform foraging processing on the artificial fish according to the current position to obtain the next position of the artificial fish; S4. Perform clustering processing and following processing on the artificial fish corresponding to the next position, and generate the target food concentration of the artificial fish according to the clustering position determined by the clustering processing and the following position determined by the following processing; S5. Update the capacity parameter according to the target position of the artificial fish corresponding to the target food concentration, and return to step S2 until the update times of the capacity parameter is equal to the preset number threshold, and generate the target capacity configuration strategy of the target power supply system according to the updated capacity parameter.

2. The method according to claim 1, wherein The generating the constraint conditions of the day-ahead energy management strategy according to the preset basic constraints and the preset reliability constraints includes: The preset basic constraints include: the power balance constraint of the target power supply system, the self-power constraint of the energy storage device in the target power supply system, the rated power constraint of the energy storage device, the power limit constraint of the target power supply system, and the 10kV distribution network load power constraint of the target power supply system; The preset reliability constraint is: Among them, is the remaining energy of the energy storage device i in the current state, N is the total number of energy storage devices, is the minimum energy limit of the energy storage device i, represents the power at the t-th time step in the traction load power sequence, and τ represents the number of time steps required for the train to pass through the power supply section.

3. The method according to claim 1, wherein The determining the objective function of the day-ahead energy management strategy includes: The objective function of the day-ahead energy management strategy is: maxR total = 365·L expec (C origin - C optim ) - C invest Among them, maxR total is the revenue value of the flexible traction power supply system, L expec is the shortest expected life in each part of the flexible traction power supply system, C origin refers to the daily operating cost of the system before the energy management strategy on the day before implementation, C optim is the daily operating cost of the system after the energy management strategy on the day before implementation, C invest is the investment cost of the flexible traction power supply system.

4. The method according to claim 1, wherein The performing foraging processing on the artificial fish according to the current position to obtain the next position of the artificial fish includes: Generate the random position of the artificial fish according to the current position and the preset random position generation algorithm, where the preset random position generation algorithm is: Among them, X h is the random position of the artificial fish, is the current position of the artificial fish k, Visual is the visual field range of the artificial fish, Rand(1) is a random number uniformly distributed between -1 and 1, and k is the identifier of the artificial fish; Generate the next position of the artificial fish according to the random position and the preset next position generation algorithm, where the preset next position generation algorithm is: Among them, is the next position of the artificial fish k, X h is the random position of the artificial fish, is the current position of the artificial fish k, Rand(1) is a random number uniformly distributed between -1 and 1, is the Euclidean distance between the random position and the current position, k is the identifier of the artificial fish that needs to perform foraging processing, and Step represents the step size.

5. The method according to claim 1, wherein The performing clustering processing and following processing on the artificial fish corresponding to the next position includes: Generate the center position of the artificial fish according to the preset center position generation algorithm and the next position, where the preset center position generation algorithm is: Among them, X C is the central position of the artificial fish, n f is the total number of artificial fish within the field of view of the current artificial fish, i is the identifier of the artificial fish that needs to perform the flocking process, and X i is the next position; Determine the foraging food concentration of the next position and the center food concentration of the center position, and judge the clustering crowding degree of the artificial fish according to the foraging food concentration and the center food concentration; Generate the clustering position of the artificial fish according to the judgment result of the clustering crowding degree judgment and the preset clustering position generation algorithm.

6. The method according to claim 1, wherein The performing clustering processing and following processing on the artificial fish corresponding to the next position includes: Determine the foraging food concentration corresponding to the next position, and generate the maximum food concentration position of the artificial fish according to the foraging food concentration and the preset maximum food concentration position generation algorithm, where the preset maximum food concentration position generation algorithm is: Among them, X MAX is the position of the maximum food concentration of the artificial fish, Y1 is the food concentration of the first artificial fish within the field of view, and n f is the total number of artificial fish within the field of view of the current artificial fish, is a set containing the food concentration values of each artificial fish within the current field of view, is to select X that maximizes the function value from the given set, and X is the position of the artificial fish; Judging the overcrowding degree of the artificial fish chasing the fish school according to the maximum food concentration corresponding to the maximum food concentration position and the foraging food concentration; Generating the chasing position of the artificial fish according to the judgment result of the overcrowding degree of the artificial fish chasing the fish school and the preset chasing position update algorithm.

7. An optimization device for power supply system capacity configuration, characterized in that, Including: A condition generation module, configured to generate constraint conditions for a day-ahead energy management strategy according to preset basic constraints and preset reliability constraints, and determine the objective function of the day-ahead energy management strategy; A revenue value generation module, configured to calculate the revenue value of the target power supply system according to the constraint conditions and the objective function; A foraging processing module, configured to determine the current position of the artificial fish in the artificial fish swarm algorithm according to the capacity parameter corresponding to the revenue value, perform foraging processing on the artificial fish according to the current position, and obtain the next position of the artificial fish; A target food concentration generation module, configured to perform clustering processing and chasing processing on the artificial fish corresponding to the next position, and generate the target food concentration of the artificial fish according to the clustering position determined by the clustering processing and the chasing position determined by the chasing processing; A configuration strategy generation module, configured to update the capacity parameter according to the target position of the artificial fish corresponding to the target food concentration, and when the update times of the capacity parameter are equal to a preset number threshold, generate a target capacity configuration strategy for the target power supply system according to the updated capacity parameter.

8. A computer device, comprising a memory, a processor, and a computer program stored on the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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