Ship optical storage diesel power system capacity optimization configuration method based on overall group optimization algorithm

Abstract

The invention provides an overall group optimization algorithm-based capacity optimization configuration method for a ship optical storage diesel power system, and the method comprises the steps: building mathematical models, including a photovoltaic array output model, an energy storage battery model and a diesel generator model, of the ship optical storage diesel power system; constructing an objective function and constraint conditions of an overall group optimization algorithm; and in combination with the objective function and the constraint condition, performing optimization solution on the established mathematical model by using an overall group optimization algorithm to obtain a capacity optimization configuration result of the ship optical storage diesel power system. According to the method, the minimum annual average total cost in the full life cycle of the ship optical storage diesel power system is taken as an optimization target, and the annual average initial investment cost, the annual operation and maintenance cost, the annual average equipment reset cost, the annual fuel cost and the annual environmental protection conversion cost are emphatically considered; resources such as photovoltaic, energy storage and diesel generators in the ship optical storage diesel power system can be fully utilized, capacity configuration of the power system is optimized, and a basis is provided for project construction of the ship optical storage diesel power system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ship power system optimization configuration, in particular, especially relates to a ship photovoltaic storage diesel power system capacity optimization configuration method based on a whole swarm optimization algorithm. BACKGROUND

[0002] With the growth of global energy demand and the improvement of environmental protection awareness, ship power systems are developing towards more efficient and environmentally friendly directions. The ship photovoltaic storage diesel power system is a hybrid power system that combines photovoltaic power generation, energy storage technology and diesel generators, aiming to fully utilize renewable energy, reduce dependence on traditional fossil fuels, reduce operating costs and environmental pollution. The system realizes the stability and economy of power supply by reasonably configuring the capacity of photovoltaic arrays, energy storage batteries and diesel generators.

[0003] Currently, the design and optimization of ship power systems mainly rely on traditional optimization methods, such as linear programming, dynamic programming, etc. These methods show certain efficiency in dealing with simple power system configuration problems, but when facing complex hybrid power systems, they often have limitations. In recent years, with the development of intelligent optimization algorithms, some swarm intelligence-based optimization algorithms, such as genetic algorithms, particle swarm optimization algorithms, etc., have begun to be applied to the optimization configuration of ship power systems. These algorithms can effectively search for global optimal solutions by simulating the group behavior in nature, improving the efficiency and accuracy of optimization.

[0004] However, existing optimization methods still have some problems when applied to ship photovoltaic storage diesel power systems. First, traditional optimization methods are difficult to consider the economy, reliability and environmental protection of the system at the same time, resulting in optimization results that may be insufficient in some aspects. Second, although existing intelligent optimization algorithms can handle complex optimization problems, they are prone to local optimal solutions when facing multi-objective, multi-constrained optimization problems such as ship photovoltaic storage diesel power systems, making it difficult to find global optimal solutions. In addition, existing methods often ignore the life cycle cost of the system during optimization, resulting in optimization results that may not be economically feasible in actual application. Therefore, developing an optimization configuration method that can comprehensively consider the life cycle cost, economy, reliability and environmental protection of the system is of great significance to the development of ship photovoltaic storage diesel power systems. SUMMARY

[0005] According to the above technical problems, a ship photovoltaic storage diesel power system capacity optimization configuration method based on a whole swarm optimization algorithm is provided. The present application establishes a system model, sets optimization objectives and constraints, and uses a whole swarm optimization algorithm to search for solutions, so as to minimize the annual total cost of the ship photovoltaic storage diesel power system throughout its life cycle, while ensuring the reliability and environmental protection of the system.

[0006] The technical means adopted by the present application are as follows: A ship photovoltaic energy storage diesel power system capacity optimization configuration method based on a whole group optimization algorithm, comprising: S1, establishing a mathematical model of the ship photovoltaic energy storage diesel power system, including a photovoltaic array output model, an energy storage battery model and a diesel generator model; S2, constructing an objective function of the whole group optimization algorithm; S3, setting constraint conditions, including system power balance constraints, device physical operation constraints, system operation strategy constraints and reliability and economy soft constraints; S4, combining the objective function and the constraint conditions, using the whole group optimization algorithm to optimize and solve the established mathematical model, and obtaining the capacity optimization configuration result of the ship photovoltaic energy storage diesel power system.

[0007] Further, in step S1, The parameters considered in establishing the photovoltaic array output model include: light, temperature, photovoltaic panel investment cost, maintenance cost, service life and single replacement cost; The parameters considered in establishing the energy storage battery model include: unit battery capacity, maximum charge and discharge power, charge and discharge efficiency, upper and lower limits of state of charge, battery investment cost, maintenance cost and single replacement cost; The parameters considered in establishing the diesel generator model include: diesel generator rated power, investment cost, maintenance cost, service life, single replacement cost and diesel fuel cost.

[0008] Further, in step S2, the objective function of the whole group optimization algorithm is constructed based on the annual average initial investment cost, the annual operation and maintenance cost, the annual average equipment replacement cost, the annual fuel cost and the annual environmental protection conversion cost, and the formula is as follows:

[0009] Wherein, is the annual average initial investment cost, is the annual operation and maintenance cost, is the annual average equipment replacement cost, is the annual fuel cost, is the annual environmental protection conversion cost, and are penalty costs.

[0010] Further, the annual average initial investment cost is calculated as follows:

[0011] Wherein, is the number of photovoltaic panels, is the number of diesel generators, is the number of batteries, is the investment cost of a single photovoltaic panel, is the investment cost of a single diesel generator, is the investment cost of a single battery, is the recovery coefficient function of photovoltaic panels, is the recovery coefficient function of diesel generators, is the recovery coefficient function of batteries, is the service life of photovoltaic panels, is the service life of diesel generators, is the service life of batteries.

[0012] Further, the annual fuel cost is calculated as follows:

[0013] wherein, is the number of hours per year, is the diesel price, and is the fuel consumption curve coefficient, is the actual output power of the diesel generator at the moment, is the rated power of a single diesel generator.

[0014] Further, in the setting constraint conditions, the system power balance constraint is set, including: At any moment , the power generation, energy storage charging and discharging, load, abandoned light and power shortage in the ship photovoltaic storage diesel power system need to meet the following balance relationship:

[0015] wherein, is the total output of photovoltaic, is the actual output of diesel generator, is the energy storage discharging power, is the energy storage charging power, is the abandoned light power, is the load power, is the power shortage.

[0016] Further, in the setting constraint conditions, the device physical operation constraint is set, including: The upper and lower limits of the state of charge of the energy storage battery are set as follows:

[0017] The maximum charging and discharging power of the energy storage battery is set as follows:

[0018]

[0019] Setting diesel generator power output constraint, when diesel generator runs, diesel generator actual power output Satisfies: .

[0020] Further, in the setting constraint condition, the system operation strategy constraint is set, including: Set power supply priority strategy constraint, when the system has power shortage, call the power supply in the following priority order: photovoltaic output, energy storage battery discharge, diesel generator output, only when all power sources reach the maximum output, there is still a shortage, then generate power shortage; Set energy consumption priority strategy constraint, when the system has power surplus, consume energy in the following priority order: energy storage battery charging, only when the energy storage battery reaches the upper limit of charging power or the upper limit of SOC, there is still surplus, then generate abandoned light power; Set diesel engine start-stop logic constraint, when the diesel generator is in running state at the previous time, the scheduling strategy at the current time prefers to keep it running to avoid frequent start-stop.

[0021] Further, in the setting constraint condition, the reliability and economy soft constraint is set, which is realized by setting the penalty term in the objective function, including: Set energy waste rate soft constraint, expect the energy waste rate of the final configuration scheme Not higher than the preset threshold; Set power shortage rate soft constraint, expect the power shortage rate of the final configuration scheme Not higher than the preset threshold.

[0022] Further, in step S4, the whole group optimization algorithm is used to optimize and solve the established mathematical model, including: S41, initialization: set the initial population containing n Individuals X , wherein each individual Represents a group of capacity configuration schemes; S42, fitness evaluation and coefficient calculation, the calculation process is as follows: Calculate the objective function value Corresponding to each individual As its fitness; Calculate the root mean square of all fitness values , the root mean square calculation formula is: , According to the difference between each fitness value and the root mean square , the normalized moving direction coefficient is calculated, and the calculation formula is as follows: S43, position update: update the position of each individual to obtain a new position :

[0023] wherein, a is a constant parameter, and b is a random value, and the sum is traversed for all individuals ; S44, selection and adaptive mutation based on adaptive simulated annealing, the process is as follows: Calculate the fitness value of the new position , if it is better than the old fitness value , then accept the new position; otherwise, calculate the acceptance probability according to the current iteration temperature and the fitness change , and decide whether to accept the new position with the probability ; According to the current iteration number, dynamically adjust the mutation rate and the mutation step size , and apply a random disturbance with a size of to the individual position with a probability ; S45, termination condition judgment: judge whether the maximum iteration number is reached, if not, return to step S42 for iteration, if yes, output the optimal individual recorded at present as the capacity optimization configuration result.

[0024] Compared with the prior art, the present application has the following advantages: 1. The ship light storage diesel power system capacity optimization configuration method based on the whole group optimization algorithm provided by the present application takes the minimization of the annual total cost in the whole life cycle as the target, comprehensively considers the cost factors such as initial investment, operation and maintenance, equipment replacement and fuel consumption, finds the cost-optimal capacity configuration scheme through the accurate optimization of the whole group optimization algorithm, and effectively reduces the long-term operation cost of the ship light storage diesel power system.

[0025] ​​2、The ship photovoltaic energy storage diesel power system capacity optimization configuration method based on the whole group optimization algorithm provided by the application fully considers the power balance of the power system, the depth of energy storage charging and discharging, the life constraint condition and the like in the optimization process, ensures that the system can stably operate under various working conditions. At the same time, through reasonable capacity configuration, the power supply reliability and anti-interference ability of the system are improved, the power failure risk caused by equipment failure or insufficient energy supply is reduced, and the reliability of the ship power system is enhanced.

[0026] 3、The ship photovoltaic energy storage diesel power system capacity optimization configuration method based on the whole group optimization algorithm provided by the application introduces the annual environmental protection conversion cost into the optimization target, encourages the system to use more clean energy (such as photovoltaic energy), reduces the dependence on fossil fuels such as diesel oil, thereby reducing the pollutant emission in the ship operation process, positively promotes the environmental protection, and improves the environmental protection of the ship photovoltaic energy storage diesel power system.

[0027] 4、The ship photovoltaic energy storage diesel power system capacity optimization configuration method based on the whole group optimization algorithm provided by the application comprehensively considers the characteristics of photovoltaic, energy storage and diesel generator and the like, realizes the optimal configuration and collaborative work among the resources through the intelligent optimization of the whole group optimization algorithm, fully gives play to the advantages of each resource, improves the resource utilization efficiency of the whole power system, and avoids the waste of resources.

[0028] 5、The ship photovoltaic energy storage diesel power system capacity optimization configuration method based on the whole group optimization algorithm provided by the application is suitable for different types of ship photovoltaic energy storage diesel power systems and has strong adaptability. Whether it is a large ship or a small ship, whether it is high-load operation or low-load operation, the most suitable capacity configuration scheme can be found through the method, and the actual needs of different ships can be met.

[0029] Based on the above reasons, the application can be widely popularized in the field of ship power system optimization configuration. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to more clearly illustrate the technical scheme in the embodiments of the application or the prior art, the drawings needed to be used in the embodiment or prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0031] Figure 1 The method flowchart of the application.

[0032] Figure 2 The curve graph of the total cost changing with the iteration number provided by the embodiment of the application. DETAILED DESCRIPTION

[0033] It should be noted that the embodiments and features of the embodiments in the present application can be combined with each other in the case of no conflict. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0034] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings of the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, but not all the embodiments. The description of the at least one exemplary embodiment is actually only illustrative, but not as any limitation on the present application and its application or use. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0035] It should be noted that the terms used herein are only intended to describe specific embodiments, and are not intended to limit the exemplary embodiments according to the present application. As used herein, the singular form is intended to include the plural form, unless the context clearly indicates otherwise, and it should also be understood that when the terms "comprise" and / or "include" are used in the specification, there is a feature, step, operation, device, component and / or combination thereof.

[0036] Unless specifically stated otherwise, the relative arrangement of components and steps, numerical expressions, and numerical values set forth in the embodiments are not meant to limit the scope of the present application. It should be apparent that the size of the various parts shown in the drawings is not to scale and that the drawings are not intended to be a precise depiction of the parts. Techniques, methods, and devices known to those of ordinary skill in the relevant art can not be discussed in detail, but should be considered as part of the specification, where appropriate. In all examples shown and discussed herein, any specific value should be interpreted as merely an example, and not as a limitation. Thus, other examples of the exemplary embodiments can have different values. It should be noted that like reference numerals and letters refer to like items in the following drawings, and thus, once an item is defined in one drawing, it need not be discussed further in subsequent drawings.

[0037] In the description of the present application, it should be understood that the orientation words such as "front, back, up, down, left, right", "transverse, vertical, perpendicular, horizontal" and "top, bottom" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and in the absence of the opposite description, these orientation words do not indicate and imply that the indicated device or element must have a particular orientation or be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the scope of protection of the present application: the orientation words "inner, outer" refer to the inner and outer of the contour of each component itself.

[0038] For the convenience of description, spatial relative terms such as "over", "above", "upper surface", "upper" and the like can be used herein to describe the spatial positional relationship of one device or feature with other devices or features as shown in the drawings. It should be understood that the spatial relative terms are intended to include different orientations in use or operation in addition to the orientation of the device described in the drawings. For example, if the device in the drawing is inverted, the device described as "above" or "over" other devices or structures will be positioned "below" or "under" other devices or structures. Thus, the exemplary term "above" can include both "above" and "below" orientations. The device can also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatial relative descriptions used herein are interpreted accordingly.

[0039] In addition, it should be noted that the use of the words "first", "second" and the like to define parts is only for the convenience of distinguishing the corresponding parts, and the above words have no special meaning unless otherwise stated, and therefore cannot be understood as a limitation on the scope of protection of the present application.

[0040] As Figure 1 shown, the present application provides a ship photovoltaic storage diesel power system capacity optimization configuration method based on a global group optimization algorithm, comprising: S1, establishing a mathematical model of the ship photovoltaic storage diesel power system, including a photovoltaic array output model, an energy storage battery model and a diesel generator model; S2, constructing an objective function of the global group optimization algorithm; S3, setting constraint conditions, including system power balance constraints, device physical operation constraints, system operation strategy constraints and reliability and economy soft constraints; S4, combining the objective function and the constraint conditions, using the global group optimization algorithm to optimize and solve the established mathematical model, and obtaining the capacity optimization configuration result of the ship photovoltaic storage diesel power system.

[0041] In implementation, as the preferred embodiment of the present application, in step S1, The parameters considered in establishing the photovoltaic array output model include: light, temperature, photovoltaic panel investment cost, maintenance cost, service life, and single replacement cost; The parameters considered in establishing the energy storage battery model include: unit battery capacity, maximum charge and discharge power, charge and discharge efficiency, upper and lower limits of state of charge, battery investment cost, maintenance cost, and single replacement cost; The parameters considered in establishing the diesel generator model include: diesel generator rated power, investment cost, maintenance cost, service life, single replacement cost, and diesel fuel cost.

[0042] In implementation, as the preferred embodiment of the present application, in step S2, the objective function of the overall group optimization algorithm is constructed based on the average annual initial investment cost, annual operation and maintenance cost, annual equipment replacement cost, annual fuel cost, and annual environmental conversion cost, and the formula is as follows:

[0043] wherein, is the average annual initial investment cost, is the annual operation and maintenance cost, is the average annual equipment replacement cost, is the annual fuel cost, is the annual environmental conversion cost, and are penalty costs.

[0044] In implementation, as the preferred embodiment of the present application, the average annual initial investment cost is calculated as follows:

[0045] wherein, is the number of photovoltaic panels, is the number of diesel generators, is the number of batteries, is the investment cost of a single photovoltaic panel, is the investment cost of a single diesel generator, is the investment cost of a single battery, is the recovery coefficient function of the photovoltaic panel, is the recovery coefficient function of the diesel generator, is the recovery coefficient function of the battery, is the service life of the photovoltaic panel, is the service life of the diesel generator, is the service life of the battery.

[0046] In particular implementation, as the preferred embodiment of the present application, the annual fuel cost The calculation formula is as follows:

[0047] Wherein, is the annual hours, is the diesel price, and is the fuel consumption curve coefficient, is the actual output power of the diesel generator at the moment, is the rated power of a single diesel generator.

[0048] In particular implementation, as the preferred embodiment of the present application, the setting constraint condition, set the system power balance constraint, including: At any moment , the power generation, energy storage charging and discharging power, load power, light abandoned power and power shortage in the ship photovoltaic and energy storage diesel power system should meet the following balance relationship:

[0049] Wherein, is the total output of photovoltaic, is the actual output of diesel generator, is the energy storage discharging power, is the energy storage charging power, is the light abandoned power, is the load power, is the power shortage.

[0050] In particular implementation, as the preferred embodiment of the present application, the setting constraint condition, set the device physical operation constraint, including: Set the upper and lower limit constraint of the state of charge (SOC) of the energy storage battery, as follows:

[0051] Set the maximum charging and discharging power constraint of the energy storage battery, as follows:

[0052]

[0053] Set the diesel generator output constraint, when the diesel generator runs, the actual output of the diesel generator Satisfies: .

[0054] In specific implementation, as the preferred embodiment of the present application, in the setting of the constraint condition, the system operation strategy constraint is set, including: The power supply priority strategy constraint is set, when the system has power shortage, the power sources are called in the following priority order: photovoltaic output, energy storage battery discharge, diesel generator output, and only when all power sources reach the maximum output and there is still a shortage, the power shortage is generated; The energy consumption priority strategy constraint is set, when the system has power surplus, the energy is consumed in the following priority order: energy storage battery charging, and only when the energy storage battery reaches the upper limit of charging power or the upper limit of SOC and there is still surplus, the light abandonment power is generated; The diesel engine start-stop logic constraint is set, when the diesel generator is in the running state at the previous time, the scheduling strategy at the current time prefers to keep it running to avoid frequent start-stop.

[0055] In specific implementation, as the preferred embodiment of the present application, in the setting of the constraint condition, the reliability and economy soft constraint is set, which is realized by setting a penalty term in the objective function, including: The energy waste rate soft constraint is set, expecting the energy waste rate of the final configuration scheme Not higher than the preset threshold 0.2; The power shortage rate soft constraint is set, expecting the power shortage rate of the final configuration scheme Not higher than the preset threshold 0.1.

[0056] In specific implementation, as the preferred embodiment of the present application, in step S4, the whole swarm optimization algorithm is used to optimize and solve the established mathematical model, including: S41, initialization: setting the initial population containing n individuals X , wherein each individual represents a set of capacity configuration schemes; S42, fitness evaluation and coefficient calculation, the calculation process is as follows: Calculate the objective function value corresponding to each individual as its fitness; Calculate the root mean square of all fitness values , the root mean square calculation formula is: , According to the difference between each fitness value and the root mean square , the normalized moving direction coefficient is calculated, the calculation formula is: ; S43, position update: updating each individual new position :

[0057] wherein, is a constant parameter, is a random value, summing over all individuals ; S44, selection and adaptive mutation based on adaptive simulated annealing, the process is as follows: calculate the fitness value of the new position , if it is better than the old fitness value , then accept the new position; otherwise, according to the current iteration temperature and the fitness change , calculate the acceptance probability , and decide whether to accept the new position with the probability; According to the current iteration number, dynamically adjust the mutation rate and mutation step , and apply a random disturbance of size to the individual position with a probability ; S45, termination condition judgment: judge whether the maximum iteration number is reached, if not, return to step S42 to continue iteration, if yes, output the optimal individual recorded at present as the capacity optimization configuration result.

[0058] In this embodiment, as shown in Figure 2 , a curve graph showing the change of total cost with iteration number is shown. In the graph, the horizontal axis represents the iteration number, and the vertical axis represents the total cost. As can be seen from the graph, with the increase of iteration number, the total cost gradually decreases and tends to be stable. This shows that the overall swarm optimization algorithm can effectively find a better capacity configuration scheme in the iteration process, so that the total cost of the ship light storage diesel power system is continuously reduced. The downward trend of the curve reflects the optimization process of the algorithm, and finally a relatively stable minimum cost value is reached after a certain number of iterations, which shows that the algorithm has successfully found an optimal capacity configuration scheme.

[0059] Finally, it should be pointed out that: the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for capacity optimization configuration of a shipboard photovoltaic-storage-diesel power system based on a global swarm optimization algorithm, characterized in that, include: S1. Establish a mathematical model for the ship's photovoltaic-storage-diesel power system, including a photovoltaic array output model, an energy storage battery model, and a diesel generator model. S2. Construct the objective function for the global swarm optimization algorithm; S3. Set constraints, including system power balance constraints, equipment physical operation constraints, system operation strategy constraints, and soft constraints on reliability and economy; S4. Combining the objective function and constraints, the global swarm optimization algorithm is used to optimize the mathematical model and obtain the capacity optimization configuration results of the ship's photovoltaic-storage-diesel power system.

2. The method for capacity optimization configuration of a shipborne photovoltaic-storage-diesel power system based on a global swarm optimization algorithm according to claim 1, characterized in that, In step S1: The parameters considered in establishing a photovoltaic array output model include: sunlight, temperature, photovoltaic panel investment cost, maintenance cost, service life, and replacement cost per unit; The parameters to be considered when establishing an energy storage battery model include: unit battery capacity, maximum charge and discharge power, charge and discharge efficiency, upper and lower limits of state of charge, battery investment cost, maintenance cost, and replacement cost per unit. The parameters considered when establishing a diesel generator model include: rated power of the diesel generator, investment cost, maintenance cost, service life, replacement cost per unit, and diesel fuel cost.

3. The method for capacity optimization configuration of a shipborne photovoltaic-storage-diesel power system based on a global swarm optimization algorithm according to claim 1, characterized in that, Step S2: Construct the objective function of the overall swarm optimization algorithm based on the average annual initial investment cost, annual operation and maintenance cost, average annual equipment replacement cost, annual fuel cost, and annual environmental protection depreciation cost. The formula is as follows: in, This represents the average annual initial investment cost. Annual operating and maintenance costs, The average annual equipment replacement cost, Annual fuel cost, Annual environmental protection cost calculation and All of these are penalties.

4. The method for capacity optimization configuration of a shipborne photovoltaic-storage-diesel power system based on a global swarm optimization algorithm according to claim 3, characterized in that, The average annual initial investment cost The calculation formula is as follows: in, For the number of photovoltaic panels, The number of diesel generators, The number of batteries, The investment cost per photovoltaic panel, The investment cost of a single diesel generator. The investment cost per battery unit. Let be the recycling coefficient function of the photovoltaic panel. For the recovery coefficient function of the diesel generator, Let be the recycling coefficient function of the battery. The lifespan of photovoltaic panels. This refers to the service life of the diesel generator. This refers to the lifespan of the battery.

5. The method for capacity optimization configuration of a shipborne photovoltaic-storage-diesel power system based on a global swarm optimization algorithm according to claim 3, characterized in that, Annual fuel cost The calculation formula is as follows: in, The total number of hours throughout the year. For diesel prices, and This is the coefficient for the fuel consumption curve. for The actual output power of the diesel generator at any given time. This refers to the rated power of a single diesel generator.

6. The method for capacity optimization configuration of a shipborne photovoltaic-storage-diesel power system based on a global swarm optimization algorithm according to claim 1, characterized in that, The set constraints include setting system power balance constraints, including: At any time The power generation, energy storage charging and discharging power, load power, curtailed power, and power shortage power in a ship's photovoltaic-storage-diesel power system must meet the following balance relationship: in, Contribute to the overall photovoltaic power generation For the actual output of the diesel generator, For energy storage discharge power, For energy storage charging power, For abandoned light power, For load power, This indicates a power shortage.

7. The method for capacity optimization configuration of a shipborne photovoltaic-storage-diesel power system based on a global swarm optimization algorithm according to claim 1, characterized in that, The set constraints include setting physical operation constraints for the equipment, including: Set the upper and lower limits of the state of charge (SOC) constraints for the energy storage battery as follows: Set the maximum charge and discharge power constraint for the energy storage battery as follows: Set output constraints for the diesel generator; when the diesel generator is running, the actual output of the diesel generator... satisfy: 。 8. The method for capacity optimization configuration of a shipborne photovoltaic-storage-diesel power system based on a global swarm optimization algorithm according to claim 1, characterized in that, The constraints set include system operation strategy constraints, including: Set power supply priority strategy constraints. When the system experiences a power shortage, the power sources will be called in the following priority order: photovoltaic output, energy storage battery discharge, and diesel generator output. Power shortage will only occur when there is still a shortage after all power sources have reached their maximum output. Set energy consumption priority strategy constraints. When the system has excess power, energy is consumed in the following priority order: energy storage battery charging. Only when the energy storage battery has excess power after reaching the charging power limit or the SOC limit will there be wasted power. Set diesel engine start-stop logic constraints. When the diesel generator was running in the previous moment, the current scheduling strategy prioritizes keeping it running to avoid frequent start-stops.

9. A method for optimizing the capacity configuration of a shipboard photovoltaic-storage-diesel power system based on a global swarm optimization algorithm as described in claim 1, characterized in that, The aforementioned constraint conditions include setting soft constraints for reliability and economy, which are achieved by setting penalty terms in the objective function, including: Set a soft constraint on energy waste rate, and expect the energy waste rate of the final configuration scheme to be... Not higher than the preset threshold; Set a soft constraint on the power outage rate, and expect the power outage rate of the final configuration scheme to be [value missing]. Not higher than the preset threshold.

10. A method for capacity optimization configuration of a shipborne photovoltaic-storage-diesel power system based on a global swarm optimization algorithm according to claim 1, characterized in that, In step S4, the established mathematical model is optimized and solved using a global swarm optimization algorithm, including: S41. Initialization: Settings include n Initial population of individuals X Each individual This represents a set of capacity configuration schemes; S42. Fitness assessment and coefficient calculation, the calculation process is as follows: Calculate each individual Corresponding objective function value As its fitness; Calculate the root mean square of all fitness values. The formula for calculating the root mean square is: ， Based on each fitness value and root mean square Differences Calculate the normalized direction of movement coefficient The calculation formula is: ； S43, Location Update: Update each individual Get a new position : in, For constant parameters, Given random values, sum them up and iterate through all individuals. ; S44. Selection and adaptive mutation based on adaptive simulated annealing, the process is as follows: Calculate the fitness value of the new location. If its fitness value is better than the old one If the position is correct, the new position is accepted; otherwise, the position is determined based on the current iteration temperature. and fitness change Calculate the probability of acceptance And use this probability to decide whether to accept a worse new position; The mutation rate is dynamically adjusted based on the current iteration number. and variable asynchronous length and with probability Apply a size of to the individual position Random perturbations; S45. Termination condition judgment: Determine whether the maximum number of iterations has been reached. If not, return to step S42 to continue iterating. If so, output the best individual currently recorded as the capacity optimization configuration result.

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