Multi-type mobile emergency power generation device group power supply optimization method

Abstract

The invention discloses a power supply optimization method for a multi-type mobile emergency power generation device group, and the method comprises the steps: monitoring a voltage amplitude and a frequency fluctuation value of an emergency load in real time through a power detection module, and synchronously obtaining the battery capacity SOC of a mobile energy storage vehicle; when the voltage fluctuation value and the frequency fluctuation value are lower than a preset threshold value and SOC is larger than or equal to a preset capacity value, a steady-state optimization strategy is started, and the power output proportion of the diesel power generation vehicle and the power output proportion of the mobile energy storage vehicle are distributed through a linear programming algorithm with the purpose of minimizing the total power generation cost; when the fluctuation value exceeds a preset threshold value or SOClt; when a capacity value is preset, a dynamic optimization strategy is started, load fluctuation suppression is taken as a constraint condition, power distribution of the diesel power generation vehicle and the mobile energy storage vehicle is dynamically adjusted through a particle swarm algorithm, the mobile energy storage vehicle preferentially responds to a fluctuation component, and the diesel power generation vehicle provides a basic load; according to the real-time fluctuation value, the SOC and the diesel capacity, power supply of the diesel power generation vehicle and power supply of the mobile energy storage vehicle are dynamically switched, and collaborative optimization of the power supply cost and the electric energy quality is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply, and in particular to a method for optimizing power supply of a group of multiple types of mobile emergency power generation devices. Background Art

[0002] Mobile emergency power generation devices, as emergency power generation equipment, provide power guarantee for emergency power work during emergency rescue and disaster relief.

[0003] The main technologies currently used include traditional diesel generator sets and new energy storage technologies. Diesel generator trucks can meet the emergency power supply needs of disaster-stricken areas in emergencies, but this method suffers from slow response, lack of standardization, and environmental pollution. Using mobile energy storage trucks for emergency power supply to disaster-stricken areas offers advantages such as rapid grid access, stable and reliable power output, and fast response times, making them more suitable for a variety of scenarios, including emergency power supply, power maintenance, and disaster relief.

[0004] Current mobile energy storage vehicles also have the following problems when providing emergency power supply: as the disaster-stricken areas are areas with relatively complex load conditions, mobile energy storage vehicles cannot simultaneously solve the problem of high load consumption in the disaster-stricken areas, and there are problems such as insufficient battery capacity and high costs; traditional mobile emergency power generation devices mostly use a single diesel generator truck or mobile energy storage truck, which cannot simultaneously take into account issues such as power generation cost and response speed. Summary of the Invention

[0005] To address the shortcomings and deficiencies of existing technologies, the present invention provides a collaborative optimization method and system for a group of multiple mobile emergency power generation devices. Through a hierarchical optimization mechanism and dynamic role switching technology, this method addresses the problem in traditional emergency power supply systems where a single device cannot balance power generation costs and power quality. Its key innovative designs include:

[0006] Hierarchical optimization mechanism: Optimization strategy switching is triggered based on the dual criteria of voltage / frequency fluctuation thresholds and the battery capacity (SOC) of the energy storage vehicle. When the load is stable, a linear programming algorithm is used to achieve optimal power supply cost. When fluctuations exceed the limit or the SOC is insufficient, the particle swarm algorithm is switched to dynamically adjust power distribution, balancing fluctuation suppression and cost optimization goals.

[0007] Dynamic role allocation: Build a collaborative power supply system between diesel generators and mobile energy storage vehicles. Dynamically allocate power supply roles based on real-time load fluctuations and SOC status. Diesel vehicles prioritize providing base load, while energy storage vehicles respond to fluctuations.

[0008] Data adaptation and linkage control: The grid-connected signal is pre-processed by a collaborative controller and the power supply mode switching mechanism driven by the SOC threshold is combined to improve the system response efficiency and operational reliability.

[0009] This solution breaks through the limitations of the traditional single power supply mode and realizes the dynamic coordinated control of multiple types of mobile power generation devices in emergency scenarios for the first time, significantly reducing power generation costs while effectively suppressing voltage fluctuations.

[0010] The present invention specifically adopts the following technical solutions:

[0011] A method for optimizing power supply for a group of multiple mobile emergency power generation devices:

[0012] The power detection module monitors the voltage amplitude and frequency fluctuation of the emergency load in real time, and simultaneously obtains the battery capacity SOC of the mobile energy storage vehicle;

[0013] When the voltage fluctuation value and the frequency fluctuation value are lower than the preset threshold value and the SOC ≥ the preset capacity value, the steady-state optimization strategy is activated to allocate the power output ratio of the diesel generator vehicle and the mobile energy storage vehicle through a linear programming algorithm with the goal of minimizing the total power generation cost;

[0014] When the fluctuation value exceeds the preset threshold or the SOC is less than the preset capacity value, a dynamic optimization strategy is initiated. With the suppression of load fluctuation as a constraint, the particle swarm algorithm is used to dynamically adjust the power distribution between the diesel generator vehicle and the mobile energy storage vehicle. The mobile energy storage vehicle is given priority to respond to the fluctuation component, and the diesel generator vehicle provides the base load.

[0015] According to the real-time fluctuation value, SOC and diesel capacity, the power supply roles of the diesel generator vehicle and the mobile energy storage vehicle are dynamically switched to achieve coordinated optimization of power supply cost and power quality.

[0016] Furthermore, the steady-state optimization strategy is implemented by a linear programming algorithm, the goal of which is to minimize the total power generation cost of the diesel generator vehicle and the mobile energy storage vehicle, specifically including:

[0017] Objective function: The total cost is the sum of the power generation costs of all diesel generators and the sum of the power generation costs of all mobile energy storage vehicles;

[0018] Diesel vehicle power constraint: The output power of each diesel generator vehicle must be within its rated power range;

[0019] Energy storage vehicle power constraints: The output power of each mobile energy storage vehicle must be within its rated power range;

[0020] Energy storage capacity constraint: The storage capacity of each mobile energy storage vehicle must be between its designed minimum and maximum capacity values.

[0021] Furthermore, the dynamic optimization strategy is implemented by a particle swarm algorithm, specifically including:

[0022] Particle dimension definition: The dimensions of each particle include the power generation and maintenance cost parameters of the diesel generator vehicle, as well as the battery capacity SOC and charging cost parameters of the mobile energy storage vehicle;

[0023] Speed and position update rules:

[0024] Particle velocity update: current velocity multiplied by inertia weight, plus the difference between the individual's historical best position and the current position, multiplied by the first learning factor and the first random number, plus the difference between the group's historical best position and the current position, multiplied by the second learning factor and the second random number;

[0025] Particle position update: current position plus updated velocity value;

[0026] Optimization goal: Minimize the total power generation cost of diesel vehicles and energy storage vehicles while satisfying load fluctuation suppression constraints.

[0027] Furthermore, the preset capacity value is 30%. When the SOC is less than 30%, the power supply function of the energy storage vehicle is turned off, and the diesel generator vehicle independently bears the load demand.

[0028] Furthermore, the collaborative controller receives the voltage and frequency data of the grid-connected controller and the battery capacity SOC data of the mobile energy storage vehicle in real time, and reduces the collected signal according to a preset ratio before the data is input. The reduced signal is used to preferentially trigger the dynamic optimization strategy, where the reduction ratio is the data collected by the grid-connected controller divided by a fixed constant.

[0029] Furthermore, the energy storage vehicle distributes the fluctuation component through the load component separation strategy in dynamic optimization, the output power of the diesel vehicle is the load fundamental component, and the output power of the energy storage vehicle is the high-frequency fluctuation component.

[0030] Furthermore, the operating status of the diesel generator truck and the mobile energy storage truck is monitored in real time, and when a fault is detected, the backup power supply device is switched to.

[0031] And, a power supply optimization system for a group of multiple types of mobile emergency power generation devices, comprising:

[0032] Power detection module, used to collect the voltage amplitude and frequency fluctuation value of the emergency load and the battery capacity SOC of the mobile energy storage vehicle in real time;

[0033] Energy management and scheduling module, including:

[0034] The energy storage management system is used to allocate power between the diesel vehicle and the energy storage vehicle through a linear programming algorithm when the fluctuation value is below the threshold and the SOC is ≥30%;

[0035] Feedback control system, used to dynamically adjust power distribution through particle swarm optimization when the fluctuation value exceeds the threshold or SOC < 30%, giving priority to energy storage vehicles to suppress fluctuations;

[0036] The mobile emergency power generation device group, including a diesel generator vehicle and a mobile energy storage vehicle, dynamically switches power supply roles according to the instructions of the energy management and scheduling module to achieve coordinated optimization of power supply cost and power quality.

[0037] And, an electronic device includes a memory, a processor, and a computer program stored in the memory and runnable on the processor, characterized in that the processor implements the steps of the above method when executing the program.

[0038] A non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0039] Compared with the prior art, the present invention and its preferred embodiments have at least the following beneficial effects:

[0040] Improve the economy and stability of emergency power supply: Through a hierarchical optimization mechanism, steady-state cost optimization and dynamic fluctuation suppression are decoupled. This prioritizes power generation costs when the load is stable, and prioritizes power quality during periods of severe fluctuations. This resolves the contradiction in traditional solutions where a single optimization objective fails to balance economy and stability.

[0041] Enhanced system responsiveness: Based on a dynamic role allocation strategy between diesel generators and mobile energy storage vehicles, the rapid response characteristics of the energy storage vehicles are leveraged to accurately compensate for high-frequency fluctuations, while the diesel vehicles provide a continuous base load, thus overcoming the response speed bottleneck in single-device power supply scenarios.

[0042] Optimize equipment coordination efficiency: Through data adaptation preprocessing and SOC threshold drive mode switching, control failures caused by insufficient data accuracy or battery over-discharge are avoided, and the reliability of coordinated operation of multiple types of mobile power generation devices is improved;

[0043] Extend the life of key equipment: The SOC threshold design is combined with the energy storage vehicle's fluctuating component directional response mechanism to reduce the number of deep charge and discharge times of the battery and reduce energy storage system losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0045] Figure 1 This is a flow chart of a power supply method for optimizing power generation costs for a group of multiple types of mobile emergency power generation devices provided by an embodiment of the present invention.

[0046] Figure 2This is a flow chart of the interaction between the energy management and scheduling module and the mobile emergency power generation device according to an embodiment of the present invention.

[0047] Figure 3 This is a flow chart of the core algorithm for energy management and scheduling according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] Hereinafter, specific embodiments of the present application will be described in detail with reference to the accompanying drawings. Based on these detailed descriptions, those skilled in the art will be able to clearly understand the present application and implement the present application. Without violating the principles of the present application, the features of different embodiments may be combined to obtain new implementations, or certain features of certain embodiments may be substituted to obtain other preferred implementations.

[0049] To make the features and advantages of the present invention more clearly understood, the following embodiments are specifically described in detail with reference to the accompanying drawings.

[0050] The purpose of the embodiments of the present invention is to provide a power supply optimization solution for a group of various mobile emergency power generation devices, which improves the response speed and reduces the power generation cost by selecting an emergency power supply solution based on the power generation cost and response speed.

[0051] Existing emergency power supply measures utilize diesel generators and mobile energy storage vehicles for power generation. A grid-connected controller monitors the current load and then directs the mobile emergency generator to connect to the grid for power. However, this approach consumes a large amount of diesel fuel year-round, and the switchover from cold standby to operational power takes a long time, slowing response times. Energy storage generators, on the other hand, are too expensive to meet the energy storage capacity requirements for high-load scenarios and when the energy storage battery is low.

[0052] Therefore, the present invention provides a cost-optimized power supply method for a group of multiple mobile emergency power generation devices. This method uses a collaborative controller to dynamically adjust the emergency power supply scheme based on the voltage and frequency of the load, as well as the capacity and voltage of the mobile energy storage vehicle. When the load is stable and the power is high, the diesel generator vehicle is primarily used for power supply; when the load fluctuates violently, the mobile energy storage vehicle is primarily used for power supply; and when the mobile energy storage vehicle's battery capacity is low, the diesel generator vehicle is used for power generation. This reduces the cost of the emergency power supply scheme and improves the response speed to the load.

[0053] Please refer to Figure 1 , Figure 2 , Figure 3 , Figure 1 A flowchart of a power supply method for optimizing power generation costs for a group of multiple types of mobile emergency power generation devices provided by an embodiment of the present invention; Figure 2 A flow chart for the interaction between the energy management and scheduling module and the mobile emergency power generation device; Figure 3 Flowchart of the core algorithm for energy management and scheduling.

[0054] The process of building and implementing the program includes the following steps:

[0055] Step 1: Build a power supply system for a group of mobile emergency power generation devices, including: power detection module, energy management and scheduling module, and mobile emergency power generation devices;

[0056] The energy management and scheduling module includes the energy storage management system (EMS system), feedback control system, fault detection module, and data management system (DMS system);

[0057] Mobile emergency power generation equipment includes diesel generators and mobile energy storage vehicles;

[0058] Step 2: The power detection module is connected to the emergency load, collects the voltage amplitude and voltage frequency of the load, and obtains the battery capacity SOC of the mobile energy storage vehicle;

[0059] Step 3: The EMS system in the energy management and scheduling module prioritizes the diesel generator truck for power supply. Simultaneously, the DMS system monitors data in real time. When the load voltage and frequency fluctuations are within the threshold range, the power generation cost steady-state optimization algorithm is activated to rationally allocate the power output ratio between the diesel generator truck and the mobile energy storage truck.

[0060] Step 4: The feedback control system and DMS system in the energy management and scheduling module detect that when the load voltage fluctuation and frequency fluctuation exceed the threshold, the mobile energy storage vehicle is activated to supply power to the fluctuation, and the dynamic optimization algorithm for power generation cost is activated. Based on parameters such as voltage and frequency fluctuation thresholds, battery capacity SOC, and diesel capacity, the power generation ratio of the diesel generator vehicle and the mobile energy storage vehicle is reasonably allocated.

[0061] The specific implementation process of the solution includes the following steps:

[0062] S101, collecting the voltage amplitude and voltage frequency of the load, and obtaining the battery capacity SOC of the mobile energy storage vehicle;

[0063] This step mainly puts the current voltage amplitude and voltage frequency data of the load collected by the grid-connected controller into the collaborative controller, and at the same time obtains the battery capacity SOC of the mobile energy storage vehicle through the collaborative controller, preparing for the subsequent optimization plan pre-action.

[0064] S102: Prioritize the diesel generator vehicle for power supply. When the load voltage fluctuation and frequency fluctuation are within the threshold range, start the power generation cost steady-state optimization algorithm to reasonably allocate the power output ratio between the diesel generator vehicle and the mobile energy storage vehicle.

[0065] Based on the previous step, this step aims to adjust the composition structure of the mobile emergency power generation device group through the load and the real-time parameters of the mobile emergency power generation device.

[0066] S103: When the load voltage fluctuation and frequency fluctuation exceed the threshold, the mobile energy storage vehicle is activated to adjust and suppress the fluctuation, and the power generation cost dynamic optimization algorithm is activated to reasonably allocate the power generation ratio between the diesel generator vehicle and the mobile energy storage vehicle based on parameters such as voltage and frequency fluctuation thresholds, battery capacity, and diesel capacity;

[0067] Based on the previous step, this step aims to screen out actions that meet the situation and execute them to switch the power supply scheme when the available capacity value of the mobile energy storage vehicle is lower than the rated standard, thereby reducing the power supply cost.

[0068] In summary, this embodiment uses a grid-connected controller and a collaborative controller to collect the voltage amplitude and voltage frequency of the load and obtain the battery capacity SOC of the mobile energy storage vehicle; the collaborative controller compares the collected data with the threshold to obtain a power supply plan for the mobile emergency power generation device group, adjusts the power supply plan according to the load fluctuation and the battery capacity SOC of the mobile energy storage vehicle, and finally obtains an emergency power supply plan with the optimal power supply cost and response speed. This method fully considers the power supply cost and response speed, and can achieve the economy and stability of emergency power generation, reduce the power supply cost and improve the response speed.

[0069] In a preferred embodiment, step 2 specifically includes:

[0070] The power detection module is connected to the temporary construction site load and uses the grid-connected controller to collect the voltage, frequency, and phase angle of the load. At the same time, the collaborative controller detects the battery capacity SOC of the mobile energy storage vehicle and reduces the data obtained by the grid-connected controller to enter the collaborative controller. The calculation formula is as follows:

[0071]

[0072] Among them, X in The data collected by the grid-connected controller; k is the reduced constant, X out is the input signal to the cooperative controller.

[0073] In a preferred embodiment, step 3 specifically includes:

[0074] The DMS system in the energy management and scheduling module analyzes the status of the emergency load and determines whether the voltage amplitude and voltage frequency of the load are within the set threshold;

[0075] If the voltage amplitude and voltage frequency of the load are within the set threshold, it is determined whether the battery capacity SOC of the mobile energy storage vehicle is greater than or equal to the set threshold;

[0076] If the battery capacity SOC of the mobile energy storage vehicle is greater than or equal to 30%, the collaborative controller in the EMS system starts the power generation cost steady-state optimization algorithm to adjust the power supply plan according to the load conditions.

[0077] If the battery capacity SOC of the mobile energy storage vehicle is less than 30%, the collaborative controller will shut down the steady-state optimization algorithm for power generation cost, and the diesel generator vehicle will be used for power supply alone.

[0078] In a preferred embodiment, step 4 specifically includes:

[0079] The DMS system and feedback control system in the energy management and scheduling module analyze the status of the emergency load and determine whether the available capacity value of the energy storage power generation vehicle is greater than or equal to the preset capacity threshold;

[0080] If the voltage amplitude and voltage frequency of the load are not within the set threshold, it is determined whether the battery capacity SOC of the mobile energy storage vehicle is greater than or equal to 30%;

[0081] If the battery capacity SOC of the mobile energy storage vehicle is greater than or equal to 30%, the diesel generator vehicle provides the DC component of the load, and the mobile energy storage vehicle provides the fluctuating component of the load.

[0082] If the battery capacity SOC of the mobile energy storage vehicle is less than 30%, the collaborative controller will start the dynamic optimization algorithm of power generation cost to adjust the power supply plan according to the load conditions.

[0083] In a preferred embodiment, in steps 3-4:

[0084] During the power supply process, the objective function of the mobile emergency power generation device group is:

[0085]

[0086] Among them C id is the power generation cost of the i-th diesel generator car, and there are m diesel generator cars in total. ib is the power generation cost of the i-th mobile energy storage vehicle, and there are k mobile energy storage vehicles in total.

[0087] When using the power generation cost optimization algorithm, the power supply cost is the primary consideration, and the goal is to keep the power supply cost as low as possible. The objective function is:

[0088] C id =f(P id ,C dp ,C dc ,C lc ,C mc ) (3)

[0089] P idis the output power (kW) of the i-th diesel generator car, C dp is the market price of diesel (RMB / L), C dc is the diesel consumption (L / kW), C lc is labor cost, C mc Maintenance costs for diesel vehicles.

[0090] C ib =f(P ib ,SOC i ,C cc ,C bc ,C mc ) (4)

[0091] P ib is the output power (kW) of the i-th mobile energy storage vehicle, SOC i is the battery capacity SOC value of the i-th mobile energy storage vehicle, C cc is the charging cost (RMB / kW), C bc is the battery cost (RMB / kWh), C mc The maintenance cost of the mobile energy storage vehicle.

[0092] In a preferred embodiment, the steady-state optimization algorithm for power generation cost includes:

[0093] The load power of the load, the power generation power of the diesel generator truck, the market price of diesel, diesel consumption, labor cost, diesel vehicle maintenance cost and the power generation power of the mobile energy storage vehicle, battery capacity SOC, charging cost, battery cost, and maintenance cost of the mobile energy storage vehicle are input into the above optimization objective function, and the power output of each mobile emergency power generation device and the SOC limit of the mobile energy storage vehicle are set to perform linear programming solution;

[0094] Set the algorithm parameters, and its constraints are:

[0095]

[0096]

[0097]

[0098] Among them E ib is the storage energy of the i-th mobile energy storage vehicle; P id is the output power of the i-th diesel generator car; P ib The output power of the i-th mobile energy storage vehicle.

[0099] In steady state:

[0100] When 30%<SOC i <100%

[0101]

[0102] When SOC i <30%,

[0103]

[0104] Among them, P load is the load power.

[0105] In a preferred embodiment, the power generation cost dynamic optimization algorithm includes:

[0106] Input the load power of the load, the power generation power of the diesel generator truck, the market price of diesel, diesel consumption, labor cost, diesel truck maintenance cost, and the power generation power of the mobile energy storage truck, battery capacity SOC, charging cost, battery cost, and maintenance cost of the mobile energy storage truck into the above optimization objective function, and set the output power and battery capacity SOC limits of each mobile emergency power generation device;

[0107] Set the algorithm parameters, and its search function is:

[0108]

[0109] in and is a ten-dimensional vector, which represents the power generation, diesel market price, diesel consumption, labor cost, diesel vehicle maintenance cost of the i-th diesel generator vehicle and the power generation, battery capacity SOC, charging cost, battery cost, and speed and position of the i-th mobile energy storage vehicle during the k-th optimization search. ω is the inertia weight, c1 and c2 are learning factors, and p id 、p gd are the local optimal power supply scheme and the global optimal power supply scheme of the particle in the optimization process, rand1 and rand2 are random numbers in the interval [0,1];

[0110] Set the algorithm parameters, and its constraints are:

[0111] In dynamic situations,

[0112]

[0113] Adjust the inertia weight and learning factor, update the position and velocity of the individual particles, and finally determine whether the data at the end of the iteration converges or meets the end condition. If not, start a new round of iteration;

[0114] Output the solution of the optimization objective function and obtain the power supply plan for the emergency power generation device group with the best economy and power quality.

[0115] Based on the same inventive concept, the present invention also provides a computer device, which includes: one or more processors and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute the program instructions stored in the memory. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is used to implement one or more instructions, specifically for loading and executing one or more instructions in a computer storage medium to implement the above method.

[0116] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium having a computer program stored thereon, which executes the above method when executed by a processor. The storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component.

[0117] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0118] The above shows and describes the basic principles, main features and advantages of the present disclosure. Those skilled in the art should understand that the present disclosure is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present disclosure. Various changes and improvements may be made to the present disclosure without departing from the spirit and scope of the present disclosure, and such changes and improvements shall fall within the scope of the present disclosure.

[0119] The present invention is not limited to the above-mentioned optimal implementation mode. Anyone can derive various other forms of a power supply optimization method for a group of multiple types of mobile emergency power generation devices under the inspiration of the present invention. All equal changes and modifications made within the scope of the patent application of the present invention should fall within the scope of the present invention.

Claims

1. A method for optimizing power supply for a group of multiple mobile emergency power generation devices, characterized by: The power detection module monitors the voltage amplitude and frequency fluctuation of the emergency load in real time, and simultaneously obtains the battery capacity SOC of the mobile energy storage vehicle; When the voltage fluctuation value and the frequency fluctuation value are lower than the preset threshold value and the SOC ≥ the preset capacity value, the steady-state optimization strategy is activated to allocate the power output ratio of the diesel generator vehicle and the mobile energy storage vehicle through a linear programming algorithm with the goal of minimizing the total power generation cost; When the fluctuation value exceeds the preset threshold or the SOC is less than the preset capacity value, a dynamic optimization strategy is initiated. With the suppression of load fluctuation as a constraint, the particle swarm algorithm is used to dynamically adjust the power distribution between the diesel generator vehicle and the mobile energy storage vehicle. The mobile energy storage vehicle is given priority to respond to the fluctuation component, and the diesel generator vehicle provides the base load. According to the real-time fluctuation value, SOC and diesel capacity, the power supply roles of the diesel generator vehicle and the mobile energy storage vehicle are dynamically switched to achieve coordinated optimization of power supply cost and power quality.

2. The method for optimizing power supply for a group of multiple types of mobile emergency power generation devices according to claim 1, characterized in that: The steady-state optimization strategy is implemented through a linear programming algorithm, whose goal is to minimize the total power generation cost of the diesel generator vehicle and the mobile energy storage vehicle. Specifically, it includes: Objective function: The total cost is the sum of the power generation costs of all diesel generators and the sum of the power generation costs of all mobile energy storage vehicles; Diesel vehicle power constraint: The output power of each diesel generator vehicle must be within its rated power range; Energy storage vehicle power constraints: The output power of each mobile energy storage vehicle must be within its rated power range; Energy storage capacity constraint: The storage capacity of each mobile energy storage vehicle must be between its designed minimum and maximum capacity values.

3. The method for optimizing power supply for a group of multiple types of mobile emergency power generation devices according to claim 1, characterized in that: The dynamic optimization strategy is implemented by particle swarm optimization, which specifically includes: Particle dimension definition: The dimensions of each particle include the power generation and maintenance cost parameters of the diesel generator vehicle, as well as the battery capacity SOC and charging cost parameters of the mobile energy storage vehicle; Speed and position update rules: Particle velocity update: current velocity multiplied by inertia weight, plus the difference between the individual's historical best position and the current position, multiplied by the first learning factor and the first random number, plus the difference between the group's historical best position and the current position, multiplied by the second learning factor and the second random number; Particle position update: current position plus updated velocity value; Optimization goal: Minimize the total power generation cost of diesel vehicles and energy storage vehicles while satisfying load fluctuation suppression constraints.

4. The method for optimizing power supply for a group of multiple mobile emergency power generation devices according to claim 1, wherein: The preset capacity value is 30%. When SOC is less than 30%, the power supply function of the energy storage vehicle is turned off, and the diesel generator vehicle independently bears the load demand.

5. The method for optimizing power supply for a group of multiple types of mobile emergency power generation devices according to claim 1, characterized in that: The collaborative controller receives the voltage and frequency data of the grid-connected controller and the battery capacity SOC data of the mobile energy storage vehicle in real time, and reduces the collected signal according to a preset ratio before the data is input. The reduced signal is used to prioritize triggering the dynamic optimization strategy, where the reduction ratio is the data collected by the grid-connected controller divided by a fixed constant.

6. The method for optimizing power supply for a group of multiple types of mobile emergency power generation devices according to claim 1, characterized in that: The energy storage vehicle distributes the fluctuation component through the load component separation strategy in dynamic optimization. The output power of the diesel vehicle is the load fundamental component, and the output power of the energy storage vehicle is the high-frequency fluctuation component.

7. The method for optimizing power supply for a group of multiple types of mobile emergency power generation devices according to claim 1, characterized in that: Monitor the operating status of the diesel generator truck and mobile energy storage truck in real time, and switch to the backup power supply device when a fault is detected.

8. A power supply optimization system for a group of multiple types of mobile emergency power generation devices, characterized in that: include: Power detection module, used to collect the voltage amplitude and frequency fluctuation value of the emergency load and the battery capacity SOC of the mobile energy storage vehicle in real time; Energy management and scheduling module, including: The energy storage management system is used to allocate power between the diesel vehicle and the energy storage vehicle through a linear programming algorithm when the fluctuation value is below the threshold and the SOC is ≥30%; Feedback control system, used to dynamically adjust power distribution through particle swarm optimization when the fluctuation value exceeds the threshold or SOC < 30%, giving priority to energy storage vehicles to suppress fluctuations; The mobile emergency power generation device group, including a diesel generator vehicle and a mobile energy storage vehicle, dynamically switches power supply roles according to the instructions of the energy management and scheduling module to achieve coordinated optimization of power supply cost and power quality.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A non-transitory 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 7 are implemented.

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