Method for evaluating regional modular reconfigurable mobile energy storage capacity demand considering multi-support demand

Abstract

This invention discloses a method for assessing the capacity demand of regional modular reconfigurable mobile energy storage considering multiple support requirements. The method includes: 1. Constructing the configuration scale and resource constraints of regional modular reconfigurable mobile energy storage; 2. Constructing a traffic network model for regional modular reconfigurable mobile energy storage; 3. Constructing a charging and discharging model for regional modular reconfigurable mobile energy storage; 4. Constructing the power flow constraints and objective function for regional modular reconfigurable mobile energy storage; 5. After transforming the power flow constraints into linearized constraints through linearization and second-order cone relaxation, solving the objective function to obtain the optimal assessment method for the capacity demand of regional modular reconfigurable mobile energy storage. This invention achieves efficient reuse of mobile energy storage resources in the spatiotemporal dimensions through modular reconfiguration, significantly reducing the total installed capacity demand while effectively improving the equipment utilization efficiency, power supply resilience, and optimal system operation performance of the distribution network.

Description

Technical Field

[0001] This invention belongs to the field of power distribution system planning technology, specifically a method for assessing the capacity demand of regional modular reconfigurable mobile energy storage that considers multiple support requirements. Background Technology

[0002] With the increasing penetration of distributed renewable energy, distribution networks face challenges such as local voltage exceedances, backflow of power, and feeder overload. Traditional methods often involve deploying fixed energy storage systems at key nodes. However, due to their immobility, fixed energy storage planning often requires redundant configuration based on peak demand at each node, leading to significant capacity redundancy and insufficient resource utilization across the entire network. This manifests as a large deployment scale and limited spatial and temporal utilization of equipment. To address these spatial and temporal adjustment challenges, mobile energy storage systems, as a mobile resource, achieve spatial and temporal reuse of energy storage resources through physical transfer between different nodes. However, existing research often treats mobile energy storage as a rigid unit integrating vehicle and electricity, neglecting the potential for functional decoupling between transportation and storage resources. In actual operation, the traditional integrated model results in the transportation unit being ineffectively occupied for extended periods while the storage module performs charging and discharging tasks, significantly increasing the amount of transportation resources required and the system scheduling burden. Furthermore, existing solutions, when assessing capacity requirements, rarely consider the assembly delay after vehicle-electricity decoupling and the complex power-traffic coupling constraints, making it difficult to accurately evaluate mobile energy storage configuration schemes under multiple support requirements. Therefore, how to construct a capacity demand assessment model that considers the characteristics of vehicle-battery separation and modular reconfiguration, and achieve efficient coordination of transportation capacity and storage resources, has become an urgent need to improve the flexibility of the power distribution network and the overall operational efficiency of the system. Summary of the Invention

[0003] To address the shortcomings of the prior art, this invention proposes a regional modular reconfigurable mobile energy storage capacity demand assessment method that considers multiple support requirements. The method aims to characterize the multi-objective support capabilities of mobile energy storage under complex operating conditions through a two-stage stochastic programming model, thereby achieving flexible resource allocation in the spatial dimension and matching of multi-scenario requirements in the temporal dimension. This will ensure voltage stability, photovoltaic absorption, and emergency support while simultaneously optimizing the overall system resource allocation scale and grid operation losses.

[0004] To achieve the above-mentioned objectives, the present invention adopts the following technical solution: This invention provides a method for assessing the capacity demand of regional modular reconfigurable mobile energy storage considering multiple support requirements. It is applied to low-voltage distribution substations, wherein a single regional modular reconfigurable mobile energy storage system consists of a mobile energy storage vehicle and its battery modules, used to meet different support requirements of the low-voltage distribution substation. The method is characterized by the following steps: Step 1: Determine the configuration scale and resource constraints of regional modular reconfigurable mobile energy storage; Step 2: Construct a transportation network model for regional modular reconfigurable mobile energy storage; Step 3: Construct a charging and discharging model for regional modular reconfigurable mobile energy storage; Step 4: Based on configuration scale and resource constraints, traffic network model, and charging / discharging model, construct a capacity demand model for regional modular reconfigurable mobile energy storage, including: power flow constraints and objective function; Step 5: After transforming the power flow constraints into linear constraints through linearization and second-order cone relaxation, solve the objective function of the capacity demand model to obtain the optimal evaluation method for the regional modular reconfigurable mobile energy storage capacity demand, including: energy storage configuration results, energy storage spatiotemporal distribution characteristics, and power distribution system operation evaluation indicators.

[0005] The characteristic of the regional modular reconfigurable mobile energy storage capacity demand assessment method considering multiple support requirements described in this invention is that step 1 is to use equations (1)-(4) to construct the configuration scale and resource constraints of regional modular reconfigurable mobile energy storage: (1) (2) (3) (4) In equations (1)-(4), This indicates the number of mobile energy storage vehicles configured. This indicates the number of battery modules configured. This indicates the unit resource occupancy weight of the mobile energy storage vehicle. This indicates the unit resource usage weight of the battery module; This indicates the total resource allocation limit. Z represents the vehicle-to-electricity ratio constraint coefficient, and Z represents a positive integer.

[0006] Furthermore, step 2 involves constructing a regional modular reconfigurable mobile energy storage transportation network model using equations (5) to (13): (5) (6) (7) (8) (9) (10) (11) (12) (13) In equations (5) to (13), express Moment Scene Lower branch road The number of battery modules transported upstream. express Moment Scene Lower branch road The number of mobile energy storage vehicles traveling on the road. This represents the speed of any mobile energy storage vehicle under zero traffic flow conditions. express Moment Scene The traffic congestion coefficient below express Moment Scene The speed of any mobile energy storage vehicle. Represents a node To the node Path distance, express Moment Scene The next mobile energy storage vehicle from the node Drive to the node Time, This indicates the assembly time for a single area of ​​modular, reconfigurable mobile energy storage. Indicates time Scene The total transfer time for modular reconfigurable mobile energy storage in a single region. This represents the total number of transition time steps after discretization. express Moment Scene Next stop at node The number of battery modules, express Moment Scene Next stop at node The number of battery modules, express Moment Scene Starting from other nodes, proceed to node The number of battery modules shipped. express Moment Scene Next node Depart, towards the node The number of battery modules shipped. express Moment Scene Next stop at node The number of mobile energy storage vehicles express Moment Scene Next stop at node The number of mobile energy storage vehicles express Moment Scene Next node Depart, head to the node The number of mobile energy storage vehicles express Moment Scene Next node Depart, towards the node The number of mobile energy storage vehicles in operation. express Always on the side road The number of battery modules transported upstream. express Always on the side road The number of mobile energy storage vehicles traveling on the road. Represents a set of nodes. Represents a set of scenes. Indicates a time interval.

[0007] Furthermore, step 3 involves constructing a charging and discharging model for regional modular reconfigurable mobile energy storage using equations (14) to (20): (14) (15) (16) (17) (18) (19) (20) In equations (14)-(20), express Moment Scene Next node The total charging power of the battery modules at that location. express Moment Scene Next node The total discharge power of the battery modules at that location. This indicates the rated charging power of a single battery module. This indicates the rated discharge power of a single battery module. express Moment Scene Next node Is the battery module at that location charging? express Moment Scene Next node Is the battery module at that location in a discharged state? Represents positive numbers. This indicates the charging efficiency of a single battery module. This indicates the discharge efficiency of a single battery module. express Moment Scene Next node The total capacity of the battery modules. express Moment Scene Next node The total capacity of the battery modules. This indicates the lower limit of the capacity of a single battery module. The maximum capacity of a single battery module.

[0008] Furthermore, step 4 includes: Step 4.1: Construct power flow constraints using equations (21)-(28): (twenty one) (twenty two) (twenty three) (twenty four) (25) (26) (27) (28) In equations (21)-(28), , They represent Moment Scene Lower branch road Branch roads Active power transmitted upstream, , They represent Moment Scene Lower branch road Branch roads Transmitted reactive power , Representing branch roads resistance, reactance , They represent Moment Scene Injection Node Active and reactive power , They represent Moment Scene Next node The active and reactive loads at the location express Moment Scene Next node The photovoltaic power station is contributing its energy. express Moment Scene Next node The amount of light discarded at that location , express Moment Scene Next node Active and reactive load shedding at the location , They represent Moment Scene Next node ,node The square of the voltage at that point, , Representing nodes respectively The safe lower and upper limits of voltage. , They represent Moment Scene Lower branch road Branch roads The square of the current, branch road The upper limit of the square term of the current, Represented by node Let be the set of end nodes corresponding to the branch at the beginning. Represented by node The first node corresponding to the last branch; Step 4.2: Construct the objective function using equations (29)-(34): (29) (30) (31) (32) (33) (34) In equations (29)-(34), This indicates the configuration scale indicator for the first phase. This indicates the operational status evaluation indicators for the second phase. Represents the time-domain equivalent reduction factor. It is the time-domain equivalent scaling factor. It refers to the lifespan of regional modular reconfigurable mobile energy storage devices. This represents the weighted probability of each scenario. This represents the system deviation and loss assessment value. This represents the quantized value of the spatial transition state. The unit evaluation weight representing line loss. The penalty gain coefficient representing the load shedding state. The penalty gain coefficient representing the abandoned light state. This represents the transfer weighting coefficient per unit mileage. express Moment Scene Next node With nodes The number of mobile energy storage vehicles traveling between them, Represents a set of distribution network lines. This represents the set of time periods for a single scheduling cycle. This represents the annualized periodic constant.

[0009] Furthermore, step 5 includes: Step 5.1: Use the quadratic terms in equations (35)-(40) , and , Transform into a linear representation: (35) (36) (37) (38) (39) (40) In equations (35)-(40), , They represent Moment Scene Lower branch road Branch roads The square of the current, , They represent Moment Scene node ,node The square of the voltage; Step 5.2: Multiply the quadratic terms in equation (40) using equation (41). After performing second-order cone relaxation, equation (41) is transformed into the standard second-order cone form using equation (42): (41) (42)

[0010] The present invention provides an electronic device, including a memory and a processor, characterized in that the memory is used to store a program that supports the processor in executing the regional modular reconfigurable mobile energy storage capacity demand assessment method considering multiple support requirements, and the processor is configured to execute the program stored in the memory.

[0011] The present invention provides a computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, performs the steps of the regional modular reconfigurable mobile energy storage capacity demand assessment method considering multiple support requirements.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention constructs a spatiotemporal reuse operation mode for mobile energy storage, utilizing its active mobility to connect the peak-shaving demands of different nodes on the time axis, effectively breaking the capacity redundancy contradiction caused by the geographical immobility limitation of fixed energy storage, and significantly reducing the total installed capacity demand of the entire network.

[0013] 2. This invention introduces a modular reconfiguration and trailer swapping transportation mode with vehicle-battery separation, which realizes deep decoupling of transportation capacity resources and storage resources in terms of physical and functional aspects, eliminates the ineffective occupation time of transportation vehicles during battery operation, thereby further optimizing resource allocation and reducing transportation costs, and overcoming the shortcomings of traditional vehicle-battery integrated rigid units in terms of scheduling flexibility.

[0014] 3. This invention establishes a multi-support requirement assessment system covering voltage regulation, absorption, loss reduction and reliability support, enabling mobile energy storage systems to simultaneously cope with complex operational problems such as local voltage over-limit, photovoltaic power flow back-feeding and feeder overload, and to provide support in extreme scenarios to improve power supply resilience, filling the gap in existing research on multi-functional integrated utilization. Attached Figure Description

[0015] Figure 1 This is an improved 33-node diagram of the present invention; Figure 2 This is a battery status diagram at each docking point of the present invention; Figure 3 This is a spatiotemporal distribution diagram of the battery module of the present invention; Figure 4 This is a comparison chart of the total charging and discharging power and net load optimization of the system of this invention; Figure 5 This is a voltage performance diagram of the system of this invention; Figure 6 This is a flowchart illustrating the implementation of the method described in this invention. Detailed Implementation

[0016] In this embodiment, a method for assessing the capacity demand of regional modular reconfigurable mobile energy storage considering multiple support requirements is described, such as... Figure 6 As shown, the specific steps are as follows: Step 1: Construct four typical scenario sets based on the energy storage needs of the transformer substation: The photovoltaic output and load curves of low-voltage distribution areas throughout the year were collected. The K-means clustering algorithm was used to divide the 365 days of the year into four typical scenarios: heavy overload scenario, photovoltaic absorption scenario, source-load balance scenario, and extreme scenario. The probability weight of each scenario was determined. The corresponding time series characteristics are shown in Table 1: Table 1

[0017] Step 2: Determine the configuration scale and resource constraints of regional modular reconfigurable mobile energy storage: (1) (2) (3) (4) In equations (1)-(4), This indicates the number of mobile energy storage vehicles configured. This indicates the number of battery modules configured. This indicates the unit resource occupancy weight of the mobile energy storage vehicle. This indicates the unit resource usage weight of the battery module; This indicates the total resource allocation limit. Z represents the vehicle-to-electricity ratio constraint coefficient, and Z represents a positive integer.

[0018] Step 3: Construct a regional modular reconfigurable mobile energy storage transportation network model: (5) (6) (7) (8) (9) (10) (11) (12) (13) In equations (5) to (13), express Moment Scene Lower branch road The number of battery modules transported upstream. express Moment Scene Lower branch road The number of mobile energy storage vehicles traveling on the road. This represents the speed of any mobile energy storage vehicle under zero traffic flow conditions. express Moment Scene The traffic congestion coefficient below express Moment Scene The speed of any mobile energy storage vehicle. Represents a node To the node Path distance, express Moment Scene The next mobile energy storage vehicle from the node Drive to the node Time, This indicates the assembly time for a single area of ​​modular, reconfigurable mobile energy storage. Indicates time Scene The total transfer time for modular reconfigurable mobile energy storage in a single region. This represents the total number of transition time steps after discretization. express Moment Scene Next stop at node The number of battery modules, express Moment Scene Next stop at node The number of battery modules, express Moment Scene Starting from other nodes, proceed to node The number of battery modules shipped. express Moment Scene Next node Depart, towards the node The number of battery modules shipped. express Moment Scene Next stop at node The number of mobile energy storage vehicles express Moment Scene Next stop at node The number of mobile energy storage vehicles express Moment Scene Next node Depart, head to the node The number of mobile energy storage vehicles express Moment Scene Next node Depart, towards the node The number of mobile energy storage vehicles in operation. express Always on the side road The number of battery modules transported upstream. express Always on the side road The number of mobile energy storage vehicles traveling on the road. Represents a set of nodes. Represents a set of scenes. Indicates a time interval.

[0019] Step 4: Construct a charging and discharging model for regional modular reconfigurable mobile energy storage: (14) (15) (16) (17) (18) (19) (20) In equations (14)-(20), Indicates at time Scene Next node The total charging power of the battery modules at that location. Indicates at time Scene Next node The total discharge power of the battery modules at that location. This indicates the rated charging power of a single battery module. This indicates the rated discharge power of a single battery module. express Scene Next node Is the battery module at that location charging? express Scene Next node Is the battery module at that location in a discharged state? Represents positive numbers. This indicates the charging efficiency of a single battery module. This indicates the discharge efficiency of a single battery module. Indicates at time Scene Next node The total capacity of the battery modules. This indicates the lower limit of the capacity of a single battery module. The maximum capacity of a single battery module.

[0020] Step 4: Constructing the power flow constraints and objective function for regional modular reconfigurable mobile energy storage. Step 4.1: Construct power flow constraints for regional modular reconfigurable mobile energy storage using equations (21)-(26): (twenty one) (twenty two) (twenty three) (twenty four) (25) (26) (27) (28) In equations (21)-(28), , Representing time respectively Scene Lower branch road Branch roads Active power transmitted upstream, , Representing time respectively Scene Lower branch road Branch roads Transmitted reactive power , Representing branch roads resistance, reactance , Representing time respectively Scene Injection Node Active and reactive power , Representing time respectively Scene Next node The active and reactive loads at the location Indicates time Scene Next node The photovoltaic power station is contributing its energy. Indicates time Scene Next node The amount of light discarded at that location , Indicates time Scene Next node Active and reactive load shedding at the location , Representing time respectively Scene Next node ,node The square of the voltage at that point, , These represent the lower and upper safe limits for node voltage, respectively. , Representing time respectively Scene Lower branch road Branch roads The square of the current, branch road The upper limit of the square term of the current, Represented by node Let be the set of end nodes corresponding to the branch at the beginning. Represented by node This refers to the starting node corresponding to the branch at the end.

[0021] Step 4.2: Construct the objective function for regional modular reconfigurable mobile energy storage using equations (29)-(34): (29) (30) (31) (32) (33) (34) In equations (29)-(34), This indicates the configuration scale indicator for the first phase. This indicates the operational status evaluation indicators for the second phase. This represents the time-domain equivalent conversion factor, used to amortize the size of a one-time configuration over a unit of computation cycle. It is the time-domain equivalent scaling factor. It refers to the lifespan of regional modular reconfigurable mobile energy storage devices. This represents the weighted probability of each scenario. This represents the system deviation and loss assessment value. This represents the quantized value of the spatial transition state. The unit evaluation weight representing line loss. The penalty gain coefficient representing the load shedding state. The penalty gain coefficient representing the abandoned light state. This represents the transfer weighting coefficient per unit mileage. express Moment Scene Next node With nodes The number of mobile energy storage vehicles traveling between them, Represents a set of distribution network lines. This represents the set of time periods for a single scheduling cycle. This represents the annualized periodic constant, used to characterize the quantitative relationship between the calculation period and the annual time scale.

[0022] Step 5: Linearization and Second-Order Cone Relaxation. After transforming the dynamic power flow constraints into linear constraints, the objective function is solved to obtain the optimal evaluation method for the regional modular reconfigurable mobile energy storage capacity demand.

[0023] Step 5.1: Use the quadratic terms in equations (35)-(40) , and , Transform into a linear representation: (35) (36) (37) (38) (39) (40) In equations (35)-(40), , Representing time respectively Scene Lower branch road Branch roads The square of the current, , Representing time respectively Scene node ,node The square of the voltage.

[0024] Step 5.2: Multiply the quadratic terms in equation (40) using equation (41). After performing second-order cone relaxation, equation (41) is transformed into the standard second-order cone form using equation (42): (41) (42) In this embodiment, an electronic device includes a memory and a processor. The memory stores a program that supports the processor in executing the above-described method, and the processor is configured to execute the program stored in the memory.

[0025] In this embodiment, a computer-readable storage medium stores a computer program, which is executed by a processor to perform the steps of the above method.

[0026] To enable those skilled in the art to better understand the present invention, the numerical example analysis includes the following components: I. Example Description and Simulation Result Analysis: Numerical simulation tests were conducted on an improved IEEE 33-bus system with a rated voltage of 12.66 kV, such as... Figure 1 As shown. The system safety voltage range is set to [0.95, 1.05] pu. Nodes 9, 13, 15, 24, and 32 are set as regional modular reconfigurable mobile energy storage access nodes. Photovoltaic generator sets with capacities of 900kVA, 800kVA, 700kVA, 600kVA, and 500kVA are installed on nodes 7, 9, 13, 15, and 27, respectively.

[0027] Active power related costs Set at 0.5 yuan / kW.h Take 6 yuan / kW.h Values ​​and equal, The rate is 0.5 / km. Each vehicle can carry a maximum of 4 batteries, each with a capacity of 20 kWh and a maximum charging / discharging power of 10 kW. The ideal speed for the mobile energy storage vehicle is 25 km / h.

[0028] The optimal configuration obtained by solving the two-stage stochastic programming model is: the system needs to be equipped with mobile energy storage vehicles. For 6 vehicles, standard battery modules There are 22 units. Among them, the vehicle-to-electricity ratio coefficient... The constraints were met, and the resource utilization indicators were achieved. and operational evaluation indicators The overall optimal solution.

[0029] like Figure 2 As shown, during the peak photovoltaic output period from 11:00 to 15:00, nodes 9, 13, and 15 underwent a concentrated unloading process and then transitioned to a high-proportion grid-connected operation. Mobile energy storage resources dynamically converged towards nodes with high photovoltaic penetration (nodes 9, 13, and 15), reaching their peak grid-connected output equivalent. Simultaneously, heavily loaded nodes (nodes 24 and 32) exhibited significant resource scarcity. Entering the evening peak load period from 18:00 to 21:00, the resource flow showed a significant reversal, with the grid-connected output curves of nodes 24 and 32 expanding substantially. These phenomena validate the temporal and spatial scheduling logic of mobile energy storage: storing surplus photovoltaic energy during midday and transferring it to heavily loaded areas via transportation networks, achieving coordinated optimization of energy in the spatiotemporal dimensions.

[0030] like Figure 3 As shown, during the typical peak photovoltaic output period of 11:00-15:00, nodes 9, 13, and 15 exhibit highly concentrated, large-scale, and high-intensity charging behavior. In stark contrast, during the peak grid load period of 17:00-21:00, the scheduling scheme shows a wide-ranging energy release (green area). In particular, heavily loaded nodes 24 and 32 maintain high levels of operation in terms of discharge duration and the equivalent of deployed batteries, indicating that the system, through precise spatiotemporal resource allocation, absorbs redundant photovoltaic output during midday and provides crucial capacity support for heavily loaded areas during the evening peak period.

[0031] like Figure 4As shown, the optimized curves of total grid charging and discharging power and net load clearly reveal the reshaping effect of mobile energy storage on system operating characteristics. During the peak photovoltaic output period from 11:00 to 15:00, the total system charging power (orange bar chart) closely matches the surplus range of distributed photovoltaic power, indicating that mobile energy storage resources are precisely dispatched to relevant nodes to perform power extraction tasks. In terms of net load, the original curve (black dashed line) drops to a negative value at noon, exhibiting typical high-penetration photovoltaic backflow characteristics; after mobile energy storage charging dispatch, the optimized net load curve (red solid line) is flattened above the zero line, effectively avoiding the risk of reverse power flow. During the peak electricity consumption period from 18:00 to 22:00, the system significantly alleviates the power supply pressure during the evening peak period by releasing the energy stored at noon.

[0032] like Figure 5 As shown, Figure 5 As shown in (a) of the figure, through the spatiotemporal coordination of mobile energy storage resources, the voltage fluctuation envelope of the entire network nodes is smoothed within an extremely narrow range of [1.00, 1.02] pu. Further combining this with the statistical probability distribution in (b) of the figure, it can be seen that more than 80% of the system's voltage deviation is controlled within 0.01 pu, and there are no instances of exceeding the limit throughout the entire cycle. This not only verifies the smoothing effect of mobile energy storage on distributed power source fluctuations but also proves the effectiveness of the optimization strategy in improving the voltage stability of the distribution network.

[0033] To verify the correctness of the proposed method for assessing the capacity demand of regional modular reconfigurable mobile energy storage considering multiple support requirements, this invention will compare and analyze four indicators—system voltage, photovoltaic absorption, network loss, and load curtailment—with the original circuit, as shown in Table 2: Table 2

[0034] As can be seen from Table 2, the method proposed in this invention achieves 100% photovoltaic absorption in the distribution network and greatly reduces network losses when there is no load cut-off, demonstrating significant advantages in many aspects.

Claims

1. A method for assessing the capacity demand of regional modular reconfigurable mobile energy storage considering multiple support requirements, applied to low-voltage distribution substations, wherein... A single-area modular reconfigurable mobile energy storage system consists of a mobile energy storage vehicle and its battery modules, used to meet different support needs of low-voltage distribution substations. Its characteristic is that the capacity demand assessment method includes the following steps: Step 1: Determine the configuration scale and resource constraints of regional modular reconfigurable mobile energy storage; Step 2: Construct a transportation network model for regional modular reconfigurable mobile energy storage; Step 3: Construct a charging and discharging model for regional modular reconfigurable mobile energy storage; Step 4: Based on configuration scale and resource constraints, traffic network model, and charging / discharging model, construct a capacity demand model for regional modular reconfigurable mobile energy storage, including: power flow constraints and objective function; Step 5: After transforming the power flow constraints into linear constraints through linearization and second-order cone relaxation, solve the objective function of the capacity demand model to obtain the optimal evaluation method for the regional modular reconfigurable mobile energy storage capacity demand, including: energy storage configuration results, energy storage spatiotemporal distribution characteristics, and power distribution system operation evaluation indicators.

2. The method for assessing regional modular reconfigurable mobile energy storage capacity demand considering multiple support requirements as described in claim 1, characterized in that, Step 1 involves using equations (1)-(4) to construct the configuration scale and resource constraints of regional modular reconfigurable mobile energy storage: (1) (2) (3) (4) In equations (1)-(4), This indicates the number of mobile energy storage vehicles configured. This indicates the number of battery modules configured. This indicates the unit resource occupancy weight of the mobile energy storage vehicle. This indicates the unit resource usage weight of the battery module; This indicates the total resource allocation limit. Z represents the vehicle-to-electricity ratio constraint coefficient, and Z represents a positive integer.

3. The method for assessing regional modular reconfigurable mobile energy storage capacity demand considering multiple support requirements as described in claim 2, characterized in that, Step 2 involves constructing a regional modular reconfigurable mobile energy storage transportation network model using equations (5) to (13): (5) (6) (7) (8) (9) (10) (11) (12) (13) In equations (5) to (13), express Moment Scene Lower branch road The number of battery modules transported upstream. express Moment Scene Lower branch road The number of mobile energy storage vehicles traveling on the road. This represents the speed of any mobile energy storage vehicle under zero traffic flow conditions. express Moment Scene The traffic congestion coefficient below express Moment Scene The speed of any mobile energy storage vehicle. Represents a node To the node Path distance, express Moment Scene The next mobile energy storage vehicle from the node Drive to the node Time, This indicates the assembly time for a single area of ​​modular, reconfigurable mobile energy storage. Indicates time Scene The total transfer time for modular reconfigurable mobile energy storage in a single region. This represents the total number of transition time steps after discretization. express Moment Scene Next stop at node The number of battery modules, express Moment Scene Next stop at node The number of battery modules, express Moment Scene Starting from other nodes, proceed to node The number of battery modules shipped. express Moment Scene Next node Depart, towards the node The number of battery modules shipped. express Moment Scene Next stop at node The number of mobile energy storage vehicles express Moment Scene Next stop at node The number of mobile energy storage vehicles express Moment Scene Next node Depart, head to the node The number of mobile energy storage vehicles express Moment Scene Next node Depart, towards the node The number of mobile energy storage vehicles in operation. express Always on the side road The number of battery modules transported upstream. express Always on the side road The number of mobile energy storage vehicles traveling on the road. Represents a set of nodes. Represents a set of scenes. Indicates a time interval.

4. The method for assessing regional modular reconfigurable mobile energy storage capacity demand considering multiple support requirements as described in claim 3, characterized in that, Step 3 involves constructing a charging and discharging model for regional modular reconfigurable mobile energy storage using equations (14) to (20): (14) (15) (16) (17) (18) (19) (20) In equations (14)-(20), express Moment Scene Next node The total charging power of the battery modules at that location. express Moment Scene Next node The total discharge power of the battery modules at that location. This indicates the rated charging power of a single battery module. This indicates the rated discharge power of a single battery module. express Moment Scene Next node Is the battery module at that location charging? express Moment Scene Next node Is the battery module at that location in a discharged state? Represents positive numbers. This indicates the charging efficiency of a single battery module. This indicates the discharge efficiency of a single battery module. express Moment Scene Next node The total capacity of the battery modules. express Moment Scene Next node The total capacity of the battery modules. This indicates the lower limit of the capacity of a single battery module. The maximum capacity of a single battery module.

5. The method for assessing regional modular reconfigurable mobile energy storage capacity demand considering multiple support requirements as described in claim 4, characterized in that, Step 4 includes: Step 4.1: Construct power flow constraints using equations (21)-(28): (21) (22) (23) (24) (25) (26) (27) (28) In equations (21)-(28), , They represent Moment Scene Lower branch road Branch roads Active power transmitted upstream, , They represent Moment Scene Lower branch road Branch roads Transmitted reactive power , Representing branch roads resistance, reactance , They represent Moment Scene Injection Node Active and reactive power , They represent Moment Scene Next node The active and reactive loads at the location express Moment Scene Next node The photovoltaic power station is contributing its energy. express Moment Scene Next node The amount of light discarded at that location , express Moment Scene Next node Active and reactive load shedding at the location , They represent Moment Scene Next node ,node The square of the voltage at that point, , Representing nodes respectively The safe lower and upper limits of voltage. , They represent Moment Scene Lower branch road Branch roads The square of the current, branch road The upper limit of the square term of the current, Represented by node Let be the set of end nodes corresponding to the branch at the beginning. Represented by node The first node corresponding to the last branch; Step 4.2: Construct the objective function using equations (29)-(34): (29) (30) (31) (32) (33) (34) In equations (29)-(34), This indicates the configuration scale indicator for the first phase. This indicates the operational status evaluation indicators for the second phase. Represents the time-domain equivalent reduction factor. It is the time-domain equivalent scaling factor. It refers to the lifespan of regional modular reconfigurable mobile energy storage devices. This represents the weighted probability of each scenario. This represents the system deviation and loss assessment value. This represents the quantized value of the spatial transition state. The unit evaluation weight representing line loss. The penalty gain coefficient representing the load shedding state. The penalty gain coefficient representing the abandoned light state. This represents the transfer weighting coefficient per unit mileage. express Moment Scene Next node With nodes The number of mobile energy storage vehicles traveling between them, Represents a set of distribution network lines. This represents the set of time periods for a single scheduling cycle. This represents the annualized periodic constant.

6. The method for assessing regional modular reconfigurable mobile energy storage capacity demand considering multiple support requirements as described in claim 5, characterized in that, Step 5 includes: Step 5.1: Use the quadratic terms in equations (35)-(40) , and , Transform into a linear representation: (35) (36) (37) (38) (39) (40) In equations (35)-(40), , They represent Moment Scene Lower branch road Branch roads The square of the current, , They represent Moment Scene node ,node The square of the voltage; Step 5.2: Multiply the quadratic terms in equation (40) using equation (41). After performing second-order cone relaxation, equation (41) is transformed into the standard second-order cone form using equation (42): (41) (42)。 7. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store a program that supports the processor in executing the regional modular reconfigurable mobile energy storage capacity demand assessment method that considers multiple support requirements as described in any one of claims 1-6, wherein the processor is configured to execute the program stored in the memory.

8. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is run by the processor, it performs the steps of the regional modular reconfigurable mobile energy storage capacity demand assessment method that considers multiple support requirements as described in any one of claims 1-6.

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