Dynamic scheduling methods, systems, equipment, and media for mobile energy storage vehicles targeting multi-regional power consumption

Abstract

A method, system, equipment, and medium for dynamic scheduling of mobile energy storage vehicles for multi-regional power consumption are disclosed. The method first constructs a dynamic scheduling optimization model for mobile energy storage vehicles with the objective of minimizing the total overload cost and total transportation cost in each region. Then, the dynamic scheduling optimization model is solved to obtain the optimal scheduling scheme for mobile energy storage vehicles among the regions. Finally, dynamic scheduling is performed based on the optimal scheduling scheme among the regions. This invention not only balances the power supply pressure in multiple regions as much as possible and improves the stability of the power system, but also reduces the complexity of the model, as the optimal scheduling scheme can be obtained by traversing the feasible set.

Description

Technical Field

[0001] This invention belongs to the field of mobile energy storage vehicle dispatching in power grids, specifically relating to a dynamic dispatching method, system, equipment, and medium for mobile energy storage vehicles targeting multi-regional power consumption. Background Technology

[0002] In modern cities, multiple areas often have significant electricity demands, and these demands fluctuate over time. High demand in a particular area puts considerable pressure on its power supply, while low demand leaves the power system underutilized, indirectly reducing energy efficiency. Mobile energy storage vehicles offer a viable solution for flexible power allocation between different areas. Through the strategic deployment of these vehicles, it's possible to draw power from areas with low demand and discharge it during peak demand periods, thus balancing electricity demand and reducing regional power overload.

[0003] However, the current model for the dynamic scheduling problem of multiple mobile energy storage vehicles in multiple regions is complex and has many variables, making it difficult to directly model and solve it using traditional methods. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned problems in the existing technology by providing a method, system, device, and medium for dynamic scheduling of mobile energy storage vehicles for multi-regional power consumption.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] In a first aspect, the present invention proposes a dynamic scheduling method for mobile energy storage vehicles targeting multi-regional electricity consumption, comprising:

[0007] S1. Construct a dynamic scheduling optimization model for mobile energy storage vehicles with the objective of minimizing the total overload cost and total transportation cost in each region;

[0008] S2. Solve the dynamic scheduling optimization model of mobile energy storage vehicles to obtain the optimal scheduling scheme of mobile energy storage vehicles in each region.

[0009] S3. Dynamic scheduling is carried out based on the optimal scheduling scheme of mobile energy storage vehicles in various regions.

[0010] In S1, the objective function of the mobile energy storage vehicle dynamic scheduling optimization model includes:

[0011]

[0012] M V-i,t =V i-t

[0013] V i-t =(P S-i-t ,P E-i-t ,T S-i-t ,T L-i-t E V-i-t B V-i-t )

[0014] In the above formula, m, n, and l represent the number of mobile energy storage vehicles, the number of areas requiring power supply, and the number of time periods, respectively, and C A-j When the electricity supplied to the j-th region exceeds the maximum electricity that can be supplied under normal circumstances, the additional overload cost per kilowatt-hour, E O-j-t C represents the portion of the power supplied by the j-th region during time period t that exceeds the maximum power available under normal circumstances. T The cost of transporting energy for one period of time for each mobile energy storage vehicle, T S-i-t Let M be the transportation state variable of the i-th mobile energy storage vehicle during time period t. V Let M be an m×l dimensional matrix for scheduling mobile energy storage vehicles. V-i,t For M V The element in V i-t Let P be the state vector of the i-th mobile energy storage vehicle at time t. S-i-t Let P be the starting area of ​​the i-th mobile energy storage vehicle during time period t. E-i-t Let T be the area that the i-th mobile energy storage vehicle plans to reach during time period t. L-i-t Let E be the transportation status label for the i-th mobile energy storage vehicle during time period t. V-i-t Let B be the total discharge of the i-th mobile energy storage vehicle during time period t. V-i-t Let t represent the battery charge of the i-th mobile energy storage vehicle after charging and discharging during time period t.

[0015] The constraints of the dynamic scheduling optimization model for mobile energy storage vehicles include:

[0016] E O-j-t =max{E A-j-t -E S-j-t -E A-max-j ,0}

[0017]

[0018] B V-i-t =B V-i-(t-1) -E V-i-t

[0019] -E V-max ≤E V-i-t ≤E V-max

[0020] 0≤B V-i-t ≤BV-max

[0021] In the above formula, E A-j-t E represents the electricity required by the j-th region during time period t. S-j-t E represents the amount of electricity provided by the mobile energy storage vehicle to the j-th area during time period t. A-max-j M represents the maximum amount of electricity that the j-th region can normally provide within a given time period. T-PS-i-(t-1),PE-i-(t-1) Let E be the number of time intervals required for the i-th mobile energy storage vehicle to travel from the departure area to the planned arrival area during time interval t-1. V-max B represents the amount of electricity a mobile energy storage vehicle can charge and discharge at maximum power within a given time period. V-max This represents the maximum battery capacity of the mobile energy storage vehicle.

[0022] The S2 solution solves the dynamic scheduling optimization model for mobile energy storage vehicles by traversing the feasible set.

[0023] Secondly, this invention proposes a dynamic scheduling system for mobile energy storage vehicles targeting multi-regional electricity consumption, including an optimization model construction module, an optimization model solving and scheduling module;

[0024] The optimization model construction module is used to construct a dynamic scheduling optimization model for mobile energy storage vehicles with the objective of minimizing the total overload cost and total transportation cost in each region.

[0025] The optimization model solving and scheduling module is used to solve the dynamic scheduling optimization model of mobile energy storage vehicles, obtain the optimal scheduling scheme of mobile energy storage vehicles in each region, and perform dynamic scheduling based on the optimal scheduling scheme of mobile energy storage vehicles in each region.

[0026] The objective function of the mobile energy storage vehicle dynamic scheduling optimization model includes:

[0027]

[0028] M V-i,t =V i-t

[0029] V i-t =(P S-i-t ,P E-i-t ,T S-i-t ,T L-i-t E V-i-t B V-i-t )

[0030] In the above formula, m, n, and l represent the number of mobile energy storage vehicles, the number of areas requiring power supply, and the number of time periods, respectively, and C A-j When the electricity supplied to the j-th region exceeds the maximum electricity that can be supplied under normal circumstances, the additional overload cost per kilowatt-hour, E O-j-tC represents the portion of the power supplied by the j-th region during time period t that exceeds the maximum power available under normal circumstances. T The cost of transporting energy for one period of time for each mobile energy storage vehicle, T S-i-t Let M be the transportation state variable of the i-th mobile energy storage vehicle during time period t. V Let M be an m×l dimensional matrix for scheduling mobile energy storage vehicles. V-i,t For M V The element in V i-t Let P be the state vector of the i-th mobile energy storage vehicle at time t. S-i-t Let P be the starting area of ​​the i-th mobile energy storage vehicle during time period t. E-i-t Let T be the area that the i-th mobile energy storage vehicle plans to reach during time period t. L-i-t Let E be the transportation status label for the i-th mobile energy storage vehicle during time period t. V-i-t Let B be the total discharge of the i-th mobile energy storage vehicle during time period t. V-i-t Let t represent the battery charge of the i-th mobile energy storage vehicle after charging and discharging during time period t.

[0031] The constraints of the dynamic scheduling optimization model for mobile energy storage vehicles include:

[0032] E O-j-t =max{E A-j-t -E S-j-t -E A-max-j ,0}

[0033]

[0034] B V-i-t =B V-i-(t-1) -E V-i-t

[0035] -E V-max ≤E V-i-t ≤E V-max

[0036] 0≤B V-i-t ≤B V-max

[0037] In the above formula, E A-j-t E represents the electricity required by the j-th region during time period t. S-j-t E represents the amount of electricity provided by the mobile energy storage vehicle to the j-th area during time period t. A-max-j Let j be the maximum amount of electricity that the j-th region can normally provide within a time period. Let E be the number of time intervals required for the i-th mobile energy storage vehicle to travel from the departure area to the planned arrival area during time interval t-1. V-max B represents the amount of electricity a mobile energy storage vehicle can charge and discharge at maximum power within a given time period. V-maxThis represents the maximum battery capacity of the mobile energy storage vehicle.

[0038] The optimization model solving and scheduling module solves the dynamic scheduling optimization model of mobile energy storage vehicles by traversing the feasible set.

[0039] Thirdly, the present invention proposes a dynamic scheduling device for mobile energy storage vehicles for multi-regional power consumption, including a memory and a processor;

[0040] The memory is used to store computer program code and transmit the computer program code to the processor;

[0041] The processor is configured to execute the aforementioned method according to instructions in the computer program code.

[0042] Fourthly, the present invention provides a computer-readable storage medium on which a computer program is stored, and which, when executed by a processor, implements the aforementioned method.

[0043] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0044] This invention proposes a dynamic scheduling method for mobile energy storage vehicles (MEVs) in multi-regional power consumption. First, it constructs a dynamic scheduling optimization model for MEVs with the objective of minimizing the total overload cost and total transportation cost in each region. Then, it solves the dynamic scheduling optimization model to obtain the optimal scheduling scheme for MEVs among different regions. Finally, it performs dynamic scheduling based on this optimal scheduling scheme. On one hand, this method selects key indicators of power consumption regions for optimization modeling, and the resulting dynamic scheduling scheme can balance the power supply pressure in multiple regions as much as possible, improving the stability of the power system. On the other hand, by discretizing continuous-time variables through time-segmentation and constructing a vector reflecting the state of each MEV in each time period, the scheduling of vehicles over time can be represented by a finite-dimensional matrix, significantly reducing the model's complexity. Since the feasible set of the problem under this model is finite, the optimal solution can be obtained by traversing the feasible set. Attached Figure Description

[0045] Figure 1 This is a flowchart of the method described in Example 1.

[0046] Figure 2 This is a structural diagram of the system described in Example 2.

[0047] Figure 3 This is a structural diagram of the device described in Example 3. Detailed Implementation

[0048] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings.

[0049] Example 1:

[0050] Consider several electricity-consuming areas, each with fluctuating electricity demand over time. Multiple mobile energy storage vehicles (MEVs) can travel between these areas to deliver some electricity. To ensure that each area maintains a relatively healthy load across different time periods, when a region experiences high electricity demand at a certain time, MEVs need to discharge there, relieving some of the power supply pressure. Conversely, when a region experiences low electricity demand, MEVs can charge at a certain power level without overloading it, thus ensuring that the load across multiple areas remains relatively healthy throughout the time period. For this scenario—the dynamic scheduling problem of MEVs in multiple electricity-consuming areas—a reasonable scheduling method is designed, such as… Figure 1 As shown, the specific implementation steps are as follows:

[0051] 2. Based on the actual situation of the power consumption area and the mobile energy storage vehicles, determine the number of mobile energy storage vehicles m and the number of areas requiring power supply n. Consider the dynamic scheduling problem of mobile energy storage vehicles over a relatively long period T. Divide the relatively long period T into l equal time periods and introduce an n×n dimensional time matrix M required for the mobile energy storage vehicles to travel between the various areas. T Its element M T-i,j This represents the number of time intervals required to travel from the i-th region to the j-th region. It is easy to see that...

[0052] For the i-th mobile energy storage vehicle, a vector V is introduced to reflect its state at time t. i-t =(P S-i-t ,P E-i-t ,T S-i-t ,T L-i-t E V-i-t B V-i-t ), where P S-i-t Let P be the starting area of ​​the i-th mobile energy storage vehicle during time period t. E-i-t Let P be the area that the i-th mobile energy storage vehicle plans to reach during time period t. S-i-t =P E-i-t This indicates that the i-th mobile energy storage vehicle remains in that area throughout time period t, thus allowing it to charge and discharge. Conversely, if P... S-i-t ≠P E-i-t This indicates that the vehicle is en route from the departure area to the planned arrival area during that time period and cannot be charged or discharged; T S-i-t Let P be the transportation state variable of the i-th mobile energy storage vehicle during time period t. S-i-t ≠P E-i-t If the i-th mobile energy storage vehicle is in a transportation state during time period t, then TS-i-t =1, conversely, if P S-i-t =P E-i-t Then T S-i-t =0;T L-i-t Let P be the transportation status label of the i-th mobile energy storage vehicle during time period t. S-i-t ≠P E-i-t Then T L-i-t ≠0 indicates that this time period is the Tth time segment during the journey from the departure area to the planned arrival area. L-i-t A period of time, namely Let T be the number of time intervals required for the i-th mobile energy storage vehicle to travel from the departure area to the planned destination area during time interval t; otherwise, T is the number of time intervals required. L-i-t =0. For example, if an hour is divided into time periods, and it takes 2 hours to travel from area A to area B, and vehicle i is charging in area A during the first hour, then P during the first hour... S-i-1 and P E-i-1 Both are in area A; if a vehicle travels from area A to area B in the 2nd and 3rd hours, then P for those two hours... S-i-t Both are in region A, P E-i-t Both are in region B because the starting point and destination of this journey (from region A to region B) are the same; the only difference between their state vectors is the transport state label T. L-i-t 1 and 2 represent the first and second time periods of this trip (from area A to area B), respectively. E V-i-t Let E be the total discharge amount of the i-th mobile energy storage vehicle during time period t, and E be the discharge amount during time period t. V-i-t When charging, E is positive. V-i-t When E is negative, it means that charging and discharging are not possible or necessary. V-i-t B is zero; V-i-t Let V be the battery charge of the i-th mobile energy storage vehicle after charging and discharging in time period t, and let V be the initial state vector of the mobile energy storage vehicle. i-0 =(P S-i-0 ,P E-i-0 ,T S-i-0 ,T L-i-0 E V-i-0 B V-i-0 ), then B V-i-t =B V-i-(t-1) -E V-i-t If P S-i-(t-1) =P E-i-(t-1) Then P S-i-t =P E-i-(t-1) If P S-i-(t-1) ≠P E-i-(t-1) ,and Then P S-i-t =P S-i-(t-1) P E-i-t =P E-i-(t-1) TL-i-t =T L-i-(t-1) +1, if P S-i-(t-1) ≠P E-i-(t-1) ,and Then P S-i-t =P E-i-(t-1) Therefore, the update rule for the status of the mobile energy storage vehicle is as follows:

[0053]

[0054] B V-i-t =B V-i-(t-1) -E V-i-t ;

[0055] The amount of electricity E that a mobile energy storage vehicle can charge and discharge at maximum power within a given time period. V-max , has -E V-max ≤E V-i-t ≤E V-max The maximum battery capacity B introduced for mobile energy storage vehicles V-max Then 0 ≤ B V-i-t ≤B V-max .

[0056] For the j-th region, we introduce the maximum electricity E that can be provided under normal circumstances within a time period. A-max-j If the actual power supply provided by the area exceeds the maximum power supply under normal circumstances, the additional overload cost per kilowatt-hour will be C. A-j Considering that the electricity demand in each region varies at different times, we introduce the electricity demand E required by the j-th region during time period t. A-j-t And the amount of electricity E supplied to the region by the mobile energy storage vehicle during that period. S-j-t Then we have:

[0057]

[0058] In reality, the area itself provides more power than the maximum power it can normally provide. O-j-t have:

[0059] E O-j-t =max{E A-j-t -E S-j-t -E A-max-j ,0}

[0060] Introducing an m×l dimensional matrix M for mobile energy storage vehicle scheduling V Its element M V-i,t =V i-t That is, matrix M V Each element in the array is a 6-dimensional vector.

[0061] 2. Based on the parameters introduced above, the following dynamic scheduling optimization model for mobile energy storage vehicles is constructed:

[0062]

[0063] M V-i,t =V i-t

[0064] V i-t =(P S-i-t ,P E-i-t ,T S-i-t ,T L-i-t E V-i-t B V-i-t )

[0065] E O-j-t =max{E A-j-t -E S-j-t -E A-max-j ,0}

[0066]

[0067] -E V-max ≤E V-i-t ≤E V-max

[0068] 0≤B V-i-t ≤B V-max

[0069] B V-i-t =B V-i-(t-1) -E V-i-t .

[0070] 3. Solve the dynamic scheduling optimization model of mobile energy storage vehicles by traversing the feasible set to obtain the optimal scheduling scheme of mobile energy storage vehicles in each region.

[0071] 4. Dynamic scheduling is carried out based on the optimal scheduling scheme of mobile energy storage vehicles in various regions.

[0072] Example 2:

[0073] A dynamic dispatching system for mobile energy storage vehicles targeting multi-regional power consumption, such as Figure 2 As shown, it includes an optimization model construction module, an optimization model solving and scheduling module.

[0074] The optimization model construction module is used to construct the following dynamic scheduling optimization model for mobile energy storage vehicles, with the objective of minimizing the total overload cost and total transportation cost in each region:

[0075]

[0076] M V-i,t =Vi-t

[0077] V i-t =(P S-i-t ,P E-i-t ,T S-i-t ,T L-i-t E V-i-t B V-i-t )

[0078] E O-j-t =max{E A-j-t -E S-j-t -E A-max-j ,0}

[0079]

[0080] -E V-max ≤E V-i-t ≤E V-max

[0081] 0≤B V-i-t ≤B V-max

[0082] B V-i-t =B V-i-(t-1) -E V-i-t

[0083] In the above formula, m, n, and l represent the number of mobile energy storage vehicles, the number of areas requiring power supply, and the number of time periods, respectively, and C A-j When the electricity supplied to the j-th region exceeds the maximum electricity that can be supplied under normal circumstances, the additional overload cost per kilowatt-hour, E O-j-t C represents the portion of the power supplied by the j-th region during time period t that exceeds the maximum power available under normal circumstances. T The cost of transporting energy for one period of time for each mobile energy storage vehicle, T S-i-t Let M be the transportation state variable of the i-th mobile energy storage vehicle during time period t. V Let M be an m×l dimensional matrix for scheduling mobile energy storage vehicles. V-i,t For M V The element in V i-t Let P be the state vector of the i-th mobile energy storage vehicle at time t. S-i-t Let P be the starting area of ​​the i-th mobile energy storage vehicle during time period t. E-i-t Let T be the area that the i-th mobile energy storage vehicle plans to reach during time period t. L-i-t Let E be the transportation status label for the i-th mobile energy storage vehicle during time period t. V-i-t Let B be the total discharge of the i-th mobile energy storage vehicle during time period t. V-i-t E represents the battery charge of the i-th mobile energy storage vehicle after charging and discharging during time period t. A-j-tE represents the electricity required by the j-th region during time period t. S-j-t E represents the amount of electricity provided by the mobile energy storage vehicle to the j-th area during time period t. A-max-j Let j be the maximum amount of electricity that the j-th region can normally provide within a time period. Let E be the number of time intervals required for the i-th mobile energy storage vehicle to travel from the departure area to the planned arrival area during time interval t-1. V-max B represents the amount of electricity a mobile energy storage vehicle can charge and discharge at maximum power within a given time period. V-max This represents the maximum battery capacity of the mobile energy storage vehicle.

[0084] The optimization model solving and scheduling module is used to solve the dynamic scheduling optimization model of mobile energy storage vehicles by traversing the feasible set, obtain the optimal scheduling scheme of mobile energy storage vehicles in each region, and perform dynamic scheduling based on the optimal scheduling scheme of mobile energy storage vehicles in each region.

[0085] Example 3:

[0086] A dynamic dispatching device for mobile energy storage vehicles targeting multi-regional power consumption, such as Figure 3 As shown, it includes a memory and a processor; the memory is used to store computer program code and transfer the computer program code to the processor;

[0087] The processor is configured to execute the method described in Example 1 according to instructions in the computer program code.

[0088] Example 4:

[0089] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in Embodiment 1.

Claims

1. A dynamic scheduling method for mobile energy storage vehicles targeting multi-regional power consumption, characterized in that, The method includes: S1. Construct a dynamic scheduling optimization model for mobile energy storage vehicles with the objective of minimizing the total overload cost and total transportation cost in each region. The objective function of this model includes: M V-i,t =V i-t V i-t =(P S-i-t ,P E-i-t ,T S-i-t ,T L-i-t ,E V-i-t ,B V-i-t ) In the above formula, m, n, and l represent the number of mobile energy storage vehicles, the number of areas requiring power supply, and the number of time periods, respectively, and C A-j When the electricity supplied to the j-th region exceeds the maximum electricity that can be supplied under normal circumstances, the additional overload cost per kilowatt-hour, E O-j-t C represents the portion of the power supplied by the j-th region during time period t that exceeds the maximum power available under normal circumstances. T The cost of transporting energy for one period of time for each mobile energy storage vehicle, T S-i-t Let M be the transportation state variable of the i-th mobile energy storage vehicle during time period t. V Let M be an m×l dimensional matrix for scheduling mobile energy storage vehicles. V-i,t For M V The element in V i-t Let P be the state vector of the i-th mobile energy storage vehicle at time t. S-i-t Let P be the starting area of ​​the i-th mobile energy storage vehicle during time period t. E-i-t Let T be the area that the i-th mobile energy storage vehicle plans to reach during time period t. L-i-t Let E be the transportation status label for the i-th mobile energy storage vehicle during time period t. V-i-t Let B be the total discharge of the i-th mobile energy storage vehicle during time period t. V-i-t The battery charge of the i-th mobile energy storage vehicle after charging and discharging during time period t; The constraints include: AND O-j-t =max{E A-j-t -AND S-j-t -AND A-max-j ,0} B V-i-t =B V-i-(t-1) -E V-i-t -AND V-max ≤E V-i-t ≤E V-max 0≤B V-i-t ≤B V-max In the above formula, E A-j-t E represents the electricity required by the j-th region during time period t. S-j-t E represents the amount of electricity provided by the mobile energy storage vehicle to the j-th area during time period t. A-max-j Let j be the maximum amount of electricity that the j-th region can normally provide within a time period. Let E be the number of time intervals required for the i-th mobile energy storage vehicle to travel from the departure area to the planned arrival area during time interval t-1. V-max B represents the amount of electricity a mobile energy storage vehicle can charge and discharge at maximum power within a given time period. V-max This represents the maximum battery capacity of the mobile energy storage vehicle. S2. Solve the dynamic scheduling optimization model of mobile energy storage vehicles to obtain the optimal scheduling scheme of mobile energy storage vehicles in each region. S3. Dynamic scheduling is carried out based on the optimal scheduling scheme of mobile energy storage vehicles in various regions.

2. The method for dynamic scheduling of mobile energy storage vehicles for multi-regional power consumption according to claim 1, characterized in that, The S2 solution solves the dynamic scheduling optimization model for mobile energy storage vehicles by traversing the feasible set.

3. A dynamic dispatching system for mobile energy storage vehicles targeting multi-regional power consumption, characterized in that, The system includes an optimization model construction module and an optimization model solving and scheduling module; The optimization model construction module is used to construct a dynamic scheduling optimization model for mobile energy storage vehicles with the objective of minimizing the total overload cost and total transportation cost in each region. The objective function of this model includes: M V-i,t =V i-t V i-t =(P S-i-t ,P E-i-t ,T S-i-t ,T L-i-t ,E V-i-t ,B V-i-t ) In the above formula, m, n, and l represent the number of mobile energy storage vehicles, the number of areas requiring power supply, and the number of time periods, respectively, and C A-j When the electricity supplied to the j-th region exceeds the maximum electricity that can be supplied under normal circumstances, the additional overload cost per kilowatt-hour, E O-j-t C represents the portion of the power supplied by the j-th region during time period t that exceeds the maximum power available under normal circumstances. T The cost of transporting energy for one period of time for each mobile energy storage vehicle, T S-i-t Let M be the transportation state variable of the i-th mobile energy storage vehicle during time period t. V Let M be an m×l dimensional matrix for scheduling mobile energy storage vehicles. V-i,t For M V The element in V i-t Let P be the state vector of the i-th mobile energy storage vehicle at time t. S-i-t Let P be the starting area of ​​the i-th mobile energy storage vehicle during time period t. E-i-t Let T be the area that the i-th mobile energy storage vehicle plans to reach during time period t. L-i-t Let E be the transportation status label for the i-th mobile energy storage vehicle during time period t. V-i-t Let B be the total discharge of the i-th mobile energy storage vehicle during time period t. V-i-t The battery charge of the i-th mobile energy storage vehicle after charging and discharging during time period t; The constraints include: AND O-j-t =max{E A-j-t -AND S-j-t -AND A-max-j ,0} B V-i-t =B V-i-(t-1) -E V-i-t -AND V-max ≤E V-i-t ≤E V-max 0≤B V-i-t ≤E V-max In the above formula, E A-j-t E represents the electricity required by the j-th region during time period t. S-j-t E represents the amount of electricity provided by the mobile energy storage vehicle to the j-th area during time period t. A-max-j Let j be the maximum amount of electricity that the j-th region can normally provide within a time period. Let E be the number of time intervals required for the i-th mobile energy storage vehicle to travel from the departure area to the planned arrival area during time interval t-1. V-max B represents the amount of electricity a mobile energy storage vehicle can charge and discharge at maximum power within a given time period. V-max This represents the maximum battery capacity of the mobile energy storage vehicle. The optimization model solving and scheduling module is used to solve the dynamic scheduling optimization model of mobile energy storage vehicles, obtain the optimal scheduling scheme of mobile energy storage vehicles in each region, and perform dynamic scheduling based on the optimal scheduling scheme of mobile energy storage vehicles in each region.

4. A dynamic dispatching system for mobile energy storage vehicles targeting multi-regional power consumption, as described in claim 3, is characterized in that... The optimization model solving and scheduling module solves the dynamic scheduling optimization model of mobile energy storage vehicles by traversing the feasible set.

5. A dynamic dispatching device for mobile energy storage vehicles targeting multi-regional power consumption, characterized in that, The device includes a memory and a processor; The memory is used to store computer program code and transmit the computer program code to the processor; The processor is configured to execute the method as described in any one of claims 1-2 according to instructions in the computer program code.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1-2.

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