Energy storage vehicle dispatching system based on model optimization

Abstract

The invention provides an energy storage vehicle scheduling system based on model optimization. The energy storage vehicle scheduling system comprises an acquisition layer, an analysis layer and a scheduling layer, the acquisition layer is used for acquiring and storing related parameters of a power distribution network system; the analysis layer establishes an energy storage vehicle scheduling optimization strategy based on conditional constraints based on the data acquired by the acquisition layer; and the scheduling layer is used for scheduling the energy storage vehicle according to the strategy provided by the analysis layer so as to optimize the load of the transformer in the transformer area. A big data theory is combined with energy storage vehicle scheduling, a data interaction relationship between an energy storage vehicle and a power grid is analyzed aiming at requirements of energy storage vehicle scheduling, data such as a heavy-load distribution transformer, a low-voltage user and photovoltaic output are fused and integrated to a unified platform, and an optimal energy storage vehicle scheduling strategy is determined through constraint conditions, a target function and decision variables. And tracking, monitoring and control of the energy storage vehicle are efficiently realized, so that the operation quality and the operation level of the energy storage vehicle are improved.

Description

Technical Field

[0001] The present invention relates to the technical fields of mobile energy storage vehicles, power distribution network emergency, and the like, and in particular to an energy storage vehicle dispatching system based on model optimization. Background Art

[0002] Mobile energy storage vehicles can provide good support for the normal operation of distribution networks due to their strong flexibility and fast response speed. In the event of a distribution network failure, they can serve as an emergency power supply to cover the faulty area, provide uninterrupted power supply to power-lost users, improve the load power supply recovery capability of the distribution network under emergency conditions, and optimize the elastic power supply of the distribution network. Resolving unstable factors in the operation of the power grid through energy storage vehicles can reduce the negative impact of loads on the power grid and reduce operating costs, while meeting the uncertainty of loads in the power grid and reducing network transmission losses.

[0003] However, there is currently a lack of reasonable emergency dispatch strategies for mobile energy storage vehicles. Dispatch decisions are mainly made manually, relying on the experience and judgment of technical personnel, and are unable to achieve the goal of fast, accurate, and efficient deployment. Summary of the invention

[0004] In view of the gaps and shortcomings in the prior art, the present invention considers combining big data theory with energy storage vehicle scheduling. According to the needs of energy storage vehicle scheduling, the data interaction relationship between the energy storage vehicle and the power grid is analyzed, and the data of heavy-load distribution transformers, low-voltage users, photovoltaic output, etc. are integrated into a unified platform. The optimal energy storage vehicle scheduling strategy is determined through constraints, objective functions, and decision variables, and the energy storage vehicle is tracked, monitored, and controlled efficiently, thereby improving the operation quality and level of the energy storage vehicle.

[0005] The present invention specifically adopts the following technical solutions:

[0006] A storage vehicle dispatching system based on model optimization, comprising: a collection layer, an analysis layer and a dispatching layer;

[0007] The acquisition layer is used to collect and save relevant parameters of the distribution network system;

[0008] The analysis layer establishes an energy storage vehicle scheduling optimization strategy based on conditional constraints based on the data obtained by the collection layer;

[0009] The dispatching layer dispatches the energy storage vehicles according to the strategy provided by the analysis layer to optimize the load of the transformer in the substation.

[0010] Furthermore, the data collected by the collection layer include: data on heavy-load distribution transformers, low-voltage users, photovoltaic output, power supply information and important loads; among which heavy-load distribution transformers refer to whether the lines and equipment are overloaded or no-loaded; low-voltage users refer to situations where the user voltage is lower than the standard voltage; photovoltaic output refers to the photovoltaic power generation in the photovoltaic area; power supply information refers to information on power supply due to line or distribution transformer failures or maintenance; important loads refer to the power supply situation of important users.

[0011] Furthermore, the collection layer collects distribution network operating parameters and transmits them back to the dispatching and analysis system master station.

[0012] Furthermore, the analysis layer is used to complete the scheduling plan preparation of energy storage vehicles, energy storage vehicle status process control and management, battery data statistical analysis and external system information exchange; by integrating the battery capacity, quantity and constraints of the energy storage vehicle, the data of each relevant system is integrated to form an energy storage vehicle management, command and monitoring system, so as to realize the management of all operating energy storage vehicles in the warehouse.

[0013] Furthermore, the analysis layer obtains an energy storage vehicle scheduling optimization strategy based on conditional constraints through a model constructed by decision variables, constraints and objective functions; wherein, the decision variables refer to the battery capacity and quantity of the energy storage vehicle, the battery capacity of each energy storage vehicle is fixed, and the number of energy storage vehicles is fixed according to the project procurement quantity; the constraints include power constraints, power constraints, time constraints, vehicle constraints and event processing priorities; the event processing priority mainly refers to scheduling energy storage vehicles according to the established event priority, and the order of vehicle dispatch is affected by the heavy-load distribution transformer, low-voltage user, important load and power protection information according to the set weight, and the original data including the transformer load data and event processing priority are used as constraint analysis objects, and the data is transmitted into the objective function; the objective function adopts the transformer load variance in the substation area.

[0014] Furthermore, mobile energy storage vehicles are dispatched to bear part of the load of the substation transformer to achieve peak shaving and valley filling. The mobile energy storage vehicles are discharged during peak load periods and charged during low load periods to reduce load peaks or fluctuations. The strategy is adjusted according to the priority of event processing so that the mobile energy storage vehicles can charge, discharge or remain idle in different time periods to optimize the load of the substation transformer.

[0015] Furthermore, at the analysis layer, based on the grid parameters, the energy storage vehicle scheduling optimization based on conditional constraints is established, and the objective function is set as follows:

[0016] The 24 hours of each day are divided into 96 time periods of 15 minutes each, and the transformer load variance S is used to calculate the load variance of the transformer area. 2As the objective function of the load optimization strategy, it describes whether the actual load curve of the transformer in the substation after connecting to the mobile energy storage vehicle is stable; since the actual load after optimization, the load of the transformer in the substation and the discharge of the mobile energy storage vehicle meet the following conditions within a certain period of time:

[0017]

[0018] The objective function is determined as:

[0019]

[0020] Where:

[0021] Δt is the time interval of each period;

[0022] P i is the load of the transformer in the substation in each period;

[0023] E i The amount of electricity that needs to be released by the mobile energy storage vehicle in each period;

[0024] is the actual load after optimization;

[0025] Considering the constraints generated by the physical constraints related to the mobile energy storage vehicle, the time priority processing level, and the substation transformer, the constraints are as follows:

[0026] 1. Power Constraints

[0027] (1) During discharge, the rated capacity of the mobile energy storage vehicle needs to be greater than the total capacity required by the transformer in the area:

[0028] E o >E n (3)

[0029] Where:

[0030] E o is the rated capacity of the mobile energy storage vehicle;

[0031] E n The total power required by the transformer in the substation area;

[0032] (2) The current total power of the mobile energy storage vehicle needs to be greater than or equal to the total power required by the transformer in the substation area, the power required by the mobile energy storage vehicle to reach the target point, and the power required by the mobile energy storage vehicle for the return trip:

[0033] E z >E n +E g +E b (4)

[0034] Where:

[0035] E z The current power of the mobile energy storage vehicle;

[0036] E g The amount of electricity required for the mobile energy storage vehicle to reach the target location;

[0037] E b The amount of electricity required for the mobile energy storage vehicle to return;

[0038] After further solving and deformation, we get:

[0039]

[0040] 2. Power Constraints

[0041] (1) To protect the safe discharge of the mobile energy storage vehicle, the discharge power of the mobile energy storage vehicle in each period shall not exceed the maximum discharge power of the mobile energy storage vehicle:

[0042]

[0043] Where:

[0044] is the maximum discharge power of the mobile energy storage vehicle;

[0045] is the discharge power of the mobile energy storage vehicle in each period;

[0046] (2) The mobile energy storage vehicle performs constant power discharge in each period;

[0047]

[0048] 3. Time constraints

[0049] (1) Discharge start time constraints of mobile energy storage vehicles:

[0050] 1≤T start ≤96 (8)

[0051] Where:

[0052] T start The discharge start period is:

[0053] 1≤T stop ≤96 (9)

[0054] Where:

[0055] T stop The discharge end period;

[0056] (3) Constraints on the relationship between the start and end time periods of discharge of mobile energy storage vehicles;

[0057] Tstop >T start (10)

[0058] 4. Vehicle restraints

[0059] When one mobile energy storage vehicle cannot meet the load demand of the transformer in the substation area, two mobile energy storage vehicles are dispatched for discharge;

[0060]

[0061] Where:

[0062] Num is the number of dispatched mobile energy storage vehicles;

[0063] P max The maximum load that the transformer in the substation can bear;

[0064] 5. Event processing priority constraints

[0065] If task T i is the highest priority, all resources can be scheduled, if T i If it is not the highest priority, the highest priority resource cannot be scheduled; the relationship between task priority and resource allocation is:

[0066]

[0067] Where:

[0068] T qosi is the business priority of the ith item, which is divided into 5 levels from 1 to 5 according to the importance, with 1 being the highest priority;

[0069] R θj is the jth resource priority;

[0070] R ωij Execute the task for the jth resource.

[0071] Furthermore, the dispatching layer dispatches the energy storage vehicle according to the instructions issued by the analysis layer, including garage parking, photovoltaic energy storage, and on-site power generation. Garage parking means that the energy storage vehicle does not need to go out to store energy or generate electricity, but is parked in the garage waiting for dispatch instructions. Photovoltaic energy storage means that the energy storage vehicle stores energy when the photovoltaic output exceeds the grid absorption line and the load is low. On-site power generation refers to power generation at the location specified by the dispatcher.

[0072] Furthermore, the dispatching layer first evaluates the actual status of the dispatched energy storage vehicle. The mobile energy storage vehicle is divided into large and small energy storage vehicles according to the capacity and rated power, and is divided into four states of discharge, charge, idle and fault according to the real-time status of the vehicle. According to the discharge amount, discharge power and status of the mobile energy storage vehicle required by the substation transformer, the appropriate mobile energy storage vehicle is selected for discharge; the dispatching layer dispatches the mobile energy storage vehicle to optimize the load of the substation transformer, so that the mobile energy storage vehicle discharges during the peak load period of the substation transformer and charges during the low load period of the substation transformer, thereby reducing the fluctuation of the substation transformer load.

[0073] And, a method for dispatching energy storage vehicles based on model optimization:

[0074] Collect and save relevant parameters of the distribution network system; and based on the collected data, establish a storage vehicle scheduling optimization strategy based on conditional constraints; use it to schedule the storage vehicle to optimize the load of the transformer in the substation.

[0075] Compared with the prior art, the present invention and its preferred solution combine big data theory with energy storage vehicle scheduling. Aiming at the needs of energy storage vehicle scheduling, the data interaction relationship between energy storage vehicles and power grids is analyzed, and data such as heavy-load distribution transformers, low-voltage users, and photovoltaic output are integrated into a unified platform. The optimal energy storage vehicle scheduling strategy is determined through constraints, objective functions, and decision variables, and the energy storage vehicle is tracked, monitored, and controlled efficiently, thereby improving the operation quality and level of the energy storage vehicle.

[0076] On the one hand, the present invention uses an analysis model of data such as overloaded distribution transformers, low-voltage users, photovoltaic output, power protection information, and important loads. After model optimization, the energy storage vehicle can be quickly dispatched to the emergency position, significantly reducing the impact of abnormalities such as overloaded distribution transformers and low voltage of users on the operation level of the power grid, improving the reliability of the power grid and ensuring stable operation of the power grid; on the other hand, the energy storage vehicle is used to solve unstable factors in the operation of the power grid, which can reduce the negative impact of the load on the power grid and reduce operating costs, while meeting the uncertainty of the load in the power grid and reducing network transmission losses.

[0077] Its main advantages include:

[0078] 1. Improved work efficiency: After model optimization, the system can quickly dispatch energy storage vehicles to emergency locations, significantly reducing the impact of abnormalities such as overloaded distribution transformers and low voltage for users on the operation of the power grid, improving the reliability of the power grid and ensuring stable operation of the power grid.

[0079] 2. Security: The system can analyze data, formulate emergency response strategies, and ensure stable operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0081] Figure 1 The overall architecture diagram of the energy storage vehicle dispatching system based on model optimization in an embodiment of the present invention. DETAILED DESCRIPTION

[0082] Hereinafter, the specific embodiments of the present application will be described in detail with reference to the accompanying drawings. According to these detailed descriptions, those skilled in the art can clearly understand the present application and can implement the present application. Without violating the principles of the present application, the features in the various embodiments can be combined to obtain new implementations, or certain features in certain embodiments can be replaced to obtain other preferred implementations.

[0083] In order to make the features and advantages of this patent more obvious and easy to understand, the following embodiments are specifically described in detail with the accompanying drawings as follows:

[0084] The embodiment of the present invention provides an energy storage vehicle scheduling system based on model optimization. The design idea of ​​the system is as follows: combining big data theory with energy storage vehicle scheduling, analyzing the data interaction relationship between the energy storage vehicle and the power grid according to the needs of energy storage vehicle scheduling, integrating the data of heavy-load distribution transformers, low-voltage users, photovoltaic output, etc. into a unified platform, determining the best energy storage vehicle scheduling strategy through constraints, objective functions, and decision variables, and efficiently realizing the tracking, monitoring, and control of energy storage vehicles, thereby improving the operation quality and level of energy storage vehicles.

[0085] like Figure 1 As shown, the overall system architecture designed by the present invention is divided into three layers: a collection layer, an analysis layer, and a scheduling layer.

[0086] Among them, the collection layer is used to collect and save data such as overloaded distribution transformers, low-voltage users, photovoltaic output, power supply information, important loads, etc., and relies on collection 2.0, FPMS3.0 and other systems to transmit various operating parameters back to the dispatching analysis system main station. Overloaded distribution transformers refer to whether the line and equipment are overloaded or no-loaded. Low-voltage users refer to users whose voltage is lower than the standard voltage. Photovoltaic output refers to the photovoltaic power generation in the photovoltaic area. Power supply information refers to power supply for line, distribution transformer failure or maintenance. Important loads refer to power supply for important users.

[0087] The analysis layer is the "nerve center" of the energy storage vehicle operation. It is responsible for formulating and implementing the dispatch plan of the energy storage vehicle, ensuring close cooperation and mutual coordination between the energy storage vehicles to jointly achieve the goal of orderly vehicle dispatching, so as to ensure the stability and safe operation of the power grid. The analysis layer mainly completes the management, command and monitoring of the dispatch plan preparation, energy storage vehicle status process control and management, battery data statistical analysis and external system information exchange. By integrating the parameters such as the battery capacity, quantity and constraints of the energy storage vehicle into the platform, the data of each related system is highly integrated to form a complete new energy storage vehicle management, command and monitoring system, so as to realize the monitoring, control, coordination, command, dispatch and management of all energy storage vehicles in the warehouse, and ensure the orderly operation of the energy storage vehicle. The analysis layer involves decision variables, constraints and objective functions. Among them, the decision variables refer to the battery capacity and quantity of the energy storage vehicle. The battery capacity of each energy storage vehicle is fixed, and the number of energy storage vehicles is fixed according to the project procurement quantity. Constraints refer to electricity constraints, power constraints, time constraints, vehicle constraints and event processing priorities. Event processing priorities mainly refer to dispatching energy storage vehicles according to the established event priority, and the order of vehicle dispatch is affected by the set weights of heavy-load distribution transformers, low-voltage users, important loads, and power supply information. The original data such as transformer load data and event processing priority are used as constraint analysis objects, and the data is transmitted into the objective function. The objective function uses the transformer load variance in the substation area.

[0088] Substation transformers may have load peaks or load fluctuations. In order to make the load of substation transformers more stable, mobile energy storage vehicles need to be dispatched to bear part of the load of substation transformers to achieve peak shaving and valley filling. Mobile energy storage vehicles are discharged during peak load periods and charged during low load periods to reduce load peaks or fluctuations. Energy storage vehicles are used to protect power for important loads, important periods, plans and faulty substations. Therefore, 24 hours a day is divided into 96 time periods of 15 minutes each. The strategy is adjusted according to the priority of event processing, so that mobile energy storage vehicles can charge, discharge or do nothing in each time period to optimize the load of substation transformers.

[0089] At the analysis level, based on the grid parameters, the energy storage vehicle dispatch optimization based on conditional constraints is established, and the objective function is set:

[0090] The transformer load variance S is used 2 As the objective function of the load optimization strategy, it describes whether the actual load curve of the rear transformer in the back-end after connecting to the mobile energy storage vehicle is stable. Since the actual load after optimization, the load of the transformer in the substation and the discharge of the mobile energy storage vehicle meet the following conditions within a certain period of time:

[0091]

[0092] The objective function is determined as:

[0093]

[0094] Where:

[0095] Δt is the time interval of each period.

[0096] P i is the load of the transformer in the substation in each time period.

[0097] E i The amount of electricity that needs to be released to move the energy storage vehicle in each period.

[0098] is the actual load after optimization.

[0099] Only the constraints generated by the physical constraints related to the mobile energy storage vehicle, the time priority processing level, and the transformer in the substation are considered. The constraints are as follows:

[0100] 1. Power Constraints

[0101] (1) During discharge, the rated capacity of the mobile energy storage vehicle needs to be greater than the total capacity required by the substation transformer.

[0102] E o >E n (3)

[0103] Where:

[0104] E o is the rated capacity of the mobile energy storage vehicle.

[0105] E n It is the total power required by the transformer in the substation.

[0106] (2) The total power of the mobile energy storage vehicle must be greater than or equal to the total power required by the substation transformer, the power required by the mobile energy storage vehicle to reach the target point, and the power required by the mobile energy storage vehicle for the return trip.

[0107] E z >E n +E g +E b (4)

[0108] Where:

[0109] E z is the current power of the mobile energy storage vehicle.

[0110] E g The amount of electricity required for the mobile energy storage vehicle to reach the target location.

[0111] E b The amount of electricity required for the mobile energy storage vehicle to return.

[0112] After solving and transforming the constraint condition, we can get:

[0113]

[0114] 2. Power Constraints

[0115] (1) To protect the safe discharge of the mobile energy storage vehicle, the discharge power of the mobile energy storage vehicle in each time period shall not exceed the maximum discharge power of the mobile energy storage vehicle.

[0116]

[0117] Where:

[0118] is the maximum discharge power of the mobile energy storage vehicle.

[0119] is the discharge power of the mobile energy storage vehicle in each period.

[0120] (2) The mobile energy storage vehicle performs constant power discharge in each period.

[0121]

[0122] 3. Time constraints

[0123] (1) Discharge start time constraints of mobile energy storage vehicles.

[0124] 1≤T star t≤96 (8)

[0125] Where:

[0126] T start Discharge start time

[0127] 1≤T stop ≤96 (9)

[0128] Where:

[0129] T stop The discharge end period.

[0130] (3) Constraints on the relationship between the start and end time periods of discharge of mobile energy storage vehicles.

[0131] T stop >T start (10)

[0132] 4. Vehicle restraints

[0133] When one mobile energy storage vehicle cannot meet the load demand of the transformer in the substation, consider dispatching two mobile energy storage vehicles for discharge.

[0134]

[0135] Where:

[0136] Num is the number of mobile energy storage vehicles dispatched.

[0137] P max It is the maximum load that the transformer in the substation can withstand.

[0138] 5. Event processing priority constraints

[0139] If task T i is the highest priority, all resources can be scheduled, if T i If it is not the highest priority, the highest priority resource cannot be scheduled. The relationship between task priority and resource allocation is:

[0140]

[0141] Where:

[0142] T qosi is the business priority of the ith item, which is divided into 5 levels from 1 to 5 according to the importance, with 1 being the highest priority.

[0143] R θj is the jth resource priority.

[0144] R ωij Execute the task for the jth resource.

[0145] The dispatching layer first evaluates the actual status of the dispatching energy storage vehicle. Mobile energy storage vehicles can be divided into large and small energy storage vehicles according to their capacity and rated power. According to the real-time status of the vehicle, they can be divided into four states: discharge, charge, idle and fault. According to the discharge amount, discharge power and status of the mobile energy storage vehicle required by the substation transformer, the appropriate mobile energy storage vehicle is selected for discharge. The dispatching layer dispatches mobile energy storage vehicles to optimize the load of the substation transformer, allowing the mobile energy storage vehicle to discharge during the peak load period of the substation transformer and charge during the low load period of the substation transformer, so as to reduce the fluctuation of the substation transformer load and make the substation transformer load more stable. Idle means that the energy storage vehicle does not need to go out to store energy or generate electricity, and is parked in the garage waiting for the dispatch instruction. Charging mainly refers to the energy storage vehicle reaching the photovoltaic output exceeding the grid absorption line and the load valley time period for energy storage. Discharging mainly refers to power generation at the location designated by the dispatcher, including heavy-load distribution transformers, power protection lines, low-voltage lines, etc. Faults mainly refer to the fault repair status of the energy storage vehicle.

[0146] The dispatching layer dispatches energy storage vehicles according to the instructions issued by the analysis layer, including garage parking, photovoltaic energy storage, and on-site power generation. Garage parking means that the energy storage vehicle does not need to go out to store energy or generate electricity, but is parked in the garage waiting for dispatch instructions. Photovoltaic energy storage mainly refers to energy storage vehicles reaching the time when the photovoltaic output exceeds the grid consumption line and the load is low. On-site power generation mainly refers to power generation at the location designated by the dispatcher, including heavy-load distribution transformers, power protection lines, low-voltage lines, etc.

[0147] The above is a preferred embodiment of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

[0148] This patent is not limited to the above-mentioned optimal implementation mode. Anyone can derive other forms of a model-optimized energy storage vehicle scheduling system based on the inspiration of this patent. All equivalent changes and modifications made according to the scope of the patent application of the present invention should be covered by this patent.

Claims

1. A storage vehicle dispatching system based on model optimization, characterized in that: include: Collection layer, analysis layer, and scheduling layer; The acquisition layer is used to collect and save relevant parameters of the distribution network system; The analysis layer establishes an energy storage vehicle scheduling optimization strategy based on conditional constraints based on the data obtained by the collection layer; The dispatching layer dispatches the energy storage vehicles according to the strategy provided by the analysis layer to optimize the load of the transformer in the substation.

2. The energy storage vehicle dispatching system based on model optimization according to claim 1, characterized in that: The data collected by the collection layer include: data on heavy-load distribution transformers, low-voltage users, photovoltaic output, power supply information and important loads; among which heavy-load distribution transformers refer to whether the lines and equipment are overloaded or no-loaded; low-voltage users refer to situations where the user voltage is lower than the standard voltage; photovoltaic output refers to the photovoltaic power generation in the photovoltaic area; power supply information refers to information on power supply due to line or distribution transformer failures or maintenance; important loads refer to the power supply status of important users.

3. The energy storage vehicle dispatching system based on model optimization according to claim 1, characterized in that: The acquisition layer collects distribution network operating parameters and transmits them back to the dispatching and analysis system master station.

4. The energy storage vehicle dispatching system based on model optimization according to claim 1, characterized in that: The analysis layer is used to complete the scheduling plan preparation of energy storage vehicles, energy storage vehicle status process control and management, battery data statistical analysis and external system information exchange; by integrating the battery capacity, quantity and constraints of the energy storage vehicle, the data of each relevant system is formed to form an energy storage vehicle management, command and monitoring system, so as to realize the management of all operating energy storage vehicles in the warehouse.

5. The energy storage vehicle dispatching system based on model optimization according to claim 1, characterized in that: The analysis layer obtains the energy storage vehicle scheduling optimization strategy based on conditional constraints through the model constructed by decision variables, constraints and objective functions; wherein, the decision variables refer to the battery capacity and quantity of the energy storage vehicle, the battery capacity of each energy storage vehicle is fixed, and the number of energy storage vehicles is fixed according to the project procurement quantity; the constraints include power constraints, power constraints, time constraints, vehicle constraints and event processing priorities; the event processing priority mainly refers to the scheduling of energy storage vehicles according to the established event priority, and the order of vehicle dispatch is affected by the heavy-load distribution transformer, low-voltage users, important loads, and power protection information according to the set weights. The original data including the transformer load data and the event processing priority are used as the constraint analysis object, and the data is transmitted into the objective function; the objective function adopts the transformer load variance in the substation area.

6. The energy storage vehicle dispatching system based on model optimization according to claim 5, characterized in that: Mobile energy storage vehicles are dispatched to bear part of the load of the substation transformer to achieve peak shaving and valley filling. The mobile energy storage vehicles are discharged during peak load periods and charged during low load periods to reduce load peaks or fluctuations. The strategy is adjusted according to the priority of event processing so that the mobile energy storage vehicles can charge, discharge or remain idle in different time periods to optimize the load of the substation transformer.

7. The energy storage vehicle dispatching system based on model optimization according to claim 5 is characterized in that: In the analysis layer, based on the grid parameters, the energy storage vehicle scheduling optimization based on conditional constraints is established, and the objective function is set as follows: The 24 hours of each day are divided into 96 time periods of 15 minutes each, and the transformer load variance S is used to calculate the load variance of the transformer area. 2 As the objective function of the load optimization strategy, it describes whether the actual load curve of the transformer in the substation after connecting to the mobile energy storage vehicle is stable; since the actual load after optimization, the load of the transformer in the substation and the discharge of the mobile energy storage vehicle meet the following conditions within a certain period of time: The objective function is determined as: Where: Δt is the time interval of each period; P i is the load of the transformer in the substation in each period; E i The amount of electricity that needs to be released by the mobile energy storage vehicle in each period; is the actual load after optimization; Considering the constraints generated by the physical constraints related to the mobile energy storage vehicle, the time priority processing level, and the substation transformer, the constraints are as follows:

1. Power Constraints (1) During discharge, the rated capacity of the mobile energy storage vehicle needs to be greater than the total capacity required by the transformer in the area: AND o >And n (3) Where: E o is the rated capacity of the mobile energy storage vehicle; E n The total power required by the transformer in the substation area; (2) The total power of the mobile energy storage vehicle must be greater than or equal to the total power required by the transformer in the substation, the power required by the mobile energy storage vehicle to reach the target point, and the power required by the mobile energy storage vehicle for the return trip: AND z >And n +E g +E b (4) Where: E z The current power of the mobile energy storage vehicle; E g The amount of electricity required for the mobile energy storage vehicle to reach the target location; E b The amount of electricity required for the mobile energy storage vehicle to return; After further solving and deformation, we get:

2. Power Constraints (1) To protect the safe discharge of the mobile energy storage vehicle, the discharge power of the mobile energy storage vehicle in each period shall not exceed the maximum discharge power of the mobile energy storage vehicle: Where: is the maximum discharge power of the mobile energy storage vehicle; is the discharge power of the mobile energy storage vehicle in each period; (2) The mobile energy storage vehicle performs constant power discharge in each period; 3. Time constraints (1) Discharge start time constraints of mobile energy storage vehicles: 1≤T start ≤96 (8) Where: T start The discharge start period is: 1≤T stop ≤96 (9) Where: T stop The discharge end period; (3) Constraints on the relationship between the start and end time periods of discharge of mobile energy storage vehicles; 4. Vehicle restraints When one mobile energy storage vehicle cannot meet the load demand of the transformer in the substation area, two mobile energy storage vehicles are dispatched for discharge; Where: Num is the number of dispatched mobile energy storage vehicles; P max The maximum load that the transformer in the substation can bear; 5. Event processing priority constraints If task T i is the highest priority, all resources can be scheduled, if T i If it is not the highest priority, the highest priority resource cannot be scheduled; The relationship between task priority and resource allocation is: Where: T qosi is the business priority of the ith item, which is divided into 5 levels from 1 to 5 according to the importance, with 1 being the highest priority; R θj is the jth resource priority; R ωij Execute the task for the jth resource.

8. The energy storage vehicle dispatching system based on model optimization according to claim 1, characterized in that: The dispatching layer dispatches the energy storage vehicle according to the instructions issued by the analysis layer, including garage parking, photovoltaic energy storage, and on-site power generation. Garage parking means that the energy storage vehicle does not need to go out to store energy or generate electricity, but is parked in the garage waiting for dispatch instructions. Photovoltaic energy storage refers to the energy storage vehicle storing energy when the photovoltaic output exceeds the grid absorption line and the load is low. On-site power generation refers to power generation at the location designated by the dispatcher.

9. The energy storage vehicle dispatching system based on model optimization according to claim 1, characterized in that: The dispatching layer first evaluates the actual status of the dispatched energy storage vehicle. The mobile energy storage vehicle is divided into large and small energy storage vehicles according to the capacity and rated power. According to the real-time status of the vehicle, it is divided into four states: discharge, charge, idle and fault. According to the discharge amount, discharge power and status of the mobile energy storage vehicle required by the substation transformer, the appropriate mobile energy storage vehicle is selected for discharge. The dispatching layer dispatches the mobile energy storage vehicle to optimize the load of the substation transformer, so that the mobile energy storage vehicle discharges during the peak load period of the substation transformer and charges during the low load period of the substation transformer, thereby reducing the fluctuation of the substation transformer load.

10. A method for dispatching energy storage vehicles based on model optimization, characterized in that: Collect and save relevant parameters of the distribution network system; and based on the collected data, establish a storage vehicle scheduling optimization strategy based on conditional constraints; use it to schedule the storage vehicle to optimize the load of the transformer in the substation.

Images