Intelligent scheduling optimization system and method comprising mobile energy storage device

Abstract

The invention discloses an intelligent scheduling optimization system comprising a mobile energy storage device. The system comprises a discrete power grid architecture construction module which collects output data of new energy power generation equipment, obtains position information of the mobile energy storage device, and establishes an electric energy transmission line to form a discrete power grid architecture; the upper-layer global optimization module constructs an upper-layer optimization model through power grid topology, load prediction and mobile energy storage device position information, and obtains a local state of a discrete power grid architecture and a charging and discharging path strategy of a mobile energy storage device through distributed solution by an alternating multiplier method; the lower-layer local execution module constructs a lower-layer optimization model through the energy storage state and position information of the mobile energy storage device, the local state of the discrete power grid architecture and output data, iteratively interacts with the upper-layer optimization model through an alternating multiplier method and edge calculation, and outputs a charging and discharging power instruction of the mobile energy storage device; and the charging and discharging power is adjusted in real time by combining a charging and discharging path The new energy utilization rate and the power grid operation efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system energy sharing and intelligent dispatching, and specifically to an intelligent dispatching optimization system and method including a mobile energy storage device. Background Art

[0002] As my country deepens its efforts to replace renewable energy with renewable energy, distributed energy resources behind the grid are experiencing explosive growth. Energy sharing, which achieves resource complementarity through the transfer of electricity usage rights, has achieved initial success in pilot energy microgrids in places like Songshan Lake, Dongguan, and Tongli, Suzhou. Current energy storage dispatching technologies primarily rely on fixed deployments, improving grid efficiency by shifting peak loads and filling valleys. However, these technologies have become increasingly limited: energy sharing relies on the capacity of fixed transmission lines, which can be easily disrupted by line congestion, geographical constraints, or natural disasters. Traditional energy storage equipment is immobile after deployment, exhibits poor scalability, and is subject to strict environmental requirements, high initial investment, and high operation and maintenance costs. Furthermore, equipment failures require complete reliance on the grid, lacking flexible backup mechanisms. Existing research focuses on centralized energy storage dispatching and lacks the design of mechanisms for building discrete energy channels for mobile energy storage. Resource coordination among multiple entities presents challenges with interest distribution and information asymmetry, leading to inefficient dispatching. Summary of the Invention

[0003] The purpose of the present invention is to provide an intelligent scheduling optimization system and method including a mobile energy storage device, which improves the utilization rate of new energy and the operation efficiency of the power grid.

[0004] To achieve this purpose, the present invention provides an intelligent scheduling optimization system including a mobile energy storage device, which includes:

[0005] The discrete grid architecture construction module is used to use the mobile energy storage devices in a set area (the entire area where the mobile energy storage devices can move) as movable nodes. The electric energy of the mobile energy storage devices is provided by the corresponding new energy power generation equipment. The new energy output data of the new energy power generation equipment is collected, and the location information of the mobile energy storage devices scheduled between the sub-areas in the set area is obtained. Based on the location information of the mobile energy storage devices and the corresponding new energy output data, an energy transmission route based on the time and space dimensions is established to form a discrete grid architecture.

[0006] The upper-level global optimization module is used to construct an upper-level optimization model by setting the grid topology and load forecast within the area and the location information of the mobile energy storage device. The upper-level optimization module uses the alternating multiplier method combined with the upper-level optimization model to perform a distributed solution on the discrete grid architecture to obtain the local state of the discrete grid architecture and the charging and discharging path strategy of the mobile energy storage device.

[0007] The lower-level local execution module is used to construct a lower-level optimization model through the energy storage status of the mobile energy storage device, the location information of the mobile energy storage device, the local state of the discrete power grid architecture, and the corresponding new energy output data. The upper-level optimization model and the lower-level optimization model are iteratively interacted through the alternating multiplier method and edge computing to output the charging and discharging power instructions of the mobile energy storage device. The charging and discharging power instructions of the mobile energy storage device are combined with the local state of the discrete power grid architecture and the charging and discharging path strategy of the mobile energy storage device to adjust the charging and discharging power of the mobile energy storage device in real time.

[0008] Preferably, the mobile energy storage device is a device with plug-and-play characteristics, which supports access to regional power grids and dynamic reconstruction of the power grid topology.

[0009] Preferably, the upper-level global optimization module is used to construct an upper-level optimization model by setting the power grid topology and load forecast within the region and the location information of the mobile energy storage device, with the goal of minimizing the operating cost of the regional power grid. The operating cost includes the power purchase cost between each sub-region, the scheduling cost generated by implementing the charging and discharging path strategy of the mobile energy storage device, and the local cost of the lower-level optimization model.

[0010] Preferably, the lower-level local execution module is used to perform constrained optimization on the energy storage scheduling cost of each sub-region through the energy storage status of the mobile energy storage device, the location information of the mobile energy storage device, the local status of the discrete power grid architecture, and the corresponding new energy output data, thereby constructing a lower-level optimization model. The lower-level optimization model updates the mobile energy storage device scheduling capacity data to the upper-level optimization model, and realizes iterative interaction between the upper-level optimization model and the lower-level optimization model through the alternating multiplier method; the dynamic update of the mobile energy storage capacity data is realized between the upper-level optimization model and the lower-level optimization model through the data interaction interface.

[0011] Preferably, by setting the power grid topology and load forecast in the region and the location information of the mobile energy storage device, the specific process of constructing the upper optimization model with the goal of minimizing the regional power grid operation cost is as follows:

[0012] A multi-energy coupling network is constructed through energy hubs to form a physical connection framework of a regional integrated energy system to form a grid topology. The grid topology defines the discrete grid architecture through nodes and edges.

[0013] Assume that electric vehicles can flow freely between sub-regions within the regional integrated energy system. The mobile energy storage device model is regarded as a transferable load. The charging and discharging behavior of the mobile energy storage device is collaboratively optimized through load forecasting. The cross-regional movement of the mobile energy storage device cluster is tracked in real time through IoT devices. The location information and number of mobile energy storage devices in each sub-region are dynamically updated. The total charging and discharging power of the mobile energy storage device in the kth sub-region is:

[0014]

[0015] Where, is the charging power of the i-th mobile energy storage device in the sub-area, is the discharge power of the i-th mobile energy storage device in the sub-area; is the charging efficiency of the i-th mobile energy storage device in the sub-area, is the discharge efficiency of the i-th mobile energy storage device in the sub-area; δ is the scheduling flag parameter, Indicates whether the i-th device in the sub-area participates in charging at time t, 1 if it participates, and 0 if it does not participate; Indicates whether the i-th device in the sub-area participates in the discharge at time t, 1 if it participates, and 0 if it does not participate; represents the net power of charging and discharging of the mobile energy storage device in the kth sub-area at time t, and the constraint can be obtained:

[0016]

[0017] Among them, n t is the number of mobile energy storage devices that can be dispatched in the sub-region at time t, n t-1 is the number of mobile energy storage devices that can be dispatched in the sub-region at time t-1, is the number of dispatchable mobile energy storage devices entering the sub-area at time t-1, is the number of dispatchable mobile energy storage devices that leave the sub-area at time t-1;

[0018] At this time, the regional integrated energy system as a whole should have:

[0019]

[0020] Where, P div,t is the total transfer load of the mobile energy storage device cluster at time t, is the net dispatch power of the mobile energy storage device in the kth sub-region at time t. This formula constrains the global power balance of the regional integrated energy system based on the energy conservation principle of the power grid topology, ensuring that the energy interaction between sub-regions meets the transmission capacity limit. Considering the maximum capacity limit of the battery, the transfer charging load of all mobile energy storage devices in the regional integrated energy system during time t is:

[0021] P div >G t -G max (4)

[0022] Where G t is the storage capacity of the mobile energy storage device cluster at time t during the scheduling period, G maxis the maximum battery capacity of the mobile energy storage device cluster, P div is the total net dispatch power of the mobile energy storage cluster at time t;

[0023] At the same time, in order to ensure that the mobile energy storage device can be transferred smoothly after discharge, record G low To ensure the minimum battery capacity for the smooth transfer of EV clusters, the formula is:

[0024] P div,t <G t -G low (5)

[0025] Among them, P div,t is the total net dispatch power of the mobile energy storage device cluster at time t;

[0026] Similarly, there are constraints:

[0027]

[0028] set up The scheduling cost generated by controlling the load of the mobile energy storage device to implement the charging and discharging path strategy of the mobile energy storage device for the kth sub-region is assumed to be proportional to the scheduling load size, which is:

[0029]

[0030] Where b is the dispatch cost coefficient;

[0031] Assume that the electricity purchase cost of the kth sub-region from the grid at time t is Then we have:

[0032]

[0033] Where d represents the unit price of electricity purchase, represents the electric power purchased from the grid by the kth sub-region at time t, is the cost of purchasing electricity from the grid for the kth sub-region;

[0034] Assume that in the kth sub-region t period, the local cost of the lower optimization model is

[0035] In summary, the upper optimization objective function is obtained with the goal of minimizing the operating cost of the regional microgrid system:

[0036]

[0037] By defining the upper optimization objective function and combining the above formulas and constraints with the local cost of the lower optimization model The upper-level optimization model is constructed.

[0038] Preferably, the specific process of performing a distributed solution on the discrete power grid architecture by combining the alternating multiplier method with the upper-level optimization model to obtain the local state of the discrete power grid architecture and the charging and discharging path strategy of the mobile energy storage device is as follows:

[0039] The alternating multiplier method optimization function that minimizes the total global scheduling cost is as follows:

[0040] min(f(x)+g(z))(11)

[0041] Wherein, variable x represents the dispatch power of mobile energy storage in each sub-region, f(x) represents the sum of the electricity purchase cost and dispatch cost in each sub-region, variable z represents the intermediate variable in the global power balance constraint, and g(z) represents the constraint penalty for each sub-region solution to satisfy the global consistency.

[0042] The global constraints of the regional power grid system are as follows:

[0043] stAx+Bz=c (12)

[0044] in, is an n-dimensional real vector; is an m-dimensional real vector; is a real matrix with p rows and n columns; is a real matrix with p rows and m columns; is a p-dimensional real vector; st indicates that the optimization problem must satisfy the subsequent constraints; the augmented Lagrangian function L can be obtained by the function f(x) and the function g(z) ρ (x,z,y):

[0045]

[0046] Where y represents the Lagrange multiplier, y T represents the transpose of the Lagrange multiplier, and ρ represents the penalty coefficient;

[0047] Define the residual as r k , r k represents the deviation of the power of all sub-regions from the global constraint formula (12), then:

[0048] r k :=Ax k +Bz k -c (14)

[0049] Among them, x k represents the dispatching power of mobile energy storage in each sub-region at the kth iteration, z k represents the local energy storage power at the kth iteration;

[0050] Define the standard dual variable uk ,Right now:

[0051] u k :=(1 / ρ)y k (15)

[0052] Among them, y k represents the Lagrange multiplier at the kth iteration;

[0053] The iterative form of the alternating multiplier method algorithm is:

[0054]

[0055] u k+1 :=u k +Ax k+1 +Bz k+1 -c (18)

[0056] Among them, k is the number of iterations of the alternating multiplier method, ρ is the penalty coefficient, and x k+1 represents the dispatching power of mobile energy storage in each sub-region at the k+1th iteration, z k+1 represents the local energy storage power at the k+1th iteration, represents the standard dual variable at the k+1th iteration;

[0057] Iteratively generate x by alternating optimization of variables x and z k+1 and z k+1 , and update the standard dual variable u k Through distributed solution, the dispatch power of mobile energy storage in each sub-region that minimizes the objective function f(x) at the k+1th iteration is obtained, and the solution of each sub-region that minimizes the objective function g(z) at the k+1th iteration satisfies the constraint penalty of global consistency, and the dispatch strategy that minimizes the total global dispatch cost is obtained, thereby obtaining the corresponding local state of the discrete power grid architecture and the charging and discharging path strategy of the mobile energy storage device;

[0058] From formula (18), it can be seen that the edge computing devices of each subsystem of the regional power grid system only need to exchange the newly generated x k+1 With z k+1 This ensures that the algorithm continues to operate until the residual is less than the specified convergence error.

[0059] Preferably, the specific process of iteratively interacting the upper-layer optimization model and the lower-layer optimization model through the alternating multiplier method and edge computing to output the charging and discharging power instructions of the mobile energy storage device is:

[0060] Assume that in the regional power grid system, the power generation modes include wind power generation and photovoltaic power generation. At time t, the photovoltaic power generation output of the kth sub-region is Wind power output is but:

[0061]

[0062] Where, Predict the output of all renewable energy sources in the kth sub-region at time t;

[0063] Assume that the depreciation cost of all energy storage devices in the region is not recorded, and the state of the i-th energy storage device in the k-th region at time t is: For any moment, There are three situations:

[0064]

[0065] Where, Indicates that the state of the i-th energy storage device in the k-th region at time t is the charging state, Indicates that the state of the i-th energy storage device in the k-th region at time t is idle. At time t, the state of the i-th energy storage device in region k is in the discharge state. Assume that there is an energy storage device with sufficient capacity in each sub-region. Its call cost increases with the increase of call capacity. The relationship between its call capacity and cost is:

[0066]

[0067] Where, is the energy storage call cost of the kth sub-region at time t, s1 is the linear cost coefficient, and s2 is the fixed cost coefficient;

[0068] Assume that the predicted load value of the kth sub-area at time t is In order to ensure that all new energy sources in the regional integrated energy system are consumed, there is a power balance constraint at time t:

[0069]

[0070] Based on all the constraints above, the objective function of minimizing the energy storage call cost, i.e. the lower-level optimization model, is:

[0071]

[0072] in represents the other operating costs of the k-th sub-region at time t;

[0073] Combining formula (10), formula (22) and formula (23), the upper optimization model is solved iteratively by the alternating multiplier method combined with edge computing, and each sub-region independently iterates to solve the P of the corresponding region. div,t , each iteration transfers the load G between different sub-regions divThey communicate with each other and substitute the local optimization calculation formula (3) of the sub-region for the next round of iteration. Through the iterative interaction between the upper optimization model and the lower optimization model, the charging and discharging power instructions of the mobile energy storage device are finally obtained.

[0074] An intelligent scheduling optimization method including a mobile energy storage device comprises the following steps:

[0075] Mobile energy storage devices in a set area are used as movable nodes. The power of the mobile energy storage devices is provided by corresponding new energy power generation equipment. New energy output data of the new energy power generation equipment is collected, and the location information of the mobile energy storage devices scheduled between sub-areas in the set area is obtained. Based on the location information of the mobile energy storage devices and the corresponding new energy output data, a power transmission route based on the time and space dimensions is established to form a discrete power grid architecture;

[0076] An upper-level optimization model is constructed by setting the grid topology and load forecast within the region and the location information of the mobile energy storage device. The local state of the discrete grid architecture and the charging and discharging path strategy of the mobile energy storage device are obtained by combining the upper-level optimization model with the alternating multiplier method to perform a distributed solution on the discrete grid architecture.

[0077] A lower-level optimization model is constructed through the energy storage status of the mobile energy storage device, the location information of the mobile energy storage device, the local status of the discrete power grid architecture, and the corresponding new energy output data. The upper-level optimization model and the lower-level optimization model are iteratively interacted through the alternating multiplier method and edge computing to output the charging and discharging power instructions of the mobile energy storage device. The charging and discharging power instructions of the mobile energy storage device are combined with the local status of the discrete power grid architecture and the charging and discharging path strategy of the mobile energy storage device to adjust the charging and discharging power of the mobile energy storage device in real time.

[0078] A computer program product comprises a computer program, wherein when the computer program is executed by a processor, the steps of the above method are implemented.

[0079] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0080] Beneficial effects of the present invention:

[0081] The present invention abstracts mobile energy storage devices into mobile nodes, breaking through the physical limitations of fixed transmission lines, and bypassing line congestion areas by dynamically constructing power transmission routes in the time and space dimensions, thereby realizing energy sharing across geographical barriers; based on the geographical aggregation characteristics of new energy power generation and energy storage equipment, it is divided into multiple sub-region interconnected structures, and the charging and discharging coordination of mobile energy storage between sub-regions is used to improve planning rationality and resource utilization; the mobile energy storage device has a plug-and-play feature that supports mobile energy storage, allowing devices to be freely connected and exited between different sub-regions, realizing dynamic reconstruction of the power grid topology, enhancing disaster tolerance capabilities for natural disasters, and through real-time interaction with mobile energy storage, dynamically regulating cross-regional charging and discharging, optimizing power grid power balance, reducing operation and maintenance complexity, and improving the operational reliability of the power grid system. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 It is a structural schematic diagram of the present invention;

[0083] Figure 2 Flowchart of the present invention. DETAILED DESCRIPTION

[0084] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0085] Example 1

[0086] An intelligent dispatching optimization system containing a mobile energy storage device, such as Figure 1 As shown, it includes:

[0087] The discrete grid architecture construction module is used to use the mobile energy storage device in the set area as a movable node. The electric energy of the mobile energy storage device is provided by the corresponding new energy power generation equipment, collect the new energy output data of the new energy power generation equipment, obtain the location information of the mobile energy storage device for scheduling between the sub-areas in the set area, and establish an energy transmission route based on the time and space dimensions according to the location information of the mobile energy storage device and the corresponding new energy output data to form a discrete grid architecture (the discrete grid architecture supports efficient energy sharing). By constructing a discrete grid architecture, this design can dynamically adjust the energy transmission route according to the real-time data of the new energy output to ensure the effective use of electricity. The mobile energy storage device can store and release electric energy at different locations, so that it can flexibly adjust its position according to the output of the new energy power generation equipment and the load demand in each sub-area. This flexibility enables the discrete grid architecture to better adapt to the intermittent and uncertain nature of new energy power generation, enhance the grid's acceptance capacity for new energy, and provide an accurate data basis for subsequent optimized scheduling.

[0088] The upper-level global optimization module is used to construct an upper-level optimization model by setting the grid topology in the area (grid topology refers to the physical connection structure and dynamic energy transmission path of the power network in the area, including the virtual connection formed by fixed transmission facilities (such as lines, substations) and mobile energy storage devices, which together constitute the network framework of energy transmission) and load forecasting (load forecasting is an estimate of the power demand of each sub-area in a certain period of time in the future, based on historical data, weather and economic factors, used to optimize the charging and discharging strategy of mobile energy storage devices) and the location information of mobile energy storage devices. The alternating multiplier method (Alternating Direction Method of ADMM is a distributed algorithm for solving constrained convex optimization problems. It is suitable for the decomposition and collaborative optimization of multivariable and large-scale problems. Its core idea is to decompose complex problems into multiple sub-problems through variable splitting and alternating optimization, and solve them in parallel, and finally converge to the global optimal solution. The upper-level optimization model is combined to perform a distributed solution on the discrete power grid architecture to obtain the local state of the discrete power grid architecture and the charging and discharging path strategy of the mobile energy storage device. By constructing an upper-level optimization model, this design can consider the influencing factors such as power grid topology, load forecasting and location information of mobile energy storage devices from the level of the entire set area, and can achieve optimal resource allocation of the entire power grid and improve the overall performance and economic benefits of the power grid. The alternating multiplier method can make full use of distributed computing resources and improve the solution speed. It is suitable for the optimization scheduling problems of large-scale complex power grids and can improve the overall operation quality of the power grid.

[0089] The lower-level local execution module is used to construct a lower-level optimization model through the energy storage status of the mobile energy storage device, the location information of the mobile energy storage device, the local state of the discrete power grid architecture, and the corresponding new energy output data. The upper-level optimization model and the lower-level optimization model are iteratively interacted through the alternating multiplier method and edge computing (the edge computing realizes cross-regional energy collaborative scheduling through hierarchical optimization. The edge computing device is deployed on the distributed device side and is responsible for sensing device information and controlling device execution strategies. Each sub-region is equipped with an upper-level edge computing device to provide the computing power, information communication and storage functions required for optimization within the sub-region) to output the charging and discharging power instructions of the mobile energy storage device, and combine the charging and discharging power instructions of the mobile energy storage device with the local state of the discrete power grid architecture and the charging and discharging path strategy of the mobile energy storage device to charge and discharge the mobile energy storage device. The discharge power is adjusted in real time (based on the upper-level optimization model and the lower-level optimization model and the alternating multiplier method, through local state perception, constraint matching, path strategy adaptation, edge real-time optimization and closed-loop feedback control, the charging and discharging power instructions of the mobile energy storage device can be dynamically combined with the discrete power grid architecture to achieve efficient and reliable real-time adjustment). This design outputs the charging and discharging power instructions of the mobile energy storage device in real time, so that the mobile energy storage device can better adapt to the dynamic changes of the power grid, improve the regulation ability and flexibility of the power grid, and combine the charging and discharging power instructions of the mobile energy storage device with the local state and charging and discharging path strategy of the discrete power grid architecture to achieve precise control of the charging and discharging power of the mobile energy storage device, which helps to improve the operation accuracy of the power grid, reduce the impact of power fluctuations, and improve the power quality of the power grid.

[0090] For the upper-level global optimization module and the lower-level local execution module, in the upper-level optimization model, the main considerations are the new energy power generation equipment (including wind turbines and photovoltaic equipment), energy storage systems and corresponding loads in each sub-region. Since the power connection between the sub-regions is not considered, under the premise of pursuing economic maximization, power outages and new energy power abandonment may occur in each sub-region. Therefore, the lower-level optimization model calculates the optimal results of each sub-region through the upper-level optimization model, and sets new scheduling targets based on the power outages or unused power of new energy in each sub-region to optimize the power grid system. Taking into account the power balance of the entire set area, the wind power, photovoltaic surplus and system power shortage obtained by the upper-level optimization model are combined with the mobile energy storage devices in each sub-region to formulate the scheduling strategy of the lower-level optimization model. The goal of the power grid system for the lower-level optimization model is the economic optimization of each sub-region under power balance.

[0091] In the above technical solution, the mobile energy storage device (a physically movable energy storage unit that is flexibly deployed at grid nodes in different geographical locations) is a device with plug-and-play characteristics that supports access to regional power grids and dynamically reconstructs the grid topology. The above design utilizes the plug-and-play characteristics of the mobile energy storage device to enable it to quickly access or disconnect from the sub-regional power grids and dynamically reconstruct the grid topology, allowing the grid to more flexibly adapt to the intermittent and uncertain nature of renewable energy generation, thereby improving the grid's regulation capabilities and stability.

[0092] In the above technical solution, the upper-level global optimization module is used to construct an upper-level optimization model with the goal of minimizing the operating costs of the regional power grid by setting the power grid topology and load forecast within the region and the location information of the mobile energy storage device. The operating costs include the power purchase costs between sub-regions, the scheduling costs generated by implementing the charging and discharging path strategy of the mobile energy storage device, and the local costs of the lower-level optimization model. The above design helps to achieve optimal resource allocation for the entire power grid by constructing an upper-level optimization model, improve the overall performance and economic benefits of the power grid, and use the alternating multiplier method to perform distributed solution on the discrete power grid architecture, which can quickly and efficiently obtain optimization results.

[0093] In the above technical solution, the lower-level local execution module is used to constrain and optimize the energy storage scheduling cost of each sub-region through the energy storage status of the mobile energy storage device, the location information of the mobile energy storage device, the local status of the discrete power grid architecture, and the corresponding new energy output data, thereby constructing a lower-level optimization model. The lower-level optimization model updates the scheduling capacity data of the mobile energy storage device to the upper-level optimization model, and realizes iterative interaction between the upper-level optimization model and the lower-level optimization model through the alternating multiplier method; the dynamic update of the mobile energy storage capacity data is realized between the upper-level optimization model and the lower-level optimization model through the data interaction interface; the above design outputs the charging and discharging power instructions of the mobile energy storage device in real time by constructing the lower-level optimization model, so that the mobile energy storage device can better adapt to the dynamic changes of the power grid and improve the regulation capability and flexibility of the power grid.

[0094] In the above technical solution, by setting the regional power grid topology and load forecast as well as the location information of the mobile energy storage device, the specific process of constructing the upper-level optimization model with the goal of minimizing the regional power grid operation cost is as follows:

[0095] A multi-energy coupling network is constructed through energy hubs to form a physical connection framework of a regional integrated energy system to form a grid topology. The grid topology defines the discrete grid architecture through nodes (including load centers and power points) and edges (including transmission lines and gas pipelines);

[0096] When scheduling mobile energy storage devices, real-time data exchange is required between the edge computing devices in each sub-area and the battery management systems (BMS) of all mobile energy storage devices in that sub-area (to obtain the battery capacity limit and current charge status of each mobile energy storage device). After the scheduling optimization calculation is completed, the edge computing device will send instructions to the BMS, including the scheduling decision and target charge status of each mobile energy storage device in the next scheduling cycle. When these mobile energy storage devices move to other sub-areas, the edge computing devices in the new sub-areas will verify the status of the battery management systems (BMS) of the mobile energy storage devices to ensure that the mobile energy storage devices can execute operations according to the established scheduling strategy. (This closed-loop scheduling mechanism ensures that the mobile energy storage translocation can accurately complete each scheduling task.)

[0097] Assume that electric vehicles can flow freely between sub-regions within the regional integrated energy system. The mobile energy storage device model is regarded as a transferable load. The charging and discharging behavior of the mobile energy storage device is collaboratively optimized through load forecasting. The cross-regional movement of the mobile energy storage device cluster is tracked in real time through IoT devices. The location information and number of mobile energy storage devices in each sub-region are dynamically updated. The total charging and discharging power of the mobile energy storage device in the kth sub-region is:

[0098]

[0099] Where, is the charging power of the i-th mobile energy storage device in the sub-area, is the discharge power of the i-th mobile energy storage device in the sub-area; is the charging efficiency of the i-th mobile energy storage device in the sub-area, is the discharge efficiency of the i-th mobile energy storage device in the sub-area; δ is the scheduling flag parameter, Indicates whether the i-th device in the sub-area participates in charging at time t. If yes, it is 1, and if not, it is 0 (a value of 1 or 0 indicates whether it participates in the optimization scheduling at time t); Indicates whether the i-th device in the sub-area participates in the discharge at time t, 1 if it participates, and 0 if it does not participate; represents the net power of charging and discharging of the mobile energy storage device in the kth sub-area at time t, and the constraint can be obtained:

[0100]

[0101] Among them, n t is the number of mobile energy storage devices that can be dispatched in the sub-region at time t, n t-1 is the number of mobile energy storage devices that can be dispatched in the sub-region at time t-1, is the number of dispatchable mobile energy storage devices entering the sub-area at time t-1, is the number of dispatchable mobile energy storage devices that leave the sub-area at time t-1;

[0102] At this time, the regional integrated energy system as a whole should have:

[0103]

[0104] Where, P div,t is the total transfer load of the mobile energy storage device cluster at time t, is the net dispatch power of the mobile energy storage device in the kth sub-region at time t. This formula constrains the global power balance of the regional integrated energy system based on the energy conservation principle of the power grid topology, ensuring that the energy interaction between sub-regions meets the transmission capacity limit. Considering the maximum capacity limit of the battery, the transfer charging load of all mobile energy storage devices in the regional integrated energy system during time t is:

[0105] P div >G t -G max (4)

[0106] Where G t is the storage capacity of the mobile energy storage device cluster at time t during the scheduling period, G max is the maximum battery capacity of the mobile energy storage device cluster, P div is the total net dispatch power of the mobile energy storage cluster at time t;

[0107] At the same time, in order to ensure that the mobile energy storage device can be transferred smoothly after discharge, record G low To ensure the minimum battery capacity for the smooth transfer of EV clusters, the formula is:

[0108] P div,t <G t -G low (5)

[0109] Among them, P div,t is the total net dispatch power of the mobile energy storage device cluster at time t;

[0110] Similarly, there are constraints:

[0111]

[0112] set up The scheduling cost generated by controlling the load of the mobile energy storage device to implement the charging and discharging path strategy of the mobile energy storage device for the kth sub-region is assumed to be proportional to the scheduling load size, which is:

[0113]

[0114] Where b is the dispatch cost coefficient;

[0115] Assume that the electricity purchase cost of the kth sub-region from the grid at time t is Then we have:

[0116]

[0117] Where d represents the unit price of electricity purchase, represents the electric power purchased from the grid by the kth sub-region at time t, is the cost of purchasing electricity from the grid for the kth sub-region;

[0118] Assume that in the kth sub-region t period, the local cost of the lower optimization model is

[0119] In summary, the upper optimization objective function is obtained with the goal of minimizing the operating cost of the regional power grid system:

[0120]

[0121] By defining the upper optimization objective function and combining the above formulas and constraints with the local cost of the lower optimization model The upper-level optimization model is constructed; the above design can ensure the feasibility of scheduling between sub-areas, reduce the risk of failure, and optimize the scheduling cost.

[0122] In the above technical solution, the specific process of performing a distributed solution on the discrete power grid architecture by combining the alternating multiplier method with the upper-level optimization model to obtain the local state of the discrete power grid architecture and the charging and discharging path strategy of the mobile energy storage device is as follows:

[0123] The alternating multiplier method optimization function that minimizes the total global scheduling cost is as follows:

[0124] min(f(x)+g(z)) (11)

[0125] Wherein, variable x represents the dispatch power of mobile energy storage in each sub-region, f(x) represents the sum of the electricity purchase cost and dispatch cost in each sub-region, variable z represents the intermediate variable in the global power balance constraint, and g(z) represents the constraint penalty for each sub-region solution to satisfy the global consistency.

[0126] The global constraints of the regional power grid system are as follows:

[0127] stAx+Bz=c (12)

[0128] in, is an n-dimensional real vector (each element is a real number); is an m-dimensional real vector (each element is a real number); is a real matrix with p rows and n columns (each element in the matrix is ​​a real number); is a real matrix with p rows and m columns (each element in the matrix is ​​a real number); is a p-dimensional real vector (each element is a real number); st indicates that the optimization problem must satisfy the subsequent constraints; the augmented Lagrangian function L can be obtained by the function f(x) and the function g(z) ρ (x,z,y):

[0129]

[0130] Where y represents the Lagrange multiplier, y T represents the transpose of the Lagrange multiplier, and ρ represents the penalty coefficient;

[0131] Define the residual as r k , r k represents the deviation of the power of all sub-regions from the global constraint formula (12), then:

[0132] r k :=Ax k +Bz k -c (14)

[0133] Among them, x k represents the dispatching power of mobile energy storage in each sub-region at the kth iteration, z k represents the local energy storage power at the kth iteration;

[0134] Define the standard dual variable u k ,Right now:

[0135] u k :=(1 / ρ)y k (15)

[0136] Among them, y k represents the Lagrange multiplier at the kth iteration;

[0137] The iterative form of the alternating multiplier method algorithm is:

[0138]

[0139] u k+1 :=u k +Ax k+1 +Bz k+1 -c (18)

[0140] Among them, k is the number of iterations of the alternating multiplier method, ρ is the penalty coefficient, and x k+1represents the dispatching power of mobile energy storage in each sub-region at the k+1th iteration, z k+1 represents the local energy storage power at the k+1th iteration, represents the standard dual variable at the k+1th iteration;

[0141] Iteratively generate x by alternating optimization of variables x and z k+1 and z k+1 , and update the standard dual variable u k Through distributed solution, the dispatch power of mobile energy storage in each sub-region that minimizes the objective function f(x) at the k+1th iteration is obtained, and the solution of each sub-region that minimizes the objective function g(z) at the k+1th iteration satisfies the constraint penalty of global consistency, and the dispatch strategy that minimizes the total global dispatch cost is obtained, thereby obtaining the corresponding local state of the discrete power grid architecture and the charging and discharging path strategy of the mobile energy storage device;

[0142] From formula (18), it can be seen that the edge computing devices of each subsystem of the regional power grid system only need to exchange the newly generated x k+1 With z k+1 This ensures that the algorithm continues to operate until the residual is less than the specified convergence error; the above design can enable the distributed optimization of the entire power grid system to have extremely high communication immediacy and operational safety.

[0143] In the above technical solution, the specific process of iteratively interacting the upper-layer optimization model and the lower-layer optimization model through the alternating multiplier method and edge computing to output the charging and discharging power instructions of the mobile energy storage device is as follows:

[0144] Assume that in the regional power grid system, the power generation modes include wind power generation and photovoltaic power generation. At time t, the photovoltaic power generation output of the kth sub-region is Wind power output is but:

[0145]

[0146] Where, Predict the output of all renewable energy sources in the kth sub-region at time t;

[0147] Assume that the depreciation cost of all energy storage devices in the region is not recorded, and the state of the i-th energy storage device in the k-th region at time t is: For any moment, There are three situations:

[0148]

[0149] Where, Indicates that the state of the i-th energy storage device in the k-th region at time t is the charging state, Indicates that the state of the i-th energy storage device in the k-th region at time t is idle. At time t, the state of the i-th energy storage device in region k is in the discharge state. Assume that there is an energy storage device with sufficient capacity in each sub-region. Its call cost increases with the increase of call capacity. The relationship between its call capacity and cost is:

[0150]

[0151] Where, is the energy storage call cost of the kth sub-region at time t, s1 is the linear cost coefficient, and s2 is the fixed cost coefficient;

[0152] Assume that the predicted load value of the kth sub-area at time t is In order to ensure that all new energy sources in the regional integrated energy system are consumed, there is a power balance constraint at time t:

[0153]

[0154] Based on all the constraints above, the objective function of minimizing the energy storage call cost, i.e. the lower-level optimization model, is:

[0155]

[0156] in represents the other operating costs of the k-th sub-region at time t;

[0157] Combining formula (10), formula (22) and formula (23), the upper optimization model is solved iteratively by the alternating multiplier method combined with edge computing, and each sub-region independently iterates to solve the P of the corresponding region. div,t , each iteration transfers the load G between different sub-regions div They communicate with each other and substitute the local optimization calculation formula (3) of the sub-region for the next round of iteration. Through the iterative interaction of the upper optimization model and the lower optimization model, the charging and discharging power instructions of the mobile energy storage device are finally obtained. In the above design, since the new energy power generation and energy storage equipment need to be planned and arranged according to the actual situation of each sub-region, they can be divided into multiple sub-regions in the form of interconnection according to the aggregation of new energy power generation equipment and mobile energy storage devices in the power grid system. The optimization problem is decomposed into each sub-region and solved step by step, which effectively improves the overall intelligence level and optimization solving ability of the power grid system. The edge computing is highly consistent with the optimization needs of the regional power grid system. While making full use of its own perception and computing capabilities to complete the optimization of each sub-region, the overall computing power coordination for the power grid system is completed through information perception and communication functions. The power grid power is effectively controlled by performing charging and discharging operations on the mobile energy storage devices in different sub-regions, which improves the power grid's control ability on the balance of the power network and optimizes the operation and maintenance level of the power grid system.

[0158] Example 2

[0159] An intelligent scheduling optimization method including a mobile energy storage device, such as Figure 2 As shown, the output data of new energy power generation equipment is collected, the location information of the mobile energy storage device is obtained, and the power transmission route is established to form a discrete power grid architecture; the upper-level optimization model is constructed through the power grid topology, load forecast and the location information of the mobile energy storage device, and the local state of the discrete power grid architecture and the charging and discharging path strategy of the mobile energy storage device are obtained through distributed solution using the alternating multiplier method; the lower-level optimization model is constructed through the energy storage state and location information of the mobile energy storage device, the local state of the discrete power grid architecture, and the output data, and the alternating multiplier method and edge computing are used to iteratively interact with the upper-level optimization model to output the charging and discharging power instructions of the mobile energy storage device, and the charging and discharging power is adjusted in real time in combination with the charging and discharging path strategy.

[0160] The specific method of intelligent scheduling optimization includes the following steps:

[0161] Mobile energy storage devices in a set area are used as movable nodes. The power of the mobile energy storage devices is provided by corresponding new energy power generation equipment. New energy output data of the new energy power generation equipment is collected, and the location information of the mobile energy storage devices scheduled between sub-areas in the set area is obtained. Based on the location information of the mobile energy storage devices and the corresponding new energy output data, a power transmission route based on the time and space dimensions is established to form a discrete power grid architecture;

[0162] An upper-level optimization model is constructed by setting the grid topology and load forecast within the region and the location information of the mobile energy storage device. The local state of the discrete grid architecture and the charging and discharging path strategy of the mobile energy storage device are obtained by combining the upper-level optimization model with the alternating multiplier method to perform a distributed solution on the discrete grid architecture.

[0163] A lower-level optimization model is constructed through the energy storage status of the mobile energy storage device, the location information of the mobile energy storage device, the local status of the discrete power grid architecture, and the corresponding new energy output data. The upper-level optimization model and the lower-level optimization model are iteratively interacted through the alternating multiplier method and edge computing to output the charging and discharging power instructions of the mobile energy storage device. The charging and discharging power instructions of the mobile energy storage device are combined with the local status of the discrete power grid architecture and the charging and discharging path strategy of the mobile energy storage device to adjust the charging and discharging power of the mobile energy storage device in real time.

[0164] Example 3

[0165] A computer program product includes a computer program, which implements the steps of the method described in Example 2 when executed by a processor.

[0166] Example 4

[0167] A computer-readable storage medium stores a computer program, which implements the steps of the method described in Example 2 when executed by a processor.

[0168] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

Claims

1. An intelligent scheduling optimization system containing a mobile energy storage device, characterized in that: It includes: The discrete grid architecture construction module is used to use mobile energy storage devices in a set area as movable nodes. The power of the mobile energy storage devices is provided by corresponding new energy power generation equipment. The module collects new energy output data of the new energy power generation equipment, obtains the location information of the mobile energy storage devices that are scheduled between sub-areas in the set area, and establishes a power transmission route based on the time and space dimensions based on the location information of the mobile energy storage devices and the corresponding new energy output data to form a discrete grid architecture. The upper-level global optimization module is used to construct an upper-level optimization model by setting the grid topology and load forecast within the area and the location information of the mobile energy storage device. The upper-level optimization module uses the alternating multiplier method combined with the upper-level optimization model to perform a distributed solution on the discrete grid architecture to obtain the local state of the discrete grid architecture and the charging and discharging path strategy of the mobile energy storage device. The lower-level local execution module is used to construct a lower-level optimization model through the energy storage status of the mobile energy storage device, the location information of the mobile energy storage device, the local state of the discrete power grid architecture, and the corresponding new energy output data. The upper-level optimization model and the lower-level optimization model are iteratively interacted through the alternating multiplier method and edge computing to output the charging and discharging power instructions of the mobile energy storage device. The charging and discharging power instructions of the mobile energy storage device are combined with the local state of the discrete power grid architecture and the charging and discharging path strategy of the mobile energy storage device to adjust the charging and discharging power of the mobile energy storage device in real time.

2. The intelligent scheduling optimization system including a mobile energy storage device according to claim 1, characterized in that: The mobile energy storage device is a device with plug-and-play characteristics, which supports access to regional power grids and dynamically reconstructs the power grid topology.

3. The intelligent scheduling optimization system including a mobile energy storage device according to claim 1, characterized in that: The upper-level global optimization module is used to construct an upper-level optimization model by setting the grid topology and load forecast within the region and the location information of the mobile energy storage device, with the goal of minimizing the operating costs of the regional grid. The operating costs include the electricity purchase costs between sub-regions, the scheduling costs generated by implementing the charging and discharging path strategy of the mobile energy storage device, and the local costs of the lower-level optimization model.

4. The intelligent scheduling optimization system including a mobile energy storage device according to claim 1, characterized in that: The lower-level local execution module is used to constrain and optimize the energy storage scheduling cost of each sub-region through the energy storage status of the mobile energy storage device, the location information of the mobile energy storage device, the local status of the discrete power grid architecture, and the corresponding new energy output data, thereby constructing a lower-level optimization model. The lower-level optimization model updates the mobile energy storage device scheduling capacity data to the upper-level optimization model, and realizes the iterative interaction between the upper-level optimization model and the lower-level optimization model through the alternating multiplier method; the dynamic update of the mobile energy storage capacity data is realized between the upper-level optimization model and the lower-level optimization model through the data interaction interface.

5. The intelligent scheduling optimization system including a mobile energy storage device according to claim 3, characterized in that: By setting the regional power grid topology and load forecast as well as the location information of the mobile energy storage device, the specific process of constructing the upper-level optimization model with the goal of minimizing the regional power grid operation cost is as follows: A multi-energy coupling network is constructed through energy hubs to form a physical connection framework of a regional integrated energy system to form a grid topology. The grid topology defines the discrete grid architecture through nodes and edges. Assume that electric vehicles can flow freely between sub-regions within the regional integrated energy system. The mobile energy storage device model is regarded as a transferable load. The charging and discharging behavior of the mobile energy storage device is collaboratively optimized through load forecasting. The cross-regional movement of the mobile energy storage device cluster is tracked in real time through IoT devices. The location information and number of mobile energy storage devices in each sub-region are dynamically updated. The total charging and discharging power of the mobile energy storage device in the kth sub-region is: Where, is the charging power of the i-th mobile energy storage device in the sub-area, is the discharge power of the i-th mobile energy storage device in the sub-area; is the charging efficiency of the i-th mobile energy storage device in the sub-area, is the discharge efficiency of the i-th mobile energy storage device in the sub-area; δ is the scheduling flag parameter, Indicates whether the i-th device in the sub-area participates in charging at time t, 1 if it participates, and 0 if it does not participate; Indicates whether the i-th device in the sub-area participates in the discharge at time t, 1 if it participates, and 0 if it does not participate; represents the net power of charging and discharging of the mobile energy storage device in the kth sub-area at time t, and the constraint can be obtained: Among them, n t is the number of mobile energy storage devices that can be dispatched in the sub-region at time t, n t-1 is the number of mobile energy storage devices that can be dispatched in the sub-region at time t-1, is the number of dispatchable mobile energy storage devices entering the sub-area at time t-1, is the number of dispatchable mobile energy storage devices that leave the sub-area at time t-1; At this time, the regional integrated energy system as a whole should have: Where, P div,t is the total transfer load of the mobile energy storage device cluster at time t, is the net dispatch power of the mobile energy storage device in the kth sub-region at time t. This formula constrains the global power balance of the regional integrated energy system based on the energy conservation principle of the power grid topology, ensuring that the energy interaction between sub-regions meets the transmission capacity limit. Considering the maximum capacity limit of the battery, the transfer charging load of all mobile energy storage devices in the regional integrated energy system during time t is: P div >G t -G max (4) Where G t is the storage capacity of the mobile energy storage device cluster at time t during the scheduling period, G max is the maximum battery capacity of the mobile energy storage device cluster, P div is the total net dispatch power of the mobile energy storage cluster at time t; At the same time, in order to ensure that the mobile energy storage device can be transferred smoothly after discharge, record G low To ensure the minimum battery capacity for the smooth transfer of EV clusters, the formula is: P div,t <G t -G low (5) Among them, P div,t is the total net dispatch power of the mobile energy storage device cluster at time t; Similarly, there are constraints: set up The scheduling cost generated by controlling the load of the mobile energy storage device to implement the charging and discharging path strategy of the mobile energy storage device for the kth sub-region is assumed to be proportional to the scheduling load size, which is: Where b is the dispatch cost coefficient; Assume that the electricity purchase cost of the kth sub-region from the grid at time t is Then we have: Where d represents the unit price of electricity purchase, represents the electric power purchased from the grid by the kth sub-region at time t, is the cost of purchasing electricity from the grid for the kth sub-region; Assume that in the kth sub-region t period, the local cost of the lower optimization model is In summary, the upper optimization objective function is obtained with the goal of minimizing the operating cost of the regional microgrid system: By defining the upper optimization objective function and combining the above formulas and constraints with the local cost of the lower optimization model The upper-level optimization model is constructed.

6. The intelligent scheduling optimization system including a mobile energy storage device according to claim 1, characterized in that: The specific process of performing a distributed solution to the discrete power grid architecture by combining the alternating multiplier method with the upper-level optimization model to obtain the local state of the discrete power grid architecture and the charging and discharging path strategy of the mobile energy storage device is as follows: The alternating multiplier method optimization function that minimizes the total global scheduling cost is as follows: min(f(x)+g(z))(11) Wherein, variable x represents the dispatch power of mobile energy storage in each sub-region, f(x) represents the sum of the electricity purchase cost and dispatch cost in each sub-region, variable z represents the intermediate variable in the global power balance constraint, and g(z) represents the constraint penalty for each sub-region solution to satisfy the global consistency. The global constraints of the regional power grid system are as follows: stAx+Bz=c (12) in, is an n-dimensional real vector; is an m-dimensional real vector; is a real matrix with p rows and n columns; is a real matrix with p rows and m columns; is a p-dimensional real vector; st indicates that the optimization problem must satisfy the subsequent constraints; the augmented Lagrangian function L can be obtained by the function f(x) and the function g(z) ρ (x,z,y): Where y represents the Lagrange multiplier, y T represents the transpose of the Lagrange multiplier, and ρ represents the penalty coefficient; Define the residual as r k , r k represents the deviation of the power of all sub-regions from the global constraint formula (12), then: r k :=Ax k +Bz k -c (14) Among them, x k represents the dispatching power of mobile energy storage in each sub-region at the kth iteration, z k represents the local energy storage power at the kth iteration; Define the standard dual variable u k ,Right now: you k :=(1 / ρ)y k (15) Among them, y k represents the Lagrange multiplier at the kth iteration; The iterative form of the alternating multiplier method algorithm is: u k+1 :=u k +Ax k+1 +Bz k+1 -c (18) Among them, k is the number of iterations of the alternating multiplier method, ρ is the penalty coefficient, and x k+1 represents the dispatching power of mobile energy storage in each sub-region at the k+1th iteration, z k+1 represents the local energy storage power at the k+1th iteration, represents the standard dual variable at the k+1th iteration; Generate x by alternating optimization of variables x and z k+1 and z k+1 , and update the standard dual variable u k Through distributed solution, the dispatch power of mobile energy storage in each sub-region that minimizes the objective function f(x) at the k+1th iteration is obtained, and the solution of each sub-region that minimizes the objective function g(z) at the k+1th iteration satisfies the constraint penalty of global consistency, and the dispatch strategy that minimizes the total global dispatch cost is obtained, thereby obtaining the corresponding local state of the discrete power grid architecture and the charging and discharging path strategy of the mobile energy storage device; From formula (18), it can be seen that the edge computing devices of each subsystem of the regional power grid system only need to exchange the newly generated x k+1 With z k+1 This ensures that the algorithm continues to operate until the residual is less than the specified convergence error.

7. The intelligent scheduling optimization system including a mobile energy storage device according to claim 1, characterized in that: The specific process of iteratively interacting the upper-layer optimization model and the lower-layer optimization model through the alternating multiplication method and edge computing to output the charging and discharging power instructions of the mobile energy storage device is as follows: Assume that in the regional power grid system, the power generation modes include wind power generation and photovoltaic power generation. At time t, the photovoltaic power generation output of the kth sub-region is Wind power output is but: Where, Predict the output of all renewable energy sources in the kth sub-region at time t; Assume that the depreciation cost of all energy storage devices in the region is not recorded, and the state of the i-th energy storage device in the k-th region at time t is: For any moment, There are three situations: Where, Indicates that the state of the i-th energy storage device in the k-th region at time t is the charging state, Indicates that the state of the i-th energy storage device in the k-th region at time t is idle. At time t, the state of the i-th energy storage device in region k is in the discharge state. Assume that there is an energy storage device with sufficient capacity in each sub-region. Its call cost increases with the increase of call capacity. The relationship between its call capacity and cost is: Where, is the energy storage call cost of the kth sub-region at time t, s1 is the linear cost coefficient, and s2 is the fixed cost coefficient; Assume that the predicted load value of the kth sub-area at time t is In order to ensure that all new energy sources in the regional integrated energy system are consumed, there is a power balance constraint at time t: Based on all the constraints above, the objective function of minimizing the energy storage call cost, i.e. the lower-level optimization model, is: in represents the other operating costs of the k-th sub-region at time t; Combining formula (10), formula (22) and formula (23), the upper optimization model is solved iteratively by the alternating multiplier method combined with edge computing, and each sub-region independently iterates to solve the P of the corresponding region. div,t , each iteration transfers the load G between different sub-regions div They communicate with each other and substitute the local optimization calculation formula (3) of the sub-region for the next round of iteration. Through the iterative interaction between the upper optimization model and the lower optimization model, the charging and discharging power instructions of the mobile energy storage device are finally obtained.

8. An intelligent scheduling optimization method including a mobile energy storage device, characterized in that: It includes the following steps: Mobile energy storage devices in a set area are used as movable nodes. The power of the mobile energy storage devices is provided by corresponding new energy power generation equipment. New energy output data of the new energy power generation equipment is collected, and the location information of the mobile energy storage devices scheduled between sub-areas in the set area is obtained. Based on the location information of the mobile energy storage devices and the corresponding new energy output data, a power transmission route based on the time and space dimensions is established to form a discrete power grid architecture; An upper-level optimization model is constructed by setting the grid topology and load forecast within the region and the location information of the mobile energy storage device. The local state of the discrete grid architecture and the charging and discharging path strategy of the mobile energy storage device are obtained by combining the upper-level optimization model with the alternating multiplier method to perform a distributed solution on the discrete grid architecture. A lower-level optimization model is constructed through the energy storage status of the mobile energy storage device, the location information of the mobile energy storage device, the local status of the discrete power grid architecture, and the corresponding new energy output data. The upper-level optimization model and the lower-level optimization model are iteratively interacted through the alternating multiplier method and edge computing to output the charging and discharging power instructions of the mobile energy storage device. The charging and discharging power instructions of the mobile energy storage device are combined with the local status of the discrete power grid architecture and the charging and discharging path strategy of the mobile energy storage device to adjust the charging and discharging power of the mobile energy storage device in real time.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to claim 8 are implemented.

10. A computer-readable storage medium storing a computer program, wherein: When the computer program is executed by a processor, the steps of the method according to claim 8 are implemented.

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