A mobile energy storage collaborative scheduling method and device considering traffic-power distribution network coupling

Abstract

The application discloses a mobile energy storage collaborative scheduling method and device considering traffic-power distribution network coupling. The method obtains the shortest transfer path and distance between access points of each substation by constructing a road traffic topology model and a road adjacency matrix and calculating by using a shortest path algorithm, and obtains traffic constraints for mobile energy storage transfer across substations. Based on the traffic constraints, a mobile energy storage space-time transfer model and an external characteristic model are constructed to form mobile energy storage operation constraints. Based on the two types of constraints, combined with substation transformer, power distribution network power flow, node voltage and branch capacity constraints, a multi-substation day-ahead collaborative scheduling model considering traffic-power distribution network coupling is constructed. The model is converted into a mixed integer quadratic programming model and solved, and the substation deployment scheme of the mobile energy storage, the cross-substation transfer path, the active and reactive power output plan and the state of charge trajectory are output. The application solves the problem of poor cross-substation collaboration in the prior art.

Description

Technical Field

[0001] This invention relates to the field of mobile energy storage dispatch and distribution network operation optimization technology, specifically to a mobile energy storage collaborative dispatch method and device that takes into account transportation-distribution network coupling. Background Technology

[0002] With the rapid integration of distributed photovoltaic (PV) power, new loads, and flexible resources into distribution networks, the operating status of distribution substations exhibits stronger spatiotemporal volatility and variability. Some substations are prone to short-term positive overload during typical periods, while in scenarios with a high proportion of distributed renewable energy integration, the net load of some substations may decrease significantly, even leading to reverse overload and voltage exceeding limits. Traditional methods of transformer expansion, distribution network upgrades, and fixed energy storage configurations either involve large investments and long construction periods, or make it difficult to flexibly share regulation capabilities among multiple substations.

[0003] Mobile energy storage systems possess the dual flexibility of time-shifting and spatial migration of power, enabling them to be transferred between different access points via transport vehicles, thereby achieving cross-regional reuse and on-demand deployment of energy storage resources. While existing research has explored mobile energy storage in areas such as active distribution network optimization, emergency power supply, and post-disaster recovery, most studies focus on the economic operation of general distribution networks, fixed charging facilities, or electric vehicle charging behavior. A multi-distribution area collaborative scheduling method that simultaneously considers road topology accessibility, shortest path constraints, transformer operation constraints, and distribution network power flow constraints has not yet been developed.

[0004] Furthermore, existing methods often fail to adequately characterize the coupling relationship between active power regulation, reactive power support, distribution area load rate optimization, and system voltage improvement during the spatiotemporal migration of mobile energy storage. This makes it difficult to achieve scheduling results that balance economy and feasibility when facing short-term positive overload management of multiple distribution areas, local reverse overload mitigation under high-penetration renewable energy sources, and active power complementarity between distribution areas.

[0005] Therefore, there is an urgent need for a mobile energy storage coordinated dispatch method that takes into account the coupling between transportation and distribution networks, in order to solve the problems of poor cross-regional coordination and difficulty in coordinating cost and voltage quality optimization in existing technologies. Summary of the Invention

[0006] To address this, the present invention provides a mobile energy storage collaborative scheduling method and device that takes into account traffic-distribution network coupling, solving the problems of existing mobile energy storage scheduling not fully considering traffic constraints, resulting in poor cross-regional coordination and difficulty in coordinating cost and voltage quality optimization.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a mobile energy storage coordinated dispatch method considering transportation-distribution network coupling, characterized in that it includes:

[0008] By collecting basic data on road networks and distribution substations, and taking the access points of distribution substations as road network nodes, a road traffic topology model and a road adjacency matrix are constructed. Based on the road traffic topology model and the road adjacency matrix, the shortest transfer path and shortest transfer distance between each substation access point are calculated using the shortest path algorithm, thereby obtaining the traffic constraints for the transfer of mobile energy storage across substations.

[0009] Based on the aforementioned traffic constraints, a spatiotemporal transfer model and an external characteristic model for mobile energy storage are constructed. The spatiotemporal transfer model describes the temporal state of mobile energy storage's residence and cross-regional transfer. The external characteristic model characterizes the charging and discharging power, reactive power, and state of charge operation characteristics of mobile energy storage, thus forming the operational constraints for mobile energy storage.

[0010] Based on the traffic constraints and the mobile energy storage operation constraints, combined with the transformer operation constraints, distribution network power flow constraints, node voltage constraints and branch capacity constraints, a multi-transformer day-ahead collaborative scheduling model considering traffic-distribution network coupling is constructed.

[0011] By linearizing the absolute value terms and nonlinear terms in the multi-region day-ahead coordinated scheduling model, the multi-region day-ahead coordinated scheduling model is transformed into a mixed integer quadratic programming model. By solving the mixed integer quadratic programming model, the deployment scheme of mobile energy storage, the cross-region transfer path, the active and reactive power output plan, and the state of charge trajectory are output.

[0012] As a preferred embodiment of a mobile energy storage coordinated dispatch method that considers traffic-distribution network coupling, the expression of the road traffic topology model is as follows:

[0013] G(V,E)

[0014] In the formula, V is the set of nodes consisting of road endpoints, intersections, and transformer access points; E is the set of edges consisting of road segments.

[0015] The expression for the road adjacency matrix is:

[0016]

[0017]

[0018] In the formula, D is the road adjacency matrix; d mn For road adjacency matrix elements; l mn is the length of the road segment between node m and node n; inf indicates that there is no direct road segment between the two nodes.

[0019] In the process of calculating the shortest transfer path and shortest transfer distance between each access point of the transformer area using the shortest path algorithm, the shortest path algorithm is Dijkstra's algorithm.

[0020] As a preferred scheme for a mobile energy storage coordinated dispatch method that considers transportation-distribution network coupling, the spatiotemporal transfer model describes the temporal state of mobile energy storage residence and cross-regional transfer as follows:

[0021]

[0022]

[0023]

[0024] In the formula, This is a set of distribution radio stations, where m and n are the radio stations deployed at road network nodes m and n, respectively. Let be the spatiotemporal transition matrix of the k-th mobile energy storage; mn represents the movement of the mobile energy storage between distribution area m and distribution area n; T represents the operating cycle of the mobile energy storage.

[0025] The operational constraints of the mobile energy storage formed by the external characteristic model include: charging and discharging power constraints, reactive power regulation constraints, and state of charge update constraints.

[0026] The expression for the charging and discharging power constraint is:

[0027]

[0028]

[0029] In the formula, , and These represent the active power and reactive power of charging and discharging of the kth mobile energy storage unit deployed in transformer area n during time period t; This represents the charging and discharging state of mobile energy storage deployed in transformer area n. When its value is equal to 1, the mobile energy storage is in a charging state; when its value is equal to 0, the mobile energy storage is in a discharging state. This represents the rated power of the k-th mobile energy storage unit.

[0030] The expression for the reactive power regulation constraint is:

[0031]

[0032] The expression for the state of charge update constraint is:

[0033]

[0034] In the formula, The duration of adjacent scheduling periods; and These are the charging efficiency and discharging efficiency of mobile energy storage, respectively. and These are the rated power and rated capacity of the k-th mobile energy storage device, respectively. The state of charge of the k-th mobile energy storage during time period t; and These are the upper and lower limits of SOC, respectively.

[0035] As a preferred scheme for a mobile energy storage coordinated dispatch method that takes into account the coupling of transportation and distribution networks, the operating constraints of the transformer substation include: active power constraints, reactive power constraints and load factor constraints of the transformer substation; the power flow constraints of the distribution network are characterized by the DistFlow model, including node injection power balance constraints and branch power balance constraints.

[0036] As a preferred embodiment of a mobile energy storage coordinated dispatch method that considers transportation-distribution network coupling, the objective function expression of the multi-region day-ahead coordinated dispatch model is:

[0037]

[0038] In the formula, f is the objective function value; This includes losses in distribution network lines and corresponding transformer losses in the distribution area. This refers to the load rate deviation of the transformer in the distribution area. Mobile energy storage relocation costs; For mobile energy storage arbitrage profits; Operating costs for charging and discharging mobile energy storage; This serves as the baseline value, used to convert cost-benefit items into per-unit values; This refers to the system voltage deviation. , , These are the weighting coefficients.

[0039] This invention also provides a mobile energy storage collaborative dispatching device considering transportation-distribution network coupling, employing the above-mentioned mobile energy storage collaborative dispatching method considering transportation-distribution network coupling, comprising:

[0040] The traffic constraint acquisition module is used to collect basic data of road network and distribution substations, and construct a road traffic topology model and road adjacency matrix with the substation access point as the road network node; based on the road traffic topology model and the road adjacency matrix, the shortest transfer path and shortest transfer distance between each substation access point are calculated by the shortest path algorithm to obtain the traffic constraint conditions for the cross-substation transfer of mobile energy storage.

[0041] The mobile energy storage operation constraint acquisition module is used to construct a spatiotemporal transfer model and an external characteristic model of mobile energy storage based on the traffic constraints; the spatiotemporal transfer model describes the temporal state of mobile energy storage residence and cross-station transfer; and the external characteristic model characterizes the charging and discharging power, reactive power and state of charge operation characteristics of mobile energy storage, thus forming the mobile energy storage operation constraints.

[0042] The multi-region day-ahead collaborative scheduling model construction module is used to construct a multi-region day-ahead collaborative scheduling model that takes into account traffic-distribution network coupling, based on the traffic constraints and the mobile energy storage operation constraints, combined with the transformer operation constraints, distribution network power flow constraints, node voltage constraints and branch capacity constraints.

[0043] The multi-region day-ahead coordinated scheduling model solution module is used to transform the multi-region day-ahead coordinated scheduling model into a mixed integer quadratic programming model by linearizing the absolute value terms and nonlinear terms in the multi-region day-ahead coordinated scheduling model; by solving the mixed integer quadratic programming model, the module outputs the mobile energy storage deployment scheme, cross-region transfer path, active and reactive power output plan, and state of charge trajectory.

[0044] As a preferred embodiment of a mobile energy storage coordinated dispatching device that considers traffic-distribution network coupling, the expression of the road traffic topology model in the traffic constraint acquisition module is as follows:

[0045] G(V,E)

[0046] In the formula, V is the set of nodes consisting of road endpoints, intersections, and transformer access points; E is the set of edges consisting of road segments.

[0047] The expression for the road adjacency matrix is:

[0048]

[0049]

[0050] In the formula, D is the road adjacency matrix; d mn For road adjacency matrix elements; l mn is the length of the road segment between node m and node n; inf indicates that there is no direct road segment between the two nodes.

[0051] In the process of calculating the shortest transfer path and shortest transfer distance between each access point of the transformer area using the shortest path algorithm, the shortest path algorithm is Dijkstra's algorithm.

[0052] As a preferred embodiment of a mobile energy storage collaborative dispatching device that considers transportation-distribution network coupling, the expression describing the temporal state of mobile energy storage residence and cross-regional transfer in the mobile energy storage operation constraint acquisition module is as follows:

[0053]

[0054]

[0055]

[0056] In the formula, This is a set of distribution radio stations, where m and n are the radio stations deployed at road network nodes m and n, respectively. Let be the spatiotemporal transition matrix of the k-th mobile energy storage; mn represents the movement of the mobile energy storage between distribution area m and distribution area n; T represents the operating cycle of the mobile energy storage.

[0057] The operational constraints of the mobile energy storage formed by the external characteristic model include: charging and discharging power constraints, reactive power regulation constraints, and state of charge update constraints.

[0058] The expression for the charging and discharging power constraint is:

[0059]

[0060]

[0061] In the formula, , and These represent the active power and reactive power of charging and discharging of the kth mobile energy storage unit deployed in transformer area n during time period t; This represents the charging and discharging state of mobile energy storage deployed in transformer area n. When its value is equal to 1, the mobile energy storage is in a charging state; when its value is equal to 0, the mobile energy storage is in a discharging state. This represents the rated power of the k-th mobile energy storage unit.

[0062] The expression for the reactive power regulation constraint is:

[0063]

[0064] The expression for the state of charge update constraint is:

[0065]

[0066] In the formula, The duration of adjacent scheduling periods; and These are the charging efficiency and discharging efficiency of mobile energy storage, respectively. and These are the rated power and rated capacity of the k-th mobile energy storage device, respectively. The state of charge of the k-th mobile energy storage during time period t; and These are the upper and lower limits of SOC, respectively.

[0067] As a preferred solution for a mobile energy storage collaborative scheduling device that takes into account the coupling of transportation and distribution networks, in the multi-region day-ahead collaborative scheduling model construction module, the operating constraints of the transformers in the distribution areas include: active power constraints, reactive power constraints, and load factor constraints of the transformers in the distribution areas; the power flow constraints of the distribution network are characterized by the DistFlow model, including node injection power balance constraints and branch power balance constraints.

[0068] As a preferred embodiment of a mobile energy storage collaborative dispatching device that considers transportation-distribution network coupling, the objective function expression of the multi-region day-ahead collaborative dispatching model construction module is as follows:

[0069]

[0070] In the formula, f is the objective function value; This includes losses in distribution network lines and corresponding transformer losses in the distribution area. This refers to the load rate deviation of the transformer in the distribution area. Mobile energy storage relocation costs; For mobile energy storage arbitrage profits; Operating costs for charging and discharging mobile energy storage; This serves as the baseline value, used to convert cost-benefit items into per-unit values; This refers to the system voltage deviation. , , These are the weighting coefficients.

[0071] The present invention has the following advantages:

[0072] First, this invention integrates road accessibility and power distribution network operation constraints into the mobile energy storage dispatch framework, which can effectively avoid unexecutable dispatch schemes caused by ignoring actual road network conditions.

[0073] Secondly, this invention can simultaneously address the short-term positive heavy overload management of multiple distribution areas, the local reverse heavy load mitigation under high-penetration renewable energy, and the active power complementarity needs between distribution areas, giving full play to the cross-regional regulation capability of mobile energy storage.

[0074] Third, based on active power support, this invention further utilizes the reactive power compensation capability of mobile energy storage to improve the voltage level of system nodes, while taking into account the optimization of transformer load rate, loss reduction and system operation economy.

[0075] Fourth, this invention can transform the model into a mixed-integer quadratic programming problem for solution, which has good engineering feasibility. Attached Figure Description

[0076] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0077] The structures, proportions, sizes, etc. illustrated in this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed herein, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0078] Figure 1 This is a flowchart illustrating a mobile energy storage collaborative scheduling method considering transportation-distribution network coupling provided in Embodiment 1 of the present invention;

[0079] Figure 2 This is a schematic diagram illustrating the specific implementation process of a mobile energy storage collaborative scheduling method considering transportation-distribution network coupling provided in Embodiment 1 of the present invention;

[0080] Figure 3 This is a schematic diagram of a portion of the main road network in one possible embodiment of Embodiment 1 of the present invention;

[0081] Figure 4 This is a schematic diagram of an improved 141-node power distribution system topology in one possible embodiment of Embodiment 1 of the present invention;

[0082] Figure 5 This is a schematic diagram of typical load conditions for three transformer substations in one possible embodiment of the present invention, as provided in Embodiment 1 of the present invention.

[0083] Figure 6 This is a schematic diagram of the multi-unit transfer path of mobile energy storage in one possible embodiment provided in Embodiment 1 of the present invention;

[0084] Figure 7 This is a schematic diagram of the typical daily operating power of each mobile energy storage device in one possible embodiment provided in Embodiment 1 of the present invention;

[0085] Figure 8This is a schematic diagram of the SOC variation curves of each mobile energy storage device in one possible embodiment provided in Embodiment 1 of the present invention;

[0086] Figure 9 This is a schematic diagram comparing the load rates of transformers in each distribution area before and after deployment in one possible embodiment of Embodiment 1 of the present invention;

[0087] Figure 10 This is a schematic diagram of the system voltage distribution before and after deployment in one possible embodiment provided in Embodiment 1 of the present invention;

[0088] Figure 11 This is a schematic diagram of the architecture of a mobile energy storage collaborative scheduling device that takes into account the coupling of transportation and power distribution networks, as provided in Embodiment 2 of the present invention. Detailed Implementation

[0089] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0090] Example 1

[0091] See Figure 1 and Figure 2 Embodiment 1 of the present invention provides a mobile energy storage coordinated scheduling method considering transportation-distribution network coupling, comprising the following steps:

[0092] S1. By collecting basic data on road networks and distribution substations, and taking the access points of distribution substations as road network nodes, a road traffic topology model and a road adjacency matrix are constructed. Based on the road traffic topology model and the road adjacency matrix, the shortest transfer path and shortest transfer distance between each substation access point are calculated using the shortest path algorithm, and the traffic constraints for the transfer of mobile energy storage across substations are obtained.

[0093] S2. Based on the traffic constraints, construct a spatiotemporal transfer model and an external characteristic model for mobile energy storage; describe the temporal state of mobile energy storage's residence and cross-regional transfer through the spatiotemporal transfer model; characterize the charging and discharging power, reactive power, and state of charge operation characteristics of mobile energy storage through the external characteristic model, thus forming the operating constraints for mobile energy storage.

[0094] S3. Based on the traffic constraints and the mobile energy storage operation constraints, and combined with the transformer operation constraints, power flow constraints, node voltage constraints and branch capacity constraints, a multi-transformer day-ahead collaborative scheduling model considering traffic-power grid coupling is constructed.

[0095] S4. By linearizing the absolute value terms and nonlinear terms in the multi-region day-ahead coordinated scheduling model, the multi-region day-ahead coordinated scheduling model is transformed into a mixed integer quadratic programming model. By solving the mixed integer quadratic programming model, the deployment scheme of mobile energy storage, the cross-region transfer path, the active and reactive power output plan, and the state of charge trajectory are output.

[0096] In this embodiment, in step S1, by collecting basic data of the road network and distribution substations, and taking the access points of the distribution substations as road network nodes, a road traffic topology model and a road adjacency matrix are constructed. Based on the road traffic topology model and the road adjacency matrix, the shortest transfer path and shortest transfer distance between each substation access point are calculated using the shortest path algorithm, thereby obtaining the traffic constraints for the cross-substation transfer of mobile energy storage.

[0097] Specifically, the transfer of mobile energy storage between multiple distribution areas involves not only the selection of the target distribution area but also constraints imposed by road topology, node connectivity, and the shortest feasible path. To describe the transfer process of mobile energy storage in a transportation network, the road network is abstracted as a graph G(V,E), where V is the set of road nodes and E is the set of road edges. An adjacency matrix D is used to describe the connectivity between nodes and the road lengths.

[0098] (1)

[0099] (2)

[0100] In the formula, D is the road adjacency matrix; d mn For road adjacency matrix elements; l mn is the length of the road segment between node m and node n; inf means there is no direct road segment between the two nodes.

[0101] In this embodiment, the shortest path algorithm used to calculate the shortest transfer path and shortest transfer distance between each access point in the transformer area is Dijkstra's algorithm.

[0102] In this embodiment, in step S2, a spatiotemporal transfer model and an external characteristic model for mobile energy storage are constructed based on the traffic constraints. The spatiotemporal transfer model describes the temporal state of mobile energy storage's residence and cross-regional transfer. The external characteristic model characterizes the charging and discharging power, reactive power, and state of charge operation characteristics of mobile energy storage, thus forming the operating constraints for mobile energy storage.

[0103] Specifically, mobile energy storage must be in a certain operating state at any given time, including being stationed in a certain distribution area or in a state of transfer between distribution areas. Equation (3) constrains mobile energy storage to be in a certain operating state at any given time; Equations (4) and (5) constrain the state of mobile energy storage between different distribution areas at adjacent times, ensuring that it must undergo the corresponding transfer state when it is transferred between the corresponding distribution areas.

[0104] (3)

[0105] (4)

[0106] (5)

[0107] In the formula, This is a set of distribution radio stations, where m and n are the radio stations deployed at road network nodes m and n, respectively. Let be the spatiotemporal transfer matrix of the k-th mobile energy storage; mn represents the movement of the mobile energy storage between substation m and substation n; and T represents the operating cycle of the mobile energy storage.

[0108] In this embodiment, the mobile energy storage operation constraints formed by the external characteristic model include: charging and discharging power constraints, reactive power regulation constraints, and state of charge update constraints.

[0109] The expression for the charging and discharging power constraint is:

[0110] (6)

[0111] (7)

[0112] In the formula, , and These represent the active power and reactive power of charging and discharging of the kth mobile energy storage unit deployed in transformer area n during time period t; This represents the charging and discharging state of mobile energy storage deployed in transformer area n. When its value is equal to 1, the mobile energy storage is in a charging state; when its value is equal to 0, the mobile energy storage is in a discharging state. This represents the rated power of the k-th mobile energy storage unit.

[0113] Equations (6) and (7) represent the charging and discharging power constraints of mobile energy storage deployed in the same transformer area n during the same time period. Therefore, the actual charging and discharging power of mobile energy storage is equal to the sum of its operating power in each transformer area, as shown in equation (8):

[0114] (8)

[0115] In this embodiment, the expression for the reactive power regulation constraint is:

[0116] (9)

[0117] The expression for the state of charge update constraint is:

[0118] (10)

[0119] In the formula, The duration of adjacent scheduling periods; and These are the charging efficiency and discharging efficiency of mobile energy storage, respectively. and These are the rated power and rated capacity of the k-th mobile energy storage device, respectively. The state of charge of the k-th mobile energy storage during time period t; and These are the upper and lower limits of SOC, respectively.

[0120] In this embodiment, in step S3, based on the traffic constraints and the mobile energy storage operation constraints, combined with the transformer operation constraints, power flow constraints, node voltage constraints and branch capacity constraints, a multi-transformer day-ahead collaborative scheduling model considering traffic-power distribution network coupling is constructed.

[0121] Specifically, after mobile energy storage is connected to the distribution transformer area, its active and reactive power outputs will affect the transformer load rate and power flow distribution of the distribution network. Distribution transformer areas mainly consist of distribution transformers, and their external characteristic constraints can be expressed as follows:

[0122] (11)

[0123] (12)

[0124] In the formula, and The active and reactive power flowing through the transformer; The total active power of the transformer's low-voltage busbar; This represents the total number of mobile energy storage units. Let n be the load rate of transformer n during time period t; Let n be the rated capacity of transformer n.

[0125] To further describe the operational constraints of the distribution network after the integration of mobile energy storage, the Distflow model is used to establish the power flow equations of the distribution network. For branch ij, its active power balance, reactive power balance, voltage drop, and current relationships are expressed as follows:

[0126] (13)

[0127] (14)

[0128] (15)

[0129] (16)

[0130] In the formula, Let i be the set of the starting nodes of the branch with node i as the ending node; Let i be the set of end nodes of the branch with node i as the first end node; and These are the resistance and reactance of AC branch ij, respectively; , These represent the active power and reactive power of branch ji during time period t; and These are the sum of active power and reactive power injected into AC node i during time period t, respectively. and Let be the branch current ij and the AC node voltage i, respectively, during time period t; and These are the active and reactive loads of the node, excluding transformer area n.

[0131] Meanwhile, the node voltage constraint and branch capacity constraint are expressed as follows:

[0132] (17)

[0133] (18)

[0134] In the formula, and These are the upper and lower voltage limits for node i, respectively; This represents the maximum carrying capacity of the line.

[0135] In this embodiment, in step S4, the absolute value term and nonlinear term in the multi-region day-ahead coordinated scheduling model are linearized to transform the multi-region day-ahead coordinated scheduling model into a mixed integer quadratic programming model. By solving the mixed integer quadratic programming model, the deployment scheme of mobile energy storage, the cross-region transfer path, the active and reactive power output plan, and the state of charge trajectory are output.

[0136] Specifically, to comprehensively consider line and transformer losses, transformer load rate deviations in the distribution area, relocation costs, arbitrage profits, charging and discharging operating costs, and system voltage deviations, the following objective function is constructed:

[0137] (19)

[0138] In the formula, f is the objective function value; This includes losses in distribution network lines and corresponding transformer losses in the distribution area. This refers to the load rate deviation of the transformer in the distribution area. Mobile energy storage relocation costs; For mobile energy storage arbitrage profits; Operating costs for charging and discharging mobile energy storage; This serves as the baseline value, used to convert cost-benefit items into per-unit values; This refers to the system voltage deviation. , , These are the weighting coefficients.

[0139] in,

[0140] (20)

[0141] In the formula, K represents the total number of distribution network nodes; Let n be the short-circuit loss of the distribution transformer n.

[0142] (twenty one)

[0143] In the formula, This represents the upper limit of the optimal load rate.

[0144] (twenty two)

[0145] In the formula, The unit mobility cost for mobile energy storage; This represents the shortest transfer distance between transformer area m and transformer area n.

[0146] (twenty three)

[0147] In the formula, The time-of-use electricity price is for period t.

[0148] (twenty four)

[0149] In the formula, Is it a cyclic decay function or a cyclic lifetime loss coefficient? Is it a static decay function or a calendar lifetime loss coefficient? and These are the average SOC and depth of discharge for mobile energy storage, respectively. The unit price is the price per kW·h of battery capacity. The model parameters are 1.952e-5, 1.75e-5, 4.9e-5, and 7.859e-6, respectively.

[0150] (25)

[0151] In the formula, and These represent the upper and lower limits of the voltage optimization range, respectively.

[0152] In this embodiment, to facilitate the solution, the absolute value terms in equations (21) and (25) are linearized. Taking equation (21) as an example, it is transformed into equation (26), and in equation (21)... replace Then we have:

[0153] (26)

[0154] In the formula, and These are intermediate variables representing the load rate.

[0155] In this embodiment, the system voltage deviation term can be linearized in the same way, thereby transforming the entire collaborative scheduling model into a mixed integer quadratic programming problem for solution, and outputting the mobile energy storage distribution area deployment scheme, cross-distribution area transfer path, active and reactive power output plan, and state of charge trajectory.

[0156] In one possible implementation, a verification example is provided as follows:

[0157] To verify the effectiveness of the proposed mobile energy storage multi-region coordinated dispatch method considering traffic-distribution network coupling, a joint verification was conducted using operational data from a portion of the main road network in a city, three typical low-voltage distribution substations, and an improved 141-node distribution system. A portion of the main road network is shown below. Figure 3 As shown, the improved 141-node distribution system topology is as follows: Figure 4 As shown, the typical load conditions of the transformers in the three distribution areas are as follows: Figure 5 As shown.

[0158] In this embodiment, the electricity price adopts a time-of-use pricing mechanism, with a price of 0.6 yuan / (kW·h) from 9:00 to 21:00 and 0.45 yuan / (kW·h) for other time periods; the upper limit of the optimal load factor is set at 0.75; and the weighting coefficient is... , and The values ​​are taken as 0.7, 0.15, and 0.15 respectively; the unit relocation cost is set at 5 yuan / km. The parameters of the three distribution transformers are shown in Table 1:

[0159] Table 1 Parameters of Distribution Transformers

[0160] The parameters of mobile energy storage are shown in Table 2:

[0161] Table 2 MESS parameters

[0162] Among them, the rated capacities of transformers in transformer substations 1, 2, and 3 are 1000 kV·A, 1250 kV·A, and 1000 kV·A, respectively, with corresponding short-circuit losses of 4.5 kW, 6 kW, and 4.5 kW, respectively; the rated capacities of mobile energy storage 1 and mobile energy storage 2 are both 600 kW·h, and the rated power is both 300 kW, while the rated capacity of mobile energy storage 3 is 1000 kW·h, and the rated power is 500 kW. Their upper and lower limits of state of charge are 0.1 and 0.9, respectively, and the unit price per unit capacity of each battery is 1059.73 yuan / (kW·h).

[0163] pass Figure 5 It can be seen that the three distribution areas exhibit significant load differences and temporal complementarity during a typical day. Distribution area 1, influenced by a high proportion of distributed photovoltaic (PV) grid connection, experiences a significant decrease in net load during specific periods, even exhibiting power backflow, demonstrating strong charging demand and energy transfer potential. Distribution area 2 shows a prominent peak load effect, making it a key target for mobile energy storage. Although distribution area 3 has a slightly lower peak load, its high load duration is relatively long, thus also possessing strong peak-shaving demand. This scenario effectively reflects the practical application requirements of multi-distribution area coordinated dispatch of mobile energy storage under transportation-distribution network coupling conditions.

[0164] After optimization using the method described in this invention, the multi-unit transfer paths of the three mobile energy storage units within a typical day are obtained, such as... Figure 6 As shown. By Figure 6 As can be seen, in this embodiment, mobile energy storage is not fixedly deployed in a single transformer substation, but rather undergoes planned spatiotemporal transfer between different substations based on substation load characteristics, distributed power output characteristics, and road accessibility constraints. For substations experiencing peak-load pressure, mobile energy storage will prioritize transferring and residing before the load increases to ensure sufficient discharge support capacity during heavy-load periods. For substations with lower net loads, mobile energy storage will enter and perform charging operations at appropriate times to absorb surplus active power and reserve energy for subsequent cross-regional support. This result demonstrates that the method proposed in this invention can achieve orderly cross-regional transfer of mobile energy storage under traffic network constraints.

[0165] Furthermore, typical daily operating power curves for each mobile energy storage device were obtained, such as... Figure 7 As shown. By Figure 7It can be seen that mobile energy storage 1 initially resides in transformer substation 3 and charges during off-peak hours, then begins discharging in subsequent periods, before transferring to transformer substation 1 to undertake evening support tasks; mobile energy storage 2 initially resides in transformer substation 1, utilizing surplus power around noon for concentrated energy replenishment, then transfers to transformer substation 3 and continues discharging in the evening; mobile energy storage 3 also initially resides in transformer substation 1 and completes large-scale energy replenishment, then transfers to transformer substation 2 and primarily undertakes peak shaving tasks during the evening rush hour. These results demonstrate that the present invention can coordinate the active power support and reactive power auxiliary regulation functions of multiple mobile energy storage units across multiple transformer substations, achieving energy complementarity between multiple substations.

[0166] The state of charge evolution process of each mobile energy storage device is as follows: Figure 8 As shown. By Figure 8 It can be seen that the SOC curves of each mobile energy storage device generally exhibit a cyclical change characteristic of first replenishing energy, then providing support, and finally restoring energy. Among them, the SOC curve of mobile energy storage device 1 shows a more obvious segmented change, reflecting its switching of operating states between multiple distribution areas; the SOC changes of mobile energy storage devices 2 and 3 are relatively stable, indicating that they mainly complete local support and short-term energy replenishment within the target distribution area. This result shows that the present invention can effectively coordinate the energy states of each mobile energy storage device while meeting traffic transfer time constraints.

[0167] Changes in transformer load rate in each distribution area before and after mobile energy storage deployment are as follows: Figure 9 As shown. By Figure 9 It can be seen that after deploying mobile energy storage, the overload problem of transformer area 2 during peak hours was significantly alleviated, the continuous overload period of transformer area 3 was significantly shortened, and transformer area 1 demonstrated its role in absorbing surplus photovoltaic power and supporting subsequent cross-regional energy. In summary, the method proposed in this invention can effectively reduce the peak load rate of transformers in multiple transformer areas, promote active power complementarity between different transformer areas, and improve the balance of transformer area operation.

[0168] Improvement in system voltage distribution before and after mobile energy storage deployment, as follows: Figure 10 As shown. By Figure 10 It can be seen that before the deployment of mobile energy storage, some nodes experienced a certain degree of low voltage during periods of heavy load. After the deployment of mobile energy storage, as the mobile energy storage outputs reactive power in the target distribution area, the voltage levels of some low-voltage nodes are raised, and the overall voltage distribution of the system becomes smoother. This result indicates that the present invention can not only alleviate transformer overload in the distribution area through active power support, but also improve the voltage quality of the distribution network through reactive power compensation.

[0169] Regarding losses and economics, before deploying mobile energy storage, the typical daily losses of the three transformer substations were approximately 112.9 kWh, and the line losses were approximately 18616.2 kWh. After deploying mobile energy storage, the transformer losses were approximately 125 kWh, and the line losses were approximately 17737.1 kWh, representing a reduction of approximately 879.1 kWh in line losses, a decrease of approximately 5%. Furthermore, the typical daily operating cost of mobile energy storage is approximately RMB 302.7, the relocation cost is approximately RMB 103.7, and the charging and discharging cost is approximately RMB 63.8. Although certain relocation and operating costs are introduced, the invention still demonstrates good overall comprehensive operational benefits by reducing line losses, optimizing transformer load rates, and improving system voltage levels.

[0170] In summary, this invention can achieve the orderly transfer and coordinated scheduling of mobile energy storage among multiple distribution areas, taking into account factors such as the shortest path constraint, the spatiotemporal transfer characteristics of mobile energy storage, the operating constraints of transformer substations, and the power flow and voltage constraints of the distribution network. This not only effectively alleviates the problems of short-term heavy load and local reverse heavy load in multiple distribution areas, but also improves the voltage level of the distribution network and enhances the overall economic efficiency of the system.

[0171] The application scenarios of this invention are as follows:

[0172] In urban multi-distribution network operation scenarios, this invention can realize cross-distribution collaborative scheduling of mobile energy storage, reduce system losses and operating costs, and improve distribution load balance and voltage quality.

[0173] In scenarios where distribution network areas have uneven loads and large voltage deviations, this invention can alleviate the problems of heavy loads and voltage exceeding limits in distribution areas through mobile energy storage for cross-regional transfer and reactive power support.

[0174] In the scenario of large-scale distributed photovoltaic grid integration, this invention can utilize mobile energy storage for cross-grid scheduling to smooth out power output fluctuations and improve the local consumption capacity of new energy.

[0175] In multi-distribution scenarios for emergency power supply and fault recovery, this invention can enhance the emergency power supply guarantee capability of the distribution network through cross-distribution transfer and coordinated power supply of mobile energy storage.

[0176] In scenarios involving distribution transformer clusters with significant time-of-use pricing differences, this invention can guide mobile energy storage to engage in cross-regional arbitrage and peak shaving / valley filling, thereby reducing overall electricity costs.

[0177] It should be noted that the method of this disclosure embodiment can be executed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method of this disclosure embodiment, and the multiple devices will interact with each other to complete the method described.

[0178] It should be noted that the above description describes some embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0179] Example 2

[0180] See Figure 11 Embodiment 2 of the present invention also provides a mobile energy storage collaborative dispatching device that takes into account the coupling of transportation and power distribution networks, comprising:

[0181] The traffic constraint acquisition module 001 is used to collect basic data of road network and distribution substations, and construct a road traffic topology model and road adjacency matrix with the substation access point as the road network node; based on the road traffic topology model and the road adjacency matrix, the shortest transfer path and shortest transfer distance between each substation access point are calculated by the shortest path algorithm to obtain the traffic constraint conditions for the cross-substation transfer of mobile energy storage.

[0182] The mobile energy storage operation constraint acquisition module 002 is used to construct a spatiotemporal transfer model and an external characteristic model of mobile energy storage based on the traffic constraints; the spatiotemporal transfer model describes the temporal state of mobile energy storage residence and cross-station transfer; and the external characteristic model characterizes the charging and discharging power, reactive power and state of charge operation characteristics of mobile energy storage, thus forming the mobile energy storage operation constraints.

[0183] The multi-region day-ahead collaborative scheduling model construction module 003 is used to construct a multi-region day-ahead collaborative scheduling model that takes into account traffic-distribution network coupling, based on the traffic constraints and the mobile energy storage operation constraints, combined with the transformer operation constraints, distribution network power flow constraints, node voltage constraints and branch capacity constraints.

[0184] The multi-region day-ahead coordinated scheduling model solution module 004 is used to transform the multi-region day-ahead coordinated scheduling model into a mixed integer quadratic programming model by linearizing the absolute value terms and nonlinear terms in the multi-region day-ahead coordinated scheduling model; by solving the mixed integer quadratic programming model, it outputs the mobile energy storage deployment scheme, cross-region transfer path, active and reactive power output plan, and state of charge trajectory.

[0185] In this embodiment, the expression of the road traffic topology model in the traffic constraint acquisition module 001 is:

[0186] G(V,E)

[0187] In the formula, V is the set of nodes consisting of road endpoints, intersections, and transformer access points; E is the set of edges consisting of road segments.

[0188] The expression for the road adjacency matrix is:

[0189]

[0190]

[0191] In the formula, D is the road adjacency matrix; d mn For road adjacency matrix elements; l mn is the length of the road segment between node m and node n; inf indicates that there is no direct road segment between the two nodes.

[0192] In the process of calculating the shortest transfer path and shortest transfer distance between each access point of the transformer area using the shortest path algorithm, the shortest path algorithm is Dijkstra's algorithm.

[0193] In this embodiment, in the mobile energy storage operation constraint acquisition module 002, the expression describing the temporal state of mobile energy storage residence and cross-regional transfer in the spatiotemporal transfer model is as follows:

[0194]

[0195]

[0196]

[0197] In the formula, This is a set of distribution radio stations, where m and n are the radio stations deployed at road network nodes m and n, respectively. Let be the spatiotemporal transition matrix of the k-th mobile energy storage; mn represents the movement of the mobile energy storage between distribution area m and distribution area n; T represents the operating cycle of the mobile energy storage.

[0198] The operational constraints of the mobile energy storage formed by the external characteristic model include: charging and discharging power constraints, reactive power regulation constraints, and state of charge update constraints.

[0199] The expression for the charging and discharging power constraint is:

[0200]

[0201]

[0202] In the formula, , and These represent the active power and reactive power of charging and discharging of the kth mobile energy storage unit deployed in transformer area n during time period t; This represents the charging and discharging state of mobile energy storage deployed in transformer area n. When its value is equal to 1, the mobile energy storage is in a charging state; when its value is equal to 0, the mobile energy storage is in a discharging state. This represents the rated power of the k-th mobile energy storage unit.

[0203] The expression for the reactive power regulation constraint is:

[0204]

[0205] The expression for the state of charge update constraint is:

[0206]

[0207] In the formula, The duration of adjacent scheduling periods; and These are the charging efficiency and discharging efficiency of mobile energy storage, respectively. and These are the rated power and rated capacity of the k-th mobile energy storage device, respectively. The state of charge of the k-th mobile energy storage during time period t; and These are the upper and lower limits of SOC, respectively.

[0208] In this embodiment, in the multi-region day-ahead collaborative scheduling model construction module 003, the transformer operation constraints include: transformer active power constraints, reactive power constraints, and load rate constraints; the power flow constraints of the distribution network are characterized by the DistFlow model, including node injection power balance constraints and branch power balance constraints.

[0209] In this embodiment, the objective function expression of the multi-station daytime collaborative scheduling model construction module 003 is as follows:

[0210]

[0211] In the formula, f is the objective function value; This includes losses in distribution network lines and corresponding transformer losses in the distribution area. This refers to the load rate deviation of the transformer in the distribution area. Mobile energy storage relocation costs; For mobile energy storage arbitrage profits; Operating costs for charging and discharging mobile energy storage; This serves as the baseline value, used to convert cost-benefit items into per-unit values; This refers to the system voltage deviation. , , These are the weighting coefficients.

[0212] It should be noted that the information interaction and execution process between the modules of the above system are based on the same concept as the method embodiment in Embodiment 1 of this application, and the resulting technical effects are the same as those in the method embodiment of this application. For details, please refer to the description in the method embodiment shown above in this application, and it will not be repeated here.

[0213] Example 3

[0214] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium storing program code of a mobile energy storage collaborative scheduling method considering transportation-distribution network coupling. The program code includes instructions for executing the mobile energy storage collaborative scheduling method considering transportation-distribution network coupling of Embodiment 1 or any possible implementation thereof.

[0215] Computer-readable storage media can be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0216] Example 4

[0217] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0218] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor can call the program instructions to execute a mobile energy storage coordinated scheduling method that takes into account transportation-distribution network coupling, as described in Embodiment 1 or any possible implementation thereof.

[0219] Specifically, a processor can be implemented in hardware or software. When implemented in hardware, the processor can be a logic circuit, an integrated circuit, etc. When implemented in software, the processor can be a general-purpose processor that reads software code stored in memory. This memory can be integrated into the processor or located outside the processor and exist independently.

[0220] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable system. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0221] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing systems. They can be centralized on a single computing system or distributed across a network of multiple computing systems. Optionally, they can be implemented using program code executable by a computing system, thereby storing them in a storage system for execution by the computing system. In some cases, the steps shown or described can be performed in a different order than those presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0222] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A mobile energy storage coordinated dispatch method considering transportation-distribution network coupling, characterized in that, include: By collecting basic data on road networks and power distribution stations, and using the access points of power distribution stations as road network nodes, a road traffic topology model and a road adjacency matrix are constructed. Based on the road traffic topology model and the road adjacency matrix, the shortest transfer path and shortest transfer distance between each transformer access point are calculated using the shortest path algorithm, thereby obtaining the traffic constraints for the cross-transformer transfer of mobile energy storage. Based on the aforementioned traffic constraints, a spatiotemporal transfer model and an external characteristic model for mobile energy storage are constructed. The spatiotemporal transfer model describes the temporal state of mobile energy storage's residence and cross-regional transfer. The external characteristic model characterizes the charging and discharging power, reactive power, and state of charge operation characteristics of mobile energy storage, thus forming the operational constraints for mobile energy storage. Based on the traffic constraints and the mobile energy storage operation constraints, combined with the transformer operation constraints, distribution network power flow constraints, node voltage constraints and branch capacity constraints, a multi-transformer day-ahead collaborative scheduling model considering traffic-distribution network coupling is constructed. By linearizing the absolute value terms and nonlinear terms in the multi-region day-ahead coordinated scheduling model, the multi-region day-ahead coordinated scheduling model is transformed into a mixed integer quadratic programming model. By solving the mixed integer quadratic programming model, the deployment scheme of mobile energy storage, the cross-region transfer path, the active and reactive power output plan, and the state of charge trajectory are output.

2. The mobile energy storage coordinated dispatch method considering transportation-distribution network coupling according to claim 1, characterized in that, The expression for the road traffic topology model is: G(V,E) In the formula, V is the set of nodes consisting of road endpoints, intersections, and transformer access points; E is the set of edges consisting of road segments. The expression for the road adjacency matrix is: ； ； In the formula, D is the road adjacency matrix; d mn For road adjacency matrix elements; l mn is the length of the road segment between node m and node n; inf indicates that there is no direct road segment between the two nodes. In the process of calculating the shortest transfer path and shortest transfer distance between each access point of the transformer area using the shortest path algorithm, the shortest path algorithm is Dijkstra's algorithm.

3. The mobile energy storage coordinated dispatch method considering transportation-distribution network coupling according to claim 2, characterized in that, The spatiotemporal transfer model describes the temporal state of mobile energy storage relocation and inter-regional transfer as follows: ； ； ； In the formula, This is a set of distribution radio stations, where m and n are the radio stations deployed at road network nodes m and n, respectively. Let m be the spatiotemporal transition matrix of the k-th mobile energy storage; mn represents the movement of the mobile energy storage between distribution area m and distribution area n. T represents the operating cycle of the mobile energy storage. The operational constraints of the mobile energy storage formed by the external characteristic model include: charging and discharging power constraints, reactive power regulation constraints, and state of charge update constraints. The expression for the charging and discharging power constraint is: ； ； In the formula, , and These represent the active power and reactive power of charging and discharging of the kth mobile energy storage unit deployed in transformer area n during time period t; This represents the charging and discharging state of mobile energy storage deployed in transformer area n. When its value is equal to 1, the mobile energy storage is in a charging state; when its value is equal to 0, the mobile energy storage is in a discharging state. This represents the rated power of the k-th mobile energy storage unit. The expression for the reactive power regulation constraint is: ； The expression for the state of charge update constraint is: ； In the formula, The duration of adjacent scheduling periods; and These are the charging efficiency and discharging efficiency of mobile energy storage, respectively. and These are the rated power and rated capacity of the k-th mobile energy storage device, respectively. The state of charge of the k-th mobile energy storage during time period t; and These are the upper and lower limits of SOC, respectively.

4. The mobile energy storage coordinated dispatch method considering transportation-distribution network coupling according to claim 3, characterized in that, The operating constraints of the transformer substations include: active power constraints, reactive power constraints, and load factor constraints of the transformer substations; the power flow constraints of the distribution network are characterized by the DistFlow model, including node injection power balance constraints and branch power balance constraints.

5. The mobile energy storage coordinated dispatch method considering transportation-distribution network coupling according to claim 4, characterized in that, The objective function expression for the multi-region daytime collaborative scheduling model is: ； In the formula, f is the objective function value; This includes losses in distribution network lines and corresponding transformer losses in the distribution area. This refers to the load rate deviation of the transformer in the distribution area. Mobile energy storage relocation costs; For mobile energy storage arbitrage profits; Operating costs for charging and discharging mobile energy storage; This serves as the baseline value, used to convert cost-benefit items into per-unit values; This refers to the system voltage deviation. , , These are the weighting coefficients.

6. A mobile energy storage collaborative dispatching device considering transportation-distribution network coupling, employing the mobile energy storage collaborative dispatching method considering transportation-distribution network coupling as described in any one of claims 1-5, characterized in that, include: The traffic constraint acquisition module is used to collect basic data of road network and distribution substations, and construct a road traffic topology model and road adjacency matrix with the substation access point as the road network node; based on the road traffic topology model and the road adjacency matrix, the shortest transfer path and shortest transfer distance between each substation access point are calculated by the shortest path algorithm to obtain the traffic constraint conditions for the cross-substation transfer of mobile energy storage. The mobile energy storage operation constraint acquisition module is used to construct a spatiotemporal transfer model and an external characteristic model of mobile energy storage based on the traffic constraints; the spatiotemporal transfer model describes the temporal state of mobile energy storage residence and cross-station transfer; and the external characteristic model characterizes the charging and discharging power, reactive power and state of charge operation characteristics of mobile energy storage, thus forming the mobile energy storage operation constraints. The multi-region day-ahead collaborative scheduling model construction module is used to construct a multi-region day-ahead collaborative scheduling model that takes into account traffic-distribution network coupling, based on the traffic constraints and the mobile energy storage operation constraints, combined with the transformer operation constraints, distribution network power flow constraints, node voltage constraints and branch capacity constraints. The multi-region day-ahead coordinated scheduling model solution module is used to linearize the absolute value terms and nonlinear terms in the multi-region day-ahead coordinated scheduling model, transforming it into a mixed integer quadratic programming model; by solving the mixed integer quadratic programming model, it outputs the mobile energy storage deployment scheme, cross-region transfer path, active and reactive power output plan, and state of charge trajectory.

7. A mobile energy storage collaborative dispatching device considering transportation-distribution network coupling according to claim 6, characterized in that, In the traffic constraint acquisition module, the expression for the road traffic topology model is: G(V,E) In the formula, V is the set of nodes consisting of road endpoints, intersections, and transformer access points; E is the set of edges consisting of road segments. The expression for the road adjacency matrix is: ； ； In the formula, D is the road adjacency matrix; d mn For road adjacency matrix elements; l mn is the length of the road segment between node m and node n; inf indicates that there is no direct road segment between the two nodes. In the process of calculating the shortest transfer path and shortest transfer distance between each access point of the transformer area using the shortest path algorithm, the shortest path algorithm is Dijkstra's algorithm.

8. A mobile energy storage collaborative dispatching device considering transportation-distribution network coupling according to claim 7, characterized in that, In the mobile energy storage operation constraint acquisition module, the expression describing the temporal state of mobile energy storage residence and cross-regional transfer in the spatiotemporal transfer model is as follows: ； ； ； In the formula, This is a set of distribution radio stations, where m and n are the radio stations deployed at road network nodes m and n, respectively. Let m be the spatiotemporal transition matrix of the k-th mobile energy storage; mn represents the movement of the mobile energy storage between distribution area m and distribution area n. T represents the operating cycle of the mobile energy storage. The operational constraints of the mobile energy storage formed by the external characteristic model include: charging and discharging power constraints, reactive power regulation constraints, and state of charge update constraints. The expression for the charging and discharging power constraint is: ； ； In the formula, , and These represent the active power and reactive power of charging and discharging of the kth mobile energy storage unit deployed in transformer area n during time period t; This represents the charging and discharging state of mobile energy storage deployed in transformer area n. When its value is equal to 1, the mobile energy storage is in a charging state; when its value is equal to 0, the mobile energy storage is in a discharging state. This represents the rated power of the k-th mobile energy storage unit. The expression for the reactive power regulation constraint is: ； The expression for the state of charge update constraint is: ； In the formula, The duration of adjacent scheduling periods; and These are the charging efficiency and discharging efficiency of mobile energy storage, respectively. and These are the rated power and rated capacity of the k-th mobile energy storage device, respectively. The state of charge of the k-th mobile energy storage during time period t; and These are the upper and lower limits of SOC, respectively.

9. A mobile energy storage collaborative dispatching device considering transportation-distribution network coupling according to claim 8, characterized in that, In the multi-region day-ahead collaborative scheduling model construction module, the transformer operation constraints of the region include: active power constraints, reactive power constraints and load rate constraints of the transformer; the power flow constraints of the distribution network are characterized by the DistFlow model, including node injection power balance constraints and branch power balance constraints.

10. A mobile energy storage collaborative dispatching device considering transportation-distribution network coupling according to claim 9, characterized in that, In the multi-station area daytime collaborative scheduling model construction module, the objective function expression of the multi-station area daytime collaborative scheduling model is: ； In the formula, f is the objective function value; This includes losses in distribution network lines and corresponding transformer losses in the distribution area. This refers to the load rate deviation of the transformer in the distribution area. Mobile energy storage relocation costs; For mobile energy storage arbitrage profits; Operating costs for charging and discharging mobile energy storage; This serves as the baseline value, used to convert cost-benefit items into per-unit values; This refers to the system voltage deviation. , , These are the weighting coefficients.

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