Multi-stage disaster recovery method considering mobile energy storage vehicle in extreme weather

Abstract

The invention discloses a multi-stage disaster recovery method considering a mobile energy storage vehicle in extreme weather, and the method comprises the steps: building a two-stage optimization model to determine the configuration number and position of mobile energy storage with the minimum sum of emergency resource configuration cost and load reduction cost as a target before a disaster; the first-stage decision variables are the configuration state and the line switch state of the emergency resources in the power distribution network, and the second-stage decision variables are the load reduction power, the distributed power supply output, the node voltage, the branch and the current. The influence of disaster weather is considered, and photovoltaic output and load are selected as uncertain variables.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of power systems, in particular to a multi-stage disaster recovery method considering mobile energy storage vehicles under extreme weather. BACKGROUND

[0002] Under the background of promoting the three-year action of tackling the root causes of work safety in high quality, in the face of extreme weather such as typhoon and high temperature, daily maintenance power failure and accident power failure, the maximum reduction of user power failure time is the internal requirement to realize the essential safety of electric power, create excellent marketing service and improve the electric power business environment. The traditional emergency diesel generator has problems such as slow response speed, large noise, large emission, low efficiency, high cost, unreasonable layout and the like, and has been unable to meet the requirements of the new type of power system "zero feeling power failure". With the development of energy storage technology, mobile energy storage vehicles have become a potential preferred tool for household emergency power supply. The research on mobile energy storage vehicle household access, layout site selection, hot plug support and power supply mode can realize the "zero feeling power failure" goal and optimize the improvement of the grid household index at a low cost, further meet the needs of household rapid power supply, and improve the power supply reliability of distribution network.

[0003] In terms of policy, the Guiding Opinions on High-quality Development of Distribution Network under the New Situation (No. 2024 of the Development and Reform Energy) promotes the construction of local emergency power supply, and coordinates the use of mobile emergency power supply; and improves the power supply guarantee capacity of key areas, key parts and important users under extreme conditions. The Implementation Plan for High-quality Development of Distribution Network (2024-2027) proposes to fill the gaps in the safety and reliability of power supply and the ability to cope with extreme disasters of distribution network.

[0004] The emergence of mobile energy storage vehicles provides a new idea for the innovation of distribution network household emergency power supply mode. Mobile energy storage vehicles have the characteristics of high flexibility, quietness, environmental protection, low cost, rapid deployment and adjustable capacity, and can quickly respond when the distribution network fails or faces an emergency. It can effectively make up for the shortcomings of traditional emergency power supply and guarantee the continuity and stability of power supply. Through in-depth research on mobile energy storage vehicle emergency resource modeling and characteristic analysis, a reasonable emergency power supply capacity configuration model is constructed, which provides a scientific basis for the application of mobile energy storage vehicles in emergency power supply. Through the research on mobile energy storage vehicles in household emergency power supply, the user failure, maintenance outage, disaster weather, extreme high temperature and other scenes are coped with, the power supply reliability of distribution network is improved, the time and household index is improved, the means of household rapid power supply under emergency need is put forward, and through reasonable capacity configuration and operation scheme, the energy utilization efficiency is improved, additional economic benefits are created, and more value is brought to the electric power department and related enterprises. SUMMARY

[0005] The application aims to provide a multi-stage disaster recovery method considering mobile energy storage vehicles under extreme weather, which can significantly reduce load reduction cost under adverse scenarios, effectively avoid system operation risks, and reasonably pre-deploy schemes to enable emergency resources to respond quickly after disasters and significantly improve resource utilization efficiency.

[0006] In order to achieve the above purpose, the application is realized by the following technical scheme: A multi-stage disaster recovery method considering mobile energy storage vehicles under extreme weather, characterized by comprising: establishing a pre-disaster pre-deployment model and a post-disaster multi-period optimization scheduling model respectively; The pre-disaster pre-deployment model aims to minimize the total cost of emergency resource configuration and load reduction, and establishes a first and second stage optimization model; The post-disaster multi-period optimization scheduling model aims to minimize the load reduction power weighted value within the fault duration; The first stage optimization model includes mobile energy storage resource constraints and distribution network radial topology constraints; The second stage optimization model includes load reduction power constraints, distributed power output constraints and distribution network operation constraints.

[0007] The pre-disaster pre-deployment model is: ; Wherein is a set of load nodes; , is a 0-1 variable, indicating the connection state of mobile energy storage and node i, and its value is 1, indicating that there is only one mobile energy storage vehicle or repair team connected to node i, and its value is 0, indicating that there is no mobile energy storage vehicle or repair team connected to node i; is the open and closed state of branch ij, and its value is 1, indicating that the line is closed, and its value is 0, indicating that it is disconnected; and are the active and reactive load reduction power respectively; and are the active and reactive power output of distributed power respectively; , are the active and reactive power of branch respectively; , are the square terms of node voltage and branch current respectively; , are the costs of pre-configured unit mobile energy storage vehicles and repair teams; is the unit load reduction cost of node i; is the photovoltaic output; is the load of node i.

[0008] The decision variable of the first-stage optimization model is the configuration state of the emergency resource in the distribution network and the line switch state, and the decision variable of the second-stage optimization model is the load reduction power, the distributed power output, the node voltage, the branch and the current.

[0009] The mobile energy storage resource constraint is: Before the disaster occurs, the mobile energy storage resource reserved is limited, and each mobile energy storage device has the same parameter, and each node is pre-configured with at most one device, so that: In the formula: is the upper limit of the number of mobile energy storages in the pre-layout stage, is the upper limit of the number of repair teams in the pre-layout stage, and the 0-1 variable , The value of limits the maximum pre-configuration of a mobile energy storage vehicle or a repair team at each node.

[0010] The distribution network radiation topology constraint is: Considering the island fusion and the power-free island, an improved single commodity flow model is used to force the distribution network to meet the radiation topology in the recovery process, a virtual source node is introduced to indicate the 0-1 variable , and the number of reconstructed islands is explicitly included in the optimization; and the Big-M technology is used for relaxation processing on the corresponding virtual power flow, so that: In the formula: is the branch set; is the total number of nodes; and are the child and parent node sets of node i , respectively; is the branch virtual power flow; is the virtual power emitted by the node; M1 is a large number.

[0011] The load reduction power constraint includes: When the load power factor is fixed, the load reduction power constraint can be expressed as follows: In the formula: and are the maximum active and reactive power of the load, respectively; The distributed power output constraint includes: The distributed power output of each node should not exceed its output upper limit, assuming that the mobile energy storage is in a full power state before the disaster occurs, the charging and discharging power upper limit is taken as the output upper limit, and there is a constraint between the active power, the reactive power and the power factor of the distributed power, wherein the photovoltaic adopts a constant power factor operation, and the distributed power output constraint can be expressed as follows: In the formula, M, D and P are node sets of the mobile energy storage, the diesel generator and the photovoltaic respectively; indicates the connection state of each type of distributed power and the node i; and are the active power output upper limit and the reactive power output upper limit of the distributed power respectively; and are the upper limit and the lower limit of the power factor of the distributed power respectively, and the upper limit and the lower limit of the power factor of the photovoltaic are the same.

[0012] The power distribution network operation constraint includes: For a radial power distribution network, the DistFlow power flow equation is adopted, and since the network topology structure changes with the line switch state, the Big-M method is used to relax the voltage equation, and thus: In the formula, and are the branch resistance and the reactance respectively; and are the upper limit and the lower limit of the node voltage square term respectively; is the upper limit of the branch current square term; For the nonlinear constraint equation between the voltage, the current and the power, the second-order cone relaxation method is used to convert it into the following second-order cone constraint: In the above deterministic model, the idea of robust optimization is introduced, and a box uncertainty set for describing uncertain variables is constructed, as follows: In the formula, and respectively represent the predicted values of photovoltaic output and load of node ; respectively represent the predicted values of photovoltaic output and load of node ; respectively represent the maximum fluctuation amplitudes of photovoltaic output and load; respectively represent the maximum fluctuation amplitudes of photovoltaic output and load; respectively represent the maximum fluctuation amplitudes of photovoltaic output and load; respectively represent the maximum fluctuation amplitudes of photovoltaic output and load; Subsequently, the pre-disposition problem is restructured into a two-stage robust optimization model, the first stage is to make pre-disaster decisions before the uncertainty is revealed, and the second stage is to determine the optimal operation adjustment strategy after the uncertainty variable is revealed, aiming to minimize the operation cost under the worst scenario, and the model is as follows: In the formula: y is a vector of first-stage decision variables, including resource allocation and binary variables of network topology; u is an uncertainty variable vector representing a specific disaster scenario; x is a vector of second-stage recourse variables for system operation.

[0013] The objective function of the post-disaster multi-period optimization scheduling model is: In the formula: is a set of fault periods; is the active load shedding power at node i at time t, and the scheduling interval is set to 1h in consideration of the time scale of scheduling under disaster conditions to describe the space-time characteristics of mobile energy storage.

[0014] The post-disaster multi-period optimization scheduling model comprises: on the basis of the space-time scheduling constraints of mobile energy storage, the online charging of mobile energy storage vehicles is increased, and the space constraints and time constraints of repair teams are added; The space-time dynamic scheduling constraint of the mobile energy storage is: In the formula: t0 is the time when the fault occurs; and are the charging and discharging flags of the mobile energy storage, respectively, and their values are 1, respectively, indicating the charging and discharging states; and are the charging and discharging active powers of the mobile energy storage; and are the charging and discharging reactive powers of the mobile energy storage; and are the upper limits of the charging and discharging active powers of the mobile energy storage; and are the upper limits of the charging and discharging reactive powers of the mobile energy storage; and are the charging and discharging efficiencies of the mobile energy storage; and are the upper and lower limits of the energy storage capacity of the mobile energy storage; The online battery replacement constraint of the mobile energy storage vehicle is: In the formula: is a 0-1 variable, and when its value is 1, it indicates that the mobile energy storage vehicle i performs a battery replacement operation at the node t in the time period j ; represents the total amount of available backup batteries; The space constraint of the repair team is: In the formula, and are both 0-1 decision variables, wherein if the path of the repair team contains a trip from the fault line to the fault line , then , otherwise, it is 0; if the fault line is assigned to the repair team Repair, then 0; in addition, represents the set of repair teams participating in the dispatch, is the set of fault lines, and are the start and end point sets of all repair teams, respectively ); The repair team time constraint is: In the formula, is the repair team arrives at the fault line ; is the time required for the repair team to repair the fault line ; is the travel time of the repair team from the fault line to the fault line ; is a 0-1 variable, if the fault line is repaired in the first time period, then it takes 1, otherwise it takes 0; is a large positive real number.

[0015] Compared with the prior art, the present application has the following advantages: 1. Facing the uncertainty of photovoltaic output and load, the present application establishes a two-stage robust optimization model in the pre-disaster prevention stage. Compared with the strategy of no pre-deployment or only single resource pre-deployment, the "mobile energy storage-repair team" collaborative pre-deployment scheme constructed in this paper can significantly reduce the load reduction cost under adverse scenarios, effectively avoiding system operation risks. At the same time, the reasonable pre-layout scheme enables emergency resources to respond quickly after the disaster, significantly improving resource utilization efficiency.

[0016] 2. In the post-disaster recovery stage, a multi-period optimization model is constructed to dynamically schedule the spatial position, discharge and exchange state of the mobile energy storage, and to be coupled with the repair process of the repair team, so as to realize the optimal allocation of electric energy in the time and space dimensions. The example results show that the dynamic recovery mode fully utilizes the multi-source collaborative advantages of "fast power supply" and "line repair", significantly reduces the load reduction loss, and speeds up the power restoration process of key loads. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is a flow chart of a multi-stage disaster recovery method considering mobile energy storage vehicles under extreme weather conditions; Figure 2 Improved IEEE33 node power distribution network structure diagram of the application; Figure 3 Pre-deployment cost diagram of four scenarios under different scheduling strategies; Figure 4a Scenario one network reconstruction result schematic diagram; Figure 4b Scenario two network reconstruction result schematic diagram; Figure 4c Scenario three network reconstruction result schematic diagram; Figure 4d Scenario three network reconstruction result schematic diagram; Figure 5 Load reduction ratio diagram under different scenarios; Figure 6 Scenario one mobile energy storage vehicle dynamic scheduling result diagram. DETAILED DESCRIPTION

[0018] The application will be further described in conjunction with the preferred specific embodiments and the accompanying drawings.

[0019] As shown in the drawings, Figure 1 An extreme weather multi-stage disaster recovery method considering mobile energy storage vehicles, comprising: establishing a pre-disaster pre-deployment model and a post-disaster multi-period optimization scheduling model respectively; the pre-disaster pre-deployment model takes the minimum sum of emergency resource configuration cost and load reduction cost as the target, and establishes a first and second stage optimization model; the post-disaster multi-period optimization scheduling model takes the minimum weighted value of load reduction power within the fault duration as the target; The first stage optimization model includes mobile energy storage resource constraints and power distribution network radial topology constraints; the second stage optimization model includes load reduction power constraints, distributed power output constraints and power distribution network operation constraints.

[0020] The pre-disaster takes the minimum sum of emergency resource configuration cost and load reduction cost as the target. Two-stage optimization model is established to determine the configuration number and position of mobile energy storage. The first stage decision variable is the configuration state of emergency resources in the power distribution network and the line switch state, and the second stage decision variable is the load reduction power, the distributed power output, the node voltage, the branch and the current. Considering the influence of disaster weather, the photovoltaic output and the load are selected as uncertain variables.

[0021] Formula (1) In the formula: is the set of load nodes; , is a 0-1 variable, indicating whether the mobile energy storage is connected to the node iThe connection status, with a value of 1 indicating that there is exactly one mobile energy storage vehicle or emergency repair team connected to the node. i A connection value of 0 indicates that there is no mobile energy storage vehicle or emergency repair team connected to the node. i connect; branch road ij The open / closed state is indicated by a value of 1 representing a closed circuit and a value of 0 representing an open circuit. and These represent the reduction of active and reactive power from the load, respectively. and These represent the active and reactive power outputs of distributed power sources, respectively. , These are the active and reactive power of the branch circuit, respectively. , These are the squares of the node voltage and the branch current, respectively. , To pre-configure the cost of mobile energy storage vehicles and emergency repair teams; For nodes i Reduce unit load cost; Contribute to photovoltaic power; For nodes i The load.

[0022] The mobile energy storage resource constraints are as follows: The mobile energy storage resources available before a disaster occur are limited, and each mobile energy storage device has the same parameters, with a maximum of one device pre-configured for each node.

[0023] (2) In the formula: This represents the upper limit for the number of mobile energy storage units during the pre-deployment phase. This represents the maximum number of repair teams during the pre-planning phase. (0-1 variable) , The value of is limited to a maximum of one mobile energy storage vehicle or emergency repair team pre-configured for each node.

[0024] The aforementioned distribution network radial topology constraints: Considering both islanded consolidation and power-free islanding scenarios, an improved single-commodity flow model is adopted to force the distribution network to conform to a radial topology during recovery. Virtual source node indicators (0–1 variables) are introduced. The number of islands after reconstruction is explicitly included in the optimization; and the corresponding virtual power flow is relaxed using Big-M technology.

[0025] (3) (4) (5) (6) wherein: is the branch set; is the total number of nodes; and are the child and parent node sets of node i respectively; is the branch virtual power flow; is the virtual power emitted by a node; M1 is a large number.

[0026] The load shedding power constraint is: Assuming the load power factor is fixed, the load shedding power constraint can be expressed as follows: (7) (8) wherein: and are the maximum active and reactive power of the load respectively.

[0027] The distributed power output constraint is: The distributed power output of each node should not exceed its output upper limit. Assuming that the mobile energy storage is in a full charge state before the disaster occurs, the charging and discharging power upper limit is taken as the output upper limit. In addition, there is a constraint between the active power, the reactive power and the power factor of the distributed power, wherein the photovoltaic adopts a fixed power factor operation. The distributed power output constraint can be expressed as follows: (9) (10) (11) wherein: M, D and P are the node sets of the mobile energy storage, the diesel generator and the photovoltaic respectively; represents the connection state of each type of distributed power and node i; and are the active power output and the reactive power output upper limit of the distributed power respectively; and are the upper and lower limits of the power factor of the distributed power, and the upper and lower limits of the power factor of the photovoltaic are the same.

[0028] The distribution network operation constraint includes: For a radial distribution network, the DistFlow power flow equation is adopted. Since the network topology structure changes with the line switch state, the Big-M method is used to relax the voltage equation.

[0029] (12) (13) (14) (15) (16) (17) where and are the branch resistance and reactance, respectively; and are the upper and lower bounds of the squared voltage at node is the upper bound of the squared current in branch

[0030] For the nonlinear constraints between voltage, current and power, we transform them into the following second-order cone constraints by the second-order cone relaxation method: (18) In actual operation, distribution networks are affected by various random factors, especially the uncertainty of photovoltaic (PV) output and load demand. The deterministic pre-dispatching model is too risky because it does not consider these fluctuations. Therefore, we introduce the idea of robust optimization into the above deterministic model to construct a box uncertainty set to describe uncertain variables, as shown in equation (19).

[0031] (19) where and represent the predicted values of PV output and load at node and represent the maximum fluctuation amplitude of PV output and load; and are variables representing the fluctuation level; and are uncertainty budgets, which adjust the overall conservatism of the model by limiting the number of variables that simultaneously reach the most adverse fluctuation level.

[0032] Subsequently, we reformulate the pre-dispatching problem as a two-stage robust optimization model. The first stage is to make pre-disaster decisions before the uncertainty is revealed. The second stage is to determine the optimal operation adjustment strategy after the uncertainty variables are revealed, aiming to minimize the operation cost in the worst-case scenario. The model is shown in equation.

[0033] (20) (21) where: y ​is the vector of first-stage decision variables, including resource allocation and binary variables of network topology; u is the vector of uncertain variables representing a specific disaster scenario; x is the vector of second-stage recourse variables for system operation.

[0034] This min-max-min problem is computationally difficult to handle. Therefore, a column-and-constraint generation (C&CG) algorithm is adopted to solve it, which decomposes the original problem into a master problem (MP) and a subproblem (SP), as shown in equation (22).

[0035] (22) (23) The master problem aims to minimize the pre-deployment cost and an auxiliary variable η , which represents the second-stage cost in the worst-case scenario. The subproblem, on the other hand, finds this worst-case cost by searching over the set of uncertainties y∗ , given a first-stage solution U . The subproblem can be solved by transforming the inner minimization problem into its dual form, resulting in a single-level maximization problem. In each iteration k , the subproblem identifies the worst-case scenario and the corresponding optimal second-stage cost. This information is used to generate a new optimality cut plane and added back to the master problem. The algorithm iterates between the master problem and the subproblem until the gap between the lower bound obtained from the master problem and the upper bound obtained from the subproblem is less than a pre-specified tolerance.

[0036] The objective function of the post-disaster multi-period optimal scheduling model is: After the disaster occurs, the distribution network loses the main network power supply. The duration of the fault can be predicted according to the disaster intensity and the number of repair resources. The objective is to minimize the weighted value of load reduction power during the fault duration. A multi-source collaborative post-disaster recovery optimization model is established to maximize the resilience of the distribution network by dynamically scheduling mobile energy storage and repair teams.

[0037] (24) where: is the set of fault periods; is the active load reduction power at node i at time t. To describe the spatiotemporal characteristics of mobile energy storage and consider the scheduling time scale under disaster conditions, the scheduling interval is set to 1 h.

[0038] Spatiotemporal dynamic scheduling constraints for mobile energy storage: (25) (26) (27) (28) (29) (30) (31) (32) (33) (34) wherein: is the pre-disaster pre-configured mobile energy storage set; t0 is the time when the fault occurs; and are the charging and discharging flags of the mobile energy storage, and their values are 1 respectively indicating the charging and discharging states; and are the charging and discharging active power of the mobile energy storage; and are the charging and discharging reactive power of the mobile energy storage; and are the upper limits of the charging and discharging active power of the mobile energy storage; and are the upper limits of the charging and discharging reactive power of the mobile energy storage; and are the charging and discharging efficiencies of the mobile energy storage; and are the upper and lower limits of the energy storage capacity of the mobile energy storage.

[0039] Formula (25) establishes the coupling relationship between the pre-disaster pre-layout and the disaster recovery stage, to ensure that the mobile energy storage is accurately located at its pre-layout position at the time when the disaster occurs; formula (26) points out that when the time interval is insufficient to cover the passing and configuration time, the mobile energy storage cannot access the node k ; formula (27) imposes a uniqueness constraint, which stipulates that the mobile energy storage can be connected to at most one node at the same time; formula (28) couples the charging and discharging states of the mobile energy storage with the spatial position state, to ensure that it must be in the connected state to charge and discharge; formula (29) and formula (30) are respectively the upper and lower limits of the charging and discharging active power of the mobile energy storage; formula (31) and formula (32) limit the range of the charging and discharging reactive power; formula (33) and formula (34) jointly define the state of charge constraint of the mobile energy storage.

[0040] Due to the bilinear term in equation (28), a linearization method is used to transform it into an equivalent linear constraint as follows: (35) (36) (37) (38) wherein: is an introduced intermediate variable, representing the movement of the mobile energy storage i and the node j is connected in the time period t~(t+1).

[0041] The present application adds the online battery replacement constraint of the mobile energy storage vehicle on the basis of the space-time scheduling constraint of the mobile energy storage.

[0042] (39) (40) (41) wherein: is a 0-1 variable, when its value is 1, it represents that the mobile energy storage vehicle i performs the battery replacement operation in the time period t at the node j . represents the total amount of available spare batteries.

[0043] Equation (39) represents that the total number of times that all the energy storage vehicles perform the battery replacement operation in the entire scheduling period cannot exceed the total amount of available spare batteries. Equation (40) is modified on the basis of equation (33), when the battery replacement occurs, the SOC should be directly restored to the full rated capacity. Equation (41) indicates that at any time, each energy storage vehicle can only perform one of the three operations of charging, discharging and battery replacement.

[0044] Repair team space constraint (42) (43) (44) (45) wherein, and are both 0-1 decision variables. Among them, if the path of the repair team contains the journey from the fault line to the fault line , then , otherwise 0; if the fault line is repaired by the repair team , otherwise 0. In addition, , otherwise 0. In addition, denotes the set of repair teams participating in the dispatch, denotes the set of fault lines, denotes the set of repair teams participating in the dispatch, denotes the set of repair teams participating in the dispatch, ).

[0045] Repair team time constraints (46) (47) where, denotes the time at which the repair team arrives at the fault line ; denotes the time required by the repair team to repair the fault line ; denotes the travel time of the repair team from the fault line to the fault line ; is a 0-1 variable that takes the value 1 if the fault line is repaired within the th time period, and 0 otherwise; is a sufficiently large positive real number.

[0046] In the post-disaster recovery phase, the load shedding power, distributed power output, and distribution network operating state of each time period still need to satisfy the corresponding constraints, and the constraint form remains consistent with the pre-disaster pre-deployment phase.

[0047] Example analysis: The improved IEEE 33-node distribution system is selected for simulation analysis, and the network structure is as shown in Figure 1 . All calculations are completed in the MATLAB environment, and the Gurobi solver is called.

[0048] Figure 2 The improved IEEE 33-node distribution network structure is as shown in Figure 2 , and the important user nodes are 1, 2, 6, 7, 8, 19, 20, 21, 22, 23, 24, 31, and 32. The system contains 10 distributed power sources, and the parameters are shown in Table 1 and Table 2.

[0049] Table 1 DEG parameters ​ Table 2 PV parameters Up to 2 MESSs with a maximum capacity of 300 kWh each and up to 2 repair teams are pre-deployed.

[0050] In order to verify the reliability of the proposed model, four different failure scenarios are established by the method described above. Scenarios one and two are failure scenarios under typhoon weather, and scenarios three and four are failure scenarios under high temperature weather.

[0051] In order to highlight the superiority of the proposed "mobile energy storage vehicle-repair team" coordinated pre-deployment strategy, the following four comparison schemes are set: Scheme A: pre-deployed; Scheme B: only pre-deploy the repair team; Scheme C: only pre-deploy the energy storage vehicle; Scheme D: no pre-deployment.

[0052] Table 3 Fault branches under different scenarios Table 4 Total pre-disaster operating costs of four scenarios under different dispatching The following is the total pre-disaster operating cost under different strategies. Among them, the pre-deployment location of mobile energy storage vehicles and repair teams under different scenarios is shown in Table 5.

[0053] Table 5 Deployment location of mobile energy storage vehicles and repair teams under different scenarios From Figure 3 It can be clearly seen that under any failure scenario, the total pre-disaster deployment cost of scheme A is the lowest. Compared with scheme D which does not pre-deploy at all, scheme A reduces the cost by 28.89%, 27.50%, 29.75% and 29.10% respectively under the four scenarios, which shows that although pre-deploying emergency resources before the disaster increases the pre-deployment cost, it can effectively reduce the load reduction cost. By comparing scheme B and scheme C, it can be found that the pre-deployment of a single type of emergency resource can also bring some improvement, but the effect is far inferior to the coordinated deployment. This proves that the combination of the rapid power supply capability of mobile energy storage and the line repair capability of repair teams, as well as the optimized layout, can maximize the utilization efficiency of emergency resources, thereby minimizing the load loss in post-disaster recovery.

[0054] After the disaster occurs, various distributed resources are used to restore the load power consumption, and the network reconstruction results under different scenarios are shown in Figures 4a to 4d , and the load reduction ratio is shown in Figure 5 .

[0055] FromFigure 5 It can be seen that in the early stage of disaster, due to a large number of line failures, the distribution network structure is severely damaged, and the load reduction ratio is relatively high. With the gradual repair of the repair team, the mobile energy storage vehicle quickly accesses the key node to provide temporary power supply, the power supply capacity of the system is quickly restored, and the load reduction ratio continues to decline. In scenario three and scenario four, the system completely restores power supply to all important loads in the 8th to 9th hour, and basically realizes zero reduction. This shows that the multi-stage post-disaster optimization scheduling model proposed in this paper can effectively coordinate mobile energy storage and repair teams, form a dynamic recovery mode, and significantly speed up the recovery process of critical loads.

[0056] To further study the recovery ability of emergency resources to the distribution network after the disaster, scenario one is taken as an example for detailed description. The dynamic scheduling result of the mobile energy storage vehicle in scenario one is shown in Figure 6

[0057] It can be seen from Figure 6 that after the disaster, the energy storage vehicles pre-deployed at nodes 7 and 23 quickly access the distribution network for discharging to provide emergency power supply for critical loads in the power failure area, and the SOC continues to decline at this time. At the same time, the repair team starts to repair the fault lines in the order of 20, 13, 35, 19, 25, 24 from the deployment point. With the repair of part of the lines, the network topology changes. The model dynamically adjusts the position of the energy storage vehicle, and MESS1 moves to node 10 and MESS2 moves to node 14 to cooperate with the recovered network structure to supply power to a larger range of loads. At the same time, the mobile energy storage vehicle carries out dynamic battery replacement at 5 o'clock and 7 o'clock. With the repair of most of the fault lines, the main network power supply is gradually restored. The energy storage vehicle moves to the new key nodes 5 and 20 again to support the power supply recovery of the local weak link. After the 9th hour, the energy storage vehicle discharges slows down, indicating that the system has basically recovered stable operation.

[0058] In summary, the multi-stage disaster recovery method considering mobile energy storage vehicles under extreme weather conditions can significantly reduce the load reduction cost under adverse scenarios, effectively avoid system operation risks, and at the same time, a reasonable pre-deployment scheme enables emergency resources to respond quickly after the disaster, significantly improving resource utilization efficiency.

[0059] ​It should be noted that in the embodiments of the present application, the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the embodiments, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second", "third" are only for the purpose of description, and cannot be understood as indicating or implying relative importance.

[0060] In the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connecting", "fixing" and the like should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or it can be integrated; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the internal communication of two elements or the interaction relationship between two elements. For those skilled in the art, the specific meaning of the above-mentioned terms in the present application can be understood according to the specific circumstances.

[0061] Although the content of the present application has been described in detail through the above preferred embodiments, it should be recognized that the above description should not be considered as limiting the present application. After reading the above content, various modifications and alternatives of the present application will be obvious to those skilled in the art. Therefore, the protection scope of the present application should be defined by the appended claims.

Claims

1. A multi-stage disaster recovery method considering mobile energy storage vehicles under extreme weather conditions, characterized in that, This includes: establishing a pre-disaster deployment model and a post-disaster multi-period optimization scheduling model; The aforementioned pre-disaster deployment model aims to minimize the sum of emergency resource allocation costs and load reduction costs, and establishes first and second phase optimization models; The post-disaster multi-period optimization scheduling model aims to minimize the weighted value of load reduction power during the duration of the fault. The first-stage optimization model includes: mobile energy storage resource constraints and distribution network radiation topology constraints; The second-stage optimization model includes: load reduction power constraints, distributed generation output constraints, and distribution network operation constraints.

2. The multi-stage disaster recovery method considering mobile energy storage vehicles under extreme weather conditions as described in claim 1, characterized in that, The aforementioned pre-disaster deployment model is as follows: ； in For the set of load nodes; , The value is a 0-1 variable, representing the connection status between the mobile energy storage vehicle and node i. A value of 1 indicates that there is one and only one mobile energy storage vehicle or emergency repair team connected to node i, and a value of 0 indicates that there is no mobile energy storage vehicle or emergency repair team connected to node i. This represents the open / closed state of branch ij, with a value of 1 indicating a closed line and a value of 0 indicating an open line. and These represent the reduction of active and reactive power from the load, respectively. and These are the active and reactive power outputs of the distributed power source, respectively. , These are the active and reactive power of the branch circuit, respectively. , These are the squares of the node voltage and the branch current, respectively. , The cost of pre-configuring mobile energy storage vehicles and emergency repair teams; Reduce the cost per unit load for node i; Contribute to photovoltaic power; Let be the load of node i.

3. The multi-stage disaster recovery method considering mobile energy storage vehicles under extreme weather conditions as described in claim 1, characterized in that, The decision variables of the first-stage optimization model are the configuration status of emergency resources in the distribution network and the status of line switches. The decision variables of the second-stage optimization model are load reduction power, distributed power output, node voltage, and branch current.

4. The multi-stage disaster recovery method considering mobile energy storage vehicles under extreme weather conditions as described in claim 1, characterized in that, The mobile energy storage resource constraints are as follows: Given that the mobile energy storage resources available before a disaster are limited, and each mobile energy storage device has identical parameters, with a maximum of one device pre-configured per node, then: In the formula: This represents the upper limit for the number of mobile energy storage units during the pre-deployment phase. The maximum number of repair teams during the pre-planning phase, a 0-1 variable. , The value of is limited to a maximum of one mobile energy storage vehicle or emergency repair team pre-configured for each node.

5. The multi-stage disaster recovery method considering mobile energy storage vehicles under extreme weather conditions as described in claim 1, characterized in that, The aforementioned distribution network radiation topology constraints are: Considering both islanded consolidation and power-free islanding scenarios, an improved single-commodity flow model is adopted to force the distribution network to conform to a radial topology during the recovery process. Virtual source node indicators (0-1 variables) are introduced. If the number of islands after reconstruction is explicitly included in the optimization, and the corresponding virtual power flow is relaxed using the Big-M technique, then: In the formula: For the set of branches; The total number of nodes; and They are nodes i The set of child and parent nodes; For branch virtual power flow; The virtual power emitted by the node; M1 It is an extremely large number.

6. The multi-stage disaster recovery method considering mobile energy storage vehicles under extreme weather conditions as described in claim 1, characterized in that, The load reduction power constraints include: Assuming a fixed load power factor, the load reduction power constraint can be expressed as follows: In the formula: and These represent the maximum active and reactive power of the load, respectively.

7. The multi-stage disaster recovery method considering mobile energy storage vehicles under extreme weather conditions as described in claim 1, characterized in that, The distributed power output constraints include: The output of distributed power sources at each node should not exceed their upper limit. Assuming that the mobile energy storage is fully charged before the disaster, the upper limit of charging and discharging power is taken as its upper limit. There are constraints on the active power, reactive power and power factor of distributed power sources. Among them, photovoltaics operates at a constant power factor. The output constraints of distributed power sources can be expressed as follows: In the formula: M, D, and P are the node sets of mobile energy storage, diesel generators, and photovoltaics, respectively; This indicates the connection status between various distributed power sources and node i; and These are the upper limits of active and reactive power output of distributed power sources, respectively. and These are the upper and lower limits of the power factor for distributed generation, respectively; the upper and lower limits of the power factor for photovoltaics are the same.

8. The multi-stage disaster recovery method considering mobile energy storage vehicles under extreme weather conditions as described in claim 1, characterized in that, The aforementioned power distribution network operation constraints include: For radial distribution networks, the DistFlow power flow equations are used. Since the network topology changes with the switching states of the lines, the Big-M method is used to relax the voltage equations, resulting in: In the formula: and These are the branch resistance and reactance, respectively. and These are the upper and lower limits of the nodal voltage square term, respectively; This is the upper limit of the square term of the branch current; For the nonlinear constraint between voltage, current, and power, it can be transformed into the following second-order cone constraint using the second-order cone relaxation method: Introducing the concept of robust optimization into the above deterministic model, we construct a box-shaped uncertainty set to describe the uncertain variables, as follows: In the formula, and Representing nodes respectively The predicted values ​​of photovoltaic output and load; and This indicates the maximum fluctuation range between photovoltaic output and load; and Variables that characterize the level of volatility; and For budgeting uncertainty, the overall conservatism of the model is adjusted by limiting the number of variables that simultaneously reach the most unfavorable volatility level; Subsequently, the pre-deployment problem is restructured into a two-stage robust optimization model. The first stage involves making pre-disaster decisions before the uncertainty is revealed, and the second stage involves determining the optimal operational adjustment strategy after the uncertainty is revealed, aiming to minimize the operational cost under the worst-case scenario. The model is as follows: In the formula: y It is a vector of decision variables for the first stage, including binary variables for resource allocation and network topology; u It is a vector of uncertain variables representing a specific disaster scenario; x It is a vector used for the second phase of system operation to trace variables.

9. The multi-stage disaster recovery method considering mobile energy storage vehicles under extreme weather conditions as described in claim 1, characterized in that, The objective function of the post-disaster multi-period optimization scheduling model is: In the formula: A set of periods of failure; Let t be the active power reduction of the load at node i at time t. To describe the spatiotemporal characteristics of mobile energy storage and take into account the dispatch time scale under disaster conditions, the dispatch interval is set to 1 hour.

10. The multi-stage disaster recovery method considering mobile energy storage vehicles under extreme weather conditions as described in claim 9, characterized in that, The post-disaster multi-period optimization scheduling model includes: on the basis of the time and air conditioning scheduling constraints of mobile energy storage, online battery swapping constraints of mobile energy storage vehicles, spatial constraints of emergency repair teams, and time constraints of emergency repair teams; The spatiotemporal dynamic scheduling constraints of the mobile energy storage are as follows: In the formula: This refers to the mobile energy storage system pre-configured before the disaster; t0 is the time the fault occurred. and These are the charging and discharging indicators for mobile energy storage, with a value of 1 indicating charging and discharging status, respectively. and These refer to the charging and discharging active power of mobile energy storage, respectively. and These refer to the charging and discharging reactive power of mobile energy storage, respectively. and These are the upper limits of the active power for charging and discharging mobile energy storage, respectively. and These are the upper limits of the reactive power for charging and discharging mobile energy storage, respectively. and These refer to the charging and discharging efficiencies of mobile energy storage, respectively. and These represent the upper and lower limits of the energy storage capacity for mobile energy storage, respectively. The constraints for online battery swapping of the mobile energy storage vehicle are as follows: In the formula: It is a 0-1 variable; when its value is 1, it represents a mobile energy storage vehicle. i During the period t At node j A battery swapping operation was performed; Indicates the total amount of available backup batteries; The space constraints for the emergency repair team are as follows: In the formula, and All are 0-1 decision variables, where, if the repair team The path includes from the faulty line Head to the faulty line The itinerary, Otherwise, it is 0; if the faulty line is Assigned to the emergency repair team Repairing it is necessary. Otherwise, it is 0; in addition, This indicates the assembly of the emergency repair teams participating in the dispatch. For the set of faulty lines, and The starting and ending points of all emergency repair teams are respectively ( ); The time constraint for the emergency repair team is as follows: In the formula, For the repair team Arrive at the faulty line The moment; For the repair team Repair faulty circuit Time required; For the repair team From the faulty line Rushing to the faulty line Passage time; For variables of 0–1, if the faulty line In the If the repair is completed within a certain time period, then 1 is used; otherwise, 0 is used. It is a sufficiently large positive real number.

Images