Mobile energy storage pre-layout and moving path two-stage optimization method and device for improving toughness of power distribution system

Abstract

The invention provides a mobile energy storage pre-layout and moving path two-stage optimization method and device for improving the toughness of a power distribution system, and the method comprises the steps: constructing a two-stage stochastic optimization model, optimizing the investment decision and initial deployment of mobile energy storage in a normal operation stage, and determining the installation position and capacity of the mobile energy storage; in the emergency stage, mobile energy storage is rapidly deployed to a disaster-affected area through an emergency operation decision; the micro-grid is dynamically reconstructed in the emergency stage, and an independent power supply area with mobile energy storage and a distributed power supply as a core is formed; and solving the two-stage stochastic optimization model by adopting an asymptotic hedging algorithm to obtain an investment and operation decision of the mobile energy storage unit. According to the invention, the economy of the power distribution system in normal operation and the recovery capability in disaster scenes are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of collaborative optimization of mobile energy storage and microgrids, and specifically to a two-stage optimization method and device for pre-layout and mobile path of mobile energy storage to enhance the resilience of distribution systems. Background Art

[0002] With the wide application of renewable energy and the growth of energy demand, traditional power grids are facing many challenges, especially in dealing with disasters, load fluctuations, and supply instability. As a flexible and reliable small-scale power system, a microgrid can provide independent operation capabilities when the traditional power grid fails by integrating distributed generation, mobile energy storage devices, and load management. However, the deployment and emergency response of microgrids still face difficulties. Introducing mobile energy storage provides greater flexibility and emergency capabilities for microgrids. Mobile energy storage can be quickly deployed to different locations to make up for the power shortage of microgrids during disasters or sudden increases in demand, ensuring the stability of power supply. By combining mobile energy storage with microgrids, not only the resilience of the power grid during disasters is improved, but also dynamic adjustment can be made according to specific needs to ensure uninterrupted power supply.

[0003] Foreign research mostly focuses on the application of mobile energy storage in post-disaster recovery, especially when the power grid is damaged by natural disasters. By quickly deploying mobile energy storage to provide temporary power support, the resilience of the power grid is enhanced. Research in the United States, Europe, Japan and other places has explored the integration of mobile energy storage and renewable energy, promoting the application and development of mobile energy storage technology. Domestic research focuses on the optimal dispatching and energy efficiency improvement of microgrids. In recent years, it has also begun to pay attention to the potential of combining mobile energy storage and microgrids, especially their applications in power dispatching optimization and emergency response. With the progress of electric vehicle and mobile energy storage technologies, mobile energy storage has gradually become an important supplement in microgrid systems. However, in practical applications, challenges such as cost, technical feasibility, and system integration still exist. Summary of the Invention

[0004] In order to optimize the investment and operation strategies of mobile energy storage, the present invention proposes a two-stage optimization method and device for pre-layout and mobile path of mobile energy storage to enhance the resilience of distribution systems, which combines the spatial flexibility of mobile energy storage with the dynamic reconfiguration of microgrids, improving the economy of the distribution system under normal operation and the recovery ability in disaster scenarios.

[0005] A two-stage optimization method for pre-layout and mobile path of mobile energy storage to enhance the resilience of distribution systems includes the following steps:

[0006] Construct a two-stage stochastic optimization model to optimize the investment decision and initial deployment of mobile energy storage during the normal operation stage, and determine the installation location and capacity of mobile energy storage; during the emergency stage, quickly deploy mobile energy storage to the affected area through emergency operation decisions;

[0007] During the emergency stage, the microgrid is dynamically reconfigured to form an independent power supply area with mobile energy storage and distributed power sources as the core;

[0008] The asymptotic hedging algorithm is used to solve the two-stage stochastic optimization model to obtain the investment and operation decisions of the mobile energy storage unit.

[0009] Furthermore, the objective function in the normal operation stage is to minimize the total cost of investment decisions and initial deployment, as shown in the following formula:

[0010]

[0011] where γ is the discount factor; IC represents the investment cost; s ∈ S represents the scenario set, and S N and S E represent the normal scenario and the emergency scenario respectively; ω s is the probability of scenario s occurring; b ∈ B represents the bus set; k ∈ K represents the mobile energy storage set; OC s represents the operation cost in the normal operation scenario; EC s represents the load shedding loss and emergency resource scheduling cost in the disaster response scenario; x k represents the mobile energy storage investment decision variable, x k = 1 indicates that the mobile energy storage k is installed, and x k = 0 indicates that it is not installed; z kb represents the fixed position variable of the mobile energy storage, z kb = 1 indicates that the mobile energy storage k is deployed on the bus b, and z kb = 0 indicates that it is not deployed; u kbts represents the transportation path variable of the mobile energy storage in the mobile mode. When the mobile energy storage k is at the bus b at time interval t in scenario s, u kbts = 1, otherwise u kbts = 0;

[0012] The relationship between IC and x in the objective function is as shown in the following formula: k The relationship between IC and x in the objective function is as shown in the following formula:

[0013]

[0014] where C p represents the rated power price of the mobile energy storage; C E represents the rated energy price of the mobile energy storage; represents the rated energy of the mobile energy storage; represents the rated active power of the mobile energy storage;

[0015] The operation cost OC in the normal operation scenario s is expressed as the following formula:

[0016]

[0017] Among them, \(t\in T\) represents the set of time intervals; represents the incremental cost of distributed power sources, represents the active output of distributed power sources; \(h\) represents the degradation slope of mobile energy storage; and are the decision variables of the active power of MES charging / discharging respectively, indicates that MES performs charging / discharging when indicates that MES does not perform charging / discharging;

[0018] Different from the normal scenario, the emergency scenario includes a load shedding item with the value of lost load as a penalty, as shown in the following formula:

[0019]

[0020] Among them, represents the value of lost load; represents the load switch status variable, which is 1 when the load of bus \(b\) is connected to the bus, and 0 otherwise;

[0021] The constraint conditions are as follows:

[0022]

[0023] The above constraints ensure that MES can only be deployed or moved after installation.

[0024] Furthermore, the objective function in the emergency stage is shown in the following formula:

[0025]

[0026] The constraint conditions include:

[0027] MES deployment quantity constraint:

[0028]

[0029] The MES deployment quantity constraint is used to limit the maximum number of mobile energy storages connected to each distribution bus \(b\);

[0030] Operation time constraint:

[0031]

[0032] The meaning of the operation time constraint is that if mobile energy storage \(k\) is at bus \(b1\) at time \(t\), then moving to \(b2\) at time \(t + 1\) needs to meet the shortest transportation time

[0033] Bind the initial position of MES, as shown in the following formula:

[0034]

[0035] At the initial moment of the emergency stage (t = 0), the position of the mobile energy storage is the same as that in the normal stage.

[0036] Furthermore, during the emergency stage, the microgrid is dynamically reconfigured to form an independent power supply area with mobile energy storage and distributed power sources as the core, specifically including:

[0037] (1) Dynamically reconfigure the microgrid and maintain the stability of power supply during emergency response through distributed power sources and load switching technology:

[0038] Based on the normal model, the microgrid line switch constraints and power flow constraints in emergency situations:

[0039]

[0040] Among them, is the line switch status variable, which is 1 when line l is closed at time interval t in scenario s and 0 when it is open; and are the active and reactive power flows of line l respectively; a lts is the square of the current of line l; R l and X l are the resistance and reactance of line l respectively;

[0041] Use line switch decisions to reconfigure the microgrid in the following way:

[0042]

[0043] Where N L is the maximum number of microgrids allowed in the power grid, N O is the number of out-of-service lines caused by disasters, N MES and N DG are the numbers of MES and DG respectively;

[0044] (2) Optimize the charge and discharge strategy of the mobile energy storage and update the energy state of the MES during emergency response;

[0045] The initial energy constraint for each MES is as follows:

[0046]

[0047] Among them, e k,t,s represents the state of charge, and e k,t0,s is the initial state of charge; is the rated energy value of the MES;

[0048] MES charge and discharge power constraint:

[0049]

[0050] Among them, and are the charging / discharging power respectively; η ch and η dis represent the charging / discharging efficiency;

[0051] MES energy state update constraint:

[0052]

[0053] Furthermore, in the process of solving the two-stage stochastic optimization model using the asymptotic hedging algorithm, the penalty coefficient is used to coordinate investment and emergency decisions, and the penalty term is designed as follows:

[0054]

[0055] Among them, and represent the maximum and minimum values of all scenarios x k in the initial iteration; and represent the maximum and minimum values of the fixed deployment variable z kb for all scenarios in the initial iteration; ρ x and ρ z are the penalty coefficients of the investment decision variable and the fixed deployment variable respectively; by dynamically adjusting the penalty coefficient, the asymptotic hedging algorithm gradually reduces the difference in decision variables between different scenarios during the iteration process and finally achieves consistency.

[0056] A two-stage optimization device for pre-layout and mobile path of mobile energy storage to improve the resilience of the distribution system, including:

[0057] A two-stage stochastic optimization model construction module, which is used to construct a two-stage stochastic optimization model, optimize the investment decision and initial deployment of mobile energy storage during the normal operation stage, and determine the installation location and capacity of mobile energy storage; during the emergency stage, quickly deploy mobile energy storage to the disaster area through emergency operation decisions;

[0058] A dynamic reconstruction module, which is used to dynamically reconstruct the microgrid during the emergency stage to form an independent power supply area with mobile energy storage and distributed power sources as the core;

[0059] A model solving module, which is used to solve the two-stage stochastic optimization model using the asymptotic hedging algorithm to obtain the investment and operation decisions of mobile energy storage units.

[0060] Furthermore, the objective function of the normal operation stage is to minimize the total cost of investment decision and initial deployment, as shown in the following formula:

[0061]

[0062] Among them, γ is the discount factor; IC represents the investment cost; s ∈ S represents the set of scenarios, where S N and S E represent the normal scenario and the emergency scenario respectively; ω s is the probability of the occurrence of scenario s; b ∈ B represents the set of buses; k ∈ K represents the set of mobile energy storages; OC s represents the operating cost under the normal operating scenario; EC s represents the load shedding loss and emergency resource scheduling cost under the disaster response scenario; x k represents the investment decision variable of the mobile energy storage, x k = 1 indicates that the mobile energy storage k is installed, and x k = 0 indicates that it is not installed; z kb represents the fixed position variable of the mobile energy storage, z kb = 1 indicates that the mobile energy storage k is deployed at the bus b, and z kb = 0 indicates that it is not deployed; u kbts represents the transportation path variable of the mobile energy storage in the mobile mode. When the mobile energy storage k is at the bus b at the time interval t under the scenario s, u kbts = 1, otherwise u kbts = 0;

[0063] The relationship between IC and x in the objective function is shown in the following formula: k

[0064]

[0065] Among them, C p represents the rated power price of the mobile energy storage; C E represents the rated energy price of the mobile energy storage; represents the rated energy of the mobile energy storage; represents the rated active power of the mobile energy storage;

[0066] The operating cost OC under the normal operating scenario s is expressed as the following formula:

[0067]

[0068] Among them, t ∈ T represents the set of time intervals; represents the incremental cost of the distributed power source, represents the active power output of the distributed power source; h represents the degradation slope of the mobile energy storage; and are the decision variables of the charging / discharging active power of the MES respectively, indicates that the MES charges / discharges when It means that MES does not charge / discharge.

[0069] Different from the normal scenario, the emergency scenario includes a load shedding item with the value of lost load as a penalty, as shown in the following formula:

[0070]

[0071] Where, represents the value of lost load; represents the load switch status variable, which is 1 when the load of bus b is connected to the bus, and 0 otherwise;

[0072] The constraint conditions are as follows:

[0073]

[0074] The above constraints ensure that MES can only be deployed or moved after installation.

[0075] Furthermore, the objective function in the emergency stage is shown in the following formula:

[0076]

[0077] The constraint conditions include:

[0078] MES deployment quantity constraint:

[0079]

[0080] The MES deployment quantity constraint is used to limit the maximum number of mobile energy storages connected to each distribution bus b;

[0081] Operation time constraint:

[0082]

[0083] The meaning of the operation time constraint is that if the mobile energy storage k is located at bus b1 at time t, then moving to b2 at time t + 1 needs to meet the shortest transportation time

[0084] Bind the initial position of MES, as shown in the following formula:

[0085]

[0086] At the initial moment of the emergency stage (t = 0), the positions of the mobile energy storages are the same as those in the normal stage.

[0087] Furthermore, the dynamic reconfiguration module dynamically reconfigures the microgrid in the emergency stage to form an independent power supply area with mobile energy storages and distributed power sources as the core, specifically including:

[0088] (1) Dynamically reconfigure the microgrid to maintain the stability of power supply during emergency response through distributed power supply and load switching technologies:

[0089] Based on the normal model, the line switch constraints and power flow constraints of the microgrid in emergency situations:

[0090]

[0091] Among them, is the line switch status variable, which is 1 when line l is closed and 0 when it is open at time interval t in scenario s; and are the active and reactive power flows of line l respectively; a lts is the square of the current of line l; R l and X l are the resistance and reactance of line l respectively;

[0092] Use line switch decisions to reconfigure the microgrid in the following way:

[0093]

[0094] Where N L is the maximum number of microgrids allowed in the power grid, N O is the number of out-of-service lines caused by disasters, N MES and N DG are the numbers of MES and DG respectively;

[0095] (2) Optimize the charging and discharging strategies of mobile energy storage to update the energy state of MES during emergency response;

[0096] The initial energy constraints of each MES are as follows:

[0097]

[0098] Among them, e k,t,s represents the state of charge, e k,t0,s is the initial state of charge; is the rated energy of MES;

[0099] MES charging and discharging power constraints:

[0100]

[0101] Among them, and are the charging / discharging powers respectively; η ch and η dis represent the charging / discharging efficiencies;

[0102] MES energy state update constraints:

[0103]

[0104] 11. Further, in the process of solving the two-stage stochastic optimization model by the asymptotic hedging algorithm in the model solving module, the penalty coefficient is used to coordinate investment and emergency decision-making, and the penalty term is designed as follows:

[0105]

[0106] Wherein, and represent the maximum and minimum values of all scenarios x k in the initial iteration; and represent the maximum and minimum values of the fixed deployment variable z kb for all scenarios in the initial iteration; ρ x and ρ z are the penalty coefficients of the investment decision variable and the fixed deployment variable respectively; by dynamically adjusting the penalty coefficient, the asymptotic hedging algorithm gradually reduces the difference of decision variables between different scenarios during the iteration process and finally realizes consistency.

[0107] The proposed mobile energy storage and microgrid integration solution significantly improves the resilience and economy of the distribution system; the deployment of mobile energy storage in the present invention enables it to be quickly deployed to the disaster area before or during the disaster, supplement electricity in real time and reduce load loss; the system can be flexibly divided into multiple microgrids, each of which is supported by mobile energy storage or distributed power sources to achieve local autonomous power supply; in addition, the present invention takes into account the economic value during normal operation and the emergency needs during disasters. Mobile energy storage reduces the operation cost through space-time arbitrage during non-disaster periods and recovers the investment through load support during disasters, forming a "dual-purpose" benefit. Compared with the traditional fixed mobile energy storage solution, the present invention performs better in terms of resource utilization efficiency, load guarantee ability and cost-benefit, providing a flexible and economic solution for the distribution system to cope with natural disasters. Description of the Drawings

[0108] Figure 1 is a flowchart of a two-stage optimization method for pre-layout and mobile path of mobile energy storage to improve the resilience of the distribution system according to an embodiment of the present invention.

[0109] Figure 2 is a structural diagram of the problem of planning mobile energy storage according to an embodiment of the present invention. Detailed Embodiment

[0110] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0111] A two-stage optimization method for mobile energy storage pre-layout and mobile path to enhance the resilience of a distribution system provided by the present invention is as Figure 1 shown. The structure of the problem of planning mobile energy storage is as Figure 2 shown. The two-stage optimization method for mobile energy storage pre-layout and mobile path of the distribution system resilience included in the present invention comprises the following steps:

[0112] First step, construct a two-stage optimization model. In the first stage, determine the installation location and capacity of mobile energy storage, and determine the installation location of mobile energy storage through an investment optimization model.

[0113] The objective function is to minimize the total cost of investment decisions and initial deployment, as shown in the following formula:

[0114]

[0115] where γ is the discount factor; IC represents the investment cost; s ∈ S represents the set of scenarios, S N and S E represent the normal scenario and the emergency scenario respectively; ω s is the probability of the occurrence of scenario s; b ∈ B represents the set of buses; k ∈ K represents the set of mobile energy storage; OC s represents the operating cost in the normal operation scenario; EC s represents the load shedding loss and emergency resource scheduling cost in the disaster response scenario; x k represents the mobile energy storage investment decision variable, x k = 1 indicates that mobile energy storage k is installed, x k = 0 indicates that it is not installed; z kb represents the mobile energy storage fixed position variable, z kb = 1 indicates that mobile energy storage k is deployed at bus b, z kb = 0 indicates that it is not deployed; u kbts represents the transportation path variable of mobile energy storage in the mobile mode. When mobile energy storage k is at bus b at time interval t in scenario s, u kbts = 1, otherwise u kbts = 0.

[0116] The relationship between IC and x in the objective function is as shown in the following formula: k as shown in the following formula:

[0117]

[0118] Among them, C p represents the rated power price of mobile energy storage; C E represents the rated energy price of mobile energy storage; represents the rated energy of mobile energy storage; represents the rated active power of mobile energy storage;

[0119] Under normal operating scenarios, the operating cost OC s can be expressed as the following formula:

[0120]

[0121] Among them, t ∈ T represents the time interval set; represents the incremental cost of distributed power sources, represents the active power output of distributed power sources; h represents the degradation slope of mobile energy storage; and are the decision variables of the active power of MES charge / discharge respectively, means MES performs charge / discharge when means MES does not perform charge / discharge.

[0122] Different from the normal scenario, the emergency scenario includes a load shedding item with the value of lost load as a penalty, as shown in the following formula:

[0123]

[0124] Among them, represents the value of lost load; represents the load switch status variable, which is 1 when the load of bus b is connected to the bus, otherwise 0.

[0125] The constraint conditions are as follows:

[0126]

[0127] The above constraints can ensure that MES can only be deployed or moved after installation (x k = 1).

[0128] Second step, the second-stage optimization model, dynamically adjusts the position of mobile energy storage according to the disaster scenario and dynamically schedules it to the disaster area.

[0129] The objective function in the emergency stage is shown in the following formula:

[0130]

[0131] Emergency operation decisions rely on the fixed-site decisions during normal operation, and its objective function is to minimize the expected emergency cost EC in the disaster scenario s , and the expression of this variable has been mentioned in the normal stage, so it will not be elaborated here

[0132] MES deployment quantity constraint:

[0133]

[0134] This formula restricts the maximum number of mobile energy storages connected to each distribution bus b

[0135] Operation time constraint:

[0136]

[0137] The above constraint can be understood as that if the mobile energy storage k is located at bus b1 at time t, then moving to b2 at time t + 1 needs to meet the shortest transportation time

[0138] In addition, the initial position of MES also needs to be bound, as shown in the following formula:

[0139]

[0140] At the initial moment of the emergency stage (t = 0), the position of the mobile energy storage must be the same as that in the normal stage

[0141] In the third step, dynamic reconfiguration of the microgrid is carried out to maintain the stability of power supply during the emergency response through distributed generation (DG) and load switching technologies

[0142] Based on the normal model, the microgrid line switch constraint and power flow constraint in the emergency situation:

[0143]

[0144] Among them, is the line switch status variable, which is 1 when the line l is closed and 0 when it is open at time interval t in scenario s; and are the active and reactive power flows of line l respectively; a lts is the square of the current of line l; R l and X l are the resistance and reactance of line l respectively. The above constraints control the on-off state of the line, limit the power flow of the faulty line, and avoid overload Using the line switch decision, the microgrid formation process is modeled in the following way:

[0145]

[0146] ​

[0147] Among them, where N L is the maximum number of microgrids allowed in the power grid, N O is the number of out-of-service lines caused by the disaster, N MES and N DG are the numbers of MES and DG respectively. The above constraints ensure that each microgrid contains at least one power source, mainly MES and DG.

[0148] Step 4: Optimize the charging and discharging strategies of mobile energy storage and update the energy state of MES during emergency response.

[0149] The initial energy constraints for each MES are as follows:

[0150]

[0151] Among them, e k,t,s represents the state of charge, while e k,t0,s is the initial state of charge; is the rated energy of MES. Here, the initial energy of each mobile energy storage at the start of the disaster is set to 50%.

[0152] MES charging and discharging power constraints:

[0153]

[0154] Among them, and are the charging / discharging powers respectively; η ch and η dis represent the charging / discharging efficiencies.

[0155] MES energy state update constraints:

[0156]

[0157] The constraints of the above formula can effectively track the energy change of MES continuously across time steps.

[0158] Step 5: Use the progressive hedging algorithm to solve the two-stage model and solve the mobile energy storage routing and microgrid configuration under each scenario in parallel.

[0159] The PH algorithm solves sub-problems in parallel by decomposing multi-scenario problems, as shown in the process of Table 1.

[0160] Table 1 Progressive hedging algorithm for installing mobile energy storage

[0161]

[0162]

[0163] During the solution process, the penalty coefficient can coordinate investment and emergency decisions, and the penalty term is designed as follows:

[0164]

[0165] Wherein, and represent the maximum and minimum values of all scenarios x k in the initial iteration; and represent the maximum and minimum values of the fixed deployment variable z kb for all scenarios in the initial iteration; ρ x and ρ z are the penalty coefficients of the investment decision variable and the fixed deployment variable respectively. By dynamically adjusting the penalty coefficient, the PH algorithm gradually reduces the difference in decision variables between different scenarios during the iteration process and finally achieves consistency.

[0166] The embodiment of the present invention also provides a two-stage optimization device for pre-layout and mobile path of mobile energy storage to improve the resilience of the distribution system, including:

[0167] A two-stage stochastic optimization model construction module, which is used to construct a two-stage stochastic optimization model, optimize the investment decision and initial deployment of mobile energy storage during the normal operation stage, and determine the installation location and capacity of mobile energy storage; during the emergency stage, quickly deploy mobile energy storage to the affected area through emergency operation decisions;

[0168] A dynamic reconstruction module, which is used to dynamically reconstruct the microgrid during the emergency stage to form an independent power supply area with mobile energy storage and distributed power sources as the core;

[0169] A model solution module, which is used to solve the two-stage stochastic optimization model by using the asymptotic hedging algorithm to obtain the investment and operation decisions of the mobile energy storage unit.

[0170] The present invention verifies the effectiveness of the method for combining mobile energy storage and microgrid through a case of a 15-bus distribution system. The impact of the combination of energy storage scheduling and microgrid on load loss during the emergency stage is shown in Table 2.

[0171] Table 2 The impact of energy storage on load loss during the emergency stage

[0172]

[0173] According to the third step in the above steps, during the emergency stage, the system is divided into multiple microgrids through line switches and mobile energy storage scheduling. As can be seen from Table 2, during the emergency stage, the load loss ratio of the mobile energy storage combined with the microgrid is nearly 10% less than that of the fixed energy storage and 29% less than that of the scheme without energy storage. In addition, the cost consumed by the mobile energy storage moving from the normal position to the disaster area during the emergency stage can be seen from Table 2. Among them, the objective function value (total cost) of the two-stage optimization model constructed based on the present invention is lower than the cost of the fixed energy storage, reflecting the balance between the economy and flexibility of the present invention.

[0174] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A two-stage optimization method for mobile energy storage pre-layout and mobile path to improve the resilience of the distribution system, characterized in that, It includes the following steps: Construct a two-stage stochastic optimization model to optimize the investment decision and initial deployment of mobile energy storage during the normal operation stage, and determine the installation location and capacity of mobile energy storage; during the emergency stage, quickly deploy mobile energy storage to the disaster area through emergency operation decisions; During the emergency stage, dynamically reconfigure the microgrid to form an independent power supply area with mobile energy storage and distributed power sources as the core; Use the asymptotic hedging algorithm to solve the two-stage stochastic optimization model to obtain the investment and operation decisions of mobile energy storage units.

2. The two-stage optimization method for mobile energy storage pre-layout and mobile path to enhance the resilience of the distribution system according to claim 1, characterized in that: The objective function of the normal operation stage is to minimize the total cost of investment decisions and initial deployment, as shown in the following formula: Among them, γ is the discount factor; IC represents the investment cost; s ∈ S represents the set of scenarios, where S N and S E represent the normal scenario and the emergency scenario respectively; ω s is the probability of the occurrence of scenario s; b ∈ B represents the set of buses; k ∈ K represents the set of mobile energy storages; OC s represents the operating cost under the normal operating scenario; EC s represents the load shedding loss and emergency resource scheduling cost under the disaster response scenario; x k represents the mobile energy storage investment decision variable. x k = 1 indicates that the mobile energy storage k is installed, and x k = 0 indicates that it is not installed; z kb represents the fixed location variable of the mobile energy storage. z kb = 1 indicates that the mobile energy storage k is deployed at the bus b, and z kb = 0 indicates that it is not deployed; u kbts represents the transportation path variable of the mobile energy storage in the mobile mode. When the mobile energy storage k is at the bus b at the time interval t under the scenario s, u kbts = 1, otherwise u kbts = 0; IC and x in the objective function k The relationship is shown in the following formula: Among them, C p represents the rated power price of mobile energy storage; C E represents the rated energy price of mobile energy storage; represents the rated energy of mobile energy storage; represents the rated active power of mobile energy storage; Operating cost OC under normal operating scenarios s Expressed as the following formula: where \(t\in T\) represents the set of time intervals; represents the incremental cost of distributed power sources; represents the active power output of distributed power sources; \(h\) represents the degradation slope of mobile energy storage; and are the decision variables for the active power of MES charging / discharging respectively, indicating that MES is charging / discharging when indicating that MES is not charging / discharging; Different from the normal scenario, the emergency scenario includes a load shedding term with the value of lost load as a penalty, as shown in the following formula: Among them, represents the value of load loss; represents the load switch status variable, which is 1 when the load of bus b is connected to the bus, and 0 otherwise; The constraint conditions are as follows: The above constraints ensure that MES can only be deployed or moved after installation.

3. The two-stage optimization method for pre-layout and mobile path of mobile energy storage to enhance the resilience of the distribution system according to claim 1, characterized in that: The objective function of the emergency stage is as shown in the following formula: The constraint conditions include: MES deployment quantity constraint: The MES deployment quantity constraint is used to limit the maximum number of mobile energy storage connected to each distribution bus b; Operating time constraint: The meaning of the running time constraint is that if the mobile energy storage k is at bus b1 at time t, then moving to b2 at time t + 1 needs to satisfy the shortest transportation time Bind the initial position of MES, as shown in the following formula: At the initial moment of the emergency stage (t = 0), the position of mobile energy storage is the same as that in the normal stage.

4. The two-stage optimization method for mobile energy storage pre-layout and mobile path to enhance the resilience of the distribution system according to claim 1, characterized in that: The dynamically reconfiguring the microgrid during the emergency stage to form an independent power supply area with mobile energy storage and distributed power sources as the core specifically includes: (1) Dynamically reconfigure the microgrid and maintain the stability of power supply during the emergency response through distributed power source and load switching technologies: Based on the normal model, the microgrid line switch constraint and power flow constraint in the emergency situation: Among them, is the line switch status variable, which is 1 when line l is closed and 0 when it is open during the time interval t in scenario s; and are the active and reactive power flows of line l respectively; a lts is the square of the current of line l; R l and X l are the resistance and reactance of line l respectively; Use the line switch decision to reconfigure the microgrid in the following way: where N L is the maximum number of microgrids allowed in the power grid, N O is the number of out-of-service lines caused by the disaster, N MES and N DG are the numbers of MES and DG, respectively; (2) Optimize the charging and discharging strategy of mobile energy storage and update the energy state of MES during the emergency response; The initial energy constraint of each MES is as follows: Among them, e k,t,s represents the state of charge, and e k,t0,s is the initial state of charge; is the energy rating of the MES; MES charging and discharging power constraint: Among them, and are the charging / discharging power respectively; η ch and η dis represent the charging / discharging efficiency; MES energy state update constraint:

5. The two-stage optimization method for pre-layout and mobile path of mobile energy storage to enhance the resilience of the distribution system according to claim 1, characterized in that: In the process of using the asymptotic hedging algorithm to solve the two-stage stochastic optimization model, coordinate the investment and emergency decisions with the penalty coefficient, and the penalty term is designed as shown below: Among them, and represent the maximum and minimum values for all scenarios x k in the initial iteration; and represent the maximum and minimum values of the fixed deployment variable z kb for all scenarios in the initial iteration; ρ x and ρ z are the penalty coefficients for the investment decision variable and the fixed deployment variable respectively; by dynamically adjusting the penalty coefficients, the asymptotic hedging algorithm gradually reduces the differences in decision variables among different scenarios during the iteration process and finally achieves consistency.

6. A two-stage optimization device for pre-layout and mobile path of mobile energy storage to enhance the resilience of the distribution system, characterized in that, It includes: A two-stage stochastic optimization model construction module for constructing a two-stage stochastic optimization model to optimize the investment decision and initial deployment of mobile energy storage during the normal operation stage, and determine the installation location and capacity of mobile energy storage; During the emergency stage, quickly deploy mobile energy storage to the disaster area through emergency operation decisions; A dynamic reconfiguration module for dynamically reconfiguring the microgrid during the emergency stage to form an independent power supply area with mobile energy storage and distributed power sources as the core; A model solving module for using the asymptotic hedging algorithm to solve the two-stage stochastic optimization model to obtain the investment and operation decisions of mobile energy storage units.

7. The two-stage optimization device for mobile energy storage pre-layout and mobile path to enhance the resilience of the distribution system according to claim 6, characterized in that, The objective function of the normal operation stage is to minimize the total cost of investment decisions and initial deployment, as shown in the following formula: where γ is the discount factor; IC represents the investment cost; s ∈ S represents the set of scenarios, and S N and S E represent the normal scenario and the emergency scenario, respectively; ω s is the probability of the occurrence of scenario s; b ∈ B represents the set of buses; k ∈ K represents the set of mobile energy storages; OC s represents the operating cost under the normal operating scenario; EC s represents the load shedding loss and emergency resource scheduling cost under the disaster response scenario; x k represents the investment decision variable of the mobile energy storage. x k = 1 indicates that the mobile energy storage k is installed, and x k = 0 indicates that it is not installed; z kb represents the fixed position variable of the mobile energy storage. z kb = 1 indicates that the mobile energy storage k is deployed at the bus b, and z kb = 0 indicates that it is not deployed; u kbts represents the transportation path variable of the mobile energy storage in the mobile mode. When the mobile energy storage k is at the bus b at the time interval t under the scenario s, u kbts = 1, otherwise u kbts = 0; IC and x in the objective function k The relationship is shown in the following formula: Among them, C p represents the rated power price of mobile energy storage; C E represents the rated energy price of mobile energy storage; represents the rated energy of mobile energy storage; represents the rated active power of mobile energy storage; Operating cost OC under normal operation scenario s It is expressed as the following formula: where \(t\in T\) represents the set of time intervals; represents the incremental cost of distributed power sources, represents the active power output of distributed power sources; \(h\) represents the degradation slope of mobile energy storage; and are the decision variables for the active power of MES charging / discharging respectively, indicating that MES charges / discharges when indicating that MES does not charge / discharge; Different from the normal scenario, the emergency scenario includes a load shedding term with the value of lost load as a penalty, as shown in the following formula: Among them, represents the value of load loss; represents the load switch status variable, which is 1 when the load of bus b is connected to the bus, and 0 otherwise; The constraint conditions are as follows: The above constraints ensure that MES can only be deployed or moved after installation.

8. The two-stage optimization device for mobile energy storage pre-layout and mobile path to improve the resilience of the power distribution system according to claim 6, characterized in that, The objective function of the emergency stage is as shown in the following formula: The constraint conditions include: MES deployment quantity constraint: The MES deployment quantity constraint is used to limit the maximum number of mobile energy storages connected to each distribution bus b; Operating time constraint: The meaning of the running time constraint is that if the mobile energy storage k is at bus b1 at time t, then when moving to b2 at time t + 1, the shortest transportation time must be satisfied. Bind the initial position of the MES as shown in the following formula: At the initial moment of the emergency phase (t = 0), the position of the mobile energy storage is the same as that in the normal phase.

9. The two-stage optimization device for pre-layout and mobile path of mobile energy storage to enhance the resilience of the power distribution system according to claim 6, wherein The dynamic reconfiguration module dynamically reconfigures the microgrid during the emergency phase to form an independent power supply area with mobile energy storage and distributed power sources as the core, specifically including: (1) Dynamically reconfigure the microgrid, and maintain the stability of power supply during the emergency response through distributed power source and load switching technologies: Based on the normal model, the microgrid line switch constraint and power flow constraint in an emergency: Among them, is the line switch status variable, which is 1 when line l is closed and 0 when it is open during the time interval t in scenario s; and are the active and reactive power flows of line l, respectively; a lts is the square of the current in line l; R l and X l are the resistance and reactance of line l, respectively; Use the line switch decision to reconfigure the microgrid in the following way: where N L is the maximum number of microgrids allowed in the power grid, N O is the number of out-of-service lines caused by the disaster, N MES and N DG are the numbers of MES and DG respectively; (2) Optimize the charge and discharge strategy of the mobile energy storage and update the MES energy state during the emergency response; The initial energy constraint of each MES is as follows: Among them, e k,t,s represents the state of charge, and e k,t0,s is the initial state of charge; is the energy rating of the MES; MES charge and discharge power constraint: Among them, and are the charging / discharging power respectively; η ch and η dis represent the charging / discharging efficiency; MES energy state update constraint:

10. The two-stage optimization device for mobile energy storage pre-layout and mobile path to enhance the resilience of the distribution system according to claim 6, characterized in that, When the model solving module solves the two-stage stochastic optimization model using the asymptotic hedging algorithm, it coordinates investment and emergency decisions with a penalty coefficient, and the penalty term is designed as shown below: Among them, and represent the maximum and minimum values for all scenarios x k in the initial iteration; and Denote the maximum and minimum values of the fixed deployment variable \(z\) across all scenarios in the initial iteration; \(\rho\) kb ; \(\rho\) x and \(\rho\) z are the penalty coefficients for the investment decision variable and the fixed deployment variable, respectively; by dynamically adjusting the penalty coefficients, the asymptotic hedging algorithm gradually reduces the differences in decision variables across different scenarios during the iteration process and finally achieves consistency.

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