Method, device and equipment for improving anti-disaster capability of power distribution network and medium

Abstract

The invention relates to a power distribution network anti-disaster capability improving method, device, equipment and medium, and the method comprises the steps: taking the minimum mobile energy storage cost, power distribution network vulnerability and energy storage input capacity as the target, constructing a power distribution network mobile energy storage optimization configuration model in a typical scene, and carrying out the solving to obtain a mobile energy storage configuration scheme; constructing a pre-disaster mobile energy storage initial position scheduling model by taking minimization of mobile energy storage scheduling cost and load reduction cost as a target according to a disaster scene, and solving to obtain a pre-scheduling scheme; and constructing an optimal recovery model of the post-disaster power distribution network by taking the minimum load shedding as a target, solving to obtain a scheduling scheme, and scheduling the mobile energy storage based on the scheduling scheme. The mobile energy storage can be optimally dispatched to improve the recovery capability of the power distribution network.

Description

Technical Field

[0001] The present invention relates to the field of power distribution network disaster resistance technology, and in particular to a method, device, equipment and medium for improving the disaster resistance capability of a power distribution network. Background Art

[0002] Research on improving the disaster resilience of distribution networks has gradually attracted attention from countries around the world. In my country, improving the power system's ability to cope with extreme events has become a major strategic need in the field of energy security.

[0003] Currently, existing research on extreme events mostly focuses on low-probability, high-loss extreme disaster events, with a focus on the grid's ability to support and recover critical loads in the event of an extreme disaster. In the context of new power systems, especially those with a high proportion of distributed generation (DG) access, the dramatic fluctuations in DG output caused by climate instability, the mismatch between output and load curves, and grid stability issues have gradually become a new focus in the field of power system disaster resilience research. Regarding research on improving the disaster resilience of distribution networks, flexibility resources are dispersed across the source, grid, load, and storage sides of the distribution network. Their synergistic effect can reduce the load loss of the distribution network caused by disaster events, thereby providing a favorable environment for the system's optimal operating state. Therefore, optimizing the utilization of flexibility resources can be used as an important method to improve disaster resilience. Studying their regulatory role in distribution network planning and operation is a key issue in the field of disaster resilience research.

[0004] Energy storage, with its flexible charging and discharging mechanism, has become an important flexibility resource for new power systems. Mobile energy storage has greater spatial flexibility in smoothing power fluctuations caused by wind and solar grid connection and ensuring power supply under extreme disasters. However, mobile energy storage is more expensive than fixed energy storage, so the configuration of mobile energy storage mainly considers economic factors. Existing research mostly maximizes investment benefits by configuring mobile energy storage, including investment and construction costs, operation and maintenance costs, etc. Some studies also consider technical and economic indicators on the grid side, mobile energy storage peak-valley arbitrage, grid slowdown benefits obtained by mobile energy storage, environmental benefits, network loss conditions, etc. Research on the role of mobile energy storage in the recovery of distribution networks under extreme disaster events mainly focuses on the goal of minimizing failure losses by temporally and spatially scheduling mobile energy storage. Existing research on mobile energy storage rarely considers the quantitative role of mobile energy storage in smoothing photovoltaic fluctuations and improving distribution network reliability after a high proportion of photovoltaic access. When considering the improvement effect of mobile energy storage scheduling on post-disaster power supply restoration, the role of mobile energy storage pre-deployment is rarely considered. Moreover, most of the research focuses on a single scenario, and there is even less research on the comprehensive role of mobile energy storage in two different scenarios. Therefore, it is necessary to comprehensively consider the research on disaster resistance improvement strategies taking into account mobile energy storage configuration and scheduling operation in both scenarios to enhance the reuse value of mobile energy storage. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method, device, equipment and medium for improving the disaster resistance of a distribution network, which can optimally dispatch mobile energy storage to improve the recovery capability of the distribution network.

[0006] The technical solution adopted by the present invention to solve the technical problem is: to provide a method for improving the disaster resistance of a distribution network, comprising the following steps:

[0007] With the goal of minimizing mobile energy storage costs, distribution network vulnerability, and energy storage investment capacity, a distribution network mobile energy storage optimization configuration model for typical scenarios is constructed. This model is then solved to obtain a mobile energy storage configuration plan.

[0008] According to the disaster scenario, with the goal of minimizing the mobile energy storage scheduling cost and the load reduction cost, a pre-disaster mobile energy storage initial position scheduling model is constructed, and the mobile energy storage configuration plan is used as the input of the pre-disaster mobile energy storage initial position scheduling model, and the pre-disaster mobile energy storage initial position scheduling model is solved to obtain a pre-dispatching plan;

[0009] With the goal of minimizing load shedding, an optimal recovery model for the post-disaster distribution network is constructed. The pre-dispatching plan is used as the input of the optimal recovery model for the post-disaster distribution network. The optimal recovery model for the post-disaster distribution network is solved to obtain a scheduling plan, and mobile energy storage is dispatched based on the scheduling plan.

[0010] The objective function of the distribution network mobile energy storage optimization configuration model is expressed as: F = min[f1, f2, f3], where F is the objective function of the distribution network mobile energy storage optimization configuration model, min[] represents the selection of the best balance solution among the three objectives, f1 is the mobile energy storage cost objective function, expressed as: f1 = -C1 - C2 + C3 + C4, and C1 is the mobile energy storage peak-valley arbitrage, expressed as: C2 is the subsidy for delayed grid construction obtained by mobile energy storage, which is expressed as: C3 is the investment cost of mobile energy storage, expressed as: C4 is the operating cost of mobile energy storage, expressed as: f2 is the distribution network vulnerability objective function, which is expressed as: V t ent is the voltage quality vulnerability of the distribution network, expressed as: V t equ is the balance degree of distribution network voltage quality vulnerability, expressed as: is the active power balance index, expressed as: ω1, ω2 and ω3 are all weight coefficients; f3 is the energy storage input capacity objective function, which is expressed as: t0 is the maximum charge or discharge start time; t0+nΔt is the maximum charge or discharge end time, P n,t is the operating power of mobile energy storage n at time t, Δt is the time interval between the two moments;

[0011] Among them, y is the usable life of mobile energy storage, D is the number of days of use of mobile energy storage throughout the year, r i is the inflation rate, r d is the discount rate, n ESM is the number of mobile energy storages, T is the time period, and are the discharge power and charging power of mobile energy storage n at time t, C(t) is the electricity price at time t, ε is the subsidy ratio for delayed construction income, is the rated power of a single mobile energy storage, b2 is the cost required to upgrade the unit power equipment, ω is the annual interest rate, C ESM is the unit capacity cost of mobile energy storage, C P is the unit power conversion cost, E is the capacity of a single mobile energy storage, n CAR is the number of transport vehicles, C CAR The unit purchase cost of the vehicle used to load and transport mobile energy storage, C m is the annual operating cost of the mobile energy storage module per unit charge and discharge power, N is the total number of nodes, V i,t is the node vulnerability index of node i at time t, V t,max and V t,min are the maximum and minimum values ​​of the node vulnerability index at time t, H i,t is the ratio of the sum of the vulnerability indices of the first i nodes to the sum of the vulnerability indices of all nodes after the vulnerability indices of the nodes at time t are sorted from small to large, x l is the reactance value of line l, P l,t is the active power transmitted by line l at time t, n line is the number of lines.

[0012] The constraints of the distribution network mobile energy storage optimization configuration model include:

[0013] The power flow constraint of the distribution network is expressed as:

[0014]

[0015] ΣP i,j ,ΣP j,k 、 and They represent the sum of the active power injected into node j, the sum of the active power flowing out, the active load forecast value, the sum of the active output of the distributed generation, and the active amount of the abandoned load; ΣQ i,j ,ΣQ j,k 、 and They represent the sum of reactive power injected into node j, the sum of reactive power outflow, reactive load forecast value, the sum of reactive output of distributed generation, and reactive amount of abandoned load respectively; and Represent the total active line loss and reactive line loss of the injected line ij, r i,j represents the resistance of line ij, x i,j represents the reactance of line ij, Represents the square of the current in line ij, m i,j is the slack variable, α i,j is a 0-1 variable, indicating the open / closed state of the line; M is a constant; is the square of the voltage at node i, is the square of the voltage at node j; P i,j represents the active power from node i to node j, Q i,j represents the reactive power from node i to node j;

[0016] Distributed photovoltaic output constraints are expressed as:

[0017]

[0018] P is the set of distributed photovoltaic access nodes; P i PV and are the distributed photovoltaic active power and reactive power connected to node i, and are the upper limit of active power output and reactive power output of distributed photovoltaic connected to node i respectively; and are the upper and lower limits of the distributed photovoltaic power factor at access node i;

[0019] The charging and discharging constraints of mobile energy storage are expressed as:

[0020]

[0021] is the charging state value of mobile energy storage n at time t, is the discharge state value of mobile energy storage n at time t; and are all 0-1 variables, representing the states of the mobile energy storage n at time t and time t+1 respectively, Ω represents the location set of the mobile energy storage, and are the charging active power and discharging active power of mobile energy storage n at time t respectively; and are the charging reactive power and discharging reactive power of mobile energy storage n at time t respectively; and are the upper limit of charging and discharging active power of mobile energy storage n respectively; and are the upper limit of charging reactive power and the upper limit of discharging reactive power of mobile energy storage n respectively; and are the charging efficiency and discharging efficiency of mobile energy storage n, respectively; and is the state of charge of the mobile energy storage n at time t and t+Δt; and are the upper and lower limits of the state of charge of the mobile energy storage n.

[0022] The objective function of the pre-disaster mobile energy storage initial position scheduling model is: Among them, f1′ is the objective function of the initial location scheduling model of mobile energy storage before the disaster, n ESM is the collection of mobile energy storage, n CAR For the collection of vehicles; is a 0-1 variable, indicating whether the mobile energy storage n is traveling on the line with the vehicle c; P i Lshd is the load active power reduction of node i; the cost of pre-dispatching a single mobile energy storage system; is the unit load loss cost of node i.

[0023] The constraints of the pre-disaster mobile energy storage initial location scheduling model include:

[0024] The mobile energy storage resource constraint is expressed as:

[0025]

[0026] is the state of mobile energy storage n at time t, N ESM,i is the maximum number of mobile energy storage installations at node i, N is the set of nodes, Indicates whether the mobile energy storage n is traveling on the line with vehicle c at time t, N ESM,c is the maximum carrying capacity of mobile energy storage of vehicle c;

[0027] The distribution network radial topology constraint is expressed as:

[0028]

[0029] v is the node other than the distributed power generation node; ε is the set of all branches; F ls and F js are the outflow powers of nodes l and j in the virtual network, respectively, and F il and F ij are the inflow powers of nodes l and j in the virtual network, δ(l) represents the outflow node set of node l, π(l) represents the inflow node set of node l, δ(j) represents the outflow node set of node j, π(j) represents the inflow node set of node j, W j is the amount of electricity provided by the distributed power sources in the virtual network; M is a constant; c ij is a variable representing the state of the branch;

[0030] The load loss power constraint is expressed as:

[0031]

[0032] P i Lshd is the load active power reduction of node i, is the reactive power reduction of the load at node i, and are the maximum active and reactive load values ​​of node i, respectively.

[0033] The objective function of the optimal restoration model of the post-disaster distribution network is expressed as: Among them, f2′ is the objective function of the optimal restoration model of the distribution network after the disaster, Ω T is the set of fault time periods, N is the set of nodes, ω i is the weight that represents the importance of the load, is the load active power reduction of node i at time t during the fault time.

[0034] The constraints of the optimal restoration model of the post-disaster distribution network include:

[0035] The dispatch constraint of mobile energy storage is expressed as:

[0036]

[0037] represents the position variable of the mobile energy storage module n at node i during period t, It represents the position variable of the mobile energy storage n placed on the vehicle c at time t and traveling on the branch road between any nodes; N is the set of nodes, n CAR is the collection of vehicles; T0 is the installation time of mobile energy storage; n jk is the number of grid edges between node j and node k; ξ is the unit time of spatial transfer of the transport vehicle between grids;

[0038] The initial position constraint of mobile energy storage is expressed as:

[0039]

[0040] t0 is the time when the disaster occurs; Ω ME A collection of mobile energy storage; Indicates whether mobile energy storage n is connected to node i at the time of disaster occurrence; It is a 0-1 variable, where its value is 1 indicating that there is only one mobile energy storage connected to node i at the time of the disaster, and its value is 0 indicating that there is no mobile energy storage connected to node i at the time of the disaster.

[0041] The technical solution adopted by the present invention to solve the technical problem is to provide a device for improving the disaster resistance of a distribution network, comprising:

[0042] The mobile energy storage configuration module is used to build a distribution network mobile energy storage optimization configuration model under typical scenarios, taking mobile energy storage cost, distribution network vulnerability, and minimum energy storage investment capacity as the goals, and solve the distribution network mobile energy storage optimization configuration model to obtain a mobile energy storage configuration plan;

[0043] A mobile energy storage pre-dispatching module is configured to construct a pre-disaster mobile energy storage initial position scheduling model based on the disaster scenario with the goal of minimizing mobile energy storage scheduling costs and load reduction costs, and to solve the pre-disaster mobile energy storage initial position scheduling model using the mobile energy storage configuration plan as input to obtain a pre-dispatching plan.

[0044] The mobile energy storage scheduling module is used to construct an optimal recovery model for the post-disaster distribution network with the goal of minimizing load shedding, use the pre-scheduling plan as the input of the optimal recovery model of the post-disaster distribution network, solve the optimal recovery model of the post-disaster distribution network, obtain a scheduling plan, and schedule the mobile energy storage based on the scheduling plan.

[0045] The objective function of the distribution network mobile energy storage optimization configuration model constructed by the mobile energy storage configuration module is: F = min[f1, f2, f3], where F is the objective function of the distribution network mobile energy storage optimization configuration model, min[] represents the selection of the best balance solution among the three objectives, f1 is the mobile energy storage cost objective function, expressed as: f1 = -C1 - C2 + C3 + C4, and C1 is the mobile energy storage peak-valley arbitrage, expressed as: C2 is the subsidy for delayed grid construction obtained by mobile energy storage, which is expressed as: C3 is the investment cost of mobile energy storage, expressed as: C4 is the operating cost of mobile energy storage, expressed as: f2 is the distribution network vulnerability objective function, which is expressed as: V t ent is the voltage quality vulnerability of the distribution network, expressed as: V t equ is the balance degree of distribution network voltage quality vulnerability, expressed as: is the active power balance index, expressed as: ω1, ω2 and ω3 are all weight coefficients; f3 is the energy storage input capacity objective function, which is expressed as: t0 is the maximum charge or discharge start time; t0+nΔt is the maximum charge or discharge end time, P n,t is the operating power of mobile energy storage n at time t, Δt is the time interval between the two moments;

[0046] Among them, y is the usable life of mobile energy storage, D is the number of days of use of mobile energy storage throughout the year, r i is the inflation rate, r d is the discount rate, n ESM is the number of mobile energy storages, T is the time period, and are the discharge power and charging power of mobile energy storage n at time t, C(t) is the electricity price at time t, ε is the subsidy ratio for delayed construction income, is the rated power of a single mobile energy storage, b2 is the cost required to upgrade the unit power equipment, ω is the annual interest rate, C ESM is the unit capacity cost of mobile energy storage, C P is the unit power conversion cost, E is the capacity of a single mobile energy storage, n CAR is the number of transport vehicles, C CAR The unit purchase cost of the vehicle used to load and transport mobile energy storage, C m is the annual operating cost of the mobile energy storage module per unit charge and discharge power, N is the total number of nodes, V i,t is the node vulnerability index of node i at time t, V t,max and V t,min are the maximum and minimum values ​​of the node vulnerability index at time t, H i,t is the ratio of the sum of the vulnerability indices of the first i nodes to the sum of the vulnerability indices of all nodes after the vulnerability indices of the nodes at time t are sorted from small to large, x l is the reactance value of line l, P l,t is the active power transmitted by line l at time t, n line is the number of lines.

[0047] The constraints of the distribution network mobile energy storage optimization configuration model constructed by the mobile energy storage configuration module include:

[0048] The power flow constraint of the distribution network is expressed as:

[0049]

[0050] ΣP i,j ,ΣP j,k 、 and They represent the sum of the active power injected into node j, the sum of the active power flowing out, the active load forecast value, the sum of the active output of the distributed generation, and the active amount of the abandoned load; ΣQ i,j ,ΣQ j,k 、 and They represent the sum of reactive power injected into node j, the sum of reactive power outflow, reactive load forecast value, the sum of reactive output of distributed generation, and reactive amount of abandoned load respectively; and Represent the total active line loss and reactive line loss of the injected line ij, r i,j represents the resistance of line ij, x i,j represents the reactance of line ij, Represents the square of the current in line ij, m i,j is the slack variable, α i,j is a 0-1 variable, indicating the open / closed state of the line; M is a constant; is the square of the voltage at node i, is the square of the voltage at node j; P i,j represents the active power from node i to node j, Q i,j represents the reactive power from node i to node j;

[0051] Distributed photovoltaic output constraints are expressed as:

[0052]

[0053] P is the set of distributed photovoltaic access nodes; P i PV and are the distributed photovoltaic active power and reactive power connected to node i, and are the upper limit of active power output and reactive power output of distributed photovoltaic connected to node i respectively; and are the upper and lower limits of the distributed photovoltaic power factor at access node i;

[0054] The charging and discharging constraints of mobile energy storage are expressed as:

[0055]

[0056] is the charging state value of mobile energy storage n at time t, is the discharge state value of mobile energy storage n at time t; and are all 0-1 variables, representing the states of the mobile energy storage n at time t and time t+1 respectively, Ω represents the location set of the mobile energy storage, and are the charging active power and discharging active power of mobile energy storage n at time t respectively; and are the charging reactive power and discharging reactive power of mobile energy storage n at time t respectively; and are the upper limit of charging and discharging active power of mobile energy storage n respectively; and are the upper limit of charging reactive power and the upper limit of discharging reactive power of mobile energy storage n respectively; and are the charging efficiency and discharging efficiency of mobile energy storage n, respectively; and is the state of charge of the mobile energy storage n at time t and t+Δt; and are the upper and lower limits of the state of charge of the mobile energy storage n.

[0057] The objective function of the pre-disaster mobile energy storage initial position scheduling model constructed by the pre-dispatching module is: Among them, f1′ is the objective function of the initial location scheduling model of mobile energy storage before the disaster, n ESM is the collection of mobile energy storage, n CAR For the collection of vehicles; is a 0-1 variable, indicating whether the mobile energy storage n is traveling on the line with the vehicle c; is the load active power reduction of node i; the cost of pre-dispatching a single mobile energy storage system; is the unit load loss cost of node i.

[0058] The constraints of the pre-disaster mobile energy storage initial position scheduling model constructed by the pre-scheduling module include:

[0059] The mobile energy storage resource constraint is expressed as:

[0060]

[0061] is the state of mobile energy storage n at time t, N ESM,i is the maximum number of mobile energy storage installations at node i, N is the set of nodes, Indicates whether the mobile energy storage n is traveling on the line with vehicle c at time t, N ESM,c is the maximum carrying capacity of mobile energy storage of vehicle c;

[0062] The distribution network radial topology constraint is expressed as:

[0063]

[0064] v is the node other than the distributed power generation node; ε is the set of all branches; F ls and F js are the outflow powers of nodes l and j in the virtual network, respectively, and F il and F ij are the inflow powers of nodes l and j in the virtual network, δ(l) represents the outflow node set of node l, π(l) represents the inflow node set of node l, δ(j) represents the outflow node set of node j, π(j) represents the inflow node set of node j, W j is the amount of electricity provided by the distributed power sources in the virtual network; M is a constant; c ij is a variable representing the state of the branch;

[0065] The load loss power constraint is expressed as:

[0066]

[0067] P i Lshd is the load active power reduction of node i, is the reactive power reduction of the load at node i, and are the maximum active and reactive load values ​​of node i, respectively.

[0068] The objective function of the optimal recovery model of the post-disaster distribution network constructed by the scheduling module is expressed as: Among them, f2′ is the objective function of the optimal restoration model of the distribution network after the disaster, Ω T is the set of fault time periods, N is the set of nodes, ω i is the weight that represents the importance of the load, is the load active power reduction of node i at time t during the fault time.

[0069] The constraints of the optimal recovery model of the post-disaster distribution network constructed by the scheduling module include:

[0070] The dispatch constraint of mobile energy storage is expressed as:

[0071]

[0072] represents the position variable of the mobile energy storage module n at node i during period t, It represents the position variable of the mobile energy storage n placed on the vehicle c at time t and traveling on the branch road between any nodes; N is the set of nodes, n CAR is the collection of vehicles; T0 is the installation time of mobile energy storage; n jk is the number of grid edges between node j and node k; ξ is the unit time of spatial transfer of the transport vehicle between grids;

[0073] The initial position constraint of mobile energy storage is expressed as:

[0074]

[0075] t0 is the time when the disaster occurs; Ω ME A collection of mobile energy storage; Indicates whether mobile energy storage n is connected to node i at the time of disaster occurrence; It is a 0-1 variable, where its value is 1 indicating that there is only one mobile energy storage connected to node i at the time of the disaster, and its value is 0 indicating that there is no mobile energy storage connected to node i at the time of the disaster.

[0076] The technical solution adopted by the present invention to solve its technical problem is: to provide an electronic device, including a memory, a processor and a computer program stored in the memory and capable of running on the processor, and when the processor executes the computer program, the steps of the above-mentioned method for improving the disaster resistance of the distribution network are implemented.

[0077] The technical solution adopted by the present invention to solve its technical problem is: providing a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned method for improving the disaster resistance of the distribution network are implemented.

[0078] Beneficial effects

[0079] Due to the adoption of the above-mentioned technical solution, the present invention has the following advantages and positive effects compared with the existing technology: the present invention proposes a two-stage disaster resistance improvement optimization model based on the initial location layout of mobile energy storage and emergency response scheduling. Before the disaster, the initial location of mobile energy storage is arranged in advance with the minimum mobile energy storage scheduling cost and load reduction cost. After the disaster, an optimization model for emergency recovery of the distribution network is established. With the minimum load reduction under fault conditions as the objective function, the mobile energy storage is optimally scheduled to improve the recovery capability of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 This is a flow chart of a method for improving the disaster resistance of a distribution network according to a first embodiment of the present invention;

[0081] Figure 2This is a flow chart for solving the distribution network mobile energy storage optimization configuration model in the first embodiment of the present invention;

[0082] Figure 3 Schematic diagram of a mobile energy storage scheduling model in two stages before and after a disaster in the first embodiment of the present invention;

[0083] Figure 4 This is a schematic diagram of the traffic grid of the distribution network in the first embodiment of the present invention;

[0084] Figure 5 IEEE 33 system topology diagram for access to distributed photovoltaics in an embodiment of the present invention;

[0085] Figure 6 This is a photovoltaic unit output prediction diagram in an embodiment of the present invention;

[0086] Figure 7 This is a schematic diagram of the probability of branch active power exceeding the reverse limit after configuring mobile energy storage;

[0087] Figure 8 This is a schematic diagram showing the severity of branch over-limit within the reverse over-limit time of active power in each branch of the system after configuring mobile energy storage.

[0088] Figure 9 This is a schematic diagram of the probability of node voltage exceeding the upper limit after configuring mobile energy storage;

[0089] Figure 10 This is a schematic diagram of the severity of node over-limit when the voltage of each node in the system exceeds the upper limit after the mobile energy storage is configured;

[0090] Figure 11 It is a schematic diagram of the failure situation of IEEE33 node system;

[0091] Figure 12 It is the load active power prediction curve;

[0092] Figure 13 It is the topological structure diagram of the distribution network island;

[0093] Figure 14 It is the charge and discharge power and state of charge diagram of the mobile energy storage module;

[0094] Figure 15 It is a diagram of the mobile energy storage dispatch process;

[0095] Figure 16 It is a schematic diagram of the load recovery ratio and load loss power in each time period. DETAILED DESCRIPTION

[0096] Below in conjunction with specific embodiment, further set forth the present invention.Should be understood that these embodiments are only used to illustrate the present invention and are not used in limiting the scope of the present invention.In addition, should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall equally within the scope limited by the appended claims of the application.

[0097] The first embodiment of the present invention relates to a method for improving the disaster resistance of distribution networks. This method configures mobile energy storage based on the demand for mobile energy storage in distribution networks under typical scenarios, with the goal of minimizing mobile energy storage costs, distribution network vulnerability, and energy storage investment capacity, so as to smooth out peak-valley differences and respond to emergencies. When extreme situations occur and power supply is interrupted in some areas, the departure positions of mobile energy storage vehicles are pre-arranged to ensure that they can be quickly put into load restoration work after the disaster. On this basis, an optimization control model is constructed in the disaster recovery stage, and by optimally scheduling mobile energy storage in time and space, the power supply to key loads is restored to the greatest extent, thereby improving the disaster resistance of distribution networks. Figure 1 As shown, the following steps are included:

[0098] Step 1: With the goal of minimizing mobile energy storage cost, distribution network vulnerability, and energy storage investment capacity, a distribution network mobile energy storage optimization configuration model under typical scenarios is constructed, and the distribution network mobile energy storage optimization configuration model is solved to obtain a mobile energy storage configuration plan.

[0099] This step considers the timing characteristics of photovoltaic output and configures energy storage modules in the distribution system with a high proportion of distributed photovoltaics. Based on the consideration of energy storage power generation efficiency and energy storage cost, three indicators (mobile energy storage cost, distribution network vulnerability, and energy storage system capacity) are selected as the objective function to obtain the optimal configuration plan for energy storage capacity and location.

[0100] Optimization goal 1: Economic efficiency of mobile energy storage investment.

[0101] The economic goal of the distribution network is to minimize the cost of mobile energy storage in the distribution network within the planning period, as shown in formula (1).

[0102] f1=-C1-C2+C3+C4 (1)

[0103] Among them, C1 is the peak-valley arbitrage of mobile energy storage, C2 is the subsidy for slow grid construction obtained by mobile energy storage, C3 is the investment cost of mobile energy storage, and C4 is the operating cost of mobile energy storage.

[0104] The peak-valley arbitrage C1 of mobile energy storage is its low storage and high generation income, and its calculation formula is shown in Equation (2) and Equation (3).

[0105]

[0106] Where y is the usable life of mobile energy storage, D is the number of days mobile energy storage is used throughout the year, r i is the inflation rate, r d is the discount rate, n ESM is the number of mobile energy storages, T is the time period, and are the discharge power and charging power of mobile energy storage n at time t, and C(t) is the electricity price at time t.

[0107] Mobile energy storage can reduce peak loads and fill valleys during normal operation of the distribution network. It can also serve as an emergency power source to supply power to load-loss nodes after an extreme disturbance event, thereby delaying the need to update the distribution network infrastructure and thus generating benefits. The subsidy C2 for delayed grid construction obtained from mobile energy storage is expressed as the calculation formula shown in Equation (4).

[0108]

[0109] Among them, ε is the subsidy ratio for delayed construction income, is the rated power of a single mobile energy storage, b2 is the cost required to upgrade the unit power equipment, and ω is the annual interest rate.

[0110] The unit investment cost C3 of mobile energy storage includes the purchase cost of energy storage modules and transport vehicles, as well as the power conversion cost. Its calculation formula is shown in formula (5).

[0111]

[0112] Where C ESM is the unit capacity cost of mobile energy storage, C P is the unit power conversion cost, E is the capacity of a single mobile energy storage, n CAR is the number of transport vehicles, C CAR Unit acquisition cost of a delivery vehicle for loading and transporting mobile energy storage.

[0113] The operating cost C4 of mobile energy storage is shown in formula (6).

[0114]

[0115] Where C m It is the annual operating cost of the mobile energy storage module per unit charge and discharge power.

[0116] Optimization objective 2: distribution network vulnerability.

[0117] Distribution network vulnerability refers to the vulnerability of a distribution network to cascading failures, potentially leading to major power outages, when a disturbance occurs. This implementation measures the resilience of a distribution network based on branch active power over-limits and node voltage over-limits. This extends the measurement to include the impact of disturbances on the active power and voltage of the distribution network. Distribution network vulnerability indices are defined. The voltage quality vulnerability index and voltage quality vulnerability balance index reflect the impact of disturbances on voltage, while the active power balance index reflects the impact of disturbances on active power.

[0118] The node voltage deviation reflects the weak link of the node operation status. The node vulnerability index calculation formula is as follows:

[0119]

[0120] Where V i,t is the node vulnerability index of node i at time t, is the per-unit voltage value of node i at time t; ΔU max is the maximum allowable voltage offset of the node.

[0121] The voltage quality vulnerability of the distribution network is calculated as follows:

[0122]

[0123] Where V t ent is the voltage quality vulnerability of the distribution network, N is the total number of nodes; V t,max and V t,min are the maximum and minimum values ​​of the node vulnerability index at time t, respectively.

[0124] If the vulnerability of node voltage quality is unevenly distributed in the distribution network, it will lead to node voltage collapse and cause large-scale power outages. Therefore, a single vulnerability indicator has limitations. The Gini coefficient is introduced to characterize the balanced degree of voltage quality vulnerability in the distribution network. The calculation formula is as follows:

[0125]

[0126] Where V t equ is the equilibrium degree of distribution network voltage quality vulnerability, H i,t It is the ratio of the sum of the vulnerability indices of the first i nodes to the sum of the vulnerability indices of all nodes at time t after the vulnerability indices of the nodes are sorted from small to large.

[0127] In addition to node voltage quality, the active power transmitted by the line is also closely related to the operation of the power grid. The access of mobile energy storage can effectively reduce the active power loss of the line, so the active power balance index is introduced. The more evenly active power is distributed in the network, the lower the vulnerability:

[0128]

[0129] Where x l is the reactance value of line l, P l,t is the active power transmitted by line l at time t, n line is the number of lines.

[0130] This implementation uses the weighted sum of the voltage quality vulnerability index, the voltage quality vulnerability balance index, and the active power balance index to obtain the comprehensive vulnerability index of the distribution network as objective function 2, as shown in Equation (11). Considering the equal importance of the three indicators, the weights ω1, ω2, and ω3 are all set to 1 / 3. The comprehensive vulnerability index value ranges from 0 to 1. The closer the index is to 1, the higher the distribution network vulnerability and the lower the grid's ability to resist disturbances.

[0131]

[0132] 3) Objective function 3: Energy storage system capacity.

[0133] The total capacity of the energy storage system is selected as objective function 3. The maximum charge / discharge capacity of the energy storage system during the operation time of the energy storage is taken as the total configuration capacity:

[0134]

[0135] Where t0 is the maximum charge or discharge start time; t0+nΔt is the maximum charge or discharge end time, P n,t is the operating power of mobile energy storage n at time t, and Δt is the time interval between the two moments.

[0136] The objective function of the distribution network mobile energy storage optimization configuration model that comprehensively considers economy, vulnerability, and energy storage system capacity is as follows:

[0137] F=min[f1,f2,f3] (13)

[0138] The constraints of the distribution network mobile energy storage optimization configuration model constructed in this step include:

[0139] (1) Distribution network flow constraints.

[0140] The radial distribution network uses the DistFlow equation to characterize the power flow operation of the distribution network. Since there are quadratic terms in the voltage balance equation, the large M method is used to relax the voltage equation and eliminate the quadratic terms to reduce the computational complexity:

[0141]

[0142] In the formula, the current flows from the starting node to the end node in the positive direction, ΣP i,j ,ΣP j,k 、 and They represent the sum of the active power injected into node j, the sum of the active power flowing out, the active load forecast value, the sum of the active output of the distributed generation, and the active amount of the abandoned load; ΣQ i,j ,ΣQ j,k 、 and They represent the sum of reactive power injected into node j, the sum of reactive power outflow, reactive load forecast value, the sum of reactive output of distributed generation, and reactive amount of abandoned load respectively; and Represent the total active line loss and reactive line loss of the injected line ij, r i,j represents the resistance of line ij, x i,j represents the reactance of line ij, Represents the square of the current in line ij, m i,j is the slack variable; α i,j is a 0-1 variable, indicating the disconnection status of the line. When the value is 1, the line is connected, and when it is 0, the line is disconnected. M is a large constant, which is 9999 in this embodiment. is the square of the voltage at node i, is the square of the voltage at node j; P i,j represents the active power from node i to node j, Q i,j represents the reactive power from node i to node j.

[0143] Line capacity, voltage, and current meet the constraints:

[0144]

[0145] Where, Represents the square of the rated voltage of the load node; S max is the maximum apparent power of the line. k is the voltage fluctuation ratio, generally set to 0.05. Nbus is the set of all nodes. NT is any time period.

[0146] Perform second-order cone relaxation on the voltage, current and power relationship constraints to obtain the formula:

[0147]

[0148] (2) Distributed photovoltaic output constraints.

[0149] The distributed photovoltaic output of each node should not exceed its output limit, which is usually represented by the rated charge and discharge power. The output constraint of distributed power generation can be expressed as follows:

[0150]

[0151] Where, P is the set of distributed photovoltaic access nodes; P i PV and are the distributed photovoltaic active power and reactive power connected to node i, and are the upper limit of active power output and reactive power output of distributed photovoltaic connected to node i respectively; and are the upper and lower limits of the distributed photovoltaic power factor at access node i, respectively.

[0152] (3) Constraints on charging and discharging of mobile energy storage.

[0153] Mobile energy storage can only be charged and discharged when it is connected to a node. The coupling relationship between the charging and discharging state of the mobile energy storage and the spatial state of its location is shown in the formula:

[0154]

[0155] Where, is the charging state value of the mobile energy storage n at time t, which is a 0-1 variable. Its value is 1, indicating that the mobile energy storage n is in the charging state during time period t; is the discharge state value of the mobile energy storage n at time t, which is a 0-1 variable. Its value is 1, indicating that the mobile energy storage n is in the discharge state during time period t; and Both are 0-1 variables, representing the status of the mobile energy storage n at time t and time t+1 respectively. The value 1 indicates that the mobile energy storage is in the driving state, and the value 0 indicates that the mobile energy storage is connected to a node. Ω represents the location set of the mobile energy storage.

[0156] The charging and discharging power of mobile energy storage is subject to upper and lower limits, as shown in formula -:

[0157]

[0158] Where, and are the charging active power and discharging active power of mobile energy storage n at time t respectively; and are the charging reactive power and discharging reactive power of mobile energy storage n at time t respectively; and are the upper limit of charging and discharging active power of mobile energy storage n respectively; and are the upper limit of charging reactive power and the upper limit of discharging reactive power of mobile energy storage n respectively.

[0159] The state of charge constraint of mobile energy storage is shown in formula:

[0160]

[0161] SOC n,0 =soc n,0 (28)

[0162] Where, and are the charging efficiency and discharging efficiency of mobile energy storage n, respectively; and is the state of charge of the mobile energy storage n at time t and t+Δt; and They are the upper and lower limits of the state of charge of the mobile energy storage n; SOC n,0 is the initial state of charge of the mobile energy storage n.

[0163] This step uses an improved multi-objective particle swarm algorithm based on the optimal decision-making of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to solve the problem and select the best balance solution among the three objectives. Compared with the shortcomings of the conventional particle swarm algorithm, such as easy to fall into the local optimal solution, lack of guidance for the global optimal solution, and many Pareto solution sets with uneven distribution, this method introduces the crossover mutation operation in the genetic algorithm into the particle swarm algorithm to improve the population diversity, uses the "one by one removal" method to update the non-inferior solution and select the global optimal solution, uses the information entropy method to judge the difference of each target value in the Pareto solution set, determines the weight of each target value, and solves the mobile energy storage location and capacity configuration results based on the TOPSIS method. The process of the algorithm to solve the mobile energy storage configuration problem is as follows: Figure 2 As shown, the steps are as follows:

[0164] Step 1: Input the grid structure, load demand, distributed photovoltaic initial value and algorithm-related parameters of the typical scenario;

[0165] Step 2: Encode the energy storage capacity and output, and randomly generate the initial position and velocity of the particles;

[0166] Step 3: Calculate the objective function value of each particle and put it into the non-inferior solution set;

[0167] Step 4: Determine the optimal solution for each particle and the global optimal solution;

[0168] Step 5: In the next iteration, the individual optimal solution is used as the initial position, the difference between each particle and the optimal particle is calculated again, the inertia weight of each particle is updated, and the position and velocity components of each particle are updated;

[0169] Step 6: Perform mutation crossover operation on each particle;

[0170] Step 7: Calculate the objective function value of each particle, solve its dense distance, and then use the "one by one elimination method" to select the particles with larger dense distances and enter the non-inferior solution set;

[0171] Step 8: Update the particle's historical optimal solution and the non-inferior solution set;

[0172] Step 9: Select the global optimal solution;

[0173] Step 10: Determine whether the number of iterations has reached the maximum. If so, go to Step 11; if not, go back to Step 6.

[0174] Step 11: Output the optimal Pareto solution set and solve the optimal configuration plan for mobile energy storage.

[0175] The mobile energy storage investment amount obtained by solving the mobile energy storage optimization configuration model constructed in this step is pre-scheduled at the initial access location of the mobile energy storage in the first stage, i.e. before the disaster, and the mobile energy storage location and charging and discharging power in each time period are scheduled in the second stage, i.e. after the disaster, to achieve rapid recovery from the fault. The two-stage mobile energy storage scheduling strategy is as follows: Figure 3 shown.

[0176] Step 2: Based on the disaster scenario, with the goal of minimizing the mobile energy storage scheduling cost and load reduction cost, a pre-disaster mobile energy storage initial position scheduling model is constructed. The mobile energy storage configuration plan is used as the input of the pre-disaster mobile energy storage initial position scheduling model, and the pre-disaster mobile energy storage initial position scheduling model is solved to obtain a pre-dispatching plan.

[0177] Before a disaster occurs, the initial position of the mobile energy storage is pre-dispatched based on the pre-dispatching scheme to ensure that the mobile energy storage can quickly participate in the important load recovery process after the disaster occurs.

[0178] The pre-disaster mobile energy storage initial location scheduling model constructed in this step aims to minimize the mobile energy storage scheduling cost and load reduction cost, as shown in formula (29).

[0179]

[0180] Where f1′ is the objective function of the pre-disaster mobile energy storage initial location scheduling model, n ESM is the collection of mobile energy storage, n CAR For the collection of vehicles; It is a 0-1 variable, indicating whether the mobile energy storage n is traveling along the line with the vehicle c. Its value is 1 when the mobile energy storage is in the driving state, and its value is 0 when the mobile energy storage is connected to a node. is the load active power reduction of node i; the cost of pre-dispatching a single mobile energy storage system; is the unit load loss cost of node i.

[0181] The constraints of the pre-disaster mobile energy storage initial location scheduling model constructed in this step include:

[0182] 1) Constraints on mobile energy storage resources.

[0183] The number of mobile energy storage modules that can be installed on a node is limited by the maximum number of mobile energy storage modules that can be installed on the node:

[0184]

[0185] Where, is the state of mobile energy storage n at time t. When its value is 1, it means that mobile energy storage n is connected to node i. When its value is 0, it means that mobile energy storage n is not connected to node i. N ESM,i is the maximum number of mobile energy storage devices installed at node i.

[0186] The number of mobile energy storage modules that can be transported by a carrier at a time cannot exceed the carrier's carrying capacity:

[0187]

[0188] Where, Indicates whether the mobile energy storage n is traveling on the line with vehicle c at time t. When its value is 1, it means that the mobile energy storage n is traveling on the line with vehicle c at time t. When its value is 0, it means that the mobile energy storage n is not traveling on the line with vehicle c at time t. N ESM,c is the maximum carrying capacity of mobile energy storage of vehicle c.

[0189] 2) Distribution network radiation topology constraints.

[0190] Considering the island division situation, a virtual network is used to achieve network connectivity and radial constraints. The radial network must meet the following two necessary and sufficient conditions:

[0191] A) Each subgraph is connected;

[0192] B) Number of line branches = number of nodes - number of subgraphs.

[0193] Design a virtual network with the same topology. Each subgraph has only one power source. All nodes except the node where the power source is located have unit load demand and act as "sinks". Then, the distribution network is islanded according to the location of the power source. The connectivity constraint formulas are as follows:

[0194]

[0195]

[0196] W j ≥1,j∈{DG} (36)

[0197] Where, v is the node other than the distributed generation node; ε is the set of all branches; F ls and F js are the outflow powers of nodes l and j in the virtual network, respectively, and F il and F ij are the inflow powers of nodes l and j in the virtual network, δ(l) represents the outflow node set of node l, π(l) represents the inflow node set of node l, δ(j) represents the outflow node set of node j, π(j) represents the inflow node set of node j; W j is the amount of electricity provided by the distributed power supply in the virtual network; M is a large constant, which is 9999 in this embodiment; c ij A 0-1 variable representing the branch status. If the value is 1, the branch is connected; if the value is 0, the branch is disconnected.

[0198] 3) Load loss power constraint.

[0199] When the load power factor is fixed, the load loss power constraint is expressed as:

[0200]

[0201] Where, P i Lshd is the load active power reduction of node i, is the reactive power reduction of the load at node i, and are the maximum active and reactive load values ​​of node i, respectively.

[0202] 4) Other constraints.

[0203] In the initial location scheduling stage of mobile energy storage before a disaster, the distribution network operation constraints and distributed power generation output constraints at each moment must be met, as shown in Equations (14) to (20).

[0204] Step 3: With minimum load shedding as the goal, construct an optimal recovery model for the post-disaster distribution network, use the pre-dispatching plan as the input of the optimal recovery model for the post-disaster distribution network, solve the optimal recovery model for the post-disaster distribution network, obtain the scheduling plan, and schedule the mobile energy storage based on the scheduling plan.

[0205] After the disaster, a fault caused power outages in parts of the distribution network. Mobile energy storage systems immediately began operating, with a dispatch interval of 0.5 hours. By dispatching mobile energy storage and determining the connection locations and charge and discharge power of mobile energy storage modules at each time period, emergency power supply can be provided to critical loads, minimizing losses from power outages and improving the network's resilience.

[0206] Extreme disasters disconnect the distribution network from the main grid, forcing it to rely solely on distributed power sources within the network. Based on the time and intensity of the disaster, the fault duration is assumed. Using minimum load shedding as the objective function, an optimal recovery model for the post-disaster distribution network is constructed to effectively dispatch mobile energy storage and optimize the output of various DGs, maximizing the distribution network's disaster resilience and enabling better load restoration. The objective function calculation formula for the optimal recovery model for the post-disaster distribution network constructed in this step is as follows:

[0207]

[0208] Where f2′ is the objective function of the optimal restoration model of the distribution network after a disaster, Ω T is the set of fault time periods, N is the set of nodes, ω i is the weight that represents the importance of the load.

[0209] The constraints of the optimal recovery model of the post-disaster distribution network constructed in this step include:

[0210] 1) Mobile energy storage dispatch constraints

[0211] In order to simplify the energy storage mobility path, the regional distribution network structure is divided into a grid, such as Figure 4 As shown in the figure, the mobile energy storage travels at a uniform speed on the grid line, and assuming that the side lengths of each grid are equal, the unit time for traveling one grid is equal.

[0212] The spatiotemporal transfer constraints of mobile energy storage are defined, and the spatiotemporal position of mobile energy storage is represented by Boolean variables. The variable representing the position of the mobile energy storage module n at node i during period t, with a value of 0-1. The variable represents the position of the mobile energy storage module n placed on the carrier vehicle c on the branch road between any nodes at time t, and its value ranges from 0 to 1. If the mobile energy storage module n is at node i, then If the mobile energy storage n moves with the carrier vehicle c, then is a binary variable, indicating that at time t, if the carrier vehicle c is traveling on the branch road l between any nodes, it is 1, otherwise it is 0. The spatiotemporal conversion constraint of mobile energy storage is shown in formula -:

[0213]

[0214] Where T0 is the installation time of mobile energy storage; n jk is the number of grid edges between nodes j and k; ξ is the unit time of the space transfer between grids for the transport vehicle. Equation (40) indicates that at time t, the mobile energy storage n has only one fixed position, and Equation (41) indicates that the mobile energy storage at node j must travel n with the transport vehicle. jk It takes time ξ to reach node k, and it takes time T0 to install to perform charging and discharging operations. Formula (42) indicates that the time the transport vehicle travels on the road cannot be less than n jk ξ, Equation (43) indicates that the carrier vehicle c loads the mobile energy storage and leaves, and Equation (44) indicates that when the carrier vehicle c transports the mobile energy storage module, the remaining mobile energy storage at other nodes can continue to charge and discharge.

[0215] In order to ensure that the mobile energy storage is located at the initial position scheduled in advance when a disaster occurs, the constraint condition is set:

[0216]

[0217] Where t0 is the time when the disaster occurs; Ω ME A collection of mobile energy storage; It is a 0-1 variable, where its value is 1 indicating that there is only one mobile energy storage connected to node i at the time of the disaster, and its value is 0 indicating that there is no mobile energy storage connected to node i at the time of the disaster.

[0218] 2) Other constraints

[0219] During the post-disaster mobile energy storage dispatching and control phase, the distribution network operation constraints and distributed power generation output constraints at each moment must be met. The constraint conditions are the same as those of the pre-disaster mobile energy storage initial position dispatching model, see Equations (14)-(28) and (30)-(38).

[0220] This implementation proposes a two-stage disaster resilience improvement optimization model based on the initial location layout of mobile energy storage and emergency response scheduling. Before a disaster, the initial location of mobile energy storage is arranged in advance to minimize the mobile energy storage scheduling cost and load reduction cost. After a disaster, an optimization model for distribution network emergency recovery is established. With the minimum load reduction under fault conditions as the objective function, mobile energy storage is optimally scheduled to improve the distribution network recovery capability.

[0221] The present invention is further described below through a specific embodiment.

[0222] In this embodiment, the 7th, 10th, 13th, 18th, 20th, 27th, and 30th nodes of the IEEE 33-node power distribution system are connected to distributed photovoltaics with a rated power of 1MW. The topology is as follows: Figure 5 The photovoltaic output power is obtained by clustering the typical output of a certain area in a certain city in central my country on sunny days in summer, as shown in Figure 2. Figure 6 shown.

[0223] The energy storage system allows for 2 to 33 access nodes. The rated power of a single energy storage module is 300 kW, and the rated capacity is 1 MW·h. A single energy storage vehicle can transport a maximum of two mobile energy storage modules. The simulation parameter settings are shown in Table 1. The basic parameters of the mobile energy storage system are shown in Table 2. The time-of-use electricity price is based on the summer time-of-use electricity price for large industrial enterprises on a 10 kV distribution network in a northern county, as shown in Table 3.

[0224] Table 1 Simulation parameter setting table

[0225]

[0226]

[0227] Table 2 Basic parameters of mobile energy storage

[0228]

[0229] Table 3 Time-of-use electricity price list

[0230] time Electricity price period Price (yuan / kWh) 7:00-10:00 Flat section 0.635 10:00-15:00 peak 0.944 15:00-18:00 Flat section 0.635 18:00-21:00 peak 0.944 21:00-23:00 Flat section 0.635 23:00-7:00 low point 0.334

[0231] (2) Application results and analysis

[0232] The following three scenarios are set to analyze the changes in operating parameters before and after energy storage is connected, as well as the impact of multiple optimization objectives on energy storage planning results:

[0233] ① Scenario 1: Energy storage integration is not considered;

[0234] ② Scenario 2: Considering energy storage integration, the optimization objective does not consider the vulnerability of the distribution network;

[0235] ③ Scenario 3: The multi-objective optimization configuration model proposed in this embodiment. The comparison of the mobile energy storage optimization configuration results in different scenarios is shown in Table 4.

[0236] Table 4 Optimal configuration results of mobile energy storage in different scenarios

[0237]

[0238]

[0239] Comparing Scenario 1 and Scenario 3, we can see that the addition of mobile energy storage reduces system vulnerability by 31.42%. A comparison of Scenario 2 and Scenario 3 shows that, because Scenario 3's optimization objective takes distribution network vulnerability into account, the overall vulnerability index of the distribution network decreases by 0.019. However, the total investment cost for distribution network energy storage increases by 223,800 yuan compared to Scenario 2. This is because the planning takes distribution network vulnerability into account, requiring the deployment of larger energy storage capacities to mitigate vulnerability, which increases the energy storage investment cost for the distribution network and the total investment cost.

[0240] (3) Analysis of improvement of anti-disturbance capability

[0241] By optimizing the configuration of mobile energy storage, the abnormal operation of the distribution network under high-proportion distributed photovoltaic disturbances can be improved. Comparing the active power over-limit severity and node voltage over-limit severity indicators, after configuring mobile energy storage (Scenario 3), the system does not have active power forward over-limit or voltage under-limit. Figure 7 and Figure 8 They are respectively the reverse over-limit probability and reverse over-limit severity of the branch active power after configuring mobile energy storage, Figure 9 and Figure 10 They are the probability and severity of node voltage exceeding the upper limit after configuring mobile energy storage.

[0242] The severity of the active power exceeding the limit of line j and the severity of the voltage exceeding the limit of node i in the system are shown in Equations (48) and (49). The probability of the active power exceeding the limit of branch l and the probability of the voltage exceeding the limit of node i are shown in Equations (48) and (49):

[0243]

[0244] Where L(S) l Indicates the severity of the active power exceeding the limit of the lth branch; F(S) s,l Indicates the severity of the active power exceeding the limit of the lth branch in the sth typical scenario; L(U) i Indicates the severity of voltage exceeding the limit at the i-th node; F(U) s,i N represents the severity of voltage over-limit at the i-th node in the s-th typical scenario; s is the number of scenes extracted; P s is the probability corresponding to the generated scene s.

[0245] The probability P of the active power exceeding the limit of branch l at time t Sl (t) is expressed as:

[0246]

[0247] Where S l(t) is the load rate of branch l at time t, that is, the ratio of the active power flowing on line l at time t to the maximum load capacity of the line; S lmax (t) is the critical value for determining whether the active power of branch l exceeds the limit at time t. Generally, 90% of the rated load rate is taken as the critical value. When the load rate is not greater than this critical value, the severity of the line active power exceeding the limit can be considered as 0. f(S) and F(S) are the probability density function and cumulative distribution function of the branch active power, respectively.

[0248] The voltage exceeding limit probability P of node i at time t Vi (t) is expressed as:

[0249]

[0250] Where V i (t) is the voltage of node i at time t; V imax (t) and V imin (t) are the upper and lower limits of the voltage violation at node i at time t, respectively. The power distribution system involved in this embodiment is mainly a medium-voltage distribution network, so the upper and lower limits of the voltage are 1.07 pu and 0.93 pu, respectively. f(V) and F(V) are the probability density function and cumulative distribution function of the voltage, respectively.

[0251] Will Figure 7 、 Figure 8 、 Figure 9 and Figure 10 Comparisons of the severity and probability of active power over-limits and the severity and probability of node voltage over-limits with those of branches without mobile energy storage reveal that: Active power forward over-limits and voltage under-limits are no longer occurring in branches and nodes of the distribution network. While the probability of active power reverse over-limits has increased, the number of branches experiencing reverse over-limits has decreased from five to three, reducing the overall severity of active power reverse over-limits by 78.22%. The number of nodes experiencing voltage over-limits has decreased from 18 to four, reducing the severity of system voltage over-limits by 98.12% and the probability of voltage over-limits by 83.72%. This has improved the distribution network's ability to withstand abnormal operations and effectively enhanced its disaster resilience.

[0252] Initial location scheduling of mobile energy storage before disasters

[0253] Select nodes and branches with poor system disaster resistance as the expected fault areas. Set branches 3, 10, 12, and 15 to fail at 10-15. Figure 11 As shown in the figure, nodes 3, 4, 6, 10, 11, 15, 17, 19, 24, 26, 28, and 33 are important user nodes, and the unit load loss cost of such nodes is 10 yuan / kW. The remaining nodes are non-important user nodes, and their unit load loss cost is 1 yuan / kW. The day-ahead load active power forecast curve is shown in Figure 12.

[0254] Based on the mobile energy storage configuration quantity obtained above (i.e., Scenario 3 in Table 4), the model in this embodiment is used to schedule the mobile energy storage vehicles at their initial positions before the disaster. The gurobi solver is used in Matlab to solve the problem. It is assumed that the mobile energy storage vehicles have sufficient time to reach the pre-disaster node, and the pre-disaster cost per unit of the mobile energy storage system is 500 yuan / MW·h.

[0255] This example compares and analyzes two schemes for the initial location scheduling of mobile energy storage before a disaster. Scheme 1 does not schedule the initial location of the mobile energy storage, and all mobile energy storage vehicles are located at the starting node. Scheme 2 pre-schedules the initial location of the mobile energy storage vehicles. A comparison of the initial location scheduling schemes is shown in Table 5. The initial location scheduling cost can be calculated using the formula.

[0256] Table 5 Comparison of initial location scheduling schemes before the disaster

[0257]

[0258] Table 5 shows that in Scheme 1, all mobile energy storage devices are located at starting node 1. Although this reduces the cost of dispatching the mobile energy storage at the initial location, it still incurs a load reduction cost of 3,454 yuan. Compared to Scheme 1, Scheme 2 applies the mobile energy storage initial location dispatch strategy proposed in this embodiment, pre-positioning the mobile energy storage at nodes 23 and 24, reducing costs by 6.2%.

[0259] Post-disaster mobile energy storage emergency recovery process

[0260] According to the mobile energy storage configuration results obtained in Table 4 and the initial position scheduling results of the mobile energy storage vehicles obtained in Table 5, it is found that mobile energy storage vehicle 1 carrying mobile energy storage module 1 and mobile energy storage module 2 is located at node 23, and mobile energy storage vehicle 2 carrying mobile energy storage module 3 and mobile energy storage module 4 is located at node 24. The mobile energy storage vehicle can travel within the distribution grid with a maximum speed of 80 km / h. The electrical distance between adjacent nodes is 2 km, and the installation and configuration time of a single mobile energy storage module is 10 minutes. After the distribution network fails, some interconnection lines are disconnected, and the distribution network is divided into the following Figure 13 The six islands shown.

[0261] By optimizing the driving route of the mobile energy storage vehicle, the mobile energy storage module can be used more flexibly to supply power to the power-off load. The charging and discharging power and charge state of the mobile energy storage module at each fault moment are as follows: Figure 14 As shown, positive power indicates module discharge, and negative power indicates module charging. The mobile energy storage scheduling process is as follows: Figure 15 shown.

[0262] from Figure 14 and Figure 15As can be seen, the distribution network experienced a fault and an islanding event starting at 10:00 AM. Mobile Energy Storage Module 1 discharged at Node 23 from 10:00 AM to 12:30 PM, then was transported to Node 7 and charged from 12:30 PM to 1:30 PM. Mobile Energy Storage Module 2 was transported by Vehicle 1 to Node 19 from 10:30 AM to 11:00 AM, discharged from 11:00 AM to 11:30 AM, then transported to Node 7 and charged until 1:30 PM. After charging, both Mobile Energy Storage Modules 1 and 2 were transported by Vehicle 1 to Node 8. Arriving at Node 8 at 2:00 PM, Mobile Energy Storage Module 2 remained at Node 8, discharging from 2:00 PM to 3:00 PM. Mobile Energy Storage Module 1 then continued on to Node 10, charging from 2:00 PM to 3:00 PM. The mobile energy storage module 3 pre-scheduled at node 24 discharges from 10:00 to 11:00, is transported to node 10 by carrier 2 from 11:00 to 11:30, charges at the node from 11:30 to 13:00, and is then transported to node 15 and continuously discharges from 13:00 to 15:00. The mobile energy storage module 4 at node 24 is transported to node 25 from 10:00 to 10:30 and continuously discharges until 12:00. From 12:00 to 12:30, carrier 2 transports the mobile energy storage module 4 to node 30, charges from 12:30 to 14:00, is transported to node 17 from 14:00 to 14:30, and then discharges until 15:00.

[0263] Load recovery ratio and load loss situation Figure 16 During the fault recovery period, the critical load recovery ratio remained above 95%, with the minimum load reduction being 184.6kW. Ultimately, the critical load recovery ratio reached 99.93%, raising the system's disaster resilience to a high level.

[0264] Effects of different strategies on improving distribution network resilience

[0265] To take advantage of the strategy for improving the disaster resistance of the distribution network proposed in this embodiment, this embodiment proposes three disaster resistance improvement schemes, and uses three indicators, namely, important load recovery speed, important load recovery rate, and load recovery rate, to reflect the disaster resistance of the distribution network. The load reduction costs and disaster resistance improvement effects of the three schemes are shown in Table 6.

[0266] 1) Important load recovery speed

[0267] This indicator reflects the amount of important loads restored per unit time during the emergency recovery period. Important loads here refer to primary and secondary loads. The higher the value, the stronger the emergency power supply capacity after the disaster. The calculation method is as follows:

[0268]

[0269] Where, Indicates the ik An important load node is The load power restored during the time period, in kW; is the important load recovery time, in h; n key is the number of important nodes; The resource scheduling time of important nodes, in hours; It is the repair time of important node equipment, in hours.

[0270] 2) Important load recovery rate

[0271] The critical load recovery rate refers to the ratio of critical loads that have been effectively restored during the emergency recovery phase to the total lost loads. The larger the index, the stronger the system's emergency power supply capability. The calculation formula is:

[0272]

[0273] Where, Represents important node i k In T h The load loss value within the time period, in kW.

[0274] 3) Load recovery rate

[0275] This indicator measures the degree of recovery of the distribution network load after a disaster. The larger the value, the stronger the overall disaster resistance of the power grid. The calculation formula is:

[0276]

[0277] Where, represents the load lost in node i; is the load restored at node i, in kW.

[0278] Option 1: Ignoring the scheduling of energy storage space, emergency recovery is achieved through fixed energy storage;

[0279] Option 2: Considering the energy storage space scheduling process, but without scheduling the mobile energy storage vehicle at the initial departure location in advance, both vehicles are located at the starting node, and the mobile energy storage vehicle is dispatched to restore power supply;

[0280] Solution 3: Adopt the disaster resistance improvement strategy proposed in this implementation method, dispatch the initial positions of each mobile energy storage in advance, and restore power supply by dispatching mobile energy storage vehicles.

[0281] Table 6 Analysis of strategies for improving the disaster resistance of distribution networks for 3 schemes

[0282] Solution 1 Option 2 Option 3 Load reduction cost / yuan 6555 3802 2708 Important load recovery speed / kW / h 108.81 110.65 111.38 Important load recovery rate / % 97.29 98.93 99.59 Load recovery rate / % 87.42 91.08 92.62

[0283] Table 6 shows that the disaster resilience enhancement strategy proposed in this implementation minimizes load reduction costs, reducing them by 58.69% and 28.77% compared to Schemes 1 and 2, respectively. Regarding critical load restoration, the strategy proposed in this implementation (Scheme 3) takes into account the spatial distribution of critical loads and distributed photovoltaics, initially scheduling mobile energy storage vehicles near key nodes. This reduces load reduction in the early stages of a fault, thereby improving the distribution network's resilience. Compared to Scheme 1, which has a fixed energy storage location and lacks flexibility, Scheme 3 fully dispatches the mobile energy storage system to achieve optimal spatial and temporal energy allocation, maximizing the distribution network's resilience.

[0284] A second embodiment of the present invention relates to a device for improving the disaster resistance of a distribution network, comprising:

[0285] The mobile energy storage configuration module is used to build a distribution network mobile energy storage optimization configuration model under typical scenarios, taking mobile energy storage cost, distribution network vulnerability, and minimum energy storage investment capacity as the goals, and solve the distribution network mobile energy storage optimization configuration model to obtain a mobile energy storage configuration plan;

[0286] A mobile energy storage pre-dispatching module is configured to construct a pre-disaster mobile energy storage initial position scheduling model based on the disaster scenario with the goal of minimizing mobile energy storage scheduling costs and load reduction costs, and to solve the pre-disaster mobile energy storage initial position scheduling model using the mobile energy storage configuration plan as input to obtain a pre-dispatching plan.

[0287] The mobile energy storage scheduling module is used to construct an optimal recovery model for the post-disaster distribution network with the goal of minimizing load shedding, use the pre-scheduling plan as the input of the optimal recovery model of the post-disaster distribution network, solve the optimal recovery model of the post-disaster distribution network, obtain a scheduling plan, and schedule the mobile energy storage based on the scheduling plan.

[0288] The objective function of the distribution network mobile energy storage optimization configuration model constructed by the mobile energy storage configuration module is: F = min[f1, f2, f3], where F is the objective function of the distribution network mobile energy storage optimization configuration model, min[] represents the selection of the best balance solution among the three objectives, f1 is the mobile energy storage cost objective function, expressed as: f1 = -C1 - C2 + C3 + C4, and C1 is the mobile energy storage peak-valley arbitrage, expressed as: C2 is the subsidy for delayed grid construction obtained by mobile energy storage, which is expressed as: C3 is the investment cost of mobile energy storage, expressed as: C4 is the operating cost of mobile energy storage, expressed as: f2 is the distribution network vulnerability objective function, which is expressed as: V t entis the voltage quality vulnerability of the distribution network, expressed as: V t equ is the balance degree of distribution network voltage quality vulnerability, expressed as: is the active power balance index, expressed as: ω1, ω2 and ω3 are all weight coefficients; f3 is the energy storage input capacity objective function, which is expressed as: t0 is the maximum charge or discharge start time; t0+nΔt is the maximum charge or discharge end time, P n,t is the operating power of mobile energy storage n at time t, Δt is the time interval between the two moments;

[0289] Among them, y is the usable life of mobile energy storage, D is the number of days of use of mobile energy storage throughout the year, r i is the inflation rate, r d is the discount rate, n ESM is the number of mobile energy storages, T is the time period, and are the discharge power and charging power of mobile energy storage n at time t, C(t) is the electricity price at time t, ε is the subsidy ratio for delayed construction income, is the rated power of a single mobile energy storage, b2 is the cost required to upgrade the unit power equipment, ω is the annual interest rate, C ESM is the unit capacity cost of mobile energy storage, C P is the unit power conversion cost, E is the capacity of a single mobile energy storage, n CAR is the number of transport vehicles, C CAR The unit purchase cost of the vehicle used to load and transport mobile energy storage, C m is the annual operating cost of the mobile energy storage module per unit charge and discharge power, N is the total number of nodes, V i,t is the node vulnerability index of node i at time t, V t,max and V t,min are the maximum and minimum values ​​of the node vulnerability index at time t, H i,t is the ratio of the sum of the vulnerability indices of the first i nodes to the sum of the vulnerability indices of all nodes after the vulnerability indices of the nodes at time t are sorted from small to large, x l is the reactance value of line l, P l,t is the active power transmitted by line l at time t, n line is the number of lines.

[0290] The constraints of the distribution network mobile energy storage optimization configuration model constructed by the mobile energy storage configuration module include:

[0291] The power flow constraint of the distribution network is expressed as:

[0292]

[0293] ΣP i,j ,ΣP j,k 、 and They represent the sum of the active power injected into node j, the sum of the active power flowing out, the active load forecast value, the sum of the active output of the distributed generation, and the active amount of the abandoned load; ΣQ i,j ,ΣQ j,k 、 and They represent the sum of reactive power injected into node j, the sum of reactive power outflow, reactive load forecast value, the sum of reactive output of distributed generation, and reactive amount of abandoned load respectively; and Represent the total active line loss and reactive line loss of the injected line ij, r i,j represents the resistance of line ij, x i,j represents the reactance of line ij, Represents the square of the current in line ij, m i,j is the slack variable, α i,j is a 0-1 variable, indicating the open / closed state of the line; M is a constant; is the square of the voltage at node i, is the square of the voltage at node j; P i,j represents the active power from node i to node j, Q i,j represents the reactive power from node i to node j;

[0294] Distributed photovoltaic output constraints are expressed as:

[0295]

[0296] P is the set of distributed photovoltaic access nodes; P i PV and are the distributed photovoltaic active power and reactive power connected to node i, and are the upper limit of active power output and reactive power output of distributed photovoltaic connected to node i respectively; and are the upper and lower limits of the distributed photovoltaic power factor at access node i;

[0297] The charging and discharging constraints of mobile energy storage are expressed as:

[0298]

[0299] is the charging state value of mobile energy storage n at time t, is the discharge state value of mobile energy storage n at time t; and are all 0-1 variables, representing the states of the mobile energy storage n at time t and time t+1 respectively, Ω represents the location set of the mobile energy storage, and are the charging active power and discharging active power of mobile energy storage n at time t respectively; and are the charging reactive power and discharging reactive power of mobile energy storage n at time t respectively; and are the upper limit of charging and discharging active power of mobile energy storage n respectively; and are the upper limit of charging reactive power and the upper limit of discharging reactive power of mobile energy storage n respectively; and are the charging efficiency and discharging efficiency of mobile energy storage n, respectively; and is the state of charge of the mobile energy storage n at time t and t+Δt; and are the upper and lower limits of the state of charge of the mobile energy storage n.

[0300] The objective function of the pre-disaster mobile energy storage initial position scheduling model constructed by the pre-dispatching module is: Among them, f1′ is the objective function of the initial location scheduling model of mobile energy storage before the disaster, n ESM is the collection of mobile energy storage, n CAR For the collection of vehicles; is a 0-1 variable, indicating whether the mobile energy storage n is traveling on the line with the vehicle c; P i Lshd is the load active power reduction of node i; the cost of pre-dispatching a single mobile energy storage system; is the unit load loss cost of node i.

[0301] The constraints of the pre-disaster mobile energy storage initial position scheduling model constructed by the pre-scheduling module include:

[0302] The mobile energy storage resource constraint is expressed as:

[0303]

[0304] is the state of mobile energy storage n at time t, N ESM,i is the maximum number of mobile energy storage installations at node i, N is the set of nodes, Indicates whether the mobile energy storage n is traveling on the line with vehicle c at time t, NESM,c is the maximum carrying capacity of mobile energy storage of vehicle c;

[0305] The distribution network radial topology constraint is expressed as:

[0306]

[0307] v is the node other than the distributed power generation node; ε is the set of all branches; F ls and F js are the outflow powers of nodes l and j in the virtual network, respectively, and F il and F ij are the inflow powers of nodes l and j in the virtual network, δ(l) represents the outflow node set of node l, π(l) represents the inflow node set of node l, δ(j) represents the outflow node set of node j, π(j) represents the inflow node set of node j, W j is the amount of electricity provided by the distributed power sources in the virtual network; M is a constant; c ij is a variable representing the state of the branch;

[0308] The load loss power constraint is expressed as:

[0309]

[0310] P i Lshd is the load active power reduction of node i, is the reactive power reduction of the load at node i, and are the maximum active and reactive load values ​​of node i, respectively.

[0311] The objective function of the optimal recovery model of the post-disaster distribution network constructed by the scheduling module is expressed as: Among them, f2′ is the objective function of the optimal restoration model of the distribution network after the disaster, Ω T is the set of fault time periods, N is the set of nodes, ω i is the weight that represents the importance of the load, is the load active power reduction of node i at time t during the fault time.

[0312] The constraints of the optimal recovery model of the post-disaster distribution network constructed by the scheduling module include:

[0313] The dispatch constraint of mobile energy storage is expressed as:

[0314]

[0315] represents the position variable of the mobile energy storage module n at node i during period t, It represents the position variable of the mobile energy storage n placed on the vehicle c at time t and traveling on the branch road between any nodes; N is the set of nodes, n CAR is the collection of vehicles; T0 is the installation time of mobile energy storage; n jk is the number of grid edges between node j and node k; ξ is the unit time of spatial transfer of the transport vehicle between grids;

[0316] The initial position constraint of mobile energy storage is expressed as:

[0317]

[0318] t0 is the time when the disaster occurs; Ω ME A collection of mobile energy storage; Indicates whether mobile energy storage n is connected to node i at the time of disaster occurrence; It is a 0-1 variable, where its value is 1 indicating that there is only one mobile energy storage connected to node i at the time of the disaster, and its value is 0 indicating that there is no mobile energy storage connected to node i at the time of the disaster.

[0319] A third embodiment of the present invention relates to an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for improving the disaster resistance of a distribution network of the first embodiment are implemented.

[0320] A fourth embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for improving the disaster resistance of a distribution network of the first embodiment are implemented.

[0321] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage, etc.) that contain computer-usable program code.

[0322] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0323] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction method, which is implemented in the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0324] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0325] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for improving the disaster resistance of a distribution network, characterized in that: The following steps are involved: With the goal of minimizing mobile energy storage costs, distribution network vulnerability, and energy storage investment capacity, a distribution network mobile energy storage optimization configuration model for typical scenarios is constructed. This model is then solved to obtain a mobile energy storage configuration plan. According to the disaster scenario, with the goal of minimizing the mobile energy storage scheduling cost and the load reduction cost, a pre-disaster mobile energy storage initial position scheduling model is constructed, and the mobile energy storage configuration plan is used as the input of the pre-disaster mobile energy storage initial position scheduling model, and the pre-disaster mobile energy storage initial position scheduling model is solved to obtain a pre-dispatching plan; With the goal of minimizing load shedding, an optimal recovery model for the post-disaster distribution network is constructed. The pre-dispatching plan is used as the input of the optimal recovery model for the post-disaster distribution network. The optimal recovery model for the post-disaster distribution network is solved to obtain a scheduling plan, and mobile energy storage is dispatched based on the scheduling plan.

2. The method for improving the disaster resistance of a distribution network according to claim 1, characterized in that: The objective function of the distribution network mobile energy storage optimization configuration model is expressed as: F = min[f1, f2, f3], where F is the objective function of the distribution network mobile energy storage optimization configuration model, min[] represents the selection of the best balance solution among the three objectives, f1 is the mobile energy storage cost objective function, expressed as: f1 = -C1 - C2 + C3 + C4, and C1 is the mobile energy storage peak-valley arbitrage, expressed as: C2 is the subsidy for delayed grid construction obtained by mobile energy storage, which is expressed as: C3 is the investment cost of mobile energy storage, expressed as: C4 is the operating cost of mobile energy storage, expressed as: f2 is the distribution network vulnerability objective function, which is expressed as: is the voltage quality vulnerability of the distribution network, expressed as: is the balance degree of distribution network voltage quality vulnerability, expressed as: is the active power balance index, expressed as: ω1, ω2 and ω3 are all weight coefficients; f3 is the energy storage input capacity objective function, which is expressed as: t0 is the maximum charge or discharge start time; t0+nΔt is the maximum charge or discharge end time, P n,t is the operating power of mobile energy storage n at time t, Δt is the time interval between the two moments; Among them, y is the usable life of mobile energy storage, D is the number of days of use of mobile energy storage throughout the year, r i is the inflation rate, r d is the discount rate, n ESM is the number of mobile energy storages, T is the time period, and are the discharge power and charging power of mobile energy storage n at time t, C(t) is the electricity price at time t, ε is the subsidy ratio for delayed construction income, is the rated power of a single mobile energy storage, b2 is the cost required to upgrade the unit power equipment, ω is the annual interest rate, C ESM is the unit capacity cost of mobile energy storage, C P is the unit power conversion cost, E is the capacity of a single mobile energy storage, n CAR is the number of transport vehicles, C CAR The unit purchase cost of the vehicle used to load and transport mobile energy storage, C m is the annual operating cost of the mobile energy storage module per unit charge and discharge power, N is the total number of nodes, V i,t is the node vulnerability index of node i at time t, V t,max and V t,min are the maximum and minimum values ​​of the node vulnerability index at time t, H i,t is the ratio of the sum of the vulnerability indices of the first i nodes to the sum of the vulnerability indices of all nodes after the vulnerability indices of the nodes at time t are sorted from small to large, x l is the reactance value of line l, P l,t is the active power transmitted by line l at time t, n line is the number of lines.

3. The method for improving the disaster resistance of a distribution network according to claim 1, characterized in that: The constraints of the distribution network mobile energy storage optimization configuration model include: The power flow constraint of the distribution network is expressed as: ΣP i,j ,ΣP j,k 、 and They represent the sum of the active power injected into node j, the sum of the active power flowing out, the active load forecast value, the sum of the active output of the distributed generation, and the active amount of the abandoned load; ΣQ i,j ,ΣQ j,k 、 and They represent the sum of reactive power injected into node j, the sum of reactive power flowing out, reactive load forecast value, the sum of reactive output of distributed generation, and reactive amount of abandoned load respectively; and Represent the total active line loss and reactive line loss of the injected line ij, r i,j represents the resistance of line ij, x i,j represents the reactance of line ij, Represents the square of the current in line ij, m i,j is the slack variable, α i,j is a 0-1 variable, indicating the open / closed state of the line; M is a constant; is the square of the voltage at node i, is the square of the voltage at node j; P i,j represents the active power from node i to node j, Q i,j represents the reactive power from node i to node j; Distributed photovoltaic output constraints are expressed as: P is the set of distributed photovoltaic access nodes; and are the distributed photovoltaic active power and reactive power connected to node i, and are the upper limit of active power output and reactive power output of distributed photovoltaic connected to node i respectively; and are the upper and lower limits of the distributed photovoltaic power factor at access node i; The charging and discharging constraints of mobile energy storage are expressed as: is the charging state value of mobile energy storage n at time t, is the discharge state value of mobile energy storage n at time t; and are all 0-1 variables, representing the states of the mobile energy storage n at time t and time t+1 respectively, Ω represents the location set of the mobile energy storage, and are the charging active power and discharging active power of mobile energy storage n at time t respectively; and are the charging reactive power and discharging reactive power of mobile energy storage n at time t respectively; and are the upper limit of charging and discharging active power of mobile energy storage n respectively; and are the upper limit of charging reactive power and the upper limit of discharging reactive power of mobile energy storage n respectively; and are the charging efficiency and discharging efficiency of mobile energy storage n, respectively; and is the state of charge of the mobile energy storage n at time t and t+Δt; and are the upper and lower limits of the state of charge of the mobile energy storage n.

4. The method for improving the disaster resistance of a distribution network according to claim 1, characterized in that: The objective function of the pre-disaster mobile energy storage initial position scheduling model is: Among them, f1′ is the objective function of the initial location scheduling model of mobile energy storage before the disaster, n ESM is the collection of mobile energy storage, n CAR For the collection of vehicles; is a 0-1 variable, indicating whether the mobile energy storage n is traveling on the line with the vehicle c; is the load active power reduction of node i; the cost of pre-dispatching a single mobile energy storage system; is the unit load loss cost of node i.

5. The method for improving the disaster resistance of a distribution network according to claim 1, characterized in that: The constraints of the pre-disaster mobile energy storage initial location scheduling model include: The mobile energy storage resource constraint is expressed as: is the state of mobile energy storage n at time t, N ESM,i is the maximum number of mobile energy storage installations at node i, N is the set of nodes, Indicates whether the mobile energy storage n is traveling on the line with vehicle c at time t, N ESM,c is the maximum carrying capacity of mobile energy storage of vehicle c; The distribution network radial topology constraint is expressed as: v is the node other than the distributed power generation node; ε is the set of all branches; F ls and F js are the outflow powers of nodes l and j in the virtual network, respectively, and F il and F ij are the inflow powers of nodes l and j in the virtual network, δ(l) represents the outflow node set of node l, π(l) represents the inflow node set of node l, δ(j) represents the outflow node set of node j, π(j) represents the inflow node set of node j, W j is the amount of electricity provided by the distributed power sources in the virtual network; M is a constant; c ij is a variable representing the branch status; The load loss power constraint is expressed as: is the load active power reduction of node i, is the reactive power reduction of the load at node i, and are the maximum active and reactive load values ​​of node i respectively.

6. The method for improving the disaster resistance of a distribution network according to claim 1, characterized in that: The objective function of the optimal restoration model of the post-disaster distribution network is expressed as: Among them, f2′ is the objective function of the optimal restoration model of the distribution network after the disaster, Ω T is the set of fault time periods, N is the set of nodes, ω i is the weight that represents the importance of the load, is the load active power reduction of node i at time t during the fault time.

7. The method for improving the disaster resistance of a distribution network according to claim 1, characterized in that: The constraints of the optimal restoration model of the post-disaster distribution network include: The dispatch constraint of mobile energy storage is expressed as: represents the position variable of the mobile energy storage module n at node i during period t, It represents the position variable of the mobile energy storage n placed on the vehicle c at time t and traveling on the branch road between any nodes; N is the set of nodes, n CAR is the collection of vehicles; T0 is the installation time of mobile energy storage; n jk is the number of grid edges between node j and node k; ξ is the unit time of spatial transfer of the transport vehicle between grids; The initial position constraint of mobile energy storage is expressed as: t0 is the time when the disaster occurs; Ω ME A collection of mobile energy storage; Indicates whether mobile energy storage n is connected to node i at the time of disaster occurrence; It is a 0-1 variable, where its value is 1 indicating that there is only one mobile energy storage connected to node i at the time of the disaster, and its value is 0 indicating that there is no mobile energy storage connected to node i at the time of the disaster.

8. A device for improving the disaster resistance of a distribution network, characterized in that: include: The mobile energy storage configuration module is used to build a distribution network mobile energy storage optimization configuration model under typical scenarios, taking mobile energy storage cost, distribution network vulnerability, and minimum energy storage investment capacity as the goals, and solve the distribution network mobile energy storage optimization configuration model to obtain a mobile energy storage configuration plan; A mobile energy storage pre-dispatching module is configured to construct a pre-disaster mobile energy storage initial position scheduling model based on the disaster scenario with the goal of minimizing mobile energy storage scheduling costs and load reduction costs, and to solve the pre-disaster mobile energy storage initial position scheduling model using the mobile energy storage configuration plan as input to obtain a pre-dispatching plan. The mobile energy storage scheduling module is used to construct an optimal recovery model for the post-disaster distribution network with the goal of minimizing load shedding, use the pre-scheduling plan as the input of the optimal recovery model of the post-disaster distribution network, solve the optimal recovery model of the post-disaster distribution network, obtain a scheduling plan, and schedule the mobile energy storage based on the scheduling plan.

9. The device for improving the disaster resistance of a distribution network according to claim 8, characterized in that: The objective function of the distribution network mobile energy storage optimization configuration model constructed by the mobile energy storage configuration module is: F = min[f1, f2, f3], where F is the objective function of the distribution network mobile energy storage optimization configuration model, min[] represents the selection of the best balance solution among the three objectives, f1 is the mobile energy storage cost objective function, expressed as: f1 = -C1 - C2 + C3 + C4, and C1 is the mobile energy storage peak-valley arbitrage, expressed as: C2 is the subsidy for delayed grid construction obtained by mobile energy storage, which is expressed as: C3 is the investment cost of mobile energy storage, expressed as: C4 is the operating cost of mobile energy storage, expressed as: f2 is the distribution network vulnerability objective function, which is expressed as: is the voltage quality vulnerability of the distribution network, expressed as: is the balance degree of distribution network voltage quality vulnerability, expressed as: is the active power balance index, expressed as: ω1, ω2 and ω3 are all weight coefficients; f3 is the energy storage input capacity objective function, which is expressed as: t0 is the maximum charge or discharge start time; t0+nΔt is the maximum charge or discharge end time, P n,t is the operating power of mobile energy storage n at time t, Δt is the time interval between the two moments; Among them, y is the usable life of mobile energy storage, D is the number of days of use of mobile energy storage throughout the year, r i is the inflation rate, r d is the discount rate, n ESM is the number of mobile energy storages, T is the time period, and are the discharge power and charging power of mobile energy storage n at time t, C(t) is the electricity price at time t, ε is the subsidy ratio for delayed construction income, is the rated power of a single mobile energy storage, b2 is the cost required to upgrade the unit power equipment, ω is the annual interest rate, C ESM is the unit capacity cost of mobile energy storage, C P is the unit power conversion cost, E is the capacity of a single mobile energy storage, n CAR is the number of transport vehicles, C CAR The unit purchase cost of the vehicle used to load and transport mobile energy storage, C m is the annual operating cost of the mobile energy storage module per unit charge and discharge power, N is the total number of nodes, V i,t is the node vulnerability index of node i at time t, V t,max and V t,min are the maximum and minimum values ​​of the node vulnerability index at time t, H i,t is the ratio of the sum of the vulnerability indices of the first i nodes to the sum of the vulnerability indices of all nodes after the vulnerability indices of the nodes at time t are sorted from small to large, x l is the reactance value of line l, P l,t is the active power transmitted by line l at time t, n line is the number of lines.

10. The device for improving the disaster resistance of a distribution network according to claim 8, characterized in that: The constraints of the distribution network mobile energy storage optimization configuration model constructed by the mobile energy storage configuration module include: The power flow constraint of the distribution network is expressed as: ΣP i,j ,ΣP j,k 、 and They represent the sum of the active power injected into node j, the sum of the active power flowing out, the active load forecast value, the sum of the active output of the distributed generation, and the active amount of the abandoned load; ΣQ i,j ,ΣQ j,k 、 and They represent the sum of reactive power injected into node j, the sum of reactive power flowing out, reactive load forecast value, the sum of reactive output of distributed generation, and reactive amount of abandoned load respectively; and Represent the total active line loss and reactive line loss of the injected line ij, r i,j represents the resistance of line ij, x i,j represents the reactance of line ij, Represents the square of the current in line ij, m i,j is the slack variable, α i,j is a 0-1 variable, indicating the open / closed state of the line; M is a constant; is the square of the voltage at node i, is the square of the voltage at node j; P i,j represents the active power from node i to node j, Q i,j represents the reactive power from node i to node j; Distributed photovoltaic output constraints are expressed as: P is the set of distributed photovoltaic access nodes; and are the distributed photovoltaic active power and reactive power connected to node i, and are the upper limit of active power output and reactive power output of distributed photovoltaic connected to node i respectively; and are the upper and lower limits of the distributed photovoltaic power factor at access node i; The charging and discharging constraints of mobile energy storage are expressed as: is the charging state value of mobile energy storage n at time t, is the discharge state value of mobile energy storage n at time t; and are all 0-1 variables, representing the states of the mobile energy storage n at time t and time t+1 respectively, Ω represents the location set of the mobile energy storage, and are the charging active power and discharging active power of mobile energy storage n at time t respectively; and are the charging reactive power and discharging reactive power of mobile energy storage n at time t respectively; and are the upper limit of charging and discharging active power of mobile energy storage n respectively; and are the upper limit of charging reactive power and the upper limit of discharging reactive power of mobile energy storage n respectively; and are the charging efficiency and discharging efficiency of mobile energy storage n, respectively; and is the state of charge of the mobile energy storage n at time t and t+Δt; and are the upper and lower limits of the state of charge of the mobile energy storage n.

11. The device for improving the disaster resistance of a distribution network according to claim 8, characterized in that: The objective function of the pre-disaster mobile energy storage initial position scheduling model constructed by the pre-dispatching module is: Among them, f1′ is the objective function of the initial location scheduling model of mobile energy storage before the disaster, n ESM is the collection of mobile energy storage, n CAR For the collection of vehicles; is a 0-1 variable, indicating whether the mobile energy storage n is traveling on the line with the vehicle c; is the load active power reduction of node i; the cost of pre-dispatching a single mobile energy storage system; is the unit load loss cost of node i.

12. The device for improving the disaster resistance of a distribution network according to claim 8, characterized in that: The constraints of the pre-disaster mobile energy storage initial position scheduling model constructed by the pre-scheduling module include: The mobile energy storage resource constraint is expressed as: is the state of mobile energy storage n at time t, N ESM,i is the maximum number of mobile energy storage installations at node i, N is the set of nodes, Indicates whether the mobile energy storage n is traveling on the line with vehicle c at time t, N ESM,c is the maximum carrying capacity of mobile energy storage of vehicle c; The distribution network radial topology constraint is expressed as: v is the node other than the distributed power generation node; ε is the set of all branches; F ls and F js are the outflow powers of nodes l and j in the virtual network, respectively, and F il and F ij are the inflow powers of nodes l and j in the virtual network, δ(l) represents the outflow node set of node l, π(l) represents the inflow node set of node l, δ(j) represents the outflow node set of node j, π(j) represents the inflow node set of node j, W j is the amount of electricity provided by the distributed power sources in the virtual network; M is a constant; c ij is a variable representing the state of the branch; The load loss power constraint is expressed as: is the load active power reduction of node i, is the reactive power reduction of the load at node i, and are the maximum active and reactive load values ​​of node i respectively.

13. The device for improving the disaster resistance of a distribution network according to claim 8, characterized in that: The objective function of the optimal recovery model of the post-disaster distribution network constructed by the scheduling module is expressed as: Among them, f2′ is the objective function of the optimal restoration model of the distribution network after the disaster, Ω T is the set of fault time periods, N is the set of nodes, ω i is the weight that represents the importance of the load, is the load active power reduction of node i at time t during the fault time.

14. The device for improving the disaster resistance of a distribution network according to claim 8, characterized in that: The constraints of the optimal recovery model of the post-disaster distribution network constructed by the scheduling module include: The dispatch constraint of mobile energy storage is expressed as: represents the position variable of the mobile energy storage module n at node i during period t, It represents the position variable of the mobile energy storage n placed on the vehicle c at time t and traveling on the branch road between any nodes; N is the set of nodes, n CAR is the collection of vehicles; T0 is the installation time of mobile energy storage; n jk is the number of grid edges between node j and node k; ξ is the unit time of spatial transfer of the transport vehicle between grids; The initial position constraint of mobile energy storage is expressed as: t0 is the time when the disaster occurs; Ω ME A collection of mobile energy storage; Indicates whether mobile energy storage n is connected to node i at the time of disaster occurrence; It is a 0-1 variable, where its value is 1 indicating that there is only one mobile energy storage connected to node i at the time of the disaster, and its value is 0 indicating that there is no mobile energy storage connected to node i at the time of the disaster.

15. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for improving the disaster resistance of the distribution network as described in any one of claims 1 to 7 are implemented.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for improving the disaster resistance of a distribution network as claimed in any one of claims 1 to 7 are implemented.

Images