Pre-disaster mobile energy storage configuration method for expressway energy system

Abstract

The invention discloses a pre-disaster mobile energy storage configuration method for an expressway energy system, and belongs to the field of pre-disaster energy storage configuration of expressways. According to the mobile energy storage system charging and discharging model, constructing a pre-disaster deterministic mobile energy storage configuration model without considering the DG output uncertainty; according to the pre-disaster deterministic mobile energy storage configuration model, considering DG output uncertainty, and constructing a pre-disaster mobile energy storage two-stage robust optimization configuration model; and on the basis of a column constraint generation algorithm, performing decoupling processing on the pre-disaster mobile energy storage two-stage robust optimization configuration model, generating a main problem model and a sub-problem model, and performing continuous iteration on the generated main problem model and sub-problem model to obtain an optimal solution of pre-disaster mobile energy storage configuration. According to the invention, the problem of large-scale power failure of the expressway energy system caused by extreme disasters is solved.

Description

Technical Field

[0001] The present invention belongs to the field of pre-disaster energy storage configuration for highways, and in particular relates to a pre-disaster mobile energy storage configuration method for highway energy systems. Background Art

[0002] In recent years, global climate change has continued to intensify, leading to an increase in the frequency and intensity of extreme disasters such as typhoons, floods, sandstorms, and snowstorms. Under the influence of these extreme disasters, the electrical equipment and related infrastructure of highway energy systems are exposed to the natural environment all year round and are susceptible to damage or failure, which not only causes economic losses, but may also cause traffic accidents or other safety hazards, endangering driving safety. This not only brings challenges to highway energy systems, but also highlights the urgency of building a resilient highway energy system.

[0003] The introduction of mobile energy storage provides flexibility and dispatchability for the system, and can provide continuous power supply when disasters occur, thereby enhancing the resilience and reliability of the system. As a new type of energy storage resource, compared with traditional fixed energy storage power stations, on the one hand, it can avoid the problem of low utilization rate of fixed energy storage power stations due to changes in load characteristics; on the other hand, due to its flexibility in time and space access, it can more effectively promote the interaction of source, grid, load and storage in the highway energy system and improve the utilization efficiency of distributed power sources and equipment. By configuring mobile energy storage, the degree to which the highway energy system is affected by disasters can be effectively reduced, and the resilience and rapid recovery capabilities of the system can be improved.

[0004] In addition to responding to the challenges of frequent disasters, highway energy systems usually need to access a variety of renewable resources, such as solar energy and wind energy. However, these renewable resources are volatile and uncertain, which will affect the stability and resilience of the system. Therefore, when configuring mobile energy storage, the uncertainty of distributed power output must be fully considered to ensure that the system can flexibly respond to the volatility of renewable resources, further improve the power supply security of highway energy systems, and enhance their resilience and disaster resistance.

[0005] Although many studies have explored the planning methods for the configuration of mobile energy storage equipment at home and abroad, there are few planning studies that apply the concept of resilience to highway energy systems and optimize the configuration of mobile energy storage before extreme disasters occur. Summary of the invention

[0006] In view of the above-mentioned deficiencies in the prior art, the present invention provides a pre-disaster mobile energy storage configuration method for a highway energy system, which solves the problem of large-scale power outages in highway energy systems caused by extreme disasters.

[0007] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a pre-disaster mobile energy storage configuration method for a highway energy system, comprising the following steps:

[0008] Construct a charging and discharging model of a mobile energy storage system, and based on the charging and discharging model of the mobile energy storage system, construct a pre-disaster deterministic mobile energy storage configuration model that does not consider the uncertainty of DG output;

[0009] According to the pre-disaster deterministic mobile energy storage configuration model, considering the uncertainty of DG output, a two-stage robust optimization configuration model of pre-disaster mobile energy storage is constructed;

[0010] Based on the column constraint generation algorithm, the two-stage robust optimization configuration model of pre-disaster mobile energy storage is decoupled to generate the main problem model and sub-problem model. The generated main problem model and sub-problem model are continuously iterated to obtain the optimal solution for the pre-disaster mobile energy storage configuration.

[0011] The beneficial effects of the present invention are as follows: by establishing a scientific two-stage robust optimization configuration model for mobile energy storage, the emergency response capability is significantly improved, ensuring the reliability of energy supply in the event of extreme disasters on highways. At the same time, the uncertainty of distributed power generation output is taken into account, making resource allocation more efficient and flexible, thereby optimizing the system economy. The flexibility and adaptability of the present invention enhance the resilience of the highway energy system and provide effective support for ensuring safe operation and promoting sustainable development.

[0012] Furthermore, the expression of the charging and discharging model of the mobile energy storage system is:

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[0020]

[0021]

[0022] in, is the mobile energy storage capacity of node i at time t+1; is the mobile energy storage capacity of node i at time t; η ch Charging efficiency for mobile energy storage; is the charging active power of mobile energy storage at node i at time t; is the active power discharged by the mobile energy storage at node i at time t; η dis is the discharge efficiency of mobile energy storage; Δt is the time difference; is a 0-1 variable, if the mobile energy storage is charged at time t, it is 1, otherwise, it is 0; The minimum active power for charging mobile energy storage; The maximum active power for charging mobile energy storage; is a 0-1 variable, if the mobile energy storage is discharged at time t, it is 1, otherwise, it is 0; It is the minimum value of the active power discharged by the mobile energy storage; It is the maximum value of the active power discharged by the mobile energy storage; is the mobile energy storage dispatch position 0-1 variable, When , it means that there is only one mobile energy storage connected to node i. Indicates that no mobile energy storage is connected to node i, and stipulates that a node can be connected to at most one mobile energy storage; Minimum value of no power for charging mobile energy storage; is the charging reactive power of the mobile energy storage at node i at time t; There is no maximum power for charging mobile energy storage; It is the minimum reactive power value of mobile energy storage discharge; is the discharged reactive power of mobile energy storage at node i at time t; It is the maximum reactive power of mobile energy storage discharge; is the state of charge of the mobile energy storage at node i at time t; E is the rated capacity of the mobile energy storage; is the initial state of charge of the mobile energy storage at time t; SOC ini Scaling factor for converting the mobile energy storage dispatch position into the actual state of charge; SOC min It is the minimum value of mobile energy storage; SOC max It is the maximum value of mobile energy storage.

[0023] The beneficial effects of the above further scheme are: optimizing the charging and discharging strategy to ensure stable power support in all time periods.

[0024] Furthermore, the objective function of the pre-disaster deterministic mobile energy storage configuration model is:

[0025] C=C1+C2

[0026]

[0027]

[0028] Among them, C is the objective function of the pre-disaster deterministic mobile energy storage configuration model; C1 is the mobile energy storage configuration cost; C2 is the load loss cost; N is the distribution network node set; C ME is the cost of a unit of mobile energy storage equipment; is the mobile energy storage dispatch position 0-1 variable, When , it means that there is only one mobile energy storage connected to node i. Indicates that no mobile energy storage is connected to node i, and stipulates that a node can be connected to at most one mobile energy storage; is the unit load shedding cost of node i at time t; is the active load removed from node i at time t.

[0029] The beneficial effects of the above further scheme are: providing a scientific basis for pre-disaster energy storage configuration and helping to improve the system's emergency response capabilities

[0030] Furthermore, the constraints of the pre-disaster deterministic mobile energy storage configuration model are:

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[0052] in, is the actual active power output of the wind turbine at node i at time t; Predict the active power output of the wind turbine at node i at time t; is the minimum value of the actual reactive power output of the wind turbine at node i; is the actual reactive power output of the wind turbine at node i at time t; is the maximum value of the actual reactive power output of the wind turbine at node i; is the actual active power output of the photovoltaic power station at the node i at time t; is the predicted active power output of photovoltaic power station i at time t; is the minimum value of the actual photovoltaic reactive power output of node i; is the actual PV reactive power output of node i at time t; is the maximum value of the actual photovoltaic reactive power output of node i; Ω N is the collection of load nodes; is the mobile energy storage dispatch position 0-1 variable, When , it means that there is only one mobile energy storage connected to node i. Indicates that no mobile energy storage is connected to node i, and stipulates that a node can be connected to at most one mobile energy storage; The upper limit of the mobile energy storage quantity in the pre-layout stage; The active power transmitted to the upper grid of node i at time t; is the active power discharged by the mobile energy storage at node i at time t; is the charging active power of mobile energy storage at node i at time t; is the active load of node i at time t; is the active load removed from node i at time t; l is the line set; ij is the line flowing from node i to node j; is the transmission active power of line ij at time t; is the transmission active power of line k'i at time t; k'i is the line flowing from node k' to node i; is the resistance of line k'i at time t; is the square of the current amplitude of line k'i at time t; is the reactive power transmitted by the upper grid of node i at time t; is the discharged reactive power of mobile energy storage at node i at time t; is the charging reactive power of the mobile energy storage at node i at time t; is the reactive load of node i at time t; is the reactive load removed from node i at time t; is the transmission reactive power of line ij at time t; is the transmission reactive power of line k'i at time t; is the reactance of line k'i at time t; is the square of the voltage amplitude at node j at time t; M is a constant; is a 0-1 variable, indicating the on / off state of the switch in line ij at time t; V i sqr,t is the square of the voltage amplitude at node i at time t; is the resistance of line ij at time t; is the reactance of line ij at time t; is the square of the current amplitude of line ij at time t; The maximum active power transmitted to the upper grid; is the maximum power transmitted by the upper power grid; P min is the minimum active power of the line; P max is the maximum active power of the line; Q min is the minimum reactive power of the line; Q max is the maximum reactive power of the line; is the minimum value of the square of the voltage amplitude; is the maximum value of the square of the voltage amplitude; is the maximum value of the square of the current amplitude; It is a radial constraint auxiliary 0-1 variable. When the line ij is connected and j is the parent node of i, otherwise, is a radial constraint auxiliary 0-1 variable. When the distribution network line ji is connected and i is the parent node of j, otherwise, N i is the set of nodes directly connected to i; It is the radial constraint auxiliary 0-1 variable of the distribution network line flowing from distribution network node 1 to distribution network node j; sub is the set of nodes connected to the substation node.

[0053] The beneficial effects of the above further scheme are: reasonably restricting the flow path of electricity, optimizing the distribution of electric energy, ensuring the efficient interaction between energy storage equipment and the power grid, and ensuring the stability and reliability of power supply.

[0054] Furthermore, according to the pre-disaster deterministic mobile energy storage configuration model, considering the uncertainty of DG output, a two-stage robust optimization configuration model for pre-disaster mobile energy storage is constructed, which is specifically:

[0055] According to the pre-disaster deterministic mobile energy storage configuration model, a box-type uncertainty set considering the uncertainty of DG output is constructed;

[0056] According to the box type uncertainty set, a two-stage robust optimization configuration model for pre-disaster mobile energy storage is constructed.

[0057] The beneficial effects of the above further scheme are: it can more reasonably configure mobile energy storage resources, improve resource utilization efficiency, and ensure the stability and reliability of the system under the worst-case scenario.

[0058] Furthermore, the expression of the box-type uncertainty set considering the uncertainty of DG output is:

[0059]

[0060] Among them, U is the box-type uncertainty set considering the uncertainty of DG output; u is the uncertain variable; is the actual active power output of the photovoltaic power station at the node i at time t; is the actual active power output of the wind turbine at node i at time t; N T is the scheduling period; is the predicted active power output of photovoltaic power station i at time t; It is the maximum fluctuation deviation allowed for photovoltaic output, which is a positive number; is the actual active power output of the wind turbine at node i at time t; Predict the active power output of the wind turbine at node i at time t; It is the maximum fluctuation deviation allowed for wind power output, which is a positive number.

[0061] The beneficial effect of the above further solution is: simulating the fluctuation of DG output within a certain range, so that the optimization model can adapt to the change of DG output, thereby improving the robustness of the system.

[0062] Furthermore, the expression of the two-stage robust optimization configuration model of pre-disaster mobile energy storage is:

[0063]

[0064] in, is the value of x when {·} is minimum; x is the optimization variable; is the mobile energy storage dispatch cost function in the first stage; is the load loss cost function of the second stage; T is the transpose; a, b, c, c1, c2, d, d1, d3, d5, e, e1, f, h and n1 are all constant column vectors; is the value of u when {·} is maximum; u is an uncertain variable; U is the box-type uncertainty set considering the uncertainty of DG output; is the value of y1 and y2 when {·} is minimum; y1 and y2 are both optimization variables; Ω(x,u) is the feasible domain of optimization variables y1 and y2; A, B, B1, B2, B3, B 3_1 , B4, B 4_1 , B5, B 5_1 , B6, B7, B8, B 10 , B 11 , B 12 , B 15 , B 16 and B 17 is the coefficient matrix of the variables under the corresponding constraints; K m is the coefficient matrix corresponding to the mth second-order cone constraint; M m is the coefficient column vector corresponding to the mth second-order cone constraint; ||·||2 is the L2 norm; is the mobile energy storage dispatch position 0-1 variable, When , it means that there is only one mobile energy storage connected to node i. Indicates that no mobile energy storage is connected to node i, and stipulates that a node can be connected to at most one mobile energy storage; is the active load removed from node i at time t; is the reactive load removed from node i at time t; is a 0-1 variable, if the mobile energy storage is charged at time t, it is 1, otherwise, it is 0; is a 0-1 variable, if the mobile energy storage is discharged at time t, it is 1, otherwise, it is 0; is a 0-1 variable, indicating the on / off state of the switch in line ij at time t; It is a radial constraint auxiliary 0-1 variable. When the line ij is connected and j is the parent node of i, otherwise, is a radial constraint auxiliary 0-1 variable. When the distribution network line ji is connected and i is the parent node of j, otherwise, V i sqr,t is the square of the voltage amplitude at node i at time t; is the square of the current amplitude of line ij at time t; is the transmission active power of line ij at time t; is the transmission reactive power of line ij at time t; The active power transmitted to the upper grid of node i at time t; is the reactive power transmitted by the upper grid of node i at time t; is the active power discharged by the mobile energy storage at node i at time t; is the charging active power of mobile energy storage at node i at time t; is the discharged reactive power of mobile energy storage at node i at time t; is the charging reactive power of the mobile energy storage at node i at time t; is the mobile energy storage capacity at node i at time t; is the actual active power output of the wind turbine at node i at time t; is the actual reactive power output of the wind turbine at node i at time t; is the actual active power output of the photovoltaic power station at the node i at time t; is the actual reactive power output of the photovoltaic node i at time t; N T The scheduling cycle.

[0065] The beneficial effects of the above further scheme are: to minimize the mobile energy storage configuration cost and the total load reduction cost, to achieve a reasonable allocation of resources, and to enhance the resilience of the distribution network in the face of extreme disasters.

[0066] Furthermore, the column constraint generation algorithm is used to decouple the two-stage robust optimization configuration model of mobile energy storage before disasters to generate a main problem model and a sub-problem model, specifically:

[0067] Based on the column constraint generation algorithm, the two-stage robust optimization configuration model of pre-disaster mobile energy storage is decomposed to obtain the main problem model; the expression of the main problem model is:

[0068]

[0069] Among them, α is an auxiliary variable; x k , and are all optimization variables obtained after the kth iteration; k is the number of iterations to generate variables and constraints; is the value of the uncertain variable u in the worst scenario obtained after the kth iteration;

[0070] The two-stage robust optimization configuration model of mobile energy storage before disaster is decoupled to obtain the sub-problem form:

[0071]

[0072] Among them, λ, υ, π, ψ, β, μ, ξ, σ, θ, δ, ω, γ m and are the dual variables of the corresponding constraints respectively;

[0073] Using strong duality theory, the minimization problem in the form of subproblems Transformed into a maximization problem and compared with the outer maximization problem Combining them, we get the dual problem:

[0074]

[0075] According to the box-type uncertainty set considering the uncertainty of DG output, the uncertainty set is obtained:

[0076]

[0077] Among them, U' is the uncertainty set; u is the uncertain variable; is the actual active power output of the photovoltaic power station at the node i at time t; is the actual active power output of the wind turbine at node i at time t; T is the transposition; N T is the scheduling period; is the predicted active power output of photovoltaic power station i at time t; It is a 0-1 variable. When the value is 1, the photovoltaic output reaches the lower limit, and when the value is 0, the photovoltaic output is equal to the predicted value; It is the maximum fluctuation deviation allowed for photovoltaic output, which is a positive number; is the actual active power output of the wind turbine at node i at time t; Predict the active power output of the wind turbine at node i at time t; It is a 0-1 variable. When the value is 1, the fan output reaches the lower limit. When the value is 0, the fan output is equal to the predicted value. is the maximum fluctuation deviation allowed for wind power output, which is a positive number; Γ PV and Γ WT All are uncertainty adjustment parameters;

[0078] Substitute the uncertainty set into the dual problem and use the big-M method to linearize it to get the subproblem model.

[0079] The beneficial effects of the above further scheme are: reducing the dimension of the problem, making the solution easier, and flexibly adjusting the conservatism of the robust optimization results; the main problem model effectively improves the performance of the distribution network in the face of uncertainty and disasters, reduces economic losses, and improves the reliability and resilience of power supply.

[0080] Furthermore, the expression of the sub-problem model is:

[0081]

[0082] in, B' DG , B DG , and are all introduced auxiliary variables; M max is the upper bound of the dual variable, a positive real number.

[0083] The beneficial effects of the above further solution are: significantly reducing the difficulty of solving and the complexity of calculation, and improving the calculation efficiency.

[0084] Furthermore, the generated main problem model and sub-problem model are continuously iterated to obtain the optimal solution of the pre-disaster mobile energy storage configuration, specifically: the main problem model and the sub-problem model are solved by using the C&CG algorithm to obtain the optimal solution of the pre-disaster mobile energy storage configuration, including the following steps:

[0085] Step 1: Set the upper and lower bounds of the objective function C of the pre-disaster deterministic mobile energy storage configuration model as UB = +∞ and LB = -∞, respectively, and give a set of initial values ​​u under the worst case, and the number of iterations k = 1;

[0086] Step 2: Based on the worst case scenario Solve the main problem model and obtain the solution α of the main problem model k 、x k , and And update the lower bound LB to α k ; is the value of the uncertain variable u in the worst scenario after the kth iteration; x k , and are the optimization variables obtained after the kth iteration; α k is the value of the auxiliary variable α obtained after the kth iteration;

[0087] Step 3: The solution x of the main problem model obtained in step 2 k Substitute it into the subproblem model to obtain the objective function value f of the subproblem model k (x k ) and the value of the uncertain variable u in the worst case of the main problem model And update the upper bound UB to min{UB,f k (x k )}:

[0088]

[0089] Among them, λ, υ, π, ψ, β, μ, ξ, σ, θ, δ, ω, γ m and are the dual variables of the corresponding constraints; c, d, d1, d3, d5, e, e1, f, h and n1 are all constant column vectors; B 12 , B 16 and B 17 is the coefficient matrix of the variables under the corresponding constraints; and B' DG All are introduced auxiliary variables; is the value of the uncertain variable u in the worst scenario after the k+1th iteration; T is the transpose;

[0090] Step 4: Given the convergence threshold value ε, if UB-LB≤ε is satisfied, stop the iteration and output the optimal solution x k and That is, the optimal solution for the mobile energy storage configuration before the disaster; otherwise, add the optimal cutting plane constraint And jump to step 2 to continue execution until the algorithm converges and meets the conditions; c2 is a constant column vector; is the optimization variable obtained after the k-1th iteration.

[0091] The beneficial effects of the above further scheme are: enhancing the robustness of the model to uncertainty, and ensuring the flexibility and convergence of the algorithm by dynamically adjusting the upper and lower bounds of the objective function and updating the constraints in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 The figure is a flow chart of the method of the present invention.

[0093] Figure 2 This is an improved IEEE 14-node system connection topology diagram in an embodiment of the present invention. DETAILED DESCRIPTION

[0094] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0095] like Figure 1 As shown, in one embodiment of the present invention, a pre-disaster mobile energy storage configuration method for a highway energy system includes the following steps:

[0096] Construct a charging and discharging model of a mobile energy storage system, and based on the charging and discharging model of the mobile energy storage system, construct a pre-disaster deterministic mobile energy storage configuration model that does not consider the uncertainty of DG output;

[0097] According to the pre-disaster deterministic mobile energy storage configuration model, considering the uncertainty of DG output, a two-stage robust optimization configuration model of pre-disaster mobile energy storage is constructed;

[0098] Based on the column constraint generation algorithm, the two-stage robust optimization configuration model of pre-disaster mobile energy storage is decoupled to generate the main problem model and sub-problem model. The generated main problem model and sub-problem model are continuously iterated to obtain the optimal solution for the pre-disaster mobile energy storage configuration.

[0099] The expression of the charging and discharging model of the mobile energy storage system is:

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[0109] in, is the mobile energy storage capacity of node i at time t+1; is the mobile energy storage capacity of node i at time t; η ch Charging efficiency for mobile energy storage; is the charging active power of mobile energy storage at node i at time t; is the active power discharged by the mobile energy storage at node i at time t; η dis is the discharge efficiency of mobile energy storage; Δt is the time difference; is a 0-1 variable, if the mobile energy storage is charged at time t, it is 1, otherwise, it is 0; The minimum active power for charging mobile energy storage; The maximum active power for charging mobile energy storage; is a 0-1 variable, if the mobile energy storage is discharged at time t, it is 1, otherwise, it is 0; It is the minimum value of the active power discharged by the mobile energy storage; It is the maximum value of the active power discharged by the mobile energy storage; is the mobile energy storage dispatch position 0-1 variable, When , it means that there is only one mobile energy storage connected to node i. Indicates that no mobile energy storage is connected to node i, and stipulates that a node can be connected to at most one mobile energy storage; Minimum value of no power for charging mobile energy storage; is the charging reactive power of the mobile energy storage at node i at time t; There is no maximum power for charging mobile energy storage; It is the minimum reactive power value of mobile energy storage discharge; is the discharged reactive power of mobile energy storage at node i at time t; It is the maximum reactive power of mobile energy storage discharge; is the state of charge of the mobile energy storage at node i at time t; E is the rated capacity of the mobile energy storage; is the initial state of charge of the mobile energy storage at time t; SOC ini Scaling factor for converting the mobile energy storage dispatch position into the actual state of charge; SOC min It is the minimum value of mobile energy storage; SOC max It is the maximum value of mobile energy storage.

[0110] The objective function of the pre-disaster deterministic mobile energy storage configuration model is:

[0111] C=C1+C2

[0112]

[0113]

[0114] Among them, C is the objective function of the pre-disaster deterministic mobile energy storage configuration model; C1 is the mobile energy storage configuration cost; C2 is the load loss cost; N is the distribution network node set; C ME is the cost of a unit of mobile energy storage equipment; is the mobile energy storage dispatch position 0-1 variable, When , it means that there is only one mobile energy storage connected to node i. Indicates that no mobile energy storage is connected to node i, and stipulates that a node can be connected to at most one mobile energy storage; is the unit load shedding cost of node i at time t; is the active load removed from node i at time t.

[0115] The constraints of the pre-disaster deterministic mobile energy storage configuration model are:

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[0137] in, is the actual active power output of the wind turbine at node i at time t; Predict the active power output of the wind turbine at node i at time t; is the minimum value of the actual reactive power output of the wind turbine at node i; is the actual reactive power output of the wind turbine at node i at time t; is the maximum value of the actual reactive power output of the wind turbine at node i; is the actual active power output of the photovoltaic power station at the node i at time t; is the predicted active power output of photovoltaic power station i at time t; is the minimum value of the actual photovoltaic reactive power output of node i; is the actual PV reactive power output of node i at time t; is the maximum value of the actual photovoltaic reactive power output of node i; Ω N is the collection of load nodes; is the mobile energy storage dispatch position 0-1 variable, When , it means that there is only one mobile energy storage connected to node i. Indicates that no mobile energy storage is connected to node i, and stipulates that a node can be connected to at most one mobile energy storage; The upper limit of the mobile energy storage quantity in the pre-layout stage; The active power transmitted to the upper grid of node i at time t; is the active power discharged by the mobile energy storage at node i at time t; is the charging active power of mobile energy storage at node i at time t; is the active load of node i at time t; is the active load removed from node i at time t; l is the line set; ij is the line flowing from node i to node j; is the transmission active power of line ij at time t; is the transmission active power of line k'i at time t; k'i is the line flowing from node k' to node i; is the resistance of line k'i at time t; is the square of the current amplitude of line k'i at time t; is the reactive power transmitted by the upper grid of node i at time t; is the discharged reactive power of mobile energy storage at node i at time t; is the charging reactive power of the mobile energy storage at node i at time t; is the reactive load of node i at time t; is the reactive load removed from node i at time t; is the transmission reactive power of line ij at time t; is the transmission reactive power of line k'i at time t; is the reactance of line k'i at time t; is the square of the voltage amplitude at node j at time t; M is a constant; is a 0-1 variable, indicating the on / off state of the switch in line ij at time t; V i sqr,t is the square of the voltage amplitude at node i at time t; is the resistance of line ij at time t; is the reactance of line ij at time t; is the square of the current amplitude of line ij at time t; The maximum active power transmitted to the upper grid; is the maximum power transmitted by the upper power grid; P min is the minimum active power of the line; P max is the maximum active power of the line; Q min is the minimum reactive power of the line; Q max is the maximum reactive power of the line; is the minimum value of the square of the voltage amplitude; is the maximum value of the square of the voltage amplitude; is the maximum value of the square of the current amplitude; It is a radial constraint auxiliary 0-1 variable. When the line ij is connected and j is the parent node of i, otherwise, is a radial constraint auxiliary 0-1 variable. When the distribution network line ji is connected and i is the parent node of j, otherwise, N i is the set of nodes directly connected to i; It is the radial constraint auxiliary 0-1 variable of the distribution network line flowing from distribution network node 1 to distribution network node j; sub is the set of nodes connected to the substation node.

[0138] According to the pre-disaster deterministic mobile energy storage configuration model, considering the uncertainty of DG output, a two-stage robust optimization configuration model for pre-disaster mobile energy storage is constructed, specifically:

[0139] According to the pre-disaster deterministic mobile energy storage configuration model, a box-type uncertainty set considering the uncertainty of DG output is constructed;

[0140] According to the box type uncertainty set, a two-stage robust optimization configuration model for pre-disaster mobile energy storage is constructed.

[0141] The expression of the box-type uncertainty set considering the uncertainty of DG output is:

[0142]

[0143] Among them, U is the box-type uncertainty set considering the uncertainty of DG output; u is the uncertain variable; is the actual active power output of the photovoltaic power station at the node i at time t; is the actual active power output of the wind turbine at node i at time t; N T is the scheduling period; is the predicted active power output of photovoltaic power station i at time t; It is the maximum fluctuation deviation allowed for photovoltaic output, which is a positive number; is the actual active power output of the wind turbine at node i at time t; Predict the active power output of the wind turbine at node i at time t; It is the maximum fluctuation deviation allowed for wind power output, which is a positive number.

[0144] The expression of the two-stage robust optimization configuration model of pre-disaster mobile energy storage is:

[0145]

[0146]

[0147] in, is the value of x when {·} is minimum; x is the optimization variable; is the mobile energy storage dispatch cost function in the first stage; is the load loss cost function of the second stage; T is the transpose; a, b, c, c1, c2, d, d1, d3, d5, e, e1, f, h and n1 are all constant column vectors; is the value of u when {·} is maximum; u is an uncertain variable; U is the box-type uncertainty set considering the uncertainty of DG output; is the value of y1 and y2 when {·} is minimum; y1 and y2 are both optimization variables; Ω(x,u) is the feasible domain of optimization variables y1 and y2; A, B, B1, B2, B3, B 3_1 , B4, B 4_1 , B5, B 5_1 , B6, B7, B8, B 10 , B 11 , B 12 , B 15 , B 16 and B 17 is the coefficient matrix of the variables under the corresponding constraints; K m is the coefficient matrix corresponding to the mth second-order cone constraint; M m is the coefficient column vector corresponding to the mth second-order cone constraint; ||·||2 is the L2 norm; is the mobile energy storage dispatch position 0-1 variable, When , it means that there is only one mobile energy storage connected to node i. Indicates that no mobile energy storage is connected to node i, and stipulates that a node can be connected to at most one mobile energy storage; is the active load removed from node i at time t; is the reactive load removed from node i at time t; is a 0-1 variable, if the mobile energy storage is charged at time t, it is 1, otherwise, it is 0; is a 0-1 variable, if the mobile energy storage is discharged at time t, it is 1, otherwise, it is 0; is a 0-1 variable, indicating the on / off state of the switch in line ij at time t; It is a radial constraint auxiliary 0-1 variable. When the line ij is connected and j is the parent node of i, otherwise, is a radial constraint auxiliary 0-1 variable. When the distribution network line ji is connected and i is the parent node of j, otherwise, V i sqr,t is the square of the voltage amplitude at node i at time t; is the square of the current amplitude of line ij at time t; is the transmission active power of line ij at time t; is the transmission reactive power of line ij at time t; The active power transmitted to the upper grid of node i at time t; is the reactive power transmitted by the upper grid of node i at time t; is the active power discharged by the mobile energy storage at node i at time t; is the charging active power of mobile energy storage at node i at time t; is the discharged reactive power of mobile energy storage at node i at time t; is the charging reactive power of the mobile energy storage at node i at time t; is the mobile energy storage capacity at node i at time t; is the actual active power output of the wind turbine at node i at time t; is the actual reactive power output of the wind turbine at node i at time t; is the actual active power output of the photovoltaic power station at the node i at time t; is the actual reactive power output of the photovoltaic node i at time t; N T The scheduling cycle.

[0148] Based on the column constraint generation algorithm, the two-stage robust optimization configuration model of mobile energy storage before disaster is decoupled to generate the main problem model and sub-problem model, which are specifically:

[0149] Based on the column constraint generation algorithm, the two-stage robust optimization configuration model of pre-disaster mobile energy storage is decomposed to obtain the main problem model; the expression of the main problem model is:

[0150]

[0151] Among them, α is an auxiliary variable; x k , and are all optimization variables obtained after the kth iteration; k is the number of iterations to generate variables and constraints; is the value of the uncertain variable u in the worst scenario obtained after the kth iteration;

[0152] The two-stage robust optimization configuration model of mobile energy storage before disaster is decoupled to obtain the sub-problem form:

[0153]

[0154] Among them, λ, υ, π, ψ, β, μ, ξ, σ, θ, δ, ω, γ m and are the dual variables of the corresponding constraints respectively;

[0155] Using strong duality theory, the minimization problem in the form of subproblems Transformed into a maximization problem and compared with the outer maximization problem Combining them, we get the dual problem:

[0156]

[0157] According to the box-type uncertainty set considering the uncertainty of DG output, the uncertainty set is obtained:

[0158]

[0159] Among them, U' is the uncertainty set; u is the uncertain variable; is the actual active power output of the photovoltaic power station at the node i at time t; is the actual active power output of the wind turbine at node i at time t; T is the transposition; N T is the scheduling period; is the predicted active power output of photovoltaic power station i at time t; It is a 0-1 variable. When the value is 1, the photovoltaic output reaches the lower limit, and when the value is 0, the photovoltaic output is equal to the predicted value; It is the maximum fluctuation deviation allowed for photovoltaic output, which is a positive number; is the actual active power output of the wind turbine at node i at time t; Predict the active power output of the wind turbine at node i at time t; It is a 0-1 variable. When the value is 1, the fan output reaches the lower limit. When the value is 0, the fan output is equal to the predicted value. is the maximum fluctuation deviation allowed for wind power output, which is a positive number; Γ PV and Γ WT All are uncertainty adjustment parameters;

[0160] Substitute the uncertainty set into the dual problem and use the big-M method to linearize it to get the subproblem model.

[0161] The expression of the sub-problem model is:

[0162]

[0163] in, B' DG , B DG , and are all auxiliary variables introduced; M max is the upper bound of the dual variable, a positive real number.

[0164] The generated main problem model and sub-problem model are continuously iterated to obtain the optimal solution of the pre-disaster mobile energy storage configuration. Specifically, the main problem model and the sub-problem model are solved by the C&CG algorithm to obtain the optimal solution of the pre-disaster mobile energy storage configuration, including the following steps:

[0165] Step 1: Set the upper and lower bounds of the objective function C of the pre-disaster deterministic mobile energy storage configuration model as UB = +∞ and LB = -∞, respectively, and give a set of initial values ​​u under the worst case, and the number of iterations k = 1;

[0166] Step 2: Based on the worst case scenario Solve the main problem model and obtain the solution α of the main problem model k 、x k , and And update the lower bound LB to α k ; is the value of the uncertain variable u in the worst scenario after the kth iteration; x k , and are the optimization variables obtained after the kth iteration; α k is the value of the auxiliary variable α obtained after the kth iteration;

[0167] Step 3: The solution x of the main problem model obtained in step 2 k Substitute it into the subproblem model to obtain the objective function value f of the subproblem model k (x k ) and the value of the uncertain variable u in the worst case of the main problem model And update the upper bound UB to min{UB,f k (x k )}:

[0168]

[0169] Among them, λ, υ, π, ψ, β, μ, ξ, σ, θ, δ, ω, γ m and are the dual variables of the corresponding constraints; c, d, d1, d3, d5, e, e1, f, h and n1 are all constant column vectors; B 12 , B 16 and B 17 is the coefficient matrix of the variables under the corresponding constraints; and B' DG All are introduced auxiliary variables; is the value of the uncertain variable u in the worst scenario after the k+1th iteration; T is the transpose;

[0170] Step 4: Given the convergence threshold value ε, if UB-LB≤ε is satisfied, stop the iteration and output the optimal solution x k and That is, the optimal solution for the mobile energy storage configuration before the disaster; otherwise, add the optimal cutting plane constraint And jump to step 2 to continue execution until the algorithm converges and meets the conditions; c2 is a constant column vector; is the optimization variable obtained after the k-1th iteration.

[0171] In this embodiment, a highway network is constructed by taking a highway in a certain area as an example. A simulation test is performed by taking two improved IEEE14 node systems as examples. Figure 2 As shown in the figure, the reference power of the system is 100MVA and the reference voltage is 12.66kv. Set the photovoltaic access nodes 1, 2, 4, 16, 18, 22, 24, and the wind turbine access nodes 8, 10, 12, 14, 15, 25, 27. Take a typical summer day as an example, assuming that the fault time is 10:00-12:00 and lasts for three hours.

[0172] In order to compare and analyze the effectiveness of the method proposed in this embodiment, two scenarios are set:

[0173] Scenario 1: No mobile energy storage is configured.

[0174] Scenario 2: Configure mobile energy storage.

[0175] The models in both scenarios are solved in Matlab by calling the CPLEX solver using the Yalmip toolbox.

[0176] Table 1 shows the two-stage robust mobile energy storage optimization configuration, with mobile energy storage configured at nodes 4, 7, 8, and 17 respectively. Table 2 shows the cost comparison results under the two scenarios. Comparing scenario 1 and scenario 2, it can be seen that the configuration of mobile energy storage increases the configuration cost, but by reducing the load loss cost by 73.206%, the total cost is reduced by 64.238%. It can be seen that by rationally planning the location of mobile energy storage, the resilience of the highway energy system is improved, thereby effectively reducing the scale of power outages caused by extreme weather in the highway energy system.

[0177] Table 1 Two-stage robust mobile energy storage optimization configuration

[0178]

[0179]

[0180] Table 2 Comparison of costs under two scenarios

[0181] result Configuration cost (10,000 yuan) Load loss cost (10,000 yuan) Total cost (ten thousand yuan) Scenario 1 0 44.60 44.60 Scenario 2 4 11.95 15.95

Claims

1. A pre-disaster mobile energy storage configuration method for highway energy systems, characterized in that: The following steps are involved: Construct a charging and discharging model of a mobile energy storage system, and based on the charging and discharging model of the mobile energy storage system, construct a pre-disaster deterministic mobile energy storage configuration model that does not consider the uncertainty of DG output; According to the pre-disaster deterministic mobile energy storage configuration model, considering the uncertainty of DG output, a two-stage robust optimization configuration model of pre-disaster mobile energy storage is constructed; Based on the column constraint generation algorithm, the two-stage robust optimization configuration model of pre-disaster mobile energy storage is decoupled to generate the main problem model and sub-problem model. The generated main problem model and sub-problem model are continuously iterated to obtain the optimal solution for the pre-disaster mobile energy storage configuration.

2. The pre-disaster mobile energy storage configuration method for highway energy systems according to claim 1 is characterized in that: The expression of the charging and discharging model of the mobile energy storage system is: in, is the mobile energy storage capacity of node i at time t+1; is the mobile energy storage capacity of node i at time t; η ch Charging efficiency for mobile energy storage; is the charging active power of mobile energy storage at node i at time t; is the active power discharged by the mobile energy storage at node i at time t; η dis is the discharge efficiency of mobile energy storage; Δt is the time difference; is a 0-1 variable, if the mobile energy storage is charged at time t, it is 1, otherwise, it is 0; The minimum active power for charging mobile energy storage; The maximum active power for charging mobile energy storage; is a 0-1 variable, if the mobile energy storage is discharged at time t, it is 1, otherwise, it is 0; It is the minimum value of the active power discharged by the mobile energy storage; It is the maximum value of the active power discharged by the mobile energy storage; is the mobile energy storage dispatch position 0-1 variable, When , it means that there is only one mobile energy storage connected to node i. Indicates that no mobile energy storage is connected to node i, and stipulates that a node can be connected to at most one mobile energy storage; Minimum value of no power for charging mobile energy storage; is the charging reactive power of the mobile energy storage at node i at time t; There is no maximum power for charging mobile energy storage; It is the minimum reactive power value of mobile energy storage discharge; is the discharged reactive power of mobile energy storage at node i at time t; It is the maximum reactive power of mobile energy storage discharge; is the state of charge of the mobile energy storage at node i at time t; E is the rated capacity of the mobile energy storage; is the initial state of charge of the mobile energy storage at time t; SOC ini Scaling factor for converting the mobile energy storage dispatch position into the actual state of charge; SOC min It is the minimum value of mobile energy storage; SOC max It is the maximum value of mobile energy storage.

3. The pre-disaster mobile energy storage configuration method for highway energy systems according to claim 1 is characterized in that: The objective function of the pre-disaster deterministic mobile energy storage configuration model is: C=C1+C2 Among them, C is the objective function of the pre-disaster deterministic mobile energy storage configuration model; C1 is the mobile energy storage configuration cost; C2 is the load loss cost; N is the distribution network node set; C ME is the cost of a unit of mobile energy storage equipment; is the mobile energy storage dispatch position 0-1 variable, When , it means that there is only one mobile energy storage connected to node i. Indicates that no mobile energy storage is connected to node i, and stipulates that a node can be connected to at most one mobile energy storage; is the unit load shedding cost of node i at time t; is the active load removed from node i at time t.

4. The pre-disaster mobile energy storage configuration method for highway energy systems according to claim 1 is characterized in that: The constraints of the pre-disaster deterministic mobile energy storage configuration model are: in, is the actual active power output of the wind turbine at node i at time t; Predict the active power output of the wind turbine at node i at time t; is the minimum value of the actual reactive power output of the wind turbine at node i; is the actual reactive power output of the wind turbine at node i at time t; is the maximum value of the actual reactive power output of the wind turbine at node i; is the actual active power output of the photovoltaic power station at the node i at time t; is the predicted active power output of photovoltaic power station i at time t; is the minimum value of the actual photovoltaic reactive power output of node i; is the actual PV reactive power output of node i at time t; is the maximum value of the actual photovoltaic reactive power output of node i; Ω N is the collection of load nodes; is the mobile energy storage dispatch position 0-1 variable, When , it means that there is only one mobile energy storage connected to node i. Indicates that no mobile energy storage is connected to node i, and stipulates that a node can be connected to at most one mobile energy storage; The upper limit of the mobile energy storage quantity in the pre-layout stage; The active power transmitted to the upper grid of node i at time t; is the active power discharged by the mobile energy storage at node i at time t; is the charging active power of mobile energy storage at node i at time t; is the active load of node i at time t; is the active load removed from node i at time t; l is the line set; ij is the line flowing from node i to node j; is the transmission active power of line ij at time t; is the transmission active power of line k'i at time t; k'i is the line flowing from node k' to node i; is the resistance of line k'i at time t; is the square of the current amplitude of line k'i at time t; is the reactive power transmitted by the upper grid of node i at time t; is the discharged reactive power of mobile energy storage at node i at time t; is the charging reactive power of the mobile energy storage at node i at time t; is the reactive load of node i at time t; is the reactive load removed from node i at time t; is the transmission reactive power of line ij at time t; is the transmission reactive power of line k'i at time t; is the reactance of line k'i at time t; is the square of the voltage amplitude at node j at time t; M is a constant; is a 0-1 variable, indicating the on / off state of the switch in line ij at time t; V i sqr,t is the square of the voltage amplitude at node i at time t; is the resistance of line ij at time t; is the reactance of line ij at time t; is the square of the current amplitude of line ij at time t; The maximum active power transmitted to the upper grid; is the maximum power transmitted by the upper power grid; P min is the minimum active power of the line; P max is the maximum active power of the line; Q min is the minimum reactive power of the line; Q max is the maximum reactive power of the line; is the minimum value of the square of the voltage amplitude; is the maximum value of the square of the voltage amplitude; is the maximum value of the square of the current amplitude; It is a radial constraint auxiliary 0-1 variable. When the line ij is connected and j is the parent node of i, otherwise, is a radial constraint auxiliary 0-1 variable. When the distribution network line ji is connected and i is the parent node of j, otherwise, N i is the set of nodes directly connected to i; It is the radial constraint auxiliary 0-1 variable of the distribution network line flowing from distribution network node 1 to distribution network node j; sub is the set of nodes connected to the substation node.

5. The pre-disaster mobile energy storage configuration method for highway energy systems according to claim 1 is characterized in that: According to the pre-disaster deterministic mobile energy storage configuration model, considering the uncertainty of DG output, a two-stage robust optimization configuration model for pre-disaster mobile energy storage is constructed, specifically: According to the pre-disaster deterministic mobile energy storage configuration model, a box-type uncertainty set considering the uncertainty of DG output is constructed; According to the box type uncertainty set, a two-stage robust optimization configuration model for pre-disaster mobile energy storage is constructed.

6. The pre-disaster mobile energy storage configuration method for highway energy systems according to claim 5 is characterized in that: The expression of the box-type uncertainty set considering the uncertainty of DG output is: Among them, U is the box-type uncertainty set considering the uncertainty of DG output; u is the uncertain variable; is the actual active power output of the photovoltaic power station at the node i at time t; is the actual active power output of the wind turbine at node i at time t; N T is the scheduling period; is the predicted active power output of photovoltaic power station i at time t; It is the maximum fluctuation deviation allowed for photovoltaic output, which is a positive number; is the actual active power output of the wind turbine at node i at time t; Predict the active power output of the wind turbine at node i at time t; It is the maximum fluctuation deviation allowed for wind power output, which is a positive number.

7. The pre-disaster mobile energy storage configuration method for highway energy systems according to claim 5 is characterized in that: The expression of the two-stage robust optimization configuration model of pre-disaster mobile energy storage is: in, is the value of x when {·} is minimum; x is the optimization variable; is the mobile energy storage dispatch cost function in the first stage; is the load loss cost function of the second stage; T is the transpose; a, b, c, c1, c2, d, d1, d3, d5, e, e1, f, h and n1 are all constant column vectors; is the value of u when {·} is maximum; u is an uncertain variable; U is the box-type uncertainty set considering the uncertainty of DG output; is the value of y1 and y2 when {·} is minimum; y1 and y2 are both optimization variables; Ω(x,u) is the feasible domain of optimization variables y1 and y2; A, B, B1, B2, B3, B 3_1 , B4, B 4_1 , B5, B 5_1 , B6, B7, B8, B 10 , B 11 , B 12 , B 15 , B 16 and B 17 is the coefficient matrix of the variables under the corresponding constraints; K m is the coefficient matrix corresponding to the mth second-order cone constraint; M m is the coefficient column vector corresponding to the mth second-order cone constraint; ||·||2 is the L2 norm; is the mobile energy storage dispatch position 0-1 variable, When , it means that there is only one mobile energy storage connected to node i. Indicates that no mobile energy storage is connected to node i, and stipulates that a node can be connected to at most one mobile energy storage; is the active load removed from node i at time t; is the reactive load removed from node i at time t; is a 0-1 variable, if the mobile energy storage is charged at time t, it is 1, otherwise, it is 0; is a 0-1 variable, if the mobile energy storage is discharged at time t, it is 1, otherwise, it is 0; is a 0-1 variable, indicating the on / off state of the switch in line ij at time t; It is a radial constraint auxiliary 0-1 variable. When the line ij is connected and j is the parent node of i, otherwise, is a radial constraint auxiliary 0-1 variable. When the distribution network line ji is connected and i is the parent node of j, otherwise, V i sqr,t is the square of the voltage amplitude at node i at time t; is the square of the current amplitude of line ij at time t; is the transmission active power of line ij at time t; is the transmission reactive power of line ij at time t; The active power transmitted to the upper grid of node i at time t; is the reactive power transmitted by the upper grid of node i at time t; is the active power discharged by the mobile energy storage at node i at time t; is the charging active power of mobile energy storage at node i at time t; is the discharged reactive power of mobile energy storage at node i at time t; is the charging reactive power of the mobile energy storage at node i at time t; is the mobile energy storage capacity at node i at time t; is the actual active power output of the wind turbine at node i at time t; is the actual reactive power output of the wind turbine at node i at time t; is the actual active power output of the photovoltaic power station at the node i at time t; is the actual reactive power output of the photovoltaic node i at time t; N T The scheduling cycle.

8. The pre-disaster mobile energy storage configuration method for highway energy systems according to claim 7 is characterized in that: Based on the column constraint generation algorithm, the two-stage robust optimization configuration model of mobile energy storage before disaster is decoupled to generate the main problem model and sub-problem model, which are specifically: Based on the column constraint generation algorithm, the two-stage robust optimization configuration model of pre-disaster mobile energy storage is decomposed to obtain the main problem model; the expression of the main problem model is: Among them, α is an auxiliary variable; x k , and are all optimization variables obtained after the kth iteration; k is the number of iterations to generate variables and constraints; is the value of the uncertain variable u in the worst scenario obtained after the kth iteration; The two-stage robust optimization configuration model of mobile energy storage before disaster is decoupled to obtain the sub-problem form: Among them, λ, υ, π, ψ, β, μ, ξ, σ, θ, δ, ω, γ m and are the dual variables of the corresponding constraints respectively; Using strong duality theory, the minimization problem in the form of subproblems Transformed into a maximization problem and compared with the outer maximization problem Combining them, we get the dual problem: According to the box-type uncertainty set considering the uncertainty of DG output, the uncertainty set is obtained: Among them, U' is the uncertainty set; u is the uncertain variable; is the actual active power output of the photovoltaic power station at the node i at time t; is the actual active power output of the wind turbine at node i at time t; T is the transposition; N T is the scheduling period; is the predicted active power output of photovoltaic power station i at time t; It is a 0-1 variable. When the value is 1, the photovoltaic output reaches the lower limit, and when the value is 0, the photovoltaic output is equal to the predicted value; It is the maximum fluctuation deviation allowed for photovoltaic output, which is a positive number; is the actual active power output of the wind turbine at node i at time t; Predict the active power output of the wind turbine at node i at time t; It is a 0-1 variable. When the value is 1, the fan output reaches the lower limit. When the value is 0, the fan output is equal to the predicted value. is the maximum fluctuation deviation allowed for wind power output, which is a positive number; Γ PV and Γ WT All are uncertainty adjustment parameters; Substitute the uncertainty set into the dual problem and use the big-M method to linearize it to get the subproblem model.

9. The pre-disaster mobile energy storage configuration method for highway energy systems according to claim 8 is characterized in that: The expression of the sub-problem model is: in, B' DG , B DG , and are all auxiliary variables introduced; M max is the upper bound of the dual variable, a positive real number.

10. The pre-disaster mobile energy storage configuration method for highway energy systems according to claim 1, characterized in that: The generated main problem model and sub-problem model are continuously iterated to obtain the optimal solution of the pre-disaster mobile energy storage configuration. Specifically, the main problem model and the sub-problem model are solved by the C&CG algorithm to obtain the optimal solution of the pre-disaster mobile energy storage configuration, including the following steps: Step 1: Set the upper and lower bounds of the objective function C of the pre-disaster deterministic mobile energy storage configuration model as UB = +∞ and LB = -∞, respectively, and give a set of initial values ​​u under the worst case, and the number of iterations k = 1; Step 2: Based on the worst case scenario Solve the main problem model and obtain the solution α of the main problem model k 、x k , and And update the lower bound LB to α k ; is the value of the uncertain variable u in the worst scenario after the kth iteration; x k , and are the optimization variables obtained after the kth iteration; α k is the value of the auxiliary variable α obtained after the kth iteration; Step 3: The solution x of the main problem model obtained in step 2 k Substitute it into the subproblem model to obtain the objective function value f of the subproblem model k (x k ) and the value of the uncertain variable u in the worst case of the main problem model And update the upper bound UB to min{UB,f k (x k )}: Among them, λ, υ, π, ψ, β, μ, ξ, σ, θ, δ, ω, γ m and are the dual variables of the corresponding constraints; c, d, d1, d3, d5, e, e1, f, h and n1 are all constant column vectors; B 12 , B 16 and B 17 is the coefficient matrix of the variables under the corresponding constraints; and B' DG All are auxiliary variables introduced; is the value of the uncertain variable u in the worst scenario after the k+1th iteration; T is the transpose; Step 4: Given the convergence threshold value ε, if UB-LB≤ε is satisfied, stop the iteration and output the optimal solution x k and That is, the optimal solution for the mobile energy storage configuration before the disaster; otherwise, add the optimal cutting plane constraint And jump to step 2 to continue execution until the algorithm converges and meets the conditions; c2 is a constant column vector; is the optimization variable obtained after the k-1th iteration.

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