Distributed robust optimal configuration method, device and equipment for mobile energy storage and medium

Abstract

The invention discloses a distribution robust optimization configuration method, device and equipment for mobile energy storage and a medium, and relates to the technical field of power management, the method is applied to a power system, the power system comprises a power transmission network and a power distribution network, and the method comprises the following steps: obtaining a power load curve of the power transmission network and the power distribution network in a future time period of a target area and a renewable energy output prediction result; determining a candidate mobile energy storage station set and a corresponding optimization decision variable, obtaining an uncertainty fuzzy set based on a renewable energy output prediction result and a corresponding generator set participation factor, and establishing a multi-scale topological structure model covering a power transmission level and a power distribution level at the same time; and determining a reconstructed target topological structure model based on a preset linearization relaxation strategy, optimizing the decision variable based on a preset target function, the target topological structure model, the uncertainty fuzzy set and the candidate mobile energy storage station set, and determining a mobile energy storage configuration scheme. According to the invention, the overall toughness, anti-interference capability and rapid recovery capability of the power grid are enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power management, in particular to a mobile energy storage distributed robust optimization configuration method, device, equipment and medium. BACKGROUND

[0002] When the traditional power system is faced with large-scale disasters (extreme natural disasters or man-made disaster events), the original static resource configuration and linear response mechanism can hardly meet the actual demand, and it is easy to cause cascading failure, leading to large-scale power failure or even systemic collapse. At the same time, the large-scale access of renewable energy makes the power transmission and distribution collaborative power system present stronger distributed characteristics, which enhances the local power supply capacity and improves the diversity of system resources, but also introduces significant operating uncertainty.

[0003] And with the large-scale access of renewable energy, the power output on the power supply side increasingly depends on weather conditions, showing strong volatility and poor predictability, further exacerbating the uncertainty of power supply before disasters. Under this background, how to reasonably configure resources with adjustment capacity before disasters has become the key to ensuring power supply continuity. SUMMARY

[0004] Therefore, the purpose of the present application is to provide a mobile energy storage distributed robust optimization configuration method, device, equipment and medium, which can face complex disaster scenarios and resource uncertainty conditions, realize efficient pre-deployment and linkage deployment of energy storage resources, enhance the overall anti-disturbance and rapid recovery ability of the power grid, improve the adaptability and robustness of the mobile energy storage configuration scheme in various operating situations, thereby improving the system resilience and reducing the system load shedding cost. The specific scheme is as follows:

[0005] In a first aspect, the present application provides a mobile energy storage distributed robust optimization configuration method applied to a power system, wherein the power system includes a power transmission grid and a power distribution grid, and the method comprises:

[0006] Based on the power load curve and the renewable energy output prediction result of the power transmission grid and the power distribution grid in the target area within the future time period, a candidate mobile energy storage site set is determined, and an optimization decision variable corresponding to each site in the candidate mobile energy storage site set is configured,

[0007] Based on the renewable energy output prediction result and the corresponding generator set participation factor, a fuzzy set is constructed to complete the modeling of renewable energy output uncertainty, and an uncertainty fuzzy set is obtained,

[0008] A multi-scale topological structure model covering the power transmission level and the power distribution level at the same time is established, and the multi-scale topological structure model is reconstructed based on a preset linearization relaxation strategy to determine a reconstructed target topological structure model,

[0009] determine a mobile energy storage configuration scheme based on the preset objective function, the target topology model, the uncertainty fuzzy set and the candidate mobile energy storage site set, and the optimization decision variable, and complete the distributed robust optimization configuration operation of the mobile energy storage according to the mobile energy storage configuration scheme.

[0010] Optionally, the candidate mobile energy storage site set is determined based on the power load curve and the renewable energy output prediction result of the power transmission network and the power distribution network in the target region in the future period, and an optimization decision variable corresponding to each site in the candidate mobile energy storage site set is configured, including:

[0011] The power load curve and the renewable energy output prediction result of the power transmission network and the power distribution network in the target region in the future period are obtained,

[0012] The operating parameters of each type of power grid element in the power transmission network and the power distribution network are obtained to determine a basic data set, which includes line transmission capacity information and generator set output upper and lower limit information,

[0013] The candidate mobile energy storage site set is determined based on the power load curve, the renewable energy output prediction result and a preset multiple constraint condition, and the preset multiple constraint condition includes a geographical location constraint condition, an accessibility constraint condition, a site space capacity constraint condition and an electrical topology constraint condition of the mobile energy storage site,

[0014] An optimization decision variable corresponding to each candidate mobile energy storage site in the candidate mobile energy storage site set is configured, and the optimization decision variable includes the deployment quantity, the rated capacity and the site selection decision parameter of the mobile energy storage component in each candidate mobile energy storage site.

[0015] Optionally, the fuzzy set construction is performed based on the renewable energy output prediction result and the corresponding generator set participation factor to complete the modeling of the renewable energy output uncertainty, including:

[0016] The prediction error aggregation is performed based on the renewable energy output prediction result, the corresponding generator set aggregate output information and the generator set participation factor to quantify the influence of the renewable energy output uncertainty on the operation of the power system, and the aggregation result is determined,

[0017] The fuzzy set construction is performed based on the aggregation result, the upper and lower limit information of the corresponding expectation and covariance, and the preset unimodal distribution assumption definition information to obtain the uncertainty fuzzy set.

[0018] Optionally, the establishing the multi-scale topology structure model covering the power transmission level and the power distribution level at the same time comprises:

[0019] Based on the key element information in the power system and the basic data set, the node power balance constraint, the line flow constraint, the generator output constraint, the line capacity constraint and the voltage constraint of the power transmission network are constructed to determine the first operation model corresponding to the power transmission network, and the key element information comprises the main trunk power transmission line related information, the power distribution branch related information and the power transmission and distribution interface node related information,

[0020] Based on the key element information and the basic data set, the node power balance constraint, the line flow constraint, the distributed generator output constraint, the line capacity constraint and the voltage constraint of the power distribution network are constructed to determine the second operation model corresponding to the power distribution network,

[0021] Based on the first operation model and the second operation model, the multi-scale topology structure model covering the power transmission level and the power distribution level at the same time is determined.

[0022] Optionally, the reconstructing the multi-scale topology structure model based on the preset linearization relaxation strategy comprises:

[0023] For the line flow constraint in the first operation model, the linear reconstruction is performed based on the preset linearization relaxation strategy to determine the reconstructed line flow constraint,

[0024] For the line capacity constraint in the first operation model, the two-square constraint reconstruction is performed based on the preset linearization relaxation strategy to determine the reconstructed line capacity constraint.

[0025] Optionally, the determining the mobile energy storage configuration scheme based on the preset objective function, the target topology structure model, the uncertainty fuzzy set and the candidate mobile energy storage site set, and the optimization decision variable comprises:

[0026] The preset objective function is obtained, and the preset objective function comprises a mobile energy storage configuration cost function, a load shedding penalty cost function and an uncertainty related cost function,

[0027] Based on the preset objective function, the target constraint condition, the candidate mobile energy storage site set, the optimization decision variable and the uncertainty fuzzy set, the target topology structure model is converted into a second-order cone programming problem to determine a conversion result,

[0028] Based on the conversion result and the solver, a mobile energy storage configuration scheme is determined, and the mobile energy storage configuration scheme comprises mobile energy storage component space layout information and mobile energy storage component capacity configuration information corresponding to a target mobile energy storage site.

[0029] Optionally, the method further comprises:

[0030] After completing the distributed robust optimization configuration operation of mobile energy storage according to the mobile energy storage configuration scheme, performance evaluation is performed based on a preset power grid resilience index and load demand of each node in the power transmission grid and the power distribution grid to determine a power grid performance evaluation result.

[0031] In a second aspect, the present application provides a distributed robust optimization configuration device for mobile energy storage, applied to a power system, wherein the power system comprises a power transmission grid and a power distribution grid, and the device comprises:

[0032] a site set determination module configured to determine a candidate mobile energy storage site set based on power load curves and renewable energy output prediction results of the power transmission grid and the power distribution grid in a target region within a future time period, and configure optimization decision variables corresponding to each site in the candidate mobile energy storage site set,

[0033] a fuzzy set determination module configured to perform fuzzy set construction based on the renewable energy output prediction results and corresponding generator set participation factors to complete modeling of renewable energy output uncertainty, and obtain an uncertainty fuzzy set,

[0034] a topological structure model construction module configured to establish a multi-scale topological structure model covering power transmission levels and power distribution levels at the same time, and reconstruct the multi-scale topological structure model based on a preset linearization relaxation strategy to determine a reconstructed target topological structure model,

[0035] a configuration scheme determination module configured to determine a mobile energy storage configuration scheme based on a preset target function, the target topological structure model, the uncertainty fuzzy set, and the candidate mobile energy storage site set and the optimization decision variables, and complete a distributed robust optimization configuration operation of mobile energy storage according to the mobile energy storage configuration scheme.

[0036] In a third aspect, the present application provides an electronic device, comprising:

[0037] a memory configured to save a computer program,

[0038] a processor configured to execute the computer program to implement the steps of the aforementioned distributed robust optimization configuration method for mobile energy storage.

[0039] In a fourth aspect, the present application provides a computer readable storage medium configured to save a computer program, wherein the computer program is executed by a processor to implement the steps of the aforementioned distributed robust optimization configuration method for mobile energy storage.

[0040] It can be seen that, in the present application, the power system includes a power transmission network and a power distribution network. The method includes: determining a candidate mobile energy storage site set based on the power load curve and the renewable energy output prediction result of the power transmission network and the power distribution network in the target area within the future period, configuring an optimization decision variable corresponding to each site in the candidate mobile energy storage site set, constructing a fuzzy set based on the renewable energy output prediction result and the corresponding generator set participation factor to complete the modeling of renewable energy output uncertainty, obtaining an uncertainty fuzzy set, establishing a multi-scale topological structure model covering the power transmission level and the power distribution level at the same time, and reconstructing the multi-scale topological structure model based on a predetermined linearization relaxation strategy to determine a reconstructed target topological structure model. Based on the predetermined objective function, the target topological structure model, the uncertainty fuzzy set, and the candidate mobile energy storage site set and the optimization decision variable, a mobile energy storage configuration scheme is determined, and a distributed robust optimization configuration operation of mobile energy storage is completed according to the mobile energy storage configuration scheme. That is, in the present application, based on the power transmission network and the power distribution network in the power system, the power load curve and the renewable energy output prediction result in the target area within the future period are determined to determine a candidate mobile energy storage site set, and the renewable energy output uncertainty is modeled based on the renewable energy output prediction result and the corresponding generator set participation factor to obtain an uncertainty fuzzy set. Then, a multi-scale topological structure model covering the power transmission level and the power distribution level at the same time is established, and the model is reconstructed using a predetermined linearization relaxation strategy to determine a target topological structure model. Then, based on the target topological structure model, the predetermined objective function, the uncertainty fuzzy set, and the candidate mobile energy storage site set and the optimization decision variable, a mobile energy storage configuration scheme is determined, and a configuration operation is triggered. In this way, the efficient pre-deployment and linkage deployment of energy storage resources can be realized under complex disaster scenarios and resource uncertainty conditions, the overall anti-disturbance and rapid recovery capability of the power grid is enhanced, the adaptability and robustness of the mobile energy storage configuration scheme under various operating situations are improved, thereby improving the system resilience and reducing the system load shedding cost. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor based on the provided drawings.

[0042] Figure 1 A flowchart of a distributed robust optimization configuration method of mobile energy storage is provided in the present application.

[0043] Figure 2 A system topology structure diagram of a power transmission and distribution network joint test case provided for the present application is provided.

[0044] Figure 3 A configuration cost and resilience index value diagram under different mobile energy storage system configuration quantities provided for the present application is provided.

[0045] Figure 4 A configuration cost and resilience index value diagram under different mobile energy storage system capacity configurations provided for the present application is provided.

[0046] Figure 5 A mobile energy storage distribution robust optimization configuration device structure diagram provided for the present application is provided.

[0047] Figure 6 An electronic device structure diagram provided for the present application is provided. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0049] When a traditional power system is faced with large-scale disasters (extreme natural disasters or man-made disaster events), the original static resource configuration and linear response mechanism are difficult to meet the actual demand, and are prone to cause cascading failures, leading to large-scale power outages and even systemic collapse. At the same time, the large-scale access of renewable energy makes the power transmission and distribution collaborative power system present stronger distributed characteristics, which enhances the local power supply capacity and improves the diversity of system resources, but also introduces significant operating uncertainty. And with the large-scale access of renewable energy, the output on the power supply side increasingly depends on weather conditions, showing strong volatility and poor predictability, further exacerbating the uncertainty of power supply before disasters. Under this background, how to reasonably configure resources with adjustment capacity before disasters has become the key to ensuring power supply continuity.

[0050] Therefore, the present application provides a mobile energy storage distribution robust optimization configuration scheme, which can face complex disaster scenarios and resource uncertainty conditions, realize efficient pre-deployment and linkage deployment of energy storage resources, enhance the overall anti-disturbance and rapid recovery ability of the power grid, improve the adaptability and robustness of the mobile energy storage configuration scheme under various operating situations, thereby improving the system resilience and reducing the system load shedding cost.

[0051] Reference is made to Figure 1As shown, the embodiment of the present application discloses a mobile energy storage distribution robust optimization configuration method, applied to a power system, the power system comprising a transmission network and a distribution network, the method comprising:

[0052] In step S11, based on the power load curve and the renewable energy output prediction result of the transmission network and the distribution network in the target region within the future period, a candidate mobile energy storage site set is determined, and optimization decision variables corresponding to each site in the candidate mobile energy storage site set are configured.

[0053] Specifically, in the embodiment, firstly, the power load curve and the renewable energy output prediction result of the transmission network and the distribution network in the target region within the future period are obtained, and the operation parameters of various types of power grid elements in the transmission network and the distribution network are obtained to determine a basic data set, the basic data set comprising line transmission capacity information, generator set output upper and lower limit information, based on the power load curve, the renewable energy output prediction result and a plurality of preset constraint conditions, the mobile energy storage site is screened to determine a candidate mobile energy storage site set, the plurality of preset constraint conditions comprising a geographical position constraint condition, an accessibility constraint condition, a site space capacity constraint condition and an electrical topology structure constraint condition of the mobile energy storage site, and optimization decision variables corresponding to each candidate mobile energy storage site in the candidate mobile energy storage site set are configured, the optimization decision variables comprising the deployment quantity, the rated capacity and the site selection decision parameter of the mobile energy storage component in each candidate mobile energy storage site.

[0054] That is, the embodiment takes the power load curve and the renewable energy output prediction result of the transmission and distribution network in the target region within the future period as the key input of the pre-disaster dispatching decision. On this basis, combined with the actual operation demand, the operation parameters of various types of power grid elements are extracted, including the generator unit output upper and lower limit, the line transmission capacity, the load response characteristic, etc., to construct a basic data set. Considering that there is a deviation between the predicted value and the actual output (true value), and the deviation has uncertainty and time variability, the influence of the prediction error needs to be fully reflected in the data construction process to provide support for the subsequent robust optimization dispatching and resource configuration strategy. Then, according to the multiple constraint conditions such as geographical position, accessibility, site space capacity and electrical topology structure, a candidate mobile energy storage site set with deployment feasibility is screened out. On this basis, further define and configure the related optimization decision variables, including the deployment quantity, the rated capacity and the site selection decision of the energy storage unit of each candidate site, as the important input of the optimization model solving process which influences the resource distribution and dispatching strategy.

[0055] Step S12, based on the renewable energy output prediction result and the corresponding generator set participation factor, fuzzy set construction is carried out to complete the modeling of renewable energy output uncertainty, and the uncertainty fuzzy set is obtained.

[0056] In this embodiment, after determining the candidate mobile energy storage station set, a unimodal assumption about the probability distribution form is introduced to realize the modeling of renewable energy output uncertainty, that is, based on the renewable energy output prediction result, the corresponding generator set output information and the generator set participation factor, the prediction error is aggregated to quantify the influence of renewable energy output uncertainty on the operation of the power system, and the aggregation result is determined, based on the aggregation result and the upper and lower limit information of the corresponding expectation and covariance, and the preset unimodal distribution assumption definition information, the fuzzy set is constructed to obtain the uncertainty fuzzy set.

[0057] Specifically, taking the photovoltaic generator set in the power system as an example, this embodiment constructs a unified error modeling framework based on the error between the photovoltaic set output and its predicted value, and quantifies the influence of uncertainty on the overall power grid operation through system-level error aggregation. On this basis, the participation factor of each adjustable unit is set so that it undertakes the adjustment task together according to the linear response relationship, so as to realize the full absorption of unbalanced power in the system and ensure the operation stability and robustness. That is, the controllable generator handles the photovoltaic uncertainty, and the control variable (such as the actual output of the generator) is designed as an affine function of the uncertain parameter, that is, a linear function plus a constant term. The actual photovoltaic output often deviates from its predicted value, and the output uncertainty of each photovoltaic generator set is represented as follows:

[0058] ;

[0059] In the formula, is the photovoltaic corresponding to the unit output prediction value, is the corresponding photovoltaic corresponding to the photovoltaic generator set output prediction error, is the photovoltaic corresponding to the actual value of the photovoltaic generator set at time t, represents the photovoltaic set. Therefore, the photovoltaic power prediction error of the whole system, that is, the system-wide uncertainty is modeled as:

[0060] ;

[0061] In the formula, T represents the transpose. The main reason for summing all prediction errors is that system operators can leverage this practical and easily manageable strategy to aggregate and address overall system uncertainty through the synergistic effect of dispatchable units. To effectively address uncertainty, an affine control strategy based on dispatchable units is used to calculate the actual output of each unit:

[0062] ;

[0063] ;

[0064] In the formula, As a participating factor, as in relation to the unit The relevant decision variables are used to characterize the degree of adjustment undertaken by the unit in response to photovoltaic uncertainties. Photovoltaic generator set Actual output value Photovoltaic generator set Power output forecast This is a collection of controllable generating units. The above formula establishes the relationship between the actual response of the units and... The linear relationship between them, and the system imbalance. It was completely absorbed internally.

[0065] Meanwhile, when modeling the uncertainty of renewable energy output, to improve the model's adaptability to complex distribution patterns, an uncertainty set is constructed by integrating structural information and statistical constraints. By introducing the assumption of unimodality regarding the probability distribution pattern, and supplementing it with upper and lower bounds on the expectation and covariance, a reasonable convergence and characterization of the potential distribution is achieved. That is, in this embodiment, to more effectively characterize uncertainty, a fuzzy set with probability distribution as its elements is introduced, which has stronger adaptability than stochastic programming and robust optimization. Constructing a suitable fuzzy set for a specific problem is key to achieving model solvability reconstruction. Therefore, a fuzzy set is constructed based on unimodality information and moment measures:

[0066] 1) - Definition of unimodality:

[0067] For any (Used to characterize the degree of unimodality of a probability distribution), if a multivariate distribution Regarding modal point 0 is - Unimodal, if and only if for any Borel set ,in For definition in The set of all Borel-measurable sets on, function exist The above is monotonically non-decreasing. Furthermore, random vectors... is - unimodal, where is the K-dimensional Euclidean real space, if and only if can be represented as where is also a random vector, is uniformly distributed over the interval (0, 1) and is mutually independent with .

[0068] 2) Definition of fuzzy set based on moments

[0069] Fuzzy set contains variable moment information (i.e. the first two moments), which is specifically as follows:

[0070] ;

[0071] In the formula: denotes the expectation of the probability distribution, is a given upper bound of the expectation , is a given lower bound of the expectation , is a given upper bound of , is a given lower bound of , is a given upper bound of , is a given lower bound of , where the subscript , denotes the number of random variables, denotes the expectation of the square of the th random variable, denotes the th random variable.

[0072] 3) Construction of uncertainty fuzzy set

[0073] By combining the unimodal feature and the moment information, the uncertainty fuzzy set is defined as follows:

[0074] ;

[0075] In the formula, denotes the set of probability distributions with reasonable - unimodality on , denotes the set of probability distributions with support set

[0076] ​​​​​Step S13, a multi-scale topology structure model covering the power transmission level and the power distribution level at the same time is established, and the multi-scale topology structure model is reconstructed based on a preset linearization relaxation strategy to determine a reconstructed target topology structure model.

[0077] In this embodiment, a multi-scale topology structure model covering the power transmission level and the power distribution level at the same time is established to depict the structural connection relationship and operational interaction between the two levels of networks. The model comprehensively considers key network components such as backbone transmission lines, important power distribution branches, and transmission and distribution interface nodes, to support subsequent resource layout and power flow distribution constraint modeling. That is, based on the key element information in the power system and the basic data set, the node power balance constraint, the line power flow constraint, the generator output constraint, the line capacity constraint, and the voltage constraint of the power transmission network are constructed to determine a first operational model corresponding to the power transmission network, the key element information includes backbone transmission line related information, power distribution branch related information, and transmission and distribution interface node related information, based on the key element information and the basic data set, the node power balance constraint, the line power flow constraint, the distributed generator output constraint, the line capacity constraint, and the voltage constraint of the power distribution network are constructed to determine a second operational model corresponding to the power distribution network, and based on the first operational model and the second operational model, a multi-scale topology structure model covering the power transmission level and the power distribution level at the same time is determined.

[0078] It should be understood that, regarding the construction of the multi-scale topology structure model, the specific process is as follows:

[0079] 1) Construction of the power transmission network operational model.

[0080] a. Node power balance constraint of the power transmission network:

[0081] According to the following equation, the algebraic sum of active (reactive) power input at each node of the power transmission network must be equal to the output:

[0082] ;

[0083] ;

[0084] In the formula, represents the active power output of the thermal power unit connected to node , represents the reactive power output of the thermal power unit connected to node , represents the active power exchange between the power transmission network and the power distribution network connected to node , represents the reactive power exchange between the power transmission network and the power distribution network connected to node , representing a node with a node between them, representing a node with a node between them; representing a set of transmission lines; representing the load supply ratio of a transmission grid node .

[0085] b. Line flow constraints of the transmission grid:

[0086] The AC power flow model is adopted to accurately represent the active and reactive power transmission in the backbone grid lines, which is expressed as follows:

[0087] .

[0088] wherein: represents the voltage amplitude of a transmission grid node , represents the voltage amplitude of a transmission grid node , represents the voltage phase angle difference between a node and a node , represents the conductance of a transmission line between a node and a node , represents the susceptance of a transmission line between a node and a node .

[0089] c. Generator output constraints of the transmission grid:

[0090] The operating state of a thermal power unit can be divided into two modes, i.e., being put into operation or being taken out of operation, and its power output needs to be strictly limited within a specific operating limit range:

[0091] .

[0092] wherein: is a binary decision variable, representing that the thermal power unit connected to the transmission node is in an operating state when , and in a shutdown state when , represents the upper limit of the active power output of the generator unit connected to the transmission node , represents the upper limit of the reactive power output of the generator unit, represents the lower limit of the reactive power output of the generator unit.

[0093] d. Line capacity constraints of the transmission network:

[0094] The power flow of the transmission line should satisfy the following capacity constraints:

[0095] ;

[0096] where, denotes the maximum apparent power capacity of the transmission line between node and node , is a binary decision variable representing the operating state of the line between node and node .

[0097] e. Voltage constraints of the transmission network:

[0098] To ensure that the voltage amplitude of each node in the transmission network is always maintained within the safe operating limit range, the following constraints must be enforced:

[0099] ;

[0100] where, denotes the upper limit of the voltage amplitude of the transmission node , denotes the lower limit of the voltage amplitude of the transmission node .

[0101] 2. Operation model construction of the distribution network:

[0102] a. Node power balance constraints of the distribution network:

[0103] ;

[0104] where, denotes the active power output of the distributed generator connected to the distribution network node , denotes the reactive power output of the distributed generator connected to the distribution network node , represents the active power exchange between the transmission and distribution networks at node , represents the reactive power exchange between the transmission and distribution networks at node , and satisfies and , denotes the active power flow on the distribution line between node and node , denotes the reactive power flow on the distribution line between node and node reactive power flow on distribution lines, for all distribution lines; denotes the load supply ratio of distribution network node .

[0105] b. Line flow constraints of distribution network:

[0106] The linearized DistFlow model is adopted to represent the power flow distribution in the distribution network. Given that the node voltage only appears in quadratic form, an auxiliary variable is introduced to eliminate the nonlinearity.

[0107] ;

[0108] where denotes a sufficiently large constant, is a binary decision variable representing the open state of the distribution line between node and node , denotes the resistance of the distribution line between node and node , denotes the reactance of the distribution line between node and node .

[0109] c. Distributed generator output constraints of distribution network:

[0110] Some distribution network nodes are connected to distributed generators, including mobile energy storage systems (MESS, deployed at the corresponding mobile energy storage sites in this embodiment), electric vehicle (EV) charging stations, diesel generators (DGE), and photovoltaic systems (PV). The active and reactive power outputs of these distributed generators are limited within certain operating limit ranges. Among them, the photovoltaic system operates at a fixed power factor, while the power factor of other distributed generators needs to be maintained within a specified limit range. The distributed generator constraints are as follows:

[0111] ;

[0112] where the superscript DG denotes the remaining distributed generators other than the photovoltaic system. denotes the upper limit of the active power output of the distributed generator connected to distribution network node , denotes the upper limit of the reactive power output of the distributed generator connected to distribution network node Upper limit of reactive power output of the connected DGs, Binary variable representing the state of the connected DGs at node Binary variable representing the state of the connected DGs at node Upper limit of the power factor angle of the connected DGs at node Upper limit of the power factor angle of the connected DGs at node Lower limit of the power factor angle of the connected DGs at node Lower limit of the power factor angle of the connected DGs at node Binary variable representing the state of the connected MESS at node Binary variable representing the state of the connected EVSE at node Binary variable representing the state of the connected diesel generator at node

[0113] d. Line capacity constraints of the distribution network:

[0114] Similar to the transmission network, the power transfer capacity of all distribution lines is limited by the following constraints:

[0115] ;

[0116] where, is the maximum apparent power capacity of the distribution line between node and node , is a binary decision variable representing the state of the line between node and node .

[0117] e. Voltage constraints of the distribution network:

[0118] Similar to the transmission network, the voltage magnitude of all distribution network nodes must satisfy the following safety operation constraints:

[0119] ;

[0120] where, is the upper limit of the square of the voltage magnitude of the distribution network node , is the lower limit of the square of the voltage magnitude of the distribution network node .

[0121] Afterwards, the multi-scale topological structure model is reconstructed based on a preset linearization relaxation strategy, that is, for the line flow constraint in the first operation model, linear reconstruction is performed based on the preset linearization relaxation strategy to determine the reconstructed line flow constraint, and for the line capacity constraint in the first operation model, two-square constraint reconstruction is performed based on the preset linearization relaxation strategy to determine the reconstructed line capacity constraint. That is, for the nonlinear and non-convex structure characteristics existing in the foregoing constructed original optimization model, appropriate linearization relaxation strategies (such as bilinear term replacement, absolute value linear reconstruction, and logic relationship transformation) are introduced to reconstruct the model into a mixed integer linear programming form that can be efficiently solved on a mainstream optimization platform, ensuring the availability and calculation stability of the model solution. The specific steps are as follows:

[0122] 1) Handling of transmission line flow constraints:

[0123] Under the approximation conditions of , and , the transmission line flow constraint can be linearly reconstructed as follows:

[0124] .

[0125] 2) Handling of transmission and distribution line capacity constraints:

[0126] In the following formula, the superscript can represent the transmission grid (T) parameter or the distribution grid (D) parameter. Since the transmission and distribution line capacity constraint is a quadratic circular constraint, it can be approximated as a two-square constraint, which is specifically expressed as follows:

[0127] .

[0128] In the formula, represents the maximum apparent power capacity of the transmission / distribution line between node and node ; is a binary decision variable representing the operation state of the transmission / distribution line between node and node ; represents the active power flow on the transmission line between node and node in the transmission / distribution grid; represents the reactive power flow on the transmission line between node and node in the transmission / distribution grid.

[0129] In step S14, based on the preset target function, the target topological structure model, the uncertain fuzzy set and the candidate mobile energy storage site set, the optimization decision variable, a mobile energy storage configuration scheme is determined, and the distribution robust optimization configuration operation of the mobile energy storage is completed according to the mobile energy storage configuration scheme.

[0130] In the embodiment, the overall system operation economy is taken as the optimization target, a weighted target function is constructed, which includes the configuration cost of the mobile energy storage device, the expected load shedding cost of the system and the uncertainty related cost. By means of a high-performance mathematical optimization solver, an optimal energy storage system spatial layout and capacity configuration scheme is solved, so as to realize the comprehensive optimization of resource cost and power supply reliability. That is, a preset target function is obtained, which includes a mobile energy storage configuration cost function, a load shedding penalty cost function and an uncertainty related cost function. Based on the preset target function, the target constraint condition, the candidate mobile energy storage site set, the optimization decision variable and the uncertain fuzzy set, the target topological structure model is converted into a second-order cone programming problem, so as to determine a conversion result. Based on the conversion result and the solver, a mobile energy storage configuration scheme is determined, which includes the mobile energy storage component spatial layout information and the mobile energy storage component capacity configuration information corresponding to the target mobile energy storage site.

[0131] It should be understood that, in the embodiment, in order to improve the disaster resistance of the transmission and distribution network under the limited investment budget, and based on the premise that the grid operator implements emergency dispatching by using own resources and infrastructure, an optimization model of the optimal capacity and distribution point of the mobile energy storage system is constructed. The model takes the minimization of the total cost as the target, and the cost composition includes the deployment cost of the mobile energy storage system, the load shedding penalty cost and the worst case expected cost. The key constraint condition requires that the service life of the existing power grid infrastructure must exceed the operation period of the deployed mobile energy storage system unit, so as to ensure the effectiveness of the energy storage configuration strategy in the whole planning period. The target function is expressed as follows:

[0132] ;

[0133] ;

[0134] .

[0135] In the formula, represents the capital recovery coefficient; represents the service life of the mobile energy storage system; is the annual discount rate; represents the investment cost of a single mobile energy storage system; represents the unit load shedding cost of the power transmission node . denotes the unit load shedding cost of distribution grid node ; denotes the load shedding amount of transmission grid node ; denotes the load shedding amount of distribution grid node ; is the rated power of the th mobile energy storage system; is the energy storage capacity of the th mobile energy storage system; denotes the set of load nodes of the transmission grid; denotes the set of load nodes of the distribution grid; denotes the worst-case expected cost of accommodating photovoltaic generation forecast error by controllable generation units; denotes the specified fuzzy set of uncertainty. is a binary decision variable, taking value 1 if the th mobile energy storage system is assigned to distribution grid node , and 0 otherwise. ensures that each mobile energy storage system can only be connected to a single distribution grid node (i.e., the target mobile energy storage site); the sup function generally refers to the supremum, which denotes the least upper bound of a set; denotes the unit generation cost of distributed generators at distribution grid node ; denotes the set of configured mobile energy storages; denotes the set of distribution grid nodes connected to mobile energy storages; denotes the unit power construction cost of mobile energy storages; denotes the unit capacity construction cost of mobile energy storages; denotes the expectation of a probability distribution.

[0136] Further, since it is assumed that photovoltaic prediction errors are mutually independent and have no correlation, we have:

[0137] ;

[0138] where is a binary variable representing the on-off state of the distributed generator connected to distribution grid node ; is the set of nodes configured with mobile energy storage systems; is the set of nodes configured with electric vehicle charging stations; are the sets of nodes configured with diesel generators, respectively. For this, we only need to solve the worst-case expectation problem , which can be equivalently transformed into the following second-order cone programming problem:

[0139] ;

[0140] ;

[0141] wherein, , , , , , are auxiliary variables; is the adjustment cost of the unit ; is the upper bound of the expectation of the square of the random variable, is the upper bound of the expectation of the random variable, is the lower bound of the expectation of the random variable; is the participation factor of the unit .

[0142] After the convexification of the original model, the optimization problem can be directly solved by a commercial solver to determine the optimal configuration scheme of the mobile energy storage system in the transmission and distribution network.

[0143] In addition, in the embodiment, after completing the distribution robust optimization configuration operation of the mobile energy storage according to the mobile energy storage configuration scheme, performance evaluation is performed based on the preset power grid resilience index and the load demand of each node in the transmission network and the distribution network to determine the power grid performance evaluation result. That is, to evaluate the effect of the mobile energy storage system on the resilience of the transmission and distribution network in the planning period, the resilience index of the transmission and distribution network is constructed. The calculation method of the specific index value is as follows:

[0144] ;

[0145] wherein: denotes the load demand of node ; denotes the power supply ratio of node , which is defined as the ratio of the actual power supply amount to the load demand of each node; is the set of load nodes; is the load shedding amount of node ; is the load supply ratio of node .

[0146] In summary, in this embodiment, by systematically selecting and configuring the mobile energy storage unit before the disaster occurs, the dynamic change characteristics of renewable energy output are fully considered to ensure that the resource configuration scheme has strong adaptability and robustness under various operating conditions, thereby improving the adaptability and robustness of resource layout. Relying on the controllable and mobile advantages of the energy storage system, the potential supply-demand gap is covered in advance and flexible coordination between multiple regions is achieved, enhancing the risk prevention capability of the power system in the pre-disaster stage and the recovery support capability in the post-disaster stage. Under the multi-source heterogeneous and dynamically changing power supply pattern, a practical, systematic and flexible proactive defense strategy framework is constructed, which can optimize the pre-deployment and coordinated deployment of power resources under complex disaster scenarios and resource uncertainty conditions, and comprehensively improve the anti-impact capability of the transmission and distribution network in the pre-disaster stage and the recovery foundation in the post-disaster stage, truly realizing the resilience closed-loop management from prevention to response.

[0147] Therefore, in this application, based on the transmission network and distribution network in the power system, the candidate mobile energy storage site set is determined based on the power load curve in the future period of the target region and the renewable energy output prediction result, the renewable energy output uncertainty is modeled based on the renewable energy output prediction result and the corresponding generator set participation factor to obtain the uncertainty fuzzy set, then the multi-scale topological structure model covering the transmission level and the distribution level at the same time is established, and the model is reconstructed using the preset linearization relaxation strategy to determine the target topological structure model, then the mobile energy storage configuration scheme is determined based on the target topological structure model, the preset objective function, the uncertainty fuzzy set, the candidate mobile energy storage site set and the optimization decision variable, and the configuration operation is triggered. In this way, efficient pre-deployment and linkage deployment of energy storage resources can be realized under complex disaster scenarios and resource uncertainty conditions, the overall anti-disturbance and rapid recovery capability of the power grid is enhanced, the adaptability and robustness of the mobile energy storage configuration scheme under various operating conditions are improved, thereby improving the system resilience and reducing the system load shedding cost.

[0148] The technical solutions of the embodiments of the application will be described in detail below with reference to the schematic diagrams disclosed in the Figures 2 to 4

[0149] Based on the power grid architecture shown in Figure 2 Figure 2 ​​G1, G2, G3, G4, G5, G6 in FIG. 1 represent generator sets. The mobile energy storage system can be fully deployed in the pre-allocated node with sufficient preparation time before the disaster event. The initial state of all mobile energy storage systems and electric vehicle charging piles connected to the charging station is full charge state. To verify the effectiveness of the deployment of the mobile energy storage system in improving the resilience of the transmission and distribution network, the following comparative cases are set up: 1) Case 1: As a benchmark scenario, no strategic pre-deployment of mobile energy storage systems is performed; 2) Case 2: Based on the resilience-driven criteria, the number and location of the mobile energy storage systems are optimized before the disaster. The comparison between the cases can be shown in Table 1.

[0150] Table 1

[0151]

[0152] On the basis of Case 2, the present research carried out a comprehensive sensitivity analysis to evaluate the system performance under different MESS configurations. As shown in the configuration cost and resilience index value diagram under different numbers of mobile energy storage system configurations Figure 3 and the configuration cost and resilience index value diagram under different mobile energy storage system capacity configurations Figure 4 , the analysis results reveal two key correlation laws: 1) the system resilience is positively correlated with the number of deployed mobile energy storage systems and the total energy storage capacity; 2) the forced load reduction cost is negatively correlated with these parameters. Specifically, whether the number of mobile energy storage systems is increased or the capacity of individual / cluster energy storage is improved, the resilience index can be quantitatively improved, and the economic loss of forced load reduction can be reduced. These findings quantitatively verify that by strategically expanding the configuration of mobile energy storage resources, the robustness of the power grid in response to interruption events can be effectively improved, and the operation cost can be optimized.

[0153] Therefore, in the pre-disaster active defense scenario, the present embodiment proposes a mobile energy storage system distribution robust optimization configuration strategy for improving the collaborative resilience of the transmission and distribution integrated power grid, which breaks through the limitations of traditional post-disaster dispatch response lag and rigid resource configuration. On the basis of constructing a unified transmission and distribution integrated modeling framework, the risk assessment and optimization decision mechanism are integrated, the efficient pre-deployment and linkage deployment of energy storage resources are realized, and the overall anti-disturbance and rapid recovery capability of the power grid is enhanced.

[0154] In view of the challenges brought by disaster disturbance and operation uncertainty, the fuzzy set description method under structural constraints is introduced to flexibly characterize the unpredictable disturbance within the interval, taking into account the model robustness and solvability, and avoiding the excessive assumptions of precise probability dependence or conservative boundaries in traditional methods. At the same time, this modeling method effectively reveals the transmission and distribution of uncertainty between levels, making the energy storage configuration more adaptive and defensive.

[0155] The above experimental results show that by optimizing the deployment position and configuration quantity of mobile energy storage, the system load shedding cost can be effectively reduced while significantly improving the resilience of the power grid; further analysis shows that increasing the capacity of energy storage or increasing the number of configuration units can bring additional cost savings and resilience enhancement, providing practical technical support for building power systems with forward-looking defense capabilities.

[0156] Referring to Figure 5 The embodiments of the present application also correspondingly disclose a mobile energy storage distributed robust optimization configuration device, applied to a power system, wherein the power system comprises a transmission grid and a distribution grid, and the device comprises:

[0157] A site set determination module 11 is configured to determine a candidate mobile energy storage site set based on power load curves and renewable energy output prediction results of the transmission grid and the distribution grid in a target area within a future time period, and configure optimization decision variables corresponding to each site in the candidate mobile energy storage site set,

[0158] A fuzzy set determination module 12 is configured to perform fuzzy set construction based on the renewable energy output prediction results and corresponding generator set participation factors to complete modeling of renewable energy output uncertainty, and obtain an uncertainty fuzzy set,

[0159] A topological structure model construction module 13 is configured to establish a multi-scale topological structure model covering the transmission level and the distribution level at the same time, and reconstruct the multi-scale topological structure model based on a preset linearization relaxation strategy to determine a reconstructed target topological structure model,

[0160] A configuration scheme determination module 14 is configured to determine a mobile energy storage configuration scheme based on a preset target function, the target topological structure model, the uncertainty fuzzy set, the candidate mobile energy storage site set, and the optimization decision variables, and complete a distributed robust optimization configuration operation of mobile energy storage according to the mobile energy storage configuration scheme.

[0161] It can be seen that, in the present application, the candidate mobile energy storage site set is determined based on the power transmission network and the power distribution network in the power system, the power load curve and the renewable energy output prediction result in the future period of the target area, the renewable energy output uncertainty is modeled based on the renewable energy output prediction result and the corresponding generator set participation factor to obtain the uncertainty fuzzy set, then the multi-scale topology structure model covering the power transmission level and the power distribution level at the same time is established, and the model is reconstructed by using the preset linearization relaxation strategy to determine the target topology structure model, and then the mobile energy storage configuration scheme is determined based on the target topology structure model, the preset target function, the uncertainty fuzzy set, the candidate mobile energy storage site set and the optimization decision variable, and the configuration operation is triggered. In this way, the efficient pre-deployment and linkage deployment of energy storage resources can be realized in the face of complex disaster scenarios and resource uncertainty conditions, the overall anti-disturbance and rapid recovery capability of the power grid is enhanced, the adaptability and robustness of the mobile energy storage configuration scheme in various operating situations are improved, thereby the system resilience is improved and the system load shedding cost is reduced.

[0162] In some embodiments, the site set determination module 11 can be specifically configured to: obtain the power load curve and the renewable energy output prediction result of the power transmission network and the power distribution network in the target area in the future period, obtain the operation parameters of various types of power grid elements in the power transmission network and the power distribution network to determine a basic data set, the basic data set including line transmission capacity information, generator set output upper and lower limit information, screening the mobile energy storage site based on the power load curve, the renewable energy output prediction result and a preset multiple constraint condition to determine a candidate mobile energy storage site set, the preset multiple constraint condition including geographical location constraint condition, accessibility constraint condition, site space capacity constraint condition and electrical topology structure constraint condition of the mobile energy storage site, configuring the optimization decision variable corresponding to each candidate mobile energy storage site in the candidate mobile energy storage site set, the optimization decision variable including the deployment quantity, rated capacity and site selection decision parameter of the mobile energy storage component in each candidate mobile energy storage site.

[0163] In some embodiments, the fuzzy set determination module 12 can be specifically configured to: based on the renewable energy output prediction result, the corresponding generator set set output information and the generator set participation factor, the prediction error is aggregated to quantify the influence of renewable energy output uncertainty on the operation of the power system, and the aggregation result is determined, based on the aggregation result and the upper and lower limit information of the corresponding expectation and covariance, and the single-peak distribution assumption definition information, the fuzzy set is constructed to obtain the uncertainty fuzzy set.

[0164] In some embodiments, the topology model construction module 13 can be specifically configured to: based on the key element information in the power system and the basic data set, construct node power balance constraints, line flow constraints, generator output constraints, line capacity constraints, and voltage constraints of the power transmission network to determine a first operation model corresponding to the power transmission network, the key element information including backbone power transmission line related information, distribution branch related information, and distribution and transmission interface node related information, based on the key element information and the basic data set, construct node power balance constraints, line flow constraints, distributed generator output constraints, line capacity constraints, and voltage constraints of the distribution network to determine a second operation model corresponding to the distribution network, and based on the first operation model and the second operation model, determine a multi-scale topology model covering the power transmission level and the distribution level at the same time.

[0165] In some embodiments, the topology model construction module 13 can be specifically configured to: for the line flow constraints in the first operation model, perform linear reconstruction based on a preset linearization relaxation strategy to determine reconstructed line flow constraints, and for the line capacity constraints in the first operation model, perform two-square constraint reconstruction based on a preset linearization relaxation strategy to determine reconstructed line capacity constraints.

[0166] In some embodiments, the configuration scheme determination module 14 can be specifically configured to: obtain a preset objective function, the preset objective function including a mobile energy storage configuration cost function, a load shedding penalty cost function, and an uncertainty related cost function, based on the preset objective function, a target constraint condition, the candidate mobile energy storage site set, the optimization decision variable, and the uncertainty fuzzy set, perform second-order cone programming problem conversion on the target topology model to determine a conversion result, and based on the conversion result and a solver, determine a mobile energy storage configuration scheme, the mobile energy storage configuration scheme including mobile energy storage component spatial layout information and mobile energy storage component capacity configuration information corresponding to a target mobile energy storage site.

[0167] In some embodiments, the mobile energy storage distribution robust optimization configuration apparatus can be further configured to: after completing the mobile energy storage distribution robust optimization configuration operation according to the mobile energy storage configuration scheme, perform performance evaluation based on a preset power grid resilience index and load demand of each node in the power transmission network and the distribution network to determine a power grid performance evaluation result.

[0168] Further, the embodiments of the present application also disclose an electronic device, Figure 6 FIG. 1 is a structural diagram of an electronic device 20 according to an example embodiment, and the content in the figure should not be considered as any limitation on the use range of the present application.

[0169] Figure 6 A structural schematic diagram of an electronic device 20 is provided in the embodiments of the present application. The electronic device 20 can specifically include at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25 and a communication bus 26. The memory 22 is configured to store a computer program, and the processor 21 is configured to load and execute the computer program to implement the related steps in the mobile energy distribution robust optimization configuration method disclosed in any of the foregoing embodiments. In addition, the electronic device 20 in the embodiments of the present application can be specifically an electronic computer.

[0170] In the embodiments of the present application, the power supply 23 is configured to provide working voltage for each hardware device on the electronic device 20, and the communication interface 24 is configured to create a data transmission channel between the electronic device 20 and external devices, and the communication protocol followed by the communication interface 24 can be any communication protocol applicable to the technical solution of the present application, which is not limited specifically herein. The input / output interface 25 is configured to obtain external input data or output data to the outside, and the specific interface type thereof can be selected according to the specific application needs, which is not limited specifically herein.

[0171] In addition, the memory 22 as a carrier for resource storage can be a read-only memory, a random access memory, a magnetic disk or an optical disk, etc., and the resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage mode can be temporary storage or permanent storage.

[0172] The operating system 221 is configured to manage and control each hardware device on the electronic device 20 and the computer program 222, and can be Windows Server, Netware, Unix, Linux, etc. In addition to the computer program capable of completing the mobile energy distribution robust optimization configuration method executed by the electronic device 20 disclosed in any of the foregoing embodiments, the computer program 222 can further include a computer program capable of completing other specific work.

[0173] Further, the present application further discloses a computer readable storage medium for storing a computer program, wherein the computer program is executed by a processor to implement the mobile energy distribution robust optimization configuration method disclosed in the foregoing embodiments. The specific steps of the method can refer to the corresponding contents disclosed in the foregoing embodiments, which will not be repeated here.

[0174] The embodiments in the present specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts of each embodiment can be referred to each other. For the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the related parts can refer to the method part.

[0175] Those skilled in the art will further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality, without referring to a specific sequence of operations for implementing the functions. The order of various illustrative blocks, modules, circuits, and steps may be re-arranged or otherwise implemented without departing from the spirit of the application, which is

[0176] The steps of a method or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can reside in random access memory (RAM), flash memory, read-only memory (ROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, hard disk can be used as a storage medium.

[0177] Finally, it should be noted that the terms "comprises", "comprising", or other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element. "Comprising" as used herein does not exclude the presence of additional elements or the possibility of additional steps.

[0178] The above detailed description has set forth various embodiments of the methods and devices disclosed herein. The descriptions are intended to be illustrative of the methods and devices, and are not intended to limit the scope of the disclosure. Those skilled in the art will recognize that modifications can be made to the methods and devices described without departing from the scope of the disclosure.

Claims

1. A method for optimizing the configuration of distributed bib rods in mobile energy storage, characterized in that, Applied to a power system, which includes a transmission network and a distribution network, the method includes: Based on the power load curves of the transmission network and the distribution network in the target area during future time periods and the renewable energy output forecast results, a set of candidate mobile energy storage sites is determined, and optimization decision variables corresponding to each site in the candidate mobile energy storage site set are configured. Based on the renewable energy output forecast results and the corresponding generator unit participation factors, a fuzzy set is constructed to complete the modeling of the uncertainty of renewable energy output, resulting in an uncertainty fuzzy set. A multi-scale topology model simultaneously covering both transmission and distribution levels is established, and the multi-scale topology model is reconstructed based on a preset linearization relaxation strategy to determine the reconstructed target topology model. Based on the preset objective function, the target topology model, the uncertain fuzzy set, the candidate mobile energy storage site set, and the optimization decision variables, a mobile energy storage configuration scheme is determined, and the mobile energy storage sub-bar optimization configuration operation is completed according to the mobile energy storage configuration scheme.

2. The method for optimizing the configuration of distributed bib rods in mobile energy storage according to claim 1, characterized in that, Based on the power load curves and renewable energy output forecasts of the transmission and distribution networks in the target area over future time periods, a set of candidate mobile energy storage sites is determined, and optimization decision variables corresponding to each site in the candidate mobile energy storage site set are configured, including: Obtain the power load curves and renewable energy output forecasts for the transmission and distribution networks in the target area over future time periods. The operating parameters of various power grid components in the transmission and distribution networks are obtained to determine the basic dataset, which includes line transmission capacity information and generator output upper and lower limits information. Based on the power load curve, the renewable energy output forecast results, and preset multiple constraints, mobile energy storage sites are screened to determine a set of candidate mobile energy storage sites. The preset multiple constraints include geographical location constraints, accessibility constraints, site space capacity constraints, and electrical topology constraints for the mobile energy storage sites. Configure optimization decision variables corresponding to each candidate mobile energy storage site in the candidate mobile energy storage site set. The optimization decision variables include the number of mobile energy storage components deployed, the rated capacity, and the site selection decision parameters for each candidate mobile energy storage site.

3. The method for optimizing the configuration of distributed bib rods in mobile energy storage according to claim 1, characterized in that, The process of constructing a fuzzy set based on the renewable energy output forecast results and the corresponding generator unit participation factors to model the uncertainty of renewable energy output includes: Based on the renewable energy output forecast results, the corresponding generator set output information, and generator set participation factors, forecast error aggregation is performed to quantify the impact of renewable energy output uncertainty on the operation of the power system, and the aggregation result is determined. Based on the aggregation results and the corresponding upper and lower bounds of expectation and covariance, as well as the preset unimodal distribution assumption definition information, a fuzzy set is constructed to obtain an uncertain fuzzy set.

4. The method for optimizing the configuration of distributed bib rods in mobile energy storage according to claim 2, characterized in that, The establishment of a multi-scale topology model that simultaneously covers both transmission and distribution levels includes: Based on the key component information of the power system and the basic dataset, node power balance constraints, line power flow constraints, generator output constraints, line capacity constraints, and voltage constraints of the transmission network are constructed to determine the first operating model corresponding to the transmission network. The key component information includes information related to trunk transmission lines, information related to distribution branches, and information related to transmission and distribution interface nodes. Based on the key component information and the basic dataset, node power balance constraints, line power flow constraints, distributed generator output constraints, line capacity constraints, and voltage constraints of the distribution network are constructed to determine the second operating model corresponding to the distribution network. Based on the first operating model and the second operating model, a multi-scale topology model that simultaneously covers the transmission and distribution levels is determined.

5. The method for optimizing the configuration of distributed bib rods in mobile energy storage according to claim 4, characterized in that, The reconstruction of the multi-scale topological structure model based on a preset linearization relaxation strategy includes: For the line power flow constraints in the first operating model, linear reconstruction is performed based on a preset linearization relaxation strategy to determine the reconstructed line power flow constraints. For the line capacity constraint in the first operating model, a bi-directional constraint reconstruction is performed based on a preset linearization relaxation strategy to determine the reconstructed line capacity constraint.

6. The method for optimal configuration of distributed bib rods in mobile energy storage according to any one of claims 1 to 5, characterized in that, The process of determining a mobile energy storage configuration scheme based on a preset objective function, the target topology model, the uncertain fuzzy set, the candidate mobile energy storage site set, and the optimization decision variables includes: Obtain a preset objective function, which includes a mobile energy storage configuration cost function, a load shedding penalty cost function, and an uncertainty-related cost function. Based on the preset objective function, objective constraints, the candidate mobile energy storage site set, the optimization decision variables, and the uncertain fuzzy set, a second-order cone programming problem is transformed into the target topology model to determine the transformation result. Based on the transformation results and the solver, a mobile energy storage configuration scheme is determined. The mobile energy storage configuration scheme includes the spatial layout information of the mobile energy storage components and the capacity configuration information of the mobile energy storage components corresponding to the target mobile energy storage site.

7. The method for optimizing the configuration of distributed bib rods in mobile energy storage according to claim 1, characterized in that, Also includes: After completing the optimized configuration of the mobile energy storage rods according to the mobile energy storage configuration scheme, a performance evaluation is performed based on the preset grid resilience index and the load demand of each node in the transmission network and the distribution network to determine the grid performance evaluation result.

8. A mobile energy storage distributed rod optimization configuration device, characterized in that, Applied to a power system, which includes a transmission network and a distribution network, the device includes: The site set determination module is used to determine a candidate mobile energy storage site set based on the power load curves and renewable energy output forecasts of the transmission network and the distribution network in the target area during future time periods, and to configure optimization decision variables corresponding to each site in the candidate mobile energy storage site set. The fuzzy set determination module is used to construct fuzzy sets based on the renewable energy output prediction results and the corresponding generator unit participation factors, in order to complete the modeling of the uncertainty of renewable energy output and obtain an uncertain fuzzy set. The topology model construction module is used to establish a multi-scale topology model that simultaneously covers the transmission and distribution levels, and to reconstruct the multi-scale topology model based on a preset linearization relaxation strategy to determine the reconstructed target topology model. The configuration scheme determination module is used to determine the mobile energy storage configuration scheme based on the preset objective function, the target topology model, the uncertain fuzzy set, the candidate mobile energy storage site set, and the optimization decision variables, and to complete the sub-bar optimization configuration operation of mobile energy storage according to the mobile energy storage configuration scheme.

9. An electronic device, characterized in that, include: Memory, used to store computer programs. A processor for executing the computer program to implement the mobile energy storage depletion bar optimization configuration method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store a computer program, which, when executed by a processor, implements the method for optimizing the configuration of distributed bristles for mobile energy storage as described in any one of claims 1 to 7.

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