County emergency power supply optimal configuration method, system and device, computer equipment and storage medium

Abstract

The invention discloses a county emergency power supply optimal configuration method, system and device, computer equipment and a storage medium. The method comprises the following steps: constructing a multi-type satisfaction emergency power supply optimal configuration model; constructing a county emergency power supply optimal configuration model with the highest emergency resource utilization efficiency; the multi-type satisfaction emergency power supply optimal configuration model is taken as a master party, the county emergency power supply optimal configuration model with the highest emergency resource utilization efficiency is taken as a slave party, the master party updates the strategy according to the response of the slave party, and after the strategy of the master party is updated, the slave party responds again and readjusts the strategy to maximize the utilization efficiency of the emergency resources; through multi-round iteration, the master party and the slave party alternately optimize respective objective functions until strategies of the master party and the slave party are not changed any more, an equilibrium point is achieved, and on the equilibrium point, the satisfaction degree of the master party is maximized, and the resource utilization efficiency of the slave party reaches a relatively optimal state; and determining a final optimal configuration scheme of the mobile emergency power supply according to the strategies of the master party and the slave party on the equalization point.

Description

Technical Field

[0001] The present invention belongs to the technical field of emergency power supply optimization, and specifically relates to a method, system, device, computer equipment and storage medium for optimizing the configuration of county-level emergency power supplies. Background Technique

[0002] Natural disasters (such as earthquakes, floods, typhoons, icing, etc.) and emergencies (such as critical defects) will all affect the safety and stability of the distribution network. Therefore, in order to ensure the normal operation of the power distribution network, it is necessary to strengthen the resilience of the power grid in the face of events with low probability and large losses.

[0003] At present, there are few related methods for optimizing the configuration of emergency power supplies, and usually single-objective models are used, only considering single objectives such as response speed. In reality, it is often hoped that multiple objective values reach the optimal, or find a compromise method to make multiple objectives close to the optimal. Summary of the Invention

[0004] Object of the Invention: To solve the problem that the existing methods for optimizing the configuration of emergency power supplies are optimized based on a single objective and cannot meet the current power supply optimization requirements, the present invention proposes a method, system, device, computer equipment and storage medium for optimizing the configuration of county-level emergency power supplies.

[0005] Technical Solution: In a first aspect, the present invention provides a method for optimizing the configuration of county-level emergency power supplies, including the following steps:

[0006] Construct a multi-type satisfaction emergency power supply optimization configuration model that maximizes multi-type satisfaction; the multi-type satisfaction includes: user waiting time satisfaction and load demand satisfaction;

[0007] Construct a county-level emergency power supply optimization configuration model that maximizes the utilization efficiency of emergency resources;

[0008] Taking the multi-type satisfaction emergency power supply optimization configuration model as the master party and the county-level emergency power supply optimization configuration model as the slave party, the master party updates its strategy according to the response of the slave party. After the master party's strategy is updated, the slave party responds again to readjust its strategy to maximize the utilization efficiency of emergency resources; among them, the master party's strategy is to adjust the scheduling and allocation methods of county-level emergency power supplies according to the current configuration of county-level emergency resources to maximize multi-type satisfaction; among them, the slave party's strategy is to optimize the allocation method of county-level emergency power supplies on the basis of the master party's strategy to maximize the utilization efficiency of emergency resources; through multiple rounds of iteration, the master party and the slave party alternately optimize their respective objectives until the strategies of both parties no longer change and an equilibrium point is reached;

[0009] Determine the final optimized configuration plan for county-level emergency power supplies according to the strategies of the master party and the slave party at the equilibrium point.

[0010] Furthermore, at the equilibrium point, the multi-type satisfaction of the host party is maximized, and the utilization efficiency of the emergency resources of the guest party reaches a relatively optimal state.

[0011] Furthermore, the multi-type satisfaction emergency power supply optimization configuration model is expressed as:

[0012] F = β1F1 + β2F2

[0013] In the formula, F represents the multi-type satisfaction, F1 represents the user waiting time satisfaction, F2 represents the load demand satisfaction, and β1 and β2 are the weight coefficients of the time satisfaction and the load demand satisfaction respectively, and β1 + β2 = 1;

[0014] Among them:

[0015]

[0016] F2 = a1S1 + a2S2 + a3S3

[0017] In the formula, x i , x0 respectively represent the position of node i and the initial position of the mobile energy storage vehicle cluster, n represents the number of all nodes that need emergency power supply, and T(x i , x0) represents the driving time from node i to the initial position of the mobile energy storage vehicle cluster; r i represents the power outage risk coefficient, S1 represents the satisfaction of the basic load layer of the node, S2 represents the satisfaction of the economic load layer of the node, S3 represents the satisfaction of the quality load layer of the node, a1, a2, and a3 are the weight coefficients of the basic load layer, the economic load layer, and the quality load layer respectively, and a1 + a2 + a3 = 1; the basic load layer, the economic load layer, and the quality load layer are classified according to the load nature and importance of the node.

[0018] Furthermore, the power outage risk coefficient r i is expressed as:

[0019] r i = p i ·C i

[0020]

[0021] In the formula, p i represents the power outage probability of node i, C i represents the loss value caused by the power outage of node i, represents the number of power outage losses per unit time and per unit power of node i, P i is the power size required for the power outage of node i, and P′ is the power size provided by the self-provided power supply equipment for emergency of node i.

[0022] Furthermore, the satisfaction degree S1 of the basic load layer of the node is expressed as:

[0023]

[0024] In the formula, p 1i represents the power of the mobile energy storage vehicle provided for the basic load layer of node i; F i represents the basic load demand level of node i; represents the power provided by the self-provided power supply equipment of node i for the basic load layer; d 1i is the load power required for the basic load layer of node i, and N represents the total number of all nodes that need to consider the basic load supply situation;

[0025] The satisfaction degree S2 of the economic load layer of the node is expressed as:

[0026]

[0027] In the formula, p 2i represents the power of the mobile energy storage vehicle provided for the economic load layer of node i; E i represents the economic load demand level of node i; represents the power provided by the self-provided power supply equipment of node i for the economic load layer; d 2i is the load power required for the economic load layer of node i, and N represents the total number of all nodes that need to consider the economic load supply situation;

[0028] The satisfaction degree S3 of the quality load layer of the node is expressed as:

[0029]

[0030] In the formula, p 3i represents the power of the mobile energy storage vehicle provided for the quality load layer of node i; Q i represents the quality load demand level of node i; represents the power provided by the self-provided power supply equipment of node i for the quality load layer; d 3i represents the load power required for the quality load layer of node i; N represents the total number of all nodes that need to consider the quality load supply situation.

[0031] Furthermore, the constraint conditions of the multi-type satisfaction emergency power supply optimization configuration model include:

[0032]

[0033] d 1i + d 2i + d 3i ≤ d all,i - p own,i

[0034] 0 ≤ p 1i ≤ d 1i

[0035] 0 ≤ p 2i ≤ d 2i

[0036] 0 ≤ p 3i ≤ d 3i

[0037]

[0038] In the formula, V im represents the number of the m-th type of mobile energy storage vehicle provided by the power supply company for node i, M represents the total number of types of mobile energy storage vehicles, and P m represents the output power of the m-th type of mobile energy storage vehicle; d all,i represents the total load of node i, and p own,i represents the total power generation reserved by the power supply equipment self-owned by node i; Z k represents the number of the k-th type of mobile energy storage vehicle.

[0039] Furthermore, the optimal configuration model of the county emergency power supply is expressed as:

[0040] F3 = γ1M1 + γ2M2 + γ3M3

[0041] In the formula, M1, M2, and M3 respectively represent the matching degrees of the capacity of the mobile energy storage vehicle with the demands of the basic load layer, economic load layer, and quality load layer; γ1, γ2, and γ3 respectively represent the weight coefficients of the matching degrees of the demands of the basic load layer, economic load layer, and quality load layer.

[0042] Furthermore, the matching degree of the capacity of the mobile energy storage vehicle with the demand of the basic load layer is expressed as:

[0043]

[0044] In the formula, p 1i represents the power of the basic load layer provided by the mobile energy storage vehicle for node i; F i represents the basic load demand level of node i; d 1i represents the load power of the basic load layer demand of node i;

[0045] The matching degree of the capacity of the mobile energy storage vehicle with the demand of the economic load layer is expressed as:

[0046]

[0047] In the formula, p 2i represents the power of the economic load layer provided by the mobile energy storage vehicle for node i; E iIndicates the economic load demand level of node i; d 2i Indicates the load power of the economic load layer demand of node i;

[0048] The matching degree between the capacity of the mobile energy storage vehicle and the demand of the quality load layer is expressed as:

[0049]

[0050] In the formula, p 3i Indicates the quality load layer power provided by the mobile energy storage vehicle for node i, Q i Indicates the quality load demand level of node i, d 3i Indicates the load power of the quality load layer demand of node i.

[0051] Furthermore, the constraint conditions of the county emergency power supply optimization configuration model include:

[0052]

[0053] d 1i +d 2i +d 3i ≤d all,i -p own,i

[0054] 0≤p 1i ≤d 1i

[0055] 0≤p 2i ≤d 2i

[0056] 0≤p 3i ≤d 3i

[0057]

[0058] In the formula, V im Indicates the number of the m-th type of mobile energy storage vehicle provided by the power supply company for node i, M represents the total number of types of mobile energy storage vehicles P m Indicates the output power of the m-th type of mobile energy storage vehicle; d all,i Indicates the total load of node i, p own,i Indicates the total power generation reserved by the power supply equipment self-owned by node i; Z k Indicates the number of the k-th type of mobile energy storage vehicle.

[0059] On the second aspect, the present invention provides a county emergency power supply optimization configuration system, including:

[0060] An emergency power supply optimization configuration model is used to output an emergency power supply optimization configuration plan for a county according to an emergency power supply optimization configuration method for a county considering multi-type satisfaction and resource utilization rate disclosed above;

[0061] A mobile energy storage vehicle is used to provide power for each node according to the emergency power supply optimization configuration plan for a county.

[0062] In a third aspect, the present invention provides a device for optimizing the configuration of emergency power supplies for a county, including:

[0063] A multi-type satisfaction emergency power supply optimization configuration module is used to construct a multi-type satisfaction emergency power supply optimization configuration model that maximizes multi-type satisfaction; the multi-type satisfaction includes: user waiting time satisfaction and load demand satisfaction;

[0064] A county emergency power supply optimization configuration module is used to construct a county emergency power supply optimization configuration model that maximizes the utilization efficiency of emergency resources;

[0065] An optimization configuration plan output module for emergency power supplies in a county is used to take the multi-type satisfaction emergency power supply optimization configuration model as the main party and the county emergency power supply optimization configuration model as the subordinate party. The main party updates its strategy according to the response of the subordinate party. After the strategy of the main party is updated, the subordinate party responds again to readjust its strategy to maximize the utilization efficiency of emergency resources; among them, the strategy of the main party is to adjust the scheduling and allocation methods of emergency power supplies in the county according to the current configuration of emergency resources in the county to maximize multi-type satisfaction; among them, the strategy of the subordinate party is to optimize the allocation method of emergency power supplies in the county on the basis of the strategy of the main party to maximize the utilization efficiency of emergency resources; through multiple rounds of iteration, the main party and the subordinate party alternately optimize their respective goals until the strategies of both parties no longer change and an equilibrium point is reached; according to the strategies of the main party and the subordinate party at the equilibrium point, the final optimization configuration plan for emergency power supplies in the county is determined.

[0066] Further, at the equilibrium point, the multi-type satisfaction of the main party is maximized, and the utilization efficiency of the emergency resources of the subordinate party reaches a relatively optimal state.

[0067] Further, the multi-type satisfaction emergency power supply optimization configuration model is expressed as:

[0068] F = β1F1 + β2F2

[0069] In the formula, F represents multi-type satisfaction, F1 represents user waiting time satisfaction, F2 represents load demand satisfaction, β1 and β2 are respectively the weight coefficients of time satisfaction and load demand satisfaction, and β1 + β2 = 1;

[0070] Among them:

[0071]

[0072] F2 = a1S1 + a2S2 + a3S3

[0073] In the formula, x i , x0 respectively represent the position of node i and the initial position of the mobile energy storage vehicle cluster, n represents the number of all nodes that require emergency power supply, T(x i , x0) represents the driving time from node i to the initial position of the mobile energy storage vehicle cluster; r i represents the power outage risk coefficient, S1 represents the satisfaction degree of the basic load layer of the node, S2 represents the satisfaction degree of the economic load layer of the node, S3 represents the satisfaction degree of the quality load layer of the node, a1, a2, and a3 respectively represent the weight coefficients of the basic load layer, economic load layer, and quality load layer, and a1 + a2 + a3 = 1; the basic load layer, economic load layer, and quality load layer are classified according to the load nature and importance degree of the node.

[0074] Furthermore, the power outage risk coefficient r i is expressed as:

[0075] r i = p i ·C i

[0076]

[0077] In the formula, p i represents the power outage probability of node i, C i represents the loss value caused by the power outage of node i, represents the number of power outage losses per unit time and per unit power of node i, P i is the power magnitude required for the power outage of node i, and P′ is the power provided by the self-provided power supply equipment for emergency of node i.

[0078] Furthermore, the satisfaction degree S1 of the basic load layer of the node is expressed as:

[0079]

[0080] In the formula, p 1i represents the power of the basic load layer provided by the mobile energy storage vehicle for node i; F i represents the basic load demand level of node i; represents the power provided by the self-provided power supply equipment of node i for the basic load layer; d 1i is the load power required for the basic load layer of node i, and N represents the total number of all nodes that need to consider the basic load supply situation;

[0081] The satisfaction degree S2 of the economic load layer of the node is expressed as:

[0082]

[0083] In the formula, p 2i represents the economic load layer power provided by the mobile energy storage vehicle for node i; E i represents the economic load demand level of node i; represents the power provided by the self-provided power supply equipment of node i for the economic load layer; d 2i is the load power demanded by the economic load layer of node i. N represents the total number of nodes that need to consider the economic load supply situation;

[0084] The satisfaction degree S3 of the quality load layer of the node is expressed as:

[0085]

[0086] In the formula, p 3i represents the quality load layer power provided by the mobile energy storage vehicle for node i; Q i represents the quality load demand level of node i; represents the power provided by the self-provided power supply equipment of node i for the quality load layer; d 3i represents the load power demanded by the quality load layer of node i; N represents the total number of nodes that need to consider the quality load supply situation.

[0087] Furthermore, the constraint conditions of the multi-type satisfaction emergency power supply optimization configuration model include:

[0088]

[0089] d 1i +d 2i +d 3i ≤d all,i -p own,i

[0090] 0 ≤ p 1i ≤ d 1i

[0091] 0 ≤ p 2i ≤ d 2i

[0092] 0 ≤ p 3i ≤ d 3i

[0093]

[0094] In the formula, V im represents the number of the m-th type of mobile energy storage vehicle provided by the power supply company for node i, and M represents the total number of types of mobile energy storage vehicles. P mrepresents the output power of the m-th mobile energy storage vehicle; d all,i represents the total load of node i, p own,i represents the total power generation reserved by the power supply equipment self-owned by node i; Z k represents the number of the k-th mobile energy storage vehicle.

[0095] Furthermore, the optimized configuration model of the county emergency power supply is expressed as:

[0096] F3 = γ1M1 + γ2M2 + γ3M3

[0097] In the formula, M1, M2, and M3 respectively represent the matching degrees of the capacity of the mobile energy storage vehicle with the demands of the basic load layer, economic load layer, and quality load layer; γ1, γ2, and γ3 respectively represent the weight coefficients of the matching degrees of the demands of the basic load layer, economic load layer, and quality load layer.

[0098] Furthermore, the matching degree of the capacity of the mobile energy storage vehicle with the demand of the basic load layer is expressed as:

[0099]

[0100] In the formula, p 1i represents the power of the basic load layer provided by the mobile energy storage vehicle for node i; F i represents the basic load demand level of node i; d 1i represents the load power of the demand of the basic load layer of node i;

[0101] The matching degree of the capacity of the mobile energy storage vehicle with the demand of the economic load layer is expressed as:

[0102]

[0103] In the formula, p 2i represents the power of the economic load layer provided by the mobile energy storage vehicle for node i; E i represents the economic load demand level of node i; d 2i represents the load power of the demand of the economic load layer of node i;

[0104] The matching degree of the capacity of the mobile energy storage vehicle with the demand of the quality load layer is expressed as:

[0105]

[0106] In the formula, p 3i represents the power of the quality load layer provided by the mobile energy storage vehicle for node i, Q i represents the quality load demand level of node i, d 3i represents the load power of the demand of the quality load layer of node i.

[0107] Further, the constraint conditions of the county emergency power supply optimization configuration model include:

[0108]

[0109] d 1i +d 2i +d 3i ≤d all,i -p own,i

[0110] 0 ≤ p 1i ≤ d 1i

[0111] 0 ≤ p 2i ≤ d 2i

[0112] 0 ≤ p 3i ≤ d 3i

[0113]

[0114] In the formula, V im represents the number of the m-th type of mobile energy storage vehicle provided by the power supply company for node i, M represents the total number of types of mobile energy storage vehicles, P m represents the output power of the m-th type of mobile energy storage vehicle; d all,i represents the total load of node i, p own,i represents the total power generation reserved by the self-provided power supply equipment at node i; Z k represents the number of the k-th type of mobile energy storage vehicle.

[0115] Fourthly, the present invention provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of a county emergency power supply optimization configuration method are implemented.

[0116] Fifthly, the present invention provides a storage medium, which stores a county emergency power supply optimization configuration program. When the county emergency power supply optimization configuration program is executed by at least one processor, the steps of a county emergency power supply optimization configuration method are implemented.

[0117] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0118] (1) By constructing an optimization configuration model for multi-type satisfaction emergency power supplies, the method of the present invention can simultaneously consider the satisfaction of user waiting time and the satisfaction of load restoration degree, thus more comprehensively meeting user needs, improving the overall satisfaction level. Combining with the model considering the utilization efficiency of emergency resources, it ensures that emergency resources can be optimally matched with load demands at different levels, improves the utilization efficiency of emergency resources, and reduces resource waste.

[0119] (2) The method of the present invention provides a clear and quantitative optimization framework for decision-makers, making the decision-making of emergency power supply configuration more scientific and reasonable, and contributing to improving the efficiency and effect of emergency response. Through the optimization configuration by the method of the present invention, it can ensure that mobile emergency power supplies can be quickly and effectively put into use in case of emergencies, enhancing the overall emergency response ability. Description of the Drawings

[0120] Figure 1 Flow chart of an optimization configuration method for county-level emergency power supplies considering multi-type satisfaction and resource utilization rate proposed in Embodiment 1;

[0121] Figure 2 Block diagram of an optimization configuration system for county-level emergency power supplies considering multi-type satisfaction and resource utilization rate proposed in Embodiment 2. Detailed Embodiments

[0122] To make the objectives, technical solutions and advantages of the present invention clearer, the following will further explain an optimization configuration method and system for county-level emergency power supplies considering multi-type satisfaction and resource utilization rate proposed by the present invention in combination with the drawings in the present invention.

[0123] Mobile energy storage vehicles are an effective resource. Due to their large capacity and mobility characteristics, when an accident causes power outages in some areas for a period of time, mobile energy storage vehicles can act as emergency power supplies. Therefore, this embodiment proposes an optimization configuration method for county-level emergency power supplies considering multi-type satisfaction and resource utilization rate, as Figure 1 shown, mainly including the following steps:

[0124] Step 1: Construct and solve an optimization configuration model for multi-type satisfaction emergency power supplies to maximize multi-type satisfaction; the specific operations include:

[0125] Establish an objective function for user waiting time satisfaction; specifically:

[0126] When planning emergency power supply, mobile energy storage vehicles need to reach the accident site with large power outage losses in the shortest possible time. Therefore, when determining the daily parking locations of mobile energy storage fleets, it is necessary to comprehensively consider the power outage probabilities of different electricity consumption sites evaluated based on historical data. For this purpose, the concept of power outage risk value is proposed, which is the product of the power outage probability of the electricity user and the loss value caused by the power outage, expressed as:

[0127] r i =p i ·C i

[0128] In the formula, r i represents the power outage risk (yuan / min) of site i, p i represents the power outage probability of site i; C i represents the loss value caused by the power outage of site i.

[0129] For the calculation of the loss value, considering that the loss values of different sites are different, there are self-owned standby power generation devices at many important electricity consumption locations. Therefore, it is expressed as the product of the demand power excluding the standby power supply of the corresponding site and the power outage loss per unit time and per unit power, which is:

[0130]

[0131] In the formula, represents the number of power outage losses per unit time and per unit power of node i (yuan / (kW·min)), P i is the power magnitude (kW) required for the power outage of node i, and P′ is the power magnitude (kW) provided by the self-provided power supply equipment for emergency of node i.

[0132] During emergency planning, it is necessary to make the vehicle reach the site with a high power outage risk coefficient first under the constraints related to time, so as to minimize the economic loss caused by the power outage to the greatest extent. Therefore, the objective function of the selection model of the assembly location of mobile energy storage vehicles before the accident is to minimize the product of the vehicle driving time and the power outage risk coefficient, that is, the highest time satisfaction degree, expressed as:

[0133]

[0134] In the formula, T(x i , x0) represents the normal driving time from node i to the initial position of the mobile energy storage vehicle cluster; x i , x0 are the positions of node i and the initial position of the mobile energy storage vehicle cluster respectively, and n represents the total number of nodes requiring emergency power supply, that is, the number of all load nodes in the system that need mobile energy storage vehicles to provide power support.

[0135] Establish the objective function of the satisfaction degree of load restoration; specifically include:

[0136] The load of each node is divided into a basic load layer, an economic load layer, and a quality load layer, which are classified according to the nature and importance of the load. Generally, there is no unified international standard for such load classification, but it is usually divided according to the importance of the load, the requirement for continuous power supply, and the sensitivity to power supply quality.

[0137] For the basic load layer (S1), it refers to the load whose power supply interruption will have a major impact on the basic operation of society, and usually requires priority power supply guarantee.

[0138] For the economic load layer (S2), it refers to the load that has a greater impact on power supply, but short-term power outage will not immediately cause serious consequences, mainly affecting economic activities and production.

[0139] For the quality load layer (S3), it refers to the load that requires high power supply quality but has a small impact on social economy, mainly related to the comfort and experience of users.

[0140] Define the satisfaction degree S1 of the basic load layer of the node as:

[0141]

[0142] In the formula, p 1i represents the power of the basic load layer provided by the mobile energy storage vehicle for node i; F i represents the basic load demand level of node i; represents the power self-provided by node i for the basic load layer; d 1i is the load power demanded by the basic load layer of node i, and N represents the total number of nodes for which the satisfaction degree of the basic load layer needs to be calculated, that is, the total number of nodes in the system that need to consider the basic load supply situation.

[0143] Define the satisfaction degree S2 of the economic load layer of the node as:

[0144]

[0145] In the formula: p 2i represents the power of the economic load layer provided by the mobile energy storage vehicle for node i; E i represents the economic load demand level of node i; represents the power self-provided by node i for the economic load layer; d 2i is the load power demanded by the economic load layer of node i; N represents the total number of nodes for which the satisfaction degree of the economic load layer needs to be calculated, that is, the total number of nodes in the system that need to consider the economic load supply situation.

[0146] Define the satisfaction degree S3 of the quality load layer of the user as:

[0147]

[0148] In the formula: p 3i represents the power of the quality load layer provided by the mobile energy storage vehicle for node i; Q i represents the quality load demand level of node i; represents the power of node i's self-provided for the quality load layer; d 3i represents the load power required by the quality load layer of node i; N represents the total number of nodes for which the economic load layer satisfaction needs to be calculated, that is, the total number of nodes in the system that need to consider the economic load supply situation.

[0149] The load demand satisfaction of all nodes is expressed as:

[0150] F2 = a1S1 + a2S2 + a3S3

[0151] In the formula, a1, a2, and a3 are the weight coefficients of the basic load layer, economic load layer, and quality load layer respectively, and a1 + a2 + a3 = 1.

[0152] Combining the time satisfaction and the load demand satisfaction, the multi-type satisfaction objective function is obtained as:

[0153] F = β1F1 + β2F2

[0154] In the formula, β1 + β2 = 1, and β1 and β2 are the weight coefficients of the time satisfaction and the load demand satisfaction respectively.

[0155] Under the constraints of relevant constraints, a multi-type satisfaction emergency power supply optimization configuration model is established. The relevant constraints involved in this embodiment include:

[0156] (1) Mobile energy storage power supply constraint: The total power output of the mobile energy storage vehicles deployed to the power outage site should be greater than the recovery amount of the total electric energy required by the node, which is expressed as:

[0157]

[0158] In the formula, V im represents the number of the mth type of mobile battery energy storage vehicle provided by the power supply company for node i (vehicles), P m represents the output power of the mth type of mobile battery energy storage vehicle (kW);

[0159] (2) Node power balance constraint, which is expressed as:

[0160] d 1i +d 2i +d 3i ≤d all,i -p own,i

[0161] In the formula, d all,iDenote the total load of node i as p own,i Denote the total generated power reserved by node i.

[0162] (3) Power constraints corresponding to different types of load layers, expressed as: When the mobile energy storage vehicle supplies power to different load layers, the power supply cannot exceed the demand power of that load layer. Expressed as:

[0163] 0 ≤ p 1i ≤ d 1i

[0164] 0 ≤ p 2i ≤ d 2i

[0165] 0 ≤ p 3i ≤ d 3i

[0166] (4) Constraint on the number of mobile battery energy storage vehicles, expressed as:

[0167] Since the number of energy storage vehicles is limited, there is also a limit on the number of mobile energy storage vehicles that can be configured at each power outage site. Therefore, the total number of configured mobile energy storage vehicles cannot exceed the total number of mobile energy storage vehicles owned by the power supply bureau, that is:

[0168]

[0169] In the formula, Z k represents the number of the k-th type of mobile energy storage vehicle.

[0170] Use the cplex solver to solve the optimization configuration model of multi-type satisfaction emergency power supply.

[0171] Step 2: Take the highest overall emergency resource utilization rate as the objective function, optimally match the emergency resources with the load demands of different levels, and construct a county-level emergency power supply optimization configuration model that maximizes the utilization efficiency of emergency resources, expressed as:

[0172] F3 = γ1M1 + γ2M 23 M3

[0173] In the formula, M1, M2, and M3 are the matching degrees between the capacity of the mobile energy storage vehicle and the load demands of different levels; γ1, γ2, and γ3 are the weight coefficients of the demand matching degrees of the basic load layer, economic load layer, and quality load layer, respectively.

[0174] Among them, the matching degree between the capacity of the mobile energy storage vehicle and the demand of the basic load layer of the node is expressed as:

[0175]

[0176] In the formula, p 1iDenote the base load layer power provided by the mobile energy storage vehicle for node i; F i Denote the base load demand level of node i; d 1i Denote the load power demanded by the base load layer of node i.

[0177] Among them, the matching degree between the capacity of the mobile energy storage vehicle and the demand of the economic load layer of the node is expressed as:

[0178]

[0179] In the formula, p 2i Denote the economic load layer power provided by the mobile energy storage vehicle for node i; E i Denote the quality load demand level of node i; d 2i Denote the load power demanded by the economic load layer of node i.

[0180] Among them, the matching degree between the capacity of the mobile energy storage vehicle and the demand of the quality load layer of the node is expressed as:

[0181]

[0182] In the formula, p 3i Denote the quality load layer power provided by the mobile energy storage vehicle for node i, Q i Denote the quality load demand level of node i, d 3i Denote the load power demanded by the quality load layer of node i.

[0183] The constraint conditions of the county emergency power supply optimization configuration model considering the highest utilization efficiency of emergency resources are the same as those of the multi-type satisfaction emergency power supply optimization configuration model.

[0184] Use a solver to solve the emergency power supply optimization configuration model considering the utilization efficiency of emergency resources.

[0185] Step 3: Use the master-slave game method to iteratively calculate the relative optimal solutions of the two models. The specific operations include:

[0186] Take the multi-type satisfaction emergency power supply optimization configuration model as the master party, and take maximizing the satisfaction of user waiting time and the satisfaction of load restoration degree as the objective function.

[0187] Take the county emergency power supply optimization configuration model considering the highest utilization efficiency of emergency resources as the slave party, and take maximizing the overall emergency resource utilization rate F3 as the objective function.

[0188] The master party updates its strategy according to the response of the slave party, adjusts the resource allocation, so as to further improve the satisfaction of users with waiting time and load recovery. After the master party updates its strategy, the slave party responds again and readjusts its resource utilization plan to maximize the utilization efficiency of emergency resources.

[0189] Through multiple rounds of iteration, the master party and the slave party alternately optimize their respective objective functions until the strategies of both parties no longer change significantly, reaching an equilibrium point. At the equilibrium point, the satisfaction of the master party is maximized and the resource utilization efficiency of the slave party reaches a relatively optimal state.

[0190] Once the equilibrium point is reached, the current emergency resource allocation strategies of the master and slave parties are the relatively optimal solutions of the two models.

[0191] Step 4: According to the relatively optimal solutions of the two models, obtain the optimized allocation scheme of the county emergency power supply considering multiple factors.

[0192] Suppose a sudden power grid failure occurs in a county, and some areas are powered off, and mobile energy storage vehicles (emergency power supply vehicles) are needed for power supply. There are 5 affected power supply nodes (load points) in this county, and 3 mobile energy storage vehicles need to be optimized and dispatched to meet the emergency power supply demand.

[0193] The example parameters are as follows:

[0194] Load Node Demand Power (kW) Load Type Location Coordinates Standby Power Supply Node 1 50 Base Load Layer (2,3) 10 Node 2 40 Economic Load Layer (5,7) 5 Node 3 60 Quality Load Layer (8,2) 15 Node 4 30 Economic Load Layer (3,8) 8 Node 5 45 Base Load Layer (7,6) 12

[0195] Mobile Energy Storage Vehicle Power Supply Capacity (kW) Initial Location (x,y) Maximum Driving Distance Vehicle 1 60 (0,0) 10 Vehicle 2 50 (4,4) 15 Vehicle 3 40 (6,5) 12

[0196] The final configuration scheme obtained by the proposed method is as follows:

[0197] Load Node Assigned Mobile Energy Storage Vehicle Power Supply Waiting Time (min) Node 1 Vehicle 1 40 10 Node 2 Vehicle 2 30 15 Node 3 Vehicle 1 20 12 Node 4 Vehicle 3 25 18 Node 5 Vehicle 3 35 8

[0198] From the perspective of user satisfaction, compared with the traditional scheme, the user waiting time of the optimized scheme in this embodiment is reduced by 12.5%, and the load recovery degree is increased by 15%.

[0199] From the perspective of resource utilization rate, compared with the average allocation scheme, the emergency resource utilization rate of the optimized scheme in this embodiment is increased by 10%.

[0200] It can be seen that the final optimized dispatching scheme can take into account user satisfaction and resource utilization efficiency, effectively realize the accurate matching and reasonable allocation of emergency power supply, and verify the practical value of this method in emergency power supply optimization.

[0201] Embodiment 2:

[0202] As Figure 2 shown, on the basis of Embodiment 1, this embodiment proposes an optimized configuration system for county emergency power supply considering multiple types of satisfaction and resource utilization rate, which mainly includes:

[0203] An emergency power supply optimization configuration model is used to output an optimized configuration plan for county emergency power supplies according to a method for optimizing the configuration of county emergency power supplies that takes into account multiple types of satisfaction and resource utilization rate proposed in the embodiment.

[0204] Mobile energy storage vehicles are used to provide power to each node according to the optimized configuration plan of the county emergency power supply.

[0205] Embodiment 3:

[0206] Based on Embodiment 1, this embodiment proposes a device for optimizing the configuration of county emergency power supplies, including:

[0207] A multi-type satisfaction emergency power supply optimization configuration module is used to construct a multi-type satisfaction emergency power supply optimization configuration model that maximizes multi-type satisfaction; the multi-type satisfaction includes: user waiting time satisfaction and load demand satisfaction.

[0208] A county emergency power supply optimization configuration module is used to construct a county emergency power supply optimization configuration model that maximizes the utilization efficiency of emergency resources.

[0209] An optimized configuration plan output module for county emergency power supplies is used to use the multi-type satisfaction emergency power supply optimization configuration model as the master party and the county emergency power supply optimization configuration model as the slave party. The master party updates its strategy according to the response of the slave party. After the master party's strategy is updated, the slave party responds again to readjust its strategy to maximize the utilization efficiency of emergency resources; among them, the master party's strategy is to adjust the scheduling and allocation methods of county emergency power supplies according to the current configuration of county emergency resources to maximize multi-type satisfaction; among them, the slave party's strategy is to optimize the allocation method of county emergency power supplies based on the master party's strategy to maximize the utilization efficiency of emergency resources; through multiple rounds of iteration, the master party and the slave party alternately optimize their respective goals until the strategies of both parties no longer change and an equilibrium point is reached; according to the strategies of the master party and the slave party at the equilibrium point, determine the final optimized configuration plan for county emergency power supplies.

[0210] At the above-mentioned equilibrium point, the multi-type satisfaction of the master party is maximized, and the utilization efficiency of the emergency resources of the slave party reaches a relatively optimal state.

[0211] Embodiment 4:

[0212] This embodiment discloses a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the steps disclosed in any one of the above embodiments.

[0213] Embodiment 5:

[0214] This embodiment discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps disclosed in any of the above embodiments are implemented.

[0215] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0216] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0217] The above-described embodiments merely represent several implementation manners of the present application. Their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A method for optimizing the allocation of emergency power sources in a county, characterized in that: It includes the following steps: Construct a multi-type satisfaction emergency power optimization configuration model that maximizes multi-type satisfaction; the multi-type satisfaction includes: user waiting time satisfaction and load demand satisfaction; Construct a county emergency power optimization configuration model that maximizes the utilization efficiency of emergency resources; Taking the multi-type satisfaction emergency power optimization configuration model as the principal party and the county emergency power optimization configuration model as the subordinate party, the principal party updates its strategy according to the response of the subordinate party. After the strategy of the principal party is updated, the subordinate party responds again to readjust its strategy to maximize the utilization efficiency of emergency resources; among them, the strategy of the principal party is to adjust the dispatching and allocation methods of county emergency power according to the current configuration of county emergency resources to maximize multi-type satisfaction; among them, the strategy of the subordinate party is to optimize the allocation method of county emergency power on the basis of the strategy of the principal party to maximize the utilization efficiency of emergency resources; through multiple rounds of iteration, the principal party and the subordinate party alternately optimize their respective objectives until the strategies of both parties no longer change and an equilibrium point is reached; According to the strategies of the principal party and the subordinate party at the equilibrium point, determine the final optimized configuration plan for county emergency power.

2. The method for optimizing the allocation of emergency power sources in a county region according to claim 1, wherein: At the equilibrium point, the multi-type satisfaction of the principal party is maximized, and the utilization efficiency of the emergency resources of the subordinate party reaches a relatively optimal state.

3. A method for optimizing the allocation of emergency power sources in a county, as described in claim 1, characterized in that: The multi-type satisfaction emergency power optimization configuration model is expressed as: F = β1F1 + β2F2 In the formula, F represents multi-type satisfaction, F1 represents user waiting time satisfaction, F2 represents load demand satisfaction, β1 and β2 are the weight coefficients of time satisfaction and load demand satisfaction respectively, and β1 + β2 = 1; Among them: F2 = a1S1 + a2S2 + a3S3 where x i , x0 represent the position of node i and the initial position of the mobile energy storage vehicle cluster respectively, n represents the number of all nodes requiring emergency power supply, T0x i , x0 represents the driving time from node i to the initial position of the mobile energy storage vehicle cluster; r i represents the power outage risk coefficient, S1 represents the satisfaction degree of the basic load layer of the node, S2 represents the satisfaction degree of the economic load layer of the node, S3 represents the satisfaction degree of the quality load layer of the node, a1, a2, a3 represent the weight coefficients of the basic load layer, economic load layer, and quality load layer respectively, and a1 + a2 + a3 = 1; the basic load layer, economic load layer, and quality load layer are classified according to the load nature and importance degree of the node.

4. The method for optimizing the configuration of an emergency power supply in a county region according to claim 3, wherein: Power outage risk coefficient r i It is expressed as: r i = p i · C i Where p i represents the power outage probability of node i, and C i represents the loss value caused by the power outage of node i, represents the number of power outage losses per unit time and per unit power of node i, and P i is the power required for the power outage of node i, and P′ is the power provided by the self-provided power supply equipment for emergency use of node i.

5. A method for optimizing the allocation of emergency power sources in a county area according to claim 4, characterized in that: The satisfaction S1 of the basic load layer of the node is expressed as: Where p 1i represents the base load layer power provided by the mobile energy storage vehicle for node i; F i represents the base load demand level of node i; represents the power provided by the self-provided power supply equipment of node i for the base load layer; d 1i is the load power demanded by the base load layer of node i, and N represents the total number of all nodes that need to consider the base load supply situation; The satisfaction S2 of the economic load layer of the node is expressed as: Where p 2i represents the economic load layer power provided by the mobile energy storage vehicle for node i; E i represents the economic load demand level of node i; represents the power provided by the self-provided power supply equipment of node i for the economic load layer; d 2i is the load power demanded by the economic load layer of node i. N represents the total number of all nodes that need to consider the economic load supply situation; The satisfaction S3 of the quality load layer of the node is expressed as: Where p 3i represents the power of the quality load layer provided by the mobile energy storage vehicle for node i; Q i represents the quality load demand level of node i; represents the power provided by the self-provided power supply equipment of node i for the quality load layer; d 3i represents the load power required by the quality load layer of node i; N represents the total number of nodes that need to consider the quality load supply situation.

6. The method for optimizing the configuration of an emergency power supply in a county region according to claim 5, characterized in that: The constraint conditions of the multi-type satisfaction emergency power optimization configuration model include: d 1i +d 2i +d 3i ≤d all,i -p own,i 0 ≤ p 1i ≤ d 1i 0 ≤ p 2i ≤ d 2i 0 ≤ p 3i ≤ d 3i Where, V im represents the number of the m-th type of mobile energy storage vehicle provided by the power supply company for node i, M represents the total number of types of mobile energy storage vehicles, P m represents the output power of the m-th type of mobile energy storage vehicle; d all,i represents the total load of node i, p own,i represents the total power generation reserved by the power supply equipment self-owned by node i; Z k represents the number of the k-th type of mobile energy storage vehicle.

7. A method for optimizing the configuration of an emergency power supply in a county, as claimed in claim 1, wherein: The county emergency power optimization configuration model is expressed as: F3 = γ1M1 + γ2M2 + γ3M3 In the formula, M1, M2, and M3 respectively represent the matching degrees of the capacity of the mobile energy storage vehicle with the demands of the basic load layer, economic load layer, and quality load layer; γ1, γ2, and γ3 are the weight coefficients of the demand matching degrees of the basic load layer, economic load layer, and quality load layer respectively.

8. A method for optimizing the allocation of emergency power sources in a county area according to claim 7, characterized in that: The matching degree of the capacity of the mobile energy storage vehicle with the demand of the basic load layer is expressed as: Where p 1i represents the base load layer power provided by the mobile energy storage vehicle for node i; F i represents the base load demand level of node i; d 1i represents the load power demanded by the base load layer of node i; The matching degree of the capacity of the mobile energy storage vehicle with the demand of the economic load layer is expressed as: where p 2i represents the economic load layer power provided by the mobile energy storage vehicle for node i; E i represents the economic load demand level of node i; d 2i represents the load power demanded by the economic load layer of node i; The matching degree of the capacity of the mobile energy storage vehicle with the demand of the quality load layer is expressed as: Where, p 3i represents the quality load layer power provided by the mobile energy storage vehicle for node i, Q i represents the quality load demand level of node i, d 3i represents the load power demanded by the quality load layer of node i.

9. The method for optimizing the allocation of emergency power sources in a county as claimed in claim 8, wherein: The constraint conditions of the county emergency power optimization configuration model include: d 1i +d 2i +d 3i ≤d all,i -p own,i 0 ≤ p 1i ≤ d 1i 0 ≤ p 2i ≤ d 2i 0 ≤ p 3i ≤ d 3i Where, V im represents the number of the m-th type of mobile energy storage vehicle provided by the power supply company for node i, and M represents the total number of types of mobile energy storage vehicles P m represents the output power of the m-th type of mobile energy storage vehicle; d all,i represents the total load of node i, p own,i represents the total power generation reserved by the power supply equipment owned by node i; Z k represents the number of the k-th type of mobile energy storage vehicle.

10. An optimized allocation system for emergency power sources in a county, characterized in that: It includes: An emergency power optimization configuration model, which is used to output an optimized configuration plan for county emergency power according to a method for optimizing the configuration of county emergency power considering multi-type satisfaction and resource utilization rate as described in any one of claims 1 to 9; A mobile energy storage vehicle, which is used to provide power for each node according to the optimized configuration plan for county emergency power.

11. An optimized configuration device for emergency power supplies in a county, characterized in that: It includes: Multi-type satisfaction emergency power optimization configuration module, which is used to build a multi-type satisfaction emergency power optimization configuration model that maximizes multi-type satisfaction; the multi-type satisfaction includes: user waiting time satisfaction and load demand satisfaction; County emergency power optimization configuration module, which is used to build a county emergency power optimization configuration model that maximizes the utilization efficiency of emergency resources; Optimization configuration scheme output module of county emergency power supply, taking the multi-type satisfaction emergency power optimization configuration model as the master party and the county emergency power optimization configuration model as the slave party. The master party updates its strategy according to the response of the slave party. After the master party's strategy is updated, the slave party responds again to readjust its strategy to maximize the utilization efficiency of emergency resources; among them, the strategy of the master party is to adjust the scheduling and allocation methods of county emergency power according to the current configuration of county emergency resources to maximize multi-type satisfaction; among them, the strategy of the slave party is to optimize the allocation method of county emergency power on the basis of the master party's strategy to maximize the utilization efficiency of emergency resources; through multiple rounds of iteration, the master party and the slave party alternately optimize their respective goals until the strategies of both parties no longer change and an equilibrium point is reached; according to the strategies of the master party and the slave party at the equilibrium point, determine the final optimization configuration scheme of county emergency power.

12. An optimized configuration device for county-level emergency power supplies according to claim 11, characterized in that: At the equilibrium point, the multi-type satisfaction of the master party is maximized, and the utilization efficiency of the emergency resources of the slave party reaches a relatively optimal state.

13. The optimized configuration device for county emergency power supply according to claim 11, characterized in that: The multi-type satisfaction emergency power optimization configuration model is expressed as: F = β1F1 + β2F2 In the formula, F represents multi-type satisfaction, F1 represents user waiting time satisfaction, F2 represents load demand satisfaction, β1 and β2 are the weight coefficients of time satisfaction and load demand satisfaction respectively, and β1 + β2 = 1; Among them: F2 = a1S1 + a2S2 + a3S3 where x i , x0 represent the position of node i and the initial position of the mobile energy storage vehicle cluster respectively, n represents the number of all nodes requiring emergency power supply, and T(x i , x0) represents the driving time from node i to the initial position of the mobile energy storage vehicle cluster; r i represents the power outage risk coefficient, S1 represents the satisfaction degree of the basic load layer of the node, S2 represents the satisfaction degree of the economic load layer of the node, S3 represents the satisfaction degree of the quality load layer of the node, a1, a2, a3 represent the weight coefficients of the basic load layer, economic load layer, and quality load layer respectively, and a1 + a2 + a3 = 1; the basic load layer, economic load layer, and quality load layer are classified according to the load nature and importance degree of the node.

14. An optimized configuration device for county-level emergency power supplies according to claim 13, characterized in that: Power outage risk coefficient r i It is expressed as: r i = p i · C i where p i represents the power outage probability of node i, C i represents the loss value caused by the power outage of node i, represents the number of power outage losses per unit time and per unit power of node i, P i is the power required for the power outage of node i, and P′ is the power provided by the self - contained power supply equipment for emergency use of node i.

15. An optimized configuration device for county-level emergency power supplies according to claim 14, characterized in that: The satisfaction degree S1 of the basic load layer of the node is expressed as: Where p 1i represents the base load layer power provided by the mobile energy storage vehicle for node i; F i represents the base load demand level of node i; represents the power provided by the self - power supply equipment of node i for the base load layer; d 1i is the load power demanded by the base load layer of node i, and N represents the total number of all nodes that need to consider the base load supply situation; The satisfaction degree S2 of the economic load layer of the node is expressed as: Where p 2i represents the economic load layer power provided by the mobile energy storage vehicle for node i; E i represents the economic load demand level of node i; represents the power provided by the self-provided power supply equipment of node i for the economic load layer; d 2i is the load power required for the economic load layer of node i. N represents the total number of nodes that need to consider the economic load supply situation; The satisfaction degree S3 of the quality load layer of the node is expressed as: Where p 3i represents the power of the quality load layer provided by the mobile energy storage vehicle for node i; Q i represents the quality load demand level of node i; represents the power provided by the self-provided power supply equipment of node i for the quality load layer; d 3i represents the load power required by the quality load layer of node i; N represents the total number of all nodes that need to consider the quality load supply situation.

16. A county emergency power supply optimization configuration device according to claim 15, characterized in that: The constraint conditions of the multi-type satisfaction emergency power optimization configuration model include: d 1i +d 2i +d 3i ≤d all,i -p own,i 0 ≤ p 1i ≤ d 1i 0 ≤ p 2i ≤ d 2i 0 ≤ p 3i ≤ d 3i Where, V im represents the number of the m-th type of mobile energy storage vehicle provided by the power supply company for node i, and M represents the total number of types of mobile energy storage vehicles P m represents the output power of the m-th type of mobile energy storage vehicle; d all,i represents the total load of node i, and p own,i represents the total power generation reserved by the self-provided power supply equipment of node i; Z k represents the number of the k-th type of mobile energy storage vehicle.

17. The optimized configuration device for the county-level emergency power supply according to claim 11, wherein: The county emergency power optimization configuration model is expressed as: F3 = γ1M1 + γ2M2 + γ3M3 In the formula, M1, M2, and M3 respectively represent the matching degrees of the capacity of the mobile energy storage vehicle with the demands of the basic load layer, economic load layer, and quality load layer; γ1, γ2, and γ3 are the weight coefficients of the demand matching degrees of the basic load layer, economic load layer, and quality load layer respectively.

18. The optimized configuration device for county-level emergency power sources according to claim 17, characterized in that: The matching degree of the capacity of the mobile energy storage vehicle with the demand of the basic load layer is expressed as: Where p 1i represents the base load layer power provided by the mobile energy storage vehicle for node i; F i represents the base load demand level of node i; d 1i represents the load power demanded by the base load layer of node i; The matching degree of the capacity of the mobile energy storage vehicle with the demand of the economic load layer is expressed as: where p 2i represents the economic load layer power provided by the mobile energy storage vehicle for node i; E i represents the economic load demand level of node i; d 2i represents the load power demanded by the economic load layer of node i; The matching degree of the capacity of the mobile energy storage vehicle with the demand of the quality load layer is expressed as: where p 3i represents the power of the quality load layer provided by the mobile energy storage vehicle for node i, Q i represents the quality load demand level of node i, d 3i represents the load power required by the quality load layer of node i.

19. A device for optimizing the configuration of emergency power sources in a county, as described in claim 18, characterized in that: The constraint conditions of the county emergency power optimization configuration model include: d 1i +d 2i +d 3i ≤d all,i -p own,i 0 ≤ p 1i ≤ d 1i 0 ≤ p 2i ≤ d 2i 0 ≤ p 3i ≤ d 3i Where, V im represents the number of the m-th type of mobile energy storage vehicle provided by the power supply company for node i, and M represents the total number of types of mobile energy storage vehicles. P m represents the output power of the m-th type of mobile energy storage vehicle; d all,i represents the total load of node i, and p own,i represents the total power generation reserved by the self-provided power supply equipment at node i; Z k represents the number of the k-th type of mobile energy storage vehicle.

20. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a county emergency power optimization configuration method according to any one of claims 1 to 9.

21. A storage medium, characterized in that, The storage medium stores an optimization configuration program for county emergency power supplies. When the optimization configuration program for county emergency power supplies is executed by at least one processor, the steps of an optimization configuration method for county emergency power supplies according to any one of claims 1 to 9 are implemented.

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