Site selection method for mobile hybrid distribution transformer based on spatial coordinate system mapping

Abstract

The present invention discloses a method for site selection of a mobile hybrid distribution transformer based on spatial coordinate system mapping, and relates to the technical field of transformer site selection. The method comprises the following steps: S1, numbering all transformer substations, and establishing a loss space coordinate system and a transfer cost space coordinate system of the mobile hybrid distribution transformer in the transformer substation; S2, calculating the system loss and travel cost during the transfer of the mobile hybrid distribution transformer; S3, constructing a site selection model of the mobile hybrid distribution transformer based on the system loss, travel cost and index system; S4, using a genetic algorithm to find the optimal solution of the site selection model of the mobile hybrid distribution transformer, and obtaining a time-space-path deployment strategy of the mobile hybrid distribution transformer. The present invention improves the economy and efficiency of the deployment of the mobile hybrid distribution transformer, and also improves the adaptability of the power grid to changes in agricultural loads, so as to support the power system to maintain high efficiency and reliability in the face of growing and changing power demand.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer site selection, and in particular to a method for site selection of a mobile hybrid distribution transformer based on spatial coordinate system mapping. Background Art

[0002] With the rapid development of the agricultural economy and the continuous expansion of cultivated areas, the demand for agricultural irrigation is growing. The agricultural irrigation load in most agricultural areas is mainly divided into spring irrigation, summer irrigation and autumn irrigation, and the irrigation load fluctuates greatly due to weather changes.

[0003] However, the diverse power demands in various regions, especially seasonal agricultural loads and emergencies, pose a higher challenge to the coping capacity of traditional power grids. Especially during the peak period of agricultural irrigation, such as summer, the centralized power supply demand causes agricultural irrigation transformers to be overloaded, causing transformers to overheat, which in turn affects their performance and shortens their service life. In addition, the long irrigation cycle also requires frequent maintenance and overhaul of transformers to ensure their long-term stable operation, which greatly increases maintenance costs. However, the high seasonality of agricultural loads leads to frequent seasonal heavy overload problems of transformers, and traditional power grids find it difficult to quickly adapt to fluctuations in power demand.

[0004] In order to improve the power distribution and supply capacity and adapt to these changes in demand, it is urgent to develop and deploy modular and mobile transformer equipment to improve heavy overload conditions. The design of modular mobile hybrid distribution transformer equipment (HDT, Hybrid Distribution Transformer) takes into account the wheeled base or draggable structure, so that it can be easily moved and deployed in complex rural terrain, thereby effectively supporting the normal and orderly progress of agricultural production. However, despite the increasing attention to HDT technology at home and abroad, the corresponding research is still in its infancy, prompting researchers to explore more flexible and rapidly deployable solutions to adapt to diverse power needs and address the high cost and low efficiency of upgrading and transforming traditional power grids. Summary of the invention

[0005] Based on this, it is necessary to provide a mobile hybrid distribution transformer site selection method based on spatial coordinate system mapping to address the above technical problems.

[0006] The present invention provides a method for selecting a site for a mobile hybrid distribution transformer based on spatial coordinate system mapping, the method comprising the following steps:

[0007] S1. Number all transformer substations and establish the loss space coordinate system and transfer cost space coordinate system of the mobile hybrid distribution transformer in the transformer substation;

[0008] S2. Calculate the system loss and travel cost during the transfer of the mobile hybrid distribution transformer;

[0009] S3. Construct a site selection model for mobile hybrid distribution transformers based on system losses, travel costs and indicator systems;

[0010] S4. Use genetic algorithm to find the optimal solution of the mobile hybrid distribution transformer site selection model and obtain the space-time-path deployment strategy of the mobile hybrid distribution transformer.

[0011] Furthermore, establishing the loss space coordinate system and the transfer cost space coordinate system of the mobile hybrid distribution transformer in the transformer area includes the following steps:

[0012] S11. Establish a loss space coordinate system of a multi-layer plane, define the x-axis and y-axis to represent the starting transformer area number of the mobile hybrid distribution transformer at the current moment and the target transformer area number to be moved at the next moment, respectively, and the z-axis represents the number of mobile hybrid distribution transformers moved from any transformer area to another transformer area;

[0013] S12. Establish a single-layer plane transfer cost space coordinate system, define the x-axis and y-axis to represent the starting transformer substation number where the mobile hybrid distribution transformer is currently located and the target transformer substation number where it will move to at the next moment, respectively, and the z-axis represents the number of mobile hybrid distribution transformers that move from one transformer substation to another.

[0014] Furthermore, the number of plane layers in the loss space coordinate system is equal to the number of mobile hybrid distribution transformers, and the position point in the loss space coordinate system is represented as (x, y, z), and the position point in the transfer cost space coordinate system is represented as (x, y, 1).

[0015] Furthermore, the calculation of system losses and travel costs during the transfer of the mobile hybrid distribution transformer includes the following steps:

[0016] S21. Define the transformer loss formula of the transformer substation, calculate the initial system loss of the m mobile hybrid distribution transformers in the initial transformer substation at time t-1, and the target system loss of the m mobile hybrid distribution transformers in the target transformer substation at time t, and use the initial system loss to subtract the target system loss to calculate the difference in system loss before and after the transfer of the m mobile hybrid distribution transformers at time t;

[0017] S22. Record the initial moving cost of a mobile hybrid distribution transformer in the starting transformer substation at time t-1 and the target moving cost of a mobile hybrid distribution transformer in the target transformer substation at time t, and subtract the target moving cost from the initial moving cost to obtain the travel cost before and after the transfer of the mobile hybrid distribution transformer at time t.

[0018] Furthermore, the transformer loss formula is expressed as:

[0019]

[0020] In the formula, represents the transformer loss of m mobile hybrid distribution transformers in transformer area s at time t; It indicates the no-load active power loss of the transformer in the transformer area No. s; It indicates the short-circuit active power loss of the transformer in the transformer area No. s; It represents the calculated load of the transformer in the transformer section No. s at time t; Indicates the rated capacity of the transformer in the transformer area No. s; It represents the no-load active power loss of the mobile hybrid distribution transformer in the transformer area No. s; It represents the short-circuit active power loss of the mobile hybrid distribution transformer in the s transformer area; It represents the calculated load of the mobile hybrid distribution transformer in the transformer area No. s at time t; It represents the rated capacity of the mobile hybrid distribution transformer in transformer area No. s; m represents the number of mobile hybrid distribution transformers in transformer area No. s.

[0021] Furthermore, based on system loss, travel cost and index system, the construction of mobile hybrid distribution transformer site selection model includes the following steps:

[0022] S31, obtaining evaluation indicators for site selection of mobile hybrid distribution transformers, constructing an indicator system, and using a data normalization method to process data included in each evaluation indicator;

[0023] S32. Based on the analytic hierarchy process, evaluate the weight coefficient of each evaluation index;

[0024] S33. Combining the social and economic benefits of mobile hybrid distribution transformers, construct an objective function;

[0025] S34, setting constraints based on the power distribution system parameters and loss costs of the transformer area;

[0026] S35. Integrate the objective function and constraints to construct a mobile hybrid distribution transformer site selection model.

[0027] Furthermore, based on the analytic hierarchy process, the weight coefficients of each evaluation index are evaluated, including the following steps:

[0028] S321. Use hierarchical analysis calculation software to establish a hierarchical structure model;

[0029] S322, construct a judgment matrix, compare all evaluation indicators in pairs, and use a scale table to score the importance of the evaluation indicators;

[0030] S323. Score the weights of the evaluation indicators and perform weighted average processing on the scores to obtain a judgment matrix; then input it into the hierarchical analysis calculation software to calculate the weight coefficients of each evaluation indicator.

[0031] Furthermore, combining the social and economic benefits of the mobile hybrid distribution transformer, constructing the objective function includes the following steps:

[0032] S331. Obtaining social parameters during the use of the mobile hybrid distribution transformer; and calculating social benefits by weighted summation based on multiple social parameters;

[0033] S332, obtaining economic parameters in the production and movement process of the mobile hybrid distribution transformer; and calculating the economic benefits by weighted summation based on multiple economic parameters;

[0034] S333. Set the weight coefficients of social benefits and economic benefits respectively, and construct an objective function, wherein the expression of the objective function is:

[0035]

[0036] In the formula, c1 represents the weight coefficient of social benefits; c2 represents the weight coefficient of economic benefits; f1 represents social benefits; and f2 represents economic benefits.

[0037] Furthermore, the economic parameters include the loss saving cost of the mobile hybrid distribution transformer, the production cost of the mobile hybrid distribution transformer and the moving cost of the mobile hybrid distribution transformer, and the calculation expressions are:

[0038]

[0039]

[0040]

[0041] In the formula, f 2,1 represents the cost savings of mobile hybrid distribution transformer losses; f 2,2 represents the production cost of mobile hybrid distribution transformer; f 2,3 represents the moving cost of the mobile hybrid distribution transformer; P Lloss Indicates network loss; P trloss represents transformer loss; n0 represents the number of mobile hybrid distribution transformers; C represents the production cost of a unit mobile hybrid distribution transformer; c L represents the mobility cost coefficient of the mobile hybrid distribution transformer; N T represents the calculation cycle; Δt represents the step length per unit time; It represents the moving cost of a unit hybrid distribution transformer at time t.

[0042] The beneficial effects of the present invention are as follows: by constructing a comprehensive optimization objective function covering social benefit cost, network loss cost, production cost of mobile hybrid distribution transformer and travel cost, an efficient mobile hybrid distribution transformer deployment strategy is provided for the power system of the transformer substation area to solve the power supply challenges caused by the rapid development of agricultural areas; the deployment strategy takes into account the fluctuations in seasonal and sudden power demand, and adopts mobile hybrid distribution transformers to enhance the flexibility and response speed of the power grid; using genetic algorithms, the present invention has achieved remarkable results in optimizing computing efficiency and processing time, especially by implementing an hourly local optimal solution strategy, effectively reducing the computing load while maintaining the quality of the solution, and effectively improving the operation efficiency of the power grid; at the same time, the present invention not only improves the economy and efficiency of the deployment of mobile hybrid distribution transformers, but also significantly improves the adaptability of the power grid to changes in agricultural loads, so as to support the power system in the face of growing and changing power demand. In the face of continuous growth and change in power demand, it can continue to maintain high efficiency and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0044] Figure 1 is a flow chart of a method for site selection of a mobile hybrid distribution transformer based on spatial coordinate system mapping according to an embodiment of the present invention;

[0045] Figure 2 is a schematic diagram of a loss space coordinate system in a method for selecting a location for a mobile hybrid distribution transformer based on space coordinate system mapping according to an embodiment of the present invention;

[0046] Figure 3 is a schematic diagram of a transfer cost space coordinate system in a method for selecting a location for a mobile hybrid distribution transformer based on space coordinate system mapping according to an embodiment of the present invention;

[0047] Figure 4 is a schematic diagram of an indicator system in a method for selecting a site for a mobile hybrid distribution transformer based on spatial coordinate system mapping according to an embodiment of the present invention;

[0048] Figure 5 is a logic flow chart of a method for selecting a location for a mobile hybrid distribution transformer based on spatial coordinate system mapping according to an embodiment of the present invention;

[0049] Figure 6 is an objective function curve under the influence of different numbers of mobile hybrid distribution transformers according to an embodiment of the present invention;

[0050] Figure 7is a path diagram for moving a hybrid distribution transformer within 24 hours according to an embodiment of the present invention;

[0051] Figure 8 is a frequency histogram of transformer substations served by a mobile hybrid distribution transformer according to an embodiment of the present invention;

[0052] Fig. 9 is a comparison diagram of transformer loads in a transformer substation area after adding a mobile hybrid distribution transformer according to an embodiment of the present invention;

[0053] Fig.10 is a comparison diagram of transformer losses in the rear area after adding a mobile hybrid distribution transformer according to an embodiment of the present invention;

[0054] Fig.11 is a load curve of a mobile hybrid distribution transformer according to an embodiment of the present invention;

[0055] Fig.12 is the loss condition of the mobile hybrid distribution transformer according to an embodiment of the present invention;

[0056] Fig.13 is an optimal fitness function diagram according to an embodiment of the present invention;

[0057] Fig.14 is a schematic diagram of error analysis according to an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0059] See also Figure 1 , a method for selecting a mobile hybrid distribution transformer site based on spatial coordinate system mapping is provided, the method comprising the following steps:

[0060] S1. Number all transformer substations and establish the loss space coordinate system and transfer cost space coordinate system of the mobile hybrid distribution transformer in the transformer substation.

[0061] Assuming T is the time step, there are n agricultural irrigation transformer substations and n0 HDTs (mobile hybrid distribution transformers), the possible HDT ratios are: There are possibilities, and these possibilities will increase exponentially with the increase of T, n, and n0.

[0062] In the description of the present invention, establishing the loss space coordinate system and the transfer cost space coordinate system of the mobile hybrid distribution transformer in the transformer area includes the following steps:

[0063] S11. Establish a multi-layer plane loss space coordinate system, define the x-axis and y-axis to represent the starting transformer substation number where the mobile hybrid distribution transformer is currently located and the target transformer substation number where it will move to next moment, respectively, and the z-axis represents the number of mobile hybrid distribution transformers that move from one transformer substation to another.

[0064] Among them, the number of plane layers in the loss space coordinate system is equal to the number of mobile hybrid distribution transformers n0, and the position point in the loss space coordinate system is represented by (x, y, z), and the corresponding values ​​​​represent the starting position, target position and number of HDTs from the starting transformer area to the target transformer area.

[0065] At time t, m HDTs are Transfer to the station area For station area ( , ,m t ) represents the difference in system loss cost before and after the transfer, and ( , ,m t )correspond express.

[0066] The schematic diagram of the loss space coordinate system at time t is as follows Figure 2 As shown, the plane Z=m is defined as a layer, and the coordinate system has a total of n0 layers.

[0067] S12. Establish a single-layer plane transfer cost space coordinate system, define the x-axis and y-axis to represent the starting transformer substation number where the mobile hybrid distribution transformer is currently located and the target transformer substation number where it will move to at the next moment, respectively, and the z-axis represents the number of mobile hybrid distribution transformers that move from one transformer substation to another.

[0068] Among them, the location point in the transfer cost space coordinate system is represented as (x, y, 1).

[0069] A spatial rectangular coordinate system model is established for the HDT travel cost in the agricultural irrigation transformer area. The x and y axes represent the agricultural irrigation transformer area number. The x and y axes represent the starting transformer area number where the HDT is currently located and the target transformer area number to be moved at the next moment, respectively. The z axis represents the number of HDTs moving from one transformer area to another. However, unlike the spatial rectangular coordinate system for optimizing the loss of the agricultural irrigation distribution system mentioned above, if multiple HDTs move from the same location to another, the travel cost they spend is multiplied by the cost caused by one HDT, that is, the coordinate system model has only one layer. The spatial rectangular coordinate system model for the HDT travel cost in the transformer area is as follows: Figure 3 shown.

[0070] S2. Calculate the system loss and travel cost during the transfer of the mobile hybrid distribution transformer.

[0071] In the description of the present invention, the calculation of system losses and travel costs during the transfer of the mobile hybrid distribution transformer includes the following steps:

[0072] S21. Define the transformer loss formula for the transformer substation area, calculate the starting system loss of m mobile hybrid distribution transformers in the starting transformer substation area at time t-1, and the target system loss of m mobile hybrid distribution transformers in the target transformer substation area at time t, and use the starting system loss to subtract the target system loss to calculate the difference in system loss before and after the transfer of the m mobile hybrid distribution transformers at time t.

[0073] Specifically, it is known that there is no HDT acting on the s transformer area, and the calculation formula for the transformer loss of the transformer area at time t is:

[0074]

[0075] Among them, ΔP 0,s is the no-load active power loss of the transformer in area s; ΔP k,s is the short-circuit active power loss of transformer in area S; Calculate the load of the transformer in area s at time t; S N,s is the rated capacity of the transformer in area S.

[0076] In the description of the present invention, the transformer loss formula is expressed as:

[0077]

[0078] In the formula, represents the transformer loss of m mobile hybrid distribution transformers in transformer area s at time t; It indicates the no-load active power loss of the transformer in the transformer area No. s; It indicates the short-circuit active power loss of the transformer in the transformer area No. s; It represents the calculated load of the transformer in the transformer section No. s at time t; Indicates the rated capacity of the transformer in the transformer area No. s; It represents the no-load active power loss of the mobile hybrid distribution transformer in the transformer area No. s; It represents the short-circuit active power loss of the mobile hybrid distribution transformer in the s transformer area; It represents the calculated load of the mobile hybrid distribution transformer in the transformer area No. s at time t; It represents the rated capacity of the mobile hybrid distribution transformer in transformer area No. s; m represents the number of mobile hybrid distribution transformers in transformer area No. s.

[0079] In addition, the difference in system loss before and after HDT transfer at time t The calculation formula is:

[0080]

[0081] S22. Record the initial moving cost of a mobile hybrid distribution transformer in the starting transformer substation at time t-1 and the target moving cost of a mobile hybrid distribution transformer in the target transformer substation at time t, and subtract the target moving cost from the initial moving cost to obtain the travel cost before and after the transfer of the mobile hybrid distribution transformer at time t.

[0082] Specifically, the corresponding values ​​of the points (x, y, 1) in the transfer cost space coordinate system represent the starting position and target position of the HDT respectively. At time t, an HDT moves from Transfer to the station area For station area ( , ,1 t ) indicates that the moving cost before and after the transfer is ( , ,1 t )correspond express.

[0083] S3. Based on system loss, travel cost and index system, a mobile hybrid distribution transformer site selection model is constructed.

[0084] In the description of the present invention, based on system loss, travel cost and index system, constructing a mobile hybrid distribution transformer site selection model includes the following steps:

[0085] S31. Obtain the evaluation index for site selection of mobile hybrid distribution transformers, construct an index system, and use data normalization method to process the data contained in each evaluation index, such as Figure 4 shown.

[0086] Specifically, considering that in the process of formulating an indicator system that reflects the urgency of the demand for mobile hybrid distribution transformers, a large amount of data needs to be used, and the various data units and sizes are different. Therefore, the data normalization method is used to process the data under each indicator. Data normalization is one of the important links in preprocessing in machine learning. Its function is to map the data to a specific range through mathematical methods, so as to avoid the impact of the dimension on data processing. Make data with different characteristics comparable. Different data have different impacts on the indicator system, and the indicators are divided into positive indicators and negative indicators.

[0087] For positive indicators:

[0088]

[0089] For negative indicators:

[0090]

[0091] In the formula, u ij represents the normalized data of the jth transformer under the i-th indicator; x ij Represents the data of the jth transformer under the i-th indicator before normalization, (i=1,2,…,n), (j=1,2,…m).

[0092] After data normalization, the weights of the impact of each indicator system data on the objective function are consistent, avoiding the problem of some data being too large and playing a dominant role, thus shielding other data.

[0093] S32. Based on the hierarchical analysis method, the weight coefficient of each evaluation index is evaluated.

[0094] In the description of the present invention, based on the hierarchical analysis method, evaluating the weight coefficients of various evaluation indicators includes the following steps:

[0095] S321. Use hierarchical analysis calculation software to establish a hierarchical structure model.

[0096] Specifically, the present invention uses yaahp software to establish a hierarchical structure model. Yaahp software is software used for hierarchical analysis method calculation and can calculate the index weight value.

[0097] S322. Construct a judgment matrix, compare all evaluation indicators in pairs, and use a scale to score the importance of the evaluation indicators.

[0098] S323, scoring the weights of the evaluation indicators, and performing weighted average processing on the scores to obtain a judgment matrix, which is then input into the hierarchical analysis calculation software to calculate the weight coefficients of each evaluation indicator.

[0099] Specifically, the weight of the indicator is scored, the filled-in scores are added and averaged to obtain the corresponding judgment matrix values, which are input into the yaahp software to calculate the consistency ratio of the judgment matrix and the weight value of each indicator. The calculation results of the first-level and second-level indicators are shown in Table 1 (n in the table represents the indicator level, and RI represents the random consistency index).

[0100] Table 1: Indicator calculation results

[0101] n RI 1 0 2 0 3 0.58 4 0.90 5 1.12 6 1.24 7 1.32 8 1.41 9 1.45 10 1.49

[0102] For each factor of the index layer of the established farmland irrigation energy urgency index system, a judgment matrix A is constructed through expert survey, as shown below:

[0103]

[0104] In the formula, a ij is the importance of factor i compared to factor j for the previous layer target (i, j = 1, 2, ..., n). Then calculate the maximum eigenvalue λ of the judgment matrix A. max And the corresponding eigenvector U, the eigenvector U is the weight value of the influencing factor, and the specific calculation steps are as follows.

[0105] 1. Normalize each column of matrix A to get the normalized matrix B, and then add the matrix B row by row to get vector C:

[0106]

[0107]

[0108] 2. By normalizing the vector C, we can get the weight U of the influencing factor:

[0109]

[0110] 3. Because the judgment matrix A is highly subjective, a consistency test is required, and the consistency index CI is introduced:

[0111]

[0112] 4. In order to measure the size of CI, the random consistency index RI is introduced. The value of RI can be obtained by looking up Table 1. If the test coefficient CR<0, the judgment matrix passes the consistency test.

[0113]

[0114] S33. Combining the social and economic benefits of mobile hybrid distribution transformers, construct an objective function.

[0115] In the description of the present invention, combining the social benefits and economic benefits of the mobile hybrid distribution transformer, constructing the objective function includes the following steps:

[0116] S331. Obtain social parameters during the use of the mobile hybrid distribution transformer, and calculate social benefits by weighted summation based on multiple social parameters.

[0117] Specifically, social benefits f1 include the extent to which the energy use problem in agricultural irrigation areas is solved after HDT is invested. 1,1 , the ability to quickly restore power supply in the face of emergencies after HDT is put into use 1,2 , the impact of HDT on rural transportation 1,3 , Villagers' attitude towards the occupation of public land resources after HDT1,4 .

[0118] The calculation formula of f1 is as follows:

[0119]

[0120] In the formula, c 1,1 represents f 1,1 The weight coefficient of 1,2 represents f 1,2 The weight coefficient of 1,3 represents f 1,3 The weight coefficient of 1,4 represents f 1,4 The weight coefficient of .

[0121] S332, obtaining economic parameters in the production and movement process of the mobile hybrid distribution transformer, and calculating the economic benefits by weighted summation based on multiple economic parameters.

[0122] Specifically, the economic benefit f2 includes the mobile hybrid distribution transformer loss savings f 2,1 , production cost of mobile hybrid distribution transformer (modular transformer system) 2,2 , the cost of moving a mobile hybrid distribution transformer (modular transformer system) 2,3 , the calculation formula of f2 is as follows:

[0123]

[0124] In the formula, c 2,1 represents f 2,1 The weight coefficient of 2,2 represents f 2,2 The weight coefficient of 2,3 represents f 2,3 The weight coefficient of .

[0125] In the description of the present invention, the economic parameters include the mobile hybrid distribution transformer loss saving cost, the mobile hybrid distribution transformer production cost and the mobile hybrid distribution transformer moving cost, and the calculation expressions are respectively:

[0126]

[0127]

[0128]

[0129] In the formula, f 2,1 represents the cost savings of mobile hybrid distribution transformer losses; f 2,2 represents the production cost of mobile hybrid distribution transformer; f 2,3represents the moving cost of the mobile hybrid distribution transformer; P Lloss Indicates network loss; P trloss represents transformer loss; n0 represents the number of mobile hybrid distribution transformers; C represents the production cost of a unit mobile hybrid distribution transformer; c L represents the mobility cost coefficient of the mobile hybrid distribution transformer; N T represents the calculation cycle; Δt represents the step length per unit time; It represents the moving cost per unit of mobile hybrid distribution transformer.

[0130] Among them, the network loss P Lloss and transformer P trloss The loss is calculated as:

[0131]

[0132]

[0133] Where N T is the calculation period; r ij is the resistance value of branch ij; I ij,t represents the branch ij current at time t; Δt is the step length per unit time; Represents the difference in system loss before and after HDT transfer at time t.

[0134] S333. Set the weight coefficients of social benefits and economic benefits respectively, and construct an objective function, wherein the expression of the objective function is:

[0135]

[0136] In the formula, c1 represents the weight coefficient of social benefits; c2 represents the weight coefficient of economic benefits; f1 represents social benefits; and f2 represents economic benefits.

[0137] S34. Set constraints based on the power distribution system parameters and loss costs of the transformer substation area.

[0138] Specifically, the constraints include system constraints and model constraints.

[0139] Among them, the system constraints are the original nonlinear linear constraints transformed into linear constraints through second-order cone relaxation, including:

[0140]

[0141]

[0142]

[0143]

[0144]

[0145]

[0146]

[0147]

[0148] Where α(j) is the set of branch terminal nodes with j as the initial node; P jk,t is the active power flowing from node j to node k at time t; Q jk,t is the reactive power flowing from node j to node k at time t; l ij,t is the square of the current in the ij branch at time t; v j,t is the square of the voltage of node j at time t; β(j) is the set of initial nodes of the branch with j as the terminal node; r ij 、x ij Indicates branch impedance; g j 、b j represents the node admittance; V j,t is the voltage at node j at time t; B is the set of all nodes; E is the set of all branches in the optimization area; E SW Represents a set of various types of tie switch branches in the reconstruction area; represents the transformer reactive load of node j in the tth period; represents the normal reactive load of node j in the tth period; represents the active load of the transformer at node j in the tth period; represents the active load of the agricultural irrigation park at node j in the tth period; represents the injected active power of node j in the tth period; represents the reactive power injected by node j in the tth period; P ij,t represents the active power of the ij branch at time t; Q ij,t represents the reactive power of the ij branch at time t; I ij,t is the current in branch ij at time t.

[0149] The node voltage constraints and branch current constraints are as follows:

[0150]

[0151]

[0152] In the formula, is the lower limit of the voltage at node j; is the upper limit of the voltage at node j; is the lower limit of the current at branch ij; are the upper limits of the current at branch ij respectively.

[0153] Model constraints, considering the timing problem, when the target position of a modular transformer system at the current moment is the starting position at the next moment, it satisfies:

[0154]

[0155] In the system loss cost coordinate system model, at the same time, in the starting transformer area and the target transformer area, only one point is selected, and the sum of the layers of all the points is the total number of HDTs, which is expressed as:

[0156]

[0157] Considering the overall manufacturing cost of the modular transformer system, there are the following cost constraints:

[0158]

[0159] In the formula, C cost For investment costs.

[0160] S35. Integrate the objective function and constraints to construct a mobile hybrid distribution transformer site selection model.

[0161] S4. Use genetic algorithm to find the optimal solution of the mobile hybrid distribution transformer site selection model and obtain the space-time-path deployment strategy of the mobile hybrid distribution transformer.

[0162] Among them, the deployment process of the space-time-path deployment strategy is as follows: first, the spatial rectangular coordinate system mapping method is used to map the data and construct an indicator system for the deployment strategy, and then the objective function is constructed based on the indicator system. Secondly, some constraints are listed based on the objective function; finally, the genetic algorithm is used to obtain the optimal solution of the hybrid distribution transformer site selection model. The solution obtained is the location coordinates of each mobile hybrid distribution transformer in 24 hours, and the space-time-path deployment strategy of each mobile hybrid distribution transformer can be obtained.

[0163] Specifically, Figure 5 As shown in the figure, a genetic algorithm is used and the hourly local optimum is considered. On this basis, an optimization model is constructed which comprehensively considers the social benefit cost, network loss cost, production cost of mobile hybrid distribution transformer and weighted minimum distance cost as the objective function to optimize the deployment strategy of HDT and adapt to seasonal and sudden changes in electricity demand.

[0164] Example

[0165] The present invention takes a certain agricultural irrigation area as an example and selects 164 agricultural irrigation transformer substations (in the present invention, agricultural irrigation transformer substation, agricultural irrigation substation and substation all represent transformer substations) to verify the effectiveness of the spatial coordinate system transformer site selection method based on genetic algorithm. Among them, the data of no-load current, short-circuit loss, rated capacity and no-load loss of the selected agricultural irrigation transformer substation are shown in Table 2:

[0166] Table 2: Transformer data

[0167] No-load current (A) Short circuit loss (W) Rated capacity (KVA) No-load loss (W) 1.24 1720 100 210

[0168] At the same time, the no-load current, short-circuit loss, rated capacity, and no-load loss of the transformer equipped by HDT are the same as the selected agricultural irrigation area transformer data. In addition, the distances between the 164 areas and the distances to the power supply stations in the area are obtained. At the same time, with a time step of 1 hour, the load conditions of the 164 areas within 1 year are clustered and the load conditions of the 164 areas within 24 hours are predicted. Furthermore, c1, c2, c 1,1 、c 1,2 、c 1,3 、c 1,4 、c 2,1 、c 2,2 With c 2,3 The value of .

[0169] The HDT site selection algorithm uses a genetic algorithm and combines it with the spatial coordinate system optimization method mentioned above to reduce the calculation time. The HDT algorithm flow chart is as follows: Figure 5 shown.

[0170] In order to analyze the optimal HDT ratio and intuitively analyze the impact of the number of HDTs on the objective function, a graph showing the impact of different numbers of HDTs on the objective function is drawn, such as Figure 6 shown.

[0171] from Figure 6 It can be clearly seen that as the number of HDT configurations increases, the value of the objective function decreases first and then increases, and the objective function reaches its minimum when the number of HDT configurations is 4, which is -110.211 yuan. That is, when 4 HDTs are added, 110.211 yuan is saved compared to before the HDT deployment. Figure 6 Based on this, we continue to analyze the spatial-location image of the deployment area where 4 HDTs are deployed in the area without considering the HDT moving time cost, such as Figure 7 shown.

[0172] Figure 7 The figure shows the changes in the substation numbers served by the four HDTs within 24 hours. As can be seen from the figure, the substation numbers served by each HDT in different time periods change significantly, which reflects the dynamic deployment and scheduling strategy of each HDT.

[0173] HDT No. 1: The transformer substations served by this HDT mainly include four parts. The transformer substations and service time intervals are: 1:00 to 5:00 for substations 9-21; 6:00 to 8:00 for substations 94-97; 9:00 to 13:00 for substations 94-97; 14:00 to 24:00 for substations 87-97. This line shows a significant upward and downward trend in the figure, indicating that the substation numbers it serves have a wide range of changes in a day. This means that the HDT is frequently reconfigured to respond to the power needs of different substations.

[0174] HDT No. 2: The service area and service time of HDT No. 2 are similar to those of HDT No. 1, and are roughly divided into 4 parts. The transformer substations and service time intervals are 1:00 to 6:00 for substations 9-19; 7:00 to 10:00 for substations 86-94; 11:00 to 13:00 for substations 11-19; 14:00 to 24:00 for substations 87-95. Especially at noon and evening, the substation number served by this HDT has a significant jump. The fact that this HDT serves the same substation area as the previous HDT indicates that the irrigation load of this area (9-21, 86-97) is large. At the same time, since the irrigation crops in the area are the same, the irrigation demand is also the same, and the temporal sequence is strong.

[0175] HDT No. 3: The agricultural irrigation area served by HDT No. 3 is different from the previous two HDTs. This transformer mainly serves the 151-162 area, but the fluctuation between 10:00 and 22:00 is large, and the HDT's service area changes frequently.

[0176] HDT No. 4: HDT No. 1 has always been in the power supply station in the figure, which indicates that the HDT is not used in the transformer substation area of ​​the agricultural irrigation area. Throughout the period, HDT No. 1 has always been located in the substation area No. 1 where the power supply station is located, indicating that this substation area may have a continuous and stable power demand. This configuration strategy ensures the stable power supply of key areas such as power supply stations to support the operation of the entire power grid. At the same time, fixing the HDT at the location of the power supply station can quickly adjust or enhance the power supply capacity of the power supply station in case of emergencies or power supply problems in other substations. Such a strategy helps to improve the power reliability of critical infrastructure. Furthermore, since the HDT is fixed in the substation area No. 1, the complexity of maintenance and monitoring may be reduced, and operation and maintenance personnel can more easily perform routine inspections and necessary maintenance work. Furthermore, in the power system, the power supply station is an extremely important node, and any power outage may affect a wide range of users. The fixed deployment of HDT No. 1 is a preventive measure to ensure a quick response in an emergency, such as increasing power supply capacity or responding quickly to system load fluctuations.

[0177] Overall, Figure 7 The dynamic dispatch and deployment efficiency of HDT is demonstrated, and each HDT moves between different stations according to grid demand and possible priorities. This analysis helps to understand the utilization efficiency and dispatch strategy of each HDT, thereby further optimizing the allocation and management of power resources. For grid operators, understanding this dynamic information is the key to optimizing operation strategies and improving service quality.

[0178] exist Figure 7 The display only shows the configuration of HDT, and it is not possible to intuitively understand the service area of ​​HDT. Therefore, the frequency of the first three HDTs serving the transformer area is counted and a histogram is drawn, such as Figure 8 shown.

[0179] In addition, Figure 7 The different line segments in the figure represent the HDT numbers 1 to 4, and the vertical axis represents the transformer area number served by the HDT. Figure 7 It can be seen that the service area of ​​HDT No. 2 is relatively concentrated. The service area of ​​​​HDT No. 90 is as high as 4 times, and the total service number is as high as 5 times. Figure 8 It can be seen that HDT's service areas change frequently, and the service areas are always changing, which better reflects HDT's flexibility.

[0180] From the above analysis, we can see that Figure 7 and Figure 8 Only the configuration of HDT is displayed, and the detailed changes of the transformer area after configuration cannot be obtained. In order to more clearly analyze the load curve of the transformer area after adding HDT and the transformer loss, take the 11, 90, and 95 areas served by HDT No. 2 as an example, and draw the load curve of the transformer area served by HDT within 24 hours and the transformer loss curve, as shown in the figure. Fig. 9 , Fig.10 shown.

[0181] Depend on Figure 7 It can be seen that the HDT serves the No. 11 transformer area at 5:00, 13:00 and 24:00; serves the No. 90 transformer area at 11:00, 19:00, 22:00 and 23:00; serves the No. 95 transformer area at 17:00 and 18:00. Fig.10 and Fig.11 As shown in the figure, when HDT serves these agricultural irrigation areas, these agricultural irrigation areas have a significant decline, indicating that whenever HDT is put into use, it can greatly alleviate the high load pressure of the agricultural irrigation area. The above analysis can only derive the load curve and transformer loss of the agricultural irrigation area after the introduction of HDT, and cannot understand the detailed load situation of HDT. Therefore, the load curves and transformer loss curves of HDT No. 1, 2, and 3 are drawn, as shown in Fig.11 , Fig.12 shown.

[0182] from Fig.11 , Fig.12 It can be clearly seen that the load rate curve trends of HDTs 1-3 are roughly the same, from which it can be concluded that the deployment strategies of HDTs are the same. At the same time, the consistency of agricultural irrigation loads determines the same trend of the load conditions of HDTs.

[0183] Since the present invention adopts a genetic algorithm, in order to verify the accuracy of the algorithm, take the curve of the total cost c1f1+c2f2 obtained after weighting at 24:00 as an example: Fig.13 As shown, the accuracy of the algorithm is analyzed.

[0184] from Fig.13 It can be seen that n g -(c1f1+c2f2) curve is very smooth, and there is no obvious fluctuation when using genetic algorithm, which proves that the genetic algorithm is suitable for the transformer site selection method in the spatial coordinate system. Because the present invention essentially considers the problem hour by hour, it is a local optimal problem, but in Fig.13 The calculation time of the algorithm cannot be obtained. Therefore, the hourly calculation local optimal time and the 24-hour overall global optimal time of the transformer site selection method using the spatial coordinate system optimization method and the method without the optimization method are statistically compared, and a table of the two calculation methods is drawn for comparison, as shown in Table 3.

[0185] Table 3: Computation time comparison

[0186]

[0187] As shown in Table 3, the overall calculation time of 24h is 72.284 times that of hourly calculation time. The difference between the two calculation times is huge. Using the local optimal method of hourly calculation can greatly reduce the time and increase the calculation efficiency. However, since the local optimal calculation method will inevitably increase the calculation error, it is necessary to compare and analyze the HDT service area under the two calculation methods and draw the cost comparison image of the two to verify the accuracy of the local optimal method of hourly calculation.

[0188] from Fig.14 It can be seen that the results obtained by the two calculation methods are almost the same, with the largest difference being at 6:00. The total cost in the hourly local calculation method is -90.203 yuan, while in the 24-hour global calculation method it is -88.241 yuan, with an error of only 2.175%, which proves the accuracy of the method used in the present invention. Fig.14Although the comparison of the calculation results under the two calculation methods can be clearly obtained, the economic and social benefits that this method brings to the region cannot be intuitively obtained. Therefore, the economic benefits, social benefits and weighted total costs are statistically analyzed, and the annual benefits are calculated separately and a comprehensive benefit analysis table is drawn, as shown in Table 4.

[0189] Table 4: Comprehensive benefit analysis table

[0190] Benefit (yuan / day) Benefit (10,000 yuan / year) Economic Benefits -98.052 -3.360 Social Benefits 51.888 1.894 <![CDATA[f min ]]> 110.211 3.924

[0191] In summary, with the help of the above technical scheme of the present invention, by constructing a comprehensive optimization objective function covering social benefit cost, network loss cost, production cost of mobile hybrid distribution transformer and travel cost, an efficient mobile hybrid distribution transformer deployment strategy is provided for the power system of the transformer substation area to solve the power supply challenges caused by the rapid development of agricultural areas; the deployment strategy takes into account the fluctuations in seasonal and sudden power demand, and adopts mobile hybrid distribution transformers to enhance the flexibility and response speed of the power grid; using genetic algorithms, the present invention has achieved remarkable results in optimizing computing efficiency and processing time, especially by implementing hourly local optimal solution strategies, effectively reducing the computing load while maintaining the quality of the solution, and effectively improving the operation efficiency of the power grid; at the same time, the present invention not only improves the economy and efficiency of the deployment of mobile hybrid distribution transformers, but also significantly improves the adaptability of the power grid to changes in agricultural loads, so as to support the power system in the face of growing and changing power demand. When it can continue to maintain high efficiency and reliability.

[0192] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

Claims

1. A method for selecting a mobile hybrid distribution transformer site based on spatial coordinate system mapping, characterized in that: The method comprises the following steps: S1. Number all transformer substations and establish the loss space coordinate system and transfer cost space coordinate system of the mobile hybrid distribution transformer in the transformer substation; S2. Calculate the system loss and travel cost during the transfer of the mobile hybrid distribution transformer; S3. Construct a site selection model for mobile hybrid distribution transformers based on system losses, travel costs and indicator systems; S4. Using genetic algorithm to find the optimal solution of the mobile hybrid distribution transformer site selection model, and obtain the time-space-path deployment strategy of the mobile hybrid distribution transformer; The establishment of the loss space coordinate system and the transfer cost space coordinate system of the mobile hybrid distribution transformer in the transformer area comprises the following steps: S11. Establish a loss space coordinate system of a multi-layer plane, define the x-axis and y-axis to represent the starting transformer area number of the mobile hybrid distribution transformer at the current moment and the target transformer area number to be moved at the next moment, respectively, and the z-axis represents the number of mobile hybrid distribution transformers moved from any transformer area to another transformer area; S12, establish a single-layer plane transfer cost space coordinate system, define the x-axis and y-axis to represent the starting transformer area number of the mobile hybrid distribution transformer at the current moment and the target transformer area number to be moved at the next moment, respectively, and the z-axis represents the number of mobile hybrid distribution transformers moved from any transformer area to another transformer area; The number of layers of the plane in the loss space coordinate system is equal to the number of mobile hybrid distribution transformers, and the position point in the loss space coordinate system is represented by (x, y, z), and the position point in the transfer cost space coordinate system is represented by (x, y, 1); The calculation of system loss and travel cost during the transfer of the mobile hybrid distribution transformer comprises the following steps: S21. Define the transformer loss formula of the transformer substation, calculate the initial system loss of the m mobile hybrid distribution transformers in the initial transformer substation at time t-1, and the target system loss of the m mobile hybrid distribution transformers in the target transformer substation at time t, and use the initial system loss to subtract the target system loss to calculate the difference in system loss before and after the transfer of the m mobile hybrid distribution transformers at time t; S22, recording the initial moving cost of a mobile hybrid distribution transformer in the initial transformer area at time t-1, and the target moving cost of a mobile hybrid distribution transformer in the target transformer area at time t, and subtracting the target moving cost from the initial moving cost to obtain the travel cost before and after the transfer of the mobile hybrid distribution transformer at time t; The construction of the mobile hybrid distribution transformer site selection model based on system loss, travel cost and index system includes the following steps: S31, obtaining evaluation indicators for site selection of mobile hybrid distribution transformers, constructing an indicator system, and using a data normalization method to process data included in each evaluation indicator; S32. Based on the analytic hierarchy process, evaluate the weight coefficient of each evaluation index; S33. Combine the social and economic benefits of the mobile hybrid distribution transformer to construct the objective function; S34. Set the constraints based on the distribution system parameters and loss costs of the transformer area; S35, integrating the objective function and the constraint conditions to construct a site selection model for a mobile hybrid distribution transformer; The method of evaluating the weight coefficients of various evaluation indicators based on the hierarchical analysis method includes the following steps: S321. Use hierarchical analysis calculation software to establish a hierarchical structure model; S322, construct a judgment matrix, compare all evaluation indicators in pairs, and use a scale table to score the importance of the evaluation indicators; S323, scoring the weights of the evaluation indicators, and performing weighted average processing on the scores to obtain a judgment matrix; then inputting the matrix into the hierarchical analysis calculation software to calculate the weight coefficients of each evaluation indicator; the objective function constructed by combining the social benefits and economic benefits of the mobile hybrid distribution transformer includes the following steps: S331. Obtaining social parameters during the use of the mobile hybrid distribution transformer; and calculating social benefits by weighted summation based on multiple social parameters; S332, obtaining economic parameters in the production and movement process of the mobile hybrid distribution transformer; and calculating the economic benefits by weighted summation based on multiple economic parameters; S333, respectively set the weight coefficients of social benefits and economic benefits, and construct the objective function; The expression of the objective function is: f min =c1f1+c2f2 In the formula, c1 represents the weight coefficient of social benefits; c2 represents the weight coefficient of economic benefits; f1 represents social benefits; f2 represents economic benefits; The economic parameters include the loss saving cost of the mobile hybrid distribution transformer, the production cost of the mobile hybrid distribution transformer and the moving cost of the mobile hybrid distribution transformer, and the calculation expressions are: f 2，1 =P Lloss +P trloss in 2，2 =n0C In the formula, f 2，1 It indicates the cost savings of mobile hybrid distribution transformer losses; f 2，2 represents the production cost of mobile hybrid distribution transformer; f 2，3 represents the cost of moving the mobile hybrid distribution transformer; P Lloss Indicates network loss; P trloss Indicates transformer loss; n0 represents the number of mobile hybrid distribution transformers; C represents the production cost of a unit mobile hybrid distribution transformer; c L represents the mobility cost coefficient of the mobile hybrid distribution transformer; N T represents the calculation cycle; Δt represents the step length per unit time; It represents the moving cost of a unit hybrid distribution transformer at time t.

2. The method for selecting a site for a mobile hybrid distribution transformer based on spatial coordinate system mapping according to claim 1 is characterized in that: The transformer loss formula is expressed as: In the formula, represents the transformer loss of m mobile hybrid distribution transformers in transformer area s at time t; ΔP 0，s It indicates the no-load active power loss of the transformer in the transformer area No. s; ΔP k，s It indicates the short-circuit active power loss of the transformer in the transformer area No. s; It represents the calculated load of the transformer in the transformer section No. s at time t; S N，s Indicates the rated capacity of the transformer in the transformer area No. s; ΔP 0，mt It represents the no-load active power loss of the mobile hybrid distribution transformer in the transformer area No. s; ΔP k，mt It represents the short-circuit active power loss of the mobile hybrid distribution transformer in the s transformer area; It represents the calculated load of the mobile hybrid distribution transformer in the transformer area No. s at time t; S N，mt It indicates the rated capacity of the mobile hybrid distribution transformer in the transformer area No. s; m represents the number of mobile hybrid distribution transformers in transformer area No. s.

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