Power distribution network transformer coordination control method and system, electronic equipment and storage medium

Abstract

The invention discloses a power distribution network transformer coordinated management and control method and system, electronic equipment and a storage medium, and relates to the technical field of power control. According to the method, after the target parameter with the influence degree on the service life of the transformer exceeding the preset standard is obtained, the transformer service life model used for representing the feasible region of the target parameter and the service life of the transformer is constructed based on the target parameter, and then the transformer service life model is subjected to convex processing to obtain the safe operation region of the transformer. Based on the safe operation domain of the transformer, a coordinated optimization scheduling model for ensuring that the actual service life of the transformer can reach an expected value is constructed, and finally, based on the coordinated optimization scheduling model, the power distribution network and the transformer are subjected to coordinated control. Therefore, the problem of how to ensure the full life cycle of the transformer under the bidirectional tidal current and control the system voltage stability is solved.

Description

Technical Field

[0001] The present invention relates to the field of power control technology, and more specifically, to a method, system, electronic device and storage medium for coordinated control of transformers in a distribution network. Background Art

[0002] The ways of connecting new energy to the power grid mainly include centralized access and distributed access. Among them, centralized access is mainly through the construction of large-scale new energy stations, connecting to 500 kV and above high-voltage power grids for long-distance transmission; while distributed access is mainly to connect new energy to 220 kV and below power grids.

[0003] In recent years, with the annual increase in wind and solar access capacity in power systems, the distributed access of a high proportion of new energy has changed the power flow distribution and coordinated operation mode of traditional power grids, forming a typical operating condition of power flow reverse transmission and frequent power flow switching in power systems:

[0004] On the one hand, the voltage of the low-voltage grid will increase at the nodes where new energy is injected, and the voltage of the feeder line will no longer show the characteristics of step-by-step decline in the traditional grid. The voltage control becomes extremely complicated and easily causes the reverse flow. On the other hand, the randomness and uncertainty of the output of new energy will cause the "source-load" imbalance of the grid, and frequent bidirectional energy flows will occur between the high-voltage and low-voltage grids. The flow can switch multiple times in a day, which will aggravate the voltage fluctuation of the distribution network nodes. In addition, the hot spot temperature and harmonic distortion rate of the main transformer will increase compared with normal conditions during reverse transmission, causing the transformer to age faster.

[0005] Therefore, in order to ensure the full life cycle of the transformer under bidirectional power flow and control the system voltage stability, it is necessary to study a distribution network-transformer coordinated control technology suitable for frequent power flow switching, so as to formulate targeted power grid dispatching and coordinated management strategies for power transformers already in operation on the grid. Summary of the invention

[0006] The object of the present invention is to provide a method, system, electronic device and storage medium for coordinated control of transformers in a distribution network, so as to solve the problem of how to ensure the full life cycle of the transformer under bidirectional power flow and control the system voltage stability.

[0007] In a first aspect, the present invention provides a method for coordinated control of transformers in a distribution network, comprising:

[0008] Obtaining a target parameter, wherein the target parameter is a parameter whose influence on the life of the transformer exceeds a preset standard;

[0009] Based on the target parameters, constructing a transformer life model for characterizing a feasible domain of the target parameters and transformer life;

[0010] Convex processing is performed on the transformer life model to obtain a safe operation domain of the transformer;

[0011] Based on the transformer safe operation domain, a coordinated optimization scheduling model is constructed to ensure that the actual service life of the transformer reaches the expected value;

[0012] Based on the coordinated optimization scheduling model, the distribution network and the transformer are coordinated and controlled.

[0013] In a preferred solution, the target parameters include: hot spot temperature and load-side harmonic content.

[0014] In a preferred solution, the step of constructing a transformer life model for characterizing a feasible domain of the target parameter and the transformer life based on the target parameter includes:

[0015] Based on the target parameters, determining the influence factor of the power flow switching condition on the life of the transformer;

[0016] Based on the influencing factors, constructing the transformer life model;

[0017] Wherein, the transformer life model is:

[0018] TLM=f(ξ,θ h )

[0019] Where ξ is the harmonic content on the load side, θ h is the hot spot temperature of the transformer, including the ambient temperature, the top oil temperature rise and the winding hot spot temperature rise.

[0020] In a preferred solution, the convexification processing of the transformer life model includes:

[0021] The transformer life model is subjected to a hyperspace projection based on the transformer life surface to obtain a life level set for characterizing the original operating domain of the transformer. The life level set is:

[0022] X(n p )={(ξ,θ h )|n≥n p}

[0023] Where X is the life level set, n is the actual usable life of the transformer, and n p is the expected life of the transformer, ξ is the harmonic content on the load side, θ h is the hot spot temperature of the transformer;

[0024] The life level set is piecewise linearized, and the original boundary of the original safe operation domain is replaced by the inner tangent represented by the linearized boundary equation. The boundary equation is:

[0025] J·ξ+W·θ h ≤Y

[0026] Wherein, J, W and Y are coefficients of the boundary equation respectively.

[0027] In a preferred solution, the load-side harmonic content constraint of the transformer life model is:

[0028] A min ≤ξ≤A max

[0029] Among them, A min is the minimum value of the ξ axis in the coordinate system where the original safe operation domain is located, A max is the intersection point of the inner tangent line and the ξ axis;

[0030] The hot spot temperature constraint of the transformer life model is:

[0031] B min ≤θ h ≤B max

[0032] Among them, B min is θ in the coordinate system of the original safe operation domain h Minimum value of axis, B max is the inner tangent line and θ h The intersection of the axes.

[0033] In a preferred solution, the coordinated optimization scheduling model for ensuring that the actual service life of the transformer can reach the expected value is constructed based on the transformer safe operation domain, including:

[0034] Input the first parameter into the day-ahead optimization dispatch model to obtain the day-ahead optimization dispatch result, wherein the first parameter includes: the day-ahead market electricity price, the output reduction compensation price of wind power and photovoltaic power, the expected life of the transformer, and the load-side load reduction compensation quotation, and the day-ahead optimization dispatch result includes: the output of the controllable distributed power source on the next day, the amount of electricity purchased from the upper power grid, and the reduction amount of the flexible load;

[0035] Input the load demand of the controllable distributed power source and the day-ahead optimization scheduling result into the intraday rolling optimization scheduling model to obtain the intraday actual output of each scheduling resource of the distribution network;

[0036] Based on the actual output within the day and the day-ahead optimization scheduling result, the node voltage of the distribution network after connecting to the controllable distributed power source and the load that can be reduced is made within a preset range, with the goal of minimizing the active power loss of the distribution network. By performing feedback correction on the rolling stage within the day, the coordinated optimization scheduling model is constructed.

[0037] In a preferred solution, the objective function of the day-ahead optimization scheduling model is:

[0038]

[0039] Among them, C DA is the total cost of the power grid operation in the day-ahead phase, T is the total dispatch period, ΔT is the time interval, N DG is the number of controllable distributed generation sources connected to the distribution network, a i , b i 、c i are the secondary output cost coefficients of the i-th distributed generation, is the output of the i-th controllable distributed generation at time t, N REG is the number of wind power and photovoltaic power connected to the distribution network, and are the predicted output and actual dispatched output of the uncontrollable distributed generation at time t the day before, is the compensation price for reducing the uncontrollable distributed power generation at time t a day ago, and are the market electricity price at time t the day before and the amount of electricity purchased from the upper power grid, and are the quotation for load reduction at time t on the next day and the amount of load reduction respectively.

[0040] In a preferred solution, the power balance constraint of the day-ahead optimization scheduling model is:

[0041]

[0042] in, is the total power demand value of the load at time t the next day;

[0043] The system reserve constraint of the day-ahead optimal scheduling model is:

[0044]

[0045] Among them, P i max is the maximum output of the i-th distributed generation, is the upper limit of the amount of electricity purchased, is the maximum load reduction that can be reduced at time t;

[0046] The power generation constraints of the day-ahead optimal dispatch model are:

[0047]

[0048] Among them, Pi min is the minimum output of the i-th distributed generation, U i,t The start and stop status of the distributed power source;

[0049] The minimum start and stop time constraint of the day-ahead optimization scheduling model is:

[0050]

[0051] Among them, MUP i and MDN i are the minimum operating time and shutdown time of distributed power sources respectively;

[0052] The climbing constraint of the day-ahead optimization scheduling model is:

[0053]

[0054] Among them, R up,i and R dn,i are the maximum and minimum climbing limits of the i-th distributed generation respectively;

[0055] The market power purchase of the day-ahead optimal dispatch model is:

[0056]

[0057] The load reduction constraint of the day-ahead optimal scheduling model is:

[0058]

[0059] The distribution network reconstruction constraints of the day-ahead optimization scheduling model are:

[0060]

[0061] Among them, V i,t is the node voltage of bus i at time t, P ij,t and Q ij,t are the active power and reactive power flowing on the line from node i to node j at time t, r ij and x ij are the resistance and reactance of the line from node i to node j, respectively, ij,t is the square of the line current at time t, M is a preset constant, α ij is the switch flag. When α ij = 0, indicating that the switch between nodes i and j is disconnected. ij =1, indicating that the switch between nodes i and j is closed;

[0062] The distribution network radiation constraint of the day-ahead optimal dispatch model is:

[0063] ∑α ij =nn s

[0064] Where n is the total number of nodes in the distribution network, n s is the number of substations in the distribution network.

[0065] In a preferred solution, the day-ahead optimization scheduling model also includes an on-load voltage regulator.

[0066] In a preferred solution, the objective function of the intraday rolling optimization scheduling model is:

[0067]

[0068] Among them, C R is the total operating cost of the power grid company during the intraday rolling dispatch phase, a i0 , b i0 、c i0 are the secondary cost coefficients of distributed generation, Forecast electricity price for period t in the rolling phase, and are the amount of electricity purchased from the upper power grid in period t obtained by the day-ahead optimization dispatch result and the amount of electricity purchased from the upper power grid in period t during the rolling phase, It is a fixed value for participating in the intraday rolling optimization scheduling. is the latest quotation for load reduction during the day in period t, is the amount of load reduction that can be achieved at time t within the day.

[0069] In a preferred solution, the power balance constraint of the intraday rolling optimization scheduling model is:

[0070]

[0071] in, ΔP t Wind , ΔP t PV , They are the change in power purchase from the power grid, the change in wind power and photovoltaic power, and the change in load at time t within the day;

[0072] The unit ramp power constraint of the intraday rolling optimization scheduling model is:

[0073]

[0074] The power purchase constraints of the intraday rolling optimization scheduling model are:

[0075]

[0076] The correction constraint of the rolling plan of the intraday rolling optimization scheduling model on the day-ahead plan is:

[0077]

[0078] in, The most recent rolling revision of the latest plan will be contributed. If no intraday revision is made, then That is the day-ahead optimization scheduling result, and P i,t +ΔP i,t within the neighborhood;

[0079] The load reduction constraint of the intraday rolling optimization scheduling model is:

[0080]

[0081] In a preferred solution, the objective function of the coordinated optimization scheduling model is:

[0082]

[0083] Among them, C RT The total cost of operation optimization at time t in the intraday real-time scheduling phase is: The electricity price is predicted for the t period in the real-time dispatch phase within the day. is the amount of electricity purchased from the large power grid during period t in the real-time dispatch phase, is the power generation of the i-th distributed generation unit at time t during the intraday real-time scheduling phase, and are the real-time quotation and load reduction amount of the load that can be reduced at time t in the real-time dispatch phase of the day, ρ gc and They are the reactive electricity price in the auxiliary market and the corresponding reactive power purchased.

[0084] In a preferred solution, the power flow constraint of the coordinated optimization scheduling model is:

[0085]

[0086] Among them, P i grid , P i DG , P i IL , P i L are the grid active power injected into node i, the active output of distributed generation, the reduction of flexible load and the load demand, Q i grid , Q i DG, Q i C , Q i L are the reactive power of the grid injected into node i, the reactive output of the distributed generation, the compensation capacity of the reactive compensator, and the reactive demand of the load;

[0087] The energy storage system constraints of the coordinated optimization scheduling model are:

[0088]

[0089] Among them, at and bt are the charging or discharging working states of the energy storage battery respectively; and are the charging and discharging power of the energy storage battery in time period t, is the output of the energy storage battery including charging and discharging in period t, P re.max and P ch.max are the maximum discharge and charge power of the energy storage battery, E t is the charge of the energy storage battery in period t, E max and E min are the upper and lower limits of the charge respectively;

[0090] The voltage constraint of the coordinated optimization dispatch model is:

[0091] V imin ≤V i ≤V imax

[0092] The reactive power compensator capacity constraint of the coordinated optimization dispatch model is:

[0093]

[0094] The output ramp constraint of the distributed power generation in the coordinated optimization scheduling model is:

[0095]

[0096] In a second aspect, the present invention provides a distribution network transformer coordinated control system, which is used to run the distribution network transformer coordinated control method provided by the first aspect of the present invention, and the system includes:

[0097] A collection unit, used to obtain a target parameter, wherein the target parameter is a parameter whose influence on the life of the transformer exceeds a preset standard;

[0098] A first construction unit is used to construct a transformer life model for characterizing a feasible domain of the target parameter and the transformer life based on the target parameter;

[0099] A processing unit, used for performing convex processing on the transformer life model to obtain a safe operation domain of the transformer;

[0100] A second construction unit constructs a coordinated optimization scheduling model for ensuring that the actual service life of the transformer reaches an expected value based on the transformer safe operation domain;

[0101] An execution unit is used to coordinate and control the distribution network and the transformer based on the coordinated optimization scheduling model.

[0102] In a third aspect, the present invention provides an electronic device, the electronic device comprising:

[0103] processor;

[0104] a memory for storing instructions executable by the processor;

[0105] The processor is used to execute the distribution network transformer coordinated control method provided by the first aspect of the present invention.

[0106] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method provided in the first aspect of the present invention are implemented.

[0107] In order to achieve the above-mentioned purpose, the coordinated control method, system, electronic device and storage medium of the distribution network transformer provided by the present invention can effectively guide the planning and operation of the distribution network and better guarantee the service life of the transformer by constructing a transformer life model under frequent power flow switching, that is, establishing the target parameters of the transformer whose influence on the transformer life exceeds the preset standard and the feasible domain of the transformer life. By convexifying the constructed transformer life model, the transformer safe operation domain is obtained, and a direct relationship between the transformer life and the transformer safe operation domain is established, so that the accuracy of solving the distribution network voltage management problem considering the transformer life constraint can be effectively improved. The transformer safe operation domain can be used for the coordinated operation and planning of the power grid, thereby improving the reliability and life of the transformer and reducing the impact of power flow switching on the performance of the built transformer. By constructing a coordinated optimization scheduling model for ensuring that the actual life of the transformer can reach the expected value based on the transformer safe operation domain, when the distribution network and the transformer are coordinated and controlled based on the model, the operation reliability of the step-down transformer can be significantly improved, and a series of problems such as abnormal temperature rise of the transformer winding, accelerated insulation aging and core excitation distortion caused by the frequent power flow switching operation of the step-down transformer can be greatly reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0108] Figure 1 A flow chart of a method for coordinated control of transformers in a distribution network provided in an embodiment of the present invention.

[0109] Figure 2 A schematic diagram of an example of obtaining a life level set for characterizing an original safe operating domain of a transformer by a hyperspace projection method provided in an embodiment of the present invention.

[0110] Figure 3 A schematic diagram of an example of replacing the original non-convex and complex life boundary of a transformer with a linearized boundary provided in an embodiment of the present invention.

[0111] Figure 4 A structural diagram of a distribution network transformer coordination control system provided in an embodiment of the present invention.

[0112] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0113] The present invention is further described in detail below through the accompanying drawings and specific embodiments.

[0114] The coordinated control method for distribution network transformers provided by the present invention is executed on an electronic device, which may be an intelligent terminal device such as a laptop, a personal computer, and a tablet computer, or may be a server on the network side.

[0115] like Figure 1 As shown, the coordinated control method of distribution network transformers provided by the embodiment of the present invention mainly includes the following steps:

[0116] 110. Get target parameters.

[0117] Among them, the target parameter is a parameter whose influence on the life of the transformer exceeds the preset standard;

[0118] 120. Based on the target parameters, a transformer life model is constructed to characterize the feasible domain of the target parameters and the transformer life;

[0119] 130. Convexify the transformer life model to obtain the transformer safe operation domain;

[0120] 140. Based on the transformer safe operation domain, a coordinated optimization scheduling model is constructed to ensure that the actual life of the transformer reaches the expected value;

[0121] 150. Based on the coordinated optimization dispatching model, the distribution network and transformers are coordinated and controlled.

[0122] In this embodiment, by coordinating and controlling the distribution network and the transformer based on the coordinated optimization scheduling model, it is possible to ensure that the actual life of the transformer can reach the expected value as the target value of the distribution network management, thereby realizing the active distribution network multi-time scale coordinated optimization scheduling.

[0123] It is understandable that the life of the transformer is closely related to the operating temperature of the oil-paper insulation. The highest internal operating temperature point is usually called the hot spot. Since the aging rate of the oil-paper insulation at this point is much greater than that at other locations, research on this point is the key to achieving accurate life assessment and prediction. In addition, the change in harmonic content on the load side caused by the access of new energy will affect the hot spot temperature, so the harmonic content on the load side is also one of the factors affecting the life of the transformer.

[0124] Based on this, in an optional embodiment, the target parameters include: hot spot temperature and load-side harmonic content.

[0125] In this embodiment, when implementing the coordinated control method of the distribution network transformer provided in this embodiment, a multi-time scale coordinated flow control model and strategy of scheduling means such as reactive compensation devices on the secondary system side, energy storage and network reconstruction can be used to affect the hot spot temperature rise of the transformer and the harmonic content on the load side, thereby adjusting the life of the transformer and promoting the integrated control of the primary and secondary sides of the power grid, thereby achieving reliable operation of equipment and networks under conditions of frequent flow switching.

[0126] Based on the content of the above embodiment and the target parameters, a transformer life model for characterizing the feasible domain of the target parameters and the transformer life is constructed, including:

[0127] Based on the target parameters, determine the factors affecting the transformer life due to the power flow switching condition;

[0128] Based on the influencing factors, a transformer life model is constructed.

[0129] Among them, the transformer life model is:

[0130] TLM=f(ξ,θ h ) (1)

[0131] Specifically, the factors affecting the transformer life under power flow switching conditions include: an increase in the harmonic content ξ on the load side and an increase in the hot spot temperature of the transformer:

[0132] Among them, as the harmonic content on the load side increases, the thermal life loss of the transformer increases with the increase of the harmonic content.

[0133] The harmonic current content injected into the substation bus is:

[0134]

[0135] The substation busbar harmonic voltage is:

[0136]

[0137] Among them, H is the maximum harmonic number set, which is generally taken as 50, and m is the total number of branches. is the fundamental current flowing through branch L, is the hth harmonic current flowing through branch L, n is the total number of nodes, is the hth harmonic voltage at node i.

[0138] Furthermore, assuming that branch L is a line from node i to node j, we have:

[0139]

[0140] in, is the admittance between nodes i and j at the hth harmonic.

[0141] Regarding the increase in transformer hot spot temperature, it can be understood that the hot spot temperature of the power transformer under any load is composed of the ambient temperature, the top oil temperature rise and the winding hot spot temperature rise:

[0142]

[0143] in, is the real-time ambient temperature of the transformer operation. is the dynamic temperature rise of the top oil relative to the environment within the time interval t, It represents the dynamic temperature rise of the winding hot spot relative to the top oil within the time interval t.

[0144] Furthermore, the transformer life model is convexified, including:

[0145] Perform hyperspace projection on the transformer life model based on the transformer life surface to obtain the life level set used to characterize the original safe operation domain of the transformer;

[0146] The life level set is:

[0147] X(n p )={(ξ,θ h )|n≥n p} (6)

[0148] Among them, X is the life level set, n is the actual useful life of the transformer, and n p is the expected life of the transformer, ξ is the harmonic content on the load side, θ h is the hot spot temperature of the transformer;

[0149] The life level set is piecewise linearized, and the original boundary of the original safe operation domain is replaced by the inner tangent represented by the linearized boundary equation;

[0150] The boundary equation is:

[0151] J·ξ+W·θ h ≤Y (7)

[0152] Where J, W, and Y are the coefficients of the boundary equations.

[0153] Specifically, combined Figure 2 As shown, by projecting the intersection line of the transformer life expectancy plane and the available life curve onto ξ and θ h The critical life curve is formed on the plane formed. Among them, Figure 2 The influencing factors 1 and 2 shown in the figure represent ξ and θ respectively. h , then the critical life curve will be ξ and θ h The feasible domain is divided into healthy operation and unhealthy operation, where "healthy" means that the life of the equipment is longer than expected. The convex approximation method is used to convexify the feasible domain of the influencing factors by drawing the inner tangent of the critical life surface, and finally the transformer life constraint can be established.

[0154] Combination Figure 3 As shown in the figure, through piecewise linearization, nonlinear problems can be converted into linearized problems for solution. Therefore, by piecewise linearization of the original safe operation domain, the original non-convex and complex boundary of the original safe operation domain can be replaced by the inner tangent represented by the linearized boundary equation, thereby reducing the computational complexity and achieving accurate evaluation of the safe operation status level of the transformer.

[0155] Based on the content of the above embodiment, the load-side harmonic content constraint of the transformer life model is:

[0156] A min ≤ξ≤A max (8)

[0157] Among them, A min is the minimum value of the ξ axis in the coordinate system of the original safe operation domain, A max is the intersection point of the inner tangent line and the ξ axis;

[0158] The hot spot temperature constraint of the transformer life model is:

[0159] B min ≤θ h ≤B max (9)

[0160] Among them, B min is θ in the coordinate system of the original safe operation domain h Minimum value of axis, B max is the inner tangent line and θ h The intersection of the axes.

[0161] In an optional embodiment, based on the transformer safe operation domain, a coordinated optimization scheduling model is constructed to ensure that the actual service life of the transformer can reach the expected value, including:

[0162] The first parameter is input into the day-ahead optimization scheduling model to obtain the day-ahead optimization scheduling result.

[0163] The first parameter includes: the day-ahead market electricity price, the output reduction compensation price of wind power and photovoltaic power, the expected life of transformers, and the load-side load reduction compensation quotation. The day-ahead optimization scheduling results include: the output of controllable distributed power sources on the next day, the amount of electricity purchased from the upper power grid, and the reduction of flexible loads;

[0164] The load demand of controllable distributed generation and the day-ahead optimization dispatching results are input into the intraday rolling optimization dispatching model to obtain the actual intraday output of each dispatching resource of the distribution network;

[0165] Based on the actual output within the day and the day-ahead optimized dispatch results, the node voltage of the distribution network connected to the controllable distributed power sources and the load that can be reduced is within the preset range. The goal is to minimize the active power loss of the distribution network. Through feedback correction in the rolling stage within the day, a coordinated optimization dispatch model is constructed.

[0166] Specifically, in this embodiment, first, in the day-ahead optimization stage, the optimization scheduling is performed according to the day-ahead market electricity price, the output reduction compensation price of wind power and photovoltaic power, and the load-side load reduction compensation quotation, etc., to determine the output of the controllable distributed power source, the amount of electricity purchased from the upper power grid, and the reduction of the flexible load on the next day. Then, considering that as the prediction time scale becomes longer, the gap between the prediction results of wind, photovoltaic and load and the actual situation gradually becomes larger, resulting in the lack of practical guidance significance for the optimization results of the long time scale, so further correction is performed in the intraday stage. The specific correction standard can be that if the superposition value of the day-ahead and intraday changes of load and wind and photovoltaic power does not exceed the preset threshold, no rolling correction is required, and the day-ahead optimization scheduling result at this time has guiding significance, and can be operated according to the day-ahead scheduling plan; and if the superposition value exceeds the preset threshold, it is rolled. Correction, that is, the load demand of the controllable distributed power source and the day-ahead optimization scheduling result are input into the intraday rolling optimization scheduling model to obtain the actual intraday output of each dispatching resource of the distribution network.

[0167] Furthermore, the construction of a coordinated optimization dispatch model is a further correction made on the basis of intraday rolling corrections during the real-time dispatch stage, that is, advanced optimization dispatch based on ultra-short-term load, photovoltaic, and wind power forecasts. For example: fine-tune the output of each part in a cycle of 5-15 minutes, and optimize the next period t=t1+1 at t=t1, so as to correct the deviation between the dispatch plan and the forecast results. By modeling the intraday real-time optimization dispatch and performing feedback correction on the intraday rolling stage, it can be ensured that the system node voltage after access to controllable distributed power sources and load reduction is within a reasonable range, and the system active power loss is minimized.

[0168] In an optional embodiment, the objective function of the day-ahead optimization scheduling model is:

[0169]

[0170] Among them, C DA is the total cost of the power grid operation in the day-ahead phase, T is the total dispatch period, ΔT is the time interval, N DG is the number of controllable distributed power sources connected to the distribution network (in this model, it is assumed that wind power and photovoltaic power are owned by the power grid company and their operating costs are ignored), a i , b i 、c i are the secondary output cost coefficients of the i-th distributed generation, is the output of the i-th controllable distributed generation at time t, N REG is the number of wind power and photovoltaic power connected to the distribution network, and are the predicted output and actual dispatched output of the uncontrollable distributed generation at time t the day before, is the compensation price for reducing the uncontrollable distributed power generation at time t a day ago, and are the market electricity price at time t the day before and the amount of electricity purchased from the upper power grid, and are the quotation for load reduction at time t on the next day and the amount of load reduction respectively.

[0171] In an optional embodiment, the power balance constraint of the day-ahead optimization scheduling model is:

[0172]

[0173] in, is the total power demand value of the load at time t the next day;

[0174] The system reserve constraint of the day-ahead optimal scheduling model is:

[0175]

[0176] Among them, P i max is the maximum output of the i-th distributed generation, is the upper limit of the amount of electricity purchased, is the maximum load reduction that can be reduced at time t;

[0177] The power generation constraints of the day-ahead optimal dispatch model are:

[0178]

[0179] Among them, P i min is the minimum output of the i-th distributed generation, U i,t The start and stop status of the distributed power supply, for example, 1 means start, and 0 means stop;

[0180] The minimum start and stop time constraint of the day-ahead optimization scheduling model is:

[0181]

[0182] Among them, MUP i and MDN i are the minimum operating time and shutdown time of distributed power sources respectively;

[0183] The ramping constraint of the day-ahead optimization scheduling model is:

[0184]

[0185] Among them, R up,i and R dn,i are the maximum and minimum climbing limits of the i-th distributed generation respectively;

[0186] The market power purchase of the day-ahead optimal dispatch model is:

[0187]

[0188] The load reduction constraint of the day-ahead optimal dispatch model is:

[0189]

[0190] The distribution network reconstruction constraints of the day-ahead optimization dispatching model are:

[0191]

[0192] Among them, V i,t is the node voltage of bus i at time t, P ij,t and Q ij,tare the active power and reactive power flowing on the line from node i to node j at time t, r ij and x ij are the resistance and reactance of the line from node i to node j, respectively, ij,t is the square of the line current at time t, M is a preset constant, which must actually be a sufficiently large number (usually greater than 1000), α ij Is the switch flag, when α ij = 0, indicating that the switch between nodes i and j is disconnected. ij =1, indicating that the switch between nodes i and j is closed;

[0193] The distribution network radiation constraint of the day-ahead optimal dispatch model is:

[0194] ∑α ij =nn s (19)

[0195] Where n is the total number of nodes in the distribution network, n s is the number of substations in the distribution network.

[0196] In an optional embodiment, the day-ahead optimization scheduling model also includes an on-load voltage regulator.

[0197] Specifically, because the on-load voltage regulator OLTC mainly adjusts the voltage value of the HV / MV low-voltage side, after adding the OLTC, the substation bus node V0 is converted into an adjustable variable, which can be replaced as follows:

[0198]

[0199] in, is the maximum node voltage, is the minimum node voltage, B OLTC is the set of substation nodes including OLTC, is the voltage value of the high voltage side of the HV / MV transformer, which is a constant value. and They are the squares of the upper and lower limits of the OLTC adjustable ratio, is the square of the OLTC transformation ratio, defined as the ratio of the secondary side to the primary side. It is actually a discrete value variable and can be further processed into the following relationship containing 0-1 variables:

[0200]

[0201] Among them, r j,s is the difference between the square of the OLTC gear ratio of gear s and gear s-1, that is, the adjacent adjustment increment, is a 0-1 identification variable. If the actual constraints such as the limit of the number of adjustments are taken into account, it can be further constrained as follows:

[0202]

[0203] in, and They are the OLTC gear adjustment change flags, which are 0-1 variables. Then the OLTC gear value in the t period is greater than the gear value in the t-1 period. similar, is the maximum range of gear position change, It is the maximum allowable adjustment times of OLTC gear position in T period.

[0204] More specifically, when the distribution transformer is used as a step-up transformer, in order to ensure the quality of the grid voltage, the voltage on the high-voltage side of the transformer must be the same as when it is used as a step-down transformer. Taking the rated load as an example, considering the voltage drop of the transformer's own impedance, the relationship between the primary side voltage and the secondary side voltage is as follows:

[0205] When used as a step-down transformer:

[0206] U HV (1-u%) = kU LV (twenty three)

[0207] When used as a step-up transformer:

[0208] U' HV =kU' LV (1-u%) (24)

[0209] Among them, U HV U is the voltage on the high-voltage side when used as a step-down transformer. LV When used as a step-down transformer, U' is the low voltage side voltage. HV is the high voltage side voltage when used as a step-up transformer, U' LV is the low voltage side voltage when used as a step-up transformer, u% is the impedance voltage of the transformer, and k is the turns ratio of the transformer.

[0210] In an optional embodiment, the objective function of the intraday rolling optimization scheduling model is:

[0211]

[0212] Among them, C R is the total operating cost of the power grid company during the intraday rolling dispatch phase, a i0 , b i0 、c i0 are the secondary cost coefficients of distributed generation, Forecast electricity price for period t in the rolling phase, and are the amount of electricity purchased from the upper power grid in period t obtained by the day-ahead optimization dispatch result and the amount of electricity purchased from the upper power grid in period t during the rolling phase, It is a fixed value for participating in the intraday rolling optimization scheduling. is the latest quotation of load that can be reduced during the day in period t. It is assumed here that the load-reducing user will update the quotation every hour during the day. is the amount of load reduction that can be achieved at time t within the day.

[0213] In an optional embodiment, the power balance constraint of the intraday rolling optimization scheduling model is:

[0214]

[0215] in, ΔP t Wind , ΔP t PV , They are the change in power purchase from the power grid, the change in wind power and photovoltaic power, and the change in load at time t within the day;

[0216] The unit ramp power constraint of the intraday rolling optimization scheduling model is:

[0217]

[0218] The power purchase constraints of the intraday rolling optimization dispatch model are:

[0219]

[0220] The correction constraint of the rolling plan of the intraday rolling optimization scheduling model on the day-ahead plan is:

[0221]

[0222] in, The most recent rolling revision of the latest plan will be contributed. If no intraday revision is made, then That is the day-ahead optimal scheduling result, and P i,t +ΔP i,t within the neighborhood;

[0223] The load reduction constraint of the intraday rolling optimization scheduling model is:

[0224]

[0225] In an optional embodiment, the objective function of the coordinated optimization scheduling model is:

[0226]

[0227] Among them, C RT The total cost of operation optimization at time t in the intraday real-time scheduling phase is: The electricity price is predicted for the t period in the real-time dispatch phase within the day. is the amount of electricity purchased from the large power grid during period t in the real-time dispatch phase, is the power generation of the i-th distributed generation unit at time t during the intraday real-time scheduling phase, and are the real-time quotation and load reduction amount of the load that can be reduced at time t in the real-time dispatch phase of the day, ρ gc and They are the reactive electricity price in the auxiliary market and the corresponding reactive power purchased.

[0228] In an optional embodiment, the power flow constraint of the coordinated optimization scheduling model is:

[0229]

[0230] Among them, P i grid , P i DG , P i IL , P i L are the grid active power injected into node i, the active output of distributed generation, the reduction of flexible load and the load demand, Q i grid , Q i DG , Q i C , Q i L are the reactive power of the grid injected into node i, the reactive output of the distributed generation, the compensation capacity of the reactive compensator, and the reactive demand of the load;

[0231] The energy storage system constraints of the coordinated optimization scheduling model are:

[0232]

[0233] Among them, a t and b t They are respectively the charging or discharging working status of the energy storage battery; and are the charging and discharging power of the energy storage battery in time period t, is the output of the energy storage battery including charging and discharging in period t, P re.max and P ch.max are the maximum discharge and charge power of the energy storage battery, E t is the charge of the energy storage battery in period t, Emax and E min are the upper and lower limits of the charge respectively;

[0234] The voltage constraint of the coordinated optimization dispatch model is:

[0235] V imin ≤V i ≤V imax (35)

[0236] The capacity constraint of the reactive power compensator in the coordinated optimization dispatch model is:

[0237]

[0238] The output ramp constraint of distributed generation in the coordinated optimization scheduling model is:

[0239]

[0240] In summary, the present invention can effectively guide the planning and operation of the distribution network and better guarantee the service life of the transformer by establishing a transformer life model under frequent power flow switching, that is, establishing factors such as hot spot temperature rise and load side harmonic content that have a greater weight on the transformer life and the feasible domain of the transformer life. By adopting the method of hyperspace projection and boundary piecewise linearization to convexify the established transformer life model to obtain the transformer safe operation domain, thereby establishing a direct relationship between the transformer life and the safe operation domain, and then effectively improving the accuracy of solving the distribution network voltage management problem considering the transformer life constraint. And the safe operation domain can be used for power grid collaborative operation and planning, thereby improving the reliability and life of the transformer and reducing the impact of power flow switching on the performance of the built transformer. Based on the transformer safe operation domain, under the premise that the time cost of each distribution network control means is different, a coordinated optimization scheduling model is established to ensure that the actual life of the transformer can reach the expected value. When the model is applied to coordinate the control of the distribution network and the transformer, the operating reliability of the step-down transformer can be significantly improved, and a series of problems such as abnormal temperature rise of the transformer winding, accelerated insulation aging and core excitation distortion caused by the frequent power flow switching operation of the step-down transformer can be greatly reduced. That is, when the coordinated control method of the distribution network transformer provided by the above embodiment of the present invention is used to coordinate the control of the distribution network transformer, not only is it ensured that the actual life of the transformer reaches the expected value, but the relationship between reliability and economy is also fully considered, thereby improving the practicality and comprehensiveness of the multi-time scale coordinated control technology of the distribution network transformer that considers the entire life cycle of the transformer under frequent power flow switching.

[0241] A distribution network transformer coordination control system provided in an embodiment of the present invention is introduced below. The distribution network transformer coordination control system described below can be considered as a module architecture for implementing the distribution network transformer coordination control method provided in an embodiment of the present invention; the content described below can be cross-referenced with the above.

[0242] Optional, see Figure 4 , Figure 4 : is a structural block diagram of a distribution network transformer coordinated control system for running the distribution network transformer coordinated control method provided by the above embodiment of the present invention, and the system may include:

[0243] The acquisition unit 10 is used to obtain a target parameter, where the target parameter is a parameter whose influence on the life of the transformer exceeds a preset standard;

[0244] A first construction unit 20 is used to construct a transformer life model for characterizing a feasible domain of target parameters and transformer life based on target parameters;

[0245] The processing unit 30 is used to perform convex processing on the transformer life model to obtain a safe operation domain of the transformer;

[0246] The second construction unit 40 is used to construct a coordinated optimization scheduling model for ensuring that the actual service life of the transformer reaches the expected value based on the transformer safe operation domain;

[0247] The execution unit 50 is used to coordinate and control the distribution network and the transformer based on the coordinated optimization scheduling model.

[0248] Optionally, target parameters include: hot spot temperature and load side harmonic content.

[0249] Optionally, the first building unit 20 is specifically used for:

[0250] Based on the target parameters, determine the factors affecting the transformer life due to the power flow switching condition;

[0251] Based on the influencing factors, a transformer life model is constructed.

[0252] Optionally, the processing unit 30 is specifically configured to:

[0253] Perform hyperspace projection on the transformer life model based on the transformer life surface to obtain the life level set used to characterize the original safe operation domain of the transformer;

[0254] The life level set is piecewise linearized, and the original boundary of the original safe operation domain is replaced by the inner tangent represented by the linearized boundary equation.

[0255] Optionally, the second construction unit 40 is specifically used for:

[0256] The first parameter is input into the day-ahead optimization dispatch model to obtain the day-ahead optimization dispatch result. The first parameter includes: the day-ahead market electricity price, the output reduction compensation price of wind power and photovoltaic power, the expected life of the transformer, and the load-side load reduction compensation quotation. The day-ahead optimization dispatch result includes: the output of the controllable distributed power source on the next day, the amount of electricity purchased from the upper power grid, and the reduction amount of the flexible load;

[0257] The load demand of controllable distributed generation and the day-ahead optimization dispatching results are input into the intraday rolling optimization dispatching model to obtain the actual intraday output of each dispatching resource of the distribution network;

[0258] Based on the actual output within the day and the day-ahead optimized dispatch results, the node voltage of the distribution network connected to the controllable distributed power sources and the load that can be reduced is within the preset range. The goal is to minimize the active power loss of the distribution network. Through feedback correction in the rolling stage within the day, a coordinated optimization dispatch model is constructed.

[0259] Optionally, the day-ahead optimization dispatch model also includes an on-load voltage regulator.

[0260] Below, reference Figure 5 To describe the electronic device provided by the embodiment of the present application, the electronic device provided by the embodiment may include: at least one processor 100, at least one communication interface 200, at least one memory 300 and at least one communication bus 400;

[0261] In the embodiment of the present invention, the number of the processor 100, the communication interface 200, the memory 300, and the communication bus 400 is at least one, and the processor 100, the communication interface 200, and the memory 300 communicate with each other through the communication bus 400; obviously, Figure 5 The communication connections shown for the processor 100, the communication interface 200, the memory 300, and the communication bus 400 are merely optional;

[0262] Optionally, the communication interface 200 may be an interface of a communication module, such as an interface of a GSM module; the processor 100 may be a central processing unit CPU, or an application specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0263] The memory 300 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0264] The processor 100 is specifically used to execute the application program in the memory to implement the steps of the above-mentioned distribution network transformer coordinated control method.

[0265] In addition, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the coordinated control method of distribution network transformers provided by the above methods is implemented.

[0266] The above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A method for coordinated control of transformers in a distribution network, characterized in that: include: Obtaining a target parameter, wherein the target parameter is a parameter whose influence on the life of the transformer exceeds a preset standard; Based on the target parameters, constructing a transformer life model for characterizing a feasible domain of the target parameters and transformer life; Convex processing is performed on the transformer life model to obtain a safe operation domain of the transformer; Based on the transformer safe operation domain, a coordinated optimization scheduling model is constructed to ensure that the actual service life of the transformer reaches the expected value; Based on the coordinated optimization scheduling model, the distribution network and the transformer are coordinated and controlled.

2. The coordinated control method for distribution network transformers according to claim 1 is characterized in that: The target parameters include: hot spot temperature and load side harmonic content.

3. The coordinated control method for distribution network transformers according to claim 2 is characterized in that: The step of constructing a transformer life model for characterizing a feasible domain of the target parameter and the transformer life based on the target parameter includes: Based on the target parameters, determining the influence factor of the power flow switching condition on the life of the transformer; Based on the influencing factors, constructing the transformer life model; Wherein, the transformer life model is: TLM=f(ξ,θ h ) Where ξ is the harmonic content on the load side, θ h is the hot spot temperature of the transformer, including the ambient temperature, the top oil temperature rise and the winding hot spot temperature rise.

4. The coordinated control method for distribution network transformers according to claim 3 is characterized in that: The convexification processing of the transformer life model comprises: The transformer life model is subjected to a hyperspace projection based on the transformer life surface to obtain a life level set for characterizing the original safe operation domain of the transformer. The life level set is: X(n p )={(ξ,θ h )∣n≥n p } Where X is the life level set, n is the actual usable life of the transformer, and n p is the expected life of the transformer, ξ is the harmonic content on the load side, θ h is the hot spot temperature of the transformer; The life level set is piecewise linearized, and the original boundary of the original safe operation domain is replaced by the inner tangent represented by the linearized boundary equation. The boundary equation is: J·ξ+W·θ h ≤Y, where J, W and Y are coefficients of the boundary equation respectively.

5. The coordinated control method for distribution network transformers according to claim 4 is characterized in that: The load-side harmonic content constraint of the transformer life model is: A min ≤ξ≤A max Among them, A min is the minimum value of the ξ axis in the coordinate system where the original safe operation domain is located, A max is the intersection point of the inner tangent line and the ξ axis; The hot spot temperature constraint of the transformer life model is: B min ≤θ h ≤B max Among them, B min is θ in the coordinate system of the original safe operation domain h Minimum value of axis, B max is the inner tangent line and θ h The intersection of the axes.

6. The coordinated control method for distribution network transformers according to claim 1, characterized in that: The coordinated optimization scheduling model for ensuring that the actual service life of the transformer can reach the expected value is constructed based on the transformer safe operation domain, including: Input the first parameter into the day-ahead optimization dispatch model to obtain the day-ahead optimization dispatch result, wherein the first parameter includes: the day-ahead market electricity price, the output reduction compensation price of wind power and photovoltaic power, the expected life of the transformer, and the load-side load reduction compensation quotation, and the day-ahead optimization dispatch result includes: the output of the controllable distributed power source on the next day, the amount of electricity purchased from the upper power grid, and the reduction amount of the flexible load; Input the load demand of the controllable distributed power source and the day-ahead optimization scheduling result into the intraday rolling optimization scheduling model to obtain the intraday actual output of each scheduling resource of the distribution network; Based on the actual output within the day and the day-ahead optimization scheduling result, the node voltage of the distribution network after connecting to the controllable distributed power source and the load that can be reduced is made within a preset range, with the goal of minimizing the active power loss of the distribution network. By performing feedback correction on the rolling stage within the day, the coordinated optimization scheduling model is constructed.

7. The coordinated control method for transformers in a distribution network according to claim 6, characterized in that: The objective function of the day-ahead optimization scheduling model is: Among them, C DA is the total cost of the power grid operation in the day-ahead phase, T is the total dispatch period, ΔT is the time interval, N DG is the number of controllable distributed generation sources connected to the distribution network, a i , b i 、c i are the secondary output cost coefficients of the i-th distributed generation, is the output of the i-th controllable distributed generation at time t, N REG is the number of wind power and photovoltaic power connected to the distribution network, and are the predicted output and actual dispatched output of the uncontrollable distributed generation at time t the day before, is the compensation price for reducing the uncontrollable distributed power generation at time t a day ago, and are the market electricity price at time t the day before and the amount of electricity purchased from the upper power grid, and are the quotation for load reduction at time t on the next day and the amount of load reduction respectively.

8. The coordinated control method for transformers in a distribution network according to claim 7, characterized in that: The power balance constraint of the day-ahead optimization scheduling model is: in, is the total power demand value of the load at time t the next day; The system reserve constraint of the day-ahead optimal scheduling model is: Among them, P i max is the maximum output of the i-th distributed generation, is the upper limit of the amount of electricity purchased, is the maximum load reduction that can be reduced at time t; The power generation constraints of the day-ahead optimal dispatch model are: Among them, P i min is the minimum output of the i-th distributed generation, U i,t The start and stop status of the distributed power source; The minimum start and stop time constraint of the day-ahead optimization scheduling model is: Among them, MUP i and MDN i are the minimum operating time and shutdown time of distributed power sources respectively; The climbing constraint of the day-ahead optimization scheduling model is: Among them, R up,i and R dn,i are the maximum and minimum climbing limits of the i-th distributed generation respectively; The market power purchase of the day-ahead optimal dispatch model is: The load reduction constraint of the day-ahead optimal scheduling model is: The distribution network reconstruction constraints of the day-ahead optimization scheduling model are: Among them, V i,t is the node voltage of bus i at time t, P ij,t and Q ij,t are the active power and reactive power flowing on the line from node i to node j at time t, r ij and x ij are the resistance and reactance of the line from node i to node j, respectively, ij,t is the square of the line current at time t, M is a preset constant, α ij Is the switch flag, when α ij = 0, indicating that the switch between nodes i and j is disconnected. ij =1, indicating that the switch between nodes i and j is closed; The distribution network radiation constraint of the day-ahead optimal dispatch model is: ∑α ij =n-n s Where n is the total number of nodes in the distribution network, n s is the number of substations in the distribution network.

9. The coordinated control method for transformers in a distribution network according to claim 8, characterized in that: The day-ahead optimization scheduling model also includes an on-load voltage regulator.

10. The coordinated control method for transformers in a distribution network according to claim 6, characterized in that: The objective function of the intraday rolling optimization scheduling model is: Among them, C R is the total operating cost of the power grid company during the intraday rolling dispatch phase, a i0 、b i0 、c i0 are the secondary cost coefficients of distributed generation, Forecast electricity price for period t in the rolling phase, and are the amount of electricity purchased from the upper power grid in period t obtained by the day-ahead optimization dispatch result and the amount of electricity purchased from the upper power grid in period t during the rolling phase, It is a fixed value for participating in the intraday rolling optimization scheduling. is the latest quotation for load reduction during the day in period t, is the amount of load reduction that can be achieved at time t within the day.

11. The coordinated control method for transformers in a distribution network according to claim 10, characterized in that: The power balance constraint of the intraday rolling optimization scheduling model is: in, ΔP t Wind , ΔP t PV , They are the change in the amount of electricity purchased from the power grid, the change in wind power and photovoltaic power, and the change in load at time t within the day; The unit ramp power constraint of the intraday rolling optimization scheduling model is: The power purchase constraints of the intraday rolling optimization scheduling model are: The correction constraint of the rolling plan of the intraday rolling optimization scheduling model on the day-ahead plan is: in, The most recent rolling revision of the latest plan will be contributed. If no intraday revision is made, then That is the day-ahead optimization scheduling result, and P i,t +ΔP i,t within the neighborhood; The load reduction constraint of the intraday rolling optimization scheduling model is:

12. The coordinated control method for transformers in a distribution network according to claim 6, characterized in that: The objective function of the coordinated optimization scheduling model is: Among them, C RT The total cost of operation optimization at time t in the intraday real-time scheduling phase is: The electricity price is predicted for the t period in the real-time dispatch phase within the day. is the amount of electricity purchased from the large power grid during period t in the real-time dispatch phase, is the power generation of the i-th distributed generation unit at time t during the intraday real-time scheduling phase, and are the real-time quotation and load reduction amount of the load that can be reduced at time t in the real-time dispatch phase of the day, ρ gc and They are the reactive electricity price in the auxiliary market and the corresponding reactive power purchased.

13. The coordinated control method for transformers in a distribution network according to claim 12, characterized in that: The power flow constraints of the coordinated optimization scheduling model are: Among them, P i grid , P i DG , P i IL , P i L are the grid active power injected into node i, the active output of distributed generation, the reduction of flexible load and the load demand, are the reactive power of the grid injected into node i, the reactive output of the distributed generation, the compensation capacity of the reactive compensator, and the reactive demand of the load; The energy storage system constraints of the coordinated optimization scheduling model are: Among them, a t and b t They are respectively the charging or discharging working status of the energy storage battery; and are the charging and discharging power of the energy storage battery in time period t, is the output of the energy storage battery including charging and discharging in period t, P re.max and P ch.max are the maximum discharge and charge power of the energy storage battery, E t is the charge of the energy storage battery in period t, E max and E min are the upper and lower limits of the charge respectively; The voltage constraint of the coordinated optimization dispatch model is: In imin ≤V i ≤V imax The reactive power compensator capacity constraint of the coordinated optimization dispatch model is: The output ramp constraint of the distributed power generation in the coordinated optimization scheduling model is:

14. A distribution network transformer coordinated control system, used to run the distribution network transformer coordinated control method according to any one of claims 1 to 13, characterized in that: include: A collection unit, used to obtain a target parameter, wherein the target parameter is a parameter whose influence on the life of the transformer exceeds a preset standard; A first construction unit is used to construct a transformer life model for characterizing a feasible domain of the target parameter and the transformer life based on the target parameter; A processing unit, used for performing convex processing on the transformer life model to obtain a safe operation domain of the transformer; A second construction unit constructs a coordinated optimization scheduling model for ensuring that the actual service life of the transformer reaches an expected value based on the transformer safe operation domain; An execution unit is used to coordinate and control the distribution network and the transformer based on the coordinated optimization scheduling model.

15. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is used to execute the distribution network transformer coordinated control method described in any one of claims 1 to 13 above.

16. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 13 are implemented.

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