Optimal site selection method of phase-shifting transformer in power system

Abstract

The invention discloses an optimal site selection method of a phase-shifting transformer in a novel electric power system, and the method comprises the steps: building a dynamic planning model which takes the photovoltaic consumption rate, the power transmission capability of a power grid, and the investment and operation cost of the phase-shifting transformer as optimization indexes based on a dual-core symmetrical phase-shifting transformer model of equivalent impedance; according to the method, a novel calculation method for the transmission capacity of the power grid containing the phase-shifting transformer under the condition that photovoltaic uncertainty is considered is established, an additional judgment link is added into an improved multi-target particle swarm optimization solution strategy to solve an optimal solution set, and an optimal optimization scheme is selected according to a comprehensive evaluation method. According to the optimal configuration scheme established by the invention, the power grid power transmission capacity of the power transmission section and the power transmission capacity of the photovoltaic transmission section are improved, the photovoltaic consumption rate of the power system is further effectively improved, the superiority and universality are high for the optimal site selection problem of the phase-shifting transformer under the novel power system, and the method is easy to implement.

Description

Technical Field

[0001] The invention belongs to the field of renewable energy power generation, and in particular relates to an optimal site selection method for a phase-shifting transformer in a novel power system. Background Art

[0002] In the current new power system, there are gradually emerging characteristics such as multi-type energy generation, increasing capacity of generator sets, high voltage, long-distance transmission and grid interconnection, which leads to the increasing complexity of the system. This complexity makes it easy for the power grid to have limited cross-section power exchange and obstructed transmission sections when the power flow is unevenly distributed. In addition, the domestic power market reform is in full swing, and the power grid operators urgently need flexible power flow control methods to cope with the diversion of heavy or overloaded lines, smooth regional electricity prices, and improve the available power transmission capacity of the power grid. The phase-shifting transformer is an effective means to control the steady-state power flow and voltage of the power grid by injecting orthogonal voltage into the line to achieve the phase shifting function, or injecting the same-phase voltage to achieve the phase adjustment function. Compared with traditional grid reinforcement solutions such as new transmission lines, the phase-shifting transformer has the advantages of lower investment cost and higher utilization rate, which can fully tap the power transmission potential of the grid and provide better power flow regulation function.

[0003] At present, phase-shifting transformers are widely used abroad, but are relatively rare in actual engineering applications in China, and are usually limited to pilot projects. Regarding the simulation research on the steady-state application of phase-shifting transformers, the existing literature mainly focuses on specific problems under traditional power systems, and establishes corresponding solutions in combination with the characteristics of phase-shifting transformers. In power systems containing new energy, the research focus is usually on solving line overload and circulating current problems caused by new energy fluctuations. However, in the power grid system after the new energy is connected to the grid, the research on the installation site selection of phase-shifting transformers is still relatively scarce. A new type of optimization site selection method is urgently needed to improve the new energy absorption rate, solve problems such as cross-section transmission obstruction, and provide guidance for actual projects. Summary of the invention

[0004] In order to solve the technical problems mentioned in the above background technology, the present invention establishes a method for optimizing the site selection of a phase-shifting transformer in a power system.

[0005] In order to achieve the above technical objectives, the technical solution of the present invention is:

[0006] An optimal site selection method for phase-shifting transformers in power systems is proposed. Based on a double-core symmetrical phase-shifting transformer with equivalent impedance, a dynamic programming model with photovoltaic absorption rate, power grid transmission capacity, phase-shifting transformer investment and operating cost as optimization indicators is established. The transmission capacity of the power grid containing the phase-shifting transformer is calculated considering photovoltaic uncertainty. An additional judgment link is added to the improved multi-objective particle swarm optimization solution strategy to solve the optimal solution set, and the best optimization scheme is selected according to the comprehensive evaluation method. The steps of establishing the optimal site selection method for phase-shifting transformers in new power systems are as follows:

[0007] (1) Establish a dynamic programming model with PV consumption rate, grid transmission capacity, phase-shifting transformer investment and operating cost as optimization indicators, and phase-shifting transformer installation location, number and gear as constraints;

[0008] (2) Establish a new calculation method for the transmission capacity of the power grid containing phase-shifting transformers under photovoltaic uncertainty. Use the clustering scenario method to generate typical photovoltaic power generation scenarios. Take each scenario as the base state and combine it with the sensitivity coefficient of the phase-shifting transformer to derive the mathematical expression of the active power of each line. Use linear programming and power flow analysis to solve the transmission capacity of the power grid between the power transmission area and the power receiving area.

[0009] (3) An additional judgment step is added to the improved multi-objective particle swarm solution strategy to test whether there is any non-convergence or unreasonable situation in the power flow, and a phase-shifting transformer optimal site selection strategy is established in combination with a comprehensive evaluation method.

[0010] Furthermore, in step (1), a dynamic programming model of a phase-shifting transformer is established based on a double-core symmetrical phase-shifting transformer with equivalent impedance. The multi-objective optimization indicators of photovoltaic absorption rate, power grid transmission capacity, phase-shifting transformer investment and operating cost are as follows:

[0011] (1) Photovoltaic absorption rate

[0012] Objective function 1 seeks the maximum photovoltaic absorption rate, and the function expression is:

[0013]

[0014] where c k is the probability of scene k, W k is the photovoltaic consumption in scenario k, W kmax is the total PV output of scenario k, and N1 is the number of PV scenarios.

[0015] (2) Grid transmission capacity

[0016] Objective function 2 seeks the transmission capacity of the power grid to characterize the transmission capacity that the power grid can transmit based on the existing transmission power foundation and the safe and stable operation of the system. Its expression is:

[0017]

[0018] Where P TTCi is the maximum transmission capacity of the power grid in scenario i, c i is the probability of scene i.

[0019] (3) Phase shifter transformer investment and operating costs

[0020] Objective function three seeks the minimum sum of phase-shifting transformer investment and operation and maintenance cost. The function expression is:

[0021]

[0022] C i =γS i

[0023] Among them C i is the investment cost of phase-shifting transformer i, N is the number of phase-shifting transformers installed, β is the annual operating cost coefficient, T i is the operating time of phase-shifting transformer i, γ is the cost coefficient, S i is the capacity of the phase-shifting transformer i.

[0024] The power system constraints are as follows:

[0025] (1) Equality constraints

[0026] The equality constraint is the power balance equation of each node:

[0027]

[0028] Where P Gi and Q Gi are the active power and reactive power output of generator i, respectively, P Li and Q Li are the active and reactive power of the load on bus i, U i is the voltage amplitude of the ith node, G ij B ij are the conductance and susceptance of line ij, n b is the number of lines connected to node i.

[0029] (2) Inequality constraints

[0030] The inequality constraints include the system's generator active and reactive output constraints, bus voltage amplitude upper and lower limit constraints, and branch active power flow constraints:

[0031]

[0032] Among them U imin and U imaxare the upper and lower limits of the voltage amplitude of node i, respectively, Gimin and P Gimax are the upper and lower limits of the active power output of generator i, Q Gimin and Q Gimax are the upper and lower limits of reactive power output of generator i, P ijmax is the thermal stability limit of line ij.

[0033] The decision variable constraints are as follows:

[0034] (1) Installation location of phase-shifting transformer

[0035] In order to avoid solving unreasonable phase-shifting transformer site selection schemes, the present invention imposes the following restrictions on the site selection range and installation quantity of phase-shifting transformers: a. The site selection range is limited to the section where the photovoltaic transmission section is located; b. At most one unit can be installed on one line, and the number of controllable phase shifters installed in one section is limited to less than 2;

[0036] l∈D

[0037] Where l is the line where the phase-shifting transformer is installed, and D is the collection of all photovoltaic transmission sections.

[0038] (2) Phase-shifting transformer gear position

[0039] T min ≤T≤T max

[0040] Where T min and T max They are the minimum and maximum adjustable gear positions respectively.

[0041] (3) Number of phase-shifting transformers installed

[0042] N≤2

[0043] (4) Photovoltaic output

[0044] 0≤P wi ≤P wimax

[0045] Where P wi is the actual output of photovoltaic field i, P wimax is the maximum output of photovoltaic field i.

[0046] Furthermore, in step (2), the clustering scenario method is used to generate typical scenarios of photovoltaic power generation. Each scenario is used as the base state, and combined with the sensitivity coefficient of the phase-shifting transformer, the mathematical expression of the active power of each line is derived. The transmission capacity of the power grid between the power transmission area and the power receiving area is solved through linear programming and power flow analysis. The specific steps are as follows:

[0047] S1: Use K-means clustering method to obtain n photovoltaic scenes, let i = 1;

[0048] S2: Select a typical grid operation scenario, substitute the photovoltaic power output in scheme i into the typical operation scenario as the base state, and perform power flow calculation. Determine whether the photovoltaic absorption rate in each scenario is 100%. If it cannot be absorbed completely, take the regional intermittent power flow size under the maximum photovoltaic absorption as P TTC ; If all can be absorbed, go to S3;

[0049] S3: Calculate the sensitivity coefficient s of line l when all generators except the balancing machine in the source area change l .

[0050]

[0051] Where ΔE is the output change of all generators in the source area except the balancing machine.

[0052] S4: Combined with the sensitivity coefficient of each phase-shifting transformer to line l, the power flow expression of each line is obtained:

[0053]

[0054] in is the sensitivity coefficient of phase-shifting transformer j to the active power of line l, P l0 is the initial active power of line l in the base state grid, α j is the phase shift angle of the phase shifting transformer j.

[0055] S5: Combine S3 and S4 to get the final power flow expression for each line:

[0056]

[0057] S6: Take the phase shift angle and ΔE of each phase shift transformer as decision variables, so that the regional transmission section P TTC Taking the maximum as the goal, build a linear programming model:

[0058]

[0059] Where Ω T is the regional contact line set, P lmax is the thermal stability limit of line l.

[0060] S7: Solve the linear programming model, obtain the phase shift angle and ΔE values ​​and substitute them into the base state power grid, and then modify their values ​​appropriately according to the continuous power flow method. According to the modified base state power grid power flow results, P TTC , let i=i+1.

[0061] S8: If i=N1, go to S9; if i<N1, return to S2.

[0062] S9: Get P in all scenes TTC After the size, calculate P TTC The expected value ETTC is expressed as:

[0063]

[0064] Furthermore, in step (3), the random generation of phase shift angle and photovoltaic output based on the improved particle swarm solution strategy will lead to non-convergence or irrationality in power flow calculation (there is a line power flow reversal or the constraint conditions are not met), which will eventually affect the accuracy of the results. An additional judgment link is added to check whether there is non-convergence or irrationality in the power flow. At the same time, combined with the power flow control characteristics of the phase-shifting transformer, the unreasonable value range of the phase shift angle of each line on the photovoltaic transmission section is further excluded, that is, the search interval is correspondingly narrowed to:

[0065]

[0066] Where Ω p It is the set of lines with the largest power flow margin on all photovoltaic transmission sections, so the phase shift angle α is set to be greater than 0 (advance adjustment to increase the power flow on the line), Ω q It is a collection of lines with the smallest power flow margin and prone to overload on all photovoltaic transmission sections, so the phase shift angle α is set to < 0 (hysteresis adjustment to reduce the power flow on the line).

[0067] The comprehensive evaluation method is used to calculate the scores of each configuration scheme of the phase-shifting transformer. Finally, all the configuration schemes are ranked based on their scores to select the optimal scheme.

[0068] Furthermore, in steps (1), (2) and (3), a computer device is established, comprising: one or more processors, a memory and one or more programs, wherein the programs are stored in the memory and configured to be executed by the processor. The one or more programs contain instructions for executing any of the methods described in the technical solution.

[0069] The beneficial effects brought by adopting the above technical solution are:

[0070] At present, the optimization site selection method of phase-shifting transformer is mainly concentrated in traditional power systems. Usually, the phase shift angle is directly used as the control variable, and the characteristics of the phase-shifting transformer impedance changing with the phase shift angle are not fully considered. In the new power system containing new energy, the traditional method may affect the accuracy and application scheme of the phase-shifting transformer site selection, and it is difficult to maximize the new energy consumption rate while alleviating grid congestion. Based on the double-core symmetrical phase-shifting transformer with equivalent impedance, the present invention establishes a dynamic programming model of the phase-shifting transformer with photovoltaic consumption rate, grid transmission capacity, and investment and operation cost of the phase-shifting transformer as optimization indicators. The model takes into account photovoltaic uncertainty and calculates the transmission capacity of the grid containing the phase-shifting transformer. The improved multi-objective particle swarm optimization solution strategy is used to solve the optimal solution set, and the best optimization scheme is selected through a comprehensive evaluation method. The method of the present invention is comprehensive, reliable, and versatile. It can provide a scientific basis and technical support for the actual engineering application of the phase-shifting transformer, and help to plan and design the grid infrastructure more accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is a flow chart of the present invention;

[0072] Figure 2 This is the flow chart of the improved multi-objective particle swarm solution of the present invention. DETAILED DESCRIPTION

[0073] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.

[0074] like Figure 1 As shown in the figure, based on the double-core symmetrical phase-shifting transformer model with equivalent impedance, a dynamic programming model with photovoltaic absorption rate, power grid transmission capacity, phase-shifting transformer investment and operating cost as optimization indicators is established, and a new calculation method for the transmission capacity of the power grid containing phase-shifting transformer under photovoltaic uncertainty is established. An additional judgment link is added to the improved multi-objective particle swarm optimization solution strategy to solve the optimal solution set, and the best optimization scheme is selected according to the comprehensive evaluation method. The steps to establish the optimal site selection method for phase-shifting transformers in new power systems are as follows:

[0075] Step 1: Establish a dynamic programming model with PV consumption rate, grid transmission capacity, phase-shifting transformer investment and operating cost as optimization indicators, and phase-shifting transformer installation location, number and gear as constraints;

[0076] Step 2: Establish a new calculation method for the transmission capacity of the power grid with phase-shifting transformers under photovoltaic uncertainty. Use the clustering scenario method to generate typical photovoltaic power generation scenarios. Take each scenario as the base state and combine the sensitivity coefficient of the phase-shifting transformer to derive the mathematical expression of the active power of each line. Solve the transmission capacity of the power grid between the power transmission area and the power receiving area through linear programming and power flow analysis.

[0077] Step 3: Add an additional judgment link to the improved multi-objective particle swarm solution strategy to check whether there is non-convergence or unreasonable situation in the power flow, and establish the optimal location strategy of the phase-shifting transformer in combination with the comprehensive evaluation method.

[0078] In this embodiment, step 1 is implemented using the following preferred solution:

[0079] Based on the double-core symmetrical phase-shifting transformer model with equivalent impedance, a dynamic programming model of the phase-shifting transformer is established. The multi-objective optimization indicators of photovoltaic consumption rate, power grid transmission capacity, phase-shifting transformer investment and operating cost are as follows:

[0080] (1) Photovoltaic absorption rate

[0081] Objective function 1 seeks the maximum photovoltaic absorption rate, and the function expression is:

[0082]

[0083] where c k is the probability of scene k, W k is the photovoltaic consumption in scenario k, W kmax is the total PV output of scenario k, and N1 is the number of PV scenarios.

[0084] (2) Grid transmission capacity

[0085] Objective function 2 seeks the transmission capacity of the power grid to characterize the transmission capacity that the power grid can transmit based on the existing transmission power foundation and the safe and stable operation of the system. Its expression is:

[0086]

[0087] Where P TTCi is the maximum transmission capacity of the power grid in scenario i, c i is the probability of scene i.

[0088] (3) Phase shifter transformer investment and operating costs

[0089] Objective function three seeks the minimum sum of phase-shifting transformer investment and operation and maintenance cost. The function expression is:

[0090]

[0091] C i =γS i

[0092] Among them C i is the investment cost of phase-shifting transformer i, N is the number of phase-shifting transformers installed, β is the annual operating cost coefficient, T i is the operating time of phase-shifting transformer i, γ is the cost coefficient, Si is the capacity of the phase-shifting transformer i.

[0093] The power system constraints are as follows:

[0094] (1) Equality constraints

[0095] The equality constraint is the power balance equation of each node:

[0096]

[0097] Where P Gi and Q Gi are the active power and reactive power output of generator i, respectively, P Li and Q Li are the active and reactive power of the load on bus i, U i is the voltage amplitude of the ith node, G ij B ij are the conductance and susceptance of line ij, n b is the number of lines connected to node i.

[0098] (2) Inequality constraints

[0099] The inequality constraints include the system's generator active and reactive output constraints, bus voltage amplitude upper and lower limit constraints, and branch active power flow constraints:

[0100]

[0101] Among them U imin and U imax are the upper and lower limits of the voltage amplitude of node i, respectively, Gimin and P Gimax are the upper and lower limits of the active power output of generator i, Q Gimin and Q Gimax are the upper and lower limits of reactive power output of generator i, P ijmax is the thermal stability limit of line ij.

[0102] The decision variable constraints are as follows:

[0103] (1) Installation location of phase-shifting transformer

[0104] In order to avoid solving unreasonable phase-shifting transformer site selection schemes, the present invention imposes the following restrictions on the site selection range and installation quantity of phase-shifting transformers: a. The site selection range is limited to the section where the photovoltaic transmission section is located; b. At most one unit can be installed on one line, and the number of controllable phase shifters installed in one section is limited to less than 2;

[0105] l∈D

[0106] Where l is the line where the phase-shifting transformer is installed, and D is the collection of all photovoltaic transmission sections.

[0107] (2) Phase-shifting transformer gear position

[0108] T min ≤T≤T max

[0109] Where T min and T max They are the minimum and maximum adjustable gear positions respectively.

[0110] (3) Number of phase-shifting transformers installed

[0111] N≤2

[0112] (4) Photovoltaic output

[0113] 0≤P wi ≤P wimax

[0114] Where P wi is the actual output of photovoltaic field i, P wimax is the maximum output of photovoltaic field i.

[0115] In this embodiment, step 2 is implemented using the following preferred solution:

[0116] The clustering scenario method is used to generate typical scenarios of photovoltaic power generation. Each scenario is used as the base state. Combined with the sensitivity coefficient of the phase-shifting transformer, the mathematical expression of the active power of each line is derived. The transmission capacity of the power grid between the power transmission area and the power receiving area is solved through linear programming and power flow analysis. The specific steps are as follows:

[0117] S1: Use K-means clustering method to obtain n photovoltaic scenes, let i = 1;

[0118] S2: Select a typical grid operation scenario, substitute the photovoltaic power output in scheme i into the typical operation scenario as the base state, and perform power flow calculation. Determine whether the photovoltaic absorption rate in each scenario is 100%. If it cannot be absorbed completely, take the regional intermittent power flow size under the maximum photovoltaic absorption as P TTC ; If all can be absorbed, go to S3;

[0119] S3: Calculate the sensitivity coefficient s of line l when all generators except the balancing machine in the source area change l .

[0120]

[0121] Where ΔE is the output change of all generators in the source area except the balancing machine.

[0122] S4: Combined with the sensitivity coefficient of each phase-shifting transformer to line l, the power flow expression of each line is obtained:

[0123]

[0124] where ξ l j is the sensitivity coefficient of phase-shifting transformer j to the active power of line l, P l0 is the initial active power of line l in the base state grid, α j is the phase shift angle of the phase shifting transformer j.

[0125] S5: Combine S3 and S4 to get the final power flow expression for each line:

[0126]

[0127] S6: Take the phase shift angle and ΔE of each phase shift transformer as decision variables, so that the regional transmission section P TTC Taking the maximum as the goal, build a linear programming model:

[0128]

[0129] Where Ω T is the regional contact line set, P lmax is the thermal stability limit of line l.

[0130] S7: Solve the linear programming model, obtain the phase shift angle and ΔE values ​​and substitute them into the base state power grid, and then modify their values ​​appropriately according to the continuous power flow method. According to the modified base state power grid power flow results, P TTC , let i=i+1.

[0131] S8: If i=N1, go to S9; if i<N1, return to S2.

[0132] S9: Get P in all scenes TTC After the size, calculate P TTC The expected value ETTC is expressed as:

[0133]

[0134] In this embodiment, step 3 is implemented using the following preferred solution:

[0135] In order to improve the convergence accuracy and speed of the algorithm in the phase-shifting transformer optimization site selection problem, the value of the inertia weight ω is improved by using the current particle position and the optimal position. The expression is as follows:

[0136]

[0137] Where t is the number of completed iterations, tmax is the maximum number of iterations, n var is the number of decision variables, x j_n is the nth objective function of the jth particle, x max_n 、x min_n are the minimum and maximum values ​​of the nth decision variable, respectively. gest_n is the optimal value of the nth decision variable at the current iteration, ω max ,ω min are the minimum and maximum values ​​of the inertia weight respectively.

[0138] A larger learning factor c1 and a smaller learning factor c2 are set for the initial operation, and the opposite is true in the later operation to enhance the diversity of the algorithm. The expressions of c1 and c2 are as follows:

[0139]

[0140] where c 1,start 、c 2,start 、c 1,end 、c 2,end are the initial and final values ​​of c1 and c2 respectively. 1,start =c 2,end =2.5, c 1,end =c 2,start =0.5.

[0141] The random generation of phase shift angle and photovoltaic output based on the improved particle swarm solution strategy will lead to non-convergence or irrationality in power flow calculation (there is a line power flow reversal or the constraint conditions are not met), which will eventually affect the accuracy of the results. An additional judgment link is added to check whether there is non-convergence or irrationality in the power flow. At the same time, combined with the power flow control characteristics of the phase-shifting transformer, the unreasonable value range of the phase shift angle of each line on the photovoltaic transmission section is further excluded, that is, the search interval is correspondingly narrowed to:

[0142]

[0143] Where Ω p It is the set of lines with the largest power flow margin on all photovoltaic transmission sections, so the phase shift angle α is set to be greater than 0 (advance adjustment to increase the power flow on the line), Ω q It is a collection of lines with the smallest power flow margin and prone to overload on all photovoltaic transmission sections, so the phase shift angle α is set to < 0 (hysteresis adjustment to reduce the power flow on the line).

[0144] Combined with the comprehensive evaluation method, the scores of each configuration scheme of the phase-shifting transformer are calculated. Finally, all the configuration schemes are ranked according to their scores to select the best one. The specific optimization site selection strategy is shown in the attached Figure 2 .

[0145] The embodiments are only for illustrating the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea established in the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for optimizing the location of a phase-shifting transformer in a power system, characterized in that: The specific steps are as follows: (1) Establish a dynamic programming model with PV consumption rate, grid transmission capacity, phase-shifting transformer investment and operating cost as optimization indicators, and phase-shifting transformer installation location, number and gear as constraints; (2) A new calculation method for the transmission capacity of a power grid with a phase-shifting transformer under photovoltaic uncertainty is proposed. The clustering scenario method is used to generate typical photovoltaic power generation scenarios. Each scenario is used as the base state. Combined with the sensitivity coefficient of the phase-shifting transformer, the mathematical expression of the active power of each line is derived. The transmission capacity of the power grid between the power transmission area and the power receiving area is solved through linear programming and power flow analysis. (3) An additional judgment step is added to the improved multi-objective particle swarm solution strategy to test whether there is any non-convergence or unreasonable situation in the power flow, and a phase-shifting transformer optimization site selection strategy is established in combination with a comprehensive evaluation method.

2. The method for optimizing the location of a phase-shifting transformer in a power system according to claim 1, characterized in that: In step (1), the optimization indicators include photovoltaic absorption rate, power grid transmission capacity and phase-shifting transformer investment and operation cost. The functional expression of the photovoltaic absorption rate is: where c k is the probability of scene k, W k is the photovoltaic consumption in scenario k, W kmax is the total photovoltaic output of scenario k, and N1 is the number of photovoltaic scenarios. The functional expression of the power transmission capacity of the power grid is: Where P TTCi is the maximum transmission capacity of the power grid in scenario i, c i is the probability of scene i. The functional expression of the investment and operation cost of the phase-shifting transformer is: C i =γS i Among them C i is the investment cost of phase-shifting transformer i, N is the number of phase-shifting transformers installed, β is the annual operating cost coefficient, T i is the operating time of phase-shifting transformer i, γ is the cost coefficient, S i is the capacity of the phase-shifting transformer i. The power system constraints include equality constraints and inequality constraints. The equality constraints are power balance equations of each node, specifically: Where P Gi and Q Gi are the active power and reactive power output of generator i, respectively, P Li and Q Li are the active and reactive power of the load on bus i, U i is the voltage amplitude of the ith node, G ij B ij are the conductance and susceptance of line ij, θ ij is the impedance angle of line ij, n b is the number of lines connected to node i; The inequality constraints include the system's generator active and reactive output constraints, bus voltage amplitude upper and lower limit constraints, and branch active power flow constraints, specifically: Among them U imin and U imax are the upper and lower limits of the voltage amplitude of node i, respectively, Gimin and P Gimax are the upper and lower limits of the active power output of generator i, Q Gimin and Q Gimax are the upper and lower limits of reactive power output of generator i, P ijmax is the thermal stability limit of line ij. The decision variable constraints include the phase-shifting transformer installation location, the phase-shifting transformer gear, the number of phase-shifting transformers installed, and the photovoltaic output. (1) Installation location of phase-shifting transformer The phase-shifting transformer is arranged at the section where the photovoltaic transmission section is located. At most one phase-shifting transformer is arranged on one line. The number of controllable phase shifters installed in one section is less than 2. l∈D Where l is the line where the phase-shifting transformer is installed, and D is the collection of all photovoltaic transmission sections. (2) Phase-shifting transformer gear position T min ≤T≤T max Where T min and T max They are the minimum and maximum adjustable gear positions respectively. (3) Number of phase-shifting transformers installed N≤2 (4) Photovoltaic output 0≤P wi ≤P wimax Where P wi is the actual output of photovoltaic field i, P wimax is the maximum output of photovoltaic field i.

3. The method for optimizing the location of a phase-shifting transformer in a power system according to claim 1, characterized in that: In step (2), the clustering scenario method is used to generate typical scenarios of photovoltaic power generation. Each scenario is used as the base state, and combined with the sensitivity coefficient of the phase-shifting transformer, the mathematical expression of the active power of each line is derived. The transmission capacity of the power grid between the power transmission area and the power receiving area is solved through linear programming and power flow analysis. The specific steps are as follows: S1: Use K-means clustering method to obtain n photovoltaic scenes, let i = 1; S2: Select a typical grid operation scenario, substitute the photovoltaic power output in scheme i into the typical operation scenario as the base state, and perform power flow calculation. Determine whether the photovoltaic absorption rate in each scenario is 100%. If it cannot be absorbed completely, take the regional intermittent power flow size under the maximum photovoltaic absorption as P TTC ; If all can be absorbed, go to S3; S3: Calculate the sensitivity coefficient s of line l when all generators except the balancing machine in the source area change l . Where ΔE is the output change value of all generators in the source area except the balancing machine; S4: Combined with the sensitivity coefficient of each phase-shifting transformer to line l, the power flow expression of each line is obtained: in is the sensitivity coefficient of phase-shifting transformer j to the active power of line l, P l0 is the initial active power of line l in the base state grid, α j is the phase shift angle of phase shift transformer j; S5: Combine S3 and S4 to get the final power flow expression for each line: S6: Take the phase shift angle of each phase shift transformer and ΔE as decision variables, so that the regional transmission section P TTC Taking the maximum as the goal, build a linear programming model: Where Ω T is the regional contact line set, P lmax is the thermal stability limit of line l; S7: Solve the linear programming model, obtain the phase shift angle and ΔE values ​​and substitute them into the base state power grid, and then modify their values ​​appropriately according to the continuous power flow method. According to the modified base state power grid power flow results, P TTC The value of , let i = i + 1; S8: if i=N1, go to S9; If i<N1, return to S2; S9: Get P in all scenes TTC After the size, calculate P TTC The expected value ETTC is expressed as:

4. The method for optimizing the location of a phase-shifting transformer in a power system according to claim 1, characterized in that: In step (3), the search interval of the judgment link is: Where Ω p is the set of lines with the largest power flow margin on all photovoltaic transmission sections, Ω q It is a collection of lines with the smallest power flow margin and prone to overload on all photovoltaic transmission sections; The comprehensive evaluation method is used to calculate the scores of each configuration scheme of the phase-shifting transformer. Finally, all the configuration schemes are ranked based on their scores to select the optimal scheme.

5. A computer device, characterized in that: The method comprises one or more processors, a memory and one or more programs, wherein the programs are stored in the memory and configured to be executed by the processor. The one or more programs contain instructions for executing any one of the methods according to claims 1-4.

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