A power grid planning method and terminal based on new energy with high penetration rate

Abstract

The present invention discloses a power grid planning method and terminal based on high-penetration new energy. With the goal of minimizing network losses and voltage and frequency fluctuations at the load in a power system with high-penetration new energy grid connection, a power grid planning model for a power system with high-penetration new energy grid connection is established. After obtaining the new energy parameters and load conditions of the power system, they are input into the power grid planning model, and an adaptive genetic simulated annealing algorithm can be used to solve the power grid planning model of the power system considering high-penetration new energy grid connection, thereby reducing uncertain energy input. Moreover, through the reliable selection of power grid planning within the power system with the characteristics of high-penetration new energy, the operation stability of the power system under the background of high-penetration new energy grid connection can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid planning, and particularly relates to a power grid planning method and a terminal based on high-penetration new energy. Background Art

[0002] With the large-scale introduction of new energy such as wind power in the power system, higher requirements are put forward for the safe and stable operation of the power system. As the main energy source at the power input end of the system, new energy power stations under the background of high penetration have extremely large power supply instability and are greatly affected by natural environmental factors, and are prone to situations such as voltage fluctuations and frequency increases in the system, resulting in a deterioration of the system's power supply capacity. As a result, different types of loads such as commercial loads, residential loads, and industrial loads are affected, that is, different degrees of load fluctuations are generated, which has a great impact on the safe and stable operation of the power system.

[0003] Current research always aims to reduce the output of new energy, thereby reducing the uncertain energy input, so as to achieve the optimal planning of new energy power stations. However, in the power system under the background of high-penetration new energy, it is of great significance to reasonably predict the output of new energy power stations and predict the power response ability of new energy power stations. Summary of the Invention

[0004] The technical problem to be solved by the present invention is: to provide a power grid planning method and a terminal based on high-penetration new energy, which can reasonably predict the output of new energy power stations in the power system under the background of high-penetration new energy.

[0005] In order to solve the above technical problem, the technical solution adopted by the present invention is:

[0006] A power grid planning method based on high-penetration new energy, comprising the steps of:

[0007] Obtain the new energy parameters and load conditions of the power system with high-penetration new energy grid connection;

[0008] Taking the minimum network loss and voltage and frequency fluctuations at the load in the power system as the goal, establish a power grid planning model for the power system with high-penetration new energy grid connection;

[0009] Input the new energy parameters and load conditions into the power grid planning model, and use the adaptive genetic simulated annealing method to solve the power grid planning model to obtain the power grid planning scheme of the power system.

[0010] In order to solve the above technical problem, another technical solution adopted by the present invention is:

[0011] A power grid planning terminal based on high-penetration new energy includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, each step of the above-mentioned power grid planning method based on high-penetration new energy is implemented.

[0012] The beneficial effects of the present invention are as follows: aiming at minimizing the network loss and the voltage and frequency fluctuations at the load in the power system with high-penetration new energy grid connection, a power grid planning model for the power system based on high-penetration new energy grid connection is established; after obtaining the new energy parameters and load conditions of the power system, inputting them into the power grid planning model, the adaptive genetic simulated annealing algorithm can be used to solve the power grid planning model of the power system considering high-penetration new energy grid connection, thereby reducing the uncertain energy input. Moreover, through the reliable selection of the power grid planning in the power system with the characteristics of high-penetration new energy, the operation stability of the power system under the background of high-penetration new energy grid connection can be effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a flowchart of a power grid planning method based on high-penetration new energy according to an embodiment of the present invention;

[0014] Figure 2 It is a schematic diagram of a power grid planning terminal based on high-penetration new energy according to an embodiment of the present invention;

[0015] Label Description:

[0016] 1. A power grid planning terminal based on high-penetration new energy; 2. Memory; 3. Processor. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] To describe in detail the technical content, achieved objectives, and effects of the present invention, the following is described in conjunction with the embodiments and accompanied by the drawings.

[0018] Please refer to Figure 1 , an embodiment of the present invention provides a power grid planning method based on high-penetration new energy, including the steps of:

[0019] Obtain the new energy parameters and load conditions of the power system with high-penetration new energy grid connection;

[0020] Taking the minimum network loss and the minimum voltage and frequency fluctuations at the load in the power system as the objectives, establish a power grid planning model for the power system based on high-penetration new energy grid connection;

[0021] Input the new energy parameters and load conditions into the power grid planning model, and use the adaptive genetic simulated annealing method to solve the power grid planning model to obtain the power grid planning scheme of the power system.

[0022] As can be seen from the above description, the beneficial effects of the present invention are as follows: aiming at minimizing the network loss and the voltage and frequency fluctuations at the load in the power system with high-penetration new energy grid connection, a grid planning model for the power system based on high-penetration new energy grid connection is established; after obtaining the new energy parameters and load conditions of the power system, inputting them into the grid planning model, the adaptive genetic simulated annealing algorithm can be used to solve the grid planning model for the power system considering high-penetration new energy grid connection, thereby reducing the uncertain energy input. Moreover, through the reliable selection of the grid planning in the power system with the characteristics of high-penetration new energy, the operation stability of the power system under the background of high-penetration new energy grid connection can be effectively improved.

[0023] Furthermore, aiming at minimizing the network loss and the voltage and frequency fluctuations at the load in the power system, establishing a grid planning model for the power system based on high-penetration new energy includes:

[0024] Establishing the objective function of the grid planning model for the power system based on high-penetration new energy:

[0025] minF=p 1 f 1 +p 2 f 2 +p 3 f 3 ;

[0026] Wherein, F represents the comprehensive planning function expression of the power system with high-penetration new energy grid connection, f 1 , f 2 , f 3 respectively represent the network loss, voltage fluctuation and frequency fluctuation in the power system, p 1 , p 2 , p 3 respectively represent the weight coefficients for converting f 1 , f 2 , f 3 into the same unit, p 1 +p 2 +p 3 =1;

[0027] The network loss in the power system is:

[0028]

[0029] Wherein, W i loss,s , W j loss,s , W k loss,sIndicates the network losses generated by the commercial load of node i, the industrial load of node j, and the residential load of node k;

[0030] The voltage frequency fluctuation at the load in the power system is:

[0031]

[0032] In the formula, U m 、U m0 represent the current voltage amplitude and the rated voltage amplitude of node m, and Ω M represents the set of all nodes, and M represents the total number of nodes;

[0033] The frequency fluctuation at the load in the power system is:

[0034]

[0035] In the formula, f m 、f m0 represent the current frequency amplitude and the rated frequency amplitude of node m.

[0036] As can be seen from the above description, a power grid planning model is established with the goals of minimizing the network losses in the power grid system with high-penetration new energy characteristics, minimizing the voltage fluctuations at the loads in the power grid system with high-penetration new energy characteristics, and minimizing the frequency fluctuations at the loads in the power grid system with high-penetration new energy characteristics, so as to reasonably predict the power output of the power system.

[0037] Furthermore, the constraint conditions of the power grid planning model include: the power flow constraint in the power system, the line power constraint, the reactive power compensation device capacity constraint, and the constraint that the rising and falling rates of the net load curve provided by the power system meet the rising and falling rates of the net load curve of the main grid.

[0038] As can be seen from the above description, for the power grid planning model of a power system with high-penetration new energy grid connection, the power flow in the power system, the line power in the system, the reactive power compensation device capacity in the system, and the rising and falling rates of the net load curve provided by the high-penetration new energy grid connection system can meet the rising and falling rates of the net load curve of the main grid as constraints, so as to improve the reliability of power grid planning.

[0039] Furthermore, the power flow constraint in the power system includes:

[0040]

[0041]

[0042] In the formula, P g 、Q g represent the active power and reactive power injected into the distribution network, Pl , Q l represents the active power and reactive power required by the distribution network, and Q s represents the reactive power output of the reactive power compensation device installed at node s of the distribution network system. N represents the number of reactive power compensation devices installed in the distribution network, and U i , U j respectively represent the voltage values corresponding to node i and node j of the distribution network, and G ij , B ij , θ ij respectively represent the conductance value, susceptance value of the distribution line model in the distribution network, and the phase angle difference between the transmission end and the load end of the distribution line.

[0043] As can be seen from the above description, obtaining the power flow constraints in the power system based on the power data of the distribution network can further improve the reliability of the power grid planning model.

[0044] Furthermore, the line power constraints in the power system include:

[0045] P l ≤ P lmax ;

[0046] In the formula, P l represents the active power transmitted on line l in the power system, and P lmax is the maximum value of the active power transmitted on line l in the distribution network;

[0047] The reactive power compensation device capacity constraints in the power system include:

[0048] 0 ≤ Q w ≤ Q max ;

[0049] In the formula, Q max represents the maximum value of the reactive power compensation device that can be installed at node i of the distribution line of the distribution network system.

[0050] As can be seen from the above description, obtaining the constraints in the power system based on the active and reactive powers and their maximum values in the power system can further improve the reliability of the power grid planning model.

[0051] Furthermore, the rising and falling rates of the net load curve provided by the power system satisfy the constraints of the rising and falling rates of the net load curve of the main grid, including:

[0052]

[0053]

[0054] In the formula, S' up , S' downDenote the rising and falling rates of the net load curve of the main grid system with high-penetration new energy grid connection as S up and S down Denote the rising and falling rates of the net load curve of the original main grid system as S 1 and S 3 Denote the rising rate of the net load curve that can be provided by the high-penetration photovoltaic power station and the high-penetration wind power station as S 2 and S 4 Denote the falling rate of the net load curve that can be provided by the high-penetration photovoltaic power station and the high-penetration wind power station.

[0055] It can be seen from the above description that the rising and falling rates of the net load curve provided can meet the rising and falling rates of the net load curve of the main grid to obtain the constraints within the power system, and can further improve the reliability of the power grid planning model.

[0056] Furthermore, establishing the power grid planning model of the power system based on high-penetration new energy includes:

[0057] Obtain the power grid planning model by establishing the output prediction model of the photovoltaic power station and the wind power station in the power system, and establishing the static voltage characteristic model of various loads.

[0058] It can be seen from the above description that the power grid planning model is obtained by combining the output prediction model of the photovoltaic power station and the wind power station and the static voltage characteristic model of various loads. Therefore, the planning model integrating the above models has high reliability.

[0059] Furthermore, establishing the output prediction model of the photovoltaic power station in the power system includes:

[0060]

[0061] In the formula, R represents the solar radiation intensity, R max represents the maximum solar radiation intensity, Γ represents the Gamma function, and α and β respectively represent the shape parameter and the scale parameter of the Beta distribution;

[0062] Predict the output of the high-penetration photovoltaic power station based on the output prediction model of the photovoltaic power station:

[0063]

[0064]

[0065] In the formula, H max,t represents the maximum output of the high-penetration photovoltaic power station at time t, H min,t represents the minimum output of the high-penetration photovoltaic power station at time t, μ t avgdenotes the expected value of the predicted output of a high-penetration PV power station at time t, σ t represents the output error of a high-penetration PV power station at time t;

[0066] Building the output prediction model of the wind power station in the power system includes:

[0067]

[0068]

[0069] In the formula, c and k respectively represent the scale parameter and shape parameter of the Weibull distribution, σ and μ respectively represent the standard deviation and expectation of the Weibull distribution, and v represents the wind speed of the wind power station;

[0070] Predict the output of the high-penetration wind power station based on the output prediction model of the wind power station:

[0071]

[0072]

[0073] In the formula, P max,t represents the maximum output of the high-penetration wind power station at time t, P min,t represents the minimum output of the high-penetration wind power station at time t, a t avg represents the expected value of the predicted output of the high-penetration wind power station at time t, b t represents the output error of the high-penetration wind power station at time t.

[0074] As can be seen from the above description, by predicting the output capabilities of PV and wind power stations, the power optimal planning of a high-penetration new energy power system can be achieved.

[0075] Furthermore, building the static voltage characteristic model of various loads includes:

[0076] Classify the loads in the system and build the static voltage characteristic models of various loads:

[0077]

[0078] In the formula, W 0 represents the overall load of the system before connecting to the new energy power supply, W so,i , W go,j , W zo,j respectively represent the commercial load power of node i, the industrial load power of node j, and the residential load power of node k before connecting to the new energy power supply, Ω I , Ω J , ΩK respectively represent the sets of various types of load nodes;

[0079] A polynomial model is adopted to establish the static voltage characteristics of the load:

[0080]

[0081]

[0082] Among them, P i0 , Q i0 respectively represent the active power and reactive power of the load at the i-th node, U N represents the voltage reference value; U i represents the actual values of the active and reactive powers of the voltage at the i-th node, a p , b p , c p , a q , b q , c q respectively represent the proportions of constant-power loads, constant-current loads, and constant-impedance loads. The coefficients satisfy a p +b p +c p =1, a q +b q +c q =1;

[0083] After the new energy power station is connected, the system load is:

[0084]

[0085] In the formula, W a represents the load situation of the system after the new energy power station is connected, and λ s , λ g , λ z represent the influence coefficients of load fluctuations in the system caused by various factors.

[0086] Please refer to Figure 2 , Another embodiment of the present invention provides a power grid planning terminal based on high-penetration new energy, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it realizes each step of the above-mentioned power grid planning method based on high-penetration new energy.

[0087] The above-mentioned power grid planning method and terminal based on high-penetration new energy of the present invention are applicable to reasonably predicting the output of new energy power stations in a power system under the background of high-penetration new energy. The following is illustrated by specific embodiments:

[0088] Embodiment 1

[0089] Please refer to Figure 1 , a power grid planning method based on high-penetration new energy, comprising the steps of:

[0090] S1. Obtain the new energy parameters and load conditions of the power system with high-penetration new energy grid connection.

[0091] S2. Taking the minimum network loss and voltage and frequency fluctuations at the load in the power system as the objective, and taking the power flow constraints, line power constraints, reactive power compensation device capacity constraints in the power system, and the rise and fall rates of the net load curve provided by the power system satisfying the rise and fall rate constraints of the main network's net load curve as the constraint conditions, establish a power grid planning model for the power system with high-penetration new energy grid connection.

[0092] S21. Establish the objective function of the power grid planning model for the power system with high-penetration new energy:

[0093] minF = p 1 f 1 + p 2 f 2 + p 3 f 3 ;

[0094] In the formula, F represents the comprehensive planning function expression of the power system with high-penetration new energy grid connection, f 1 , f 2 , f 3 respectively represent the network loss, voltage fluctuation and frequency fluctuation in the power system, p 1 , p 2 , p 3 respectively represent the weight coefficients for converting f 1 , f 2 , f 3 into the same unit, p 1 + p 2 + p 3 = 1.

[0095] S211. The network loss f 1 in the power system is:

[0096]

[0097] In the formula, W i loss,s , W j loss,s , W k loss,s represent the network losses generated by the commercial load of node i, the industrial load of node j and the residential load of node k.

[0098] S212. The voltage frequency fluctuation f at the load in the power system is 2 as follows:

[0099]

[0100] where U m and U m0 represent the current voltage amplitude and the rated voltage amplitude of node m, Ω M represents the set of all nodes, and M represents the total number of nodes.

[0101] S213. The frequency fluctuation f at the load in the power system is 3 as follows:

[0102]

[0103] where f m and f m0 represent the current frequency amplitude and the rated frequency amplitude of node m.

[0104] S214. The power flow constraints in the power system include:

[0105]

[0106]

[0107] where P g and Q g represent the active power and reactive power injected into the distribution network, P l and Q l represent the active power and reactive power required by the distribution network, Q s represents the reactive power output of the reactive power compensation device installed at node s of the distribution network system, N represents the number of reactive power compensation devices installed in the distribution network, U i and U j respectively represent the voltage values corresponding to nodes i and j of the distribution network, G ij , B ij , and θ ij respectively represent the conductance value, susceptance value of the distribution line model in the distribution network, and the phase angle difference between the transmission end and the load end of the distribution line.

[0108] S215. The line power constraints in the power system include:

[0109] P l ≤P lmax ;

[0110] where P l represents the active power transmitted on line l in the power system, P lmaxis the maximum value of the active power transmitted on line l in the distribution network;

[0111] S216. The reactive power compensation device capacity constraint in the power system includes:

[0112] 0 ≤ Q w ≤ Q max ;

[0113] In the formula, Q max represents the maximum value of the reactive power compensation device that can be installed at node i of the distribution line in the distribution network system.

[0114] S217. The constraints on the rising and falling rates of the net load curve provided by the power system to meet those of the main network include:

[0115]

[0116]

[0117] In the formula, S' up 、S' down represent the rising and falling rates of the net load curve of the main network system with high-penetration new energy grid connection, S up 、S down represent the rising and falling rates of the net load curve of the original main network system, S 1 、S 3 represent the rising rate of the net load curve that can be provided by high-penetration photovoltaic power plants and high-penetration wind power plants, S 2 、S 4 represent the falling rate of the net load curve that can be provided by high-penetration photovoltaic power plants and high-penetration wind power plants.

[0118] S22. By establishing the output prediction model of the photovoltaic power plants and wind power plants in the power system, and establishing the static voltage characteristic model of various loads, the power grid planning model is obtained.

[0119] S221. Establish the output prediction model of the photovoltaic power plants in the power system:

[0120]

[0121] In the formula, R represents the solar radiation intensity, R max represents the maximum solar radiation intensity, Γ represents the Gamma function, and α and β represent the shape parameter and scale parameter of the Beta distribution respectively;

[0122] Based on the output prediction model of the photovoltaic power plants, predict the output of high-penetration photovoltaic power plants:

[0123]

[0124]

[0125] Wherein, H max,t represents the maximum output of the high-penetration photovoltaic power station at time t, and H min,t represents the minimum output of the high-penetration photovoltaic power station at time t, and μ t avg represents the expected value of the predicted output of the high-penetration photovoltaic power station during the t period, and σ t represents the output error of the high-penetration photovoltaic power station during the t period.

[0126] S222. Establish an output prediction model for the fan power station of the power system:

[0127]

[0128]

[0129] Wherein, c and k respectively represent the scale parameter and shape parameter of the Weibull distribution, σ and μ respectively represent the standard deviation and expectation of the Weibull distribution, and v represents the wind speed of the fan power station;

[0130] Predict the output of the high-penetration fan power station based on the output prediction model of the fan power station:

[0131]

[0132]

[0133] Wherein, P max,t represents the maximum output of the high-penetration fan power station at time t, and P min,t represents the minimum output of the high-penetration fan power station at time t, and a t avg represents the expected value of the predicted output of the high-penetration fan power station during the t period, and b t represents the output error of the high-penetration fan power station during the t period.

[0134] S223. Establish a static voltage characteristic model for various loads:

[0135]

[0136] Wherein, W 0 represents the overall load of the system before connecting to the new energy power supply, and W so,i , W go,j , W zo,j respectively represent the commercial load power of node i, the industrial load power of node j, and the residential load power of node k before connecting to the new energy power supply, and ΩI , Ω J , Ω K respectively represent the sets of various types of load nodes;

[0137] A polynomial model is used to establish the static voltage characteristics of the load:

[0138]

[0139]

[0140] Among them, P i0 , Q i0 respectively represent the active power and reactive power of the load at the i-th node, U N represents the voltage reference value; U i represents the actual values of the active and reactive powers of the voltage at the i-th node, a p , b p , c p , a q , b q , c q respectively represent the proportions of constant power loads, constant current loads, and constant impedance loads. The coefficients satisfy, a p + b p + c p = 1, a q + b q + c q = 1;

[0141] After the new energy power station is connected, the system load is:

[0142]

[0143] In the formula, W a represents the load situation of the system after the new energy power station is connected, λ s , λ g , λ z represent the influence coefficients of load fluctuations in the system caused by various factors.

[0144] S3. Input the new energy parameters and load situation into the power grid planning model, and use the adaptive genetic simulated annealing method to solve the power grid planning model to obtain the power grid planning scheme of the power system.

[0145] Among them, the obtained power grid planning scheme is the output situation of the high-penetration new energy power system at this moment.

[0146] In this embodiment, by collecting the sunlight intensity, the maximum solar radiation intensity, and the wind farm wind speed in real time, converting them into digital quantities, and then transmitting them as inputs to the power system planning model for high-penetration new energy grid connection, combined with the relevant parameters of the power system lines for high-penetration new energy grid connection, the load size, and the load change situation, a power system planning scheme for high-penetration new energy grid connection is obtained, providing a reference for the output situation of the high-penetration new energy power system.

[0147] To better verify the effectiveness of the method proposed in the present invention, on a certain day in summer in a certain area, the sunlight intensity is 6.5*10 4 lx, the maximum solar radiation intensity is 7.7*10 4 lx, and the wind speed is 1 m / s. As the model input quantity, combined with the relevant parameters of the power system lines for high-penetration new energy grid connection, the load size, and the load change situation, the total active power of the wind turbines is obtained as 1401.3 kW, and the total active power of the photovoltaic panels is 1206.7 kW.

[0148] Embodiment 2

[0149] Please refer to Figure 2 , a power grid planning terminal 1 based on high-penetration new energy, includes a memory 2, a processor 3, and a computer program stored on the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, it realizes each step of a power grid planning method based on high-penetration new energy in Embodiment 1.

[0150] In summary, a power grid planning method and terminal provided by the present invention aim to minimize the network loss and the voltage and frequency fluctuations at the load in the power system for high-penetration new energy grid connection, establish a power grid planning model for the power system for high-penetration new energy grid connection; after obtaining the new energy parameters and load situation of the power system, input them into the power grid planning model, and can use the adaptive genetic simulated annealing algorithm to solve the power grid planning model for the power system considering high-penetration new energy grid connection, thereby reducing the uncertain energy input. And, through the reliable selection of the power grid planning within the power system with the characteristics of high-penetration new energy, the operation stability of the power system under the background of high-penetration new energy grid connection can be effectively improved.

[0151] The above are only the embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in the relevant technical fields, shall be included in the patent protection scope of the present invention by the same token.

Claims

1. A power grid planning method based on new energy with high penetration rate, characterized in that, it includes the steps of: Obtaining the new energy parameters and load conditions of the power system with new energy integrated into the grid with high penetration rate; Taking the minimum network loss and the minimum voltage and frequency fluctuations at the load in the power system as the objectives, establishing a power grid planning model for the power system with new energy integrated into the grid with high penetration rate; Inputting the new energy parameters and load conditions into the power grid planning model, and using the adaptive genetic simulated annealing method to solve the power grid planning model to obtain the power grid planning scheme for the power system; Taking the minimum network loss and the minimum voltage and frequency fluctuations at the load in the power system as the objectives, establishing a power grid planning model for the power system with new energy with high penetration rate includes: Establishing the objective function of the power grid planning model for the power system with new energy with high penetration rate: minF = p 1 f 1 + p 2 f 2 + p 3 f 3 ; In the formula, F represents the comprehensive planning function expression of the power system with high - permeability new - energy grid connection, and f 1 , f 2 , f 3 respectively represent the network loss, voltage fluctuation and frequency fluctuation in the power system, p 1 , p 2 , p 3 respectively represent the weight coefficients for converting f 1 , f 2 , f 3 into the same unit, and p 1 +p 2 +p 3 = 1; The network loss in the power system is: ； Where, W i loss,s , W j loss,s , W k loss,s represent the network losses generated by the commercial load of node i, the industrial load of node j, and the residential load of node k; The voltage and frequency fluctuations at the load in the power system are: ； where U m and U m0 represent the current voltage magnitude and the rated voltage magnitude of node m, Ω M represents the set of all nodes, and M represents the total number of nodes; The frequency fluctuations at the load in the power system are: ； where f m and f m0 represent the current frequency amplitude and the rated frequency amplitude of node m respectively.

2. A power grid planning method based on new energy with high penetration rate according to claim 1, characterized in that, The constraint conditions of the power grid planning model include: the power flow constraint, the line power constraint, the reactive power compensation device capacity constraint in the power system, and the constraint that the rising and falling rates of the net load curve provided by the power system meet the rising and falling rates of the net load curve of the main grid.

3. A power grid planning method based on new energy with high penetration rate according to claim 2, characterized in that, The power flow constraint in the power system includes: ； ； Wherein, P g , Q g represent the active power and reactive power injected into the distribution network, P l , Q l represent the active power and reactive power required by the distribution network, Q s represents the reactive power output of the reactive power compensation device installed at the node s of the distribution network system, N represents the number of reactive power compensation devices installed in the distribution network, U i , U j respectively represent the voltage values corresponding to the i-node and j-node of the distribution network, G ij , B ij , θ ij respectively represent the conductance value, susceptance value of the distribution line model in the distribution network, and the phase angle difference between the transmission end and the load end of the distribution line.

4. A power grid planning method based on new energy with high penetration rate according to claim 2, characterized in that, The line power constraint in the power system includes: P l ≤P lmax ; Wherein, P l represents the active power transmitted on line l in the power system, and P lmax is the maximum value of the active power transmitted on line l in the distribution network; The reactive power compensation device capacity constraint in the power system includes: 0 ≤ Q w ≤ Q max ; where Q max represents the maximum value of the reactive power compensation device that can be installed at node i of the distribution line in the distribution network system.

5. A power grid planning method based on new energy with high penetration rate according to claim 2, characterized in that, The constraint that the rising and falling rates of the net load curve provided by the power system meet the rising and falling rates of the net load curve of the main grid includes: ； ； where S' up and S' down represent the rising and falling rates of the net load curve of the main grid system with high-penetration new energy grid connection, S up and S down represent the rising and falling rates of the net load curve of the original main grid system, S 1 and S 3 represent the rising rate of the net load curve that can be provided by the high-penetration photovoltaic power station and the high-penetration wind power station, S 2 and S 4 represent the falling rate of the net load curve that can be provided by the high-penetration photovoltaic power station and the high-penetration wind power station.

6. A power grid planning method based on new energy with high penetration rate according to claim 1, characterized in that, Establishing a power grid planning model for the power system with new energy with high penetration rate includes: Obtaining the power grid planning model by establishing the output power prediction models of the photovoltaic power station and the wind power station in the power system, and establishing the static voltage characteristic models of various loads.

7. A power grid planning method based on new energy with high penetration rate according to claim 1, characterized in that, Establishing the output power prediction model of the photovoltaic power station in the power system includes: ； where R represents the solar radiation intensity, R max represents the maximum solar radiation intensity, Γ represents the Gamma function, and α and β represent the shape parameter and the scale parameter of the Beta distribution, respectively; Predicting the output power of the high-penetration photovoltaic power station based on the output power prediction model of the photovoltaic power station; ； ； where, H max,t represents the maximum output of the high-penetration PV power station at time t, and H min,t represents the minimum output of the high-penetration PV power station at time t, and μ t avg represents the expected value of the predicted output of the high-penetration PV power station during period t, and σ t represents the output error of the high-penetration PV power station during period t; Establishing the output power prediction model of the wind power station in the power system includes: ； ； Wherein, c and k respectively represent the scale parameter and the shape parameter of the Weibull distribution, σ and μ respectively represent the standard deviation and the expectation of the Weibull distribution, and v represents the wind speed of the wind power station; Predicting the output power of the high-penetration wind power station based on the output power prediction model of the wind power station: ； ； Where, P max,t represents the maximum output of the high - permeability wind power plant at time t, P min,t represents the minimum output of the high - permeability wind power plant at time t, a t avg represents the expected value of the predicted output of the high - permeability wind power plant during time period t, b t represents the output error of the high - permeability wind power plant during time period t.

8. A power grid planning method based on high-penetration new energy according to claim 1, characterized in that, the establishment of the static voltage characteristic models of various types of loads includes: classifying the loads in the system and establishing the static voltage characteristic models of various types of loads: ； Where, W 0 represents the overall load of the system before connecting to the new energy power supply, and W so,i , W go,j , and W zo,j respectively represent the commercial load power of node i, the industrial load power of node j, and the residential load power of node k before connecting to the new energy power supply, and Ω I , Ω J , and Ω K respectively represent the sets of various load nodes; using a polynomial model to establish the load static voltage characteristic: ； ； Among them, P i0 and Q i0 respectively represent the active power and reactive power of the load at the i-th node, and U N represents the voltage reference value; U i represents the actual values of the active and reactive power of the voltage at the i-th node. a p , b p , c p , a q , b q , c q respectively represent the proportions of constant power load, constant current load, and constant impedance load. The coefficients satisfy a p +b p +c p =1, a q +b q +c q =1; the system load after the access of the new energy power station is: ； Where, W a represents the load condition of the system after the access of the new energy power station, and λ s , λ g , λ z represent the influence coefficients of load fluctuations in the system caused by various factors.

9. A power grid planning terminal based on high-penetration new energy, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the power grid planning method based on high-penetration new energy according to any one of claims 1 to 8 above.

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