New energy-containing power distribution network power flow optimization method and system

Abstract

The invention discloses a power flow optimization method and system containing a new energy power distribution network, and relates to the technical field of power flow optimization of a power distribution system. The method comprises the following steps: firstly, establishing a model for representing uncertain wind power and photovoltaic output, establishing a steady-state power flow model containing a new energy distribution network system, and realizing power flow calculation of the new energy distribution network; then, equality constraints and inequality constraints are determined, a multi-target optimal power flow model is established with the minimum economic cost and the minimum network loss as targets, and a Pareto set evaluation model is established; and finally, adopting a fast non-dominated sorting genetic (NSGA-II) algorithm with an elitist strategy to carry out power flow optimization solution on the power distribution system containing the new energy. The result shows that the method can effectively balance a plurality of targets, the optimized power flow distribution remarkably improves the operation stability and economy of the system, and a feasible optimization scheme can be provided for a power distribution system containing high-proportion renewable energy sources in the future.

Description

Technical Field

[0001] The present invention belongs to the field of power flow optimization calculation of distribution network system, and in particular relates to a power flow optimization method and system for a distribution network containing new energy. Background Art

[0002] With the decreasing fossil energy, its unique non-renewable nature and the increasingly obvious environmental problems caused by traditional fossil energy power generation, new energy sources led by wind power and photovoltaics have entered the stage of the power system. At present, the global demand for low-carbon energy is growing, and the proportion of renewable energy such as wind power and photovoltaics in the distribution network is gradually increasing. However, considering the significant volatility and uncertainty of wind power and photovoltaics, especially when meteorological conditions change, it brings new challenges to the stability and power flow control of the power grid. This not only makes the optimal power flow problem of the power system more complicated, but also puts higher requirements on the distribution network operation mode of the traditional power system. Therefore, the power system not only needs to optimize the traditional power generation cost and power loss, but also must strengthen the stability of node voltage and the dynamic regulation capability of reactive power.

[0003] The traditional power flow optimization solution for the distribution network system is the key to improving the planning and scheduling of the distribution network system, and in order to ensure the practicality of the power flow optimization solution for the distribution network system, it is necessary to consider the access of new energy and the optimization of multiple objectives. At present, there is little consideration of the multi-objective power flow optimization of the distribution network system containing new energy, which is difficult to meet the needs of the development of the distribution network system. The present invention solves the power flow optimization problem of the improved distribution network system containing new energy based on the NSGA-II algorithm, completes the optimal power flow taking into account multiple objectives in the distribution network system containing new energy, meets the needs of scheduling and planning in the distribution network system, has the characteristics of fast solution speed, high accuracy, and avoidance of falling into local optimum. The present invention solves the problems of long solution time and poor optimization results in the traditional power flow optimization solution. Summary of the invention

[0004] The purpose of the present invention is to provide a power flow optimization method for a distribution network containing new energy, which is used to solve the power flow optimization method for a distribution network containing new energy and improve the system.

[0005] To achieve the above purpose, the present invention adopts the following scheme: first, a model characterizing the uncertain wind power and photovoltaic output is established, and a steady-state power flow model of a distribution network system containing new energy is established to realize the power flow calculation of the distribution network containing new energy; then, its equality constraints and inequality constraints are determined, and a multi-objective optimal power flow model is established with the minimum economic cost and the minimum network loss as the goals, and an evaluation model of the Pareto set is established; finally, a fast non-dominated sorting genetic algorithm with an elite strategy (NSGA-II) is used to optimize the power flow of the distribution system containing new energy.

[0006] On one hand, the present invention provides a method for optimizing power flow in a power distribution network containing new energy, characterized in that it comprises the following steps: Step 1: Based on the Monte Carlo method, the probability density function of the wind speed distribution is obtained by sampling and simulating the Weibull distribution function. W The probability model of wind turbine output power is obtained; the Beta distribution function is used to characterize the active output of photovoltaic generator sets, and the probability model of active output of photovoltaic generator sets is obtained; Step 2: After modeling the uncertainty of wind turbine and photovoltaic power generation, determine the parameters required for steady-state power flow calculation of the distribution network system containing new energy: the input active power P, input reactive power Q, node voltage amplitude V and inter-node voltage phase angle δ in each node; Step 3: Select the PQ node and PV node calculation formula as the equality constraints of the distribution network system containing new energy, select the generator output constraint, node voltage amplitude constraint, and node power constraint as the inequality constraints of the distribution network system containing new energy; select the system minimum economic cost and the system minimum network loss as the objective function of the optimization calculation of the distribution network system containing new energy; establish the objective function evaluation index of the Pareto solution set to obtain the optimal power flow of the distribution network system of new energy; Step 4: Modify the traditional IEEE 14-node system, add wind turbine and photovoltaic unit nodes, and form an improved IEEE 14-node system containing wind power and photovoltaic new energy; Step 5: Use the NSGA-II algorithm and set the power flow optimization parameters to solve the power flow optimization of the improved IEEE 14-bus system containing wind power and photovoltaic new energy.

[0007] Furthermore, in Step 1: Step 1.1: The probability model of wind turbine output power is:

[0008] ; Where v is the actual wind speed, k is the shape parameter of the Weibull distribution function, and c is the scale parameter; P W is the active output power of the fan, and the expression is: ,in, , , P W , P N are the output power and rated power of the wind turbine respectively, v in 、v out 、v N The corresponding wind speeds are represented by the fan's cut-in, cut-out and rated wind speed.

[0009] Step 1.2: The probability model of active output of photovoltaic units is:

[0010] , Among them, N PV represents the number of photovoltaic units, r represents the solar radiation intensity, P PVN Indicates the rated power of the photovoltaic unit, R C Represents the radiation point; R STD is the solar radiation intensity under standard radiation, where R C With R STD Both take standard values, 150W / m 2 , 1000W / m 2 .

[0011] Furthermore, in Step 2: Step 2.1: Establish a power flow calculation model for the distribution network containing new energy. The specific parameter solution method is as follows: Analyze the i-th node of the distribution network containing new energy. The active power P involved in the i-th AC node i It can be expressed as: ; The reactive power Q involved in the i-th AC node i It is expressed as: ; Among them, V i 、V j represents the voltage amplitude of nodes i and j; G ij , B ij represents the admittance value between nodes i and j; θ ij represents the voltage phase angle difference between nodes i and j; Step 2.2: Establish the Jacobian matrix of the system to be solved, and use the Newton-Raphson method to perform Taylor series expansion on the selected nonlinear unbalanced equation (ignore quadratic terms and higher-order terms). The corrected matrix equation of the steady-state power flow model of the new energy distribution network system is expressed as: ; Among them, △f=[…,△P m ,△Q m ,△P m+1 ,△V 2 m+1 ,…] T , K is the Jacobian matrix of the AC system, △y=[…,△θ m , V m / △V m ,△θ m-1 , V m+1 / △V m+1 ,…] T .

[0012] Furthermore, in Step 3, the constraints in the optimization of power flow calculation of the distribution network containing new energy are divided into an equality set and an inequality set: Step 3.1: The equality constraints are the unbalanced equations of the PQ node and the PV node; among them, the unbalanced equation expression of the PQ node is: ; The expression of PV node unbalance equation is: ; Step 3.2: The inequality constraints are the generator set output constraint, node voltage amplitude constraint, and node power constraint. The generator set output constraint expression is as follows: , , ; Among them, V Gk , P Gk , G Gk They represent the terminal voltage, active power and reactive power of the kth generator respectively. The subscripts min and max represent the upper and lower limits of the corresponding parameters. The node voltage amplitude constraint expression is: , where U i represents the voltage amplitude of the i-th node; the node current amplitude constraint expression is: , where I i represents the current amplitude of the i-th node; the node power constraint expression is: , , where P i , G i They represent the active power and reactive power of the i-th node respectively.

[0013] Step 3.3: The system minimum economic cost F is represented by the power generation cost, expressed as:

[0014] ; Among them, N G Indicates the number of generator sets, P Gk represents the active output of the kth generator; a k 、b k 、c k represents the cost coefficient of the kth generator; Step 3.4: The minimum network loss E of the system is characterized by the AC active power loss, and the expression is:

[0015] ; Among them, U a , U b represents the node voltage at both ends of lines a and b, g k Indicates the conductance at both ends of the line; sets the deviation weights for the minimum economic cost of the system and the minimum network loss of the system , , weight , The value range is [0,1] and satisfies ; Step 3.5: Construct the Pareto frontier set of power flow optimization under different weights. The expression of the corresponding power flow optimization set H is as follows: ; Step 3.6: The evaluation index C is expressed as follows:

[0016] ; Among them, the subscripts max and min represent the maximum and minimum values ​​of the corresponding target set in the Pareto set, respectively.

[0017] Step 3.7: The two objective function values ​​are normalized and weighted, and the evaluation of the set H is completed through the objective function evaluation index C to obtain the optimal solution for power flow optimization. The minimum value of the evaluation index is the optimal solution for power flow optimization.

[0018] Furthermore, in Step 4, the traditional IEEE 14-node system is modified by changing the generator corresponding to node 3 to a wind turbine, and the generator set corresponding to node 8 to a photovoltaic set, thereby forming an IEEE 14-node system including wind power and photovoltaic new energy.

[0019] Furthermore, in Step 5, according to the equality constraints and inequality constraints, the active output of the generator set, the terminal voltage, the transformer ratio, and the switching capacity of the reactive compensation capacitor are selected as the control variables for power flow optimization, and the optimization solution of the multi-objective power flow model of the improved IEEE 14-bus system containing wind power and photovoltaic new energy is realized based on the NSGA-II algorithm.

[0020] Another aspect of the present invention provides a power flow optimization system for a new energy distribution network, specifically comprising: Node system parameter input module: used to obtain line impedance parameters, admittance parameters, traditional generator set parameters, wind turbine set parameters, photovoltaic set parameters, traditional generator set node location parameters, wind turbine set node location parameters, photovoltaic set node location parameters, load node parameters, and load parameters in the node system; Constraint input module: used to obtain equality constraints and inequality constraints, including PQ node unbalanced equation constraints, PV node unbalanced equation constraints, generator set output constraints, node voltage amplitude constraints, and node power constraints; Optimization solution input module: used to obtain the population parameters, iteration number parameters, crossover probability parameters, mutation probability parameters, and convergence parameters of the NSGA-II algorithm; Weight coefficient input module: used to obtain cost weight coefficient and loss cost coefficient; Result display module: used to obtain the data of minimum economic cost and minimum network loss of the system obtained by optimizing the input node system.

[0021] Furthermore, the node system parameter input module is used to obtain line impedance parameters, admittance parameters, traditional generator set parameters, wind turbine set parameters, photovoltaic set parameters, traditional generator set node location parameters, wind turbine set node location parameters, photovoltaic set node location parameters, load node parameters, and load parameters in the node system.

[0022] Furthermore, the constraint input module is used to obtain equality constraints and inequality constraints including PQ node unbalance equation constraints, PV node unbalance equation constraints, generator set output constraints, node voltage amplitude constraints, and node power constraints.

[0023] Furthermore, the optimization solution input module is used to obtain the population parameters, iteration number parameters, crossover probability parameters, mutation probability parameters, and convergence parameters of the NSGA-II algorithm.

[0024] Furthermore, the weight coefficient input module is used to obtain the cost weight coefficient and the loss cost coefficient.

[0025] Furthermore, the result display module is used to obtain data on the minimum economic cost of the system and the minimum network loss of the system obtained by optimizing the input node system.

[0026] In summary, the power flow optimization method of the distribution network containing new energy first establishes a steady-state power flow model of the distribution network system containing new energy. According to the equality constraints and inequality constraints, a multi-objective optimal power flow model is established with the minimum economic cost of the system and the minimum network loss of the system, and an evaluation model of the Pareto set is established; then the NSGA-II algorithm is used and the power flow optimization parameters are set to solve the power flow optimization of the new energy distribution network system; finally, the optimal power flow result is obtained based on the improved IEEE 14-node system containing wind power and photovoltaic new energy. The power flow optimization system of the distribution network containing new energy consists of a node system parameter input module, a constraint condition input module, an optimization solution input module, a weight coefficient input module, and a result display module, thereby realizing the power flow optimization calculation of the node system.

[0027] The beneficial effects of the present invention are:

[0028] (1) The NSGA-II algorithm can effectively balance multiple objectives. The optimized power flow distribution significantly improves the operating stability and economy of the system, providing a feasible optimization solution for future distribution networks with a high proportion of renewable energy.

[0029] (2) It has the characteristics of fast solution speed, high accuracy and avoidance of falling into local optimum, which solves the problems of long solution time and poor optimization results in traditional flow optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to better understand the above technical solution, the exemplary embodiments of the present invention will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art. The accompanying drawings of the present invention are as follows: Figure 1 A flow chart for solving the power flow optimization problem of the power distribution network system containing new energy provided by the present invention; Figure 2 It is a system diagram for solving the power flow optimization of the new energy distribution network system provided by the present invention; Figure 3 It is an improved IEEE 14-node system diagram including wind power and photovoltaic new energy provided by the present invention; Figure 4 It is a Pareto optimal solution distribution diagram obtained after optimization of the NSGA-II algorithm provided by the present invention with a cost weight coefficient of 0.5 and a loss weight coefficient of 0.5; Figure 5 It is a Pareto optimal solution distribution diagram obtained after optimization of the NSGA-II algorithm provided by the present invention with a cost weight coefficient of 0.7 and a loss weight coefficient of 0.3. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and do not limit the present invention. In addition, the technical features designed in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0032] Improved IEEE 14-node system including wind power and photovoltaic new energy Figure 2 As shown in the figure, the optimization calculation steps of the power flow of the distribution network containing new energy are as follows: Figure 1 As shown, specifically: Step 1: Based on the Monte Carlo method, the probability density function of the wind speed distribution is obtained by sampling and simulating the Weibull distribution function. WThe probability model of wind turbine output power is obtained. The Beta distribution function is used to characterize the active output of photovoltaic generator sets, and its probability density function and the active output model of photovoltaic generator sets are obtained; Step 2: After modeling the uncertainty of wind turbine and photovoltaic power generation, determine the parameters required for steady-state power flow calculation of the distribution network system containing new energy: the input active power P, input reactive power Q, node voltage amplitude V and inter-node voltage phase angle δ in each node; Step 3: Select the PQ node and PV node calculation formula as the equality constraints of the distribution network system containing new energy, select the generator output constraint, node voltage amplitude constraint, and node power constraint as the inequality constraints of the distribution network system containing new energy; select the system minimum economic cost and the system minimum network loss as the objective function of the optimization calculation of the distribution network system containing new energy; establish the objective function evaluation index of the Pareto solution set to obtain the optimal power flow of the distribution network system of new energy; Step 4: Modify the traditional IEEE 14-node system, add wind turbine and photovoltaic unit nodes, and form an improved IEEE 14-node system containing wind power and photovoltaic new energy; Step 5: Use the NSGA-II algorithm and set the power flow optimization parameters to solve the power flow optimization of the improved IEEE 14-bus system containing wind power and photovoltaic new energy.

[0033] Embodiment 1: The control variables for optimal power flow optimization are set, and the range of the active output change rate of the generator set is [0, 1]; the range of the terminal voltage change rate is [0.9, 1.1]; reactive compensation is added at nodes 7 and 9, and the capacitor switching capacity range is [0, 0.6]; the transformation ratio taps of the three transformers are discrete variables, the number of gears required is 5, the range is [0.95, 1.05], and the step size is determined to be 0.025; the wind speed, light intensity, etc. involved in the wind turbine set and photovoltaic set collected at any time node are selected to obtain the power output of the wind turbine set and photovoltaic set at the corresponding time.

[0034] Set the initial parameters of the NSGA-II algorithm, the population size is 150; the maximum number of iterations is 100; randomly generate 200 sets of data within a given range, and set the crossover probability and mutation probability of the NSGA-II algorithm.

[0035] The cost and loss weight coefficients are 0.5 and 0.5 respectively. In order to maximize the economic and reliable operation of the power system, the NSGA-II algorithm is used to calculate the multi-objective optimal power flow of the improved IEEE14-node system taking into account wind and solar renewable energy. The Pareto optimal solution distribution obtained after the NSGA-II algorithm optimization is as follows: Figure 3 As shown below; according to the evaluation index, the effective solution in Pareto is obtained as shown below.

[0036]

[0037] After the NSGA-II algorithm optimized the power flow of the improved IEEE14-node system, it was found that the minimum cost and minimum loss of the system were improved compared with before optimization, among which the cost index was optimized by 19.67% and the loss index was reduced by about 72.54%.

[0038] Example 2: In order to further analyze the impact of different weight coefficients on the results, the weight coefficient ratio in the evaluation criteria is changed, the cost coefficient is set to 0.7, and the loss coefficient is set to 0.3. The distribution of the Pareto optimal solution after optimization under this weight is as follows Figure 4 As shown; the effective solution in Pareto obtained according to the evaluation index is shown below.

[0039]

[0040] After changing the weight coefficient, the cost index decreased by 25.41% compared with before optimization; the loss index decreased by 18.87% compared with before optimization. It is not difficult to find that after changing the optimization weight of the cost index, the cost index was further optimized and decreased by 5.74% again. At the same time, because the weight of the loss index decreased, the optimization of the system loss decreased compared with before changing the weight coefficient, but the effect is still better than before optimization, and the performance of the system operation is still improved.

[0041] After selecting different evaluation criteria, the two indicators to be optimized in the system are significantly improved. At the same time, the optimization algorithm can be applied to scenarios with different requirements. The indicators in the determined system can be optimized by simply changing the weight coefficients.

[0042] The above two optimization examples show that the NSGA-II algorithm has a very powerful global search capability and will hardly fall into the local optimum. At the same time, according to the Pareto solution set obtained by optimization, the key optimization indicators required by the system are combined with the evaluation model to obtain the optimal effective solution that meets the requirements. In summary, the simulation verifies the feasibility of the proposed method and meets the economic and reliability operation requirements of the power system.

[0043] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for optimizing power flow in a distribution network containing new energy, characterized in that: The following steps are involved: Step 1: Based on the Monte Carlo method, the probability density function of the wind speed distribution is obtained by sampling and simulating the Weibull distribution function. W The probability model of wind turbine output power is obtained; the Beta distribution function is used to characterize the active output of photovoltaic generator sets, and the probability model of active output of photovoltaic generator sets is obtained; Step 2: After modeling the uncertainty of wind turbine and photovoltaic power generation, determine the parameters required for steady-state power flow calculation of the distribution network system containing new energy: the input active power P, input reactive power Q, node voltage amplitude V and inter-node voltage phase angle δ in each node; Step 3: Select the PQ node and PV node calculation formula as the equality constraints of the distribution network system containing new energy, select the generator output constraint, node voltage amplitude constraint, and node power constraint as the inequality constraints of the distribution network system containing new energy; select the system minimum economic cost and the system minimum network loss as the objective function of the optimization calculation of the distribution network system containing new energy; establish the objective function evaluation index of the Pareto solution set to obtain the optimal power flow of the distribution network system of new energy; Step 4: Modify the traditional IEEE 14-node system, add wind turbine and photovoltaic unit nodes, and form an improved IEEE 14-node system containing wind power and photovoltaic new energy; Step 5: Use the NSGA-II algorithm and set the power flow optimization parameters to solve the power flow optimization of the improved IEEE 14-bus system containing wind power and photovoltaic new energy.

2. The method for optimizing power flow of a power distribution network containing new energy sources according to claim 1, characterized in that: In Step 1, the probability model of the wind turbine output power is: ； Where v is the actual wind speed, k is the shape parameter of the Weibull distribution function, and c is the scale parameter; P W is the active output power of the fan, and the expression is: ,in, , , P W , P N are the output power and rated power of the wind turbine respectively, v in 、v out 、v N The corresponding wind speeds are represented by the fan's cut-in, cut-out and rated wind speed.

3. The power flow optimization method for a distribution network containing new energy according to claim 1, characterized in that: In Step 1, the probability model of active power output of the photovoltaic unit is: , where N PV represents the number of photovoltaic units, r represents the solar radiation intensity, P PVN Indicates the rated power of the photovoltaic unit, R C Represents the radiation point; R STD is the solar radiation intensity under standard radiation, where R C With R STD Both take standard values, 150W / m 2 , 1000W / m 2 .

4. The method for optimizing power flow of a power distribution network containing new energy sources according to claim 1, characterized in that: In Step 2, the output of wind power and photovoltaic power at the corresponding time is regarded as a known quantity, and a power flow calculation model containing a new energy distribution network is established. The specific parameter solution method is as follows: the i-th node of the new energy distribution network is analyzed, and the active power P involved in the i-th AC node is i It can be expressed as: ; The reactive power Q involved in the i-th AC node i It is expressed as: ; Among them, V i 、V j represents the voltage amplitude of nodes i and j, G ij , B ij represents the admittance value between nodes i and j, θ ij Represents the voltage phase angle difference between nodes i and j; establishes the Jacobian matrix of the system to be solved, and uses the Newton-Raphson method to perform Taylor series expansion on the selected nonlinear unbalanced equation (ignoring quadratic terms and higher-order terms). The corrected matrix equation of the steady-state power flow model of the new energy distribution network system is expressed as: ; Among them, △f=[…,△P m ,△Q m ,△P m+1 ,△V 2 m+1 ,…] T , K is the Jacobian matrix of the AC system, △y=[…,△θ m , V m / △V m ,△θ m-1 , V m+1 / △V m+1 ,…] T .

5. The method for optimizing power flow of a power distribution network containing new energy sources according to claim 1, characterized in that: In the Step 3, the constraints in the optimization of power flow calculation of the new energy distribution network are divided into an equation set and an inequality set, wherein the equation constraints are the unbalanced equations of the PQ nodes and the PV nodes; wherein the expression of the unbalanced equation of the PQ node is: ; The expression of PV node unbalance equation is: ; The inequality constraints are the generator set output constraint, node voltage amplitude constraint, and node power constraint. The generator set output constraint expression is as follows: , , ; Among them, V Gk , P Gk , G Gk They represent the terminal voltage, active power and reactive power of the kth generator respectively. The subscripts min and max represent the upper and lower limits of the corresponding parameters. The node voltage amplitude constraint expression is: , where U i represents the voltage amplitude of the i-th node; the node current amplitude constraint expression is: , where I i represents the current amplitude of the i-th node; the node power constraint expression is: , , where P i , G i They represent the active power and reactive power of the i-th node respectively.

6. The power flow optimization method for a distribution network containing new energy sources according to claim 1 is characterized in that: The multi-objective power flow optimization model containing the new energy distribution network in Step 3 is composed of the system minimum economic cost, the system minimum network loss and the Pareto evaluation index; the system minimum economic cost F is represented by the power generation cost, and the expression is: ; Among them, N G Indicates the number of generator sets, P Gk represents the active output of the kth generator; a k 、b k 、c k represents the cost coefficient of the kth generator; the minimum network loss E of the system is characterized by the AC active power loss, and the expression is: Among them, U a , U b represents the node voltage at both ends of lines a and b, g k Indicates the conductance at both ends of the line; sets the deviation weights for the minimum economic cost of the system and the minimum network loss of the system , , weight , The value range is [0,1] and satisfies ; Construct the Pareto frontier set of power flow optimization under different weights. The expression of the corresponding power flow optimization set H is as follows: ; The two objective function values ​​obtained are normalized and weighted, and the evaluation of set H is completed through the objective function evaluation index C to obtain the optimal solution for power flow optimization. The minimum value of the evaluation index is the optimal solution for power flow optimization; the expression of the evaluation index C is as follows: ; Among them, the subscripts max and min represent the maximum and minimum values ​​of the corresponding target set in the Pareto set, respectively.

7. The power flow optimization method for a power distribution network containing new energy according to claim 1, characterized in that: In the Step 4, the traditional IEEE 14-node system is modified by changing the generator corresponding to the node 3 to a wind turbine, and the generator set corresponding to the node 8 to a photovoltaic set, thereby forming an IEEE 14-node system including wind power and photovoltaic new energy.

8. The method for optimizing power flow of a power distribution network containing new energy sources according to claim 1, characterized in that: In the Step 5, according to the equality constraints and inequality constraints, the active output of the generator set, the terminal voltage, the transformer ratio, and the switching capacity of the reactive compensation capacitor are selected as the control variables for power flow optimization, and the optimization solution of the multi-objective power flow model of the improved IEEE 14-node system containing wind power and photovoltaic new energy is realized based on the NSGA-II algorithm.

9. A power flow optimization system for a new energy distribution network, characterized in that: The power flow optimization system comprises: Node system parameter input module: used to obtain line impedance parameters, admittance parameters, traditional generator set parameters, wind turbine set parameters, photovoltaic set parameters, traditional generator set node location parameters, wind turbine set node location parameters, photovoltaic set node location parameters, load node parameters, and load parameters in the node system; Constraint input module: used to obtain equality constraints and inequality constraints, including PQ node unbalanced equation constraints, PV node unbalanced equation constraints, generator set output constraints, node voltage amplitude constraints, and node power constraints; Optimization solution input module: used to obtain the population parameters, iteration number parameters, crossover probability parameters, mutation probability parameters, and convergence parameters of the NSGA-II algorithm; Weight coefficient input module: used to obtain cost weight coefficient and loss cost coefficient; Result display module: used to obtain the data of minimum economic cost and minimum network loss of the system obtained by optimizing the input node system.

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