Method for improving new energy bearing capacity of power distribution network by rotating power flow controller

Abstract

The invention is suitable for the technical field of improving the new energy bearing capacity of a power distribution network, and provides a method for improving the new energy bearing capacity of the power distribution network through a rotating power flow controller, and the method comprises the steps: constructing a rotating power flow controller and a demand response model under a novel power system; constructing a distributed new energy bearing capacity double-layer optimization model which takes maximization of the bearing capacity of the power distribution network to the new energy as an upper layer objective function and minimization of the comprehensive operation cost of the system as a lower layer objective function; and solving the model by adopting a hybrid optimization algorithm based on a grey wolf algorithm and second-order cone programming to obtain the carrying capacity of the power distribution network under the condition of considering the demand response measure and the rotating power flow controller. The new energy bearing capacity level of the power distribution network can be improved when the rotating power flow controller and the load demand response are considered.

Description

Technical Field

[0001] The present application belongs to the technical field of improving the carrying capacity of distribution networks in power systems, and specifically relates to a method for improving the new energy carrying capacity of distribution networks using a rotating power flow controller. Background Art

[0002] Distributed renewable energy has become an important tool for achieving the "dual carbon" goal due to its clean, green, renewable and pollution-free characteristics. Its access ratio in the distribution network system is increasing, but the indirectness and randomness of its output will bring problems such as bidirectional flow of power and voltage over-limit to the distribution network. In addition, the existing distribution network grid structure is relatively weak, and the local load distribution is uneven, resulting in overload of some lines, imbalance of system energy, and aggravation of wind and solar power abandonment.

[0003] Power electronic flexible interconnection devices are mainly composed of power electronic devices, which have the disadvantages of high operation and maintenance costs and weak impact resistance, making it difficult to achieve large-scale application. As an electromagnetic flexible interconnection device, the rotary power flow controller has low cost, simple control method, strong impact resistance and good stability. It can continuously and accurately control the power between feeders, improve the voltage quality of system nodes, optimize the network power flow distribution, and effectively improve the distribution network's carrying capacity for distributed new energy.

[0004] The existing methods for optimizing and improving the carrying capacity of distribution networks lack means of combining demand response and the investment of rotating power flow controllers, and there is little research on the carrying capacity of distributed renewable energy by comprehensively considering the system operating costs. On the other hand, the optimization of distributed renewable energy carrying capacity and the reconstruction of distribution networks considering the access of rotating power flow controllers are essentially large-scale mixed integer nonlinear programming problems. Traditional mathematical methods are difficult to handle complex large-scale distribution network models due to dimensionality problems, and intelligent algorithms such as genetic algorithms and particle swarm algorithms have poor optimization accuracy and slow convergence speed. Summary of the invention

[0005] The purpose of the present invention is to provide a method for improving the new energy carrying capacity of a distribution network by using a rotating power flow controller in view of the deficiencies of the above-mentioned prior art; the method effectively utilizes the rotating power flow controller, demand response and network reconstruction to maximize the carrying capacity of the distribution network for distributed new energy; and in terms of model solving, a hybrid optimization algorithm based on the grey wolf algorithm and cone programming is used to transform the distribution network model to obtain a mixed integer convex programming model.

[0006] In order to achieve the above object, the present invention provides a method for improving the new energy carrying capacity of a distribution network by using a rotating power flow controller, comprising the following steps:

[0007] Construct a rotating power flow controller and a demand response model for a new power system; construct a two-layer optimization model for the distributed new energy carrying capacity with maximizing the distribution network's carrying capacity for new energy as the upper objective function and minimizing the system's comprehensive operating costs as the lower objective function; based on the two-layer optimization model of the distributed new energy carrying capacity, a hybrid optimization algorithm based on the grey wolf algorithm and second-order cone programming is used to solve the above model to obtain the distribution network carrying capacity considering demand response measures and a rotating power flow controller.

[0008] In realizing the method of improving the new energy carrying capacity of the distribution network by considering the rotating power flow controller, the upper objective function is constructed to maximize the carrying capacity of the distribution network for new energy and optimize the access capacity of new energy, including:

[0009] The objective function of the upper optimization model is to maximize the access capacity of distributed photovoltaic and wind turbines, and its mathematical expression is:

[0010]

[0011] Where: S is the carrying capacity of the distribution network; is the wind power access capacity of the i-th node at time t; is the photovoltaic access capacity of the i-th node at time t; φ wind is the node set connected to wind power in the distribution system; PV is the set of nodes connected to photovoltaic in the system; T represents a scheduling cycle; ΔT is a time period within the scheduling cycle.

[0012] Furthermore, in realizing the method of improving the new energy carrying capacity of the distribution network by considering the rotating power flow controller, the upper objective function is characterized by maximizing the carrying capacity of the distribution network for new energy. The constraints are mainly the upper and lower limits of the output of distributed new energy, including:

[0013]

[0014] Where: and are the active and reactive outputs of distributed renewable energy at node i in the system at time t respectively; and are the upper and lower limits of the active power output of distributed renewable energy at node i; and The upper and lower limits of the reactive power output of distributed renewable energy.

[0015] In realizing the method of improving the new energy carrying capacity of the distribution network by considering the rotating power flow controller, the construction is to minimize the comprehensive operating cost of the system and adjust the operating output of each device as the lower objective function, including:

[0016] Furthermore, the lower optimization model considers the comprehensive operation cost of the distribution network under various constraints, in which the investment cost of the power flow controller is regarded as the value converted to each day, that is, only its operation cost is considered in the model without considering its investment cost; therefore, the comprehensive operation cost of the system mainly includes the network loss cost f loss 、Wind abandonment cost wind , abandoned light cost PV , Power flow controller operating cost f RPFC and the switching cost f switch ,Right now:

[0017]

[0018] Where: f is the comprehensive operating cost of the distribution network; φ b is the set of branches in the distribution network; φ RPFC C is the set of nodes connected to the power flow controller in the system; loss , C wind , C PV , C RPFC and C switch are the network loss cost coefficient, wind abandonment penalty coefficient, solar abandonment penalty coefficient, power flow controller operation cost coefficient and switch action cost coefficient respectively; I ij,t is the current flowing through branch ij at time t; r ij is the resistance on branch ij; and are the active power generated by the wind turbine and photovoltaic at node i at time t; and is the actual power injected into the system by the wind turbine and photovoltaic at node i at time t; is the power loss on the power flow controller at node i at time t; ε ij,0 and ε ij They are the on-off status of the system branches before and after network reconstruction.

[0019] Furthermore, in realizing the method of improving the new energy carrying capacity of the distribution network by considering the rotating power flow controller, the construction is characterized by minimizing the comprehensive operating cost of the system and adjusting the operating output of each device as the lower objective function, and its constraints are demand response constraints and power flow controller constraints, including:

[0020] Demand response reduces the peak-to-valley difference of load by partially shifting the load in different time periods of the day, promotes the consumption of distributed new energy, reduces network losses, and reduces the comprehensive operating cost of the distribution network; the load power elasticity constraint, total load capacity constraint, and electricity price constraint considering demand response are shown in the following formula:

[0021]

[0022]

[0023]

[0024]

[0025]

[0026] Where: t is the electricity price elasticity coefficient at time t; P t and P t cur is the load power of the system before and after the implementation of demand response measures at time t; ρ t and are the electricity prices before and after demand response at time t; T peak is the time period to which the peak electricity price belongs; ρ peak is the peak electricity price; T valley is the time period to which the off-peak electricity price belongs; ρ valley The off-peak electricity price; and The upper and lower limits of electricity prices.

[0027] Furthermore, in the power distribution system, the power flow controller can transfer power from the heavily loaded lines to the lightly loaded lines to achieve functions such as power flow control and power mutual assistance. Its constraints are shown in the following formula:

[0028]

[0029]

[0030]

[0031]

[0032]

[0033]

[0034] Where: and are the active and reactive powers transmitted by nodes i and j of the power flow controller at time t, respectively; and is the active power loss of the power flow controller of nodes i and j at time t; μ i and μ j are the reactive power constraint coefficients of ports i and j of the power flow controller respectively; S RPFC is the capacity of the power flow controller.

[0035] Furthermore, in order to realize the method of improving the new energy carrying capacity of the distribution network by considering the rotating power flow controller, a hybrid optimization algorithm based on the grey wolf algorithm and the second-order cone programming is used to solve the above model, including:

[0036] The Gray Wolf Algorithm, as the framework of the hybrid algorithm, is used to determine the access capacity of renewable energy and the access position of the power flow controller. The access capacity of renewable energy and the input position of the power flow controller are regarded as individual wolves in the gray wolf group, and the upper objective function is regarded as the fitness function of the gray wolf group. In each iteration of the Gray Wolf Algorithm, the second-order cone relaxation is used to solve the model in a convex manner to obtain the optimal operation mode of the distribution network system in each scenario, thereby calculating the fitness of the individuals in the Gray Wolf Algorithm. When the fitness function value no longer changes after multiple updates of the gray wolf position, it is considered that the optimal solution to the problem is obtained.

[0037] Step 1: Input relevant parameters of power distribution network, energy storage, distributed power source, etc.

[0038] Step 2: Taking the maximum new energy carrying capacity of the distribution network as the optimization goal, the distributed new energy access capacity and the node location of the power flow controller access as the decision variables, the gray wolf algorithm is used to solve the upper optimization problem;

[0039] Step 3: Taking the minimization of the comprehensive operating cost of the distribution network system as the optimization objective, the operating output of wind, solar and storage resources and the power flow controller in the distribution network as decision variables, and using the second-order cone relaxation optimization problem. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Provide a two-tier optimization process for distributed renewable energy carrying capacity.

[0041] Figure 2 This is the IEEE 33-node power distribution system diagram.

[0042] Figure 3 It is the load power before and after the implementation of demand response.

[0043] Figure 4 This is the energy storage charging and discharging power diagram.

[0044] Figure 5 It is the energy storage capacity change curve.

[0045] Figure 6 Diagram of the power delivered for the rotating power flow controller. DETAILED DESCRIPTION

[0046] In the following description of the present invention, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0047] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0048] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0049] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0050] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0051] The present application is described in detail below with reference to the accompanying drawings and specific implementation methods.

[0052] Figure 1 For the double-layer optimization flow chart of distributed new energy carrying capacity, refer to Figure 1 , the method is described in detail as follows:

[0053] Construct a rotating power flow controller and demand response model under a new power system; construct a two-level optimization model of distributed new energy carrying capacity with maximizing the distribution network's carrying capacity for new energy as the upper objective function and minimizing the system's comprehensive operating costs as the lower objective function; based on the two-level optimization model of distributed new energy carrying capacity, a hybrid optimization algorithm based on the gray wolf algorithm and second-order cone programming is used to solve the above model, and the distribution network carrying capacity considering demand response measures and rotating power flow controller is obtained, including:

[0054] In realizing the method of improving the new energy carrying capacity of the distribution network by considering the rotating power flow controller, the upper objective function is constructed to maximize the carrying capacity of the distribution network for new energy and optimize the access capacity of new energy, including:

[0055] For example, the objective function of the upper optimization model is to maximize the access capacity of distributed photovoltaic and wind turbines, and its mathematical expression is:

[0056]

[0057] Where: S is the carrying capacity of the distribution network; is the wind power access capacity of the i-th node at time t; is the photovoltaic access capacity of the i-th node at time t; φ wind is the node set connected to wind power in the distribution system; PV is the set of nodes connected to photovoltaic in the system; T represents a scheduling cycle; ΔT is a time period within the scheduling cycle.

[0058] Optionally, in realizing the method of improving the new energy carrying capacity of the distribution network by considering the rotating power flow controller, the upper objective function is characterized by maximizing the carrying capacity of the distribution network for new energy. The constraints are mainly the upper and lower limit constraints of the output of distributed new energy, including:

[0059]

[0060] Where: and are the active and reactive outputs of distributed renewable energy at node i in the system at time t respectively; and are the upper and lower limits of the active power output of distributed renewable energy at node i; and The upper and lower limits of the reactive power output of distributed renewable energy.

[0061] Exemplarily, in realizing the method of improving the new energy carrying capacity of the distribution network by considering the rotating power flow controller, the construction is to minimize the comprehensive operating cost of the system and adjust the operating output of each device as the lower objective function, including:

[0062] Optionally, the lower optimization model considers the comprehensive operation cost of the distribution network under various constraints, where the investment cost of the power flow controller is regarded as the value converted to each day, that is, only its operation cost is considered in the model without considering its investment cost; therefore, the comprehensive operation cost of the system mainly includes the network loss cost f loss 、Wind abandonment cost wind , abandoned light cost PV , Power flow controller operating cost f RPFC and the switching cost f switch ,Right now:

[0063]

[0064] Where: f is the comprehensive operating cost of the distribution network; φ b is the set of branches in the distribution network; φ RPFC C is the set of nodes connected to the power flow controller in the system; loss , C wind , C PV , C RPFC and C switch are the network loss cost coefficient, wind abandonment penalty coefficient, solar abandonment penalty coefficient, power flow controller operation cost coefficient and switch action cost coefficient respectively; I ij,t is the current flowing through branch ij at time t; r ij is the resistance on branch ij; and are the active power generated by the wind turbine and photovoltaic at node i at time t; and is the actual power injected into the system by the wind turbine and photovoltaic at node i at time t; is the power loss on the power flow controller at node i at time t; ε ij,0 and ε ij They are the on-off status of the system branches before and after network reconstruction.

[0065] Exemplarily, in realizing the method of improving the new energy carrying capacity of the distribution network by considering the rotating power flow controller, the construction is characterized by minimizing the comprehensive operating cost of the system and adjusting the operating output of each device as the lower objective function, and its constraints are demand response constraints and power flow controller constraints, including:

[0066] For example, demand response reduces the peak-to-valley difference of load by partially shifting the load in different time periods of the day, promotes the consumption of distributed renewable energy, reduces network losses, and reduces the comprehensive operating cost of the distribution network; the load power elasticity constraint, total load capacity constraint, and electricity price constraint considering demand response are shown in the following formula:

[0067]

[0068]

[0069]

[0070]

[0071]

[0072] Where: t is the electricity price elasticity coefficient at time t; P t and P t cur is the load power of the system before and after the implementation of demand response measures at time t; ρ t and are the electricity prices before and after demand response at time t; T peak is the time period to which the peak electricity price belongs; ρ peak is the peak electricity price; T valley is the time period to which the off-peak electricity price belongs; ρ valley The off-peak electricity price; and The upper and lower limits of electricity prices.

[0073] Furthermore, in the power distribution system, the power flow controller can transfer power from the heavily loaded lines to the lightly loaded lines to achieve functions such as power flow control and power mutual assistance. Its constraints are shown in the following formula:

[0074]

[0075]

[0076]

[0077]

[0078]

[0079]

[0080] Where: and are the active and reactive powers transmitted by nodes i and j of the power flow controller at time t, respectively; and is the active power loss of the power flow controller of nodes i and j at time t; μ i and μ j are the reactive power constraint coefficients of ports i and j of the power flow controller respectively; S RPFC is the capacity of the power flow controller.

[0081] Furthermore, in order to realize the method of improving the new energy carrying capacity of the distribution network by considering the rotating power flow controller, a hybrid optimization algorithm based on the grey wolf algorithm and the second-order cone programming is used to solve the above model, including:

[0082] Exemplarily, the Gray Wolf Algorithm is used as a framework of a hybrid algorithm to determine the access capacity of new energy and the access location of a power flow controller. The access capacity of new energy and the input location of a power flow controller are regarded as individual wolves in a gray wolf group, and the upper-level objective function is regarded as the fitness function of the gray wolf group. In each iteration of the Gray Wolf Algorithm, the model is converted into a convex solution using second-order cone relaxation to obtain the optimal operation mode of the distribution network system in each scenario, thereby calculating the fitness of individuals in the Gray Wolf Algorithm. When the fitness function value no longer changes after multiple updates of the gray wolf position, it is considered that the optimal solution to the problem is obtained.

[0083] Step 1: Input relevant parameters of power distribution network, energy storage, distributed power source, etc.

[0084] Step 2: Taking the maximum new energy carrying capacity of the distribution network as the optimization goal, the distributed new energy access capacity and the node location of the power flow controller access as the decision variables, the gray wolf algorithm is used to solve the upper optimization problem;

[0085] Step 3: Taking the minimization of the comprehensive operating cost of the distribution network system as the optimization objective, the operating output of wind, solar and storage resources and the power flow controller in the distribution network as decision variables, and using the second-order cone relaxation optimization problem.

[0086] Example

[0087] The IEEE 33-node power distribution system is used as an example for analysis. The system structure is as follows: Figure 2 As shown in Figure 2, the system active and reactive loads are 3715kW and 2300kvar respectively; the configuration of distributed new energy and energy storage is as follows: Figure 2 As shown, the tie lines S1 to S5 are the candidate locations for the rotating power flow controllers, and the capacities of the two rotating power flow controllers pre-invested are both 0.5 MVA.

[0088] There are 5 distributed renewable energy access points in the system. The maximum output power of photovoltaic and wind turbines in each period is obtained by multiplying their corresponding installed capacity and time series value, while the load in each period is obtained by multiplying the basic load of the IEEE33 node system and the corresponding time series value. According to the current peak-valley electricity price policy of a certain city, the system power consumption peak period T peak 8:00-14:00 and 19:00-21:00, the electricity price is 1 yuan / kWh; the off-peak period T valley From 23:00 to 5:00 the next day, the electricity price is 0.25 yuan / kWh; the normal electricity price is 0.7 yuan / kWh; the load elasticity coefficient is 0.5.

[0089] Taking into account the rotating power flow controller, load demand response and network reconstruction of the system, the distribution network disconnects the switches of lines L10-11, L13-14, L9-15, L8-21 and L25-29, and the rotating power flow controller is put into use on the tie lines S1 and S4; the optimized distributed energy carrying capacity of the IEEE33-node distribution system is 2812.2kW, and the total operating cost of the system is 2310.4 yuan. That is, by adopting the strategy of considering the rotating power flow controller and demand response, when the comprehensive operating cost of the operating layer system is minimized, the maximum capacity configuration of new energy with a penetration rate of 75.7% can be achieved in the system.

[0090] Figure 3 Load power consumption curve before and after the implementation of demand response; Under the condition of ensuring the total power consumption and power consumption constraints in each time period, the system allocates part of the power demand in peak hours (8:00-14:00 and 19:00-21:00) to the power off-peak period (23:00-5:00 the next day); The load peak-to-valley difference before the demand response strategy is 0.533 (per unit), and the load peak-to-valley difference after the demand response is 0.332 (per unit); Therefore, demand response can adjust the power consumption behavior and partially transfer the power demand in different time periods, thereby reducing the system's load peak-to-valley difference and improving the absorption rate of distributed new energy.

[0091] Energy storage charging and discharging power Figure 4 As shown in , the value is positive during discharge and negative during charge. Figure 5 As shown in the figure, the upper limit of the capacity of ESS1 is 1MW, and the upper limit of the capacity of ESS2 is 1.2MW; it can be seen from the figure that energy storage can be discharged as an active power source or charged as an active load. For ESS1, charging is performed at 12:00-16:00h, and discharging is performed at 5:00-6:00h, 19:00h-22:00h and 24:00h, that is, when the light conditions are good and the load is not enough to consume all the photovoltaic power, ESS1 absorbs the excess power generated by PV to reduce the phenomenon of abandoned light; when the light conditions are poor, ESS1 transmits power to the distribution network to meet the power demand of the system load; for ESS2, charging is performed at 1:00-4:00h, 6:00h and 23:00-24:00h, and discharging is performed at 10:00-18:00h, storing the power at the peak of wind power and releasing it at the peak of load power consumption, weakening the impact of wind power anti-peak regulation characteristics in the system and reducing wind abandonment.

[0092] Figure 6The figure shows the active power curve transmitted by the rotating power flow controller; RPFC1 is put into operation at the interconnection line S1, and there is a photovoltaic access at its port 2 (node ​​22). Therefore, more active power is transmitted on the rotating power flow controller during the peak period of photovoltaic power generation (9:00-15:00), and less active power is transmitted during the low period of photovoltaic power generation (20:00-5:00 the next day); RPFC2 is put into operation at the interconnection line S4. During the peak period of wind power generation (19:00-8:00 the next day), active power is transmitted along port 2 (node ​​33) to port 1 (node ​​18). During 10:00-17:00, photovoltaic power generation is at its peak period, and the power flow flows from port 1 to port 2 through RPFC2. Therefore, the rotating power flow controller can improve the system's ability to absorb new energy by regulating the line power flow.

[0136] The present application provides a method for improving the new energy carrying capacity of a distribution network by using a rotating power flow controller, the method comprising: constructing a rotating power flow controller and a demand response model under a new power system; constructing a two-layer optimization model for distributed new energy carrying capacity with maximizing the distribution network's carrying capacity for new energy as an upper objective function and minimizing the system's comprehensive operating costs as a lower objective function; using a hybrid optimization algorithm based on the grey wolf algorithm and second-order cone programming to solve the above model, thereby improving the distribution network carrying capacity level when considering demand response measures and a rotating power flow controller.

[0093] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0094] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for improving the new energy carrying capacity of a distribution network by using a rotating power flow controller, characterized in that: include: Construct a rotating power flow controller and demand response model for a new power system; Construct a two-level optimization model for distributed renewable energy carrying capacity, with maximizing the distribution network's renewable energy carrying capacity as the upper objective function and minimizing the system's comprehensive operating costs as the lower objective function; Based on the two-layer optimization model of distributed renewable energy carrying capacity, a hybrid optimization algorithm based on the grey wolf algorithm and second-order cone programming is used to solve the above model, and the distribution network carrying capacity considering demand response measures and rotating power flow controller is obtained.

2. A method for improving the new energy carrying capacity of a distribution network by using a rotating power flow controller according to claim 1, characterized in that: The goal is to increase the carrying capacity of new energy in the distribution network and reduce the operating costs of the distribution network.

3. A method for improving the new energy carrying capacity of a distribution network by using a rotating power flow controller according to claim 1, characterized in that: The construction is based on maximizing the carrying capacity of the distribution network for new energy and optimizing the access capacity of new energy as the upper objective function, including: The objective function of the upper optimization model is to maximize the access capacity of distributed photovoltaic and wind turbines, and its mathematical expression is: Where: S is the carrying capacity of the distribution network; is the wind power access capacity of the i-th node at time t; is the photovoltaic access capacity of the i-th node at time t; φ wind is the node set connected to wind power in the distribution system; PV is the set of nodes connected to photovoltaic in the system; T represents a scheduling cycle; ΔT is a time period within the scheduling cycle.

4. According to claim 3, the characteristic of maximizing the carrying capacity of the distribution network for new energy as the upper objective function, the constraint conditions are mainly the upper and lower limit constraints of the output of distributed new energy, including: Where: and are the active and reactive outputs of distributed renewable energy at node i in the system at time t respectively; and are the upper and lower limits of the active power output of distributed renewable energy at node i; and The upper and lower limits of the reactive power output of distributed renewable energy.

5. A method for improving the new energy carrying capacity of a distribution network by using a rotating power flow controller according to claim 1, characterized in that: The construction is to minimize the comprehensive operating cost of the system and adjust the operating output of each device as the lower-level objective function, including: The lower optimization model considers the comprehensive operation cost of the distribution network under various constraints. The investment cost of the power flow controller is regarded as the value converted to each day, that is, only its operation cost is considered in the model without considering its investment cost. Therefore, the comprehensive operation cost of the system mainly includes the network loss cost f loss 、Wind abandonment cost wind , abandoned light cost PV , Power flow controller operating cost f RPFC and the switching cost f switch ,Right now: Where: f is the comprehensive operating cost of the distribution network; φ b is the set of branches in the distribution network; φ RPFC C is the set of nodes connected to the power flow controller in the system; loss , C wind , C PV , C RPFC and C switch are the network loss cost coefficient, wind abandonment penalty coefficient, solar abandonment penalty coefficient, power flow controller operation cost coefficient and switch action cost coefficient respectively; I ij,t is the current flowing through branch ij at time t; r ij is the resistance on branch ij; and are the active power generated by the wind turbine and photovoltaic at node i at time t; and is the actual power injected into the system by the wind turbine and photovoltaic at node i at time t; is the power loss on the power flow controller at node i at time t; ε ij,0 and ε ij They are the on-off status of the system branches before and after network reconstruction.

6. According to claim 5, the characteristic of adjusting the operating output of each device to minimize the comprehensive operating cost of the system as the lower objective function, the constraint conditions are demand response constraints and power flow controller constraints, including: Demand response reduces the peak-to-valley difference of load by partially shifting the load in different time periods of the day, promotes the consumption of distributed new energy, reduces network losses, and reduces the comprehensive operating cost of the distribution network; the load power elasticity constraint, total load capacity constraint, and electricity price constraint considering demand response are shown in the following formula: Where: t is the electricity price elasticity coefficient at time t; P t and P t cur is the load power of the system before and after the implementation of demand response measures at time t; ρ t and are the electricity prices before and after demand response at time t; T peak is the time period to which the peak electricity price belongs; ρ peak is the peak electricity price; T valley is the time period to which the off-peak electricity price belongs; ρ valley The off-peak electricity price; and The upper and lower limits of electricity prices; In the power distribution system, the power flow controller can transfer power from the heavily loaded lines to the lightly loaded lines to achieve functions such as power flow control and power mutual assistance. Its constraints are shown in the following formula: Where: and are the active and reactive powers transmitted by nodes i and j of the power flow controller at time t, respectively; and is the active power loss of the power flow controller of nodes i and j at time t; μ i and μ j are the reactive power constraint coefficients of ports i and j of the power flow controller respectively; S RPFC is the capacity of the power flow controller.

7. A method for improving the new energy carrying capacity of a distribution network by using a rotating power flow controller according to claim 1, characterized in that: The above model is solved by a hybrid optimization algorithm based on the grey wolf algorithm and second-order cone programming, including: The gray wolf algorithm is used as a framework of the hybrid algorithm to determine the access capacity of new energy and the access position of the power flow controller. The access capacity of new energy and the input position of the power flow controller are regarded as individual wolves in the gray wolf group, and the upper objective function is regarded as the fitness function of the gray wolf group. In each iteration of the gray wolf algorithm, the second-order cone relaxation is used to convert the model into a convex solution to obtain the optimal operation mode of the distribution network system in each scenario, so as to calculate the fitness of the individuals in the gray wolf algorithm. When the fitness function value no longer changes after multiple updates of the gray wolf position, it is considered that the optimal solution to the problem is obtained. Step 1: Input relevant parameters of power distribution network, energy storage, distributed power source, etc. Step 2: Taking the maximum new energy carrying capacity of the distribution network as the optimization goal, the distributed new energy access capacity and the node location of the power flow controller access as the decision variables, the gray wolf algorithm is used to solve the upper optimization problem; Step 3: Taking the minimization of the comprehensive operating cost of the distribution network system as the optimization objective, the operating output of wind, solar and storage resources and the power flow controller in the distribution network as decision variables, and using the second-order cone relaxation optimization problem.

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