Electric energy layered fuzzy scheduling method for accessing distributed power supply to photovoltaic power distribution network

Abstract

The invention discloses an electric energy layered fuzzy scheduling method for a distributed power supply connected to a photovoltaic power distribution network. The method comprises the following steps: S10, carrying out hierarchical scheduling architecture analysis; s20, constructing an upper-layer optimization scheduling model based on the optimal power flow, and setting constraint conditions; s30, constructing a lower-layer optimization scheduling model based on model prediction; s40, performing fuzzification processing on the optimization objective function of the upper-layer scheduling model and the comprehensive objective function of the lower-layer scheduling model; and S50, determining a non-dominated optimal solution set, and calculating an optimal solution in the non-dominated optimal solution set. According to the invention, by constructing the hierarchical optimization scheduling model and carrying out fuzzy processing and solving on the target function, the disturbance influence of the distributed power supply and photovoltaic output is avoided, the power distribution network is enabled to operate stably, the energy loss and the total operation cost in the electric energy transmission process can be significantly reduced, the discontinuity and fluctuation of the distributed power supply are effectively inhibited, and the reliability of the power distribution network is improved. And the development of a novel power system is facilitated.

Description

Technical Field

[0001] This invention relates to the field of power system dispatching technology, and in particular to a hierarchical fuzzy dispatching method for distributed power sources connected to a photovoltaic distribution network. Background Technology

[0002] Under the major trend of energy transition, the development and utilization of renewable energy has become a key direction for global energy development. Among them, photovoltaic power generation, as a clean, efficient, and sustainable energy form, has broad development prospects. In recent years, my country's photovoltaic power generation industry has shown rapid development, with installed capacity continuously increasing and its proportion in the energy structure gradually rising. However, the intermittent and unstable characteristics of photovoltaic power generation bring many challenges to the dispatch and operation of the distribution network after its integration. With the large-scale integration of distributed power sources, especially the intermittent output of photovoltaic power generation, the operating characteristics of the distribution network have changed significantly. Traditional dispatching methods are difficult to adapt to its complex and ever-changing operating conditions, greatly increasing the difficulty of dispatching.

[0003] Before the integration of distributed generation into the distribution network, the dispatching of the distribution network mainly relied on centralized dispatching methods. This method achieves stable operation of the power system by optimizing generation plans and load allocation. However, with the large-scale integration of distributed generation, especially the intermittent output of photovoltaic (PV) power, the limitations of traditional centralized dispatching methods have gradually become apparent. First, the integration of distributed generation increases the number of power sources in the distribution network, and their uneven distribution increases the complexity of dispatching. Second, the intermittent and unstable nature of PV power generation makes it difficult to accurately predict its output, posing challenges to the formulation of dispatching plans. Furthermore, traditional centralized dispatching methods cannot fully consider the characteristics of distributed generation and the electricity demand of users, resulting in insufficient adaptability and flexibility in dispatching results, as well as significant energy losses and dispatching costs. Summary of the Invention

[0004] The main objective of this invention is to provide a hierarchical fuzzy scheduling method for distributed power sources connected to a photovoltaic power distribution network, in order to solve the technical problems of high power loss and scheduling cost in existing power scheduling methods.

[0005] To achieve the above objectives, the present invention provides a hierarchical fuzzy scheduling method for distributed power sources connected to a photovoltaic distribution network, the method comprising the following steps: S10, perform hierarchical scheduling architecture analysis; S20, Construct an upper-level optimization scheduling model based on optimal power flow and set constraints; S30, Construct a lower-level optimization scheduling model based on model prediction; S40, fuzzification processing is performed on the optimization objective function of the upper-level scheduling model and the comprehensive objective function of the lower-level optimization scheduling model; S50, determine the set of non-dominated optimal solutions, and calculate the optimal solution in the set of non-dominated optimal solutions.

[0006] Optionally, step S10 includes the following steps: S110, when distributed power sources are connected to the AC / DC hybrid photovoltaic distribution network, the distribution network is divided into two areas: DC and AC. S120, determine the hierarchical scheduling mode.

[0007] Optionally, step S20 includes the following steps: S210, with the objective function being to minimize the dispatch network loss between AC and DC in the photovoltaic distribution network; S220, Minimize the objective function to construct an upper-level optimization scheduling model based on optimal power flow: S230, Set constraints for the upper-level optimized scheduling model.

[0008] Alternatively, the expression for minimizing the objective function is as follows:

[0009] In the formula, P all,loss The minimum power loss of distributed power sources connected to the photovoltaic distribution network within the dispatch range; I is the total number of photovoltaic distribution network nodes; T is time; P loss,i,t P load,i,t P g,i,t These represent the power loss, active power consumed by the load, and active power output by the generator at node i at time t, respectively; F g,i Let be the power generation cost of node i; Set a fixed network loss coefficient for each node; This is the node network loss correction factor; The fixed cost coefficient for node power generation.

[0010] Optionally, the constraints include power flow constraints, the expressions of which are as follows:

[0011] In the formula, Let be the active power transmitted by node i in the distribution network at time t; Let be the active power consumed by the generator at node i at time t; and These represent the reactive power consumed by the generator, distribution network transmission, and load at node i at time t. These represent the voltage fluctuation ranges at the locations of nodes i and j at time t, respectively. These represent the conductance, susceptance, and voltage phase angle difference of the line between node i and node j, respectively.

[0012] Optionally, the constraints further include safe operation constraints, the expressions of which are as follows:

[0013] In the formula, These are the maximum and minimum active and reactive power of the generator at node i at time t, respectively. These are the maximum and minimum values ​​of the generator reactive power at node i at time t, respectively. These represent the maximum and minimum values ​​of the generator voltage fluctuation range at node i at time t, respectively. This represents the maximum power that the branch between node i and node j can withstand.

[0014] Optionally, step S30 includes the following steps: S310 takes minimizing the exchange power between the distribution network area and the external power grid and the error of the upper-level optimization results, and minimizing the cost of controllable distributed power sources, energy storage and flexible load dispatch within the area as its objective function. S320 combines two objective functions; S330, Set constraints, including: power balance constraints, flexible load constraints, energy storage device constraints, and controllable distributed power source constraints.

[0015] Optionally, step S40 includes the following steps: S410, the membership function based on the decreasing semi-Γ-shaped distribution, is the optimization objective function of the upper-level scheduling model. The blurring process is performed using the following expression:

[0016] In the formula, min P all,loss For constraint P all,loss The minimum value below; This represents the first membership function; S420, based on the membership function of the decreasing semi-Γ-shaped distribution, fuzzifies the comprehensive objective function F of the lower-level optimization scheduling model, as shown in the following expression:

[0017] In the formula, F represents the comprehensive objective function of the lower-level optimization scheduling model; This represents the second membership function.

[0018] Optionally, step S50 includes the following steps: S510, obtain the non-dominated optimal solution set obtained in each hierarchical optimization scheduling iteration of the photovoltaic distribution network; S520, combine the non-dominated optimal solution set and calculate the optimal solution in the non-dominated optimal set.

[0019] Optionally, the optimal solution in the non-dominated optimal set is calculated based on the following formula:

[0020] In the formula, It is the optimal solution in the non-dominated optimal set; The target number of the photovoltaic distribution network optimization scheduling model; , respectively, represent the weight and membership degree of the k-th objective in the photovoltaic power distribution network optimization scheduling model; m and n represent the number of non-dominated solutions and the number of dominated solutions, respectively.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows: The power hierarchical fuzzy scheduling method for distributed power sources connected to photovoltaic distribution networks provided in this application embodiment constructs a hierarchical optimization scheduling model (upper-level optimization scheduling model and lower-level optimization scheduling model), performs fuzzy processing on the objective function and solves it, avoids the disturbance effects of distributed power sources and photovoltaic output, ensures stable operation of the distribution network, and can significantly reduce energy loss and total operating cost during power transmission, effectively suppress the discontinuity and fluctuation of distributed power sources, and contribute to the development of new power systems. Attached Figure Description

[0022] Figure 1 A flowchart of a hierarchical fuzzy scheduling method for distributed power sources connected to a photovoltaic distribution network, provided in an embodiment of this application.

[0023] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0024] It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of the application. Rather, these embodiments are provided to make the disclosure more thorough and complete, and to fully convey the scope of the disclosure to those skilled in the art.

[0025] To address the aforementioned technical problems, embodiments of this application provide a hierarchical fuzzy scheduling method for distributed power sources connected to a photovoltaic distribution network, such as... Figure 1 As shown, the method may include the following steps: S10, perform hierarchical scheduling architecture analysis.

[0026] In an exemplary embodiment, step S10 includes the following steps: S110, when distributed power sources are connected to the AC / DC hybrid photovoltaic distribution network, the distribution network is divided into two areas: DC and AC. S120, determine the hierarchical scheduling mode.

[0027] Specifically, the hierarchical scheduling mode is as follows: the upper layer performs global scheduling to determine the goals and strategies for optimal scheduling; the lower layer optimizes the scheduling of each controllable resource to suppress the fluctuations and discontinuities of uncontrollable photovoltaic resources.

[0028] S20. Construct an upper-level optimization scheduling model based on optimal power flow and set constraints.

[0029] In an exemplary embodiment, step S20 includes the following steps: S210, with the objective function being to minimize the dispatch network loss between AC and DC in the photovoltaic distribution network; S220, Minimize the objective function to construct an upper-level optimization scheduling model based on optimal power flow: S230, Set constraints for the upper-level optimized scheduling model.

[0030] In an exemplary embodiment, the expression for minimizing the objective function is as follows:

[0031] In the formula, P all,loss The minimum power loss of distributed power sources connected to the photovoltaic distribution network within the dispatch range; I is the total number of photovoltaic distribution network nodes; T is time; P loss,i,t P load,i,t P g,i,t These represent the power loss, active power consumed by the load, and active power output by the generator at node i at time t, respectively; F g,i Let be the power generation cost of node i; Set a fixed network loss coefficient for each node; This is the node network loss correction factor; The fixed cost coefficient for node power generation.

[0032] The power system is a complex engineering system whose operation is subject to various physical, technical, economic and safety factors, and requires some constraints to make the model more realistic.

[0033] The constraints will now be explained in detail: In an exemplary embodiment, the constraints include power flow constraints to ensure power balance and voltage stability, and the expressions for the power flow constraints are as follows:

[0034] In the formula, Let be the active power transmitted by node i in the distribution network at time t; Let be the active power consumed by the generator at node i at time t; and These represent the reactive power consumed by the generator, distribution network transmission, and load at node i at time t. These represent the voltage fluctuation ranges at the locations of nodes i and j at time t, respectively. These represent the conductance, susceptance, and voltage phase angle difference of the line between node i and node j, respectively.

[0035] In an exemplary embodiment, the constraints further include safe operation constraints to limit the generator power and voltage range, and the expression for the safe operation constraints is as follows:

[0036] In the formula, Let be the maximum and minimum active and reactive power of the generator at node i at time t, respectively; These are the maximum and minimum values ​​of the generator reactive power at node i at time t, respectively. These represent the maximum and minimum values ​​of the generator voltage fluctuation range at node i at time t, respectively. This represents the maximum power that the branch between node i and node j can withstand.

[0037] S30, Construct a lower-level optimized scheduling model based on model prediction.

[0038] In an exemplary embodiment, step S30 includes the following steps: S310 takes minimizing the exchange power between the distribution network area and the external power grid and the error of the upper-level optimization results, and minimizing the cost of controllable distributed power sources, energy storage and flexible load dispatch within the area as its objective function. S320 combines two objective functions; S330, Set constraints, including: power balance constraints, flexible load constraints, energy storage device constraints, and controllable distributed power source constraints.

[0039] Specifically, the expression of this exemplary embodiment is as follows: Minimum switching power error:

[0040] In the formula, This represents the actual switching power. To exchange power as desired.

[0041] Lowest scheduling cost:

[0042] In the formula, The objective function is the scheduling cost. These refer to the number of flexible loads, controllable distributed power sources, and energy storage devices, respectively. These are their respective scheduling costs.

[0043] Combined objective function: Combining the two objective functions above, the formula is:

[0044] In the formula, The objective synthesis function; is the conversion factor.

[0045] Furthermore, to help the optimization algorithm find the optimal solution while meeting the system's operational requirements, the following constraints are set: Power balance constraint: Ensures power balance within the region. The formula is:

[0046] In the formula, It is the sum of flexible load and rigid load; Reactive load within the optimized range of the photovoltaic distribution network; The number of distributed power sources; Let t be the reactive power output of the first distributed power source at time t; The active and reactive power are respectively; a represents the number of dispatchable flexible loads; b represents the number of shearable flexible loads; and s represents the number of energy storage devices.

[0047] Flexible load constraint: Limits the power range of the flexible load. The formula is:

[0048] In the formula, These are the actual, minimum, and maximum values ​​of the nth flexible load, respectively.

[0049] Energy storage device constraints: These limit the power and state of charge of the energy storage device. The formula is:

[0050] In the formula, These are the minimum and maximum power values ​​of the energy storage device when the battery is operating normally; It is in a charged state.

[0051] Controllable distributed generation constraints: Limiting the power range of distributed generation sources. The formula is:

[0052] In the formula, These represent the active power output, minimum output value, and maximum output value of the l-th distributed power source at time t, respectively.

[0053] S40, fuzzification processing is performed on the optimization objective function of the upper-level scheduling model and the comprehensive objective function of the lower-level optimization scheduling model.

[0054] In an exemplary embodiment, step S40 includes the following steps: S410, the membership function based on the decreasing semi-Γ-shaped distribution, is the optimization objective function of the upper-level scheduling model. The blurring process is performed using the following expression:

[0055] In the formula, min P all,loss For constraint P all,loss The minimum value below; This represents the first membership function; S420, based on the membership function of the decreasing semi-Γ-shaped distribution, fuzzifies the comprehensive objective function F of the lower-level optimization scheduling model, as shown in the following expression:

[0056] In the formula, F represents the comprehensive objective function of the lower-level optimization scheduling model; This represents the second membership function.

[0057] To balance the conflict between multiple objectives, it is necessary to find a set of solutions that are optimal for all objectives. In this embodiment, Pareto optimality is used to find the non-dominated solution. The solution process will be described in detail below.

[0058] S50, determine the set of non-dominated optimal solutions, and calculate the optimal solution in the set of non-dominated optimal solutions.

[0059] In a multi-objective optimization problem, if a feasible solution X1 dominates X2, then the following conditions must be met:

[0060] In the formula, The number of objective functions; These are the dominated set and non-dominated set of the objective function in the hierarchical dispatch of power in the photovoltaic power distribution network for distributed generation.

[0061] In an exemplary embodiment, step S50 includes the following steps: S510, obtain the non-dominated optimal solution set obtained in each hierarchical optimization scheduling iteration of the photovoltaic distribution network; S520, combine the non-dominated optimal solution set and calculate the optimal solution in the non-dominated optimal set.

[0062] In an exemplary embodiment, the optimal solution in the non-dominated optimal set is calculated based on the following formula:

[0063] In the formula, It is the optimal solution in the non-dominated optimal set; The target number of the photovoltaic distribution network optimization scheduling model; , respectively, represent the weight and membership degree of the k-th objective in the photovoltaic power distribution network optimization scheduling model; m and n represent the number of non-dominated solutions and the number of dominated solutions, respectively.

[0064] The above embodiments will now be explained again in conjunction with practical applications: The effectiveness of the proposed method is verified using a simulation example of an IEEE 33-node distribution network system. The average voltage amplitude at each node in the IEEE 33-node distribution network system is 0.9540 pu, and the average voltage phase angle is 0.0027. The photovoltaic power generation capacity in the IEEE 33-node distribution network system is 50 kW, the conductor resistance is 0.2 Ω / km, the conductor loss is 1.2 kW, and the minimum network loss is 10 kW. The power generation cost of the photovoltaic distribution network is 0.15 yuan / (kW·h). Each distribution network contains 32 branches, 5 tie-line branches, and 1 power supply network. Its starting-end reference voltage is 12.66 kV, the three-phase power standard is 10 MV·A, and the total network load is (5084.26 + j2547.32) kV·A. Two photovoltaic generators with an installed capacity of 100 kW and a battery capacity of 200 kW·h are connected to the system.

[0065] The above data was calculated as follows. For a power of 10kW, after completing the construction of the upper and lower layer optimization models according to steps S20 and S30, the Pareto optimal solution is calculated according to steps S40 and S50. The optimal solution is calculated according to step S50 using the non-dominated optimal solution set obtained from each layer optimization scheduling iteration.

[0066] In the above embodiments of this application, by constructing a hierarchical optimization scheduling model (upper-level optimization scheduling model and lower-level optimization scheduling model), the objective function is fuzzy-processed and solved, avoiding the disturbance effects of distributed power sources and photovoltaic output, ensuring the stable operation of the distribution network, and significantly reducing energy loss and total operating costs during power transmission. It also effectively suppresses the discontinuity and fluctuations of distributed power sources, which is conducive to the development of new power systems.

[0067] In the description of this application, it should be noted that the terms "first", "second", and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0068] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0069] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0070] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0071] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0072] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0073] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the technical scope disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

[0074] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

Claims

1. A hierarchical fuzzy scheduling method for distributed power sources connected to a photovoltaic distribution network, characterized in that, The method includes the following steps: S10, perform hierarchical scheduling architecture analysis; S20, Construct an upper-level optimization scheduling model based on optimal power flow and set constraints; S30, Construct a lower-level optimization scheduling model based on model prediction; S40, fuzzification processing is performed on the optimization objective function of the upper-level scheduling model and the comprehensive objective function of the lower-level optimization scheduling model; S50, determine the set of non-dominated optimal solutions, and calculate the optimal solution in the set of non-dominated optimal solutions.

2. The hierarchical fuzzy scheduling method for distributed power generation connected to a photovoltaic distribution network according to claim 1, characterized in that, Step S10 includes the following steps: S110, when distributed power sources are connected to the AC / DC hybrid photovoltaic distribution network, the distribution network is divided into two areas: DC and AC. S120, determine the hierarchical scheduling mode.

3. The hierarchical fuzzy scheduling method for distributed power generation connected to a photovoltaic distribution network according to claim 1, characterized in that, Step S20 includes the following steps: S210, with the objective function being to minimize the dispatch network loss between AC and DC in the photovoltaic distribution network; S220, Minimize the objective function to construct an upper-level optimization scheduling model based on optimal power flow: S230, Set constraints for the upper-level optimized scheduling model.

4. The hierarchical fuzzy scheduling method for distributed power sources connected to a photovoltaic distribution network according to claim 3, characterized in that, The expression for minimizing the objective function is as follows: ； In the formula, P all,loss The minimum power loss of distributed power sources connected to the photovoltaic distribution network within the dispatch range; I is the total number of photovoltaic distribution network nodes; T is time; P loss,i,t P load,i,t P g,i,t These represent the power loss, active power consumed by the load, and active power output by the generator at node i at time t, respectively; F g,i Let be the power generation cost of node i; Set a fixed network loss coefficient for each node; This is the node network loss correction factor; The fixed cost coefficient for node power generation.

5. The hierarchical fuzzy scheduling method for distributed power sources connected to a photovoltaic distribution network according to claim 3, characterized in that, The constraints include power flow constraints, the expressions of which are as follows: ； In the formula, Let be the active power transmitted by node i in the distribution network at time t; Let be the active power consumed by the generator at node i at time t; and These represent the reactive power consumed by the generator, distribution network transmission, and load at node i at time t. These represent the voltage fluctuation ranges at the locations of nodes i and j at time t, respectively. These represent the conductance, susceptance, and voltage phase angle difference of the line between node i and node j, respectively.

6. The hierarchical fuzzy scheduling method for distributed power generation connected to a photovoltaic distribution network according to claim 3, characterized in that, The constraints also include safe operation constraints, the expressions of which are as follows: ； In the formula, Let be the maximum and minimum active and reactive power of the generator at node i at time t, respectively; These are the maximum and minimum values ​​of the generator reactive power at node i at time t, respectively. These represent the maximum and minimum values ​​of the generator voltage fluctuation range at node i at time t, respectively. This represents the maximum power that the branch between node i and node j can withstand.

7. The hierarchical fuzzy scheduling method for distributed power generation connected to a photovoltaic distribution network according to claim 1, characterized in that, Step S30 includes the following steps: S310 takes minimizing the exchange power between the distribution network area and the external power grid and the error of the upper-level optimization results, and minimizing the cost of controllable distributed power sources, energy storage and flexible load dispatch within the area as its objective function. S320 combines two objective functions; S330, Set constraints, including: power balance constraints, flexible load constraints, energy storage device constraints, and controllable distributed power source constraints.

8. The hierarchical fuzzy scheduling method for distributed power generation connected to a photovoltaic distribution network according to claim 1, characterized in that, Step S40 includes the following steps: S410, the membership function based on the decreasing semi-Γ-shaped distribution, is the optimization objective function of the upper-level scheduling model. The blurring process is performed using the following expression: ； In the formula, min P all,loss For constraint P all,loss The minimum value below; This represents the first membership function; S420, based on the membership function of the decreasing semi-Γ-shaped distribution, fuzzifies the comprehensive objective function F of the lower-level optimization scheduling model, as shown in the following expression: ； In the formula, F represents the comprehensive objective function of the lower-level optimization scheduling model; This represents the second membership function.

9. The hierarchical fuzzy scheduling method for distributed power generation connected to a photovoltaic distribution network according to claim 1, characterized in that, Step S50 includes the following steps: S510, obtain the non-dominated optimal solution set obtained in each hierarchical optimization scheduling iteration of the photovoltaic distribution network; S520, combine the non-dominated optimal solution set and calculate the optimal solution in the non-dominated optimal set.

10. The hierarchical fuzzy scheduling method for distributed power generation connected to a photovoltaic distribution network according to claim 1, characterized in that, The optimal solution in the non-dominated optimal set is calculated based on the following formula: ； In the formula, It is the optimal solution in the non-dominated optimal set; The target number of the photovoltaic distribution network optimization scheduling model; , respectively, represent the weight and membership degree of the k-th objective in the photovoltaic power distribution network optimization scheduling model; m and n represent the number of non-dominated solutions and the number of dominated solutions, respectively.

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