A method for joint optimization and configuration of optical storage resources considering power quality treatment

Abstract

This invention discloses a method for joint optimization allocation of photovoltaic and energy storage resources considering power quality management, comprising the following steps: (1) calculating power quality indicators; (2) comprehensively evaluating power quality and obtaining comprehensive evaluation results; (3) constructing a joint optimization allocation model for photovoltaic and energy storage resources considering power quality management, comprising the following steps: setting an objective function; setting constraints; the objective function includes the power quality optimization function of distributed photovoltaic access points and the function of minimizing total distribution operating costs; the constraints include energy storage operation constraints, photovoltaic curtailment constraints, and network topology constraints; the power quality optimization function of distributed photovoltaic access points is calculated based on the comprehensive evaluation results; (4) solving the optimal solution using the joint optimization allocation model, comprising the following steps: selecting the configuration scheme with the optimal power quality indicators. This invention not only considers the total operating cost but also focuses on the optimal power quality to select the optimal configuration scheme, thus realizing the management of power quality.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic and energy storage resource allocation technology, and more specifically, to a method for joint optimization allocation of photovoltaic and energy storage resources that takes into account power quality management. Background Technology

[0002] Existing power quality management methods mainly rely on network reconfiguration and equipment compensation. These methods are primarily applied on the user side, and with the large-scale integration of distributed photovoltaic power into the distribution network, the existing measures are not economically viable and are difficult to implement in practice.

[0003] Photovoltaic (PV) power generation is significantly affected by sunlight intensity, and its grid-connected power exhibits intermittent, fluctuating, and random characteristics, posing a considerable challenge to power grid quality. Furthermore, the use of power electronic devices in PV grid operation introduces harmonic injection, a major cause of power quality degradation. While numerous power quality indicators exist, they only assess one aspect of voltage, current, and frequency quality. Distributed PV grid integration introduces multiple power quality issues, necessitating a comprehensive power quality indicator to serve as a basis for effective management.

[0004] Existing research on the optimal allocation of photovoltaic (PV) and energy storage resources mainly focuses on maximizing PV capacity or minimizing total investment cost, with few considering power quality in the optimization models. However, the impact of PV integration on the power quality of the distribution network is becoming increasingly significant, making the use of PV and energy storage resources to improve power quality through configuration a key research focus. Therefore, there is an urgent need for a joint optimal allocation method for PV and energy storage resources that considers power quality management, taking into account not only total operating costs but also comprehensive power quality assessment to obtain the optimal allocation scheme. Summary of the Invention

[0005] The purpose of this invention is to provide a method for the joint optimization of photovoltaic and energy storage resources that takes into account power quality management. This method not only considers the total operating cost, but also focuses on selecting the optimal configuration scheme with the best power quality, thereby achieving power quality management.

[0006] To achieve the above objectives, a method for joint optimization allocation of photovoltaic and energy storage resources considering power quality governance is provided, comprising the following steps:

[0007] (1) Calculate power quality indicators; set several power quality indicators and calculate them according to the corresponding calculation methods; the power quality indicators include total current harmonic distortion rate (THDI), voltage deviation (δV), voltage fluctuation (Δd), and photovoltaic power generation penetration rate (η);

[0008] (2) A comprehensive evaluation of power quality is conducted, which includes the following steps: the weight of each power quality index is calculated using the intuitionistic fuzzy cross-entropy algorithm, and the sum of the power quality index multiplied by the corresponding weight is used to obtain the comprehensive evaluation result;

[0009] (3) Construct a joint optimization allocation model for photovoltaic and energy storage resources that considers power quality governance, including the following steps: setting the objective function; setting constraints;

[0010] The objective function includes the optimal power quality function for distributed photovoltaic (PV) grid connection points and the minimum total operating cost function for power distribution; the constraints include energy storage operation constraints, PV curtailment constraints, and network topology constraints; the optimal power quality function for distributed PV grid connection points is calculated based on the comprehensive evaluation results.

[0011] (4) Solving the optimal solution of the joint optimization configuration model includes the following steps: using a non-dominated sorting genetic algorithm with an elite strategy to solve the joint optimization configuration model of photovoltaic and energy storage resources established in step (3) to obtain a set; calculating the power quality index and cost under the configuration scheme corresponding to the set; selecting the configuration scheme with acceptable cost; and selecting the configuration scheme with the best power quality index from the above configuration schemes.

[0012] Specifically, the total current harmonic distortion rate (THDI) is calculated as follows:

[0013]

[0014] In the formula: I1 is the fundamental current; I h It is a high-order harmonic current.

[0015] Specifically, the voltage deviation δV is calculated as follows:

[0016]

[0017] In the formula: V i The real-time voltage of grid-connected node i can be obtained from power flow calculations; V i,n Let be the nominal voltage value of node i.

[0018] Specifically, the voltage fluctuation Δd is calculated as follows:

[0019]

[0020] In the formula: V N ΔI is the rated voltage; ΔI is the current change at the connection point caused by the change in output power; Z is the equivalent impedance of the two-port network distribution network as seen at the connection point.

[0021] Specifically, the method for calculating the photovoltaic power generation penetration rate η is as follows:

[0022]

[0023] In the formula: P PV For photovoltaic grid connection capacity; P total This represents the total power generation of the system.

[0024] Specifically, the method for calculating the weight of each power quality index using the intuitionistic fuzzy cross-entropy algorithm in step (2) is as follows:

[0025] 1) Let δj represent the power quality index, where j = 1, 2, 3, 4; according to the definition of intuitionistic fuzzy numbers, the importance, unimportance, and hesitation degree of index δj are represented by αj, βj, and γj, respectively, and their calculation methods are as follows, and an objective evaluation value O is defined. j = (αj, βj);

[0026]

[0027]

[0028]

[0029] In the formula: u1 = 0, u4 = 1, u2, u3 are the trisection points;

[0030] 2) The objective evaluation value O j The matrix Sj = (αj, βj) and the decision-maker's subjective preference value Sj = (σj, ρj) are concatenated to form a group intuitive fuzzy evaluation matrix M containing both subjective and objective evaluation information. j As shown in equation (8); furthermore, the intuitive fuzzy entropy of each power quality index is solved, as shown in equation (9);

[0031]

[0032]

[0033] In the formula: E(Mj) is the intuitionistic fuzzy entropy of the j-th index; σ j ρ j These represent the importance and unimportance of the subjective preference value for the j-th indicator, respectively, which can be obtained from surveys or expert ratings.

[0034] 3) Calculate the weight ω of the j-th indicator based on the intuitionistic fuzzy entropy of power quality indicators. j :

[0035]

[0036] Specifically, the optimal power quality function of the distributed photovoltaic access point is:

[0037]

[0038] In the formula: nPV is the set of photovoltaic access points; Hpq,i is the comprehensive power quality assessment result of the i-th photovoltaic access point;

[0039] The minimum function of the total operating cost of power distribution is:

[0040] min C total =C invest +C maint +C loss +C purch (13);

[0041] Among them, the converted cost of photovoltaic and energy storage configuration C invest Operation and maintenance costs C maint Network loss cost C loss and the cost of purchasing electricity from the main grid (C) purch ,

[0042]

[0043] Among them, C total The total cost of the distribution network within one operating cycle; T is the number of operating hours; Ω ess and Ω PV These are the sets of all nodes configured with energy storage and photovoltaics, respectively; r ess and r PV These are the discount rates for energy storage and photovoltaics, respectively; y ess and y PV The service life of energy storage and photovoltaics are respectively; c invest,ess and c invest,PV These represent the unit capacity investment costs for energy storage and photovoltaics, respectively; E ess,i and E PV,i The energy storage and photovoltaic capacities configured for node i are respectively; c maint,ess and c maint,PV These represent the annual operating and maintenance costs per unit capacity for energy storage and photovoltaics, respectively; Ω b C is the set of all branches of the distribution network; loss Cost per unit of network loss; I ij,t Let r be the current in branch ij at time t; ij P is the resistance of branch ij; t The amount of electricity purchased by the distribution network from the upstream main network at any given time.

[0044] Specifically, the energy storage operation constraints include state of charge continuity constraints and energy balance constraints, which are as follows:

[0045]

[0046]

[0047] In the formula, μ i μ is a 0-1 variable. i =1 indicates that ESS is configured at node i, μ i =0 indicates that no ESS is configured at node i; and P represents the maximum charging and discharging power of the ESS. i,t Let e ​​be the switching power of the ESS and the distribution network at node i at time t; i,t Let be the capacity of the ESS at node i at time t; Let represent the charge state of the ESS at node i at time t. and These represent the upper and lower limits of the State of Charge (SOC); T is one operating cycle; E ess,i Configure the ESS capacity at node i;

[0048] The photovoltaic curtailment constraint is

[0049]

[0050] Where: ΔP PV,t Let t represent the amount of solar power wasted during the t-th period. This represents the maximum amount of solar power that can be wasted during the t-th time period.

[0051] The network topology constraints include power balance constraints, power flow constraints, node voltage constraints, and branch current constraints, which are as follows:

[0052] P G,t +P PV,t +P ess,t +P purch,t =P L,t (18)

[0053]

[0054] V i,min ≤V i,t ≤V i,max (20)

[0055]

[0056] In the formula P G,t P PV,t and P ess,t P represents the output of generators, photovoltaic systems, and energy storage in the distribution network at time t, respectively. purch,t P represents the power transmitted from the upstream main grid to the distribution network at time t. L,t P represents the load at time t; ess,i,t and Q ess,i,tLet P be the active and reactive power outputs of the energy storage at node i at time t, respectively; L,i,t and Q L,i,t V represents the active and reactive power consumed by the load at node i at time t, respectively; i,t and V j,t G represents the voltage amplitude at nodes i and j at time t, respectively; ij and B ij These represent the conductance and susceptance of branch ij, respectively; θ i,t and θ j,t V represents the voltage phase angles at nodes i and j at time t, respectively; i,min and V i,max Let I be the minimum and maximum allowable voltage amplitudes at node i, respectively; ij,t Let I be the current flowing through branch ij at time t. ij,max Let be the maximum allowable current for branch ij.

[0057] Specifically, the method for solving the optimal solution using the joint optimization configuration model is as follows: The non-dominated sorting genetic algorithm is set to use real number encoding, and the i-th individual in the population is...

[0058] X i ={P PV,i ,P ess,i} (twenty two)

[0059] In the formula: P PV,i P ess,i These represent the photovoltaic configuration capacity and energy storage configuration capacity of the i-th node in the distribution network, respectively.

[0060] A set of Pareto optimal solutions is obtained based on the joint optimization configuration model of photovoltaic and energy storage resources and the non-dominated sorting genetic algorithm. The power quality index and cost of each element in the Pareto optimal solution set are calculated under the corresponding configuration scheme. The configuration scheme with acceptable cost is selected. The configuration scheme with the best power quality index is selected from the above configuration schemes.

[0061] The beneficial effects of the present invention are as follows:

[0062] This invention proposes a method for the joint optimization allocation of photovoltaic (PV) and energy storage resources that considers power quality management. First, an intuitionistic fuzzy cross-entropy algorithm is used to assign weights to the sub-indices to obtain a comprehensive power quality evaluation index, thereby quantifying the power quality of the distribution network. Second, with the optimal power quality of the distribution network and the minimum total operating cost as objective functions, a joint optimization allocation model for PV and energy storage resources is constructed, taking into account the output characteristics and operational constraints of PV and energy storage resources. Finally, a genetic algorithm is used to solve the model, and the optimal allocation scheme is selected with a focus on optimizing power quality, thus achieving power quality management. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is an overall flowchart of an embodiment of the present invention.

[0065] Figure 2 This is a flowchart of step (4) of an embodiment of the present invention. Detailed Implementation

[0066] The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby providing a clearer and more explicit definition of the scope of protection of the present invention.

[0067] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0068] It should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use. They are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," and "third," etc., are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0069] Furthermore, terms such as "horizontal," "vertical," and "sag" do not imply that components must be absolutely horizontal or suspended, but rather that they can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal relative to "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0070] like Figure 1As shown, photovoltaic (PV) power generation is significantly affected by sunlight intensity, and its grid-connected power exhibits intermittent, fluctuating, and random characteristics, posing a considerable challenge to the power quality of the grid. Furthermore, since PV power generation often utilizes power electronic devices in grid-connected operation, the resulting harmonic injection is also a major cause of power quality degradation. Therefore, this invention provides a method for the joint optimization allocation of PV and energy storage resources, considering power quality management, comprising the following steps:

[0071] (1) Calculate power quality indicators; set several power quality indicators and calculate them according to the corresponding calculation methods. Power quality indicators include total current harmonic distortion (THDI), voltage deviation (δV), voltage fluctuation (Δd), and photovoltaic power generation penetration rate (η).

[0072] Photovoltaic inverters contain numerous power electronic components that generate harmonics during the inverter process, causing harmonic pollution to the power grid. Therefore, after photovoltaic power is connected, it is necessary to reduce the total harmonic content of the line current. The calculation method for Total Harmonic Distortion (THDI) is as follows:

[0073]

[0074] In the formula: I1 is the fundamental current; I h It is a high-order harmonic current.

[0075] In photovoltaic (PV) power generation systems, the inverter does not actively participate in voltage regulation. When a distributed PV system operating in unity power factor mode is connected to the distribution network, the voltage of that branch will increase. When the connected capacity exceeds the maximum limit, it will cause voltage over-limit, reducing the reliability of the power grid. Therefore, it is necessary to examine the voltage deviation at the PV grid connection point. The voltage deviation is defined as the percentage of the difference between the actual voltage at a node in the system and the system's nominal voltage, relative to the system's nominal voltage. The calculation method for voltage deviation δV is as follows:

[0076]

[0077] In the formula: V i The real-time voltage of grid-connected node i can be obtained from power flow calculations; V i,n Let be the nominal voltage value of node i.

[0078] Changes in solar radiation intensity caused by cloud movement lead to variations in the output power of photovoltaic power generation systems, which is the main cause of voltage fluctuations. The calculation method for voltage fluctuation Δd at the connection point after grid connection is as follows:

[0079]

[0080] In the formula: V NΔI is the rated voltage; ΔI is the current change at the connection point caused by the change in output power; Z is the equivalent impedance of the two-port network distribution network as seen at the connection point.

[0081] The impact of distributed photovoltaic (PV) power generation on the power quality of the distribution network is closely related to the PV penetration rate (i.e., the proportion of connected capacity to the total power generation of the power system). The calculation method for the PV penetration rate η is as follows:

[0082]

[0083] In the formula: P PV For photovoltaic grid connection capacity; P total This represents the total power generation of the system.

[0084] (2) A comprehensive evaluation of power quality is conducted. The comprehensive evaluation includes the following steps: the weight of each power quality indicator is calculated using the intuitionistic fuzzy cross-entropy algorithm, and the sum of the power quality indicators multiplied by their corresponding weights is used to obtain the comprehensive evaluation result. This method can ensure the integrity of the indicators, reflect the differences between different indicators, and fully consider the objective evaluation and subjective preferences of decision-makers, thus ensuring the reliability and stability of the comprehensive evaluation of power quality.

[0085] The specific method for calculating the weight of each power quality index using the intuitionistic fuzzy cross-entropy algorithm in step (2) is as follows:

[0086] 1) Let δj represent the power quality index, where j = 1, 2, 3, 4; according to the definition of intuitionistic fuzzy numbers, the importance, unimportance, and hesitation degree of index δj are represented by αj, βj, and γj, respectively, and their calculation methods are as follows, and an objective evaluation value O is defined. j = (αj, βj);

[0087]

[0088]

[0089]

[0090] In the formula: u1 = 0, u4 = 1, u2, u3 are the trisection points;

[0091] 2) The objective evaluation value O j The matrix Sj = (αj, βj) and the decision-maker's subjective preference value Sj = (σj, ρj) are concatenated to form a group intuitive fuzzy evaluation matrix M containing both subjective and objective evaluation information. j As shown in equation (8); furthermore, the intuitive fuzzy entropy of each power quality index is solved, as shown in equation (9);

[0092]

[0093]

[0094] In the formula: E(Mj) is the intuitionistic fuzzy entropy of the j-th index; σ j ρ j These represent the importance and unimportance of the subjective preference value for the j-th indicator, respectively, which can be obtained from surveys or expert ratings.

[0095] 3) Calculate the weight ω of the j-th indicator based on the intuitionistic fuzzy entropy of power quality indicators. j :

[0096]

[0097] Specifically, the optimal power quality function of the distributed photovoltaic access point is:

[0098]

[0099] In the formula: nPV is the set of photovoltaic access points; Hpq,i is the comprehensive power quality assessment result of the i-th photovoltaic access point;

[0100] The minimum function of the total operating cost of power distribution is:

[0101] min C total =C invest +C maint +C loss +C purch (13);

[0102] Among them, the converted cost of photovoltaic and energy storage configuration C invest Operation and maintenance costs C maint Network loss cost C loss and the cost of purchasing electricity from the main grid (C) purch ,

[0103]

[0104] Among them, C total The total cost of the distribution network within one operating cycle; T is the number of operating hours; Ω ess and Ω PV These are the sets of all nodes configured with energy storage and photovoltaics, respectively; r ess and r PV These are the discount rates for energy storage and photovoltaics, respectively; y ess and y PV The service life of energy storage and photovoltaics are respectively; c invest,ess and c invest,PV These represent the unit capacity investment costs for energy storage and photovoltaics, respectively; E ess,i and E PV,i The energy storage and photovoltaic capacities configured for node i are respectively; cmaint,ess and c maint,PV These represent the annual operating and maintenance costs per unit capacity for energy storage and photovoltaics, respectively; Ω b C is the set of all branches of the distribution network; loss Cost per unit of network loss; I ij,t Let r be the current in branch ij at time t; ij P is the resistance of branch ij; t The amount of electricity purchased by the distribution network from the upstream main network at any given time.

[0105] (3) Construct a joint optimization allocation model for photovoltaic and energy storage resources that considers power quality governance, including the following steps: setting the objective function; setting constraints;

[0106] The objective functions include the optimal power quality function for distributed photovoltaic (PV) grid connection points and the minimum total distribution operating cost function; the constraints include energy storage operation constraints, PV curtailment constraints, and network topology constraints. The optimal power quality function for distributed PV grid connection points is calculated based on the comprehensive evaluation results.

[0107] Energy storage operation constraints include state of charge continuity constraints and energy balance constraints, which are as follows:

[0108]

[0109]

[0110] In the formula, μ i μ is a 0-1 variable. i =1 indicates that ESS is configured at node i, μ i =0 indicates that no ESS is configured at node i; and P represents the maximum charging and discharging power of the ESS. i,t Let e ​​be the switching power of the ESS and the distribution network at node i at time t; i,t Let be the capacity of the ESS at node i at time t; Let represent the charge state of the ESS at node i at time t. and These represent the upper and lower limits of the State of Charge (SOC); T is one operating cycle; E ess,i Configure the ESS capacity at node i;

[0111] The photovoltaic curtailment constraint is

[0112]

[0113] Where: ΔP PV,t Let t represent the amount of solar power wasted during the t-th period. This represents the maximum amount of solar power that can be wasted during the t-th time period.

[0114] The network topology constraints include power balance constraints, power flow constraints, node voltage constraints, and branch current constraints, which are as follows:

[0115] P G,t +P PV,t +P ess,t +P purch,t =P L,t (18)

[0116]

[0117] V i,min ≤V i,t ≤V i,max (20)

[0118]

[0119] In the formula P G,t P PV,t and P ess,t P represents the output of generators, photovoltaic systems, and energy storage in the distribution network at time t, respectively. purch,t P represents the power transmitted from the upstream main grid to the distribution network at time t. L,t P represents the load at time t; ess,i,t and Q ess,i,t Let P be the active and reactive power outputs of the energy storage at node i at time t, respectively; L,i,t and Q L,i,t V represents the active and reactive power consumed by the load at node i at time t, respectively; i,t and V j,t G represents the voltage amplitude at nodes i and j at time t, respectively; ij and B ij These represent the conductance and susceptance of branch ij, respectively; θ i,t and θ j,t V represents the voltage phase angles at nodes i and j at time t, respectively; i,min and V i,max Let I be the minimum and maximum allowable voltage amplitudes at node i, respectively; ij,t Let I be the current flowing through branch ij at time t. ij,max Let be the maximum allowable current for branch ij.

[0120] (4) Solving the optimal solution of the joint optimization configuration model includes the following steps: using a non-dominated sorting genetic algorithm with an elite strategy to solve the joint optimization configuration model of photovoltaic and energy storage resources established in step (3) to obtain a set; calculating the power quality index and cost under the configuration scheme corresponding to the set; selecting the configuration scheme with acceptable cost; and selecting the configuration scheme with the best power quality index from the above configuration schemes.

[0121] like Figure 2As shown, the specific method for solving the optimal solution using the joint optimization configuration model is as follows: The non-dominated sorting genetic algorithm is set to use real number encoding, and the i-th individual in the population is...

[0122] X i ={P PV,i ,P ess,i} (twenty two)

[0123] In the formula: P PV,i P ess,i These represent the photovoltaic configuration capacity and energy storage configuration capacity of the i-th node in the distribution network, respectively.

[0124] A set of Pareto optimal solutions is obtained based on the joint optimization configuration model of photovoltaic and energy storage resources and the non-dominated sorting genetic algorithm. The power quality index and cost of each element in the Pareto optimal solution set are calculated under the corresponding configuration scheme. The configuration scheme with acceptable cost is selected. The configuration scheme with the best power quality index is selected from the above configuration schemes.

[0125] Although embodiments of the present invention have been described in conjunction with the accompanying drawings, the patent owner may make various modifications or alterations within the scope of the appended claims, as long as they do not exceed the protection scope described in the claims of the present invention, they shall be within the protection scope of the present invention.

Claims

1. A method for joint optimization and configuration of optical storage resources considering power quality governance, characterized in that: Includes the following steps: (1) Calculate power quality indicators; set several power quality indicators and calculate them according to the corresponding calculation methods; the power quality indicators include total current harmonic distortion rate (THDI), voltage deviation (δV), voltage fluctuation (Δd), and photovoltaic power generation penetration rate (η); (2) A comprehensive evaluation of power quality is conducted, which includes the following steps: the weight of each power quality index is calculated using the intuitionistic fuzzy cross-entropy algorithm, and the sum of the power quality index multiplied by the corresponding weight is used to obtain the comprehensive evaluation result; (3) Construct a joint optimization allocation model for photovoltaic and energy storage resources that considers power quality governance, including the following steps: setting the objective function; setting constraints; The objective function includes the optimal power quality function for distributed photovoltaic (PV) grid connection points and the minimum total operating cost function for power distribution; the constraints include energy storage operation constraints, PV curtailment constraints, and network topology constraints; the optimal power quality function for distributed PV grid connection points is calculated based on the comprehensive evaluation results. (4) Solving the optimal solution of the joint optimization configuration model includes the following steps: using a non-dominated sorting genetic algorithm with an elite strategy to solve the joint optimization configuration model of photovoltaic and energy storage resources established in step (3) to obtain a set; calculating the power quality index and cost under the configuration scheme corresponding to the set; selecting the configuration scheme with acceptable cost; and selecting the configuration scheme with the best power quality index from the above configuration schemes. The specific method for calculating the weight of each power quality index using the intuitionistic fuzzy cross-entropy algorithm in step (2) is as follows: 1) Use power quality indicators Let δj = 1, 2, 3, 4; according to the definition of intuitionistic fuzzy numbers, the importance, unimportance, and hesitation of the index δj are respectively represented by δj. , and The calculation method is as follows, and an objective evaluation value is defined. =( , ); ; ； (7)； In the formula: =0, =1, , It is the point that divides the data into three equal parts; 2) Objective evaluation value =( , ) and the decision-maker's subjective preference value =( , The matrices are concatenated and merged to form a group intuitive fuzzy evaluation matrix containing both subjective and objective evaluation information. As shown in equation (8); furthermore, the intuitive fuzzy entropy of each power quality index is solved, as shown in equation (9); (8)； (9)； In the formula: Let be the intuitive fuzzy entropy of the j-th index; These represent the importance and unimportance of the subjective preference value for the j-th indicator, respectively, which can be obtained from surveys or expert ratings. 3) Calculate the weight of the j-th indicator based on the intuitionistic fuzzy entropy of power quality indicators. : (j=1,2,3,4)(10)； The optimal power quality function for the distributed photovoltaic access point is: (12); In the formula: A collection of photovoltaic access points; This represents the comprehensive power quality assessment result for the i-th photovoltaic access point. The minimum function of the total operating cost of power distribution is: (13)； Among them, the converted cost of photovoltaic and energy storage configuration Operation and maintenance costs Network loss cost and the cost of purchasing electricity from the main grid , (14); in, The total cost of the distribution network within one operating cycle; T represents the number of operating hours; and These are the sets of all nodes configured with energy storage and photovoltaics, respectively. and These are the discount rates for energy storage and photovoltaics, respectively. and These refer to the service life of energy storage and photovoltaic systems, respectively. and These are the unit capacity investment costs for energy storage and photovoltaics, respectively. and These are the energy storage and photovoltaic capacities configured for node i, respectively. and These are the annual operating and maintenance costs per unit capacity for energy storage and photovoltaic, respectively. This is the set of all branches of the distribution network; Cost per unit of network loss; Let be the current in branch ij at time t; Let be the resistance of branch ij; The amount of electricity purchased by the distribution network from the upstream main network at any given time.

2. The method for joint optimization allocation of photovoltaic and energy storage resources considering power quality management according to claim 1, characterized in that: The total current harmonic distortion rate The calculation method is as follows: ； In the formula: It is the fundamental current; It is a high-order harmonic current.

3. The method for joint optimization allocation of photovoltaic and energy storage resources considering power quality management according to claim 1, characterized in that: The voltage deviation δV is calculated as follows: In the formula: The real-time voltage of grid-connected node i can be obtained from power flow calculations; Let be the nominal voltage value of node i.

4. The method for joint optimization allocation of photovoltaic and energy storage resources considering power quality management according to claim 1, characterized in that: The calculation method for the voltage fluctuation Δd is as follows: (3); In the formula: Rated voltage; Z represents the current change at the connection point caused by the change in output power; Z is the equivalent impedance of the two-port distribution network as seen at the connection point.

5. The method for joint optimization allocation of photovoltaic and energy storage resources considering power quality management according to claim 1, characterized in that: The photovoltaic power generation penetration rate η is calculated as follows: (4)； In the formula: For photovoltaic grid connection capacity; This represents the total power generation of the system.

6. The method for joint optimization allocation of photovoltaic and energy storage resources considering power quality management according to claim 1, characterized in that: The energy storage operation constraints include a state of charge continuity constraint and a charge balance constraint, which are as follows: (15); (16)； In the formula, For 0-1 variables, =1 indicates that ESS is configured at node i. =0 indicates that no ESS is configured at node i; and These represent the maximum charging and discharging power of the ESS, respectively. Let be the switching power between the ESS and the distribution network at node i at time t; Let be the capacity of the ESS at node i at time t; Let represent the charge state of the ESS at node i at time t. and These represent the upper and lower limits of the SOC, respectively; T is one operating cycle. Configure the ESS capacity at node i; The photovoltaic curtailment constraint is (17)； In the formula: Let t represent the amount of solar power wasted during the t-th period. This represents the maximum amount of solar power that can be wasted during the t-th time period. The network topology constraints include power balance constraints, power flow constraints, node voltage constraints, and branch current constraints, which are as follows: (18) (19) (20) (21) In the formula , and These represent the output of generators, photovoltaic systems, and energy storage in the distribution network at time t, respectively. Let t be the power transmitted from the upstream main grid to the distribution network; The load at time t; and These represent the active and reactive power outputs of the energy storage at node i at time t, respectively. and These represent the active and reactive power consumed by the load at node i at time t, respectively. Let i and j be the voltage amplitudes at nodes i and j at time t, respectively. and These are the conductance and susceptance of branch ij, respectively; and Let be the voltage phase angles at nodes i and j at time t, respectively; and These are the minimum and maximum allowable voltage amplitudes at node i, respectively; Let be the current flowing through branch ij at time t. Let be the maximum allowable current for branch ij.

7. The method for joint optimization allocation of photovoltaic and energy storage resources considering power quality management according to claim 1, characterized in that: The specific method for solving the optimal solution using the joint optimization configuration model is as follows: The non-dominated sorting genetic algorithm is set to use real number encoding, and the i-th individual in the population is... (22) In the formula: , These represent the photovoltaic configuration capacity and energy storage configuration capacity of the i-th node in the distribution network, respectively. A set of Pareto optimal solutions is obtained based on the joint optimization configuration model of photovoltaic and energy storage resources and the non-dominated sorting genetic algorithm. The power quality index and cost of each element in the Pareto optimal solution set are calculated under the corresponding configuration scheme. The configuration scheme with acceptable cost is selected. The configuration scheme with the best power quality index is selected from the above configuration schemes.

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