Optimization method, device, equipment, medium and program product for optical storage capacity of power distribution network

Abstract

The application relates to a power distribution network light storage capacity optimization method, device, equipment, medium and program product. The method solves a pre-constructed light storage joint optimization model according to power distribution network basic data to obtain initial capacity configuration parameters of the power distribution network, and then solves a pre-constructed target planning model according to the initial capacity configuration parameters to obtain target capacity configuration parameters. The above method first determines the initial capacity configuration parameters with the minimum total system economic cost as the target, and then determines the target capacity configuration parameters with the minimum total system economic cost and the minimum collaborative distance as the target. The light storage capacity configuration parameters can consider the difference between the economic optimal scheduling and the power quality optimal scheduling on the basis of ensuring the economy, avoid the forced switching of the system between the economic priority and the quality priority in the long-term operation, and improve the collaboration of the power distribution network economy and high-quality operation and the light storage capacity optimization accuracy.

Description

Technical Field

[0001] This application relates to the field of power distribution network planning technology, and in particular to a method, apparatus, equipment, medium, and program product for optimizing the photovoltaic and energy storage capacity of a power distribution network. Background Technology

[0002] Distributed photovoltaic (PV) power generation, due to its clean and renewable characteristics, has been widely integrated and scaled up in distribution networks. However, the volatility and indirectness of distributed power output pose significant challenges to power quality during large-scale grid-connected operation, threatening the safe and stable operation of the grid. To mitigate the impact of PV output volatility and intermittency on the grid, energy storage systems are typically combined with PV systems to form integrated PV-storage systems, thereby improving the distribution network's capacity to absorb new energy and its operational flexibility. In this context, the rational configuration of the capacity of PV-storage systems is crucial to ensuring the safe and economical operation of the distribution network.

[0003] However, the optimization methods for photovoltaic and energy storage capacity in distribution networks in related technologies suffer from inaccurate optimization. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, equipment, medium, and program product for optimizing the photovoltaic and energy storage capacity of distribution networks that can improve the accuracy of optimization, in order to address the above-mentioned technical problems.

[0005] Firstly, this application provides a method for optimizing the photovoltaic and energy storage capacity of a power distribution network, the method comprising:

[0006] The pre-constructed photovoltaic-storage joint optimization model is solved based on the basic data of the distribution network to obtain the initial capacity configuration parameters of the distribution network. The photovoltaic-storage joint optimization model includes a first optimization objective function and optimization constraints. The first optimization objective function aims to minimize the total economic cost of the system. The optimization constraints include photovoltaic-storage installation capacity constraints, renewable energy utilization rate constraints, energy storage operation constraints, system power flow constraints, photovoltaic output constraints, and external grid power purchase constraints.

[0007] The target capacity configuration parameters are obtained by solving the pre-constructed target planning model based on the initial capacity configuration parameters. The target planning model includes a second optimization objective function and optimization constraints. The second optimization objective function aims to minimize the total economic cost of the system and the coordination distance. The coordination distance is used to characterize the difference in operation between the economically optimal scheduling scheme and the power quality optimal scheduling scheme under the same capacity configuration parameters. The target capacity configuration parameters include the installed capacity of photovoltaic equipment and energy storage equipment at each grid connection point in the distribution network.

[0008] In some embodiments, the target capacity configuration parameters are obtained by solving a pre-built target planning model based on initial capacity configuration parameters, including:

[0009] Based on the initial capacity configuration parameters, the system output parameters of the target planning model are adjusted according to the economic objectives and the power quality objectives, respectively, to obtain the first economic information, the first power quality information and the first output information under the economically optimal scheduling scheme, as well as the second economic information, the second power quality information and the second output information under the power quality optimal scheduling scheme.

[0010] The coordination distance is determined based on the first economic information, the first power quality information, the first output information, the second economic information, the second power quality information, and the second output information.

[0011] Based on the cooperative distance and the total economic cost of the system, the capacity configuration parameters of the target planning model are adjusted. Then, based on the adjusted capacity configuration parameters, the steps of adjusting the system output parameters of the target planning model with economic objectives and power quality objectives are returned until the cooperative distance and the total economic cost of the system are minimized, thus obtaining the target capacity configuration parameters.

[0012] In some embodiments, based on initial capacity configuration parameters, the system output parameters of the target planning model are adjusted according to economic and power quality objectives to obtain first economic information, first power quality information, and first output information under the economically optimal scheduling scheme, as well as second economic information, second power quality information, and second output information under the power quality optimal scheduling scheme, including:

[0013] The first lower-level operation model is solved based on the initial capacity configuration parameters to obtain the first economic information, the first power quality information, and the first output information under the economically optimal scheduling scheme. The first lower-level operation model includes an economic objective function and planning constraints. The economic objective function aims to minimize the sum of the typical daily solar-storage operation cost and the typical daily electricity purchase cost. The planning constraints include system power flow constraints, photovoltaic output constraints, and external grid electricity purchase constraints.

[0014] The pre-constructed second lower-level operation model is solved based on the initial capacity configuration parameters to obtain the second economic information, second power quality information, and second output information under the optimal power quality scheduling scheme. The second lower-level operation model includes a power quality objective function and planning constraints. The power quality objective function aims to minimize the power quality assessment value. The power quality assessment value is the sum of the products of multiple power quality indicators and their corresponding weight coefficients. The power quality indicators include voltage deviation, voltage fluctuation, and voltage harmonics.

[0015] In some embodiments, the cooperative distance is determined based on first economic information, first power quality information, first output information, second economic information, second power quality information, and second output information, including:

[0016] Substitute the first economic information, the first power quality information, the second economic information, and the second power quality information into the distance relationship of the operation results to calculate the Euclidean distance of the operation results;

[0017] Substitute the first and second output information into the operating mode distance relationship formula to calculate the operating mode Euclidean distance;

[0018] The collaborative distance is determined based on the Euclidean distance of the operation results and the Euclidean distance of the operation mode.

[0019] In some embodiments, the first optimization objective function includes: the total economic cost of the system is the sum of the photovoltaic and energy storage investment cost, the total annual operating cost, and the total annual electricity purchase cost; the photovoltaic and energy storage investment cost is the sum of the photovoltaic investment cost and the energy storage investment cost; the total annual operating cost is the sum of the products of the number of days occupied by each typical day and the photovoltaic and energy storage operating cost of each typical day; and the total annual electricity purchase cost is the sum of the products of the number of days occupied by each typical day and the electricity purchase and operating cost of each typical day.

[0020] In some embodiments, energy storage operation constraints include: mutual exclusion constraints for energy storage charging and discharging, energy storage discharge power constraints, energy storage charging power constraints, and energy storage state of charge timing update constraints; system power flow constraints include: node active power balance constraints, node reactive power balance constraints, node voltage amplitude recursion constraints, and line transmission capacity constraints.

[0021] Secondly, this application also provides a photovoltaic-storage capacity optimization device for a power distribution network, the device comprising:

[0022] The initial planning module is used to solve the pre-constructed photovoltaic-storage joint optimization model based on the basic data of the distribution network to obtain the initial capacity configuration parameters of the distribution network. The photovoltaic-storage joint optimization model includes a first optimization objective function and optimization constraints. The first optimization objective function aims to minimize the total economic cost of the system. The optimization constraints include photovoltaic-storage installation capacity constraints, renewable energy utilization rate constraints, energy storage operation constraints, system power flow constraints, photovoltaic power output constraints, and external grid power purchase constraints.

[0023] The target planning module is used to solve the pre-built target planning model based on the initial capacity configuration parameters to obtain the target capacity configuration parameters. The target planning model includes a second optimization objective function and optimization constraints. The second optimization objective function aims to minimize the total economic cost of the system and the coordination distance. The coordination distance is used to characterize the difference in operation between the economically optimal scheduling scheme and the power quality optimal scheduling scheme under the same capacity configuration parameters. The target capacity configuration parameters include the installed capacity of photovoltaic equipment and energy storage equipment at each grid connection point in the distribution network.

[0024] Thirdly, this application also provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method of any of the embodiments in the first aspect described above.

[0025] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method of any of the embodiments in the first aspect described above.

[0026] Fifthly, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the method in any of the embodiments of the first aspect described above.

[0027] The aforementioned method, device, equipment, medium, and program products for optimizing the photovoltaic (PV) and energy storage (ESS) capacity in the distribution network involve solving a pre-constructed PV-ESS joint optimization model based on the distribution network's basic data to obtain initial capacity configuration parameters. Then, based on these initial parameters, a pre-constructed target planning model is solved to obtain target capacity configuration parameters. The PV-ESS joint optimization model includes a first optimization objective function and optimization constraints. The first objective function aims to minimize the total economic cost of the system. The optimization constraints include PV-ESS installation capacity constraints, renewable energy utilization rate constraints, energy storage operation constraints, system power flow constraints, PV output constraints, and external grid power purchase constraints. The target planning model includes a second optimization objective function and optimization constraints. The second objective function aims to minimize both the total economic cost of the system and the coordination distance. The coordination distance characterizes the operational differences between the economically optimal dispatch scheme and the power quality-optimal dispatch scheme under the same capacity configuration parameters. The target capacity configuration parameters include the installed capacity of PV equipment and the installed capacity of energy storage equipment at each grid connection point in the distribution network. In the above method, the initial capacity configuration parameters are first determined with the goal of minimizing the total economic cost of the system. Then, the target capacity configuration parameters are determined with the goals of minimizing both the total economic cost and the coordination distance. This ensures that the photovoltaic and energy storage capacity configuration parameters, while maintaining economic efficiency, incorporate the difference between economically optimal scheduling and power quality-optimal scheduling into the optimization considerations. This effectively avoids the system being forced to passively switch between two extreme modes of prioritizing economy and quality during long-term operation, improving the long-term coordination capability of economic and high-quality operation of the distribution network, and thus enhancing the accuracy of photovoltaic and energy storage capacity optimization decisions. Simultaneously, by setting constraints on photovoltaic and energy storage installation capacity, renewable energy utilization rate, energy storage operation, system power flow, photovoltaic output, and external grid power purchase, the feasibility of the configuration scheme in actual grid operation is ensured, ultimately achieving continuous coordinated optimization of distribution network economy and power quality. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 These are internal structural diagrams of the computer device in some embodiments;

[0030] Figure 2 This is one of the flowcharts illustrating the method for optimizing the photovoltaic and energy storage capacity of a distribution network in some embodiments;

[0031] Figure 3 This is a mapping diagram of the execution results of different targets in some embodiments;

[0032] Figure 4 This is a mapping diagram of different target operation modes in some embodiments;

[0033] Figure 5 This is a flowchart illustrating the process of determining target capacity parameters in some embodiments.

[0034] Figure 6 This is the second flowchart illustrating the method for optimizing the photovoltaic and energy storage capacity of a distribution network in some embodiments;

[0035] Figure 7 This is a schematic diagram showing the location of the photovoltaic-storage grid connection point in some embodiments;

[0036] Figure 8 Here are some examples of typical daily total load, photovoltaic curves, and peak-valley electricity prices;

[0037] Figure 9 The voltage amplitude at a typical solar-energy storage grid connection point is shown in some embodiments;

[0038] Figure 10 The voltage harmonic distortion rate at a typical solar-energy storage grid connection point is shown in some embodiments.

[0039] Figure 11 The results and Euclidean distances for different iteration numbers and running methods in some embodiments;

[0040] Figure 12 This is a structural block diagram of a power distribution network photovoltaic storage capacity optimization device in some embodiments. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0042] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more, and other quantifiers are similar. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The term "and / or" used in this application refers to one of the solutions, or any combination of multiple solutions. In the embodiments of this application, the term "at least one" means one or more.

[0043] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0044] With the introduction of dual-carbon goals, building a clean energy utilization system has become a trend in my country's energy development. Distributed power sources, due to their clean and renewable characteristics, have broad application prospects. However, the volatility and indirectness of distributed power generation output have led to increasingly prominent power quality problems in the power grid as a large number of distributed power sources are connected, seriously affecting the safe and stable operation of the power grid.

[0045] To address the aforementioned issues, traditional research methods often optimize photovoltaic-storage capacity under a single objective, such as minimizing economic cost or optimizing power quality indicators. They rarely systematically incorporate power quality assessment into the planning framework, making it difficult for configuration schemes to achieve the expected comprehensive benefits in actual operation. Therefore, to address the shortcomings of traditional research methods, existing studies have incorporated power quality indicators as objectives or constraints into traditional planning models, enabling planning results to balance economy and power quality to some extent. However, due to the overlap and differences in the feasible domains of economic operation and high-quality operation under different photovoltaic-storage configuration schemes, these two aspects are not always contradictory. Existing research has failed to fully explore the synergistic potential between distribution network economy and power quality levels in long-term operation, neglecting the contradictions and potential connections between them. This leads to planning schemes often being forced to passively switch between two extreme modes of "economy priority" and "quality priority" in long-term operation, resulting in a lack of consistency in operating modes and making it difficult to achieve long-term synergistic operation of economy and power quality levels. In addition, existing studies often use single indicators such as voltage deviation and harmonics as representative indicators of power quality. However, power quality problems caused by photovoltaic access actually have multi-dimensional coupling characteristics, covering multiple dimensions such as voltage deviation, voltage fluctuation, and harmonic distortion. A single indicator is difficult to fully characterize the comprehensive impact of photovoltaic and energy storage operation on the power grid, and it cannot support the planning level to accurately judge the power quality under different configuration schemes, resulting in inaccurate optimization of photovoltaic and energy storage capacity.

[0046] Therefore, there is an urgent need to establish a set of photovoltaic and energy storage capacity optimization methods that are oriented towards the synergy of power quality improvement and economic efficiency. This will reduce the economic cost of distribution network operation while maximizing the power quality level of the distribution network, and achieve long-term synergy between economic operation and high-quality operation of the distribution network.

[0047] In view of this, this application proposes a method, device, equipment, medium, and program product for optimizing the photovoltaic and energy storage capacity of a distribution network. On the one hand, addressing the lack of a long-term coordination mechanism between economic efficiency and power quality in existing technologies, this application uses Euclidean distance to measure the difference in operating strategies under the two objectives of optimal economic efficiency and optimal power quality. It incorporates quantitative indicators of coordination into the optimization framework, studies the coordination mechanism between economic and high-quality operation of the distribution network, and breaks through the limitations of traditional static weighted or single-layer optimization. This enables the capacity configuration scheme to dynamically coordinate economic efficiency and power quality over a long-term operational scale, improving the matching degree between power quality and economic efficiency in the "planning-operation" two-stage process, avoiding passive switching between two extreme modes, and ultimately achieving optimal coordination between the two. On the other hand, addressing the deficiency of single power quality indicators in existing technologies, this application uses the analytic hierarchy process (AHP) to construct a comprehensive power quality evaluation model covering multiple dimensions such as voltage deviation, voltage fluctuation, and harmonic distortion. This model is used as one of the objective functions for lower-level operational optimization, achieving a comprehensive quantitative assessment of the power quality level of the distribution network.

[0048] It should be noted that the beneficial effects or technical problems solved by the embodiments of this application are not limited to this one, but may also be other implicit or related problems. For details, please refer to the description of the embodiments below.

[0049] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0050] In some embodiments, the photovoltaic and energy storage capacity optimization method for distribution networks provided in this application can be applied to, for example... Figure 1 The computer device shown can be a server or a terminal, and its internal structure diagram can be as follows: Figure 1 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a method for optimizing the photovoltaic and energy storage capacity of a power distribution network. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0051] Those skilled in the art will understand that Figure 1 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0052] In some embodiments, such as Figure 2 As shown, a method for optimizing the photovoltaic and energy storage capacity of a distribution network is provided, and this method is applied to... Figure 1 Taking a computer device as an example, the explanation includes the following steps:

[0053] S201, based on the basic data of the distribution network, solve the pre-constructed photovoltaic-storage joint optimization model to obtain the initial capacity configuration parameters of the distribution network.

[0054] The photovoltaic-storage joint optimization model includes a first optimization objective function and optimization constraints. The first optimization objective function aims to minimize the total economic cost of the system. The optimization constraints include photovoltaic-storage installation capacity constraints, renewable energy utilization rate constraints, energy storage operation constraints, system power flow constraints, photovoltaic output constraints, and external grid power purchase constraints.

[0055] Distribution network basic data refers to the original parameters and operating data of the distribution network required for optimizing the configuration of photovoltaic and energy storage capacity. These include, but are not limited to: the distribution network topology, line impedance parameters, system reference voltage, load data of each node, typical daily power generation characteristics of distributed photovoltaics, charging and discharging efficiency and lifespan parameters of energy storage devices, unit capacity investment cost, unit operating cost, annual discount rate, and connection relationship with the external power grid and electricity purchase price.

[0056] Initial capacity configuration parameters refer to the preliminary capacity configuration results obtained after solving the photovoltaic-storage joint optimization model, including the capacity of photovoltaic equipment and energy storage equipment installed at each grid connection point in the distribution network.

[0057] Computer equipment can pre-build an optimization model for a power distribution network system. This model includes an upper-level optimization model and a lower-level optimization model. The upper-level optimization model includes the upper-level objective function and upper-level constraints, while the lower-level operational model includes the lower-level objective function and lower-level constraints.

[0058] The upper-level objective function is based on the total economic cost of the system. Minimize costs, including photovoltaic and energy storage investment costs. Total annual operating costs Total annual electricity purchase cost The relationship is as follows:

[0059] (1)

[0060] (2)

[0061] (3)

[0062] (4)

[0063] In the formula: The discount rate; and These refer to the lifespan of distributed photovoltaic and energy storage devices, respectively. and These refer to the operational capacity of distributed photovoltaic and energy storage devices at their respective grid connection points; and The unit capacity investment cost of distributed photovoltaic and energy storage equipment; and These are collections of grid connection points for distributed photovoltaic and energy storage devices, respectively. For typical days Number of days occupied; A collection of typical days.

[0064] Upper-level constraints include photovoltaic and energy storage installation capacity constraints and renewable energy utilization rate constraints.

[0065] The relationship between the photovoltaic and energy storage installation capacity constraints is as follows:

[0066] (5)

[0067] In the formula: and These represent the maximum installed capacity of distributed photovoltaic and energy storage, respectively.

[0068] The relationship for the renewable energy utilization rate constraint is as follows:

[0069] (6)

[0070] In the formula: and These are the upper and lower limits of the annual weighting, respectively. This represents the total annual power generation of distributed photovoltaic power. Total annual electricity consumption; This is the reserve factor for new energy sources.

[0071] The lower-level objective function uses typical solar power storage operating costs. Electricity purchase cost and power quality assessment level The objective is to minimize it, and the relationship is as follows:

[0072] (7)

[0073] (8)

[0074] (9)

[0075] In the formula: and These are the unit operating costs of distributed photovoltaic power and energy storage, respectively; subscript and These represent a typical day and a specific time, respectively. Contributing to the active power of distributed photovoltaic power generation; and These are the discharge and charging power of energy storage, respectively. The unit price for time-of-use electricity; Power purchased from external power grids; A set of connection points to the external network; For the time set.

[0076] Due to the randomness and volatility of distributed photovoltaic (PV) output, voltage fluctuations, voltage deviations, and voltage harmonics are prominent issues in the distribution network when PV is connected to the grid. Therefore, this paper constructs a comprehensive power quality evaluation system for distributed PV grid-connected points, using these three power quality indicators as the object. The power quality evaluation value is determined by multiple power quality indicators and their corresponding weighting coefficients. These power quality indicators include voltage deviation, voltage fluctuations, and voltage harmonics, and the relationships are as follows:

[0077] The weighting coefficients of each power quality indicator are determined using the aforementioned analytic hierarchy process (AHP), and the relationships are as follows:

[0078] (10)

[0079] In the formula: , and The weighting coefficients for the evaluation indicators.

[0080] The voltage deviation is calculated based on the relative deviation between the actual operating voltage at the photovoltaic grid connection point and the nominal voltage of the system, using the following formula:

[0081] (11)

[0082] In the formula: For photovoltaic grid connection points The actual voltage; For nodes The reference voltage.

[0083] The relationship for voltage fluctuations is as follows:

[0084] (12)

[0085] In the formula: For photovoltaic grid connection points Voltage fluctuations; For nodes A set; For nodes Resistance of each line; This represents the maximum daily output of a typical distributed photovoltaic system. This is the system's rated voltage.

[0086] The relationship between voltage harmonics is as follows:

[0087] (13)

[0088] In the formula: and Photovoltaic grid connection points Total harmonic voltage content and total harmonic distortion of voltage; For photovoltaic grid connection points The Subharmonic voltage content; Photovoltaic grid connection point The fundamental voltage.

[0089] The lower-level constraints include energy storage operation constraints, system power flow constraints, photovoltaic power output constraints, and external grid power purchase constraints.

[0090] The expression for the energy storage operation constraints is as follows:

[0091] (14)

[0092] In the formula: and 0-1 variables representing the charge and discharge states of the energy storage system; and These are the minimum discharge power for energy storage and the charging power, respectively. The amount of electrical energy stored for energy storage; and These represent the energy storage charging and discharging efficiency, respectively.

[0093] The expressions for system power flow constraints are as follows:

[0094] (15)

[0095] (16)

[0096] In the formula: For nodes The load value; For nodes For the first node This refers to the line power at the end; and respectively with This is a combination of the terminal and the first segment of the line; Line current; and For line resistance and reactance; , and These are reactive power generated by distributed photovoltaic power, external purchases, and capacitors, respectively. The reactive power consumed by the load; For nodes The actual voltage.

[0097] The expression for the photovoltaic output constraint is as follows:

[0098] (17)

[0099] The expression for the external power grid purchase constraint is as follows:

[0100] (18)

[0101] In the formula: and These represent the maximum active and reactive power purchased from the external power grid, respectively.

[0102] In this embodiment, the photovoltaic-storage joint optimization model and the distribution network system optimization model have the same architecture, including an upper-level optimization model and a lower-level optimization model, but the objective functions are different. The first optimization objective function in the photovoltaic-storage joint optimization model is the upper-level objective function in the distribution network system optimization model, that is, the first optimization objective function aims to minimize the total economic cost of the system. The optimization constraints include upper-level constraints and lower-level constraints, namely, the optimization constraints include photovoltaic and storage installation capacity constraints, renewable energy utilization rate constraints, energy storage operation constraints, system power flow constraints, photovoltaic power output constraints, and external grid power purchase constraints.

[0103] Specifically, the first optimization objective function includes the total economic cost of the system being the sum of the photovoltaic and energy storage investment cost, the total annual operating cost, and the total annual electricity purchase cost, i.e., the above-mentioned relationship (1). The photovoltaic and energy storage investment cost is the sum of the photovoltaic investment cost and the energy storage investment cost, i.e., the above-mentioned relationship (2); the photovoltaic investment cost is the sum of the equivalent annual value conversion factor, the photovoltaic unit capacity investment cost at each node of the distribution network, and the photovoltaic equipment installation capacity, i.e., the first term in relationship (2); the energy storage investment cost is the sum of the equivalent annual value conversion factor, the energy storage unit capacity investment cost at each node of the distribution network, and the energy storage equipment installation capacity, i.e., the second term in relationship (2).

[0104] The total annual operating cost is the sum of the product of the number of days occupied by each typical day and the operating cost of photovoltaic storage on each typical day, i.e., the above relationship (3); the operating cost of photovoltaic storage on a typical day is the sum of the photovoltaic operating cost and the energy storage operating cost, i.e., the above relationship (7); the photovoltaic operating cost is the sum of the product of the photovoltaic unit operating cost and the photovoltaic active power output of each node of the distribution network at each time period, i.e., the first term in the above relationship (7); the energy storage operating cost is the sum of the product of the energy storage unit operating cost and the energy storage active power output of each node at each time period, i.e., the second term in the above relationship (7);

[0105] The total annual electricity purchase cost is the sum of the products of the number of days occupied by each typical day and the electricity purchase and operation cost of each typical day, i.e., the above relationship (4); the electricity purchase and operation cost of a typical day is the sum of the products of the time-of-use electricity purchase price and the external grid purchase power of each node in each time period, i.e., the above relationship (8); the time-of-use electricity purchase price is the electricity purchase price per unit time period, and the external grid purchase power is the power purchased from the external grid by each node of the distribution network in the corresponding time period.

[0106] The power quality assessment level is the sum of the power quality indicators of all distribution network nodes and all operating periods, i.e., the above relationship (10); the power quality assessment value is the sum of the products of multiple power quality indicators and their corresponding weight coefficients. The power quality indicators include voltage deviation, voltage fluctuation and voltage harmonics.

[0107] The photovoltaic and energy storage installation capacity constraints include: the installed capacity of photovoltaic equipment shall not exceed the upper limit of photovoltaic capacity, and the installed capacity of energy storage equipment shall not exceed the upper limit of energy storage capacity, i.e., the above relationship (5). The photovoltaic and energy storage installation capacity constraints are used to limit the installed capacity of photovoltaic equipment and energy storage equipment at each grid connection point in the distribution network to not exceed their respective preset maximum allowable values, so as to ensure that the capacity configuration parameters are feasible in engineering.

[0108] The renewable energy utilization rate constraint includes the ratio of the annual effective photovoltaic power generation of the distribution network to the total annual photovoltaic power consumption, which is between the preset minimum penetration rate and the preset maximum penetration rate, i.e., the above relationship (6). The renewable energy utilization rate constraint is used to limit the proportion of distributed photovoltaic annual power generation in the total annual power consumption of the distribution network.

[0109] Energy storage operation constraints include: energy storage charge and discharge mutual exclusion constraints (i.e., the first clause in the above relation (14)), energy storage discharge power constraints (i.e., the second clause in the above relation (14)), energy storage charging power constraints (i.e., the third clause in the above relation (14)), and energy storage state of charge timing update constraints (i.e., the fourth clause in the above relation (14)). Energy storage operation constraints are used to limit the charging and discharging behavior of energy storage devices during operation, and to ensure the safe and stable operation of energy storage devices.

[0110] System power flow constraints include: node active power balance constraints (i.e., the first condition in equation (15) above), node reactive power balance constraints (i.e., the second condition in equation (15) above), node voltage magnitude recursion constraints (i.e., the first condition in equation (16) above), and line transmission capacity constraints (i.e., the second condition in equation (16) above). System power flow constraints are used to ensure that the active and reactive power of each node in the distribution network remain balanced.

[0111] Photovoltaic output constraints include: the real-time active power output of photovoltaics is between the lower limit and the upper limit of active power output, i.e., the above relationship (17). Photovoltaic output constraints are used to limit the actual active power output of photovoltaics at each grid connection point from not exceeding the maximum output value corresponding to its installed capacity.

[0112] External grid power purchase constraints include: the active power purchased from the external grid by each node in each time period is between the lower limit and the upper limit of active power purchase; the reactive power purchased from the external grid by each node in each time period is between the lower limit and the upper limit of active power purchase; i.e., the above relationship (18). External grid power purchase constraints are used to limit the active and reactive power purchased by the distribution network from the external grid to not exceed their respective maximum allowable values.

[0113] In this embodiment, the computer equipment can first acquire basic data of the distribution network. Specifically, the computer equipment reads the network topology and line impedance parameters of the distribution network from the distribution network energy management system, and obtains the system reference voltage from the system or system configuration file; it extracts typical daily load curves of each load node from the historical database as the load data of each node in the basic data of the distribution network; it obtains typical daily power generation characteristic data of distributed photovoltaics from the meteorological database; and it reads the economic and technical parameters of photovoltaic equipment and energy storage equipment from the equipment parameter table, including the charging and discharging efficiency and lifespan parameters of energy storage equipment, the unit capacity investment cost of photovoltaic equipment and energy storage equipment, the unit operating cost of each equipment, the annual discount rate, and the connection relationship with the external power grid and the electricity purchase price.

[0114] Optionally, the distribution network basic data can be preprocessed. Specifically, the acquired distribution network basic data undergoes preprocessing operations such as data cleaning, format unification, and normalization to eliminate noise and outliers in the data and ensure that the data format matches the input interface of the photovoltaic-storage joint optimization model. Data cleaning includes removing obviously erroneous records and filling in missing values; format unification refers to converting data from different sources into a unified dimension and data structure; normalization refers to standardizing the load data and photovoltaic output data of each node according to the reference value corresponding to the system reference voltage.

[0115] After acquiring the aforementioned basic data of the distribution network, the computer equipment can first generate candidate capacity configuration parameters through the upper-level optimization model. Specifically, the upper-level optimization model takes minimizing the total economic cost of the system as its primary optimization objective, uses the photovoltaic (PV) installation capacity and energy storage installation capacity at each grid connection point as decision variables, and employs a particle swarm optimization algorithm or a genetic algorithm to generate an initial population. The initial population contains multiple sets of candidate capacity configuration parameters, each set including the PV installation capacity and energy storage installation capacity at each grid connection point in the distribution network. When generating the initial population, the computer equipment verifies the parameters based on PV and energy storage installation capacity constraints and renewable energy utilization rate constraints. For each set of candidate capacity configuration parameters that meets the above constraints, the upper-level optimization model passes it to the lower-level operation model for simulation evaluation.

[0116] Then, the computer equipment simulates and evaluates the candidate capacity configuration parameters through the lower-level operation model. Specifically, based on the candidate capacity configuration parameters passed from the upper-level optimization model, and with the goal of minimizing the comprehensive cost of photovoltaic and energy storage operation, power purchase cost, and power quality assessment level on a typical day, a 24-hour operation simulation is performed on each typical day. Typical days include three scenarios: sunny, cloudy, and rainy, each corresponding to different photovoltaic output and load curves. The number of days each typical day occupies in the year is determined based on historical meteorological data of the distribution network area. At each moment, the lower-level operation model decides on the following scheduling variables: the actual active power output of photovoltaic grid-connected points, the energy storage charging and discharging power of each energy storage grid-connected point, the state of charge of each energy storage grid-connected point, and the power purchased from the external grid. During the optimization scheduling process, the lower-level operation model must satisfy the following energy storage operation constraints, system power flow constraints, photovoltaic output constraints, and external grid power purchase constraints. The lower-level operation model uses a mixed-integer linear programming algorithm for solution, where the energy storage charging and discharging state is represented by 0-1 variables, and the charging and discharging power and state of charge are continuous variables. After the solution is obtained, the lower-level operating model feeds back the daily operating cost and daily electricity purchase cost of each group of candidate capacity configuration parameters on each typical day to the upper-level optimization model.

[0117] Finally, the computer equipment calculates the total economic cost of the system and determines convergence through the upper-level optimization model. Specifically, after receiving the daily operating costs and daily electricity purchase costs for each typical day from the lower-level operation model, the upper-level optimization model calculates the total economic cost of the system for each set of candidate capacity configuration parameters, taking into account the unit capacity investment cost and lifespan of photovoltaic and energy storage. The upper-level optimization model selects the parameter with the minimum total economic cost from all candidate capacity configuration parameters as the current optimal parameter and determines whether the convergence condition is met. The convergence condition is: the difference in the total economic cost of the system for the optimal parameter obtained in two adjacent iterations is less than a preset threshold (e.g., 0.1%), or the number of iterations reaches a preset maximum number of iterations (e.g., 50). If the convergence condition is met, convergence is determined, and the current optimal parameter is output as the initial capacity configuration parameter. The initial capacity configuration parameters include the photovoltaic installation capacity and energy storage installation capacity at each grid connection point in the distribution network. If the convergence condition is not met, the upper-level optimization model updates the current population using a particle swarm optimization algorithm or a genetic algorithm based on the total system economic cost of each group of candidate capacity configuration parameters, generates a new generation of multiple candidate capacity configuration parameters, and then passes them back to the lower-level running model for simulation evaluation. This process is repeated iteratively until convergence.

[0118] Optionally, the linear programming solution tool can be a simplex method solver, an interior point method solver, or a branch and bound method solver. Optionally, the convergence accuracy threshold can be set according to the actual computational accuracy requirements.

[0119] S202, Solve the pre-built target planning model based on the initial capacity configuration parameters to obtain the target capacity configuration parameters.

[0120] The target programming model includes a second optimization objective function and optimization constraints. The second optimization objective function aims to minimize the total economic cost of the system and the coordination distance. The coordination distance characterizes the difference in operation between the economically optimal scheduling scheme and the power quality optimal scheduling scheme under the same capacity configuration parameters, reflecting the synergy between economic efficiency and power quality. The smaller the difference, the higher the degree of synergy between economic efficiency and power quality. The target capacity configuration parameters refer to the final capacity configuration results obtained after solving the target programming model, including the installed capacity of photovoltaic equipment and energy storage equipment at each grid connection point in the distribution network. The optimization constraints are the same as those of the photovoltaic-storage joint optimization model, including photovoltaic-storage installation capacity constraints, renewable energy utilization rate constraints, energy storage operation constraints, system power flow constraints, photovoltaic output constraints, and external grid power purchase constraints.

[0121] In this embodiment, the target programming model and the distribution network system optimization model have the same architecture, including an upper-level optimization model and a lower-level optimization model, but the objective functions are different. The second optimization objective function in the target programming model is the sum of the upper-level objective function in the distribution network system optimization model and the cooperative distance, with the objectives of minimizing the total economic cost of the system and minimizing the cooperative distance.

[0122] At the operational level, the study focuses on the synergy between operational results and operational modes under two types of objectives (economic objectives and power quality objectives). It investigates the differences between economic costs and power quality assessment results under the two objectives of economic and power quality, as well as the similarity of similar units in their operational modes. Using the Euclidean distance between operational results and operational modes as a benchmark for synergy measurement, the study investigates the synergistic mechanism between economic operation and high-quality operation of the distribution network.

[0123] Specifically, at the operational level, the operating costs of photovoltaic and energy storage and the cost of electricity purchase are separately taken as economic objectives, and the power quality assessment level is separately taken as a power quality objective. After obtaining the initial capacity configuration parameters according to S201, the economic information of the current capacity configuration parameters under the economic objectives is calculated by combining equations (7) to (9). Power quality information And output information, as well as economic information under power quality targets. Power quality information And output information.

[0124] From the perspective of operational results, economic information under the two objectives (economic and low-carbon) will be analyzed. Power quality information under two objectives Normalize the data according to their respective maximum values, and map the normalized data onto a two-dimensional plane, where the horizontal axis represents the mapping distance of power quality level, and the vertical axis represents the mapping distance of economic cost, such as... Figure 3 As shown in the figure, 'a' represents the result under the economic objective, and 'b' represents the result under the power quality objective. In this two-dimensional plane, the Euclidean distance between the two points characterizes the combined difference between the two operating results in the normalized space.

[0125] From the perspective of operation mode, according to the relationship (19), the power of each distributed photovoltaic and energy storage device under the two objectives is uniformly normalized according to the maximum output of each type of unit, and the normalization results are mapped to a one-dimensional coordinate system with the mapping distance between the output of distributed photovoltaic and the output of energy storage devices as the horizontal axis, such as Figure 4 As shown, this allows us to map the operational modes of the two objectives onto the same dimension for collaborative analysis and research.

[0126] (19)

[0127] In the formula: and These refer to the output of distributed photovoltaic and energy storage under economic and power quality targets, respectively.

[0128] The Euclidean distance relationship between the operating results (economic information, power quality information) and the operating mode (output information) under the two objectives is as follows:

[0129] (20)

[0130] (twenty one)

[0131] Since both the execution result and the execution method calculate the Euclidean distance in a normalized form, they can be directly added together. Therefore, the expression for the total Euclidean distance of the execution result and the execution method is as follows:

[0132] (twenty two)

[0133] In the formula: and These are the Euclidean distances between the running results and the running methods under the two objectives, respectively. and These are the Euclidean distances between the output power of distributed photovoltaic and energy storage, respectively. This represents the total Euclidean distance.

[0134] It can be seen that when The smaller the value, the closer the economic and high-quality operation results are, enabling long-term synergy between economic and high-quality operation of the distribution network.

[0135] The total Euclidean distance between the operation of economic objectives and the operation of power quality objectives is fed back into the upper-level optimization model to obtain a target programming model with the objectives of minimizing total economic cost and minimizing cooperative distance.

[0136] The second objective function in the goal programming model is expressed as follows:

[0137] (twenty three).

[0138] In this embodiment, after obtaining the initial capacity configuration parameters, the computer device uses these parameters as the initial input to the objective planning model and calls a pre-built cooperative distance calculation module and a multi-objective optimization solver to solve the objective planning model. The specific process of the computer device solving the objective planning model includes: First, under the current capacity configuration parameters, minimizing the sum of photovoltaic-storage operation costs and electricity purchase costs is used as the objective function. Combining energy storage operation constraints, system power flow constraints, photovoltaic output constraints, and external grid electricity purchase constraints, economically optimal scheduling is solved to obtain the economically optimal scheduling scheme and the corresponding economic cost and power quality assessment results. Simultaneously, under the current capacity configuration parameters, minimizing the comprehensive power quality assessment level is used as the objective function. Combining the same optimization constraints, power quality optimal scheduling is solved to obtain the power quality optimal scheduling scheme and the corresponding economic cost and power quality assessment results. Then, the computer device calculates the cooperative distance between the economically optimal scheduling scheme and the power quality optimal scheduling scheme. Finally, the computer device adds the total system economic cost under the current capacity configuration parameters to the cooperative distance to obtain the second optimization objective function value. The computer equipment uses a multi-objective evolutionary algorithm to iteratively solve the objective planning model. In each iteration, the capacity configuration parameters are updated and the cooperative distance is recalculated until the convergence condition is met. The final capacity configuration parameters are then output as the target capacity configuration parameters.

[0139] Furthermore, in the process of solving the second optimization objective function, since the total economic cost of the system and the cooperative distance are two objectives with different dimensions and scales, the computer equipment can use a fuzzy membership function to classify the Pareto front solution set. The solution with the highest comprehensive membership function value is the optimal objective solution, which is used as the final objective capacity configuration parameter. The specific implementation process is as follows: First, the initial capacity configuration parameter is used as the initial input to the objective programming model, and a multi-objective optimization algorithm is called to iteratively solve the objective programming model to obtain the Pareto front solution set. The Pareto front solution set contains multiple non-dominated solutions, each corresponding to a capacity configuration parameter, and each capacity configuration parameter corresponding to a total economic cost objective function value and a cooperative distance objective function value. Then, for each non-dominated solution in the Pareto front solution set, the function values ​​of each non-dominated solution under the total economic cost objective function and the cooperative distance objective function are obtained, as well as the maximum and minimum values ​​of the total economic cost objective function and the cooperative distance objective function in the Pareto front solution set are obtained. Next, for the z-th non-dominated solution and the m-th objective function, the membership value of the non-dominated solution under the m-th objective function is calculated based on the function value of the non-dominated solution under the m-th objective function and the maximum and minimum values ​​of the m-th objective function in the Pareto front solution set. Then, the membership values ​​of each objective function are summed to obtain the comprehensive membership value of each non-dominated solution. The non-dominated solution with the largest comprehensive membership value is determined as the optimal decision vector, and the capacity configuration parameter corresponding to the optimal decision vector is output as the target capacity configuration parameter.

[0140] Specifically: for the z-th decision variable, the... Membership function of objective function The relationship is as follows:

[0141] (twenty four)

[0142] In the formula: For the z-th decision vector, the th... The optimal solution of each objective function; and The Pareto Frontiers The maximum and minimum values ​​of each objective function.

[0143] The optimal solution is determined using the following formula:

[0144] (25)

[0145] In the formula: The number of objective functions in the Pareto front solution set; This is the optimal decision vector.

[0146] The photovoltaic (PV) and energy storage (ESS) capacity optimization method for distribution networks provided in this application involves solving a pre-constructed PV-ESS joint optimization model based on basic distribution network data to obtain initial capacity configuration parameters. Then, based on these initial capacity configuration parameters, a pre-constructed target planning model is solved to obtain target capacity configuration parameters. The PV-ESS joint optimization model includes a first optimization objective function and optimization constraints. The first optimization objective function aims to minimize the total economic cost of the system. The optimization constraints include PV-ESS installation capacity constraints, renewable energy utilization rate constraints, energy storage operation constraints, system power flow constraints, PV output constraints, and external grid power purchase constraints. The target planning model includes a second optimization objective function and optimization constraints. The second optimization objective function aims to minimize the total economic cost of the system and the coordination distance. The coordination distance characterizes the operational differences between the economically optimal dispatch scheme and the power quality-optimal dispatch scheme under the same capacity configuration parameters. The target capacity configuration parameters include the installed capacity of PV equipment and the installed capacity of energy storage equipment at each grid connection point in the distribution network. In the above method, the initial capacity configuration parameters are first determined with the goal of minimizing the total economic cost of the system. Then, the target capacity configuration parameters are determined with the goals of minimizing both the total economic cost and the coordination distance. This ensures that the photovoltaic and energy storage capacity configuration parameters, while maintaining economic efficiency, incorporate the difference between economically optimal scheduling and power quality-optimal scheduling into the optimization considerations. This effectively avoids the system being forced to passively switch between two extreme modes of prioritizing economy and quality during long-term operation, improving the long-term coordination capability of economic and high-quality operation of the distribution network, and thus enhancing the accuracy of photovoltaic and energy storage capacity optimization decisions. Simultaneously, by setting constraints on photovoltaic and energy storage installation capacity, renewable energy utilization rate, energy storage operation, system power flow, photovoltaic output, and external grid power purchase, the feasibility of the configuration scheme in actual grid operation is ensured, ultimately achieving continuous coordinated optimization of distribution network economy and power quality.

[0147] In some embodiments, a specific implementation method for solving a pre-built goal programming model is also provided, such as... Figure 5 As shown, the phrase "solving the pre-built target planning model based on the initial capacity configuration parameters to obtain the target capacity configuration parameters" in S202 above includes:

[0148] S301, based on the initial capacity configuration parameters, adjust the system output parameters of the target planning model according to the economic target and the power quality target respectively, to obtain the first economic information, the first power quality information and the first output information under the economically optimal scheduling scheme, as well as the second economic information, the second power quality information and the second output information under the power quality optimal scheduling scheme.

[0149] Among them, the system output parameters are the following scheduling variables decided by the lower-level operation model at each moment, namely, the actual active power output of photovoltaic grid-connected points, the energy storage charging power and discharging power of each energy storage grid-connected point, the state of charge of each energy storage grid-connected point, and the power purchased from the external grid under the given capacity configuration parameters. The economic objective is to minimize the system photovoltaic-storage operation cost and power purchase cost. The power quality objective is to optimize the system power quality assessment level (i.e., minimize the comprehensive power quality assessment value). The economically optimal scheduling scheme is the economic information, power quality information, and output information obtained by optimization under the economic objective. The power quality optimal scheduling scheme is the economic information, power quality information, and output information obtained by optimization under the power quality objective. The first economic information is the typical daily photovoltaic-storage operation cost and typical daily power purchase operation cost under the economically optimal scheduling scheme (i.e., the above-mentioned relationships (7) and (8)). The first power quality information is the system power quality assessment level under the economically optimal scheduling scheme (i.e., the above-mentioned relationship (10)). The first output information consists of the photovoltaic active power output, energy storage charging power, and energy storage discharging power of each device at each time under the optimal economic dispatch scheme. The second economic information consists of the typical daily operating cost of photovoltaic-energy storage and the typical daily electricity purchase operating cost under the optimal power quality dispatch scheme. The second power quality information consists of the system power quality assessment level under the optimal power quality dispatch scheme. The second output information consists of the photovoltaic active power output, energy storage charging power, and energy storage discharging power of each device at each time under the optimal power quality dispatch scheme.

[0150] In this embodiment, after obtaining the initial capacity configuration parameters, the computer device uses the initial capacity configuration parameters as the current capacity configuration parameters, and adjusts the system output parameters of the target planning model according to the economic target and the power quality target respectively, to obtain economic information, power quality information and output information under two sets of scheduling schemes. That is, it obtains the first economic information, the first power quality information and the first output information under the economically optimal scheduling scheme, and the second economic information, the second power quality information and the second output information under the power quality optimal scheduling scheme.

[0151] Specifically, S301 above includes:

[0152] S3011, based on the initial capacity configuration parameters, solves the pre-constructed first lower-level operation model to obtain the first economic information, the first power quality information and the first output information under the economically optimal scheduling scheme.

[0153] The architecture of the lower-level operation model of the first lower-level operation model and the lower-level operation model of the distribution network system optimization model are the same, only the objective function and constraints are different. The first lower-level operation model includes an economic objective function and planning constraints. The economic objective function aims to minimize the sum of the typical daily operating cost of photovoltaic and energy storage and the typical daily electricity purchase cost. The planning constraints include system power flow constraints, photovoltaic output constraints, and external grid electricity purchase constraints.

[0154] In this embodiment, the computer equipment inputs the photovoltaic installation capacity and energy storage installation capacity of each grid-connected point as fixed values ​​from the initial capacity configuration parameters into the first lower-level operation model. The first lower-level operation model, with an economic objective function as its goal and a time step of 1 hour, performs 24-hour optimized scheduling for each typical day, satisfying system power flow constraints, photovoltaic output constraints, and external grid power purchase constraints. The decision variables for optimized scheduling are the actual active power output of each photovoltaic grid-connected point at each time, the charging and discharging power of each energy storage grid-connected point, the state of charge of each energy storage grid-connected point, and the external grid power purchase at each time. The first lower-level operation model is solved using a mixed-integer linear programming algorithm, where the energy storage charging and discharging state is represented by 0-1 variables, and the charging and discharging power and state of charge are continuous variables. After the solution is completed, the computer equipment obtains the output value of each device at each time under the economically optimal scheduling scheme and records it as the first output information. It also records the sum of the photovoltaic and energy storage operating costs and power purchase costs under this scheme as the first economic information, and records the comprehensive power quality assessment value under this scheme as the first power quality information.

[0155] S3012, based on the initial capacity configuration parameters, solves the pre-built second lower-level operation model to obtain the second economic information, second power quality information and second output information under the optimal power quality scheduling scheme.

[0156] The second lower-level operation model shares the same architecture as the lower-level operation model of the distribution network system optimization model, differing only in its objective function and constraints. The second lower-level operation model includes a power quality objective function and planning constraints. The power quality objective function aims to minimize the power quality assessment value, which is the sum of the products of multiple power quality indicators and their corresponding weighting coefficients. These power quality indicators include voltage deviation, voltage fluctuation, and voltage harmonics.

[0157] In this embodiment, the computer equipment inputs the photovoltaic installation capacity and energy storage installation capacity of each grid-connected point from the initial capacity configuration parameters as fixed values ​​into the second lower-level operation model. The second lower-level operation model uses the power quality objective function as its objective, with a time step of 1 hour, to perform 24-hour optimized scheduling for each typical day, under the premise of satisfying system power flow constraints, photovoltaic output constraints, and external grid power purchase constraints. The power quality assessment value is calculated as follows: first, the voltage deviation, voltage fluctuation, and voltage harmonic distortion rate of each photovoltaic grid-connected point are calculated; then, each indicator value is multiplied by the corresponding weight coefficients determined by the analytic hierarchy process (AHP) and summed to obtain the power quality assessment value of each grid-connected point; finally, the assessment values ​​of each grid-connected point are summed to obtain the system power quality assessment value. After the solution is completed, the computer equipment obtains the output value of each device at each time under the optimal power quality scheduling scheme and records it as the second output information; the sum of the photovoltaic and energy storage operating costs and power purchase costs under this scheme is recorded as the second economic information; and the power quality assessment value under this scheme is recorded as the second power quality information.

[0158] It should be noted that the optimized scheduling in both operating modes adopts the same typical day set (including three scenarios: sunny, cloudy and rainy days) and the same time step (1 hour), and both must meet the constraints of energy storage operation, system power flow, photovoltaic output and external grid power purchase.

[0159] S302, determine the cooperative distance based on the first economic information, the first power quality information, the first output information, the second economic information, the second power quality information, and the second output information.

[0160] The coordination distance, a quantitative indicator based on Euclidean distance, characterizes the degree of operational difference between the economically optimal scheduling scheme and the power quality optimal scheduling scheme under the same capacity configuration parameters. A smaller coordination distance indicates that the operational results and modes are more similar under the two extreme operating modes, meaning that the coordination between economical and high-quality operation is higher for the capacity configuration parameters. The coordination distance comprises two parts: the Euclidean distance at the operational result level and the Euclidean distance at the operational mode level. The former measures the difference in results between the two scheduling schemes in the two-dimensional target space of "economic cost-power quality," while the latter measures the strategic differences between the two scheduling schemes in the output timing of each device.

[0161] In this embodiment of the application, after obtaining the economic information, power quality information and output information under two sets of scheduling schemes, the computer device substitutes the first economic information, the first power quality information, the first output information, the second economic information, the second power quality information and the second output information into the distance relationship formula to calculate and determine the cooperative distance under the current capacity configuration parameters.

[0162] Specifically, S302 above includes:

[0163] S3021, substitute the first economic information, the first power quality information, the second economic information, and the second power quality information into the distance relationship of the operation result to calculate the Euclidean distance of the operation result.

[0164] Among them, the Euclidean distance of the operating results is used to measure the degree of difference between the economically optimal dispatch scheme and the power quality optimal dispatch scheme at the level of operating results. The operating result distance relationship is constructed based on the two-dimensional Euclidean distance formula, where the two dimensions are the normalized economic cost and the normalized power quality assessment value, respectively. The operating result distance relationship is the above relationship (20).

[0165] In this embodiment, the computer device normalizes the first economic information and the second economic information by taking the maximum value of the two, resulting in normalized first economic cost and second economic cost. The first power quality information and the second power quality information are also normalized by taking the maximum value of the two, resulting in normalized first power quality assessment value and second power quality assessment value. The normalized data are mapped onto a two-dimensional plane, where the horizontal axis represents the normalized value of the power quality assessment level, and the vertical axis represents the normalized value of the economic cost.

[0166] S3022, substitute the first output information and the second output information into the operating mode distance relationship formula for calculation to obtain the operating mode Euclidean distance.

[0167] Among them, the Euclidean distance of the operation mode is used to measure the degree of difference between the economically optimal dispatch scheme and the power quality optimal dispatch scheme at the operation mode level. The operation mode distance relationship includes two parts: the calculation of the Euclidean distance between photovoltaic outputs and the calculation of the Euclidean distance between energy storage outputs, which are used to measure the strategic differences in the output timing of photovoltaic equipment and energy storage equipment under the two dispatch schemes. The operation mode distance relationship is the above relationship (21).

[0168] In this embodiment, the computer device normalizes the output power of each distributed photovoltaic (PV) and energy storage device in the first and second output information according to the maximum output value of each type of unit in two modes. For PV devices, the Euclidean distance between the normalized output power of each PV grid-connected point at each time step is calculated in both modes; for energy storage devices, the Euclidean distance between the normalized absolute values ​​of charge and discharge power of each energy storage grid-connected point at each time step is calculated in both modes. The Euclidean distance between PV output power and the Euclidean distance between energy storage output power are added together to obtain the Euclidean distance at the operating mode level.

[0169] S3023, determine the cooperative distance based on the Euclidean distance of the operation results and the Euclidean distance of the operation mode.

[0170] The collaborative distance is the sum of the Euclidean distance of the running result and the Euclidean distance of the running mode. Since both are calculated in a normalized form, they can be directly added together. That is, the above relationship (22).

[0171] In this embodiment of the application, the computer device adds the Euclidean distance of the running result to the Euclidean distance of the running mode to obtain the cooperative distance under the current capacity configuration parameters.

[0172] S303, adjust the capacity configuration parameters of the target planning model based on the cooperative distance and the total economic cost of the system, and return to the steps of adjusting the system output parameters of the target planning model with economic objectives and power quality objectives respectively, until the cooperative distance and the total economic cost of the system are minimized, and obtain the target capacity configuration parameters.

[0173] In this model, the cooperative distance and the total economic cost of the system constitute the two optimization objectives, which are contradictory: reducing the total economic cost may lead to an increase in the cooperative distance (i.e., a greater difference between the economically optimal and power quality-optimal operating modes), while reducing the cooperative distance may require sacrificing some economic efficiency. The target capacity configuration parameter is the capacity configuration scheme that achieves the optimal balance between the total economic cost of the system and the cooperative distance.

[0174] In this embodiment, after determining the cooperative distance under the current capacity configuration parameters, the computer device uses the cooperative distance and the total economic cost of the system as two optimization objectives in the second optimization objective function to adjust the capacity configuration parameters. Specifically, the computer device treats the current capacity configuration parameters as an individual and generates a population containing multiple sets of candidate capacity configuration parameters using a multi-objective evolutionary algorithm (such as multi-objective particle swarm optimization or other algorithms). For each set of candidate capacity configuration parameters in the population, the system output parameters are adjusted according to steps S301 and S302, respectively, with economic objectives and power quality objectives, to obtain economic information, power quality information, and output information under two sets of scheduling schemes, and calculates the cooperative distance and total economic cost of the system corresponding to the candidate capacity configuration parameter. Then, with the minimum total economic cost of the system and the minimum cooperative distance as dual objectives, the computer device performs multi-objective non-dominated sorting on all individuals in the current population to determine the Pareto level of each individual. Based on the Pareto level, dominant individuals are selected, and crossover and mutation operations are performed to generate a new generation of capacity configuration parameter population, i.e., the capacity configuration parameters are adjusted. Based on the adjusted capacity configuration parameters, the process returns to the step of adjusting the system output parameters according to economic and power quality objectives (i.e., returning to S301), and recalculates the cooperative distance and total system economic cost corresponding to each group of candidate capacity configuration parameters in the new generation population. This process is repeated, with dominant individuals from the previous generation population retained and inferior individuals eliminated in each iteration, generating new candidate solutions. After each iteration, the computer determines whether the convergence condition is met: when the average cooperative distance change of the non-dominated solution set obtained in two adjacent iterations is less than a preset threshold and the average total system economic cost change is less than a preset threshold, or when the number of iterations reaches a preset maximum number of iterations, it is determined that convergence has reached the Pareto front. At this point, the cooperative distance and total system economic cost corresponding to the non-dominated solutions in the current population are both at their current minimum, and the computer outputs the solution with the highest comprehensive membership value in the non-dominated solution set as the target capacity configuration parameter.

[0175] The method described in this application comprehensively measures the degree of difference between two extreme scheduling modes from both the perspectives of operational results and operational modes, achieving precise quantification of the synergy of capacity configuration parameters and avoiding the one-sidedness of single-indicator evaluation. The synergy distance is determined based on the Euclidean distance of the operational results and the Euclidean distance of the operational modes, enabling a quantitative assessment of the difference between economically optimal scheduling and power quality-optimal scheduling under the same capacity configuration parameters. A smaller synergy distance indicates a more convergent operational strategy between the two extreme operating modes, and a higher synergy between economic and high-quality operation of the capacity configuration parameters. The capacity configuration parameters are iteratively adjusted based on the synergy distance and the total economic cost of the system, incorporating the synergy distance and economic objectives into a unified optimization framework for multi-objective synergistic optimization. This ensures that the final target capacity configuration parameters achieve an optimal balance between the total economic cost of the system and the synergy distance, effectively preventing the distribution network from being forced to passively switch between the two extreme modes of "economic priority" and "quality priority" during long-term operation, and achieving long-term synergy between economic and high-quality operation of the distribution network.

[0176] In summary, based on all the above embodiments, a method for optimizing the photovoltaic and energy storage capacity of a distribution network is also provided, such as... Figure 6 As shown, the method includes:

[0177] S401, solve the pre-constructed photovoltaic-storage joint optimization model based on the basic data of the distribution network to obtain the initial capacity configuration parameters of the distribution network; the photovoltaic-storage joint optimization model includes a first optimization objective function and optimization constraints. The first optimization objective function aims to minimize the total economic cost of the system. The optimization constraints include photovoltaic-storage installation capacity constraints, renewable energy utilization rate constraints, energy storage operation constraints, system power flow constraints, photovoltaic power output constraints, and external grid power purchase constraints.

[0178] S402, Solve the pre-constructed target planning model based on the initial capacity configuration parameters to obtain the target capacity configuration parameters; The target planning model includes a second optimization objective function and optimization constraints. The second optimization objective function aims to minimize the total economic cost of the system and the coordination distance. The coordination distance is used to characterize the difference in operation between the economically optimal scheduling scheme and the power quality optimal scheduling scheme under the same capacity configuration parameters; The target capacity configuration parameters include the installed capacity of photovoltaic equipment and the installed capacity of energy storage equipment at each grid connection point in the distribution network.

[0179] The above S402 includes:

[0180] S4021, based on the initial capacity configuration parameters, solve the pre-constructed first lower-level operation model to obtain the first economic information, first power quality information and first output information under the economically optimal dispatch scheme; the first lower-level operation model includes an economic objective function and planning constraints. The economic objective function aims to minimize the sum of the typical daily solar-storage operation cost and the typical daily electricity purchase cost. The planning constraints include system power flow constraints, photovoltaic output constraints and external grid electricity purchase constraints.

[0181] S4022, based on the initial capacity configuration parameters, solve the pre-constructed second lower-level operation model to obtain the second economic information, second power quality information and second output information under the optimal power quality scheduling scheme; the second lower-level operation model includes a power quality objective function and planning constraints. The power quality objective function aims to minimize the power quality assessment value. The power quality assessment value is the sum of the products of multiple power quality indicators and their corresponding weight coefficients. The power quality indicators include voltage deviation, voltage fluctuation and voltage harmonics;

[0182] S4023, substitute the first economic information, the first power quality information, the second economic information, and the second power quality information into the distance relationship of the operation result to calculate the Euclidean distance of the operation result;

[0183] S4024, Substitute the first output information and the second output information into the operating mode distance relationship formula for calculation to obtain the operating mode Euclidean distance;

[0184] S4025, determine the cooperative distance based on the Euclidean distance of the operation results and the Euclidean distance of the operation mode;

[0185] S4026, adjust the capacity configuration parameters of the target planning model according to the cooperative distance and the total economic cost of the system, and return to the steps of adjusting the system output parameters of the target planning model according to the economic target and the power quality target respectively, until the cooperative distance and the total economic cost of the system are minimized, and the target capacity configuration parameters are obtained.

[0186] To better illustrate this embodiment, a specific example will be provided below for detailed explanation:

[0187] Taking the IEEE-33 standard node system as an example, four photovoltaic grid-connected points are set up, and energy storage is configured in two of the photovoltaic units, such as... Figure 7 As shown in Table 1, the network voltage level is set to 10kV. Distribution network node 1 is connected to the external power grid, and the impact of electricity market price fluctuations on system operating costs is ignored.

[0188] Table 1

[0189]

[0190] The annual discount rate is set at 8%, the penetration rate of new energy power generation is no less than 50% and no more than 80%, and the typical daily total load, photovoltaic output, and peak-valley electricity price are as follows: Figure 8 As shown. The energy storage capacity is set as an integer multiple of the power capacity, with the minimum capacity required to discharge at maximum power for 1 hour and the maximum not exceeding 10 hours.

[0191] The impact of the proposed planning model on system economy and power quality is analyzed based on the following two capacity configuration parameters.

[0192] Option 1: Ignoring the coordination distance between economic efficiency and power quality, we only use power quality indicators as normal optimization constraints and objective functions in the traditional planning model;

[0193] Option 2: Consider the synergistic distance between economic efficiency and power quality.

[0194] The effectiveness of the proposed optimized configuration scheme will be verified from the following three perspectives:

[0195] (1) From the perspective of economic efficiency and power quality assessment results: Table 2 shows the comparison results of economic efficiency and power quality assessment of the two schemes. It can be seen that the total economic cost and power quality assessment value of Scheme 2 are better than those of Scheme 1, indicating that the photovoltaic storage capacity optimization configuration method proposed in this patent can effectively improve the system economy while ensuring the power quality of the system.

[0196] Table 2

[0197]

[0198] (2) From the perspective of power quality indicators at the grid connection point: Taking voltage fluctuation and voltage harmonic distortion rate as examples, the analysis results of voltage amplitude and harmonic distortion rate at the photovoltaic-storage grid connection point on a typical day are as follows: Figure 9 and Figure 10 As shown, the overall voltage fluctuation and amplitude of Scheme 2 are smaller than those of Scheme 1, and the total harmonic distortion rate of Scheme 2 is smaller than that of Scheme 1 when the photovoltaic output is large, which effectively improves the power quality level at the grid connection point.

[0199] (3) The effectiveness of the proposed model is verified from the perspective of the convergence of the optimal configuration model: the total Euclidean distance of the running results and running modes under different iteration numbers is calculated, and the results are as follows: Figure 11 As shown, when the number of iterations reaches seven or more, the overall trend of the Euclidean distance remains unchanged, but the operating results of each device are still changing. When the number of iterations reaches ten and eleven, the Euclidean distance remains unchanged before and after two adjacent iterations in both cases, reaching the convergence condition and proving the convergence of the planning model.

[0200] The beneficial effects of the method described in this application include: (1) improving the long-term coordinated operation capability of distribution network economy and power quality. In view of the problem that the two objectives are difficult to balance in the long term in the existing technology, by introducing Euclidean distance to measure the difference in operation strategy and constructing a two-way iterative feedback mechanism, the capacity configuration parameters can adaptively and dynamically coordinate the contradiction between economic optimization and quality optimization. The technical effect is that the system is no longer forced to passively switch between two extreme modes in long-term operation, but can maintain the continuity and consistency of the operation mode, which significantly improves the continuous coordinated operation level of economy and power quality. (2) realizing a comprehensive quantitative evaluation of the power quality level of distribution network. In view of the shortcomings of the existing technology that the power quality indicators are single and difficult to characterize multi-dimensional coupling characteristics, by constructing a multi-dimensional power quality comprehensive evaluation model based on the analytic hierarchy process, multiple indicators such as voltage deviation, voltage fluctuation, and harmonic distortion are organically integrated. The technical effect is that the planning layer can accurately judge the comprehensive impact of different photovoltaic and energy storage configuration schemes on the power quality of the power grid, providing a scientific and comprehensive evaluation basis for capacity optimization and avoiding planning deviations caused by one-sided indicators.

[0201] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0202] Based on the same inventive concept, this application also provides a photovoltaic-storage capacity optimization device for implementing the aforementioned method for optimizing the photovoltaic-storage capacity of a distribution network. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the photovoltaic-storage capacity optimization device for distribution networks provided below can be found in the limitations of the photovoltaic-storage capacity optimization method for distribution networks described above, and will not be repeated here.

[0203] In some embodiments, such as Figure 12 As shown, a photovoltaic-storage capacity optimization device for a power distribution network is provided, comprising:

[0204] The initial planning module 11 is used to solve the pre-constructed photovoltaic-storage joint optimization model based on the basic data of the distribution network to obtain the initial capacity configuration parameters of the distribution network. The photovoltaic-storage joint optimization model includes a first optimization objective function and optimization constraints. The first optimization objective function aims to minimize the total economic cost of the system. The optimization constraints include photovoltaic-storage installation capacity constraints, renewable energy utilization rate constraints, energy storage operation constraints, system power flow constraints, photovoltaic output constraints, and external grid power purchase constraints.

[0205] The target planning module 12 is used to solve the pre-built target planning model based on the initial capacity configuration parameters to obtain the target capacity configuration parameters. The target planning model includes a second optimization objective function and optimization constraints. The second optimization objective function aims to minimize the total economic cost of the system and the coordination distance. The coordination distance is used to characterize the difference in operation between the economically optimal scheduling scheme and the power quality optimal scheduling scheme under the same capacity configuration parameters. The target capacity configuration parameters include the installed capacity of photovoltaic equipment and the installed capacity of energy storage equipment at each grid connection point in the distribution network.

[0206] In some embodiments, the target planning module described above includes:

[0207] The first adjustment unit is used to adjust the system output parameters of the target planning model based on the initial capacity configuration parameters, with economic objectives and power quality objectives respectively, to obtain the first economic information, the first power quality information and the first output information under the economically optimal scheduling scheme, as well as the second economic information, the second power quality information and the second output information under the power quality optimal scheduling scheme.

[0208] The determining unit is used to determine the cooperative distance based on the first economic information, the first power quality information, the first output information, the second economic information, the second power quality information, and the second output information;

[0209] The second adjustment unit is used to adjust the capacity configuration parameters of the target planning model according to the cooperative distance and the total economic cost of the system, and return to execute the steps of adjusting the system output parameters of the target planning model according to the economic target and the power quality target respectively, until the cooperative distance and the total economic cost of the system are minimized, and the target capacity configuration parameters are obtained.

[0210] In some embodiments, the first adjustment unit includes:

[0211] The first regulation subunit is used to solve the pre-constructed first lower-level operation model based on the initial capacity configuration parameters to obtain the first economic information, the first power quality information, and the first output information under the economically optimal dispatch scheme. The first lower-level operation model includes an economic objective function and planning constraints. The economic objective function aims to minimize the sum of the typical daily solar-storage operation cost and the typical daily electricity purchase cost. The planning constraints include system power flow constraints, photovoltaic output constraints, and external grid electricity purchase constraints.

[0212] The second regulation subunit is used to solve the pre-constructed second lower-level operation model based on the initial capacity configuration parameters to obtain the second economic information, second power quality information and second output information under the optimal power quality scheduling scheme. The second lower-level operation model includes a power quality objective function and planning constraints. The power quality objective function aims to minimize the power quality assessment value. The power quality assessment value is the sum of the products of multiple power quality indicators and their corresponding weight coefficients. The power quality indicators include voltage deviation, voltage fluctuation and voltage harmonics.

[0213] In some embodiments, the determining unit includes:

[0214] The first calculation subunit is used to substitute the first economic information, the first power quality information, the second economic information, and the second power quality information into the distance relationship of the operation result to calculate and obtain the Euclidean distance of the operation result.

[0215] The first calculation subunit is used to substitute the first output information and the second output information into the operating mode distance relationship to calculate and obtain the operating mode Euclidean distance;

[0216] The third calculation subunit is used to determine the cooperative distance based on the Euclidean distance of the running results and the Euclidean distance of the running mode.

[0217] In some embodiments, the first optimization objective function includes: the total economic cost of the system is the sum of the photovoltaic and energy storage investment cost, the total annual operating cost, and the total annual electricity purchase cost; the photovoltaic and energy storage investment cost is the sum of the photovoltaic investment cost and the energy storage investment cost; the total annual operating cost is the sum of the product of the number of days occupied by each typical day and the photovoltaic and energy storage operating cost of each typical day; and the total annual electricity purchase cost is the sum of the product of the number of days occupied by each typical day and the electricity purchase and operating cost of each typical day.

[0218] In some embodiments, energy storage operation constraints include: mutual exclusion constraints for energy storage charging and discharging, energy storage discharge power constraints, energy storage charging power constraints, and energy storage state of charge timing update constraints; system power flow constraints include: node active power balance constraints, node reactive power balance constraints, node voltage amplitude recursion constraints, and line transmission capacity constraints.

[0219] Each module in the aforementioned power distribution network photovoltaic-storage capacity optimization device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0220] In some embodiments, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in any of the above embodiments.

[0221] In some embodiments, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the above embodiments.

[0222] In some embodiments, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described in any of the above embodiments.

[0223] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0224] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0225] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for optimizing the photovoltaic and energy storage capacity of a power distribution network, characterized in that, The method includes: The pre-constructed photovoltaic-storage joint optimization model is solved based on the basic data of the distribution network to obtain the initial capacity configuration parameters of the distribution network. The photovoltaic-storage joint optimization model includes a first optimization objective function and optimization constraints. The first optimization objective function aims to minimize the total economic cost of the system. The optimization constraints include photovoltaic-storage installation capacity constraints, renewable energy utilization rate constraints, energy storage operation constraints, system power flow constraints, photovoltaic output constraints, and external grid power purchase constraints. The target capacity configuration parameters are obtained by solving the pre-constructed target planning model based on the initial capacity configuration parameters. The target planning model includes a second optimization objective function and optimization constraints. The second optimization objective function aims to minimize the total economic cost of the system and the coordination distance. The coordination distance is used to characterize the difference in operation between the economically optimal scheduling scheme and the power quality optimal scheduling scheme under the same capacity configuration parameters. The target capacity configuration parameters include the installed capacity of photovoltaic equipment and the installed capacity of energy storage equipment at each grid connection point in the distribution network.

2. The method according to claim 1, characterized in that, The step of solving the pre-built target planning model based on the initial capacity configuration parameters to obtain the target capacity configuration parameters includes: Based on the initial capacity configuration parameters, the system output parameters of the target planning model are adjusted according to the economic target and the power quality target, respectively, to obtain the first economic information, the first power quality information and the first output information under the economic optimal scheduling scheme, as well as the second economic information, the second power quality information and the second output information under the power quality optimal scheduling scheme. The cooperative distance is determined based on the first economic information, the first power quality information, the first output information, the second economic information, the second power quality information, and the second output information; Based on the cooperative distance and the total economic cost of the system, the capacity configuration parameters of the target planning model are adjusted, and the steps of adjusting the system output parameters of the target planning model with economic objectives and power quality objectives are returned to the adjusted capacity configuration parameters until the cooperative distance and the total economic cost of the system are minimized, thus obtaining the target capacity configuration parameters.

3. The method according to claim 2, characterized in that, The process involves adjusting the system output parameters of the target planning model based on the initial capacity configuration parameters, using economic and power quality objectives, to obtain first economic information, first power quality information, and first output information under the economically optimal scheduling scheme, as well as second economic information, second power quality information, and second output information under the power quality optimal scheduling scheme, including: Based on the initial capacity configuration parameters, the pre-constructed first lower-level operation model is solved to obtain the first economic information, first power quality information, and first output information under the economically optimal scheduling scheme. The first lower-level operation model includes an economic objective function and planning constraints. The economic objective function aims to minimize the sum of the typical daily solar-storage operation cost and the typical daily electricity purchase cost. The planning constraints include the system power flow constraints, the photovoltaic output constraints, and the external grid electricity purchase constraints. Based on the initial capacity configuration parameters, the pre-constructed second lower-level operation model is solved to obtain the second economic information, second power quality information, and second output information under the optimal power quality scheduling scheme. The second lower-level operation model includes a power quality objective function and the planning constraints. The power quality objective function aims to minimize the power quality assessment value. The power quality assessment value is the sum of the products of multiple power quality indicators and their corresponding weight coefficients. The power quality indicators include voltage deviation, voltage fluctuation, and voltage harmonics.

4. The method according to claim 2, characterized in that, Determining the cooperative distance based on the first economic information, the first power quality information, the first output information, the second economic information, the second power quality information, and the second output information includes: Substitute the first economic information, the first power quality information, the second economic information, and the second power quality information into the distance relationship of the operation result to calculate the Euclidean distance of the operation result; Substitute the first output information and the second output information into the operating mode distance relationship formula for calculation to obtain the operating mode Euclidean distance; The cooperative distance is determined based on the Euclidean distance of the operation results and the Euclidean distance of the operation mode.

5. The method according to any one of claims 1-4, characterized in that, The first optimization objective function includes: the total economic cost of the system is the sum of the photovoltaic and energy storage investment cost, the total annual operating cost, and the total annual electricity purchase cost; the photovoltaic and energy storage investment cost is the sum of the photovoltaic investment cost and the energy storage investment cost; the total annual operating cost is the sum of the products of the number of days occupied by each typical day and the photovoltaic and energy storage operating cost of each typical day; and the total annual electricity purchase cost is the sum of the products of the number of days occupied by each typical day and the electricity purchase and operating cost of each typical day.

6. The method according to any one of claims 1-4, characterized in that, The energy storage operation constraints include: mutual exclusion constraints for energy storage charging and discharging, energy storage discharge power constraints, energy storage charging power constraints, and energy storage state of charge timing update constraints; the system power flow constraints include: node active power balance constraints, node reactive power balance constraints, node voltage amplitude recursion constraints, and line transmission capacity constraints.

7. A photovoltaic-storage capacity optimization device for a power distribution network, characterized in that, The device includes: The initial planning module is used to solve the pre-constructed photovoltaic-storage joint optimization model based on the basic data of the distribution network to obtain the initial capacity configuration parameters of the distribution network. The photovoltaic-storage joint optimization model includes a first optimization objective function and optimization constraints. The first optimization objective function aims to minimize the total economic cost of the system. The optimization constraints include photovoltaic-storage installation capacity constraints, renewable energy utilization rate constraints, energy storage operation constraints, system power flow constraints, photovoltaic output constraints, and external grid power purchase constraints. The target planning module is used to solve the pre-constructed target planning model based on the initial capacity configuration parameters to obtain the target capacity configuration parameters. The target planning model includes a second optimization objective function and the optimization constraints. The second optimization objective function aims to minimize the total economic cost of the system and the coordination distance. The coordination distance is used to characterize the difference in operation between the economically optimal scheduling scheme and the power quality optimal scheduling scheme under the same capacity configuration parameters. The target capacity configuration parameters include the installed capacity of photovoltaic equipment and the installed capacity of energy storage equipment at each grid connection point in the distribution network.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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