A new energy power generation equipment optimization configuration method, system, device and medium

Abstract

The application discloses a new energy power generation equipment optimization configuration method, system, device and medium, basic parameters of a radiation type power distribution network are acquired, power flow analysis is performed on the radiation type power distribution network based on the basic parameters, power data and power loss data of each node in the radiation type power distribution network are obtained, the stability, power loss and of the radiation type power distribution network are determined according to the power data and the power loss data, the gas emission of the radiation type power distribution network is determined through the released power of a new energy power generation equipment, a multi-objective function of the radiation type power distribution network is constructed based on the stability, the power loss and and the gas emission, configuration constraint conditions of the new energy power generation equipment are acquired, the multi-objective function is solved through a preset optimization algorithm, and an optimization configuration scheme of the new energy power generation equipment is obtained, so that the construction and operation cost of the power distribution network is reduced, and economic benefits are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to a new energy power generation equipment optimization configuration method, system, device and medium. BACKGROUND

[0002] With the serious environmental consequences brought by the wide use of internal combustion engines in vehicles, electric vehicles are increasingly accepted and adopted. However, the increase in the use of electric vehicles, the high initial cost and the insufficient infrastructure, especially the limited availability of electric vehicle charging stations, pose a huge obstacle to widespread adoption, and the charging of electric vehicles depends on the power grid, and the construction of charging stations affects the operation and planning of radial distribution networks, including increased energy loss, voltage curve drop and potential overload, which pose challenges to the reliability and quality of power supply.

[0003] Therefore, how to effectively integrate electric vehicle charging stations into existing distribution networks to reduce the negative impact on the power grid has become a technical problem to be solved by those skilled in the art. SUMMARY

[0004] The present application provides a new energy power generation equipment optimization configuration method, system, device and medium, which solves the problem of how to effectively integrate electric vehicle charging stations into existing distribution networks to reduce the negative impact on the power grid.

[0005] To solve the above technical problems, the present application provides a new energy power generation equipment optimization configuration method, comprising:

[0006] Obtain the basic parameters of the radial distribution network, and perform power flow analysis on the radial distribution network based on the basic parameters to obtain power data and power loss data of each node in the radial distribution network;

[0007] Determine the stability, power loss and gas emission of the radial distribution network according to the power data and the power loss data, and determine the gas emission of the radial distribution network through the release power of the new energy power generation equipment;

[0008] Construct a multi-objective function of the radial distribution network based on the stability, power loss and gas emission;

[0009] Obtain the configuration constraints of the new energy power generation equipment, and solve the multi-objective function through a preset optimization algorithm to obtain an optimized configuration scheme for the new energy power generation equipment.

[0010] Compared with the prior art, the beneficial effects of the present application embodiment are at least one of the following:

[0011] (1) By acquiring the basic parameters of the radial distribution network and performing power flow calculation, the power demand and distribution of each node can be determined, and combined with the optimal configuration of new energy power generation equipment, the supply and demand relationship of the power grid can be more accurately balanced, the frequency and voltage fluctuation of the power grid caused by unbalanced supply and demand can be reduced, the stability of the power grid is improved, the reliability and continuity of power supply are ensured, and more stable power service is provided for users;

[0012] (2) When constructing the objective function, the loss, stability and pollution of the radial distribution network are fully considered, so that the configuration of the new energy power generation equipment obtained by the optimization algorithm is more reasonable, the energy utilization efficiency is improved, the operation cost of the power grid is reduced, and strong support is provided for the sustainable development of the power grid. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the present application, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0014] Figure 1 is a flowchart of a new energy power generation equipment optimization configuration method provided by an embodiment of the present application;

[0015] Figure 2 is a schematic diagram of two interconnected buses in a radial distribution network provided by an embodiment of the present application;

[0016] Figure 3 is an output data diagram of RDG in 24 hours provided by an embodiment of the present application;

[0017] Figure 4 is an active and reactive power demand curve diagram provided by an embodiment of the present application;

[0018] Figure 5 is a RDG power generation power curve diagram provided by an embodiment of the present application;

[0019] Figure 6 is an EVCS power demand / power generation capacity diagram provided by an embodiment of the present application;

[0020] Figure 7 is a structure diagram of a new energy power generation equipment optimization configuration system provided by an embodiment of the present application;

[0021] Figure 8 is a structure diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings and embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments of the present application. The purpose of providing these embodiments is to make the disclosure of the present application more thorough and complete. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort belong to the scope of protection of the present application.

[0023] In the description of the present application, the terms "first", "second", "third", etc. are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second", "third", etc. can explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise specified, the meaning of "a plurality of" is two or more.

[0024] In the description of the present application, it should be noted that, unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connecting" should be understood in a broad sense, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium, or it can be the communication inside two elements. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used in this paper are only for the purpose of description, and cannot be understood as indicating or implying that the system or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present application. The term "and / or" used in this paper includes any and all combinations of one or more related listed items. For those skilled in the art, the specific meaning of the above terms in the present application can be understood in specific cases.

[0025] In the description of the present application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present application have the same meaning as understood by those skilled in the art of the present technology. The terms used in the specification of the present application are only for the purpose of describing the specific embodiments, and are not intended to limit the present application. For those skilled in the art, the specific meaning of the above terms in the present application can be understood in specific cases.

[0026] In an embodiment, as shown in Figure 1 The first aspect of the present application provides a method for optimizing the configuration of a new energy power generation device, comprising:

[0027] S1, obtain basic parameters of a radial power distribution network, and perform power flow analysis on the radial power distribution network based on the basic parameters to obtain power data and power loss data of each node in the radial power distribution network; specifically, the basic parameters of the radial power distribution network are first obtained, including the position impedance of each node, line distribution, line impedance, transformer parameters, load distribution, voltage level, etc., and the load demand, state of charge, load demand, initial configuration and operating state of new energy power generation equipment in various scenarios (such as before and after installation of power generation equipment) are determined, and these data are preprocessed, such as removing duplicate values, filling missing values, etc., to ensure the accuracy of the data.

[0028] In an embodiment, the power flow analysis on the radial power distribution network based on the basic parameters to obtain the power data and the power loss data of each node in the radial power distribution network comprises:

[0029] According to the basic parameters, the forward-backward scanning method is used to perform power flow analysis on the radial power distribution network in a preset scenario to obtain first power data and first loss data of each node; wherein the preset scenario is a scenario in which the radial power distribution network is not installed with the new energy power generation equipment;

[0030] Based on the basic parameters, each load bus in the radial power distribution network is sequentially taken as a generator bus to perform the power flow analysis step on the radial power distribution network in the preset scenario by the forward-backward scanning method until a preset iteration number is reached, and the power data and the loss data in the iteration process are taken as second power data and second loss data of each node, respectively;

[0031] The first power data and the second power data, and the first loss data and the second loss data are combined, respectively, to obtain the power data and the power loss data of each node.

[0032] Specifically, the forward-backward scanning method (Backward / Forward Sweep, BFS) includes two different calculation stages, in the forward scanning process, the voltage is calculated from the reference node to the terminal node of the power distribution network, and in the backward scanning, the current or power flow solution is started at the terminal node of the power distribution network and is propagated to the branch connected to the reference node; in the whole scanning process, the voltage remains constant during the backward scanning, while the current or power flow remains constant during the forward scanning, and the convergence of the power flow solution is checked after each iteration to ensure the accuracy. The schematic diagram of two interconnected buses in the radial power distribution network is shown in Figure 2 As shown in the figure, bus j is taken as the sending end, and bus j+1 is taken as the receiving end, the active power P j,j+1 and the reactive power Q j,j+1 between bus j and bus j+1 can be represented as follows:

[0033]

[0034]

[0035] where P j+1,eff and Q j+1,eff are the total effective active and reactive power supplied at bus j+1 respectively; P Loss(j,j+1) and Q Loss(j,j+1) are the active and reactive power losses between buses j and j+1 respectively; then the current flow between these two buses can be expressed as:

[0036]

[0037]

[0038] where V j and V j+1 are the voltage magnitudes at buses j and j+1 respectively; and are the voltage angles at buses j and j+1 respectively; R j,j+1 and X j,j+1 are the resistance and reactance of the line segment connecting buses j and j+1.

[0039] From the two current flow equations, we have:

[0040]

[0041] If we equate the real and imaginary parts of both sides of the above equations and sum the squares of the resulting two equations, the process and result are as follows:

[0042]

[0043]

[0044]

[0045] The active and reactive power losses in the line segment connecting buses j and j+1 are then determined as:

[0046]

[0047]

[0048]

[0049]

[0050] And the total active loss data P L and the total reactive loss data Q L can be calculated by adding up the losses of all line sections, as shown in the following formula:

[0051]

[0052]

[0053] In the formula, N b is the total number of branches in the radial distribution network.

[0054] The present application uses the forward-backward scanning method to perform power flow analysis on the radial distribution network to determine the active and reactive power data and power loss data of the radial distribution network, including:

[0055] First, the forward-backward scanning method is used to perform power flow analysis on the radial distribution network without new energy power generation equipment, that is, according to the topology data of the radial distribution network, a line is selected from any line, and the forward scanning is performed according to the remaining data in the basic parameters, that is, starting from the power supply end of the line, the voltage and power distribution of each node on the line are calculated; the backward scanning is performed, that is, starting from the load end of the line, the current and power loss of each node on the line are calculated in reverse, the line is updated, and the scanning calculation process of the updated line is repeated, until all lines in the radial distribution network are iterated, that is, the first power data and the first loss data of each node in the radial distribution network can be obtained.

[0056] Then, taking the number of iterations as the stopping condition, or setting the convergence threshold of power or loss, according to the basic parameters of the radial distribution network, each load bus in the radial distribution network is temporarily regarded as a generator bus in sequence, that is, it is assumed that these load buses can generate a certain power output during the iteration process to simulate the influence of different load distribution on the power flow of the power grid. For each load node set as a generator bus, the forward-backward scanning method is used to perform power flow analysis again, and the power and loss data of each node are updated in each iteration, and the power data and loss data in each iteration process are recorded as the second power data and the second loss data of each node in the radial distribution network.

[0057] Finally, the first power data and the second power data are combined, and the first loss data and the second loss data are combined, and the combination process can be realized by taking the average value, the maximum value, the minimum value or selecting data according to a specific rule, that is, the power data and the power loss data of each node can be obtained to facilitate subsequent stability analysis, power loss calculation and optimization of new energy power generation equipment.

[0058] By applying the forward and backward scanning method, the present invention can more accurately calculate the power and loss data of each node in the radial distribution network, and the iterative process further considers the impact of different load distributions on the grid current, so that the analysis results are closer to the actual situation, so as to more effectively evaluate the stability of the grid, help to discover the weak links in the grid, and provide a basis for subsequent grid optimization and transformation; by comparing the power and loss data under different numbers of iterations, the impact of new energy power generation equipment on the grid current after being connected to the grid can be analyzed, which helps to optimize the configuration of new energy power generation equipment and achieve the dual goals of economy and environmental protection of the grid.

[0059] S2. Determine the stability and power loss of the radial distribution network based on the power data and the power loss data, and determine the gas emissions of the radial distribution network based on the power released by the new energy power generation equipment;

[0060] The new energy generation equipment mentioned includes renewable distributed generation (RDG), electric vehicle charging stations (EVCS), and battery energy storage systems (BESS). These generation devices play a key role in modern energy infrastructure. RDG includes solar arrays, biomass, and wind turbines, which generate electricity locally using renewable energy. RDG models are crucial for conducting grid integration assessments and evaluating the impact on voltage stability, power quality, and grid resilience. Advanced modeling techniques can incorporate dynamic properties to simulate real-time responses and optimize RDG dispatch strategies in radial distribution networks.

[0061] Solar-based distributed generation (SDG) uses photovoltaic panels to convert sunlight directly into electricity. The technology's appeal lies in its scalability, adaptability to diverse environments, and minimal environmental impact, which has led to widespread adoption. Its sustainable development goals—enabling communities and businesses to generate clean energy locally, reducing reliance on centralized power grids, reducing carbon footprints, and improving efficiency and affordability—have further reinforced its importance in global sustainable energy initiatives. In radial distribution network modeling, SDG power output depends on the level of solar irradiance and the effectiveness of the solar panels and inverters. The active power generated by SDG is typically expressed as:

[0062]

[0063] Where, is the active power of solar distributed power generation equipment; N SP(j) is the number of solar panels; A SP is the area of ​​a single solar panel; η SP is the efficiency of the photovoltaic panel; η INV is the efficiency of the inverter; G is the solar irradiance (in W / m²).

[0064] Wind-based distributed generation (WDG) uses wind turbines to harness the natural energy of the wind to generate electricity. This renewable energy source is widely available in different geographical regions, providing a reliable and cost-effective alternative to traditional energy sources. WDGs play a key role in diversifying energy supply and enhancing energy resilience, especially in remote and rural areas. Their ability to operate independently or in conjunction with existing power grids enhances their versatility in advancing sustainable development goals. WDGs actively promote clean energy conversion by injecting active and reactive power into radial distribution networks. The active power of WDG at bus j is and reactive power The generation depends on the specific operating characteristics and environmental conditions, as shown in the following formula:

[0065]

[0066]

[0067] Where η WG is the efficiency of the wind turbine, which determines the efficiency of converting wind kinetic energy into electrical energy; ρ is the air density (kg / m³), whose value is affected by the large amount of air flowing through the turbine blades; A is the swept area of ​​the wind turbine blades (unit: m²), which plays an important role in capturing wind energy; ν is the wind speed; C p is the power coefficient, which indicates the efficiency of the wind turbine in extracting power from the wind; is the phase angle, which represents the relationship between the voltage and current phases in power generation.

[0068] Due to the abundant availability of biomass feedstocks such as wood, agricultural crops, and landfill gas, biomass-based distributed generation systems offer significant advantages for rural power systems. Unlike traditional diesel generators, biomass distributed generation (BDG) utilizes organic materials to generate electricity, promoting sustainability and reducing environmental emissions. It is known for its low emissions, high reliability, and effective steady-state frequency regulation, making it a reliable alternative energy source for improving the performance and living standards of rural communities. The present invention proposes integrating BDG into a radial distribution network to achieve optimal performance, with a synchronous motor model for BDG, controlling reactive power through armature current and field current, and BDG injecting active and reactive power into the radial distribution network at bus j and reactive power as follows:

[0069]

[0070]

[0071] where V BDG is the terminal voltage of BDG; I BDG is the current output of BDG; is the phase angle, representing the relationship between voltage and current phase.

[0072] The active power of RDG is obtained by adding the active power of the aforementioned three types of solar, biomass, and wind energy, and the reactive power of RDG is similarly obtained. As shown in Figure 3 , which shows the output data of RDG within 24 hours, with the output in units on the y-axis and time on the x-axis, it can be seen that the load curve captures factors such as solar irradiance, wind speed, and used biomass.

[0073] Battery energy storage systems (BESS) are an important part of modern radial distribution networks, providing critical energy storage capabilities to manage fluctuating renewable energy and improve grid reliability. BESS captures excess energy generated by RDG (such as solar and wind systems) during high production periods and releases stored energy into the grid during peak demand periods or when RDG output is low, effectively smoothing fluctuations and ensuring stable power supply. By storing excess energy and releasing it when needed, BESS supports grid stability, optimizes energy use, and reduces dependence on fossil fuel generation. Their deployment in radial distribution networks helps to more effectively integrate renewable energy, mitigate grid imbalances, and contribute to overall energy efficiency and sustainability goals.

[0074] The BESS stores the excess power of the RDG and discharges when needed, the RDG charges the BESS when excess power is available, the BESS discharges to the grid when extra power or peak demand is needed, the BESS in charging and discharging is given by the following formula:

[0075]

[0076]

[0077] where, and are the charging and discharging capacity of the BESS, respectively; is the maximum power capacity of the BESS, representing the highest rate at which energy can be stored or released; E BESS is the stored energy in the BESS, which fluctuates according to the charging and discharging cycles; P D is the power demand from the grid, representing the amount of electricity needed at a given time; is the available power of the BESS.

[0078] Electric vehicle charging stations are non-negligible facilities of the radial distribution network, and through perfect charging infrastructure, they promote the integration of electric vehicles with the grid. In the grid-to-vehicle (G2V) mode, EVCS usually obtains power from the grid during off-peak hours when power demand is low to charge the batteries of electric vehicles. This not only supports the widespread adoption of electric vehicles but also optimizes grid utilization by balancing load patterns and utilizing excess generation capacity; conversely, in the vehicle-to-grid (V2G) mode, electric vehicles can discharge stored power back to the grid during peak demand periods or when renewable energy generation is insufficient. This bidirectional capability enhances grid stability, reduces peak load demand, and supports renewable energy integration, thus fostering a more resilient and sustainable energy ecosystem.

[0079] During off-peak hours, electric vehicles charge from the grid at charging stations; while V2G technology plays a crucial role in improving grid reliability and efficiency, especially during peak power demand periods. V2G enables bidirectional interaction between electric vehicles and the power system by allowing electric vehicles to discharge power back to the grid, thus facilitating better load management and reducing pressure on traditional energy sources. As a result, the total power exchange between electric vehicle charging stations and the grid can be represented as:

[0080]

[0081]

[0082]

[0083] wherein, , , Ptotal, Pgrid, Pgridin, Pgridoutare the total exchange power between EVCS and grid, power obtained from grid, power discharged to grid, respectively; P EV Pchargeis the power required to charge each EV, representing the energy needed to replenish the EV battery; N G2V Ngridis the number of EVCS operating in G2V mode, where electrical energy is transferred from grid to EVs; N V2G Nevis the number of EVs capable of V2G operation.

[0084] As EVs can feed stored energy back to the grid when it is most needed, this capability can significantly reduce the grid's peak load demand. The implementation of V2G technology not only improves the overall efficiency of energy distribution, but also provides economic incentives for EV owners, as they can be compensated for supplying power to the grid. In addition, V2G operation can be strategically adjusted to accommodate periods of high electricity demand or low renewable energy generation, further supporting grid stability and reducing dependence on fossil fuel power generation, which is crucial for cultivating a sustainable energy ecosystem and maximizing the benefits of EVs.

[0085] And the efficient operation of EVCS and RDG is crucial for the reliability and energy optimization of the system, the main operation strategies include:

[0086] (1) Energy consumption priority: RDG directly powers EVCS to meet its real-time demand, with the remaining portion directly powering BESS; (2) Surplus power management: excess RDG power is stored in BESS for later use or output to the grid to optimize resource consumption and support grid stability; (3) Insufficient new energy generation: when RDG output is insufficient, the system prioritizes the use of BESS power, and if both are insufficient, additional power is provided from the grid to ensure continuous operation of EVCS; (4) Dynamic adjustment: continuous monitoring of RDG output or adaptive adjustment of BESS power storage to effectively match energy supply and EVCS demand; (5) Grid integration: integration with energy markets to participate in selling electricity and grid support services, improving overall system efficiency and reliability. These strategies ensure the optimal operation, grid integration and resilience of the hybrid energy system, supporting the transition to sustainable energy solutions.

[0087] In addition, introducing any new component into the radial distribution network, especially the component related to active and reactive power generation, will significantly affect the voltage stability index (VSI) and cause a significant change in its value. The voltage stability of each bus depends on the size of the reference bus voltage and the active and reactive power received at the bus. The stability of the radial distribution network is as follows:

[0088]

[0089] In the formula, VSI(j) is the stability of the radial distribution network.

[0090] Reducing total network loss is a key goal of intelligent radial distribution network design. Loss is an important indicator of power grid performance. The loss will be calculated by the hour, and the total amount will be added to determine the daily loss. This ensures a comprehensive assessment of energy efficiency within the power grid over 24 hours. The power loss of the radial distribution network is as follows:

[0091]

[0092] In the formula, is the power loss of the radial distribution network.

[0093] In an embodiment, the gas emission amount of the radial distribution network is determined by the release power of the new energy power generation equipment, comprising:

[0094] The active output power of the renewable distributed power generation equipment in the running state is taken as the first release power, and the active output power of the battery energy storage equipment in the discharge mode is taken as the second release power;

[0095] The active output power of the electric vehicle charging station in the vehicle-to-grid mode is taken as the third release power, and the first release power, the second release power, and the third release power are combined to obtain the release power of the new energy power generation equipment;

[0096] The gas emission amount is quantified based on the grid demand amount of the radial distribution network and the release power amount of the new energy power generation equipment.

[0097] Specifically, when the new energy power generation equipment in the radiative power distribution network is configured and optimized, the gas emission (carbon dioxide emission) is considered, the active power of the renewable distributed power generation equipment in the running state is taken as the first release power, the active power of the battery energy storage equipment in the discharge mode is taken as the second release power, the active power of the electric vehicle charging station in the vehicle-to-grid (V2G) mode is taken as the third release power, and the three active powers are combined to obtain the total release power of the new energy power generation equipment to calculate the gas emission with the demand of the radiative power distribution network to the power grid, which is expressed by the following formula:

[0098]

[0099] In the formula, is the gas emission of the radiative power distribution network, which is also the carbon dioxide emission; is the related emission of the actual power generation per unit hour of the power grid, which is 0.910 TonCO2 / MWh 2 ; N is a node in the radiative power distribution network; P grid is the power purchased from the power grid, i.e. the demand of the power grid; P RDG is the power generated by the RDG, i.e. the active power; t is time.

[0100] By combining the power outputs of the renewable distributed power generation equipment, the battery energy storage equipment and the electric vehicle charging station, the power grid demand can be more flexibly met, thereby improving the energy utilization efficiency and significantly reducing the gas emission; it is helpful to promote the development and application of renewable energy and reduce the dependence on traditional fossil energy, thereby promoting sustainable development; the power output of the battery energy storage equipment and the electric vehicle charging station in a specific mode can provide additional adjustment capacity, which is helpful to improve the stability and reliability of the power grid, and provides a feasible path for energy transformation, and through the integration of various new energy power generation equipment and energy storage technologies, the transformation from traditional energy to new energy is gradually realized.

[0101] S3, constructing a multi-objective function of the radiative power distribution network based on the stability, the power loss and the gas emission;

[0102] In an embodiment, step S3 comprises:

[0103] constructing a first objective function based on the stability of the radiative power distribution network before and after the installation of the renewable distributed power generation equipment;

[0104] constructing a second objective function based on the daily power loss of the radiative power distribution network before and after the installation of the new energy power generation equipment;

[0105] construct a third objective function based on the gas emission amount of the radiation type power distribution network before and after installation of the new energy power generation equipment;

[0106] weight the first objective function, the second objective function and the third objective function to obtain the multi-objective function.

[0107] Specifically, the strategy allocation of single and combined equipment in the radiation type power distribution network can profoundly affect the system performance by reducing power loss, improving stability margin and reducing emissions. In the competitive market environment, the goal of the equipment owner is to obtain financial benefits, which prompts them to invest in cutting-edge technology. Therefore, careful planning of the configuration of the equipment is needed to maximize the technical performance and economic benefits. The present application considers these factors, and the multi-objective function combines the technical factors of power loss and stability with the resource factors of CO2 emissions to achieve the best system performance.

[0108] The present application takes the value obtained by dividing the stability of the radiation type power distribution network after installation of the renewable distributed power generation equipment by the stability before installation as the first objective function, takes the value obtained by dividing the daily system loss of the radiation type power distribution network after installation of the new energy power generation equipment by the daily system loss before installation as the second objective function, and takes the value obtained by dividing the gas emission amount of the radiation type power distribution network after installation of the new energy power generation equipment by the gas emission amount before installation as the third objective function. The first objective function, the second objective function and the third objective function are weighted by using adjustable constants to obtain a multi-objective function, which is represented by the following formula:

[0109]

[0110] In the formula, OF1, OF2 and OF3 are the first objective function, the second objective function and the third objective function respectively; α1, α2 and α3 represent adjustable constants for measuring the influence of each factor on the overall multi-objective function, which allows optimization according to specific standards.

[0111] The present application considers the changes in stability, power loss and gas emission amount of the radiation type power distribution network before and after installation of the new energy power generation equipment, constructs a multi-objective function, which can more comprehensively evaluate the performance of the power grid, helps to find the weak links and potential problems in the power grid, and provides strong support for optimizing resource allocation. By focusing on the changes in gas emission amount, the application and promotion of new energy power generation equipment can be promoted, thereby promoting energy saving and emission reduction and environmental protection, and providing a scientific basis for power grid planning and decision-making.

[0112] S4, obtaining the configuration constraint condition of the new energy power generation equipment, and solving the multi-objective function by using a preset optimization algorithm to obtain an optimized configuration scheme of the new energy power generation equipment;

[0113] In an embodiment, step S4 comprises:

[0114] obtaining configuration constraints of the new energy power generation equipment and constraint boundaries of the multi-objective function, and initializing the number of meerkats based on the configuration constraints and the constraint boundaries; one meerkat is an optimal configuration scheme of a new energy power generation equipment;

[0115] According to the multi-objective function, the fitness of each meerkat is evaluated, a decreasing density factor is constructed based on the current iteration number, and the mining phase and the honey collecting phase of the meerkat optimization algorithm are executed through the fitness of each meerkat according to the decreasing density factor, so as to update the position of each meerkat, and obtain the optimal meerkat and its position;

[0116] The updated position of each meerkat is evaluated, and the mining phase and the honey collecting phase are iteratively executed according to the evaluation result until a preset iteration number is reached, and the final output optimal meerkat position is taken as the optimal configuration scheme of the new energy power generation equipment.

[0117] Specifically, the configuration constraints of the new energy power generation equipment include the number of RDGs to be installed in the power grid, and the overall size, dimension, and maximum iteration limit of optimization.

[0118] The layout of EVCS and RDG is affected by multiple factors, such as infrastructure, geographical conditions, and regulatory standards. These considerations determine the appropriate installation location based on available space, grid connection, distance to demand centers, and other factors. The initial system conditions of the radial distribution network include voltage level, load demand, state of charge (SOC), and the operating state of the equipment. RDG is assigned a specific actual power capacity, while EVCS operates within defined SOC limits, charging and discharging efficiency, and battery capacity limits. These factors collectively ensure the effective integration and operation of energy resources within the radial distribution network, enhancing system stability and optimizing overall performance. The constraint boundaries of the multi-objective function include:

[0119] Power balance constraint, which maintains the balance between total power loss, demand, and supply of the radial distribution network in different operating modes within 24 hours, as follows:

[0120]

[0121]

[0122] In the formula, P L , P TL is the power loss of the radial distribution network in G2V and V2G modes; P D is the power demand of the radial distribution network.

[0123] Voltage amplitude constraint, as follows:

[0124]

[0125] where, and are the minimum and maximum limits of the voltage amplitude at bus j.

[0126] Actual power compensation, as follows:

[0127]

[0128] where, and are the minimum and maximum actual power produced by the RDG at bus j.

[0129] SOC limit of the EVCS, which is crucial for maintaining the battery health of the electric vehicles connected to the EVCS and ensuring efficient operation, as follows:

[0130]

[0131] where, SOC min and SOC max are the minimum and maximum limits of the electric vehicle SOC at each charging station within 24 hours at bus j.

[0132] The improved meerkat optimization algorithm employed by the present application utilizes the hunting behavior of meerkats, i.e., meerkats use their acute senses to locate prey and dig into the soil to capture prey, mimicking this process for global optimization. In the process of searching for food, meerkats can dig 50 holes a day, with a hole spacing of at least 40 kilometers, demonstrating extraordinary perseverance. Meerkats strive to find beehives, and they cooperate with honeyguides that can find beehives but cannot access the honey. In this symbiotic relationship, the birds guide the meerkats into the beehives. This algorithm incorporates these behaviors into its method, known as the digging phase and the honey phase, respectively, to enhance its optimization capabilities.

[0133] Like other population-based algorithms, this algorithm starts with the initialization of a random population (including population size, algorithm parameters, initial speed of each meerkat, etc.), so that the position and speed of the meerkats during the optimization process are within the above constraints, while mathematically:

[0134]

[0135] where, and are the lower and upper limits of the search space, respectively; µ1 is a random number uniformly distributed between 0 and 1; is the position of the xyth candidate or meerkat.

[0136] The movement of each honey badger is affected by the smell intensity of the food source, which is calculated according to the concentration of the prey and the distance between the honey badger and the food source, and the smell intensity Jx is calculated by using the inverse square law, that is, Jx=1 / (Dx)2, where Dx is the distance between the honey badger and the food source. x which can be expressed as:

[0137]

[0138] D x =(f s -f x )

[0139] L=(f x -f x-1 ) 2

[0140] In the formula, L is the intensity of the position or concentration of the prey; the variable µ2 is a random number from 0 to 1, and D x is the distance between the food source f s and the xth honey badger f x .

[0141] Then, the fitness of each honey badger is evaluated by a multi-objective function to reflect the pros and cons of the configuration scheme in meeting the constraint conditions and optimization targets, and in order to ensure a smooth transition from exploration to exploitation and effectively manage fluctuations over time, a decreasing density factor is introduced in the process based on the current iteration number, which is used to adjust the exploration and utilization capabilities of the algorithm, and as the iteration number increases, the density factor gradually decreases, so that the algorithm changes from extensive exploration to fine utilization, and the decreasing density factor is represented by the following formula:

[0142]

[0143] In the formula, β is the decreasing density factor; a is the current iteration number; A is the maximum iteration number; κ is a constant, which is usually set to 2 by default to ensure that the algorithm avoids falling into local optimum.

[0144] The position update formula of the original honey badger optimization algorithm is improved by using the decreasing density factor, and the new update formula is used in the mining stage and the honey collecting stage based on the fitness values of each honey badger to update the position of each honey badger, so as to obtain the optimal honey badger and its position, that is, the mining stage is to use the improved position update formula to make the honey badger randomly explore new configuration schemes in the search space; wherein the improved position update formula is represented by the following formula:

[0145]

[0146]

[0147] In the formula, fn is the updated position; f p is the best position of the prey found so far; the parameter γ>1 represents the hunting ability of the meerkat, and the default value is usually set to 6; the flag FL is used to modify the search direction, facilitating adjustment to promote effective exploration of the search space.

[0148] The digging behavior of the meerkat is strongly influenced by various factors: the strength of the prey, the distance from the prey, and the time-dependent factor β affecting its search. In the digging process using the improved position updating formula for position updating, the meerkat may also encounter interference marked as "FL", which helps it locate the prey more effectively.

[0149] The honey gathering stage simulates the behavior of the meerkat locating the beehive following the honeyguide, enabling the meerkat to perform fine search in the neighborhood of the current best configuration scheme to find a better configuration scheme, which is represented by the following formula:

[0150]

[0151] In the formula, µ3, µ4, µ5, µ6, µ7 are random numbers from 0 to 1.

[0152] The meerkat uses the strategy affected by the random factors represented by µ3 to µ7 to explore its environment and gathers honey by the above formula, guided by the distance information D x , the meerkat concentrates its search around the previously discovered prey position f p , and this behavior is further formed by the time-dependent search pattern represented by β, and the two stages are coordinated by the decreasing density factor and fitness to balance the global search ability and local optimization ability of the algorithm.

[0153] According to the results of the digging stage and the honey gathering stage, the position of each meerkat (i.e. the configuration scheme) is updated, and the fitness of the updated meerkat position is evaluated, and the digging stage and the honey gathering stage are continued to be iteratively executed according to the evaluation results, and this process is repeated until a preset number of iterations is reached, so as to output the final optimal meerkat position as the optimal configuration scheme of the new energy power generation equipment. The present application introduces a decreasing density factor in the meerkat position updating process to enhance the exploration of the search space, uses the flag FL to adjust the search direction to promote thorough exploration and maximize the opportunity to identify the optimal solution; by balancing the global search and local optimization ability, the optimal configuration scheme of the new energy power generation equipment can be efficiently found under complex constraint conditions; by outputting the optimal configuration scheme, scientific basis and decision support are provided for the planning, design and operation of the new energy power generation equipment.

[0154] In an embodiment, step S4 further comprises:

[0155] According to the use, the area to which the radial power distribution network belongs is divided to obtain a plurality of basic areas, and the penetration rate of the electric vehicle load in each basic area is obtained;

[0156] Optionally, one basic area is taken as a target area, the configuration constraint condition of the new energy power generation equipment and the constraint boundary of the multi-objective function are obtained as the constraint condition of the target area, and the particle swarm is initialized based on the constraint condition; each particle represents an optimized configuration scheme of the new energy power generation equipment;

[0157] The fitness value of each particle is quantified according to the multi-objective function, and the penetration rate of the target area is taken as an adjustment factor to adjust the original position and speed updating formula of the particle swarm algorithm to obtain an improved updating formula;

[0158] Based on the improved updating formula, the personal optimal position and the global optimal position of each particle are updated through the fitness value, and the fitness value quantification step and the optimal position updating step are repeatedly executed until a preset repetition number is reached, and the final global optimal position is output as the optimized configuration scheme of the new energy power generation equipment of the target area;

[0159] The target area is updated, and the particle swarm initialization step, the fitness value quantification step and the optimal position updating step are repeatedly executed based on the constraint condition of the updated target area until a preset repetition number is reached, and the final global optimal position is output as the optimized configuration scheme of the new energy power generation equipment of the updated target area;

[0160] The optimized configuration schemes of the new energy power generation equipment of each target area are combined to obtain the optimized configuration scheme of the new energy power generation equipment.

[0161] Specifically, when the layout of the new energy power generation equipment in the radial power distribution network is optimized and configured, the influence of the electric vehicle load penetration rate on the configuration scheme is considered, and the overall optimized configuration scheme is obtained through the division and iterative optimization of the basic area, including:

[0162] According to the use, the area to which the radial power distribution network belongs is divided to obtain a plurality of basic areas, such as industry, commerce, residence and the like, and the penetration rate of the electric vehicle load in each basic area is obtained as an important parameter in the subsequent optimization process; wherein the penetration rate is determined based on historical data, prediction model and the like;

[0163] Then, one basic area is taken as a target area, the configuration constraint condition of the new energy power generation equipment in the target area and the constraint boundary of the multi-objective function are obtained, and then the particle swarm is initialized based on the constraint condition of the target area to generate an initial scheme, and each particle represents an optimized configuration scheme of the new energy power generation equipment;

[0164] Then the fitness value of each particle is quantified according to the multi-objective function, and the position of the particle is updated based on the calculated fitness value, and in the updating process, the electric vehicle load penetration rate of the target area is taken as the adjusting factor to adjust the original position and speed updating formula of the particle swarm algorithm, so that the improved updating formula is obtained, so that the algorithm is more suitable for the basic area with different penetration rates, wherein the improved updating formula is represented by the following formula:

[0165]

[0166] In the formula, , Vi(t) and Vi(t+1) are the speed of particle i in the dth dimension at the tth and t+1th generation respectively, w is the inertia weight, which controls the tendency of the particle to maintain the previous speed, c1 and c2 are learning factors, which represent the weight of the particle learning from its historical optimal position and global optimal position respectively, r1 and r2 are random numbers between 0 and 1, which are used to increase the randomness of the search; Pbesti(t) is the historical optimal position of particle i in the dth dimension at the tth generation; Xi(t) is the position of particle i in the dth dimension at the tth generation; Gbest(t) is the global optimal position at the tth generation; λ is an adjusting coefficient, which is used to control the influence of penetration rate on speed updating; PR is the electric vehicle load penetration rate of the target area; r3 is a random number between 0 and 1, which is used to randomize the influence of penetration rate, in order to avoid the algorithm falling into local optimum; the role of r3-0.5 is to make the influence of penetration rate fluctuate between positive and negative, and increase the diversity of search.

[0167] Using the improved speed updating formula, the personal optimal position and global optimal position of each particle are updated through the fitness value of each particle, and the fitness value quantification step and optimal position updating step are repeatedly executed until the preset repetition number is reached, and then the target area is updated to iterate and optimize in the above manner until all the basic areas are optimized and configured, or the preset total repetition number is reached, finally the optimization configuration scheme of new energy power generation equipment of each target area is combined to obtain the overall new energy power generation equipment optimization configuration scheme.

[0168] The present application uses electric vehicle load penetration rate as an adjusting factor to make the optimization configuration scheme more in line with actual demand and future development trend; through the division and iterative optimization of the basic area, the optimization configuration of each area is realized, and the overall coordination is realized through the combination of the final scheme; the improved particle swarm optimization algorithm can adaptively adjust according to the constraint conditions and electric vehicle load penetration rate of different areas, improving the applicability and accuracy of the algorithm; through iterative optimization and global search strategy, a better new energy power generation equipment configuration scheme can be quickly found, improving the optimization efficiency.

[0169] In one embodiment, the present application employs an IEEE 69 radial distribution network modified with three different radial distribution networks, including three interconnected radial distribution networks, serving residential, commercial, and industrial customers, each radial distribution network can operate independently, the active and reactive power demand curves of the distribution network are shown in Figure 4 The above method is simulated in MATLAB to solve the problem of effective deployment of EVCS, specifically to manage the actual power and reactive power in a hybrid energy framework, which uses the IEEE 69 bus system as its base model, integrates multiple types of RDG units and BESS, and combines multiple load models to reflect real scenarios. The operating parameters of the system are: actual power demand is 3.725 MW, reactive power demand is 2.095 MVAr, and operates at a line voltage of 12.66 kV to calculate the actual and reactive power loss and bus voltage value without compensation measures.

[0170] This example uses the improved meerkat optimization algorithm to solve the configuration problem of electric vehicles in the radial distribution network, the scheme involves managing 100 electric vehicles distributed in residential (10 electric vehicles), commercial (70 electric vehicles) and industrial (20 electric vehicles), each electric vehicle has a charging rate of 20 kW, three EVCS with multiple charging points have been arranged, and various types of RDG devices are optimized according to the requirements of each radial distribution network; the load of electric vehicles is dynamically affected by the number of electric vehicles in G2V and V2G modes, and the charging rate and discharging rate need to be considered; in order to better optimize the objective function, each radial distribution network includes at least one RDG and EVCS, the improved meerkat optimization algorithm is used for RDG capacity planning and point arrangement; the BESS and the RDG unit in the radial distribution network are located in the same location, and the size of the BESS depends on the RDG capacity in the radial distribution network.

[0171] The present example uses the optimal configuration method of the new energy power generation equipment to reduce the influence of the EVCS in the radiation distribution network, while optimizing the multi-objective function, which promotes the bidirectional power transmission between the EVCS and the intelligent radiation distribution network by using the V2G technology, thereby reducing the dependence on external grid procurement. The model and optimization framework of the radiation distribution network are illustrated during the 24-hour operation period, and the model and optimization framework are selected to consider the uncertainty of the RDG unit and the load fluctuation of the BESS, considering the influence of the EVCS on the active power loss and the system parameters, and the efficient use of the EVCS in the radiation distribution network needs to be arranged at the key bus position, and the position of the RDG and the EVCS can reduce these losses. Before determining the positions of the EVCS and the RDG, the baseline loss in the system is determined based on the BFS-based power flow analysis, the EVCS is distributed to the 6th, 30th and 39th buses in the intelligent radiation distribution network, considering the characteristics and availability of each type of distributed energy source, the position of the RDG unit is arranged at the 18th (wind distributed power generation), 29th (solar distributed power generation) and 61st (biomass distributed power generation) buses after optimization by the HBOA, and the BESS device is located at the same position as the RDG device to store the surplus energy during the off-peak period of the EVCS and discharge during the peak period to support the operation of the radiation distribution network.

[0172] In the field of developing distribution systems, intelligent microgrids play a key role in supporting radiation distribution networks by providing necessary ancillary services. Intelligent microgrids (also for radiation distribution networks) are crucial in meeting real and reactive power demand, enhancing voltage stability, minimizing power loss, and reducing CO2 emissions; while new energy power generation equipment, such as solar, wind, and biomass-based distributed power generation systems, exhibit unique daily output patterns, with solar distributed power generation peaking at noon and gradually decreasing after sunset, wind distributed power generation experiencing unpredictable changes due to wind conditions, and biomass distributed power generation providing stable output throughout the day, depending on stable biomass supply, while the RDG power generation curve is shown in Figure 5 Figure 6 The RDG and EVCS actual power generation within 24 hours is illustrated in detail in the figure. The present example integrates EVCS to analyze its impact on RDG operation, and during the RDG peak period (11 am to 3 pm), the EVCS uses the surplus RDG power to charge (G2V mode), and conversely, during the peak demand period (6 pm to 10 pm), the EVCS discharges (V2G mode) to help stabilize the grid through electric vehicle power supply.

[0173] ​The embodiment of the application is based on how to effectively integrate the electric vehicle charging station into the existing power distribution network to reduce the negative impact on the power grid. A new energy power generation equipment optimization configuration method is designed, which realizes obtaining basic parameters of a radial power distribution network, performing power flow analysis on the radial power distribution network based on the basic parameters, obtaining power data and power loss data of each node in the radial power distribution network, determining the stability, power loss and and of the radial power distribution network according to the power data and the power loss data, and determining the gas emission of the radial power distribution network through the released power of the new energy power generation equipment; a multi-objective function of the radial power distribution network is constructed based on the stability, the power loss and and and the gas emission; the configuration constraint conditions of the new energy power generation equipment are obtained, and the multi-objective function is solved through a preset optimization algorithm to obtain a technical solution of the optimization configuration scheme of the new energy power generation equipment; and the construction and operation cost of the power distribution network is reduced, and the economic benefit is improved.

[0174] It should be noted that although each step in the above flowchart is displayed in sequence according to the direction of the arrow, these steps are not necessarily executed in sequence according to the direction of the arrow. Unless otherwise stated herein, the execution of these steps has no strict sequence limitation, and these steps can be executed in other orders.

[0175] In another embodiment, as shown in FIG. 2, the second aspect of the application provides a new energy power generation equipment optimization configuration system, which comprises: Figure 7

[0176] The power flow analysis module 10 is configured to obtain basic parameters of a radial power distribution network, perform power flow analysis on the radial power distribution network based on the basic parameters, and obtain power data and power loss data of each node in the radial power distribution network.

[0177] The data processing module 20 is configured to determine the stability, power loss and and of the radial power distribution network according to the power data and the power loss data, and determine the gas emission of the radial power distribution network through the released power of the new energy power generation equipment.

[0178] The function construction module 30 is configured to construct a multi-objective function of the radial power distribution network based on the stability, the power loss and and and the gas emission.

[0179] The scheme generation module 40 is configured to obtain the configuration constraint conditions of the new energy power generation equipment, solve the multi-objective function through a preset optimization algorithm, and obtain an optimization configuration scheme of the new energy power generation equipment.

[0180] ​It should be noted that the various modules in the above-mentioned optimization configuration system of new energy power generation equipment can be realized by software, hardware and combinations thereof, in whole or in part. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above-mentioned modules. For the specific limitation of the optimization configuration system of new energy power generation equipment, refer to the limitation of the optimization configuration method of new energy power generation equipment, both have the same function and effect, and will not be repeated here.

[0181] The third aspect of the application provides an electronic device, comprising:

[0182] a processor, a memory and a bus;

[0183] the bus, for connecting the processor and the memory;

[0184] the memory, for storing operation instructions;

[0185] the processor, for executing the operations corresponding to the optimization configuration method of new energy power generation equipment as shown in the first aspect of the application by calling the operation instructions.

[0186] In an optional embodiment, an electronic device is provided, as shown in Figure 8 The electronic device 5000 shown in Figure 8 The electronic device 5000 shown in

[0187] The processor 5001 can be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can realize or execute various exemplary logical blocks, modules and circuits described in combination with the disclosure. The processor 5001 can also be a combination of computing functions, such as one or more microprocessor combinations, combinations of DSP and microprocessor, etc.

[0188] The bus 5002 can include a channel for transmitting information between the above-mentioned components. The bus 5002 can be a PCI bus or an EISA bus, etc. The bus 5002 can be divided into an address bus, a data bus, a control bus, etc. For the sake of expression, Figure 8Only one bus or type of bus might exist but implementations that have more than one bus or type of bus are possible.

[0189] The memory 5003 can be a ROM, or other type of static storage that can store static information and instructions; a RAM, or other type of dynamic storage that can store information and instructions; an EEPROM, CD-ROM or other optical disk storage, magneto-optical storage or other optical storage; a magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to this.

[0190] The memory 5003 is configured to store application codes for implementing the solutions of the present application, and the processor 5001 is configured to control the execution. The processor 5001 is configured to execute the application codes stored in the memory 5003 to implement the content shown in any of the foregoing method embodiments.

[0191] The electronic device includes, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (Personal Digital Assistant), a PAD (Tablet Personal Computer), a PMP (Portable Multimedia Player), a car terminal (for example, a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like.

[0192] The fourth aspect of the present application provides a computer readable storage medium, and the computer readable storage medium stores a computer program, and the program is executed by a processor to implement the method for optimizing the configuration of a new energy power generation device according to the first aspect of the present application.

[0193] Another embodiment of the present application provides a computer readable storage medium, and the computer readable storage medium stores a computer program, and when the computer program is run on a computer, the computer can execute the corresponding content in the foregoing method embodiments.

[0194] In addition, an embodiment of the present application also provides a computer readable storage medium, and the computer readable storage medium stores a computer program, and the program is executed by a processor to implement the steps of the foregoing method.

[0195] In summary, the present invention relates to the field of computer technology, and discloses a method, system, device, and medium for optimizing the configuration of new energy power generation equipment. By obtaining basic parameters of a radial distribution network and performing a flow analysis on the radial distribution network based on the basic parameters, power data and power loss data of each node in the radial distribution network are obtained; the stability and the sum of power losses of the radial distribution network are determined according to the power data and the power loss data, and the gas emissions of the radial distribution network are determined by the released power of the new energy power generation equipment; a multi-objective function of the radial distribution network is constructed based on the stability, the sum of power losses, and the gas emissions; the configuration constraints of the new energy power generation equipment are obtained, and the multi-objective function is solved by a preset optimization algorithm to obtain an optimized configuration scheme for the new energy power generation equipment, so as to reduce the construction and operation costs of the distribution network and improve economic benefits.

[0196] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the various technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various 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 specification.

[0197] The above-described embodiments merely represent several preferred implementations of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art could make several improvements and substitutions without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be based on the scope of protection of the claims.

Claims

1. A method for optimizing the configuration of new energy power generation equipment, characterized in that: include: Obtaining basic parameters of a radial distribution network, and performing a power flow analysis on the radial distribution network based on the basic parameters to obtain power data and power loss data of each node in the radial distribution network; Determining the stability and power loss of the radial distribution network according to the power data and the power loss data, and determining the gas emissions of the radial distribution network according to the power released by the new energy power generation equipment; Constructing a multi-objective function of the radial distribution network based on the stability, the power loss and the gas emission; Obtaining configuration constraints for the new energy power generation equipment, and solving the multi-objective function using a preset optimization algorithm to obtain an optimal configuration solution for the new energy power generation equipment; The obtaining of the configuration constraints of the new energy power generation equipment and solving the multi-objective function by a preset optimization algorithm to obtain an optimized configuration scheme for the new energy power generation equipment includes: Obtaining configuration constraints of the new energy power generation equipment and constraint boundaries of the multi-objective function, and initializing the number of honey badgers based on the configuration constraints and the constraint boundaries; one honey badger is an optimization configuration scheme for a new energy power generation equipment; The fitness of each honey badger is evaluated according to the multi-objective function, a decreasing density factor is constructed based on the current number of iterations, and the mining phase and the honey collection phase of the honey badger optimization algorithm are executed through each fitness according to the decreasing density factor to update the position of each honey badger and obtain the optimal honey badger and its position; The fitness of each honey badger's updated position is evaluated, and the excavation phase and the honey collection phase are iteratively executed according to the evaluation results until a preset number of iterations is reached, and the optimal honey badger position finally outputted is used as the optimized configuration scheme for the new energy power generation equipment.

2. The method for optimizing configuration of new energy power generation equipment according to claim 1, characterized in that: The performing of power flow analysis on the radial distribution network based on the basic parameters to obtain power data and power loss data of each node in the radial distribution network includes: According to the basic parameters, a power flow analysis is performed on the radial distribution network in a preset scenario by a forward and backward scanning method to obtain first power data and first loss data of each node; Based on the basic parameters, each load bus in the radial distribution network is sequentially used as a generator bus to iteratively perform a power flow analysis step on the radial distribution network under the preset scenario through a forward and backward scanning method until a preset number of iterations is reached, and power data and loss data during the iteration process are respectively used as second power data and second loss data of each of the nodes; The first power data and the second power data, as well as the first loss data and the second loss data are respectively combined to obtain power data and power loss data of each node.

3. The method for optimizing configuration of new energy power generation equipment according to claim 2, characterized in that: The preset scenario is a scenario in which the radial distribution network is not installed with the new energy power generation equipment.

4. The method for optimizing configuration of new energy power generation equipment according to claim 1, characterized in that: The new energy power generation equipment includes renewable distributed power generation equipment, electric vehicle charging stations and battery energy storage equipment; The method of determining the gas emissions of the radial distribution network by using the released power of the new energy power generation equipment includes: Using the active output power of the renewable distributed power generation device in the operating state as the first release power, and using the active output power of the battery energy storage device in the discharge mode as the second release power; using the active output power of the electric vehicle charging station in the vehicle-to-grid mode as the third released power, and combining the first released power, the second released power, and the third released power to obtain the released power of the new energy power generation equipment; The gas emissions are quantified based on the grid demand of the radial distribution network and the released power of the new energy power generation equipment.

5. The method for optimizing configuration of new energy power generation equipment according to claim 4, characterized in that: The multi-objective function of constructing the radial distribution network based on the stability, the power loss and the gas emission includes: constructing a first objective function based on the stability of the radial distribution network before and after the installation of the renewable distributed power generation equipment; Constructing a second objective function based on the sum of daily power losses before and after the installation of the new energy power generation equipment in the radial distribution network; constructing a third objective function based on the gas emissions before and after the installation of the new energy power generation equipment in the radial distribution network; The first objective function, the second objective function and the third objective function are weighted to obtain the multi-objective function.

6. An optimization configuration system for new energy power generation equipment, characterized in that: include: A power flow analysis module, configured to obtain basic parameters of a radial distribution network, and perform power flow analysis on the radial distribution network based on the basic parameters to obtain power data and power loss data of each node in the radial distribution network; a data processing module, configured to determine the stability and power loss of the radial distribution network based on the power data and the power loss data, and determine the gas emissions of the radial distribution network based on the power released by the new energy power generation equipment; a function construction module, configured to construct a multi-objective function of the radial distribution network based on the stability, the power loss, and the gas emission; A solution generation module is used to obtain the configuration constraints of the new energy power generation equipment and solve the multi-objective function through a preset optimization algorithm to obtain an optimized configuration solution for the new energy power generation equipment; The obtaining of the configuration constraints of the new energy power generation equipment and solving the multi-objective function by a preset optimization algorithm to obtain an optimized configuration scheme for the new energy power generation equipment includes: Obtaining configuration constraints of the new energy power generation equipment and constraint boundaries of the multi-objective function, and initializing the number of honey badgers based on the configuration constraints and the constraint boundaries; one honey badger is an optimization configuration scheme for a new energy power generation equipment; The fitness of each honey badger is evaluated according to the multi-objective function, a decreasing density factor is constructed based on the current number of iterations, and the mining phase and the honey collection phase of the honey badger optimization algorithm are executed through each fitness according to the decreasing density factor to update the position of each honey badger and obtain the optimal honey badger and its position; The fitness of each honey badger's updated position is evaluated, and the excavation phase and the honey collection phase are iteratively executed according to the evaluation results until a preset number of iterations is reached, and the optimal honey badger position finally outputted is used as the optimized configuration scheme for the new energy power generation equipment.

7. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the optimization configuration method of the new energy power generation equipment according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the optimization configuration method of the new energy power generation equipment according to any one of claims 1 to 5 is implemented.

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