Optimal configuration method, system and equipment for new energy power generation equipment and medium

Abstract

The invention discloses an optimal configuration method and system for new energy power generation equipment, equipment and a medium, and the method comprises the steps: obtaining basic parameters of a radiation type power distribution network, carrying out the power flow analysis of the radiation type power distribution network based on the basic parameters, and obtaining the power data and power loss data of each node in the radiation type power distribution network; determining the stability and the power loss sum of the radiation type power distribution network according to the power data and the power loss data, and determining the gas emission amount of the radiation type power distribution network according to the release power of new energy power generation equipment; constructing a multi-objective function of the radiation type power distribution network based on the stability, the power loss and the gas emission amount; and obtaining a configuration constraint condition of the new energy power generation equipment, and solving the multi-objective function through a preset optimization algorithm to obtain an optimal configuration scheme of the new energy power generation equipment so as to reduce construction and operation costs of a power distribution network and improve economic benefits.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to an optimization configuration method, system, equipment and medium for new energy power generation equipment. Background Art

[0002] With the serious environmental consequences of the widespread use of internal combustion engines in vehicles, electric vehicles are increasingly accepted and adopted. However, the increase in the use of electric vehicles, high initial costs and insufficient infrastructure, especially the limited availability of electric vehicle charging stations, pose a huge obstacle to widespread adoption. In addition, 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 losses, voltage curve drops and potential overloads, which pose challenges to the reliability and quality of power supply.

[0003] It can be seen that how to effectively integrate electric vehicle charging stations into the existing power distribution network to reduce the negative impact on the power grid has become a technical problem that technical personnel in this field need to solve urgently. Summary of the invention

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

[0005] In order to solve the above technical problems, the present invention provides a method for optimizing the configuration of new energy power generation equipment, comprising: Acquire 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; Determine the stability and power loss 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 by 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; 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 optimal configuration scheme for the new energy power generation equipment.

[0006] Compared with the prior art, the embodiments of the present invention have the following advantages: (1) By obtaining the basic parameters of the radial distribution network and performing power flow calculations, the power demand and distribution of each node can be clearly identified. 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, reducing the frequency and voltage fluctuations of the power grid caused by the imbalance between supply and demand, improving the stability of the power grid, and ensuring the reliability and continuity of power supply, providing users with more stable power services; (2) When constructing the objective function, the loss, stability and environmental pollution of the radial distribution network are fully considered, making the configuration of the new energy power generation equipment obtained by the optimization algorithm more reasonable, which not only improves the energy utilization efficiency, but also reduces the operating cost of the power grid, providing strong support for the sustainable development of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the implementation mode will be briefly introduced below. Obviously, the drawings described below are only some implementation modes of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0008] Figure 1 It is a flow chart of a method for optimizing configuration of a new energy power generation equipment provided by a certain embodiment of the present invention; Figure 2 is a schematic diagram of two interconnected buses in a radial distribution network provided by an embodiment of the present invention; Figure 3 is an output data graph of RDG within 24 hours provided by an embodiment of the present invention; Figure 4 is an active and reactive power demand curve diagram provided by a certain embodiment of the present invention; Figure 5 is a RDG power generation curve diagram provided by an embodiment of the present invention; Figure 6 is an EVCS power demand / generation diagram provided by an embodiment of the present invention; Figure 7 It is a structural diagram of an optimization configuration system for a new energy power generation equipment provided by a certain embodiment of the present invention; Figure 8 It is a structural diagram of an electronic device provided by a certain embodiment of the present invention. DETAILED DESCRIPTION

[0009] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings and embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0010] In the description of this application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0011] In the description of the present application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two components. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are for illustrative purposes only, and do not indicate or imply that the system or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0012] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0013] In one embodiment, if Figure 1 As shown, the first aspect of the present invention provides a method for optimizing configuration of a new energy power generation device, comprising: S1. Obtain basic parameters of a radial distribution network, and perform 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. Specifically, the present invention first obtains basic parameters of a radial distribution network, including location impedance, line distribution, line impedance, transformer parameters, load distribution, voltage level, etc. of each node, and determines load demand, charge state, load demand, initial configuration and operating status of new energy power generation equipment in various scenarios (such as before and after the installation of power generation equipment), and preprocesses these data, such as removing duplicate values, filling missing values, etc., to ensure the accuracy of the data.

[0014] In one embodiment, the performing 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; wherein the preset scenario is a scenario in which the radial distribution network is not installed with the new energy power generation equipment; Based on the basic parameters, each load bus in the radial distribution network is used as a generator bus in sequence, so as to iteratively perform the power flow analysis step of the radial distribution network under the preset scenario by a forward and backward scanning method until a preset number of iterations is reached, and the power data and loss data in the iterative process are used as the second power data and the second loss data of each of the nodes respectively; The first power data and the second power data, and the first loss data and the second loss data are respectively combined to obtain power data and power loss data of each of the nodes.

[0015] Specifically, the Backward / Forward Sweep (BFS) method includes two different calculation stages. In the forward sweep process, the voltage is calculated sequentially from the reference node to the terminal node of the distribution network. In the backward sweep, the current or power flow solution is started at the terminal node of the distribution network and propagates to the branch connected to the reference node. During the entire sweep process, the voltage remains constant during the backward sweep, while the current or power flow remains constant during the forward sweep. The convergence of the power flow solution is checked after each iteration to ensure accuracy. The schematic diagram of two interconnected buses in a radial distribution network is shown in Figure 1. Figure 2 As shown, bus j is used as the transmitting end and bus j+1 is used as the receiving end. The active power P between bus j and j+1 is j,j+1 And reactive power Q j,j+1 It can be expressed as follows: Where P j+1,eff and Q j+1,eff are the total effective active power and total effective reactive power supplied at bus j+1 respectively; P Loss(j,j+1) and Q Loss(j,j+1) are the active loss and reactive loss between busbars j and j+1 respectively; then the current flow between these two buses can be expressed as: Where V j and V j+1 are the voltage amplitudes at nodes j and j+1 respectively; and are the voltage angles at nodes j and j+1 respectively; R j,j+1 and X j,j+1 are the resistance and reactance of the line segment connecting busbars j and j+1 respectively.

[0016] According to the two formulas of current flow, we can get: If the real and imaginary parts of both sides of the above formula are equal, and the two formulas are squared and summed, the process and result are as follows: Then the active power loss and reactive power loss in the line segment connecting busbars j and j+1 are determined as follows: The total active power loss data P in the radial distribution network L And the total reactive power loss data Q L It can be calculated by adding the losses of all line segments, as shown below: Where N b is the total number of branches in the radial distribution network.

[0017] The present invention adopts a forward and backward scanning method to perform power flow analysis on a radial distribution network to determine the active and reactive power data and power loss data of the radial distribution network, including: First, a power flow analysis is performed on the radial distribution network without the installation of new energy power generation equipment by the forward and backward scanning method, that is, a line is selected from the radial distribution network according to the topological data of the radial distribution network, and a forward scan is performed according to the remaining data in the basic parameters - starting from the power supply end of the line, the voltage and power distribution of each node on the line are calculated; backward scanning - starting from the load end of the line, the current and power loss of each node on the line are reversely calculated, the line is updated, and the scanning calculation process of the updated line is repeated until all the lines in the radial distribution network are iterated, and the first power data and the first loss data of each node in the radial distribution network can be obtained; Then, the number of iterations is used as a stop condition, or a convergence threshold of power or loss is set. Each load bus in the radial distribution network is temporarily regarded as a generator bus in sequence according to the basic parameters of the radial distribution network. That is, it is assumed that these load buses can generate a certain power output during the iteration process to simulate the impact of different load distributions on the power grid flow. For each load node set as a generator bus, the forward and backward scanning method is used to re-perform the power flow analysis. Each iteration will update the power and loss data of each node, 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 respectively. Finally, the first power data and the second power data are combined, and the first loss data and the second loss data are combined. The combination process can be achieved by taking the average value, maximum value, minimum value or selecting data according to specific rules, so as to obtain the power data and power loss data of each node, so as to facilitate the subsequent stability analysis, power loss calculation and optimal configuration of new energy power generation equipment.

[0018] The present invention can more accurately calculate the power and loss data of each node in the radial distribution network through the application of the forward and backward scanning method, and the iterative process further considers the impact of different load distributions on the power grid flow, so that the analysis result is closer to the actual situation, so as to more effectively evaluate the stability of the power grid, help to find the weak links in the power grid, and provide a basis for subsequent power grid optimization and transformation; by comparing the power and loss data under different iteration times, the impact of new energy power generation equipment on the power grid flow after being connected to the power 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 power grid.

[0019] S2. Determine the stability and power loss 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 by the power released by the new energy power generation equipment; The new energy generation equipment includes renewable distributed generation (RDG), electric vehicle charging stations (EVCS) and battery energy storage systems (BESS); these power generation equipment play a key role in modern energy infrastructure; RDG includes solar arrays, biomass and wind turbines, which use renewable energy to generate electricity locally. RDG models are essential for grid integration assessments and assessments of the impact on voltage stability, power quality and grid resilience. Advanced modeling techniques can combine dynamic properties to simulate real-time responses and optimize RDG dispatch strategies in radial distribution networks.

[0020] Solar-based distributed generation (SDG) uses photovoltaic panels to convert sunlight directly into electricity. The appeal of this technology lies in its scalability, adaptability to different environments, and minimal environmental impact, which promotes widespread adoption; its sustainable development goals enable communities and businesses to produce clean energy locally, reduce dependence on centralized power grids, reduce carbon footprints, and improve efficiency and affordability, further strengthening their importance in global sustainable energy initiatives. In the modeling of radial distribution networks, the power output of SDG depends on the level of solar irradiance and the effectiveness of solar panels and inverters. The active power generated by SDG is usually expressed as: In the formula, 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).

[0021] Wind-based distributed generation (WDG) uses wind turbines to generate electricity from natural wind energy. This renewable energy source is widely available in different geographical areas, providing a reliable and cost-effective alternative to traditional energy sources. WDG plays 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. WDG actively promotes 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 below: 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 factor, which indicates how efficiently the wind turbine extracts power from the wind; is the phase angle, which represents the relationship between the voltage and current phases in power generation.

[0022] Due to the abundant availability of biomass raw materials (such as wood, crops, and landfill gas), biomass-based distributed generation systems offer significant advantages to rural power systems. Unlike traditional diesel generators, biomass-based distributed generation (BDG) uses organic materials to generate electricity, promoting sustainability and reducing environmental emissions. It uses a steam cycle to convert biomass into steam to drive turbines, or uses biogas to drive gas turbines. It is known for its low emissions, high reliability, and effective steady-state frequency regulation, making it a reliable alternative energy source to improve the performance and living standards of rural communities. The present invention proposes to integrate BDG into a radial distribution network to achieve optimal efficiency. The BDG model is a synchronous motor model. The reactive power is controlled by the armature current and the excitation current. The BDG injects the active power of the radial distribution network at bus j. and reactive power As shown below: Where V BDG is the terminal voltage of BDG; IBDG is the current output of BDG; is the phase angle, which represents the relationship between the voltage and current phases.

[0023] The active power of RDG is obtained by adding the active power of the above three types of solar energy, biomass and wind energy, and the reactive power of RDG is the same. Figure 3 As shown, it shows the output data of RDG within 24 hours, expressed as the per-unit value of the output on the y-axis and time on the x-axis. It can be seen that the load curve captures the influence of factors such as solar irradiance, wind speed and biomass used.

[0024] Battery energy storage systems (BESS) are an important component of modern radial distribution networks, providing critical energy storage capabilities to manage fluctuating renewable energy and improve grid reliability. BESS captures surplus energy generated by RDG (such as solar and wind systems) during high-production periods, and discharges the stored energy into the grid during peak demand periods or when RDG output is low, effectively smoothing out fluctuations and ensuring a stable power supply. By storing excess energy and releasing it when needed, BESS supports grid stability, optimizes energy use, and reduces reliance on fossil fuel power generation. Their deployment in radial distribution networks helps integrate renewable energy more efficiently, alleviates grid imbalances, and helps achieve overall energy efficiency and sustainability goals.

[0025] BESS stores excess power from RDG and discharges it when needed. When excess power is available, RDG charges BESS, and BESS discharges to the grid when additional power is needed or when peak demand occurs. The BESS during charging and discharging is given by the following formula: In the formula, and are the charging and discharging amounts of BESS respectively; is the maximum power capacity of the BESS, indicating the highest rate at which energy can be stored or released; E BESS is the current energy stored in the BESS, which fluctuates according to the charge and discharge cycles; P D is the electricity demand from the grid, which represents the amount of electricity required at a given time; is the available power of BESS.

[0026] Electric vehicle charging stations are indispensable facilities for radial distribution networks, which promote the integration of electric vehicles with the grid through a complete charging infrastructure. In the Grid to Vehicle (G2V) mode, EVCS usually obtains electricity from the grid to charge the batteries of electric vehicles during off-peak hours when electricity demand is low. 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 electricity back to the grid during peak demand periods for grid electricity or when renewable energy generation is insufficient. This two-way capability enhances grid stability, reduces peak load demand, and supports renewable energy integration, thereby fostering a more resilient and sustainable energy ecosystem.

[0027] During off-peak hours, electric vehicles are charged from the grid at charging stations; V2G technology plays a vital role in improving grid reliability and efficiency, especially during peak electricity demand periods. V2G enables two-way interaction between electric vehicles and the power system by allowing electric vehicles to discharge electricity back to the grid, thereby promoting 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 expressed as: In the formula, , , are the total exchange power between the electric vehicle charging station and the power grid, the power obtained from the power grid, and the power discharged to the power grid; P EV The power required to charge each electric vehicle represents the energy required to replenish the electric vehicle battery; N G2V is the number of EVCS operating in G2V mode, where electric energy is transmitted from the grid to charge EVs; N V2G is the number of electric vehicles capable of V2G operation.

[0028] Since electric vehicles can feed stored energy back into the grid when it is most needed, this capability can significantly reduce peak load demands on the grid. The implementation of V2G technology not only improves the overall efficiency of energy distribution, but also provides financial incentives for electric vehicle owners as they can be compensated for supplying power to the grid. In addition, V2G operations can be strategically adjusted to accommodate periods of high electricity demand or low renewable energy generation, further supporting grid stability and reducing reliance on fossil fuel generation. This integration is critical to fostering a sustainable energy ecosystem and maximizing the benefits of electric vehicles.

[0029] The efficient operation of EVCS and RDG is crucial to the reliability and energy optimization of the system. The main operation strategies include: (1) Energy consumption priority: RDG directly supplies power to EVCS to meet its real-time demand, and the remaining power is directly supplied to BESS; (2) Power surplus 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 renewable energy generation: When RDG output is insufficient, the system gives priority to using the power supply of BESS. If both are insufficient, additional power is provided from the grid to ensure continuous operation of EVCS; (4) Dynamic adjustment: Continuously monitor RDG output or adaptive adjustment of BESS power storage to effectively match energy supply and EVCS demand; (5) Grid integration: Integration with the energy market enables participation in electricity sales and grid support services, improving overall system efficiency and reliability. These strategies ensure the optimized operation, grid integration and resilience of the hybrid energy system, and support the transition to sustainable energy solutions.

[0030] In addition, the introduction of any new components in the radial distribution network, especially those involving active and reactive power generation, will significantly affect the voltage stability index (VSI), causing its value to change significantly. The voltage stability of each bus depends on the magnitude 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: Where VSI(j) is the stability of the radial distribution network.

[0031] Reducing total network loss is a key goal of the design of intelligent radial distribution network. Loss is an important indicator of grid performance. The loss will be calculated hourly, and the total amount will be added to determine the daily loss, thereby ensuring a comprehensive assessment of the energy efficiency in the grid within 24 hours. The power loss of the radial distribution network is as follows: In the formula, is the power loss and of the radial distribution network.

[0032] In one embodiment, determining the gas emission of the radial distribution network by the released power of the new energy power generation equipment includes: Using the active output power of the renewable distributed power generation equipment in the operating state as the first released power, and using the active output power of the battery energy storage equipment in the discharge mode as the second released power; The active output power of the electric vehicle charging station in the vehicle-to-grid mode is used as the third released power, and the first released power, the second released power and the third released power are combined to obtain the released power of the new energy power generation equipment; The gas emission is quantified based on the grid demand of the radial distribution network and the released power of the new energy power generation equipment.

[0033] Specifically, the present invention takes into account gas emissions (carbon dioxide emissions) when optimizing the configuration of new energy power generation equipment in the radial distribution network. The active power of the renewable distributed power generation equipment in the operating state is used as the first release power, the active power of the battery energy storage equipment in the discharge mode is used as the second release power, and the active power of the electric vehicle charging station in the vehicle-to-grid (V2G) mode is used as the third release power. The three active powers are combined to obtain the total release power of the new energy power generation equipment to calculate the gas emissions with the demand of the radial distribution network for the power grid, which is expressed by the following formula: In the formula, It is the gas emission of the radial distribution network, which is also the carbon dioxide emission; is the emission related to the actual power generation per unit hour of the grid, which is 0.910 TonCO2 / MWh 2 ; N is the node in the radial distribution network; P grid is the electricity purchased from the grid, i.e., the grid demand; P RDG is the power generated by RDG, that is, active power; t is time.

[0034] The present invention combines the power outputs of renewable distributed power generation equipment, battery energy storage equipment and electric vehicle charging stations to more flexibly meet the needs of the power grid, thereby improving energy utilization efficiency and significantly reducing gas emissions; it helps to promote the development and application of renewable energy, reduce dependence on traditional fossil energy, and thus promote sustainable development; the power outputs of battery energy storage equipment and electric vehicle charging stations in specific modes can provide additional regulation capabilities, help improve the stability and reliability of the power grid, and provide a feasible path for energy transformation. By integrating a variety of new energy power generation equipment and energy storage technologies, the transition from traditional energy to new energy can be gradually realized.

[0035] S3, constructing a multi-objective function of the radial distribution network based on the stability, the power loss and the gas emission; In one embodiment, step S3 includes: Constructing a first objective function through the stability of the radial distribution network before and after the installation of the renewable distributed generation equipment; Constructing a second objective function according to the sum of daily power losses before and after the radial distribution network is installed with the new energy power generation equipment; Constructing a third objective function based on the gas emissions before and after the radial distribution network is installed with the new energy power generation equipment; The first objective function, the second objective function and the third objective function are weighted to obtain the multi-objective function.

[0036] Specifically, the strategic allocation of individual and combined devices in radial distribution networks can profoundly affect system performance by reducing power loss, improving stability margins, and reducing emissions. In a highly competitive market environment, the goal of equipment owners is to obtain financial gains, which motivates them to invest in cutting-edge technologies. Therefore, the configuration of equipment needs to be carefully planned to maximize technical performance and economic benefits. The present invention takes these factors into account, and the formulation of a multi-objective function combines technical factors such as power loss and stability with resource factors such as CO2 emissions to achieve optimal system performance.

[0037] The present invention divides the stability of the radial distribution network after the installation of renewable distributed power generation equipment by the stability before the installation as the first objective function, divides the daily system loss of the radial distribution network after the installation of the new energy power generation equipment by the daily system loss before the installation as the second objective function, divides the gas emission of the radial distribution network after the installation of the new energy power generation equipment by the gas emission before the installation as the third objective function, and uses an adjustable constant to weight the first objective function, the second objective function and the third objective function to obtain a multi-objective function, which is expressed by the following formula: Wherein, OF1, OF2, OF3 are the first objective function, the second objective function, and the third objective function respectively; α1, α2, and α3 represent adjustable constants used to measure the influence of each factor on the overall multi-objective function, allowing optimization according to specific criteria.

[0038] The present invention constructs a multi-objective function by comprehensively considering the changes in stability, power loss, gas emissions and other aspects of the radial distribution network before and after the installation of new energy power generation equipment. It can more comprehensively evaluate the performance of the power grid, help to discover weak links and potential problems in the power grid, and provide strong support for optimizing resource allocation. By paying attention to the changes in gas emissions, it can promote the application and promotion of new energy power generation equipment, thereby promoting energy conservation, emission reduction and environmental protection, and providing a scientific basis for power grid planning and decision-making.

[0039] S4, obtaining the configuration constraint conditions of the new energy power generation equipment, and solving the multi-objective function through a preset optimization algorithm to obtain an optimal configuration scheme for the new energy power generation equipment; In one embodiment, step S4 includes: Obtaining the configuration constraints of the new energy power generation equipment and the constraint boundaries of the multi-objective function, and initializing the number of honey badgers based on the configuration constraints and the constraint boundaries; a 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 to obtain the optimal honey badger and its position; The fitness of each of the honey badgers' updated positions is evaluated, and the mining phase and the honey collecting 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 an optimized configuration scheme for the new energy power generation equipment.

[0040] Specifically, the configuration constraints of the renewable energy generation equipment include the number of RDGs to be installed in the grid, as well as the overall size, dimension, and maximum iteration limit of the optimization.

[0041] 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 from demand center and other factors. The initial system conditions of the radial distribution network include voltage level, load demand, state of charge (SOC) and operating status of the equipment. RDG is assigned a specific actual power capacity, while EVCS operates within the defined SOC limit, charging and discharging efficiency and battery capacity limit. These factors together ensure the effective integration and operation of energy resources within the radial distribution network, enhance system stability and optimize overall performance. The constraint boundaries of multiple objective functions include: The power balance constraint, which maintains the balance between the total power loss, demand and supply of the radial distribution network in different operation modes within 24 hours, is as follows: Where P L , P TL is the power loss of the radial distribution network in G2V and V2G modes; P D The power demand of radial distribution network.

[0042] The voltage amplitude constraint is as follows: In the formula, and are the minimum and maximum limits of the voltage amplitude at bus j.

[0043] The actual power compensation is as follows: In the formula, and is the minimum and maximum actual power generated by RDG at bus j.

[0044] The SOC limit of EVCS is crucial to maintain the battery health of electric vehicles connected to EVCS and ensure efficient operation, as shown in the following formula: In the formula, SOC min and SOC max The minimum maximum limit of the electric vehicle SOC at each charging station within 24 hours at bus j.

[0045] The improved honey badger optimization algorithm used in the present invention utilizes the hunting behavior of honey badgers, that is, honey badgers use their keen senses to locate prey and dig in the soil to capture prey, and imitates this process for global optimization. In the process of searching for food, honey badgers can dig 50 holes a day, with a distance of at least 40 kilometers between the holes, showing extraordinary perseverance. Honey badgers work hard to find beehives. They cooperate with honeybirds that can find beehives but cannot get close to honey. In this symbiotic relationship, the birds guide the badgers into the beehives. The algorithm incorporates these behaviors into its method, respectively called the digging stage and the honey stage, to enhance its optimization ability.

[0046] Like other population-based algorithms, this algorithm starts with initializing a random population (including population size, algorithm parameters, initial speed of each honey badger, etc.), so that the position and speed of the honey badgers are within the above constraints during the optimization process. Mathematically: In the formula, 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 honey badger.

[0047] The movement of each honey badger is affected by the intensity of the food source's odor. This odor intensity Jx is calculated based on the concentration of prey and the distance between the honey badger and the food source. Using the inverse square law, the odor intensity J x It can be expressed as: D x =(f s -f x ) L=(f x -f x-1 ) 2 Where L is the prey location or concentration intensity; variable µ2 is a random number from 0 to 1, and D x For food source s and the xth honey badger f x The distance between.

[0048] Then, the fitness of each honey badger is evaluated through a multi-objective function to reflect the pros and cons of the configuration scheme in satisfying the constraints and optimizing the objectives. In order to ensure a smooth transition from exploration to exploitation and effectively manage fluctuations over time, a decreasing density factor is introduced in this process based on the current number of iterations. It is used to adjust the exploration and utilization capabilities of the algorithm. As the number of iterations increases, the density factor gradually decreases, so that the algorithm shifts from extensive exploration to sophisticated utilization. The decreasing density factor is expressed by the following formula: Where β is the decreasing density factor; a is the current number of iterations; A is the maximum number of iterations; κ is a constant, usually set to 2 by default to ensure that the algorithm avoids falling into the local optimum.

[0049] The position update formula of the original honey badger optimization algorithm is improved by using the decreasing density factor, and a new update formula is used based on each fitness value to perform the mining stage and the honey collection stage to update the position of each honey badger and obtain the optimal honey badger and its position. In other words, the mining stage uses the improved position update formula to allow the honey badger to randomly explore new configuration schemes in the search space; the improved position update formula is expressed as follows: In the formula, f n 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 honey badger, and the default value is usually set to 6; the flag FL is used to modify the search direction, which is easy to adjust to promote effective exploration of the search space.

[0050] The digging behavior of honey badgers is strongly affected by multiple factors: the intensity of the prey, the distance to the prey, and the time-dependent factor β that affects its search. During the digging process using the improved position update formula for position updating, the honey badger may also encounter interference marked as "FL", which helps it locate the prey more effectively.

[0051] The honey collection stage simulates the behavior of the honey badger following the honey bird to locate the beehive, so that the honey badger conducts a fine search based on the neighborhood of the current optimal configuration to find a better configuration, which is expressed by the following formula: Wherein, µ3, µ4, µ5, µ6, and µ7 are all random numbers from 0 to 1.

[0052] The honey badger uses a strategy influenced by random factors represented by µ3 to µ7 to explore its environment and collect honey using the above formula. x Guided by the , the honey badger will focus its search on the location of previously discovered prey. p Nearby, this behavior is further shaped by the time-dependent search pattern represented by β. These two stages are coordinated by the decreasing density factor and fitness to balance the global search ability and local optimization ability of the algorithm.

[0053] According to the results of the excavation phase and the honey collection phase, the position of each honey badger (i.e., the configuration plan) is updated, and the fitness of the updated honey badger position is evaluated, and the excavation phase and the honey collection phase are iteratively executed according to the evaluation results, and this process is repeated until the preset number of iterations is reached, so that the optimal honey badger position outputted at the end is used as the optimal configuration plan for the new energy power generation equipment. The present invention introduces a decreasing density factor in the honey badger position update process to enhance the exploration of the search space, uses the flag FL to adjust the search direction, promotes thorough exploration, and maximizes the chance of identifying the optimal solution; by balancing the global search and local optimization capabilities, it is possible to efficiently find the optimal configuration plan for the new energy power generation equipment under complex constraints; by outputting the optimal configuration plan, it provides a scientific basis and decision support for the planning, design, and operation of the new energy power generation equipment.

[0054] In one embodiment, step S4 further includes: Dividing the area to which the radial distribution network belongs according to the purpose to obtain a plurality of basic areas, and obtaining the penetration rate of electric vehicle load in each of the basic areas; Select any basic area as the target area, obtain the configuration constraint conditions of the new energy power generation equipment and the constraint boundary of the multi-objective function as the constraint conditions of the target area, and initialize the particle swarm based on the constraint conditions; each particle represents an optimal configuration scheme of the new energy power generation equipment; quantifying the fitness value of each particle according to the multi-objective function, and using the permeability of the target area as an adjustment factor to adjust the original position and speed update formula of the particle swarm algorithm to obtain an improved update formula; Based on the improved update formula, the personal optimal position and the global optimal position of each particle are updated by each fitness value, and the fitness value quantization step and the optimal position update step are repeatedly performed until a preset number of repetitions is reached, and the final global optimal position is output as an optimized configuration scheme for the new energy power generation equipment in the target area; The target area is updated, and the particle swarm initialization step, the fitness value quantization step, and the optimal position update step are repeatedly executed based on the constraint conditions of the updated target area until a preset number of repetitions is reached, and a final global optimal position is output as an optimization configuration scheme for the new energy power generation equipment in the updated target area; The optimal configuration schemes for the new energy power generation equipment in each of the target areas are combined to obtain an optimal configuration scheme for the new energy power generation equipment.

[0055] Specifically, the present invention takes into account the impact of electric vehicle load penetration on the configuration scheme when optimizing the layout of new energy power generation equipment in the radial distribution network, and obtains an overall optimized configuration scheme by dividing the basic area and iterative optimization, including: Divide the area to which the radial distribution network belongs according to its purpose to obtain several basic areas, such as industrial, commercial, residential, etc., and obtain the penetration rate of electric vehicle load in each basic area as an important parameter in the subsequent optimization process; the penetration rate is determined based on historical data, prediction models, etc.; Then, a basic area is selected as the target area, and the configuration constraints of the new energy power generation equipment in the target area and the constraint boundaries of the multi-objective function are obtained. Then, the particle swarm is initialized based on the constraints of the target area to generate an initial solution, and each particle represents an optimal configuration solution for a new energy power generation equipment. 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 values. During the updating process, the electric vehicle load penetration rate of the target area is used as an adjustment factor to adjust the original position and speed update formula of the particle swarm algorithm to obtain an improved update formula, which makes the algorithm more suitable for basic areas with different penetration rates; the improved update formula is expressed by the following formula: In the formula, , are the velocities of particle i in the tth and t+1th generations in the dth dimension; w is the inertia weight, which controls the tendency of the particle to maintain its previous velocity; c1 and c2 are learning factors, which represent the weights of the particle learning from its own historical optimal position and the global optimal position respectively; r1 and r2 are random numbers between [0, 1], which are used to increase the randomness of the search; is the historical optimal position of particle i in the tth generation in the dth dimension; is the position of particle i in the tth generation in the dth dimension; is the global optimal position of the tth generation; λ is an adjustment coefficient used to control the influence of permeability on speed update; PR is the electric vehicle load penetration rate in the target area; r3 is a random number between [0, 1], which is used to randomize the influence of permeability to avoid the algorithm falling into the local optimum; the role of r3-0.5 is to make the influence of permeability fluctuate between positive and negative, increasing the diversity of search.

[0056] Using the improved speed update formula, the personal optimal position and the global optimal position of each particle are updated through the fitness value of each particle, and the fitness value quantization step and the optimal position update step are repeated until the preset number of repetitions is reached, and then the target area is updated to perform iterative optimization in the above manner until all basic areas are optimized or the preset total number of repetitions is reached. Finally, the optimization configuration schemes of the new energy power generation equipment in each target area are combined to obtain the overall optimization configuration scheme of the new energy power generation equipment.

[0057] The present invention uses the electric vehicle load penetration rate as a regulating factor to make the optimization configuration scheme more in line with actual needs and future development trends; through the division of basic areas and iterative optimization, the optimal configuration of each area is achieved, and overall coordination is achieved through the combination of final schemes; the improved particle swarm optimization algorithm can be adaptively adjusted according to the constraints of different areas and the electric vehicle load penetration rate, thereby improving the applicability and accuracy of the algorithm; through iterative optimization and global search strategies, a better configuration scheme for new energy power generation equipment can be quickly found, thereby improving the optimization efficiency.

[0058] In one embodiment, the present invention adopts the IEEE 69 radial distribution network modified by three different radial distribution networks, which includes three interconnected radial distribution networks serving residential, commercial and industrial users, each radial distribution network can operate independently, and the active and reactive power demand curves of the distribution network are as follows: Figure 4 As shown in the figure, in order to capture the expected daily load changes, the above method is used to simulate in MATLAB to solve the problem of effective deployment of EVCS, specifically managing real power and reactive power within a hybrid energy framework. The test system used in this framework adopts the IEEE 69 bus system as its basic model. The test system integrates various types of RDG units and BESS, and combines various load models to reflect real scenarios. The operating parameters of the system are: real power demand is 3.725 MW, reactive power demand is 2.095 MVAr, and it operates at a line voltage of 12.66 kV to calculate the real and reactive losses and bus voltage values ​​without compensation measures.

[0059] This example uses the improved Honey Badger optimization algorithm to solve the configuration problem of electric vehicles in the radial distribution network. The solution involves managing 100 electric vehicles distributed in residential (10 electric vehicles), commercial (70 electric vehicles) and industrial (20 electric vehicles). The charging rate of each electric vehicle is 20 kW. Three EVCS with multiple charging points have been deployed. The location of various types of RDG devices is optimized according to the regional requirements in each radial distribution network. Among them, the load of electric vehicles is dynamically affected by the number of electric vehicles in G2V and V2G modes, and their charging rate and discharge rate need to be considered. In order to better optimize the objective function, each radial distribution network includes at least one RDG and EVCS, and the improved Honey Badger optimization algorithm is used for RDG capacity planning and point layout. Among them, the BESS is located at the same location as the RDG unit in the radial distribution network, and the size of the BESS depends on the capacity of the RDG in the radial distribution network.

[0060] This example adopts the optimization configuration method of the new energy power generation equipment described in the present invention to reduce the impact of EVCS in the radial distribution network, and optimizes the multi-objective function at the same time. It uses V2G technology to promote two-way power transmission between EVCS and intelligent radial distribution network, thereby reducing the dependence on external power grid procurement. The model and optimization framework of the radial distribution network are explained during the 24-hour operation. The model and optimization framework are selected to consider the uncertainty of RDG units and the load fluctuations affecting BESS. Considering the impact of EVCS on active power loss and system parameters, the efficient use of EVCS in the radial distribution network requires layout at key bus positions. The location of RDG and EVCS can reduce these losses. Before determining the locations of EVCS and RDG, the baseline losses in the system are determined based on the power flow analysis of BFS. The EVCS is allocated to the 6th, 30th and 39th buses in the smart radial distribution network. Considering the characteristics and availability of each type of distributed energy, the locations of the RDG units are optimized by HBOA and arranged at the 18th (wind distributed generation), 29th (solar distributed generation) and 61st (biomass distributed generation) buses. The BESS device is located at the same location as the RDG device to store the surplus energy during the off-peak EVCS period and discharge it during peak hours to support the operation of the radial distribution network.

[0061] In the evolving field of power distribution systems, smart microgrids play a key role in supporting radial distribution networks by providing essential ancillary services. Smart microgrids (also radial distribution networks) are essential in meeting real and reactive power demands, enhancing voltage stability, minimizing power losses and reducing CO2 emissions; while renewable energy generation equipment, such as solar, wind and bio-based distributed generation systems, show unique daily output patterns. Solar distributed generation peaks at noon and gradually weakens after sunset, while wind distributed generation varies unpredictably due to wind conditions. Biomass distributed generation provides stable output and relies on a stable biomass supply throughout the day, while RDG power generation power curves are as follows: Figure 5 As shown, the EVCS power demand / generation is as follows Figure 6 As shown in the figure, the actual power generation of RDG and EVCS within 24 hours is detailed. This study integrates EVCS to analyze its impact on RDG operation. During the RDG peak period (11 am to 3 pm), EVCS uses the remaining RDG power to charge (G2V mode). On the contrary, during the peak demand period (6 pm to 10 pm), EVCS discharges (V2G mode) to help stabilize the grid through electric vehicle power supply.

[0062] In the embodiment of the present application, based on the problem of how to effectively integrate electric vehicle charging stations into the existing distribution network to reduce the negative impact on the power grid, a method for optimizing the configuration of new energy power generation equipment is designed, which realizes obtaining the basic parameters of the radial distribution network, and performing power flow analysis on the radial distribution network based on the basic parameters to obtain the power data and power loss data of each node in the radial distribution network; determining the stability and the power loss sum 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 through the released power of the new energy power generation equipment; constructing a multi-objective function of the radial distribution network based on the stability, the power loss sum and the gas emissions; obtaining the configuration constraints of the new energy power generation equipment, and solving the multi-objective function through a preset optimization algorithm to obtain a technical solution for the optimal configuration scheme of the new energy power generation equipment; so as to reduce the construction and operation costs of the distribution network and improve economic benefits.

[0063] It should be noted that although the steps in the above flowchart are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders.

[0064] In another embodiment, if Figure 7As shown, the second aspect of the present invention provides an optimization configuration system for new energy power generation equipment, comprising: The power flow analysis module 10 is used to obtain 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; A data processing module 20, for 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 emission of the radial distribution network by the power released by the new energy power generation equipment; A function construction module 30, configured to construct a multi-objective function of the radial distribution network based on the stability, the power loss and the gas emission; The solution generation module 40 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.

[0065] It should be noted that each module in the above-mentioned optimization configuration system of a new energy power generation equipment can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules. For the specific definition of the optimization configuration system of a new energy power generation equipment, please refer to the definition of the optimization configuration method of a new energy power generation equipment mentioned above. The two have the same functions and effects, which will not be repeated here.

[0066] A third aspect of the present invention provides an electronic device, the electronic device comprising: processor, memory, and bus; The bus is used to connect the processor and the memory; The memory is used to store operation instructions; The processor is used to call the operation instruction, and the executable instruction enables the processor to perform operations corresponding to the optimization configuration method of a new energy power generation equipment as shown in the first aspect of the present application.

[0067] In an alternative embodiment, an electronic device is provided, such as Figure 8 As shown, Figure 8The electronic device 5000 shown includes: a processor 5001 and a memory 5003. The processor 5001 and the memory 5003 are connected, such as through a bus 5002. Optionally, the electronic device 5000 may also include a transceiver 5004. It should be noted that in actual applications, the transceiver 5004 is not limited to one, and the structure of the electronic device 5000 does not constitute a limitation on the embodiments of the present application.

[0068] Processor 5001 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA or other programmable logic device, a transistor logic device, a hardware component or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. Processor 5001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0069] The bus 5002 may include a path to transmit information between the above components. The bus 5002 may be a PCI bus or an EISA bus, etc. The bus 5002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 8 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0070] The memory 5003 may be a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, or an EEPROM, a CD-ROM or other optical disk storage, an optical disk storage (including a compressed optical disk, a laser disk, an optical disk, a digital versatile disk, a Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0071] The memory 5003 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 5001. The processor 5001 is used to execute the application code stored in the memory 5003 to implement the content shown in any of the above method embodiments.

[0072] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc.

[0073] The fourth aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the method for optimizing the configuration of a new energy power generation equipment shown in the first aspect of the present application is implemented.

[0074] Another embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0075] In addition, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented.

[0076] 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 a new energy power generation device. The method obtains the basic parameters of a radial distribution network, and performs 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; determines the stability and the power loss sum of the radial distribution network according to the power data and the power loss data, and determines the gas emission of the radial distribution network by the released power of the new energy power generation device; constructs a multi-objective function of the radial distribution network based on the stability, the power loss sum and the gas emission; obtains the configuration constraint conditions of the new energy power generation device, and solves the multi-objective function by a preset optimization algorithm to obtain an optimized configuration scheme for the new energy power generation device, so as to reduce the construction and operation costs of the distribution network and improve the economic benefits.

[0077] 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 technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not 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.

[0078] The above-mentioned embodiments only express several preferred implementation modes of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in the technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be based on the protection scope of the claims.

Claims

1. A method for optimizing the configuration of new energy power generation equipment, characterized in that: include: Acquire 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; Determine the stability and power loss 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 by 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; 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 optimal configuration scheme for the new energy power generation equipment.

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

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

4. The optimization configuration method of a 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; Wherein, determining the gas emission of the radial distribution network by the released power of the new energy power generation equipment includes: Using the active output power of the renewable distributed power generation equipment in the operating state as the first released power, and using the active output power of the battery energy storage equipment in the discharge mode as the second released power; The active output power of the electric vehicle charging station in the vehicle-to-grid mode is used as the third released power, and the first released power, the second released power and the third released power are combined to obtain the released power of the new energy power generation equipment; The gas emission is quantified based on the grid demand of the radial distribution network and the released power of the new energy power generation equipment.

5. The optimization configuration method of a 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 through the stability of the radial distribution network before and after the installation of the renewable distributed generation equipment; Constructing a second objective function according to the sum of daily power losses before and after the radial distribution network is installed with the new energy power generation equipment; Constructing a third objective function based on the gas emissions before and after the radial distribution network is installed with the new energy power generation equipment; The first objective function, the second objective function and the third objective function are weighted to obtain the multi-objective function.

6. The optimization configuration method of a new energy power generation equipment according to claim 1, characterized in that: The obtaining of the configuration constraint conditions of the new energy power generation equipment and solving the multi-objective function by a preset optimization algorithm to obtain an optimal configuration scheme for the new energy power generation equipment includes: Obtaining the configuration constraints of the new energy power generation equipment and the constraint boundaries of the multi-objective function, and initializing the number of honey badgers based on the configuration constraints and the constraint boundaries; a 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 to obtain the optimal honey badger and its position; The fitness of each of the honey badgers' updated positions is evaluated, and the mining phase and the honey collecting 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 an optimized configuration scheme for the new energy power generation equipment.

7. The method for optimizing configuration of new energy power generation equipment according to claim 1, characterized in that: The obtaining of the configuration constraint conditions of the new energy power generation equipment and solving the multi-objective function by a preset optimization algorithm to obtain an optimal configuration scheme for the new energy power generation equipment also includes: Dividing the area to which the radial distribution network belongs according to the purpose to obtain a plurality of basic areas, and obtaining the penetration rate of electric vehicle load in each of the basic areas; Select any basic area as the target area, obtain the configuration constraint conditions of the new energy power generation equipment and the constraint boundary of the multi-objective function as the constraint conditions of the target area, and initialize the particle swarm based on the constraint conditions; each particle represents an optimal configuration scheme of the new energy power generation equipment; quantifying the fitness value of each particle according to the multi-objective function, and using the permeability of the target area as an adjustment factor to adjust the original position and speed update formula of the particle swarm algorithm to obtain an improved update formula; Based on the improved update formula, the personal optimal position and the global optimal position of each particle are updated by each fitness value, and the fitness value quantization step and the optimal position update step are repeatedly performed until a preset number of repetitions is reached, and the final global optimal position is output as an optimized configuration scheme for the new energy power generation equipment in the target area; The target area is updated, and the particle swarm initialization step, the fitness value quantization step, and the optimal position update step are repeatedly executed based on the constraint conditions of the updated target area until a preset number of repetitions is reached, and a final global optimal position is output as an optimization configuration scheme for the new energy power generation equipment in the updated target area; The optimal configuration schemes for the new energy power generation equipment in each of the target areas are combined to obtain an optimal configuration scheme for the new energy power generation equipment.

8. An optimization configuration system for new energy power generation equipment, characterized in that: include: A power flow analysis module, used to obtain 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; A data processing module, used to determine the stability and power loss 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 released power of the new energy power generation equipment; A function construction module, used for constructing a multi-objective function of the radial distribution network based on the stability, the power loss and the gas emission; The 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.

9. 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 as described in any one of claims 1 to 7.

10. 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 as described in any one of claims 1 to 7 is implemented.

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