Method and device for improving bearing capacity of electric vehicle in power distribution network and electronic equipment

Abstract

The invention discloses a power distribution network electric vehicle bearing capacity improving method and device and electronic equipment. The method comprises the following steps: determining an evaluation index for evaluating the bearing capacity of the electric vehicle in the power distribution network, and constructing an evaluation system; based on the region types corresponding to the one or more regions in the power distribution network, establishing charging load models of the electric vehicles corresponding to the one or more regions in the power distribution network; constructing a multi-objective optimization model of the power distribution network based on the evaluation system and the charging load models of the electric vehicles respectively corresponding to the one or more areas in the power distribution network; and based on the multi-objective optimization model, obtaining charging pile configurations corresponding to the one or more areas and capable of improving the bearing capacity of the electric vehicle of the power distribution network. The technical problems that the bearing capacity of the power distribution network is impacted due to large-scale charging pile access, but the bearing capacity of the power distribution network is not comprehensively considered in charging pile configuration are solved.

Description

Technical Field

[0001] The present invention relates to the field of electric power, and in particular to a method, a device and an electronic device for improving the carrying capacity of electric vehicles in a distribution network. Background Art

[0002] With the popularity of electric vehicles (EVs), the access of large-scale charging piles has brought new challenges to the carrying capacity of existing distribution networks. Traditional distribution network planning and operation methods are difficult to cope with the load impact brought by electric vehicles. The distribution network charging pile configuration method in related technologies cannot improve the distribution network's carrying capacity for electric vehicles.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present invention provide a method, device and electronic device for improving the carrying capacity of electric vehicles in a distribution network, so as to at least solve the technical problem that the access of large-scale charging piles brings an impact on the carrying capacity of the distribution network, but the configuration of the charging piles does not comprehensively consider the carrying capacity of the distribution network.

[0005] According to one aspect of an embodiment of the present invention, a method for improving the carrying capacity of electric vehicles in a distribution network is provided, comprising: determining an evaluation index for evaluating the carrying capacity of electric vehicles in the distribution network, and constructing an evaluation system, wherein the distribution network includes one or more areas, and the evaluation system includes: a safety evaluation index, a stability evaluation index, and a quality evaluation index; based on the area types corresponding to the one or more areas in the distribution network, a charging load model of the electric vehicles corresponding to the one or more areas in the distribution network is established; based on the evaluation system and the charging load model of the electric vehicles corresponding to the one or more areas in the distribution network, a multi-objective optimization model of the distribution network is constructed; based on the multi-objective optimization model, a configuration of charging piles corresponding to the one or more areas that can improve the carrying capacity of electric vehicles in the distribution network is obtained.

[0006] Optionally, the evaluation system includes: safety evaluation indicators, stability evaluation indicators, and quality evaluation indicators, wherein: the safety evaluation indicators include at least one of the following: distribution transformer heavy load rate, distribution transformer light load rate, line heavy load rate, line light load rate; the stability evaluation indicators include at least one of the following: net load deviation rate, voltage deviation rate, voltage fluctuation rate, power flow imbalance; the quality evaluation indicators include at least one of the following: distribution network acceptance capacity, electric vehicle instantaneous network penetration rate and average network penetration rate.

[0007] Optionally, the establishing of the charging load model of the electric vehicles corresponding to the one or more areas in the distribution network based on the area types corresponding to the one or more areas in the distribution network includes: calculating the charging demand quantity of the electric vehicles in the one or more areas in each time period based on the area types corresponding to the one or more areas, according to the charging demand quantity distribution curve of the electric vehicles corresponding to the area types, and the peak parking quantity of the electric vehicles in the one or more areas; calculating the selection probability of each type of charging pile in the one or more areas in each time period based on the area types corresponding to the one or more areas, according to the residence time distribution curve of the electric vehicles corresponding to the area types, and the current charge state of each electric vehicle in the one or more areas; obtaining the demand quantity of each type of charging pile for the electric vehicles in the one or more areas in each time period based on the charging demand quantity and the selection probability of each type of charging pile in the area; establishing the charging load model of the electric vehicles in the one or more areas based on the demand quantity of each type of charging pile and the rated power of each type of charging pile.

[0008] Optionally, the multi-objective optimization model of the distribution network is constructed based on the evaluation system and the charging load model of the electric vehicles corresponding to the one or more areas in the distribution network, including: determining one or more objective functions based on the evaluation system; determining one or more constraint conditions based on the charging load model of the electric vehicles corresponding to the one or more areas in the distribution network; and constructing the multi-objective optimization model of the distribution network based on the one or more objective functions and the one or more constraint conditions.

[0009] Optionally, determining one or more objective functions based on the evaluation system includes: determining a first objective function for maximizing the electric vehicle carrying capacity of the distribution network based on the evaluation system; determining a second objective function for minimizing the annualized economic cost of the distribution network based on the annualized economic cost of the distribution network; and determining the one or more objective functions based on the first objective function and the second objective function.

[0010] Optionally, the one or more constraints include at least one of the following: system flow constraint, node power balance constraint, voltage amplitude constraint, branch current constraint, reactive compensation equipment output constraint, wind and solar output constraint, charging pile quantity constraint in the charging station, node charging power constraint, and generator output constraint.

[0011] Optionally, after obtaining the charging pile configurations corresponding to the one or more areas that can improve the electric vehicle carrying capacity of the distribution network based on the multi-objective optimization model, it also includes: calculating the evaluation value corresponding to the evaluation index according to the charging pile configuration; and obtaining the electric vehicle carrying capacity of the distribution network according to the evaluation value.

[0012] According to another aspect of the present invention, there is provided a device for configuring charging piles in a distribution network, comprising: a determination module for determining evaluation indicators for evaluating the electric vehicle carrying capacity of the distribution network and constructing an evaluation system, wherein the distribution network comprises one or more regions, and the evaluation system comprises: a safety evaluation indicator, a stability evaluation indicator, and a quality evaluation indicator; an establishment module for establishing, based on the region types respectively corresponding to the one or more regions in the distribution network, a charging load model of the electric vehicle respectively corresponding to the one or more regions in the distribution network; a construction module for constructing a multi-objective optimization model of the distribution network based on the evaluation system and the charging load model of the electric vehicle respectively corresponding to the one or more regions in the distribution network; and a configuration module for obtaining the charging pile configurations respectively corresponding to the one or more regions in the distribution network based on the multi-objective optimization model.

[0013] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned methods for improving the carrying capacity of electric vehicles in the distribution network.

[0014] According to another aspect of the present invention, there is provided an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the method for improving the carrying capacity of electric vehicles in a distribution network described above is executed when the program is running.

[0015] According to another aspect of the present invention, there is provided a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of any one of the methods for improving the carrying capacity of electric vehicles in a distribution network are implemented.

[0016] In the embodiment of the present invention, a method of reasonably deploying multi-power charging pile configuration is adopted. Through multi-power charging pile planning, the purpose of improving the carrying capacity of the distribution network under the existing distribution network framework is achieved, thereby achieving the technical effect of combining the improvement of the carrying capacity of the distribution network with the optimization planning of charging piles, and further solving the impact of large-scale charging pile access on the carrying capacity of the distribution network. However, the configuration of charging piles does not comprehensively consider the technical problem of the carrying capacity of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0018] Figure 1 is a flow chart of a method for improving the carrying capacity of electric vehicles in a distribution network according to an embodiment of the present invention;

[0019] Figure 2 is a schematic diagram of a method for improving the electric vehicle carrying capacity of a distribution network considering multi-power charging pile planning according to an optional implementation mode of the present invention;

[0020] Figure 3 is a schematic diagram of a typical day electric vehicle charging load modeling method according to an optional embodiment of the present invention;

[0021] Figure 4 is a schematic diagram of a grid structure according to an optional embodiment of the present invention;

[0022] Figure 5 is a schematic diagram of a power distribution network coupled with actual geography according to an optional embodiment of the present invention;

[0023] Figure 6 is a schematic diagram of a distribution curve of the number of electric vehicle charging demands corresponding to each area type according to an optional implementation manner of the present invention;

[0024] Figure 7 is a schematic diagram of a distribution curve of electric vehicle residence time corresponding to each area type according to an optional implementation manner of the present invention;

[0025] Figure 8 is a schematic diagram of charging pile planning according to three schemes of optional implementation modes of the present invention;

[0026] Fig. 9 It is a schematic diagram comparing the total number of charging piles installed in the distribution network according to three optional implementation schemes of the present invention;

[0027] Fig.10 It is a radar chart of the distribution of indicators in each dimension of three solutions according to the optional implementation mode of the present invention;

[0028] Fig.11 The present invention is a structural block diagram of a device for improving the carrying capacity of electric vehicles in a distribution network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] First, some nouns or terms that appear in the description of the embodiments of the present application are subject to the following explanations:

[0032] Electric Vehicle (EV): refers to a vehicle that uses electricity as its main power source. Unlike traditional internal combustion engine vehicles, electric vehicles store electricity through on-board batteries and drive the vehicle through electric motors. Electric vehicles include battery electric vehicles (BEV), plug-in hybrid electric vehicles (PHEV) and fuel cell electric vehicles (FCEV).

[0033] Current state of charge (SOC): is a parameter that describes the ratio of the current remaining power of the battery to the total capacity of the battery, usually expressed as a percentage. In the field of electric vehicles, SOC is very critical information because it directly affects the vehicle's range and charging requirements.

[0034] With the increase in the penetration rate of new energy and the development of electric energy substitution technology, the scale growth of electric vehicles, electric heating and other power loads has gradually transformed the traditional distribution network into a more complex new distribution network system. However, the development of the distribution network has brought not only opportunities but also challenges, especially new problems in the management of the load side.

[0035] The transformation of the global energy structure and the promotion of the concept of sustainable development have made electric vehicles an important part of the green transformation of various countries. Countries have promoted the large-scale popularization of electric vehicles to reduce dependence on fossil fuels and reduce greenhouse gas emissions to achieve carbon neutrality goals. Electric vehicles not only play a key role in the transportation field as a representative of clean energy applications, but their bidirectional charging and discharging characteristics also make them an important distributed energy storage unit in the future smart energy system, forming a two-way interaction with the power system. This makes the role of electric vehicles in the energy system more important and also puts forward new requirements for the distribution network.

[0036] The rapid popularization of electric vehicles has brought new challenges to the carrying capacity of existing distribution networks. Especially when multiple electric vehicles are connected to charge at the same time, the distribution network may be overloaded and the power supply pressure may increase sharply, thus affecting the safety and stability of the power grid. Traditional distribution network planning and operation methods are difficult to cope with the load impact brought by electric vehicles, especially the charging demand during peak hours, which will cause local overload, voltage fluctuations and other operating risks in the distribution network. Therefore, how to improve the distribution network's carrying capacity for electric vehicle loads through scientific and reasonable charging pile planning has become an urgent problem that needs to be solved.

[0037] The deployment of charging piles in related technologies usually only focuses on location planning and user habits, and does not fully consider the carrying capacity of the distribution network. This single consideration leads to uneven load distribution when electric vehicles are charging, and the power load in some areas is too high, which cannot fully utilize the distribution network's carrying potential for electric vehicles. In addition, the failure to reasonably match charging piles of different powers with the charging needs of various regions has further increased the pressure on the distribution network. Therefore, how to optimize the carrying capacity of the distribution network while ensuring user needs by reasonably planning the deployment location, charging power and charging method of charging piles has become a key issue that needs to be urgently solved in the current development of power systems and electric vehicles.

[0038] In the field of research on the carrying capacity of electric vehicles in distribution networks, most of the relevant technologies focus on the evaluation of carrying capacity, while there are relatively few specific technical solutions for improving carrying capacity. A large number of literature focuses on how to evaluate the carrying capacity of existing distribution networks when electric vehicles are connected, and a variety of evaluation models and optimization methods are proposed, but research on actually improving carrying capacity is relatively scarce. A small number of related technologies involve improving carrying capacity, and local optimization is performed through grid transformation, energy storage equipment deployment, demand response, etc. However, most of these methods are accompanied by high implementation costs, complex technical requirements, and user participation, and are difficult to be widely promoted in the short term.

[0039] In other words, the research on the carrying capacity of distribution networks in related technologies mainly focuses on the evaluation of electric vehicle access. Most literature and technical solutions only provide evaluation results without proposing feasible plans to improve carrying capacity. Related technologies cannot provide systematic planning tools to effectively improve the overall carrying capacity of distribution networks. In addition, traditional capacity expansion and grid transformation methods are costly and involve large-scale infrastructure construction, which is difficult to meet short-term needs and sustainable development requirements.

[0040] At the same time, most of the charging pile planning in related technologies focuses on the economic efficiency of charging stations or the reasonable layout of charging piles, but has limited effect in improving the overall carrying capacity of the distribution network. The lack of comprehensive consideration of electric vehicle charging behavior, load distribution characteristics and distribution network carrying capacity has led to problems such as load concentration and local overload in some areas, and has failed to effectively improve the safety and stability of the distribution network. This single charging pile layout planning method has failed to fully utilize the carrying potential of the existing distribution network.

[0041] Therefore, the embodiment of the present invention proposes a method for improving the carrying capacity of the distribution network through multi-power charging pile planning. This method does not require large-scale transformation of the existing distribution network, nor does it require users to participate in a complex demand response system. It can improve the distribution network's access capacity for electric vehicles by only rationally deploying multi-power charging piles. This method combines the improvement of the distribution network's carrying capacity with the optimization planning of charging piles, comprehensively considers both economic factors and comprehensive indicators of the distribution network's carrying capacity, and provides an economical and effective systematic solution to improve the distribution network's electric vehicle carrying capacity, effectively solving the shortcomings of related technologies in improving carrying capacity.

[0042] According to an embodiment of the present invention, an embodiment of a method for improving the carrying capacity of electric vehicles in a distribution network is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0043] Figure 1is a flow chart of a method for improving the carrying capacity of electric vehicles in a distribution network according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0044] Step S102, determining evaluation indicators for evaluating the electric vehicle carrying capacity of the distribution network, and constructing an evaluation system, wherein the distribution network includes one or more regions, and the evaluation system includes: safety evaluation indicators, stability evaluation indicators, and quality evaluation indicators.

[0045] As an optional embodiment, the execution subject of the method of this embodiment can be a terminal or server used to configure the charging piles of the distribution network and thus improve the electric vehicle carrying capacity of the distribution network. For example, when applied to the terminal, it can easily realize the configuration of the charging piles under a simple distribution network; for example, when applied to the server, the rich computing resources of the server can be called, and the charging piles under a complex distribution network can be configured more accurately. It should be noted that the types of the above-mentioned terminals can be various, for example, it can be a mobile terminal with certain computing capabilities, or it can be a fixed computer device with recognition capabilities. The regional types of the above-mentioned servers can also be various, for example, it can be a local server or a virtual cloud server. According to the computing power, the server can be a single computer device or a computer cluster in which multiple computer devices are integrated together.

[0046] As an optional embodiment, the above-mentioned distribution network is responsible for distributing electricity from the high-voltage transmission network to end users, covering geographical areas such as cities, towns or specific industrial areas, which can be further subdivided into one or more regions based on load characteristics and geographical distribution. Among them, the characteristics of each region (such as load type, load density, grid structure, etc.) are different, so the electric vehicle charging demand in each region and the impact of each region on the overall distribution network carrying capacity are also different. In addition, the regional division in the distribution network can be adopted in a variety of ways. For example, it can be divided according to geographical location or according to functional areas. Among them, according to the way of functional area division, due to the consistency of functions within the region, the charging behavior patterns of electric vehicles in the region are also relatively similar, which is more conducive to evaluating and optimizing the configuration of charging piles in the distribution network according to the region.

[0047] As an optional embodiment, the above-mentioned evaluation system includes: safety evaluation index, stability evaluation index, and quality evaluation index. Among them, the above-mentioned safety evaluation index can be used to evaluate the ability of the distribution network to withstand disturbances and keep various operating parameters within safety constraints, including at least one of the following: distribution transformer heavy load rate, distribution transformer light load rate, line heavy load rate, and line light load rate. Among them, the above-mentioned distribution transformer heavy load rate is an indicator to measure whether the operating load of the distribution transformer is close to or exceeds its rated capacity within a specific time or a certain period, reflecting the operating state of the distribution system under high load conditions. The above-mentioned distribution transformer light load rate is another indicator for evaluating the operating state of the distribution transformer, reflecting the operating frequency of the distribution transformer under light load conditions. The light load state usually refers to the load rate of the transformer being far lower than its rated capacity, such as the case where the load rate is less than 20%. The above-mentioned line heavy load rate is an indicator to measure whether the power transmitted by the power line in a specific time period is close to or exceeds its rated transmission capacity. In the power system, a high line heavy load rate means that the line may face the risk of overload, which will not only reduce the safety of the power system, but also may damage the line equipment and affect the quality of power service. The above-mentioned line light load rate refers to the operating frequency or time ratio when the actual transmission power of the power line is far lower than its rated transmission capacity during operation, which may be a situation where the line load rate is lower than 20%.

[0048] The above stability assessment index can reflect the ability of the distribution network to maintain stable operation after being disturbed, including at least one of the following: net load deviation rate, voltage deviation rate, voltage fluctuation rate, and power flow imbalance. Among them, the above net load deviation rate is an indicator to measure the difference between the actual net load and the predicted net load of the power system. The net load refers to the difference between the total power generation and power consumption of the power system within a certain period of time. The above voltage deviation rate is an indicator to measure the degree to which the actual operating voltage deviates from its rated voltage in the power system, which can be expressed as the deviation ratio between the voltage in the distribution network area and the rated voltage. The above voltage fluctuation rate is a key indicator to measure the degree of voltage change stability in the power system, reflecting the amplitude of voltage fluctuation in a short period of time. When the load or power generation in the system changes rapidly, such as a large number of electric vehicles connected to charge at the same time, it may cause system voltage instability and voltage fluctuation. High voltage fluctuation rate will not only affect the quality of power, but also may cause damage to equipment and users in the power system. The above-mentioned power flow imbalance can be used to evaluate the balance of electric energy flow in the system, reflecting whether the distribution of power transmission between different lines is reasonable and whether there is a potential overload risk, especially when considering the distribution network's carrying capacity for electric vehicle charging loads. When the load distribution of each line in the power system is uneven, some lines may be overloaded while other lines may be underloaded. This phenomenon is called power flow imbalance, which can be calculated based on the difference between the actual power flow of each line in the power grid and the average power flow or the maximum allowable power flow.

[0049] The above-mentioned quality evaluation indicators can reflect the ability of the distribution network to accept electric vehicles, including at least one of the following: the distribution network acceptance capacity, the instantaneous penetration rate of electric vehicles and the average penetration rate. Among them, the above-mentioned distribution network acceptance capacity refers to the maximum capacity of the distribution network to accept and process new loads or distributed energy without large-scale transformation and without significantly affecting the power quality and safe and stable operation of the system. The above-mentioned instantaneous penetration rate of electric vehicles refers to the proportion of electric vehicle charging load to the total load of the power grid at a certain moment, which can reflect the impact of electric vehicle charging on the distribution network. The above-mentioned average penetration rate refers to the average proportion of electric vehicle charging load to the total load of the power grid within a certain time range, which can evaluate the overall impact of electric vehicle charging on the distribution network, and the above-mentioned average penetration rate can be expressed by the average value of the above-mentioned instantaneous penetration rate over a period of time.

[0050] Step S104: establishing a charging load model of the electric vehicle corresponding to the one or more areas in the distribution network based on the area types corresponding to the one or more areas in the distribution network.

[0051] As an optional embodiment, the area types corresponding to the one or more areas in the above-mentioned distribution network can be determined by the basis for dividing the areas of the above-mentioned distribution network. For example, when the above-mentioned distribution network is divided into areas based on functions, the above-mentioned area types can be determined according to the functions of the target areas. For example, the areas can be divided based on functions such as residence, office, and commercial activities, and the corresponding area types are residential areas, office areas, and commercial areas. Through the above-mentioned division method, different area types can correspond to different travel purposes, that is, to different charging behaviors of electric vehicles.

[0052] As an optional embodiment, when establishing the charging load model of the electric vehicles corresponding to the one or more areas in the distribution network based on the area types corresponding to the one or more areas in the distribution network, multiple methods can be used. For example, based on the area types corresponding to the one or more areas, according to the distribution curve of the number of charging demands of electric vehicles corresponding to the area types, and the peak parking number of electric vehicles in the one or more areas, the charging demand number of electric vehicles in the one or more areas in each time period can be calculated; based on the area types corresponding to the one or more areas, according to the distribution curve of the number of stay time of electric vehicles corresponding to the area types, and the current state of charge of each electric vehicle in the one or more areas, the selection probability of each type of charging pile in the one or more areas in each time period is calculated; based on the charging demand number and the selection probability of each type of charging pile in the one or more areas, the demand number of each type of charging pile for electric vehicles in the one or more areas in each time period is obtained; based on the demand number of each type of charging pile and the rated power of each type of charging pile, the charging load model of electric vehicles in the one or more areas is established.

[0053] Among them, the above-mentioned charging demand quantity distribution curve can be obtained based on a certain area of ​​the same type as the above-mentioned area type, and the historical charging data of the certain area in different time periods, that is, the number of electric vehicles that need to be charged, is counted, and a benchmark value is set. According to the ratio of all data to the benchmark value, the above-mentioned charging demand quantity distribution curve is formed. Because the relationship between the charging data of electric vehicles is mainly affected by the area type, the charging demand quantity distribution curve of a certain area of ​​the same type as the above-mentioned area type is also applicable to the above-mentioned one or more areas. Similarly, the above-mentioned residence time distribution curve of a certain area of ​​the same type as the above-mentioned area type is also applicable to the above-mentioned one or more areas. Through the above-mentioned area type, the most suitable charging pile type can be matched more accurately for each electric vehicle in the above-mentioned one or more areas, and then the charging load model of the electric vehicle in the above-mentioned one or more areas can be established. For example, the charging behavior of the electric vehicle can be predicted based on the remaining power of the electric vehicle and the expected stay time at the destination, and the appropriate charging pile can be selected for charging. The charging pile that can fully charge the battery within the stay time is preferred. If the power of multiple charging piles can meet the charging demand, the charging pile with the smallest charging power can be selected, and the charging load model is established according to the selected charging pile type.

[0054] For another example, based on the above-mentioned regional types and the historical charging data corresponding to the regional types, regression analysis or machine learning algorithms can be used to predict future charging loads. By determining the regional types corresponding to the above-mentioned one or more regions, the relevant information of the charging behavior patterns of electric vehicles corresponding to the above-mentioned one or more regions can be determined, and then the charging load model of electric vehicles in the above-mentioned one or more regions can be established. That is, the specific attributes of the target area can be considered more comprehensively, and the charging demand of electric vehicles in the above-mentioned one or more regions can be predicted more accurately, ensuring that the configuration of charging pile resources in the above-mentioned one or more regions matches the above-mentioned charging demand, and avoiding overload or insufficient power supply of the distribution network in the above-mentioned one or more regions.

[0055] Step S106: construct a multi-objective optimization model of the distribution network based on the evaluation system and the charging load models of the electric vehicles corresponding to the one or more areas in the distribution network.

[0056] As an optional embodiment, when constructing the multi-objective optimization model of the distribution network based on the above-mentioned evaluation system and the charging load model of the electric vehicle corresponding to the above-mentioned one or more areas in the above-mentioned distribution network, a variety of methods can be used. For example, one or more objective functions can be determined based on the above-mentioned evaluation system; one or more constraints can be determined based on the charging load model of the electric vehicle corresponding to the above-mentioned one or more areas in the above-mentioned distribution network; based on the above-mentioned one or more objective functions and the above-mentioned one or more constraints, the above-mentioned multi-objective optimization model of the above-mentioned distribution network is constructed. The multi-objective optimization model constructed by the objective function and the constraint condition can satisfy all objective functions at the same time and achieve a balance between one or more objective functions. It is not just to optimize a single goal, but to plan the configuration of the distribution network charging piles in a more comprehensive and reasonable manner, thereby improving the carrying capacity of electric vehicles in the distribution network. Considering the above-mentioned one or more constraints, it can ensure that the distribution network charging pile configuration scheme obtained by the multi-objective optimization model will not exceed the safe and stable operation range of the power grid in actual operation, which helps to prevent overload, voltage fluctuations and other problems and ensure the safety of the power system.

[0057] As an optional embodiment, when determining one or more objective functions based on the above evaluation system, multiple methods can be used. For example, only the above evaluation system can be considered for determination. The objective function determined in this way can ensure that the multi-objective optimization model finds a balanced solution between the safety, stability and quality of the distribution network. That is, based on the above evaluation system, a comprehensive evaluation of the carrying capacity of the distribution network can be obtained, and one or more objective functions can be determined based on the above comprehensive evaluation of the carrying capacity of the distribution network. The above one or more objective functions can be obtained in a variety of ways. For example, the indicators in the above evaluation system can be divided into positive indicators and negative indicators, and the evaluation indicators can be normalized to avoid different indicators corresponding to different dimensions, which will affect the evaluation results. Among them, a variety of methods can be used for normalization, such as minimum-maximum normalization, Z-Score standardization, decimal calibration normalization, quantile normalization, etc.; then the normalized evaluation indicators are converted into scores, and there are also many conversion methods, such as power coefficient method, linear proportional conversion, nonlinear mapping, segmented scoring, etc.; then the distribution network carrying capacity is evaluated based on the converted multiple scores, and the multiple scores can be weighted and summed to obtain a comprehensive score of the distribution network carrying capacity. Through the weighted summation method, the evaluation results of different dimensions can be integrated into a whole to ensure that in the final score, the contribution of each scoring indicator matches its importance; finally, the objective function can be determined based on the above distribution network carrying capacity, that is, maximizing the above distribution network carrying capacity is one of the goals. By integrating multiple evaluation indicators in terms of safety, stability and quality, the performance and status of the distribution network can be evaluated from different perspectives, ensuring that the evaluation results cover all key aspects and providing a more comprehensive and multi-dimensional capacity evaluation, thereby supporting more optimized decision-making and improving the electric vehicle carrying capacity of the distribution network.

[0058] For another example, the objective function can be determined in combination with other indicators while considering the above-mentioned evaluation system. For example, it can be combined with economic indicators, such as the annual economic cost of the distribution network. Among them, the above-mentioned annual economic cost can include a variety of costs, such as annual investment and construction costs, annual operation and maintenance costs, electricity purchase costs from superior power grids, network loss costs, etc. By combining other indicators to determine the objective function, the charging pile configuration scheme obtained by the multi-objective optimization model can not only improve the carrying capacity of the above-mentioned distribution network, but also reduce the economic cost of the above-mentioned distribution network as much as possible. That is, by considering the above-mentioned evaluation system and combining other indicators to determine the objective function, the overall performance of the distribution network under the charging load of electric vehicles can be optimized, ensuring that the distribution network can operate safely and stably, improving the acceptance capacity of electric vehicles, and reducing the annual economic cost.

[0059] As an optional embodiment, when determining one or more constraints based on the charging load model of the electric vehicle corresponding to the above one or more areas in the above distribution network, multiple methods can be used. For example, the above one or more constraints may include one or more of the following: system flow constraints, node power balance constraints, voltage amplitude constraints, branch current constraints, reactive compensation equipment output constraints, wind and solar output constraints, charging pile quantity constraints in charging stations, node charging power constraints, generator output constraints. Among them, the system flow constraint can ensure that the power flow of the distribution network complies with the laws of physics in any time period, that is, the power flows from the high-voltage side to the low-voltage side, ensuring the correct distribution of power in the network. The node power balance constraint can ensure that at each node, the inflowing active power and reactive power are balanced with the outflowing active power and reactive power and the load demand of the node. The voltage amplitude constraint can keep the voltage of each node in the network within the specified range, prevent overvoltage or undervoltage phenomena, and damage the electrical equipment and power system hardware. Branch current constraints can limit the current flowing through the line to not exceed its rated current, avoid line overload, prevent the line from heating and damaging due to excessive current, or trigger the protection device to cause network interruption. Reactive power compensation device output constraints can ensure that the output power of the reactive power compensation device is within the set range and maintain the voltage level of the power grid. Wind and solar power output constraints can ensure that the output of renewable energy does not exceed the range that the system can handle, and avoid instability of the power grid due to excessive variable power input. The number of charging piles in the charging station can ensure that the number of charging piles at each charging station location meets the charging needs of electric vehicles, while limiting the maximum number to control investment costs. Node charging power constraints can ensure that charging behavior does not exceed the load level that the system can bear. Generator output constraints can ensure the safe operation of power generation equipment while controlling the cost of power generation. By determining one or more constraints from the above constraints, it can ensure that the multi-objective optimization model, while pursuing specific goals (such as improving carrying capacity, reducing costs, and other objective functions), avoids various extreme situations or failures that may occur in system operation, ensures the feasibility and practicality of the optimization results, and maintains the safety and economy of the distribution network.

[0060] Step 108, based on the multi-objective optimization model, obtain the charging pile configurations corresponding to the one or more areas that can improve the electric vehicle carrying capacity of the distribution network.

[0061] As an optional embodiment, based on the above multi-objective optimization model, under the joint action of the above one or more objective functions and the above one or more constraints, the charging pile configuration corresponding to the above one or more areas in the above distribution network can be obtained. The charging pile configuration obtained by the multi-objective optimization model can comprehensively consider various objectives and constraints to ensure that the charging pile configuration not only maximizes the carrying capacity of the distribution network, but also maintains the power balance and voltage stability of the system, prevents overload and voltage fluctuations, thereby improving the efficiency and reliability of the overall system operation. Among them, one or more objective functions allow the multi-objective optimization model to adjust the charging pile configuration according to actual needs and changes in external conditions to ensure that the configuration scheme can adapt to various situations; one or more constraints enable the charging pile configuration to accurately match the charging needs and network conditions of each area. Therefore, the charging pile configuration obtained based on the multi-objective optimization model can ensure the safe and stable operation of the system while taking into account the economy of the distribution network and the carrying capacity of the distribution network for electric vehicles, reduce costs, improve carrying capacity, and promote the efficient integration of electric vehicle charging facilities and distribution networks.

[0062] As an optional embodiment, after obtaining the configuration of the charging piles corresponding to the above one or more areas in the above distribution network based on the above multi-objective optimization model, the evaluation value corresponding to the above evaluation index can also be calculated according to the above charging pile configuration; according to the above evaluation value, the electric vehicle carrying capacity of the above distribution network is obtained. After constructing the above multi-objective optimization model based on the above evaluation index and the charging load model of the above electric vehicle, and obtaining the configuration of the charging pile, the evaluation value corresponding to the above evaluation index is calculated according to the obtained charging pile configuration scheme, which can verify whether the charging pile configuration scheme has truly effectively improved the carrying capacity of the distribution network and evaluate the actual feasibility and effect of the output of the multi-objective optimization model. From the above charging pile matching, it can be known that the specific performance of the charging pile configuration in different areas and different numbers can be known, providing data support for the refined management and resource optimization of the distribution network. At the same time, the above evaluation value can also be used as feedback for the iteration and adjustment of the multi-objective optimization model. If the evaluation value shows that some indicators do not achieve the expected effect, the model parameters and constraints can be adjusted, and the multi-objective optimization model can be re-run until a better charging pile configuration scheme is found, which helps to continuously improve the carrying capacity of the distribution network and ensure that the system can cope with higher electric vehicle charging needs in the future.

[0063] Through the above steps, a multi-dimensional distribution network evaluation index system covering safety, stability and quality can be constructed, which can not only comprehensively evaluate the distribution network's carrying capacity for electric vehicles, but also optimize the configuration of charging piles in the distribution network as a goal of improving carrying capacity. By dividing the region, according to the electric vehicle charging demand quantity curve and residence time distribution curve corresponding to the regional type, the electric vehicle charging load model corresponding to different regions can be established. Based on this, a multi-objective optimization function can be constructed to consider the balance between economic cost and carrying capacity improvement, and achieve a better configuration of charging piles.

[0064] Since the relevant research on the carrying capacity of the distribution network is mostly concentrated in the evaluation stage, there is a lack of specific and feasible improvement plans. The planning of charging piles and the design of the distribution network often focus on the economy or the reasonable layout of charging piles, while ignoring the impact of charging piles on the overall carrying capacity of the distribution network, resulting in limited carrying capacity of the distribution network for electric vehicles, or causing safety and stability problems in the operation of the distribution network after the electric vehicle load is connected. In combination with the above-mentioned embodiments and optional embodiments, an optional implementation method is provided. In this optional implementation method, a method for improving the electric vehicle carrying capacity of the distribution network considering the planning of multi-power charging piles is provided. The method comprehensively evaluates the carrying capacity of the distribution network for electric vehicles from multiple dimensions such as safety and stability by considering the optimized planning of multi-power charging piles. Without large-scale infrastructure transformation, the carrying capacity of the distribution network can be significantly improved by reasonably arranging charging piles, effectively solving the limitations in related technologies and avoiding the high cost of large-scale transformation of the distribution network. Figure 2 is a schematic diagram of a method for improving the electric vehicle carrying capacity of a distribution network considering multi-power charging pile planning according to an optional implementation mode of the present invention, such as Figure 2 As shown, the method includes the following processing.

[0065] S1. Determine the comprehensive carrying capacity index. Starting from the multi-dimensional attributes such as safety, stability and quality, an index system for evaluating the electric vehicle carrying capacity of the distribution network is constructed, including the heavy load rate of the distribution transformer, the light load rate of the distribution transformer, the heavy load rate of the line, the light load rate of the line, the net load deviation rate, the voltage deviation rate, the voltage fluctuation rate, the power flow imbalance, the distribution network acceptance capacity and the electric vehicle penetration rate, so as to comprehensively evaluate the operating status and carrying capacity of the distribution network.

[0066] The access of electric vehicles increases the uncertainty and volatility in the operation of distribution networks, affecting the quality and safety of power, as well as the reliability of grid load, node voltage and power supply facilities. In order to comprehensively evaluate and improve the carrying capacity of distribution networks under the charging load of electric vehicles, a more comprehensive, reasonable and complete set of evaluation indicators is constructed based on multi-dimensional attributes such as safety, stability and quality, so as to comprehensively evaluate the impact of large-scale access of new elements such as electric vehicles to the grid on the operating status of distribution networks.

[0067] S11, safety index. The safety of the distribution network refers to the ability of the distribution network to withstand disturbances and keep the operating parameters within the safety constraints. This includes the heavy load rate of the distribution transformer, the light load rate of the distribution transformer, the heavy load rate of the line, and the light load rate of the line.

[0068] The distribution transformer overload rate is the ratio of the operating time when the distribution transformer power is greater than or equal to 80% of the rated capacity of the distribution transformer to the total operating time.

[0069] F sub,weight =T sub,weight / T

[0070] Where: F sub,weight is the distribution transformer overload factor; T sub,weight It is the time when the distribution transformer runs under heavy load.

[0071] The light load rate of the distribution transformer is the ratio of the operating time when the distribution transformer power is less than or equal to 20% of the rated capacity of the distribution transformer to the total operating time.

[0072] F sub,light =T sub,light / T

[0073] Where: F sub,light is the light load rate of the distribution transformer; T sub,light It is the time when the distribution transformer runs under light load.

[0074] The line overload rate is the ratio of the number of lines whose line transmission power is greater than or equal to 80% of the rated transmission capacity of the line to the total number of lines.

[0075]

[0076] Where: F line,weight is the line overload rate; n line,weight,t N is the number of lines running overloaded at time t; line is the total number of lines.

[0077] The line light load rate is the ratio of the number of lines whose line transmission power is less than or equal to 20% of the rated transmission capacity of the line to the total number of lines.

[0078]

[0079] Where: F line,light is the line light load rate; n line,light,t is the number of lines running with light load at time t.

[0080] S12, stability index. The stability of the distribution network can reflect the ability of the distribution network to maintain stable operation after being disturbed. It includes net load deviation rate, voltage deviation rate, voltage fluctuation rate and power flow imbalance.

[0081] The net load deviation rate is the deviation ratio between the actual net load power of the distribution network and the predicted net load power.

[0082]

[0083] Where: F NLR is the net load deviation rate; P NL,t is the net load power at time t; P NL,f,t is the predicted net load power at time t.

[0084] The voltage deviation rate is the deviation ratio between the node voltage of the distribution network and the rated voltage. The above-mentioned node is the node area, that is, the aforementioned area.

[0085]

[0086] Where: F R,ΔU is the voltage deviation rate; U N is the rated voltage; U i,t is the node voltage of the i-th node at time t.

[0087] The voltage fluctuation rate is the change ratio of the voltage at the distribution network node at adjacent moments.

[0088]

[0089] Where: F R,ΔU,T is the voltage fluctuation rate; U i,t+1 is the node voltage of the i-th node at time t+1.

[0090] The power flow imbalance is the ratio of the difference between the distribution network line transmission power and the line average transmission power to the line maximum transmission power.

[0091]

[0092] Where: F ΔP,line is the tidal current imbalance; P line,i,t is the line transmission power of the i-th branch at time t; P line,ave,t is the average line transmission power at time t; P line,max is the maximum line transmission power.

[0093] S13, quality index. The quality of the distribution network can reflect the ability of the distribution network to accept electric vehicles and is an important manifestation of the distribution network's carrying capacity. It includes the distribution network's acceptance capacity, the instantaneous penetration rate of electric vehicles into the network, and the average penetration rate of electric vehicles into the network.

[0094] The distribution network's acceptance capacity is expressed as the total load of electric vehicles that the distribution network can connect to in one day.

[0095]

[0096] Where: is the electric vehicle charging load of the i-th node at time t, It is the total load of electric vehicles in the entire network during the maximum time when the distribution network accepts electric vehicles throughout the year.

[0097] The instantaneous grid penetration rate is expressed as the instantaneous proportion of the electric vehicle charging load in the total load of the distribution network, which can reflect the instantaneous penetration degree of electric vehicles into the overall load of the distribution network.

[0098]

[0099] Where: F IPR is the instantaneous network penetration rate, P i,t is the total load of the ith node at time t. By calculating the instantaneous network penetration rate throughout the year, the maximum value of the instantaneous network penetration rate can be obtained: and minimum value

[0100] The average grid penetration rate is the time average of the instantaneous grid penetration rate. It can comprehensively evaluate the grid penetration of electric vehicles throughout the year and reflect the impact of electric vehicles on the distribution network over a long period of time.

[0101]

[0102] Where: F IPR,avg is the average network penetration rate.

[0103] S14, bearing capacity index scoring. Each evaluation index has different dimensions and physical meanings. In order to facilitate calculation and use a unified evaluation standard for comprehensive bearing capacity evaluation, each index needs to be dimensionless, such as using the maximum-minimum normalization method. The specific formula is shown below.

[0104] For positive indicators, the larger the value, the better the performance:

[0105]

[0106] For negative indicators, the smaller the value, the better the performance:

[0107]

[0108] Where: x i ′ is the normalized value, x i is the actual value of index i, are the maximum and minimum values ​​of index i respectively.

[0109] In order to compare the advantages and disadvantages of indicators, the efficacy coefficient method is used to convert the indicators into scores.

[0110] gi =a+bx i

[0111] Where: g i is the score of indicator i, a and b can be 60 and 40 respectively.

[0112] The final bearing capacity score is obtained by weighted summation of each indicator:

[0113]

[0114] Where: N index is the number of electric vehicle carrying capacity indicators.

[0115] S2, Modeling of electric vehicle charging load on a typical day. Using historical data and forecasting models, the electric vehicle charging demand at each time point in each region on a typical day is calculated in detail. Based on the electric vehicle charging demand quantity curve and the residence time distribution curve, combined with the land type and peak parking quantity in each region, the number of vehicles requiring slow charging, fast charging, and ultra-fast charging is calculated, thereby obtaining the electric vehicle load distribution.

[0116] The charging behavior of electric vehicles is significantly correlated with the land use and attributes of their destination areas. Different types of destinations (such as residential areas, shopping malls, and office areas) correspond to different travel purposes, resulting in significant differences in the number of electric vehicle charging demands and the distribution of dwell time in these areas. Therefore, three common typical area types are selected: residential areas, commercial areas, and office areas. Based on the historical data collected from each type of area, the electric vehicle charging demand quantity curve and the residence time distribution curve are fitted. Taking into account the large differences in the travel behavior of electric vehicles on weekdays and weekends, the data for weekdays and weekends are processed independently. In order to achieve accurate modeling and calculation of electric vehicle charging demand, it is necessary to consider factors such as the types of distribution networks, the distribution curve of electric vehicle charging demand quantity, and the residence time distribution curve. Figure 3 is a schematic diagram of a typical daily electric vehicle charging load modeling method according to an optional embodiment of the present invention, such as Figure 3 As shown, T is the calculation time period, N b is the number of network distribution nodes.

[0117] First, the type of each area, as well as the distribution curve of the number of electric vehicle charging demands (i.e., the arrival number distribution curve) and the residence time distribution curve are input. The peak parking quantity is used as a decision variable and will be determined in the optimization process of S3.

[0118] S21, for each area, according to its type and the corresponding charging demand quantity distribution curve, combined with the peak parking quantity, calculate the charging demand quantity of electric vehicles in the area in each time period, and the calculation formula is:

[0119]

[0120] Where: is the number of electric vehicle charging demands in region i at time t, is the peak parking number in area i, A i,t is the distribution of electric vehicle charging demand in region i at time t, with a value between 0 and 1.

[0121] S22, make reasonable assumptions about the charging behavior of electric vehicles. Assume that electric vehicles select appropriate charging piles for charging based on their remaining power and expected stay time at the destination, and give priority to charging piles that can fully charge the battery within the stay time. If the power of multiple charging piles can meet the charging demand, the charging pile with the smallest charging power will be selected. Three types of charging piles are considered here, namely slow charging piles, fast charging piles and super fast charging piles, to meet the selection of electric vehicles with different needs.

[0122] For each area, according to its type and corresponding residence time distribution curve, combined with the current state of charge of electric vehicles, the number of charging piles of each type required in the area in each time period is calculated. And the charging load of electric vehicles is calculated based on the number of charging piles required for each type and the rated power of each type of charging pile. The calculation formula is:

[0123]

[0124] Where: N s,i,t 、N f,i,t 、N u,i,t are the number of vehicles that select slow charging pile, fast charging pile and ultra-fast charging pile for node i at time t, respectively; is the battery capacity of vehicle k; and are the remaining power and residence time of vehicle k at node i at time t, Obeys uniform distribution in the interval [0-1]; P s , P f , P u They are the rated power of slow, fast and ultra-fast charging piles respectively; are the probabilities of an electric vehicle choosing a slow, fast, or ultra-fast charging station at node i at time t, respectively; are the number of charging piles required by electric vehicles at node i at time t, is the charging power of the electric vehicle at node i at time t.

[0125] S3, construct a distribution network EV carrying capacity improvement model. Based on the aforementioned carrying capacity evaluation indicators, a multi-objective optimization model is constructed. This model considers the comprehensive carrying capacity indicators of distribution network electric vehicles proposed in S1, with the optimization objectives of maximizing the comprehensive carrying capacity of the distribution network and minimizing the annualized total equipment cost. Through the optimal charging pile optimization configuration plan, the safe and stable operation of the distribution network is ensured under the condition of large-scale access of electric vehicles, so as to improve the comprehensive carrying capacity of the distribution network for electric vehicles.

[0126] S31, in order to improve the comprehensive carrying capacity of electric vehicles in the distribution network, a multi-objective optimization model can be constructed to comprehensively consider the evaluation indicators of the distribution network carrying capacity. The objective function aims to optimize the overall performance of the distribution network under the electric vehicle charging load to ensure its safe and stable operation. The improvement model takes the comprehensive score of carrying capacity as one of the objectives:

[0127] maxf1=G

[0128] While improving the comprehensive carrying capacity of electric vehicles in the distribution network, economic efficiency is also an important consideration. Therefore, the annualized economic cost will be included in the optimization model as one of the multi-objective functions:

[0129] minf2=C invest +C opera +C sub +C loss

[0130] Where: C invest represents the annualized investment and construction cost of electric vehicle charging stations, C opera represents the annual operation and maintenance cost of the electric vehicle charging station, C sub Indicates the cost of purchasing electricity from the upper grid, C loss Represents the network loss cost. The specific expressions of each variable in the formula are as follows.

[0131] Annual investment and construction cost of electric vehicle charging stations C invest It is expressed as:

[0132]

[0133] Where: c invest,s 、c invest,f 、c invest,u They represent the unit investment and construction costs of slow charging piles, fast charging piles, and ultra-fast charging piles, respectively. are the number of slow, fast, and ultra-fast charging piles installed at node j, R d is the annualized auxiliary variable, d is the discount rate, l EV is the economic life of the charging pile, and its calculation formula is as follows:

[0134]

[0135] Annual operation and maintenance cost of electric vehicle charging station C opera It is expressed as:

[0136]

[0137] Where: c opera,s 、c opera,f 、c opera,u These are the annual operation and maintenance costs of slow, fast and ultra-fast charging piles respectively.

[0138] Cost of purchasing electricity from the upper grid C sub It is expressed as:

[0139]

[0140] Where: c p,i,t is the energy transaction price at time t on a typical day i, P g,i,t is the active power injected into the transformer at time t on a typical day i, D typical,d It is the number of days in a year as a typical day, including weekdays and weekends in the four seasons of spring, summer, autumn and winter.

[0141] Network loss cost C loss It is expressed as:

[0142]

[0143] Where: P loss,t,l is the branch line loss of line l at time t.

[0144] S32, constraints. The constraints of the above multi-objective optimization function may include the following constraints:

[0145] The system flow constraint is described by a simplified power flow model (Dist-Flow model), which is used to analyze the flow of power in the power grid to ensure safe operation under the conditions of meeting voltage and power constraints. The system flow constraint can be calculated by the following formula:

[0146]

[0147] Where u(j) and ν(j) represent the set of nodes connected to node j and located downstream and upstream of node j, respectively. ij,t and Q ij,t represents the active power and reactive power flowing through branch ij at time t, and They represent the equivalent active load and reactive load of node j at time t, U i,t is the voltage amplitude of node i at time t, R ij and Xij are branch resistance and reactance respectively, U sub The voltage amplitude at the substation node, usually taken as 1.0pu. Among them, pu is the abbreviation of "per unit", which means "per unit" or "per unit value". The per unit value system is a relative value system used in power system analysis and design to simplify calculations and provide more intuitive data comparisons. In the per unit value system, all physical quantities are normalized relative to a selected reference value. For voltage, "1.0pu" usually means that the voltage amplitude is equal to the selected reference voltage.

[0148] Node power balance constraints:

[0149]

[0150] Where: and They represent the active load and reactive load of node j at time t, respectively. represents the output of the photovoltaic power station at node j at time t, represents the output of the wind power station at node j at time t, represents the active load of electric vehicle charging at node j at time t, is the reactive power output of the reactive power compensation device at node j.

[0151] Voltage amplitude constraint:

[0152] U i.min ≤U i ≤U i.max

[0153] Where: U i.min , U i.max are the lower and upper limits of the voltage at node i, respectively.

[0154] Branch current constraints:

[0155] I ij ≤I ij.max

[0156] Where: I ij.max is the upper limit of the current amplitude of line ij.

[0157] Reactive power compensation equipment output constraints:

[0158]

[0159] Where: and They respectively represent the lower and upper limits of the output of the reactive power compensation equipment at node i.

[0160] Wind and solar power output constraints:

[0161]

[0162] Where: and They respectively represent the upper limits of photovoltaic and wind power output, that is, their capacity limitations.

[0163] In an electric vehicle charging station, the number of all installed charging piles must meet the charging demand in each time period. If the number of charging piles of a certain type is insufficient, the electric vehicles to be charged can use higher-power charging piles to maintain charging efficiency. Therefore, the number of the highest-power super-fast charging piles must at least meet the charging demand of that type in any time period. Constraints on the number of charging piles in an electric vehicle charging station:

[0164]

[0165] In the formula, They represent the number of slow, fast and ultra-fast charging piles installed at charging location node j respectively.

[0166] Node charging power constraints:

[0167]

[0168] In the formula, is the upper limit of the charging load that the charging facility at node i can accommodate for electric vehicles.

[0169] Generator output constraints:

[0170]

[0171] In the formula, is the maximum output of the generator at time t.

[0172] Based on the above optional implementation, a specific optional implementation is provided. This method uses the IEEE33 node power distribution system for testing. Figure 4 is a schematic diagram of a grid structure according to an optional embodiment of the present invention, such as Figure 4 shown. Figure 5 is a schematic diagram of a distribution network coupled with actual geography according to an optional implementation mode of the present invention, such as Figure 5 As shown, the power distribution system is coupled with the actual geography, and is divided into different types based on the areas to which the power distribution system nodes belong, namely three typical areas (residential area, supermarket area, and office area).

[0173] According to the above regional divisions, Figure 6 is a schematic diagram of a distribution curve of the number of electric vehicle charging demands corresponding to each area type according to an optional implementation manner of the present invention, such as Figure 6 As shown, Figure 6 (a) represents a residential area, Figure 6 (b) represents the supermarket area, Figure 6 (c) represents the office area. Figure 7 is a schematic diagram of a distribution curve of electric vehicle residence time corresponding to each area type according to an optional implementation manner of the present invention, such as Figure 7 As shown, Figure 7 (a) represents a residential area, Figure 7 (b) represents the supermarket area, Figure 7 (c) represents the office area.

[0174] At the same time, three typical electric vehicle charging piles are selected as representatives, namely slow charging piles, fast charging piles, and super fast charging piles, and their relevant information is shown in Table 1.

[0175] Table 1 Parameters of electric vehicle charging piles

[0176]

[0177] In order to verify the method for improving the carrying capacity of the distribution network proposed in this optional implementation method, three schemes are designed for comparative analysis as shown in Table 2. Scheme 1 is a traditional charging pile planning scheme, which is mainly based on the user's charging needs and car usage habits. The goal is to meet the user's charging needs and minimize the annualized total cost. It focuses on the investment and maintenance cost optimization of charging piles, and does not consider the carrying capacity of the distribution network and other related factors. Scheme 2 focuses on improving the electric vehicle acceptance capacity of the distribution network. The goal is to maximize the access load of electric vehicles. Other key factors of carrying capacity are not considered, such as the safety, stability and power quality of the distribution network. Scheme 3 is a comprehensive optimization scheme proposed in this optional implementation method. The goal is not only to consider the economy, but also to comprehensively consider the carrying capacity evaluation indicators of the distribution network, including key factors such as safety, stability and quality.

[0178] Table 2 Scheme design

[0179]

[0180] According to the above schemes, the charging pile planning corresponding to each scheme is obtained. Figure 8 is a schematic diagram of charging pile planning according to three optional implementation schemes of the present invention, such as Figure 8 shown. Fig. 9 is a schematic diagram comparing the total number of charging piles installed in the distribution network according to three optional implementation schemes of the present invention, such as Fig. 9As shown. In the figure, Schemes 1, 2, and 3 are represented by S1, S2, and S3 respectively, and the slow, fast, and super-speed charging piles are represented by s, f, and u respectively. Through analysis, it can be seen that different schemes have obvious differences in the number and layout of charging piles. Scheme 1 is mainly planned based on user needs and economic efficiency. The number of installed charging piles is relatively small, which fully reflects the goal of economic optimization. Scheme 2 does not consider other key factors of the distribution network's carrying capacity. It aims to maximize the acceptance capacity of electric vehicles. Therefore, a large number of charging piles are installed in some areas, and some nodes far exceed Scheme 3. The total number of charging piles installed in Scheme 3 can be achieved. It is not much different from Scheme 2, but its layout and distribution strategy of charging piles is more balanced. Through scientific site selection and capacity planning, it takes into account the improvement of the distribution network's carrying capacity, reflecting a more reasonable distribution method.

[0181] The above-mentioned carrying capacity evaluation indicators are used to evaluate the three schemes, and the electric vehicle carrying capacity evaluation results are shown in Table 3. Fig.10 is a radar chart showing the distribution of indicators in each dimension of three schemes according to the optional implementation mode of the present invention, such as Fig.10 As shown in Table 3. It can be seen from Table 3 that Scheme 1 performs best in terms of economy, which is reflected in the lowest installation and maintenance costs of charging piles. However, Scheme 1 ignores the safety and stability of the distribution network and relies only on user demand, resulting in its limited ability to carry electric vehicles. Although Scheme 2 aims to maximize the access of electric vehicles, it does not take safety and stability into consideration, resulting in poor performance of its line overload rate and voltage deviation rate, and may cause problems such as distribution network overload and voltage instability. In contrast, Scheme 3 achieves the best balance between safety, stability and economy. By rationally planning the number and location of charging piles, Scheme 3 effectively improves the carrying capacity of the distribution network, while controlling the economy within a reasonable range, ensuring the long-term safe operation of the distribution network. From Fig.10 It can be seen that Scheme 3 has the largest comprehensive area, indicating that it performs well in all dimensional evaluation indicators. Overall, Scheme 3 takes into account the multi-dimensional factors of the distribution network carrying capacity and adopts a scientific site selection and capacity determination strategy. It not only approaches the number of charging piles installed in Scheme 2, but also significantly improves the safety, stability and economy of the system, achieving the optimal carrying capacity improvement effect.

[0182] Table 3 Scores of electric vehicle evaluation indicators for three schemes

[0183]

[0184]

[0185] Therefore, in this optional implementation mode, in the model of improving the electric vehicle carrying capacity of the distribution network through charging pile planning, multiple dimensions of the distribution network's carrying capacity for electric vehicles, including safety, stability and quality, are comprehensively considered. Its key evaluation indicators include distribution transformer heavy load rate, distribution transformer light load rate, line heavy load rate, line light load rate, net load deviation rate, voltage deviation rate, voltage fluctuation rate, power flow imbalance, distribution network acceptance capacity and electric vehicle access penetration rate. By building a comprehensive evaluation system, the carrying capacity of the distribution network under the large-scale access of electric vehicles can be accurately measured. Not only has the overall carrying capacity of the distribution network been greatly improved, but also the operational stability and power quality of electric vehicles after access have been significantly enhanced. Unlike the traditional single indicator evaluation method, the multi-dimensional comprehensive evaluation can more accurately reflect the complexity of the system, while ensuring the improvement of the carrying capacity of electric vehicles, it also guarantees the safe and stable operation of the system.

[0186] Based on multi-dimensional evaluation indicators, this optional implementation method proposes an innovative method for improving the carrying capacity of the distribution network considering the planning of multi-power charging piles. This method takes maximizing the comprehensive carrying capacity of the distribution network and minimizing the annualized total equipment cost as the optimization goal. It not only ensures the safety and stability of the distribution network under the large-scale access of electric vehicles, but also significantly improves the access capacity of electric vehicles. By reasonably optimizing the configuration and layout of charging piles, without large-scale infrastructure transformation, the efficient matching of electric vehicle charging demand and distribution network carrying capacity is achieved, and the resources and load margin of the existing distribution network are fully utilized. It overcomes the high cost and complexity problems brought about by large-scale transformation of distribution networks in related technologies. By simultaneously optimizing the comprehensive carrying capacity and economy of the distribution network, the carrying capacity of electric vehicles is effectively improved, and the safety and stability of the system during operation are ensured, while also having certain economic advantages.

[0187] According to an embodiment of the present invention, a method and device for improving the carrying capacity of electric vehicles in a distribution network is provided. Fig.11 is a structural block diagram of a method and device for improving the carrying capacity of electric vehicles in a distribution network according to an embodiment of the present invention. Fig.11 As shown, the device includes: a determination module 1102, an establishment module 1104, a construction module 1106, and a configuration module 1108. The device is described below.

[0188] A determination module 1102 is used to determine evaluation indicators for evaluating the electric vehicle carrying capacity of the distribution network and to construct an evaluation system, wherein the distribution network includes one or more areas, and the evaluation system includes: safety evaluation indicators, stability evaluation indicators, and quality evaluation indicators; an establishment module 1104 is connected to the determination module 1102 and is used to establish a charging load model of the electric vehicles corresponding to the one or more areas in the distribution network based on the area types corresponding to the one or more areas in the distribution network; a construction module 1106 is connected to the establishment module 1104 and is used to construct a multi-objective optimization model of the distribution network based on the evaluation system and the charging load model of the electric vehicles corresponding to the one or more areas in the distribution network; a configuration module 1108 is connected to the construction module 1106 and is used to obtain, based on the multi-objective optimization model, the configuration of charging piles corresponding to the one or more areas that can improve the electric vehicle carrying capacity of the distribution network.

[0189] It should be noted here that the above-mentioned determination module 1102, establishment module 1104, construction module 1106, and configuration module 1108 correspond to steps S102 to S108 in the embodiment, and the instances and application scenarios implemented by the multiple modules and corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned embodiments.

[0190] As an optional embodiment, the above-mentioned evaluation system includes: safety evaluation indicators, stability evaluation indicators, and quality evaluation indicators, wherein: the above-mentioned safety evaluation indicators include at least one of the following: distribution transformer heavy load rate, distribution transformer light load rate, line heavy load rate, line light load rate; the above-mentioned stability evaluation indicators include at least one of the following: net load deviation rate, voltage deviation rate, voltage fluctuation rate, power flow imbalance; the above-mentioned quality evaluation indicators include at least one of the following: distribution network acceptance capacity, electric vehicle instantaneous network penetration rate and average network penetration rate.

[0191] As an optional embodiment, the establishment module 1104 includes: a first calculation unit, a second calculation unit, a obtaining unit, and an establishment unit. The first calculation unit is used to calculate the number of charging demands of electric vehicles in the one or more areas in each time period based on the area types corresponding to the one or more areas, the distribution curve of the number of charging demands of electric vehicles corresponding to the area types, and the peak parking number of electric vehicles in the one or more areas; the second calculation unit is connected to the first calculation unit, and is used to calculate the selection probability of each type of charging pile in the one or more areas in each time period based on the area types corresponding to the one or more areas, the distribution curve of the number of stay time of electric vehicles corresponding to the area types, and the current state of charge of each electric vehicle in the one or more areas; the obtaining unit is connected to the second calculation unit, and is used to obtain the number of charging piles required by electric vehicles in the one or more areas for each type of charging pile in each time period based on the number of charging demands and the selection probability of charging piles of each type of area; the establishment unit is connected to the obtaining unit, and is used to establish the charging load model of electric vehicles in the one or more areas based on the number of charging piles required and the rated power of each type of charging pile.

[0192] As an optional embodiment, the construction module 1106 includes: a first determination unit, a second determination unit, and a construction unit. The first determination unit is used to determine one or more objective functions based on the evaluation system; the second determination unit is connected to the first determination unit and is used to determine one or more constraints based on the charging load model of the electric vehicle corresponding to the one or more areas in the distribution network; the construction unit is connected to the second determination unit and is used to construct the multi-objective optimization model of the distribution network based on the one or more objective functions and the one or more constraints.

[0193] As an optional embodiment, the first determination unit includes: a first determination subunit, a second determination subunit, and a third determination subunit. The first determination subunit is used to determine the first objective function for maximizing the electric vehicle carrying capacity of the distribution network based on the evaluation system; the second determination subunit is connected to the first determination subunit and is used to determine the second objective function for minimizing the annualized economic cost of the distribution network based on the annualized economic cost of the distribution network; the third determination subunit is connected to the second determination subunit and is used to determine the one or more objective functions based on the first objective function and the second objective function.

[0194] As an optional embodiment, the above-mentioned one or more constraints include at least one of the following: system flow constraint, node power balance constraint, voltage amplitude constraint, branch current constraint, reactive compensation equipment output constraint, wind and solar output constraint, charging pile quantity constraint in the charging station, node charging power constraint, and generator output constraint.

[0195] As an optional embodiment, the device further includes: a calculation module and an acquisition module. The calculation module is used to calculate the evaluation value corresponding to the evaluation index according to the configuration of the charging pile; the acquisition module is connected to the calculation module and is used to obtain the electric vehicle carrying capacity of the distribution network according to the evaluation value.

[0196] According to an embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned methods for improving the carrying capacity of electric vehicles in the distribution network.

[0197] According to an embodiment of the present invention, an electronic device is provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes any one of the above-mentioned methods for improving the carrying capacity of electric vehicles in a distribution network when running.

[0198] According to an embodiment of the present invention, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the steps of any one of the above methods are implemented.

[0199] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0200] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0201] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0202] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0203] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0204] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0205] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for improving the carrying capacity of electric vehicles in a distribution network, characterized in that: include: Determine evaluation indicators for evaluating the electric vehicle carrying capacity of the distribution network, and construct an evaluation system, wherein the distribution network includes one or more regions, and the evaluation system includes: safety evaluation indicators, stability evaluation indicators, and quality evaluation indicators; Based on the area types respectively corresponding to the one or more areas in the distribution network, a charging load model of the electric vehicle respectively corresponding to the one or more areas in the distribution network is established; Based on the evaluation system and the charging load models of electric vehicles corresponding to the one or more areas in the distribution network, a multi-objective optimization model of the distribution network is constructed; Based on the multi-objective optimization model, the charging pile configurations corresponding to the one or more areas that can improve the electric vehicle carrying capacity of the distribution network are obtained.

2. The method according to claim 1, characterized in that The evaluation system includes: safety evaluation index, stability evaluation index, and quality evaluation index, among which: The safety assessment index includes at least one of the following: distribution transformer overload rate, distribution transformer underload rate, line overload rate, line underload rate; The stability evaluation index includes at least one of the following: net load deviation rate, voltage deviation rate, voltage fluctuation rate, and power flow imbalance; The quality evaluation index includes at least one of the following: distribution network acceptance capacity, instantaneous electric vehicle access penetration rate and average access penetration rate.

3. The method according to claim 1, characterized in that The step of establishing charging load models for electric vehicles corresponding to the one or more areas in the distribution network based on the area types corresponding to the one or more areas in the distribution network comprises: Based on the area types corresponding to the one or more areas, according to the distribution curve of the number of charging demands of electric vehicles corresponding to the area types, and the peak parking number of electric vehicles in the one or more areas, calculating the number of charging demands of electric vehicles in the one or more areas in each time period; Based on the area type corresponding to the one or more areas, according to the residence time distribution curve of the electric vehicles corresponding to the area type, and the current charge state of each electric vehicle in the one or more areas, calculating the selection probability of each type of charging pile in the one or more areas in each time period; Based on the charging demand quantity and the selection probability of the charging piles of each type in each area, obtaining the demand quantity of each type of charging piles for electric vehicles in the one or more areas in each time period; A charging load model for electric vehicles in the one or more areas is established based on the required number of charging piles of each type and the rated power of the charging piles of each type.

4. The method according to claim 1, characterized in that: The multi-objective optimization model of the distribution network is constructed based on the evaluation system and the charging load models of the electric vehicles corresponding to the one or more areas in the distribution network, including: Based on the evaluation system, determining one or more objective functions; Determining one or more constraint conditions based on charging load models of electric vehicles corresponding to the one or more areas in the distribution network; Based on the one or more objective functions and the one or more constraints, the multi-objective optimization model of the power distribution network is constructed.

5. The method according to claim 4, characterized in that Determining one or more objective functions based on the evaluation system includes: Based on the evaluation system, determining a first objective function for maximizing the electric vehicle carrying capacity of the distribution network; Determining, according to the annualized economic cost of the distribution network, a second objective function for minimizing the annualized economic cost of the distribution network; The one or more objective functions are determined according to the first objective function and the second objective function.

6. The method according to claim 4, characterized in that The one or more constraints include at least one of the following: system flow constraint, node power balance constraint, voltage amplitude constraint, branch current constraint, reactive compensation equipment output constraint, wind and solar output constraint, charging pile quantity constraint in the charging station, node charging power constraint, and generator output constraint.

7. The method according to any one of claims 1 to 4, characterized in that After obtaining the charging pile configurations corresponding to the one or more areas in the distribution network based on the multi-objective optimization model, the method further includes: According to the configuration of the charging pile, calculating the evaluation value corresponding to the evaluation index; According to the evaluation value, the electric vehicle carrying capacity of the distribution network is obtained.

8. A device for improving the carrying capacity of electric vehicles in a distribution network, characterized in that: include: A determination module, used to determine evaluation indicators for evaluating the electric vehicle carrying capacity of the distribution network, and to construct an evaluation system, wherein the distribution network includes one or more regions, and the evaluation system includes: a safety evaluation indicator, a stability evaluation indicator, and a quality evaluation indicator; An establishing module, used to establish a charging load model of the electric vehicle corresponding to the one or more areas in the distribution network based on the area types corresponding to the one or more areas in the distribution network; A construction module, configured to construct a multi-objective optimization model of the distribution network based on the evaluation system and charging load models of electric vehicles corresponding to the one or more areas in the distribution network; The configuration module is used to obtain the charging pile configurations corresponding to the one or more areas that can improve the electric vehicle carrying capacity of the distribution network based on the multi-objective optimization model.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the method for improving the carrying capacity of electric vehicles in the distribution network according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: A memory storing an executable program; A processor is used to run the program, wherein when the program is running, the method for improving the carrying capacity of electric vehicles in the distribution network as described in any one of claims 1 to 7 is executed.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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