Power transmission network planning method and system considering multiple change domains

Abstract

The invention discloses a power transmission network planning method and system considering multiple change domains, and belongs to the technical field of power grid planning, and the method comprises the steps: obtaining time-sharing energy consumption through the division of obtained urban power consumption data, and determining the power generation amount of a generator through the time-sharing energy consumption; predicting the generating capacity of the new energy single equipment, constructing a new energy installed capacity and an initial energy storage capacity according to the generating capacity of the new energy single equipment and the time-sharing energy consumption, and constructing a first planning function by using the generating capacity of the generator, the new energy installed capacity and the energy storage capacity; constructing a second planning function through the line number expression parameter and the generator operation cost; under the constraint of the formulated planning function constraint conditions, simultaneous solution is carried out on the two planning functions, and a current-year planning solution is obtained. According to the scheme, multiple change domains are considered, system integration is carried out, the fluctuation and load stability requirements of new energy output can be balanced, and the environment change domain of new energy output can be effectively coped with.

Description

Technical Field

[0001] This application belongs to the field of power grid planning technology, and specifically relates to a transmission network planning method and system that considers multiple variation domains. Background Technology

[0002] With the acceleration of urbanization and the advancement of new power system construction, urban electricity load is characterized by "total growth and widening peak-valley difference." Simultaneously, the large-scale integration of new energy sources such as wind power and photovoltaics into the transmission network presents urban transmission network planning with multiple challenges, including "dynamic load changes, fluctuations in new energy output, and tightening cost constraints." Traditional planning methods are no longer adequate for complex operational scenarios. Existing transmission network planning methods often focus on analyzing single-dimensional influencing factors, lacking a systematic integration of multiple change domains across "source-grid-load-intermediate-storage-policy." Furthermore, the identification of change domains is fragmented, failing to form an analytical framework covering "influence dimension-time scale-uncertainty intensity."

[0003] Current urban power transmission network planning technology has the following main limitations: Insufficient load-generation matching: Traditional planning is mostly based on annual or monthly average load data to formulate power generation plans without dividing the load into time periods. This leads to a disconnect between generator power generation and actual time-of-use energy consumption demand, which can easily result in power supply gaps during peak hours or power waste during off-peak hours, and cannot adapt to the time-varying characteristics of the load.

[0004] Lack of coordinated planning for new energy and energy storage: New energy power generation is significantly affected by weather (wind speed, sunlight). Existing plans have low accuracy in predicting new energy power generation and do not coordinate the construction of new energy installed capacity and energy storage capacity. It is difficult to balance the volatility of new energy output with the demand for load stability and fails to effectively address the environmental change domain problem of new energy output. Summary of the Invention

[0005] To address the aforementioned issues, this application provides a power grid planning method and system that considers multiple variation domains, effectively balancing the volatility of renewable energy output with load stability requirements and addressing the environmental variation domain of renewable energy output.

[0006] This application provides a power grid planning method considering multiple variation domains, including: S1: Obtain urban electricity consumption data, divide the urban electricity consumption data into time-of-use energy consumption, and determine the generator output through time-of-use energy consumption. S2: Determine the rated power and weather data of the standard new energy power generation equipment, predict the power generation of a single new energy equipment based on the weather data and rated power, construct the new energy installed capacity and initial energy storage capacity based on the power generation of a single new energy equipment and time-of-use energy consumption, and construct a first planning function with the goal of minimizing the function value using the generator power generation, new energy installed capacity and energy storage capacity, wherein the initial value of the energy storage capacity is the initial energy storage capacity. S3: Determine the generator operating cost. Construct a line quantity expression parameter based on line density, new energy installed capacity and the number of lines required for the generator. Construct a second planning function with the goal of minimizing the function value using the line quantity expression parameter and the generator operating cost. S4: Formulate the constraints of the planning function, and solve the first and second planning functions simultaneously under the constraints of the planning function to obtain the planning solution for the year, which includes the ratio of the number of various new energy power generation equipment, the optimized energy storage capacity and the number of transmission lines.

[0007] Furthermore, time-of-use energy consumption includes peak energy consumption and off-peak energy consumption; Obtain urban electricity consumption data, divide the urban electricity consumption data into time-of-use energy consumption, and determine the generator output based on the time-of-use energy consumption, including: Obtain urban electricity consumption data, divide the urban electricity consumption data by hourly electricity consumption to obtain off-peak energy consumption, and determine the generator power generation based on off-peak energy consumption.

[0008] Furthermore, new energy power generation equipment includes: wind power generation equipment and solar power generation equipment; time-of-use energy consumption includes morning peak energy consumption, evening peak energy consumption, and off-peak energy consumption, and S2 specifically includes: S21: Determine the rated power of the standard new energy power generation equipment, obtain weather data, input the weather data into the prediction model, and obtain the power generation of a single new energy equipment. The output layer of the prediction model is embedded with the rated power of the standard new energy power generation equipment. S22: Construct the new energy installed capacity, including wind power installed capacity and solar power installed capacity, based on the ratio of power generation of a single new energy device, morning peak energy consumption and evening peak energy consumption. S23: Construct the initial energy storage capacity based on the installed capacity of new energy sources and urban electricity consumption data; construct the first planning function using generator power generation, installed capacity of new energy sources, and energy storage capacity.

[0009] Furthermore, the first planning function constructed is:

[0010] Here, min() indicates finding the minimum value within the parentheses. This represents the construction cost of the generator, allocated to the generator's power output. This indicates the generator's power output. Let represent the discount rate for the generator, and t represent the current year as the t-th year in the planning process. This represents the operating and construction costs of new energy power generation equipment allocated to the total new energy power generation. This represents the total power generation from new energy sources. This represents the discount rate for new energy power generation equipment. This represents the operating and construction costs of energy storage equipment allocated to the energy storage capacity. Indicates energy storage capacity, This represents the discount rate for energy storage equipment.

[0011] Furthermore, S3 specifically includes: S31: Determine the generator cost characteristic function, and determine the generator operating cost based on the generator cost characteristic function; S32: Determine the line density parameters, including generator line density, wind power line density, and solar power line density. Construct line quantity expression parameters based on the line density parameters, the installed capacity of new energy sources, and the number of lines required for generators. Construct a second planning function using the line quantity expression parameters and generator operating costs.

[0012] Furthermore, the constructed second programming function is as follows:

[0013] in, The generator's operating costs this year Let represent the discount rate for the generator, and t represent the current year as the t-th year in the planning process. The construction cost of a power transmission line, The parameter is used to express the number of lines. This is the discount rate for the route.

[0014] Furthermore, S4 specifically includes: S41: Formulate the programming function constraints, including the first programming function constraints and the second programming function constraints; S42: Combine the first and second programming functions, and find the Pareto optimal solution under the constraints of the programming functions to obtain the current year's programming solution, which includes the ratio of the number of various new energy power generation devices, energy storage capacity, and the number of transmission lines.

[0015] Furthermore, following S4, it also includes: S5: Predict and update the power generation of a single new energy device to obtain the updated power generation of a single new energy device. Based on the updated power generation of a single new energy device, obtain the future planning solution. Complete the incremental planning of the urban power transmission network by combining the current year's planning solution and the future planning solution.

[0016] Furthermore, S5 specifically includes: The growth curve of power generation per new energy unit is predicted. Based on the growth curve of power generation per new energy unit, the updated power generation per new energy unit is obtained. Future urban power consumption data is predicted. Using the updated power generation per new energy unit and future urban power consumption data, the future planning solution is obtained. The incremental planning of the urban power transmission network is completed by combining the current year's planning solution and the future planning solution.

[0017] Furthermore, the first planning function constraints include that the proportion of wind power generation equipment in the installed capacity of new energy sources must not be lower than a preset threshold, the energy storage capacity must not be lower than a set proportion of the total power generation of new energy sources, and the power output of new energy power generation equipment, generators and energy storage equipment at any time must meet the time-of-use energy consumption at any time; the second planning function constraints include that the load of each transmission line must not exceed the upper limit of the load of a single transmission line, the power of each node in the transmission line can be balanced, and the voltage phase angle of each transmission line is within a set range.

[0018] This application also provides a power grid planning system that considers multiple variation domains, including: The generator power generation determination module is used to acquire urban power consumption data, divide the urban power consumption data into time-of-use energy consumption, and determine the generator power generation through time-of-use energy consumption. The first planning function construction module is used to determine the rated power and weather data of the standard new energy power generation equipment, predict the power generation of a single new energy equipment based on the weather data and rated power, construct the new energy installed capacity and initial energy storage capacity based on the power generation of a single new energy equipment and time-of-use energy consumption, and construct a first planning function with the goal of minimizing the function value using the generator power generation, new energy installed capacity and energy storage capacity, wherein the initial value of the energy storage capacity is the initial energy storage capacity. The second planning function construction module is used to determine the generator operating cost. It constructs a line quantity expression parameter based on line density, new energy installed capacity and the number of lines required for the generator. It then constructs a second planning function with the goal of minimizing the function value using the line quantity expression parameter and the generator operating cost. The annual planning module is used to formulate the constraints of the planning function. Under the constraints of the planning function, the first planning function and the second planning function are solved simultaneously to obtain the annual planning solution, which includes the ratio of the number of various new energy power generation equipment, the optimized energy storage capacity, and the number of transmission lines.

[0019] Furthermore, time-of-use energy consumption includes peak energy consumption and off-peak energy consumption; The generator power generation determination module is specifically used to acquire urban power consumption data, divide the urban power consumption data by hourly power consumption to obtain off-peak energy consumption, and determine the generator power generation based on off-peak energy consumption.

[0020] Furthermore, new energy power generation equipment includes: wind power generation equipment and solar power generation equipment; time-of-use energy consumption includes morning peak energy consumption, evening peak energy consumption, and off-peak energy consumption; The first planning function construction module specifically determines the power generation capacity of the standard new energy power generation equipment, acquires weather data, inputs the weather data into the prediction model, and obtains the power generation of a single new energy equipment. Based on the power generation of a single new energy equipment, the ratio of morning peak energy consumption to evening peak energy consumption, the module constructs the new energy installed capacity, including wind power installed capacity and solar power installed capacity. Based on the new energy installed capacity and urban electricity consumption data, the module constructs the initial energy storage capacity. The module then uses the generator power generation, new energy installed capacity, and energy storage capacity to construct the first planning function, where the initial value of the energy storage capacity is the initial energy storage capacity.

[0021] Furthermore, it also includes: The future planning determination module is used to predict and update the power generation of single renewable energy devices, obtain the updated power generation of single renewable energy devices, and obtain the future planning solution based on the updated power generation of single renewable energy devices. The gradual planning of the urban power transmission network is completed by combining the current year's planning solution and the future planning solution.

[0022] This application also provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; The processor implements the above method when executing programs stored in memory.

[0023] This application also provides a computer storage medium storing a computer program, which, when executed by a processor, implements any of the methods described above.

[0024] This application also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer is able to perform any of the methods described above.

[0025] Compared with the prior art, this application has the following advantages: This application provides a power grid planning method considering multiple variation domains. By statistically analyzing urban electricity consumption data at different times, and combining this with weather data, it constructs a first planning function for planning new energy equipment, including the installed capacity of renewable energy sources and energy storage capacity. A second planning function is then constructed to evaluate the cost of power line construction. Solving the first and second planning functions simultaneously integrates information from multiple variation domains (source-grid-load-intermediate-storage-policy) for comprehensive planning of power grid equipment construction. Furthermore, considering future advancements in renewable energy equipment and the planning of future power generation equipment operating costs, this application enables progressive planning of future power grid construction over time.

[0026] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 A flowchart illustrating a transmission network planning method considering multiple variation domains according to an embodiment of this application is shown. Figure 2 A schematic diagram of the structure of a power grid planning system considering multiple variation domains according to an embodiment of this application is shown; Figure 3 A schematic diagram of an electronic device structure according to an embodiment of this application is shown. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0030] Figure 1 A flowchart illustrating a transmission network planning method considering multiple variation domains according to an embodiment of this application is shown, as follows: Figure 1As shown, the power grid planning method considering multiple variation domains in this application includes: S1: Obtain urban electricity consumption data, divide the urban electricity consumption data into time-of-use energy consumption, and determine the generator output through time-of-use energy consumption.

[0031] Furthermore, the objective of this stage is to determine the generator output by dividing the city's electricity consumption data into time-of-use energy consumption. Specifically, in step S1, after obtaining the city's electricity consumption data, the data is divided according to hourly electricity consumption to obtain the time-of-use energy consumption, which includes off-peak energy consumption. The generator output is then constructed based on the off-peak energy consumption.

[0032] The generator power output mentioned herein refers to the generator power output of non-new energy power generation equipment.

[0033] The specific implementation method for the above steps in this embodiment is as follows: First, we need to obtain the city's electricity consumption data, which is the specific data on the city's electricity consumption over a recent period. This data is then averaged to obtain the city's electricity consumption data. In this embodiment, the city's electricity consumption data is presented as the city's average electricity consumption per hour throughout the day. Next, the city's electricity consumption data is divided according to hourly electricity consumption, specifically identifying peak electricity consumption periods. The hourly average energy consumption during these peak periods is defined as peak energy consumption. Typically, a city has two peak electricity consumption periods: morning and the first half of the night. These two peaks have different characteristics, so the hourly average energy consumption for each peak needs to be calculated separately to obtain morning peak energy consumption and evening peak energy consumption. Additionally, the hourly average energy consumption during off-peak periods also needs to be calculated to obtain off-peak energy consumption. Then, we can construct a variable for generator output based on off-peak energy consumption. Here, generator output is the electricity generated by the generator per hour, and it must be ensured that its minimum output is not lower than the off-peak energy consumption.

[0034] S2: Predict the power generation of a single new energy device. Based on the power generation of a single new energy device and the time-of-use energy consumption, construct the installed capacity of new energy and the initial energy storage capacity. Use the power generation of the generator, the installed capacity of new energy, and the energy storage capacity to construct the first planning function with the goal of minimizing the function value.

[0035] Specifically, based on determined weather data and the rated power of standard new energy power generation equipment, the power generation of a single new energy unit is predicted.

[0036] Wherein, the initial value of the energy storage capacity is the initial energy storage capacity; New energy power generation equipment includes wind power generation equipment and solar power generation equipment. The purpose of this step is to predict the power generation of a single new energy device, construct the installed capacity of new energy and the initial energy storage capacity, and thus construct the first planning function. Specifically, step S2 further includes: S21: Determine the rated power of the standard new energy power generation equipment, obtain weather data, input the weather data into the prediction model, and obtain the power generation of a single new energy equipment; This application constructs a prediction model. First, the determined rated power of the equipment is used as a core constraint and embedded into the output layer of the prediction model. Then, the acquired weather data is input into the prediction model, and the actual output power of the equipment is calculated through the prediction model algorithm, such as combining the irradiance-power curve and the wind speed-power curve, to finally obtain the predicted power generation of a single new energy device. It should be noted that the prediction result must not exceed the maximum theoretical power generation corresponding to the rated power. S22: The time-of-use energy consumption includes morning peak energy consumption and evening peak energy consumption. The new energy installed capacity, including wind power installed capacity and solar power installed capacity, is constructed based on the ratio of the power generation of the single new energy device, the morning peak energy consumption, and the evening peak energy consumption. S23: Construct the initial energy storage capacity based on the installed capacity of new energy sources and the urban power consumption data; construct the first planning function using the generator output, the installed capacity of new energy sources, and the energy storage capacity.

[0037] The initial value of the energy storage capacity is the initial energy storage capacity.

[0038] The specific implementation method for the above steps in this embodiment is as follows: First, it's necessary to identify the new energy power generation equipment, including wind power and solar power equipment. The power output of each type of equipment depends on the external environment, specifically the weather. Weather conditions significantly impact the power output of individual wind and solar power units. Therefore, it's essential to obtain long-term weather data for the city and input it into a specially trained prediction model. This allows the model to predict the average hourly power output of the wind and solar power equipment in the city, representing the power output per unit. Since different models of wind and solar power equipment have varying power outputs, a standard model must be selected. This standard model will have the same average hourly power output, and its predicted hourly power output in the city will be used as the power output per unit. When the equipment model changes, its hourly power output will be converted to the equivalent of the standard wind and solar power equipment.

[0039] Time-of-use energy consumption includes morning peak energy consumption and evening peak energy consumption. Morning peak energy consumption is caused by morning commutes, factory and business operations, and school openings. During this time, equipment in factories and businesses starts up collectively, requiring higher energy consumption for preheating, resulting in a peak in electricity demand. Evening peak energy consumption is due to residents using multiple appliances simultaneously after returning home, such as cooking, lighting, air conditioning, televisions, and washing machines, combined with the electricity consumption of commercial establishments like shopping malls, restaurants, and office buildings, as well as the evening shift demand from some businesses and factories. Comparatively, evening peak energy consumption is higher than morning peak energy consumption.

[0040] For new energy power generation equipment, wind power equipment can generate electricity all day, while solar power equipment can only generate electricity during the day. Electricity demand is higher during the evening peak. Therefore, to meet the evening peak electricity demand and avoid power shortages during this period, it is necessary to increase the proportion of wind power equipment, that is, to increase the proportion of wind power installed capacity. Here, we first need to determine the difference between the city's daily electricity demand and the generator output to obtain the electricity demand gap. Then, we need to ensure that the total daily power generation of new energy power generation equipment equals the electricity demand gap, and that the ratio of the daily power generation of wind power equipment to solar power equipment equals the ratio of evening peak energy consumption to morning peak energy consumption. Finally, combining this with the power generation of individual new energy equipment, we can calculate the new energy installed capacity, which includes the installed capacity of both solar and wind power equipment.

[0041] Since the highest power generation efficiency of renewable energy generation equipment is in the afternoon and evening, while peak electricity consumption is concentrated in the morning and early night, and solar power generation equipment does not generate electricity at night, energy storage equipment is needed to store excess electricity generated by renewable energy generation equipment and supplement it during peak periods. Here, the maximum amount of excess electricity generated by renewable energy generation equipment can be calculated based on the installed capacity, power generation of a single renewable energy unit, and time-of-use energy consumption, and the energy storage capacity can be set according to this maximum amount of excess electricity. Here, the maximum amount of excess electricity can be set to 80% of the energy storage capacity to allow for redundancy and ensure the lifespan of the energy storage equipment. This allows us to construct the first planning function:

[0042] Here, min() indicates finding the minimum value within the parentheses. This represents the construction cost of the generator, allocated to the generator's power output. This indicates the generator's power output. Let represent the discount rate for the generator, and t represent the current year as the t-th year in the planning process. This represents the operating and construction costs of new energy power generation equipment allocated to the total new energy power generation, including the costs of solar power generation equipment and wind power generation equipment. This represents the total power generation from renewable energy sources, which can be obtained based on the installed capacity of renewable energy plants. This represents the discount rate for new energy power generation equipment. This represents the operating and construction costs of energy storage equipment allocated to the energy storage capacity. This represents the energy storage capacity, with its initial value being the initial energy storage capacity. This represents the discount rate for energy storage equipment.

[0043] When adjusting the first programming function, the ratio of daily power generation of wind power generation equipment and solar power generation equipment, energy storage capacity, etc. can be adjusted to make the first programming function take the minimum value. The preliminary settings of parameters such as the ratio of daily power generation of wind power generation equipment and solar power generation equipment, energy storage capacity, etc., made in the previous text can greatly save computing resources in the subsequent process of obtaining the programming solution for the year.

[0044] S3: Determine the generator operating cost. Construct a line quantity expression parameter based on line density, new energy installed capacity, and the number of lines required for the generator. Construct a second planning function with the goal of minimizing the function value using the line quantity expression parameter and the generator operating cost.

[0045] Furthermore, the objective of this stage is to construct parameters representing the number of lines, thereby constructing the second planning function. Specifically, step S3 further includes: S31: Determine the generator cost characteristic function, and determine the generator operating cost based on the generator cost characteristic function; S32: Determine the line density parameters, including generator line density, wind power line density, and solar power line density; construct line quantity expression parameters based on the line density parameters, the installed capacity of new energy sources, and the number of lines required for generators; and construct a second planning function using the line quantity expression parameters and the generator operating cost.

[0046] The specific implementation method for the above steps in this embodiment is as follows: First, it is necessary to fit historical data on generator operating costs at the power plant to determine the generator cost characteristic function. Based on this function, the generator operating cost for the current year can be determined. Subsequently, line density parameters were determined, including generator line density, wind power line density, and solar power line density. Generator line density is the average number of lines required to generate one megawatt of electricity; wind power line density is the average number of lines required to generate one megawatt of electricity; and solar power line density is the average number of lines required to generate one megawatt of electricity. Thus, based on the line density parameters and the installed capacity of new energy sources, the current required number of lines can be obtained, which is the line quantity expression parameter. This allows us to construct the following second programming function:

[0047] in, The discount rate for the route. The construction cost of a power transmission line.

[0048] S4: Define the constraints of the planning function, and solve the first and second planning functions simultaneously under the constraints of the planning function to obtain the planning solution for the year.

[0049] Furthermore, the objective of this stage is to formulate the constraints of the planning function, thereby solving the first and second planning functions simultaneously to obtain the planning solution for the current year. Specifically, step S4 further includes: S41: Formulate the planning function constraints, including the first planning function constraints and the second planning function constraints; S42: Combine the first and second planning functions, and find the Pareto optimal solution under the constraints of the planning functions to obtain the planning solution for the current year.

[0050] The specific implementation method for the above steps in this embodiment is as follows: Since the parameters representing the number of lines in the second programming function are directly related to the installed capacity of solar and wind power equipment implied in the cost of solar and wind power equipment in the first programming function, a simultaneous solution is required. To achieve this, constraints need to be defined for the programming functions to avoid obtaining solutions unusable in practical applications. These constraints are divided into first programming function constraints for the first programming function and second programming function constraints for the second programming function. The first programming function constraints include: the proportion of wind power equipment in the installed capacity of new energy sources must not be lower than a preset threshold; the energy storage capacity must not be lower than a set proportion of the total power generation of new energy sources; and the power output of new energy power generation equipment, generators, and energy storage equipment at any given time must meet the time-of-use energy consumption requirements at any time.

[0051] The second programming function constraint requires that the load on each transmission line must not exceed the upper limit of the single transmission line load, the power of each node in the transmission line can be balanced, and the voltage phase angle of each transmission line must be within a set range. Then, the first and second programming functions are simultaneously solved, and under the constraints of the programming function, the ratio of the number of wind power generation devices to the number of solar power generation devices, the optimized energy storage capacity, and the number of transmission lines are obtained. This is used as the programming solution for that year, and the energy storage capacity is the capacity of the energy storage devices.

[0052] Considering that existing planning schemes are mostly static and one-time formulations, and do not dynamically update and adjust future plans according to the power generation of new energy single equipment, they lack the ability to progressively optimize the long-term operation of the transmission network and are difficult to adapt to the long-term changing needs of urban power systems. In order to overcome this problem, the scheme of this application also includes the following S5 step.

[0053] S5: Predict and update the power generation of a single new energy device to obtain the updated power generation of a single new energy device. Based on the updated power generation of a single new energy device, obtain the future planning solution. Complete the incremental planning of the urban power transmission network by combining the current year's planning solution and the future planning solution.

[0054] Furthermore, the objective of this stage is to obtain the future planning solution, thereby completing the incremental planning of the urban power grid. Specifically, in step S5, the growth curves of the total power generation of new energy sources and the power generation of new energy single equipment are predicted. Based on the growth curves of the power generation of new energy single equipment, the updated power generation of new energy single equipment is obtained. Future urban power consumption data is predicted. The future planning solution is obtained using the updated power generation of new energy single equipment and the future urban power consumption data.

[0055] The specific implementation method for the above steps in this embodiment is as follows: Due to the rapid advancements in new energy power generation equipment and the rapid growth in urban electricity demand driven by urban development and construction, it is necessary to predict the growth curve of single-equipment power generation based on these advancements. This forecast determines the potential increase in single-equipment power generation over the next few years, allowing for the estimation of future single-equipment power generation – the updated single-equipment power generation. Based on urban electricity consumption data and development plans, future urban electricity consumption can be predicted. The increased future electricity consumption relative to current consumption can then be calculated by substituting the updated single-equipment power generation and future urban electricity consumption data into the first and second planning functions. This yields the current year's planning solution – the future planning solution. These solutions guide the construction of the power system and facilitate future development and construction planning, ultimately completing the progressive planning of the urban power transmission network.

[0056] This application integrates "source," which includes power sources such as generators and new energy power generation equipment; "grid," which is the power grid composed of transmission lines; "load," which is the load caused by urban power consumption; "medium," which is the external medium environment, mainly external weather; "storage," which is energy storage capacity; and "strategy," which is the overall planning strategy. It provides a more comprehensive and realistic power grid planning scheme by comprehensively planning the urban power grid across multiple dimensions and multiple domains of "source-grid-load-medium-storage-strategy."

[0057] The following describes the power grid planning system considering multiple variation domains provided in this application. The incremental planning system for power grids considering multiple variation domains described below can be referred to in correspondence with the novel incremental planning method for urban power grids considering multiple variation domains described above. Figure 2 A block diagram of a transmission network planning system considering multiple variation domains according to an embodiment of this application is shown, such as... Figure 2 As shown, a transmission network planning system considering multiple variation domains includes: The generator power generation determination module 100 is used to acquire urban power consumption data, divide the urban power consumption data into time-of-use energy consumption, and determine the generator power generation through time-of-use energy consumption. First planning function construction module 200, The system determines the rated power and weather data of the standard new energy power generation equipment, predicts the power generation of a single new energy equipment based on the weather data and rated power, constructs the new energy installed capacity and initial energy storage capacity based on the power generation of a single new energy equipment and time-of-use energy consumption, and constructs a first planning function with the objective of minimizing the function value using the generator power generation, new energy installed capacity, and energy storage capacity; the second planning function construction module 300 is used to determine the generator operating cost, constructs a line quantity expression parameter based on line density, new energy installed capacity, and the number of lines required for the generator, and constructs a second planning function with the objective of minimizing the function value using the line quantity expression parameter and the generator operating cost; The annual plan identifies module 400, which is used to formulate the constraints of the planning function. Under the constraints of the planning function, the first planning function and the second planning function are solved simultaneously to obtain the annual planning solution, which includes the ratio of the number of various new energy power generation equipment, the optimized energy storage capacity, and the number of transmission lines.

[0058] Furthermore, the time-of-use energy consumption includes peak energy consumption and off-peak energy consumption; The generator power generation determination module 500 is specifically used to acquire the city's power consumption data, divide the city's power consumption data by hourly power consumption to obtain off-peak energy consumption, and determine the generator power generation based on the off-peak energy consumption.

[0059] Furthermore, the time-of-use energy consumption includes morning peak energy consumption, evening peak energy consumption, and off-peak energy consumption; The first planning function construction module 200 is specifically used to determine the rated power of the standard new energy power generation equipment, acquire weather data, input the weather data into the prediction model to obtain the power generation of a single new energy equipment; construct the new energy installed capacity including wind power installed capacity and solar power installed capacity based on the power generation of the single new energy equipment, the ratio of the morning peak energy consumption and the evening peak energy consumption; construct the initial energy storage capacity based on the new energy installed capacity and the urban electricity consumption data; and construct the first planning function using the generator power generation, the new energy installed capacity and the energy storage capacity, wherein the initial value of the energy storage capacity is the initial energy storage capacity.

[0060] The second planning function construction module 300 is specifically used to determine the generator cost characteristic function, determine the generator operating cost based on the generator cost characteristic function, determine the line density parameters including generator line density, wind power line density and solar power line density, construct line quantity expression parameters based on the line density parameters and the new energy installed capacity, and construct the second planning function through the line quantity expression parameters and the generator operating cost.

[0061] Furthermore, it also includes: The future planning module 500 is used to predict and update the power generation of single new energy equipment, obtain the updated power generation of single new energy equipment, and obtain the future planning solution based on the updated power generation of single new energy equipment. The gradual planning of the urban power transmission network is completed by combining the current year's planning solution and the future planning solution.

[0062] Based on the same inventive concept disclosed above, this application also provides an electronic device, such as... Figure 3 As shown. An electronic device according to an embodiment of this application includes at least one processor and at least one memory electrically connected to the processor. The memory stores instructions executable by the at least one processor, which, when executed, enable the at least one processor to perform the method described above.

[0063] It should be noted that the electrical connection between the above-mentioned units does not necessarily mean the connection between lines. The indirect connection method can be applied to the embodiments of this application as long as it achieves the purpose of this application.

[0064] Based on the same inventive concept, this application also provides a computer storage medium storing a computer program, which, when executed by a processor, implements the above-described method.

[0065] Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A power transmission network planning method considering multiple variation domains, characterized in that, The method includes: S1: Obtain urban electricity consumption data, divide the urban electricity consumption data into time-of-use energy consumption, and determine the generator output through time-of-use energy consumption. S2: Determine the rated power and weather data of the standard new energy power generation equipment, predict the power generation of a single new energy equipment based on the weather data and rated power, construct the new energy installed capacity and initial energy storage capacity based on the power generation of a single new energy equipment and time-of-use energy consumption, and construct a first planning function with the goal of minimizing the function value using the generator power generation, new energy installed capacity and energy storage capacity, wherein the initial value of the energy storage capacity is the initial energy storage capacity. S3: Determine the generator operating cost. Construct a line quantity expression parameter based on line density, new energy installed capacity and the number of lines required for the generator. Construct a second planning function with the goal of minimizing the function value using the line quantity expression parameter and the generator operating cost. S4: Formulate the constraints of the planning function, and solve the first and second planning functions simultaneously under the constraints of the planning function to obtain the planning solution for the year, which includes the ratio of the number of various new energy power generation equipment, the optimized energy storage capacity and the number of transmission lines.

2. The method according to claim 1, characterized in that, The time-of-use energy consumption includes peak energy consumption and off-peak energy consumption; Obtain urban electricity consumption data, divide the urban electricity consumption data into time-of-use energy consumption, and determine the generator output based on the time-of-use energy consumption, including: The city's electricity consumption data is obtained, and the city's electricity consumption data is divided according to the hourly electricity consumption to obtain off-peak energy consumption. The generator power generation is determined based on the off-peak energy consumption.

3. The method according to claim 1, characterized in that, New energy power generation equipment includes: wind power generation equipment and solar power generation equipment; the time-of-use energy consumption includes morning peak energy consumption, evening peak energy consumption, and off-peak energy consumption, and S2 specifically includes: S21: Determine the rated power of the standard new energy power generation equipment, obtain weather data, input the weather data into the prediction model, and obtain the power generation of a single new energy equipment, wherein the output layer of the prediction model is embedded with the rated power of the standard new energy power generation equipment. S22: Construct a new energy installed capacity including wind power installed capacity and solar power installed capacity based on the ratio of the power generation of the new energy single equipment, the energy consumption during the morning peak and the energy consumption during the evening peak; S23: Construct the initial energy storage capacity based on the installed capacity of new energy sources and the urban power consumption data; construct the first planning function using the generator output, the installed capacity of new energy sources, and the energy storage capacity.

4. The method according to claim 3, characterized in that, The first planning function constructed is: Here, min() indicates finding the minimum value within the parentheses. This represents the construction cost of the generator, allocated to the generator's power output. This indicates the generator's power output. Let represent the discount rate for the generator, and t represent the current year as the t-th year in the planning process. This represents the operating and construction costs of new energy power generation equipment allocated to the total new energy power generation. This represents the total power generation from new energy sources. This represents the discount rate for new energy power generation equipment. This represents the operating and construction costs of energy storage equipment allocated to the energy storage capacity. Indicates energy storage capacity, This represents the discount rate for energy storage equipment.

5. The method according to claim 1, characterized in that, S3 specifically includes: S31: Determine the generator cost characteristic function, and determine the generator operating cost based on the generator cost characteristic function; S32: Determine the line density parameters, including generator line density, wind power line density, and solar power line density; construct line quantity expression parameters based on the line density parameters, the installed capacity of new energy sources, and the number of lines required for generators; and construct a second planning function using the line quantity expression parameters and the generator operating cost.

6. The method according to claim 1, characterized in that, The second planning function constructed is: in, The generator's operating costs this year Let represent the discount rate for the generator, and t represent the current year as the t-th year in the planning process. The construction cost of a power transmission line, The parameter is used to express the number of lines. This is the discount rate for the route.

7. The method according to claim 1, characterized in that, S4 specifically includes: S41: Formulate the planning function constraints, including the first planning function constraints and the second planning function constraints; S42: Combine the first planning function and the second planning function, and find the Pareto optimal solution under the constraints of the planning function to obtain the current year's planning solution, which includes the ratio of the number of various new energy power generation devices, energy storage capacity, and the number of transmission lines.

8. The method according to claim 1, characterized in that, Following S4, it also includes: S5: Predict and update the power generation of a single new energy device to obtain the updated power generation of a single new energy device. Based on the updated power generation of a single new energy device, obtain the future planning solution. Complete the incremental planning of the urban power transmission network by combining the current year's planning solution and the future planning solution.

9. The method according to claim 8, characterized in that, S5 specifically includes: The growth curve of power generation per renewable energy unit is predicted, and the updated power generation per renewable energy unit is obtained based on the growth curve. Future urban power consumption data is predicted, and the future planning solution is obtained using the updated power generation per renewable energy unit and the future urban power consumption data. The incremental planning of the urban power transmission network is completed by using the current year's planning solution and the future planning solution.

10. The method according to claim 7, characterized in that, The first planning function constraints include that the proportion of wind power generation equipment in the new energy installed capacity shall not be lower than the preset threshold, the energy storage capacity shall not be lower than the set proportion of the total new energy power generation, and the power output of new energy power generation equipment, generators and energy storage equipment at any time must meet the time-of-use energy consumption at any time. The second planning function constraints include: the load of each transmission line must not exceed the upper limit of the load of a single transmission line, the power of each node in the transmission line can be balanced, and the voltage phase angle of each transmission line is within the set range.

11. A power transmission network planning system considering multiple variation domains, characterized in that, include: The generator power generation determination module is used to acquire urban power consumption data, divide the urban power consumption data into time-of-use energy consumption, and determine the generator power generation through time-of-use energy consumption. The first planning function construction module is used to determine the rated power and weather data of the standard new energy power generation equipment, predict the power generation of a single new energy equipment based on the weather data and rated power, construct the new energy installed capacity and initial energy storage capacity based on the power generation of a single new energy equipment and time-of-use energy consumption, and construct a first planning function with the goal of minimizing the function value using the generator power generation, new energy installed capacity and energy storage capacity, wherein the initial value of the energy storage capacity is the initial energy storage capacity. The second planning function construction module is used to determine the generator operating cost. It constructs a line quantity expression parameter based on line density, new energy installed capacity and the number of lines required for the generator. It then constructs a second planning function with the goal of minimizing the function value using the line quantity expression parameter and the generator operating cost. The annual planning module is used to formulate the constraints of the planning function. Under the constraints of the planning function, the first planning function and the second planning function are solved simultaneously to obtain the annual planning solution, which includes the ratio of the number of various new energy power generation equipment, the optimized energy storage capacity, and the number of transmission lines.

12. The system according to claim 11, characterized in that, The time-of-use energy consumption includes peak energy consumption and off-peak energy consumption; The generator power generation determination module is specifically used to acquire the city's power consumption data, divide the city's power consumption data by hourly power consumption to obtain off-peak energy consumption, and determine the generator power generation based on the off-peak energy consumption.

13. The system according to claim 11, characterized in that, New energy power generation equipment includes: wind power generation equipment and solar power generation equipment; the time-of-use energy consumption includes morning peak energy consumption, evening peak energy consumption and off-peak energy consumption; The first planning function construction module specifically determines the power generation capacity of the standard new energy power generation equipment, acquires weather data, inputs the weather data into the prediction model, and obtains the power generation of a single new energy equipment; constructs a new energy installed capacity including wind power installed capacity and solar power installed capacity based on the power generation of the single new energy equipment, the ratio of the morning peak energy consumption and the evening peak energy consumption; constructs the initial energy storage capacity based on the new energy installed capacity and the urban electricity consumption data; and constructs a first planning function using the generator power generation, the new energy installed capacity, and the energy storage capacity, wherein the initial value of the energy storage capacity is the initial energy storage capacity.

14. The system according to claim 11, characterized in that, Also includes: The future planning determination module is used to predict and update the power generation of single renewable energy devices, obtain the updated power generation of single renewable energy devices, and obtain the future planning solution based on the updated power generation of single renewable energy devices. The gradual planning of the urban power transmission network is completed by combining the current year's planning solution and the future planning solution.

15. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; The memory is used to store computer programs; When the processor executes a program stored in the memory, it implements the method described in any one of claims 1-10.

16. A computer storage medium, characterized in that, The computer storage medium stores a computer program, which, when executed by a processor, implements the method described in any one of claims 1-10.

17. A computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, characterized in that, When the program instructions are executed by a computer, the computer is able to perform the method described in any one of claims 1-10.

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