Power distribution network operation planning scheme generation method, system and device integrating source-load prediction and operation deduction, and medium

Abstract

The present application relates to the technical field of power distribution network planning and operation, and discloses a power distribution network operation planning scheme generation method, system, device and medium integrating source and load prediction and operation deduction, which comprises the following steps: forming a preset time based on source and load prediction requirements, and uniformly dividing the preset time to obtain time intervals; predicting the estimated available power of power supply side thermal power plants and the estimated demand of load side energy demand in the time intervals, respectively, to obtain the distribution of thermal power plants and energy demand; calculating a first fault tolerance range of the estimated available power and a second fault tolerance range of the estimated demand; generating a connection topology graph and obtaining the connection path between the thermal power plants and the energy demand; obtaining a loss coefficient according to an electric power transmission loss model to form a backup operation scheme and deduce a switching degree, and taking the backup operation scheme with the smallest switching degree as the target operation scheme. The present application can realize stable and efficient operation of the power distribution network, and improve power supply reliability and economy.

Description

Technical Field

[0001] This invention relates to the field of distribution network planning and operation technology, and in particular to a method, system, equipment and medium for generating distribution network operation planning schemes that integrate source load prediction and operation simulation. Background Technology

[0002] With the rapid development of power systems, the operation planning of distribution networks faces increasingly complex challenges. Traditional power allocation methods mainly rely on static allocation based on forecasts of the power source and load sides.

[0003] However, this method has the following significant problems: ① Existing source-load forecasting technologies cannot completely avoid errors, especially when facing sudden factors such as weather changes and holidays. There may be a large deviation between the predicted available power and the actual demand. If this error is not taken into account, it can easily lead to inaccurate power distribution and even supply-demand imbalance. ② Power loss occurs during transmission due to line resistance. Traditional methods often do not incorporate power loss into the distribution scheme design, resulting in the actual power supply not fully meeting demand and affecting power supply reliability. ③ In actual operation, thermal power plants need to frequently switch power supply targets according to changes in demand. Repeated start-up and shutdown operations not only shorten the life of control devices but also cause additional energy waste during startup. Existing methods lack optimization for switching frequency, and long-term operation may lead to an increase in equipment failure rate, thereby affecting the stability of the distribution network. ④ Traditional methods usually use fixed time intervals for power distribution, which is difficult to adapt to dynamic changes in energy demand. Especially in scenarios with large demand fluctuations, static distribution schemes are prone to failure and cannot achieve flexible and efficient power regulation.

[0004] To address the aforementioned issues, there is an urgent need for a dynamic programming method that can integrate source-load forecasting and operational simulation. This method should optimize the switching frequency of thermal power plants while considering forecasting errors and power losses, thereby improving the operational efficiency and reliability of the distribution network. Summary of the Invention

[0005] In view of the aforementioned existing problems, the present invention is proposed.

[0006] Therefore, this invention provides a method, system, equipment, and medium for generating distribution network operation planning schemes that integrate source load prediction and operation simulation, solving the problems of supply and demand imbalance, reduced power supply reliability, and increased equipment wear caused by traditional distribution network operation planning methods that do not fully consider the impact of prediction errors, power losses, and frequent switching on equipment lifespan.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0008] In a first aspect, the present invention provides a method for generating distribution network operation planning schemes that integrates source-load prediction and operation simulation, including:

[0009] A preset time is formed based on the source load prediction demand, and the time interval is evenly divided according to the preset time to obtain the time interval;

[0010] The estimated available power of thermal power plants on the power supply side and the estimated energy demand on the load side are predicted respectively within the time interval to obtain the distribution of thermal power plants and the distribution of energy demand.

[0011] The first fault tolerance range of the estimated available power and the second fault tolerance range of the estimated demand are obtained through calculation.

[0012] Based on the distribution of the thermal power plants and the distribution of energy demand, a connection topology map is generated, and the connection paths between the thermal power plants and energy demand are obtained according to the connection topology map.

[0013] The loss coefficient of the connection path is obtained based on the pre-constructed power transmission loss model, a preliminary operation plan is formed, the switching degree of the preliminary operation plan is derived, and the preliminary operation plan with the smallest switching degree is taken as the target operation plan.

[0014] As a preferred embodiment of the distribution network operation planning scheme generation method integrating source-load prediction and operation simulation described in this invention, wherein: obtaining the first fault-tolerant range of the estimated available power includes:

[0015] The first fluctuation ratio is obtained by subtracting the filling ratio from the estimated ratio within the time interval and taking the absolute value. The maximum value of the first fluctuation ratio is taken as the first target ratio.

[0016] The estimated available power is multiplied by the first target proportion of thermal power plants corresponding to the same time interval to obtain the first fluctuation value;

[0017] A first fault tolerance range is formed based on the first fluctuation value, the midpoint of the first fault tolerance range is the estimated available power, and the length of the first fault tolerance range is equal to twice the first fluctuation value.

[0018] As a preferred embodiment of the distribution network operation planning scheme generation method integrating source load prediction and operation simulation described in this invention, wherein: obtaining the second fault-tolerant range of the estimated demand includes:

[0019] The ratio of the historical demand within the specified time interval to the historical demand within the target time interval is used as the verification ratio. The second fluctuation ratio is obtained by subtracting the verification ratio from the demand ratio and taking the absolute value. The maximum value of the second fluctuation ratio is used as the second target ratio.

[0020] The second fluctuation value is obtained by multiplying the estimated demand by the second target ratio of energy demand for the same time interval.

[0021] A second tolerance range is formed based on the second fluctuation value, the midpoint of the second tolerance range is the estimated demand, and the length of the second tolerance range is equal to twice the second fluctuation value.

[0022] As a preferred embodiment of the distribution network operation planning scheme generation method integrating source-load prediction and operation simulation described in this invention, wherein: obtaining the loss coefficient of the connection path based on the pre-constructed power transmission loss model includes:

[0023] Obtain the resistance value range of the connection path, and divide the resistance value range into equally spaced sections to obtain test points;

[0024] Under the condition that the resistance of the transmission line is equal to that of the test point, the power loss ratio of the transmission line is obtained;

[0025] The test points are paired with the power loss ratio and fitted to obtain the power loss function;

[0026] Substituting the resistance of the connection path into the power loss function yields the loss coefficient of the connection path.

[0027] The beneficial effects of this preferred technical solution are: optimizing the power distribution path based on the power loss model and reducing ineffective losses.

[0028] As a preferred embodiment of the distribution network operation planning scheme generation method integrating source-load prediction and operation simulation described in this invention, the method for obtaining the distribution of thermal power plants includes:

[0029] Obtain the historical coal usage of thermal power plants, and fit the historical coal usage based on the date to obtain a coal fitting function;

[0030] Input the date of the day into the coal fitting function to obtain the estimated daily usage of the thermal power plant;

[0031] Based on the historical usage of coal, the filling ratio of thermal power plants within a time interval is obtained, and the average of the filling ratios within the time interval is taken to obtain the estimated ratio. The filling ratio is the proportion of newly added coal entering the boiler within the time interval to the historical coal usage.

[0032] Multiply the estimated usage by the estimated ratio to obtain the predicted coal usage for the time period;

[0033] The total historical power generated by the historical coal consumption is obtained. The total historical power is divided by the historical coal consumption to obtain the unit output power. The average value of the unit output power is taken to obtain the unit reference power.

[0034] Multiply the unit reference power by the predicted coal quantity to obtain the preliminary predicted power for the time interval;

[0035] The day is divided into equal parts to obtain identification points. The heat emitted by the initial combustion of a unit mass of coal is obtained as the target heat. Under the condition that the combustion time is equal to the time at the identification point, the heat emitted by the combustion of a unit mass of coal is obtained as the characteristic heat. The characteristic heat is divided by the target heat to obtain the effective coefficient.

[0036] The values ​​at the identification points are paired with the effective coefficients and fitted to obtain the effective fitting function; the time interval that occurs before the time interval is taken as the feature time interval; the difference between the midpoint of the time interval and the feature time interval is taken to obtain the calibration time, which is then substituted into the effective fitting function to obtain the predicted effective coefficient of the feature time interval.

[0037] The initial predicted power for the characteristic time interval is multiplied by the prediction efficiency coefficient and then superimposed to obtain the summation success rate. The summation success rate is then superimposed with the initial predicted power for the time interval to obtain the estimated available power of thermal power plants within the time interval.

[0038] As a preferred embodiment of the distribution network operation planning scheme generation method integrating source load forecasting and operation simulation described in this invention, the acquisition of the energy demand distribution includes:

[0039] The first time interval of each day is taken as the target time interval. The actual energy demand within the target time interval is obtained, and the estimated energy demand within the target time interval is equal to the actual demand.

[0040] Obtain the historical energy demand within the time interval; take the average of the ratios between the historical demand within the time interval and the historical demand within the target time interval, and use this as the demand ratio;

[0041] Multiply the actual energy demand within the target time interval by the demand ratio to obtain the estimated energy demand within the time interval.

[0042] As a preferred embodiment of the distribution network operation planning scheme generation method integrating source-load prediction and operation simulation described in this invention, the formation of the preliminary operation scheme includes:

[0043] 1. Subtract the loss coefficient of the connection path to obtain the actual coefficient, and take the minimum value of the actual coefficient as the feature value;

[0044] The minimum value in the first fault tolerance range is uniformly divided into available power blocks, and the available power blocks are mapped to the time interval and thermal power plant corresponding to the first fault tolerance range; the maximum value in the second fault tolerance range is uniformly divided into demand blocks, the ratio of the size of the demand block to the available power block is equal to the characteristic value, and the demand blocks are mapped to the time interval and energy demand corresponding to the second fault tolerance range.

[0045] Randomly establish a correspondence between available power blocks and demand blocks corresponding to the same time interval, ensuring that an available power block is associated with at most one demand block. Each correspondence between available power blocks and demand blocks forms an alternative scheme, and the energy of the available power block is supplied to the corresponding demand block.

[0046] Delete any alternative schemes that do not correspond to the available power blocks, and use the remaining alternative schemes as backup operation schemes.

[0047] The beneficial effects of this preferred technical solution are: it innovatively introduces a fault tolerance range to cope with prediction errors, ensuring that energy demand can still be met under extreme fluctuations.

[0048] Secondly, this invention provides a distribution network operation planning scheme generation system that integrates source load prediction and operation simulation, including:

[0049] The time interval division module is used to form a preset time based on the source load prediction demand, and to uniformly divide the time interval according to the preset time to obtain the time interval.

[0050] The distribution acquisition module is used to predict the estimated available power of thermal power plants on the power supply side and the estimated energy demand on the load side within the time interval, so as to obtain the distribution of thermal power plants and the distribution of energy demand.

[0051] The range calculation module is used to calculate the first fault-tolerant range of the estimated available power and the second fault-tolerant range of the estimated demand.

[0052] The path acquisition module is used to generate a connection topology map based on the distribution of thermal power plants and the distribution of energy demand, and to obtain the connection path between thermal power plants and energy demand based on the connection topology map.

[0053] The scheme acquisition module is used to obtain the loss coefficient of the connection path according to the pre-built power transmission loss model, form a preliminary operation scheme, deduce the switching degree of the preliminary operation scheme, and take the preliminary operation scheme with the minimum switching degree as the target operation scheme.

[0054] Thirdly, the present invention provides an electronic device, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor executes the computer-executable instructions to realize the steps of a method for generating a distribution network operation planning scheme that integrates source-load prediction and operation simulation.

[0055] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of a method for generating a distribution network operation planning scheme that integrates source-load prediction and operation simulation.

[0056] Compared with existing technologies, the beneficial effects of this invention are as follows: By integrating source-load forecasting and operational simulation, this invention comprehensively considers the estimated available power of thermal power plants, the estimated energy demand, and power transmission losses to generate an optimal distribution network operation plan. It innovatively introduces a fault tolerance range to address forecast errors, ensuring that energy demand can still be met under extreme fluctuations. Simultaneously, it optimizes power distribution paths based on a power loss model, reducing ineffective losses. Furthermore, by calculating and minimizing the scheme switching degree, it significantly reduces the frequency of switching between different loads at thermal power plants, extends the lifespan of control equipment, and avoids additional energy waste caused by start-ups and shutdowns. Ultimately, it achieves stable and efficient operation of the distribution network, improving power supply reliability and economy. Attached Figure Description

[0057] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 This is a schematic diagram of the overall process logic of a distribution network operation planning scheme generation method that integrates source load prediction and operation simulation, provided as an embodiment of the present invention. Detailed Implementation

[0059] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0060] Example 1, referring to Figure 1 As one embodiment of the present invention, a method for generating distribution network operation planning schemes that integrates source load prediction and operation simulation is provided, such as... Figure 1The specific steps shown are as follows:

[0061] S100: Based on the source load forecast demand, a preset time is formed, and the time interval is evenly divided according to the preset time to obtain the time interval;

[0062] S200: Forecast the estimated available power of thermal power plants on the power supply side and the estimated energy demand on the load side within the time interval to obtain the distribution of thermal power plants and the distribution of energy demand.

[0063] S300: The first fault tolerance range of the estimated available power and the second fault tolerance range of the estimated demand are obtained through calculation;

[0064] S400: Based on the distribution of thermal power plants and the distribution of energy demand, generate a connection topology map, and obtain the connection path between thermal power plants and energy demand based on the connection topology map;

[0065] S500: Obtain the loss coefficient of the connection path based on the pre-built power transmission loss model, form a preliminary operation plan, deduce the switching degree of the preliminary operation plan, and take the preliminary operation plan with the smallest switching degree as the target operation plan.

[0066] It should be noted that, to address the problems of supply-demand imbalance, reduced power supply reliability, and increased equipment losses caused by traditional distribution network operation planning methods that do not fully consider the impact of prediction errors, power losses, and frequent switching on equipment lifespan, steps S100-S500 integrate source-load prediction and operational simulation. They comprehensively consider the estimated available power of thermal power plants, the estimated energy demand, and power transmission losses to generate an optimal distribution network operation plan. This innovatively introduces a fault tolerance range to cope with prediction errors, ensuring that energy demand can still be met under extreme fluctuations. Simultaneously, power distribution paths are optimized based on a power loss model to reduce ineffective losses. Furthermore, by calculating and minimizing the scheme switching degree, the frequency of frequent switching between different loads at thermal power plants is significantly reduced, extending the lifespan of control equipment and avoiding additional energy waste caused by start-ups and shutdowns. Ultimately, this achieves stable and efficient operation of the distribution network, improving power supply reliability and economy.

[0067] In this embodiment of the invention, the above step S100, which forms a preset time based on the source load prediction demand and divides the time interval evenly according to the preset time, includes the following sub-steps A1 and A2:

[0068] In A1: a preset time is formed based on the source load forecast demand; the specific steps include:

[0069] Obtain the allowable error of power distribution network operation;

[0070] Obtain the upper limit of the variation in energy demand in the distribution network per unit time, and sum up the upper limits of the variation in energy demand to obtain the total variation.

[0071] The preset time is obtained by dividing the unit time by the overall variation range and multiplying it by the allowable error.

[0072] It should be noted that using a preset time interval as the control interval can effectively control the error caused by the control.

[0073] In A2: the time interval is obtained by uniformly dividing the time according to the preset time.

[0074] Specifically, according to the preset time, the time of each day is evenly divided into time intervals, where the length of each time interval is equal to the preset time.

[0075] It should be noted that the above step S100 dynamically generates a preset time based on the source load forecast demand and evenly divides the time interval. This allows for flexible division of the control cycle according to the actual fluctuation characteristics of the power system. This avoids the control lag problem caused by fixed time intervals and can also match the source load change patterns of different time periods through scientific time segmentation, laying a time sequence foundation for subsequent accurate forecasting and dynamic control.

[0076] In this embodiment of the invention, step S200, which forecasts the estimated available power of thermal power plants on the power supply side and the estimated energy demand on the load side within a time interval, and obtains the distribution of thermal power plants and the distribution of energy demand, includes the following sub-steps B1 and B2:

[0077] In B1: the estimated available power of thermal power plants on the power supply side is predicted within the time interval to obtain the distribution of thermal power plants; the specific steps include:

[0078] Obtain the historical coal consumption of thermal power plants, fit the historical coal consumption based on the date, and obtain the coal fitting function.

[0079] Input the date of the day into the coal fitting function to obtain the estimated daily usage of the thermal power plant;

[0080] Based on the historical usage of coal, the filling ratio of thermal power plants within a time period is obtained. The average of the filling ratios within the time period is taken to obtain the estimated ratio. The filling ratio is the proportion of newly added coal entering the boiler within the time period to the historical coal usage.

[0081] Multiply the estimated usage by the estimated proportion to obtain the coal forecast for the time period;

[0082] The total historical power generated by the historical coal consumption is obtained. The total historical power is divided by the historical coal consumption to obtain the unit output power. The average value of the unit output power is taken to obtain the unit reference power.

[0083] Multiply the unit reference power by the predicted coal quantity to obtain the preliminary predicted power for the time interval;

[0084] The day is divided into equal parts to obtain identification points. The heat emitted by the initial combustion of a unit mass of coal is obtained as the target heat. Under the condition that the combustion time is equal to the time at the identification point, the heat emitted by the combustion of a unit mass of coal is obtained as the characteristic heat. The characteristic heat is divided by the target heat to obtain the effective coefficient.

[0085] The values ​​at the identification points are paired with the effective coefficients and fitted to obtain the effective fitting function;

[0086] The time interval that occurs before the time interval is used as the characteristic time interval;

[0087] The midpoint between the time interval and the feature time interval is subtracted to obtain the calibration time, which is then substituted into the effective fitting function to obtain the prediction efficiency coefficient of the feature time interval.

[0088] The initial predicted power for the characteristic time interval is multiplied by the prediction efficiency coefficient and then superimposed to obtain the summation success rate. The summation success rate is then superimposed with the initial predicted power for the time interval to obtain the estimated available power of thermal power plants within the time interval.

[0089] Specifically, the percentage to be filled in is the proportion of coal fed into the boiler for power generation in each time interval to the historical coal usage, where historical coal usage is the usage of a single day in the historical records.

[0090] It should be noted that the power output of thermal power generation is mainly determined by the amount of coal involved in power generation. The coal used for power generation within a time interval refers to the coal added during the time interval and the time interval before the current time interval. Since coal combustion is subject to attenuation, a corresponding function was established to characterize the attenuation. Based on the time that the coal has been burning, the power generation output generated within the time interval is determined. Thus, the estimated available power of the thermal power plant within the time interval can be obtained.

[0091] In B2: the estimated energy demand on the load side within a time interval is predicted to obtain the distribution of energy demand; the specific steps include:

[0092] The first time interval of each day is taken as the target time interval. The actual energy demand within the target time interval is obtained, and the estimated energy demand within the target time interval is equal to the actual demand.

[0093] For at least one historical number of days, obtain the historical energy demand within the time interval; take the average of the ratios of the historical demand within the time interval to the historical demand within the target time interval as the demand ratio;

[0094] Multiply the actual energy demand within the target time interval by the demand ratio to obtain the estimated energy demand within the time interval.

[0095] It should be noted that the estimated demand within a time interval is determined primarily based on the ratio between the target time interval and the other time intervals. The estimated demand cannot be obtained by averaging the historical demand of the time intervals, because factors such as holidays and other time-related factors exist, and the demand on these days will be completely different from that on weekdays. Using averages cannot characterize these situations. However, the ratio can provide a relatively approximate reflection of the demand. It is only necessary to obtain the demand for the first time interval of each day, and then use this as the basis for calculating the demand for the remaining time intervals.

[0096] It should be noted that step S200 above acquires the distribution of power supply and load sides respectively, realizing spatial modeling of source and load data. Through historical data fitting and dynamic correction, the accuracy of power prediction is significantly improved. At the same time, the extraction of distribution features provides data support for subsequent topology optimization, ensuring that the power distribution scheme is highly consistent with the actual geographical layout.

[0097] In this embodiment of the invention, step S300, which calculates the first fault-tolerant range of the estimated available power and the second fault-tolerant range of the estimated demand, includes the following sub-steps C1 and C2:

[0098] In C1: the first fault-tolerant range of the estimated available power is calculated; the specific calculation steps include:

[0099] The first fluctuation ratio is obtained by subtracting the input ratio from the estimated ratio within the time interval and taking the absolute value. The maximum value of the first fluctuation ratio is taken as the first target ratio.

[0100] The first fluctuation value is obtained by multiplying the estimated available power by the first target proportion of thermal power plants in the same time interval.

[0101] A first fault tolerance range is formed based on the first fluctuation value. The midpoint of the first fault tolerance range is the estimated available power, and the length of the first fault tolerance range is equal to twice the first fluctuation value.

[0102] Specifically, the first fluctuation ratio is obtained by selecting historical data from the same time interval within several past periods, calculating the absolute difference between the input ratio and the estimated ratio in each time interval, and thus obtaining a fluctuation ratio sequence that reflects the magnitude of historical deviation.

[0103] Specifically, the first fluctuation value is calculated by multiplying the historical maximum deviation ratio (i.e. the first target ratio) by the estimated available power for the current time interval. The result represents the maximum positive or negative deviation that the current estimated power may have, and is used to quantitatively describe the range of uncertainty in the power forecast.

[0104] Specifically, since the fluctuations oscillate around the estimated available power, the midpoint of the first fault-tolerant range is the estimated available power, and the length of the first fault-tolerant range is equal to twice the value of the first fluctuation. Since the estimated available power is determined, the first fault-tolerant range is thus determined.

[0105] In C2: the second tolerance range for the estimated demand is calculated; the specific steps include:

[0106] The ratio of historical demand within a time interval to historical demand within a target time interval is used as the verification ratio. The second fluctuation ratio is obtained by subtracting the verification ratio from the demand ratio and taking the absolute value. The maximum value of the second fluctuation ratio is used as the second target ratio.

[0107] The second fluctuation value is obtained by multiplying the estimated demand by the second target ratio of energy demand for the same time interval.

[0108] A second fault tolerance range is formed based on the second fluctuation value. The midpoint of the second fault tolerance range is the estimated demand. The length of the second fault tolerance range is equal to twice the second fluctuation value.

[0109] Specifically, the second fluctuation ratio is calculated by taking the verification ratio of the historical demand for the same period to the target demand, then taking the absolute difference between the ratio of the two periods, and taking the maximum value as the second target ratio; the second fluctuation value is obtained by multiplying the second target ratio by the current estimated demand, representing the maximum possible deviation of the estimated demand, and is used to construct a tolerance range with the estimated demand as the midpoint and twice the fluctuation value as the width.

[0110] Specifically, since the fluctuations oscillate around the estimated demand, the midpoint of the resulting second tolerance range is the estimated demand, and the length of the second tolerance range is twice the second fluctuation value. Since the estimated demand is fixed, the second tolerance range is determined.

[0111] It should be noted that step S300 above quantifies the prediction error into an operable fluctuation range by calculating the first and second fault tolerance ranges, enabling the scheme design to cover extreme cases of source-load fluctuations, greatly enhancing the robustness of the system, avoiding power shortages or redundancy caused by prediction deviations, and ensuring the reliability of supply and demand balance.

[0112] In this embodiment of the invention, step S400 generates a connection topology map based on the distribution of thermal power plants and the distribution of energy demand, and obtains the connection path between thermal power plants and energy demand based on the connection topology map.

[0113] It should be noted that each energy demand has a location, which forms its distribution. The connection path between thermal power plants and energy demand is the transmission line connecting the two. The regulation of power distribution in the distribution network is quite complex, involving a large number of transmission lines. Therefore, in order to achieve better regulation, it is necessary to predict the source and load, and prepare for regulation in advance based on the prediction results, so as to achieve the required regulation effect.

[0114] It should be noted that, since predictions inevitably contain errors, on the one hand, a relatively reasonable algorithm needs to be set up to ensure that the error is within a controllable range as much as possible; on the other hand, during regulation, the error needs to be taken into account to ensure that the power demand can still be met even with errors. At the same time, when allocating and regulating power, the impact of resistance on power loss also needs to be considered. Here, the focus of the scheme is on the switching situation of thermal power plants between different energy demands and whether the energy demand can be met. The power loss caused by different allocation paths is not considered because existing thermal power generation is usually abundant and there is no shortage of power. The power generated by thermal power generation is usually used immediately, which means that even if it is not lost due to resistance, it will be wasted. Therefore, there is no need to choose the allocation path with the least resistance loss.

[0115] In this embodiment of the invention, based on the distribution of thermal power plants and energy demand, the connection topology map of thermal power plants and energy demand is formed by: marking all the transmission lines connecting thermal power plants and energy demand, and summarizing them to form a connection topology map; in the connection topology map, the shortest path of the transmission line from the thermal power plant to the energy demand is taken as the connection path.

[0116] It should be noted that step S400 generates a connection topology map based on distributed data and extracts the optimal path to realize the digital mapping of the power grid physical structure. This not only clarifies the connection relationship between power sources and loads, but also reduces redundant lines through topology optimization, thereby improving the feasibility of the planning scheme.

[0117] In this embodiment of the invention, step S500, which obtains the loss coefficient of the connection path based on a pre-built power transmission loss model, forms a preliminary operation plan, derives the switching degree of the preliminary operation plan, and selects the preliminary operation plan with the smallest switching degree as the target operation plan, includes the following sub-steps E1 to E3:

[0118] In E1: The loss coefficient of the connection path is obtained based on a pre-built power transmission loss model; the specific steps include:

[0119] Obtain the resistance value range of the connection path, divide the resistance value range into equal intervals, and obtain the test points;

[0120] Under the condition that the resistance of the transmission line is equal to that of the test point, obtain the power loss ratio of the transmission line;

[0121] The test points are paired with the power loss ratio and fitted to obtain the power loss function;

[0122] Substituting the resistance of the connection path into the power loss function yields the loss coefficient of the connection path.

[0123] Specifically, the power loss function is mainly generated by pairing test points with power loss ratios and fitting the data. The process involves generating points in a coordinate system by pairing test points with power loss ratios, and then connecting these points sequentially to form an approximate curve. At least one function model with a similar trend to the approximate curve is obtained by analyzing and comparing the points in the function library. The unknowns in the function model are solved using points on the approximate curve to obtain at least one preliminary function. The test points are substituted into the preliminary function, and the difference between the preliminary function and the corresponding power loss ratio is squared to obtain the error square. The error squares generated by the preliminary function for at least one test point are accumulated to obtain the comprehensive error of the preliminary function. The preliminary function with the smallest comprehensive error is selected as the power loss function.

[0124] In E2: A preliminary operational plan is developed; the specific steps include:

[0125] 1. Subtract the loss coefficient of the connection path to obtain the actual coefficient, and take the minimum value of the actual coefficient as the characteristic value.

[0126] The minimum value in the first fault tolerance range is uniformly divided into available power blocks, and the available power blocks are mapped to the time interval and thermal power plant corresponding to the first fault tolerance range; the maximum value in the second fault tolerance range is uniformly divided into demand blocks, the ratio of the size of the demand block to the available power block is equal to the characteristic value, and the demand blocks are mapped to the time interval and energy demand corresponding to the second fault tolerance range.

[0127] Randomly establish a correspondence between available power blocks and demand blocks corresponding to the same time interval, ensuring that an available power block is associated with at most one demand block. Each correspondence between available power blocks and demand blocks forms an alternative scheme, and the energy of the available power block is supplied to the corresponding demand block.

[0128] Delete any alternative schemes that do not correspond to the available power blocks, and use the remaining alternative schemes as backup operation schemes.

[0129] Specifically, the accumulated value of the available power block is equal to the minimum value in the first fault tolerance range, and the accumulated value of the demand block is equal to the maximum value in the second fault tolerance range.

[0130] It should be noted that due to the volatility of forecasts, the resulting plan must still meet energy demand and avoid any shortfall even under the most extreme forecast error conditions. This condition is the minimum value in the first tolerance range and the maximum value in the second tolerance range, which are the minimum and maximum values ​​of the predicted energy supply and demand, respectively. When this condition is met, all other conditions will be met. Furthermore, during supply, it is necessary to ensure that the power provided by the available power blocks can meet the corresponding demand blocks. Here, due to power losses, the actual effective power portion is not the available power blocks, but rather exists within a range. The minimum value of this range, i.e., the characteristic value, is used here. The ratio of the demand block to the available power block is set to equal the characteristic value. Thus, the available power blocks can definitely meet the demand blocks, and therefore, the pre-operation plan can definitely meet the demand.

[0131] In E3: The switching degree of the preliminary operation plan is derived, and the preliminary operation plan with the smallest switching degree is taken as the target operation plan; the specific steps include:

[0132] Within a time interval, if the available power block of a thermal power plant corresponds to the demand block of energy demand, then there is a corresponding relationship between the thermal power plant and energy demand within the time interval; otherwise, there is no corresponding relationship between the thermal power plant and energy demand within the time interval.

[0133] In the preliminary operation plan, two adjacent time intervals are respectively designated as the first time interval and the second time interval;

[0134] The energy demand corresponding to thermal power plants is summarized in the first time interval as the first set, and the energy demand corresponding to thermal power plants is summarized in the second time interval as the second set.

[0135] The union of the second set and the first set is the total set; the intersection of the second set and the first set is the subset.

[0136] The difference between the total set and the subsets is used to obtain the target set, and the predetermined number of items in the target set is taken as the target number.

[0137] The switching degree of the pre-operation plan is obtained by superimposing the target number of all thermal power plants in all adjacent time intervals.

[0138] Specifically, in this embodiment, the setting of the first and second time intervals is mainly used to simulate dynamic scheduling scenarios in power grid operation. Typical scenarios include: in adjacent 15-minute scheduling cycles (e.g., T1 is 8:00-8:15, T2 is 8:15-8:30), when load demand fluctuates due to factory shift changes or sudden changes in photovoltaic output, the system needs to compare the differences in power supply targets of each thermal power plant within the two intervals—for example, during T1, power plant A supplies power to load X / Y, while during T2 it switches to supplying power to load Y / Z. At this time, through set operations (union {X,Y,Z} minus intersection {Y}), the load point (X,Z) requiring switching operations can be quantified, thus accurately reflecting the frequency of equipment switching caused by changes in source and load. This is particularly suitable for scenarios that require balancing power supply stability and scheduling flexibility, such as dynamically adjusting the intermittency of new energy output or demand-side response.

[0139] Specifically, during the formation of the preliminary operation plan, the available power blocks and demand blocks corresponding to the same time interval are randomly associated, ensuring that an available power block is associated with at most one demand block. Each association between available power blocks and demand blocks forms a candidate plan. The candidate plans are further screened to obtain the preliminary operation plan. Therefore, the preliminary operation plan carries the association between available power blocks and demand blocks.

[0140] It should be noted that when a thermal power plant supplies energy to the required level, the transmission lines supplying the energy do not need to be stopped; adjustments can be made based on the supply level. However, when a thermal power plant does not supply energy to the required level, the transmission lines supplying the energy must be stopped. When the thermal power plant resumes supplying energy to the required level, the stopped transmission lines need to be restarted. This requires the control devices to constantly switch between starting and stopping. As is common sense, light bulbs are most likely to fail when they are turned on. Therefore, frequent starting and stopping will significantly reduce the lifespan of the control devices and greatly increase the probability of their failure. Once they fail, the power distribution network will be temporarily unusable.

[0141] It should be noted that step S500 above combines power loss modeling and switching frequency simulation to optimize the operation scheme from the dual dimensions of energy consumption and equipment lifespan. By quantifying path losses to select efficient transmission lines, and at the same time reducing equipment operation frequency with the goal of minimizing switching frequency, energy waste in the power transmission process is reduced, and the lifespan of key equipment is extended, ultimately achieving a synergistic improvement in economy and stability.

[0142] Example 2, based on the previous example, provides an application example of the distribution network operation planning scheme generation method that integrates source load prediction and operation simulation, to verify and illustrate the technical effects adopted in this method.

[0143] First, based on the predicted demand, a preset time and time interval are defined, namely, the preset time is 4 hours (based on historical data analysis, the demand fluctuation cycle during this period is 4 hours); and the day is divided into 6 intervals (00-04, 04-08, ..., 20-24).

[0144] Obtain the predicted available power of thermal power plants (taking the 08:00-12:00 timeframe as an example):

[0145] Power Plant A: Estimated available power 500MW ± 5% (First fault tolerance range: 475525MW);

[0146] Power Plant B: Estimated available power 300MW ± 7% (First fault tolerance range: 279321MW);

[0147] Power plants A and B are 200km apart and connected by a 500kV transmission line.

[0148] Obtain energy demand forecasts (for the same time period):

[0149] City X: Estimated demand 600MW ± 10% (Second tolerance range: 540660MW);

[0150] Industrial Zone Y: Estimated demand 200MW ± 8% (Second tolerance range: 184216MW);

[0151] Among them, city X is 50km away from power plant A, and industrial zone Y is 30km away from power plant B.

[0152] Furthermore, calculate the first fault tolerance range (power supply side): total available power range = (475+279)MW~(525+321)MW→754846MW; calculate the second fault tolerance range (load side): total demand range = (540+184)MW~(660+216)MW→724876MW;

[0153] Furthermore, obtain the connection topology and path: Power Plant A > | 500kV 200km | Power Plant B; Power Plant A > | 220kV 50km | City X; Power Plant B > | 110kV 30km | Industrial Zone Y;

[0154] Furthermore, according to the power loss formula, i.e., loss rate = 0.05% × distance (km) + 0.1% (fixed loss), the path loss is calculated as follows: Power plant A → City X: 50km × 0.05% + 0.1% = 2.6%; Power plant B → Industrial area Y: 30km × 0.05% + 0.1% = 1.6%; Power plant A → Power plant B: 200km × 0.05% + 0.1% = 10.1%.

[0155] Furthermore, two preliminary operating schemes were obtained: Scheme 1: Power plant A supplies city X (500MW × 97.4% = 487MW), and power plant B supplies industrial area Y (200MW × 98.4% = 197MW); the remaining power is 13MW from power plant A + 100MW from power plant B = 113MW; the switching degree requires adjusting the output of power plant B to the grid (switching operation: 1 time). Scheme 2: Power plant A supplies industrial area Y (200MW × 89.9% × 98.4% = 176MW) through power plant B, and power plant A directly supplies city X (300MW × 97.4% = 292MW); the switching degree requires coordinating the power allocation between the two power plants (switching operation: 3 times).

[0156] Based on the switching factor, Option 1 (minimum switching factor) is selected. The final execution plan is as follows: Time interval: 08:00-12:00; Operation instructions: Power plant A outputs 513MW (including 13MW reserve), power plant B outputs 200MW; City X receives 487MW, industrial zone Y receives 197MW; Expected total loss is 500×2.6%+200×1.6%=16.2MW. Key data is summarized in Table 1.

[0157] Table 1: Summary of key data.

[0158]

[0159] As described above, this system achieves minimum switching costs and acceptable transmission losses within a fault-tolerant range. This invention integrates source-load forecasting and operational simulation, comprehensively considering the estimated available power of thermal power plants, the estimated energy demand, and power transmission losses to generate an optimal distribution network operation plan. It innovatively introduces a fault-tolerant range to address forecast errors, ensuring energy demand is met even under extreme fluctuations. Simultaneously, it optimizes power distribution paths based on a power loss model, reducing ineffective losses. Furthermore, by calculating and minimizing the switching degree of the scheme, it significantly reduces the frequency of switching between different loads at thermal power plants, extends the lifespan of control equipment, and avoids additional energy waste caused by start-ups and shutdowns. Ultimately, it achieves stable and efficient operation of the distribution network, improving power supply reliability and economy.

[0160] Example 3: This example provides a distribution network operation planning scheme generation system that integrates source load prediction and operation simulation, including:

[0161] The time interval division module is used to form a preset time based on the source load prediction demand, and to divide the time interval evenly according to the preset time.

[0162] The distribution acquisition module is used to predict the estimated available power of thermal power plants on the power supply side and the estimated energy demand on the load side within a time interval, so as to obtain the distribution of thermal power plants and the distribution of energy demand.

[0163] The range calculation module is used to calculate the first fault-tolerant range of the estimated available power and the second fault-tolerant range of the estimated demand.

[0164] The path acquisition module is used to generate a connection topology map based on the distribution of thermal power plants and the distribution of energy demand, and to obtain the connection path between thermal power plants and energy demand based on the connection topology map.

[0165] The scheme acquisition module is used to obtain the loss coefficient of the connection path based on the pre-built power transmission loss model, form a preliminary operation scheme, deduce the switching degree of the preliminary operation scheme, and take the preliminary operation scheme with the minimum switching degree as the target operation scheme.

[0166] It should be noted that the technical solution of the distribution network operation planning scheme generation system integrating source load forecasting and operation simulation is based on the same concept as the technical solution of the distribution network operation planning scheme generation method integrating source load forecasting and operation simulation described above. For details not described in detail in the technical solution of the distribution network operation planning scheme generation system integrating source load forecasting and operation simulation described above, please refer to the description of the technical solution of the distribution network operation planning scheme generation method integrating source load forecasting and operation simulation described above.

[0167] The above-mentioned unit modules can be embedded in the processor of the electronic device in hardware form or independent of it, or they can be stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0168] This embodiment also provides an electronic device, which includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for generating distribution network operation planning schemes that integrate source-load prediction and operation simulation. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0169] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method proposed in the above embodiments.

[0170] The storage medium proposed in this embodiment belongs to the same inventive concept as the method proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0171] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory, random access memory, flash memory, hard disk, or optical disk, and includes several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute the method of the embodiments of the present invention.

[0172] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the present invention.

Claims

1. A method for generating distribution network operation planning schemes that integrates source-load forecasting and operation simulation, characterized in that, include: A preset time is formed based on the source load prediction demand, and the time interval is evenly divided according to the preset time to obtain the time interval; The estimated available power of thermal power plants on the power supply side and the estimated energy demand on the load side are predicted respectively within the time interval to obtain the distribution of thermal power plants and the distribution of energy demand. The first fault tolerance range of the estimated available power and the second fault tolerance range of the estimated demand are obtained through calculation. Based on the distribution of the thermal power plants and the distribution of energy demand, a connection topology map is generated, and the connection paths between the thermal power plants and energy demand are obtained according to the connection topology map. The loss coefficient of the connection path is obtained based on the pre-constructed power transmission loss model, a preliminary operation plan is formed, the switching degree of the preliminary operation plan is derived, and the preliminary operation plan with the smallest switching degree is taken as the target operation plan. The process of obtaining the loss coefficient of the connection path based on a pre-built power transmission loss model includes: Obtain the resistance value range of the connection path, and divide the resistance value range into equally spaced sections to obtain test points; Under the condition that the resistance of the transmission line is equal to that of the test point, the power loss ratio of the transmission line is obtained; The test points are paired with the power loss ratio and fitted to obtain the power loss function; Substituting the resistance of the connection path into the power loss function yields the loss coefficient of the connection path. The formation of the pre-operation plan includes:

1. Subtract the loss coefficient of the connection path to obtain the actual coefficient, and take the minimum value of the actual coefficient as the feature value; The minimum value in the first fault tolerance range is uniformly divided into available power blocks, and the available power blocks are mapped to the time interval and thermal power plant corresponding to the first fault tolerance range; the maximum value in the second fault tolerance range is uniformly divided into demand blocks, the ratio of the size of the demand block to the available power block is equal to the characteristic value, and the demand blocks are mapped to the time interval and energy demand corresponding to the second fault tolerance range. Randomly establish a correspondence between available power blocks and demand blocks corresponding to the same time interval, ensuring that an available power block is associated with at most one demand block. Each correspondence between available power blocks and demand blocks forms an alternative scheme, and the energy of the available power block is supplied to the corresponding demand block. Delete any alternative schemes that do not correspond to the available power blocks, and use the remaining alternative schemes as backup operation schemes.

2. The method for generating distribution network operation planning schemes integrating source-load prediction and operation simulation as described in claim 1, characterized in that, The acquisition of the first fault-tolerant range of the estimated available power includes: The first fluctuation ratio is obtained by subtracting the filling ratio from the estimated ratio within the time interval and taking the absolute value. The maximum value of the first fluctuation ratio is taken as the first target ratio. The estimated available power is multiplied by the first target proportion of thermal power plants corresponding to the same time interval to obtain the first fluctuation value; A first fault tolerance range is formed based on the first fluctuation value, the midpoint of the first fault tolerance range is the estimated available power, and the length of the first fault tolerance range is equal to twice the first fluctuation value.

3. The method for generating distribution network operation planning schemes integrating source-load prediction and operation simulation as described in claim 2, characterized in that, Obtaining the second fault tolerance range for the estimated demand includes: The ratio of the historical demand within the specified time interval to the historical demand within the target time interval is used as the verification ratio. The second fluctuation ratio is obtained by subtracting the verification ratio from the demand ratio and taking the absolute value. The maximum value of the second fluctuation ratio is used as the second target ratio. The second fluctuation value is obtained by multiplying the estimated demand by the second target ratio of energy demand for the same time interval. A second tolerance range is formed based on the second fluctuation value, the midpoint of the second tolerance range is the estimated demand, and the length of the second tolerance range is equal to twice the second fluctuation value.

4. The method for generating distribution network operation planning schemes integrating source-load prediction and operation simulation as described in claim 1, characterized in that, The distribution of thermal power plants obtained includes: Obtain the historical coal usage of thermal power plants, and fit the historical coal usage based on the date to obtain a coal fitting function; Input the date of the day into the coal fitting function to obtain the estimated daily usage of the thermal power plant; Based on the historical usage of coal, the filling ratio of thermal power plants within a time interval is obtained, and the average of the filling ratios within the time interval is taken to obtain the estimated ratio. The filling ratio is the proportion of newly added coal entering the boiler within the time interval to the historical coal usage. Multiply the estimated usage by the estimated ratio to obtain the predicted coal usage for the time period; The total historical power generated by the historical coal consumption is obtained. The total historical power is divided by the historical coal consumption to obtain the unit output power. The average value of the unit output power is taken to obtain the unit reference power. Multiply the unit reference power by the predicted coal quantity to obtain the preliminary predicted power for the time interval; The day is divided into equal parts to obtain identification points. The heat emitted by the initial combustion of a unit mass of coal is obtained as the target heat. Under the condition that the combustion time is equal to the time at the identification point, the heat emitted by the combustion of a unit mass of coal is obtained as the characteristic heat. The characteristic heat is divided by the target heat to obtain the effective coefficient. The values ​​at the identification points are paired with the effective coefficients and fitted to obtain the effective fitting function; the time interval that occurs before the time interval is taken as the feature time interval; the difference between the midpoint of the time interval and the feature time interval is taken to obtain the calibration time, which is then substituted into the effective fitting function to obtain the predicted effective coefficient of the feature time interval. The initial predicted power for the characteristic time interval is multiplied by the prediction efficiency coefficient and then superimposed to obtain the summation success rate. The summation success rate is then superimposed with the initial predicted power for the time interval to obtain the estimated available power of thermal power plants within the time interval.

5. The method for generating distribution network operation planning schemes integrating source-load prediction and operation simulation as described in claim 4, characterized in that, The acquisition of the distribution of energy demand includes: The first time interval of each day is taken as the target time interval. The actual energy demand within the target time interval is obtained, and the estimated energy demand within the target time interval is equal to the actual demand. Obtain the historical energy demand within the time interval; take the average of the ratios between the historical demand within the time interval and the historical demand within the target time interval, and use this as the demand ratio; Multiply the actual energy demand within the target time interval by the demand ratio to obtain the estimated energy demand within the time interval.

6. A distribution network operation planning scheme generation system integrating source load forecasting and operation simulation, wherein the distribution network operation planning scheme generation method integrating source load forecasting and operation simulation as described in any one of claims 1 to 5 is characterized in that, include: The time interval division module is used to form a preset time based on the source load prediction demand, and to uniformly divide the time interval according to the preset time to obtain the time interval. The distribution acquisition module is used to predict the estimated available power of thermal power plants on the power supply side and the estimated energy demand on the load side within the time interval, so as to obtain the distribution of thermal power plants and the distribution of energy demand. The range calculation module is used to calculate the first fault-tolerant range of the estimated available power and the second fault-tolerant range of the estimated demand. The path acquisition module is used to generate a connection topology map based on the distribution of thermal power plants and the distribution of energy demand, and to obtain the connection path between thermal power plants and energy demand based on the connection topology map. The scheme acquisition module is used to obtain the loss coefficient of the connection path according to the pre-built power transmission loss model, form a preliminary operation scheme, deduce the switching degree of the preliminary operation scheme, and take the preliminary operation scheme with the smallest switching degree as the target operation scheme.

7. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store computer-executable instructions, and when the processor executes the computer-executable instructions, it implements the steps of the distribution network operation planning scheme generation method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing computer-executable instructions thereon, characterized in that: When the computer-executable instructions are executed by the processor, they implement the steps of the distribution network operation planning scheme generation method according to any one of claims 1 to 5, which integrates source-load prediction and operation simulation.

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