Power transmission curve and power source installation collaborative optimization method and system of an energy base

Abstract

This invention relates to a method and system for the coordinated optimization of power transmission curves and power generation capacity in energy bases, belonging to the fields of power system planning and new energy consumption technology. It includes the following steps: collecting source load and transmission channel parameters, calculating the available transmission capacity of new energy and standardizing the source load, and identifying the evening peak hours for each month; generating monthly daily average composite reference curves through time-segmented weighted fusion, extracting inflection points through first-order difference and dividing the curves into three power intervals; constructing a dual objective function of goodness of fit and supply guarantee deviation, using a sequential quadratic programming algorithm to solve for the optimal power parameters, and reconstructing the initial power transmission curve; and using a particle swarm optimization algorithm with wind power capacity as the variable for reverse optimization, outputting the optimal capacity scheme and power transmission curve. This invention can achieve coordinated planning of transmission curves and power generation capacity, improving the matching degree between the sending and receiving ends and the evening peak supply guarantee capability, and is suitable for large-scale new energy base planning scenarios.

Description

Technical Field

[0001] This invention relates to a method and system for coordinating the optimization of power transmission curves and power generation capacity in energy bases, belonging to the fields of power system planning and new energy consumption technology. Background Technology

[0002] Driven by the "dual carbon" goals, the global energy structure is accelerating its transition to clean energy, with the construction of large-scale new energy bases, primarily based on wind and solar power, showing a comprehensive acceleration. High-capacity DC transmission lines, with their efficient and stable power transmission capabilities, have become the backbone for optimizing the allocation of cross-regional power resources. Under these circumstances, scientifically and rationally formulating the power transmission curves for these lines is of paramount importance for improving the absorption of new energy sources, ensuring reliable power supply, and promoting the sustainable development of the energy system, and has become one of the core tasks of planning large-scale new energy bases.

[0003] On the one hand, existing technologies have significant shortcomings in the selection of normalization benchmarks: most current methods simply use the total capacity of transmission channels or the total installed capacity of new energy sources as the normalization benchmark. However, this approach does not fully consider the actual occupancy of transmission space by the guaranteed output of adjustable energy sources. To ensure the stable operation of the power system, adjustable energy sources need to reserve a certain amount of guaranteed output, which occupies a certain amount of transmission space. If this is not deducted, the actual usable transmission space of new energy sources will inevitably be underestimated, thus affecting the effective transmission of new energy power generation. On the other hand, the coordination depth between the sending and receiving ends is severely insufficient: during power transmission, the output of new energy sources at the sending end is random and intermittent, while the load shape at the receiving end is also complex and variable. Existing technologies lack an effective mechanism for continuous quantitative weighted fusion of the characteristics of new energy output at the sending end and the load shape at the receiving end, making it impossible to achieve accurate matching and efficient coordination of power supply and demand at both ends. Furthermore, the handling of evening peak supply constraints is too crude: power demand surges during evening peak hours, placing higher demands on the stability and reliability of power supply. However, existing methods generally treat peak-hour power supply constraints as a post-processing correction step. This ex-post adjustment approach often fails to ensure that the optimization results closely match the supply requirements, potentially leading to power shortages or even failure to meet demand in actual operation. Furthermore, there is a lack of a coordinated optimization mechanism between power generation capacity and transmission curves: power generation capacity determines the power supply capacity, while the transmission curve reflects the timing and power distribution of power transmission; the two are interconnected and mutually influential. However, current technologies cannot achieve bidirectional coordinated planning of curves and installed capacity, resulting in unreasonable power generation capacity allocation and the inability of the transmission curve to fully realize its intended effectiveness, thus impacting the overall operating efficiency and economic benefits of the power system.

[0004] Therefore, there is an urgent need for a method that can reasonably select a normalized benchmark, enhance the coordination depth between the sending and receiving ends, finely handle the evening peak supply guarantee constraints, and achieve coordinated optimization of power generation capacity and power transmission curves, in order to formulate a more reasonable channel power transmission curve. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention proposes a method and system for co-optimizing the power transmission curve and power generation capacity of an energy base.

[0006] The technical solution of the present invention is as follows: On the one hand, this invention proposes a method for coordinating the optimization of power transmission curves and power generation capacity of energy bases, including the following steps: Collect parameters of new energy sources, adjustable energy sources, loads at the receiving end, and transmission channels; calculate the available capacity of new energy sources; normalize the output of new energy sources and the loads at the receiving end to obtain normalized source-load data; and identify the evening peak hours for each month. During non-peak hours, the standardized source load data is weighted and fused, while during peak hours, the standardized receiving end load is directly retained. The data is aggregated monthly to generate the monthly average composite reference curve. The inflection point is extracted hourly by first-order difference from the daily average composite reference curve, and the day is divided into three continuous power intervals: the valley flat section, the peak plateau, and the evening peak plateau. A comprehensive objective function is constructed with the goal of maximizing the goodness of fit between the segmented power transmission curve and the daily average synthetic reference curve and minimizing the supply deviation during the evening peak. The optimal power parameters for each interval are solved iteratively using a sequential quadratic programming algorithm, and the initial optimal power transmission curve is obtained after dimensional restoration. Using wind power installed capacity as the optimization variable, the particle swarm optimization algorithm is used to iteratively find the optimal solution. Each particle corresponds to a set of wind and solar power installed capacity ratios and completes the power transmission curve generation process. The fitness is calculated to update the particle state, and the optimal power installed capacity scheme and the optimal power transmission curve are output.

[0007] Preferably, the steps of calculating the available transmission capacity of new energy sources and normalizing the new energy output and receiving-end load to obtain normalized source-load data include: The fixed minimum output of adjustable energy is calculated based on the configuration parameters of thermal power and energy storage; The available capacity for new energy transmission is obtained by subtracting the fixed minimum output from the rated capacity of the DC transmission channel. The total output is calculated based on the installed capacity of wind and solar power and hourly output data. The total output is normalized by using the space of new energy transmission capacity as the normalization denominator to obtain the normalized new energy output sequence. The fixed minimum output is deducted from the actual hourly receiving end load, and the maximum value is taken from zero. Then, the normalized receiving end load sequence is obtained by normalizing the denominator with the space of new energy transmission capacity.

[0008] Preferably, the steps for identifying evening rush hour periods for each month include: The arithmetic mean of the actual load at the same time for all natural days in each month is taken to generate the typical 24-hour load curve for each month. In the afternoon to nighttime period of the typical day's 24-hour load curve, identify local high-load intervals and determine the start and end times of the intervals as the start and end times of the evening peak for the month, respectively. If there is no independent evening peak pattern in the corresponding month, the first difference minimum point of the typical day's 24-hour load curve is taken as the steepest drop moment, and this moment is determined as the end time of the evening peak for that month.

[0009] Preferably, during non-peak hours, the standardized source-load data is weighted and fused, while during peak hours, the standardized receiving-end load is directly retained. Monthly aggregation generates monthly average composite reference curves. The steps of extracting key inflection points from the hourly first-order difference of the daily average composite reference curves and dividing the day into three continuous power intervals include: During non-peak hours, the standardized new energy output sequence and the standardized receiving load sequence are weighted and fused based on the weighting coefficients of the sending and receiving ends. During peak hours, the standardized receiving load sequence is directly used to obtain the value. The hourly reference curve value for the whole year is synthesized and aggregated by monthly daily average to generate a monthly daily average composite reference curve. The first-order difference operation is performed on the monthly daily average composite reference curve. The time corresponding to the maximum value of the first-order difference is identified as the steepest rise time, and the time corresponding to the minimum value of the first-order difference is identified as the steepest fall time. Together with the end time of the evening peak, they are used as key inflection points. Based on key inflection points, the entire 24 hours are divided into three continuous power ranges: the low-valley flat period, the peak plateau, and the evening peak plateau.

[0010] Preferably, the steps for constructing the comprehensive objective function include: Calculate the Pearson correlation coefficient between the segmented power transmission curve and the daily average composite reference curve, and use the square of the Pearson correlation coefficient to characterize the goodness of fit. Based on the supply guarantee constraint rate, channel rated capacity, fixed minimum output and new energy transmission capacity space, the per-unit value of the supply guarantee lower limit of the evening peak platform is derived and calculated. Based on the relative relationship between the peak platform power parameters and the per-unit value of the supply guarantee lower limit, the peak supply guarantee deviation is calculated. By introducing a fitting accuracy penalty factor and a peak supply margin penalty factor, the objectives of maximizing fitting goodness and minimizing supply deviation are transformed into a comprehensive objective function in minimization form.

[0011] Preferably, the steps of iteratively solving for the optimal power parameters in each interval using a sequential quadratic programming algorithm and obtaining the initial optimal power transmission curve after dimensional restoration include: Initialize the power parameters of the low-valley flat segment, peak plateau, and evening peak plateau. Within the feasible region that satisfies the intraday time ordering constraint, the upper limit constraint of the evening peak, the lower limit constraint of the evening peak, and the annual utilization hours constraint of the channel, use a sequential quadratic programming algorithm to iteratively update the power parameters to obtain the optimal power parameters of the three segments for each month. By utilizing the available capacity of new energy sources and the fixed minimum output, the optimal three-stage power parameters for each month are reconstructed in terms of dimensions, and a power transmission curve covering the entire year's time sequence is formed, and the annual utilization hours of the channel are calculated. If the annual utilization hours of the channel do not meet the preset target range, the total deviation is calculated and evenly distributed to all times throughout the year. The values ​​of the three power parameters are shifted in the same direction and the curve is reconstructed iteratively until the annual utilization hours of the channel converge to the target range.

[0012] Preferably, the steps of using the wind power installed capacity as the optimization variable and employing the particle swarm optimization algorithm for iterative optimization include: Set the effective search range and capacity search step size for wind power installed capacity, and initialize the position and movement speed of each particle in the particle swarm within the search range; Each particle, based on the wind power installed capacity corresponding to its position and the photovoltaic installed capacity obtained by subtracting the wind power installed capacity from the total new energy installed capacity, executes the power transmission curve generation process in its entirety and outputs the power transmission curve under the corresponding ratio. Construct a comprehensive fitness function that includes the average goodness of fit for each month, the penalty for breach of supply guarantee during the evening peak, and the penalty for breach of channel utilization hours per year, and calculate the fitness value of each particle. By combining the individual historical best position and the global best position, the velocity and position of each particle are iteratively updated, and the wind power installed capacity value is aligned to the nearest search step size grid point until the maximum number of iterations is reached, at which point the globally optimal installed capacity scheme is output.

[0013] On the other hand, the present invention also proposes a power transmission curve and power generation capacity coordinated optimization system for energy bases, comprising the following modules: The data processing module is used to collect parameters of new energy sources, adjustable energy sources, loads at the receiving end, and transmission channels at the sending end; calculate the transmittable capacity of new energy sources; normalize the output of new energy sources and the loads at the receiving end to obtain normalized source-load data; and identify the evening peak hours for each month. The curve construction module is used to perform weighted fusion of standardized source load data during non-peak hours, and directly retains standardized receiving load during peak hours. It aggregates data monthly to generate monthly average composite reference curves. It is also used to extract key inflection points by hourly first-order difference of the daily average composite reference curves, and divides the day into three continuous power intervals: low valley flat section, peak plateau and evening peak plateau. The parameter solving module is used to construct a comprehensive objective function with the goal of maximizing the goodness of fit between the segmented power transmission curve and the daily average synthetic reference curve and minimizing the supply deviation during the evening peak. The optimal power parameters for each interval are solved iteratively using a sequential quadratic programming algorithm, and the initial optimal power transmission curve is obtained after dimensional restoration. The collaborative optimization module uses wind power installed capacity as the optimization variable, employs particle swarm optimization algorithm to iteratively find the optimal solution, controls each particle to correspond to a set of wind and solar installed capacity ratios and fully executes the power transmission curve generation process, calculates the fitness to update the particle state, and finally outputs the optimal power installed capacity scheme and the optimal power transmission curve.

[0014] In another aspect, the present invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method as described in any embodiment of the present invention.

[0015] In another aspect, the present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any embodiment of the present invention.

[0016] The present invention has the following beneficial effects: 1. This invention proposes a method and system for coordinating the power transmission curve and power generation capacity optimization of energy bases. By using the renewable energy transmission capacity space after deducting the adjustable energy minimum output as a normalization benchmark, it truly reflects the actual available power transmission space of renewable energy and improves the accuracy of the matching of sending and receiving end characteristics. At the same time, it introduces the weighting coefficients of sending and receiving ends to realize the continuous quantitative weighted fusion of sending end output and receiving end load characteristics. The receiving end load characteristics are independently retained during the evening peak period, which can flexibly adapt to planning scenarios with different degrees of dominance. This solves the problems of normalization benchmark deviation and insufficient coordination depth of sending and receiving ends in traditional methods.

[0017] 2. This invention proposes a method and system for the coordinated optimization of power transmission curves and power generation capacity in energy bases. By constructing a comprehensive objective function that takes into account both curve fitting goodness and peak-hour power supply deviation, a sequential quadratic programming algorithm is used to iteratively solve the three-segment power parameters. The peak-hour power supply constraint is integrated into the optimization process in advance, replacing the traditional ex-post correction method, and systematically ensuring the hard constraint of peak-hour power supply. Simultaneously, an iterative verification mechanism based on the annual utilization hours of the channel is combined to improve curve matching accuracy and channel resource utilization efficiency while ensuring supply capacity. The optimization results are more feasible for engineering.

[0018] 3. This invention proposes a method and system for the coordinated optimization of power transmission curves and power generation capacity in energy bases. By employing a particle swarm optimization algorithm with wind power capacity as a variable for reverse optimization, the complete power transmission curve generation process is embedded as an inner loop in the optimization process, realizing closed-loop coordinated planning of power transmission curve shape and wind and solar power ratio. This breaks the limitation of the separation between power generation configuration and power transmission curve in traditional planning, and can output the comprehensive optimal power generation capacity scheme and power transmission curve. It is applicable to various energy base scenarios such as supporting thermal power, energy storage or pure new energy. Attached Figure Description

[0019] Figure 1 This is a flowchart of the method for coordinating and optimizing the power transmission curve and power generation capacity of an energy base, as proposed in this invention. Figure 2 This is a schematic diagram of the adaptive normalization proposed in this invention; Figure 3 This is a comparison image before and after the proposed independent treatment of the evening peak hour; Figure 4 This is a schematic diagram of the inflection point identification proposed in this invention; Figure 5 This is a comparison chart of the optimization results of the initial optimal power transmission curves for 12 months proposed in this invention; Figure 6 This is a comparison chart of the optimization results of the 12-month optimal power transmission curve proposed in this invention; Figure 7 This is a schematic diagram of the wind and solar power output curves at the sending end of the Shagohuang power transmission base proposed in Embodiment 2 of the present invention; Figure 8 This is a schematic diagram of the curve corresponding to the receiving-end load characteristics of the Shagohuang power transmission base proposed in Embodiment 2 of the present invention; Figure 9 This is the typical daily receiving-end load characteristic curve of the Shagohuang power transmission base in all four seasons, as proposed in Embodiment 2 of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0022] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0023] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0024] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0025] Example 1: See Figure 1 This embodiment proposes a method for coordinating the optimization of power transmission curves and power generation capacity of energy bases, including the following steps: S100. Conduct comprehensive basic engineering data collection. The basic data sources collected are divided into five categories: the total planned installed capacity of new energy at the sending end, the hourly real-time output time series data of wind power and photovoltaic power for 8760 hours throughout the year, the set of adjustable energy configuration parameters, the hourly electricity load time series curve of the receiving end area throughout the year, and the rated capacity parameters of the cross-regional DC transmission channel. Among them, adjustable energy is an optional configuration item for the project. The project can choose not to match any adjustable power source, or it can match one of thermal power and energy storage alone, or a combination of thermal power and energy storage. Among them, under the condition of supporting thermal power, the adjustable energy detailed parameters include the installed capacity of a single thermal power unit, the total number of operating units, and the minimum stable output coefficient of the thermal power unit. The total installed capacity of thermal power is calculated by multiplying the capacity of a single unit by the number of units, and then multiplying the total installed capacity of thermal power by the minimum output coefficient to finally obtain the fixed minimum output of thermal power. Under the condition of supporting energy storage, the rated charging and discharging power, rated energy storage capacity and comprehensive charging and discharging efficiency of the energy storage system are collected. The upper limit of the adjustable power limit of the energy storage is determined based on the rated power, and the available adjustable capacity of the energy storage throughout the entire cycle is determined based on the rated capacity. If the project has no adjustable energy infrastructure, the system's overall minimum guaranteed output will be set to 0.

[0026] See Figure 2 Furthermore, by obtaining the rated capacity and adjustable minimum energy output of the DC transmission channel, the available capacity for new energy transmission is calculated, expressed by the formula: ; In the formula, This indicates the available capacity for new energy transmission. Indicates the rated capacity of the DC power transmission channel. This indicates the minimum output of the adjustable energy source; when there is no matching adjustable energy source, this value is 0. Using the available renewable energy transmission capacity as the normalized denominator, the hourly renewable energy output for the entire year of 8760 hours is normalized. For the sending-end renewable energy output, calculations are first performed based on the wind power installed capacity, photovoltaic installed capacity, and corresponding hourly output time series data. Total new energy output at any given time, then calculate The per-unit value of renewable energy output at any given time is expressed by the formula: ; ; ; ; In the formula, Indicates the time index. express The combined output of wind and solar power at any given moment. express The actual wind power output at any given moment. express The actual output value of photovoltaic power at any given time. Indicates wind power installed capacity. Indicates photovoltaic installed capacity. express Time-series data of wind power output per unit capacity at any given moment. express Time-series data of photovoltaic power output per unit capacity at any given moment. express The per-unit value of new energy power output at any given time. This indicates the minimum value operation; By combining the hourly per-unit values ​​of renewable energy output for 8760 hours throughout the year in chronological order, a curve of per-unit renewable energy output is obtained.

[0027] Furthermore, for the receiving-end load, after deducting the load support portion corresponding to the adjustable energy reserve output, it is standardized based on the available capacity of new energy transmission. The per-unit value of the load at the receiving end at any given time is expressed by the formula: ; In the formula, express The per-unit value of the receiving-end load at a given time. express The actual load value at the receiving end at any given time. This indicates the operation of finding the maximum value. The per-unit load values ​​of the receiving end are calculated hourly for each of the 8760 hours of the year, and then combined in chronological order to obtain the per-unit load curve of the receiving end throughout the year.

[0028] When an adjustable energy source is not provided, the adjustable energy source will provide a minimum output. The per-unit load curve at the receiving end can be simplified to: .

[0029] Obtain the receiving-end load time-series curve and conduct peak-valley characteristic analysis at the monthly granularity: Take the arithmetic mean of the load at the same time for all natural days within each month to generate the typical 24-hour load curve for each month; Identify local high-load intervals within the afternoon to nighttime period of the typical monthly load curve, and determine the starting time of these intervals as the starting time of the evening peak for that month. The end time of the interval is determined as the end time of the evening peak of the current month. ; If the load curve for a certain month does not exhibit a distinct evening peak pattern, then the steepest drop in the load curve on a typical day of that month is taken as the end time of the evening peak. .

[0030] S200, see also Figure 3 Based on the start and end times of the evening peak hours in each season, the per-unit values ​​of new energy output and receiving-end load are weighted and superimposed. A composite reference curve value is calculated from 8760 hours of data throughout the year, expressed by the formula: ; In the formula, Indicates the composite reference curve value. Indicates the weighting coefficients of the sending and receiving ends. Indicates the start time of the evening rush hour in each season. Indicates the end time of the evening rush hour in each season; Among them, the weighting coefficients of the sending and receiving ends ;when At that time, the synthetic reference curve is entirely dominated by the receiving-end load; when At that time, the synthetic reference curve was entirely dominated by the receiving-end load; After calculating the hourly reference curve values ​​for 8760 hours throughout the year, daily average aggregation processing is performed on a monthly basis: the arithmetic mean of the reference curve values ​​at the same time for all natural days within each month is taken to obtain a 24-hour curve that represents the typical sunrise power pattern of that month, which is the daily average composite reference curve for that month. .

[0031] Furthermore, for the 24-hour daily average composite reference curve of each month... Hourly first-order difference calculations are performed, and the key turning points of the curve shape are located by the difference extreme values. Based on this, the 24 hours of the day are divided into three continuous power plateau intervals. Each interval corresponds to an optimization parameter to be solved, thus realizing the variable definition of the three-segment transmission curve. See Figure 4 Three key inflection points can be identified through first-order difference operations: steepest ascent The moment when the first difference of the synthetic reference curve reaches its maximum value represents the dividing point where the intraday power output transitions from a trough to a rapid upward phase. steepest descent moment The moment when the first difference of the synthetic reference curve reaches its minimum value represents the dividing point where the intraday power output transitions from the peak stage to the rapid decline stage. Evening rush hour If there is an independent evening peak period in a given month, the end time of the evening peak period will be used. If there is no independent evening peak period in a given month, the steepest descent time will be taken. This ensures that the curve segmentation logic is complete in all months. Based on the three inflection points mentioned above, the 24-hour period is divided into three power plateau intervals, each corresponding to a per-unit power parameter to be optimized, wherein: The time interval corresponding to the trough and flat period is Corresponding optimization parameters This represents the per-unit value of the power transmitted during the intraday low point. The time interval corresponding to the peak platform is Corresponding optimization parameters , representing the per-unit value of the power transmitted during the daytime peak phase; The time range corresponding to the evening peak platform is Corresponding optimization parameters , representing the per-unit value of the power transmitted during the evening peak period.

[0032] S300, see also Figure 5 With the core objective of maximizing the goodness of fit between the optimized curve and the synthetic reference curve, and introducing a peak-hour supply deviation penalty term, a minimized objective function is constructed. This ensures that the power transmitted during the peak hours meets the supply requirements while improving the curve shape matching degree. The formula is as follows: ; In the formula, This represents the objective function of the optimization model. This represents the fitting accuracy penalty factor. This represents the Pearson correlation coefficient between the optimization curve to be solved and the synthetic reference curve. Indicates the penalty factor for supply margin during the evening peak period. This indicates the deviation in supply during the evening peak hours; Among them, the evening peak supply deviation is calculated based on the relative relationship between the evening peak platform value and the supply lower limit, and is expressed by the formula: ; In the formula, This represents the per-unit value of the supply guarantee limit for the evening peak platform; The minimum standard value of supply guarantee on the evening peak platform The formula is derived from the supply constraint rate combined with the rated capacity of the channel, the minimum output capacity, and the available capacity of new energy transmission, and is expressed as follows: ; In the formula, Indicates the supply constraint rate; Furthermore, four types of constraints are set to jointly limit the feasible region of the optimization variables, wherein: The first type is intraday time-ordering constraints, which satisfy... This conforms to the pattern of daily load and output gradually increasing from trough to peak and then to evening peak; The second category is the evening peak platform lower limit constraint, which satisfies... Ensure that the power output during the evening peak hours is not lower than the hard constraints for supply guarantee; The third category is the evening peak platform upper limit constraint, which meets the requirements. ,in This is the per-unit value of the average monthly evening peak load at the receiving end, to avoid the transmitted power exceeding the actual load demand at the receiving end. The fourth category is the constraint on the annual utilization hours of the transmission channel. A reasonable target range for the annual utilization hours TD of the transmission channel is set to ensure that the resource utilization level of the transmission channel is within the reasonable range of the project.

[0033] Furthermore, the above optimization model is iteratively solved using the Supersimal Suspension-Laser Quadratic Programming (SLSQP) algorithm. The calculations are performed independently on a monthly basis, with the optimal three-segment power parameters obtained for each month. Specifically: a. Initialize the optimization variables for each month, i.e., the trough and flat period values. Peak platform value Evening Peak Platform Value The initial value; b. In each iteration, construct a time-by-time optimization curve based on the current three parameters and the inflection point, and substitute it into the objective function of the optimization model to calculate the current objective function value. ; c. Based on the current gradient information, the next search direction is approximately determined by the Hessian matrix, and the values ​​of the three parameters are updated within the feasible region that satisfies all constraints. d. Repeat the above iterative process until the objective function of the optimization model converges, that is, the difference between the objective values ​​of two adjacent iterations is less than a preset threshold, or the preset maximum number of iterations is reached, and output the optimal parameter triplet for that month. .

[0034] Based on the optimal three-segment parameters for each month, combined with the identified inflection point moments for each month... , , Construct a three-step hourly per-unitized power transmission curve for each month. This can be expressed as a formula: ; In the formula, express The per-unit power transmission curve value at any given time; Furthermore, the per-unit power transmission curve will be standardized. The transmission curve, converted to actual power dimensions, is expressed by the following formula: ; In the formula, express The actual power transmission curve value at any given time; The actual power transmission curves after 12 months of restoration are summarized to form an initial power transmission curve covering the time sequence characteristics of 8760 hours throughout the year, and the annual channel utilization hours TD are calculated. If the calculated annual channel utilization hours do not meet the preset target range, the total deviation will be evenly distributed across the entire year of 8760 hours, resulting in an overall shift in the same direction. The values ​​of the three parameters are used to re-execute the curve construction and dimension restoration process, and the annual utilization hours of the channel are recalculated; this process is repeated until the annual utilization hours converge to the target interval. During the iterative adjustment process, the peak evening platform value is always maintained. The value shall not be lower than the per-unit value of the supply guarantee minimum. To ensure that supply constraints during the evening peak remain in effect.

[0035] S400, see also Figure 6 The total installed capacity of new energy sources and the search step size for installed capacity are obtained. Wind power installed capacity is used as the optimization variable, and photovoltaic installed capacity is indirectly determined by subtracting wind power installed capacity from the total installed capacity of new energy sources. The effective search range for wind power installed capacity is set to 10% to 90% of the total installed capacity. Within the search range, the position and initial movement speed of each particle in the particle swarm are initialized to complete the swarm initialization. The relationship between installed capacity and capacity is expressed by the formula: ; In the formula, Indicates photovoltaic installed capacity. This indicates the total installed capacity of new energy sources. Indicates wind power installed capacity; Each particle in the population, based on the wind power capacity and photovoltaic capacity corresponding to its position, fully executes the entire process from steps S100 to S300: First, in step S100, the corresponding new energy output time sequence is generated based on the current installed capacity ratio, and normalization processing and evening peak period identification are completed; Subsequently, in step S200, a synthetic reference curve for this ratio is generated and divided into three intervals; Finally, in step S300, the three-segment delivery curve that satisfies the constraints is obtained. Based on the results of the entire process, the fitness of the particle is calculated, expressed by the formula: ; In the formula, This represents the fitness value of a particle. This represents the average of the squared Pearson correlation coefficients between the monthly optimized curves and the corresponding synthetic reference curves. This indicates the penalty coefficient for breach of supply guarantee during the evening peak hours. This indicates the penalty coefficient for breach of contract based on the annual utilization hours of the channel. This indicates the number of months in which supply guarantee measures were breached during the evening rush hour. This indicates the number of hours the channel is used annually and the amount of penalty for breach of contract. Based on the standard update mechanism of the particle swarm optimization algorithm, the velocity and position of each particle are iteratively updated by combining the individual's historical best position and the global best position, as expressed by the formula: ; In the formula, Indicates particle index, Indicates the iteration count index. Indicates the first The particle in the first The speed of each iteration Indicates the first The particle in the first The speed of each iteration Indicates inertia weight, Represents cognitive learning factors, Represents social learning factors. and To show obedience Uniformly distributed random numbers, Indicates the first The optimal position of each particle in its historical records. This represents the globally optimal position of the population. Indicates the first The particle in the first The position of the next iteration; After the particle position is updated, the wind power installed capacity value is aligned to the nearest search step grid point and restricted to the preset effective search range to avoid exceeding the reasonable boundary of the installed capacity ratio. After each iteration, the individual optimal position of each particle and the global optimal position of the population are updated. Fitness calculation and particle velocity and position update are performed cyclically until the preset maximum number of iterations is reached, and the iteration process terminates. After the iteration terminates, the wind power installed capacity corresponding to the globally optimal location is determined as the optimal wind power installed capacity. The optimal photovoltaic installed capacity is calculated accordingly. At the same time, it outputs the comprehensive optimized power supply curve obtained through the whole process optimization under this installed capacity ratio, which is the final optimal power supply installed capacity scheme and optimal power supply curve.

[0036] Furthermore, a multi-dimensional quantitative evaluation was conducted on the final generated optimal channel transmission curve. Three indicators were selected: the squared Pearson correlation coefficient, the mean absolute error, and the root mean square error. These were used to evaluate the optimization effect from two perspectives: morphological matching degree and absolute deviation magnitude. The mean absolute error (MAE) is expressed by the formula: ; In the formula, This represents the average absolute value of the hourly transmitted power. This represents the total number of time series data points participating in the evaluation. This represents the composite reference curve in actual power dimensions; The root mean square error (RMSE) is expressed by the formula: ; In the formula, The root mean square error represents the hourly deviation of the transmitted power. It should be noted that the square of the Pearson correlation coefficient reflects the degree of matching of the curve fluctuation trend, while the mean absolute error and root mean square error quantitatively characterize the absolute deviation of the hourly output. The combination of these three indicators can achieve a comprehensive quantitative evaluation of the final optimization result.

[0037] Example 2: Based on Example 1, this example takes a large-scale new energy base in Northwest China with supporting thermal power plants as an example to illustrate the specific implementation steps of the power transmission curve generation method that takes into account both the sending and receiving ends and the supply guarantee needs: See Figure 7A comprehensive assessment and survey of the wind and solar resources of the energy base, the supporting adjustable energy configuration, and the parameters of the external transmission channel were conducted. The results showed that the total installed capacity of new energy was 1200MW, with a total of 4 thermal power units, each with a capacity of 66MW, and a total installed capacity of 264MW for thermal power. The minimum output coefficient of thermal power was taken as 0.2. The rated capacity of the DC transmission channel was 800MW. The initial planning scheme sets the wind power installed capacity at 400MW and the photovoltaic installed capacity at 800MW. Simultaneously, the annual simulated output curves and utilization hours data of the corresponding wind power and photovoltaic are obtained for 8760 hours, which serve as the basic input for subsequent full-process calculations.

[0038] See Figure 8-9 To obtain the time-series load characteristics data of the receiving end, load peak-valley characteristic analysis is conducted for each month to determine the evening peak period, providing a time boundary for the subsequent construction of the three-segment DC transmission curve. Considering that the inflection points of the receiving end load characteristics curves are similar in each month within the same season, they are combined and used according to the season in engineering applications. The specific settings for the evening peak period in each season are as follows: There are no separate evening rush hours during spring (March to May) and autumn (September to November); During the summer months of June to August, the evening rush hour is from 5 PM to 8 PM. During winter, specifically December and January to February, the evening rush hour is from 4 PM to 7 PM. Based on the above fundamental parameters, the available capacity for renewable energy transmission is calculated. Using this as a normalization benchmark, the renewable energy output at the sending end and the load at the receiving end are normalized separately. Specifically: The minimum output of thermal power is calculated as the product of the total installed capacity of thermal power and the minimum output coefficient, that is, 264MW multiplied by 0.2 to get 52.8MW; Subtracting the minimum output of thermal power from the rated capacity of the transmission channel, the available capacity for new energy transmission is 747.2MW. Using this spatial value as the denominator, we perform per-unit calculations on the total wind and solar power output and the hourly receiving-end load throughout the year to obtain the per-unit curves of new energy power output and receiving-end load. Considering the planning demand and a greater emphasis on the load characteristics at the receiving end, a weighting coefficient of 0.3 was selected for both the sending and receiving ends. The non-peak and peak hours were processed separately, and then weighted and superimposed to obtain a composite reference curve, where: During non-peak hours, the per-unit values ​​of new energy output and receiving load are weighted and integrated according to the weighting coefficient. During peak hours from 5 pm to 8 pm in summer and from 4 pm to 7 pm in winter, the per-unit values ​​of receiving load are directly retained and are not affected by the weighting coefficient. Based on this, daily averages are aggregated monthly to obtain the daily average composite reference curve for each month's 24 hours.

[0039] Furthermore, hourly first-order difference operations are used on the monthly average synthetic reference curve to identify three key inflection points: the steepest rise, the steepest fall, and the end of the evening peak. This divides the 24-hour period into three continuous intervals: the trough plateau, the peak plateau, and the evening peak plateau, corresponding to three power parameters to be optimized, thus completing the interval division of the three-segment curve.

[0040] The SLSQP algorithm is used to optimize and solve the three-segment power parameters for each month. For each month, the following steps are performed: As the initial solution, construct the hourly target curve based on the current parameters, substitute it into the objective function to calculate the target value and its gradient with respect to the optimization variables, iteratively update the parameters within the constraints, and output the optimal three-segment parameters for each month after convergence.

[0041] The constraints are set as follows: During summer and winter months when there is an evening rush hour, the evening rush hour plateau value The constraint range is ; In spring and autumn, there is no independent evening peak, so the evening peak plateau value is... With peak platform value equal; In constructing the evening peak supply guarantee constraint, the supply guarantee constraint rate is taken. This means that the actual power transmitted during the evening peak hours should not be less than 50% of the channel's rated capacity. Based on this, the per-unit value of the lower limit for supply guarantee during the evening peak hours is calculated. This corresponds to the lower limit of the actual external power transmission; The per-unit power transmission curves obtained from the solution for each month are restored to their original dimensions to obtain the actual power transmission curves for each month.

[0042] The annual utilization hours TD of the transmission channel are calculated by summarizing the power transmission plans for 12 months, and the constraint range of TD is set as follows. If the calculated result does not meet the target range for the hourly interval, the deviation will be evenly distributed over the entire year of 8760 hours, resulting in an overall shift in the same direction. The values ​​of the three parameters are used to reconstruct the curve and calculate the annual utilization hours. The process is iterated repeatedly until it converges to the target interval, and finally the initial optimal power transmission curve is obtained.

[0043] Using the initial optimal power transmission curve as a reference, the PSO algorithm is employed to perform a reverse iterative optimization of the wind and solar power installed capacity ratio, specifically as follows: The search range for wind power installed capacity is set to 120MW to 1080MW, and the search step size is set to 10MW to ensure that the installed capacity is rounded up. Each particle corresponds to a set of wind and solar power installed capacity ratios. The entire process of normalization, synthetic reference curve construction, and three-stage parameter optimization is fully executed. The corresponding fitness value is calculated, and the particle swarm velocity and position update mechanism is used for iterative search. After the iteration is completed, the globally optimal wind and solar power installed capacity ratio and the corresponding optimal power transmission curve are output. The Pearson correlation coefficient squared, mean absolute error (MAE), and root mean square error (RMSE) were calculated for the generated optimal export curve. A comprehensive performance evaluation was conducted, and the comparison results of the indicators between the initial and optimal schemes are presented in a table: Table 1. Comparison of Indicators between Initial and Optimal Solutions

[0044] As can be seen from the comparison of the indicators, the optimal solution improved the goodness of fit from 0.9768 to 0.9821, the mean absolute error decreased from 0.0401 to 0.0256, and the root mean square error decreased from 0.0471 to 0.0326 compared with the initial solution. The curve matching accuracy and supply guarantee effect were significantly improved, which verified the synergistic optimization effect of the proposed method.

[0045] Example 3: This embodiment proposes a system for coordinating and optimizing the power transmission curve and power generation capacity of an energy base, which includes the following modules: The data processing module is used to collect parameters of new energy sources, adjustable energy sources, loads at the receiving end, and transmission channels at the sending end; calculate the transmittable capacity of new energy sources; normalize the output of new energy sources and the loads at the receiving end to obtain normalized source-load data; and identify the evening peak hours for each month. The curve construction module is used to perform weighted fusion of standardized source load data during non-peak hours, and directly retains standardized receiving load during peak hours. It aggregates data monthly to generate monthly average composite reference curves. It is also used to extract key inflection points by hourly first-order difference of the daily average composite reference curves, and divides the day into three continuous power intervals: low valley flat section, peak plateau and evening peak plateau. The parameter solving module is used to construct a comprehensive objective function with the goal of maximizing the goodness of fit between the segmented power transmission curve and the daily average synthetic reference curve and minimizing the supply deviation during the evening peak. The optimal power parameters for each interval are solved iteratively using a sequential quadratic programming algorithm, and the initial optimal power transmission curve is obtained after dimensional restoration. The collaborative optimization module uses wind power installed capacity as the optimization variable, employs particle swarm optimization algorithm to iteratively find the optimal solution, controls each particle to correspond to a set of wind and solar installed capacity ratios and fully executes the power transmission curve generation process, calculates the fitness to update the particle state, and finally outputs the optimal power installed capacity scheme and the optimal power transmission curve.

[0046] Example 4: This embodiment proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method described in any embodiment of the present invention.

[0047] Example 5: This embodiment proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in any embodiment of the present invention.

[0048] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0049] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0050] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0051] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0052] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for co-optimizing the power transmission curve and power generation capacity of an energy base, characterized in that, Includes the following steps: Collect parameters of new energy sources, adjustable energy sources, loads at the receiving end, and transmission channels; calculate the available capacity of new energy sources; normalize the output of new energy sources and the loads at the receiving end to obtain normalized source-load data; and identify the evening peak hours for each month. During non-peak hours, the standardized source load data is weighted and fused, while during peak hours, the standardized receiving end load is directly retained. The data is aggregated monthly to generate the monthly average composite reference curve. The inflection point is extracted hourly by first-order difference from the daily average composite reference curve, and the day is divided into three continuous power intervals: the valley flat section, the peak plateau, and the evening peak plateau. A comprehensive objective function is constructed with the goal of maximizing the goodness of fit between the segmented power transmission curve and the daily average synthetic reference curve and minimizing the supply deviation during the evening peak. The optimal power parameters for each interval are solved iteratively using a sequential quadratic programming algorithm, and the initial optimal power transmission curve is obtained after dimensional restoration. Using wind power installed capacity as the optimization variable, the particle swarm optimization algorithm is used to iteratively find the optimal solution. Each particle corresponds to a set of wind and solar power installed capacity ratios and completes the power transmission curve generation process. The fitness is calculated to update the particle state, and the optimal power installed capacity scheme and the optimal power transmission curve are output.

2. The method for coordinated optimization of power transmission curves and power generation capacity of an energy base according to claim 1, characterized in that, The steps for calculating the available transmission capacity of new energy sources and normalizing the new energy output and receiving-end load to obtain normalized source-load data include: The fixed minimum output of adjustable energy is calculated based on the configuration parameters of thermal power and energy storage; The available capacity for new energy transmission is obtained by subtracting the fixed minimum output from the rated capacity of the DC transmission channel. The total output is calculated based on the installed capacity of wind and solar power and hourly output data. The total output is normalized by using the space of new energy transmission capacity as the normalization denominator to obtain the normalized new energy output sequence. The fixed minimum output is deducted from the actual hourly receiving end load, and the maximum value is taken from zero. Then, the normalized receiving end load sequence is obtained by normalizing the denominator with the space of new energy transmission capacity.

3. The method for coordinated optimization of power transmission curves and power generation capacity of an energy base according to claim 1, characterized in that, The steps to identify evening rush hour times for each month include: The arithmetic mean of the actual load at the same time for all natural days in each month is taken to generate the typical 24-hour load curve for each month. In the afternoon to nighttime period of the typical day's 24-hour load curve, identify local high-load intervals and determine the start and end times of the intervals as the start and end times of the evening peak for the month, respectively. If there is no independent evening peak pattern in the corresponding month, the first difference minimum point of the typical day's 24-hour load curve is taken as the steepest drop moment, and this moment is determined as the end time of the evening peak for that month.

4. The method for coordinated optimization of power transmission curves and power generation capacity of an energy base according to claim 1, characterized in that, During non-peak hours, the standardized source load data is weighted and fused, while during peak hours, the standardized receiving end load is directly retained, and the monthly aggregated data is used to generate the monthly daily average composite reference curve. The steps for extracting key inflection points by hourly first-order difference of the daily average composite reference curve and dividing the day into three continuous power intervals include: During non-peak hours, the standardized new energy output sequence and the standardized receiving load sequence are weighted and fused based on the weighting coefficients of the sending and receiving ends. During peak hours, the standardized receiving load sequence is directly used to obtain the value. The hourly reference curve value for the whole year is synthesized and aggregated by monthly daily average to generate a monthly daily average composite reference curve. The first-order difference operation is performed on the monthly daily average composite reference curve. The time corresponding to the maximum value of the first-order difference is identified as the steepest rise time, and the time corresponding to the minimum value of the first-order difference is identified as the steepest fall time. Together with the end time of the evening peak, they are used as key inflection points. Based on key inflection points, the entire 24 hours are divided into three continuous power ranges: the low-valley flat period, the peak plateau, and the evening peak plateau.

5. The method for coordinated optimization of power transmission curves and power generation capacity of an energy base according to claim 1, characterized in that, The steps to construct the comprehensive objective function include: Calculate the Pearson correlation coefficient between the segmented power transmission curve and the daily average composite reference curve, and use the square of the Pearson correlation coefficient to characterize the goodness of fit. Based on the supply guarantee constraint rate, channel rated capacity, fixed minimum output and new energy transmission capacity space, the per-unit value of the supply guarantee lower limit of the evening peak platform is derived and calculated; Based on the relative relationship between the peak platform power parameters and the per-unit value of the supply guarantee lower limit, the peak supply guarantee deviation is calculated. By introducing a fitting accuracy penalty factor and a peak supply margin penalty factor, the objectives of maximizing fitting goodness and minimizing supply deviation are transformed into a comprehensive objective function in minimization form.

6. The method for coordinated optimization of power transmission curves and power generation capacity of an energy base according to claim 1, characterized in that, The steps for iteratively solving for the optimal power parameters in each interval using a sequential quadratic programming algorithm, and obtaining the initial optimal power transmission curve after dimensional restoration, include: Initialize the power parameters of the low-valley flat segment, peak plateau, and evening peak plateau. Within the feasible region that satisfies the intraday time ordering constraint, the upper limit constraint of the evening peak, the lower limit constraint of the evening peak, and the annual utilization hours constraint of the channel, use a sequential quadratic programming algorithm to iteratively update the power parameters to obtain the optimal power parameters of the three segments for each month. By utilizing the available capacity of new energy sources and the fixed minimum output, the optimal three-stage power parameters for each month are reconstructed in terms of dimensions, and a power transmission curve covering the entire year's time sequence is formed, and the annual utilization hours of the channel are calculated. If the annual utilization hours of the channel do not meet the preset target range, the total deviation is calculated and evenly distributed to all times throughout the year. The values ​​of the three power parameters are shifted in the same direction and the curve is reconstructed iteratively until the annual utilization hours of the channel converge to the target range.

7. The method for coordinated optimization of power transmission curves and power generation capacity of an energy base according to claim 1, characterized in that, Using wind power installed capacity as the optimization variable, the steps of iterative optimization using the particle swarm optimization algorithm include: Set the effective search range and capacity search step size for wind power installed capacity, and initialize the position and movement speed of each particle in the particle swarm within the search range; Each particle, based on the wind power installed capacity corresponding to its position and the photovoltaic installed capacity obtained by subtracting the wind power installed capacity from the total new energy installed capacity, executes the power transmission curve generation process in its entirety and outputs the power transmission curve under the corresponding ratio. Construct a comprehensive fitness function that includes the average goodness of fit for each month, the penalty for breach of supply guarantee during the evening peak, and the penalty for breach of channel utilization hours per year, and calculate the fitness value of each particle. By combining the individual historical best position and the global best position, the velocity and position of each particle are iteratively updated, and the wind power installed capacity value is aligned to the nearest search step size grid point until the maximum number of iterations is reached, at which point the globally optimal installed capacity scheme is output.

8. A system for co-optimizing power transmission curves and installed power capacity in an energy base, characterized in that, Includes the following modules: The data processing module is used to collect parameters of new energy sources, adjustable energy sources, loads at the receiving end, and transmission channels at the sending end; calculate the transmittable capacity of new energy sources; normalize the output of new energy sources and the loads at the receiving end to obtain normalized source-load data; and identify the evening peak hours for each month. The curve construction module is used to perform weighted fusion of standardized source load data during non-peak hours, and directly retains standardized receiving load during peak hours. It aggregates data monthly to generate monthly average composite reference curves. It is also used to extract key inflection points by hourly first-order difference of the daily average composite reference curves, and divides the day into three continuous power intervals: low valley flat section, peak plateau and evening peak plateau. The parameter solving module is used to construct a comprehensive objective function with the goal of maximizing the goodness of fit between the segmented power transmission curve and the daily average synthetic reference curve and minimizing the supply deviation during the evening peak. The optimal power parameters for each interval are solved iteratively using a sequential quadratic programming algorithm, and the initial optimal power transmission curve is obtained after dimensional restoration. The collaborative optimization module uses wind power installed capacity as the optimization variable, employs particle swarm optimization algorithm to iteratively find the optimal solution, controls each particle to correspond to a set of wind and solar installed capacity ratios and fully executes the power transmission curve generation process, calculates the fitness to update the particle state, and finally outputs the optimal power installed capacity scheme and the optimal power transmission curve.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 7.

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