Two-stage optimization method and system for high-voltage direct-current external sending curve of new energy base

Abstract

The application discloses a new energy base high-voltage direct current external sending curve two-stage optimization method and system, belongs to the high-voltage direct current transmission technical field, and is based on annual power generation curve and annual load curve to calculate annual net load curve; at least four typical days are extracted based on the annual net load curve; through the first-stage optimization model established, the minimum system annual operation cost is taken as an objective function, wherein the operation constraints of coal-fired generating units, new energy generating units and energy storage devices are considered, the step-shaped operation constraints of LCC-HVDC external sending power curve are not considered, and the preliminary external sending curve of each typical day is acquired; through the second-stage optimization model established, the minimum error between the generated fitting external sending curve and the preliminary external sending curve is taken as an objective function, wherein the step-shaped operation constraints of LCC-HVDC external sending power curve are considered, the operation constraints of coal-fired generating units, new energy generating units and energy storage devices are not considered, and the fitting external sending curve of each typical day meeting the step-shaped operation constraints is acquired.

Description

Technical Field

[0001] This invention relates to the field of high-voltage direct current (HVDC) transmission technology for large-scale new energy bases, and more specifically, to a two-stage optimization method and system for determining the HVDC transmission curve of a new energy base. Background Technology

[0002] Line-commutated converter based high-voltage direct current (LCC-HVDC) technology has become an important means of transmitting electricity from renewable energy bases due to its economic advantages in long-distance, large-capacity power transmission. However, current LCC-HVDC power transmission projects generally suffer from insufficient utilization. This low utilization not only leads to idle transmission resources but also increases the risk of losing high investment costs. Therefore, it is necessary to optimize the high-voltage direct current transmission curve to improve transmission efficiency and the utilization rate of transmission channels.

[0003] The optimization of the high-voltage direct current (HVDC) transmission curve for large-scale new energy bases requires consideration not only of the power supply composition, energy storage equipment operating characteristics, and load levels in both the sending and receiving areas, but also the stepped operation mode of the LCC-HVDC transmission power curve. This includes constraints such as transmission power holding time, adjustment amounts, and the number of adjustments. These factors introduce a large number of binary variables into the transmission curve optimization model, making the model solution time-consuming.

[0004] Prior art 1 provides an operation planning method based on experience / setpoint transmission curves. In many existing engineering practices, HVDC transmission curves are typically formulated manually based on experience rules, setpoint plans, or historical typical load days for annual / seasonal / monthly plans. Typical implementation steps of the scheme disclosed in prior art 1 include: (1) Generate new energy output time series by season / month / day based on the historical capacity factor and installed capacity of the sending end; generate load time series based on the historical load curve and load forecast of the receiving end; (2) Determine the daily / hourly output limit and scheduling plan of the transmission channel based on empirical ratios or simple rules (e.g., by installed capacity ratio or fixed power allocation); (3) Deviations are corrected at the scheduling execution level through manual adjustment or online rescheduling.

[0005] However, the prior art 1 has the following drawbacks: (1) Poor matching: The experience / set value scheme cannot dynamically reflect the fluctuations in the output of new energy at the sending end and the changes in the load at the receiving end, resulting in a large error between the transmission plan and the actual output / load, causing wind and solar curtailment or idle channels. The reason for the defect is that the method ignores the systematic optimization of time-series coupling and energy storage regulation capabilities.

[0006] (2) Low utilization rate and poor economic efficiency: Due to the rigidity of the plan, the transmission corridor is difficult to fully utilize during periods of high output and also difficult to reasonably reduce demand during periods of low demand, thereby reducing the utilization rate of transmission assets and increasing the leveling cost. The root cause is the lack of optimization based on the overall cost target for the whole year or typical days.

[0007] (3) Frequent manual adjustments and poor repeatability: Relying on manual experience for correction leads to poor repeatability and response delay, which is not conducive to large-scale, multi-channel automated scheduling.

[0008] Existing technology 2 provides a single-stage mixed integer programming (MIP) joint optimization model. The implementation steps of existing technology 2 are typically as follows: (1) Construct an overall time series optimization model that includes constraints on coal-fired power unit output, renewable energy output, energy storage charging and discharging constraints, node power balance and HVDC channel power constraints; (2) Model the HVDC step-shaped operation characteristics (e.g., minimum adjustment amount, holding time, number of adjustments) as integer / binary variables and directly incorporate them into the objective function and constraints; (3) Use commercial solvers (such as CPLEX, Gurobi) or custom branch and bound methods to solve the system and obtain the joint optimal scheduling and outbound curves for the whole year or typical days.

[0009] However, the prior art 2 has the following drawbacks: (1) Large computational scale and time-consuming solution: Explicitly modeling the HVDC ladder constraint as a binary variable leads to an explosion in problem size, especially in the annual time series optimization with hourly granularity, where the solution time and memory consumption increase significantly. This is because the number of mixed integer variables increases with the time step and the number of channels, affecting the efficiency of branch and bound.

[0010] (2) Limited engineering feasibility: In order to reduce the amount of computation, the model often needs to be coarsened or truncated in engineering (such as shortening the time window or reducing the resolution), which will result in a loss of planning accuracy and may produce outgoing curves that cannot be directly implemented. The root cause of the defects is that single-stage models are difficult to balance between ensuring global optimality and engineering feasibility.

[0011] (3) Insufficient robustness and scalability: When more adjustable resources (multi-channel, multi-energy storage, multiple units) or higher resolution prediction are introduced, the scalability and robustness of the model decrease significantly.

[0012] Existing technology 3 provides an optimization model that ignores or oversimplifies the HVDC stepped operation constraints, including: establishing an optimization model with continuous or simplified discrete constraints. In the model, only the economic operation constraints of the system (such as unit combination, renewable energy consumption, power balance, etc.) are considered, while the HVDC transmission curve is regarded as an ideal power curve that can be continuously, without delay, and adjusted infinitely, or only simple upper and lower power constraints are applied. By solving this greatly simplified model, a smooth, continuously changing "optimal" transmission curve is directly obtained, and this is used as the output result.

[0013] However, the existing technology 3 has the following drawbacks: The optimization results lack engineering feasibility, ignoring the rigid requirements of actual HVDC engineering operation. Actual DC transmission power cannot be continuously and rapidly adjusted like AC line power; each power adjustment requires a certain stabilization period, and the number of adjustments per day is limited. Therefore, while the continuous transmission curve generated by existing technology 3 may be economically optimal, it is divorced from engineering reality. The dispatch center cannot directly execute this curve and must perform secondary corrections based on manual experience. This not only increases the workload but also fails to guarantee the economic efficiency of the corrected curve, potentially leading to low channel utilization and a decline in renewable energy consumption. Summary of the Invention

[0014] To address the aforementioned problems, this invention provides a two-stage optimization method and system for determining the high-voltage direct current transmission curve of a new energy base, the method comprising: Obtain the annual power generation curve of the sending-end renewable energy power plant and the annual load curve of the receiving end; calculate the annual net load curve based on the annual power generation curve and the annual load curve; extract at least 4 typical days based on the annual net load curve; The first-stage optimization model is established with the objective function of minimizing the annual operating cost of the system. It takes into account the operating constraints of coal-fired units, new energy units, and energy storage equipment, but does not consider the step-shaped operating constraints of the LCC-HVDC power transmission curve, and obtains the preliminary transmission curve for each typical day. The second-stage optimization model is established with the objective function being to minimize the error between the generated fitted transmission curve and the initial transmission curve. The model considers the stepped operation constraints of the LCC-HVDC transmission power curve, but does not consider the operation constraints of coal-fired units, new energy units, and energy storage equipment. The model obtains the fitted transmission curve that satisfies the stepped operation constraints for each typical day.

[0015] Preferably, the step of obtaining the annual power generation curve of the sending-end renewable energy power plant and the annual load curve of the receiving end, and calculating the annual net load curve based on the annual power generation curve and the annual load curve, includes: The annual power generation curve is obtained by multiplying the annual capacity factor of the sending-end renewable energy power plant by the renewable energy installed capacity. The annual load curve is obtained by multiplying the annual load ratio coefficient of the receiving area by the load peak. The annual net load curve is obtained by subtracting the annual power generation curve from the annual load curve.

[0016] Preferably, the extraction of at least four typical days based on the annual net load curve includes: At least four typical days were extracted from the annual net load curve using the K-Means clustering method.

[0017] Preferably, the first-stage optimization model established includes: The objective function of the first-stage optimization model is to minimize the annual operating cost of the system. The constraints include the operating constraints of coal-fired power units, new energy units, and energy storage equipment. By solving the first-stage optimization model, the preliminary transmission curve for each typical day is obtained.

[0018] Preferably, the objective function of the first-stage optimization model is expressed as:

[0019] in, The weighting coefficient for a typical day ranges from [0, 1]. , and These represent the index and set of a typical day, respectively. and These represent the output power of coal-fired power units and the absorption capacity of new energy power units, respectively. ,in g and These represent the index and set of coal-fired power units in the system, respectively. ,in t and These represent the hour index and set for a given day, respectively. and These represent the electricity costs of coal-fired power units and renewable energy units, respectively. This is an index of the new energy generating units in the sending-end new energy power plant.

[0020] Preferably, the operating constraints of the coal-fired unit are expressed as follows:

[0021] in, and These represent the upper and lower limits of the operating power of coal-fired power units, respectively. It is a binary variable representing the on / off state of a coal-fired power unit.

[0022] Preferably, the operating constraints of the new energy unit are expressed as follows:

[0023] in, and These represent the electricity consumed and the electricity abandoned by new energy generating units, respectively. ,in and These represent the index and set of new energy generating units, respectively. This represents the power generation capacity of new energy power plants on each typical day; The utilization rate of new energy sources must be higher than 90%, expressed as: .

[0024] Preferably, the operating constraints of the energy storage device include: charging and discharging power constraints, constraints that cannot be charged and discharged simultaneously, storage level constraints at the beginning and end of a typical day, dynamic changes in storage level constraints, upper and lower limits of storage level constraints, node power balance constraints of the network, and transmission power constraints of the external transmission lines in the network. The charging and discharging power constraint is expressed as follows:

[0025] in, and These represent discharge and charge power, respectively; binary variables. and This indicates the charging and discharging status of the energy storage device. If the sum of the two values ​​is less than 1, it means that the energy storage device cannot be charged and discharged simultaneously. This indicates the maximum operating power of the energy storage device; ,in s and These represent the indexes and sets of energy storage devices in the system, respectively. Storage levels at the end of each typical day It must be greater than or equal to its initial storage level. The storage level constraints for the start and end times of a typical day are expressed as follows:

[0026] in, This indicates the percentage of the initial storage level relative to the rated capacity. Indicates the rated capacity of the energy storage device; The dynamic change constraint of the storage level is expressed as follows:

[0027] in, and Each typical day represents a different day. t Storage levels for the hour and the previous hour; and Indicates the charging and discharging efficiency of energy storage devices; The upper and lower limits of the storage level are expressed as follows:

[0028] in, and They represent The upper and lower limits of the proportion of the rated capacity of energy storage; The node power balance constraint of the network is expressed as:

[0029] Subscript n ( g ), n ( w )and n ( s ) respectively represent the nodes coal-fired power units New energy units and energy storage equipment s ; and These represent the starting and ending nodes in the network, respectively. The route; , It represents the set of all lines in the system; The active power of the line; The power transmission constraints of lines in a network are expressed as follows:

[0030] in, Indicates the rated capacity of the line; Indicates a DC transmission line between the sending and receiving areas; Indicates the communication lines within the sending and receiving areas; It is determined that the power generation of coal-fired power units in new energy bases is less than 50% of their external power transmission, and the total coal-fired power generation of all new energy bases is less than 50% of the total external power transmission through all transmission channels, as expressed as:

[0031] Subscript Indicates location in the delivery channel coal-fired power units at the sending end g .

[0032] Preferably, the objective function of the established second-stage optimization model is expressed as:

[0033] in, This is represented as the fitted export curve; This is represented as a preliminary outbound curve; The weighting coefficient for a typical day ranges from [0, 1]. , and These represent the index and set of a typical day, respectively. , Indicates a DC transmission line between the sending and receiving areas; ,in t and These represent the hour index and set for a day, respectively.

[0034] Preferably, the step-shaped operation constraints of the second-stage optimization model include: upper and lower limit constraints of the fitted transmission curve of the transmission line, upper and lower adjustment amount constraints, state maintenance duration constraints, power adjustment frequency constraints in a day, and constraints that the daily transmission volume of the fitted transmission curve is not less than the daily transmission volume of the initial transmission curve.

[0035] Preferably, the upper and lower limit constraints of the fitted transmission curve of the transmission line are expressed as follows: .

[0036] Preferably, the upper and lower adjustment constraints of the fitted transmission curve of the transmission line are expressed as follows:

[0037] in, and These represent the minimum and maximum adjustment amounts for a DC line, respectively. and The two variables represent the upper and lower adjustment flags, respectively, and neither can be equal to 1 at the same time.

[0038] Preferably, the constraint on the duration of the state retention of the fitted transmission curve of the transmission line is expressed as follows:

[0039] in, This indicates the duration of the state maintained by the fitted export curve; and The two variables represent the upper and lower adjustment flags, respectively, and neither can be equal to 1 at the same time.

[0040] Preferably, the constraint on the number of power adjustment times per day for the fitted transmission curve of the transmission line is expressed as:

[0041] in, and The two variables represent the upper and lower adjustment flags respectively, and neither can be 1 at the same time; This indicates the maximum number of times the transmission power can be adjusted.

[0042] Preferably, the constraint that the daily transmission capacity of the fitted transmission curve is not less than the daily transmission capacity of the initial transmission curve is expressed as follows: .

[0043] Based on another aspect of the present invention, the present invention provides a two-stage optimization system for determining the high-voltage direct current transmission curve of a new energy base, the system comprising: The initial unit is used to acquire the annual power generation curve of the sending-end renewable energy power plant and the annual load curve of the receiving end, calculate the annual net load curve based on the annual power generation curve and the annual load curve, and extract at least 4 typical days based on the annual net load curve; The first acquisition unit is used to obtain the preliminary transmission curve for each typical day by using the established first-stage optimization model, with the objective function of minimizing the annual operating cost of the system, taking into account the operating constraints of coal-fired units, new energy units, and energy storage equipment, but not considering the step-shaped operating constraints of the LCC-HVDC power transmission curve. The second acquisition unit is used to obtain the fitted transmission curve that satisfies the stepped operation constraints for each typical day by using the established second-stage optimization model with the objective function of minimizing the error between the generated fitted transmission curve and the preliminary transmission curve. The model considers the stepped operation constraints of the LCC-HVDC transmission power curve, but does not consider the operation constraints of coal-fired units, new energy units, and energy storage equipment.

[0044] This invention provides a two-stage optimization method and system for determining the high-voltage direct current (HVDC) transmission curve of a new energy base. The method includes: Step 101: Obtaining the annual power generation curve of the sending-end new energy power plant and the annual load curve of the receiving end to calculate the annual net load curve, and extracting at least four typical days based on the annual net load curve; Step 102: Using the established first-stage optimization model, with the objective function of minimizing the annual operating cost of the system, considering the operating constraints of coal-fired units, new energy units, and energy storage equipment, but not considering the step-shaped operating constraints of the LCC-HVDC transmission power curve, obtaining the preliminary transmission curve for each typical day; Step 103: Using the established second-stage optimization model, with the objective function of minimizing the error between the generated fitted transmission curve and the preliminary transmission curve, considering the step-shaped operating constraints of the transmission power curve, but not considering the operating constraints of coal-fired units, new energy units, and energy storage equipment, obtaining the fitted transmission curve for each typical day that satisfies the step-shaped operating constraints. This invention improves the optimization efficiency of the HVDC transmission curve and the channel utilization rate. Attached Figure Description

[0045] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures: Figure 1 This is a flowchart of a two-stage optimization method for determining the high-voltage direct current transmission curve of a new energy base according to a preferred embodiment of the present invention. Figure 2 This is a schematic diagram of an example system for the sending and receiving ends according to a preferred embodiment of the present invention; Figure 3 A schematic diagram of the preliminary and fitted transmission curves of a high-voltage direct current (HVDC) line in a typical day according to a preferred embodiment of the present invention; and Figure 4 A system structure diagram for two-stage optimization of the high-voltage direct current transmission curve of a new energy base according to a preferred embodiment of the present invention is proposed. Detailed Implementation

[0046] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0047] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0048] Figure 1 This is a flowchart of a two-stage optimization method for determining the high-voltage direct current transmission curve of a new energy base according to a preferred embodiment of the present invention.

[0049] This invention aims to efficiently optimize and formulate a high-voltage direct current (HVDC) transmission curve that is both economical and meets engineering availability requirements, solve the coordination problem between large-scale new energy sources on the "source" side and large-capacity transmission channels on the "grid" side, and thereby improve the level of large-scale consumption of new energy and the utilization rate of inter-regional transmission channels.

[0050] This invention proposes a two-stage optimization method for determining the high-voltage direct current (HVDC) transmission curve of a new energy base. The core innovation of this invention lies in achieving a collaborative mechanism of "optimization first, then fitting" through model decoupling: the first stage obtains a globally economically optimal continuous transmission curve without considering the constraints of DC stepped operation; the second stage performs stepped fitting based on the operating characteristics of the DC system. This method significantly improves the model's solution speed and stability, and the generated transmission curve possesses both economic feasibility and engineering implementability.

[0051] This invention's method can be widely applied to the operation planning and scheduling optimization of large-scale renewable energy base transmission projects. It is applicable to multiple existing and under-construction UHVDC transmission projects, such as the Jiquan and Harbin-Chongqing DC transmission channels. This invention can take into account the annual time-series interaction characteristics of the source and load at both the sending and receiving ends, efficiently formulating DC transmission curves and improving the utilization rate of transmission channels and the level of renewable energy absorption. With the advancement of my country's new power system construction and the expansion of high-proportion renewable energy grid connection, the technical solution of this invention can serve as an important component of power grid planning and dispatch decision support systems, supporting the coordinated configuration and optimized operation of multiple bases, multiple channels, and multiple energy storage systems, demonstrating good economic benefits and promising social application prospects.

[0052] like Figure 1 As shown, this invention provides a two-stage optimization method for determining the high-voltage direct current transmission curve of a new energy base. The method includes: Step 101: Obtain the annual power generation curve of the sending-end renewable energy power plant and the annual load curve of the receiving end; calculate the annual net load curve based on the annual power generation curve and the annual load curve; extract at least 4 typical days based on the annual net load curve; Preferably, the annual power generation curve of the sending-end renewable energy power plant and the annual load curve of the receiving end are obtained, and the annual net load curve is calculated based on the annual power generation curve and the annual load curve, including: The annual power generation curve is obtained by multiplying the annual capacity factor of the sending-end renewable energy power plant by the renewable energy installed capacity. The annual load curve is obtained by multiplying the annual load ratio coefficient of the receiving area by the load peak. The annual net load curve is obtained by subtracting the annual load curve from the annual power generation curve.

[0053] Preferably, at least four typical days are extracted based on the annual net load curve, including: At least four typical days were extracted from the annual net load curve using the K-Means clustering method. Step 102: Using the established first-stage optimization model, with the minimum annual operating cost of the system as the objective function, the operational constraints of coal-fired units, new energy units, and energy storage equipment are considered, but the step-shaped operational constraints of the LCC-HVDC power transmission curve are not considered, and the preliminary power transmission curve for each typical day is obtained. Preferably, the first-stage optimization model is established with the objective function of minimizing the annual operating cost of the system. This model considers the operational constraints of coal-fired power units, new energy units, and energy storage equipment, but does not consider the step-shaped operational constraints of the LCC-HVDC power transmission curve. The preliminary transmission curve for each typical day is obtained, including: The objective function of the first-stage optimization model is to minimize the annual operating cost of the system, and the constraints include the operating constraints of coal-fired units, new energy units, and energy storage equipment. By solving the first-stage optimization model, the preliminary export curve for each typical day is obtained.

[0054] Preferably, the objective function of the first-stage optimization model is expressed as:

[0055] in, The weighting coefficient for a typical day ranges from [0, 1]. , and These represent the index and set of a typical day, respectively. and These represent the output power of coal-fired power units and the absorption capacity of new energy power units, respectively. ,in g and These represent the index and set of coal-fired power units in the system, respectively. ,in t and These represent the hour index and set for a given day, respectively. and These represent the electricity costs of coal-fired power units and renewable energy units, respectively. This is an index for the new energy generating units at the sending end.

[0056] Preferably, the operating constraints of the coal-fired unit are expressed as follows:

[0057] in, and These represent the upper and lower limits of the operating power of coal-fired power units, respectively. It is a binary variable representing the on / off state of a coal-fired power unit.

[0058] Preferably, the operating constraints of the new energy unit are expressed as follows:

[0059] in, and These represent the electricity consumed and the electricity abandoned by renewable energy units, respectively. ,in and These represent the index and set of new energy generating units, respectively. This indicates the power generation capacity of a new energy power plant; The utilization rate of new energy sources must be higher than 90%, expressed as: .

[0060] Preferably, the operational constraints of the energy storage device include: charging and discharging power constraints, constraints that cannot be charged and discharged simultaneously, storage level constraints at the beginning and end of a typical day, dynamic changes in storage level constraints, upper and lower limits of storage level constraints, node power balance constraints of the network, and transmission power constraints of lines in the network. The charging and discharging power constraint is expressed as follows:

[0061] in, and These represent the discharge and charging power, respectively; the two variables represent the charging and discharging state of the energy storage device, and the sum of the two is less than 1, indicating that the energy storage device cannot be charged and discharged simultaneously. This indicates the maximum operating power of the energy storage device; ,in s and These represent the indexes and sets of energy storage devices in the system, respectively. Storage levels at the end of each typical day It must be greater than or equal to its initial storage level. The storage level constraints for the start and end times of a typical day are expressed as follows:

[0062] in, This indicates the percentage of the initial storage level relative to the rated capacity. Indicates the rated capacity of the energy storage device; The dynamic change constraint of storage level is expressed as:

[0063] in, and Each typical day represents a different day. t Storage levels for the hour and the previous hour; and Indicates the charging and discharging efficiency of energy storage devices; Storage level upper and lower limits constraints are expressed as follows:

[0064] in, and They represent The upper and lower limits of the proportion of the rated capacity of energy storage; The node power balance constraint of the network is expressed as:

[0065] Subscript n ( g ), n ( w )and n ( s ) respectively represent the nodes coal-fired power units New energy units and energy storage equipment s ; and These represent the starting and ending nodes in the network, respectively. The route; , It represents the set of all lines in the network; The active power of the line; The power transmission constraints of lines in a network are expressed as follows:

[0066] in, Indicates the rated capacity of the line; Indicates a DC transmission line between the sending and receiving areas; Indicates the communication lines within the sending and receiving areas; It is determined that the power generation of coal-fired power units in new energy bases is less than 50% of their external power transmission, and the total coal-fired power generation of all new energy bases is less than 50% of the total external power transmission through all transmission channels, as expressed as:

[0067] Subscript Indicates location in the delivery channel coal-fired power units at the sending end g .

[0068] This invention establishes a first-stage optimization model: with the minimum annual operating cost of the system as the objective function, which takes into account the operating constraints of coal-fired units, new energy units, and energy storage equipment, but does not consider the step-shaped operating constraints of the LCC-HVDC power transmission curve. Then, the model is solved to obtain the preliminary transmission curve of each typical day of the LCC-HVDC channel.

[0069] Step 103: Using the established second-stage optimization model, obtain the fitted transmission curve for each typical day based on the preliminary transmission curve for each typical day.

[0070] Preferably, through the established second-stage optimization model, based on the preliminary export curve for each typical day, the fitted export curve for each typical day is obtained, including: The objective function of the second-stage optimization model is determined to minimize the error between the fitted export curve and the initial export curve, expressed as:

[0071] in, This is represented as the fitted export curve; This is represented as a preliminary outbound curve; The weighting coefficient for a typical day ranges from [0, 1]. , and These represent the index and set of a typical day, respectively. , Indicates a DC transmission line between the sending and receiving areas; ,in t and These represent the hour index and set for a day, respectively.

[0072] Preferably, the step-shaped operation constraints of the second-stage optimization model include: upper and lower limit constraints of the fitted transmission curve of the transmission line, upper and lower adjustment amount constraints, state maintenance duration constraints, power adjustment frequency constraints in a day, and constraints that the daily transmission volume of the fitted transmission curve is not less than the daily transmission volume of the initial transmission curve.

[0073] Preferably, the upper and lower limit constraints of the fitted transmission curve of the transmission line are expressed as follows: .

[0074] Preferably, the upper and lower adjustment constraints of the fitted transmission curve of the transmission line are expressed as follows:

[0075] in, and These represent the minimum and maximum adjustment amounts for a DC line, respectively. and The two variables represent the upper and lower adjustment flags, respectively, and neither can be equal to 1 at the same time.

[0076] Preferably, the constraint on the duration of the state retention of the fitted transmission curve of the transmission line is expressed as follows:

[0077] in, This indicates the duration of the state maintained by the fitted export curve; and The two variables represent the upper and lower adjustment flags, respectively, and neither can be equal to 1 at the same time.

[0078] Preferably, the constraint on the number of power adjustments per day in a typical day for the fitted transmission curve of the transmission line is expressed as:

[0079] in, and The two variables represent the upper and lower adjustment flags respectively, and neither can be 1 at the same time; This indicates the maximum number of times the transmission power can be adjusted.

[0080] Preferably, the constraint that the daily transmission capacity of the fitted transmission curve is not less than the daily transmission capacity of the initial transmission curve is expressed as follows: .

[0081] This invention establishes a second-stage optimization model: the objective function is to minimize the error between the fitted curve and the initial transmission curve. The model considers the step-shaped operation constraint of the LCC-HVDC transmission power curve, but does not consider the operation constraints of coal-fired units, new energy units, energy storage equipment, etc. The model is then solved to obtain the fitted transmission curve for each typical day of the LCC-HVDC channel.

[0082] The proposed method will be illustrated in detail below through a simulation example. The simulation example uses a two-area system with sending and receiving ends, and its topology is as follows: Figure 2As shown, the sending-end region has 7000 MW of wind power, 3000 MW of photovoltaic power, 4000 MW of coal-fired power units, and 2000 MW×4 h of energy storage. The peak load of the receiving-end region is approximately 9600 MW, with 8000 MW of coal-fired power units. The total installed capacity of wind and solar power accounts for approximately 45.45%, and the renewable energy penetration rate under ideal conditions (no curtailment) is approximately 42.23%. K-Means clustering was used to extract 12 typical days from the annual net load curve. The sending-end renewable energy base transmits power to the load center through an 8000 MW LCC-HVDC line. The power ratio of sending-end wind power, photovoltaic power, coal-fired power, energy storage, and transmission channels is 7:3:4:2:8. The maximum and minimum power adjustments of the transmission channels are... and The minimum holding time for the line's operating power status is set at 40% and 10% of the line capacity, respectively. The maximum number of line power adjustments is set to 2 hours. The data is set at 4 times / day. Based on the above data, the first-stage optimization model of the system is established and solved to obtain the preliminary transmission curve for each typical day of the LCC-HVDC channel. Then, the second-stage optimization model is established and solved to obtain the fitted transmission curve for each typical day of the LCC-HVDC channel.

[0083] Taking a typical day as an example, the preliminary and fitted transmission curves of the LCC-HVDC line are as follows: Figure 3 As shown in the figure, the solid dot curve is the initial economically optimal curve obtained in the first stage, while the stepped hollow dot curve is the fitted curve of the final output in the second stage. The fitted curve closely follows the initial curve in its overall trend, indicating that the economy is preserved to the maximum extent. At the same time, the fitted curve presents a stepped shape, which strictly meets the engineering constraints such as the power adjustment amount and holding time of the DC transmission channel. In addition, the computational efficiency is improved by about 12.5 times compared with the single-stage transmission curve optimization method, verifying the correctness and effectiveness of the proposed method.

[0084] Regarding the "Two-Stage Optimization Method and System for Determining High-Voltage DC Transmission Curves for New Energy Bases" provided by this invention, those skilled in the art can substitute or adjust some technical steps without departing from the core concept of this invention. These substitutions can also achieve the invention's objective of improving optimization efficiency and channel utilization. Specific details are as follows: 1. Alternatives to typical daily extraction methods In step 101, the present invention exemplarily employs the K-Means clustering method to extract at least four typical days from the annual net load curve. Other time-series data feature extraction and scene generation methods known in the art can be used as alternatives to achieve the same purpose. For example, hierarchical clustering, DBSCAN, fuzzy C-means (FCM), or correlation analysis based on representative day matching, etc.

[0085] 2. Alternatives in the first-stage optimization model: (1) The objective function can be replaced by the minimum system carbon emissions, the minimum amount of abandoned electricity, or a comprehensive economic-environmental multi-objective function; (2) Mixed integer linear programming (MILP), quadratic programming (QP) or heuristic algorithms (such as particle swarm optimization and genetic algorithm) can be used to solve the problem to further improve computational efficiency.

[0086] 3. Alternatives in the second-stage fitting model: (1) In addition to minimizing the fitting error, minimizing the annual mean square error or minimizing the maximum deviation can be adopted; (2) The constraints on the number of power adjustments and the holding time can be implemented with flexible control by introducing a penalty function or a slack variable.

[0087] 4. Substitution of system composition and operational constraints: In addition to considering coal-fired power units, wind power, photovoltaics and energy storage, multi-energy coordinated operation units such as hydropower, gas power, pumped storage or compressed air energy storage can also be introduced.

[0088] 5. Alternatives to the two-stage solution framework: (1) The two-stage model can be merged into a single multi-objective collaborative optimization model, through the multi-level weighting method or ε - Solve using the constraint method; (2) Instead of using two-stage decomposition, we directly construct a mixed integer programming model with full constraints and solve it based on the Benders decomposition algorithm.

[0089] This invention significantly improves the utilization rate and economy of high-voltage direct current (HVDC) transmission channels: Through a two-stage optimization method, this invention refines the HVDC transmission curve that best matches the power supply and load characteristics of the sending and receiving ends, while ensuring the safe operation of the system. This maximizes the utilization of transmission capacity, reduces the cost per unit of transmitted power, and improves the return on investment of transmission assets.

[0090] This invention effectively improves the solution efficiency of complex models containing a large number of integer variables: It creatively decomposes the complex mixed-integer programming problem into two consecutive sub-problems. The first stage ignores the step-shaped operational constraints of the LCC-HVDC export curve and its related discrete variables for preliminary optimization. The second stage, based on the first stage, focuses on curve fitting, greatly reducing the computational complexity and solution time of the model, and improving the engineering feasibility of year-round optimization calculations for large-scale systems.

[0091] This invention balances economic efficiency and engineering practicality: the method provided by this invention ensures the economic efficiency of system operation through global optimization in the first stage, while satisfying the engineering constraints of the LCC-HVDC stepped operation mode through curve fitting in the second stage. The resulting final transmission curve is not only economical and efficient, but also a solution that fully conforms to the actual operating characteristics of DC projects and can be directly adopted by the power grid dispatching system.

[0092] This invention promotes the large-scale and efficient consumption of new energy sources and supports the construction of new power systems: By optimizing the transmission curve, this invention guides the sending-end base to transmit more energy during periods of high new energy generation and to transmit more energy during periods of high load at the receiving end, thus smoothing the power fluctuations of the sending-end power grid, alleviating the problem of wind and solar curtailment, and effectively meeting the clean energy demand of the receiving-end power grid.

[0093] Figure 4 A system structure diagram for two-stage optimization of the high-voltage direct current transmission curve of a new energy base according to a preferred embodiment of the present invention is proposed.

[0094] like Figure 4 As shown, this invention provides a two-stage optimization system for determining the high-voltage direct current transmission curve of a new energy base. The system includes: The initial unit 401 is used to acquire the annual power generation curve of the sending-end renewable energy power plant and the annual load curve of the receiving end, calculate the annual net load curve based on the annual power generation curve and the annual load curve, and extract at least 4 typical days based on the annual net load curve. Preferably, the annual power generation curve of the sending-end renewable energy power plant and the annual load curve of the receiving end are obtained, and the annual net load curve is calculated based on the annual power generation curve and the annual load curve, including: The annual power generation curve is obtained by multiplying the annual capacity factor of the sending-end renewable energy power plant by the renewable energy installed capacity. The annual load curve is obtained by multiplying the annual load ratio coefficient of the receiving area by the load peak. The annual net load curve is obtained by subtracting the annual load curve from the annual power generation curve.

[0095] Preferably, at least four typical days are extracted based on the annual net load curve, including: At least four typical days were extracted from the annual net load curve using the K-Means clustering method. The first acquisition unit 402 is used to acquire the preliminary transmission curve for each typical day by using the established first-stage optimization model, with the minimum annual operating cost of the system as the objective function, taking into account the operating constraints of coal-fired units, new energy units, and energy storage equipment, but not considering the step-shaped operating constraints of the LCC-HVDC power transmission curve. Preferably, the first-stage optimization model is used to obtain the preliminary transmission curve for each typical day by taking the minimum annual operating cost of the system as the objective function, considering the operating constraints of coal-fired units, new energy units, and energy storage equipment, but not considering the step-shaped operating constraint of the LCC-HVDC power transmission curve, including: The objective function of the first-stage optimization model is to minimize the annual operating cost of the system, and the constraints include the operating constraints of coal-fired units, new energy units, and energy storage equipment. By solving the first-stage optimization model, the preliminary export curve for each typical day is obtained.

[0096] Preferably, the objective function of the first-stage optimization model is expressed as:

[0097] in, The weighting coefficient for a typical day ranges from [0, 1]. , and These represent the index and set of a typical day, respectively. and These represent the output power of coal-fired power units and the absorption capacity of new energy power units, respectively. , where g and These represent the index and set of coal-fired power units in the system, respectively. ,in t and These represent the hour index and set for a given day, respectively. and These represent the electricity costs of coal-fired power units and renewable energy units, respectively. This is an index for the sending-end new energy generating units.

[0098] Preferably, the operating constraints of the coal-fired unit are expressed as follows:

[0099] in, and These represent the upper and lower limits of the operating power of coal-fired power units, respectively. It is a binary variable representing the on / off state of a coal-fired power unit.

[0100] Preferably, the operating constraints of the new energy unit are expressed as follows:

[0101] in, and These represent the electricity consumed and the electricity abandoned by renewable energy units, respectively. ,in and These represent the index and set of new energy generating units, respectively. This indicates the power generation capacity of a new energy power plant; The utilization rate of new energy sources must be higher than 90%, expressed as:

[0102] Preferably, the operational constraints of the energy storage device include: charging and discharging power constraints, constraints that cannot be charged and discharged simultaneously, storage level constraints at the beginning and end of a typical day, dynamic changes in storage level constraints, upper and lower limits of storage level constraints, node power balance constraints of the network, and transmission power constraints of lines in the network. The charging and discharging power constraint is expressed as follows:

[0103] in, and These represent discharge and charge power, respectively; binary variables. and This indicates the charging and discharging status of the energy storage device. If the sum of the two values ​​is less than 1, it means that the energy storage device cannot be charged and discharged simultaneously. This indicates the maximum operating power of the energy storage device; ,in s and These represent the indexes and sets of energy storage devices in the system, respectively. Storage levels at the end of each typical day It must be greater than or equal to its initial storage level. The storage level constraints for the start and end times of a typical day are expressed as follows:

[0104] in, This indicates the percentage of the initial storage level relative to the rated capacity. Indicates the rated capacity of the energy storage device; The dynamic change constraint of storage level is expressed as:

[0105] in, and Each typical day represents a different day. t Storage levels for the hour and the previous hour; and Indicates the charging and discharging efficiency of energy storage devices; Storage level upper and lower limits constraints are expressed as follows:

[0106] in, and They represent The upper and lower limits of the proportion of the rated capacity of energy storage; The node power balance constraint of the network is expressed as:

[0107] Subscript n ( g ), n ( w )and n ( s ) respectively represent the nodes coal-fired power units New energy units and energy storage equipment s ; and These represent the starting and ending nodes in the network, respectively. The route; , It represents the set of all lines in the system; The active power of the line; The power transmission constraints of lines in a network are expressed as follows:

[0108] in, Indicates the rated capacity of the line; Indicates a DC transmission line between the sending and receiving areas; Indicates the communication lines within the sending and receiving areas; It is determined that the power generation of coal-fired power units in new energy bases is less than 50% of their external power transmission, and the total coal-fired power generation of all new energy bases is less than 50% of the total external power transmission through all transmission channels, as expressed as:

[0109] Subscript Indicates location in the delivery channel coal-fired power units at the sending end g .

[0110] The second acquisition unit 403 is used to acquire the fitted transmission curve that satisfies the stepped operation constraints for each typical day by using the established second-stage optimization model with the objective function of minimizing the error between the generated fitted transmission curve and the preliminary transmission curve. The model considers the stepped operation constraints of the LCC-HVDC transmission power curve, but does not consider the operation constraints of coal-fired units, new energy units, and energy storage equipment.

[0111] Preferably, the second-stage optimization model, with the objective function being the minimum error between the generated fitted transmission curve and the initial transmission curve, considers the stepped operation constraint of the LCC-HVDC transmission power curve, but does not consider the operation constraints of coal-fired units, new energy units, and energy storage equipment. The fitted transmission curve satisfying the stepped operation constraint for each typical day includes: The objective function of the second-stage optimization model is determined to minimize the error between the fitted export curve and the initial export curve, expressed as:

[0112] in, This is represented as the fitted export curve; This is represented as a preliminary outbound curve; The weighting coefficient for a typical day ranges from [0, 1]. , and These represent the index and set of a typical day, respectively. , Indicates a DC transmission line between the sending and receiving areas; ,in t and These represent the hour index and set for a day, respectively.

[0113] Preferably, the constraints for determining the second-stage optimization model include: upper and lower limit constraints of the fitted transmission curve of the transmission line, upper and lower adjustment amount constraints, state maintenance duration constraints, power adjustment frequency constraints in a day, and the constraint that the daily transmission volume of the fitted transmission curve is not less than the daily transmission volume of the initial transmission curve.

[0114] Preferably, the upper and lower limit constraints of the fitted transmission curve of the transmission line are expressed as follows: .

[0115] Preferably, the upper and lower adjustment constraints of the fitted transmission curve of the transmission line are expressed as follows:

[0116] in, and These represent the minimum and maximum adjustment amounts for a DC line, respectively. and The two variables represent the upper and lower adjustment flags, respectively, and neither can be equal to 1 at the same time.

[0117] Preferably, the constraint on the duration of the state retention of the fitted transmission curve of the transmission line is expressed as follows:

[0118] in, This indicates the duration of the state maintained by the fitted export curve; and The two variables represent the upper and lower adjustment flags, respectively, and neither can be equal to 1 at the same time.

[0119] Preferably, the constraint on the number of power adjustments per day for fitting the transmission curve of the transmission line is expressed as:

[0120] in, and The two variables represent the upper and lower adjustment flags respectively, and neither can be 1 at the same time; This indicates the maximum number of times the transmission power can be adjusted.

[0121] Preferably, the constraint that the daily transmission capacity of the fitted transmission curve is not less than the daily transmission capacity of the initial transmission curve is expressed as follows: .

[0122] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as object-oriented programming languages ​​like Java and Python, and interpreted scripting languages ​​like JavaScript.

[0123] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0124] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0125] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0126] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0127] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0128] The invention has been described with reference to a few embodiments. However, as will be known to those skilled in the art, and as defined in the appended claims, other embodiments besides those disclosed above fall equivalently within the scope of the invention.

[0129] Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the art, unless otherwise expressly defined herein. All references to “a / the / the [device, component, etc.]” ​​are openly interpreted as at least one instance of said device, component, etc., unless otherwise expressly stated. The steps of any method disclosed herein need not be performed in the exact order disclosed unless explicitly stated otherwise.

Claims

1. A two-stage optimization method for determining the high-voltage direct current transmission curve of a new energy base, the method comprising: Obtain the annual power generation curve of the sending-end renewable energy power plant and the annual load curve of the receiving end; calculate the annual net load curve based on the annual power generation curve and the annual load curve; extract at least 4 typical days based on the annual net load curve; The first-stage optimization model is established with the goal of minimizing the annual operating cost of the system. It takes into account the operating constraints of coal-fired units, new energy units, and energy storage equipment, but does not consider the stepped operating constraints of the LCC-HVDC power transmission curve based on line commutation converter. The preliminary power transmission curve for each typical day is obtained. The second-stage optimization model, with the objective function being the minimum error between the generated fitted transmission curve and the initial transmission curve, considers the stepped operation constraint of the LCC-HVDC transmission power curve, but does not consider the operation constraints of coal-fired units, new energy units, and energy storage equipment. The fitted transmission curve satisfying the stepped operation constraint is obtained for each typical day. The objective function of the second-stage optimization model is confirmed to be: in, This is represented as the fitted export curve; This is represented as a preliminary outbound curve; The weighting coefficient for a typical day ranges from [0, 1]. , and These represent the index and set of a typical day, respectively. , Indicates a DC transmission line between the sending and receiving areas; ,in t and These represent the hour index and set for a given day, respectively. The step-shaped operating constraints of the second-stage optimization model include: upper and lower limit constraints of the fitted transmission curve of the transmission line, upper and lower adjustment amount constraints, state maintenance duration constraints, power adjustment frequency constraints, and the constraint that the daily transmission volume of the fitted transmission curve is not less than the daily transmission volume of the initial transmission curve.

2. The method according to claim 1, wherein obtaining the annual power generation curve of the sending-end renewable energy power plant and the annual load curve of the receiving end, and calculating the annual net load curve based on the annual power generation curve and the annual load curve, comprises: The annual power generation curve is obtained by multiplying the annual capacity factor of the sending-end renewable energy power plant by the renewable energy installed capacity. The annual load curve is obtained by multiplying the annual load ratio coefficient of the receiving area by the load peak. The annual net load curve is obtained by subtracting the annual power generation curve from the annual load curve.

3. The method according to claim 1, wherein extracting at least four typical days based on the annual net load curve includes: At least four typical days were extracted from the annual net load curve using the K-Means clustering method.

4. The method according to claim 1, wherein the objective function of the first-stage optimization model is expressed as: in, The weighting coefficient for a typical day ranges from [0, 1]. , and These represent the index and set of a typical day, respectively. and These represent the output power of coal-fired power units and the absorption capacity of new energy power units, respectively. ,in g and These represent the index and set of coal-fired power units in the system, respectively. ,in t and These represent the hour index and set for a given day, respectively. and These represent the electricity costs of coal-fired power units and renewable energy units, respectively. This is an index for the sending-end new energy generating units.

5. The method according to claim 4, wherein the operating constraints of the coal-fired unit are expressed as follows: in, and These represent the upper and lower limits of the operating power of coal-fired power units, respectively. It is a binary variable representing the on / off state of a coal-fired power unit.

6. The method according to claim 4, wherein the operating constraints of the new energy unit are expressed as follows: in, and These represent the electricity consumed and the electricity abandoned by new energy generating units, respectively. ,in and These represent the index and set of new energy generating units, respectively. This indicates the power generation capacity of a new energy power plant; The utilization rate of new energy sources must be higher than 90%, expressed as: 。 7. The method according to claim 4, wherein the operating constraints of the energy storage device include: Charge and discharge power constraints, simultaneous charge and discharge constraints, storage level constraints at the beginning and end of a typical day, dynamic changes in storage level constraints, upper and lower limits of storage level constraints, node power balance constraints in the network, and transmission power constraints of lines in the network. The charging and discharging power constraint is expressed as follows: in, and These represent discharge and charge power, respectively; binary variables. and This indicates the charging and discharging status of the energy storage device. If the sum of the two values ​​is less than 1, it means that the energy storage device cannot be charged and discharged simultaneously. This indicates the maximum operating power of the energy storage device; ,in s and These represent the indexes and sets of energy storage devices in the system, respectively. Storage levels at the end of each typical day It must be greater than or equal to its initial storage level. The storage level constraints for the start and end times of a typical day are expressed as follows: in, This indicates the percentage of the initial storage level relative to the rated capacity. Indicates the rated capacity of the energy storage device; The dynamic change constraint of the storage level is expressed as follows: in, and Each typical day represents a different day. t Storage levels for the hour and the previous hour; and Indicates the charging and discharging efficiency of energy storage devices; The upper and lower limits of the storage level are expressed as follows: in, and They represent The upper and lower limits of the proportion of the rated capacity of energy storage; The node power balance constraint of the network is expressed as: Subscript n ( g ), n ( w )and n ( s ) respectively represent the nodes coal-fired power units New energy units and energy storage equipment s ; and These represent the starting and ending nodes in the network, respectively. The route; , It represents the set of all lines in the system; The active power of the line; The power transmission constraints of lines in a network are expressed as follows: in, The rated capacity of the line; Indicates a DC transmission line between the sending and receiving areas; Indicates the communication lines within the sending and receiving areas; It is determined that the power generation of coal-fired power units in new energy bases is less than 50% of their external power transmission, and the total coal-fired power generation of all new energy bases is less than 50% of the total external power transmission through all transmission channels, as expressed as: Subscript Indicates location in the delivery channel coal-fired power units at the sending end g .

8. The method according to claim 1, wherein the upper and lower limit constraints of the fitted transmission curve of the transmission line are expressed as follows: 。 9. The method according to claim 1, wherein the upper and lower adjustment constraints of the fitted transmission curve of the transmission line are expressed as follows: in, and These represent the minimum and maximum adjustment amounts for a DC line, respectively. and The two variables represent the upper and lower adjustment flags, respectively, and neither can be equal to 1 at the same time.

10. The method according to claim 1, wherein the state retention time constraint of the fitted transmission curve of the transmission line is expressed as: in, This indicates the duration of the state maintained by the fitted export curve; and The two variables represent the upper and lower adjustment flags, respectively, and neither can be equal to 1 at the same time.

11. The method according to claim 1, wherein the constraint on the number of power adjustments per day for the fitted transmission curve of the transmission line is expressed as: in, and The two variables represent the upper and lower adjustment flags respectively, and neither can be 1 at the same time; This indicates the maximum number of times the transmission power can be adjusted.

12. According to the method of claim 1, the constraint that the daily transmission capacity of the fitted transmission curve is not less than the daily transmission capacity of the initial transmission curve is expressed as: 。 13. A two-stage optimization system for determining the high-voltage direct current transmission curve of a new energy base, the system comprising: The initial unit is used to acquire the annual power generation curve of the sending-end renewable energy power plant and the annual load curve of the receiving end, calculate the annual net load curve based on the annual power generation curve and the annual load curve, and extract at least 4 typical days based on the annual net load curve; The first acquisition unit is used to obtain the preliminary transmission curve for each typical day by using the established first-stage optimization model, with the minimum annual operating cost of the system as the objective function, taking into account the operating constraints of coal-fired units, new energy units, and energy storage equipment, but not considering the step-shaped operating constraints of the LCC-HVDC power transmission curve. The second acquisition unit is used to obtain the fitted transmission curve that satisfies the step-shaped operation constraint for each typical day by using the established second-stage optimization model, with the objective function being the minimum error between the generated fitted transmission curve and the initial transmission curve. This objective function considers the step-shaped operation constraint of the LCC-HVDC transmission power curve but does not consider the operation constraints of coal-fired units, new energy units, or energy storage equipment. The objective function of the second-stage optimization model is confirmed to be: in, This is represented as the fitted export curve; This is represented as a preliminary outbound curve; The weighting coefficient for a typical day ranges from [0, 1]. , and These represent the index and set of a typical day, respectively. , Indicates a DC transmission line between the sending and receiving areas; ,in t and These represent the hour index and set for a given day, respectively. The step-shaped operating constraints of the second-stage optimization model include: upper and lower limit constraints of the fitted transmission curve of the transmission line, upper and lower adjustment amount constraints, state maintenance duration constraints, power adjustment frequency constraints, and the constraint that the daily transmission volume of the fitted transmission curve is not less than the daily transmission volume of the initial transmission curve.

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