Cross-region direct current transmission curve fitting method and system for improving comprehensive consumption rate of sending and receiving ends

Abstract

This application relates to the fields of power system dispatching and operation and new energy power generation technology, and particularly to a method and system for fitting cross-regional DC power transmission curves to improve the comprehensive absorption rate at both the sending and receiving ends. The method includes: acquiring the power output characteristic curves of new energy at the sending end and the load characteristic curves at the receiving end, generating a wind-solar hybrid power output characteristic curve; averaging the wind-solar hybrid power output values ​​at each time point by month and performing curve fitting to obtain an initial hourly power transmission coefficient, and determining the monthly average power coefficient for each month; differentially adjusting the initial hourly power transmission coefficient according to the intraday load distribution at the receiving end to generate an initial power transmission curve; establishing a time-series production simulation optimization model with maximizing new energy absorption as the objective function, substituting the initial power transmission curve into the model for solution, and outputting the final power transmission curve through closed-loop iterative feedback optimization. Through the decoupling and coupling of the "monthly-intraday" dual-time granularity, a synergistic improvement in the comprehensive absorption rate of new energy on both the sending and receiving ends is achieved.

Description

Technical Field

[0001] This application relates to the fields of power system dispatching and operation and new energy power generation technology, and in particular to a method and system for fitting cross-regional DC power transmission curves to improve the comprehensive absorption rate at the sending and receiving ends. Background Technology

[0002] Since the "dual carbon" target was proposed, China has actively promoted the low-carbon transformation of its energy and power sector. Large-scale energy bases, focusing on desert, Gobi, and arid regions, have been a top priority for new energy development during the 15th Five-Year Plan period. The development and construction of inter-regional DC transmission lines have provided a platform for absorbing the energy from these large-scale energy bases. However, the mismatch between the characteristics of new energy sources at the sending end and the characteristics of the load at the receiving end presents a challenge to the formulation of power transmission curves.

[0003] The existing methods for determining power transmission curves have the following main shortcomings: (1) The power transmission curve is not comprehensively considered. In the existing technology, the power transmission curve is mostly only considered in terms of the output of new energy at the sending end or only in terms of a single aspect of the load at the receiving end. Han Peidong et al. proposed a multi-objective joint optimization configuration method for wind, solar and storage capacity of new energy bases based on multi-objective bi-layer optimization and time-series production simulation, but it mainly focuses on capacity configuration; Wang Jinshi et al. proposed a typical configuration of wind, solar, thermal and storage multi-energy complementary system and day-ahead dispatch optimization model in the "Shagohuang" area, but it still has not fully solved the problem of coordinated optimization between the power transmission curve and the characteristics of the receiving end load.

[0004] (2) Lack of coordinated optimization between wind, solar, thermal, and energy storage systems and the receiving-end power grid. In existing technologies, inter-regional DC power transmission plans mostly adopt a binary choice between "sender-end priority" and "receiver-end priority." For example, the existing two modes, "sender-end does not participate in receiving-end peak shaving" and "sender-end partially participates in receiving-end peak shaving," fail to achieve decoupling and coupling of sender-end and receiver-end characteristics at different time scales. Existing technologies lack a complete method for formulating power transmission curves based on the characteristics of the sender-end and receiver-end at both the annual hourly scale and the monthly and daily time granularities, respectively. Therefore, the mismatch between the seasonal peak of renewable energy generation at the sender-end (spring) and the peak electricity consumption at the receiver-end (summer and winter), as well as the daily mismatch between the midday peak of photovoltaic power generation at the sender-end and the evening peak at the receiver-end, has not been effectively solved in existing technologies.

[0005] Therefore, there is an urgent need for a method and system for fitting cross-regional DC power transmission curves to improve the overall absorption rate at both the sending and receiving ends. This involves coupling the characteristics of renewable energy at the sending end and the load characteristics at the receiving end to construct power transmission curves suitable for both sides. A time-series production simulation optimization model with the goal of maximizing renewable energy absorption should be established to improve the overall absorption rate of renewable energy at both the sending and receiving ends while meeting system operation constraints. Summary of the Invention

[0006] The purpose of this application is to provide a method and system for fitting cross-regional DC power transmission curves to improve the comprehensive absorption rate of the sending and receiving ends. By coupling the characteristics of new energy sources at the sending end and the load characteristics at the receiving end, a power transmission curve suitable for both the sending and receiving ends is constructed. A time-series production simulation optimization model with the goal of maximizing the absorption of new energy sources is established to improve the comprehensive absorption rate of new energy sources on both the sending and receiving ends under the premise of meeting the system operation constraints.

[0007] The first embodiment of this application discloses a method for fitting cross-regional DC power transmission curves to improve the overall absorption rate at the sending and receiving ends. The sending-end system to which the method is applied includes wind power, photovoltaic power, thermal power, and energy storage power sources. The method includes the following steps: Step S1: Obtain the power output characteristic curve of the sending end renewable energy source and the load characteristic curve of the receiving end. The power output characteristic curve of the sending end renewable energy source includes the power output characteristic curve of the sending end wind power source and the power output characteristic curve of the sending end photovoltaic power source. Step S2: Based on the wind power output characteristic curve and the photovoltaic power output characteristic curve at the sending end, and combined with the wind and solar scale ratio, generate a wind and solar hybrid power output characteristic curve. Step S3: Based on the wind-solar hybrid power output characteristic curve, the wind-solar hybrid power output values ​​at each moment are averaged by month to obtain the monthly average power output value at each moment in each month; curve fitting is performed based on the monthly average power output values ​​at each moment in each month to obtain the initial hourly power transmission coefficient; the monthly average power coefficient for each month is determined based on the monthly average power output values ​​at each moment in each month; within each month, using the monthly average power coefficient as a benchmark, the initial hourly power transmission coefficient is differentially adjusted according to the intraday distribution of the receiving-end load characteristic curve within that month to obtain the adjusted hourly power transmission coefficient, such that the hourly power transmission coefficient during peak receiving-end load periods is higher than the monthly average power coefficient for that month, and the hourly power transmission coefficient during off-peak receiving-end load periods is lower than the monthly average power coefficient for that month; an initial power transmission curve is generated based on the adjusted hourly power transmission coefficient and the maximum transmission power of the DC transmission channel; Step S4: Establish a time-series production simulation optimization model with the objective function of maximizing the consumption of new energy, and including at least the power balance constraints of the sending end, the power balance constraints of the receiving end, the power output constraints of the power source, and the energy storage operation constraints. Step S5: Substitute the initial power transmission curve as a boundary condition into the time-series production simulation optimization model for solution to obtain the new energy consumption result; Step S6: Determine whether the initial power transmission curve meets the preset target based on the new energy consumption result. If it does not meet the target, adjust the initial power transmission curve and repeat steps S4 to S6. If it meets the target, output the final power transmission curve.

[0008] In another preferred embodiment, in step S2, the wind-solar hybrid power output characteristic curve is calculated using the following formula: Where 'a' represents the scale of wind power and 'b' represents the scale of photovoltaic power. This is the wind power output characteristic curve. This is the photovoltaic power output characteristic curve. This is the output characteristic curve of the wind-solar hybrid power generation system.

[0009] In another preferred embodiment, in step S3, the initial power supply curve is generated by the following formula: in, Let t be the power transmitted by the DC transmission channel at time t. This represents the maximum transmission power of the DC transmission channel. The time-by-time power delivery coefficient after the differential adjustment is given, and satisfies the following conditions: .

[0010] In another preferred embodiment, step S3, the step of "determining the monthly average power coefficient of each month based on the monthly average output value at each time of each month" includes the following sub-steps: The power amplitude level of each month is determined based on the monthly average output value at each time of each month. The monthly average power coefficient of the month with the largest power amplitude level is set to 1 p.u., and the monthly average power coefficient of the remaining months is determined according to the ratio of the power amplitude level of the month to the power amplitude level of the largest month.

[0011] In another preferred embodiment, step S3, the step of "differentiating the initial hourly power supply coefficient", includes the following sub-steps: Within the same month, the power transmission coefficient is adjusted at hourly intervals based on the intraday distribution of the receiving-end load characteristic curve within that month, so that the power transmission coefficient during peak receiving-end load periods is higher than the monthly average power coefficient for that month, and the power transmission coefficient during off-peak receiving-end load periods is lower than the monthly average power coefficient for that month.

[0012] In another preferred embodiment, in step S4, the objective function is: Where Q represents the total amount of renewable energy generation during the entire optimization period. Let t be the power output of the new energy generation at the sending end. Let t be the power generation of the receiving end of the new energy source, and T be the total number of optimization periods.

[0013] In another preferred embodiment, in step S4, the power balance constraint at the sending end is: The power balance constraint at the receiving end is: in, and These represent the power generation of new energy sources at the sending and receiving ends at time t. and Let be the power generation of the i-th thermal power unit at the sending and receiving ends, respectively, at time t. and Let be the discharge power of the k-th energy storage at the sending and receiving ends at time t, respectively. and Let be the charging power of the k-th energy storage at the sending and receiving ends at time t, respectively. The exchange power of external power from the sending-end grid at time t. Let t be the power transmitted by the DC transmission channel at time t. and These represent the total number of thermal power units at the sending and receiving ends, respectively. and These represent the total energy storage capacity at the sending and receiving ends, respectively. This is the load characteristic curve at the receiving end.

[0014] In another preferred embodiment, in step S4, the power output constraint includes: a new energy power output upper limit constraint and a thermal power unit power output constraint; The upper limit constraint on the output of the new energy source is: The output constraint of the thermal power unit is: in, and These represent the power generation of new energy sources at the sending and receiving ends at time t. and These represent the upper limits of renewable energy power generation at the sending and receiving ends at time t, respectively. and Let be the power generation of the i-th thermal power unit at the sending and receiving ends, respectively, at time t. and These are the lower limits of the output of the i-th thermal power unit at the sending and receiving ends, respectively. and , respectively, represent the upper limit of the output of the i-th thermal power unit at the sending and receiving ends, and T represents the total number of optimization periods.

[0015] In another preferred embodiment, in step S4, the energy storage operation constraints include: energy storage charging and discharging power constraints, energy storage capacity constraints, and charging and discharging state coordination constraints. The energy storage charging and discharging power constraint is: The energy storage capacity constraint is: The charging and discharging state coordination constraint is as follows: in, and Let be the discharge power of the k-th energy storage at the sending and receiving ends at time t, respectively. and Let be the charging power of the k-th energy storage at the sending and receiving ends at time t, respectively. and Let be the discharge state variables of the k-th energy storage at the sending and receiving ends at time t, respectively. and Let be the charging state variables of the k-th energy storage at the sending and receiving ends at time t, respectively. and These represent the maximum discharge power of the k-th energy storage unit at the sending and receiving ends, respectively. and These represent the maximum charging power of the k-th energy storage unit at the sending and receiving ends, respectively. and Let be the stored energy of the k-th energy storage unit at the sending and receiving ends at time t, respectively. and Let be the stored energy of the k-th energy storage unit at the sending and receiving ends at time t+1, respectively. and These are the minimum storage capacities of the k-th energy storage unit at the sending and receiving ends, respectively. and These represent the maximum energy storage capacity of the k-th energy storage unit at the sending and receiving ends, respectively. and Let be the charging efficiency of the k-th energy storage unit at the sending and receiving ends, respectively. and , respectively, are the discharge efficiencies of the k-th energy storage at the sending and receiving ends.

[0016] The second embodiment of this application also discloses a cross-regional DC power transmission curve fitting system to improve the overall absorption rate at the sending and receiving ends. The sending-end system to which the curve fitting system is applied includes wind power, photovoltaic power, thermal power, and energy storage power sources. The curve fitting system includes: The data acquisition module is used to acquire the power output characteristic curve of the sending end renewable energy and the load characteristic curve of the receiving end. The power output characteristic curve of the sending end renewable energy includes the power output characteristic curve of the sending end wind power and the power output characteristic curve of the sending end photovoltaic power. The wind-solar hybrid power generation module is used to generate a wind-solar hybrid power generation characteristic curve based on the wind power output characteristic curve and the photovoltaic power output characteristic curve at the sending end, combined with the wind and solar scale ratio. The power transmission curve formulation module is used to: average the wind and solar hybrid power output values ​​at each moment according to the wind-solar hybrid power output characteristic curve, to obtain the monthly average power output value at each moment of each month; perform curve fitting based on the monthly average power output value at each moment of each month to obtain the initial hourly power transmission coefficient; determine the monthly average power coefficient for each month based on the monthly average power output value at each moment of each month; within each month, using the monthly average power coefficient as a benchmark, and based on the intraday distribution of the receiving-end load characteristic curve within that month, differentiate the initial hourly power transmission coefficient to obtain the adjusted hourly power transmission coefficient, such that the hourly power transmission coefficient during peak receiving-end load periods is higher than the monthly average power coefficient for that month, and the hourly power transmission coefficient during off-peak receiving-end load periods is lower than the monthly average power coefficient for that month; and generate the initial power transmission curve based on the adjusted hourly power transmission coefficient. The time-series production simulation optimization module is used to establish a time-series production simulation optimization model with the objective function of maximizing the amount of renewable energy consumed, and including at least power balance constraints, power output constraints, and energy storage operation constraints; the initial power transmission curve is substituted into the time-series production simulation optimization model as boundary conditions for solving to obtain the renewable energy consumption results; The feedback iterative optimization module is used to determine whether the initial power transmission curve meets the preset target based on the new energy consumption result. If it does not meet the target, the initial power transmission curve is adjusted and the timing production simulation optimization module and the feedback iterative optimization module are restarted. If the target is met, the final power transmission curve is output.

[0017] The embodiments of this application also disclose a cross-regional DC power transmission curve fitting device for improving the overall absorption rate at the sending and receiving ends, comprising: Memory, used to store computer-executable instructions; and, A processor, coupled to the memory, is configured to implement the steps of the method described above when executing the computer-executable instructions.

[0018] Embodiments of this application also disclose a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps in the method described above.

[0019] Embodiments of this application also disclose a computer program product including computer-executable instructions that, when executed by a processor, implement the steps in the method described above.

[0020] The main differences and effects of the implementation method of this application compared with the prior art are as follows: The implementation method of this application couples the characteristics of new energy sources at the sending end and the characteristics of loads at the receiving end. The monthly inter-regional power transmission curve is mainly based on the characteristics of new energy generation, while the intraday inter-regional power transmission curve is mainly based on the characteristics of the load of the receiving end power grid. This makes the proposed power transmission curve match the output of new energy sources at the sending end in terms of monthly characteristics and the load demand at the receiving end in terms of daily characteristics. By comprehensively considering the characteristics of both the sending and receiving ends, it can more accurately match the characteristics of both ends and more closely resemble the actual power transmission curve, which is conducive to improving the overall absorption rate.

[0021] Furthermore, by decoupling and coupling the "monthly-intraday" dual time granularity, the seasonal patterns of renewable energy at the sending end (e.g., high generation in spring and low generation in winter) are matched at the monthly scale, while the peak-valley rhythm of the receiving end load (e.g., high photovoltaic generation at midday and peak electricity consumption in the evening) is tracked at the intraday scale. This ensures that the power transmission curve respects both the resource conditions at the sending end and the peak-shaving needs at the receiving end, avoiding the one-sidedness of existing technologies that "focus solely on either the sending or receiving end." Compared to traditional methods, this approach can more accurately match the characteristics of both the sending and receiving ends, which is beneficial for improving the overall absorption rate.

[0022] Furthermore, by simultaneously incorporating renewable energy sources from both the sending and receiving ends into the objective function, and by independently modeling both sides using power balance constraints at the sending and receiving ends respectively, the consumption demands of both sides can be comprehensively considered. Compared to schemes that only consider consumption on one side, this approach avoids the trade-off between "excessive power generation at the sending end and difficulties in peak shaving at the receiving end" or "reduced peak shaving pressure at the receiving end but increased wind and solar curtailment at the sending end," truly achieving a synergistic improvement in the overall consumption rate of both the sending and receiving ends.

[0023] Furthermore, through a closed-loop iteration of "formulation-simulation-feedback-adjustment," the power delivery curve can be gradually optimized under the boundary conditions of system operation constraints until the preset target is met. Compared with open-loop schemes, it is possible to achieve the transformation from a "feasible scheme" to an "optimal scheme" through multiple iterations.

[0024] Furthermore, the technical solution of this application constructs a time-series production simulation optimization model for the "wind, solar, thermal, and storage" system, which includes equipment-type constraints such as upper and lower limits of thermal power unit output, energy storage capacity constraints, charging and discharging power constraints, and charging and discharging state coordination constraints. Compared with existing technologies for "hydro, wind, and solar", it can more accurately simulate the actual operating characteristics of the wind, solar, thermal, and storage system, providing a more reliable simulation basis for optimizing the power transmission curve. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating a method for fitting cross-regional DC power transmission curves to improve the overall absorption rate at the sending and receiving ends, according to the first embodiment of this application. Figure 2This is a flowchart illustrating a preferred embodiment of the first embodiment of this application; Figure 3 This is a schematic diagram of a cross-regional DC power transmission curve fitting system for improving the overall absorption rate at the sending and receiving ends, according to the second embodiment of this application. Detailed Implementation

[0026] In the following description, numerous technical details are presented to facilitate the reader's understanding of this application. However, those skilled in the art will understand that the technical solutions claimed in the claims of this application can be implemented even without these technical details and with various variations and modifications based on the following embodiments.

[0027] Explanation of some concepts: 1. Regulating power sources: These are power sources that can proactively and flexibly adjust their power output according to the grid's demand. When electricity load changes, or when the output of new energy sources (wind power, solar power) fluctuates significantly due to weather conditions, the grid's frequency and voltage become unstable. In such cases, regulating power sources need to respond quickly, increasing or decreasing power generation to maintain a real-time balance between power generation and consumption, ensuring the safe and stable operation of the grid. Examples include thermal power, hydropower, and energy storage.

[0028] 2. Non-regulating power sources: These are power sources whose output is largely determined by natural resources and is difficult or impossible to significantly alter by humans; they are beyond human control. Examples include wind power and solar power.

[0029] 3. Sending end: This refers to the "generating end" (power source side) of electricity, typically the location of large energy bases, such as new energy bases in deserts, Gobi, and arid regions. These areas have a large concentration of wind power, solar power, and thermal power plants. Part of the generated electricity is consumed locally, while the other part is transmitted to other regions via ultra-high-voltage direct current (UHVDC) and other high-voltage transmission lines. For the sending end, its characteristics are dominated by power source properties; its output is greatly affected by natural resources (such as wind and solar resources), exhibiting volatility and uncertainty.

[0030] 4. Receiving end: This refers to the "receiving party" (load side) of electricity, typically referring to economically developed and densely populated areas such as the eastern coastal regions, including the Yangtze River Delta, Beijing-Tianjin-Hebei region, and Pearl River Delta. Land resources are precious here, and local power generation is limited, but electricity demand is enormous, requiring the import of large amounts of electricity to fill power gaps. For the receiving end, load characteristics dominate. Their electricity demand exhibits regular peak-valley variations (e.g., high during the day, low at night, high in summer and winter), and the power grid focuses on how to safely and smoothly receive this electricity from afar.

[0031] 5. In a wind-solar-thermal-storage system, thermal power provides stable foundational support, while energy storage offers rapid and flexible regulation capabilities. The characteristics of thermal power units, such as ramp rate, minimum start-up and shutdown time, and minimum technical output, must be considered, resulting in complex constraints. Energy storage offers rapid response, flexible intraday charging and discharging, and flexible dispatching. It is suitable for large-scale renewable energy bases in areas with scarce water resources or abundant thermal power resources. The goal is to leverage the stability of thermal power and the flexibility of energy storage to solve the problems of grid integration and peak shaving for large-scale renewable energy.

[0032] 6. In hydropower-wind-solar power systems, hydropower (especially regulating hydropower stations with reservoir capacity) plays a leading regulatory role, with energy storage as a supplement. Utilizing the seasonal and intra-day regulating capabilities of hydropower, fluctuations in wind and solar power output are smoothed out. However, due to hydrological constraints, complex hydrological boundary conditions such as inflow forecasts, reservoir water levels, and ecological flows must be considered. This approach is suitable for river basins with abundant water resources and existing cascade hydropower station clusters. The goal is to maximize the utilization of hydropower's natural regulating reservoir capacity, packaging fluctuating renewable energy sources into stable and smooth high-quality power for transmission.

[0033] 7. Output refers to the actual active power generated by the power generation equipment (unit: MW or kW).

[0034] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0035] The first embodiment of this application relates to a method for fitting cross-regional DC power transmission curves to improve the overall absorption rate at the sending and receiving ends. The sending-end systems to which the method is applied include wind power, photovoltaic power, thermal power, and energy storage power sources. Figure 1 This is a flowchart illustrating the method for fitting the cross-regional DC power transmission curve to improve the overall absorption rate at both the sending and receiving ends.

[0036] Specifically, such as Figure 1 As shown, the method for fitting the cross-regional DC power transmission curve to improve the overall absorption rate at both the sending and receiving ends includes the following steps: In step S1, the power output characteristic curve of the sending end and the load characteristic curve of the receiving end are obtained. The power output characteristic curve of the sending end includes the power output characteristic curve of the sending end wind power and the power output characteristic curve of the sending end photovoltaic power.

[0037] Then proceed to step S2, where a wind-solar hybrid power output characteristic curve is generated based on the wind power output characteristic curve and the photovoltaic power output characteristic curve at the sending end, combined with the wind-solar scale ratio.

[0038] In this embodiment, preferably, in step S2 above, the wind-solar hybrid power output characteristic curve is calculated using the following formula: Where 'a' represents the scale of wind power and 'b' represents the scale of photovoltaic power. This is the wind power output characteristic curve. This is the photovoltaic power output characteristic curve. This is the output characteristic curve of the wind-solar hybrid power generation system.

[0039] Then, proceed to step S3. Based on the wind-solar hybrid power output characteristic curve, the wind-solar hybrid power output value at each moment is averaged by month to obtain the monthly average power output value at each moment of each month. Based on the monthly average power output value at each moment of each month, curve fitting is performed to obtain the initial hourly power transmission coefficient. Based on the monthly average power output value at each moment of each month, the monthly average power coefficient for each month is determined. Within each month, based on the monthly average power coefficient, the initial hourly power transmission coefficient is differentially adjusted according to the intraday distribution of the receiving-end load characteristic curve within that month to obtain the adjusted hourly power transmission coefficient, such that the hourly power transmission coefficient during the peak period of the receiving-end load is higher than the monthly average power coefficient for that month, and the hourly power transmission coefficient during the off-peak period of the receiving-end load is lower than the monthly average power coefficient for that month. Based on the adjusted hourly power transmission coefficient and the maximum transmission power of the DC transmission channel, an initial power transmission curve is generated.

[0040] In this embodiment, preferably, in step S3 above, The initial hourly power transmission coefficient is obtained by fitting the power transmission curve based on the time-of-use monthly average of the new energy characteristic curve. Determine the annual power transmission volume And according to the received-end load characteristic curve The initial hourly power transmission coefficient is adjusted to obtain the adjusted hourly power transmission coefficient. ; The initial power supply curve is generated using the following formula: in, Let t be the power transmitted by the DC transmission channel at time t. This represents the maximum transmission power of the DC transmission channel. The time-by-time power delivery coefficient after the differential adjustment is given, and satisfies the following conditions: , This is the initial power transmission curve obtained through coupling.

[0041] The step of "determining the monthly average power coefficient of each month based on the monthly average output value at each time of each month" includes the following sub-steps: The power amplitude level of each month is determined based on the monthly average output value at each time of each month. The monthly average power coefficient of the month with the largest power amplitude level is set to 1 p.u., and the monthly average power coefficient of the remaining months is determined according to the ratio of the power amplitude level of the month to the power amplitude level of the largest month.

[0042] It should be noted that the above-mentioned pu is a reference value in the power system per unit system, and in this application it represents the rated transmission capacity of the DC transmission channel.

[0043] For example, assuming a DC transmission channel has a rated capacity of 1000MW, then: When the actual transmission power is 700MW, the per-unit value is 700 / 1000 = 0.7pu; When transmitting 1000MW at full power, the per-unit value is 1000 / 1000=1p.u.

[0044] Continuing with the example of the aforementioned channel, assuming its capacity is 1000MW and the ratio of wind power to photovoltaic power is 1:1, we statistically analyze the monthly average output characteristics of the wind-solar hybrid system throughout the year. The highest monthly average is 0.85 pu in April, followed by 0.40 pu in December.

[0045] Based on the steps outlined in this application, the monthly average power factor for April, the month with the largest power output, is set to 1 p.u. (i.e., the transmission channel delivers a full 1000 MW). The monthly average power factor for December is proportionally set to 0.40 / 0.85 ≈ 0.471 p.u. (i.e., the average monthly power output for that month is 471 MW). This process is repeated to obtain the monthly average power factor for all 12 months of the year. This factor reflects the seasonal pattern of renewable energy output at the sending end and serves as a benchmark for subsequent intraday differentiated adjustments.

[0046] The “monthly average output value” refers to the absolute value of the wind and solar power output at the sending end (e.g., 0.65 pu), and the “monthly average power coefficient” refers to the relative value after “normalization of the maximum month” (the maximum month is 1 p.u., and the other months are converted proportionally).

[0047] Therefore, at the monthly level of the technical solution in this application, there is a seasonal mismatch between the peak power generation of wind and solar power at the sending end (spring) and the peak electricity consumption at the receiving end (summer / winter). The solution is to determine the average power factor for each month based on the power output characteristics of the sending end.

[0048] The step of "differentiating the initial hourly power transmission coefficient" includes the following sub-steps: Within the same month, the power transmission coefficient is adjusted at hourly intervals based on the intraday distribution of the receiving-end load characteristic curve within that month, so that the power transmission coefficient during peak receiving-end load periods is higher than the monthly average power coefficient for that month, and the power transmission coefficient during off-peak receiving-end load periods is lower than the monthly average power coefficient for that month.

[0049] In other words, the adjusted hourly power supply coefficient = monthly average power coefficient + intraday differential adjustment amount.

[0050] Therefore, at the intraday level of the technical solution in this application, there is an intraday mismatch between the peak photovoltaic power generation during the Dragon Boat Festival and the peak evening electricity demand at the receiving end. The solution is to adjust the hourly power output "on an hourly basis within each month" according to the intraday distribution of the receiving end load.

[0051] In the technical solution of this application, the monthly power curve is mainly determined based on the characteristics of renewable energy generation at the sending end. Specifically, during the peak renewable energy generation period in spring, the average power transmission volume for that month is set higher; during the winter when power output is lower, it is adjusted accordingly, thus resolving the problem of "mismatch between the peak renewable energy generation season at the sending end and the peak electricity consumption season at the receiving end". The intraday curve is mainly adjusted based on the load characteristics of the receiving end power grid. Specifically, during the peak electricity consumption period of the receiving end power grid during the day, more power is transmitted; during the off-peak electricity consumption period at night, less power is transmitted, thus resolving intraday contradictions such as "the sending end generates a lot of photovoltaic power at noon, but the receiving end may not need that much power during the midday period".

[0052] Then proceed to step S4, establish a time-series production simulation optimization model with the objective function of maximizing the consumption of new energy, and including at least sending-end power balance constraints, receiving-end power balance constraints, power output constraints, and energy storage operation constraints.

[0053] In this embodiment, preferably, in step S4 above, The objective function is: Where Q represents the total amount of renewable energy generation during the entire optimization period. Let t be the power output of the new energy generation at the sending end. Let t be the power generation of the receiving end of the new energy source, and T be the total number of optimization periods.

[0054] The power balance constraint at the sending end is: The power balance constraint at the receiving end is: in, and These represent the power generation of new energy sources at the sending and receiving ends at time t. and Let be the power generation of the i-th thermal power unit at the sending and receiving ends, respectively, at time t. and Let be the discharge power of the k-th energy storage at the sending and receiving ends at time t, respectively. and Let be the charging power of the k-th energy storage at the sending and receiving ends at time t, respectively. The exchange power of external power from the sending-end grid at time t. Let t be the power transmitted by the DC transmission channel at time t. and These represent the total number of thermal power units at the sending and receiving ends, respectively. and These represent the total energy storage capacity at the sending and receiving ends, respectively. This is the load characteristic curve at the receiving end.

[0055] By simultaneously incorporating renewable energy sources from both the sending and receiving ends into the objective function, and by independently modeling both sides using power balance constraints at the sending and receiving ends respectively, the consumption demand on both sides can be comprehensively considered. Compared with schemes that only consider consumption on one side, this approach avoids the trade-off between "excessive power generation at the sending end and difficulties in peak shaving at the receiving end" or "reduced peak shaving pressure at the receiving end but increased wind and solar curtailment at the sending end," truly achieving a synergistic improvement in the overall consumption rate of both the sending and receiving ends.

[0056] The power output constraints include: upper limit constraints on the output of new energy sources and constraints on the output of thermal power units. The upper limit constraint on the output of the new energy source is: The output constraint of the thermal power unit is: in, and These represent the power generation of new energy sources at the sending and receiving ends at time t. and These represent the upper limits of renewable energy power generation at the sending and receiving ends at time t, respectively. and Let be the power generation of the i-th thermal power unit at the sending and receiving ends, respectively, at time t. and These are the lower limits of the output of the i-th thermal power unit at the sending and receiving ends, respectively. and , respectively, represent the upper limit of the output of the i-th thermal power unit at the sending and receiving ends, and T represents the total number of optimization periods.

[0057] The energy storage operation constraints include: energy storage charging and discharging power constraints, energy storage capacity constraints, and charging and discharging state coordination constraints. The energy storage charging and discharging power constraint is: The energy storage capacity constraint is: The charging and discharging state coordination constraint is as follows: in, and Let be the discharge power of the k-th energy storage at the sending and receiving ends at time t, respectively. and Let be the charging power of the k-th energy storage at the sending and receiving ends at time t, respectively. and Let be the discharge state variables of the k-th energy storage at the sending and receiving ends at time t, respectively. and Let be the charging state variables of the k-th energy storage at the sending and receiving ends at time t, respectively. and These represent the maximum discharge power of the k-th energy storage unit at the sending and receiving ends, respectively. and These represent the maximum charging power of the k-th energy storage unit at the sending and receiving ends, respectively. and Let be the stored energy of the k-th energy storage unit at the sending and receiving ends at time t, respectively. and Let be the stored energy of the k-th energy storage unit at the sending and receiving ends at time t+1, respectively. and These are the minimum storage capacities of the k-th energy storage unit at the sending and receiving ends, respectively. and These represent the maximum energy storage capacity of the k-th energy storage unit at the sending and receiving ends, respectively. and Let be the charging efficiency of the k-th energy storage unit at the sending and receiving ends, respectively. and , respectively, are the discharge efficiencies of the k-th energy storage at the sending and receiving ends.

[0058] The technical solution proposed in this application is a time-series production simulation optimization model for the "wind, solar, thermal, and energy storage" system. It includes equipment-type constraints such as upper and lower limits of thermal power unit output, energy storage capacity constraints, charging and discharging power constraints, and charging and discharging state coordination constraints. Compared with existing technologies for "hydro, wind, and solar", it can more accurately simulate the actual operating characteristics of the wind, solar, thermal, and energy storage system, and provide a more reliable simulation basis for optimizing the power transmission curve.

[0059] Then proceed to step S5, where the initial power transmission curve is substituted as a boundary condition into the time-series production simulation optimization model for solution, and the result of new energy consumption is obtained.

[0060] In the technical solution of this application, a time-series production simulation optimization model is established with the goal of maximizing the absorption of new energy and considering the constraints of the operation mode of system source, grid, load and storage. Then, the theoretical power sequence of new energy and load for each time period of the year is used as boundary conditions. By solving the optimization model, the optimal power generation results of new energy and conventional power sources are obtained, thereby obtaining the optimal absorption of new energy for the whole year.

[0061] Then proceed to step S6, where the initial power transmission curve is judged to meet the preset target based on the new energy consumption result. If it does not meet the target, the initial power transmission curve is adjusted and steps S4 to S6 are repeated. If it meets the target, the final power transmission curve is output.

[0062] Furthermore, through a closed-loop iteration of "formulation-simulation-feedback-adjustment," the power delivery curve can be gradually optimized under the boundary conditions of system operation constraints until the preset target is met. Compared with open-loop schemes, it is possible to achieve the transformation from a "feasible scheme" to an "optimal scheme" through multiple iterations.

[0063] This process will then end.

[0064] In summary, the implementation method of this application, by coupling the characteristics of new energy sources at the sending end and the characteristics of loads at the receiving end, determines the monthly inter-regional power transmission curve based primarily on the characteristics of new energy generation, and the intraday inter-regional power transmission curve based primarily on the load characteristics of the receiving end power grid. This ensures that the determined power transmission curves match the output of new energy sources at the sending end in terms of monthly characteristics and the load demand at the receiving end in terms of daily characteristics. By comprehensively considering the characteristics of both the sending and receiving ends, the characteristics of both ends can be matched more accurately, resulting in a power transmission curve that is closer to the actual power transmission curve and is conducive to improving the overall absorption rate.

[0065] To better understand the technical solution of this application, a preferred embodiment will be described below. The details listed in this preferred embodiment are mainly for ease of understanding and are not intended to limit the scope of protection of this application.

[0066] This preferred embodiment aims to construct a method for formulating a cross-regional DC power transmission curve that couples the characteristics of the sending-end renewable energy and the characteristics of the receiving-end load. It also combines renewable energy time-series production simulation technology to construct a simulation model aimed at improving the renewable energy absorption rate, and explores a better power transmission curve while meeting the absorption rate requirements.

[0067] The main objective of this preferred embodiment is: (1) By coupling the characteristics of the sending and receiving ends, the power transmission curve of the new energy base is formulated to achieve the highest comprehensive absorption rate.

[0068] (2) Establish new energy time-series production simulation technology through mathematical model and build a collaborative optimization model of sending power source and receiving power grid.

[0069] The core logic of this preferred embodiment is as follows: 1. Complementary Analysis of Sending and Receiving Ends The power transmission curve formulation mainly includes monthly and intraday curves. In order to transmit as much renewable energy as possible, the monthly average output of the inter-regional power transmission curve is mainly based on the characteristics of renewable energy generation. In order to minimize the pressure on the receiving-end grid units for peak shaving and intraday start-up and shutdown, the intraday inter-regional power transmission curve is mainly based on the load characteristics of the receiving-end grid.

[0070] Seasonal matching: Statistical results of power generation characteristics at the sending end and load characteristics at the receiving end show that the peak generation season of renewable energy at the sending end is mainly concentrated in spring, while the demand for peak capacity at the receiving end is mismatched between summer and winter.

[0071] Daily matching performance: Statistical results from the sending-end power supply characteristics and the receiving-end load characteristics (seasonal load characteristics and daily load characteristics) show that the peak generation periods of renewable energy at the sending end are all during the daytime photovoltaic peak generation period, reducing the matching performance between the sending and receiving ends. However, the power supply guarantee capacity can be improved through midday charging of energy storage and peak-hour charging in the evening. At the same time, the configuration of renewable energy with more wind than solar power can provide a certain peak-hour capacity during the evening peak period and at night, improving the matching performance between the sending and receiving ends.

[0072] 2. Principles for determining the power transmission curve (1) In accordance with the requirements put forward by the power grid dispatching and operation department, the DC power adjustment shall not exceed 6 times per day, and a step-type daily hourly operation curve shall be formulated; (2) Draft the DC curves for each month according to different months, and only consider the transmission power of the month with the largest power as 1 p.u.; (3) Ensure that the annual utilization hours of DC are reasonable, taking into account the characteristics of wind and solar resources at the sending end and improving the utilization rate of DC power transmission channels; (4) The power transmission curves are proposed according to three cases: considering only the characteristics of the new energy source at the sending end, considering only the characteristics of the load at the receiving end, and considering the characteristics of the coupled sending and receiving ends.

[0073] 3. Specific steps: The implementation process of this preferred embodiment mainly includes five steps: first, formulating a power transmission curve based on input parameters; second, obtaining the wind, solar and energy storage configuration scale through power balancing; third, simulating the time-series production of new energy sources; and fourth, adjusting and optimizing the power transmission curve scheme according to boundary requirements such as consumption and grid supply to obtain the final result that meets the target. Figure 2 This is a flowchart illustrating the preferred embodiment.

[0074] Key features of this preferred embodiment include: (1) Method for formulating power transmission curves that couple the characteristics of the sending and receiving ends: This invention collects the characteristic curves of the new energy source at the sending end and the characteristic curves at the receiving end, and couples them to form power transmission curves applicable to both ends. Specifically, the monthly characteristics match the output of the new energy source at the sending end, and the daily characteristics match the load demand at the receiving end.

[0075] (2) Based on the simulation model of new energy production with the maximum absorption rate: This invention establishes a simulation optimization model of new energy production with the goal of maximizing the absorption of new energy and considering the constraints of the operation mode of the system source, grid, load and storage. Then, the theoretical power sequence of new energy and load throughout the year is used as the boundary condition. The optimal power generation results of new energy and conventional power sources are obtained by solving the optimization model, thereby obtaining the optimal absorption of new energy throughout the year.

[0076] This preferred embodiment achieves the following excellent technical effects: (1) The power transmission curve is more reasonably proposed. Compared to traditional curve formulation methods, this method takes into account the characteristics of both the sending and receiving ends, making it more accurate and closer to the actual curve in production simulation, and more beneficial for the comprehensive absorption of both ends.

[0077] (2) Construction of a time-series production simulation model based on the new energy source with the highest absorption rate This method can systematically consider the constraints of the operation modes of power sources, grids, loads, and storage. It uses the theoretical power sequence of new energy sources and loads throughout the year as boundary conditions, and obtains the optimal power generation results of new energy sources and conventional power sources by solving the optimization model.

[0078] The second embodiment of this application relates to a cross-regional DC power transmission curve fitting system that improves the overall absorption rate at both the sending and receiving ends. Figure 3 This is a schematic diagram of the cross-regional DC power transmission curve fitting system that improves the overall absorption rate at both the sending and receiving ends.

[0079] Specifically, such as Figure 3 As shown, the cross-regional DC power transmission curve fitting system for improving the overall absorption rate at both the sending and receiving ends utilizes wind power, photovoltaic power, thermal power, and energy storage power sources. The curve fitting system includes: The data acquisition module is used to acquire the power output characteristic curve of the sending end renewable energy and the load characteristic curve of the receiving end. The power output characteristic curve of the sending end renewable energy includes the power output characteristic curve of the sending end wind power and the power output characteristic curve of the sending end photovoltaic power. The wind-solar hybrid power generation module is used to generate a wind-solar hybrid power generation characteristic curve based on the wind power output characteristic curve and the photovoltaic power output characteristic curve at the sending end, combined with the wind and solar scale ratio. The power transmission curve formulation module is used to: average the wind and solar hybrid power output values ​​at each moment according to the wind-solar hybrid power output characteristic curve, to obtain the monthly average power output value at each moment of each month; perform curve fitting based on the monthly average power output value at each moment of each month to obtain the initial hourly power transmission coefficient; determine the monthly average power coefficient for each month based on the monthly average power output value at each moment of each month; within each month, using the monthly average power coefficient as a benchmark, and based on the intraday distribution of the receiving-end load characteristic curve within that month, differentiate the initial hourly power transmission coefficient to obtain the adjusted hourly power transmission coefficient, such that the hourly power transmission coefficient during peak receiving-end load periods is higher than the monthly average power coefficient for that month, and the hourly power transmission coefficient during off-peak receiving-end load periods is lower than the monthly average power coefficient for that month; and generate the initial power transmission curve based on the adjusted hourly power transmission coefficient. The time-series production simulation optimization module is used to establish a time-series production simulation optimization model with the objective function of maximizing the amount of renewable energy consumed, and including at least power balance constraints, power output constraints, and energy storage operation constraints; the initial power transmission curve is substituted into the time-series production simulation optimization model as boundary conditions for solving to obtain the renewable energy consumption results; The feedback iterative optimization module is used to determine whether the initial power transmission curve meets the preset target based on the new energy consumption result. If it does not meet the target, the initial power transmission curve is adjusted and the timing production simulation optimization module and the feedback iterative optimization module are restarted. If the target is met, the final power transmission curve is output.

[0080] It should be noted that the components or modules mentioned in the second embodiment of this application are all logical modules. Physically, a logical module can be a physical module, a part of a physical module, or a combination of multiple physical modules. The physical implementation of these logical modules is not the most important factor; rather, the combination of functions implemented by these logical modules is the key to solving the technical problem proposed in this application. Furthermore, to highlight the innovative aspects of this application, the above embodiments of this application do not introduce components or modules that are not closely related to solving the technical problem proposed in this application. This does not mean that the above embodiments do not contain other components or modules.

[0081] The first embodiment is a method embodiment corresponding to this embodiment. The technical details in the first embodiment can be applied to this embodiment, and the technical details in this embodiment can also be applied to the first embodiment.

[0082] Accordingly, embodiments of this application also provide a cross-regional DC power transmission curve fitting device for improving the overall absorption rate at the sending and receiving ends, including a memory for storing computer-executable instructions, and a processor; the processor is used to implement the steps in the above-described method embodiments when executing the computer-executable instructions in the memory. The processor may be a Central Processing Unit (CPU), Graphics Processing Unit (GPU), Digital Signal Processor (DSP), Microcontroller Unit (MCU), Neural Processing Unit (NPU), Application Specific Integrated Circuit (ASIC), Field Programmable Gate Array (FPGA), or other programmable logic devices. The aforementioned memory may be read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or solid-state drive, etc. The steps of the methods disclosed in the various embodiments of the present invention can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.

[0083] Furthermore, embodiments of this application also provide a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the various method embodiments of this application. Computer-readable storage media include permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device. As defined herein, computer-readable storage media does not include transient media, such as modulated data signals and carrier waves.

[0084] Furthermore, embodiments of this application also provide a computer program product, including computer-executable instructions that, when executed by a processor, implement the steps in the above-described method embodiments.

[0085] It should be noted that in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. In this application, if it refers to performing an action according to an element, it means performing the action at least according to that element, including two cases: performing the action only according to that element, and performing the action according to that element and other elements. Expressions such as "multiple," "repeatedly," and "various" include two, two times, two kinds, and more than two, more than two times, and more than two kinds.

[0086] The numbering used in describing the steps of a method does not inherently limit the order of these steps. For example, a step with a higher number does not necessarily have to be executed after a step with a lower number; it can be executed first and then second, or even in parallel, as long as this execution order is reasonable to someone skilled in the art. Similarly, multiple steps with consecutively numbered sequences (e.g., step 101, step 102, step 103, etc.) do not restrict other steps from being executed between them; for example, there can be other steps between step 101 and step 102.

[0087] This specification includes combinations of various embodiments described herein. Individual references to embodiments are made (e.g., "one embodiment," "some embodiments," or "preferred embodiments"); however, these embodiments are not mutually exclusive unless indicated to be mutually exclusive or are readily apparent to those skilled in the art. It should be noted that the word "or" is used in a non-exclusive sense throughout this specification unless the context explicitly indicates or requires it.

[0088] All references to this specification are considered to be incorporated integrally into the disclosure of this application so that they can serve as the basis for modifications if necessary. Furthermore, it should be understood that the above descriptions are merely preferred embodiments of this specification and are not intended to limit the scope of protection of this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this specification should be included within the scope of protection of one or more embodiments of this specification.

[0089] In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for fitting curves in cross-regional DC power transmission to improve the overall absorption rate at both the sending and receiving ends, characterized in that, The sending-end system to which the method is applied includes wind power, photovoltaic power, thermal power, and energy storage power sources. The method includes the following steps: Step S1: Obtain the power output characteristic curve of the sending end renewable energy source and the load characteristic curve of the receiving end. The power output characteristic curve of the sending end renewable energy source includes the power output characteristic curve of the sending end wind power source and the power output characteristic curve of the sending end photovoltaic power source. Step S2: Based on the wind power output characteristic curve and the photovoltaic power output characteristic curve at the sending end, and combined with the wind and solar scale ratio, generate a wind and solar hybrid power output characteristic curve. Step S3: Based on the wind-solar hybrid power output characteristic curve, the wind-solar hybrid power output values ​​at each moment are averaged by month to obtain the monthly average power output value at each moment in each month; curve fitting is performed based on the monthly average power output values ​​at each moment in each month to obtain the initial hourly power transmission coefficient; the monthly average power coefficient for each month is determined based on the monthly average power output values ​​at each moment in each month; within each month, using the monthly average power coefficient as a benchmark, the initial hourly power transmission coefficient is differentially adjusted according to the intraday distribution of the receiving-end load characteristic curve within that month to obtain the adjusted hourly power transmission coefficient, such that the hourly power transmission coefficient during peak receiving-end load periods is higher than the monthly average power coefficient for that month, and the hourly power transmission coefficient during off-peak receiving-end load periods is lower than the monthly average power coefficient for that month; an initial power transmission curve is generated based on the adjusted hourly power transmission coefficient and the maximum transmission power of the DC transmission channel; Step S4: Establish a time-series production simulation optimization model with the objective function of maximizing the consumption of new energy, and including at least the power balance constraints of the sending end, the power balance constraints of the receiving end, the power output constraints of the power source, and the energy storage operation constraints. Step S5: Substitute the initial power transmission curve as a boundary condition into the time-series production simulation optimization model for solution to obtain the new energy consumption result; Step S6: Determine whether the initial power transmission curve meets the preset target based on the new energy consumption result. If it does not meet the target, adjust the initial power transmission curve and repeat steps S4 to S6. If it meets the target, output the final power transmission curve.

2. The method for fitting the cross-regional DC power transmission curve to improve the overall absorption rate at the sending and receiving ends, as described in claim 1, is characterized in that... In step S2, the wind-solar hybrid power output characteristic curve is calculated using the following formula: Where 'a' represents the scale of wind power and 'b' represents the scale of photovoltaic power. This is the wind power output characteristic curve. This is the photovoltaic power output characteristic curve. This is the output characteristic curve of the wind-solar hybrid power generation system.

3. The method for fitting the cross-regional DC power transmission curve to improve the overall absorption rate at the sending and receiving ends, as described in claim 1, is characterized in that... In step S3, the initial power supply curve is generated using the following formula: in, Let t be the power transmitted by the DC transmission channel at time t. This represents the maximum transmission power of the DC transmission channel. The time-by-time power delivery coefficient after the differential adjustment is given, and satisfies the following conditions: .

4. The method for fitting the cross-regional DC power transmission curve to improve the overall absorption rate at the sending and receiving ends, as described in claim 1, is characterized in that... In step S3, the step of "determining the monthly average power coefficient of each month based on the monthly average output value at each time of each month" includes the following sub-steps: The power amplitude level of each month is determined based on the monthly average output value at each time of each month. The monthly average power coefficient of the month with the largest power amplitude level is set to 1 p.u., and the monthly average power coefficient of the remaining months is determined according to the ratio of the power amplitude level of the month to the power amplitude level of the largest month.

5. The method for fitting the cross-regional DC power transmission curve to improve the overall absorption rate at the sending and receiving ends, as described in claim 1, is characterized in that... In step S3, the step of "differentiating the initial hourly power transmission coefficient" includes the following sub-steps: Within the same month, the power transmission coefficient is adjusted at hourly intervals based on the intraday distribution of the receiving-end load characteristic curve within that month, so that the power transmission coefficient during peak receiving-end load periods is higher than the monthly average power coefficient for that month, and the power transmission coefficient during off-peak receiving-end load periods is lower than the monthly average power coefficient for that month.

6. The method for fitting the cross-regional DC power transmission curve to improve the overall absorption rate at the sending and receiving ends, as described in claim 1, is characterized in that... In step S4, the objective function is: Where Q represents the total amount of renewable energy generation during the entire optimization period. Let t be the power output of the new energy generation at the sending end. Let t be the power generation of the receiving end of the new energy source, and T be the total number of optimization periods.

7. The method for fitting the cross-regional DC power transmission curve to improve the overall absorption rate at the sending and receiving ends, as described in claim 1, is characterized in that... In step S4, the power balance constraint at the sending end is: The power balance constraint at the receiving end is: in, and These represent the power generation of new energy sources at the sending and receiving ends at time t. and Let be the power generation of the i-th thermal power unit at the sending and receiving ends, respectively, at time t. and Let be the discharge power of the k-th energy storage at the sending and receiving ends at time t, respectively. and Let be the charging power of the k-th energy storage at the sending and receiving ends at time t, respectively. The exchange power of external power from the sending-end grid at time t. Let t be the power transmitted by the DC transmission channel at time t. and These represent the total number of thermal power units at the sending and receiving ends, respectively. and These represent the total energy storage capacity at the sending and receiving ends, respectively. This is the load characteristic curve at the receiving end.

8. The method for fitting the cross-regional DC power transmission curve to improve the overall absorption rate at the sending and receiving ends, as described in claim 1, is characterized in that... In step S4, the power output constraints include: upper limit constraints on new energy power output and power output constraints on thermal power units; The upper limit constraint on the output of the new energy source is: The output constraint of the thermal power unit is: in, and These represent the power generation of new energy sources at the sending and receiving ends at time t. and These represent the upper limits of renewable energy power generation at the sending and receiving ends at time t, respectively. and Let be the power generation of the i-th thermal power unit at the sending and receiving ends, respectively, at time t. and These are the lower limits of the output of the i-th thermal power unit at the sending and receiving ends, respectively. and , respectively, represent the upper limit of the output of the i-th thermal power unit at the sending and receiving ends, and T represents the total number of optimization periods.

9. The method for fitting the cross-regional DC power transmission curve to improve the overall absorption rate at the sending and receiving ends, as described in claim 1, is characterized in that... In step S4, the energy storage operation constraints include: energy storage charging and discharging power constraints, energy storage capacity constraints, and charging and discharging state coordination constraints. The energy storage charging and discharging power constraint is: The energy storage capacity constraint is: The charging and discharging state coordination constraint is as follows: in, and Let be the discharge power of the k-th energy storage at the sending and receiving ends at time t, respectively. and Let be the charging power of the k-th energy storage at the sending and receiving ends at time t, respectively. and Let be the discharge state variables of the k-th energy storage at the sending and receiving ends at time t, respectively. and Let be the charging state variables of the k-th energy storage at the sending and receiving ends at time t, respectively. and These represent the maximum discharge power of the k-th energy storage unit at the sending and receiving ends, respectively. and These represent the maximum charging power of the k-th energy storage unit at the sending and receiving ends, respectively. and Let be the stored energy of the k-th energy storage unit at the sending and receiving ends at time t, respectively. and Let be the stored energy of the k-th energy storage unit at the sending and receiving ends at time t+1, respectively. and These are the minimum storage capacities of the k-th energy storage unit at the sending and receiving ends, respectively. and These represent the maximum energy storage capacity of the k-th energy storage unit at the sending and receiving ends, respectively. and Let be the charging efficiency of the k-th energy storage unit at the sending and receiving ends, respectively. and , respectively, are the discharge efficiencies of the k-th energy storage at the sending and receiving ends.

10. A cross-regional DC power transmission curve fitting system for improving the overall absorption rate at both the sending and receiving ends, characterized in that, The curve fitting system is used in sending-end systems including wind power, photovoltaic power, thermal power, and energy storage power sources. The curve fitting system includes: The data acquisition module is used to acquire the power output characteristic curve of the sending end renewable energy and the load characteristic curve of the receiving end. The power output characteristic curve of the sending end renewable energy includes the power output characteristic curve of the sending end wind power and the power output characteristic curve of the sending end photovoltaic power. The wind-solar hybrid power generation module is used to generate a wind-solar hybrid power generation characteristic curve based on the wind power output characteristic curve and the photovoltaic power output characteristic curve at the sending end, combined with the wind and solar scale ratio. The power transmission curve formulation module is used to: average the wind and solar hybrid power output values ​​at each moment according to the wind-solar hybrid power output characteristic curve, to obtain the monthly average power output value at each moment of each month; perform curve fitting based on the monthly average power output value at each moment of each month to obtain the initial hourly power transmission coefficient; determine the monthly average power coefficient for each month based on the monthly average power output value at each moment of each month; within each month, using the monthly average power coefficient as a benchmark, and based on the intraday distribution of the receiving-end load characteristic curve within that month, differentiate the initial hourly power transmission coefficient to obtain the adjusted hourly power transmission coefficient, such that the hourly power transmission coefficient during peak receiving-end load periods is higher than the monthly average power coefficient for that month, and the hourly power transmission coefficient during off-peak receiving-end load periods is lower than the monthly average power coefficient for that month; and generate the initial power transmission curve based on the adjusted hourly power transmission coefficient. The time-series production simulation optimization module is used to establish a time-series production simulation optimization model with the objective function of maximizing the amount of renewable energy consumed, and including at least power balance constraints, power output constraints, and energy storage operation constraints; the initial power transmission curve is substituted into the time-series production simulation optimization model as boundary conditions for solving to obtain the renewable energy consumption results; The feedback iterative optimization module is used to determine whether the initial power transmission curve meets the preset target based on the new energy consumption result. If it does not meet the target, the initial power transmission curve is adjusted and the timing production simulation optimization module and the feedback iterative optimization module are restarted. If the target is met, the final power transmission curve is output.

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