A new energy power generation power distribution optimization method and device

Abstract

The present application relates to the technical field of new energy power generation control, and particularly provides a new energy power generation power distribution optimization method and device, aiming to solve the technical problem that the traditional short-circuit ratio index cannot reflect the influence of the interaction between multiple new energy stations on the system voltage strength, and cannot effectively represent the influence of the change of the power generation power of each new energy station on the system stability. The new energy power generation power distribution optimization method and device provided by the present application adjust the power generation power of each new energy station in the to-be-optimized new energy station by using the comprehensive sensitivity of each new energy station, can obtain an optimal distribution control scheme of the new energy power generation power under the requirement of safe and stable operation of the system, so as to realize the full utilization of renewable energy on the basis of ensuring safety and stability.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation control, and specifically to a method and apparatus for optimizing the allocation of new energy power generation. Background Technology

[0002] Due to resource distribution limitations, most wind and solar resource-rich areas are far from conventional power sources and load centers. Large-scale wind and solar power are typically connected to the end of the grid. In these areas, the local grid strength is relatively weak compared to the amount of new energy connected. Furthermore, because new energy units are generally connected to the grid through power electronic devices, their regulation and support capabilities are weak. This leads to problems such as transient overvoltages and broadband oscillations when new energy is connected to the grid, becoming a significant factor restricting the capacity for new energy integration and affecting the safe and stable operation of the grid. With the introduction of the "dual carbon" target and the further acceleration of new energy development, this issue will gradually become a key challenge affecting the green development of the power grid.

[0003] To address system stability issues such as transient overvoltages and broadband oscillations, the mainstream approach is to explore the impact of changes in the power generation of renewable energy units on various system parameters, thereby optimizing high-proportion renewable energy integration schemes. References such as "Accommodation capacity for wind farm based on the static security constraints in bulk power systems" and "Method for determining the maximum allowable penetration level of distributed generation without steady-state voltage violations" determine the power generation limit by quantitatively calculating the sensitivity of each unit's output to voltage rises to avoid static stability problems such as voltage exceeding limits. Furthermore, the impact of power generation on system reliability and power supply economy has been analyzed. Reference "Penetration level optimization for DG considering reliable action of relay protection device constrains" considers the impact of different renewable energy power generation on the reliable operation of system relay protection devices, thereby calculating the maximum penetration rate of renewable energy generation in the system. Reference "An analytical method for the sizing and sitting of distributed generators in radial systems" proposes a renewable energy location and capacity determination strategy with the goal of minimizing system network losses. However, the above studies mainly focus on the static stability, reliability, and economy of power systems, and have not conducted in-depth research on the optimal allocation of new energy power plants under the quantitative constraints of system stability levels.

[0004] The system stability level comprehensively reflects the grid's capacity to support various renewable energy sources and should be used as a primary reference indicator for evaluating optimized allocation schemes in systems with a high proportion of renewable energy centralized access. In large-scale renewable energy centralized grid-connected systems, to intuitively and effectively measure the voltage intensity of the system connected to multiple renewable energy power plants and quantitatively assess the scale of renewable energy access, experts have proposed the short-circuit ratio (SCR) index. As a static analysis method, the SCR, with its simplicity and intuitiveness, provides important reference for grid planning and operation. However, the traditional SCR index cannot reflect the impact of interactions between multiple renewable energy power plants on system voltage intensity, nor can it effectively characterize the impact of changes in the power generation of each renewable energy power plant on system stability. Summary of the Invention

[0005] To overcome the above-mentioned shortcomings, this invention is proposed to provide a new energy power allocation optimization method and apparatus that solves or at least partially solves the technical problem that the traditional short-circuit ratio index cannot reflect the impact of the interaction between multiple new energy power plants on the system voltage intensity, and cannot effectively characterize the impact of changes in the power generation of each new energy power plant on the system stability.

[0006] Firstly, a method for optimizing the allocation of new energy power generation capacity is provided, the method comprising:

[0007] Step 1. Obtain the comprehensive sensitivity of each new energy power station in the new energy power stations to be optimized, select the new energy power station with the smallest absolute value of comprehensive sensitivity, and determine whether its output has reached the maximum output. If so, remove the new energy power station from the new energy power stations to be optimized and repeat Step 1. Otherwise, go to Step 2.

[0008] Step 2. Increase the power generation capacity of the new energy power station with the smallest absolute value of the overall sensitivity by one adjustment step;

[0009] Step 3. Calculate the short-circuit ratio of each new energy power station in the new energy power station to be optimized, and obtain the difference between the minimum short-circuit ratio and the preset short-circuit ratio reference value;

[0010] Step 4. If the difference is not greater than the preset deviation value or the number of optimization iterations is reached, the operation ends; otherwise, return to step 1.

[0011] Preferably, the formula for calculating the overall sensitivity of each new energy power station is as follows:

[0012]

[0013] In the above formula, D c.i Let be the comprehensive sensitivity of the i-th renewable energy power station, α be the weighting coefficient of the system average sensitivity, and D be the comprehensive sensitivity of the i-th renewable energy power station. sys.i Let β be the average sensitivity of the short-circuit ratio of all power stations in the system to the power of the i-th renewable energy power station, and let β be the weighting coefficient of the sensitivity of the short-circuit ratio of the lowest renewable energy power station. Let be the sensitivity of the minimum short-circuit ratio of the renewable energy power station to the i-th renewable energy power station, where i∈[1,n] and n is the total number of renewable energy power stations among those to be optimized.

[0014] Furthermore, the formula for calculating the average sensitivity of the short-circuit ratio of all power stations in the system to the power of the i-th new energy power station is as follows:

[0015]

[0016] In the above formula, P reiM represents the active power injected into the grid-side access point / grid connection point of the power generation equipment in the i-th renewable energy power station. i Let be the short-circuit ratio of the i-th renewable energy power station.

[0017] Furthermore, the formula for calculating the short-circuit ratio of the i-th renewable energy power station is as follows:

[0018]

[0019] In the above formula, S aci Z represents the injected three-phase short-circuit capacity at the grid-side access point / grid connection point of the power generation equipment in the i-th renewable energy power station. ij This is the power conversion factor between the i-th and j-th renewable energy power stations.

[0020] Furthermore, the calculation formula for the power conversion factor of the i-th new energy power station and the j-th new energy power station is as follows:

[0021]

[0022] In the above formula, Z eqij Z represents the equivalent AC grid impedance between the grid-connected busbars of the i-th and j-th renewable energy power plants. eqii Let U be the equivalent impedance of the AC power grid at the grid connection bus of the i-th renewable energy power station. i Let U be the grid-connected bus voltage of the i-th renewable energy power station. j Let be the grid-connected bus voltage of the j-th renewable energy power station.

[0023] Furthermore, calculate using the following formula.

[0024]

[0025] Secondly, a new energy power generation allocation optimization device is provided, the new energy power generation allocation optimization device comprising:

[0026] The first judgment module is used to obtain the comprehensive sensitivity of each new energy power station in the new energy power station to be optimized, select the new energy power station with the smallest absolute value of comprehensive sensitivity, and determine whether its output has reached the maximum output. If so, the new energy power station is removed from the new energy power station to be optimized and the first judgment module is re-executed; otherwise, it is transferred to the optimization module.

[0027] The optimization module is used to increase the power generation of the new energy power station with the smallest absolute value of the comprehensive sensitivity by an adjustment step size;

[0028] The calculation module is used to calculate the short-circuit ratio of each new energy power station in the new energy power station to be optimized, and to obtain the difference between the minimum short-circuit ratio and the preset short-circuit ratio reference value.

[0029] The second judgment module is used to end the operation if the difference is not greater than the preset deviation value or the number of optimization iterations is reached; otherwise, it returns to the first judgment module.

[0030] Preferably, the formula for calculating the overall sensitivity of each new energy power station is as follows:

[0031]

[0032] In the above formula, D c.i Let be the comprehensive sensitivity of the i-th renewable energy power station, α be the weighting coefficient of the system average sensitivity, and D be the comprehensive sensitivity of the i-th renewable energy power station. sys.i Let β be the average sensitivity of the short-circuit ratio of all power stations in the system to the power of the i-th renewable energy power station, and let β be the weighting coefficient of the sensitivity of the short-circuit ratio of the lowest renewable energy power station. Let be the sensitivity of the minimum short-circuit ratio of the renewable energy power station to the i-th renewable energy power station, where i∈[1,n] and n is the total number of renewable energy power stations among those to be optimized.

[0033] Thirdly, a storage medium is provided, the storage medium including a stored program, wherein, when the program is running, the device where the storage medium is located is controlled to execute the new energy power generation allocation optimization method.

[0034] Fourthly, a processor is provided for running a program, wherein the program executes the new energy power generation allocation optimization method during runtime.

[0035] The above-described technical solutions of the present invention have at least one or more of the following beneficial effects:

[0036] This invention provides a method for optimizing the allocation of renewable energy power generation. The method includes: Step 1. Obtaining the comprehensive sensitivity of each renewable energy power station in the renewable energy power station to be optimized, selecting the renewable energy power station with the smallest absolute value of comprehensive sensitivity, and determining whether its output has reached the maximum output capacity. If so, the renewable energy power station is removed from the renewable energy power station to be optimized and Step 1 is repeated; otherwise, proceed to Step 2; Step 2. Increasing the power generation of the renewable energy power station with the smallest absolute value of comprehensive sensitivity by an adjustment step size; Step 3. Calculating the short-circuit ratio of each renewable energy power station in the renewable energy power station to be optimized, and obtaining the difference between the minimum short-circuit ratio and the preset short-circuit ratio reference value; Step 4. If the difference is not greater than the preset deviation value or reaches the number of optimization iterations, the operation ends; otherwise, return to Step 1. This technical solution comprehensively considers the relationship between grid stability and renewable energy capacity after renewable energy integration, as well as the impact of the comprehensive short-circuit ratio sensitivity of multiple renewable energy power plants on their integrated capacity. Based on the iterative determination of renewable energy unit output using this comprehensive short-circuit ratio sensitivity, the maximum renewable energy generation capacity can be obtained after successive iterations, ensuring the short-circuit ratio meets the requirements of the region. Furthermore, a power allocation scheme for renewable energy power plants can be derived using this method. The solution is validated using a real power system, demonstrating that the renewable energy allocation optimization scheme based on quantitative assessment of system stability is entirely feasible. The calculation results show that the renewable energy allocation optimization scheme based on quantitative assessment of system stability can effectively improve the scale of renewable energy grid connection and the system stability under disturbance faults, illustrating the rationality of the technical solution provided by this invention compared to the allocation method based on installed capacity ratio. Attached Figure Description

[0037] Figure 1 This is a schematic flowchart of the main steps of a new energy power generation power allocation optimization method according to an embodiment of the present invention;

[0038] Figure 2 This is a schematic diagram of the actual power grid structure in the XJZD area in this embodiment of the invention;

[0039] Figure 3 This is a diagram showing the voltage change curve of the unit during DC blocking in an embodiment of the present invention;

[0040] Figure 4 This is a schematic diagram of the short-circuit ratio iteration process in an embodiment of the present invention;

[0041] Figure 5 This is a main structural block diagram of a new energy power generation power allocation optimization device according to an embodiment of the present invention. Detailed Implementation

[0042] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] The short-circuit ratio of multiple renewable energy power plants takes into account the mutual influence among renewable energy sources and can characterize the degree of influence of renewable energy power generation on the stability level of the power system. Through extensive simulation analysis, when the short-circuit ratio of multiple renewable energy power plants is above 1.5, the renewable energy enters a stable state. This invention utilizes this index, combined with real-time renewable energy power and power generation forecasts, to propose an optimization method for renewable energy power allocation to address the renewable energy power optimization allocation problem, which can fully utilize renewable energy on the basis of ensuring safety and stability.

[0045] This method can reasonably formulate optimized control schemes for the distribution of renewable energy power generation based on the sensitivity of the short-circuit ratio of multiple power plants. While ensuring the short-circuit ratio requirements of the power system and avoiding transient voltage control and phase-locked synchronization stability issues, it can achieve the largest-scale grid connection of renewable energy and realize the full utilization of renewable energy.

[0046] See appendix Figure 1 , Figure 1 This is a schematic flowchart illustrating the main steps of a new energy power generation allocation optimization method according to an embodiment of the present invention. Figure 1 As shown, the new energy power generation allocation optimization method in this embodiment of the invention mainly includes the following steps:

[0047] Step 1. Obtain the comprehensive sensitivity of each new energy power station in the new energy power stations to be optimized, select the new energy power station with the smallest absolute value of comprehensive sensitivity, and determine whether its output has reached the maximum output. If so, remove the new energy power station from the new energy power stations to be optimized and repeat Step 1. Otherwise, go to Step 2.

[0048] Step 2. Increase the power generation capacity of the new energy power station with the smallest absolute value of the overall sensitivity by one adjustment step;

[0049] Step 3. Calculate the short-circuit ratio of each new energy power station in the new energy power station to be optimized, and obtain the difference between the minimum short-circuit ratio and the preset short-circuit ratio reference value;

[0050] Step 4. If the difference is not greater than the preset deviation value or the number of optimization iterations is reached, the operation ends; otherwise, return to step 1.

[0051] The comprehensive sensitivity considers the impact of increased power from renewable energy plants on the average stability level of the system, while also taking into account the impact on the renewable energy plant with the lowest stability level in the system. To avoid the weakest link effect, as renewable energy power increases, the renewable energy plant with the lowest stability level will be the first to become unstable. The sensitivity weighting coefficient of the renewable energy plant with the lowest stability level is higher than that of the average stability level sensitivity. In this embodiment, the comprehensive sensitivity calculation formula for each renewable energy plant is as follows:

[0052]

[0053] In the above formula, D c.i Let be the comprehensive sensitivity of the i-th renewable energy power station, α be the weighting coefficient of the system average sensitivity, and D be the comprehensive sensitivity of the i-th renewable energy power station. sys.i Let β be the average sensitivity of the short-circuit ratio of all power stations in the system to the power of the i-th renewable energy power station, and let β be the weighting coefficient of the sensitivity of the short-circuit ratio of the lowest renewable energy power station. Let be the sensitivity of the minimum short-circuit ratio of the renewable energy power station to the i-th renewable energy power station, where i∈[1,n] and n is the total number of renewable energy power stations among those to be optimized.

[0054] In one implementation, the formula for calculating the average sensitivity of the short-circuit ratio of all power stations in the system to the power of the i-th renewable energy power station is as follows:

[0055]

[0056] In the above formula, P rei M represents the active power injected into the grid-side access point / grid connection point of the power generation equipment in the i-th renewable energy power station. i Let be the short-circuit ratio of the i-th renewable energy power station.

[0057] In this embodiment of the invention, the short-circuit ratio of the i-th renewable energy power station is calculated as follows:

[0058]

[0059] In the above formula, S aci Z represents the injected three-phase short-circuit capacity at the grid-side access point / grid connection point of the power generation equipment in the i-th renewable energy power station. ij This is the power conversion factor between the i-th and j-th renewable energy power stations.

[0060] In one embodiment, the formula for calculating the power conversion factor between the i-th and j-th renewable energy power stations is as follows:

[0061]

[0062] In the above formula, Z eqijZ represents the equivalent AC grid impedance between the grid-connected busbars of the i-th and j-th renewable energy power plants. eqii Let U be the equivalent impedance of the AC power grid at the grid connection bus of the i-th renewable energy power station. i Let U be the grid-connected bus voltage of the i-th renewable energy power station. j Let be the grid-connected bus voltage of the j-th renewable energy power station.

[0063] In one implementation, the following formula is used for calculation.

[0064]

[0065] To verify the feasibility and effectiveness of the optimal power allocation method for renewable energy power plants with equal short-circuit ratio sensitivity proposed above, a case study is taken using the actual power grid in the XJZD region. Figure 2 As shown, the proposed method and the equal-capacity allocation method are compared and verified through simulation. The XJZD region is rich in wind resources, with a renewable energy installed capacity of 5.5 million kilowatts. Large-scale renewable energy is connected to the 750 kV grid via a step-up process from low-voltage 690 / 400 volts, increasing the voltage by over a thousand times, and then transmitted via DC. The electrical distance between wind power and the main grid is relatively long, and the short-circuit capacity near the connection point is low due to the lack of conventional energy sources. This leads to significant transient overvoltage problems when a DC fault occurs in the near area. To prevent renewable energy from being disconnected from the grid due to transient overvoltage, under the equal-capacity allocation method, when the near-area wind power generation exceeds 3 million kilowatts, some wind turbines will experience transient overvoltage problems and disconnect from the grid after a DC fault. Therefore, the near-area wind power quota is only 3 million kilowatts, resulting in severe wind power limitations in the region during high-wind seasons.

[0066] Table 1 shows a comparison of power allocation schemes for new energy power plants using different methods. As can be seen from the table, the grid-connected capacity of new energy plants based on the equal sensitivity method reaches 3.54 million kilowatts, which is much higher than the grid-connected capacity of new energy plants allocated according to the installed capacity ratio.

[0067] Table 1 Comparison of power allocation schemes for new energy power plants using different methods

[0068]

[0069] like Figure 3The figure shows the voltage change curve of the generating units during DC blocking. When allocating 3 million kilowatts of renewable energy power according to the installed capacity ratio, the highest transient overvoltage at the renewable energy collection station after a DC fault is 1.286 pu. Simultaneously, when allocating 3 million kilowatts of renewable energy power according to the equal sensitivity criterion, the lowest short-circuit ratio is 1.26 pu. Compared to the allocation scheme based on installed capacity, the system stability level is also improved for the same power generation. This verifies the feasibility and effectiveness of the proposed method. When allocating renewable energy power to the renewable energy station using the equal sensitivity method with the lowest short-circuit ratio as the target, with an allocated capacity of 3.5 million kilowatts, the transient overvoltage at the renewable energy collection station after a DC fault is also lower than the transient overvoltage of allocating 3 million kilowatts according to the installed capacity ratio, at 1.273 pu.

[0070] like Figure 4 The figure shows the short-circuit ratio iteration process in the embodiment. As can be seen from the figure, at the same short-circuit ratio, the quantitative evaluation method based on system stability level allows for a larger amount of new energy to be connected than the traditional method, thus maximizing the access of new energy while ensuring safety and stability.

[0071] Based on the same inventive concept, this invention provides a new energy power generation power distribution optimization device, such as... Figure 5 As shown, the new energy power generation allocation optimization device includes:

[0072] The first judgment module is used to obtain the comprehensive sensitivity of each new energy power station in the new energy power station to be optimized, select the new energy power station with the smallest absolute value of comprehensive sensitivity, and determine whether its output has reached the maximum output. If so, the new energy power station is removed from the new energy power station to be optimized and the first judgment module is re-executed; otherwise, it is transferred to the optimization module.

[0073] The optimization module is used to increase the power generation of the new energy power station with the smallest absolute value of the comprehensive sensitivity by an adjustment step size;

[0074] The calculation module is used to calculate the short-circuit ratio of each new energy power station in the new energy power station to be optimized, and to obtain the difference between the minimum short-circuit ratio and the preset short-circuit ratio reference value.

[0075] The second judgment module is used to end the operation if the difference is not greater than the preset deviation value or the number of optimization iterations is reached; otherwise, it returns to the first judgment module.

[0076] Preferably, the formula for calculating the overall sensitivity of each new energy power station is as follows:

[0077]

[0078] In the above formula, D c.i Let be the comprehensive sensitivity of the i-th renewable energy power station, α be the weighting coefficient of the system average sensitivity, and D be the comprehensive sensitivity of the i-th renewable energy power station. sys.iLet β be the average sensitivity of the short-circuit ratio of all power stations in the system to the power of the i-th renewable energy power station, and let β be the weighting coefficient of the sensitivity of the short-circuit ratio of the lowest renewable energy power station. Let be the sensitivity of the minimum short-circuit ratio of the renewable energy power station to the i-th renewable energy power station, where i∈[1,n] and n is the total number of renewable energy power stations among those to be optimized.

[0079] Furthermore, the formula for calculating the average sensitivity of the short-circuit ratio of all power stations in the system to the power of the i-th new energy power station is as follows:

[0080]

[0081] In the above formula, P rei M represents the active power injected into the grid-side access point / grid connection point of the power generation equipment in the i-th renewable energy power station. i Let be the short-circuit ratio of the i-th renewable energy power station.

[0082] Furthermore, the formula for calculating the short-circuit ratio of the i-th renewable energy power station is as follows:

[0083]

[0084] In the above formula, S aci Z represents the injected three-phase short-circuit capacity at the grid-side access point / grid connection point of the power generation equipment in the i-th renewable energy power station. ij This is the power conversion factor between the i-th and j-th renewable energy power stations.

[0085] Furthermore, the calculation formula for the power conversion factor of the i-th new energy power station and the j-th new energy power station is as follows:

[0086]

[0087] In the above formula, Z eqij Z represents the equivalent AC grid impedance between the grid-connected busbars of the i-th and j-th renewable energy power plants. eqii Let U be the equivalent impedance of the AC power grid at the grid connection bus of the i-th renewable energy power station. i Let U be the grid-connected bus voltage of the i-th renewable energy power station. j Let be the grid-connected bus voltage of the j-th renewable energy power station.

[0088] Furthermore, calculate using the following formula.

[0089]

[0090] Furthermore, the present invention also provides a storage medium, the storage medium including a stored program, wherein, when the program is running, it controls the device where the storage medium is located to execute the new energy power generation allocation optimization method.

[0091] Furthermore, the present invention also provides a processor for running a program, wherein the program executes the new energy power generation allocation optimization method during runtime.

[0092] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied 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.

[0093] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0094] 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.

[0095] 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.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A new energy power generation power distribution optimization method, characterized in that, The method includes: Step 1. Obtain the comprehensive sensitivity of each new energy power station in the new energy power stations to be optimized, select the new energy power station with the smallest absolute value of comprehensive sensitivity, and determine whether its output has reached the maximum output. If so, remove the new energy power station from the new energy power stations to be optimized and repeat Step 1. Otherwise, go to Step 2. Step 2. Increase the power generation capacity of the new energy power station with the smallest absolute value of the overall sensitivity by one adjustment step; Step 3. Calculate the short-circuit ratio of each new energy power station in the new energy power station to be optimized, and obtain the difference between the minimum short-circuit ratio and the preset short-circuit ratio reference value; Step 4. If the difference is not greater than the preset deviation value or the number of optimization iterations is reached, the operation ends; otherwise, return to step 1. The formula for calculating the overall sensitivity of each new energy power station is as follows: In the above formula, is the comprehensive sensitivity of the i th new energy station, is the weight coefficient of the system average sensitivity, is the average sensitivity of the short-circuit ratio of all stations in the system to the power of the i th new energy station, is the weight coefficient of the minimum new energy station short-circuit ratio sensitivity, is the sensitivity of the minimum new energy station short-circuit ratio to the i th new energy station, n is the total number of new energy stations in the new energy stations to be optimized. The formula for calculating the average sensitivity of the short-circuit ratio of all power stations in the system to the power of the i-th renewable energy power station is as follows: In the above formula, is the active power injected by the grid-side access point / station interconnection point of the power generation equipment in the i-th new energy station, is the short-circuit ratio of the i-th new energy station.

2. The method of claim 1, wherein, The formula for calculating the short-circuit ratio of the i-th renewable energy power station is as follows: In the above formula, is the three-phase short-circuit capacity of the injection of the grid-side access point / station interconnection point of the power generation equipment in the i-th new energy station, is the power conversion factor of the i-th new energy station and the j-th new energy station.

3. The method of claim 2, wherein, The formulas for calculating the power conversion factors of the i-th and j-th renewable energy power stations are as follows: In the above formula, is the AC grid equivalent impedance between the grid-connected bus of the i-th new energy station and the grid-connected bus of the j-th new energy station, is the AC grid equivalent impedance at the grid-connected bus of the i-th new energy station, is the grid-connected bus voltage of the i-th new energy station, is the grid-connected bus voltage of the j-th new energy station.

4. The method as described in claim 3, characterized in that, Calculate according to the formula : 。 5. An apparatus based on the new energy power generation power allocation optimization method according to any one of claims 1-4, characterized in that, The device includes: The first judgment module is used to obtain the comprehensive sensitivity of each new energy power station in the new energy power station to be optimized, select the new energy power station with the smallest absolute value of comprehensive sensitivity, and determine whether its output has reached the maximum output. If so, the new energy power station is removed from the new energy power station to be optimized and the first judgment module is re-executed; otherwise, it is transferred to the optimization module. The optimization module is used to increase the power generation of the new energy power station with the smallest absolute value of the comprehensive sensitivity by an adjustment step size; The calculation module is used to calculate the short-circuit ratio of each new energy power station in the new energy power station to be optimized, and to obtain the difference between the minimum short-circuit ratio and the preset short-circuit ratio reference value. The second judgment module is used to end the operation if the difference is not greater than the preset deviation value or the number of optimization iterations is reached; otherwise, it returns to the first judgment module.

6. A storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is running, it controls the device where the storage medium is located to execute the new energy power generation allocation optimization method according to any one of claims 1 to 4.

7. A processor, characterized in that, The processor is used to run a program, wherein the program executes the new energy power generation power allocation optimization method according to any one of claims 1 to 4.

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