A capacity matching data generation method and related device

Abstract

The application provides a capacity matching data generation method and related equipment, the method comprises the following steps: taking the time-sharing output of multiple energy as the first decision variable, taking the maximum net electricity sales income of the base in a preset time period as the first objective function, and determining the first time-sharing output scheme corresponding to the first capacity matching scheme; taking the capacity matching of multiple energy as the second decision variable, taking the maximum total investment internal rate of return of the base as the second objective function, and determining the second capacity matching scheme, wherein the total investment internal rate of return of the base is determined according to the net electricity sales income of the base in the preset time period; and obtaining the capacity matching data of each energy in the multiple energy corresponding to the second capacity matching scheme. In this way, from the perspective of a long-term operation period, the total investment internal rate of return and the net electricity sales income are comprehensively considered, so that the capacity matching data of multiple energy in the finally generated optimal capacity matching scheme has high accuracy, and the base can be continuously and stably developed in the long-term operation.

Description

Technical Field

[0001] This invention relates to the field of power technology, and in particular to a method for generating capacity allocation data and related equipment. Background Technology

[0002] With the vigorous promotion of the clean and low-carbon transformation of the energy structure, it is necessary to increase the proportion of new energy sources in the power supply structure. Therefore, more and more integrated energy bases are being planned and constructed. However, current research on the optimization of capacity allocation in integrated energy bases is insufficient. Existing methods establish single-objective or multi-objective optimization models, aiming at minimizing the curtailment rate of wind and solar power or maximizing the net income of the base, with research periods mostly limited to one year. They do not consider the revenue throughout the entire operating cycle of the base, and the resulting capacity optimization configuration may not be optimal throughout the entire cycle; furthermore, they do not consider the influence of the electricity spot market, leading to poor accuracy in the optimization results of the base capacity allocation. Summary of the Invention

[0003] This invention provides a method and related equipment for generating capacity allocation data to solve the problem of poor accuracy in the base capacity allocation optimization results determined by existing methods.

[0004] This invention provides a method for generating capacity allocation data, the method comprising:

[0005] Using the time-of-use output of multiple energy sources as the first decision variable, and maximizing the net electricity sales revenue of the base within a preset time period as the first objective function, a first time-of-use output scheme corresponding to a first capacity allocation scheme is determined. The first capacity allocation scheme is used to indicate the capacity allocation data of the multiple energy sources, and the first time-of-use output scheme is used to indicate the time-of-use output data of the multiple energy sources in the first capacity allocation scheme. The time-of-use output data of the multiple energy sources is used to calculate the net electricity sales revenue of the base within the preset time period. The base is the area where the multiple energy sources are to be deployed.

[0006] Using the capacity ratio of the various energy sources as the second decision variable, and maximizing the internal rate of return on the total investment of the base as the second objective function, a second capacity ratio scheme is determined. The internal rate of return on the total investment of the base is determined based on the net electricity sales revenue of the base within a preset time period. There is at least one energy source with different capacity ratio data between the second capacity ratio scheme and the first capacity ratio scheme.

[0007] If the internal rate of return on total investment of the base corresponding to the second capacity allocation scheme is greater than or equal to the internal rate of return on total investment of the base corresponding to the first capacity allocation scheme, obtain the capacity allocation data of each energy source among the multiple energy sources corresponding to the second capacity allocation scheme.

[0008] This invention also provides a capacity allocation data generation device, the device comprising:

[0009] The first determining module is used to determine the first time-of-use output scheme corresponding to the first capacity allocation scheme, with the time-of-use output of multiple energy sources as the first decision variable and the maximum net electricity sales revenue of the base within a preset time period as the first objective function. The first capacity allocation scheme is used to indicate the capacity allocation data of the multiple energy sources, and the first time-of-use output scheme is used to indicate the time-of-use output data of the multiple energy sources in the first capacity allocation scheme. The time-of-use output data of the multiple energy sources is used to calculate the net electricity sales revenue of the base within the preset time period. The base is the area where the multiple energy sources are to be deployed.

[0010] The second determining module is used to determine a second capacity allocation scheme with the capacity ratio of the multiple energy sources as the second decision variable and the maximum internal rate of return on the total investment of the base as the second objective function. The internal rate of return on the total investment of the base is determined based on the net electricity sales revenue of the base within a preset time period. There is at least one energy source with different capacity allocation data between the second capacity allocation scheme and the first capacity allocation scheme.

[0011] The first acquisition module is used to acquire the capacity ratio data of each energy source among the multiple energy sources corresponding to the second capacity ratio scheme when the total internal rate of return on investment of the base corresponding to the second capacity ratio scheme is greater than or equal to the total internal rate of return on investment of the base corresponding to the first capacity ratio scheme.

[0012] This invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the capacity ratio data generation method described above.

[0013] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the capacity ratio data generation method described above.

[0014] This invention also provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the capacity ratio data generation method described above.

[0015] In this embodiment of the invention, the capacity allocation optimization problem of the base is broken down into two problems: maximizing the base's net electricity sales revenue within a preset time period and maximizing the base's total internal rate of return (IRR). By using the time-of-use output of multiple energy sources as the first decision variable, a first time-of-use output scheme corresponding to the first capacity allocation scheme with the maximum net electricity sales revenue is determined. This first time-of-use output scheme guides the output power of each energy source at different times to match the base's participation in the electricity spot market. Furthermore, by using the capacity allocation of multiple energy sources as the second decision variable, a second capacity allocation scheme with the maximum IRR is determined. This second capacity allocation scheme determines the capacity allocation data of each energy source to guide the base's initial investment planning, improving the base's IRR throughout its entire operating cycle. Finally, an iterative approach is used to continuously adjust the energy capacity allocation. From the perspective of the long-term operating cycle, the IRR and net electricity sales revenue are comprehensively considered, ensuring that the capacity allocation data of multiple energy sources in the final optimal capacity allocation scheme has high accuracy, guaranteeing the base's continuous and stable development in long-term operation. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the capacity ratio data generation method provided in an embodiment of the present invention;

[0018] Figure 2 This is a schematic diagram of the structure of the two-layer optimization model provided in an embodiment of the present invention;

[0019] Figure 3 This is a flowchart illustrating the improved genetic algorithm for the elite retention strategy provided in this embodiment of the invention.

[0020] Figure 4 This is a schematic diagram of the capacity ratio data generation device provided in an embodiment of the present invention;

[0021] Figure 5 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0022] 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, not all, of the embodiments of the present invention. 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.

[0023] The terms "first," "second," etc., used in the specification and claims of this invention are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such structures can be interchanged where appropriate so that embodiments of the invention can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, the first object can be one or more.

[0024] like Figure 1 The diagram shown is a flowchart illustrating the capacity allocation data generation method provided in this embodiment of the invention. The capacity allocation data generation method in this embodiment specifically includes the following steps:

[0025] Step 101: Using the time-of-use output of multiple energy sources as the first decision variable and maximizing the net electricity sales revenue of the base within a preset time period as the first objective function, determine the first time-of-use output scheme corresponding to the first capacity allocation scheme. The first capacity allocation scheme is used to indicate the capacity allocation data of the multiple energy sources, and the first time-of-use output scheme is used to indicate the time-of-use output data of the multiple energy sources in the first capacity allocation scheme. The time-of-use output data of the multiple energy sources is used to calculate the net electricity sales revenue of the base within the preset time period. The base is the area where the multiple energy sources are to be deployed.

[0026] In one example, multiple energy sources can include wind power, solar power, and energy storage. Regions rich in wind and solar resources can be selected, and a certain scale of electrochemical energy storage can be added to establish a comprehensive wind-solar-storage energy base. Using the time-of-use output of wind power, solar power, and energy storage as the primary decision variable, the time-of-use output data can reflect the actual output power of these energy devices at different times. For example, wind speed and solar irradiance vary at different times of the day, causing wind and solar power output to change accordingly, while energy storage charges and discharges according to system supply and demand, and its output also changes over time.

[0027] The first objective function is to maximize the net electricity sales revenue of the base within a preset time period. The calculation of net electricity sales revenue considers multiple factors, including the correlation between electricity sales revenue and market time-of-use electricity prices, as well as the time-of-use output of wind power, solar power, and energy storage. Under the constraints of the first objective function, a first capacity allocation scheme is determined (assuming the capacity allocation of wind power, solar power, and energy storage is M). W1 M S1 and M B1 The first time-of-use power output scheme corresponds to the first time-of-use power output scheme, which clarifies the specific output power of wind power, photovoltaic and energy storage at different times. It can be used to guide the deployment of wind power, photovoltaic and energy storage operation mode (i.e. time-of-use power output) so that the base can maximize net electricity sales revenue within the preset time period.

[0028] Step 102: Using the capacity ratio of the various energy sources as the second decision variable and the maximum internal rate of return on the total investment of the base as the second objective function, determine the second capacity ratio scheme. The internal rate of return on the total investment of the base is determined based on the net electricity sales revenue of the base within a preset time period. There is at least one energy source with different capacity ratio data between the second capacity ratio scheme and the first capacity ratio scheme.

[0029] Using the capacity allocation ratio of wind power, solar power, and energy storage as the second decision variable, the data reflects the investment allocation in these three areas. A higher capacity allocation ratio for a particular energy source will result in a larger proportion of investment in that energy source, which is of great guiding significance for the initial investment planning of the base and also affects subsequent operating costs and returns. For example, a relatively high wind power capacity allocation ratio indicates that the base focuses more on utilizing wind energy resources in its energy supply; a large solar power capacity allocation ratio indicates a high degree of dependence on solar energy resources; and the size of the energy storage capacity allocation ratio reflects the base's emphasis on energy storage regulation capabilities to cope with the intermittency and volatility of energy supply.

[0030] The second objective function is to maximize the internal rate of return (IRR) of the base's total investment. The IRR of the base's total investment is correlated with the maximum net electricity sales revenue within the preset time period in step 101. This can be achieved by calculating the correlation between the capacity allocation of wind power, solar power, and energy storage and their corresponding unit investment costs. Using the entire operating cycle of the base (i.e., multiple preset time periods) as the calculation dimension, and under the constraints of the second objective function, a second capacity allocation scheme can be determined (assuming the capacity allocation of wind power, solar power, and energy storage is M). W2 M S2 and M B2Furthermore, at least one energy source has a different capacity allocation data between the second capacity allocation scheme and the first capacity allocation scheme. The second capacity allocation scheme further optimizes the data on the capacity allocation of wind power, photovoltaics, and energy storage in the base, and can be used to guide the deployment of the capacity allocation of wind power, photovoltaics, and energy storage in order to maximize the internal rate of return on the total investment of the base.

[0031] Step 103: If the total internal rate of return on investment of the base corresponding to the second capacity allocation scheme is greater than or equal to the total internal rate of return on investment of the base corresponding to the first capacity allocation scheme, obtain the capacity allocation data of each energy source among the multiple energy sources corresponding to the second capacity allocation scheme.

[0032] Compare the internal rate of return (IRR) of total investment for the base corresponding to the second capacity allocation scheme with that corresponding to the first capacity allocation scheme. If the IRR of total investment for the second capacity allocation scheme is greater than or equal to that for the first capacity allocation scheme, then obtain the capacity allocation data (i.e., M) of each energy source in the second capacity allocation scheme. W2 M S2 and M B2 These data represent the optimal capacity allocation scheme for the integrated wind, solar, and energy storage base under current conditions, which can be used to guide the actual planning and construction of the base. This improves the internal rate of return (IRR) of the base's total investment throughout its entire operating cycle. Furthermore, the calculation of the IRR comprehensively considers the base's net electricity sales revenue within a preset time period, making the optimized capacity allocation scheme for the integrated wind, solar, and energy storage base more adaptable to the new situation in the electricity market.

[0033] For example, the capacity ratio data (i.e., M) of each energy source in the multiple energy sources corresponding to the second capacity ratio scheme is obtained through the above steps. W2 M S2 and M B2 After that, based on the obtained wind power capacity allocation data M W2 This allows us to determine the quantity and model of the wind turbines that need to be purchased. For example, M W2 For a capacity of 100MW, considering the single-unit capacity of the selected wind turbine model (e.g., 2MW), 50 wind turbines will be procured and installed. Based on the obtained photovoltaic capacity allocation data M... S2 This allows us to determine the number of photovoltaic panels to be installed and the array arrangement. For example, M S2 For an 80MW capacity, based on a typical photovoltaic panel power output (e.g., 0.5MW), 160 photovoltaic panels would be procured and installed. This would then inform the planning of the photovoltaic power plant's site area, support structure installation, and other related engineering. Energy storage capacity allocation data M B2This is used to determine the capacity and specifications of energy storage equipment, such as how much capacity of battery packs to purchase, as well as supporting equipment such as inverters, and to reasonably arrange the installation location and connection method of the energy storage system.

[0034] Furthermore, based on the capacity allocation data of each energy source, time-of-use (TOU) power output schemes for wind power, solar power, and energy storage can be further planned. The operation modes of deployed wind turbines, solar panels, and energy storage devices can be adjusted according to the TOU corresponding to the second capacity allocation scheme. For example, during periods of abundant wind and solar resources and high electricity market demand, the power generation capacity of wind and solar power can be fully utilized; when resources are insufficient or market demand is low, energy storage can be used for energy storage or release to maintain the stability of the base's power output. During peak daytime electricity consumption and when electricity prices are high, if the solar capacity allocation is appropriate, solar power generation can be fully utilized and sold; at night or during off-peak hours, energy storage can be charged to store low-priced electricity, which can then be discharged and sold when electricity prices rise.

[0035] Thus, the capacity allocation data determined by the capacity allocation data generation method provided by the present invention improves the accuracy of the optimization results. When guiding the actual planning and construction of the base, it can improve the return on investment of the base, improve energy utilization efficiency and stability, and enhance its adaptability and competitiveness in the electricity market.

[0036] In this embodiment of the invention, the capacity allocation optimization problem of the base is broken down into two problems: maximizing the base's net electricity sales revenue within a preset time period and maximizing the base's total internal rate of return on investment. By using the time-of-use output of multiple energy sources as the first decision variable, a first time-of-use output scheme corresponding to the first capacity allocation scheme with the maximum net electricity sales revenue is determined. This first time-of-use output scheme can guide the output power of each energy source at different times to match the scenario of the base participating in the electricity spot market. (For example, time-of-use electricity prices change at different times depending on electricity supply and demand; for instance, prices are higher during peak hours and lower during off-peak hours. The time-of-use output scheme can guide the output power of each energy source (wind power, photovoltaic, energy storage) based on these price fluctuations.) The system adjusts output power at different times to effectively respond to electricity market price signals. By using the capacity ratio of various energy sources as the second decision variable, a second capacity ratio scheme with the highest internal rate of return (IRR) is determined. Based on this second scheme, the capacity ratio data for each energy source can be determined to guide the initial investment planning of the base, improving the IRR of the base throughout its entire operating cycle. Finally, an iterative approach is used to continuously adjust the energy capacity ratio. From a long-term operating cycle perspective, considering both the IRR of the total investment and net electricity sales revenue, the capacity ratio data for various energy sources in the final optimal capacity ratio scheme has high accuracy, ensuring the base's sustainable and stable development in the long term.

[0037] Optionally, before step 101, determining the first time-sharing output scheme corresponding to the first capacity allocation scheme, the method further includes:

[0038] A two-layer optimization model is constructed, comprising an upper-layer optimization model and a lower-layer optimization model that are coupled together. The lower-layer optimization model is used to determine the time-of-use output scheme corresponding to the capacity allocation scheme, with the time-of-use output of multiple energy sources as the first decision variable and the maximization of the net electricity sales revenue of the base within a preset time period as the first objective function. The upper-layer optimization model is used to determine the capacity allocation scheme, with the capacity allocation of the multiple energy sources as the second decision variable and the maximization of the internal rate of return on the total investment of the base as the second objective function.

[0039] In this embodiment, as Figure 2 As shown, a two-level optimization model is constructed to decompose the base capacity allocation optimization problem into two optimization problems: an upper-level model and a lower-level model. In the lower-level optimization model, the time-of-use output of various energy sources is used as the first decision variable, and maximizing the base's net electricity sales revenue within a preset time period is used as the first objective function to determine the time-of-use output scheme corresponding to the capacity allocation scheme. In the upper-level optimization model, the capacity allocation of various energy sources is used as the second decision variable, and maximizing the base's total investment internal rate of return is used as the second objective function to determine the capacity allocation scheme. The lower-level optimization addresses the problem of maximizing the base's net electricity sales revenue within a preset time period under market mechanisms; the upper-level optimization addresses the problem of maximizing the base's total investment internal rate of return over the entire operating cycle. The lower-level and upper-level optimization models each have their own objective functions, decision variables, and related constraints, and there is a hierarchical relationship between the two levels. The optimization of the upper-level problem depends on the optimization results of the lower-level problem, and the optimization results of the lower-level problem depend on the decision variables of the upper-level problem. In this way, the energy capacity allocation is adjusted through continuous iteration. From the perspective of long-term operation cycle, the internal rate of return on total investment and net electricity sales revenue are comprehensively considered. This ensures that the capacity allocation data of various energy sources in the final optimal capacity allocation scheme has high accuracy, and ensures that the base can continue to develop stably in the long-term operation.

[0040] Optionally, the two-layer optimization model is expressed as:

[0041]

[0042] Where x is the first decision variable, y is the second decision variable, H(x,y) is the first objective function, R(x,y) is the second objective function, h(x,y) is the constraint condition of the first objective function, and r(x,y) is the constraint condition of the second objective function.

[0043] In this example, the capacity allocation optimization problem of a wind-solar-storage integrated energy base is decomposed into two interrelated optimization problems: an upper-level problem and a lower-level problem. The upper-level and lower-level optimization models are coupled through decision variables x (time-of-use output of various energy sources) and y (capacity allocation of various energy sources), as well as objective functions H(x,y) (maximizing net electricity sales revenue within a preset time period) and R(x,y) (maximizing the internal rate of return on total investment in the base). This coupling relationship means that the optimization of the upper-level model depends on the time-of-use output scheme information provided by the lower-level model, while the optimization of the lower-level model is influenced by the capacity allocation determined by the upper-level model. Through iterative calculations, both gradually approach the optimal solution.

[0044] Using the time-of-use (TOU) output of various energy sources, x, as the first decision variable, the aim is to maximize the base's net electricity sales revenue within a preset time period (e.g., one year) by adjusting the output power of wind power, photovoltaic (PV), and energy storage at different times. The setting of the first objective function is directly related to the base's short-term economic benefits, as net electricity sales revenue is the most direct economic indicator for the base's market operation. By optimizing TOU output, the base can better adapt to electricity market price fluctuations, increasing power generation output during periods of higher electricity prices and rationally arranging energy storage charging and discharging or adjusting energy output during periods of lower electricity prices, thereby increasing overall electricity sales revenue. The constraint h(x,y)≤0 of the first objective function restricts the lower-level optimization process, ensuring the feasibility of the TOU output scheme. These constraints may include constraints on the output characteristics of wind power and PV, ensuring that the TOU output of wind power and PV is within the limits allowed by their resource conditions; and constraints related to energy storage and state of charge constraints, ensuring the safe and reasonable operation of energy storage equipment. These constraints comprehensively consider the physical characteristics of energy supply, infrastructure limitations, and equipment operating requirements, ensuring that the TOU output scheme obtained from the lower-level optimization can be realized in practice.

[0045] Using the capacity allocation ratio y of various energy sources as the second decision variable, the second objective function is to maximize the internal rate of return (IRR) of the base's total investment. The IRR is a crucial indicator considering the investment and returns throughout the base's entire operational cycle (e.g., multiple years). By optimizing the capacity allocation, from a long-term strategic perspective, the optimal combination of wind power, solar power, and energy storage installation scales is determined to maximize the base's return on investment over its entire operational cycle. This decision considers the investment costs of different energy sources (e.g., wind power investment costs, solar power investment costs, energy storage investment costs), long-term operation and maintenance costs (e.g., the individual operation and maintenance costs of wind power, solar power, and energy storage), and electricity sales revenue, balancing the economic benefits of different energy sources in long-term operation to achieve sustainable development and maximize investment returns for the base. The constraint r(x,y)≤0 on the second objective function restricts the upper-level optimization process, including limitations on the installed capacity of wind power, solar power, and energy storage. These constraints consider the resource conditions of the base site and the available land area, limiting the overall developable scale of the base. Ensure that the capacity allocation scheme obtained from the upper-level optimization is feasible in the actual construction of the base, and avoid over-planning that leads to resource waste or inability to implement the plan.

[0046] In this way, by constructing such a two-layer optimization model and clarifying the objective functions and constraints of the upper and lower layers, the capacity allocation optimization problem of wind-solar-storage integrated energy bases under the electricity market mechanism can be systematically and comprehensively solved, taking into account both short-term benefits and long-term investment returns, while also considering various practical operation and construction constraints, thus providing a scientific and reasonable decision-making basis for the planning, construction and operation of the base.

[0047] Optionally, after determining the second capacity ratio scheme, the method further includes:

[0048] When the total investment internal rate of return of the base corresponding to the second capacity allocation scheme is less than the total investment internal rate of return of the base corresponding to the first capacity allocation scheme, the first capacity allocation scheme in the first objective function is iterated based on the second capacity allocation scheme to obtain the second time-sharing power output scheme corresponding to the second capacity allocation scheme.

[0049] Based on the net electricity sales revenue of the base corresponding to the second time-sharing power output scheme within a preset time period, the input data of the second objective function is iterated to obtain the third capacity allocation scheme. The input data of the second objective function includes the net electricity sales revenue of the base corresponding to the first time-sharing power output scheme within a preset time period.

[0050] If the internal rate of return on total investment of the base corresponding to the third capacity allocation scheme is greater than or equal to the internal rate of return on total investment of the base corresponding to other capacity allocation schemes, the capacity allocation data of each energy source among the multiple energy sources corresponding to the third capacity allocation scheme are obtained.

[0051] In this embodiment, when it is found that the internal rate of return (IRR) of the total investment of the base corresponding to the second capacity allocation scheme is less than that corresponding to the first capacity allocation scheme, it indicates that the current optimization process has not yet reached the optimal state and further adjustments are needed. Through iteration, the capacity allocation and time-sharing power output schemes can be gradually improved to increase the IRR of the base's total investment and bring it closer to the optimal solution.

[0052] Specifically, using the second capacity allocation scheme as a new foundation, the first capacity allocation scheme in the first objective function is iterated. This means that while keeping the capacity allocation of various energy sources unchanged in the second capacity allocation scheme, the time-of-use output of various energy sources is re-optimized to find a time-of-use output scheme that can achieve greater net electricity sales revenue for the base within a preset time period (e.g., one year) under this capacity allocation, thus obtaining the second time-of-use output scheme corresponding to the second capacity allocation scheme. This step mainly explores the potential to improve short-term economic benefits through a more reasonable time-of-use output arrangement under the current capacity structure, given a fixed capacity allocation.

[0053] Using the net electricity revenue from the base corresponding to the newly obtained second time-of-use power output scheme within a preset time period, the input data of the second objective function (aiming to maximize the internal rate of return on total investment) is iteratively applied. Here, the input data is updated to the net electricity revenue corresponding to the second time-of-use power output scheme, and then optimization calculations are performed again to obtain the third capacity allocation scheme. This step, after improving the time-of-use power output scheme and obtaining more accurate short-term net electricity revenue data, re-examines the long-term investment strategy and adjusts the capacity allocation of various energy sources to pursue a higher internal rate of return on total investment. In this way, the short-term time-of-use power output optimization results are fed back into the long-term capacity allocation optimization, achieving mutual influence and synergistic improvement between short-term and long-term optimizations.

[0054] Finally, the internal rate of return (IRR) of total investment corresponding to the third capacity allocation scheme is compared with the IRR of total investment corresponding to other capacity allocation schemes (including the previous first and second capacity allocation schemes, etc.). If the IRR of total investment of the third capacity allocation scheme is greater than or equal to that of other schemes, then a better (or optimal) capacity allocation scheme is considered to have been found. At this time, the capacity allocation data of each energy source corresponding to the third capacity allocation scheme are obtained. These data are the capacity configuration schemes suitable for the integrated energy base determined after multiple iterations of optimization. They can be used to guide the actual construction and operation of the base, maximize the return on investment of the base throughout the entire operating cycle, and adapt to various requirements under the electricity market mechanism.

[0055] In this way, by continuously optimizing and adjusting the capacity allocation and time-of-use output, the relationship between short-term and long-term benefits is fully considered, as well as the feedback effect between different optimization results. This allows for a more comprehensive and accurate solution to the capacity allocation optimization problem of integrated wind, solar and energy storage bases, thereby improving the overall performance and economic benefits of the bases.

[0056] In one embodiment, when evaluating the return on project investment, two indicators can be used: Net Present Value (NPV) or Internal Return Rate (IRR). However, comparing the NPV of different projects under different investment scales is meaningless. Therefore, to make the method proposed in this invention more universal, IRR is chosen as the optimization objective of the upper-level optimization model. The upper-level optimization model uses the capacity ratio of wind power, photovoltaics, and energy storage as the second decision variable, and maximizing the internal return rate of the base's total investment as the second objective function. Optionally, the second objective function is expressed as:

[0057]

[0058] Where IRR is the internal rate of return on total investment of the base, TI is the initial investment, and NR is the internal rate of return. i The net electricity sales revenue of the base during the i-th preset time period is n, and the operating years of the base are n;

[0059] Where TI = M W C W +M S C S +M B C B M W For wind power capacity allocation data, M S For photovoltaic capacity allocation data, M B For energy storage capacity allocation data, C W C represents the unit investment cost per kilowatt of wind power. SC represents the unit investment cost per kilowatt of photovoltaic power. B The second decision variable, M, represents the unit kilowatt investment cost for energy storage. W M S and M B .

[0060] The constraints of the upper-level optimization model (i.e., the constraints of the second objective function) mainly concern the installed capacity of wind power and photovoltaic power. For a comprehensive energy base, the overall developable scale of the base is limited by the resource conditions of the site selection and the available land area. The specific formulas for the constraints are as follows:

[0061]

[0062] M max It is the total installed capacity planned for the entire base, M Wmax and M Smax These are the upper limits of the maximum exploitable scale for wind power and solar power, respectively.

[0063] In one embodiment, the lower-level optimization model optimizes the time-of-use output of wind power, photovoltaic power, and energy storage at a base within a preset time period (e.g., one year), given the scale of wind power, photovoltaic power, and energy storage. Optionally, the first objective function is expressed as:

[0064] max(NR i )=max[(R Wi -V Wi -F Wi )+(R Si -V Si -F Si )+(R Bi -V Bi -F Bi )];

[0065] Among them, NR i R represents the net electricity sales revenue of the base during the i-th preset time period. Wi R represents the net electricity revenue from wind power sales during the i-th preset time period. Si R represents the net electricity revenue from photovoltaic power generation during the i-th preset time period. Bi V represents the net electricity revenue from energy storage during the i-th preset time period. Wi V represents the variable operation and maintenance cost of wind power within the i-th preset time period. Si V represents the variable operation and maintenance cost of photovoltaic power generation within the i-th preset time period. Bi F represents the variable operation and maintenance cost of energy storage within the i-th preset time period. Wi F represents the fixed operation and maintenance cost of wind power within the i-th preset time period. SiF represents the fixed operation and maintenance cost of photovoltaic power generation within the i-th preset time period. Bi The fixed operation and maintenance cost of energy storage within the i-th preset time period;

[0066] The variable operation and maintenance costs (O&M) of wind power, solar power, and energy storage are typically close to zero; therefore, they are not considered in this embodiment's calculations. Without considering variable O&M costs, electricity sales revenue is related to the time-of-use output of the power base and the market electricity price, as detailed below:

[0067] EP(t) is the market time-of-use electricity price, P W (t) represents the time-of-use power output data of wind power, P S (t) represents the time-of-use power output data of the photovoltaic system, P B (t) represents the time-of-use output data of energy storage, and the first decision variable includes P. W (t), P S (t) and P B (t). For energy storage, P during charging. B (t)<0, P during discharge B (t)>0.

[0068] The constraints of the lower-level optimization model (i.e., the constraints of the first objective function) mainly include the output characteristics constraints of wind power and solar power, the constraints of the base's external transmission channels, and energy storage-related constraints. The output characteristics constraints of wind power and solar power are mainly determined by the wind and solar resource conditions of the base, and the time-of-use output of wind power and solar power should satisfy:

[0069]

[0070] In the formula, P Wmax (t) and P Smax (t) represents the output characteristics of wind power and photovoltaic power. The output characteristics of wind power mainly depend on the local wind speed, while the output characteristics of photovoltaic power mainly depend on the local irradiance, temperature, weather conditions, etc.

[0071] Among them, the power constraint P of the base's external transmission channel T This refers to the fact that the power transmitted from the base cannot exceed the transmission power limit of the transmission channel: P W (t)+P S (t)+P B (t)≤P T .

[0072] Among these, energy storage-related constraints mainly refer to the constraints on state of charge (SOC) and charge / discharge power. The charge / discharge power of the energy storage device must not exceed its rated charge / discharge power.

[0073]

[0074] In the formula, R dch It is the rated discharge power of the energy storage, R ch It is the rated discharge power of the energy storage.

[0075] Among these, the state of charge (SOC) must not exceed the battery's over-discharge protection rate (SOC). min The maximum overcharge rate cannot exceed the battery's state of charge (SOC). max The details are as follows:

[0076]

[0077] In the formula, E(t) is the remaining energy stored at time t. max It is the rated capacity of energy storage.

[0078] In this way, by constructing a two-layer optimization model and clarifying the objective functions and constraints of the upper and lower layers, the capacity allocation optimization problem of wind-solar-storage integrated energy bases under the electricity market mechanism can be systematically and comprehensively solved, taking into account both short-term benefits and long-term investment returns, while also considering various practical operation and construction constraints, thus providing a scientific and reasonable decision-making basis for the planning, construction and operation of the base.

[0079] Optionally, an improved genetic algorithm with an elite retention strategy can be used to solve the bi-level optimization model.

[0080] In this example, considering that traditional genetic algorithms are prone to getting trapped in local optima and have slow convergence speeds, this invention proposes an elite retention strategy to address this problem. Elite retention means replacing the worst-fitting offspring with the best-fitting individual from the parent generation, thereby maximizing the retention of highly fit individuals in the population. The improved algorithm does not directly replace parent individuals with offspring; instead, it adaptively calculates the crossover and mutation probabilities of excellent individuals from the parent generation, appropriately increasing the crossover probability while decreasing the mutation probability, so that excellent individuals in each generation can be passed on to the next generation to the greatest extent possible. Figure 3 As shown, the specific steps include the following:

[0081] Generate and encode the initial population;

[0082] In this step, an initial population is randomly generated, with each individual representing a possible wind-solar-storage capacity ratio or time-of-use power output scheme (depending on whether the current problem is an upper-level or lower-level optimization problem). These individuals are then encoded, transforming them into a form that the algorithm can process. For example, binary encoding or real-number encoding can be used to convert the capacity ratio or time-of-use power output data into a string of numbers or character sequences, allowing subsequent genetic operations (crossover, mutation, etc.) to be performed on these codes.

[0083] Selecting elite groups based on fitness;

[0084] In this step, for each individual in the population, its fitness is calculated based on its corresponding capacity allocation or time-of-use power distribution scheme. Fitness is calculated using the objective function of the two-level optimization model. If solving the lower-level optimization problem (aiming to maximize net electricity sales revenue), fitness is related to net electricity sales revenue; if solving the upper-level optimization problem (aiming to maximize the internal rate of return on total investment), fitness is related to the internal rate of return on total investment. After calculating the fitness, the individuals are ranked according to their fitness, and the top 10% of individuals are identified as the elite group. These individuals have high fitness and represent relatively better solutions in the current population. The remaining individuals are considered the non-elite group.

[0085] Adaptive calculation of crossover and mutation probabilities in elite populations;

[0086] In this step, when performing crossover and mutation operations, the crossover and mutation probabilities of the elite population will be calculated adaptively, while the mutation and crossover probabilities of the non-elite population will remain at their initial values. The specific calculation formula is as follows:

[0087]

[0088] In the formula, P m (x) and P c (x) represent the probabilities of mutation and crossover in the nth generation, respectively, P m0 and P c0 These are the initial settings for mutation and crossover probabilities, respectively, where x is the nth generation individual. It is an elite group, n is the current generation, and N is the maximum number of generations the algorithm can evolve.

[0089] Thus, as the number of generations n increases, e (n-N) The value gradually increases by 1-e (n-N) The value of gradually decreases, meaning that the mutation probability of elite individuals gradually decreases during the evolutionary process. The purpose of this is to provide elite individuals with some mutation opportunities in the early stages of the algorithm to explore a better solution space; while in the later stages, to reduce mutations in elite individuals to prevent the destruction of already found better solutions. Furthermore, as the number of generations n increases, The value of P gradually increases, meaning the crossover probability among elite individuals gradually increases. This is to enhance information exchange among elite individuals in the later stages of the algorithm, promoting the generation of better solutions. For the non-elite group, the mutation and crossover probabilities remain at the initial value P. m0 and P c0 The process should be conducted in a way that maintains a degree of exploratory nature, avoiding the algorithm from getting trapped in local optima.

[0090] Perform crossover and mutation operations;

[0091] In this step, crossover and mutation operations are performed on individuals in the population based on the calculated crossover and mutation probabilities. Crossover involves exchanging some genes between two individuals to create new individuals, simulating gene recombination in biological heredity. This helps introduce new gene combinations into the population, increasing solution diversity. Mutation, on the other hand, randomly alters certain genes in individuals, simulating gene mutation in biological heredity. This introduces new genes into the population and prevents the algorithm from prematurely converging to a local optimum. Through these operations, a new population is formed, whose individuals inherit some characteristics from the previous generation while also possessing certain new traits.

[0092] Determine the termination condition and output the result;

[0093] The algorithm checks if the termination condition is met. The termination condition could be reaching the predetermined maximum number of generations N, or the population's fitness having converged (i.e., the fitness change over several consecutive generations is less than a certain threshold). If the termination condition is not met, the algorithm returns to the previous state and continues to calculate fitness, divide the population, adaptively calculate crossover and mutation probabilities, and perform crossover and mutation operations on the new population, continuously iterating and optimizing. If the termination condition is met, the algorithm outputs the optimal individual in the current population. The capacity allocation or time-sharing power output scheme represented by this optimal individual is the approximate optimal solution (or optimal solution) of the two-layer optimization model, which can be used to guide the capacity configuration and operation strategy formulation of integrated wind, solar, and energy storage bases.

[0094] In this way, the improved genetic algorithm with the elite preservation strategy can accelerate the convergence to the global optimum or near-optimal solution by adaptively adjusting the crossover and mutation probabilities of the elite population while maintaining population diversity. This effectively solves the complexity and local optima problems in the solution process of the two-level optimization model, and improves the accuracy and reliability of the optimization results.

[0095] In one specific embodiment, a wind-solar-storage integrated energy base is planned to be established in a region in Northwest China. The total scale of wind and solar power in the base will be in the range of 200MW-400MW, and a certain scale of electrochemical energy storage will be provided for peak shaving. The entire base is designed to operate for 20 years. The region boasts excellent wind and solar resources, with wind resource utilization hours reaching 3600 hours and solar resource utilization hours reaching 1900 hours annually. Regarding economic costs, the investment cost for wind power is considered to be 6000 yuan / kW, and the operation and maintenance cost is considered to be 160 yuan / kW; the investment cost for solar power is considered to be 3700 yuan / kW, and the operation and maintenance cost is considered to be 120 yuan / kW; the investment cost per kilowatt-hour for energy storage is considered to be 1200 yuan / kWh, and the operation and maintenance cost is considered to be 50 yuan / kW.

[0096] Using the capacity allocation data generation method proposed in this invention, capacity allocation optimization was performed on integrated energy bases with total scales of 200MW, 300MW, and 400MW, respectively. To verify the effectiveness of this invention, the results were compared with traditional research methods that do not consider market mechanisms and the entire operating cycle. The comparison results are shown in Table 1.

[0097] Table 1 Comparison of Optimization Results

[0098]

[0099] By comparing the results, it can be found that compared with traditional research methods, the capacity allocation data determined by the capacity allocation data generation method provided by this invention improves the internal rate of return on total investment by about 2% to 3% when calculating the internal rate of return on total investment. This can better guide the actual planning and construction of the base, thereby improving the internal rate of return on total investment of the base throughout the entire operating cycle.

[0100] like Figure 4 As shown, this embodiment of the invention also provides a capacity ratio data generation device, the capacity ratio data generation device 400 comprising:

[0101] The first determining module 401 is used to determine the first time-of-use output scheme corresponding to the first capacity allocation scheme, with the time-of-use output of multiple energy sources as the first decision variable and the maximum net electricity sales revenue of the base within a preset time period as the first objective function. The first capacity allocation scheme is used to indicate the capacity allocation data of the multiple energy sources, and the first time-of-use output scheme is used to indicate the time-of-use output data of the multiple energy sources in the first capacity allocation scheme. The time-of-use output data of the multiple energy sources is used to calculate the net electricity sales revenue of the base within the preset time period. The base is the area where the multiple energy sources are to be deployed.

[0102] The second determining module 402 is used to determine a second capacity allocation scheme with the capacity ratio of the multiple energy sources as the second decision variable and the maximum internal rate of return on the total investment of the base as the second objective function. The internal rate of return on the total investment of the base is determined based on the net electricity sales revenue of the base within a preset time period. There is at least one energy source with different capacity allocation data between the second capacity allocation scheme and the first capacity allocation scheme.

[0103] The first acquisition module 403 is used to acquire the capacity ratio data of each energy source among the multiple energy sources corresponding to the second capacity ratio scheme when the total investment internal rate of return of the base corresponding to the second capacity ratio scheme is greater than or equal to the total investment internal rate of return of the base corresponding to the first capacity ratio scheme.

[0104] Optionally, the capacity ratio data generation device 400 further includes:

[0105] The first iteration module is used to iterate the first capacity allocation scheme in the first objective function based on the second capacity allocation scheme when the total investment internal rate of return of the base corresponding to the second capacity allocation scheme is less than the total investment internal rate of return of the base corresponding to the first capacity allocation scheme, so as to obtain the second time-sharing power output scheme corresponding to the second capacity allocation scheme.

[0106] The second iteration module is used to iterate the input data of the second objective function based on the net electricity sales revenue of the base corresponding to the second time-sharing power output scheme within a preset time period to obtain a third capacity allocation scheme. The input data of the second objective function includes the net electricity sales revenue of the base corresponding to the first time-sharing power output scheme within a preset time period.

[0107] The second acquisition module is used to acquire the capacity ratio data of each energy source among the multiple energy sources corresponding to the third capacity ratio scheme when the total internal rate of return on investment of the base corresponding to the third capacity ratio scheme is greater than or equal to the total internal rate of return on investment of the base corresponding to other capacity ratio schemes.

[0108] Optionally, the multiple energy sources include wind power, photovoltaic power, and energy storage, and the first objective function is expressed as:

[0109] max(NR i )=max[(R Wi -V Wi -F Wi )+(R Si -V Si -F Si )+(R Bi -V Bi -F Bi )];

[0110] Among them, NR i R represents the net electricity sales revenue of the base during the i-th preset time period. Wi R represents the net electricity revenue from wind power sales during the i-th preset time period. Si R represents the net electricity revenue from photovoltaic power generation during the i-th preset time period. Bi V represents the net electricity revenue from energy storage during the i-th preset time period. Wi V represents the variable operation and maintenance cost of wind power within the i-th preset time period. Si V represents the variable operation and maintenance cost of photovoltaic power generation within the i-th preset time period. Bi F represents the variable operation and maintenance cost of energy storage within the i-th preset time period. Wi F represents the fixed operation and maintenance cost of wind power within the i-th preset time period. SiF represents the fixed operation and maintenance cost of photovoltaic power generation within the i-th preset time period. Bi The fixed operation and maintenance cost of energy storage within the i-th preset time period;

[0111] in, EP(t) is the market time-of-use electricity price, P W (t) represents the time-of-use power output data of wind power, P S (t) represents the time-of-use power output data of the photovoltaic system, P B (t) represents the time-of-use output data of energy storage, and the first decision variable includes P. W (t), P S (t) and P B (t).

[0112] Optionally, the multiple energy sources include wind power, photovoltaic power, and energy storage, and the second objective function is expressed as:

[0113]

[0114] Where IRR is the internal rate of return on total investment of the base, TI is the initial investment, and NR is the internal rate of return. i The net electricity sales revenue of the base during the i-th preset time period is n, and the operating years of the base are n;

[0115] Where TI = M W C W +M S C S +M B C B M W For wind power capacity allocation data, M S For photovoltaic capacity allocation data, M B For energy storage capacity allocation data, C W C represents the unit investment cost per kilowatt of wind power. S C represents the unit investment cost per kilowatt of photovoltaic power. B The second decision variable, M, represents the unit kilowatt investment cost for energy storage. W M S and M B .

[0116] Optionally, the capacity ratio data generation device 400 further includes:

[0117] A construction module is used to construct a two-layer optimization model, which includes an upper-layer optimization model and a lower-layer optimization model that are coupled together. The lower-layer optimization model is used to determine the time-of-use output scheme corresponding to the capacity allocation scheme by taking the time-of-use output of multiple energy sources as the first decision variable and maximizing the net electricity sales revenue of the base within a preset time period as the first objective function. The upper-layer optimization model is used to determine the capacity allocation scheme by taking the capacity allocation of the multiple energy sources as the second decision variable and maximizing the internal rate of return on the total investment of the base as the second objective function.

[0118] Optionally, the two-layer optimization model is expressed as:

[0119]

[0120] Where x is the first decision variable, y is the second decision variable, H(x,y) is the first objective function, R(x,y) is the second objective function, h(x,y) is the constraint condition of the first objective function, and r(x,y) is the constraint condition of the second objective function.

[0121] It should be noted that the capacity ratio data generation device 400 provided in this embodiment of the invention can achieve the following: Figure 1 The entire technical process of the capacity ratio data generation method shown in the embodiment, and achieving the same technical effect, will not be repeated here to avoid duplication.

[0122] This invention also provides an electronic device, including: a processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the above-described functionality. Figure 1 The various processes of the method embodiments shown can achieve the same technical effect, and will not be described again here to avoid repetition.

[0123] For details, see Figure 5 As shown, this embodiment of the invention also provides an electronic device, including a bus 501, a transceiver 502, an antenna 503, a bus interface 504, a processor 505, and a memory 506.

[0124] In this embodiment, the electronic device further includes a computer program stored in the memory 506 and executable on the processor 505. Wherein, when the electronic device is a first server, the computer program, when executed by the processor 505, can achieve the following: Figure 1 The various processes of the method shown in the embodiments can achieve the same technical effect, and will not be described again here to avoid repetition.

[0125] exist Figure 5In this document, a bus architecture (represented by bus 501) is used. Bus 501 can include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 505 and memory represented by memory 506. Bus 501 can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 504 provides an interface between bus 501 and transceiver 502. Transceiver 502 can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 505 is transmitted over a wireless medium via antenna 503, which further receives data and transmits it to processor 505.

[0126] Processor 505 manages bus 501 and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory 506 can be used to store data used by processor 505 during operation.

[0127] Optionally, the processor 505 can be a CPU, ASIC, FPGA, or CPLD.

[0128] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the above-described functions. Figure 1 The various processes of the illustrated method embodiments achieve the same technical effect, and to avoid repetition, they will not be described again here. The computer-readable storage medium mentioned includes, for example, ROM, RAM, magnetic disk, or optical disk.

[0129] This invention also provides a computer program product, including computer instructions, which, when executed by a processor, implement the above-described... Figure 1 The various processes of the method embodiments shown can achieve the same technical effect, and will not be described again here to avoid repetition.

[0130] It should be noted that, in this document, 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 that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of the present invention is not limited to performing functions in the order discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0131] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of the present invention.

Claims

1. A method for generating capacity ratio data, characterized in that, The method includes: Using the time-of-use output of multiple energy sources as the first decision variable, and maximizing the net electricity sales revenue of the base within a preset time period as the first objective function, a first time-of-use output scheme corresponding to a first capacity allocation scheme is determined. The first capacity allocation scheme is used to indicate the capacity allocation data of the multiple energy sources, and the first time-of-use output scheme is used to indicate the time-of-use output data of the multiple energy sources in the first capacity allocation scheme. The time-of-use output data of the multiple energy sources is used to calculate the net electricity sales revenue of the base within the preset time period. The base is the area where the multiple energy sources are to be deployed. Using the capacity ratio of the various energy sources as the second decision variable, and maximizing the internal rate of return on the total investment of the base as the second objective function, a second capacity ratio scheme is determined. The internal rate of return on the total investment of the base is determined based on the net electricity sales revenue of the base within a preset time period. There is at least one energy source with different capacity ratio data between the second capacity ratio scheme and the first capacity ratio scheme. If the internal rate of return on total investment of the base corresponding to the second capacity allocation scheme is greater than or equal to the internal rate of return on total investment of the base corresponding to the first capacity allocation scheme, obtain the capacity allocation data of each energy source among the multiple energy sources corresponding to the second capacity allocation scheme.

2. The method according to claim 1, characterized in that, After determining the second capacity ratio scheme, the method further includes: When the total investment internal rate of return of the base corresponding to the second capacity allocation scheme is less than the total investment internal rate of return of the base corresponding to the first capacity allocation scheme, the first capacity allocation scheme in the first objective function is iterated based on the second capacity allocation scheme to obtain the second time-sharing power output scheme corresponding to the second capacity allocation scheme. Based on the net electricity sales revenue of the base corresponding to the second time-sharing power output scheme within a preset time period, the input data of the second objective function is iterated to obtain the third capacity allocation scheme. The input data of the second objective function includes the net electricity sales revenue of the base corresponding to the first time-sharing power output scheme within a preset time period. If the internal rate of return on total investment of the base corresponding to the third capacity allocation scheme is greater than or equal to the internal rate of return on total investment of the base corresponding to other capacity allocation schemes, the capacity allocation data of each energy source among the multiple energy sources corresponding to the third capacity allocation scheme are obtained.

3. The method according to claim 1, characterized in that, The various energy sources include wind power, photovoltaic power, and energy storage, and the first objective function is expressed as: max(NR i )=max[(R Wi -V Wi -F Wi )+(R Si -V Si -F Si )+(R Bi -V Bi -F Bi )]; Among them, NR i R represents the net electricity sales revenue of the base during the i-th preset time period. Wi R represents the net electricity revenue from wind power sales during the i-th preset time period. Si R represents the net electricity revenue from photovoltaic power generation during the i-th preset time period. Bi V represents the net electricity revenue from energy storage during the i-th preset time period. Wi V represents the variable operation and maintenance cost of wind power within the i-th preset time period. Si V represents the variable operation and maintenance cost of photovoltaic power generation within the i-th preset time period. Bi F represents the variable operation and maintenance cost of energy storage within the i-th preset time period. Wi F represents the fixed operation and maintenance cost of wind power within the i-th preset time period. Si F represents the fixed operation and maintenance cost of photovoltaic power generation within the i-th preset time period. Bi The fixed operation and maintenance cost of energy storage within the i-th preset time period; in, EP(t) is the market time-of-use electricity price, P W (t) represents the time-of-use power output data of wind power, P S (t) represents the time-of-use power output data of the photovoltaic system, P B (t) represents the time-of-use output data of energy storage, and the first decision variable includes P. W (t), P S (t) and P B (t).

4. The method according to claim 1, characterized in that, The various energy sources include wind power, photovoltaic power, and energy storage, and the second objective function is expressed as: Where IRR is the internal rate of return on total investment of the base, TI is the initial investment, and NR is the internal rate of return. i The net electricity sales revenue of the base during the i-th preset time period is n, and the operating years of the base are n; Where TI = M W C W +M S C S +M B C B M W For wind power capacity allocation data, M S For photovoltaic capacity allocation data, M B For energy storage capacity allocation data, C W C represents the unit investment cost per kilowatt of wind power. S C represents the unit investment cost per kilowatt of photovoltaic power. B The second decision variable, M, represents the unit kilowatt investment cost for energy storage. W M S and M B .

5. The method according to claim 1, characterized in that, Before determining the first time-sharing output scheme corresponding to the first capacity allocation scheme, the method further includes: A two-layer optimization model is constructed, comprising an upper-layer optimization model and a lower-layer optimization model that are coupled together. The lower-layer optimization model is used to determine the time-of-use output scheme corresponding to the capacity allocation scheme, with the time-of-use output of multiple energy sources as the first decision variable and the maximization of the net electricity sales revenue of the base within a preset time period as the first objective function. The upper-layer optimization model is used to determine the capacity allocation scheme, with the capacity allocation of the multiple energy sources as the second decision variable and the maximization of the internal rate of return on the total investment of the base as the second objective function.

6. The method according to claim 5, characterized in that, The two-layer optimization model is expressed as follows: Where x is the first decision variable, y is the second decision variable, H(x,y) is the first objective function, R(x,y) is the second objective function, h(x,y) is the constraint condition of the first objective function, and r(x,y) is the constraint condition of the second objective function.

7. A capacity ratio data generation device, characterized in that, The device includes: The first determining module is used to determine the first time-of-use output scheme corresponding to the first capacity allocation scheme, with the time-of-use output of multiple energy sources as the first decision variable and the maximum net electricity sales revenue of the base within a preset time period as the first objective function. The first capacity allocation scheme is used to indicate the capacity allocation data of the multiple energy sources, and the first time-of-use output scheme is used to indicate the time-of-use output data of the multiple energy sources in the first capacity allocation scheme. The time-of-use output data of the multiple energy sources is used to calculate the net electricity sales revenue of the base within the preset time period. The base is the area where the multiple energy sources are to be deployed. The second determining module is used to determine a second capacity allocation scheme with the capacity ratio of the multiple energy sources as the second decision variable and the maximum internal rate of return on the total investment of the base as the second objective function. The internal rate of return on the total investment of the base is determined based on the net electricity sales revenue of the base within a preset time period. There is at least one energy source with different capacity allocation data between the second capacity allocation scheme and the first capacity allocation scheme. The first acquisition module is used to acquire the capacity ratio data of each energy source among the multiple energy sources corresponding to the second capacity ratio scheme when the total internal rate of return on investment of the base corresponding to the second capacity ratio scheme is greater than or equal to the total internal rate of return on investment of the base corresponding to the first capacity ratio scheme.

8. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the capacity proportioning data generation method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the capacity proportioning data generation method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, It includes computer instructions that, when executed by a processor, implement the steps of the capacity proportioning data generation method as described in any one of claims 1 to 6.

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