Supply and demand balance modeling method for extremely-high-proportion renewable energy power system based on frequency domain analysis

Abstract

The invention provides a frequency domain analysis-based supply and demand balance modeling method for an extremely-high-proportion renewable energy power system, and the method comprises the steps: converting the time sequence data of supply and demand resources of the system into a power spectrum density, constructing the constraints of various adjustment resources, constructing a supply and demand balance optimization model in a frequency domain, and carrying out the modeling of the supply and demand balance. Therefore, efficient solving and resource optimization distribution of multi-time-scale supply and demand balance of the extremely-high-proportion renewable energy power system are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and in particular to a supply and demand balance modeling method for an extremely high proportion of renewable energy power systems based on frequency domain analysis. Background Art

[0002] With the large-scale access of renewable energy (such as wind and solar energy), wind and solar power generation have brought severe challenges to the supply and demand balance of the power system due to the strong randomness and volatility of wind and solar resources. As wind and solar and other volatile power sources become the main power supply, the intrinsic characteristics of the power system have changed, "base load" power plants have basically disappeared, conventional thermal power units are quickly started and stopped during the day, and through the adjustment of multiple types of flexible resources such as hydropower plants, gas power plants, energy storage and elastic loads, the random fluctuations of renewable energy are complemented together. Flexible balancing capabilities have become the core of planning and operation.

[0003] Traditional time domain methods face problems such as large computational complexity and complex models when dealing with multiple time scales and uncertainties. The frequency domain analysis method provides a new approach to solving this problem by treating system supply and demand as a random process and using power spectrum density to characterize its statistical characteristics. However, the application of this method at the system level is still in the exploratory stage and requires further modeling and optimization. Summary of the invention

[0004] Therefore, in response to the gaps in the prior art, the present invention proposes a supply and demand balance modeling method for extremely high renewable energy proportion power systems based on frequency domain analysis, aiming to solve the difficult problems of multi-time scale supply and demand balance and uncertainty processing in extremely high renewable energy proportion systems.

[0005] This method converts the time series data of both the supply and demand resources of the system into power spectrum density, constructs constraints on various types of regulation resources, and builds an optimization model of supply and demand balance in the frequency domain to achieve efficient solution of multi-time scale supply and demand balance and optimal resource allocation for extremely high proportion of renewable energy power systems. This method solves the difficulties of time domain methods in dealing with multi-time scales and uncertainty modeling, and can effectively deal with the uncertainty of large-scale wind / solar power sources and elastic loads.

[0006] The present invention specifically adopts the following technical solutions:

[0007] A supply and demand balance modeling method for extremely high renewable energy proportion power systems based on frequency domain analysis, by converting the time series data of both the supply and demand resources of the system into power spectrum density and constructing constraints for various types of regulation resources, an optimization model of supply and demand balance is constructed in the frequency domain, so as to achieve efficient solution of multi-time scale supply and demand balance and optimal resource allocation for extremely high renewable energy proportion power systems.

[0008] Furthermore, based on the power spectral density modeling of the system supply resources and the power spectral density modeling of the system demand resources, a frequency domain production simulation and optimization model for obtaining the system supply and demand balance is constructed.

[0009] Furthermore, the power spectral density modeling of the system supply resources is specifically as follows: by collecting the time series data of the main supply resources of the extremely high proportion system and preprocessing, the time series data of the supply resources are converted into power spectral density using frequency domain transformation, thereby modeling the power spectral density of the main supply resources of the extremely high proportion of renewable energy power system, wherein the main supply resources include temperature control loads, battery energy storage / electric vehicle loads, electric hydrogen production loads, and hydropower loads with storage capacity.

[0010] The power spectrum density of the temperature control load is modeled to obtain the power spectrum density of the temperature control load in the cooling situation:

[0011]

[0012] For the heating shape, the power spectrum density is:

[0013]

[0014] The power spectral density obtained by the power spectral density modeling of the battery energy storage / electric vehicle load is:

[0015]

[0016] The power spectrum density obtained by the power spectrum density modeling of the electric hydrogen production load is:

[0017]

[0018] The power spectrum density obtained by the power spectrum density modeling of the hydropower load with storage capacity is:

[0019]

[0020] Furthermore, the power spectral density modeling of the system demand resources obtains the load and wind / solar power generation hourly power curves used for calculation based on actual data obtained from the existing network operation, and obtains the net load time series of the extremely high proportion system; wherein the net load is defined as the difference between the system load and the wind / solar power generation; the net load time series data is transformed in the frequency domain to obtain the power spectral density of the system demand resources.

[0021] Furthermore, the frequency domain production simulation and optimization model of the system supply and demand balance is a frequency domain optimization model constructed based on power spectral density; the frequency domain optimization model includes optimization variables corresponding to the power spectral density of various types of resources at different frequencies, as well as objective functions and constraints; the constraints include upper and lower limit constraints, adjustment power limit constraints, power climbing limit constraints, continuous adjustment power deviation limit constraints, and associated physical quantity fluctuation limit constraints.

[0022] Furthermore, the objective function of the frequency domain optimization model is:

[0023]

[0024] in: The power spectrum density of each type of regulation resource at each frequency is solved by modeling the power spectrum density of the system supply resource; S D (ω) is the power spectrum density obtained by frequency domain transformation based on the net load time series curve, and is solved by power spectrum density modeling of the system demand resources; As the optimization variable, S D (ω) as a variable.

[0025] Furthermore, the constraint formula of the constraint condition is expressed based on power spectrum density, and each constraint formula corresponds to a timing constraint respectively.

[0026] The upper and lower limit constraints are:

[0027] 0≤S X (ω)≤S D (ω)

[0028] The power limit constraint is a power limit constraint based on a single-sided spectrum:

[0029]

[0030] The power ramp limit constraint is a power ramp limit constraint based on a single-sided spectrum:

[0031]

[0032] The continuous regulation power deviation limit constraint is a continuous regulation power deviation limit constraint based on a single-sided spectrum:

[0033]

[0034] in:

[0035] The associated physical quantity fluctuation limit constraint is an associated physical quantity fluctuation limit constraint based on a unilateral spectrum form:

[0036]

[0037] Furthermore, by calculating the optimization model in the frequency domain, the optimal power spectrum density distribution of various types of resources at different frequencies is obtained to optimize the supply and demand balance in the frequency domain.

[0038] And, an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of a method for modeling supply and demand balance of a power system with a very high proportion of renewable energy based on frequency domain analysis as described above are implemented.

[0039] A non-transitory computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a supply and demand balance modeling method for a very high proportion of renewable energy power system based on frequency domain analysis as described above.

[0040] Compared with the prior art, the present invention and its preferred solution transform the time domain problem into the frequency domain problem through frequency domain transformation, reduce the model complexity, and characterize the uncertainty of supply and demand through power spectrum density; based on the frequency domain optimization results, the regulation capacity of various resources is reasonably allocated to improve the economy and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0042] Figure 1 It is a schematic diagram of the overall flow of the modeling method according to an embodiment of the present invention;

[0043] Figure 2 It is a schematic diagram of a specific modeling process of an embodiment of the present invention. DETAILED DESCRIPTION

[0044] Hereinafter, the specific embodiments of the present application will be described in detail with reference to the accompanying drawings. According to these detailed descriptions, those skilled in the art can clearly understand the present application and can implement the present application. Without violating the principles of the present application, the features in the various embodiments can be combined to obtain new implementations, or certain features in certain embodiments can be replaced to obtain other preferred implementations.

[0045] In order to make the features and advantages of this patent more obvious and easy to understand, the following embodiments are specifically described in detail with the accompanying drawings as follows:

[0046] like Figure 1 As shown, the basic design of a supply and demand balance modeling method for a very high proportion of renewable energy power system based on frequency domain analysis provided by an embodiment of the present invention is as follows:

[0047] Step 1: Modeling the power spectrum density of the system supply resources. Model the power spectrum density of the main supply resources of the extremely high proportion of renewable energy power system, which mainly include temperature control loads, battery energy storage / electric vehicle loads, electric hydrogen loads, and hydropower loads with storage capacity. This step will collect and pre-process the time series data of the main supply resources of the extremely high proportion of renewable energy power system, and use the frequency domain transformation algorithm to convert the time series data of the supply resources into power spectrum density.

[0048] Step 2: Modeling the power spectrum density of system demand resources. Based on the actual grid operation time series data, the load and wind / solar power generation hourly power curves used for calculation are calculated, and the net load time series data is transformed into the frequency domain to obtain the power spectrum density of system demand resources.

[0049] Step 3: Construct a frequency domain production simulation and optimization model to obtain system supply and demand balance. Based on the power spectrum density, a frequency domain optimization model is constructed. The model includes optimization variables (power spectrum density of various resources at different frequencies), objective functions and constraints. The constraints include upper and lower limit constraints, power adjustment limit constraints, power climbing limit constraints, continuous power adjustment deviation limit constraints, and associated physical quantity fluctuation limit constraints. The setting of constraints must fully consider the physical characteristics and operation requirements of various resources. The optimization algorithm is used to perform optimization calculations in the frequency domain to obtain the optimal power spectrum density distribution of various resources at different frequencies.

[0050] like Figure 2 As shown, the specific implementation process of the above modeling method is as follows:

[0051] Step 1: Power Spectral Density Modeling of System Supply Resources

[0052] Considering that the flexibility demand of extremely high-proportion systems mainly comes from the random fluctuations of wind / solar power generation and the random changes of rigid loads, a large number of elastic loads will participate in responding to the power and energy fluctuations of wind / solar power generation. Electric vehicles and temperature control loads (P2H and P2C) were the main considerations in the past research on demand-side response. The tracking of power supply fluctuations by loads through P2G (mainly referring to hydrogen production by electricity) has received attention in recent years. Hydrogen production loads are considered to be an important resource that follows the large fluctuations in photovoltaic power generation. However, there are significant differences in the scenarios and time scales of different flexibility resources, and the ability to provide power regulation and energy regulation is different.

[0053] The following is a modeling of the power spectrum density of the main supply resources of the extremely high proportion of renewable energy power system. The main process is: collect the time series data of the main supply resources of the system and preprocess them, and use the frequency domain transformation algorithm to convert the time series data of the supply resources into power spectrum density. The main supply resources are temperature control loads, battery energy storage / electric vehicle loads, electric hydrogen production loads, and hydropower loads with storage capacity.

[0054] 1. Power spectral density modeling of temperature control loads

[0055] Taking refrigeration as an example, the power spectrum density H1(jω) of the temperature control load can be solved as follows:

[0056]

[0057] Where: T i is the indoor temperature, T0 is the outdoor temperature, Q' is the heating power from the human body, electrical appliances, etc., k is the efficiency coefficient, P is the input electrical power, C is the building heat capacity, and R is the building thermal resistance.

[0058] Defining variables P baseline , for

[0059]

[0060] It can be further deduced that:

[0061]

[0062] Define variables γ and β as

[0063]

[0064] It can be further deduced that:

[0065]

[0066] Based on frequency domain transformation, we can get:

[0067]

[0068] That is, the power spectrum density H1(jω) of the temperature control load is

[0069]

[0070] Similarly, for the heating shape, the power spectrum density H1(jω) is

[0071]

[0072] 2. Power Spectral Density Modeling of Battery Energy Storage / Electric Vehicle Loads

[0073] The power spectrum density H2(jω) of the battery energy storage / electric vehicle load can be solved as follows:

[0074]

[0075] Where: α is the battery power loss coefficient, Pch is the charging power, η ch is the charging efficiency, P dis is the discharge efficiency, η dis is the discharge power.

[0076] Because it is difficult to distinguish the difference between charging and discharging states in the frequency domain, η ch and η dis Taking it as 1, we can get:

[0077]

[0078] Where: P = P ch -P dis

[0079] Defining variables for

[0080]

[0081] You can get:

[0082]

[0083] Based on frequency domain transformation, we can get

[0084]

[0085] The power spectrum density H2(jω) of the battery energy storage / electric vehicle load is:

[0086]

[0087] 3. Power Spectral Density Modeling of Hydrogen Production Load

[0088] Based on the actual operation data, it can be considered that the hydrogen production rate of the electrolyzer is proportional to the electrolyzer current. Considering the energy loss of the produced hydrogen after compression or liquefaction storage, the relationship between the rate of hydrogen injection into the hydrogen storage device and the input power of the electrolyzer can be approximately obtained as follows:

[0089]

[0090] Where: n t is the rate of hydrogen injection into the hydrogen storage device; P t is the electric power input to the electrolyzer; η is the gas production efficiency, k0 is the relationship coefficient between the hydrogen production rate of the electrolyzer and the electrolyzer current, and Z is the electrolyzer impedance.

[0091] Define the variable y t , k is

[0092]

[0093] It can be deduced that:

[0094] y t =kP t

[0095] In order to eliminate the nonlinear term about P, ​​a physical quantity Y reflecting the hydrogen storage capacity is defined as

[0096]

[0097] Defining variables for

[0098]

[0099] It can be deduced that:

[0100]

[0101] The power spectrum density H3(jω) of the electric hydrogen production load is:

[0102]

[0103] 4. Power Spectral Density Modeling of Hydropower Load with Reservoir Capacity

[0104] Based on actual operation data, it is approximately believed that the hydropower generation power is proportional to the reservoir discharge flow, considering the reservoir water balance relationship:

[0105]

[0106] Where: V t+1 is the reservoir capacity at time t+1, V t is the reservoir capacity at time t, is the inflow flow in period t, k is the water consumption coefficient for power generation, P t For hydroelectric power generation, is the abandoned water flow rate, and Δt is the time between every two moments.

[0107] Define variable P baseline,t 、V baseline,t , for

[0108]

[0109] It can be deduced that:

[0110]

[0111] The power spectrum density H(jω) of the hydropower load with reservoir capacity is:

[0112]

[0113] Step 2: Power Spectral Density Modeling of System Requirement Resources

[0114] In extremely high-ratio systems, wind / solar power characteristics dominate system operation. Flexibility is a new issue of concern for high-ratio systems. The sharp increase in demand for flexible power and energy regulation resources will be a key contradiction in extremely high-ratio systems.

[0115] In an application example of the present invention, based on the actual data of each province within the business scope of State Grid Corporation of China in 2020, the load and wind / solar power generation hourly power curves used for calculation are obtained, and based on the capacity planning data in 2060, the net load time series of the extremely high proportion system can be obtained. Among them, the net load is defined as the difference between the system load and the wind / solar power generation. The net load time series data is transformed in the frequency domain to obtain the power spectrum density of the system demand resources.

[0116] Step 3: Frequency domain production simulation and optimization model for system supply and demand balance

[0117] Based on the power spectrum density, a frequency domain optimization model is constructed. The model includes optimization variables (power spectrum density of various resources at different frequencies), objective functions, and constraints. The setting of constraints needs to fully consider the physical characteristics and operation requirements of various resources. Using the optimization algorithm, optimization calculations are performed in the frequency domain to obtain the optimal power spectrum density distribution of various resources at different frequencies.

[0118] The objective function of the overall optimization model is as follows:

[0119]

[0120] in: is the power spectrum density of each type of regulation resource at each frequency, which is solved by modeling the power spectrum density of the system supply resource in step 1. D (ω) is the power spectrum density obtained by frequency domain transformation based on the net load time series curve, which is solved by the power spectrum density modeling of the system demand resources in step 2. In this model, As the optimization variable, S D (ω) as a variable.

[0121] For this model, there are five constraints, including upper and lower limit constraints, adjustment power limit constraints, power ramp limit constraints, continuous adjustment power deviation limit constraints, and associated physical quantity fluctuation limit constraints. The QOS (quality of service) constraint of the optimization problem is expressed based on the power spectrum density. Each constraint corresponds to the timing constraint. The following describes the method of transforming the relationship from the time domain to the frequency domain power spectrum density. The derivation premise is that the active output and net load demand of each flexible adjustment resource can be regarded as a wide stationary random process with a mean of zero.

[0122] 1. Upper and lower limit constraints

[0123] 0≤S X (ω)≤S D (ω)

[0124] 2. Adjust power limit constraints

[0125] Starting from the time domain deterministic constraints:

[0126]

[0127] Where: P baseline (t) is the mean of P(t), such that The mean of is 0.

[0128] Consider the probabilistic constraints in the time domain:

[0129]

[0130] The following can be derived from Chebyshev's inequality:

[0131]

[0132] From the relationship between the variance function and the correlation function of the random process, we can get the expression based on the correlation function:

[0133]

[0134] After frequency domain transformation, the expression based on power spectrum density can be obtained:

[0135]

[0136] Finally, the regulation power limit constraint based on the single-sided spectrum can be obtained:

[0137]

[0138] 3. Power ramp limit constraint

[0139] Starting from the time domain deterministic constraints:

[0140]

[0141] Consider the probabilistic constraints in the time domain:

[0142]

[0143] The following can be derived from Chebyshev's inequality:

[0144]

[0145] From the relationship between the variance function and the correlation function of the random process, we can get the expression based on the correlation function:

[0146]

[0147] After frequency domain transformation, the expression based on power spectrum density can be obtained:

[0148]

[0149] Finally, the power ramp limit constraint based on the single-sided spectrum can be obtained:

[0150]

[0151] 4. Continuously adjust the power deviation limit constraint

[0152] Starting from the time domain deterministic constraints:

[0153]

[0154] Consider the probabilistic constraints in the time domain:

[0155]

[0156] The following can be derived from Chebyshev's inequality:

[0157]

[0158] From the relationship between the variance function and the correlation function of the random process, we can get the expression based on the correlation function:

[0159]

[0160] After frequency domain transformation, the expression based on power spectrum density can be obtained:

[0161]

[0162] Finally, the continuous regulation power deviation limit constraint based on the unilateral spectrum can be obtained:

[0163]

[0164] in:

[0165] 5. Constraints on fluctuation limits of associated physical quantities

[0166] Starting from the time domain deterministic constraints:

[0167]

[0168] Among them: θ(t) can represent the indoor temperature, the water storage capacity of the reservoir, and the gas storage capacity of the hydrogen storage tank considering the time domain probabilistic constraints:

[0169]

[0170] The following can be derived from Chebyshev's inequality:

[0171]

[0172] From the relationship between the variance function and the correlation function of the random process, we can get the expression based on the correlation function:

[0173]

[0174] After frequency domain transformation, the expression based on power spectrum density can be obtained:

[0175]

[0176] Finally, the fluctuation limit constraints of the associated physical quantities based on the unilateral spectrum can be obtained:

[0177]

[0178] By setting five constraints and calculating the optimization model in the frequency domain, the optimal power spectral density allocation of various resources at different frequencies can be obtained.

[0179] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0180] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0181] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0182] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0183] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0184] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any technician familiar with the profession may use the above disclosed technical content to change or modify it into an equivalent embodiment with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention still belongs to the protection scope of the technical solution of the present invention.

[0185] This patent is not limited to the above-mentioned optimal implementation mode. Anyone can derive various other forms of supply and demand balance modeling methods for extremely high proportion of renewable energy power systems based on frequency domain analysis under the inspiration of this patent. All equal changes and modifications made according to the scope of the patent application of this invention should be covered by this patent.

Claims

1. A supply and demand balance modeling method for a very high proportion of renewable energy power system based on frequency domain analysis, characterized by: By converting the time series data of both the supply and demand sides of the system into power spectral density and building constraints for various types of regulation resources, an optimization model of supply and demand balance is constructed in the frequency domain to achieve efficient solution and optimal resource allocation of multi-time scale supply and demand balance in power systems with extremely high proportions of renewable energy.

2. The supply and demand balance modeling method of a very high proportion of renewable energy power system based on frequency domain analysis according to claim 1 is characterized by: Based on the power spectral density modeling of system supply resources and the power spectral density modeling of system demand resources, a frequency domain production simulation and optimization model is constructed to obtain the system supply and demand balance.

3. The supply and demand balance modeling method of a very high proportion of renewable energy power system based on frequency domain analysis according to claim 2 is characterized by: The power spectrum density modeling of the system supply resources is specifically as follows: by collecting the time series data of the main supply resources of the extremely high proportion system and preprocessing, the time series data of the supply resources are converted into power spectrum density using frequency domain transformation, thereby modeling the power spectrum density of the main supply resources of the extremely high proportion renewable energy power system, wherein the main supply resources include temperature control loads, battery energy storage / electric vehicle loads, electric hydrogen production loads, and hydropower loads with storage capacity.

4. The supply and demand balance modeling method of a very high proportion of renewable energy power system based on frequency domain analysis according to claim 2 is characterized by: The power spectral density modeling of the system demand resources obtains the load and wind / solar power generation hourly power curves used for calculation based on the actual data obtained from the current network operation, and obtains the net load time series of the extremely high proportion system; wherein the net load is defined as the difference between the system load and the wind / solar power generation; the net load time series data is transformed in the frequency domain to obtain the power spectral density of the system demand resources.

5. The supply and demand balance modeling method of a very high proportion of renewable energy power system based on frequency domain analysis according to claim 2 is characterized by: The frequency domain production simulation and optimization model of the system supply and demand balance is a frequency domain optimization model constructed based on power spectrum density; the frequency domain optimization model includes optimization variables corresponding to the power spectrum density of various types of resources at different frequencies, as well as objective functions and constraints; the constraints include upper and lower limit constraints, adjustment power limit constraints, power climbing limit constraints, continuous adjustment power deviation limit constraints, and associated physical quantity fluctuation limit constraints.

6. The supply and demand balance modeling method of a very high proportion of renewable energy power system based on frequency domain analysis according to claim 5 is characterized by: The objective function of the frequency domain optimization model is: in: The power spectrum density of each type of regulation resource at each frequency is solved by modeling the power spectrum density of the system supply resource; S D (ω) is the power spectrum density obtained by frequency domain transformation based on the net load time series curve, and is solved by power spectrum density modeling of the system demand resources; As the optimization variable, S D (ω) as a variable.

7. The supply and demand balance modeling method of a very high proportion of renewable energy power system based on frequency domain analysis according to claim 5 is characterized by: The constraint formula of the constraint condition is expressed based on power spectrum density, and each constraint formula corresponds to a timing constraint respectively.

8. The supply and demand balance modeling method of a very high proportion of renewable energy power system based on frequency domain analysis according to claim 5 is characterized by: By calculating the optimization model in the frequency domain, the optimal power spectrum density allocation of various resources at different frequencies is obtained.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of a supply and demand balance modeling method for a very high proportion of renewable energy power system based on frequency domain analysis as described in any one of claims 1 to 8 are implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for modeling supply and demand balance of a very high proportion of renewable energy power system based on frequency domain analysis as described in any one of claims 1 to 8 are implemented.

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