Wind and light cluster frequency modulation control parameter optimization method considering wind and light combined frequency modulation

Abstract

The invention belongs to the technical field of power system frequency stability control, and discloses a wind-solar cluster frequency modulation control parameter optimization method considering wind-solar combined frequency modulation, comprising the following steps: S1, respectively describing frequency response characteristics of wind power, photovoltaic, thermal power and energy storage power stations, and establishing a wind-solar cluster frequency response model comprising an amplitude limiting link; s2, obtaining a discretization form of a wind and light cluster frequency response model based on a difference method; and S3, according to the power generation cost and the station reserve capacity, establishing a frequency modulation parameter optimization model by taking maximization of the frequency supporting capacity of the cluster as a target. According to the wind and light cluster frequency modulation control parameter optimization method considering wind and light combined frequency modulation, frequency modulation parameter optimization calculation is performed on a power generation system containing different frequency modulation resources of wind, light and storage, so that various frequency modulation resources are reasonably utilized, cooperative complementation of various frequency modulation resources is realized, the active frequency supporting capability of a wind and light cluster is improved, and the power generation efficiency is improved. And safe and stable operation of a power system is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power system frequency stability control, and in particular to a method for optimizing wind-solar cluster frequency regulation control parameters considering wind-solar joint frequency regulation. Background Art

[0002] New energy is showing a large-scale, high-growth development trend around the world. The power supply structure of the power system is changing. Although traditional thermal and hydropower still dominate, new energy units are gradually replacing more and more conventional power sources and occupying a more important position in the power grid. In recent years, the development and utilization of new energy in many regions has grown rapidly. At the same time, the rising proportion of new energy installed capacity has brought challenges to the safe operation of the power system, and the stability and recovery of the system frequency have become more difficult.

[0003] On the one hand, traditional power generation sources have mechanical inertia. When extreme disturbances occur, these mechanical inertias can provide inertial support for the system, which helps stabilize and restore the system frequency. New energy sources are connected to the power system through power electronic interfaces. These power electronic devices enable new energy units to control the output of electric energy more flexibly, but at the same time reduce mechanical inertia, resulting in weak inertia characteristics; and new energy units usually operate in maximum power tracking mode to maximize power output. This control strategy helps new energy units to follow changes more actively when the grid frequency changes, instead of maintaining stable inertial support like traditional units; in addition, the output power of new energy units is affected by changes in natural resources (such as wind speed, sunshine, etc.), resulting in large fluctuations in power output. In order to improve the grid's ability to adapt to this volatility, new energy units are usually designed to respond more flexibly to grid demand, which also results in weak inertia or no inertia characteristics.

[0004] On the other hand, the increasing proportion of high-power, long-distance DC transmission lines has increased the risk of safe operation of the power system. Once a DC blocking fault occurs, there will be a serious imbalance between the actual power generation of the power grid and the load demand, greatly increasing the probability of system frequency instability. Therefore, the increasing installed capacity of new energy and the access of large-scale high-voltage DC transmission lines have brought challenges to the safe and stable operation of the power system, and the frequency stability problem under the new energy power system needs to be solved urgently.

[0005] As an important means to maintain frequency stability, the frequency regulation capability of primary frequency regulation depends on the active regulation characteristics of the units involved in frequency regulation, such as frequency regulation parameters such as differential coefficient, reheat time constant, high-pressure turbine coefficient, amplitude limiting, dead zone, etc. The current standards for primary frequency regulation of grid-connected power sources in my country clearly stipulate the adjustable parameter ranges and dynamic performance indicators of primary frequency regulation of various power sources, and the dynamic response characteristics of power supply speed regulators, frequency regulation dead zones and differential regulation rates are relatively fixed, while the adjustable range of speed regulator amplitude limiting parameters is relatively flexible. Therefore, how to optimize the allocation of frequency regulation resources for primary frequency regulation to enhance the system frequency active support capability has become an important issue facing new energy sources participating in system frequency regulation. Summary of the invention

[0006] The purpose of the present invention is to provide a method for optimizing the frequency regulation control parameters of a wind-solar cluster taking into account the joint frequency regulation of wind and solar power, and to perform frequency regulation parameter optimization calculations for a power generation system containing different frequency regulation resources such as wind, solar power and storage, so as to rationally utilize various frequency regulation resources, realize the coordinated complementarity of various frequency regulation resources, improve the frequency active support capability of the wind-solar cluster, and ensure the safe and stable operation of the power system.

[0007] To achieve the above object, the present invention provides a method for optimizing wind-solar cluster frequency modulation control parameters considering wind-solar joint frequency modulation, comprising the following steps:

[0008] S1. Characterize the frequency response characteristics of wind power, photovoltaic power, thermal power and energy storage power stations respectively, and establish a wind-solar cluster frequency response model including a limiting link;

[0009] S2. Obtain the discretized form of the wind-solar cluster frequency response model based on the difference method;

[0010] S3. Based on the power generation cost and site spare capacity, a frequency regulation parameter optimization model is established with the goal of maximizing the frequency support capability of the cluster.

[0011] Preferably, in step S1, a wind-solar cluster frequency response model including a limiting link is established, and the specific process is as follows:

[0012] S11. Based on the external response characteristics of various power sources, the frequency response characteristics of wind power, photovoltaic power, thermal power and energy storage power stations are described as follows:

[0013]

[0014] Where ΔP G , ΔP W , ΔP P , ΔP B Respectively represent the active power adjustment of thermal power, wind power, photovoltaic power and electrochemical energy storage; Respectively represent the active power adjustment of thermal power, wind power, photovoltaic power and electrochemical energy storage after the limit link; T RRepresents the reheating time constant of the equivalent thermal power unit; F H Indicates the power ratio of the high-pressure cylinder of the equivalent unit; T W , T P , T B They represent the equivalent first-order inertia delay constants of the frequency control links of wind power, photovoltaic power, and electrochemical energy storage, respectively; s is the complex variable of Laplace transform;

[0015] S12. Based on the influence of the limiting links of various power sources on the dynamic response of frequency, a frequency response model of the wind and solar cluster is established as follows:

[0016]

[0017] Among them, H S represents the total inertia of the system, D represents the damping coefficient of the system, ΔP e Indicates the change in system active power; Respectively represent the frequency regulation and standby active power adjustment limits of thermal power, wind power, photovoltaic power, and electrochemical energy storage; K G , K W , K P , K B Respectively represent the equivalent primary frequency modulation gains of thermal power, wind power, photovoltaic power, and electrochemical energy storage; H W , H P , H B They represent the equivalent inertia time constants of wind power, photovoltaic power, and electrochemical energy storage respectively; Δf represents the frequency deviation of the wind and solar cluster after a disturbance occurs.

[0018] Preferably, in step S2, obtaining a discretized form of the wind-solar cluster frequency response model based on a difference method includes the following steps:

[0019] S21. Based on the difference method, the difference equation of the system frequency deviation is established as follows:

[0020]

[0021] Where Δf n represents the system frequency deviation of the nth differential step, dn represents the differential step, ΔP G,n-1 , ΔP W,n-1 , ΔP P,n-1 , ΔP B,n-1 They represent the active power adjustment of thermal power, wind power, photovoltaic power and electrochemical energy storage at the n-1th differential step respectively;

[0022] S22. Based on the differential method, the differential equations of active power adjustment after various power supply disturbances are established as follows:

[0023]

[0024] in, They represent the active power adjustment of thermal power, wind power, photovoltaic power and electrochemical energy storage after the limiting link at the n-1th differential step;

[0025] S23. Since the introduction of the limiting link leads to a nonlinear link, the large M method is used to linearize the nonlinear link, as shown below:

[0026]

[0027] The frequency deviation is added to the objective function, and the nonlinear constraint link including the min operation function is linearized.

[0028] Preferably, in step S3, a frequency modulation parameter optimization model is established, and the specific process is as follows:

[0029] S31. Based on the power generation cost and the station spare capacity, with the goal of maximizing the frequency support capability of the cluster, a frequency modulation parameter optimization objective function is established as follows:

[0030]

[0031] min(F1+F2+F3)(21)

[0032]

[0033] Among them, F1 is the system power generation cost, F2 is the system frequency regulation and standby cost, and F3 is the frequency deviation; Represent the power generation costs of thermal power, wind power, photovoltaic power, and electrochemical energy storage respectively; Respectively represent the frequency regulation and backup costs of wind power, photovoltaic power, and electrochemical energy storage; Respectively represent the frequency regulation reserve of wind power, photovoltaic power and electrochemical energy storage at the nth differential step; C ffr Represents the frequency deviation coefficient;

[0034] S32. Establish constraints based on the system's operational safety boundaries and frequency support requirements, as shown below:

[0035] P G +P W +P P +P B =P e (25)

[0036]

[0037]

[0038]

[0039] Δf min ≤Δf≤Δf max (38)

[0040] K W,min ≤K W ≤K W,max (39)

[0041] H W,min ≤H W ≤H W,max (40)

[0042] K P,min ≤K P ≤K P,max (41)

[0043] H P,min ≤H P ≤H P,max (42)

[0044] K B,min ≤K B ≤K B,max (43)

[0045] H B,min ≤H B ≤H B,max (44)

[0046] Among them, P G , P W , P P , P W Respectively represent the active output of thermal power, wind power, photovoltaic power, and electrochemical energy storage; P e Indicates the active power shortage of the power system; Respectively represent the upper and lower frequency regulation standby of thermal power, wind power, photovoltaic power, and electrochemical energy storage; Respectively represent the upper and lower limits of active output of thermal power, wind power, photovoltaic power, and electrochemical energy storage; Δf max , Δf min Respectively represent the upper and lower limits of frequency deviation; K W,max , K W,min , K p,max , K P,min , K B,max , K B,min Respectively represent the upper and lower limits of the equivalent primary frequency modulation gain of thermal power, wind power, photovoltaic power, and electrochemical energy storage; H W,max , H W,min , H p,max , H P,min , H B,max , HB,min Respectively represent the upper and lower limits of the inertia time constant of thermal power, wind power, photovoltaic power, and electrochemical energy storage;

[0047] Based on the above steps, the construction of the wind-solar cluster frequency regulation control parameter optimization model considering the wind-solar joint frequency regulation is completed. The model is solved by the commercial software GUROBI to obtain the frequency regulation control parameter optimization setting results of various power sources in the system.

[0048] Therefore, the present invention adopts the above-mentioned wind-solar cluster frequency regulation control parameter optimization method considering the joint frequency regulation of wind and solar, and performs frequency regulation parameter optimization calculation for the power generation system containing different frequency regulation resources of wind, solar and storage, so as to rationally utilize various frequency regulation resources, realize the coordinated complementarity of various frequency regulation resources, improve the frequency active support capability of the wind-solar cluster, and ensure the safe and stable operation of the power system.

[0049] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of a method for optimizing wind-solar cluster frequency modulation control parameters considering wind-solar joint frequency modulation of the present invention;

[0051] Figure 2 A system frequency response dynamic model block diagram of a wind-solar cluster frequency modulation control parameter optimization method considering wind-solar joint frequency modulation of the present invention;

[0052] Figure 3 A diagram showing the dynamic response result of the power system frequency in an embodiment of the present invention;

[0053] Figure 4 It is a result diagram of the total active power adjustment amount of the power system in an embodiment of the present invention;

[0054] Figure 5 This is a result diagram of wind power active power adjustment in an embodiment of the present invention;

[0055] Figure 6 This is a result diagram of photovoltaic active power adjustment in an embodiment of the present invention;

[0056] Figure 7 This is a diagram showing the result of energy storage active power adjustment in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0058] like Figure 1 As shown, a method for optimizing wind-solar cluster frequency regulation control parameters considering wind-solar joint frequency regulation includes the following steps:

[0059] S1. Characterize the frequency response characteristics of wind power, photovoltaic power, thermal power and energy storage power stations respectively, and establish a wind-solar cluster frequency response model including a limiting link.

[0060] S11, such as Figure 2 As shown, according to the external response characteristics of various power sources, the frequency response characteristics of wind power, photovoltaic power, thermal power and energy storage power stations are characterized respectively, as shown below:

[0061]

[0062] Where ΔP G , ΔP W , ΔP P , ΔP B Respectively represent the active power adjustment of thermal power, wind power, photovoltaic power and electrochemical energy storage; Respectively represent the active power adjustment of thermal power, wind power, photovoltaic power and electrochemical energy storage after the limit link; T R Represents the reheating time constant of the equivalent thermal power unit; F H Indicates the power ratio of the high-pressure cylinder of the equivalent unit; T W , T P , T B They represent the equivalent first-order inertia delay constants of the frequency control links of wind power, photovoltaic power, and electrochemical energy storage respectively; s is the complex variable of the Laplace transform.

[0063] S12. Considering the impact of the limiting links of various power sources on the dynamic frequency response, a frequency response model of the wind and solar cluster is established as follows:

[0064]

[0065]

[0066] Among them, H S represents the total inertia of the system, D represents the damping coefficient of the system, ΔP e Indicates the change in system active power; Respectively represent the frequency regulation and standby active power adjustment limits of thermal power, wind power, photovoltaic power, and electrochemical energy storage; K G , K W , K P , K B Respectively represent the equivalent primary frequency modulation gains of thermal power, wind power, photovoltaic power, and electrochemical energy storage; H W , H P , H B They represent the equivalent inertia time constants of wind power, photovoltaic power, and electrochemical energy storage respectively; Δf represents the frequency deviation of the wind and solar cluster after a disturbance occurs.

[0067] S2. Obtain the discretized form of the wind-solar cluster frequency response model based on the difference method.

[0068] S21. Based on the difference method, the difference equation of the system frequency deviation is established as follows:

[0069]

[0070] Where Δf n represents the system frequency deviation of the nth differential step, dn represents the differential step, ΔP G,n-1 , ΔP W,n-1 , ΔP P,n-1 , ΔP B,n-1 They respectively represent the active adjustment of thermal power, wind power, photovoltaic power and electrochemical energy storage at the n-1th differential step.

[0071] S22. Based on the differential method, the differential equations of active power adjustment after various power supply disturbances are established as follows:

[0072]

[0073]

[0074] in, They respectively represent the active adjustment of thermal power, wind power, photovoltaic power, and electrochemical energy storage after the limiting link at the n-1th differential step.

[0075] S23. Due to the introduction of the limiting link in the model, a nonlinear link appears. The large M method is used to linearize the nonlinear link, as shown below:

[0076]

[0077] The frequency deviation is added to the objective function, and the nonlinear constraint link including the min operation function is linearized.

[0078] S3. Considering the power generation cost and site spare capacity, a frequency regulation parameter optimization model is established with the goal of maximizing the frequency support capability of the cluster.

[0079] S31. Considering the power generation cost and the station spare capacity, with the goal of maximizing the frequency support capability of the cluster, the frequency modulation parameter optimization objective function is established as follows:

[0080]

[0081] min(F1+F2+F3)(21)

[0082]

[0083]

[0084] Among them, F1 is the system power generation cost, F2 is the system frequency regulation and standby cost, and F3 is the frequency deviation; Represent the power generation costs of thermal power, wind power, photovoltaic power, and electrochemical energy storage respectively; Respectively represent the frequency regulation and backup costs of wind power, photovoltaic power, and electrochemical energy storage; Respectively represent the frequency regulation reserve of wind power, photovoltaic power and electrochemical energy storage at the nth differential step; C ffr Indicates the frequency deviation coefficient.

[0085] S32. Establish constraints based on the system's operational safety boundaries and frequency support requirements, as shown below:

[0086] P G +P W +P P +P B =P e (25)

[0087]

[0088]

[0089]

[0090] Δf min ≤Δf≤Δf max (38)

[0091] K W,min ≤K W ≤K W,max (39)

[0092] H W,min ≤H W ≤H W,max (40)

[0093] K P,min ≤K P ≤K P,max (41)

[0094] H P,min ≤H P ≤H P,max (42)

[0095] K B,min ≤K B ≤K B,max (43)

[0096] H B,min ≤H B ≤H B,max(44)

[0097] Among them, P G , P W , P P , P W Respectively represent the active output of thermal power, wind power, photovoltaic power, and electrochemical energy storage; P e Indicates the active power shortage of the power system; Respectively represent the upper and lower frequency regulation standby of thermal power, wind power, photovoltaic power, and electrochemical energy storage; Respectively represent the upper and lower limits of active output of thermal power, wind power, photovoltaic power, and electrochemical energy storage; Δf max , Δf min Respectively represent the upper and lower limits of frequency deviation; K W,max , K W,min , K p,max , K P,min , K B,max , K B,min Respectively represent the upper and lower limits of the equivalent primary frequency modulation gain of thermal power, wind power, photovoltaic power, and electrochemical energy storage; H W,max , H W,min , H p,max , H P,min , H B,max , H B,min They respectively represent the upper and lower limits of the inertia time constants of thermal power, wind power, photovoltaic power, and electrochemical energy storage.

[0098] Based on the above steps, the construction of the wind-solar cluster frequency regulation control parameter optimization model considering the wind-solar joint frequency regulation is completed. The model is solved by the commercial software GUROBI to obtain the frequency regulation control parameter optimization setting results of various power sources in the system.

[0099] Example

[0100] This embodiment is a simulation of the primary frequency regulation scenario of the wind and solar cluster. The parameters of the system and frequency regulation function are set as follows: the total capacity is 1066MW, thermal power 400MW, electrochemical energy storage 33MW·2h, wind power 500MW, photovoltaic 100MW. The operating points are: thermal power 280MW, electrochemical energy storage 15MW·2h, wind power 300MW, photovoltaic 60MW. The DC channel transmits a total of 6100MW. At this time, the maximum wind power can generate 420MW, and the maximum photovoltaic can generate 80MW; the reserve capacity is increased: thermal power 120MW, electrochemical energy storage 18MW·2h, wind power 120MW, photovoltaic 20MW. The fixed parameters of the equivalent primary frequency regulation gain of each type of power source are set to 33, the fixed parameters of the equivalent inertia time constant of each type of power source are set to 8s, the reheating time constant of the equivalent unit of thermal power is set to 13s, and the high-pressure cylinder power ratio of the equivalent unit is set to 0.3. The equivalent first-order inertia delay constants of the frequency control links of wind power, photovoltaic power, and electrochemical energy storage are set to 1.2s, 0.9s, and 0.03s, respectively; the disturbance is set to 20% of the power loss at the sending end.

[0101] A system frequency response dynamic model is established for the wind-solar cluster simulation system, and the discretization form of the model is obtained by the differential method to obtain the discrete expressions of the system frequency deviation after disturbance and the active power adjustment of various power sources. In this embodiment, the differential step size is 0.02s. When calling a commercial solver for optimization calculation, each variable needs to be initialized. In this embodiment, the initial system frequency deviation is 0, and the initial additional active power of the equivalent synchronous unit is 0.

[0102] Constraints are established based on the system frequency stability requirements. In this embodiment, the maximum system frequency deviation constraint is set to 0.2 Hz, the upper and lower limits of the equivalent primary frequency modulation gains of various power supplies are set to 50 and 10, and the upper and lower limits of the equivalent inertia time constant are set to 12s and 5s.

[0103] The commercial solver is called to optimize the objective function, calculate the optimal solution of each variable that meets the constraints, and output the calculation results of each differential step, as shown in Table 1.

[0104] Table 1 Optimization results of wind-solar cluster frequency modulation control parameters and some key parameter values ​​of frequency response curve

[0105] Fixed parameters Parameter Optimization <![CDATA[K W ]]> 33 50 <![CDATA[K P ]]> 33 50 <![CDATA[K B ]]> 33 47.62 <![CDATA[H W ]]> 8 12 <![CDATA[H P ]]> 8 12 <![CDATA[H B ]]> 8 12

[0106] At the same time, the dynamic response results of the power system frequency are as follows: Figure 3 As shown; the total active power of the wind and solar cluster power system, wind power active power, photovoltaic active power and energy storage active power adjustment results are generated, as shown Figure 4 , Figure 5 , Figure 6 and Figure 7 shown.

[0107] Therefore, the present invention adopts the above-mentioned wind-solar cluster frequency regulation control parameter optimization method considering the joint frequency regulation of wind and solar, and performs frequency regulation parameter optimization calculation for the power generation system containing different frequency regulation resources of wind, solar and storage, so as to rationally utilize various frequency regulation resources, realize the coordinated complementarity of various frequency regulation resources, improve the frequency active support capability of the wind-solar cluster, and ensure the safe and stable operation of the power system.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for optimizing wind-solar cluster frequency modulation control parameters considering wind-solar joint frequency modulation, characterized in that: The following steps are involved: S1. Characterize the frequency response characteristics of wind power, photovoltaic power, thermal power and energy storage power stations respectively, and establish a wind-solar cluster frequency response model including a limiting link; S2. Obtain the discretized form of the wind-solar cluster frequency response model based on the difference method; S3. Based on the power generation cost and site spare capacity, a frequency regulation parameter optimization model is established with the goal of maximizing the frequency support capability of the cluster.

2. The method for optimizing wind-solar cluster frequency modulation control parameters considering wind-solar joint frequency modulation according to claim 1 is characterized in that: In step S1, a wind-solar cluster frequency response model including a limiting link is established. The specific process is as follows: S11. Based on the external response characteristics of various power sources, the frequency response characteristics of wind power, photovoltaic power, thermal power and energy storage power stations are described as follows: Among them, ΔP G , ΔP W , ΔP P , ΔP B Respectively represent the active power adjustment of thermal power, wind power, photovoltaic power and electrochemical energy storage; Respectively represent the active power adjustment of thermal power, wind power, photovoltaic power and electrochemical energy storage after the limit link; T R Represents the reheating time constant of the equivalent thermal power unit; F H Indicates the power ratio of the high-pressure cylinder of the equivalent unit; T W , T P , T B They represent the equivalent first-order inertia delay constants of the frequency control links of wind power, photovoltaic power, and electrochemical energy storage, respectively; s is the complex variable of Laplace transform; S12. Based on the influence of the limiting links of various power sources on the dynamic response of frequency, a frequency response model of the wind and solar cluster is established as follows: Among them, H S represents the total inertia of the system, D represents the damping coefficient of the system, ΔP e Indicates the change in system active power; Respectively represent the frequency regulation and standby active power adjustment limits of thermal power, wind power, photovoltaic power, and electrochemical energy storage; K G , K W , K P , K B Respectively represent the equivalent primary frequency modulation gains of thermal power, wind power, photovoltaic power, and electrochemical energy storage; H W , H P , H B They represent the equivalent inertia time constants of wind power, photovoltaic power, and electrochemical energy storage respectively; Δf represents the frequency deviation of the wind and solar cluster after a disturbance occurs.

3. The method for optimizing wind-solar cluster frequency modulation control parameters considering wind-solar joint frequency modulation according to claim 1 is characterized in that: In step S2, a discretized form of the wind-solar cluster frequency response model is obtained based on a difference method, including the following steps: S21. Based on the difference method, the difference equation of the system frequency deviation is established as follows: Where Δf n represents the system frequency deviation of the nth differential step, dn represents the differential step, ΔP G,n-1 , ΔP W,n-1 , ΔP P,n-1 , ΔP B,n-1 Respectively represent the active adjustment of thermal power, wind power, photovoltaic power, and electrochemical energy storage at the n-1th differential step; S22. Based on the differential method, the differential equations of active power adjustment after various power supply disturbances are established as follows: in, They represent the active power adjustment of thermal power, wind power, photovoltaic power and electrochemical energy storage after the limiting link at the n-1th differential step; S23. Since the introduction of the limiting link leads to a nonlinear link, the large M method is used to linearize the nonlinear link, as shown below: The frequency deviation is added to the objective function, and the nonlinear constraint link including the min operation function is linearized.

4. The method for optimizing wind-solar cluster frequency modulation control parameters considering wind-solar joint frequency modulation according to claim 1 is characterized in that: In step S3, a frequency modulation parameter optimization model is established, and the specific process is as follows: S31. Based on the power generation cost and the station spare capacity, with the goal of maximizing the frequency support capability of the cluster, a frequency modulation parameter optimization objective function is established as follows: min(F1+F2+F3)(21) Among them, F1 is the system power generation cost, F2 is the system frequency regulation and standby cost, and F3 is the frequency deviation; Represent the power generation costs of thermal power, wind power, photovoltaic power, and electrochemical energy storage respectively; Respectively represent the frequency regulation and backup costs of wind power, photovoltaic power, and electrochemical energy storage; Respectively represent the frequency regulation reserve of wind power, photovoltaic power and electrochemical energy storage at the nth differential step; C ffr Represents the frequency deviation coefficient; S32. Establish constraints based on the system's operational safety boundaries and frequency support requirements, as shown below: P G +P W +P P +P B =P e (25) Δf min ≤Δf≤Δf max (38) K W,min ≤K W ≤K W,max (39) H W,min ≤H W ≤H W,max (40) K P,min ≤K P ≤K P,max (41) H P,min ≤H P ≤H P,max (42) K B,min ≤K B ≤K B,max (43) H B,min ≤H B ≤H B,max (44) Among them, P G , P W , P P , P W Respectively represent the active output of thermal power, wind power, photovoltaic power, and electrochemical energy storage; P e Indicates the active power shortage of the power system; Respectively represent the upper and lower frequency regulation standby of thermal power, wind power, photovoltaic power, and electrochemical energy storage; Respectively represent the upper and lower limits of active output of thermal power, wind power, photovoltaic power, and electrochemical energy storage; Δf max , Δf min Respectively represent the upper and lower limits of frequency deviation; K W,max , K W,min , K p,max , K P,min , K B,max , K B,min Respectively represent the upper and lower limits of the equivalent primary frequency modulation gain of thermal power, wind power, photovoltaic power, and electrochemical energy storage; H W,max , H W,min , H p,max , H P,min , H B,max , H B,min Respectively represent the upper and lower limits of the inertia time constant of thermal power, wind power, photovoltaic power, and electrochemical energy storage; Based on the above steps, the construction of the wind-solar cluster frequency regulation control parameter optimization model considering the wind-solar joint frequency regulation is completed. The model is solved by the commercial software GUROBI to obtain the frequency regulation control parameter optimization setting results of various power sources in the system.

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