Double-layer optimization control method for multiple energy storage power stations participating in peak and frequency regulation

Abstract

This paper proposes a two-tiered optimization control method for multiple energy storage plants participating in peak and frequency regulation. First, to address the significant resource waste associated with battery energy storage systems (BESSs) that only participate in peak or frequency regulation, a two-tiered optimization control method for multiple BESSs is proposed. This method divides each BESS into multiple operating zones based on its SOC (System On Capacity) (SOC) status, thereby improving BESS utilization and economic efficiency. Then, during BESS peak regulation, a model is established to minimize net load deviation and optimize BESS peak regulation economics. This reduces peak-to-valley load variations in the power system, reduces wind curtailment, and achieves cost-effective peak regulation. Finally, during BESS frequency regulation, SOC recovery and BESS output deviation penalties are comprehensively considered to improve frequency regulation accuracy and the SOC status of each BESS, thereby maintaining system frequency stability.

Description

Technical Field

[0001] The present invention relates to the technical field of multiple energy storage power stations participating in peak-shaving and frequency regulation, and in particular to a double-layer optimization control method for multiple energy storage power stations participating in peak-shaving and frequency regulation. Background Art

[0002] In recent years, the rapid development of renewable energy, represented by wind power and photovoltaics, has effectively alleviated global energy shortages and environmental pollution, creating opportunities for energy structure transformation. However, the large-scale integration of renewable energy into the grid has posed severe challenges to the grid's peak and frequency regulation, threatening its safe and stable operation. Large-scale battery energy storage technology, with its excellent static characteristics, can mitigate peak-to-valley variations by combining low storage with high generation, effectively alleviating the grid's peak-to-peak regulation pressure. Furthermore, battery energy storage systems (BESS) offer extremely fast response speeds, enabling rapid response to grid frequency changes, resulting in more precise and efficient regulation. The excellent active power regulation capabilities of BESS can comprehensively enhance the peak and frequency regulation capabilities of power grids in large, renewable energy-rich regions, thereby increasing renewable energy consumption and generating significant economic benefits. However, BESSs are currently primarily used for single peak and frequency regulation scenarios, operating only during specific periods and remaining idle at other times, significantly wasting BESS resources. Therefore, it is necessary to develop a rational, optimized control strategy for BESS participation in grid peak and frequency regulation to effectively implement peak and frequency regulation while minimizing system economic costs. Furthermore, the frequent charging and discharging of BESSs during peak and frequency regulation shortens their cycle life. Therefore, when developing an optimized control strategy for BESSs participating in grid peak and frequency regulation, it is necessary to establish a model that accurately reflects both the BESS lifecycle cost and the effectiveness of frequency and peak regulation.

[0003] The existing technology has the following defects and deficiencies:

[0004] First, existing BESS peak-shaving and frequency-regulation control strategies are characterized by decentralized, independent control, often targeting a single BESS. However, in reality, regional power grids contain multiple BESSs. Therefore, controlling only a single BESS for peak-shaving and frequency-regulation is not practical. Second, existing multi-BESS control strategies are only applied to single peak-shaving or frequency-regulation scenarios, significantly wasting BESS resources. Finally, for peak-shaving and frequency-regulation control strategies involving multiple BESSs, existing methods fail to coordinate the charging and discharging power of all BESSs in a region, potentially leading to unnecessary interoperability between multiple BESSes and unnecessary charging and discharging. Furthermore, it is impossible to compare the operating costs of different BESSes, making it difficult to develop the most economically efficient frequency-regulation and peak-shaving solutions. Summary of the Invention

[0005] To address the shortcomings and deficiencies of existing technologies, the present invention proposes a dual-tiered optimization control method for peak and frequency regulation involving multiple energy storage power stations. First, addressing the significant waste of resources associated with battery energy storage systems (BESSs) that only participate in peak or frequency regulation, a dual-tiered optimization control method for peak and frequency regulation involving multiple BESSs is proposed. This method divides the BESSs into multiple operating zones based on their SOC status, thereby improving BESS utilization and economic efficiency. Subsequently, during BESS peak regulation, a model is established to minimize net load deviation and optimize BESS peak regulation economics. This reduces the peak-to-valley load variation in the power system, reduces wind curtailment, and achieves cost-effective peak regulation. Finally, during BESS frequency regulation, SOC recovery and BESS output deviation penalties are comprehensively considered to improve frequency regulation accuracy and the SOC status of each BESS, thereby maintaining system frequency stability.

[0006] Its design points include:

[0007] (1) The present invention proposes a dual-layer optimization control method for peak and frequency regulation of multiple BESSs, which divides each BESS into multiple working areas according to its state of charge (SOC), thereby improving the utilization rate of the BESSs.

[0008] (2) During the BESS peak load regulation process, a model with minimum net load deviation and optimal BESS peak load regulation economy is established, which reduces the peak-to-valley difference of the power system load.

[0009] (3) During the BESS participation in frequency regulation, the SOC recovery and BESS output deviation penalty are comprehensively considered to improve the frequency regulation accuracy and the SOC status of each BESS.

[0010] The present invention specifically adopts the following technical solutions:

[0011] A dual-layer optimization control method for multiple energy storage power stations participating in peak and frequency regulation, characterized by comprising the following steps:

[0012] Step S1: Obtain the net load of the power grid, input the operating parameters of the conventional units and the parameters of the multi-energy storage power station BESS;

[0013] Step S2: Determine the peak shaving and valley filling area based on the net load data, and determine the frequency difference based on the real-time load disturbance data;

[0014] Step S3: When the net load data is outside the peak shaving line and the valley filling line, it indicates that the BESS is needed for peak shaving. If the SOC of the BESS is between 0.2 and 0.8, the BESS peak shaving power is allocated according to the upper-level objective function to determine the output and SOC values of multiple BESSs.

[0015] Step S4: The SOCs of multiple BESSs are obtained based on the upper-level objective function, and a determination is made as to whether the frequency deviation exceeds the dead zone. If so, all BESSs participating in peak shaving are frequency-regulated, and BESS frequency regulation power is allocated based on the lower-level objective function. If the number of BESSs does not meet the frequency regulation requirements, other BESSes are mobilized, and the frequency regulation power of the multiple BESSs is allocated directly based on the lower-level objective function to determine the output and SOC of each BESS.

[0016] Step S5: Output the optimal output and SOC value of all BESSs;

[0017] The upper model takes maximizing the BESS peak-shaving operation benefit and minimizing the net load fluctuation as the objective function. The operation benefit includes BESS loss cost, wind curtailment cost and environmental benefit.

[0018] The lower-level model passes on the peak-shaving output and SOC of each BESS from the upper-level model, and continues to optimize the BESS output according to the frequency regulation constraints and objective functions. The lower-level objective functions include: SOC recovery and frequency regulation tracking accuracy.

[0019] Furthermore, the upper layer objective function is constructed as follows:

[0020] maxF1=I v -P SDv (1)

[0021]

[0022]

[0023]

[0024] Where, F1 is the upper layer objective function; I v is the operating income of BESS; P SDv is the net load deviation; P netload,t is the net load power at time t; P netload,ver is the average net load; I is the total number of BESS; C i is the total cost of the i-th BESS; Benefits of BESS operation environment; C ωo is the wind curtailment cost; T is the total number of sampling points in the dispatching day;

[0025] Considering that economic goals and peak load regulation goals are two goals of different dimensions, the economic conversion coefficient ω1 is introduced to project the peak load reduction and valley filling goals into the economic dimension, thereby transforming the multi-objective optimization model into a single-objective optimization model. At this time, the upper-level objective function F1 is expressed as:

[0026] maxF1=I v-ω1P SDv (5).

[0027] Furthermore, the upper layer model includes:

[0028] Loss costs, including energy loss costs and life-cycle loss costs Specifically, as shown in formulas (6)-(8):

[0029]

[0030]

[0031]

[0032] Where, E rate,i is the rated capacity of the i-th BESS, c e is the on-grid electricity price; Δt is the scheduling time step, which is 1 minute; η C ,η D are the charging and discharging efficiency of the i-th BESS respectively; N 0,i is the equivalent cycle number of the i-th BESS at 100% charge and discharge depth; k p is a constant, which can be obtained by fitting the relationship between BESS cycle number and discharge depth using the actual operating data provided by the battery manufacturer;

[0033] Cost of curtailed wind power:

[0034]

[0035] Where, θ is the wind abandonment penalty coefficient; P wind,t is the wind power at time t; P windjn,t is the wind power grid-connected power at time t;

[0036] Environmental benefits, the formula is as follows:

[0037]

[0038] Where: K is the total amount of pollutants emitted by the unit to produce electricity; ζ PO,k is the emission density of the kth pollutant per unit of electricity produced; P price,k is the unit emission cost of the kth pollutant;

[0039] In the upper model, the BESS should also meet the rated power constraint and the SOC upper and lower limit constraints, as shown in equations (11)-(13), where SOC i (t) is the actual SOC value of the i-th BESS at time t;

[0040] Rated power constraints:

[0041]

[0042] Where, P C,i and P D,i is the maximum charge and discharge power of the i-th BESS; and They are the charging and discharging state variables of the energy storage in period t, which are 0-1 variables. In the discharge state

[0043] SOC constraints:

[0044]

[0045] Where, and are the lower and upper limits of the SOC of the i-th BESS; SOC i (t) is the SOC of the i-th BESS at time t;

[0046] Balance constraints of BESS charge and discharge capacity:

[0047] For a BESS that only participates in peak-shaving scenarios, the amount of electricity released by peak shaving must be equal to the amount of electricity absorbed by valley filling on a daily basis. This means the BESS must complete a complete charge-discharge balance within a day, meaning the SOC of the BESS must return to its initial state. The charge-discharge balance constraint is shown in the following equation:

[0048]

[0049] Where t1 is the total charging time of BESS per day, and t2 is the total discharging time of BESS per day.

[0050] Furthermore, the construction of the lower-level objective function first introduces the SOC weight to realize the distribution of the frequency modulation power between each BESS according to their respective SOC values, as shown in formula (14). In this way, the BESS with a higher SOC value has a larger discharge weight and a smaller charging weight, while the BESS with a lower SOC value has a larger charging weight and a smaller discharge weight, so that the BESS with a low power is given priority for charging and the BESS with a high power is given priority for discharging, thereby restoring the SOC value of the BESS:

[0051]

[0052] Where, are the charge and discharge SOC weights of unit j in power station i at time t;

[0053] Then normalize the weights:

[0054]

[0055] Where, are the normalized charge and discharge SOC weights respectively;

[0056] The SOC of BESS at time t and the ideal state SOC ideal The objective function is the one with the smallest absolute value of the difference, as shown in formula (16):

[0057]

[0058] Set BESS output deviation penalty for:

[0059]

[0060] Where c dev The penalty price for unit power shortage is greater than the on-grid electricity price. It is the total automatic frequency regulation power generation control command of BESS;

[0061] According to the processing method of the upper-level objective function, the economic conversion coefficient ω2 is introduced to transform the multi-objective optimization model into a single-objective optimization model; the objective function F2 is finally expressed as:

[0062]

[0063] Furthermore, the constraints of the lower-level model are that the BESS needs to meet the rated power constraint and the SOC upper and lower limit constraints when participating in frequency regulation:

[0064] Rated power constraints:

[0065]

[0066] Where, P C,i and P D,i is the maximum charge and discharge power of the i-th BESS; and They are the charging and discharging state variables of the energy storage in period t, which are 0-1 variables. In the discharge state

[0067] SOC constraints:

[0068]

[0069] Where, SOC i (t) is the actual SOC value of the i-th BESS at time t; and are the lower and upper limits of the SOC of the i-th BESS; SOC i(t) is the SOC of the i-th BESS at time t.

[0070] The beneficial effects of the present invention and its preferred embodiments include:

[0071] 1) To address the problem that BESS only participates in peak shaving or frequency regulation, which greatly wastes its resources, multiple BESSs can be implemented to perform reasonable peak shaving and frequency regulation according to their SOC sizes, thereby improving the utilization and economy of BESS.

[0072] 2) It can comprehensively consider peak-shaving economy and net load deviation, convert multiple objectives into a single objective by introducing weights, reduce wind curtailment, and achieve economically effective peak-shaving.

[0073] 3) It can achieve that the BESS with a higher SOC value has a larger discharge weight and a smaller charging weight, and the BESS with a lower SOC value has a larger charging weight and a smaller discharge weight, and maximizes the accuracy of tracking the frequency modulation signal, so as to ensure the accuracy of restoring the SOC value of each BESS and tracking the frequency modulation signal, and maintain the stability of the system frequency. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0075] Figure 1 Schematic diagram of the SOC area according to an embodiment of the present invention.

[0076] Figure 2 This is a flow chart of multiple BESSs participating in peak load regulation and frequency regulation control according to an embodiment of the present invention. DETAILED DESCRIPTION

[0077] Hereinafter, specific embodiments of the present application will be described in detail with reference to the accompanying drawings. Based on these detailed descriptions, those skilled in the art will be able to clearly understand the present application and implement the present application. Without violating the principles of the present application, the features of different embodiments may be combined to obtain new implementations, or certain features of certain embodiments may be substituted to obtain other preferred implementations.

[0078] To make the features and advantages of this patent more clearly understood, the following embodiments are specifically described in detail with reference to the accompanying drawings:

[0079] In this embodiment, first, to address the significant waste of resources caused by battery energy storage systems (BESSs) only participating in peak or frequency regulation, a dual-tiered optimization control method for peak and frequency regulation of multiple BESSs is proposed. This method divides the BESSs into multiple operating zones based on their SOC status, thereby improving BESS utilization and economic efficiency. Then, during BESS peak regulation, a model is established to minimize net load deviation and optimize BESS peak regulation economics. This reduces the peak-to-valley load difference in the power system, reduces wind curtailment, and achieves economically efficient peak regulation. Finally, during BESS frequency regulation, SOC recovery and BESS output deviation penalties are comprehensively considered to improve frequency regulation accuracy and the SOC status of each BESS, thereby maintaining system frequency stability.

[0080] 1 Upper model

[0081] The upper-level model mainly takes maximizing the BESS peak-shaving operation benefits and minimizing the net load fluctuation as the objective function. The operation benefits mainly include BESS loss costs, wind curtailment costs and environmental benefits.

[0082] maxF1=I v -P SDv (1)

[0083]

[0084]

[0085]

[0086] Where F1 is the objective function; I v is the operating income of BESS; P SDv is the net load deviation; P netload,t is the net load power at time t; P netload,ver is the average net load; I is the total number of BESS; C i is the total cost of the i-th BESS; Benefits of BESS operation environment; C ωo is the cost of wind curtailment; T is the total number of sampling points in the scheduling day.

[0087] Since economic goals and peak load regulation goals are two different dimensions, we introduce the economic conversion coefficient ω1 to project the peak load reduction and valley filling goals into the economic dimension, thereby transforming the multi-objective optimization model into a single-objective optimization model. The objective function F1 can be expressed as:

[0088] maxF1=I v -ω1P SDv (5)

[0089] 1) Loss cost

[0090] When BESS participates in grid peak regulation and frequency regulation, power loss is an important factor that cannot be ignored, affecting the charging and discharging behavior of BESS and the frequency regulation and peak regulation effects. The main loss cost is energy loss cost. and life-cycle loss costs Specifically, as shown in formulas (6)-(8):

[0091]

[0092]

[0093]

[0094] Where, E rate,i is the rated capacity of the i-th BESS, c e is the on-grid electricity price, which is 520 yuan / MWh in this embodiment, without considering the impact of time-of-use electricity prices on frequency regulation costs; Δt is the scheduling time step, which is 1 minute; η C ,η D are the charging and discharging efficiency of the i-th BESS respectively; N 0,i is the equivalent cycle number of the i-th BESS at 100% charge and discharge depth; k p is a constant, which can be obtained by fitting the relationship between BESS cycle number and discharge depth using actual operating data provided by the battery manufacturer. It is generally between 0.8 and 2.1, and is set to 1 in this embodiment.

[0095] 2) Wind curtailment costs

[0096] In order to increase the acceptance of wind power and reduce the system wind curtailment rate, a wind curtailment penalty cost is established.

[0097]

[0098] Where, θ is the wind abandonment penalty coefficient; P wind,t is the wind power at time t; P windjn,t is the wind power grid connected at time t.

[0099] 3) Environmental benefits

[0100] The environmental benefit formula is as follows:

[0101]

[0102] Where: K is the total amount of pollutants emitted by the unit to produce electricity; ζ PO,k is the emission density of the kth pollutant per unit of electricity produced; P price,k is the unit emission cost of the kth pollutant.

[0103] (2) Constraints

[0104] The BESS should also meet the rated power constraint and the SOC upper and lower limit constraints, as shown in equations (11)-(13), where SOC i (t) is the actual SOC value of the i-th BESS at time t.

[0105] 1) Rated power constraints

[0106]

[0107] Where, P C,i and P D,i is the maximum charging and discharging power of the i-th BESS, which is generally equal to the rated power of the energy storage station; and They are the charging and discharging state variables of the energy storage in period t, which are 0-1 variables. In the discharge state

[0108] 2) SOC Constraint

[0109]

[0110] Where, and are the lower and upper limits of the SOC of the i-th BESS; SOC i (t) is the SOC of the i-th BESS at time t;

[0111] 3) Balance constraints of BESS charge and discharge

[0112] For a BESS that only participates in peak-shaving scenarios, the amount of energy released by peak-shaving must be equal to the amount of energy absorbed by valley-filling on a daily basis. This means that the BESS must complete a complete charge-discharge balance within a day, meaning that the BESS's SOC must return to its initial state. The charge-discharge balance constraint is shown in Equation (13).

[0113]

[0114] Where t1 is the total charging time of BESS per day, and t2 is the total discharging time of BESS per day.

[0115] 2 Lower model

[0116] The lower layer mainly optimizes the output of BESS based on the peak-shaving output and SOC of each BESS transmitted through the upper layer model and the frequency regulation constraints and objective function.

[0117] 1) Objective function

[0118] The lower-level objective function mainly includes two parts: SOC recovery and frequency modulation tracking accuracy. First, the SOC weight is introduced to realize the frequency modulation power distribution among each BESS according to their respective SOC values, as shown in formula (14). This formula can make the BESS with a higher SOC value have a larger discharge weight and a smaller charging weight, and the BESS with a lower SOC value have a larger charging weight and a smaller discharge weight, so that the BESS with a low power is given priority for charging and the BESS with a high power is given priority for discharging, thereby restoring the SOC value of the BESS:

[0119]

[0120] Where, are the charge and discharge SOC weights of unit j in power station i at time t. The weights are normalized as follows:

[0121]

[0122] Where, are the normalized charge and discharge SOC weights respectively.

[0123] The SOC of BESS at time t and the ideal state SOC ideal The objective function is the one with the smallest absolute value of the difference, as shown in formula (16):

[0124]

[0125] In the formula, in order to improve the bidirectional regulation capability of energy storage, SOC ideal Take 0.5.

[0126] Secondly, if the BESS output deviates from the AGC instructions, not only will it incur frequency regulation costs, it will also generate a series of auxiliary service costs such as standby, and even endanger the frequency security of the power grid. Therefore, it is necessary to punish the deviation power, and the penalty should be severe, so as to approximately represent the adverse impact of the BESS deviation from the dispatch plan on the power grid.

[0127] BESS output deviation penalty for:

[0128]

[0129] Where c dev The penalty price for unit power shortage is twice the on-grid electricity price, i.e. 1040 yuan / MW. It is the total automatic frequency regulation power generation control command of BESS.

[0130] According to the processing method of the upper objective function, the economic conversion coefficient ω2 is introduced to transform the multi-objective optimization model into a single-objective optimization model. The objective function F2 can be expressed as

[0131]

[0132] 2) Constraints

[0133] When participating in frequency regulation, BESS needs to meet the rated power constraint and the SOC upper and lower limit constraints, as shown in Equations (11) and (12).

[0134] 3 Method flow

[0135] like Figure 1 、 Figure 2 As shown, based on the design of the above model, the specific steps of the multi-BESS peak and frequency regulation optimization control method provided in this embodiment are as follows:

[0136] Step S1: First, obtain the net load of the power grid and input the operating parameters of the conventional units and the parameters of the BESS;

[0137] Step S2: Determine the peak shaving and valley filling area based on the net load data, and determine the frequency difference based on the real-time load disturbance data;

[0138] Step S3: When the net load data is outside the peak shaving line and the valley filling line, it means that BESS is needed to perform peak shaving. According to the SOC area diagram, Figure 1 ,If the SOC of BESS is between 0.2 and 0.8, the BESS peak-shaving power is allocated according to the upper-level objective function, and the values of multiple BESS outputs and SOCs are determined;

[0139] Step S4: The SOCs of multiple BESSs are obtained based on the upper-level objective function, and a determination is made as to whether the frequency deviation exceeds the dead zone. If so, all BESSs participating in peak shaving are frequency-regulated, and BESS frequency regulation power is allocated based on the lower-level objective function. If the number of BESSs does not meet the frequency regulation requirements, other BESSes are mobilized, and the frequency regulation power of the multiple BESSs is allocated directly based on the lower-level objective function to determine the output and SOC of each BESS.

[0140] Step S5: Output the optimal output and SOC values of all BESSs.

[0141] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

[0142] This patent is not limited to the above-mentioned optimal implementation method. Anyone can derive various other forms of multi-energy storage power stations participating in peak-shaving and frequency-regulating dual-layer optimization control methods based on the inspiration of this patent. All equivalent changes and modifications made within the scope of the patent application of this invention should be covered by this patent.

Claims

1. A dual-layer optimization control method for multiple energy storage power stations participating in peak and frequency regulation, characterized in that: The following steps are involved: Step S1: Obtain the net load of the power grid, input the operating parameters of the conventional units and the parameters of the multi-energy storage power station BESS; Step S2: Determine the peak shaving and valley filling area based on the net load data, and determine the frequency difference based on the real-time load disturbance data; Step S3: When the net load data is outside the peak shaving line and the valley filling line, it indicates that the BESS is needed for peak shaving. If the SOC of the BESS is between 0.2 and 0.8, the BESS peak shaving power is allocated according to the upper-level objective function to determine the output and SOC values of multiple BESSs. Step S4: The SOCs of multiple BESSs are obtained based on the upper-level objective function, and a determination is made as to whether the frequency deviation exceeds the dead zone. If so, all BESSs participating in peak shaving are frequency-regulated, and BESS frequency regulation power is allocated based on the lower-level objective function. If the number of BESSs does not meet the frequency regulation requirements, other BESSes are mobilized, and the frequency regulation power of the multiple BESSs is allocated directly based on the lower-level objective function to determine the output and SOC of each BESS. Step S5: Output the optimal output and SOC value of all BESSs; The upper model takes maximizing the BESS peak-shaving operation benefit and minimizing the net load fluctuation as the objective function. The operation benefit includes BESS loss cost, wind curtailment cost and environmental benefit. The lower-level model uses the peak-shaving output and SOC of each BESS transmitted by the upper-level model to optimize the BESS output according to the frequency regulation constraints and objective functions. The lower-level objective functions include: SOC recovery and frequency regulation tracking accuracy. The upper objective function is constructed as follows: maxF1=I v -P SDv (1) Where, F1 is the upper layer objective function; I v is the operating income of BESS; P SDv is the net load deviation; P netload,t is the net load power at time t; P netload,ver is the average net load; I is the total number of BESS; C i is the total cost of the i-th BESS; Benefits of BESS operation environment; C ωo is the wind curtailment cost; T is the total number of sampling points in the dispatching day; Considering that economic goals and peak load regulation goals are two goals of different dimensions, the economic conversion coefficient ω1 is introduced to project the peak load reduction and valley filling goals into the economic dimension, thereby transforming the multi-objective optimization model into a single-objective optimization model. At this time, the upper-level objective function F1 is expressed as: maxF1=I v -ω1P SDv (5)。 2. The dual-layer optimization control method for multiple energy storage power stations participating in peak and frequency regulation according to claim 1 is characterized in that: The upper-level model includes: Loss costs, including energy loss costs and life-cycle loss costs Specifically, as shown in formulas (6)-(8): Where, E rate,i is the rated capacity of the i-th BESS, c e is the on-grid electricity price; Δt is the scheduling time step, which is 1 minute; η C ,η D are the charging and discharging efficiency of the i-th BESS respectively; N 0,i is the equivalent cycle number of the i-th BESS at 100% charge and discharge depth; k p is a constant, which can be obtained by fitting the relationship between BESS cycle number and discharge depth using the actual operating data provided by the battery manufacturer; Cost of curtailed wind power: Where, θ is the wind abandonment penalty coefficient; P wind,t is the wind power at time t; P windjn,t is the wind power grid-connected power at time t; Environmental benefits, the formula is as follows: Where: K is the total amount of pollutants emitted by the unit to produce electricity; ζ PO,k is the emission density of the kth pollutant per unit of electricity produced; P price,k is the unit emission cost of the kth pollutant; In the upper model, the BESS should also meet the rated power constraint and the SOC upper and lower limit constraints, as shown in equations (11)-(13), where SOC i (t) is the actual SOC value of the i-th BESS at time t; Rated power constraints: Where, P C,i and P D,i is the maximum charge and discharge power of the i-th BESS; and They are the charging and discharging state variables of the energy storage in period t, which are 0-1 variables. In the discharge state SOC constraints: Where, and are the lower and upper limits of the SOC of the i-th BESS; SOC i (t) is the SOC of the i-th BESS at time t; Balance constraints of BESS charge and discharge capacity: For BESS that only participates in peak load regulation, the amount of electricity released by peak load reduction needs to be equal to the amount of electricity absorbed by valley load filling on a daily basis. That is, the BESS needs to complete a complete charge-discharge balance within one day, that is, the SOC of the BESS needs to return to its initial state. The charge-discharge balance constraint is shown in Equation (13). Where t1 is the total charging time of BESS per day, and t2 is the total discharging time of BESS per day.

3. The dual-layer optimization control method for multiple energy storage power stations participating in peak and frequency regulation according to claim 1 is characterized in that: The construction of the lower-level objective function first introduces the SOC weight to realize the distribution of the frequency modulation power between each BESS according to their respective SOC values, as shown in formula (14). In this way, the BESS with a higher SOC value has a larger discharge weight and a smaller charging weight, while the BESS with a lower SOC value has a larger charging weight and a smaller discharge weight. This realizes that the BESS with low power is charged first and the BESS with high power is discharged first, thereby restoring the SOC value of the BESS: Where, are the charge and discharge SOC weights of unit j in power station i at time t; Then normalize the weights: Where, are the normalized charge and discharge SOC weights respectively; The SOC of BESS at time t and the ideal state SOC ideal The objective function is the one with the smallest absolute value of the difference, as shown in formula (16): Set BESS output deviation penalty for: Where c dev The penalty price for unit power shortage is higher than the on-grid electricity price. It is the total automatic frequency regulation power generation control command of BESS; According to the processing method of the upper-level objective function, the economic conversion coefficient ω2 is introduced to transform the multi-objective optimization model into a single-objective optimization model; the objective function F2 is finally expressed as:

4. The dual-layer optimization control method for multiple energy storage power stations participating in peak and frequency regulation according to claim 3 is characterized in that: The constraints of the lower-level model are that the BESS must meet the rated power constraint and the SOC upper and lower limit constraints when participating in frequency regulation: Rated power constraints: Where, P C,i and P D,i is the maximum charge and discharge power of the i-th BESS; and They are the charging and discharging state variables of the energy storage in period t, which are 0-1 variables. In the discharge state SOC constraints: Where, SOC i (t) is the actual SOC value of the i-th BESS at time t; and are the lower and upper limits of the SOC of the i-th BESS respectively; SOC i (t) is the SOC of the i-th BESS at time t.

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