Energy storage and conventional generator set frequency modulation capacity collaborative scheduling optimization method

Abstract

The application discloses a kind of energy storage and conventional generator set frequency modulation capacity collaborative scheduling optimization method, it is related to the technical field of power system automatic generation control and auxiliary service optimization.The method includes: constructing regional frequency response model, carrying out load disturbance simulation, multiple simulation simulation, multiple scene extraction performance boundary, constructing segmented linear mapping relationship and using mixed integer linear programming (MILP) optimization model to optimize solution process, etc.;Wherein, based on the energy storage and conventional power capacity collaborative scheduling method of CPS1, by explicit modeling CF1 performance index, frequency control standard is included in capacity optimization process in a verifiable form, improves the compliance and engineering applicability of auxiliary service scheme;Capacity boundary is extracted based on the simulation simulation of multiple disturbance scenes, and segmented linear function is fitted to model using data-driven, effectively avoids the defects of nonlinear control modeling in solving efficiency and stability.

Description

Technical Field

[0001] This application relates to the field of power system automatic generation control and ancillary service optimization technology, and in particular to a method for coordinated scheduling optimization of energy storage and conventional generator frequency regulation capacity. Background Technology

[0002] With the increasing penetration of renewable energy in the power system and the growing global demand for stable and clean power system operation, the traditional reliance on thermal power units for frequency regulation is facing unprecedented challenges. The intermittent and highly volatile nature of new energy resources such as wind and solar power significantly exacerbates system frequency fluctuations, posing a threat to the safe operation of the power grid.

[0003] Existing frequency regulation mechanisms primarily rely on automatic generation control (AGC) responses from conventional generating units. While these mechanisms offer advantages such as high stability and continuity, they suffer from slow response times and low regulation efficiency. In contrast, energy storage systems (ESS) possess advantages like high regulation accuracy and fast response speeds, making them a significant representative of the next generation of flexible regulation resources in power systems. However, the energy capacity of energy storage systems is limited, making it difficult to independently undertake long-term frequency support tasks. Therefore, how to achieve complementary advantages between energy storage and conventional generating units to construct an efficient, economical, and reliable frequency regulation system has become a key issue in current power grid operation and dispatch.

[0004] Under the CPS1 control performance standard framework proposed by NERC, frequency control must not only meet steady-state error requirements, but also statistically satisfy the coupling constraints between frequency offset and control error, thereby improving the quantitative evaluation capability of system regulation resource performance. However, the control performance standards of existing frequency scheduling methods have not been modeled and optimized in the system planning stage, thus weakening the practicality and engineering feasibility of the scheduling scheme. Summary of the Invention

[0005] Therefore, it is necessary to provide a method for the coordinated scheduling optimization of energy storage and conventional generator frequency regulation capacity to address the above-mentioned technical problems.

[0006] The following technical solution is adopted in this specification: Based on the NERC control performance standard CPS1, construct control performance evaluation indicators for the compliance of power system frequency regulation resource allocation; A linear two-region frequency response model is constructed; wherein the two regions include a target region and a background region, and the target region includes an energy storage system (ESS) and a conventional generator set (CG). Multiple load disturbance sequences were generated using a Gaussian random walk model to simulate the frequency shift of the two-region frequency response model. Obtain the maximum available ESS and CG power capacity values ​​in the power system; The maximum available ESS power capacity value is divided into multiple discrete values. Based on each of the discrete values, a two-region frequency response model is used to simulate the frequency response of each load disturbance sequence to obtain the CF1 sequence. Based on the control performance evaluation index, the compliance of the CF1 sequence is judged to obtain a multidimensional dataset of the minimum capacity configuration required to achieve the NERC control performance standard CPS1 under load disturbance. Based on the multidimensional dataset, the load disturbance sequence is compressed using a compression algorithm to obtain a representative disturbance sequence. By assigning probability weights to each representative disturbance sequence, the minimum CG power capacity and the expected value of the ESS energy capacity under all representative disturbances are obtained for each fixed ESS power capacity. Piecewise linear function relationships between the expected values ​​of the ESS power capacity and CG power capacity, and between the expected values ​​of the ESS power capacity and ESS energy capacity, are respectively fitted to establish the SOS2 interpolation structure. Based on the ESS power capacity, CG power capacity, and SOS2 interpolation structure, a MILP model is constructed; and the MILP model is optimized and solved using a quantum coupling optimization algorithm to obtain the optimal frequency regulation scheduling decision result.

[0007] Furthermore, the construction process of the control performance evaluation index includes: Based on the CPS1 control performance standard proposed by NERC, a one-minute compliance factor is introduced. CF 1 [n] And based on the system frequency deviation and area control error, the... CF 1 [n] The calculation is performed using the following formula: ; in, This represents the average ACE per minute, expressed in megawatts (MW). This represents the average frequency deviation per minute, in Hz. This is the frequency deviation coefficient, with units of MW / 0.1Hz; The reference frequency offset value specified by CPS1; Based on a monthly rolling calculation method, each minute acquired within the continuous period of the previous 12 months... The average value is used to calculate the CPS1 score. The calculation formula is as follows: ; in, The CPS1 score for the system over 12 months; For all one-minute compliance factors over 12 months The mean.

[0008] Furthermore, the process of constructing the linearized two-region frequency response model includes: The target area participates in automatic generation control, and performs ESS and CG frequency regulation capacity scheduling. The frequency response equation is as follows: ; in, For the frequency deviation of the target area; , The system inertia and damping coefficients for the target region; This is expressed as the increase in power generation in the target area; This represents an increase in the power generation capacity of energy storage. Indicates load disturbance; This indicates the amount of power exchanged between two regions; The background region bit system provides frequency-supported inertia, and the frequency response equation is: ; in, This represents the frequency deviation in the background region. , The system inertia and damping coefficients for the background region; This represents the increase in power generation in the background area. The formula for calculating the increase in power generation in the background area is: ; in, This represents the power sensitivity coefficient of the connecting line between regions 1 and 2; The output of the conventional frequency regulation units in the two regions is expressed as follows: ; Where i = 1, 2, and when i = 2... ; This represents the primary frequency modulation proportional gain of the speed controller in region i. , These are the time constants of the governor and the turbine in region i, respectively. For control inputs, such as the output of a PI controller.

[0009] Furthermore, the load disturbance sequence is represented as: ; in, Let j be the j-th load disturbance sequence.

[0010] Furthermore, the calculation process of the CF1 sequence specifically includes: Based on the maximum available ESS and CG power capacity obtained from the system, the maximum available ESS power capacity value is divided into multiple discrete values; For each ESS power capacity For each load disturbance sequence Capacity feasibility simulation was performed to calculate the CF1 sequence. ; The vector representing the plurality of discrete values ​​arranged in ascending order is as follows: ; in, Indicates the power of the ESS; and ; The representative value in the CF1 sequence satisfies the following condition: ; in, It is a small tolerance factor; This indicates that q% of the j-th values ​​conform to the CPS1 specification.

[0011] Furthermore, the construction process of the cube with the minimum capacity configuration includes: Compliance judgment is performed on the values ​​in the CF1 sequence: if all the values ​​in the CF1 sequence comply with the CPS1 requirements, then the CG power capacity is recorded as the minimum feasible value, and the energy change range of ESS in this process is also recorded. Based on the discrete data of ESS power, a multidimensional dataset is constructed to determine the minimum capacity required to achieve the CPS1 standard under a certain load disturbance: ; ; in, Let be the CG power set for the j-th load disturbance, where each CG power corresponds to a corresponding ESS power; The ESS power under the j-th load disturbance is The energy variation range of ESS at that time and These represent the maximum and minimum values ​​of the ESS energy change, respectively.

[0012] Furthermore, the calculation process for the expected value of the minimum CG power capacity and the expected value of the ESS energy capacity for each fixed ESS power capacity includes: Scene compression is performed using the fast-forward compression algorithm to compress the load sequence, resulting in K representative perturbation sequences. And calculate the probability distribution corresponding to each perturbation sequence. Assign probability weights to them; Calculate the power capacity of each fixed ESS separately. The expected values ​​of the minimum CG power capacity and ESS energy capacity under all representative perturbation sequences are calculated using the following formula: ; ; in, and These represent the corresponding expected CG power and expected ESS energy capacity, respectively.

[0013] Furthermore, the establishment of the SOS2 interpolation structure includes: Piecewise linear functions were fitted to the expected values ​​of ESS power and CG power, as well as the expected value of ESS energy capacity, and vector functions were introduced. Establish the SOS2 interpolation structure; Wherein, the vector The constraints are: ; ; In the MILP model, the piecewise linear relationship curve of ESS energy is modeled by the following formula: ; ; in, and These are variables in MILP, representing the power configuration and energy capacity configuration of the ESS, respectively; A piecewise linear relationship curve representing the capacity relationship of CG is defined by constraints, and the formula is: ; in, It is another variable in MILP, representing the power configuration of CG.

[0014] Furthermore, the calculation process for the optimal frequency modulation scheduling decision result is as follows: Construct a MILP model with the goal of minimizing the power capacity cost of ESS and CG; The MILP model is optimized and solved using the QCO algorithm to obtain the frequency regulation scheduling decision results; The frequency modulation scheduling decision result is the optimal configuration that meets the CPS1 control standard and has the lowest cost. The objective function of the MILP model is: ; in, and These are the unit power capacity costs for CG and ESS, respectively.

[0015] Furthermore, the optimization solution of the MILP model using the QCO algorithm specifically includes: S101: Initialize the quantum system, determine the number of quantum particles, initial particle coordinates, and quantum energy level of each particle, and select the three particles with the best energy levels as the quantum optimizer; S102: Encode the particle coordinates into quantum states; S103: Decode the coordinates encoded by the quantum state into classical quantum coordinates; S104: Adjust particle coordinates: Check if any classical particle coordinates exceed the specified upper and lower bounds of each optimization parameter. If a coordinate exceeds these limits, correct it. S105: Calculate the quantum energy level at each particle's coordinates; S106: Calculate and update the quantum energy levels in the optimal quantum optimizer; S107: Adjust and update the coordinates of the particle in the quantum potential field. S108: Calculate the quantum energy level of the particle during quantum tunneling in wave function collapse, compare it with the quantum energy level in the three quantum optimizers, update the optimal quantum energy level and save it in the quantum optimizer; S109: The particle coordinates are updated by quantization according to the quantum rotation gate to obtain the latest particle coordinates; S1010: Determine if the maximum number of iterations has been reached; if the number of iterations has not reached the maximum value, proceed to step S103; if the number of iterations has reached the maximum value, output the coordinates of the particle in the optimal quantum optimizer obtained by the iteration as the optimal solution to the optimization problem, and end the optimization algorithm.

[0016] The above-mentioned technical solutions adopted in this specification can achieve the following beneficial effects: The proposed method for coordinated scheduling of energy storage and conventional power generation capacity based on CPS1 improves the compliance and engineering applicability of ancillary service solutions by explicitly modeling the CF1[n] performance index and incorporating frequency control standards into the capacity optimization process in a verifiable form.

[0017] Furthermore, based on simulation of multi-perturbation scenarios, the capacity boundary is extracted, and a data-driven piecewise linear function is used for fitting modeling, which effectively avoids the shortcomings of nonlinear control modeling in terms of solution efficiency and stability. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0019] Figure 1 This document provides a flowchart illustrating a method for the coordinated scheduling and optimization of frequency regulation capacity of energy storage and conventional generator sets. Figure 2 This is a two-region frequency response model diagram provided in this specification; Figure 3 This document provides a system optimization flowchart. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this specification clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in this specification without creative effort are within the scope of protection of this application.

[0021] Existing methods for collaborative allocation of frequency control resources still face several technical bottlenecks: 1. The lack of system modeling and evaluation methods for capacity matching between energy storage systems and the frequency regulation capabilities of conventional generating units can easily lead to resource redundancy or performance gaps; 2. Traditional optimization models do not consider the impact of disturbance uncertainties on control performance and cannot provide robust and reliable capacity boundaries; 3. The control performance standards were not modeled and optimized during the system planning stage, which weakened the practicality and engineering feasibility of the scheduling scheme.

[0022] This proposal puts forward a new method for frequency regulation resource collaborative scheduling that integrates CPS1 performance evaluation mechanism, energy storage and conventional unit capacity complementarity modeling, and uncertainty analysis of disturbance scenarios, which meets the dual objectives of frequency security and economy of power system operation under the background of high proportion of new energy access.

[0023] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0024] like Figure 1 and Figure 3 The proposed method for coordinated scheduling and optimization of energy storage and conventional generator frequency regulation capacity includes the following steps: S101: Based on the CPS1 control performance standard proposed by NERC, the following control performance evaluation index is introduced: one-minute compliance factor ( CF1 [n] ).

[0025] The process of constructing CPS1 control performance evaluation indicators includes: Based on the CPS1 control performance standard proposed by NERC, a one-minute compliance factor is introduced. CF 1 [n] The one-minute compliance factor is calculated based on system frequency deviation and area control error (ACE). CF 1 [n] The mathematical expression is specifically represented as follows: ; in, The average ACE per minute is expressed in megawatts (MW). This represents the average frequency deviation per minute, in Hz. This is the frequency deviation coefficient, with units of (MW / 0.1Hz). This is the reference frequency offset value specified by CPS1, typically taken as 0.021 Hz.

[0026] Based on a monthly rolling calculation method, each minute acquired within the continuous period of the previous 12 months... The average value is used to calculate the CPS1 score, and the calculation formula is as follows: ; in, The system's CPS1 score over 12 months is used; the higher the score, the better the system's control performance meets the CPS1 standard. For all one-minute compliance factors over 12 months CF 1 [n] The mean.

[0027] According to the CPS1 score calculation formula, if each If all scores are limited to below 1, the 12-month average will inevitably be below 1, thus ensuring that the monthly CPS1 score exceeds 100%, and the system's control performance "exceeds" the CPS1 standard. According to this method, even within one minute... CF 1 [n] The values ​​fluctuate significantly, but after averaging a large number of samples over 12 months, the system still meets the CPS1 standard.

[0028] S102: As Figure 2 As shown, a linear two-region frequency response model is constructed, wherein the two regions include the target region and the background region.

[0029] The target area is designated as Area 1, which includes an energy storage system (ESS) and a conventional generator set (CG). It participates in automatic generation control (AGC) and acts as the main regulator for AGC control, performing frequency regulation capacity scheduling of the ESS and CG.

[0030] Frequency response equation for region 1: ; in: For the frequency deviation of region 1; , Let be the system inertia and damping coefficients for region 1; This is expressed as the increase in power generation capacity of Region 1 (MW). This represents an increase in the power generation capacity of energy storage. Indicates load disturbance (MW); This indicates the amount of power exchanged between two regions.

[0031] The background area is designated as Region 2. It does not participate in AGC regulation and only provides inertial support for the system.

[0032] Frequency response equation for region 2: ; in: For the frequency deviation in region 2; , The system inertia and damping coefficients for region 2; This is expressed as the increase in power generation in Region 2 (MW).

[0033] The calculation formula is: ; in, This represents the power sensitivity coefficient of the connecting line between regions 1 and 2.

[0034] The output of the conventional frequency regulation units in the two regions is expressed as follows: ; Where i = 1, 2, and when i = 2... ; This represents the primary frequency modulation proportional gain of the speed controller in region i. , These are the time constants of the governor and the turbine in region i, respectively. For control inputs, such as the output of a PI controller.

[0035] ACE calculation formula: ; The formula for the PI controller in region 1 is: ; in, and These are the proportional and integral coefficients of the PI controller.

[0036] Energy storage system response model in Region 1: ; State of charge: ; ; ; in, The state of charge of energy storage; and These represent the minimum and maximum states of charge for energy storage, respectively. This indicates the maximum power output of the energy storage system; and These are the proportional and integral coefficients of the energy storage controller; This is the output response gain for energy storage.

[0037] S103: Using a Gaussian random walk model, generate multiple typical load disturbance sequences and simulate possible frequency shifts.

[0038] Among them, a Gaussian random walk model was used to generate multiple typical load disturbance sequences for region 1 with a time length of 1 hour and a time resolution of 1 second; A typical load disturbance sequence is represented as follows: ; in, Let j be the j-th load disturbance sequence.

[0039] S104: Obtain the maximum available energy storage system and conventional generator power capacity in the system, and divide the maximum available ESS power capacity value into multiple discrete values.

[0040] The maximum available ESS power capacity is divided into multiple discrete values, and these discrete values ​​are represented by a vector arranged in ascending order: ; in, Indicates the power of the ESS; and .

[0041] S105: Based on each ESS power capacity value, calculate the CF1 sequence through frequency response simulation, and determine the compliance of each configuration group based on the CF1[n] index.

[0042] For each ESS power capacity For each load disturbance sequence Perform capacity feasibility simulation and calculate the CF1 sequence , where the condition formula that the values of the CF1 sequence need to satisfy is: ; Among them, is a small tolerance coefficient; represents that q% of the values of the jth meet the provisions of CPS1.

[0043] Based on the CF1[n] index, determine whether each group of configurations is compliant. If the index meets the above formula, record the CG power capacity as the minimum feasible value, and at the same time record the energy change range of the ESS during this process.

[0044] S106: Extract the data under each scenario and construct a multidimensional dataset with the minimum capacity that meets the requirements.

[0045] Extract the data under each scenario and construct a multidimensional dataset with the minimum capacity required to meet the CPS1 standard under a certain load disturbance: ; ; Among them, is the CG power set of the jth load disturbance, and each CG power in it corresponds to a corresponding ESS power; is the energy change range of the ESS when the ESS power is under the jth load disturbance, and represent the maximum and minimum values of the ESS energy change respectively.

[0046] S107: By performing compression processing on the load sequence, obtain the disturbance sequence and perform probability weighting on it; calculate the expected values of the minimum CG power capacity and ESS energy capacity for each fixed ESS power capacity under all representative disturbances.

[0047] First, use the fast-forward compression algorithm for scenario compression, perform compression processing on the load sequence, and obtain K (K < M) representative disturbance sequences , and calculate the probability distribution corresponding to each disturbance sequence Perform probability weighting on it.

[0048] Calculate the expected values of the minimum CG power capacity and ESS energy capacity for each fixed ESS power capacity under all representative disturbance sequences respectively. The calculation formula is: ; ; in, and These represent the corresponding expected CG power and expected ESS energy capacity, respectively.

[0049] S108: Fit piecewise linear functions to the expected values ​​of ESS power and CG power, and to the expected value of ESS energy capacity, respectively, and introduce vector... Establish the SOS2 interpolation structure.

[0050] Where, vector The constraints are: ; ; The piecewise linear relationship curve of ESS energy is modeled in MILP, and the modeling formula is as follows: ; ; in, and These are variables in MILP, representing the power configuration and energy capacity configuration of the ESS, respectively.

[0051] The piecewise linear relationship curve used to represent the capacity relationship of CG is defined by constraints, the formula of which is: ; in, It is another variable in MILP, representing the power configuration of CG.

[0052] S109: Construct the MILP model and perform optimization to obtain the optimal configuration as the frequency regulation scheduling decision result.

[0053] First, a MILP model is constructed with the objective of minimizing the power capacity cost of ESS and CG. The MILP model includes: Objective function: ; The constraints are shown in S108; in, and The unit power capacity costs for CG and ESS are respectively (RMB / MW) and (RMB / MW).

[0054] Then, the QCO algorithm is used to optimize and solve the MILP model, outputting the optimal configuration that satisfies the CPS1 control criterion and has the lowest cost. As a result of the frequency modulation scheduling decision, the specific steps are as follows: (1) Initialize the quantum system, determine the number of quantum particles, the initial particle coordinates and the quantum energy level of each particle, and select the three particles with the best energy levels as the quantum optimizer.

[0055] (2) Encode the particle coordinates using quantum states.

[0056] (3) Decode the coordinates encoded by the quantum state into classical quantum coordinates.

[0057] (4) Adjust the position of particle coordinates: Check if any classical particle coordinates exceed the specified upper and lower bounds of each optimization parameter. If a coordinate exceeds these limits, it is corrected.

[0058] (5) Calculate the quantum energy level of each particle's coordinates.

[0059] (6) Calculate and update the quantum energy levels in the optimal quantum optimizer.

[0060] (7) Adjust and update the coordinates of the particle in the quantum potential field.

[0061] (8) Calculate the quantum energy level of the particle during quantum tunneling in wave function collapse, compare it with the quantum energy level in the three quantum optimizers, and update the optimal quantum energy level and store it in the quantum optimizer.

[0062] (9) The particle coordinates are updated by quantization according to the quantum rotation gate to obtain the latest particle coordinates.

[0063] (10) Determine if the maximum number of iterations has been reached. If the number of iterations has not reached the maximum value, go to (3); if the number of iterations has reached the maximum value, output the coordinates of the particle in the optimal quantum optimizer obtained by the iteration as the optimal solution to the optimization problem, and end the optimization algorithm.

[0064] Compared with the prior art, the beneficial effects of the technical solution of the present invention are: The proposed method for coordinated scheduling of energy storage and conventional power generation capacity based on CPS1 introduces control performance constraints into frequency regulation resource allocation, achieving a balance between system dynamic response capability and economic objectives. On one hand, by explicitly modeling the CF1[n] performance index, frequency control standards are incorporated into the capacity optimization process in a verifiable form, improving the compliance and engineering applicability of ancillary service schemes. On the other hand, capacity boundaries are extracted based on simulations of multiple disturbance scenarios, and data-driven piecewise linear functions are used for fitting modeling, effectively avoiding the shortcomings of nonlinear control modeling in terms of solution efficiency and stability. Furthermore, this method fully leverages the advantages of energy storage systems in response speed, forming a synergistic complement with the continuous regulation capabilities of conventional units, significantly enhancing the power system's frequency regulation support capability against short-term disturbances, and possessing good scalability and practical application prospects.

[0065] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A method for coordinated scheduling and optimization of energy storage and conventional generator frequency regulation capacity, characterized in that, include: Based on the NERC control performance standard CPS1, construct control performance evaluation indicators for the compliance of power system frequency regulation resource allocation; A linear two-region frequency response model is constructed; wherein the two regions include a target region and a background region, and the target region includes an energy storage system (ESS) and a conventional generator set (CG). Multiple load disturbance sequences were generated using a Gaussian random walk model to simulate the frequency shift of the two-region frequency response model. Obtain the maximum available ESS and CG power capacity values ​​in the power system; The maximum available ESS power capacity value is divided into multiple discrete values. Based on each of the discrete values, a two-region frequency response model is used to simulate the frequency response of each load disturbance sequence to obtain the CF1 sequence. Based on the control performance evaluation index, the compliance of the CF1 sequence is judged to obtain a multidimensional dataset of the minimum capacity configuration required to achieve the NERC control performance standard CPS1 under load disturbance. Based on the multidimensional dataset, the load disturbance sequence is compressed using a compression algorithm to obtain a representative disturbance sequence. By assigning probability weights to each representative disturbance sequence, the minimum CG power capacity and the expected value of the ESS energy capacity under all representative disturbances are obtained for each fixed ESS power capacity. Piecewise linear function relationships between the expected values ​​of the ESS power capacity and CG power capacity, and between the expected values ​​of the ESS power capacity and ESS energy capacity, are respectively fitted to establish the SOS2 interpolation structure. Based on the ESS power capacity, CG power capacity, and SOS2 interpolation structure, a MILP model is constructed; and the MILP model is optimized and solved using a quantum coupling optimization algorithm to obtain the optimal frequency regulation scheduling decision result.

2. The method for coordinated scheduling and optimization of energy storage and conventional generator frequency regulation capacity as described in claim 1, characterized in that, The construction process of the control performance evaluation index includes: Based on the CPS1 control performance standard proposed by NERC, a one-minute compliance factor is introduced. CF 1 [n] And based on the system frequency deviation and area control error, the... CF 1 [n] The calculation is performed using the following formula: ； in, This represents the average ACE per minute, expressed in megawatts (MW). This represents the average frequency deviation per minute, in Hz. This is the frequency deviation coefficient, with units of MW / 0.1Hz; The reference frequency offset value specified by CPS1; Based on a monthly rolling calculation method, each minute acquired within the continuous period of the previous 12 months... The average value is used to calculate the CPS1 score. The calculation formula is as follows: ； in, The CPS1 score for the system over 12 months; For all one-minute compliance factors over 12 months The mean.

3. The method for coordinated scheduling and optimization of energy storage and conventional generator frequency regulation capacity as described in claim 1, characterized in that, The process of constructing the linearized two-region frequency response model includes: The target area participates in automatic generation control, and performs ESS and CG frequency regulation capacity scheduling. The frequency response equation is as follows: ； in, For the frequency deviation of the target area; , The system inertia and damping coefficients for the target region; This is expressed as the increase in power generation in the target area; This represents an increase in the power generation capacity of energy storage. Indicates load disturbance; This indicates the amount of power exchanged between two regions; The background region bit system provides frequency-supported inertia, and the frequency response equation is: ； in, This represents the frequency deviation in the background region. , The system inertia and damping coefficients for the background region; This represents the increase in power generation in the background area; The formula for calculating the increase in power generation in the background area is: ； in, This represents the power sensitivity coefficient of the connecting line between regions 1 and 2; The output of the conventional frequency regulation units in the two regions is expressed as follows: ； Where i = 1, 2, and when i = 2... ; This represents the primary frequency modulation proportional gain of the speed controller in region i. , These are the time constants of the governor and the turbine in region i, respectively. For control inputs, such as the output of a PI controller.

4. The method for coordinated scheduling and optimization of energy storage and conventional generator frequency regulation capacity as described in claim 1, characterized in that, The load disturbance sequence is represented as follows: ； in, Let j be the j-th load disturbance sequence.

5. The method for coordinated scheduling and optimization of energy storage and conventional generator frequency regulation capacity as described in claim 1, characterized in that, The calculation process of the CF1 sequence specifically includes: Based on the maximum available ESS and CG power capacity obtained from the system, the maximum available ESS power capacity value is divided into multiple discrete values; For each ESS power capacity For each load disturbance sequence Capacity feasibility simulation was performed to calculate the CF1 sequence. ; The vector representing the plurality of discrete values ​​arranged in ascending order is as follows: ； in, Indicates the power of the ESS; and ; The representative value in the CF1 sequence satisfies the following condition: ； in, It is a small tolerance factor; This indicates that q% of the j-th values ​​conform to the CPS1 specification.

6. The method for coordinated scheduling and optimization of energy storage and conventional generator frequency regulation capacity as described in claim 1, characterized in that, The process of constructing the minimum capacity configuration of the cube includes: Compliance judgment is performed on the values ​​in the CF1 sequence: if all the values ​​in the CF1 sequence comply with the CPS1 requirements, then the CG power capacity is recorded as the minimum feasible value, and the energy change range of ESS in this process is also recorded. Based on the discrete data of ESS power, a multidimensional dataset is constructed to determine the minimum capacity required to achieve the CPS1 standard under a certain load disturbance: ； ； in, Let be the CG power set for the j-th load disturbance, where each CG power corresponds to a corresponding ESS power; The ESS power under the j-th load disturbance is The energy variation range of ESS at that time and These represent the maximum and minimum values ​​of the ESS energy change, respectively.

7. The method for coordinated scheduling and optimization of energy storage and conventional generator frequency regulation capacity as described in claim 1, characterized in that, The calculation process for the expected value of the minimum CG power capacity and the expected value of the ESS energy capacity for each fixed ESS power capacity includes: Scene compression is performed using the fast-forward compression algorithm to compress the load sequence, resulting in K representative perturbation sequences. And calculate the probability distribution corresponding to each perturbation sequence. Assign probability weights to them; Calculate the power capacity of each fixed ESS separately. The expected values ​​of the minimum CG power capacity and ESS energy capacity under all representative perturbation sequences are calculated using the following formula: ； ； in, and These represent the corresponding expected CG power and expected ESS energy capacity, respectively.

8. The method for coordinated scheduling and optimization of energy storage and conventional generator frequency regulation capacity as described in claim 1, characterized in that, The establishment of the SOS2 interpolation structure includes: Piecewise linear functions were fitted to the expected values ​​of ESS power and CG power, as well as the expected value of ESS energy capacity, and vector functions were introduced. Establish the SOS2 interpolation structure; Wherein, the vector The constraints are: ； ； In the MILP model, the piecewise linear relationship curve of ESS energy is modeled by the following formula: ； ； in, and These are variables in MILP, representing the power configuration and energy capacity configuration of the ESS, respectively; A piecewise linear relationship curve representing the capacity relationship of CG is defined by constraints, and the formula is: ； in, It is another variable in MILP, representing the power configuration of CG.

9. The method for coordinated scheduling and optimization of energy storage and conventional generator frequency regulation capacity as described in claim 1, characterized in that, The calculation process for the optimal frequency modulation scheduling decision result is as follows: Construct a MILP model with the goal of minimizing the power capacity cost of ESS and CG; The MILP model is optimized and solved using the QCO algorithm to obtain the frequency regulation scheduling decision results; The frequency modulation scheduling decision result is the optimal configuration that meets the CPS1 control standard and has the lowest cost. The objective function of the MILP model is: ； in, and These are the unit power capacity costs for CG and ESS, respectively.

10. The method for coordinated scheduling and optimization of energy storage and conventional generator frequency regulation capacity as described in claim 9, characterized in that, The optimization and solution of the MILP model using the QCO algorithm specifically includes: S101: Initialize the quantum system, determine the number of quantum particles, initial particle coordinates, and quantum energy level of each particle, and select the three particles with the best energy levels as the quantum optimizer; S102: Encode the particle coordinates into quantum states; S103: Decode the coordinates encoded by the quantum state into classical quantum coordinates; S104: Adjust particle coordinates: Check if any classical particle coordinates exceed the specified upper and lower bounds of each optimization parameter. If a coordinate exceeds these limits, correct it. S105: Calculate the quantum energy level at each particle's coordinates; S106: Calculate and update the quantum energy levels in the optimal quantum optimizer; S107: Adjust and update the coordinates of the particle in the quantum potential field. S108: Calculate the quantum energy level of the particle during quantum tunneling in wave function collapse, compare it with the quantum energy level in the three quantum optimizers, update the optimal quantum energy level and save it in the quantum optimizer; S109: The particle coordinates are updated by quantization according to the quantum rotation gate to obtain the latest particle coordinates; S1010: Determine if the maximum number of iterations has been reached; if the number of iterations has not reached the maximum value, proceed to step S103; if the number of iterations has reached the maximum value, output the coordinates of the particle in the optimal quantum optimizer obtained by the iteration as the optimal solution to the optimization problem, and end the optimization algorithm.

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