A capacity optimization configuration method participating in fast frequency regulation of a power system

Abstract

The present application relates to the technical field of frequency modulation energy storage, in particular to a capacity optimization configuration method for participating in the fast frequency modulation of a power system, which comprises the following steps: S1, collecting historical load data of the power system through a historical database and analyzing the load fluctuation law; S2, predicting future load demand and fluctuation range by using an ARIMA model; S3, introducing a carbon emission environmental variable into a minimized total system cost objective function by calculating the minimized total system cost objective function and considering the influence of the carbon emission factor, and optimizing the capacity configuration of the frequency modulation resource; S4, substituting the optimization result into the Euler method for dynamic simulation to verify whether all constraint conditions are met. By constructing the minimized total system cost objective function and combining the constraint conditions to optimize the capacity configuration of the frequency modulation resource, the comprehensive balance of economy, environmental protection and stability is realized, which is the core decision-making link of the whole method.

Description

Technical Field

[0001] This invention relates to the field of frequency regulation energy storage technology, and more specifically, to a capacity optimization configuration method for participating in rapid frequency regulation of power systems. Background Technology

[0002] A capacity optimization configuration method for power system rapid frequency regulation aims to enhance the power system's frequency regulation capability in the face of load fluctuations or sudden events through scientifically sound energy storage systems or flexible load capacity design. This method is typically based on the power system's active power-frequency dynamic characteristic model, combined with the technical parameters of energy storage devices and operational constraints, and utilizes advanced optimization algorithms to determine the optimal energy storage configuration. Simultaneously, this method needs to comprehensively consider frequency regulation demands across multiple time scales to achieve efficient synergy of energy storage systems in different scenarios, balancing economic efficiency and technical performance to ensure that system stability requirements are met while reducing investment and operating costs. Furthermore, with the high proportion of renewable energy integrated into the grid, this method needs to further incorporate renewable energy output forecasting, load characteristic analysis, and uncertainty modeling, thereby providing crucial support for improving the flexibility and sustainable development of modern power systems.

[0003] In existing capacity optimization allocation methods for power system fast frequency regulation, traditional methods suffer from insufficient historical data mining, low load forecasting accuracy, focus on system performance degradation due to a single objective, incomplete consideration of constraints, inadequate frequency stability considerations, low optimization algorithm efficiency, lack of dynamic simulation verification, and insufficient comprehensiveness and flexibility. Therefore, this paper proposes a capacity optimization allocation method for power system fast frequency regulation. Summary of the Invention

[0004] The purpose of this invention is to provide a capacity optimization configuration method for participating in fast frequency regulation of power systems, in order to solve the problems mentioned in the background art. In the existing capacity optimization configuration methods for participating in fast frequency regulation of power systems, traditional methods have the following defects: insufficient mining of historical data, low load forecasting accuracy, system performance degradation caused by focusing only on a single objective, incomplete consideration of constraints, insufficient consideration of frequency stability, low efficiency of optimization algorithms, lack of dynamic simulation verification, and insufficient comprehensiveness and flexibility.

[0005] To achieve the above objectives, the present invention aims to provide a capacity optimization configuration method for participating in fast frequency regulation of a power system, comprising the following steps: S1. Collect historical load data of the power system through historical databases and analyze load fluctuation patterns; S2. Use the ARIMA model to predict future load demand and its fluctuation range; S3. By calculating the objective function that minimizes the total system cost and taking into account the impact of carbon emission factors, the environmental variable of carbon emission is introduced into the objective function that minimizes the total system cost to optimize the capacity configuration of frequency regulation resources. S4. Substitute the optimization results into the Euler method for dynamic simulation to verify whether all constraints are met.

[0006] As a further improvement to this technical solution, in step S1, historical load data of the power system is collected through a historical database, and the load fluctuation pattern is analyzed. The load fluctuation characteristics are extracted using the mean and standard deviation calculation method. Historical load data includes power capacity, energy capacity, and charge / discharge efficiency.

[0007] As a further improvement to this technical solution, the specific steps in S2 for predicting future load demand and fluctuation range using the ARIMA model are as follows: S2.1 Construct an ARIMA model to predict future load demand; S2.2 Calculate the predicted load fluctuation range as the frequency regulation demand input; Among them, load fluctuation range prediction: ; In the formula, This represents the predicted range of load fluctuations. This represents the maximum power value of the load forecast; This represents the minimum power value for the load forecast.

[0008] As a further improvement to this technical solution, the specific construction process of building the ARIMA model for predicting future load demand in S2.1 is as follows: Historical load data is divided into training and test sets, and then divided in chronological order. Use the ADF test and KPSS test to determine whether a time series is stationary; When the surface is non-stationary, a difference operation is performed to make it stationary. The difference formula is: ; In the formula, express Order difference; Indicates the lag operator; This indicates that time series data is at a specific point in time. The value; The parameters of the ARIMA model are determined by analysis of the autocorrelation function (ACF) and the partial autocorrelation function (PACF). Based on the selected parameters , , Building an ARIMA model: ; In the formula, This represents the load forecast value; Represents a constant term; Represents the autoregressive coefficient; Indicates the moving average coefficient; Represents white noise; Indicates the order of autoregression; Indicates the order of difference; Indicates the order of the moving average; To evaluate the predictive ability of a model using test set data, common metrics include: Mean square error: ; In the formula, This represents the total number of data points; Indicates mean square error; Mean absolute error: ; In the formula, Indicates the mean absolute error; Mean absolute percentage error: ; In the formula, Indicates the mean absolute percentage error; Finally, output load forecast values .

[0009] As a further improvement to this technical solution, in step S3, by calculating and minimizing the total system cost objective function, and considering the impact of carbon emission factors, the carbon emission environmental variable is introduced into the minimization of the total system cost objective function to optimize the capacity configuration of frequency regulation resources. The specific process is as follows:

[0010] S3.1 Construct an objective function to minimize the total system cost; S3.2 Define decision variables; The decision variables specifically include: No. A frequency modulation resource in time capacity configuration ; No. A frequency modulation resource in time Enabled status ;

[0011] No. A frequency modulation resource in time Reserve capacity ratio ; S3.3, Set constraints; S3.4. Use an evolutionary algorithm to solve for the optimal frequency modulation resource capacity configuration; S3.5 Output the optimal capacity configuration and the minimum target value for each frequency modulation resource.

[0012] As a further improvement to this technical solution, in step S3.1, the objective function constructed to minimize the total system cost is as follows: The total cost of the system includes operating costs and investment costs; To minimize the total system cost, optimize the capacity configuration of frequency modulation resources: ; In the formula, This represents the target value to be minimized; Indicates time Operating costs; Indicates time Investment costs; To address the issue of additional costs arising from the dynamic adjustment of frequency modulation resources, time is considered when calculating the objective function to minimize the total system cost. The dynamic adjustment variable of the internal frequency modulation resource is introduced into the objective function of minimizing the total system cost for optimization: ; In the formula, This represents the minimum objective value after introducing dynamically adjusted weights; This indicates that the weights are dynamically adjusted to reflect the differences in frequency modulation performance requirements at different times. Indicates time Dynamic adjustment costs; Considering the trade-off between carbon emission environmental factors and the objective function of minimizing the total system cost, a carbon emission environmental variable is introduced to optimize the objective function of minimizing the total system cost. ; In the formula, This represents the minimum target value after introducing the environmental variable of carbon emissions; Indicates the weight of environmental impact; This represents the environmental variable of carbon emissions, and also represents the first... A frequency modulation resource in time Carbon emissions.

[0013] As a further improvement to this technical solution, the constraints in S3.3 specifically include: Frequency modulation (FM) requirement constraints: ; In the formula, Indicates time The system frequency regulation requirements; Indicates any time; Indicates the total number of frequency modulation resources; Indicates frequency modulation resource variables; Resource capacity limit: ; In the formula, Indicates the first The maximum capacity of each frequency modulation resource; Represents arbitrary frequency modulation resources; Reserve capacity constraints: ; In the formula, Indicates the first A frequency modulation resource in time Backup requirements; Dynamically adjust constraints: ; In the formula, Indicates the first A frequency modulation resource in time The adjustment amount; Indicates the first The maximum adjustment rate of each frequency modulation resource; Nonnegativity constraint: ; A series of constraints are set to ensure the stability and economy of the system.

[0014] As a further improvement to this technical solution, considering the impact of the dynamic behavior of frequency deviation on system stability in the dynamic behavior of the power system, a penalty term is added to minimize the fluctuation of frequency deviation: The final optimized objective function that minimizes the total system cost is: ; In the formula, This represents the final optimized objective function value that minimizes the total system cost. Indicates the frequency deviation weight; Indicates system frequency deviation; Indicates the entire time range The integral of the square of the internal frequency deviation is used to measure the magnitude of frequency fluctuation; Then add dynamic behavior constraints: ; In the formula, Represents the system's inertial time constant; This represents the rate of change of the system frequency deviation over time. Indicates the damping coefficient; This represents the power-frequency gain coefficient. This indicates the real-time power change.

[0015] As a further improvement to this technical solution, the specific steps in S3.4 for using an evolutionary algorithm to obtain the optimal frequency modulation resource capacity configuration are as follows: S3.41. Initialize the population; Specifically, initializing the population involves: Define population size ; Randomly generated One candidate solution: ; In the formula, Indicates the first The initial solution for each individual; Indicates the first A frequency modulation resource in time Initial capacity configuration; Represents an individual index variable; S3.42. Calculate the fitness value of each individual based on the objective function; Specifically, the fitness value of each individual is calculated based on the objective function as follows: ; In the formula, Indicates the first The fitness value of each individual; S3.43. Perform constraint checks on each individual; The specific process for checking the constraints for each individual is as follows: When individual If a constraint is not met, subtract the penalty term from its fitness value. : ; In the formula, This represents the fitness value after considering the penalty factor; Indicates a penalty item; S3.44. Select outstanding individuals from the current population as the parents of the next generation by using roulette wheel selection; The specific options for roulette are as follows: ; In the formula, Indicates the first The probability of an individual being selected; This represents the sum of the fitness values ​​of all individuals in the current population; S3.45. Generate new offspring individuals through crossover operations, and combine some genes from two parent individuals to form a new solution; The crossover operation specifically refers to: according to Two individuals are randomly selected from the population as parents. and ; Cross operation: ; In the formula, Represents offspring individuals; Indicates the crossover ratio; S3.46. By performing mutation operations, a small-scale random perturbation is made to the genes of some offspring individuals to increase population diversity; The mutation operation specifically includes: ; In the formula, Indicates the result after mutation operation. Capacity configuration of each frequency modulation resource; Represents the original first Capacity configuration of each frequency modulation resource; Indicates variable-length asynchronous operation; Represents a random disturbance term; S3.47. Merge parent and offspring individuals and select the one with the highest fitness. Individuals form a new generation population. When the maximum number of iterations is reached, the optimal solution is output; otherwise, return to step S3.42.

[0016] As a further improvement to this technical solution, in step S4, the specific steps for substituting the optimization results into the Euler method for dynamic simulation to verify whether all constraints are met are as follows: The Euler method is used to solve the problem, and the frequency deviation at each time step is calculated step by step: ; In the formula, Indicates the system's next time step Frequency deviation; Indicates the time step; Check if the constraints are met: When all constraints are satisfied, the simulation results are output. If certain constraints are not met, the type, time point, and degree of constraint violation are recorded and returned to the optimization algorithm for adjustment.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. In this capacity optimization allocation method for rapid frequency regulation in the power system, load fluctuation patterns are analyzed through historical databases to provide basic data support for subsequent prediction and optimization, ensuring a thorough understanding of the power system's operating characteristics. The ARIMA model is used to predict future load demand and fluctuation ranges, providing accurate input conditions for the capacity allocation of frequency regulation resources and enhancing the foresight and adaptability of the optimization scheme.

[0018] 2. In this capacity optimization allocation method for rapid frequency regulation in power systems, the core decision-making step of the entire method is to construct a target function that minimizes the total system cost and optimizes the capacity allocation of frequency regulation resources in combination with constraints, achieving a comprehensive balance between economy, environmental protection, and stability. The optimization results are then substituted into the Euler method for dynamic simulation to verify whether all constraints are met, ensuring the feasibility and reliability of the optimization scheme in a real dynamic environment, thus forming a closed-loop verification mechanism. Attached Figure Description

[0019] Figure 1 This is a flowchart of the overall method of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figure 1 As shown in the figure, this embodiment provides a capacity optimization configuration method for participating in fast frequency regulation of the power system, including the following steps: S1. Collect historical load data of the power system through historical databases and analyze load fluctuation patterns; In this example, historical load data of the power system is collected through a historical database, and the load fluctuation pattern is analyzed by using the mean and standard deviation calculation method to extract the load fluctuation characteristics. Historical load data includes power capacity, energy capacity, and charge / discharge efficiency.

[0022] Specifically, the load fluctuation characteristics are extracted using the mean and standard deviation calculation method; Mean and standard deviation calculation: ; ; In the formula, This represents the average load power. This represents the standard deviation of load power; Indicates a point in time The load power value; Indicates the number of time intervals; Indicates a time point index variable; Load fluctuation range: ; In the formula, Indicates the range of load fluctuations; This represents the maximum power value in historical load data; This represents the minimum power value in historical load data.

[0023] Specifically, load fluctuation characteristics are extracted using the mean and standard deviation calculation method, and the load fluctuation range is calculated using the maximum and minimum power values, thereby quantifying the load variation characteristics. These analytical results can help accurately assess the system's frequency regulation needs, ensuring that the input parameters of subsequent prediction and optimization algorithms are more reliable, and ultimately achieving a balance between the economy and effectiveness of frequency regulation resource capacity allocation.

[0024] S2. Use the ARIMA model to predict future load demand and its fluctuation range; In this example, the specific steps for using the ARIMA model to predict future load demand and its fluctuation range are as follows: S2.1 Construct an ARIMA model to predict future load demand; In this example, the specific process of building an ARIMA model to predict future load demand is as follows: Historical load data is divided into training and test sets, and then divided in chronological order. Use the ADF test and KPSS test to determine whether a time series is stationary; When the surface is non-stationary, a difference operation is performed to make it stationary. The difference formula is: ; In the formula, express Order difference; Indicates the lag operator; This indicates that time series data is at a specific point in time. The value; The parameters of the ARIMA model are determined by analysis of the autocorrelation function (ACF) and the partial autocorrelation function (PACF). The parameters of the ARIMA model were determined based on ACF and PACF analyses. , , : The truncation position is determined by PACF. : Determined by ACF, the truncation position corresponds; Determined based on the degree of difference; Based on the selected parameters , , Building an ARIMA model: ; In the formula, This represents the load forecast value; Represents a constant term; Represents the autoregressive coefficient; Indicates the moving average coefficient; Represents white noise; Indicates the order of autoregression; Indicates the order of difference; Indicates the order of the moving average; To evaluate the predictive ability of a model using test set data, common metrics include: Mean square error: ; In the formula, This represents the total number of data points; Indicates mean square error; Mean absolute error: ; In the formula, Indicates the mean absolute error; Mean absolute percentage error: ; In the formula, Indicates the mean absolute percentage error; Finally, output load forecast values .

[0025] Specifically, the ARIMA model ensures the reliability and accuracy of its predictions through stationarity testing of time series data, differencing, and parameter selection. Furthermore, the model's performance is evaluated using metrics such as MSE, MAE, and MAPE, further validating the effectiveness of the predictions. The final output load forecasts and fluctuation ranges provide crucial input for constructing the subsequent optimization objective function and setting constraints, thereby achieving precise planning and efficient allocation of frequency regulation resources.

[0026] S2.2 Calculate the predicted load fluctuation range as the frequency regulation demand input; Among them, load fluctuation range prediction: ; In the formula, This represents the predicted range of load fluctuations. This represents the maximum power value of the load forecast; This represents the minimum power value for the load forecast.

[0027] Specifically, in a capacity optimization allocation method for power system fast frequency regulation, step S2 uses an ARIMA model to predict future load demand and fluctuation range, providing crucial input for subsequent capacity optimization of frequency regulation resources. Specifically, S2.1 constructs an ARIMA model to analyze historical load data and predict future load demand, ensuring accurate and reliable prediction results; S2.2 calculates the load fluctuation range based on the predicted load values, quantifying the magnitude of future load changes, thereby clarifying the system's frequency regulation requirements. This process not only provides precise data support for optimizing the frequency regulation resource capacity allocation in the objective function but also helps set reasonable constraints, ensuring that frequency regulation resources can effectively cope with load fluctuations, ultimately achieving a balance between the power system's economy and stability.

[0028] S3. By calculating the objective function that minimizes the total system cost and taking into account the impact of carbon emission factors, the environmental variable of carbon emission is introduced into the objective function that minimizes the total system cost to optimize the capacity configuration of frequency regulation resources. In this example, by calculating the objective function that minimizes the total system cost and considering the impact of carbon emissions, the environmental variable of carbon emissions is introduced into the objective function to optimize the capacity allocation of frequency regulation resources. The specific process is as follows: S3.1 Construct an objective function to minimize the total system cost; In this example, the objective function to minimize the total system cost is constructed as follows: The total cost of the system includes operating costs and investment costs; To minimize the total system cost, optimize the capacity configuration of frequency modulation resources: ; In the formula, This represents the target value to be minimized; Indicates time Operating costs; Indicates time Investment costs; To address the issue of additional costs arising from the dynamic adjustment of frequency modulation resources, time is considered when calculating the objective function to minimize the total system cost. The dynamic adjustment variable of the internal frequency modulation resource is introduced into the objective function of minimizing the total system cost for optimization: ; In the formula, This represents the minimum objective value after introducing dynamically adjusted weights; This indicates that the weights are dynamically adjusted to reflect the differences in frequency modulation performance requirements at different times. Indicates time Dynamic adjustment costs; Specifically, and , for: Operating costs: ; In the formula, Indicates the first A frequency modulation resource in time Fuel cost coefficient; Indicates the first A frequency modulation resource in time Maintenance cost coefficient; Investment costs: ; In the formula, Indicates the first A frequency modulation resource in time The unit capacity investment cost coefficient; Dynamically adjust costs: ; In the formula, Indicates the first The start / stop cost coefficient for each frequency modulation resource; Indicates the first The adjustment cost coefficient of each frequency modulation resource; Indicates the first A frequency modulation resource in time Changes in the enabled status; Indicates the first A frequency modulation resource in time The adjustment amount; Considering the trade-off between carbon emission environmental factors and the objective function of minimizing the total system cost, a carbon emission environmental variable is introduced to optimize the objective function of minimizing the total system cost. ; In the formula, This represents the minimum target value after introducing the environmental variable of carbon emissions; Indicates the weight of environmental impact; This represents the environmental variable of carbon emissions, and also represents the first... A frequency modulation resource in time Carbon emissions.

[0029] Specifically, by defining the total cost of the system (including operating and investment costs) and gradually introducing dynamically adjusted weights and carbon emission environmental variables, the objective function can comprehensively balance the relationship between economic efficiency, frequency regulation performance, and environmental benefits. Specifically, this objective function not only optimizes the capacity allocation of frequency regulation resources to reduce overall costs, but also adapts to frequency regulation demands at different times by dynamically adjusting weights, and incorporates environmental impact weights to consider carbon emissions. This process ensures that the frequency regulation resource allocation scheme meets the needs of rapid frequency regulation while taking into account economic benefits and environmental sustainability, thus providing a scientific basis for the efficient, stable, and green operation of the power system.

[0030] S3.2 Define decision variables; The decision variables specifically include: No. A frequency modulation resource in time capacity configuration The problem is the same as above. No. A frequency modulation resource in time Enabled status The problem is the same as above. No. A frequency modulation resource in time Reserve capacity ratio ; S3.3, Set constraints; In this example, the constraints specifically include: Frequency modulation (FM) requirement constraints: ; In the formula, Indicates time The system frequency regulation requirements; Indicates any time; Indicates the total number of frequency modulation resources; Indicates frequency modulation resource variables; Specifically, system frequency regulation requirements It refers to time In order to maintain the frequency stability of the system, the total capacity of frequency regulation resources required is usually determined based on the predicted value of the load fluctuation range. The predicted value of load fluctuation range With system frequency modulation requirements The association is: ; In the formula, This represents the proportionality coefficient by which load fluctuations affect frequency regulation demand. Resource capacity limit: ; In the formula, Indicates the first The maximum capacity of each frequency modulation resource; Represents arbitrary frequency modulation resources; Reserve capacity constraints: ; In the formula, Indicates the first A frequency modulation resource in time Backup requirements; Dynamically adjust constraints: ; In the formula, Indicates the first A frequency modulation resource in time The adjustment amount; Indicates the first The maximum adjustment rate of each frequency modulation resource; Nonnegativity constraint: ; A series of constraints are set to ensure the stability and economy of the system.

[0031] Specifically, in a capacity optimization allocation method for rapid frequency regulation in power systems, the setting of constraints plays a crucial role in ensuring system stability and economy. Specifically, frequency regulation demand constraints ensure that the total capacity of frequency regulation resources can meet the system's frequency regulation demand at any given time; resource capacity limits prevent individual frequency regulation resources from operating beyond their maximum capacity, thus protecting equipment safety; reserve capacity constraints ensure that frequency regulation resources have sufficient reserve capacity to cope with emergencies; dynamic adjustment constraints limit the adjustment rate of frequency regulation resources, preventing system instability caused by frequent or excessively rapid adjustments; and non-negativity constraints clarify the reasonable range of variable values, ensuring the physical feasibility and mathematical rationality of the optimization problem. Through the comprehensive application of these constraints, the optimization model can achieve efficient utilization and maximized economy of frequency regulation resources while meeting system operation requirements, providing a reliable solution for rapid frequency regulation in power systems.

[0032] In this example, considering the impact of the dynamic behavior of frequency deviation on system stability within the dynamic behavior of the power system, a penalty term is added to minimize the fluctuation of frequency deviation: The final optimized objective function that minimizes the total system cost is: ; In the formula, This represents the final optimized objective function value that minimizes the total system cost. Indicates the frequency deviation weight; Indicates system frequency deviation; Indicates the entire time range The integral of the square of the internal frequency deviation is used to measure the magnitude of frequency fluctuation; Then add dynamic behavior constraints: ; In the formula, Represents the system's inertial time constant; This represents the rate of change of the system frequency deviation over time. Indicates the damping coefficient; This represents the power-frequency gain coefficient. This indicates the real-time power change.

[0033] Specifically, in a capacity optimization allocation method for power system fast frequency regulation, the addition of a frequency deviation penalty term and dynamic behavioral constraints plays a crucial role in improving system stability and dynamic performance. By introducing the square integral of the frequency deviation and its corresponding weight into the objective function, the optimization model can effectively suppress frequency fluctuations and reduce system instability caused by load changes or disturbances. Simultaneously, the dynamic behavioral constraints describe the dynamic characteristics of the system frequency deviation, ensuring that the optimization results meet the actual dynamic response requirements of the power system. This improvement not only strengthens the support capability of frequency regulation resources for frequency stability but also achieves comprehensive optimization of economy, environmental protection, and stability, providing a more comprehensive guarantee for the efficient operation of the power system.

[0034] S3.4. Use an evolutionary algorithm to solve for the optimal frequency modulation resource capacity configuration; In this example, the specific steps for using an evolutionary algorithm to obtain the optimal frequency modulation resource capacity configuration are as follows: S3.41. Initialize the population; Specifically, initializing the population involves: Define population size ; Randomly generated One candidate solution: ; In the formula, Indicates the first The initial solution for each individual; Indicates the first A frequency modulation resource in time Initial capacity configuration; Represents an individual index variable; S3.42. Calculate the fitness value of each individual based on the objective function; Specifically, the fitness value of each individual is calculated based on the objective function as follows: ; In the formula, Indicates the first The fitness value of each individual; S3.43. Perform constraint checks on each individual; The specific process for checking the constraints for each individual is as follows: When individual If a constraint is not met, subtract the penalty term from its fitness value. : ; In the formula, This represents the fitness value after considering the penalty factor; Indicates a penalty item; S3.44. Select outstanding individuals from the current population as the parents of the next generation by using roulette wheel selection; The specific options for roulette are as follows: ; In the formula, Indicates the first The probability of an individual being selected; This represents the sum of the fitness values ​​of all individuals in the current population; S3.45. Generate new offspring individuals through crossover operations, and combine some genes from two parent individuals to form a new solution; The crossover operation specifically refers to: according to Two individuals are randomly selected from the population as parents. and ; Cross operation: ; In the formula, Represents offspring individuals; Indicates the crossover ratio; S3.46. By performing mutation operations, a small-scale random perturbation is made to the genes of some offspring individuals to increase population diversity; The mutation operation specifically includes: ; In the formula, Indicates the result after mutation operation. Capacity configuration of each frequency modulation resource; Represents the original first Capacity configuration of each frequency modulation resource; Indicates variable-length asynchronous operation; Represents a random disturbance term; S3.47. Merge parent and offspring individuals and select the one with the highest fitness. Individuals form a new generation population. When the maximum number of iterations is reached, the optimal solution is output; otherwise, return to step S3.42.

[0035] Specifically, in a capacity optimization allocation method for power system rapid frequency regulation, the steps of using an evolutionary algorithm to solve for the optimal frequency regulation resource capacity allocation play a crucial role in efficiently solving complex optimization problems. Through steps such as population initialization, fitness value calculation, constraint checking, roulette wheel selection, crossover operations, and mutation operations, this method can comprehensively explore the solution space, balance global search and local optimization capabilities, and incorporate penalty terms to handle constraints, ensuring the feasibility of the solution. Finally, after multiple generations of iterative evolution, it outputs the optimal frequency regulation resource capacity allocation scheme that meets the requirements of system stability, economy, and environmental protection, providing scientific and reliable decision support for rapid frequency regulation of power systems and significantly improving the practicality and robustness of the optimization results.

[0036] S3.5 Output the optimal capacity configuration and the minimum target value for each frequency modulation resource.

[0037] S4. Substitute the optimization results into the Euler method for dynamic simulation to verify whether all constraints are met.

[0038] In this example, the specific steps for substituting the optimization results into the Euler method for dynamic simulation to verify whether all constraints are satisfied are as follows: The Euler method is used to solve the problem, and the frequency deviation at each time step is calculated step by step: ; In the formula, Indicates the system's next time step Frequency deviation; Indicates the time step; Check if the constraints are met: When all constraints are satisfied, the simulation results are output. If certain constraints are not met, the type, time point, and degree of constraint violation are recorded and returned to the optimization algorithm for adjustment.

[0039] Specifically, the frequency deviation at each time step is calculated using the Euler method, simulating the dynamic behavior of the power system in actual operation and ensuring that the optimization results meet the requirements of frequency stability and other constraints. If all constraints are satisfied during the simulation, the effectiveness of the optimization scheme is verified; if any constraints are not satisfied, the violation is recorded and fed back to the optimization algorithm for adjustment, thus achieving a closed-loop iteration between optimization and simulation. This process not only ensures the theoretical optimality of the frequency regulation resource capacity configuration scheme but also verifies its feasibility and stability in a real dynamic environment, providing an important guarantee for the reliable operation of the power system.

[0040] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for optimizing capacity allocation in power system fast frequency regulation, characterized in that, Includes the following steps: S1. Collect historical load data of the power system through historical databases and analyze load fluctuation patterns; S2. Use the ARIMA model to predict future load demand and its fluctuation range; S3. By calculating the objective function that minimizes the total system cost and taking into account the impact of carbon emission factors, the environmental variable of carbon emission is introduced into the objective function that minimizes the total system cost to optimize the capacity configuration of frequency regulation resources. S4. Substitute the optimization results into the Euler method for dynamic simulation to verify whether all constraints are met. In step S2, the specific steps for using the ARIMA model to predict future load demand and fluctuation range are as follows: S2.1 Construct an ARIMA model to predict future load demand; S2.2 Calculate the predicted load fluctuation range as the frequency regulation demand input; Among them, load fluctuation range prediction: ； In the formula, This represents the predicted range of load fluctuations. This represents the maximum power value of the load forecast; This represents the minimum power value for load forecasting; In S2.1, the specific construction process for building the ARIMA model to predict future load demand is as follows: Historical load data is divided into training and test sets, and then divided in chronological order. Use the ADF test and KPSS test to determine whether a time series is stationary; When the surface is non-stationary, a difference operation is performed to make it stationary. The difference formula is: ； In the formula, express Order difference; Indicates the lag operator; This indicates that time series data is at a specific point in time. The value; The parameters of the ARIMA model are determined by analysis of the autocorrelation function (ACF) and the partial autocorrelation function (PACF). Based on the selected parameters , , Building an ARIMA model: ； In the formula, This represents the load forecast value; Represents a constant term; Represents the autoregressive coefficient; Indicates the moving average coefficient; Represents white noise; Indicates the order of autoregression; Indicates the order of difference; Indicates the order of the moving average; To evaluate the predictive ability of a model using test set data, common metrics include: Mean square error: ； In the formula, This represents the total number of data points; Indicates mean square error; Mean absolute error: ； In the formula, Indicates the mean absolute error; Mean absolute percentage error: ； In the formula, Indicates the mean absolute percentage error; Finally, output load forecast values .

2. The capacity optimization configuration method for participating in fast frequency regulation of the power system according to claim 1, characterized in that: In step S1, historical load data of the power system is collected through a historical database, and the load fluctuation pattern is analyzed. The load fluctuation characteristics are extracted using the mean and standard deviation calculation method. Historical load data includes power capacity, energy capacity, and charge / discharge efficiency.

3. The capacity optimization configuration method for participating in fast frequency regulation of the power system according to claim 2, characterized in that: In step S3, the specific process of optimizing the capacity allocation of frequency regulation resources by calculating and minimizing the total system cost objective function, and considering the impact of carbon emission factors, by incorporating the environmental variable of carbon emissions into the total system cost objective function, is as follows: S3.1 Construct an objective function to minimize the total system cost; S3.2 Define decision variables; The decision variables specifically include: No. A frequency modulation resource in time capacity configuration ; No. A frequency modulation resource in time Enabled status ; No. A frequency modulation resource in time Reserve capacity ratio ; S3.3, Set constraints; S3.

4. Use an evolutionary algorithm to solve for the optimal frequency modulation resource capacity configuration; S3.5 Output the optimal capacity configuration and the minimum target value for each frequency modulation resource.

4. The capacity optimization configuration method for participating in fast frequency regulation of the power system according to claim 3, characterized in that: In step S3.1, the objective function is constructed to minimize the total system cost as follows: The total cost of the system includes operating costs and investment costs; To minimize the total system cost, optimize the capacity configuration of frequency modulation resources: ； In the formula, This represents the target value to be minimized; Indicates time Operating costs; Indicates time Investment costs; To address the issue of additional costs arising from the dynamic adjustment of frequency modulation resources, time is considered when calculating the objective function to minimize the total system cost. The dynamic adjustment variable of the internal frequency modulation resource is introduced into the objective function of minimizing the total system cost for optimization: ； In the formula, This represents the minimum objective value after introducing dynamically adjusted weights; This indicates that the weights are dynamically adjusted to reflect the differences in frequency modulation performance requirements at different times. Indicates time Dynamic adjustment costs; Considering the trade-off between carbon emission environmental factors and the objective function of minimizing the total system cost, a carbon emission environmental variable is introduced to optimize the objective function of minimizing the total system cost. ； In the formula, This represents the minimum target value after introducing the environmental variable of carbon emissions; Indicates the weight of environmental impact; This represents the environmental variable of carbon emissions, and also represents the first... A frequency modulation resource in time Carbon emissions.

5. The capacity optimization configuration method for participating in fast frequency regulation of the power system according to claim 4, characterized in that: In S3.3, the constraints specifically include: Frequency modulation (FM) requirement constraints: ； In the formula, Indicates time The system frequency regulation requirements; Indicates any time; Indicates the total number of frequency modulation resources; Indicates frequency modulation resource variables; Resource capacity limit: ； In the formula, Indicates the first The maximum capacity of each frequency modulation resource; Represents arbitrary frequency modulation resources; Reserve capacity constraints: ； In the formula, Indicates the first A frequency modulation resource in time Backup requirements; Dynamically adjust constraints: ； In the formula, Indicates the first A frequency modulation resource in time The adjustment amount; Indicates the first The maximum adjustment rate of each frequency modulation resource; Nonnegativity constraint: ； A series of constraints are set to ensure the stability and economy of the system.

6. The capacity optimization configuration method for participating in fast frequency regulation of the power system according to claim 5, characterized in that: Considering the impact of frequency deviation dynamics on system stability within the dynamic behavior of the power system, a penalty term is added to minimize frequency deviation fluctuations: The final optimized objective function that minimizes the total system cost is: ； In the formula, This represents the final optimized objective function value that minimizes the total system cost. Indicates the frequency deviation weight; Indicates system frequency deviation; Indicates the entire time range The integral of the square of the internal frequency deviation is used to measure the magnitude of frequency fluctuation; Then add dynamic behavior constraints: ； In the formula, Represents the system's inertial time constant; This represents the rate of change of the system frequency deviation over time. Indicates the damping coefficient; This represents the power-frequency gain coefficient. This indicates the real-time power change.

7. The capacity optimization configuration method for participating in fast frequency regulation of the power system according to claim 6, characterized in that: In step S3.4, the specific steps for obtaining the optimal frequency modulation resource capacity configuration using an evolutionary algorithm are as follows: S3.

41. Initialize the population; Specifically, initializing the population involves: Define population size ; Randomly generated One candidate solution: ； In the formula, Indicates the first The initial solution for each individual; Indicates the first A frequency modulation resource in time Initial capacity configuration; Represents an individual index variable; S3.

42. Calculate the fitness value of each individual based on the objective function; Specifically, the fitness value of each individual is calculated based on the objective function as follows: ； In the formula, Indicates the first The fitness value of each individual; S3.

43. Perform constraint checks on each individual; The specific process for checking the constraints for each individual is as follows: When individual If a constraint is not met, subtract the penalty term from its fitness value. : ； In the formula, This represents the fitness value after considering the penalty factor; Indicates a penalty item; S3.

44. Select outstanding individuals from the current population as the parents of the next generation by using roulette wheel selection; The specific options for roulette are as follows: ； In the formula, Indicates the first The probability of an individual being selected; This represents the sum of the fitness values ​​of all individuals in the current population; S3.

45. Generate new offspring individuals through crossover operations, and combine some genes from two parent individuals to form a new solution; The crossover operation specifically refers to: according to Two individuals are randomly selected from the population as parents. and ; Cross operation: ； In the formula, Represents offspring individuals; Indicates the crossover ratio; S3.

46. By performing mutation operations, a small-scale random perturbation is made to the genes of some offspring individuals to increase population diversity; The mutation operation specifically includes: ； In the formula, Indicates the result after mutation operation. Capacity configuration of each frequency modulation resource; Represents the original first Capacity configuration of each frequency modulation resource; Indicates variable-length asynchronous operation; Represents a random disturbance term; S3.

47. Merge parent and offspring individuals and select the one with the highest fitness. Individuals form a new generation population. When the maximum number of iterations is reached, the optimal solution is output; otherwise, return to step S3.

42.

8. The capacity optimization configuration method for participating in fast frequency regulation of the power system according to claim 1, characterized in that: In step S4, the specific steps for substituting the optimization results into the Euler method for dynamic simulation to verify whether all constraints are met are as follows: The Euler method is used to solve the problem, and the frequency deviation at each time step is calculated step by step: ； In the formula, Indicates the system's next time step Frequency deviation; Indicates the time step; Check if the constraints are met: When all constraints are satisfied, the simulation results are output. If certain constraints are not met, the type, time point, and degree of constraint violation are recorded and returned to the optimization algorithm for adjustment.

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