Capacity optimization configuration method and apparatus for wind-gas-storage virtual power plant, and device

Abstract

A capacity optimization configuration method and apparatus for a wind-gas-storage virtual power plant, and a device, relating to the technical field of virtual power plants. The method comprises: performing filtering processing on a load power deviation curve to obtain a first frequency signal and a second frequency signal, calculating a minimum rated capacity of a gas turbine generator set on the basis of the first frequency signal, and calculating a minimum rated capacity of a battery energy storage system on the basis of the second frequency signal; calculating an operating cost of the wind-gas-storage virtual power plant on the basis of power of the gas turbine generator set and power of the battery energy storage system, and constructing an objective function on the basis of the operating cost and an environmental cost of the wind-gas-storage virtual power plant; and using a particle swarm optimization algorithm to solve the objective function to obtain optimized power of the gas turbine generator set and optimized power of the battery energy storage system. The defect that a reasonable capacity planning configuration of a current virtual power plant fails to consider the output characteristics of wind power, gas turbines and energy storage batteries, which may lead to unstable power supply during peak load periods, is overcome.

Description

Methods, apparatus and equipment for capacity optimization configuration of wind, gas and energy storage virtual power plants

[0001] Cross-reference to related applications

[0002] This application claims the benefit of Chinese Patent Application No. 202411963604.6, filed on December 30, 2024, the contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of virtual power plant technology, specifically to a capacity optimization configuration method for a wind-gas-storage virtual power plant, a capacity optimization configuration device for a wind-gas-storage virtual power plant, an electronic device, a machine-readable storage medium, and a computer program product. Background Technology

[0004] In recent years, with the continuous adjustment of the power energy supply-side structure, the proportion of renewable energy integrated into the power grid has been increasing. Renewable energy generation, represented by wind power, exhibits volatility and randomness, with weak controllability and difficulty in accurate prediction; this volatility and randomness pose significant challenges to the safe operation of the power system. Virtual power plants, through advanced control, measurement, and communication technologies, aggregate distributed power sources, controllable loads, and energy storage distributed across different regions, and achieve coordinated and optimized operation of multiple energy sources through a higher-level software architecture. This not only effectively solves the problems of random and uncontrollable wind power output but also achieves better economic and environmental benefits. Reasonable capacity planning and configuration of virtual power plants are a prerequisite for fully realizing their potential and a crucial link in ensuring the effectiveness of system investment and operational economy.

[0005] However, current virtual power plant capacity planning and configuration do not consider the output characteristics of wind power, gas turbines, and energy storage batteries, which may lead to power supply instability during peak load periods. Therefore, existing construction task scheduling methods cannot balance solution speed and solution quality. Summary of the Invention

[0006] The purpose of this application is to provide a method, apparatus, and equipment for optimizing the capacity configuration of a wind-gas-storage virtual power plant, in order to solve the defect that the current reasonable capacity planning and configuration of virtual power plants does not take into account the output characteristics of wind power, gas turbines, and energy storage batteries, which may lead to unstable power supply during peak load periods.

[0007] To achieve the above objectives, this application provides a capacity optimization configuration method for a wind-gas-storage virtual power plant, applied to a wind-gas-storage virtual power plant, wherein the wind-gas-storage virtual power plant includes a wind turbine generator set, a gas turbine generator set, and a battery energy storage system, and the method includes:

[0008] Obtain the power grid load curve and the wind power load curve, and determine the load power deviation curve based on the power grid load curve and the wind power load curve;

[0009] The load power deviation curve is filtered to obtain a first frequency signal and a second frequency signal. The minimum rated capacity of the gas turbine generator set is calculated based on the first frequency signal, and the minimum rated capacity of the battery energy storage system is calculated based on the second frequency signal. The minimum value of the second frequency signal is greater than the maximum value of the first frequency signal.

[0010] The operating cost of the wind-gas-storage virtual power plant is calculated based on the power of the gas turbine generator set and the power of the battery energy storage system. An objective function is constructed based on the operating cost and the environmental cost of the wind-gas-storage virtual power plant. The objective function represents the minimum total cost of the wind-gas-storage virtual power plant.

[0011] The objective function is solved using the particle swarm optimization algorithm to obtain the optimized power of the gas turbine generator set and the optimized power of the battery energy storage system.

[0012] Based on the optimized power of the gas turbine generator set, the minimum rated capacity of the gas turbine generator set, the optimized power of the battery energy storage system, and the minimum rated capacity of the battery energy storage system, the capacity optimization configuration value of the wind-gas-storage virtual power plant is determined.

[0013] Optionally, the step of filtering the load power deviation curve to obtain a first frequency signal and a second frequency signal, and calculating the minimum rated capacity of the gas turbine generator set based on the first frequency signal and the minimum rated capacity of the battery energy storage system based on the second frequency signal, includes:

[0014] The load power deviation curve is subjected to a first-order low-pass filter to obtain a first frequency signal and a second frequency signal.

[0015] The maximum value of the first frequency signal is taken as the minimum rated capacity of the gas turbine generator set;

[0016] Based on the second frequency signal, an integral calculation is performed to obtain the energy change curve of the battery energy storage system, and the minimum rated capacity of the battery energy storage system is calculated based on the energy change curve and the state of charge of the battery energy storage system.

[0017] Optionally, the minimum rated capacity of the battery energy storage system is calculated based on the energy change curve and the state of charge of the battery energy storage system using the following formula:

[0018] Among them, C batE represents the minimum rated capacity of the battery energy storage system. bat (t) represents the energy change curve, maxE bat (t) represents the maximum value of the energy change curve, minE bat (t) represents the minimum value of the energy change curve, SOC max The SOC represents the maximum value of the state of charge of the battery energy storage system. min This represents the minimum state of charge of the battery energy storage system.

[0019] Optionally, the calculation of the operating cost of the wind-gas-storage virtual power plant based on the power of the gas turbine generator set and the power of the battery energy storage system, and the construction of an objective function based on the operating cost and the environmental cost of the wind-gas-storage virtual power plant, include:

[0020] The operating cost of a gas turbine generator set is calculated based on the unit power investment cost, power of the gas turbine generator set, annual operation and maintenance cost coefficient, lifespan, discount rate, natural gas price, and natural gas consumption.

[0021] The operating cost of a battery energy storage system is calculated based on the unit capacity investment cost, the actual operating capacity, the unit power investment cost, the power, the annual operation and maintenance cost coefficient, the lifespan, and the discount rate of the battery energy storage system.

[0022] The operating cost of the wind-gas-storage virtual power plant is calculated based on the operating cost of the gas turbine generator set and the operating cost of the battery energy storage system.

[0023] The environmental cost of a wind-gas-storage virtual power plant is calculated based on the cost of pollutant treatment, the amount of pollutants emitted during the operation of the gas turbine generator set, and the actual operating capacity of the gas turbine generator set.

[0024] The objective function is constructed based on the sum of the operating cost and the environmental cost of the wind-gas-storage virtual power plant.

[0025] Optionally, the operating cost of the gas turbine generator set is calculated based on the unit power investment cost, power of the gas turbine generator set, annual operation and maintenance cost coefficient, lifespan, discount rate, natural gas price, and natural gas consumption, using the following formula:

[0026] Among them, C gt c represents the operating cost of a gas turbine generator set.gt P represents the unit power investment cost of a gas turbine generator set. gt c represents the power of the gas turbine generator set. μ The annual operation and maintenance cost coefficient of the gas turbine generator set is represented by r, the discount rate of the gas turbine generator set is represented by n, and the service life of the gas turbine generator set is represented by c. NG V represents the price of natural gas. NG Indicates natural gas consumption;

[0027] The operating cost of a battery energy storage system is calculated based on the unit capacity investment cost, actual operating capacity, unit power investment cost, power, annual maintenance cost coefficient, lifespan, and discount rate. The calculation is performed using the following formula:

[0028] Among them, C ess c represents the operating cost of a battery energy storage system. e c represents the unit capacity investment cost of a battery energy storage system. ess c represents the actual operating capacity of the battery energy storage system. p P represents the unit power investment cost of a battery energy storage system. ess c represents the power of the battery energy storage system. μ denoted by r, which represents the annual operation and maintenance cost coefficient of the battery energy storage system; and denoted by r, which represents the discount rate of the battery energy storage system; and denoted by n, which represents the lifespan of the battery energy storage system.

[0029] The environmental cost of the wind-gas-storage virtual power plant is calculated based on the pollutant treatment cost, the pollutant emissions during the operation of the gas turbine generator set, and the actual operating capacity of the gas turbine generator set, using the following formula:

[0030] Where f2 represents the environmental cost of the wind-gas-storage virtual power plant, and C k The treatment cost for pollutant class k; γ k P represents the emission of pollutant of type k during the operation of the gas turbine; GT (t) represents the actual operating capacity of the gas turbine generator set;

[0031] The calculation of the operating cost of the wind-gas-storage virtual power plant based on the operating cost of the gas turbine generator set and the operating cost of the battery energy storage system includes:

[0032] The operating cost of the wind-gas-storage virtual power plant is calculated based on the sum of the operating costs of the gas turbine generator set and the battery energy storage system.

[0033] Optionally, the step of solving the objective function using the particle swarm optimization algorithm to obtain the optimized power of the gas turbine generator set and the optimized power of the battery energy storage system includes:

[0034] The time constant for filtering the load power deviation curve for each particle in the particle swarm is randomly selected within a set range, so as to calculate the power of the gas turbine generator set and the power of the battery energy storage system for each example using the time constant.

[0035] Determine the initial individual optimal value and the initial global optimal value for all particles in the particle swarm;

[0036] Repeat the following steps to calculate the objective function value of all particles in the particle swarm until the set stopping condition is met:

[0037] If the power of the first gas turbine generator set and the power of the first battery energy storage system corresponding to the time constant of the first particle meet the set constraints, the power of the first gas turbine generator set and the power of the first battery energy storage system are substituted into the objective function for calculation to obtain the objective function value;

[0038] If the objective function value is less than the initial individual optimal value, the objective function value is taken as the latest individual optimal value; and if the objective function value is less than the initial global optimal value, the objective function value is taken as the latest global optimal value.

[0039] The first particle is any particle in the particle swarm; the set stopping condition is that the absolute value of the difference between the objective function values ​​of two particles in two adjacent iterations is greater than a set threshold.

[0040] On the other hand, embodiments of this application also provide a capacity optimization configuration device for a wind-gas-storage virtual power plant, comprising:

[0041] The acquisition module is used to acquire the power grid load curve and the wind power load curve, and to determine the load power deviation curve based on the power grid load curve and the wind power load curve;

[0042] The filtering module is used to filter the load power deviation curve to obtain a first frequency signal and a second frequency signal, and to calculate the minimum rated capacity of the gas turbine generator set based on the first frequency signal and the minimum rated capacity of the battery energy storage system based on the second frequency signal; the minimum value of the second frequency signal is greater than the maximum value of the first frequency signal.

[0043] A construction module is used to calculate the operating cost of the wind-gas-storage virtual power plant based on the power of the gas turbine generator set and the power of the battery energy storage system, and to construct an objective function based on the operating cost and the environmental cost of the wind-gas-storage virtual power plant; the objective function represents the minimum total cost of the wind-gas-storage virtual power plant.

[0044] The solution module is used to solve the objective function using the particle swarm optimization algorithm to obtain the optimized power of the gas turbine generator set and the optimized power of the battery energy storage system.

[0045] The determination module is used to determine the capacity optimization configuration value of the wind-gas-storage virtual power plant based on the optimized power of the gas turbine generator set, the minimum rated capacity of the gas turbine generator set, the optimized power of the battery energy storage system, and the minimum rated capacity of the battery energy storage system.

[0046] Optionally, the step of filtering the load power deviation curve to obtain a first frequency signal and a second frequency signal, and calculating the minimum rated capacity of the gas turbine generator set based on the first frequency signal and the minimum rated capacity of the battery energy storage system based on the second frequency signal, includes:

[0047] The load power deviation curve is subjected to a first-order low-pass filter to obtain a first frequency signal and a second frequency signal.

[0048] The maximum value of the first frequency signal is taken as the minimum rated capacity of the gas turbine generator set;

[0049] Based on the second frequency signal, an integral calculation is performed to obtain the energy change curve of the battery energy storage system, and the minimum rated capacity of the battery energy storage system is calculated based on the energy change curve and the state of charge of the battery energy storage system.

[0050] Optionally, the minimum rated capacity of the battery energy storage system is calculated based on the energy change curve and the state of charge of the battery energy storage system using the following formula:

[0051] Among them, C bat E represents the minimum rated capacity of the battery energy storage system. bat (t) represents the energy change curve, maxE bat (t) represents the maximum value of the energy change curve, minE bat (t) represents the minimum value of the energy change curve, SOC max The SOC represents the maximum value of the state of charge of the battery energy storage system. min This represents the minimum state of charge of the battery energy storage system.

[0052] Optionally, the calculation of the operating cost of the wind-gas-storage virtual power plant based on the power of the gas turbine generator set and the power of the battery energy storage system, and the construction of an objective function based on the operating cost and the environmental cost of the wind-gas-storage virtual power plant, include:

[0053] The operating cost of a gas turbine generator set is calculated based on the unit power investment cost, power of the gas turbine generator set, annual operation and maintenance cost coefficient, lifespan, discount rate, natural gas price, and natural gas consumption.

[0054] The operating cost of a battery energy storage system is calculated based on the unit capacity investment cost, the actual operating capacity, the unit power investment cost, the power, the annual operation and maintenance cost coefficient, the lifespan, and the discount rate of the battery energy storage system.

[0055] The operating cost of the wind-gas-storage virtual power plant is calculated based on the operating cost of the gas turbine generator set and the operating cost of the battery energy storage system.

[0056] The environmental cost of a wind-gas-storage virtual power plant is calculated based on the cost of pollutant treatment, the amount of pollutants emitted during the operation of the gas turbine generator set, and the actual operating capacity of the gas turbine generator set.

[0057] The objective function is constructed based on the sum of the operating cost and the environmental cost of the wind-gas-storage virtual power plant.

[0058] Optionally, the operating cost of the gas turbine generator set is calculated based on the unit power investment cost, power of the gas turbine generator set, annual operation and maintenance cost coefficient, lifespan, discount rate, natural gas price, and natural gas consumption, using the following formula:

[0059] Among them, C gt c represents the operating cost of a gas turbine generator set. gt P represents the unit power investment cost of a gas turbine generator set. gt c represents the power of the gas turbine generator set. μ The annual operation and maintenance cost coefficient of the gas turbine generator set is represented by r, the discount rate of the gas turbine generator set is represented by n, and the service life of the gas turbine generator set is represented by c. NG V represents the price of natural gas. NG Indicates natural gas consumption;

[0060] The operating cost of a battery energy storage system is calculated based on the unit capacity investment cost, actual operating capacity, unit power investment cost, power, annual maintenance cost coefficient, lifespan, and discount rate. The calculation is performed using the following formula:

[0061] Among them, C ess c represents the operating cost of a battery energy storage system. e c represents the unit capacity investment cost of a battery energy storage system. ess c represents the actual operating capacity of the battery energy storage system. p P represents the unit power investment cost of a battery energy storage system. ess c represents the power of the battery energy storage system. μ denoted by r, which represents the annual operation and maintenance cost coefficient of the battery energy storage system; and denoted by r, which represents the discount rate of the battery energy storage system; and denoted by n, which represents the lifespan of the battery energy storage system.

[0062] The environmental cost of the wind-gas-storage virtual power plant is calculated based on the pollutant treatment cost, the pollutant emissions during the operation of the gas turbine generator set, and the actual operating capacity of the gas turbine generator set, using the following formula:

[0063] Where f2 represents the environmental cost of the wind-gas-storage virtual power plant, and C k The treatment cost for pollutant class k; γ k P represents the emission of pollutant of type k during the operation of the gas turbine; GT (t) represents the actual operating capacity of the gas turbine generator set;

[0064] The calculation of the operating cost of the wind-gas-storage virtual power plant based on the operating cost of the gas turbine generator set and the operating cost of the battery energy storage system includes:

[0065] The operating cost of the wind-gas-storage virtual power plant is calculated based on the sum of the operating costs of the gas turbine generator set and the battery energy storage system.

[0066] Optionally, the step of solving the objective function using the particle swarm optimization algorithm to obtain the optimized power of the gas turbine generator set and the optimized power of the battery energy storage system includes:

[0067] The time constant for filtering the load power deviation curve for each particle in the particle swarm is randomly selected within a set range, so as to calculate the power of the gas turbine generator set and the power of the battery energy storage system for each example using the time constant.

[0068] Determine the initial individual optimal value and the initial global optimal value for all particles in the particle swarm;

[0069] Repeat the following steps to calculate the objective function value of all particles in the particle swarm until the set stopping condition is met:

[0070] If the power of the first gas turbine generator set and the power of the first battery energy storage system corresponding to the time constant of the first particle meet the set constraints, the power of the first gas turbine generator set and the power of the first battery energy storage system are substituted into the objective function for calculation to obtain the objective function value;

[0071] If the objective function value is less than the initial individual optimal value, the objective function value is taken as the latest individual optimal value; and if the objective function value is less than the initial global optimal value, the objective function value is taken as the latest global optimal value.

[0072] The first particle is any particle in the particle swarm; the set stopping condition is that the absolute value of the difference between the objective function values ​​of two particles in two adjacent iterations is greater than a set threshold.

[0073] On the other hand, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-mentioned capacity optimization configuration method for a wind-gas-storage virtual power plant.

[0074] On the other hand, this application also provides a machine-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described capacity optimization configuration method for a wind-gas-storage virtual power plant.

[0075] On the other hand, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-mentioned capacity optimization configuration method for a wind-gas-storage virtual power plant.

[0076] Through the above technical solution, this application embodiment filters the load power deviation curve, decomposes the power signal into a first frequency signal and a second frequency signal, calculates the minimum rated capacity of the gas turbine generator set based on the lower frequency first frequency signal, and calculates the minimum rated capacity of the battery energy storage system based on the higher frequency second frequency signal. Finally, using the minimum total cost of the wind-gas-storage virtual power plant throughout its entire life cycle as the objective function, the capacity configuration scheme of the wind-gas-storage virtual power plant is obtained through particle swarm optimization. This application embodiment, based on the output characteristics of wind power, gas turbine, and energy storage battery, combines wind turbine generator sets, gas turbine generator sets, and battery energy storage systems to form a wind-gas-storage virtual power plant. The gas turbine generator set handles the low-frequency portion of load fluctuations, while the battery energy storage system handles the high-frequency portion of load fluctuations. This fully utilizes the complementary characteristics of wind power generation, gas turbine, and energy storage battery, smooths wind power output, and improves power supply stability.

[0077] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0078] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:

[0079] Figure 1 is a flowchart illustrating the capacity optimization configuration method for the wind-gas-storage virtual power plant provided in this application;

[0080] Figure 2 is a schematic diagram of a typical daily power output curve of the wind turbine generator set provided in this application;

[0081] Figure 3 is a schematic diagram of the power grid load curve provided in this application;

[0082] Figure 4 is a schematic diagram of the power target value undertaken by the gas turbine generator set and battery energy storage system provided in this application;

[0083] Figure 5 is a schematic diagram illustrating the effect of different time constants on the filtering effect provided in this application;

[0084] Figure 6 is a schematic diagram illustrating the impact of different time constants provided in this application on the capacity configuration of a wind, gas, and energy storage virtual power plant;

[0085] Figure 7 is a schematic diagram of the power curve of the gas turbine generator set provided in this application;

[0086] Figure 8 is a schematic diagram of the power curve of the battery energy storage system provided in this application;

[0087] Figure 9 is a schematic diagram of the SOC curve of the battery energy storage system provided in this application;

[0088] Figure 10 is one of the structural schematic diagrams of the capacity optimization configuration device of the wind-gas-storage virtual power plant provided in this application;

[0089] Figure 11 is a second structural schematic diagram of the capacity optimization configuration device of the wind-gas-storage virtual power plant provided in this application;

[0090] Figure 12 is the third structural schematic diagram of the capacity optimization configuration device of the wind-gas-storage virtual power plant provided in this application;

[0091] Figure 13 is a schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

[0092] The specific embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the embodiments of this application.

[0093] Method Implementation Examples

[0094] Referring to Figure 1, this application provides a capacity optimization configuration method for a wind-gas-storage virtual power plant, applied to a wind-gas-storage virtual power plant. The wind-gas-storage virtual power plant includes a wind turbine generator set, a gas turbine generator set (hereinafter referred to as a gas turbine), and a battery energy storage system. The method includes:

[0095] Step 100: Obtain the power grid load curve and the wind power load curve, and determine the load power deviation curve based on the power grid load curve and the wind power load curve.

[0096] The electronic equipment acquires the grid load power curve and wind power load power curve for a typical day, and obtains the load power deviation curve ΔP(t) by subtracting the grid load power curve from the wind power load power curve. The electronic equipment can also acquire other power and load data, including: wind power generation, gas turbine generator power, battery energy storage system power, and battery energy storage system state of charge for a typical day. In addition, the electronic equipment can acquire meteorological parameters and unit operation status monitoring data, including: other operation status parameters of wind turbine generators, gas turbine generators, and battery energy storage systems. It should be noted that this application embodiment will judge the correctness of the collected data; if the collected parameter value is greater than three times the standard deviation, it is considered an outlier and discarded.

[0097] Step 200: Filter the load power deviation curve to obtain a first frequency signal and a second frequency signal, and calculate the minimum rated capacity of the gas turbine generator set based on the first frequency signal, and calculate the minimum rated capacity of the battery energy storage system based on the second frequency signal; the minimum value of the second frequency signal is greater than the maximum value of the first frequency signal.

[0098] This application embodiment can use various low-pass filtering methods to filter the load power deviation curve, obtaining a first frequency signal as a low-frequency signal and a second frequency signal as a high-frequency signal. Then, based on the first frequency signal, the minimum rated capacity of the gas turbine generator set is calculated, and based on the second frequency signal, the minimum rated capacity of the battery energy storage system is calculated, thus enabling the gas turbine generator set to handle the low-frequency portion of load fluctuations, and the battery energy storage system to handle the high-frequency portion of load fluctuations.

[0099] In one embodiment, step 200, filtering the load power deviation curve to obtain a first frequency signal and a second frequency signal, and calculating the minimum rated capacity of the gas turbine generator set based on the first frequency signal and the minimum rated capacity of the battery energy storage system based on the second frequency signal, includes:

[0100] Step 210: Perform a first-order low-pass filter on the load power deviation curve to obtain a first frequency signal and a second frequency signal.

[0101] Step 220: Take the maximum value of the first frequency signal as the minimum rated capacity of the gas turbine generator set.

[0102] Step 230: Perform integration calculation based on the second frequency signal to obtain the energy change curve of the battery energy storage system, and calculate the minimum rated capacity of the battery energy storage system based on the energy change curve and the state of charge of the battery energy storage system.

[0103] Specifically, the Automatic Gain Control (AGC) of electronic equipment, based on the AGC command load and wind power load, obtains the load power deviation curve ΔP(t) by subtracting the grid load power curve from the wind power generation power curve. Then, a first-order low-pass filter is used to filter the load power deviation curve ΔP(t) to obtain the low-frequency signal P. L (t)(first frequency signal); compare the load power deviation curve ΔP(t) with the low frequency signal P L The difference between (t) and (t) is used to obtain the high-frequency signal P. H (t)(second frequency signal), i.e.: P L (t)=αΔP(t-Δt)+(1-α)ΔP(t); P H (t)=ΔP(t)-P L (t);

[0104] Where Δt is the time interval for power sampling, and T is the time constant of the first-order low-pass filter.

[0105] low frequency signal P L (t) is undertaken by the gas turbine generator set in the wind-gas-storage virtual power plant, therefore the minimum rated capacity PGT of the gas turbine generator set is: PGT = Max P L (t); that is, this application will use the low-frequency signal P L The maximum value of (t) is taken as the minimum rated capacity of the gas turbine generator set.

[0106] High-frequency signal P H (t) is handled by the battery energy storage system in the wind-gas-storage virtual power plant, with high-frequency signal P. H The mean μ and variance σ of (t) are respectively:

[0107] Where N represents the number of samples, P H (t) represents the charging and discharging power of the battery energy storage system at time t. The rated power P of the battery energy storage system... bat For: P bat =max{|μ-3σ|,|μ+3σ|};

[0108] This application embodiment addresses the high-frequency signal P. H Integrating over the interval from 0 to t yields the energy change curve E of the battery energy storage system. bat (t). To improve the service life of the energy storage battery system, this application embodiment controls the state of charge (SOC) of the battery energy storage system within a certain range. Therefore, this application calculates the minimum rated capacity of the battery energy storage system based on the energy change curve and the SOC of the battery energy storage system. Thus, the minimum rated capacity C of the battery energy storage system... bat for:

[0109] Among them, C bat E represents the minimum rated capacity of the battery energy storage system. bat (t) represents the energy change curve, maxE bat (t) represents the maximum value of the energy change curve, minE bat (t) represents the minimum value of the energy change curve, SOC max This represents the maximum (upper limit) state of charge (SOC) of the battery energy storage system, which can be taken as 90%. min This represents the minimum (lower limit) state of charge of the battery energy storage system, which can be taken as 20%.

[0110] Step 300: Calculate the operating cost of the wind-gas-storage virtual power plant based on the power of the gas turbine generator set and the power of the battery energy storage system, and construct an objective function based on the operating cost and the environmental cost of the wind-gas-storage virtual power plant.

[0111] The electronic device constructs the objective function based on the sum of the operating cost and the environmental cost of the wind-gas-storage virtual power plant. The objective function represents minimizing the total cost of the wind-gas-storage virtual power plant. This application embodiment constructs a mathematical model for the capacity optimization configuration of a wind-gas-storage virtual power plant involving the entire life-cycle operating cost and environmental protection cost, with the objective function being: min C = f1 + f2;

[0112] Where C, f1, and f2 represent the total cost, operating cost, and environmental cost of the virtual power plant throughout its entire lifecycle, respectively. Solving the objective function yields the optimal power outputs of the gas turbine generator set and the battery energy storage system that minimize the total cost.

[0113] The operating cost of the aforementioned wind-gas-storage virtual power plant is calculated through the following steps:

[0114] Step 310: Calculate the operating cost of the gas turbine generator set based on the unit power investment cost, power of the gas turbine generator set, annual operation and maintenance cost coefficient of the gas turbine generator set, lifespan of the gas turbine generator set, discount rate of the gas turbine generator set, natural gas price and natural gas consumption.

[0115] In one embodiment, the operating cost of a gas turbine generator set is calculated based on the unit power investment cost, power output, annual maintenance cost coefficient, lifespan, discount rate, natural gas price, and natural gas consumption. This calculation is performed using the following formula:

[0116] Among them, C gt c represents the operating cost of a gas turbine generator set. gt This represents the unit power investment cost of a gas turbine generator set (unit: yuan / kW), for example, 2500 yuan / kW; P gt c represents the power of the gas turbine generator set. μ represents the annual operation and maintenance cost coefficient of the gas turbine generator set, for example, 0.06; r represents the discount rate of the gas turbine generator set, for example, 0.1; n represents the service life of the gas turbine generator set, for example, 30 years; c NG This represents the price of natural gas, for example, 2.4 yuan / Nm³. 3 V NG This indicates natural gas consumption, in Nm³. 3 / d. That is, the daily cost of a gas turbine generator set over its entire life cycle mainly includes energy costs and operation and maintenance costs.

[0117] Step 320: Calculate the operating cost of the battery energy storage system based on the unit capacity investment cost, actual operating capacity, unit power investment cost, power, annual operation and maintenance cost coefficient, lifespan, and discount rate of the battery energy storage system.

[0118] In one embodiment, the operating cost of the battery energy storage system is calculated based on the unit capacity investment cost, the actual operating capacity, the unit power investment cost, the power, the annual operation and maintenance cost coefficient, the lifespan, and the discount rate. This calculation is performed using the following formula:

[0119] Among them, C ess c represents the operating cost of a battery energy storage system. e This represents the unit capacity investment cost of a battery energy storage system (unit: yuan / kW·h), for example, 1600 yuan / kW; c ess c represents the actual operating capacity of the battery energy storage system. p This represents the unit power investment cost of a battery energy storage system (unit: yuan / kW), for example, 1200 yuan / kW; P ess c represents the power of the battery energy storage system. μ represents the annual operation and maintenance cost coefficient of the battery energy storage system, for example, it can be taken as 0.05; r represents the discount rate of the battery energy storage system, for example, it can be taken as 0.1; n represents the lifespan of the battery energy storage system, for example, it can be taken as 5 years.

[0120] Step 330: Calculate the operating cost of the wind-gas-storage virtual power plant based on the operating cost of the gas turbine generator set and the operating cost of the battery energy storage system.

[0121] The electronic device calculates the operating cost of the wind-gas-storage virtual power plant based on the sum of the operating costs of the gas turbine generator set and the battery energy storage system. That is, the expression for the operating cost of the virtual power plant over its entire lifecycle is: f1 = C ess +C gt；

[0122] Where f1 represents the operating cost of the wind-gas-storage virtual power plant, which is equal to the sum of the operating cost of the gas turbine generator set and the operating cost of the battery energy storage system.

[0123] In addition, the operating cost of the wind-gas-storage virtual power plant is calculated through the following steps: based on the pollutant treatment cost, the pollutant emissions during the operation of the gas turbine generator set, and the actual operating capacity of the gas turbine generator set, the environmental cost of the wind-gas-storage virtual power plant is calculated.

[0124] In one embodiment, the environmental cost of the wind-gas-storage virtual power plant is calculated based on the pollutant treatment cost, the pollutant emissions during the operation of the gas turbine generator set, and the actual operating capacity of the gas turbine generator set, using the following formula:

[0125] Where f2 represents the environmental cost of the wind-gas-storage virtual power plant, and C k The treatment cost for pollutant class k; γ k P represents the emission of pollutant of type k during the operation of the gas turbine; GT (t) represents the actual operating capacity of the gas turbine generator set. Where k = 1, 2, 3, representing the three main pollutants emitted by the gas turbine generator set: CO2, NO2, NO3, and NO4. X And SO2.

[0126] Step 400: Solve the objective function using the particle swarm optimization algorithm to obtain the optimized power of the gas turbine generator set and the optimized power of the battery energy storage system.

[0127] It should be noted that the following constraints should be considered when configuring the capacity of gas turbine generator sets and battery energy storage systems:

[0128] (1) Energy conservation constraint. The sum of the output from wind power, gas, and energy storage should be consistent with the power load of the power grid. P WT (t)+P GT (t)+P bat (t)=P load (t);

[0129] Among them, P WT (t) represents the output value of the wind turbine generator set, P GT (t) represents the output value of the gas turbine generator set, P bat (t) Output value of the battery energy storage system, P load (t) represents the power of the grid load.

[0130] (2) Constraints on grid-connected power fluctuations. Grid-connected power fluctuations must meet the relevant national standards or set values. ε < ε max ;

[0131] Where ε represents grid-connected power fluctuation, ε max This indicates the maximum value of grid-connected power fluctuation that meets the corresponding national standards or set values.

[0132] (3) Power and SOC constraints of battery energy storage system:

[0133] Among them, P m inbat P maxbat These represent the lower and upper limits of the power output of a battery energy storage system; SOC min SOC max These represent the lower and upper limits of the battery energy storage system capacity, respectively.

[0134] (4) Constraints on gas turbine output and gradeability:

[0135] Among them, P GT (t) represents the output value of the gas turbine generator set, P min GT P max GT These represent the lower and upper limits of the output of the gas turbine generator set, respectively; λ GT This is the upper limit for the gradient of the gas turbine generator set.

[0136] In one embodiment, step 400, solving the objective function using a particle swarm optimization algorithm to obtain the optimized power of the gas turbine generator set and the optimized power of the battery energy storage system, includes:

[0137] Step 410: The time constant for filtering the load power deviation curve of each particle in the particle swarm is randomly selected within a set range, so as to calculate the power of the gas turbine generator set and the power of the battery energy storage system for each example through the time constant.

[0138] Step 420: Determine the initial individual optimal value and the initial global optimal value of all particles in the particle swarm.

[0139] Repeat the following steps to calculate the objective function value of all particles in the particle swarm until the set stopping condition is met:

[0140] Step 430: If the power of the first gas turbine generator set and the power of the first battery energy storage system corresponding to the time constant of the first particle meet the set constraints, substitute the power of the first gas turbine generator set and the power of the first battery energy storage system into the objective function for calculation to obtain the objective function value.

[0141] Step 440: If the objective function value is less than the initial individual optimal value, the objective function value is taken as the latest individual optimal value; and if the objective function value is less than the initial global optimal value, the objective function value is taken as the latest global optimal value.

[0142] The first particle is any particle in the particle swarm; the set stopping condition is that the absolute value of the difference between the objective function values ​​of two particles in two adjacent iterations is greater than a set threshold.

[0143] This application embodiment utilizes the particle swarm optimization algorithm to solve for the capacity optimization configuration of the wind-gas-storage virtual power plant, obtaining the optimal configuration scheme. Specifically, the capacity optimization configuration process based on the particle swarm optimization algorithm is as follows:

[0144] Step (1): Initialize the particle swarm, where each particle contains a time constant T of a first-order low-pass filter. Calculate the corresponding two parameters based on the time constant: the power of the gas turbine generator set and the power of the battery energy storage system. Within the range of the gas turbine and battery energy storage system capacities, each particle is randomly assigned a value; and the individual optimal value pbest and the global optimal value gbest are initialized based on the random values ​​of the particles.

[0145] Step (2): Calculate the power of the gas turbine and the power of the battery energy storage system for each particle, as well as the state of charge of the battery energy storage system. Compare these with the constraints mentioned above to determine whether they meet the power demand and power quality requirements of the power grid. If they do, calculate the objective function.

[0146] Step (3): Compare the objective function corresponding to each particle value with pbest and gbest. If the current objective function value is better than pbest, then the current value is taken as the new pbest; if the current objective function value is better than gbest, then the current value is taken as the new gbest. This is to update the values ​​of the gas turbine generator power and battery energy storage system power in each particle, and to adjust the search direction and speed of the particles in the optimization algorithm.

[0147] Step (4): Determine the objective function |minC i+1 -minC i If |≤e is satisfied, that is, the minimum cost that meets the capacity configuration conditions obtained in the (i+1)th iteration is compared with the result of the i-th iteration. If the set precision e is satisfied, the calculation ends; otherwise, proceed to step (2).

[0148] Therefore, this embodiment uses a particle swarm optimization algorithm to solve for the time constant T of the first-order low-pass filter element that satisfies the optimal objective function value, and then solves for the optimized power of the gas turbine generator set and the optimized power of the battery energy storage system under the optimal time constant T of the first-order low-pass filter element. This allows for the configuration of the gas turbine capacity and the battery energy storage system capacity. It should be noted that in other embodiments, optimization algorithms such as genetic algorithms can also be used to solve the objective function.

[0149] Step 500: Based on the optimized power of the gas turbine generator set, the minimum rated capacity of the gas turbine generator set, the optimized power of the battery energy storage system, and the minimum rated capacity of the battery energy storage system, determine the capacity optimization configuration value of the wind-gas-storage virtual power plant.

[0150] In this embodiment, step 200 obtains the minimum rated capacity of the gas turbine generator set and the minimum rated capacity of the battery energy storage system, thereby determining the required minimum rated capacity (configuration capacity lower limit) of the gas turbine generator set and the required minimum rated capacity (configuration capacity lower limit) of the battery energy storage system. If the optimized power of the gas turbine generator set obtained through particle swarm optimization is greater than or equal to the minimum rated capacity of the gas turbine generator set, the optimized power of the gas turbine generator set is determined as the capacity optimization configuration value of the gas turbine generator set in the wind-gas-storage virtual power plant. Similarly, if the optimized power of the battery energy storage system obtained through particle swarm optimization is greater than or equal to the minimum rated capacity of the battery energy storage system, the optimized power of the battery energy storage system is determined as the capacity optimization configuration value of the battery energy storage system in the wind-gas-storage virtual power plant. In one embodiment, the time constant T of the first-order low-pass filter element that satisfies the optimal objective function value is 8 minutes. At this time, the rated power of the gas turbine generator set after capacity optimization configuration is 70.06 MW, and the rated power and rated capacity of the battery energy storage system are 4.98 MW and 8.38 MWh, respectively.

[0151] The following specific examples illustrate how the time constant T of the first-order low-pass filter affects the capacity of the gas turbine generator set and the battery energy storage system.

[0152] First, typical power data from a wind turbine generator set throughout the year is selected as the research object. As shown in Figure 2, based on the active power sampling data of a typical day in autumn for this wind turbine generator set, the capacity configuration of a virtual power plant using wind turbine, gas turbine, and battery storage is performed. The power grid load of a certain region is shown in Figure 3. The target power value ΔP required by the gas turbine and battery storage is shown in Figure 4.

[0153] Next, we consider the effect of the smoothing time constant on the filtering effect. Figure 5 shows the output curves of ΔP(t) after smoothing by the filtering stage when the low-pass filter smoothing time constant is T = 5 min, T = 1 h, and T = 24 h. As shown in Figure 5, the output curve of ΔP(t) becomes smoother as the time constant T increases. When T = 24 h, the power difference after the low-pass filter stage is approximately a decreasing straight line.

[0154] Next, we consider the impact of the smoothing time constant on the capacity of the gas turbine and the energy storage battery. Taking the time constant T of the low-pass filter stage as 1 min, 2 min, 5 min, 8 min, 10 min, and 30 min, the changing trends of the gas turbine's rated power PGT, the battery energy storage system's rated power PB, and rated capacity EB are shown in Figure 6. As shown in Figure 6, increasing the time constant T of the first-order low-pass filter stage results in a smaller maximum fluctuation after filtering, gradually reducing the required rated power of the gas turbine, while increasing the required rated power and rated capacity of the battery energy storage system. When the time constant of the low-pass filter stage increases from 8 min to 30 min, the rated power of the gas turbine almost no longer changes. Therefore, a time constant of 8 min for the low-pass filter stage achieves a relatively ideal filtering effect, simultaneously considering the economic efficiency of both the gas turbine and battery energy storage system capacity configurations. At this point, the rated power of the gas turbine is 70.06 MW, and the rated power and rated capacity of the battery energy storage system are 4.98 MW and 8.38 MWh, respectively. With a time constant of 8 minutes for the low-pass filter, the trends of gas turbine power, battery energy storage system power, and battery energy storage system SOC over time are shown in Figures 7-9. The horizontal axis in Figures 7-9 is in hours. Figure 7 shows a schematic diagram of the gas turbine generator power changing over time. Figure 8 shows a schematic diagram of the battery energy storage system power changing over time. Figure 9 shows a schematic diagram of the battery energy storage system SOC changing over time.

[0155] Based on the characteristics of the power supply side, energy storage side, and load side, this application embodiment designs a power allocation strategy for a wind-gas-storage virtual power plant based on the first-order low-pass filtering principle. With the goal of minimizing the total cost of the wind-gas-storage virtual power plant, the capacity of the wind-gas-storage virtual power plant is optimized, achieving efficient utilization and economical operation of clean energy.

[0156] This application embodiment filters the load power deviation curve, decomposing the power signal into a first frequency signal and a second frequency signal. The minimum rated capacity of the gas turbine generator set is calculated based on the lower-frequency first frequency signal, and the minimum rated capacity of the battery energy storage system is calculated based on the higher-frequency second frequency signal. Finally, using the minimum total lifecycle cost of the wind-gas-storage virtual power plant as the objective function, the capacity configuration scheme of the wind-gas-storage virtual power plant is obtained through particle swarm optimization. This application embodiment, based on the output characteristics of wind power, gas turbine, and energy storage batteries, combines wind turbine generator sets, gas turbine generator sets, and battery energy storage systems to form a wind-gas-storage virtual power plant. The gas turbine generator set handles the low-frequency portion of load fluctuations, while the battery energy storage system handles the high-frequency portion. This fully utilizes the complementary characteristics of wind power generation, gas turbine, and energy storage batteries, smoothing wind power output and improving power supply stability.

[0157] Device Examples

[0158] Referring to Figure 10, in another aspect, this application embodiment also provides a capacity optimization configuration device for a wind-gas-storage virtual power plant, comprising:

[0159] The acquisition module 1001 is used to acquire the power grid load curve and the wind power load curve, and to determine the load power deviation curve based on the power grid load curve and the wind power load curve;

[0160] The filtering module 1002 is used to filter the load power deviation curve to obtain a first frequency signal and a second frequency signal, and to calculate the minimum rated capacity of the gas turbine generator set based on the first frequency signal and the minimum rated capacity of the battery energy storage system based on the second frequency signal; the minimum value of the second frequency signal is greater than the maximum value of the first frequency signal.

[0161] Module 1003 is used to calculate the operating cost of the wind-gas-storage virtual power plant based on the power of the gas turbine generator set and the power of the battery energy storage system, and to construct an objective function based on the operating cost and the environmental cost of the wind-gas-storage virtual power plant; the objective function represents the minimum total cost of the wind-gas-storage virtual power plant.

[0162] The solution module 1004 is used to solve the objective function using the particle swarm optimization algorithm to obtain the optimized power of the gas turbine generator set and the optimized power of the battery energy storage system.

[0163] The determination module 1005 is used to determine the capacity optimization configuration value of the wind-gas-storage virtual power plant based on the optimized power of the gas turbine generator set, the minimum rated capacity of the gas turbine generator set, the optimized power of the battery energy storage system, and the minimum rated capacity of the battery energy storage system.

[0164] Optionally, the step of filtering the load power deviation curve to obtain a first frequency signal and a second frequency signal, and calculating the minimum rated capacity of the gas turbine generator set based on the first frequency signal and the minimum rated capacity of the battery energy storage system based on the second frequency signal, includes:

[0165] The load power deviation curve is subjected to a first-order low-pass filter to obtain a first frequency signal and a second frequency signal.

[0166] The maximum value of the first frequency signal is taken as the minimum rated capacity of the gas turbine generator set;

[0167] Based on the second frequency signal, an integral calculation is performed to obtain the energy change curve of the battery energy storage system, and the minimum rated capacity of the battery energy storage system is calculated based on the energy change curve and the state of charge of the battery energy storage system.

[0168] Optionally, the minimum rated capacity of the battery energy storage system is calculated based on the energy change curve and the state of charge of the battery energy storage system using the following formula:

[0169] Among them, C bat E represents the minimum rated capacity of the battery energy storage system. bat (t) represents the energy change curve, maxE bat (t) represents the maximum value of the energy change curve, minE bat (t) represents the minimum value of the energy change curve, SOC max The SOC represents the maximum value of the state of charge of the battery energy storage system. min This represents the minimum state of charge of the battery energy storage system.

[0170] Optionally, the calculation of the operating cost of the wind-gas-storage virtual power plant based on the power of the gas turbine generator set and the power of the battery energy storage system, and the construction of an objective function based on the operating cost and the environmental cost of the wind-gas-storage virtual power plant, include:

[0171] The operating cost of a gas turbine generator set is calculated based on the unit power investment cost, power of the gas turbine generator set, annual operation and maintenance cost coefficient, lifespan, discount rate, natural gas price, and natural gas consumption.

[0172] The operating cost of a battery energy storage system is calculated based on the unit capacity investment cost, the actual operating capacity, the unit power investment cost, the power, the annual operation and maintenance cost coefficient, the lifespan, and the discount rate of the battery energy storage system.

[0173] The operating cost of the wind-gas-storage virtual power plant is calculated based on the operating cost of the gas turbine generator set and the operating cost of the battery energy storage system.

[0174] The environmental cost of a wind-gas-storage virtual power plant is calculated based on the cost of pollutant treatment, the amount of pollutants emitted during the operation of the gas turbine generator set, and the actual operating capacity of the gas turbine generator set.

[0175] The objective function is constructed based on the sum of the operating cost and the environmental cost of the wind-gas-storage virtual power plant.

[0176] Optionally, the operating cost of the gas turbine generator set is calculated based on the unit power investment cost, power of the gas turbine generator set, annual operation and maintenance cost coefficient, lifespan, discount rate, natural gas price, and natural gas consumption, using the following formula:

[0177] Among them, C gt c represents the operating cost of a gas turbine generator set. gt P represents the unit power investment cost of a gas turbine generator set. gt c represents the power of the gas turbine generator set. μ The annual operation and maintenance cost coefficient of the gas turbine generator set is represented by r, the discount rate of the gas turbine generator set is represented by n, and the service life of the gas turbine generator set is represented by c. NG V represents the price of natural gas. NG Indicates natural gas consumption;

[0178] The operating cost of a battery energy storage system is calculated based on the unit capacity investment cost, actual operating capacity, unit power investment cost, power, annual maintenance cost coefficient, lifespan, and discount rate. The calculation is performed using the following formula:

[0179] Among them, C ess c represents the operating cost of a battery energy storage system. e c represents the unit capacity investment cost of a battery energy storage system. ess c represents the actual operating capacity of the battery energy storage system. p P represents the unit power investment cost of a battery energy storage system. ess c represents the power of the battery energy storage system. μ denoted by r, which represents the annual operation and maintenance cost coefficient of the battery energy storage system; and denoted by r, which represents the discount rate of the battery energy storage system; and denoted by n, which represents the lifespan of the battery energy storage system.

[0180] The environmental cost of the wind-gas-storage virtual power plant is calculated based on the pollutant treatment cost, the pollutant emissions during the operation of the gas turbine generator set, and the actual operating capacity of the gas turbine generator set, using the following formula:

[0181] Where f2 represents the environmental cost of the wind-gas-storage virtual power plant, and C k The treatment cost for pollutant class k; γ k P represents the emission of pollutant of type k during the operation of the gas turbine; GT(t) represents the actual operating capacity of the gas turbine generator set;

[0182] The calculation of the operating cost of the wind-gas-storage virtual power plant based on the operating cost of the gas turbine generator set and the operating cost of the battery energy storage system includes:

[0183] The operating cost of the wind-gas-storage virtual power plant is calculated based on the sum of the operating costs of the gas turbine generator set and the battery energy storage system.

[0184] Optionally, the step of solving the objective function using the particle swarm optimization algorithm to obtain the optimized power of the gas turbine generator set and the optimized power of the battery energy storage system includes:

[0185] The time constant for filtering the load power deviation curve for each particle in the particle swarm is randomly selected within a set range, so as to calculate the power of the gas turbine generator set and the power of the battery energy storage system for each example using the time constant.

[0186] Determine the initial individual optimal value and the initial global optimal value for all particles in the particle swarm;

[0187] Repeat the following steps to calculate the objective function value of all particles in the particle swarm until the set stopping condition is met:

[0188] If the power of the first gas turbine generator set and the power of the first battery energy storage system corresponding to the time constant of the first particle meet the set constraints, the power of the first gas turbine generator set and the power of the first battery energy storage system are substituted into the objective function for calculation to obtain the objective function value;

[0189] If the objective function value is less than the initial individual optimal value, the objective function value is taken as the latest individual optimal value; and if the objective function value is less than the initial global optimal value, the objective function value is taken as the latest global optimal value.

[0190] The first particle is any particle in the particle swarm; the set stopping condition is that the absolute value of the difference between the objective function values ​​of two particles in two adjacent iterations is greater than a set threshold.

[0191] The capacity optimization configuration device of the wind-gas-storage virtual power plant includes a processor and a memory. The aforementioned acquisition module, filtering module, construction module, solution module, and determination module are all stored as program units in the memory. The processor executes the aforementioned program units stored in the memory to realize the corresponding functions.

[0192] The acquisition module 1001 can be used as a data processing module: this module is responsible for collecting and processing data from the power supply side, energy storage side and load side, including grid load data, meteorological parameters, parameters of wind power equipment, gas turbine equipment and energy storage equipment, etc.

[0193] The filter module 1002 can be used as a power distribution module: based on the information provided by the data processing module, it calculates the rated capacity of the gas turbine according to the low-frequency power signal and the minimum rated capacity of the battery energy storage system according to the high-frequency power signal.

[0194] The construction module 1003, the solution module 1004, and the determination module 1005 can be combined as a capacity optimization configuration module: to determine the optimal capacity configuration of the wind, gas, and energy storage virtual power plant to achieve the lowest total cost.

[0195] Referring to Figures 11 and 12, the data processing module collects grid load data, meteorological parameters, and parameters of wind power equipment, gas turbine equipment, and energy storage equipment, and sends these parameters to the power allocation module and the capacity optimization configuration module. The power allocation module performs first-order low-pass filtering on the power signal, decomposing it into low-frequency and high-frequency power signals. It calculates the minimum rated capacity of the gas turbine based on the low-frequency power signal and the minimum rated capacity of the battery energy storage system based on the high-frequency power signal. The capacity optimization configuration module uses the lowest total life-cycle cost of the virtual power plant as its objective function and finally obtains the capacity configuration scheme of the wind-gas-storage virtual power plant through an optimization algorithm.

[0196] A processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured.

[0197] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0198] Figure 13 illustrates a schematic diagram of the physical structure of an electronic device. As shown in Figure 13, the electronic device may include: a processor 1310, a communication interface 1320, a memory 1330, and a communication bus 1340. The processor 1310, communication interface 1320, and memory 1330 communicate with each other via the communication bus 1340. The processor 1310 can call logical instructions in the memory 1330 to execute a capacity optimization configuration method for a wind-gas-storage virtual power plant. This method includes: acquiring a grid load power curve and a wind power load power curve; determining a load power deviation curve based on the grid load power curve and the wind power load power curve; filtering the load power deviation curve to obtain a first frequency signal and a second frequency signal; calculating the minimum rated capacity of the gas turbine generator set based on the first frequency signal; and calculating the minimum rated capacity of the battery energy storage system based on the second frequency signal. The minimum value of the second frequency signal is greater than the minimum value of the first frequency signal. The operating cost of the wind-gas-storage virtual power plant is calculated based on the power of the gas turbine generator set and the power of the battery energy storage system. An objective function is constructed based on the operating cost and the environmental cost of the wind-gas-storage virtual power plant. The objective function represents minimizing the total cost of the wind-gas-storage virtual power plant. The objective function is solved using a particle swarm optimization algorithm to obtain the optimized power of the gas turbine generator set and the optimized power of the battery energy storage system. Based on the optimized power of the gas turbine generator set, the minimum rated capacity of the gas turbine generator set, the optimized power of the battery energy storage system, and the minimum rated capacity of the battery energy storage system, the optimal capacity configuration value of the wind-gas-storage virtual power plant is determined.

[0199] Furthermore, the logical instructions in the aforementioned memory 1330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0200] On the other hand, this application also provides a computer program product, which includes a computer program that can be stored on a machine-readable storage medium. When the computer program is executed by a processor, the computer can execute a capacity optimization configuration method for a wind-gas-storage virtual power plant. This method includes: acquiring a grid load power curve and a wind power load power curve; determining a load power deviation curve based on the grid load power curve and the wind power load power curve; filtering the load power deviation curve to obtain a first frequency signal and a second frequency signal; calculating the minimum rated capacity of the gas turbine generator set based on the first frequency signal; and calculating the minimum rated capacity of the battery energy storage system based on the second frequency signal. The minimum value of the second frequency signal is greater than the maximum value of the first frequency signal; the operating cost of the wind-gas-storage virtual power plant is calculated based on the power of the gas turbine generator set and the power of the battery energy storage system, and an objective function is constructed based on the operating cost and the environmental cost of the wind-gas-storage virtual power plant; the objective function represents the minimum total cost of the wind-gas-storage virtual power plant; the objective function is solved using the particle swarm optimization algorithm to obtain the optimized power of the gas turbine generator set and the optimized power of the battery energy storage system; based on the optimized power of the gas turbine generator set, the minimum rated capacity of the gas turbine generator set, the optimized power of the battery energy storage system, and the minimum rated capacity of the battery energy storage system, the capacity optimization configuration value of the wind-gas-storage virtual power plant is determined.

[0201] Furthermore, this application also provides a machine-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a capacity optimization configuration method for a wind-gas-storage virtual power plant. The method includes: acquiring a grid load power curve and a wind power load power curve; determining a load power deviation curve based on the grid load power curve and the wind power load power curve; filtering the load power deviation curve to obtain a first frequency signal and a second frequency signal; calculating the minimum rated capacity of the gas turbine generator set based on the first frequency signal; and calculating the minimum rated capacity of the battery energy storage system based on the second frequency signal; wherein the minimum value of the second frequency signal is greater than... The operating cost of the wind-gas-storage virtual power plant is calculated based on the maximum value of the first frequency signal, the power of the gas turbine generator set, and the power of the battery energy storage system. An objective function is constructed based on the operating cost and the environmental cost of the wind-gas-storage virtual power plant. The objective function represents the minimum total cost of the wind-gas-storage virtual power plant. The objective function is solved using a particle swarm optimization algorithm to obtain the optimized power of the gas turbine generator set and the optimized power of the battery energy storage system. Based on the optimized power of the gas turbine generator set, the minimum rated capacity of the gas turbine generator set, the optimized power of the battery energy storage system, and the minimum rated capacity of the battery energy storage system, the optimal capacity configuration value of the wind-gas-storage virtual power plant is determined.

[0202] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0203] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0204] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A capacity optimization configuration method for a wind-gas-storage virtual power plant, applied to a wind-gas-storage virtual power plant, wherein the wind-gas-storage virtual power plant includes a wind turbine generator set, a gas turbine generator set, and a battery energy storage system, the method comprising: Obtain the power grid load curve and the wind power load curve, and determine the load power deviation curve based on the power grid load curve and the wind power load curve; The load power deviation curve is filtered to obtain a first frequency signal and a second frequency signal. The minimum rated capacity of the gas turbine generator set is calculated based on the first frequency signal, and the minimum rated capacity of the battery energy storage system is calculated based on the second frequency signal. The minimum value of the second frequency signal is greater than the maximum value of the first frequency signal. The operating cost of the wind-gas-storage virtual power plant is calculated based on the power of the gas turbine generator set and the power of the battery energy storage system. An objective function is constructed based on the operating cost and the environmental cost of the wind-gas-storage virtual power plant. The objective function represents the minimum total cost of the wind-gas-storage virtual power plant. The objective function is solved using the particle swarm optimization algorithm to obtain the optimized power of the gas turbine generator set and the optimized power of the battery energy storage system. Based on the optimized power of the gas turbine generator set, the minimum rated capacity of the gas turbine generator set, the optimized power of the battery energy storage system, and the minimum rated capacity of the battery energy storage system, the capacity optimization configuration value of the wind-gas-storage virtual power plant is determined.

2. The capacity optimization configuration method for a wind-gas-storage virtual power plant according to claim 1, wherein filtering the load power deviation curve to obtain a first frequency signal and a second frequency signal, and calculating the minimum rated capacity of the gas turbine generator set based on the first frequency signal, and calculating the minimum rated capacity of the battery energy storage system based on the second frequency signal, comprises: The load power deviation curve is subjected to a first-order low-pass filter to obtain a first frequency signal and a second frequency signal. The maximum value of the first frequency signal is taken as the minimum rated capacity of the gas turbine generator set; Based on the second frequency signal, an integral calculation is performed to obtain the energy change curve of the battery energy storage system, and the minimum rated capacity of the battery energy storage system is calculated based on the energy change curve and the state of charge of the battery energy storage system.

3. The capacity optimization configuration method for a wind-gas-storage virtual power plant according to claim 2, wherein the minimum rated capacity of the battery energy storage system is calculated based on the energy change curve and the state of charge of the battery energy storage system using the following formula: in, C bat E represents the minimum rated capacity of the battery energy storage system. bat (t) represents the energy change curve, maxE bat (t) represents the maximum value of the energy change curve, minE bat (t) represents the minimum value of the energy change curve, SOC max The SOC represents the maximum value of the state of charge of the battery energy storage system. min This represents the minimum state of charge of the battery energy storage system.

4. The capacity optimization configuration method for a wind-gas-storage virtual power plant according to claim 1, wherein calculating the operating cost of the wind-gas-storage virtual power plant based on the power of the gas turbine generator set and the power of the battery energy storage system, and constructing an objective function based on the operating cost and the environmental cost of the wind-gas-storage virtual power plant, includes: The operating cost of a gas turbine generator set is calculated based on the unit power investment cost, power of the gas turbine generator set, annual operation and maintenance cost coefficient, lifespan, discount rate, natural gas price, and natural gas consumption. The operating cost of a battery energy storage system is calculated based on the unit capacity investment cost, the actual operating capacity, the unit power investment cost, the power, the annual operation and maintenance cost coefficient, the lifespan, and the discount rate of the battery energy storage system. The operating cost of the wind-gas-storage virtual power plant is calculated based on the operating cost of the gas turbine generator set and the operating cost of the battery energy storage system. The environmental cost of a wind-gas-storage virtual power plant is calculated based on the cost of pollutant treatment, the amount of pollutants emitted during the operation of the gas turbine generator set, and the actual operating capacity of the gas turbine generator set. The objective function is constructed based on the sum of the operating cost and the environmental cost of the wind-gas-storage virtual power plant.

5. The capacity optimization configuration method for a wind-gas-storage virtual power plant according to claim 4, wherein the operating cost of the gas turbine generator set is calculated based on the unit power investment cost of the gas turbine generator set, the power of the gas turbine generator set, the annual operation and maintenance cost coefficient of the gas turbine generator set, the service life of the gas turbine generator set, the discount rate of the gas turbine generator set, the price of natural gas, and the consumption of natural gas, using the following formula: in, C gt c represents the operating cost of a gas turbine generator set. gt P represents the unit power investment cost of a gas turbine generator set. gt c represents the power of the gas turbine generator set. μ The annual operation and maintenance cost coefficient of the gas turbine generator set is represented by r, the discount rate of the gas turbine generator set is represented by n, and the service life of the gas turbine generator set is represented by c. NG V represents the price of natural gas. NG Indicates natural gas consumption; The operating cost of a battery energy storage system is calculated based on the unit capacity investment cost, actual operating capacity, unit power investment cost, power, annual maintenance cost coefficient, lifespan, and discount rate. The calculation is performed using the following formula: Among them, C ess c represents the operating cost of a battery energy storage system. e c represents the unit capacity investment cost of a battery energy storage system. ess c represents the actual operating capacity of the battery energy storage system. p P represents the unit power investment cost of a battery energy storage system. ess c represents the power of the battery energy storage system. μ denoted by r, which represents the annual operation and maintenance cost coefficient of the battery energy storage system; and denoted by r, which represents the discount rate of the battery energy storage system; and denoted by n, which represents the lifespan of the battery energy storage system. The environmental cost of the wind-gas-storage virtual power plant is calculated based on the pollutant treatment cost, the pollutant emissions during the operation of the gas turbine generator set, and the actual operating capacity of the gas turbine generator set, using the following formula: Where f2 represents the environmental cost of the wind-gas-storage virtual power plant, and C k The treatment cost for pollutant class k; γ k P represents the emission of pollutant of type k during the operation of the gas turbine; GT (t) represents the actual operating capacity of the gas turbine generator set; The calculation of the operating cost of the wind-gas-storage virtual power plant based on the operating cost of the gas turbine generator set and the operating cost of the battery energy storage system includes: The operating cost of the wind-gas-storage virtual power plant is calculated based on the sum of the operating costs of the gas turbine generator set and the battery energy storage system.

6. The capacity optimization configuration method for a wind-gas-storage virtual power plant according to claim 1, wherein solving the objective function using a particle swarm optimization algorithm to obtain the optimized power of the gas turbine generator set and the optimized power of the battery energy storage system includes: The time constant for filtering the load power deviation curve for each particle in the particle swarm is randomly selected within a set range, so as to calculate the power of the gas turbine generator set and the power of the battery energy storage system for each example using the time constant. Determine the initial individual optimal value and the initial global optimal value for all particles in the particle swarm; Repeat the following steps to calculate the objective function value of all particles in the particle swarm until the set stopping condition is met: If the power of the first gas turbine generator set and the power of the first battery energy storage system corresponding to the time constant of the first particle meet the set constraints, the power of the first gas turbine generator set and the power of the first battery energy storage system are substituted into the objective function for calculation to obtain the objective function value; If the objective function value is less than the initial individual optimal value, the objective function value is taken as the latest individual optimal value; and if the objective function value is less than the initial global optimal value, the objective function value is taken as the latest global optimal value. The first particle is any particle in the particle swarm; the set stopping condition is that the absolute value of the difference between the objective function values ​​of two particles in two adjacent iterations is greater than a set threshold.

7. A capacity optimization configuration device for a wind-gas-storage virtual power plant, comprising: The acquisition module is used to acquire the power grid load curve and the wind power load curve, and to determine the load power deviation curve based on the power grid load curve and the wind power load curve; The filtering module is used to filter the load power deviation curve to obtain a first frequency signal and a second frequency signal, and to calculate the minimum rated capacity of the gas turbine generator set based on the first frequency signal and the minimum rated capacity of the battery energy storage system based on the second frequency signal. The minimum value of the second frequency signal is greater than the maximum value of the first frequency signal; A construction module is used to calculate the operating cost of the wind-gas-storage virtual power plant based on the power of the gas turbine generator set and the power of the battery energy storage system, and to construct an objective function based on the operating cost and the environmental cost of the wind-gas-storage virtual power plant; the objective function represents the minimum total cost of the wind-gas-storage virtual power plant. The solution module is used to solve the objective function using the particle swarm optimization algorithm to obtain the optimized power of the gas turbine generator set and the optimized power of the battery energy storage system. The determination module is used to determine the capacity optimization configuration value of the wind-gas-storage virtual power plant based on the optimized power of the gas turbine generator set, the minimum rated capacity of the gas turbine generator set, the optimized power of the battery energy storage system, and the minimum rated capacity of the battery energy storage system.

8. The capacity optimization configuration device for a wind-gas-storage virtual power plant according to claim 7, wherein the step of filtering the load power deviation curve to obtain a first frequency signal and a second frequency signal, and calculating the minimum rated capacity of the gas turbine generator set based on the first frequency signal, and calculating the minimum rated capacity of the battery energy storage system based on the second frequency signal, comprises: The load power deviation curve is subjected to a first-order low-pass filter to obtain a first frequency signal and a second frequency signal. The maximum value of the first frequency signal is taken as the minimum rated capacity of the gas turbine generator set; Based on the second frequency signal, an integral calculation is performed to obtain the energy change curve of the battery energy storage system, and the minimum rated capacity of the battery energy storage system is calculated based on the energy change curve and the state of charge of the battery energy storage system.

9. The capacity optimization configuration device for the wind-gas-storage virtual power plant according to claim 8, wherein the minimum rated capacity of the battery energy storage system is calculated based on the energy change curve and the state of charge of the battery energy storage system using the following formula: in, C bat E represents the minimum rated capacity of the battery energy storage system. bat (t) represents the energy change curve, maxE bat (t) represents the maximum value of the energy change curve, minE bat (t) represents the minimum value of the energy change curve, SOC max The SOC represents the maximum value of the state of charge of the battery energy storage system. min This represents the minimum state of charge of the battery energy storage system.

10. The capacity optimization configuration device for a wind-gas-storage virtual power plant according to claim 7, wherein calculating the operating cost of the wind-gas-storage virtual power plant based on the power of the gas turbine generator set and the power of the battery energy storage system, and constructing an objective function based on the operating cost and the environmental cost of the wind-gas-storage virtual power plant, comprises: The operating cost of a gas turbine generator set is calculated based on the unit power investment cost, power of the gas turbine generator set, annual operation and maintenance cost coefficient, lifespan, discount rate, natural gas price, and natural gas consumption. The operating cost of a battery energy storage system is calculated based on the unit capacity investment cost, the actual operating capacity, the unit power investment cost, the power, the annual operation and maintenance cost coefficient, the lifespan, and the discount rate of the battery energy storage system. The operating cost of the wind-gas-storage virtual power plant is calculated based on the operating cost of the gas turbine generator set and the operating cost of the battery energy storage system. The environmental cost of a wind-gas-storage virtual power plant is calculated based on the cost of pollutant treatment, the amount of pollutants emitted during the operation of the gas turbine generator set, and the actual operating capacity of the gas turbine generator set. The objective function is constructed based on the sum of the operating cost and the environmental cost of the wind-gas-storage virtual power plant.

11. The capacity optimization configuration device for the wind-gas-storage virtual power plant according to claim 10, wherein the calculation of the operating cost of the gas turbine generator set based on the unit power investment cost of the gas turbine generator set, the power of the gas turbine generator set, the annual operation and maintenance cost coefficient of the gas turbine generator set, the service life of the gas turbine generator set, the discount rate of the gas turbine generator set, the price of natural gas, and the consumption of natural gas is performed by the following formula: in, C gt c represents the operating cost of a gas turbine generator set. gt P represents the unit power investment cost of a gas turbine generator set. gt c represents the power of the gas turbine generator set. μ The annual operation and maintenance cost coefficient of the gas turbine generator set is represented by r, the discount rate of the gas turbine generator set is represented by n, and the service life of the gas turbine generator set is represented by c. NG V represents the price of natural gas. NG Indicates natural gas consumption; The operating cost of a battery energy storage system is calculated based on the unit capacity investment cost, actual operating capacity, unit power investment cost, power, annual maintenance cost coefficient, lifespan, and discount rate. The calculation is performed using the following formula: Among them, C ess c represents the operating cost of a battery energy storage system. e c represents the unit capacity investment cost of a battery energy storage system. ess c represents the actual operating capacity of the battery energy storage system. p P represents the unit power investment cost of a battery energy storage system. ess c represents the power of the battery energy storage system. μ denoted by r, which represents the annual operation and maintenance cost coefficient of the battery energy storage system; and denoted by r, which represents the discount rate of the battery energy storage system; and denoted by n, which represents the lifespan of the battery energy storage system. The environmental cost of the wind-gas-storage virtual power plant is calculated based on the pollutant treatment cost, the pollutant emissions during the operation of the gas turbine generator set, and the actual operating capacity of the gas turbine generator set, using the following formula: Where f2 represents the environmental cost of the wind-gas-storage virtual power plant, and C k The treatment cost for pollutant class k; γ k P represents the emission of pollutant of type k during the operation of the gas turbine; GT (t) represents the actual operating capacity of the gas turbine generator set; The calculation of the operating cost of the wind-gas-storage virtual power plant based on the operating cost of the gas turbine generator set and the operating cost of the battery energy storage system includes: The operating cost of the wind-gas-storage virtual power plant is calculated based on the sum of the operating costs of the gas turbine generator set and the battery energy storage system.

12. The capacity optimization configuration device for a wind-gas-storage virtual power plant according to claim 7, wherein solving the objective function using a particle swarm optimization algorithm to obtain the optimized power of the gas turbine generator set and the optimized power of the battery energy storage system includes: The time constant for filtering the load power deviation curve for each particle in the particle swarm is randomly selected within a set range, so as to calculate the power of the gas turbine generator set and the power of the battery energy storage system for each example using the time constant. Determine the initial individual optimal value and the initial global optimal value for all particles in the particle swarm; Repeat the following steps to calculate the objective function value of all particles in the particle swarm until the set stopping condition is met: If the power of the first gas turbine generator set and the power of the first battery energy storage system corresponding to the time constant of the first particle meet the set constraints, the power of the first gas turbine generator set and the power of the first battery energy storage system are substituted into the objective function for calculation to obtain the objective function value; If the objective function value is less than the initial individual optimal value, the objective function value is taken as the latest individual optimal value; and if the objective function value is less than the initial global optimal value, the objective function value is taken as the latest global optimal value. The first particle is any particle in the particle swarm; the set stopping condition is that the absolute value of the difference between the objective function values ​​of two particles in two adjacent iterations is greater than a set threshold.

13. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the capacity optimization configuration method of the wind-gas-storage virtual power plant according to any one of claims 1 to 6.

14. A machine-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the capacity optimization configuration method of the wind-gas-storage virtual power plant according to any one of claims 1 to 6.

15. A computer program product comprising a computer program that, when executed by a processor, implements the capacity optimization configuration method for a wind-gas-storage virtual power plant as described in any one of claims 1 to 6.

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