A fractional-order optimization control method for the load frequency of an integrated energy system incorporating green hydrogen.

Abstract

A fractional-order optimization control method for the load frequency of an integrated energy system incorporating green hydrogen includes the following steps: Step 1: Establishing a mathematical model for load frequency control involving an electro-hydrogen-electric conversion link including an alkaline water electrolyzer, a hydrogen storage tank, and a hydrogen fuel cell; Step 2: Establishing a load frequency control system model for an integrated energy microgrid incorporating green hydrogen; Step 3: Designing a fractional-order PI... λ D μ Controller, Step 4: Establish the objective function for frequency control optimization of fractional-order loads; Step 5: Propose a controller parameter optimization strategy based on an improved sparrow search algorithm. The method of this invention can help improve the photovoltaic absorption capacity of integrated energy systems, thereby improving the frequency stability of the system.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system optimization control technology, specifically relating to a fractional-order optimization control method for the load frequency of an integrated energy system incorporating green hydrogen. Background Technology

[0002] New power systems with high penetration of renewable energy typically face problems such as supply-demand imbalances and severe frequency fluctuations. As the share of thermal power units gradually decreases and the existing power system structure becomes constrained, peak-shaving and frequency regulation technologies based on traditional thermal power units are increasingly unable to meet the peak-valley regulation capabilities required by new power systems. Therefore, the instability of system frequency under random fluctuations and discontinuous operating conditions has become one of the key issues hindering the widespread application of renewable energy power generation technologies.

[0003] To improve the system stability and load response flexibility of integrated energy systems with high renewable energy penetration under full absorption conditions, peak-valley regulation technologies based on various energy storage methods have been extensively studied. Examples include the application of pumped storage power plants and large-scale vanadium redox flow batteries (VRFs) in peak shaving / dispatch. However, pumped storage power plants suffer from geographical limitations and relatively slow load response speeds. Electrochemical energy storage power plants offer more flexible site selection and faster load response characteristics, but their installed capacity is relatively low, and their service life is relatively short with higher maintenance costs. Therefore, it is necessary to develop load frequency regulation methods for integrated energy systems based on novel energy storage approaches.

[0004] Hydrogen-based energy storage devices are characterized by high energy density, and hydrogen can be stored in large quantities and in multiple ways over long periods, making hydrogen energy an ideal candidate for clean, sustainable, and zero-carbon energy. Existing research has extensively discussed the development and application of green hydrogen technology. However, with the increasing penetration of renewable energy in integrated energy systems, the imbalance between power supply and demand is becoming more pronounced, creating a more urgent need for flexible resources with acceptable controllability. Therefore, it is necessary to further investigate how to deeply integrate the production and storage processes of green hydrogen with the control processes of integrated energy systems to improve system frequency stability and enhance energy utilization efficiency.

[0005] Currently, load frequency control (LFC) in power systems is typically based on PID control methods, relying on experience and the dynamic characteristics of the frequency regulating units to tune controller parameters. This neglects the disturbance source characteristics of renewable energy units in the system, thus limiting the control performance of the frequency regulating units. Furthermore, traditional PID controllers only have three adjustable parameters, restricting the parameter tuning range and consequently limiting the performance of the controlled object. Therefore, Professor I. Podlubny proposed a fractional-order PID controller... λ D μThe concept of a controller is introduced by expanding the dimensionality and range of its adjustable parameters through the inclusion of fractional derivative order λ and fractional integral order μ. In recent years, scholars both domestically and internationally have attempted to incorporate fractional PI... λ D μ The controller was applied to the LFC of the integrated energy system, and the system using this controller was found to have superior performance. Summary of the Invention

[0006] This invention provides a fractional-order optimization control method for the load frequency of an integrated energy system incorporating green hydrogen, which can help improve the photovoltaic absorption capacity of the integrated energy system and thus improve the frequency stability of the system.

[0007] The technical solution adopted in this invention is as follows:

[0008] A fractional-order optimization control method for the load frequency of an integrated energy system incorporating green hydrogen includes the following steps:

[0009] Step 1: Establish a mathematical model for load frequency control involving the electro-hydrogen-electric conversion process, which includes an alkaline water electrolyzer, a hydrogen storage tank, and a hydrogen fuel cell;

[0010] Step 2: Establish a load frequency control system model for an integrated energy microgrid incorporating green hydrogen;

[0011] Step 3: Design a fractional-order PI λ D μ Controller

[0012] Step 4: Establish the objective function for fractional-order load frequency control optimization;

[0013] Step 5: Propose a controller parameter optimization strategy based on an improved sparrow search algorithm.

[0014] The model established in step 1 includes:

[0015] 1.1: Mathematical model of working voltage for alkaline water electrolyzer:

[0016]

[0017] In equation (1), is the terminal voltage of the electrolytic cell; is the reverse voltage of the electrolytic cell, which is generally taken as 1.229V under standard atmospheric pressure; and are the ohmic resistance parameters of the electrolyte; is the area of ​​the electrolytic module; is the working current of the electrolytic cell; is the electrode overvoltage coefficient; t1, t2, and t3 are all temperature influence coefficients of the electrolytic cell; is the working temperature of the electrolytic cell.

[0018] 1.2: Mathematical model for power regulation in alkaline water electrolyzers participating in load frequency control:

[0019] P ele =Nele I ele U ele (2);

[0020] In equation (2), P ele Indicates the power consumed by the electrolytic cell; N ele This indicates the number of electrolytic cell units; in this invention, 240 units are selected.

[0021] 1.3: Mathematical model of hydrogen evolution rate during alkaline water electrolyzer operation:

[0022]

[0023] In equation (3), is the hydrogen evolution rate; is the number of electrolyzer units; is the number of transferred electrons, with a value of 2; is the Faraday constant, with a value of 96487 C / mol; and and are both Faraday efficiency coefficients.

[0024] 1.4: Mathematical model of the terminal voltage at the maximum efficiency operating point of a hydrogen fuel cell:

[0025]

[0026] In equation (4), E Nernest For Nethers voltage; U act For activation overvoltage; U ohmic For ohmic loss voltage; U con ΔG is the concentration overvoltage; ΔS is the Gibbs energy change; R is the entropy change; T is the gas constant. fc T represents the operating temperature of the fuel cell; ref This is the reference temperature for the fuel cell; and These are the partial pressures of hydrogen and oxygen required for a fuel cell, respectively; I fc ξ1, ξ2, ξ3, and ξ4 are the operating current of the hydrogen fuel cell; ξ1, ξ2, ξ3, and ξ4 are empirical coefficients for the fuel cell. R represents the oxygen concentration at the cathode gas-liquid surface. ohm The internal resistance of the fuel cell is l; the thickness of the proton exchange membrane is r. M R is the resistivity of the membrane; C α is the impedance of the proton membrane; α1 is the proportionality constant; i d =I fc / A fc Let A be the current density, where A is the current density. fc The effective cross-sectional area of ​​the proton exchange membrane is denoted by K, which is the mass transfer constant determined by the fuel cell and its operating state; i max That is the maximum current density.

[0027] 1.5: Mathematical model of power regulation of hydrogen fuel cells in load frequency control:

[0028] P fc =N fc U fc I fc (5);

[0029] In equation (5), P fc Indicates the power provided by the hydrogen fuel cell; U fc N is the terminal voltage of the fuel cell; fc The number of hydrogen fuel cell units selected in this invention is 340.

[0030] 1.6: Mathematical model of hydrogen consumption rate during hydrogen fuel cell operation:

[0031]

[0032] In equation (6), R is the gas constant; 273.15 K represents the molar mass of hydrogen; 273.15 K represents 0 °C at standard atmospheric pressure. It is the hydrogen supply pressure.

[0033] 1.7: Mathematical model of hydrogen storage capacity and hydrogen pressure in hydrogen storage tank:

[0034]

[0035] In equation (7), T represents the initial hydrogen storage capacity. sto V represents the temperature of the hydrogen storage tank. sto For the volume of the hydrogen storage tank, when At this time, the electrolyzer and fuel cell maintain normal operating conditions; This represents the maximum pressure that the hydrogen storage tank can withstand. This indicates the amount of hydrogen in the hydrogen storage tank at time t. This represents the rate at which hydrogen is produced in the electrolyzer at time τ. This represents the rate at which the hydrogen fuel cell consumes hydrogen at time τ.

[0036] The model established in step 2 includes:

[0037] 2.1: Mathematical Model and Dynamic Model of Photovoltaic Power Generation Unit:

[0038]

[0039] In equation (8), η is the conversion efficiency of the photovoltaic cell; S is the area of ​​the photovoltaic array; Φ is the solar radiation on the surface of the photovoltaic cell; T a It is the ambient temperature; K PV and T PV These are the gain constant and time constant of the photovoltaic unit, respectively. 25℃ represents normal temperature under standard atmospheric pressure; P PV GPV These represent the first-order transfer function models of photovoltaic power output and photovoltaic power generation unit dynamics, respectively.

[0040] 2.2: Mathematical Model of Pumped Storage System:

[0041]

[0042] In equation (9), T W and T GH These are the time constants of the turbine and the governor, respectively; T R R is the reset time constant; R is the droop coefficient of the hydropower unit; R T The transient rate of decline; ΔP Pd The power consumed in pumping is m; the number of units in pumped storage operation is m; P P This refers to the rated power of the pumped storage unit.

[0043] In step 3, by transforming the integer-order derivative and integral of the error in a traditional integer-order PID controller into fractional-order λ-order integral and fractional-order μ-order derivative, a fractional-order PID controller can be constructed. λ D μ The controller is shown in equation (10) below:

[0044]

[0045] In equation (10), K P K I and K D These are the proportional coefficient, integral coefficient, and differential coefficient, respectively; 1 / s λ and s μ These are the integral operator and the differential operator, respectively.

[0046] In step 4, the objective function is constructed as follows: the minimum integral of time multiplied by absolute error (ITAE) of the system frequency deviation Δ is taken as the optimization objective.

[0047]

[0048] In equation (11), T sim is the total duration of the optimization process, J represents the objective function, and Δf represents the system frequency deviation.

[0049] In step 5, a controller parameter optimization strategy based on the improved sparrow search algorithm is designed:

[0050] The proportional, integral, and derivative coefficients K of the fractional-order controller P K I KD The fractional integral order λ and the fractional derivative order μ are the adjustable parameters of the system. The improved sparrow search algorithm based on the golden sine law and adaptive t-distribution strategy is used, with the objective function for fractional load frequency control optimization established in step 4 as the fitness function, to optimize the controller parameters. Step 5 includes the following steps:

[0051] Step 5.1: Parameter initialization. First, randomly generate sparrow positions in the parameter optimization space, i.e., a set of controller parameters [K]. P ,K I ,K D ,λ,μ], and calculate the objective function value for each sparrow, i.e., the ITAE value, retaining the optimal parameters and the corresponding optimal ITAE value;

[0052] Step 5.2: Based on the objective function ITAE value obtained from the initialization parameters, select the top 60% of sparrows in the entire population as discoverers, the remaining 40% as followers, and then randomly generate 20% of the entire sparrow population as vigilants. Update the controller parameters, i.e. the sparrow positions, according to the position update method of these three roles.

[0053] Step 5.3: Calculate the objective function value for each sparrow, i.e., each group of controller parameters, after the update, and update the optimal position and its corresponding ITAE value;

[0054] Step 5.4: Introduce a dynamic selection probability p to adjust the number of iterations of the algorithm as the degree of freedom of the adaptive t-distribution mutation operator. Specifically, in each iteration of the algorithm, a random number rand∈[0,1] is generated. When rand>p, t-distribution mutation is performed.

[0055] Step 5.5: Calculate the ITAE value of the mutated sparrow individual and update the optimal position and its corresponding ITAE value;

[0056] Step 5.6: Return to step 5.2, optimize the parameters of the next generation of sparrows, and determine whether the maximum number of iterations of the algorithm has been reached;

[0057] Step 5.7: When the maximum number of iterations of the algorithm is reached, output the optimized control parameter results and the program ends.

[0058] This invention provides a fractional-order optimization control method for the load frequency of an integrated energy system incorporating green hydrogen, with the following technical advantages:

[0059] 1) This invention comprehensively considers the characteristics of photovoltaic output and load fluctuations, system power demand characteristics, and the mechanism of the electricity-hydrogen-electricity conversion process in integrated energy systems. It constructs a green hydrogen-integrated integrated energy system model that integrates green hydrogen production, storage, and system load frequency regulation. Compared to traditional integrated energy systems that include pumped hydro storage units, the integrated energy system incorporating green hydrogen units adds a hydrogen storage component, enabling the system to respond faster to random fluctuations in photovoltaic output and step load disturbances. This helps improve the system's photovoltaic absorption capacity and frequency stability.

[0060] 2) This invention proposes a load frequency fractional-order control scheme based on an improved sparrow search algorithm for optimizing control parameters. This is achieved by introducing a fractional-order PI... λ D μ The controller adds a dimension to the system load frequency control parameters; by using the proposed optimization method to optimize the control parameters, a lower system frequency deviation can be obtained.

[0061] 3) For integrated energy systems facing random fluctuations in photovoltaic output and large-scale step disturbances in load, this invention introduces a green hydrogen unit and adopts a fractional-order load frequency control and optimization scheme, resulting in better system frequency stability, improved robustness, and faster response speed. Attached Figure Description

[0062] Figure 1 A fractional-order load frequency control model for green hydrogen integrated energy systems.

[0063] Figure 2 For fractional order PI λ D μ Controller block diagram.

[0064] Figure 3 Flowchart for optimizing frequency control parameters for fractional-order loads.

[0065] Figure 4 Power output of the photovoltaic units.

[0066] Figure 5 A comparison of convergence curves for optimizing the objective function of different LFC schemes.

[0067] Figure 6 This is a comparison chart of system frequency deviations under photovoltaic random discontinuous fluctuation conditions.

[0068] Figure 7 This is a step disturbance in the load.

[0069] Figure 8 This is a comparison chart of system frequency deviations under load step disturbance conditions.

[0070] Figure 9This is a system structure diagram of the present invention. Detailed Implementation

[0071] A fractional-order optimization control method for the load frequency of an integrated energy system incorporating green hydrogen is proposed. First, a model of an integrated energy microgrid system considering non-reheat thermal power units, pumped storage power stations, photovoltaic power units, water electrolysis hydrogen production devices, proton exchange membrane hydrogen fuel cells, and hydrogen storage devices is designed. Then, a fractional-order control method for the system load frequency is proposed, along with a control parameter optimization strategy based on an improved sparrow search algorithm. Finally, a test environment considering the random discontinuity of photovoltaic output and load fluctuation characteristics is established. Starting with the system frequency deviation index, the effectiveness and superiority of the proposed method in improving the system load frequency control performance are verified.

[0072] like Figure 1 , Figure 9 As shown, the integrated energy microgrid load frequency fractional-order system control model incorporating green hydrogen consists of a non-reheat thermal power frequency regulation unit, photovoltaic units, pumped storage units, a hydrogen storage system, and a fractional-order control system. The hydrogen storage system comprises three stages: hydrogen production (electrolyzer), hydrogen storage (hydrogen tank), and hydrogen-to-electricity conversion (fuel cell). The pumped storage system and hydrogen storage system are used to absorb excess photovoltaic power generation and assist traditional frequency regulation units in participating in system frequency regulation. The fractional-order control system issues power command requirements to each unit according to the control optimization strategy. The specific implementation steps are as follows:

[0073] Step 1: Establish a mathematical model for load frequency control involving the electro-hydrogen-electric conversion process, including an alkaline water electrolyzer, a hydrogen storage tank, and a hydrogen fuel cell, including:

[0074] 1.1: Mathematical model of working voltage for alkaline water electrolyzer:

[0075]

[0076] In equation (1), is the terminal voltage of the electrolytic cell; is the reverse voltage of the electrolytic cell, which is generally taken as 1.229V under standard atmospheric pressure; r1 and r2 are both ohmic resistance parameters of the electrolyte; A ele I represents the area of ​​the electrolysis module. ele t1 is the operating current of the electrolytic cell; v is the electrode overvoltage coefficient; t1, t2, and t3 are all temperature influence coefficients of the electrolytic cell; T ele This refers to the operating temperature of the electrolytic cell.

[0077] 1.2: Mathematical model for power regulation in alkaline water electrolyzers participating in load frequency control:

[0078] P ele =N ele I ele U ele (2);

[0079] In equation (2), P ele Indicates the power consumed by the electrolytic cell; N ele This indicates the number of electrolytic cell units; in this invention, 240 units are selected.

[0080] 1.3: Mathematical model of hydrogen evolution rate during alkaline water electrolyzer operation:

[0081]

[0082] In equation (3), The hydrogen evolution rate; N ele denoted as the number of electrolytic cell units; z is the number of transferred electrons, with a value of 2; F is the Faraday constant, with a value of 96487 C / mol; f1 and f2 are both Faraday efficiency coefficients.

[0083] 1.4: Mathematical model of the terminal voltage at the maximum efficiency operating point of a hydrogen fuel cell:

[0084]

[0085] In equation (4), E Nernest For Nethers voltage; U act For activation overvoltage; U ohmic For ohmic loss voltage; U con ΔG is the concentration overvoltage; ΔS is the Gibbs energy change; R is the entropy change; T is the gas constant. fc T represents the operating temperature of the fuel cell; ref This is the reference temperature for the fuel cell; and These are the partial pressures of hydrogen and oxygen required for a fuel cell, respectively; I fc ξ1, ξ2, ξ3, and ξ4 are the operating current of the hydrogen fuel cell; ξ1, ξ2, ξ3, and ξ4 are empirical coefficients for the fuel cell. R represents the oxygen concentration at the cathode gas-liquid surface. ohm The internal resistance of the fuel cell is l; the thickness of the proton exchange membrane is r. M R is the resistivity of the membrane; C α is the impedance of the proton membrane; α1 is the proportionality constant; i d =I fc / A fc Let A be the current density, where A is the current density. fc The effective cross-sectional area of ​​the proton exchange membrane is denoted by i; K is the mass transfer constant, which is determined by the fuel cell and its operating state; i max That is the maximum current density.

[0086] 1.5: Mathematical model of power regulation of hydrogen fuel cells in load frequency control:

[0087] P fc =Nfc U fc I fc (5);

[0088] In equation (5), P fc Indicates the power provided by the hydrogen fuel cell; U fc N is the terminal voltage of the fuel cell; fc The number of hydrogen fuel cell units selected in this invention is 340.

[0089] 1.6: Mathematical model of hydrogen consumption rate during hydrogen fuel cell operation:

[0090]

[0091] In equation (6), R is the gas constant; 273.15 K represents the molar mass of hydrogen; 273.15 K represents 0 °C at standard atmospheric pressure. It is the absolute supply pressure of hydrogen.

[0092] 1.7: Mathematical model of hydrogen storage capacity and hydrogen pressure in hydrogen storage tank:

[0093]

[0094] In equation (7), This represents the initial hydrogen storage capacity. st＝ V represents the temperature of the hydrogen storage tank. st＝ For the volume of the hydrogen storage tank, when At this time, the electrolyzer and fuel cell maintain normal operating conditions; This represents the maximum pressure that the hydrogen storage tank can withstand. This indicates the amount of hydrogen in the hydrogen storage tank at time t. This represents the rate at which hydrogen is produced in the electrolyzer at time τ. This represents the rate at which the hydrogen fuel cell consumes hydrogen at time τ.

[0095] The model established in step 2 includes:

[0096] 2.1: Mathematical Model and Dynamic Model of Photovoltaic Power Generation Unit:

[0097]

[0098] In equation (8), η is the conversion efficiency of the photovoltaic cell; S is the area of ​​the photovoltaic array; Φ is the solar radiation on the surface of the photovoltaic cell; T a It is the ambient temperature; K PV and T PV These are the gain constant and time constant of the photovoltaic unit, respectively. 25℃ represents normal temperature under standard atmospheric pressure; P PV G PVThese represent the first-order transfer function models of photovoltaic power output and photovoltaic power generation unit dynamics, respectively.

[0099] 2.2: Mathematical Model of Pumped Storage System:

[0100]

[0101] In equation (9), T W and T GH These are the time constants of the turbine and the governor, respectively; T R R is the reset time constant; R is the droop coefficient of the hydropower unit; R T The transient rate of decline; ΔP P0 The power consumed in pumping is m; the number of units in pumped storage operation is m; P P This refers to the rated power of the pumped storage unit.

[0102] Step 3: Design a fractional-order PI λ D μ Controller:

[0103] Based on the fractional-order Riemann-Liouville (RL) definition, a load frequency control method for integrated energy systems with green hydrogen intervention is established. Figure 2 The fractional-order PID controller model is shown. By transforming the integer-order derivative and integral of the error in the traditional integer-order PID controller into fractional-order λ-order integral and fractional-order μ-order derivative, a fractional-order PID controller can be constructed. λ D μ The controller is shown in equation (10) below:

[0104]

[0105] In equation (10), K P K I and K D These are the proportional coefficient, integral coefficient, and differential coefficient, respectively; 1 / s λ and s μ These are the integral operator and the differential operator, respectively.

[0106] Step 4: Establish the objective function for fractional-order load frequency control optimization:

[0107] The objective function for fractional-order load frequency control optimization is constructed with the goal of minimizing the integral of time multiplied by absolute error (ITAE) of the system frequency deviation Δ, as shown in equation (11):

[0108]

[0109] In equation (11), is the total duration of the optimization process. J represents the objective function; Δf represents the system frequency deviation.

[0110] Step 5: Design a controller parameter optimization strategy based on the improved sparrow search algorithm:

[0111] The proportional, integral, and derivative coefficients K of the fractional-order controller P K I K D The fractional integral order λ and the fractional derivative order μ are the adjustable parameters of the system. Using the improved sparrow search algorithm based on the golden sine law and adaptive t-distribution strategy, and with the objective function for fractional load frequency control optimization established in step 4 as the fitness function, the controller parameters are optimized. The optimization flowchart is shown below. Figure 3 As shown, it includes the following steps:

[0112] Step 5.1: Parameter initialization. First, randomly generate sparrow positions in the parameter optimization space, i.e., a set of controller parameters [K]. P ,K I ,K D ,λ,μ], and calculate the objective function value for each sparrow, i.e., the ITAE value, retaining the optimal parameters and the corresponding optimal ITAE value;

[0113] Step 5.2: Based on the objective function ITAE value obtained from the initialization parameters, select the top 60% of sparrows in the entire population as discoverers, the remaining 40% as followers, and then randomly generate 20% of the entire sparrow population as vigilants. Update the controller parameters, i.e. the sparrow positions, according to the position update method of these three roles.

[0114] Step 5.3: Calculate the objective function value for each sparrow, i.e., each group of controller parameters, after the update, and update the optimal position and its corresponding ITAE value;

[0115] Step 5.4: Introduce a dynamic selection probability p to adjust the number of iterations of the algorithm as the degree of freedom of the adaptive t-distribution mutation operator. Specifically, in each iteration of the algorithm, a random number rand∈[0,1] is generated. When rand>p, t-distribution mutation is performed.

[0116] Step 5.5: Calculate the ITAE value of the mutated sparrow individual and update the optimal position and its corresponding ITAE value;

[0117] Step 5.6: Return to step 5.2, optimize the parameters of the next generation of sparrows, and determine whether the maximum number of iterations of the algorithm has been reached;

[0118] Step 5.7: When the maximum number of iterations of the algorithm is reached, output the optimized control parameter results and the program ends.

[0119] Step 6, the present invention will be further explained below with two scenario analyses:

[0120] according to Figure 1 A simulation model of load frequency control for a green hydrogen integrated energy system was constructed, and the parameters of the system model are listed in Table 1. The model considers the fluctuation of photovoltaic output and the step disturbance of load. The simulation results of the proposed fractional-order load frequency control and optimization strategy are compared with the results of various other schemes. To ensure a fair evaluation, the population size and maximum number of iterations for all optimization strategies were set to 50 during the simulation and comparison process.

[0121] Table 1. Parameters of the Integrated Energy System with Green Hydrogen Incorporation

[0122] parameter numerical values parameter numerical values parameter numerical values <![CDATA[K P ]]> 120Hz / puMW <![CDATA[T P ]]> 20s <![CDATA[α H ]]> 0.2 <![CDATA[T G ]]> 0.08s <![CDATA[T T ]]> 0.3s <![CDATA[α F ]]> 0.2 <![CDATA[T GH ]]> 48.7s <![CDATA[T R ]]> 5s <![CDATA[α E ]]> 0.5 <![CDATA[T RH ]]> 0.513s <![CDATA[T W ]]> 0.5s <![CDATA[α P ]]> 0.5 <![CDATA[T PV ]]> 1.2s <![CDATA[K PV ]]> 1Hz / puMW <![CDATA[T e ]]> 0.6s <![CDATA[R H ]]> 2.4Hz / puMW <![CDATA[R G ]]> 2.4Hz / puMW <![CDATA[T f ]]> 0.6s B -0.425 puMW / Hz <![CDATA[α G ]]> 0.6

[0123] Scenario 1 only considers the change in the output of the photovoltaic unit. Figure 4 The random power output of the photovoltaic unit is given, taking into account the random fluctuations in power output with light intensity. Figure 5 The convergence characteristic curves of the fitness function obtained by four different LFC methods are shown below. Figure 5 It can be seen that when the green hydrogen unit participates in the frequency regulation of the integrated energy system, the fitness function of the ISSA-FOPID method proposed in this invention has the minimum value after 50 iterations of optimization, which shows the superiority of the proposed scheme in quickly obtaining the optimal target ITAE value.

[0124] The frequency deviations of the system dynamic response obtained by four different LFC schemes are as follows: Figure 6 As shown, from Figure 6 The comparison shows that using the solution of this invention can not only reduce the system frequency deviation, but also... Figure 4 The peak value of the photovoltaic unit output during discontinuous fluctuations is shown, and the system frequency can be restored to the nominal value in the shortest time, thereby improving the power quality of the system.

[0125] Scenario 2 considers the impact of photovoltaic power output fluctuations and load step disturbances on the system's secondary frequency adjustment. The photovoltaic power output fluctuations are the same as in test scenario 1, i.e. Figure 4 As shown, the step disturbance change of the load within 100 seconds is as follows: Figure 7 As shown, under the conditions of random photovoltaic output and load step disturbance, the system frequency deviation response of four different LFC schemes is compared as follows: Figure 8 As shown, from Figure 8As can be seen, when using the scheme proposed in this invention, the overshoot of the system frequency deviation is reduced, and the impact of random fluctuations in photovoltaic output and load disturbances on the system frequency is smaller. Therefore, the controller used in this invention has strong robustness. In addition, under the same LFC scheme, the intervention of the green hydrogen unit further reduces the frequency overshoot and accelerates the system response speed, enabling it to track the reference frequency in a shorter time.

[0126] The results of the numerical analysis and comparison show that the present invention can effectively suppress system frequency fluctuations and shorten the adjustment time required to complete load frequency control, thereby improving the utilization rate of photovoltaic power generation.

Claims

1. A fractional-order optimization control method for the load frequency of an integrated energy system incorporating green hydrogen, characterized in that... Includes the following steps: Step 1: Establish a mathematical model for load frequency control involving the electro-hydrogen-electric conversion process, which includes an alkaline water electrolyzer, a hydrogen storage tank, and a hydrogen fuel cell; Step 2: Establish a load frequency control system model for an integrated energy microgrid incorporating green hydrogen; Step 3: Design a fractional-order PI λ D μ Controller Step 4: Establish the objective function for fractional-order load frequency control optimization; Step 5: Propose a controller parameter optimization strategy based on an improved sparrow search algorithm; In step 4, the system frequency deviation is used as the basis for the measurement. The optimization objective is to minimize the integral of time multiplied by absolute error (ITAE). The objective function is constructed as shown in equation (11): (11)； In equation (11), It is the total time of the optimization process. Represent the objective function; Indicates system frequency deviation; Step 5 includes the following steps: Step 5.1: Parameter initialization. First, randomly generate sparrow positions in the parameter optimization space, which are a set of controller parameters. And calculate the objective function value for each sparrow, i.e., the ITAE value, retaining the optimal parameters and the corresponding optimal ITAE value; Step 5.2: Based on the objective function ITAE value obtained from the initialization parameters, select the top 60% of sparrows in the entire population as discoverers, the remaining 40% as followers, and then randomly generate 20% of the entire sparrow population as vigilants. Update the controller parameters, i.e. the sparrow positions, according to the position update method of these three roles. Step 5.3: Calculate the objective function value for each sparrow, i.e., each group of controller parameters, after the update, and update the optimal position and its corresponding ITAE value; Step 5.4: Introduce dynamic selection probability To adjust the number of algorithm iterations as the degree of freedom adaptively The distribution mutation operator is used in the following way: a random number is generated in each iteration of the algorithm. ,when When, execute Distribution variation; Step 5.5: Calculate the ITAE value of the mutated sparrow individual and update the optimal position and its corresponding ITAE value; Step 5.6: Return to step 5.2, optimize the individual parameters of the next generation of sparrows, and determine whether the maximum number of iterations of the algorithm has been reached; Step 5.7: When the maximum number of iterations of the algorithm is reached, output the optimized control parameter results and the program ends.

2. The fractional-order optimization control method for the load frequency of an integrated energy system incorporating green hydrogen according to claim 1, characterized in that: The model established in step 1 includes: 1.1: Mathematical model of working voltage for alkaline water electrolyzer: (1)； In equation (1), This is the terminal voltage of the electrolytic cell; This is the reverse voltage of the electrolytic cell; and These are all parameters related to the ohmic resistance of the electrolyte; The area of ​​the electrolysis module; This refers to the operating current of the electrolytic cell; Electrode overvoltage coefficient; , , All are coefficients related to the temperature influence of the electrolytic cell. This refers to the operating temperature of the electrolytic cell. 1.2: Mathematical model for power regulation in alkaline water electrolyzers participating in load frequency control: (2)； In equation (2), This indicates the power consumption of the electrolytic cell; Indicates the number of electrolytic cell units; 1.3: Mathematical model of hydrogen evolution rate during alkaline water electrolyzer operation: (3)； In equation (3), This represents the hydrogen evolution rate; This refers to the number of electrolytic cell units; The number of electrons transferred; It is Faraday's constant; and All are Faraday efficiency coefficients; 1.4: Mathematical model of the terminal voltage at the maximum efficiency operating point of a hydrogen fuel cell: (4)； In equation (4), For Nethers voltage; To activate overvoltage; For ohmic loss voltage; Concentration overvoltage; Gibbs can change; R is the entropy change; R is the gas constant. This refers to the operating temperature of the fuel cell. This is the reference temperature for the fuel cell; and These are the partial pressures of hydrogen and oxygen required for fuel cells, respectively. This is the operating current for the hydrogen fuel cell; , , and These are all empirical coefficients for fuel cells; The oxygen concentration at the cathode gas-liquid surface; It is the internal resistance of the fuel cell; The thickness of the proton exchange membrane; The resistivity of the membrane; The impedance of the proton membrane; This is the proportionality coefficient; Let be the current density, where This refers to the effective cross-sectional area of ​​the proton exchange membrane. It is the mass transfer constant, determined by the fuel cell and its operating state; It is the maximum current density; 1.5: Mathematical model of power regulation of hydrogen fuel cells in load frequency control: (5)； In equation (5), This indicates the power provided by the hydrogen fuel cell; This refers to the terminal voltage of the fuel cell. Number of hydrogen fuel cell units; 1.6: Mathematical model of hydrogen consumption rate during hydrogen fuel cell operation: （6）； In equation (6), It is the gas constant; This refers to the molar mass of hydrogen gas. It is the hydrogen supply pressure; 1.7: Mathematical model of hydrogen storage capacity and hydrogen pressure in hydrogen storage tank: (7)； In equation (7), This represents the initial hydrogen storage capacity. Temperature of the hydrogen storage tank; For the volume of the hydrogen storage tank, when At this time, the electrolyzer and fuel cell maintain normal operating conditions; This represents the maximum pressure that the hydrogen storage tank can withstand. This indicates the amount of hydrogen in the hydrogen storage tank at time t. express The rate at which hydrogen is produced in the electrolyzer at any given time. express The rate at which hydrogen is consumed by a hydrogen fuel cell at any given time.

3. The fractional-order optimization control method for the load frequency of an integrated energy system incorporating green hydrogen as described in claim 1, characterized in that: The model established in step 2 includes: 2.1: Mathematical Model and Dynamic Model of Photovoltaic Power Generation Unit: （8）； In equation (8), It refers to the conversion efficiency of photovoltaic cells; It is the area of ​​the photovoltaic array; It is solar radiation on the surface of the photovoltaic cell; It is the ambient temperature; and These are the gain constant and time constant of the photovoltaic unit, respectively. , These represent the first-order transfer function models of photovoltaic power output and photovoltaic power generation unit dynamics, respectively. 2.2: Mathematical Model of Pumped Storage System: (9)； In equation (9), and These are the time constants of the turbine and the governor, respectively. This is the reset time constant; This refers to the droop coefficient of the hydroelectric generator unit; The transient decline rate; The power consumed in pumping water; This refers to the number of units operating under pumped storage conditions. This refers to the rated power of the pumped storage unit.

4. The fractional-order optimization control method for the load frequency of an integrated energy system incorporating green hydrogen as described in claim 1, characterized in that: In step 3, the fractional order PI λ D μ The controller is shown in equation (10) below: (10)； In equation (10), , and These are the proportional coefficient, integral coefficient, and differential coefficient, respectively. and These are the integral operator and the differential operator, respectively.

5. The fractional-order optimization control method for the load frequency of an integrated energy system incorporating green hydrogen according to claim 1, characterized in that: In step 5, a controller parameter optimization strategy based on the improved sparrow search algorithm is designed: The proportional, integral, and derivative coefficients of the fractional-order controller , , and fractional integral order Fractional order of differential The system is adjustable parameters; the improved sparrow search algorithm based on the golden sine law and adaptive t-distribution strategy is used to optimize the controller parameters with the objective function of the fractional-order load frequency control optimization established in step 4 as the fitness function.

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