An energy internet event triggering frequency control method, medium and system

Abstract

The application discloses an energy internet event triggering frequency control method, medium and system, comprising the following steps: establishing a frequency control state space equation of the energy internet; designing a bounded adaptive event triggering communication scheme; establishing a frequency control closed-loop state space equation of the energy internet considering transmission delay and rotating reserve of power generation equipment; calculating a derivative of a constructed Lyapunov energy function, and making the derivative not greater than 0 to obtain design constraints of the frequency control closed-loop state space equation and the bounded adaptive event triggering communication scheme; and taking a weighted sum of an energy internet communication triggering rate and a frequency deviation rate as an optimization target, and determining optimal controller gains and performance weight matrices to control the frequency. The application saves network resources, and the controller still maintains a sensitive monitoring capability for the microgrid operation state after the frequency deviation is well inhibited.

Description

Technical Field

[0001] The present invention relates to the field of energy internet information control technology, and in particular to an energy internet event trigger frequency control method, medium and system. Background Art

[0002] Continued concern about carbon emissions has driven the development of distributed generation technologies based on solar and wind power. Accordingly, an energy internet integrating large-scale distributed generators has become a future trend in power systems. However, the uncertain output of renewable energy active power and the random fluctuations in load have led to frequent imbalances in power supply and demand within the energy internet, adversely affecting frequency stability. Due to the limited capacity of power generation equipment and the general lack of inertia support provided by diesel generators, the combination of the uncertain output of renewable energy active power and the random fluctuations in load demand can lead to imbalances in active power supply and demand within the energy internet exceeding the spinning reserve capacity of the power generation equipment.

[0003] On the other hand, as the scale and complexity of the Energy Internet continue to grow, utilizing communication networks to transmit state variables and control commands over long distances has become an inevitable option. However, communication networks inevitably suffer from uncertain network-induced delays. For the frequency control system of the Energy Internet, uncertain network-induced delays can lead to a range of consequences, including reduced stability margins, increased frequency deviations, and even instability.

[0004] In recent years, scholars both domestically and internationally have conducted extensive research on networked frequency control for the Energy Internet. Existing event-triggered frequency control methods for the Energy Internet generally ignore the constraints of microgrid spinning reserve. Spinning reserve is typically 10% to 20% of the rated total capacity. In fact, when the power supply-demand imbalance exceeds the current Energy Internet spinning reserve, power generation equipment can only participate in frequency control at its maximum adjustable power. In this scenario, the power supply-demand imbalance can only be reduced to a limited extent, resulting in persistent frequency deviations. In this scenario, the control center must quickly adjust the control input command to the maximum spinning reserve to minimize the frequency deviation while preventing the frequency deviation from exceeding the allowable range. Controller design methods that fail to consider this constraint may risk exceeding the frequency deviation limit or slow recovery. Summary of the Invention

[0005] Embodiments of the present invention provide an energy internet event trigger frequency control method, medium, and system to solve the problem of the existing lack of prediction technology for the surface flashover voltage of high-speed rail roof insulators in extreme environments such as strong airflow.

[0006] In a first aspect, a method for controlling the frequency of triggering an energy internet event is provided, comprising:

[0007] Based on the number of diesel generators and energy storage devices involved in the frequency control of the energy internet, the frequency control state space equation of the energy internet is established;

[0008] Design bounded adaptive event-triggered communication schemes;

[0009] Based on the frequency control state space equation of the energy internet and the bounded adaptive event-triggered communication scheme, a frequency control closed-loop state space equation of the energy internet is established that takes into account transmission delay and spinning reserve of power generation equipment;

[0010] Calculating the derivative of the constructed Lyapunov energy function and setting the derivative to be no greater than 0, thereby obtaining the frequency control closed-loop state space equation and the design constraints of the bounded adaptive event-triggered communication scheme;

[0011] Based on the frequency control closed-loop state space equation of the energy internet and the design constraints, the optimal controller gain and performance weight matrix are determined to control the frequency with the minimum weighted sum of the energy internet communication trigger rate and the frequency deviation rate as the optimization goal.

[0012] In a second aspect, a computer-readable storage medium is provided, on which computer program instructions are stored; when the computer program instructions are executed by a processor, the energy Internet event trigger frequency control method as described in the embodiment of the first aspect above is implemented.

[0013] In a third aspect, an energy internet event trigger frequency control system is provided, comprising: a computer-readable storage medium as described in the embodiment of the second aspect above.

[0014] In this way, the embodiment of the present invention not only saves network resources, but also allows the controller to maintain the ability to sensitively monitor the operating status of the microgrid after the frequency deviation is well suppressed. By fully considering the active power output constraints of the energy storage device and the uncertain delay in the communication interaction process, the microgrid frequency control system is described as a nonlinear model with a saturation term. Based on the Lyapunov stability theory, the design criteria of the event-triggered control parameters under power constraints, random delays and parameter uncertainties are strictly derived to facilitate frequency control. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0016] Figure 1is a flow chart of a method for controlling the frequency of energy internet event triggering according to an embodiment of the present invention;

[0017] Figure 2 It is a structural diagram of the energy internet of the present invention in a real-time example;

[0018] Figure 3 This is a diagram of the frequency control structure of the energy internet in a real-time example of the present invention;

[0019] Figure 4 : This is a curve showing changes in new energy output and load demand within the energy internet of an application example of the present invention;

[0020] Figure 5 is a frequency deviation diagram of different control strategies of an application example of the present invention;

[0021] Figure 6 Schematic diagram of the spinning reserve injected into the energy internet under different control strategies of the application examples of the present invention;

[0022] Figure 7 It is a schematic diagram of the cumulative communication trigger times of different event trigger control strategies in the application example of the present invention. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments derived by ordinary technicians in this field based on the embodiments of the present invention without creative work are within the scope of protection of the present invention.

[0024] The embodiment of the present invention discloses a method for controlling the frequency of triggering of energy internet events. Figure 1 As shown, the method of the embodiment of the present invention includes the following steps:

[0025] Step S101: Establish the frequency control state space equation of the energy internet according to the number of diesel generators and energy storage devices participating in the frequency control of the energy internet.

[0026] Energy Internet structure Figure 2 As shown, the frequency control structure of the energy Internet is as follows Figure 3 shown.

[0027] The frequency control state space equation includes the active power output of new energy, the active output of energy storage equipment, the grid load demand, etc. Specifically, the frequency control state space equation is as follows:

[0028]

[0029] Among them, A, B, C, and E are matrix coefficients.

[0030] Specifically,

[0031] in, T gn 、T tn are the time constants of the diesel engine speed governor and synchronous generator of the nth diesel generator, T ESSm is the time constant of the inverter of the mth energy storage device, R DGn is the droop coefficient of the nth diesel generator, D is the equivalent damping coefficient of the energy internet, H is the equivalent inertia coefficient of the energy internet, n = 1,…, N, m = 1,…, M.

[0032] Specifically,

[0033] in, R ESS is the droop coefficient of the mth energy storage device, α n , β m are the frequency modulation participation factors of the nth diesel generator and the mth energy storage device, satisfying Σα n +Σβ m =1.

[0034] Specifically, C = [1, 0, 0, 0, 0], where "0" represents a dimensionally appropriate zero vector.

[0035] Specifically,

[0036] Specifically, x(t)=[Δf,ΔP DG,1 ,…,ΔP DG,n ,…,ΔP DG,N , ΔP v1 ,…,ΔP vn ,…,ΔP vN , ΔP ref ESS,1 ,…,ΔP ref ESS,m ,…,ΔP ref ESS,M , ΔP ESS,1 ,…,ΔP ESS,m ,…,ΔP ESS,M ,∫Δf] T .

[0037] Where Δf is the frequency deviation, ΔP DG,n , ΔP ESS,m are the active output increments of the nth diesel generator and the mth energy storage device, ΔP vnis the governor valve opening of the nth diesel generator, ΔP ref ESS,m Output power instruction for the inverter of the mth energy storage device.

[0038] Specifically, y(t) is Δf.

[0039] Specifically, u(t) is the spinning reserve power injected into the energy internet;

[0040] Specifically, w(t) is the external power disturbance. Where w(t) = ΔP WT +ΔP PV -ΔP d , ΔP WT , ΔP PV , ΔP d They are wind turbines, photovoltaics and load fluctuations respectively.

[0041] Step S102: Design a bounded adaptive event-triggered communication solution.

[0042] Specifically, the bounded adaptive event-triggered communication scheme is as follows:

[0043]

[0044] Among them, t h is the adaptive event triggering moment, Φ is the performance weight matrix, T s is the sampling time, l is a natural number, Z is a natural number set, δ(t h T s ) is the event triggering threshold.

[0045] Among them, e(i l T s )=x(i l T s )-x(t h T s ), i l T s =t h T s +lT s ,δ(t h T s )=δ m +λδ(t h-1 T s ).

[0046]

[0047] Among them, μ is the adaptive coefficient, δ m is the lower limit of the event trigger threshold, μ∈(0,1), δ m∈(0,1), initial condition p=0, δ(t0T s )=δ m .

[0048] Through the bounded adaptive event-triggered communication scheme designed above, it can be seen that when the frequency deviation is effectively reduced, that is, ||y(t h+1 T s )||<||y(t h T s )||, λ gradually changes from 0 to μ. Correspondingly, the event trigger threshold gradually changes from δ m Change to (1+μ)δ m , which enables the Energy Internet Control Center to control the frequency adjustment equipment at a lower trigger frequency, thereby saving communication resources; on the contrary, when the frequency deviation increases, that is, ||y(t h+1 T s )||≥||y(t h T s )||, λ will jump to 0, and the event trigger threshold will drop to δ m , that is, the Energy Internet Control Center controls the power generation equipment at a higher communication frequency to ensure frequency stability. Therefore, the adjustment range of the event trigger threshold is [δ m ,(1+μ)δ m Compared with existing adaptive adjustment schemes with no upper bound on the trigger threshold, the embodiments of the present invention ensure that after the frequency deviation is effectively controlled, the control center can still maintain its monitoring capability of the power generation equipment at a lower communication frequency. Therefore, it not only ensures the monitoring sensitivity of the energy internet control center to the power generation equipment but also effectively saves communication resources.

[0049] Step S103: Based on the frequency control state space equation of the energy internet and the bounded adaptive event-triggered communication scheme, a frequency control closed-loop state space equation of the energy internet is established that takes into account transmission delay and spinning reserve of power generation equipment.

[0050] Specifically, the frequency control closed-loop state space equation includes:

[0051]

[0052] Where K is the controller gain, τ(t) is the uncertain transmission delay, τ(t)∈(0,d], d is the upper bound of the transmission delay, is the nonlinearity between the ideal spinning reserve and the actual spinning reserve constraint of the power generation equipment,

[0053] According to the event-triggered communication scheme designed in step S102, the control instructions remain unchanged within the interval between two adjacent event-triggered communications. Therefore, in an ideal state, the spinning reserve power injected into the microgrid (in the form of state feedback) is Then according to the sampling period T s The interval between two adjacent event triggering times is divided into several subsets, namely i h T s =t h T s +lT s . Define τ(t) = ti h T s , according to e(i l T s )=Kx(t h T s )=x(i l T s )-x(t h T s ), we get x(t h T s )=x(i h T s )-e(i h T s )=x(t-τ(t))-e(i h T s ). Therefore, we get In addition, Inside, there d is the upper bound of the delay.

[0054] However, due to the combined effects of wind power, photovoltaic power, and load demand fluctuations, spinning reserve is often limited due to considerations such as DG ramp rate, inverter capacity, and economic efficiency. This can cause the power supply and demand imbalance in the Energy Internet to exceed the maximum allowable spinning reserve of the power generation equipment participating in frequency regulation. When the power supply and demand imbalance exceeds the actual maximum spinning reserve, each participating frequency regulation unit can only provide the maximum reserve capacity to inject power into the microgrid or absorb excess power. Therefore, the spinning reserve actually injected into the Energy Internet by the microgrid is described by a saturated nonlinear model, as follows:

[0055]

[0056] Where ΔP rmax Indicates the microgrid spare capacity.

[0057] Step S104: Calculate the derivative of the constructed Lyapunov energy function and set the derivative to be no greater than 0, thereby obtaining the frequency control closed-loop state space equation and the design constraints of the bounded adaptive event-triggered communication scheme.

[0058] The Lyapunov energy function is constructed by the Lyapunov stability analysis method as follows:

[0059]

[0060] By calculating the derivative of the above equation and setting the derivative dV(t) / dt≤0, the design constraints can be determined as follows:

[0061] as well as,

[0062]

[0063] in, δ M =(1+μ)δ m ,ρ,γ,δ M are scalars, ρ, γ, δ M ≥0, λ is the slack variable introduced to deal with the microgrid spinning reserve constraint, λ∈[0,1], Γ, is a matrix, Λ, Ψ, is a symmetric positive definite matrix, and I is the identity matrix. It should be understood that the asterisk "*" indicates that the matrix at that position is the transposed matrix of the matrix that is symmetric to the matrix at that position on the diagonal.

[0064] Therefore, when given scalars ρ, γ, δ M ≥0,λ∈[0,1], if there exists a matrix Γ, Symmetric positive definite matrices Λ, Ψ, The above constraints are met, so that the closed-loop frequency system is asymptotically stable and the controller gain is K = ΓΛ -1 , the performance weight matrix is

[0065] Step S105: Based on the frequency control closed-loop state space equation and design constraints of the energy internet, with the weighted sum of the energy internet communication trigger rate and the frequency deviation rate minimized as the optimization goal, the optimal controller gain and performance weight matrix are determined to control the frequency.

[0066] Since satisfactory dynamic performance often requires more frequent information exchange, control performance and network resource utilization are conflicting design requirements. Therefore, an optimization design is performed with the goal of minimizing the weighted sum of the energy internet communication trigger rate and the frequency deviation rate. This method uses a random direction search approach to determine the optimal controller gains and performance weight matrix. The optimized controller gains are used as controller parameters, and the performance weight matrix is ​​used in an event-triggered communication scheme to control frequency.

[0067] Specifically, this step includes the following process:

[0068] Step 1: Establish the optimization objective function of frequency control of energy Internet.

[0069] Specifically, the optimization objective function is as follows:

[0070]

[0071] Among them, J represents the value of the objective function, σ, are weight coefficients respectively, satisfying C N When entering and maintaining |Δf|≤0.1%|Δf| max The number of control cycles of the response within the deviation band, count is the number of event-triggered communications within the control cycle, This is the energy internet communication trigger rate, |Δf| max is the maximum allowable frequency deviation, |Δf| max_rated To allow the frequency to deviate from the rated value, This is the frequency deviation rate.

[0072] Step 2: Input matrix coefficients A, B, C, E, and microgrid reserve capacity ΔP rmax , the upper bound of transmission delay d, the maximum allowable frequency deviation |Δf| max , H ∞ Performance coefficient γ, and weight coefficient σ,

[0073] Step 3: Initialize the lower limit of the event trigger threshold δ m and adaptive coefficient μ, set search step θ, scaling coefficient ε min and ε max , and, the iteration termination condition χ.

[0074] It should be understood that the lower limit of the initial event trigger threshold δ m and adaptive coefficient μ are both ∈ (0,1), and the initial θ> 0. Scaling coefficient ε min and ε max Satisfy 0<ε respectively min <1,ε max>1.

[0075] Step 4: Calculate the lower limit of the current event trigger threshold δ based on the design constraints m The initial matrix Φ of the controller gain K and performance weight matrix corresponding to the adaptive coefficient μ.

[0076] Step 5: Calculate the maximum allowable frequency deviation |Δf| under the controller gain K and performance weight matrix Φ based on the current frequency control closed-loop state space equation. max .

[0077] That is, y(t), i.e., Δf, is calculated by the frequency control closed-loop state space equation, and the maximum absolute value of the deviation of Δf is obtained, i.e., |Δf| max .

[0078] Step 6: Based on the current calculated maximum allowable frequency deviation |Δf| max , calculate the value J of the objective function in the current iteration loop.

[0079] It should be understood that this process will calculate the corresponding count and C N , and calculate the value through the aforementioned optimization objective function.

[0080] Step 7: Randomly generate a set of preset number of unit vectors {(g mh ,η h )|h=1,2,…,L}.

[0081] Among them, g in each unit vector element of the unit vector set mh and η h The square root of the sum of the squares is equal to 1, that is L is the preset quantity.

[0082] Step 8: Use each group of unit vector elements (g mh η h ), get the updated lower limit of the event trigger threshold δ mh and the adaptive coefficient μ h , and let δ m =δ mh , μ=μ h After that, if 0≤δ mh ,μ h ≤1, then return to step 4 and perform steps 4 to 6, otherwise the lower limit of the event trigger threshold obtained by updating the set of unit vector elements δ mh and the adaptive coefficient μ h rounding.

[0083] It should be understood that if 0≤δ is not satisfied mh ,μ hIf ≤1, it indicates that the event-triggered communication mechanism is not satisfied, so it is discarded.

[0084] Among them, the lower limit δ used to update the event trigger threshold mh and the adaptive coefficient μ h The calculation formula includes:

[0085] δ mh =δ m +θg mh ;

[0086] μ h =μ+θη h .

[0087] After updating, use the updated δ m Recalculate and μ to get the value of the objective function J h , a total of up to L objective function values ​​J are obtained h .

[0088] Step 9: The value J of the preset number of objective functions obtained from the calculation h Get the minimum value of the objective function J l .

[0089] It should be understood that l∈{1,2,…,H}

[0090] Step 10: If the minimum value of the objective function J l Less than the current objective function value J, and the minimum objective function value J l The absolute value of the difference between the current objective function value J is not less than the iteration termination condition, then let δ m =δ ml , μ=μ l , θ=ε max After θ, return to step 4 and perform steps 4 to 9.

[0091] Step 11: If the minimum value of the objective function J l Not less than the current objective function value J, and the minimum objective function value J l The absolute value of the difference between the current objective function value J is not less than the iteration termination condition, then let θ = ε min After θ, return to step 4 and perform steps 4 to 9.

[0092] Step 12: If the minimum value of the objective function J l If the absolute value of the difference from the current objective function value J is less than the iteration termination condition χ, the current controller gain K and performance weight matrix Φ are output.

[0093] Through the above steps, when the iteration termination condition is not met, the corresponding parameters are updated and the value of the objective function is calculated again; when the iteration termination condition is met, the process is terminated and the current controller gain K and performance weight matrix Φ are output.

[0094] An embodiment of the present invention further discloses a computer-readable storage medium having computer program instructions stored thereon; when the computer program instructions are executed by a processor, the energy internet event trigger frequency control method as described in the above embodiment is implemented.

[0095] An embodiment of the present invention further discloses an energy internet event trigger frequency control system, comprising: a computer-readable storage medium as described in the above embodiment.

[0096] The technical solution of the embodiment of the present invention is further illustrated below with a specific application example.

[0097] To verify the feasibility of the energy internet frequency control method proposed in this embodiment of the present invention, which considers spinning reserves and uncertain delays, we tested an energy internet frequency control system shown in Table 1. The comparison methods selected were: 1) a periodic triggering scheme that does not consider spinning reserves; 2) a periodic triggering scheme that considers spinning reserves; 3) a fixed threshold event triggering scheme that does not consider spinning reserves; 4) a fixed threshold event triggering scheme that considers spinning reserves; and 5) an unbounded adaptive threshold event triggering scheme that does not consider spinning reserves.

[0098] Table 1 Parameter values ​​of the energy internet frequency control system

[0099]

[0100]

[0101] Figure 4 Shows the fluctuations in renewable energy output and load demand. Figure 5 The frequency deviations for different control methods are shown. Figure 6 Active power instructions injected into the microgrid under different control schemes. Figure 7 The triggering time and interval of different event-triggered communication schemes are shown in Table 2. The frequency deviation variance, the final number of triggers, and the absolute integral of the frequency deviation are selected as evaluation indicators.

[0102] Table 2 Performance indicators of different control schemes

[0103]

[0104] according to Figure 5 and Figure 6Because the power fluctuation exceeds the microgrid's spinning reserve limit, only the maximum spinning reserve power can be injected into the microgrid within 20s-40s and 60-80s. Accordingly, the frequency deviation cannot be restored to zero during these two periods. Compared with the periodic control scheme, although the frequency fluctuation variance of the method proposed in the embodiment of the present invention increased by 17.7%, the number of communication triggers was significantly reduced by 84.4%. Compared with other ETC schemes, the method proposed in the embodiment of the present invention takes into account both control performance and network bandwidth utilization. Therefore, the frequency deviation variance and IAE index are reduced by at least 13.11% and 6.077% respectively, reflecting that the method proposed in the embodiment of the present invention has better frequency deviation recovery capability.

[0105] It should be noted that although the number of triggering times of the adaptive ETC solution without an upper limit on the trigger threshold can be further reduced by more than 49% compared with the solution proposed in the embodiment of the present invention, Figure 4 It can be seen that the frequency deviation of the ETC scheme without an upper trigger threshold was not effectively damped during the 40-60 second period, and within the 80-100 second period, the frequency deviation exceeded 0.2 Hz (the maximum allowable frequency deviation for the Energy Internet). This is because when the frequency deviation begins to decay, the trigger threshold of the ETC scheme without an upper trigger threshold increases rapidly, making subsequent triggering more difficult. This, in turn, compromises the control center's ability to monitor frequency deviation.

[0106] In summary, the embodiments of the present invention not only save network resources, but also enable the controller to maintain the ability to sensitively monitor the operating status of the microgrid after the frequency deviation is well suppressed. By fully considering the active power output constraints of the energy storage equipment and the uncertain delay in the communication interaction process, the microgrid frequency control system is described as a nonlinear model with a saturation term. Based on the Lyapunov stability theory, the design criteria of event-triggered control parameters under power constraints, random delays, and parameter uncertainties are rigorously derived to facilitate frequency control.

[0107] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for controlling the frequency of triggering an energy internet event, characterized in that: include: Based on the number of diesel generators and energy storage devices involved in the frequency control of the energy internet, the frequency control state space equation of the energy internet is established; Design bounded adaptive event-triggered communication schemes; Based on the frequency control state space equation of the energy internet and the bounded adaptive event-triggered communication scheme, a frequency control closed-loop state space equation of the energy internet is established that takes into account transmission delay and spinning reserve of power generation equipment; Calculating the derivative of the constructed Lyapunov energy function and setting the derivative to be no greater than 0, thereby obtaining the frequency control closed-loop state space equation and the design constraints of the bounded adaptive event-triggered communication scheme; Based on the frequency control closed-loop state space equation of the energy internet and the design constraints, with minimizing the weighted sum of the energy internet communication trigger rate and the frequency deviation rate as the optimization objective, the optimal controller gain and performance weight matrix are determined to control the frequency; The bounded adaptive event-triggered communication solution includes: ； in, t h is the adaptive event triggering moment, Φ is the performance weight matrix, T s is the sampling time, l is a natural number, Z is the set of natural numbers, δ ( t h T s ) is the event trigger threshold, e ( i l T s )= x ( i l T s )- x ( t h T s ), i l T s = t h T s + lT s , δ ( t h T s )= δ m + λδ ( t h-1 T s ); in, , μ is the adaptive coefficient, δ m is the lower limit of the event trigger threshold, μ ∈(0, 1), δ m ∈(0, 1), initial conditions p =0, δ ( t 0 T s )= δ m ; The frequency control closed-loop state space equation includes: ； in, K is the controller gain, τ ( t ) is the uncertain transmission delay, τ ( t )∈(0, d ], d is the upper bound of transmission delay, is the nonlinearity between the ideal spinning reserve and the actual spinning reserve constraint of the power generation equipment, , the saturated nonlinear model used to describe the actual spinning reserve injected into the energy internet by power generation equipment includes: ； in, Indicates the microgrid spare capacity.

2. The energy internet event trigger frequency control method according to claim 1, characterized in that: The frequency control state space equation includes: ； in, A 、 B 、 C 、 E is the matrix coefficient; in, , , , , , , , , T gn 、 T tn Respectively n The time constants of the diesel engine speed governor and synchronous generator of each diesel generator, T ESSm For the m The time constant of the inverter of the energy storage device, R DGn For the n The droop coefficient of a diesel generator, D is the equivalent damping coefficient of the Energy Internet, H is the equivalent inertia coefficient of the Energy Internet, n =1,…, N , m =1,…, M ; in, , , , R ESS For the m The droop coefficient of each energy storage device is α n 、 β m Respectively n A diesel generator and m Frequency modulation participation factor of each energy storage device, Σ α n +Σ β m =1; in, C =[1, 0, 0, 0, 0]; in, ; in, x ( t )=[Δ f , Δ P DG, 1 ,…,Δ P DG, n ,…,Δ P DG, N , Δ P v1 ,…,Δ P vn ,…,Δ P vN , Δ P ref ESS, 1 ,…,Δ P ref ESS,m ,…,Δ P ref ESS, M , Δ P ESS, 1 ,…,Δ P ESS, m ,…,Δ P ESS, M , ∫Δ f ] T , Δ f is the frequency deviation, Δ P DG, n , Δ P ESS, m Respectively n A diesel generator and m The active output increment of each energy storage device, Δ P vn For the n The opening of the governor valve of a diesel generator, Δ P ref ESS,m For the m The inverter output power instruction of each energy storage device; in, y ( t ) is Δ f ; in, u ( t ) is the spinning reserve power injected into the Energy Internet; in, w ( t ) is the external power disturbance, w ( t )=Δ P WT +Δ P PV -Δ P d , Δ P WT , Δ P PV , Δ P d They are wind turbines, photovoltaics and load fluctuations respectively.

3. The energy internet event trigger frequency control method according to claim 1, characterized in that: The Lyapunov energy function includes: 。 4. The energy internet event trigger frequency control method according to claim 3, characterized in that: The design constraints include: ;as well as, ； in, , , , , , , , , , , , , , , , , , , , , , ρ , γ , δ M is a scalar, ρ , γ , δ M ≥0, λ The slack variables introduced to deal with the microgrid spinning reserve constraints are: λ ∈[0, 1],Γ、 is a matrix, 、 、 、 、 is a symmetric positive definite matrix, I is the identity matrix.

5. The energy internet event trigger frequency control method according to claim 4, characterized in that: The step of determining the optimal controller gain and performance weight matrix comprises: Step 1: Establish the optimization objective function of frequency control of energy internet, where the optimization objective function is , J is the value of the objective function, σ 、 φ are weight coefficients, σ + φ =1, C N When entering and maintaining |Δ f |≤0.1%|Δ f | max The number of control cycles of the response within the deviation band, count is the number of event-triggered communications within the control cycle, Δ f | max is the maximum allowable frequency deviation, |Δ f | max_rated is the allowable frequency deviation rated value; Step 2: Input the matrix coefficients A, B, C, E, and the microgrid spare capacity , the upper bound of the transmission delay d , the maximum allowable frequency deviation |Δ f | max , H ∞ Coefficient of performance γ , and the weight coefficient σ 、 φ ; Step 3: Initialize the lower limit of the event trigger threshold δ m and the adaptive coefficient μ , set the search step θ , scaling factor ε min and ε max , and, the iteration termination condition χ ; Step 4: Calculate the lower limit of the current event trigger threshold based on the design constraints δ m and the adaptive coefficient μ The corresponding controller gain K and the performance weight matrix Φ; Step 5: Calculate the controller gain based on the current frequency control closed-loop state space equation K and the maximum allowable frequency deviation |Δ under the performance weight matrix Φ f | max ; Step 6: Based on the current calculated maximum allowable frequency deviation |Δ f | max , calculate the value of the objective function in the current iteration loop J ; Step 7: Randomly generate a preset number of unit vector sets: {( g mh , η h )| h =1, 2, …, L }; wherein, in each group of unit vector elements of the unit vector set g mh and η h The square root of the sum of the squares of is equal to 1. L is the preset quantity; Step 8: Use each group of unit vector elements ( g mh η h ), obtain the updated lower limit of the event trigger threshold δ mh and the adaptive coefficient μ h , and order δ m = δ mh , μ = μ h After that, if 0≤ δ mh , μ h ≤1, then return to step 4 and perform steps 4 to 6, otherwise update the set of unit vector elements to obtain the lower limit of the event trigger threshold δ mh and the adaptive coefficient μ h rounding; Step 9: The value of the objective function of the preset number obtained by calculation J h Obtain the minimum value of the objective function J l ; Step 10: If the value of the minimum objective function J l Less than the current value of the objective function J , and the minimum value of the objective function J l With the current value of the objective function J The absolute value of the difference is not less than the iteration termination condition, then let δ m = δ ml , μ = μ l , θ = ε max θ Then, return to step 4 and perform steps 4 to 9; Step 11: If the value of the minimum objective function J l Not less than the current value of the objective function J , and the minimum value of the objective function J l With the current value of the objective function J The absolute value of the difference is not less than the iteration termination condition, then let θ = ε minθ , return to step 4, and perform steps 4 to 9; Step 12: If the value of the minimum objective function J l With the current value of the objective function J The absolute value of the difference is less than the iteration termination condition χ , then the current controller gain is output K and the performance weight matrix Φ.

6. The energy internet event trigger frequency control method according to claim 5, characterized in that: Used to update the lower limit of the event trigger threshold δ mh and the adaptive coefficient μ h The calculation formula includes: δ mh = δ m + θg mh ； μ h = μ + θη h 。 7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by the processor, the energy internet event trigger frequency control method according to any one of claims 1 to 6 is implemented.

8. An energy internet event trigger frequency control system, characterized in that: include: The computer-readable storage medium of claim 7.

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