Wind-wave-storage combined system multi-time scale model predictive energy management method

Abstract

A wind-wave-storage combined system multi-time scale model prediction energy management method, according to the current frequency, the storage SOC and the wind wave power margin, respectively through the active adjustment wind wave combined power generation unit motor opening and the reference power response of the storage unit, the output power is adjusted quickly, at the same time, the frequency optimization and the storage state adjustment are carried out, so that the system energy management optimization is realized.The present application can realize the power management of ms level, realize the system frequency optimization, and realize the SOC management of hour level, improve the long-term operation stability of the system, fully tap the potential of wind wave renewable energy in energy management, and improve the stability and survivability of the system.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of island energy management, and particularly relates to a wind-wave-storage combined system multi-time scale model prediction energy management method. BACKGROUND

[0002] The wind-wave-storage microgrid system is an important energy supply mode of future island energy systems. However, the intermittent and fluctuating characteristics of wind and wave energy lead to frequent power fluctuations of the system, which is not conducive to the stable control of the system frequency, and the small-capacity energy storage system cannot guarantee stable output for a long time under extreme working conditions, which is not conducive to the stable operation of the system in the ms and h multi-time scale. SUMMARY

[0003] The application proposes a wind-wave-storage combined system multi-time scale model prediction energy management method, which can simultaneously realize ms-level power management to optimize the system frequency and h-level SOC management to improve the long-term operation stability of the system, fully tap the potential of wind and wave renewable energy in energy management, and improve the stability and survivability of the system.

[0004] The application is implemented through the following technical scheme:

[0005] The application relates to a wind-wave-storage combined system multi-time scale model prediction energy management method, which adjusts the motor opening degree of the wind-wave combined power generation unit and the reference power of the energy storage unit according to the current frequency, the energy storage SOC and the wind-wave power margin to simultaneously adjust the output power, optimize the frequency and adjust the energy storage state, so as to realize the optimization of system energy management.

[0006] The application relates to a wind-wave-storage combined power supply system for realizing the above method, which comprises a wind-wave combined power generation unit and an energy storage unit, wherein the wind-wave combined power generation unit is a hydraulic transmission power generation device, two hydraulic motors are coaxially connected to realize the superposition of wind energy and wave energy and drive a synchronous power generation stage to directly output alternating current energy, the unit adjusts the output power by controlling the opening degrees of the two hydraulic motors, and the energy storage unit adopts droop control and is connected to the system bus, and the output power is adjusted by controlling the reference power.

[0007] TECHNICAL EFFECT

[0008] This invention provides a refined modeling of the state space of a wind-wave-storage island microgrid system, encompassing frequency, motor operation, energy storage reference power, and energy storage state of charge (SOC). It clarifies the system variables affecting the overall system operation characteristics and their interaction mechanisms, and establishes predictive models for system frequency and energy storage SOC. Based on the control requirements of the microgrid system at different time scales, a dual-mode energy management control strategy is proposed. At the millisecond time scale, frequency optimization is the primary objective, while at the hourly time scale, energy storage SOC regulation is the primary objective. A multi-time-scale operating mode switching method is designed, adaptively adjusting the weight coefficients of the cost function based on the system frequency deviation to achieve flexible switching between multiple operating modes. Compared with existing technologies, the present invention provides more precise system state constraints and operational constraints, enabling coordinated allocation of wind-wave-storage output power during multi-timescale energy management. It also utilizes surplus wind and wave energy to regulate the storage SOC, fully tapping the potential of wind and waves in system energy management. This further enhances the stability and economy of the island microgrid system while allowing for real-time adjustment of control strategies based on system state, achieving flexible switching between millisecond-level power management and h-level state management, and balancing millisecond-level system frequency control with h-level system energy optimization. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the invention;

[0010] Figure 2 This is a schematic diagram of a combined wind and wave power generation system.

[0011] Figure 3 A schematic diagram illustrating the variation characteristics of the correction coefficient for an energy storage unit;

[0012] Figure 4 A schematic diagram of the model-predictive energy management algorithm flow;

[0013] Figure 5 Schematic diagram of frequency modulation performance for different networking schemes;

[0014] Figure 6 This is a schematic diagram of the system state changes during a transient process;

[0015] In the figure: (a) system state variables and output power of each unit, (b) motor opening degree and energy storage reference power;

[0016] Figure 7 A diagram showing the performance comparison of different networking schemes when wind and wave power is insufficient;

[0017] In the figure: (a) system frequency, (b) energy storage output power;

[0018] Figure 8 A diagram showing the frequency modulation performance comparison of different MPC methods;

[0019] In the diagram: (a) wind and wave power is sufficient, (b) wind and wave power is insufficient;

[0020] Figure 9 A schematic diagram of the wind-wave-load time distribution of a certain island over 72 hours;

[0021] In the figure: (a) wind and wave resources, (b) source load power;

[0022] Figure 10 A schematic diagram illustrating the 72-hour SOC (State of Charge) change of an island's energy storage.

[0023] Figure 11 Schematic diagram of the operating performance of different control strategies;

[0024] In the figure: (a) System frequency, (b) Energy storage SOC. Detailed Implementation

[0025] like Figure 1 As shown in the figure, this embodiment relates to a multi-timescale model predictive energy management method for a wind-wave-storage integrated system. Based on the current system frequency, energy storage SOC, and wind and wave power margin, the output power is rapidly adjusted by actively regulating the motor opening of the wind-wave integrated power generation unit and the reference power response of the energy storage unit, while simultaneously optimizing the frequency and adjusting the energy storage state, thereby achieving system energy management optimization.

[0026] like Figure 2 As shown, the wind and wave combined power generation unit includes: a bidirectional hydraulic cylinder ①, a one-way valve ②, an air-filled accumulator ③, a hydraulic tank ④, and a pressure relief valve ⑤ located on the wave energy unit side; a variable displacement hydraulic motor ⑥ and a synchronous generator ⑨ connected to both ends of the coupling respectively; and a constant displacement hydraulic pump ⑦ and a wind turbine ⑧ located on the wind power unit side. The wind turbine drives the constant displacement hydraulic pump to rotate and pump out hydraulic oil, which then drives the variable displacement motor through the hydraulic circuit. The wave energy unit captures wave energy through the bidirectional hydraulic cylinder and drives the hydraulic motor through the one-way valve rectifier circuit. The air-filled accumulator in the circuit reduces output power fluctuations by compressing internal air to smooth out rapid changes in flow and pressure in the circuit. When the pressure in the circuit exceeds the set threshold of the pressure relief valve, the excess energy is discharged from the system in the form of hydraulic energy.

[0027] The coupling is equivalent to an inertia-spring-damping system, which realizes the synchronization of the speeds of the two variable displacement hydraulic motors and the superposition of mechanical power for shaft engagement.

[0028] The system energy management mentioned above refers to power management at the millisecond level and energy storage SOC management at the h level, based on the time scale. Power management refers to the real-time coordinated allocation of the output power of wind, wave, and energy storage multiple power sources according to the microgrid frequency deviation to achieve system frequency optimization. Energy storage SOC management refers to adjusting the energy storage reference power and utilizing the surplus wind and wave power to participate in energy storage state regulation on the basis of achieving stable frequency control.

[0029] The power management mentioned above specifically includes: P wd P we P L f and f represent the output power of the combined wind and wave power generation unit, the output power of the wave energy unit, the load demand power, and the system frequency, respectively, where f0 is the rated system frequency. The output power of the combined wind and wave power generation unit is... η is the energy transmission efficiency of the hydraulic motor, p is the hydraulic circuit pressure, ω is the motor speed, and V is the voltage. max S represents the maximum displacement of the hydraulic motor, S represents the opening degree of the hydraulic motor, and the subscripts i = 1 and 2 correspond to the combined wind and wave power generation unit and the wave energy unit, respectively. The energy storage unit output power is...

[0030] P b =P0-m(f-f0), where P0 is the reference power of the energy storage unit, m is the droop coefficient, and the system moment of inertia is the sum of the moments of inertia of the rotating equipment directly connected to the synchronous machine, specifically: J1, J2 and J e The moments of inertia, N and P, of the rotors of wind turbines, wave motors, and synchronous machines, respectively. N This refers to the transmission ratio of the coupling and the number of pole pairs of the synchronous generator.

[0031] The opening degree of the hydraulic motor can be adjusted arbitrarily within (0-1) to achieve stepless transmission and ensure smooth and continuous power conversion.

[0032] When the system frequency is disturbed, the system coordinates and controls Pwd, Pwe, and Pb to quickly respond to the frequency change and achieves rapid frequency recovery through millisecond-level power management.

[0033] The aforementioned energy storage SOC management specifically includes: Among them: W b To determine the energy storage capacity, the reference power P0 of the energy storage unit is set as the controlled variable, and the droop coefficient m is set as a constant. When the system frequency is stable at the rated value, the energy storage output power is equal to the reference power P0. By actively adjusting the reference power P0 of the energy storage unit according to the output power margin of the wind and wave unit, the energy storage SOC regulation can be achieved.

[0034] The aforementioned multi-time-scale model is derived from the system frequency-motor operation-energy storage reference power state-space equation, specifically as follows: Where: I is the identity matrix, T s S is the sampling period, k is the sampling time, Y(k+n|k) and ΔU(k) ​​are the system output prediction sequence and control increment sequence, respectively, and S is the sampling period. x S u S d represents the state prediction matrix, control prediction matrix, and non-control prediction matrix, respectively; Δ represents the state difference between two sampling times; and x(k) and y(k) are the system state vector and output vector at time k, respectively.

[0035]

[0036] The system frequency-motor opening-energy storage reference power state-space equation is as follows:

[0037] Where: Pm is the combined wind and wave output power, T m Let ω be the time constant of the hydraulic motor response. c The rotational speed of the coupling is the speed of the coupling. The coupling is equivalent to an inertia-spring-damping unit, which connects the hydraulic motor of the wind and wave combined power generation unit and the wave energy unit to achieve speed synchronization and power superposition.

[0038] The active adjustment of motor opening and reference power response to rapidly regulate output power while simultaneously optimizing frequency and adjusting energy storage status is achieved through the following methods:

[0039] Step 1: Construct the cost function for system operation, and establish the system energy management objective function with the minimum cost function as the control objective. Penalize the system output deviation and the increment of the controlled variable. Comprehensively consider the impact of frequency deviation, energy storage SOC deviation, motor opening increment, and energy storage reference power increment on the overall system performance, so that the system can achieve frequency regulation and energy storage state adjustment in energy management with minimal actions. Specifically:

[0040]

[0041] Where: α, β, λ, μ and All are penalty coefficients, P b0 and R b0 The rated output power and rated ramp rate of the energy storage unit. and It is a function of the SOC of the energy storage unit, and represents the correction coefficients for discharge power, charging power, discharge ramp rate and charging ramp rate, respectively. The subscript i indicates the sampling point, ref represents the reference value, k represents the sampling time, and P represents the control time domain.

[0042] Step 2: Construct the actual operating constraints of the system, including constraints on the range and rate of change of motor opening, the range and rate of change of reference power, the range of energy storage output power, and the ramp rate. Specifically:

[0043]

[0044] Step 3: Transform the control problem into a quadratic optimization problem with the control increment sequence ΔU(k) ​​as the optimization variable, and solve it quickly using the quadratic programming solver (Quadprog) function, specifically:

[0045] Where: U is the independent variable, H is the coefficient of the quadratic term, G is the coefficient of the linear term, Q and R are the weight matrices of the system output deviation and control increment in the cost function, respectively, and Ref is the output reference value matrix.

[0046] like Figure 4 As shown, the energy management optimization includes: a power management mode, which optimizes control only for frequency, and a State of Charge (SOC) management mode, which adjusts the energy storage state while maintaining frequency stability. Specifically, the frequency deviation is used as the basis for mode switching, and the SOC deviation weight β is adjusted to achieve the following: when the frequency deviation exceeds the limit, β = 0, and the system operates in frequency optimization mode. At this time, the deviation between SOC and the reference value is not penalized, and the system does not actively adjust SOC; when the frequency deviation does not exceed the limit, β ≠ 0, and the operating mode switches to SOC adjustment mode. At this time, the controller will actively adjust the energy storage state to achieve the goal of minimizing the cost function.

[0047] The SOC reference value is calculated based on the current wind and wave margin, specifically as follows:

[0048] Wherein: P1 and P2 are the output power of the wind and wave combined power generation unit and the wave energy unit, respectively.

[0049] Based on specific practical experiments and the equipment parameters of the Wenzhou Nanji Island microgrid project (as shown in Table 1), a typical wind-wave-storage island microgrid system RT-LAB hardware-in-the-loop simulation model, including wind and wave compensation devices, hydraulic transmission devices, couplings, energy storage, typical loads, and control units, was used as the object. The simulation platform employed included the RT-LAB OP7000 hardware simulation real-time simulator, AN706 controller, XLINX FPGA ZYNQ7020 development board, and Tektronix MSO44 oscilloscope. By sampling state signals such as system frequency and power supply through the controller, the FPGA quickly solved the optimal solution for model predictive control, realizing hardware-in-the-loop simulation of frequency optimization and energy storage state management, thereby verifying the effectiveness of the proposed system and method.

[0050] Table 1 Parameters of the Island Microgrid Power System

[0051]

[0052]

[0053] This embodiment uses FPGA to solve the model predictive control optimal solution online. To ensure the real-time performance and accuracy of the calculation, the sampling step size T is... s The time limit was set to 0.02s, with a step size of 5 steps. The system's operational and state constraints were selected based on real microgrid data, as shown in Table 2.

[0054] Table 2 System State Constraints and Weighting Coefficients

[0055]

[0056] Comparison of Frequency Regulation Performance of Different Networking Schemes: To compare the frequency regulation performance of different networking schemes, a comparative analysis of the three schemes was conducted under the same operating conditions and constraints. The results are as follows: Figure 5 As shown in Table 3. The environmental input and load requirements are set as follows: the input for the combined wind and wave power generation unit is a mean of 8 m / s and a variance of 0.25 m / s. 2 / s 2 The random wind; the wave energy unit input is a periodic wave force with a period of 2πs and an amplitude of 400kN; the load demand is an increase of 20kW of load within 1s.

[0057] Table 3 Comparison of Frequency Characteristics of Different Networking Schemes

[0058]

[0059]

[0060] All three networking schemes can achieve frequency regulation: the system with wind-wave networking alone achieves frequency regulation by controlling the generator speed, but due to the large rotational inertia of the system, the speed regulation is slow, so the transient time and overshoot of the frequency response exceed the limit; the system with energy storage networking alone has a fast response, but there is a steady-state deviation and the requirements for energy storage capacity are relatively high; in contrast, the wind-wave-storage combined networking system combines the advantages of both, and has superior performance in frequency optimization, reducing the maximum frequency deviation by 87.6% and 44.4% respectively, and the frequency recovery time is also reduced by 74.4% and 16.7% respectively, which is conducive to the system reaching stability faster.

[0061] When the frequency drops rapidly, the system switches to frequency optimization mode. At this time, the energy storage unit rapidly outputs power through droop control to ensure the lowest frequency point does not exceed the limit. Simultaneously, the wind, wave, and energy storage units respond to frequency changes by adjusting motor opening and reference power to increase system power output until the system returns to steady state. Afterward, the system resumes SOC regulation mode, with surplus wind and wave energy used to regulate the energy storage state. The system state change characteristics at each stage are as follows: Figure 6 As shown in Table 4, the power supply output power characteristics are as follows.

[0062] Table 4. Power output power variation characteristics at different stages

[0063]

[0064] In addition, changes in marine weather may lead to insufficient wind and wave power generation. To verify the frequency regulation performance of this method under this condition, the experiment simulated a scenario where the wind and wave input was reduced by 50% at a certain moment. At this time, the wind and wave output power was less than the load demand, the wind and wave combined power generation unit operated at maximum power, and the power gap was supported by the energy storage unit.

[0065] The operational results of the three networking schemes are as follows: Figure 7 As shown. The wind-wave grid system includes a hydraulic energy storage structure, which can quickly release power when power is insufficient to slow down frequency drops, but its output power is uncontrollable; after the wind and wave output decreases, the energy storage grid needs to take on more power output, resulting in a larger frequency deviation; in contrast, the wind-wave-energy storage combined grid system has a stronger output capability, with a maximum frequency of only 0.06Hz, which is 57.1% and 97.2% lower than the previous two schemes, respectively. At the same time, the recovery time to steady state is also reduced by 65.5% and 89.4%, respectively. Although the energy storage unit is still outputting power at this time, its reference power is also adjusted during the frequency regulation process, which is equivalent to a secondary frequency regulation of the system, so there is no frequency deviation in steady state.

[0066] Energy Storage State Management Performance Comparison: Under the same operating conditions, the control performance differences of the two MPC methods are compared at two different time scales: frequency optimization and SOC regulation. The frequency optimization performance of the two methods is examined over a short time scale. Simulations are performed under two scenarios: sufficient wind and wave power and insufficient wind and wave power (same as in Example 1), simulating a 20kW increase in island load within 1 second. The results are as follows: Figure 8 As shown.

[0067] When wind and wave power is sufficient, both MPC methods can respond to frequency fluctuations in real time, adjust the power output of each unit, and achieve frequency optimization. In contrast, the MPC method that takes into account the energy storage state can adjust the reference power according to the frequency deviation based on the energy storage droop control, which helps the frequency recover quickly, reduces the maximum frequency deviation by 21.1%, and reduces the frequency recovery time by 37.5%.

[0068] When wind and wave power is insufficient, energy storage units need to supplement the power gap. Since droop control that only considers primary frequency regulation will cause stability deviation in the system frequency, the MPC method that does not take into account the energy storage state cannot restore the frequency to the rated value when the system is stable, but changes with the fluctuation of wind and wave output power. In contrast, the MPC method that takes into account the energy storage state can actively adjust the reference power point in response to frequency fluctuations, perform secondary frequency regulation of the system, and the adjustment speed is greater than the speed of wind and wave power change, ensuring that the frequency can be restored to the rated value when the system is stable.

[0069] The energy storage SOC regulation performance of the two methods was examined over a long time scale. A simulation of the operating conditions of an island over a 3-day (72-hour) period was performed. The source-load power fluctuations and SOC operation results during this period are as follows: Figure 9 and 10 As shown.

[0070] The wind and wave resources on the island changed constantly within 72 hours, and there were multiple instances of insufficient wind and wave power. At this time, the energy storage unit needed to continuously output to make up for the power gap. The MPC method, which did not take into account the energy storage state, could not convert the excess power into electrical energy storage when the power was sufficient, and therefore could not restore the energy storage state. In contrast, the MPC method that took into account the energy storage state could convert the excess wind and wave power into electrical energy storage by adjusting the reference power point. The energy storage SOC reduction under the control of this method was reduced by 88.5%.

[0071] Performance Comparison of Different Control Strategies: The comparison strategy constructs a two-layer control system with top-level energy management and bottom-level power regulation. Based on minimum operating cost and energy storage state balance, it uses PI control to achieve voltage control and SOC regulation of the DC microgrid. The case study sets the same system operation and constraints, comparing the system frequency and energy storage SOC variation characteristics under different PI parameters.

[0072] Table 5 PI Parameter Settings

[0073]

[0074] The comparative strategy calculates the penalty term for each unit's operation using a cost function, considering the overall system benefit. In contrast, this strategy employs different control functions for frequency optimization and energy storage state management scenarios, and can quickly switch according to the system's operating state, making it more targeted and thus exhibiting better performance in specific scenarios. Performance comparison of different control strategies is provided below. Figure 11 As shown in Table 6, specifically, the proposed strategy has a faster frequency recovery speed and a smaller maximum frequency deviation during frequency transient optimization; and a faster power response and more complete energy conversion during energy storage state management.

[0075] Table 6 Comparison of Frequency and SOC for Different Control Strategies

[0076]

[0077]

[0078] When examining the frequency optimization performance, for the two-layer control strategy based on PI regulation, as the proportional gain increases, the system response speed continuously improves, and the maximum frequency deviation and frequency recovery time decrease. However, an excessively large proportional gain can lead to frequency overshoot, which is detrimental to system stability. Compared with PI 1, which has the best overall performance, the proposed strategy shortens the frequency recovery time by 43.3% and the maximum frequency deviation by 46.4%.

[0079] When examining the SOC regulation performance, the comparative strategy calculates the power distribution of each power source through the top-level energy management system and then realizes the power output through PI control. The power source response is not fast enough, resulting in the energy storage being unable to efficiently absorb excess wind and wave power. In contrast, the proposed strategy updates the SOC reference value in real time based on the current power margin and directly adjusts control quantities such as motor opening and energy storage reference power. Under the premise of ensuring frequency stability, it maximizes the use of excess wind and wave power to regulate the energy storage state and improves the energy storage absorption efficiency by 34.7%.

[0080] Compared with existing technologies, this method monitors the system status in real time, uses whether the frequency deviation exceeds the limit as the switching criterion, and actively adjusts the weight coefficient of the cost function to achieve flexible switching between power management and SOC management operating modes, thereby realizing multi-objective optimized energy management of system operation.

[0081] The above-described specific implementations can be partially adjusted by those skilled in the art in different ways without departing from the principles and purpose of the present invention. The scope of protection of the present invention is defined by the claims and is not limited to the above-described specific implementations. All implementation schemes within the scope of the claims are bound by the present invention.

Claims

1. A method for predictive energy management of a wind-wave-storage integrated system using a multi-timescale model, characterized in that, Based on the current system frequency, energy storage SOC, and wind and wave power margin, the output power is rapidly adjusted by actively regulating the motor opening of the wind and wave combined power generation unit and the reference power response of the energy storage unit, while simultaneously optimizing the frequency and adjusting the energy storage status, thereby achieving system energy management optimization. The aforementioned method of rapidly adjusting the output power by actively regulating the motor opening of the wind and wave combined power generation unit and the reference power response of the energy storage unit is achieved through the following means: Step 1: Construct the cost function for system operation, and establish the system energy management objective function with the minimum cost function as the control objective. Penalize the system output deviation and the increment of the controlled variable. Comprehensively consider the impact of frequency deviation, energy storage SOC deviation, motor opening increment, and energy storage reference power increment on the overall system performance, so that the system can achieve frequency regulation and energy storage state adjustment in energy management with minimal actions. Specifically: ， ， Where: α, β, λ, μ and All are penalty coefficients, P b0 and R b0 The rated output power and rated ramp rate of the energy storage unit. , , and It is a function of the SOC of the energy storage unit, and the correction coefficients are the discharge power, charging power, discharge ramp rate and charging ramp rate, respectively. The subscript i indicates the sampling point, ref represents the reference value, k represents the sampling time, P represents the control time domain, and x(k) is the system state vector at time k. Step 2: Construct the actual operating constraints of the system, including constraints on the range and speed of motor opening variation, the range and speed of reference power variation, the range of energy storage output power, and the ramp rate. Step 3: Transform the control problem into a quadratic optimization problem with the control increment sequence ΔU(k) ​​as the optimization variable, and solve it quickly using the quadratic programming solver (Quadprog) function; The energy management optimization includes: a power management mode, which optimizes control only for frequency, and a State of Charge (SOC) management mode, which adjusts the energy storage state while maintaining frequency stability. Specifically, the mode switching is based on frequency deviation, and is achieved by adjusting the weight β of the SOC deviation: when the frequency deviation exceeds the limit, β=0, and the system operates in frequency optimization mode. In this mode, the deviation between SOC and the reference value is not penalized, and the system does not actively adjust SOC; when the frequency deviation does not exceed the limit, β≠0, and the operating mode switches to SOC adjustment mode. In this mode, the controller actively adjusts the energy storage state to achieve the goal of minimizing the cost function.

2. The energy management method for multi-timescale model prediction of the wind-wave-storage integrated system according to claim 1, characterized in that, The wind and wave combined power generation unit includes: a bidirectional hydraulic cylinder, a one-way valve, an air-filled accumulator, a hydraulic tank and a pressure relief valve located on the wave energy unit side; a hydraulic motor and a synchronous generator connected to both ends of the coupling respectively; and a constant displacement hydraulic pump and a wind turbine located on the wind energy unit side. Specifically, the wind turbine drives the constant displacement hydraulic pump to rotate and pump out hydraulic oil, which then drives the hydraulic motor through a hydraulic circuit. The wave energy unit captures wave energy through the bidirectional hydraulic cylinder and drives the hydraulic motor through a one-way valve rectifier circuit. The air-filled accumulator in the circuit reduces output power fluctuations by compressing internal air to smooth out rapid changes in flow and pressure within the circuit. When the pressure in the circuit exceeds the set threshold of the pressure relief valve, excess energy is discharged from the system in the form of hydraulic energy.

3. The energy management method for multi-timescale model prediction of the wind-wave-storage integrated system according to claim 1, characterized in that, The system energy management mentioned above refers to power management at the millisecond level and energy storage SOC management at the h level, based on the time scale. Power management refers to the real-time coordinated allocation of the output power of wind, wave, and energy storage multiple power sources according to the microgrid frequency deviation to achieve system frequency optimization. Energy storage SOC management refers to adjusting the energy storage reference power and utilizing the surplus wind and wave power to participate in energy storage state regulation on the basis of achieving stable frequency control.

4. The energy management method for multi-timescale model prediction of the wind-wave-storage integrated system according to claim 3, characterized in that, The power management mentioned above specifically includes: Among them: wind power unit output power Wave energy unit output power P L Where is the load demand power, f is the system frequency, f0 is the system rated frequency, η is the energy transfer efficiency of the hydraulic motor, p is the hydraulic circuit pressure, ω is the motor speed, and V max S represents the maximum displacement of the hydraulic motor, S represents the opening degree of the hydraulic motor, and the subscripts i=1 and 2 correspond to the wind energy unit and the wave energy unit, respectively. The energy storage unit output power is... P0 is the reference power of the energy storage unit, m is the droop coefficient, and the system moment of inertia is the sum of the moments of inertia of the rotating equipment directly connected to the synchronous machine, specifically: J1, J2 and J e The moments of inertia, N and P, of the rotors of the wind turbine, wave turbine, and synchronous motor, respectively. N This refers to the transmission ratio of the coupling and the number of pole pairs of the synchronous generator.

5. The multi-timescale model predictive energy management method for wind-wave-storage integrated systems according to claim 4, characterized in that, The aforementioned energy storage SOC management specifically includes: , where: W b To determine the energy storage capacity, the reference power P0 of the energy storage unit is set as the controlled variable, and the droop coefficient m is set as a constant. When the system frequency is stable at the rated value, the energy storage output power is equal to the reference power P0. By actively adjusting the reference power P0 of the energy storage unit according to the output power margin of the wind and wave combined power generation unit, the energy storage SOC regulation can be achieved.

6. The multi-timescale model predictive energy management method for wind-wave-storage integrated systems according to claim 4, characterized in that, The aforementioned multi-time-scale model is derived from the system frequency-motor operation-energy storage reference power state-space equation, specifically as follows: Where: I is the identity matrix, T s S is the sampling period, k is the sampling time, Y(k+n|k) and ΔU(k) ​​are the system output prediction sequence and control increment sequence, respectively, and S is the sampling period. x S u S d Let represent the state prediction matrix, control prediction matrix, and non-control prediction matrix, respectively; Δ represent the state difference between two sampling times; and x(k) and y(k) represent the system state vector and output vector at time k, respectively.

7. The energy management method for multi-timescale model prediction of the wind-wave-storage integrated system according to claim 6, characterized in that, The system frequency-motor opening-energy storage reference power state-space equation is as follows: Among them: W b Where Pm is the energy storage capacity, Pm is the combined wind and wave output power, and T is the energy storage capacity. m Let ω be the time constant of the hydraulic motor response. c The rotational speed of the coupling is the speed of the coupling. The coupling is equivalent to an inertia-spring-damping unit, which connects the hydraulic motor of the wind and wave combined power generation unit and the wave energy unit to achieve speed synchronization and power superposition.

8. The energy management method for multi-timescale model prediction of the wind-wave-storage integrated system according to claim 1, characterized in that, The SOC reference value is calculated based on the current wind and wave margin, specifically as follows: Where: P1 and P2 are the output powers of the wind energy unit and the wave energy unit, respectively, and T s For the sampling period, W b S represents the capacity of the energy storage unit, S represents the opening degree of the hydraulic motor, the subscripts i=1 and 2 correspond to the wind energy unit and the wave energy unit, and k represents the sampling time.

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