A multi-mode switching control method applicable to a hydrogen-electric combined energy storage system

Abstract

The present invention discloses a multi-mode switching control method applicable to a hydrogen-electric combined energy storage system, including: coupling a plurality of control units through a DC bus to obtain a system architecture; according to the operating mode of the system, coordinating the power output of the energy storage units by using a dynamic cooperative optimization method based on virtual synchronous machine technology and variable droop coefficients, and introducing a dynamic energy distribution model and dynamic energy distribution constraints to obtain a power balance framework for the energy storage units; using an optimization model based on cooperative game and a dynamic power regulation model to perform cooperative optimization and joint optimization on several energy storage units in the power balance framework to obtain a joint dynamic balance mechanism; using a distributed dynamic optimization algorithm to perform iterative optimization and improvement on the joint dynamic balance mechanism to obtain a hydrogen-electric combined energy storage system. The present invention relates to hydrogen-electric combined energy storage control technology, and solves the technical problems of difficult coordination and insufficient dynamic response in the multi-mode switching process of the existing hydrogen-electric combined energy storage system.

Description

Technical Field

[0001] The present invention belongs to the field of new energy system control, relates to multi-mode control technology for hydrogen-electric combined energy storage, and specifically is a multi-mode switching control method applicable to a hydrogen-electric combined energy storage system. Background Art

[0002] With the accelerating transformation of the global energy structure towards low-carbonization, the large-scale access of renewable energy represented by wind energy and photovoltaic energy to the power grid has become an inevitable trend. However, the inherent volatility and intermittency of renewable energy pose severe challenges to the power grid stability and energy dispatching ability. Especially in scenarios with frequent load fluctuations, it is difficult for traditional power systems to achieve dynamic supply-demand balance. Therefore, energy storage technology has become a key means to suppress the fluctuations of new energy and improve the flexibility of the power grid. Among them, hydrogen energy storage systems and electrochemical energy storage systems have received extensive attention due to their complementary characteristics.

[0003] Hydrogen energy storage systems achieve the conversion of electrical energy and chemical energy through electrolytic water hydrogen production and fuel cell power generation, and have the advantages of long-term energy storage and large-scale energy regulation. However, their dynamic response speed is relatively slow, and it is difficult to meet the demand for short-term power fluctuations. Although electrochemical energy storage systems have a millisecond-level fast response ability, they are limited by capacity constraints and cannot meet the long-term energy support requirements. Existing technologies mostly focus on the internal optimization of a single energy storage system. For example, by improving the efficiency of hydrogen energy storage systems or enhancing the charge and discharge performance of batteries. However, such solutions still have significant limitations when dealing with complex and changeable load scenarios: on the one hand, a single energy storage mode is difficult to balance the suppression of short-term power fluctuations and long-term energy requirements; on the other hand, during the switching process between different operating modes (such as off-grid and grid-connected), there is a lack of an effective dynamic coordination mechanism, resulting in system response lag and low operating efficiency. In addition, current research generally ignores the collaborative optimization problem between hydrogen storage units and electrical storage units under multi-mode switching. Especially during load mutations or external power grid interactions, existing control strategies are difficult to achieve real-time optimization of power distribution, and are prone to causing bus voltage instability or a sharp increase in operating costs. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; for this purpose, the present invention proposes a multi-mode switching control method applicable to a hydrogen-electric combined energy storage system, which is used to solve the technical problems of difficult dynamic coordination, insufficient dynamic response, and poor stability of hydrogen storage units and electrical storage units in the multi-mode switching process of existing hydrogen-electric combined energy storage systems.

[0005] To achieve the above object, the present invention provides a multi-mode switching control method applicable to a hydrogen-electric combined energy storage system, including:

[0006] S1, coupling a plurality of control units through a DC bus to obtain an energy storage system architecture;

[0007] S2. According to the operation mode of the energy storage system, use the dynamic collaborative optimization method based on virtual synchronous machine technology and variable droop coefficient to coordinate the output power of the energy storage units, and introduce a dynamic energy distribution model and dynamic energy distribution constraints to obtain the power balance framework of the energy storage units;

[0008] S3. Use the optimization model based on cooperative game and the dynamic power regulation model to perform collaborative optimization and joint optimization on several energy storage units in the power balance framework to obtain the joint dynamic balance mechanism;

[0009] S4. Use the distributed dynamic optimization algorithm to perform iterative optimization and improvement on the joint dynamic balance mechanism to obtain the hydrogen-electricity hybrid energy storage system with multi-mode switching control.

[0010] Furthermore, the system architecture includes a wind-solar power generation unit, a hydrogen storage unit, an electric energy storage unit, and a load unit, where the hydrogen storage unit and the electric energy storage unit are collectively referred to as the energy storage units.

[0011] Furthermore, the use of the dynamic collaborative optimization method based on virtual synchronous machine technology and variable droop coefficient to coordinate the output power of the energy storage units includes:

[0012] S2-1. In the off-grid mode, construct the virtual inertia enhancement model as: , and use the virtual inertia enhancement model to control the hydrogen storage unit; where, represents the output power of the hydrogen storage unit at time t, represents the equivalent inertia constant, , respectively represent the real-time frequency and the reference frequency of the system, represents the damping coefficient;

[0013] S2-2. Construct the variable droop coefficient dynamic regulation model as: , and in the grid-connected mode, use the variable droop coefficient dynamic regulation model to control the electric energy storage unit; where, represents the output power of the electric energy storage unit at time t, represents the real-time voltage at time t, represents the reference voltage, represents the droop coefficient, and the adjustment formula of the droop coefficient is: , represents the reference voltage and the real-time voltage the difference between them, represents the initial droop coefficient, and a represents the adjustment coefficient of the initial droop coefficient;

[0014] S2-3. According to the energy storage constraint condition: , constrain the charge and discharge states of the electric energy storage unit and the hydrogen storage unit; where, Indicates the moment The power of the electrical energy storage unit, and is restricted by constraints: , and respectively represent the minimum and maximum storage powers of the electrical energy storage unit, , respectively represent the charging power and discharging power of the electrical energy storage unit at time t, , respectively represent the charging efficiency and discharging efficiency of the electrical energy storage unit, Indicates the moment The power of the hydrogen energy storage unit, and respectively represent the charging power and discharging power of the hydrogen energy storage unit at time t, and respectively represent the charging efficiency and discharging efficiency of the hydrogen energy storage unit, Indicates the time step;

[0015] S2-4, according to the dynamic optimization objective: , perform power distribution and voltage regulation of the energy storage unit; among them, , respectively represent the unit operating cost coefficients of the hydrogen energy storage unit and the electrical energy storage unit, , respectively represent the output powers of the hydrogen energy storage unit and the electrical energy storage unit at the moment , Represents the voltage deviation penalty coefficient, Represents the square of the bus voltage deviation;

[0016] S2-5, according to the balance condition of the energy storage system: , restrict the output power of the energy storage unit; among them, Represents the load demand power of the energy storage system at time t.

[0017] The virtual inertia model ensures dynamic balance under load fluctuations by adjusting the output power of the hydrogen energy storage unit in real time to respond to system frequency changes; the variable droop control enables the electrical energy storage unit to quickly adjust its output power under different load conditions to avoid bus voltage instability caused by voltage fluctuations. And under dynamic load conditions, the dynamic optimization objective can reduce voltage fluctuations, improve the steady-state performance of the power system, and ensure the long-term operation reliability of the energy storage device.

[0018] Furthermore, the dynamic energy distribution model includes: ; among them, , respectively represent the output powers of the i-th hydrogen energy storage unit and the i-th electrical energy storage unit at time t, , ​​​​respectively represent the maximum output power of the i-th hydrogen energy storage unit and the i-th electrical energy storage unit, represents the stored electricity of the i-th hydrogen energy storage unit at time t, represents the i-th electrical energy storage unit at time of the stored electricity, represents the total demand power of the energy storage system at time t, represents the time when the load demand power of the energy storage system, represents the energy conversion efficiency of the hydrogen energy storage unit, 、 respectively represent the charging efficiency and discharging efficiency of the electrical energy storage unit, represents the charging power of the i-th electrical energy storage unit at time t, represents the discharging power of the i-th electrical energy storage unit at time t, where i represents the index of the hydrogen energy storage unit and the electrical energy storage unit, represents the time step.

[0019] Through the above model, the hydrogen energy storage unit and the electrical energy storage unit can achieve coordinated operation in different modes to cope with load demand fluctuations and energy dynamic distribution problems, and improve the operation flexibility and stability of the system.

[0020] Furthermore, the dynamic energy distribution constraint includes:

[0021] The dynamic energy distribution constraint of the energy storage system is: ; where, represents the time output power of the hydrogen energy storage unit, represents the time output power of the electrical energy storage unit, represents the time load demand power of the energy storage system, represents the time interaction power between the energy storage system and the power grid, where a positive value represents selling electricity to the power grid and a negative value represents purchasing electricity from the power grid;

[0022] The output power constraints of the hydrogen energy storage unit and the electrical energy storage unit are: , to obtain the first power constraint; where, 、 respectively represent the minimum and maximum output powers of the hydrogen energy storage unit, 、 respectively represent the minimum and maximum output powers of the electrical energy storage unit;

[0023] The energy balance constraints of the hydrogen energy storage unit and the electrical energy storage unit are: where, represents the stored electricity of the i-th hydrogen energy storage unit at time t, Indicates the stored power of the i-th electrical energy storage unit at time . Indicates the energy conversion efficiency of the hydrogen storage unit, Indicates the time step, Indicates the discharge power of the i-th electrical energy storage unit at time t.

[0024] Furthermore, the optimization model based on cooperative game includes:

[0025] S31-1. Construct the power allocation objective function based on the cooperative game method as: ; where Indicates the operating cost of the i-th hydrogen storage unit at time, N represents the total number of hydrogen storage units and electrical energy storage units in the energy storage system, and i represents the energy storage unit index;

[0026] S31-2. Construct the constraint conditions of the power allocation objective function as: , obtaining the second power constraint; where Indicates the load demand power of the load unit at time;

[0027] S31-3. Adjust the power allocation ratio of the hydrogen storage unit and the electrical energy storage unit according to the power allocation objective function and the constraint conditions of the power allocation objective function, and perform collaborative optimization of the energy storage units.

[0028] In the power allocation objective function based on cooperative game, according to the marginal contribution value and operating cost of each energy storage unit, dynamically adjust the power allocation ratio between each unit to achieve collaborative management in "energy balance and real-time dynamic scheduling", and at the same time achieve the economic goal of "minimizing operating cost".

[0029] Furthermore, the dynamic power regulation model includes:

[0030] S32-1. Construct the dynamic power regulation model as: ; where and respectively represent the hydrogen storage unit regulation coefficient and the electrical energy storage unit regulation coefficient, which are used to balance the deviation between the hydrogen storage unit, the electrical energy storage unit and the overall power output of the system respectively, and T represents the total operating time of the energy storage system;

[0031] S32-2. Define the constraint conditions of the dynamic power regulation model as: ; where represents the change in the output power of the hydrogen storage unit between consecutive times t, represents the change in the output power of the electrical energy storage unit between consecutive times t, , respectively represent the minimum and maximum output powers of the hydrogen energy storage unit, and respectively represent the minimum and maximum output powers of the electrical energy storage unit;

[0032] S32-3. Adjust the output powers of the hydrogen energy storage unit and the electrical energy storage unit according to the dynamic power regulation model and the constraint conditions of the dynamic power regulation model to perform joint optimization of the energy storage unit.

[0033] Furthermore, the joint dynamic balance mechanism includes:

[0034] S41-1. Define the comprehensive optimization objective function of the energy storage system: ; where and and respectively represent the weight coefficients of the optimization objectives, which are used to balance cost, efficiency, and load demand, and respectively represent the operating costs of the hydrogen energy storage unit and the electrical energy storage unit, and T represents the total operating time of the energy storage system;

[0035] S41-2. Determine the power balance relationship of the energy storage system as: ; where represents the interactive power between the energy storage system and the power grid at time ;

[0036] S41-3. Construct the constraint conditions of the comprehensive optimization objective function according to the first power constraint and the second power constraint as: ; where η t represents the power allocation ratio of the hydrogen energy storage unit.

[0037] Furthermore, the comprehensive optimization objective function is iteratively optimized through a distributed dynamic optimization algorithm, and when the iteration result satisfies the convergence condition: or reaches the maximum number of iterations , the iterative optimization is completed; where represents the preset iterative convergence threshold, and k represents the number of iterations.

[0038] Furthermore, the obtained hydrogen-electricity combined energy storage system with multi-mode switching control includes:

[0039] S42-1. Based on the improved joint dynamic balance mechanism, obtain the joint optimization objective function as: ; where and and represent the optimization weight coefficients of each item, represents the output power of the hydrogen energy storage unit at time represents the output power of the electrical energy storage unit at a moment, represents the load demand power of the load unit at a moment;

[0040] S42-2. Define the constraint conditions of the joint optimization objective function, including:

[0041] (1) Dynamic power distribution constraint: ; where, and respectively represent the dynamic adjustment coefficients of the hydrogen energy storage unit and the electrical energy storage unit, and respectively represent the change amounts of the output powers of the hydrogen energy storage unit and the electrical energy storage unit between consecutive moments;

[0042] (2) Power range constraint of the energy storage unit: ; where, , respectively represent the minimum and maximum output powers of the hydrogen energy storage unit, , respectively represent the minimum and maximum output powers of the electrical energy storage unit;

[0043] (3) Dynamic compensation for the output power deviation of the energy storage unit: ; where, represents the dynamic regulation error tolerance range between the output power and the load demand power;

[0044] S42-3. Use the distributed optimization algorithm to optimize the output power of the energy storage unit through the joint iteration optimization formula: and stop the optimization when the convergence condition: or the maximum number of iterations is reached, and obtain the hydrogen-electricity combined energy storage system with multi-mode switching control; where, k represents the number of iterations, and T represents the total operation time of the energy storage system.

[0045] Through the joint optimization objective function model, the energy storage system can not only dynamically respond to the power fluctuations under complex load conditions, but also maintain the overall operation efficiency and stability of the system while reducing the operation costs of the hydrogen energy storage unit and the electrical energy storage unit.

[0046] Compared with the prior art, the beneficial effects of the present invention are:

[0047] (1) In the prior art, a single energy storage system has limitations. The hydrogen energy storage system has a slow response, and the electrical energy storage system has a limited capacity. The present invention enhances the dynamic inertia response of the hydrogen storage unit through virtual synchronous machine technology, and combines variable droop coefficient control to enable the electrical storage unit to quickly respond to bus voltage fluctuations. In the off-grid mode, it can effectively cope with load fluctuations, ensure system stability, solve the problem of insufficient dynamic response of the energy storage system in the prior art, and improve the response ability of the energy storage system in a complex energy environment;

[0048] (2) Aiming at the insufficient research on dynamic coordination during the multi-mode switching process of the hydrogen-electric combined energy storage system in the prior art, the present invention adopts a dynamic cooperative optimization algorithm. Combining time-of-use electricity prices and load fluctuations, it coordinates the energy output distribution of the hydrogen storage and electrical storage units, gives priority to balancing the output power of photovoltaic and wind turbine units, and suppresses power fluctuations. In the scenario of high renewable energy penetration, it makes full use of renewable energy, reduces energy waste, and improves energy utilization efficiency;

[0049] (3) The present invention constructs a multi-mode system operation cost minimization model, considering the costs of multiple operation entities. Through a resource allocation method based on cooperative game, it dynamically adjusts the power allocation ratio of the hydrogen storage unit and the electrical storage unit, reducing the overall operation cost of the system. In practical applications, after the implementation of the multi-mode switching strategy, the total operation cost, the operation cost of the fuel cell, and the hydrogen production cost are all significantly reduced, demonstrating good economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0051] Figure 1 It is a technical flow chart of a multi-mode switching control method applicable to a hydrogen-electric combined energy storage system provided by the present invention;

[0052] Figure 2 It is a schematic diagram of the hydrogen-electric combined energy storage system architecture provided by the present invention;

[0053] Figure 3 It is a virtual inertia control block diagram of a DC power grid based on a virtual synchronous machine provided by the present invention;

[0054] Figure 4 It is a dynamic response characteristic diagram of each unit in the simulation results provided by the present invention;

[0055] Figure 5 It is a dynamic energy distribution result diagram under different mode switching conditions provided by the present invention. Detailed implementation manners

[0056] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0057] Please refer to Figures 1 - 5 , an embodiment of the first aspect of the present invention provides a multi-mode switching control method applicable to a hydrogen-electric combined energy storage system, including:

[0058] S1, coupling a plurality of control units through a DC bus to obtain a system architecture; the system architecture is as Figure 2 shown, and each unit includes the following modules:

[0059] Hydrogen storage unit: The hydrogen storage unit consists of an electrolyzer, a hydrogen storage tank and a fuel cell. The electrolyzer stores the excess electric energy by converting it into hydrogen, and when the load demand increases, the fuel cell reduces the hydrogen into electric energy to provide supplementary energy for the system;

[0060] Electric energy storage unit: including a photovoltaic power generation unit and a wind power generation unit, which are responsible for converting light energy and wind energy into direct current and inputting it into the bus to ensure the stability of the system in a new energy scenario with large fluctuations;

[0061] Load unit: mainly provides power demand for the system, and dynamically interacts with the electric energy and hydrogen storage units to achieve supply-demand balance;

[0062] Wind-solar power generation unit: composed of a photovoltaic power generation unit and a wind power generation unit. The photovoltaic power generation unit converts solar energy into direct current by using the photovoltaic effect, and the wind power generation unit converts wind energy into direct current by driving a generator with wind power. The two input the generated direct current into the bus to provide the main electric energy source for the system;

[0063] And in Figure 2Among them, AC / DC represents the conversion from alternating current to direct current, which is used to convert an AC power supply into direct current and is suitable for charging a battery or an energy storage system; DC / DC is a DC voltage conversion module used to adjust DC currents of different voltages. G_VSC and W_VSC respectively represent the voltage source converters connected to the power grid and the wind power generation system, which are used to regulate the power flow between the power grids and the conversion of wind energy; PV_DC represents the DC output of photovoltaic power generation, which is used to provide the electricity converted from solar energy; EL_DC and FC_DC are respectively the DC parts of the water electrolysis hydrogen production system and the fuel cell system. The former is used for hydrogen production, and the latter converts hydrogen into electricity; B_DC is the DC part of the battery energy storage system, which is used to store electrical energy; L_DC is the DC part at the load end, which receives the outputs of various power sources. The PEM system is a proton exchange membrane fuel cell system, which provides the function of efficiently converting hydrogen into electricity. P load represents the load power demand of the system; P bat is the power output of the battery energy storage system; P el is the power output of the electrolyzer, which is used for hydrogen production; P fc is the power output of the fuel cell; P sto is the power output of the energy storage system; P grid represents the power exchanged with the power grid; P wind and P pv respectively represent the power outputs of wind energy and photovoltaic power generation;

[0064] It should be noted that in this embodiment, the hydrogen storage unit and the electrical storage unit can be collectively referred to as the energy storage unit.

[0065] S2. According to the operation mode of the energy storage system, use the dynamic cooperative optimization method based on virtual synchronous machine technology and variable droop coefficient to coordinate the output power of the energy storage unit, and introduce a dynamic energy distribution model and dynamic energy distribution constraints to obtain the power balance framework of the energy storage unit;

[0066] In this embodiment, the energy storage system realizes the dynamic response of the hydrogen storage unit and the electrical storage unit through virtual synchronous machine technology and variable droop coefficient control in the off-grid mode, constructs a virtual inertia enhancement model and a dynamic power distribution model, and combines energy storage constraint conditions and optimization algorithms to ensure the stable operation of the system. Specifically, it includes:

[0067] S2-1. In the off-grid mode, construct a virtual inertia enhancement model as: , and use the virtual inertia enhancement model to control the hydrogen storage unit; where, represents the output power of the hydrogen storage unit at time t, represents the equivalent inertia constant, 、 respectively represent the real-time frequency and the reference frequency of the system, represents the damping coefficient;

[0068] S2-2, construct the variable droop coefficient dynamic adjustment model as: , and in the grid-connected mode, the variable droop coefficient dynamic regulation model is used to control the power storage unit; Represents the output power of the power storage unit, represents the real-time voltage at time t, represents the reference voltage, It represents the droop coefficient, and the adjustment formula of the droop coefficient is: , which represents the default droop coefficient of the system when it is stable; a represents the adjustment coefficient of the initial droop coefficient, which is used to control the influence of voltage deviation on the droop coefficient and is determined based on historical experimental experience;

[0069] S2-3, according to the energy storage constraint: , constraining the charge and discharge states of the electric storage unit and the hydrogen storage unit; wherein, Indicates time The amount of electricity stored in the storage unit, subject to constraints: , and Respectively represent the minimum and maximum storage capacity of the storage unit, , They represent the charging power and discharging power of the storage unit at time t, respectively. , Respectively represent the charging efficiency and discharging efficiency of the storage unit, Indicates time The amount of electricity in the hydrogen storage unit, and They represent the charging power and discharging power of the hydrogen storage unit at time t, and They represent the charging efficiency and discharging efficiency of the hydrogen storage unit, represents the time step;

[0070] S2-4, according to the dynamic optimization goal: , to perform power distribution and voltage regulation of energy storage units; wherein, , Represent the unit operating cost coefficients of the hydrogen storage unit and the electricity storage unit, , Respectively represent the hydrogen storage unit and the electricity storage unit at time The output power, represents the voltage deviation penalty coefficient, Denote the square of the bus voltage deviation; this optimization objective belongs to the local optimization model in the multi-mode switching control method, which mainly targets the power distribution and voltage regulation of the energy storage unit; it will be part of the final joint optimization strategy, and its optimization variables and are subject to the global optimization constraints and are solved in the final joint optimization model;

[0071] S2-5, according to the balance condition of the energy storage system: , constrain the output power of the energy storage unit; where, denotes the load demand power of the energy storage system at time t.

[0072] In this embodiment, the dynamic energy distribution model in step S2 is used to achieve the coordinated operation of different hydrogen storage units and electrical storage units, and meet the dynamic balance requirements of the system under multi-mode switching, specifically including: ; where, 、 respectively represent the output powers of the i-th hydrogen storage unit and the electrical storage unit at time t, 、 respectively represent the maximum output powers of the i-th hydrogen storage unit and the i-th electrical storage unit, represents the stored electrical quantity of the i-th hydrogen storage unit at time t, represents the stored electrical quantity of the i-th electrical storage unit at time t, represents the total demand power of the energy storage system at time t, represents the time when the load demand power of the energy storage system, represents the energy conversion efficiency of the hydrogen storage unit, 、 respectively represent the charging efficiency and discharging efficiency of the electrical storage unit, represents the charging power of the i-th electrical storage unit at time t, represents the discharging power of the i-th electrical storage unit at time t, i represents the index of the hydrogen storage unit and the electrical storage unit, represents the time step.

[0073] As Figure 3 shown, it is the virtual inertia control block diagram of the DC power grid based on the virtual synchronous machine provided by this embodiment; in Figure 3 , represents the change in DC voltage, which is used to represent voltage error or disturbance, and is regulated by the current regulator to keep the voltage at the desired value; represents the gain coefficient, which is used to adjust the response speed of the virtual synchronous generator (VSG) control loop. By controlling this gain, the voltage regulation ability of the system can be optimized. Represents the reference current signal, which is input as an input signal to the control system to help the system adjust the current output.

[0074] S represents the Laplace operator, which is commonly used in transfer functions to represent the dynamic response of the system. Represents the actual current output, which is affected by the control signal and the reference current, and adjusts the power output of the virtual synchronous generator. Represents the damping coefficient of the virtual synchronous generator, which is used to adjust the system stability and prevent system oscillation. Represents the virtual capacitance. Represents the steady-state value of the DC voltage in the system, which is used as the target voltage input. Represents the DC voltage reference value, which is used as the target value and compared with the actual output of the system for the control system to adjust the output. Represents the output voltage of the DC power supply in the current control loop, and this coefficient is the actual value of the DC voltage. The system adjusts , and compares it with the target voltage for voltage control. Is the second-order difference of the current change, and this coefficient represents the dynamic response of the current. This signal is used for adjustment in the current control loop to avoid excessive current fluctuations and ensure smooth current change. Represents the feedforward gain coefficient, which is used to optimize the control response and enhance the system stability. Represents the current error signal, specifically the deviation between the actual current and the reference current. Represents the gain coefficient, which is used to adjust the control loop. Through these gain parameters, the system can accurately control the voltage and current;

[0075] AC / DC represents the conversion from alternating current to direct current. PI (Proportional-Integral Controller) represents the proportional-integral controller, which is used to adjust the balance between DC current and AC current. PLL (Phase-Locked Loop) represents the phase-locked loop, which is used to synchronize the frequency and phase of the system. abc / dq represents the conversion between the three-phase coordinate system (abc) and the direct-axis quadrature-axis coordinate system (dq). The abc coordinate system is commonly used to describe three-phase AC systems, while the dq coordinate system is useful for simplifying control design, especially in current decoupling and voltage control. SVPWM (Space Vector Pulse Width Modulation) represents space vector pulse width modulation, which is used to generate the modulation signal for controlling the output voltage of the inverter. It optimizes the control of voltage and current by adjusting the ratio of voltage vectors. 、 Represent the current components on the direct current axis (d-axis) and the alternating current axis (q-axis) respectively, which are used to control the current of the system. Represents the target current. and Represent the voltage errors on the DC and AC axes respectively, which are usually used by the controller to adjust the output voltage. Represents the frequency of the power grid, usually the reference frequency of the power grid, which is used for system synchronization together with other parameters. and and Represent the error terms of the three-phase voltage, which are used to adjust the control output. Represents resistance, which may be the load resistance in the circuit or other resistances related to inductance and current. Represents the phase angle, which is used for synchronization with other signals. Represents the target current of the direct current axis (d-axis). Represents the target current of the alternating current axis (q-axis). Represents inductance, which is usually a parameter related to the change of current in the circuit and affects the regulation and stability of current. Represents the reference voltage of the direct current axis (d-axis). Represents the actual current of phase A of the three-phase power supply. Represents the actual current of phase B of the three-phase power supply. Represents the actual current of phase C of the three-phase power supply;

[0076] In the off-grid mode, the energy storage system relies on virtual inertia control technology to enhance the stability of the system. Specifically, when the photovoltaic power generation and wind power generation cannot meet the load demand, the hydrogen storage unit conducts rapid energy replenishment through fuel cells. When there is surplus in new energy power generation, the excess energy is converted into hydrogen through the electrolyzer and stored in the hydrogen storage tank. Through such a dynamic response mechanism, the system can maintain the balance between supply and demand without relying on the large power grid and achieve energy self-sufficiency.

[0077] In the grid-connected mode, the system further optimizes the energy flow through interaction and scheduling with the large power grid. In this mode, the system dynamically adjusts the output power of each unit according to real-time electricity price signals, load demand, and new energy fluctuations. For example, when the electricity price is high, the system preferentially sells new energy electricity to the large power grid; when the electricity price is low, it stores energy through the electrolyzer for subsequent peak load demand. In addition, by adjusting the operation mode of the fuel cell, the carbon emissions in the system can be further reduced and the operation cost can be lowered.

[0078] In this embodiment, the operation cost minimization model of the energy storage system includes multiple operation entities. Its goal is to minimize the operation cost on the premise of meeting the dynamic demand and operation constraints of the energy storage. The specific operation cost model is:

[0079]

[0080] In the formula: is the operating cost of microgrid j, is the power purchase cost of the interaction between the microgrid and the large power grid, , , , are respectively the operating costs of the gas turbine, fuel cell, photovoltaic power generation unit and wind power generation unit in the microgrid. is the carbon emission cost, and its calculation formula is:

[0081]

[0082] In the formula: is the unit carbon emission treatment cost coefficient, , is the unit power generation carbon emission coefficient. The power purchase cost and power sale income of the microgrid satisfy the following relationship:

[0083]

[0084] And the operating costs of each power generation unit in the microgrid can be further decomposed into:

[0085]

[0086]

[0087]

[0088]

[0089] In the formula: , , , are respectively the operating cost coefficients of the gas turbine, fuel cell, photovoltaic and wind turbine power generation units, , , , is the output power at time t.

[0090] To achieve the multi-mode switching collaborative optimization goal of the hydrogen-electricity combined energy storage system, the present invention proposes an optimization strategy based on dynamic adjustment. This strategy combines the characteristics of the hydrogen storage unit and the electric storage unit, while meeting the load demand, further improves the safety and stability of the system, and reduces the operating cost. The system operation needs to meet the following dynamic energy distribution constraint conditions:

[0091] (1) Dynamic energy distribution constraint of the energy storage system: ; where represents the output power of the hydrogen storage unit at time ​ Indicates the moment Output power of the electrical energy storage unit Indicates the moment Load demand power of the energy storage system Indicates the moment Interactive power between the energy storage system and the power grid, where a positive value indicates selling electricity to the power grid and a negative value indicates purchasing electricity from the power grid;

[0092] (2) Output power constraints of the hydrogen energy storage unit and the electrical energy storage unit: , obtaining the first power constraint; where 、 respectively represent the minimum and maximum output powers of the hydrogen energy storage unit, 、 respectively represent the minimum and maximum output powers of the electrical energy storage unit;

[0093] (3) The energy balance constraints of the hydrogen energy storage unit and the electrical energy storage unit are: Where represents the stored electricity of the i-th hydrogen energy storage unit at moment t, represents the stored electricity of the i-th electrical energy storage unit at moment of, represents the energy conversion efficiency of the hydrogen energy storage unit, represents the time step, represents the discharge power of the i-th electrical energy storage unit at moment t.

[0094] Through the dynamic energy distribution model and dynamic energy distribution constraints, the hydrogen-electricity combined energy storage system can achieve dynamic energy distribution among hydrogen energy storage units and electrical energy storage units in the dynamic mode, balance the load demand and optimize the system operation efficiency.

[0095] S3. Use the optimization model based on cooperative game and the dynamic power regulation model to perform collaborative optimization and joint optimization on several energy storage units in the power balance framework, and obtain the joint dynamic balance mechanism;

[0096] To achieve the collaborative optimization among multiple hydrogen energy storage units and electrical energy storage units in the joint energy storage system, in this embodiment, a resource allocation method based on cooperative game is proposed. In different operation modes, the hydrogen energy storage unit and the electrical energy storage unit achieve dynamic power distribution through joint optimization decisions, so as to reduce the overall operation cost of the system and improve the energy utilization efficiency. The specific steps include:

[0097] 1) Establish an optimization model based on cooperative game. The objective function of this model is established on the basis of the detailed derivation of the aforementioned operating cost model and energy balance constraints. By calculating the product of the unit operating cost of each energy storage unit (including hydrogen storage unit and electric energy storage unit) and its output power at each moment, and fully considering the system dynamic balance condition, the marginal contribution value of each energy storage unit is determined, and then the optimization objective function is constructed;

[0098] 2) Coordinate the output power of the hydrogen storage unit and the electric energy storage unit through a distributed optimization algorithm, and collect the operating status of each energy storage unit in real time to ensure that the load demand is met under the system dynamic balance condition;

[0099] 3) Dynamically adjust the power distribution ratio between the hydrogen storage unit and the electric energy storage unit. This optimization objective function is one of the optimization objectives included in the final joint optimization objective function. In the above-mentioned cooperative game model, according to the marginal contribution value and operating cost of each energy storage unit, the power distribution ratio between each unit is dynamically adjusted to achieve collaborative management in terms of "energy balance and real-time dynamic scheduling", and at the same time achieve the economic goal of "minimizing operating costs".

[0100] Specifically, the optimization model based on cooperative game includes:

[0101] S31-1. Construct the power distribution objective function based on the cooperative game method as: ; where, represents the operating cost of the i-th hydrogen storage unit at time, N represents the total number of hydrogen storage units and electric energy storage units in the energy storage system, and i represents the energy storage unit index;

[0102] S31-2. The constraint condition for constructing the power distribution objective function is: , to obtain the second power constraint; where, represents the load demand power of the load unit at time; at any time when the situation is the sum of the power outputs of the hydrogen storage unit and the electric energy storage unit must be equal to or exceed the power demand of the system load unit

[0103] S31-3. Adjust the power distribution ratio of the hydrogen storage unit and the electric energy storage unit according to the power distribution objective function and the constraint conditions of the power distribution objective function, and conduct collaborative optimization of the energy storage units.

[0104] In this embodiment, a joint optimization of the hydrogen storage unit and the electrical energy storage unit in the power balance framework is carried out through a dynamic power regulation model to achieve the rapid response of the energy storage system and the maximization of the operation efficiency, and at the same time meet the balance requirements of dynamic loads;

[0105] The dynamic power regulation model in step S3 includes:

[0106] S32-1, constructing the dynamic power regulation model as: ; where and respectively represent the regulation coefficient of the hydrogen storage unit and the regulation coefficient of the electrical energy storage unit, which are obtained based on historical experience;

[0107] S32-2, defining the constraint conditions of the dynamic power regulation model as: ; where represents the change in the output power of the hydrogen storage unit between consecutive time instants (unit: W), represents the change in the output power of the electrical energy storage unit between consecutive time instants (unit: W);

[0108] S32-3, adjusting the output powers of the hydrogen storage unit and the electrical energy storage unit according to the dynamic power regulation model and the constraint conditions of the dynamic power regulation model, and performing joint optimization of the energy storage units.

[0109] It should be noted that when determining the values of and these two regulation coefficients, a method based on system requirements and performance optimization is usually adopted. First, the initial values can be set according to the load fluctuation characteristics of the system, the dynamic response ability of the energy storage unit, and the energy conversion efficiency. For example, if the electrical energy storage unit has a fast response speed, it may be necessary to appropriately increase β to enhance its adaptability to load fluctuations; for the hydrogen storage unit with a relatively slow response, a smaller α value can be set. Then, based on the dynamic simulation model, α and β are gradually adjusted, and the values of the regulation coefficients are optimized by minimizing the power regulation error and the system operation cost. Through multiple simulations and tests, the optimal regulation coefficients of the system under different load conditions can be obtained, so as to ensure the stability and high efficiency of the system while meeting the load requirements.

[0110] Through the above dynamic optimization mechanism, the hydrogen storage unit and the electrical energy storage unit can quickly adjust the power output according to the load change, so as to achieve the efficient operation and response stability of the combined energy storage system in a complex dynamic environment.

[0111] S4, using a distributed dynamic optimization algorithm to iteratively optimize and improve the joint dynamic balance mechanism, and obtaining a hydrogen-electric combined energy storage system with multi-mode switching control;

[0112] To further improve the collaborative optimization ability of the hydrogen storage unit and the electric energy storage unit under complex load conditions, a joint dynamic balance mechanism is proposed. By establishing a multi-objective optimization model, this mechanism comprehensively considers the system operation cost, energy efficiency, and load balance to achieve dynamic regulation and joint optimization between the hydrogen storage unit and the electric energy storage unit. To construct a reasonable optimization strategy, the comprehensive optimization objective function of the energy storage system is first defined as follows: ; where , , respectively represent the weight coefficients of the optimization objectives, which are used to balance the cost, efficiency, and load demand. , respectively represent the operation costs of the hydrogen storage unit and the electric energy storage unit;

[0113] The comprehensive optimization objective function reflects the demand for minimizing the comprehensive operation cost of the energy storage system under different load states, including core factors such as the hydrogen storage unit, the electric energy storage unit, micro-grid power purchase and sale, and environmental costs. This function ensures load balance during system operation, can adjust the power output in real time to match the load demand, and guarantees the stable operation of the system. By introducing multi-objective weight coefficients, the objective function can flexibly adjust the priority between various objectives according to different operation modes (such as grid-connected or off-grid) to achieve overall optimization and ensure the economy, stability, and high efficiency of the system in a complex dynamic environment;

[0114] To ensure that the optimization objective function can effectively guide the power distribution of the system, the power balance of the energy storage system needs to satisfy the following relationship at each time step t: ; where represents the interaction power between the energy storage system and the power grid at time t;

[0115] This power balance equation ensures the matching of power supply and demand among the hydrogen storage unit, the electric energy storage unit, and the power grid, that is, the dynamic power balance of the entire system. To enable the optimization objective function to be used for actual solution, it is necessary to construct the constraint conditions of the comprehensive optimization objective function according to the first power constraint and the second power constraint, specifically including: ; where represents the power distribution ratio of the hydrogen storage unit.

[0116] In this embodiment, based on the above comprehensive optimization objective function and constraint conditions, the final optimization problem can be further expressed as the following specific optimization model: ;

[0117] Then, a distributed dynamic optimization algorithm is used to iteratively optimize the optimization models of the hydrogen storage unit and the electric energy storage unit to achieve the minimization of the overall system operation cost and the efficient utilization of energy. During the iteration process, the system judges whether the optimization reaches the stop criterion through the convergence condition. The specific convergence conditions are as follows:

[0118]

[0119] If the above convergence condition is not met during the iteration process, the system continues to adjust the power distribution until the convergence requirement is satisfied or the preset maximum number of iterations is reached and then terminates, obtaining the optimized joint dynamic balance mechanism; where: is the iteration convergence threshold.

[0120] During the multi-mode switching control process, to further improve the efficiency and stability of the system operation, a dynamic adjustment mechanism based on load fluctuation compensation is proposed. This mechanism realizes the dynamic balance of energy and minimizes the system operation cost by jointly optimizing the scheduling strategies of the hydrogen storage unit and the electrical energy storage unit. To achieve the above goals, in this embodiment, based on the optimized joint dynamic balance mechanism, further improvement is carried out, and the joint optimization objective function is obtained as: ; where , , represent the optimization weight coefficients, which are used to balance the deviations between the hydrogen storage unit, the electrical energy storage unit and the overall system power output respectively, and are obtained through experimental experience;

[0121] Compensate the load fluctuation through the joint optimization objective function to ensure the stable operation of the system in a dynamic load environment; at the same time, to ensure that the output powers of the hydrogen storage unit and the electrical energy storage unit can operate stably under the influence of load fluctuation, the system needs to meet the following constraint conditions:

[0122] (1) Dynamic power distribution constraint: ; where and represent the dynamic adjustment coefficients of the hydrogen storage unit and the electrical energy storage unit respectively, and are determined based on historical experimental experience;

[0123] (2) Power range constraint of the energy storage unit: ;

[0124] (3) Dynamic compensation for the output power deviation of the energy storage unit: ; where represents the dynamic adjustment error tolerance range between the power output and the load demand. Usually used in control systems, it refers to the deviation tolerance of the system power, which is used to characterize the allowable difference between the output powers of the hydrogen storage unit and the electrical energy storage unit and the load power, and is set based on historical experience;

[0125] In the joint optimization objective function, the output powers of the hydrogen storage unit and the electrical energy storage unit are adjusted through the following joint iterative optimization formula:

[0126] , and when the convergence condition is satisfied: or the maximum number of iterations is reached the optimization stops, and a hydrogen-electric hybrid energy storage system with multi-mode switching control is obtained, which can not only dynamically respond to power fluctuations under complex load conditions, but also maintain the overall operation efficiency and stability of the system while reducing the operation cost of the energy storage unit.

[0127] In a hydrogen-electric hybrid energy storage system, multi-mode switching is an important means to ensure the stability and economy of the system under different operating conditions. In this embodiment, by combining the operating requirements in the off-grid mode and the grid-connected mode, a control strategy for multi-mode switching is designed to achieve efficient coordination of the hydrogen storage unit and the electric storage unit under different operating conditions. The off-grid mode is mainly for the island operation scenario without external grid support. In this mode, by combining the hydrogen storage unit and the electric storage unit, the advantages of both are utilized to cope with different load conditions.

[0128] The off-grid mode is divided into the following states:

[0129] 1) Light load state. When the load is low, the system mainly relies on the photovoltaic power generation unit and the wind turbine power generation unit to provide power output. The hydrogen storage unit is in the standby state and only receives quick response support from the electric storage unit during voltage fluctuations. The system goal in this state is to minimize energy loss, and the formula is as follows:

[0130]

[0131] In the formula: is the photovoltaic power generation, is the wind turbine power generation, is the load power.

[0132] 2) Medium load state. When the load is medium, the fuel cell part of the hydrogen storage unit intervenes in operation, and the insufficient new energy is supplemented by the output power of the fuel cell. To maintain system stability, the outputs of the hydrogen storage unit and the electric storage unit need to meet the following dynamic balance condition:

[0133]

[0134] Among them, is the output power of the fuel cell, is the output power of the electric storage unit.

[0135] 3) Heavy load state. Under the condition of high load demand, the system preferentially schedules the hydrogen storage unit and places the electric storage unit in the auxiliary response mode, and at the same time starts a dynamic power distribution algorithm to optimize the energy distribution. In this state, the system goal is to ensure the stability of the bus voltage, and the relevant power distribution formula is as follows:

[0136]

[0137] The grid-connected mode aims to achieve the coordinated operation of the system with the external power grid. In this mode, the system economy is optimized through the time-of-use electricity price mechanism and the dynamic load adjustment strategy, and it is divided into low electricity price periods and high electricity price periods. When the electricity price of the external power grid is low, the system preferentially uses cheap electric energy for hydrogen production operations and outputs excess electric energy through the photovoltaic power generation unit and the wind turbine power generation unit. The power demand of the electrolyzer satisfies the following constraints:

[0138]

[0139] In the formula: is the input power of the electrolyzer, is the power obtained from the power grid. When the electricity price of the external power grid is high, the optimization goal of this system is to maximize the economic benefit. The system increases the power sold through the fuel cell and reduces the operation time of the electrolyzer to reduce the operation cost. Its objective function is:

[0140]

[0141] In the formula: is the power sold, is the power purchased, , are the electricity selling price and the electricity purchasing price respectively.

[0142] As Figure 5 shown, the simulation analysis shows the performance of the system in dynamic energy distribution under different mode switching conditions; among them, the vertical coordinate Power represents power, the unit is watt (W), DC bus voltage represents the DC bus voltage, the horizontal coordinate Time represents time, the unit is second s, and (a) and (b) show that when the energy storage unit is switched at 8:00, the changes of the system power and the DC bus voltage with time, (c) and (d) show that when the energy storage unit is switched at 9:00, the changes of the system power and the DC bus voltage with time;

[0143] Therefore, from Figure 5 it can be seen that when the switching point occurs, the electrical energy storage unit can quickly respond and suppress the voltage fluctuation within a short time, while the hydrogen energy storage unit provides long-term steady-state power support, verifying the operation reliability and stability of the strategy of the present invention during the mode switching process.

[0144] The dynamic cooperative optimization strategy of this embodiment shows excellent flexibility and adaptability in various operating scenarios. The following are the cooperative control methods under different typical application scenarios:

[0145] Scenario 1 is a scenario with a high renewable energy penetration rate. In the case of a high proportion of renewable energy, the power generation of photovoltaic power generation and wind turbines fluctuates greatly. Through dynamic collaborative optimization, the output power of photovoltaic units and wind turbine units can be preferentially balanced in real-time scheduling, and at the same time, the fast response characteristics of battery units are used to suppress power fluctuations.

[0146] Figure 4 It shows the dynamic response characteristics of each unit in the simulation results. The ordinate DC bus voltage represents the DC bus voltage, Battery current represents the battery current, and the unit is ampere (A). I bat represents the battery discharge current, and the unit is ampere (A). SOC represents the state of charge of the battery, and the unit is percentage (%). The abscissa Time represents time, and the unit is second s;

[0147] As Figure 4 shown, in (a), the output power of the photovoltaic unit and the wind turbine unit is dynamically adjusted according to the load demand, while the lithium battery unit suppresses short-term power fluctuations through fast charge and discharge; (b) shows the stability of the DC bus voltage during fluctuations, verifying the reliability of the strategy; (c) reflects the SOC and current change characteristics of the lithium battery, and its fast compensation ability ensures the smooth operation of the system in a short time;

[0148] Scenario 2 is an off-grid island mode. In this scenario, the energy distribution and load response of the system depend on the coordinated cooperation between different energy storage units. The hydrogen storage unit provides power supplementation through a fuel cell when the load demand is high, and the electrical energy storage unit quickly responds to voltage fluctuations. It adjusts short-term load fluctuations through charge and discharge to ensure the stable operation of the system. Figure 5 It shows the dynamic energy distribution results in this mode, reflecting how the system dynamically adjusts energy distribution according to load changes and optimizes the charge and discharge paths of the hydrogen storage unit and the electrical energy storage unit. Figure 5 The data in shows that the electrical energy storage unit can quickly respond during mode switching, suppressing voltage fluctuations in a short time while the hydrogen storage unit provides long-term steady-state power support, verifying the operation reliability and stability of the strategy of the present invention during mode switching.

[0149] It should be noted that Figure 4 and Figure 5 the meanings of the parameters in are as follows:

[0150] P wind and P pv respectively represent the power outputs of wind energy and photovoltaic power generation; P el is the power output of the electrolyzer, which is used for hydrogen production; P fc is the power output of the fuel cell; P load represents the load power demand of the energy storage system; Pbat is the power output of the battery energy storage system; P grid represents the power exchanged with the power grid.

[0151] The dynamic cooperative optimization strategy of this embodiment has high feasibility in practical engineering applications. In terms of its technical implementation, the system architecture can be compatible with existing energy controllers, hydrogen storage units, and electric energy storage units, has good hardware adaptability, and is easy to integrate with existing equipment. At the same time, the dynamic programming algorithm is adopted, which has a low computational complexity and can operate efficiently on an embedded platform. Economically, through the optimization of the multi-mode switching strategy, the system operation cost has been significantly reduced, reflecting good economic benefits. As shown in the following table, it is the economic analysis under the multi-mode switching strategy:

[0152]

[0153] Through the implementation strategy of dynamic cooperative optimization, the system demonstrates high efficiency and flexibility in various operating modes. The simulation results show that this method can significantly reduce the system operation cost and has high application potential in practical engineering.

[0154] Some of the data in the above formula are taken as numerical values after removing the dimension. The formula is obtained by software simulation of a large amount of collected data to get a formula closest to the actual situation. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0155] The working principle of the present invention:

[0156] 1) In the off-grid mode, based on the virtual synchronous machine technology, enhance the inertial response of the hydrogen storage unit, and adjust the output power of the electric energy storage unit through the dynamic optimization algorithm to ensure the short-term power fluctuation balance;

[0157] 2) In the grid-connected mode, combined with the time-of-use electricity price and load fluctuation demand, establish an optimization model for power distribution of the hydrogen storage unit and the electric energy storage unit, and dynamically distribute the output power of the hydrogen storage unit and the electric energy storage unit through the real-time optimization algorithm to improve the system operation economy;

[0158] 3) Adopt the dynamic cooperative optimization method to balance the long-term load support ability of the hydrogen storage unit and the fast response characteristics of the electric energy storage unit, optimize the energy utilization efficiency of the hydrogen storage unit and the electric energy storage unit, and improve the overall performance of the system.

[0159] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A multi-mode switching control method applicable to a hydrogen-electric combined energy storage system, characterized in that Including: S1. Coupling a number of control units through a DC bus to obtain an energy storage system architecture; wherein, the system architecture includes a wind-solar power generation unit, a hydrogen storage unit, an electrical energy storage unit, and a load unit, and the hydrogen storage unit and the electrical energy storage unit are collectively referred to as the energy storage unit; S2. According to the operation mode of the energy storage system, using a dynamic cooperative optimization method based on virtual synchronous machine technology and variable droop coefficient to coordinate the output power of the energy storage unit, and introducing a dynamic energy distribution model and dynamic energy distribution constraints to obtain a power balance framework of the energy storage unit; S3. Using an optimization model based on cooperative game and a dynamic power regulation model to perform cooperative optimization and joint optimization on several energy storage units in the power balance framework to obtain a joint dynamic balance mechanism; S4. Using a distributed dynamic optimization algorithm to iteratively optimize and improve the joint dynamic balance mechanism to obtain a hydrogen-electricity combined energy storage system with multi-mode switching control; Among them, the obtained hydrogen-electricity combined energy storage system with multi-mode switching control includes: S42-1 is improved based on the optimized joint dynamic balance mechanism, and the joint optimization objective function is obtained as follows: ; where , , represent the optimization weight coefficients of each item, represents the output power of the hydrogen storage unit at time represents the output power of the electrical energy storage unit at time represents the load demand power of the load unit at time; S42-2. Defining the constraint conditions of the joint optimization objective function, including: (1) Dynamic power distribution constraint: ; where and respectively represent the dynamic adjustment coefficients of the hydrogen storage unit and the electrical energy storage unit, and respectively represent the changes in the output powers of the hydrogen storage unit and the electrical energy storage unit between consecutive moments; (2)Power range constraint of the energy storage unit: ; where , represent the minimum and maximum output powers of the hydrogen storage unit respectively, , represent the minimum and maximum output powers of the electrical energy storage unit respectively; (3)Dynamic compensation for the output power deviation of the energy storage unit: ; where represents the dynamic regulation error tolerance range between the output power and the load demand power; S42-3, using a distributed optimization algorithm to optimize the output power of the energy storage unit through the joint iterative optimization formula: for optimization, and when the convergence condition is met: or the maximum number of iterations is reached stop the optimization to obtain a hydrogen-electric hybrid energy storage system with multi-mode switching control; where k represents the number of iterations and T represents the total operating time of the energy storage system.

2. The multimode switching control method applicable to a hydrogen-electric combined energy storage system according to claim 1, wherein The method of using a dynamic cooperative optimization method based on virtual synchronous machine technology and variable droop coefficient to coordinate the output power of the energy storage unit includes: S2-1. In the off-grid mode, the virtual inertia enhancement model is constructed as follows: , and the hydrogen storage unit is controlled using the virtual inertia enhancement model; where represents the output power of the hydrogen storage unit at time t, represents the equivalent inertia constant, and represent the system real-time frequency and the reference frequency respectively, represents the damping coefficient; S2-2, the dynamic droop coefficient adjustment model is constructed as follows: , and in the grid-connected mode, the dynamic droop coefficient adjustment model is used to control the electrical energy storage unit; where represents the output power of the electrical energy storage unit at time t, represents the real-time voltage at time t, represents the reference voltage, represents the droop coefficient, and the adjustment formula for the droop coefficient is: , represents the reference voltage and the real-time voltage the difference between them, represents the initial droop coefficient, and a represents the adjustment coefficient of the initial droop coefficient; S2-3. According to the energy storage constraint conditions: , the charge and discharge states of the electrical energy storage unit and the hydrogen energy storage unit are constrained; where represents the moment the electrical energy of the electrical energy storage unit, and is subject to the constraint: , and represent the minimum and maximum electrical energy storage of the electrical energy storage unit respectively, , represent the charging power and discharging power of the electrical energy storage unit at time t respectively, , represent the charging efficiency and discharging efficiency of the electrical energy storage unit respectively, represents the moment the electrical energy of the hydrogen energy storage unit, and represent the charging power and discharging power of the hydrogen energy storage unit at time t respectively, and represent the charging efficiency and discharging efficiency of the hydrogen energy storage unit respectively, represents the time step; S2-4, according to the dynamic optimization objective: , perform power distribution and voltage regulation of the energy storage unit; where 、 respectively represent the unit operating cost coefficients of the hydrogen storage unit and the electrical energy storage unit, 、 respectively represent the output powers of the hydrogen storage unit and the electrical energy storage unit at time , represents the voltage deviation penalty coefficient, represents the square of the bus voltage deviation. S2-5. According to the balance condition of the energy storage system: , the output power of the energy storage unit is constrained; where represents the load demand power of the energy storage system at time t.

3. The multimode switching control method applicable to a hydrogen-electric combined energy storage system according to claim 2, wherein, The described dynamic energy allocation model includes: ; where and respectively represent the output power of the i-th hydrogen storage unit and the i-th electrical energy storage unit at time t, and respectively represent the maximum output power of the i-th hydrogen storage unit and the i-th electrical energy storage unit, represents the stored electrical energy of the i-th hydrogen storage unit at time t, represents the stored electrical energy of the i-th electrical energy storage unit at time , represents the total demand power of the energy storage system at time t, represents the time when the load demand power of the energy storage system, represents the energy conversion efficiency of the hydrogen storage unit, and respectively represent the charging efficiency and discharging efficiency of the electrical energy storage unit, represents the charging power of the i-th electrical energy storage unit at time t, represents the discharging power of the i-th electrical energy storage unit at time t, where i represents the index of the hydrogen storage unit and the electrical energy storage unit, represents the time step.

4. A multi-mode switching control method applicable to a hydrogen-electric combined energy storage system according to claim 3, characterized in that, The dynamic energy distribution constraints include: The dynamic energy distribution constraints of the energy storage system are as follows: ; where represents the output power of the hydrogen energy storage unit at time ; represents the output power of the electrical energy storage unit at time ; represents the load demand power of the energy storage system at time ; represents the interactive power between the energy storage system and the power grid at time , where a positive value indicates selling electricity to the power grid and a negative value indicates purchasing electricity from the power grid; The output power constraints of the hydrogen storage unit and the electrical energy storage unit are as follows: , obtaining the first power constraint; where , respectively represent the minimum and maximum output powers of the hydrogen storage unit, , respectively represent the minimum and maximum output powers of the electrical energy storage unit; The energy balance constraints of the hydrogen storage unit and the electrical storage unit are as follows: Among them, represents the stored electricity of the i-th hydrogen storage unit at time t, represents the i-th electrical storage unit at time the stored electricity of, represents the energy conversion efficiency of the hydrogen storage unit, represents the time step, represents the discharge power of the i-th electrical storage unit at time t.

5. The multimode switching control method applicable to a hydrogen-electric combined energy storage system according to claim 4, wherein The dynamic power regulation model includes: S32-1, the dynamic power regulation model is constructed as follows: ; where and respectively represent the regulation coefficient of the hydrogen storage unit and the regulation coefficient of the electrical energy storage unit, which are used to balance the deviations between the hydrogen storage unit, the electrical energy storage unit and the overall power output of the system respectively, and T represents the total operating time of the energy storage system; S32-2, define the constraint conditions of the dynamic power regulation model as follows: ; where represents the change in the output power of the hydrogen storage unit between consecutive time instants, represents the change in the output power of the electrical energy storage unit between consecutive time instants, , respectively represent the minimum and maximum output powers of the hydrogen storage unit, , respectively represent the minimum and maximum output powers of the electrical energy storage unit; S32-3. Adjusting the output power of the hydrogen storage unit and the electrical energy storage unit according to the dynamic power regulation model and the constraint conditions of the dynamic power regulation model to perform joint optimization of the energy storage unit.

6. The multimode switching control method applicable to a hydrogen-electric combined energy storage system according to claim 5, wherein The joint dynamic balance mechanism includes: S41-1, define the comprehensive optimization objective function of the energy storage system: ; among which, , , respectively represent the weight coefficients of the optimization objectives, which are used to balance cost, efficiency and load demand, , respectively represent the operating costs of the hydrogen storage unit and the electrical storage unit, and T represents the total operating time of the energy storage system; S41-2, determine that the power balance relationship of the energy storage system is: ; where represents the interactive power between the energy storage system and the power grid at time t; For S41-3, the constraint conditions for constructing the comprehensive optimization objective function are as follows: ; where represents the power distribution ratio of the hydrogen storage unit.

7. A multi-mode switching control method applicable to a hydrogen-electric combined energy storage system according to claim 6, characterized in that The comprehensive optimization objective function is iteratively optimized by a distributed dynamic optimization algorithm, and when the iteration result satisfies the convergence condition: or reaches the maximum number of iterations then the iterative optimization is completed; where represents a preset iterative convergence threshold, and k represents the number of iterations.

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