Multi-mode switching control method suitable for hydrogen-electricity combined energy storage system

Abstract

The invention discloses a multi-mode switching control method suitable for a hydrogen-electricity combined energy storage system, and the method comprises the steps: carrying out the coupling of a plurality of control units through a DC bus, and obtaining a system architecture; according to the operation mode of the system, coordinating the power output of the energy storage unit by using a dynamic collaborative optimization method based on a virtual synchronous machine technology and a variable droop coefficient, and introducing a dynamic energy distribution model and a dynamic energy distribution constraint to obtain a power balance framework of the energy storage unit; performing collaborative optimization and joint optimization on a plurality of energy storage units in the power balance framework by using the optimization model based on the cooperative game and the dynamic power regulation model to obtain a joint dynamic balance mechanism; and iteratively optimizing and improving the combined dynamic balance mechanism by using a distributed dynamic optimization algorithm to obtain the hydrogen-electricity combined energy storage system. The invention relates to a hydrogen-electricity combined energy storage control technology, and solves the technical problems that an existing hydrogen-electricity combined energy storage system is difficult to coordinate and insufficient in dynamic response in the multi-mode switching process.

Description

Technical Field

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

[0002] As the global energy structure accelerates its transition to a low-carbon one, the large-scale access of renewable energy, represented by wind power and photovoltaics, to the power grid has become an inevitable trend. However, the inherent volatility and intermittency of renewable energy pose severe challenges to the stability of the power grid and the energy dispatching capabilities. In particular, in scenarios with frequent load fluctuations, it is difficult for traditional power systems to achieve dynamic supply and demand balance. Therefore, energy storage technology has become a key means to smooth out the fluctuations of new energy and enhance the flexibility of the power grid. Among them, hydrogen energy storage systems and electrochemical energy storage systems have received widespread attention due to their complementary characteristics.

[0003] The hydrogen energy storage system realizes the conversion of electrical energy and chemical energy through water electrolysis and fuel cell power generation. It has the advantages of long-term energy storage and large-scale energy regulation, but its dynamic response speed is slow and it is difficult to adapt to short-term power fluctuations. Although the electrochemical energy storage system has a millisecond-level rapid response capability, it is limited by capacity constraints and cannot meet long-term energy support needs. Existing technologies mostly focus on the internal optimization of a single energy storage system, such as improving the efficiency of the hydrogen energy storage system or improving the battery charging and discharging performance, but 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 take into account short-term power fluctuation suppression and long-term energy balance requirements; on the other hand, there is a lack of effective dynamic coordination mechanism during the switching process of different operating modes (such as off-grid and grid-connected), resulting in delayed system response and low operating efficiency. In addition, current research generally ignores the problem of coordinated optimization of hydrogen storage units and electric storage units under multi-mode switching. Especially when the load changes suddenly or the external power grid interacts, the existing control strategy is difficult to achieve real-time optimization of power distribution, which can easily cause bus voltage instability or surge 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; to this end, the present invention proposes a multi-mode switching control method suitable for a hydrogen-electricity combined energy storage system, which is used to solve the technical problems of difficult dynamic coordination between hydrogen storage units and electricity storage units, insufficient dynamic response and poor stability in the multi-mode switching process of the existing hydrogen-electricity combined energy storage system.

[0005] To achieve the above objectives, the present invention provides a multi-mode switching control method applicable to a hydrogen-electricity combined energy storage system, comprising: S1, coupling several control units through a DC bus to obtain an energy storage system architecture; S2, according to the operation mode of the energy storage system, the output power of the energy storage unit is coordinated by using the dynamic collaborative optimization method based on virtual synchronous machine technology and variable droop coefficient, and the dynamic energy distribution model and dynamic energy distribution constraints are introduced to obtain the power balance framework of the energy storage unit; S3, using the optimization model based on cooperative game and the dynamic power regulation model to coordinate and jointly optimize several energy storage units in the power balance framework, and obtain a joint dynamic balance mechanism; S4, using the distributed dynamic optimization algorithm to iteratively optimize and improve the joint dynamic balance mechanism, and obtain a hydrogen-electricity combined energy storage system with multi-mode switching control.

[0006] Furthermore, the system architecture includes a wind and solar power generation unit, a hydrogen storage unit, an electricity storage unit and a load unit, wherein the hydrogen storage unit and the electricity storage unit are collectively referred to as an energy storage unit.

[0007] Furthermore, the method of coordinating the output power of the energy storage unit by using a dynamic collaborative optimization method based on virtual synchronous machine technology and variable droop coefficient includes: S2-1, in off-grid mode, the virtual inertia enhancement model is constructed as: , using a virtual inertia enhancement model to control the hydrogen storage unit; where, represents the output power of the hydrogen storage unit, represents the equivalent inertia constant, , Respectively represent the system real-time frequency and reference frequency, represents the damping coefficient; 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, Indicates the real-time voltage. represents the reference voltage, It represents the droop coefficient, and the adjustment formula of the droop coefficient is: , Indicates the reference voltage The actual voltage The difference between represents the initial droop coefficient, represents the adjustment factor of the initial droop factor; 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, , Respectively represent the charging power and discharging power of the storage unit, , Respectively represent the charging efficiency and discharging efficiency of the storage unit, Indicates time The amount of electricity in the hydrogen storage unit, and Respectively represent the charging power and discharging power of the hydrogen storage unit, and They represent the charging efficiency and discharging efficiency of the hydrogen storage unit, represents the time step; 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 power output, represents the voltage deviation penalty coefficient, Indicates the square of bus voltage deviation; S2-5, according to the balance condition of the energy storage system: , constrains the output power of the energy storage unit; where P load Represents the load power demand of the energy storage system.

[0008] The virtual inertia model responds to system frequency changes by adjusting the output power of the hydrogen storage unit in real time to ensure dynamic balance under load fluctuations; variable droop control enables the storage unit to quickly adjust the output power under different load conditions to avoid bus voltage instability caused by voltage fluctuations. And under dynamic load conditions, the dynamic optimization target can reduce voltage fluctuations, improve the steady-state performance of the power system, and ensure the long-term operation reliability of energy storage equipment.

[0009] Furthermore, the dynamic energy allocation model includes: ;in, , Respectively represent the output power of the hydrogen storage unit and the electricity storage unit at time t, , Respectively represent the maximum output power limits of the hydrogen storage unit and the electricity storage unit, , Respectively represent the hydrogen storage unit and the electricity storage unit at time The energy state, represents the total power demand of the energy storage system, Indicates time The load demand of the energy storage system is Indicates the energy conversion efficiency of the hydrogen storage unit.

[0010] Through the above model, the hydrogen storage unit and the electricity storage unit can achieve coordinated operation in different modes to cope with load demand fluctuations and dynamic energy allocation problems, and improve the system's operational flexibility and stability.

[0011] Furthermore, the dynamic energy allocation constraint includes: The dynamic energy allocation constraint of the energy storage system is: ;in, Indicates time The output power of the hydrogen storage unit, Indicates time The output power of the storage unit, Indicates time The actual load demand of the energy storage system, Indicates time The interactive power between the energy storage system and the grid, where a positive value indicates purchasing electricity from the grid and a negative value indicates selling electricity to the grid; The output power constraints of the hydrogen storage unit and the electricity storage unit are: , get the first power constraint; where, , Respectively represent the minimum and maximum output power of the hydrogen storage unit, , Respectively represent the minimum and maximum output power of the power storage unit; The energy balance constraints of the hydrogen storage unit and the electricity storage unit are: ; where i represents the index, represents the storage capacity of the hydrogen storage unit at time t, Indicates the power storage unit at time The storage capacity, represents the energy conversion efficiency of the hydrogen storage unit, represents the time step, Represents the discharge power of the storage unit at time t.

[0012] Furthermore, the optimization model based on cooperative game includes: S31-1, based on the cooperative game method, the power allocation objective function is constructed as follows: ;in, Hydrogen storage unit The running cost at the time, express The output power of the hydrogen storage unit at the moment, express The output power of the electric storage unit at the moment, N represents the total number of hydrogen storage units and electric storage units in the energy storage system, and i represents the energy storage unit index; S31-2, the constraints for constructing the power allocation objective function are: , and obtain the second power constraint; where, express The load demand power of the load unit at the moment; S31-3, adjusting the power allocation ratio of the hydrogen storage unit and the electricity storage unit according to the power allocation objective function and the constraints of the power allocation objective function, and performing collaborative optimization of the energy storage units.

[0013] In the power allocation objective function based on cooperative game, the power allocation ratio between each unit is dynamically adjusted according to the marginal contribution value of each energy storage unit and its operating cost, so as to realize the coordinated management of "energy balance and real-time dynamic scheduling" and achieve the economic goal of "minimizing operating costs" at the same time.

[0014] Furthermore, the dynamic power regulation model includes: S32-1, construct the dynamic power regulation model as follows: ;in, and They represent the hydrogen storage unit adjustment coefficient and the electricity storage unit adjustment coefficient, respectively, and are used to balance the deviations between the hydrogen storage unit, the electricity storage unit and the overall power output of the system, respectively. T represents the total operating time of the energy storage system; S32-2, define the constraints of the dynamic power regulation model as: ; where ∆P HESS Indicates the change in the power output of the hydrogen storage unit between consecutive moments, ∆P ESS Indicates the change in power output of the power storage unit between consecutive moments; S32-3, adjusting the output power of the hydrogen storage unit and the electricity storage unit according to the dynamic power regulation model and the constraints of the dynamic power regulation model, and performing joint optimization of the energy storage units.

[0015] Furthermore, the combined dynamic balance mechanism includes: S41-1, define the comprehensive optimization objective function of the energy storage system: ;in, , , They represent the weight coefficients of the optimization objectives, which are used to balance cost, efficiency and load demand. , They represent the operating costs of the hydrogen storage unit and the electricity storage unit, respectively, and T represents the total operating time of the energy storage system; S41-2, determine the power balance relationship of the energy storage system as: ; Among them, P gird Represents the power exchange between the grid and the energy storage system; S41-3, the constraint conditions of constructing a comprehensive optimization objective function according to the first power constraint and the second power constraint are: ;in, η t Indicates the power allocation ratio of the hydrogen storage unit.

[0016] Furthermore, the comprehensive optimization objective function is iteratively optimized by a distributed dynamic optimization algorithm, and when the iterative result meets the convergence condition: Or the maximum number of iterations is reached When , the iterative optimization is completed; represents the preset iterative convergence threshold, and k represents the number of iterations.

[0017] Furthermore, the multi-mode switching controlled hydrogen-electricity combined energy storage system comprises: S42-1, based on the optimized joint dynamic balance mechanism, the joint optimization objective function is obtained as follows: ;in, , , Represents the optimization weight coefficients; S42-2, define the constraints of the joint optimization objective function, including: (1) Dynamic power allocation constraints: ;in, and represent the dynamic adjustment coefficients of the hydrogen storage unit and the electricity storage unit respectively; (2) Power range constraints of energy storage units: ; (3) Dynamic compensation of energy storage unit output power deviation: ;in, Indicates the dynamic regulation error tolerance range between power output and load demand; S42-3, using a distributed optimization algorithm to optimize the output power of the energy storage unit through a joint iterative optimization formula: using a distributed optimization algorithm to optimize the output power of the energy storage unit through a joint iterative optimization formula: Optimize and when the convergence condition is met: Or the maximum number of iterations is reached The optimization is stopped when , and a hydrogen-electricity combined energy storage system with multi-mode switching control is obtained.

[0018] By jointly optimizing the objective function model, the energy storage system can not only dynamically respond to power fluctuations under complex load conditions, but also maintain the overall operating efficiency and stability of the system while reducing the operating costs of the hydrogen storage unit and the electricity storage unit.

[0019] Compared with the prior art, the present invention has the following beneficial effects: (1) The single energy storage system in the prior art has limitations, the hydrogen energy storage system has a slow response, and the electric 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 electric storage unit to respond quickly to bus voltage fluctuations. In off-grid mode, it can effectively respond to load fluctuations and ensure system stability, solving the problem of insufficient dynamic response of energy storage systems in the prior art and improving the response capability of energy storage systems in complex energy environments; (2) In view of the fact that the existing technology has insufficient research on dynamic coordination during the multi-mode switching process of hydrogen-electric combined energy storage systems, the present invention adopts a dynamic collaborative optimization algorithm. Combining time-of-use electricity prices and load fluctuations, the energy output distribution of hydrogen storage and electricity storage units is coordinated, the output power of photovoltaic and wind turbine units is balanced first, and power fluctuations are suppressed. In the scenario of high renewable energy penetration, renewable energy is fully utilized, energy waste is reduced, and energy utilization efficiency is improved; (3) The present invention constructs a multi-mode system operation cost minimization model, taking into account the costs of various operating entities. Through a resource allocation method based on cooperative game, the power allocation ratio of the hydrogen storage unit and the power storage unit is dynamically adjusted to reduce the overall operation cost of the system. In practical applications, after the implementation of the multi-mode switching strategy, the total operation cost, fuel cell operation cost and hydrogen production cost are significantly reduced, reflecting good economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 A technical flow chart of a multi-mode switching control method applicable to a hydrogen-electricity combined energy storage system provided by the present invention; Figure 2 A schematic diagram of the architecture of the hydrogen-electricity combined energy storage system provided by the present invention; Figure 3 A block diagram of virtual inertia control of a DC power grid based on a virtual synchronous machine provided by the present invention; Figure 4 A dynamic response characteristic diagram of each unit in the simulation result provided by the present invention; Figure 5 This is a diagram of dynamic energy allocation results under different mode switching conditions provided by the present invention. DETAILED DESCRIPTION

[0022] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] See also Figure 1-Figure 5 The first embodiment of the present invention provides a multi-mode switching control method applicable to a hydrogen-electricity combined energy storage system, comprising: S1, a number of control units are coupled through a DC bus to obtain a system architecture; the system architecture is as follows Figure 2 As shown, each unit includes the following modules: Hydrogen storage unit: The hydrogen storage unit consists of an electrolyzer, a hydrogen storage tank and a fuel cell. The electrolyzer converts excess electrical energy into hydrogen for storage, and when the load demand increases, the fuel cell converts the hydrogen into electrical energy to provide supplementary energy for the system; Power storage unit: including photovoltaic power generation unit and wind turbine power generation unit, responsible for converting light energy and wind energy into direct current input bus, ensuring the stability of the system in the scenario of new energy with large fluctuations; Load unit: mainly provides power demand for the system and dynamically interacts with the power and hydrogen storage units to achieve supply and demand balance; Wind and solar power generation unit: It consists of photovoltaic power generation unit and wind turbine power generation unit. The photovoltaic power generation unit uses the photoelectric effect to convert solar energy into direct current, while the wind turbine power generation unit uses the wind-driven generator to convert wind energy into direct current. The two will input the generated direct current into the bus, providing the main source of power for the system; And in Figure 2In the figure, AC / DC represents the conversion of alternating current to direct current, which is used to convert AC power into direct current, suitable for charging batteries or energy storage systems; DC / DC is a direct current voltage conversion module, which is used to adjust direct current of different voltages. G_VSC and W_VSC represent the voltage source converters with the power grid and wind power generation system, respectively, which are used to regulate the power flow between the power grids and the conversion of wind energy; PV_DC represents the direct current output of photovoltaic power generation, which is used to provide electricity converted from solar energy; EL_DC and FC_DC are the direct current parts of the water electrolysis hydrogen production system and the fuel cell system, respectively, the former is used for hydrogen production, and the latter converts hydrogen into electricity; B_DC is the direct current part of the battery energy storage system, which is used to store electrical energy; L_DC is the direct current part of the load end, which receives the output of each power source. The PEM system is a proton exchange membrane fuel cell system, which provides efficient hydrogen-to-electricity conversion function. 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 Indicates the power exchanged with the grid; P wind and P pv represent the power output of wind and photovoltaic power generation respectively; It should be noted that, in this embodiment, the hydrogen storage unit and the electricity storage unit may be collectively referred to as an energy storage unit.

[0024] S2, according to the operation mode of the energy storage system, the output power of the energy storage unit is coordinated by using the dynamic collaborative optimization method based on virtual synchronous machine technology and variable droop coefficient, and the dynamic energy distribution model and dynamic energy distribution constraints are introduced to obtain the power balance framework of the energy storage unit; In this embodiment, the energy storage system realizes the dynamic response of the hydrogen storage unit and the electric storage unit through virtual synchronous machine technology and variable droop coefficient control in off-grid mode, constructs a virtual inertia enhancement model and a dynamic power allocation model, and combines energy storage constraints and optimization algorithms to ensure stable operation of the system, including: S2-1, in off-grid mode, the virtual inertia enhancement model is constructed as: , using a virtual inertia enhancement model to control the hydrogen storage unit; where, represents the output power of the hydrogen storage unit, represents the equivalent inertia constant, , Respectively represent the system real-time frequency and reference frequency, represents the damping coefficient; 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, Indicates the real-time voltage. 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; The adjustment coefficient of the initial droop coefficient is used to control the influence of voltage deviation on the droop coefficient and is determined based on historical experimental experience; 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, , Respectively represent the charging power and discharging power of the storage unit, , Respectively represent the charging efficiency and discharging efficiency of the storage unit, Indicates time The amount of electricity in the hydrogen storage unit, and Respectively represent the charging power and discharging power of the hydrogen storage unit, and They represent the charging efficiency and discharging efficiency of the hydrogen storage unit, represents the time step; 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 power output, represents the voltage deviation penalty coefficient, Represents the square of bus voltage deviation; this optimization target belongs to the local optimization model in the multi-mode switching control method, which mainly targets the power distribution and voltage regulation of energy storage units; it will be a component of the final joint optimization strategy, and its optimization variable and It is subject to global optimization constraints and solved in the final joint optimization model; S2-5, according to the balance condition of the energy storage system: , constrains the output power of the energy storage unit; where P load Represents the load power demand of the energy storage system.

[0025] In this embodiment, the dynamic energy allocation model in step S2 is used to achieve the coordinated operation of different hydrogen storage units and electricity storage units to meet the dynamic balance requirements of the system under multi-mode switching, specifically including: ;in, , Respectively represent the output power of the hydrogen storage unit and the electricity storage unit at time t, , Respectively represent the maximum output power limits of the hydrogen storage unit and the electricity storage unit, , Respectively represent the hydrogen storage unit and the electricity storage unit at time energy state, represents the total power demand of the energy storage system, Indicates time The load demand of the energy storage system is Indicates the energy conversion efficiency of the hydrogen storage unit.

[0026] like Figure 3 As shown, it is a block diagram of virtual inertia control of a DC grid based on a virtual synchronous machine provided in this embodiment; Figure 3 middle, Indicates the change in DC voltage, used to indicate voltage error or disturbance, and is adjusted by the current regulator to keep the voltage at the desired value; Represents the gain factor, which is used to adjust the response speed of the virtual synchronous generator (VSG) control loop. By controlling this gain, the voltage regulation capability of the system can be optimized. Represents the reference current signal, which is input to the control system as an input signal to help the system adjust the current output.

[0027] S represents the Laplace operator, which is often used in transfer functions to represent the dynamic response of the system. It 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. It represents the damping coefficient of the virtual synchronous generator, which is used to adjust the system stability and prevent system oscillation. Represents virtual capacitance. Indicates the steady-state value of the DC voltage in the system and is used as the target voltage input. Represents the DC voltage reference value, which is used as the target value to compare with the actual system output and is used to control the system to adjust the output. Indicates the output voltage of the DC power supply in the current control loop. This coefficient is the actual value of the DC voltage. , and the target voltage Voltage control is performed by comparison. It is the second-order difference of the current change. This coefficient represents the dynamic response of the current. This signal is used for regulation in the current control loop to avoid excessive current fluctuations and ensure smooth changes in current. Represents the feedforward gain coefficient, which is used to optimize the control response and enhance the system stability. Represents the current error signal, which is specifically expressed as 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; AC / DC refers to the conversion of alternating current to direct current. PI (Proportional-Integral Controller) refers to the proportional-integral controller, which is used to adjust the balance between direct current and alternating current. PLL (Phase-Locked Loop) refers to the phase-locked loop, which is used to synchronize the frequency and phase of the system. abc / dq refers to the conversion between the three-phase coordinate system (abc) and the direct-axis quadrature-axis coordinate system (dq). The abc coordinate system is often used to describe three-phase AC systems, while the dq coordinate system is used to simplify control design, especially in current decoupling and voltage control. SVPWM (Space Vector Pulse Width Modulation) refers to space vector pulse width modulation, which is used to generate a modulation signal to control the output voltage of the inverter. It optimizes the control of voltage and current by adjusting the ratio of the voltage vector. , They represent the current components on the DC axis (d axis) and the AC axis (q axis), respectively, and are used to control the current of the system. Indicates the target current. , Represents the voltage error on the DC and AC axes respectively, and is usually used by the controller to adjust the output voltage. Indicates the frequency of the power grid, usually the reference frequency of the power grid, used for system synchronization together with other parameters.

[0028] , , Represents the error term of the three-phase voltage and is used to adjust the control output. Represents resistance, which may be a load resistor in a circuit or some other resistance associated with inductance and current. Indicates the phase angle, used for synchronization with other signals. Indicates the target current of the DC axis (d axis). Indicates the target current of the AC axis (q axis). Represents inductance, which is usually a parameter related to current changes in a circuit, affecting the regulation and stability of current. Indicates the reference voltage of the DC axis (d-axis). Indicates the actual current of phase A of the three-phase power supply. Indicates the actual current of phase B of the three-phase power supply. Indicates the actual current of phase C of the three-phase power supply; In off-grid mode, the energy storage system relies on virtual inertial control technology to enhance the stability of the system. Specifically, when photovoltaic power generation and wind turbine power generation cannot meet the load demand, the hydrogen energy unit uses fuel cells to quickly replenish energy, and when new energy power generation is surplus, 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 supply and demand balance without relying on the large power grid and achieve energy self-sufficiency.

[0029] In the grid-connected mode, the system further optimizes the energy flow through interactive scheduling with the large power grid. In this mode, the system dynamically adjusts the output power of each unit according to the real-time electricity price signal, load demand and fluctuations in new energy. For example, when the electricity price is high, the system gives priority to selling new energy electricity to the large power grid; when the electricity price is low, the system stores energy through the electrolyzer to prepare for subsequent peak load demand. In addition, by adjusting the operating mode of the fuel cell, the carbon emissions in the system can be further reduced and the operating cost can be reduced.

[0030] In this embodiment, the operation cost minimization model of the energy storage system includes multiple operation entities, and its goal is to minimize the operation cost under the premise of meeting the dynamic demand of energy storage and operation constraints. The specific operation cost model is: Where: is the operating cost of microgrid j, The electricity purchase cost for the interaction between the microgrid and the large power grid, , , , They are the operating costs of the gas turbine, fuel cell, photovoltaic power generation unit and wind power generation unit in the microgrid respectively. is the carbon emission cost, and the calculation formula is: Where: is the unit carbon emission governance cost coefficient, , is the carbon emission coefficient per unit of electricity generation. The electricity purchase cost and electricity sales revenue of the microgrid satisfy the following relationship: And the operating cost of each power generation unit in the microgrid can be further decomposed into: Where: , , , are the unit operating cost coefficients of gas turbine, fuel cell, photovoltaic and wind turbine power generation, , , , is the output power at time t.

[0031] In order to achieve the multi-mode switching coordinated optimization goal of the hydrogen-electric combined energy storage system, this paper proposes an optimization strategy based on dynamic adjustment. This strategy combines the characteristics of the hydrogen storage unit and the electricity storage unit to further improve the safety and stability of the system while meeting the load demand and reducing the operating cost. The system operation must meet the following dynamic energy allocation constraints: (1) Dynamic energy allocation constraints of energy storage systems: ;in, Indicates time The output power of the hydrogen storage unit, Indicates time The output power of the storage unit, Indicates time The actual load demand of the energy storage system, Indicates time The interactive power between the energy storage system and the grid, where a positive value indicates purchasing electricity from the grid and a negative value indicates selling electricity to the grid; (2) Output power constraints of hydrogen storage units and electricity storage units: , get the first power constraint; where, , Respectively represent the minimum and maximum output power of the hydrogen storage unit, , Respectively represent the minimum and maximum output power of the power storage unit; (3) Energy balance constraints of hydrogen storage unit and electricity storage unit: ;in, represents the storage capacity of the hydrogen storage unit at time t, Indicates the power storage unit at time The storage capacity, represents the energy conversion efficiency of the hydrogen storage unit, represents the time step, Represents the discharge power of the storage unit at time t.

[0032] Through dynamic energy distribution models and dynamic energy distribution constraints, the hydrogen-electricity combined energy storage system can realize dynamic energy distribution between hydrogen storage units and electricity storage units in dynamic mode, balance load demand and optimize system operation efficiency.

[0033] S3, using the optimization model based on cooperative game and the dynamic power regulation model to coordinate and jointly optimize several energy storage units in the power balance framework, and obtain a joint dynamic balance mechanism; In order to achieve the coordinated optimization between multiple hydrogen storage units and electric storage units in the joint energy storage system, a resource allocation method based on cooperative game is proposed in this embodiment. Under different operating modes, the hydrogen storage unit and the electric storage unit realize the dynamic allocation of power through joint optimization decision-making to reduce the overall operating cost of the system and improve energy utilization efficiency. The specific steps include: 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 electricity storage unit) and the output power at each moment, and taking full account of the dynamic balance conditions of the system, the marginal contribution value of each energy storage unit is determined, thereby forming the optimization objective function; 2) Coordinate the output power of hydrogen storage units and electricity storage units through distributed optimization algorithms, collect the operating status of each energy storage unit in real time, and ensure that the load demand is met under the condition of dynamic balance of the system; 3) Dynamically adjust the power allocation ratio between hydrogen storage units and electricity storage units. This optimization objective function is one of the optimization objectives included in the final joint optimization objective function. In the above cooperative game model, the power allocation ratio between each unit is dynamically adjusted according to the marginal contribution value of each energy storage unit and its operating cost, to achieve collaborative management in terms of "energy balance and real-time dynamic scheduling" and achieve the economic goal of "minimizing operating costs" at the same time.

[0034] Specifically, the optimization model based on cooperative game includes: S31-1, based on the cooperative game method, the power allocation objective function is constructed as follows: ;in, Hydrogen storage unit The running cost at the time, express The output power of the hydrogen storage unit at the moment, express The output power of the electric storage unit at the moment, N represents the total number of hydrogen storage units and electric storage units in the energy storage system, and i represents the energy storage unit index; S31-2, the constraints for constructing the power allocation objective function are: , and obtain the second power constraint; where, express The load demand power of the load unit at any time; Hydrogen storage unit With power storage unit The sum of the power outputs of the load units must equal or exceed the power requirements of the system load units. , realize the efficient utilization of resources between hydrogen storage unit and electricity storage unit, ensure the stable operation of the system and minimize the cost in complex dynamic environment; S31-3, adjusting the power allocation ratio of the hydrogen storage unit and the electricity storage unit according to the power allocation objective function and the constraints of the power allocation objective function, and performing collaborative optimization of the energy storage units.

[0035] In this embodiment, the hydrogen storage unit and the electricity storage unit are jointly optimized in the power balance framework through the dynamic power regulation model to achieve rapid response and maximize the operating efficiency of the energy storage system, and at the same time meet the balance requirements of the dynamic load; The dynamic power adjustment model in step S3 includes: S32-1, construct the dynamic power regulation model as follows: ;in, and They represent the regulation coefficient of hydrogen storage unit and the regulation coefficient of electricity storage unit respectively, which are obtained based on historical experience; S32-2, define the constraints of the dynamic power regulation model as: ; where ∆P HESS Indicates the change in power output of the hydrogen storage unit between consecutive moments (unit: W), ∆P ESS Indicates the change in power output of the power storage unit between consecutive moments (unit: W); S32-3, adjusting the output power of the hydrogen storage unit and the electricity storage unit according to the dynamic power regulation model and the constraints of the dynamic power regulation model, and performing joint optimization of the energy storage units.

[0036] It should be noted that in determining and When determining the values ​​of these two adjustment coefficients, a method based on system requirements and performance optimization is usually adopted. First, the initial value can be set according to the load fluctuation characteristics of the system, the dynamic response capability of the energy storage unit, and the energy conversion efficiency. For example, if the response speed of the electric storage unit is fast, β may need to be appropriately increased to enhance its adaptability to load fluctuations; while 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 to optimize the value of the adjustment coefficient by minimizing the power regulation error and system operating cost. Through multiple simulations and tests, the optimal adjustment coefficient of the system under different load conditions can be obtained, thereby ensuring that the system maintains stability and efficiency while meeting load requirements.

[0037] Through the above-mentioned dynamic optimization mechanism, the hydrogen storage unit and the electricity storage unit can quickly adjust the power output according to load changes, thereby realizing efficient operation and response stability of the combined energy storage system in a complex dynamic environment.

[0038] S4, using the distributed dynamic optimization algorithm to iteratively optimize and improve the joint dynamic balance mechanism, and obtain a hydrogen-electricity combined energy storage system with multi-mode switching control; In order to further improve the collaborative optimization capability of hydrogen storage units and electric storage units under complex load conditions, a joint dynamic balance mechanism is proposed. This mechanism establishes a multi-objective optimization model, comprehensively considers system operating costs, energy efficiency and load balance, and realizes dynamic adjustment and joint optimization between hydrogen storage units and electric storage units. In order to construct a reasonable optimization strategy, the comprehensive optimization objective function of the energy storage system is first defined as follows: ;in, , , They represent the weight coefficients of the optimization objectives, which are used to balance cost, efficiency and load demand. , denote the operating costs of the hydrogen storage unit and the electricity storage unit, respectively; The comprehensive optimization objective function reflects the need to minimize the comprehensive operating cost of the energy storage system under different load conditions, including core factors such as hydrogen storage units, electricity storage units, micro-grid electricity sales, and environmental costs; this function ensures the load balance of the system during operation, and can adjust the power output in real time to match the load demand to ensure stable operation of the system. And by introducing multi-objective weight coefficients, the objective function can flexibly adjust the priority between each goal according to different operating modes (such as grid-connected or off-grid), achieve overall optimization, and ensure the economy, stability, and efficiency of the system in a complex dynamic environment; In order to ensure that the optimization objective function can effectively guide the power allocation of the system, at each time step The power balance of the energy storage system must satisfy the following relationship: ; Among them, P gird Represents the power exchange between the grid and the energy storage system; The power balance equation ensures the power supply and demand matching of the hydrogen storage unit, the electricity storage unit and the power grid, that is, the dynamic power balance of the entire system. In order to make the optimization objective function usable for actual solution, it is necessary to construct the constraint conditions of the comprehensive optimization objective function based on the first power constraint and the second power constraint, including: ;in, η t Indicates the power allocation ratio of the hydrogen storage unit.

[0039] In this embodiment, based on the above comprehensive optimization objective function and constraints, the final optimization problem can be further expressed as the following specific optimization model: ; Then, the distributed dynamic optimization algorithm is used to iteratively optimize the optimization models of the hydrogen storage unit and the electricity storage unit to minimize the overall operating cost of the system and achieve efficient use of energy; during the iteration process, the system determines whether the optimization reaches the stopping standard through the convergence conditions. The specific convergence conditions are as follows: If the above convergence conditions are not met during the iteration process, the system continues to adjust the power allocation until the convergence requirements are met or the preset maximum number of iterations is reached. When it is terminated, the optimized joint dynamic balance mechanism is obtained; where: is the iterative convergence threshold.

[0040] In the multi-mode switching control process, in order to further improve the efficiency and stability of the system operation, a dynamic adjustment mechanism based on load fluctuation compensation is proposed. This mechanism achieves dynamic balance of energy and minimizes system operation costs by jointly optimizing the scheduling strategies of hydrogen storage units and electricity storage units. To achieve the above goals, in this embodiment, further improvements are made based on the optimized joint dynamic balance mechanism, and the joint optimization objective function is obtained as follows: ;in, , , represents the optimization weight coefficient, which is used to balance the deviation between the hydrogen storage unit, the electric storage unit and the overall power output of the system, and is obtained through experimental experience; The load fluctuation is compensated by jointly optimizing the objective function to ensure the stable operation of the system under dynamic load environment. At the same time, in order to ensure that the output power of the hydrogen storage unit and the electricity storage unit can operate stably under the influence of load fluctuation, the system needs to meet the following constraints: (1) Dynamic power allocation constraints: ;in, and They represent the dynamic adjustment coefficients of the hydrogen storage unit and the electricity storage unit, respectively, which are determined based on historical experimental experience; (2) Power range constraints of energy storage units: ; (3) Dynamic compensation of energy storage unit output power deviation: in, Indicates the dynamic adjustment error tolerance range between power output and load demand. It is usually used in control systems and refers to the deviation tolerance of system power. It is used to characterize the allowable difference between the output power of hydrogen storage units and electric storage units and the load power, and is set based on historical experience. In the joint optimization objective function, the output power of the hydrogen storage unit and the electricity storage unit is adjusted by the following joint iterative optimization formula: , and when the convergence condition is met: Or the maximum number of iterations is reached The optimization is stopped when the load is too high, and a multi-mode switching control hydrogen-electric combined energy storage system is obtained, which can not only dynamically respond to power fluctuations under complex load conditions, but also maintain the overall operating efficiency and stability of the system while reducing the operating cost of the energy storage unit.

[0041] In the hydrogen-electric combined energy storage system, multi-mode switching is an important means to ensure the stability and economy of the system under different operating conditions. This embodiment designs a multi-mode switching control strategy by combining the operating requirements in the off-grid mode and the grid-connected mode to achieve efficient coordination of the hydrogen storage unit and the electric storage unit under different operating conditions. The off-grid mode is mainly aimed at isolated island operation scenarios without external grid support. In this mode, by combining the hydrogen storage unit and the electric storage unit, the advantages of both are brought into play to cope with different load conditions.

[0042] The off-grid mode is divided into the following states: 1) Light load state. When the load is low, the system mainly relies on photovoltaic power generation units and wind turbine power generation units to provide power output. The hydrogen storage unit is in standby state and only provides rapid response support from the power storage unit when the voltage fluctuates. In this state, the system goal is to minimize energy loss. The formula is as follows: Where: is the photovoltaic power generation power, is the wind turbine power generation, is the load power.

[0043] 2) Medium load state. Under medium load conditions, the fuel cell part of the hydrogen storage unit intervenes in operation, and the output power of the fuel cell supplements the shortage of new energy. To maintain system stability, the output of the hydrogen storage unit and the power storage unit must meet the following dynamic balance conditions: in, is the fuel cell output power, Output power to the power storage unit.

[0044] 3) Heavy load state. Under the condition of high load demand, the system prioritizes the hydrogen storage unit and puts the electric storage unit into auxiliary response mode, while starting the dynamic power allocation algorithm to optimize energy distribution. In this state, the system goal is to ensure the stability of the bus voltage. The relevant power allocation formula is as follows: The grid-connected mode aims to achieve coordinated operation between the system and the external power grid. In this mode, the economic efficiency of the system is optimized through the time-of-use electricity price mechanism and dynamic load adjustment strategy, and is divided into low-price periods and high-price periods. When the external power grid electricity price is low, the system gives priority to using cheap electricity for hydrogen production operations, and outputs excess electricity through photovoltaic power generation units and wind turbine power generation units. The power demand of the electrolyzer meets the following constraints: Where: Input power to the electrolyzer, is the power obtained from the grid. When the external grid electricity price is high, the optimization goal of this system is to maximize economic benefits. The system increases the power sold by the fuel cell and reduces the operating time of the electrolyzer to reduce the operating cost. Its objective function is: Where: For selling electricity, For the purchased power, , They are the electricity sales price and the electricity purchase price respectively.

[0045] like Figure 5 As shown, the simulation analysis shows the performance of the system in dynamic energy distribution under different mode switching conditions; wherein, the vertical axis Power represents power in watts (W), the DC bus voltage represents the DC bus voltage, the horizontal axis Time represents time in seconds, and (a) and (b) represent the changes of system power and DC bus voltage over time when the energy storage unit is switched at 8:00, and (c) and (d) represent the changes of system power and DC bus voltage over time when the energy storage unit is switched at 9:00; Therefore, by Figure 5 It can be seen that when the switching point occurs, the electricity storage unit can respond quickly and smooth out the voltage fluctuations in a short period of time, while the hydrogen storage unit provides long-term steady-state power support, verifying the operational reliability and stability of the strategy of the present invention during the mode switching process.

[0046] The dynamic collaborative optimization strategy of this embodiment demonstrates excellent flexibility and adaptability in a variety of operating scenarios. The following are collaborative control methods in different typical application scenarios: Scenario 1 is a scenario with high renewable energy penetration. When the proportion of renewable energy is high, the power of photovoltaic power generation and wind turbine power generation fluctuates greatly. Through dynamic collaborative optimization, the output power of photovoltaic units and wind turbine units can be prioritized in real-time scheduling, while the rapid response characteristics of battery units can be used to suppress power fluctuations.

[0047] Figure 4The dynamic response characteristics of each unit in the simulation results are shown, and the vertical axis DC bus voltage represents the DC bus voltage, and the battery current represents the battery current in amperes (A). bat Indicates the battery discharge current in ampere (A), SOC indicates the battery state of charge in percentage (%), and the horizontal axis Time indicates the time in seconds (s); like Figure 4 As 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 by fast charging and discharging; (b) shows the stability of the DC bus voltage during fluctuations, verifying the reliability of the strategy; (c) reflects the SOC and current variation characteristics of the lithium battery, and its fast compensation capability ensures the stability of the system operation in a short time; Scenario 2 is the island mode of off-grid operation. In this scenario, the energy distribution and load response of the system depend on the coordination and cooperation between different energy storage units. The hydrogen storage unit provides power supplement through fuel cells when the load demand is high, and the electric storage unit responds quickly to voltage fluctuations. It adjusts short-term load fluctuations through charging and discharging to ensure the stable operation of the system. Figure 5 The dynamic energy distribution results in this mode are demonstrated, reflecting how the system dynamically adjusts energy distribution according to load changes and optimizes the charging and discharging paths of the hydrogen storage unit and the electricity storage unit. Figure 5 The data in the data show that the electricity storage unit can respond quickly when switching modes, smoothing out voltage fluctuations in a short period of time while the hydrogen storage unit provides long-term steady-state power support, verifying the operational reliability and stability of the strategy of the present invention during the mode switching process.

[0048] It should be noted that Figure 4 and Figure 5 The meanings of the parameters are as follows: P wind and P pv Represent the power output of wind power and photovoltaic power generation respectively; 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; P bat is the power output of the battery energy storage system; P grid Indicates the power exchanged with the grid.

[0049] The dynamic collaborative optimization strategy of this embodiment has high feasibility in actual engineering applications. In terms of its technical implementation, the system architecture is compatible with existing energy controllers, hydrogen storage units, and electricity storage units, has good hardware adaptability, and is easy to integrate with existing equipment; at the same time, it adopts a dynamic programming algorithm, which has low computational complexity and can run efficiently on embedded platforms. In terms of economy, through the optimization of the multi-mode switching strategy, the system operating cost has been significantly reduced, reflecting good economic benefits. As shown in the following table, this is the economic analysis under the multi-mode switching strategy:

[0050] Through the implementation strategy of dynamic collaborative optimization, the system demonstrates high efficiency and flexibility in multiple operation modes. Simulation results show that this method can significantly reduce the system operation cost and has high application potential in actual engineering.

[0051] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0052] Working principle of the present invention: 1) In off-grid mode, the inertial response of the hydrogen storage unit is enhanced based on the virtual synchronous machine technology, and the output power of the electric storage unit is adjusted through the dynamic optimization algorithm to ensure the balance of short-term power fluctuations; 2) In the grid-connected mode, a power allocation optimization model for hydrogen storage units and electric storage units is established by combining time-of-use electricity prices with load fluctuation requirements. The output power of hydrogen storage and electric storage units is dynamically allocated through a real-time optimization algorithm to improve the economic efficiency of system operation; 3) Use dynamic collaborative optimization methods to balance the long-term load support capacity of the hydrogen storage unit and the rapid response characteristics of the electricity storage unit, optimize the energy utilization efficiency of the hydrogen storage unit and the electricity storage unit, and improve the overall performance of the system.

[0053] 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 skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A multi-mode switching control method suitable for a hydrogen-electricity combined energy storage system, characterized in that: include: S1, coupling several control units through a DC bus to obtain an energy storage system architecture; S2, according to the operation mode of the energy storage system, the output power of the energy storage unit is coordinated by using the dynamic collaborative optimization method based on virtual synchronous machine technology and variable droop coefficient, and the dynamic energy distribution model and dynamic energy distribution constraints are introduced to obtain the power balance framework of the energy storage unit; S3, using the optimization model based on cooperative game and the dynamic power regulation model to coordinate and jointly optimize several energy storage units in the power balance framework, and obtain a joint dynamic balance mechanism; S4, using the distributed dynamic optimization algorithm to iteratively optimize and improve the joint dynamic balance mechanism, and obtain a hydrogen-electricity combined energy storage system with multi-mode switching control.

2. A multi-mode switching control method suitable for a hydrogen-electricity combined energy storage system according to claim 1, characterized in that: The system architecture includes a wind and solar power generation unit, a hydrogen storage unit, an electricity storage unit and a load unit, wherein the hydrogen storage unit and the electricity storage unit are collectively referred to as an energy storage unit.

3. A multi-mode switching control method suitable for a hydrogen-electricity combined energy storage system according to claim 2, characterized in that: The method of coordinating the output power of the energy storage unit by using a dynamic collaborative optimization method based on virtual synchronous machine technology and variable droop coefficient includes: S2-1, in off-grid mode, the virtual inertia enhancement model is constructed as: , using a virtual inertia enhancement model to control the hydrogen storage unit; where, represents the output power of the hydrogen storage unit, represents the equivalent inertia constant, , Respectively represent the system real-time frequency and reference frequency, represents the damping coefficient; 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, Indicates the real-time voltage. represents the reference voltage, It represents the droop coefficient, and the adjustment formula of the droop coefficient is: , Indicates the reference voltage The actual voltage The difference between represents the initial droop coefficient, represents the adjustment factor of the initial droop factor; 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 in the storage unit, subject to constraints: , and Respectively represent the minimum and maximum storage capacity of the storage unit, , Respectively represent the charging power and discharging power of the storage unit, , Respectively represent the charging efficiency and discharging efficiency of the storage unit, Indicates time The amount of electricity in the hydrogen storage unit, and Respectively represent the charging power and discharging power of the hydrogen storage unit, and They represent the charging efficiency and discharging efficiency of the hydrogen storage unit, represents the time step; 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 power output, represents the voltage deviation penalty coefficient, Indicates the square of bus voltage deviation; S2-5, according to the balance condition of the energy storage system: , constrains the output power of the energy storage unit; where P load Represents the load power demand of the energy storage system.

4. A multi-mode switching control method suitable for a hydrogen-electricity combined energy storage system according to claim 3, characterized in that: The dynamic energy allocation model comprises: ;in, , Respectively represent the output power of the hydrogen storage unit and the electricity storage unit at time t, , Respectively represent the maximum output power limits of the hydrogen storage unit and the electricity storage unit, , Respectively represent the hydrogen storage unit and the electricity storage unit at time energy state, represents the total power demand of the energy storage system, Indicates time The load demand of the energy storage system is represents the energy conversion efficiency of the hydrogen storage unit, , Respectively represent the charging efficiency and discharging efficiency of the storage unit, represents the discharge power of the electric storage unit at time t, i represents the index of the hydrogen storage unit and the electric storage unit, Represents the time step.

5. A multi-mode switching control method applicable to a hydrogen-electricity combined energy storage system according to claim 4, characterized in that: The dynamic energy allocation constraint includes: The dynamic energy allocation constraint of the energy storage system is: ;in, Indicates time The output power of the hydrogen storage unit, Indicates time The output power of the storage unit, Indicates time The actual load demand of the energy storage system, Indicates time The interactive power between the energy storage system and the grid, where a positive value indicates the purchase of electricity from the grid, a negative value indicates the sale of electricity to the grid, and i indicates the index of the hydrogen storage unit and the electricity storage unit; The output power constraints of the hydrogen storage unit and the electricity storage unit are: , get the first power constraint; where, , Respectively represent the minimum and maximum output power of the hydrogen storage unit, , Respectively represent the minimum and maximum output power of the power storage unit; The energy balance constraints of the hydrogen storage unit and the electricity storage unit are: ; where i represents the index, represents the storage capacity of the hydrogen storage unit at time t, Indicates the power storage unit at time The storage capacity, represents the energy conversion efficiency of the hydrogen storage unit, represents the time step, Represents the discharge power of the storage unit at time t.

6. A multi-mode switching control method applicable to a hydrogen-electricity combined energy storage system according to claim 5, characterized in that: The optimization model based on cooperative game includes: S31-1, based on the cooperative game method, the power allocation objective function is constructed as follows: ;in, Indicates hydrogen storage order The running cost at the time, express The output power of the hydrogen storage unit at the moment, express The output power of the electric storage unit at the moment, N represents the total number of hydrogen storage units and electric storage units in the energy storage system, and i represents the energy storage unit index; S31-2, the constraints for constructing the power allocation objective function are: , and obtain the second power constraint; where, express The load demand power of the load unit at the moment; S31-3, adjusting the power allocation ratio of the hydrogen storage unit and the electricity storage unit according to the power allocation objective function and the constraints of the power allocation objective function, and performing collaborative optimization of the energy storage units.

7. A multi-mode switching control method applicable to a hydrogen-electricity combined energy storage system according to claim 6, characterized in that: The dynamic power regulation model includes: S32-1, construct the dynamic power regulation model as follows: ;in, and They represent the hydrogen storage unit adjustment coefficient and the electricity storage unit adjustment coefficient, respectively, and are used to balance the deviations between the hydrogen storage unit, the electricity storage unit and the overall power output of the system, respectively. T represents the total operating time of the energy storage system; S32-2, define the constraints of the dynamic power regulation model as: ; where ∆P HESS Indicates the change in the power output of the hydrogen storage unit between consecutive moments, ∆P ESS Indicates the change in power output of the storage unit between consecutive moments. , Respectively represent the minimum and maximum output power of the hydrogen storage unit, , denote the minimum and maximum output powers of the electric storage unit, respectively, and i denotes the index of the hydrogen storage unit and the electric storage unit; S32-3, adjusting the output power of the hydrogen storage unit and the electricity storage unit according to the dynamic power regulation model and the constraints of the dynamic power regulation model, and performing joint optimization of the energy storage units.

8. A multi-mode switching control method applicable to a hydrogen-electricity combined energy storage system according to claim 7, characterized in that: The combined dynamic balance mechanism comprises: S41-1, define the comprehensive optimization objective function of the energy storage system: ;in, , , They represent the weight coefficients of the optimization objectives, which are used to balance cost, efficiency and load demand. , They represent the operating costs of the hydrogen storage unit and the electricity storage unit, respectively, and T represents the total operating time of the energy storage system; S41-2, determine the power balance relationship of the energy storage system as: ; Among them, P gird Represents the power exchange between the grid and the energy storage system; S41-3, constructing the constraint condition of the comprehensive optimization objective function according to the first power constraint and the second power constraint: ;in, η t Indicates the power allocation ratio of the hydrogen storage unit.

9. A multi-mode switching control method applicable to a hydrogen-electricity combined energy storage system according to claim 8, characterized in that: The comprehensive optimization objective function is iteratively optimized through a distributed dynamic optimization algorithm, and when the iterative result meets the convergence condition: Or the maximum number of iterations is reached When , the iterative optimization is completed; represents the preset iterative convergence threshold, and k represents the number of iterations.

10. A multi-mode switching control method applicable to a hydrogen-electricity combined energy storage system according to claim 2, characterized in that: The multi-mode switching controlled hydrogen-electricity combined energy storage system comprises: S42-1, based on the optimized joint dynamic balance mechanism, the joint optimization objective function is obtained as follows: ;in, , , Represents the optimization weight coefficients, express The output power of the hydrogen storage unit at the moment, express The output power of the storage unit at the moment, express The load demand power of the load unit at the moment; S42-2, define the constraints of the joint optimization objective function, including: (1) Dynamic power allocation constraints: ;in, and Respectively represent the dynamic adjustment coefficients of the hydrogen storage unit and the electricity storage unit, and Respectively represent the changes in the output power of the hydrogen storage unit and the electricity storage unit between consecutive moments t; (2) Power range constraints of energy storage units: ;in, , They represent the minimum and maximum output power of the hydrogen storage unit at time t, , Respectively represent the minimum and maximum output power of the storage unit at time t; 3) Dynamic compensation of energy storage unit output power deviation: ;in, Indicates the dynamic regulation error tolerance range between power output and load demand; S42-3, using the distributed optimization algorithm to optimize the output power of the energy storage unit through a joint iterative optimization formula: Optimize and when the convergence condition is met: Or the maximum number of iterations is reached The optimization is stopped when , and a hydrogen-electricity combined energy storage system with multi-mode switching control is obtained; where represents the number of iterations, and T represents the total operating time of the energy storage system.

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