Control method and system of electric-hydrogen coupling system based on distributed model predictive control

Abstract

The control method and system of the electric-hydrogen coupling system based on distributed model predictive control collect the operation data of the electric-hydrogen hybrid energy storage system, establish the physical layer model and control layer model of the system; establish the joint constraint conditions of the system based on the physical layer model and the control layer model; establish the control conditions of the system operation scenario; according to the performance indicators of the electric energy storage system, the performance indicators of the hydrogen energy storage system and the control conditions of the system operation scenario, respectively establish a first target model representing the optimal performance of the electric energy storage system and a second target model representing the optimal performance of the hydrogen energy storage system; establish the control model of the electric-hydrogen coupling system with the first target model, the second target model and the joint constraint conditions; iteratively solve the control model of the electric-hydrogen coupling system to obtain the predicted values ​​of the operation parameters and the predicted values ​​of the control parameters of the system; control the system based on the predicted values ​​of the operation parameters and the predicted values ​​of the control parameters, give full play to the role of the hybrid energy storage system, and improve the new energy absorption capacity.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy system control, and specifically, relates to a study on a control strategy of an electric-hydrogen coupling system based on distributed model predictive control, and designs and implements a control strategy based on distributed model predictive control for electric-hydrogen hybrid energy storage. Background Art

[0002] As the penetration rate of renewable energy and DC loads continues to increase, the application of DC microgrids has become more extensive. At the same time, due to the high cost of power transmission in remote areas, the importance of electric-hydrogen hybrid energy storage systems has increased significantly. The access to energy storage systems can effectively support the stable operation of electric-hydrogen hybrid energy storage systems and improve the capacity to absorb new energy. Compared with a single type of energy storage, hybrid energy storage systems are usually composed of high energy density energy storage and high power density energy storage, which can take into account both large-capacity storage and rapid response. Hydrogen energy storage has the characteristics of high energy density, long life, easy storage and transportation, and is an important energy carrier for large-capacity storage. Compared with the traditional single-unit system in which hydrogen energy storage fuel cells and electrolysis hydrogen production devices are separated, the unitized regenerative fuel cell (URFC) system is highly integrated, using reversible stacks to achieve bidirectional conversion of electric energy and hydrogen, and has the advantage of higher energy density. In scenarios of rapid adjustment and frequent start and stop, URFC can be combined with high-power-density lithium batteries to form an electric-hydrogen hybrid energy storage system to achieve large-capacity rapid response. In order to give full play to the advantages of hybrid energy storage, smooth out power fluctuations and ensure system stability, existing technologies are controlled based on decentralized, centralized and distributed coordination strategies respectively.

[0003] Among them, the decentralized coordinated control method is implemented based on the local controller, including a method for realizing the coordination of hybrid electric-hydrogen energy storage based on the common DC bus voltage, matching high and low frequency loads with the energy storage response speed, but the bus voltage always has deviations. At the same time, decentralized control is difficult to implement complex control strategies due to the lack of communication links. Its computational complexity increases rapidly with the increase in the type and number of equipment, and its application scope is limited to a certain extent. Centralized control includes central processor control and local processor control, including variable weight model predictive control strategy, real-time change of energy storage grid controller power reference value, can maintain a good SOCe of the battery, but does not consider the voltage instability caused by the difference in the response speed of electric and hydrogen energy storage. There is also a deep reinforcement learning method to solve the problem of electricity and hydrogen coordination, but the computational burden is large and it is difficult to run in real time. In addition, centralized control needs to ensure real-time and fast communication between the central processor and the local processor, and there is a risk of single point failure, and the reliability is relatively low. Compared with decentralized and centralized control, each controller in distributed control works independently, avoiding the risk of single point failure, high redundancy, and can achieve voltage difference-free control. Distributed control regards the dispatchable source system as an intelligent body. Each dispatch source controller is linked through a communication network, receiving local information and collecting data from adjacent intelligent bodies, which can achieve collaborative control. The power distribution of electric-hydrogen energy storage is determined based on the marginal cost consensus (MCC) strategy to minimize the instantaneous power generation cost, but the service life of lithium batteries is not considered. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention provides a control method and system for an electric-hydrogen coupling system based on distributed model predictive control. In view of the problem that a single type of energy storage unit is difficult to meet the needs of smoothing new energy fluctuations and storing excess electric energy due to limitations such as response speed and capacity, it is proposed to combine URFC with an electric energy storage system to form short-term and long-term hybrid energy storage applications. At the same time, controllers based on distributed model predictive control (DMPC) are designed for the electric and hydrogen subsystems respectively, so as to achieve the goal of hydrogen energy storage supporting low-frequency loads and electric energy storage supporting high-frequency loads, reduce the number of charge and discharge cycles of electric energy storage, extend the service life of lithium batteries, give full play to the rapid response and large-capacity support of the hybrid energy storage system, and realize the improvement of the new energy consumption capacity of the electric-hydrogen hybrid energy storage system.

[0005] The present invention adopts the following technical solution.

[0006] The present invention proposes a control method for an electric-hydrogen coupling system based on distributed model predictive control, which is applicable to an electric-hydrogen hybrid energy storage system. The electric-hydrogen hybrid energy storage system includes an electric energy storage system and a hydrogen energy storage system; and includes:

[0007] Collect the operating data of the electric-hydrogen hybrid energy storage system, and establish the physical layer model and control layer model of the electric-hydrogen hybrid energy storage system; based on the physical layer model and control layer model of the electric-hydrogen hybrid energy storage system, establish the joint constraint conditions of the electric-hydrogen hybrid energy storage system;

[0008] Establish control conditions for the operation scenarios of the electric-hydrogen hybrid energy storage system;

[0009] According to the performance indicators of the electric energy storage system, the performance indicators of the hydrogen energy storage system and the control conditions of the operation scenario of the electric-hydrogen hybrid energy storage system, a first target model representing the optimal performance of the electric energy storage system and a second target model representing the optimal performance of the hydrogen energy storage system are respectively established; a control model of the electric-hydrogen coupling system is established with the first target model, the second target model and the joint constraint conditions;

[0010] The control model of the electric-hydrogen coupling system is solved iteratively to obtain the predicted values ​​of the operating parameters and the predicted values ​​of the control parameters of the electric-hydrogen hybrid energy storage system; based on the predicted values ​​of the operating parameters and the predicted values ​​of the control parameters, the electric-hydrogen hybrid energy storage system is controlled.

[0011] Preferably, collecting the operating data of the electric-hydrogen hybrid energy storage system and establishing a physical layer model and a control layer model of the electric-hydrogen hybrid energy storage system include:

[0012] Based on system graph theory, the physical layer and control layer of the electric-hydrogen hybrid energy storage system are established; the control layer adopts a hierarchical control architecture;

[0013] Collecting the operating data of the electric-hydrogen hybrid energy storage system to establish a physical layer model of the electric-hydrogen hybrid energy storage system and a control layer model of the electric-hydrogen hybrid energy storage system; the control layer model includes: an electric energy storage dynamic model, a hydrogen energy storage dynamic model, an electric energy storage state model, and a hydrogen energy storage state model;

[0014] Preferably, the physical layer model satisfies the following relationship:

[0015] ,

[0016] In the formula, For the period No. The output power of distributed power sources is For the period No. The output voltage of the distributed power supply is For the period No. The current on the DC bus side of a distributed power supply, For the period No. The voltage estimation value of the line coupling resistance corresponding to each distributed power source satisfies: , For the The line coupling resistance corresponding to each distributed power source.

[0017] Preferably, based on the droop control, an electric energy storage dynamic model is established according to the dynamic behavior of the electric energy storage, satisfying the following relationship:

[0018] ,

[0019] In the formula, is the system rated voltage, For the period No. The voltage setting value of each energy storage based on droop control, For the period No. The output power of the electric energy storage The droop coefficient, For the period No. The electric energy storage is used to realize the secondary control signal of average voltage recovery, battery state of charge balance, and battery state of charge recovery;

[0020] Based on VSG control, a hydrogen energy storage dynamic model is established according to the dynamic behavior of hydrogen energy storage, satisfying the following relationship:

[0021] ,

[0022] In the formula, is the system rated power, For the period No. The output power of hydrogen energy storage is For the period No. The voltage setting value of each hydrogen storage is based on the VSG control. For the period No. The hydrogen energy storage is used to realize the secondary control signal of average voltage recovery, hydrogen storage tank capacity state balance, and hydrogen storage tank capacity state recovery. is the damping coefficient, is the virtual inertia;

[0023] The electric energy storage state model satisfies the following relationship:

[0024] ,

[0025] In the formula, For the period Battery charge status, For the initial period Battery charge status, is the battery output power, is the battery capacity (in Ah), is the battery voltage, which remains unchanged during a calculation period;

[0026] The hydrogen energy storage state model satisfies the following relationship:

[0027] ,

[0028] In the formula, For the period The capacity status of the hydrogen storage tank, For the initial period The capacity status of the hydrogen storage tank, and are the gas constant and Faraday constant, respectively. is the number of electrons transferred per reaction, is the hydrogen storage tank temperature, is the volume of the hydrogen storage tank, is the upper limit of hydrogen storage tank pressure, is the hydrogen production efficiency, Output power for the hydrogen storage tank, is the URFC voltage, which remains unchanged during a calculation period.

[0029] Preferably, based on the physical layer model and control layer model of the electric-hydrogen hybrid energy storage system, a joint constraint condition of the electric-hydrogen hybrid energy storage system is established, including:

[0030] Discretize the physical layer model and the control layer model;

[0031] Based on the discretized physical layer model, electric energy storage dynamic model and hydrogen energy storage dynamic model, the joint constraints of the electric-hydrogen hybrid energy storage system are established; the joint constraints of the electric-hydrogen hybrid energy storage system include: equality constraints and inequality constraints;

[0032] Among them, the equality constraints include: process constraints on the average output voltage, terminal constraints on the average output voltage, average SOC constraints on the electric energy storage, and average SOC constraints on the hydrogen energy storage; the inequality constraints include: constraints on the output voltage of distributed power sources, constraints on the output power of distributed power sources, SOC safety range, constraints on the control action change rate, and constraints on the response speed.

[0033] Preferably, the process constraint condition of the output voltage average value satisfies the following relationship:

[0034] ,

[0035] In the formula, At the sampling time No. The average value of the output voltage of the distributed power supply, At the sampling time No. The output voltage of the distributed power supply is At the sampling time Characterization The first distributed power source and the The constant of the communication channel between distributed generation sources, At the sampling time No. The output voltage of a distributed power supply; It is a collection of distributed power sources;

[0036] The terminal constraint condition of the output voltage average value satisfies the following relationship:

[0037] ,

[0038] In the formula, At the sampling time No. The average value of the output voltage of the distributed power supply, The number of samples in the time domain is controlled for prediction;

[0039] Based on the local approximation algorithm, the average SOC constraint conditions of the electric energy storage and the average SOC constraint conditions of the hydrogen energy storage both satisfy the following relationship:

[0040] ,

[0041] In the formula, At the sampling time No. The type is The average SOC value of the distributed power supply, The first distributed power source and the The types of distributed power sources are the same. represents the electric energy storage system, Represents hydrogen energy storage system, distributed power source collection , electric energy storage system collection , Hydrogen Energy Storage System Collection , is the number of electric energy storage systems, is the number of distributed generation sources.

[0042] Preferably, the constraint condition of the output voltage of the distributed power supply satisfies the following relationship:

[0043] ,

[0044] In the formula, , For the The lower and upper limits of the output voltage of a distributed power supply, At the sampling time No. The output voltage of a distributed power supply;

[0045] The constraints of the distributed power output power satisfy the following relationship:

[0046] ,

[0047] In the formula, , Respectively The lower and upper limits of the output power of a distributed power generation unit, At the sampling time No. Output power of distributed power sources;

[0048] The SOC safety range satisfies the following relationship:

[0049] ,

[0050] In the formula, , Respectively The type is The lower and upper limits of the distributed power supply SOC, At the sampling time No. The type is SOC of distributed power supply;

[0051] The constraint condition of the change rate of the secondary control signal satisfies the following relationship:

[0052] ,

[0053] In the formula, , Respectively The type is The lower and upper limits of the rate of change of the secondary control signal of the distributed power supply, At the sampling time No. The type is The rate of change of the secondary control signal of the distributed power source;

[0054] 5) The response speed constraint condition satisfies the following relationship:

[0055] ,

[0056] In the formula, , Respectively The lower and upper limits of the response speed of a distributed power source, At the sampling time No. The response speed of a distributed power source.

[0057] Preferably, the control conditions for establishing the operation scenario of the electric-hydrogen hybrid energy storage system include:

[0058] The first Boolean variable is used as a start-stop control variable of the hydrogen energy storage system, and the second Boolean variable is used as a SOC warning variable of the electric energy storage system;

[0059] Establish the initial judgment conditions for scenario triggering of the electric-hydrogen hybrid energy storage system;

[0060] Based on the first Boolean variable, the second Boolean variable and the scene triggering initial judgment condition of the electric-hydrogen hybrid energy storage system, the control conditions of the operation scene of the electric-hydrogen hybrid energy storage system are determined, wherein the control conditions include scene maintenance conditions and scene switching conditions.

[0061] Preferably, when the first Boolean variable is 1, it indicates that the hydrogen energy storage system is started, and when the first Boolean variable is 0, it indicates that the hydrogen energy storage system is shut down;

[0062] When the second Boolean variable is 1, it indicates that the SOC of the electric energy storage system is in a warning state, and when the second Boolean variable is 0, it indicates that the SOC of the electric energy storage system is normal;

[0063] The initial judgment conditions for triggering the scenario of the electric-hydrogen hybrid energy storage system are: ,in is the error limit, Indicates the total power of the electric energy storage system; if the initial judgment conditions for the scenario trigger are met, it indicates that the electric-hydrogen hybrid energy storage system is in a steady-state normal operation state; otherwise, it indicates that the hydrogen energy storage system is in a shutdown state or the hydrogen energy storage system has transient fluctuations.

[0064] Preferably, the operation scenarios of the electric-hydrogen hybrid energy storage system include but are not limited to: normal operation scenario, hydrogen energy storage startup scenario, and electric energy storage warning scenario;

[0065] The scene switching conditions of the normal operation scene include: when the scene trigger initial judgment condition is met, and the first Boolean variable is 1 and the second Boolean variable is 0;

[0066] The scene maintenance condition of the normal operation scene includes: when the scene switching condition of the normal operation scene is met, and the first Boolean variable is 1 and the second Boolean variable is 0;

[0067] The scene switching conditions of the hydrogen energy storage startup scene include: when the initial judgment condition of the scene trigger is met, and the second Boolean variable is 0, the first Boolean variable changes from 0 to 1 after a delay;

[0068] The scene maintenance condition of the hydrogen energy storage startup scene includes: when the scene switching condition of the hydrogen energy storage startup scene is met, and the first Boolean variable is 1 and the second Boolean variable is 0;

[0069] The scene switching condition from the hydrogen energy storage startup scene to the electric energy storage warning scene includes: when the scene switching condition of the hydrogen energy storage startup scene is met but the scene triggering initial judgment condition is not met, and the second Boolean variable changes from 0 to 1;

[0070] The scene switching conditions from the electric energy storage warning scene to the normal operation scene include: when the scene switching conditions of the hydrogen energy storage startup scene are met and the scene triggering initial judgment conditions are met, and the second Boolean variable changes from 1 to 0.

[0071] Preferably, according to the performance indicators of the electric energy storage system, the performance indicators of the hydrogen energy storage system and the control conditions of the operation scenario of the electric-hydrogen hybrid energy storage system, a first target model characterizing the optimal performance of the electric energy storage system and a second target model characterizing the optimal performance of the hydrogen energy storage system are respectively established, including:

[0072] Based on the cost function model, according to the performance indicators of the electric energy storage system and the control conditions of the operation scenario of the electric-hydrogen hybrid energy storage system, a first objective model that characterizes the optimal performance of the electric energy storage system is established;

[0073] Based on the cost function model, according to the performance indicators of the hydrogen energy storage system and the control conditions of the operation scenario of the electric-hydrogen hybrid energy storage system, a second objective model that characterizes the optimal performance of the hydrogen energy storage system is established;

[0074] Based on the sparse communication consistency algorithm, the battery state of charge between each electric energy storage system is optimized, and the first target model is updated with the optimized battery state of charge balance index;

[0075] The control model of the electric-hydrogen coupling system is established based on the updated first objective model, second objective model and joint constraints.

[0076] Preferably, the performance indicators of the electric energy storage system include: an average voltage recovery indicator, a battery power state balance indicator, an output power recovery indicator, a battery power state recovery indicator, and a control performance indicator;

[0077] Based on the cost function model, the linear weighted method is adopted, and the first Boolean variable and the second Boolean variable are introduced. The first objective model that characterizes the optimal performance of the electric energy storage system satisfies the following relationship:

[0078] ,

[0079] In the formula, At the sampling time No. The cost function of an electric energy storage system is , , , , All are The weight of the cost function of each electric energy storage system; The number of samples in the prediction control time domain; the electric energy storage system collection , is the number of electric energy storage systems; the first Boolean variable As the start-stop control variable of the hydrogen energy storage system, the second Boolean variable As a SOC warning variable for electric energy storage systems;

[0080] middle, At the sampling time No. The average output voltage of the energy storage system, is the system rated voltage, the first term is the number of samples in the predictive control time domain Neidi The average output voltage recovery value of each electric energy storage system is the predicted value of the average voltage recovery index;

[0081] middle, At the sampling time No. The SOC of an electric energy storage system, At the sampling time No. The SOC of the energy storage system Electric energy storage system The balance is updated only with the predicted values ​​transmitted from the neighboring energy storage system, which depends on the Communication channel constants ; At the sampling time No. The electric energy storage system and The sparse communication factor between the energy storage systems, the energy storage system set , is the number of electric energy storage systems; the second is the battery state of charge balance indicator;

[0082] middle, At the sampling time No. The output power of the electric energy storage system is obtained by introducing the control conditions of the operation scenario of the electric-hydrogen hybrid energy storage system. The third term represents the output power of the electric energy storage system. The minimum output power of the energy storage system, and the third item is the output power recovery index;

[0083] middle, For the prediction time No. The average SOC value of the energy storage system is is the recovery reference value of SOC; the fourth item is Electric energy storage system The recovery item is a battery power status recovery indicator;

[0084] middle, At the sampling time No. The fifth item is the rate of change of the secondary control signal of the energy storage system. The minimum value of the control action change rate of an electric energy storage system is a control performance indicator.

[0085] Preferably, the performance indicators of the hydrogen energy storage system include: average voltage recovery indicator, control performance indicator;

[0086] Based on the cost function model, the linear weighted method is adopted, and the first Boolean variable and the second Boolean variable are introduced. The second objective model that characterizes the optimal performance of the hydrogen energy storage system satisfies the following relationship:

[0087] ,

[0088] In the formula, At the sampling time No. The cost function of a hydrogen energy storage system is: , All are The weight of the cost function of each hydrogen energy storage system;

[0089] middle, At the sampling time No. The average output voltage of the hydrogen energy storage system, is the system rated voltage, the first term is the number of samples in the predictive control time domain Neidi The average output voltage recovery value of each hydrogen energy storage system is the predicted value of the average voltage recovery index;

[0090] middle, At the sampling time No. The second term is the change rate of the secondary control signal of the hydrogen energy storage system. The minimization value of the control action change rate of a hydrogen energy storage system is the control performance indicator.

[0091] Preferably, based on the sparse communication consistency algorithm, the SOC balancing optimization between the electric energy storage systems satisfies the following relationship:

[0092] ,

[0093] In the formula, At the sampling time No. The predicted value of the SOC of an electric energy storage system, At the sampling time No. The actual value of the SOC of the energy storage system, For the The electric energy storage system and Sparse communication factor between electric energy storage systems; is the convergence coefficient; At the sampling time Characterization The first distributed power source and the The constant of the communication channel between distributed power sources.

[0094] Preferably, iteratively solving the electric-hydrogen coupling system control model to obtain the predicted values ​​of the operating parameters and the predicted values ​​of the control parameters of the electric-hydrogen hybrid energy storage system includes:

[0095] Iteratively solve the control model of the electric-hydrogen coupling system to obtain the predicted time The control parameter prediction values ​​of the electric-hydrogen hybrid energy storage system include: prediction time No. The rate of change of the secondary control signal of the energy storage system and prediction time No. The change rate of the secondary control signal of a hydrogen energy storage system ;

[0096] Based on the prediction time The predicted value of the control parameter of the electric-hydrogen hybrid energy storage system is iteratively solved to solve the control model of the electric-hydrogen coupling system and obtain the predicted time The predicted values ​​of the operating parameters of the electric-hydrogen hybrid energy storage system include: No. Prediction of the average output voltage of an energy storage system , predict the time No. Predicted value of the output voltage of a distributed power source , predict the time No. The type is The predicted value of the average SOC value of the distributed power supply , predict the time No. The type is Prediction value of distributed power supply SOC , predict the time No. The predicted value of the output power of distributed generation ;in, , Controls the number of time-domain samples for prediction.

[0097] The present invention also proposes a control system for an electric-hydrogen coupling system based on distributed model predictive control, comprising: a joint constraint condition establishment module, an operation scenario control condition establishment module, an electric-hydrogen coupling system control model establishment module, and a prediction value solution module;

[0098] A joint constraint condition establishment module is used to collect the operating data of the electric-hydrogen hybrid energy storage system and establish the physical layer model and control layer model of the electric-hydrogen hybrid energy storage system; based on the physical layer model and control layer model of the electric-hydrogen hybrid energy storage system, establish the joint constraint conditions of the electric-hydrogen hybrid energy storage system;

[0099] An operation scenario control condition establishment module is used to establish control conditions for the operation scenarios of the electric-hydrogen hybrid energy storage system;

[0100] The electric-hydrogen coupling system control model establishment module is used to establish a first target model representing the optimal performance of the electric energy storage system and a second target model representing the optimal performance of the hydrogen energy storage system according to the performance indicators of the electric energy storage system, the performance indicators of the hydrogen energy storage system and the control conditions of the electric-hydrogen hybrid energy storage system operation scenario; and establish the electric-hydrogen coupling system control model with the first target model, the second target model and the joint constraint conditions;

[0101] The prediction value solving module is used to iteratively solve the electric-hydrogen coupling system control model to obtain the operating parameter prediction values ​​and control parameter prediction values ​​of the electric-hydrogen hybrid energy storage system; based on the operating parameter prediction values ​​and control parameter prediction values, the electric-hydrogen hybrid energy storage system is controlled.

[0102] A terminal comprises a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0103] A computer-readable storage medium stores a computer program, which implements the steps of the method when executed by a processor.

[0104] The beneficial effect of the present invention is that, compared with the prior art, at least hydrogen energy, which will occupy an important position in the future energy system, is taken into account, and a distributed model predictive control method for electric-hydrogen hybrid energy storage is proposed for the electric-hydrogen hybrid energy storage system, which makes full use of the characteristics of fast response speed of electric energy storage and high capacity density of hydrogen energy storage, and designs electric energy storage to support high-frequency loads under normal working conditions, while hydrogen energy storage supports low-frequency loads, thereby reducing the number of charge and discharge times of electric energy storage and extending its service life. At the same time, the cold and hot start characteristics of hydrogen energy storage are also taken into account, and the electric energy storage is designed to support the load alone during the switching process of hydrogen energy storage mode, so as to realize the effectiveness of the proposed control strategy under different working conditions. In addition, the present invention designs hydrogen energy storage to use virtual synchronous machine (VSG) control, so that it is more in line with the characteristics of slow response speed of hydrogen energy storage, thereby improving the inertia and stability of the system. In this way, efficient coordination of hybrid energy storage is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] Figure 1 It is a flow chart of a control method of an electric-hydrogen coupling system based on distributed model predictive control proposed by the present invention.

[0106] FIG2 is a simulation result diagram of a battery energy storage deep charging area in an embodiment of the present invention, wherein FIG2(a) is a simulation result diagram of a microgrid unit output power in a battery energy storage deep charging area, FIG2(b) is a simulation result diagram of a dispatchable distributed power supply output voltage and average output voltage in a battery energy storage deep charging area, and FIG2(c) is a battery charge state in a battery energy storage deep charging area;

[0107] FIG3 is a diagram showing the simulation results of the deep discharge area of ​​the battery energy storage in an embodiment of the present invention, wherein FIG3 (a) is a diagram showing the simulation results of the output power of the microgrid unit in the deep discharge area of ​​the battery energy storage, FIG3 (b) is a diagram showing the simulation results of the output voltage and average output voltage of the dispatchable distributed power supply in the deep discharge area of ​​the battery energy storage, and FIG3 (c) is a diagram showing the battery charge state in the deep discharge area of ​​the battery energy storage. DETAILED DESCRIPTION

[0108] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0109] The present invention proposes a control method for an electric-hydrogen coupling system based on distributed model predictive control, which is applicable to electric-hydrogen hybrid energy storage systems, such as Figure 1 As shown, including:

[0110] Step 1: Collect the operating data of the electric-hydrogen hybrid energy storage system, and establish a physical layer model and a control layer model of the electric-hydrogen hybrid energy storage system; based on the physical layer model and the control layer model of the electric-hydrogen hybrid energy storage system, establish joint constraints of the electric-hydrogen hybrid energy storage system.

[0111] Specifically, step 1 includes:

[0112] Step 1.1, based on system graph theory, establish the physical layer and control layer of the electric-hydrogen hybrid energy storage system; the control layer adopts a hierarchical control architecture;

[0113] Specifically, the electric-hydrogen hybrid energy storage system includes photovoltaics, batteries, unitized renewable fuel cells (URFC), loads and their interface converters, and distributed control units. The electric-hydrogen hybrid energy storage system is connected to the island DC microgrid bus through the interface converter and filter. Among them, photovoltaics are non-dispatched distributed generation (DG), which works in grid-following mode under maximum power point tracking control to maximize the use of renewable energy. Lithium batteries and URFC are both dispatchable DGs, which work in grid-building mode under a hierarchical control framework to achieve functions such as precise voltage recovery and power distribution.

[0114] Specifically, the electric-hydrogen hybrid energy storage system includes DG, the DG set is expressed as , DG set It is a collection of electric energy storage systems and hydrogen energy storage system integration The union of is the number of electric energy storage systems that meet . Communication topology of electric-hydrogen hybrid energy storage system is undirectedly connected, and the set of communication links is expressed as ; Among them, the adjacency matrix By characterization The first distributed power source and the The constant of the communication channel between distributed power sources constitute, Indicates that the communication channel exists. indicates that the communication channel does not exist; therefore, the Laplacian matrix in the undirected graph satisfies the following relationship:

[0115] ,

[0116] In the formula, Indicates The number of neighbor nodes of a node, where No. The node corresponds to Distributed power sources to meet .

[0117] Step 1.2, establishing a physical layer model of the electric-hydrogen hybrid energy storage system based on the operating data of the electric-hydrogen hybrid energy storage system;

[0118] Specifically, in the physical layer of the electric-hydrogen hybrid energy storage system, the DG is connected to the DC bus through a DC / DC interface converter, a capacitor filter, a line coupling resistor, etc. The physical layer collects state quantity signals in real time and sends them to the control layer for calculation. The physical layer model is established with the dispatchable DG output power, satisfying the following relationship:

[0119] ,

[0120] In the formula, For the period No. The output power of distributed power sources is For the period No. The output voltage of the distributed power supply is For the period No. The current on the DC bus side of a distributed power supply, For the period No. The voltage estimation value of the line coupling resistance corresponding to each distributed power source satisfies: , For the The line coupling resistance corresponding to each distributed power source.

[0121] Step 1.3, establishing a control layer model of the electric-hydrogen hybrid energy storage system according to the operating data of the electric-hydrogen hybrid energy storage system; the control layer model includes: an electric energy storage dynamic model, a hydrogen energy storage dynamic model, an electric energy storage state model, and a hydrogen energy storage state model;

[0122] In the primary control layer of the electric-hydrogen hybrid energy storage system, both electric energy storage and hydrogen energy storage are designed as grid-type control, using droop control and virtual synchronous generator (VSG) control respectively to provide virtual inertia for the isolated microgrid, supporting voltage stability and reliable power supply. The VP characteristic curves of droop control and VSG control are both represented in the form of affine functions; current and voltage control, droop control or VSG control, and sinusoidal pulse width modulation constitute the primary control layer. Since current and voltage control has a faster response speed than droop control or VSG control, the dynamic behavior of each energy storage in the primary control layer is mainly determined by droop control or VSG control.

[0123] Specifically, based on droop control, an electric energy storage dynamic model is established according to the dynamic behavior of the electric energy storage, satisfying the following relationship:

[0124] ,

[0125] In the formula, is the rated voltage of the system, For the period No. The voltage setting value of each energy storage based on droop control, For the period No. The output power of the electric energy storage The droop coefficient, For the period No. The energy storage is used to realize the secondary control signal of average voltage recovery, battery state of charge balance and battery state of charge recovery.

[0126] Specifically, based on VSG control, a hydrogen energy storage dynamic model is established according to the dynamic behavior of hydrogen energy storage, satisfying the following relationship:

[0127] ,

[0128] In the formula, is the system rated power, For the period No. The output power of hydrogen energy storage is For the period No. The voltage setting value of each hydrogen storage is based on the VSG control. For the period No. The hydrogen energy storage is used to realize the secondary control signal of average voltage recovery, hydrogen storage tank capacity state balance, and hydrogen storage tank capacity state recovery. is the damping coefficient, is the virtual inertia.

[0129] The control layer of the electric-hydrogen hybrid energy storage system includes but is not limited to a primary control layer and a secondary control layer. Therefore, the electric energy storage dynamic model and the hydrogen energy storage dynamic model of the electric-hydrogen hybrid energy storage system both include secondary control signals.

[0130] The electric energy storage state model satisfies the following relationship:

[0131] ,

[0132] In the formula, For the period Battery charge status, For the initial period Battery charge status, is the battery output power, is the battery capacity (in Ah), is the battery voltage, which remains unchanged during a calculation period.

[0133] The hydrogen energy storage state model satisfies the following relationship:

[0134] ,

[0135] In the formula, For the period The capacity status of the hydrogen storage tank, For the initial period The capacity status of the hydrogen storage tank, and are the gas constant and Faraday constant, respectively. is the number of electrons transferred per reaction, is the hydrogen storage tank temperature, is the volume of the hydrogen storage tank, is the upper limit of hydrogen storage tank pressure, is the hydrogen production efficiency, Output power for the hydrogen storage tank, is the URFC voltage, which remains unchanged during a calculation period.

[0136] The energy storage state model includes: an electric energy storage state model and a hydrogen energy storage state model; therefore, the energy storage state model satisfies the following relationship:

[0137] ,

[0138] In the formula, is the electric energy storage coefficient, satisfying ; is the hydrogen storage coefficient, satisfying ;

[0139] use Characterize the energy storage state model.

[0140] Step 1.4, based on the physical layer model and control layer model of the electric-hydrogen hybrid energy storage system, establish the joint constraint conditions of the electric-hydrogen hybrid energy storage system;

[0141] Specifically, step 1.4 includes:

[0142] Step 1.4.1, discretize the physical layer model and the control layer model;

[0143] After discretization of the physical layer model, the following relationship is obtained:

[0144] ,

[0145] In the formula, , At the sampling time , No. The output power of distributed power sources is , At the sampling time , No. The output voltage of the distributed power supply is For the The line coupling resistance corresponding to each distributed power source is: At the sampling time No. The voltage estimation value of the line coupling resistance corresponding to each distributed power source.

[0146] After discretization of the electric energy storage dynamic model and the hydrogen energy storage dynamic model, the following relationship is obtained:

[0147] ,

[0148] In the formula, , At the sampling time , No. The voltage setting value of each energy storage based on droop control, , At the sampling time , No. The output power of the electric energy storage is At the sampling time No. The energy storage is used to realize the secondary control signal of average voltage recovery, battery state of charge balance and battery state of charge recovery. The change in the amount of satisfy ;

[0149] At the sampling time No. The voltage setting value of each hydrogen storage is based on the VSG control. At the sampling time No. Hydrogen energy storage output power, At the sampling time No. The hydrogen storage is used to realize the secondary control signal of average voltage recovery, hydrogen storage tank capacity state balance, and hydrogen storage tank capacity state recovery. The change in the amount of satisfy ;

[0150] The system control problem is converted into the rate of change of the secondary control signal of the electric energy storage And hydrogen energy storage secondary control signal change rate The control optimization problem is solved to eliminate the steady-state error.

[0151] Step 1.4.2, based on the discretized physical layer model, the electric energy storage dynamic model and the hydrogen energy storage dynamic model, establish the joint constraints of the electric-hydrogen hybrid energy storage system; the joint constraints of the electric-hydrogen hybrid energy storage system include: equality constraints and inequality constraints;

[0152] Among them, the equation constraints include: process constraints of the average output voltage, terminal constraints of the average output voltage, average electric energy storage SOC constraints, and average hydrogen energy storage SOC constraints;

[0153] Specifically, the equation constraints of the electric-hydrogen hybrid energy storage system are established, including:

[0154] 1) Sampling time As the reference time, obtain the Distributed power supply output voltage , No. The type is SOC value of distributed power supply , No. Distributed power output power , For the The type is The voltage of the distributed power supply, where Represents electrical energy storage, Represents hydrogen energy storage; the various values ​​at the reference time are the initial constraint values ​​of the model predictive control.

[0155] 2) The process constraint condition of the output voltage average value satisfies the following relationship:

[0156] ,

[0157] In the formula, At the sampling time No. The average value of the output voltage of the distributed power supply, At the sampling time No. The output voltage of the distributed power supply is At the sampling time Characterization The first distributed power source and the The constant of the communication channel between distributed generation sources, At the sampling time No. The output voltage of a distributed power supply; A collection of distributed power sources.

[0158] 3) The terminal constraint condition of the output voltage average value satisfies the following relationship:

[0159] ,

[0160] In the formula, At the sampling time No. The average value of the output voltage of the distributed power supply, Controls the number of time-domain samples for prediction.

[0161] It means that the average output voltage converges to the rated voltage value at the end of the predictive control time domain.

[0162] 4) The average SOC constraint conditions of electric energy storage and the average SOC constraint conditions of hydrogen energy storage both satisfy the following relationship:

[0163] ,

[0164] In the formula, At the sampling time No. The type is The SOC value of the distributed power supply, For type The coefficient of distributed power generation, is the switching cycle, At the sampling time No. Output power of distributed power sources;

[0165] Based on the local approximation algorithm, the average SOC constraint conditions of the electric energy storage and the average SOC constraint conditions of the hydrogen energy storage both satisfy the following relationship:

[0166] ,

[0167] In the formula, At the sampling time No. The type is The average SOC value of the distributed power supply, The first distributed power source and the The types of distributed power sources are the same. represents the electric energy storage system, Represents hydrogen energy storage system, distributed power source collection , electric energy storage system collection , Hydrogen Energy Storage System Collection , is the number of electric energy storage systems, is the number of distributed generation sources.

[0168] It can be seen that the process constraints of the output voltage average value, the electric energy storage SOC average value constraints and the hydrogen energy storage SOC average value constraints all depend on the constants of the communication channel. .

[0169] In order to limit the solution space and improve the transient response of the controller, the inequality constraints of the electric-hydrogen hybrid energy storage system are established, including: the constraints of the output voltage of the distributed power source, the constraints of the output power of the distributed power source, the SOC safety range, the constraints of the control action change rate, and the constraints of the response speed;

[0170] 1) The constraints of the output voltage of the distributed power supply satisfy the following relationship:

[0171] ,

[0172] In the formula, , For the The lower and upper limits of the output voltage of a distributed power supply, At the sampling time No. The output voltage of a distributed power supply;

[0173] 2) The constraints of the output power of distributed power sources satisfy the following relationship:

[0174] ,

[0175] In the formula, , Respectively The lower and upper limits of the output power of a distributed power generation unit, At the sampling time No. Output power of distributed power sources;

[0176] 3) SOC safety range satisfies the following relationship:

[0177] ,

[0178] In the formula, , Respectively The type is The lower and upper limits of the distributed power supply SOC, At the sampling time No. The type is SOC of distributed power supply;

[0179] 4) The constraint condition of the change rate of the secondary control signal satisfies the following relationship:

[0180] ,

[0181] In the formula, , Respectively The type is The lower and upper limits of the rate of change of the secondary control signal of the distributed power supply, At the sampling time No. The type is The rate of change of the secondary control signal of the distributed power source;

[0182] 5) The response speed constraint condition satisfies the following relationship:

[0183] ,

[0184] In the formula, , Respectively The lower and upper limits of the response speed of a distributed power source, At the sampling time No. The response speed of a distributed power source.

[0185] Step 2: Establish control conditions for the operation scenario of the electric-hydrogen hybrid energy storage system.

[0186] Specifically, step 2 includes:

[0187] Step 2.1, take the first Boolean variable As the start-stop control variable of the hydrogen energy storage system, the second Boolean variable As the SOC warning variables of the electric energy storage system, they satisfy the following relationship:

[0188] ,

[0189] In a non-limiting preferred embodiment, an upper hysteresis comparator and a lower hysteresis comparator are provided. Greater than the upper hysteresis comparator high threshold or less than the lower threshold of the lower hysteresis comparator When Not less than the lower threshold of the hysteresis comparator and is not greater than the high threshold of the upper hysteresis comparator The warning can be lifted in time to avoid frequent switching of the warning status of the electric energy storage system.

[0190] Step 2.2, establishing the initial judgment conditions for triggering the scenario of the electric-hydrogen hybrid energy storage system;

[0191] Specifically, the initial judgment condition for scene triggering is: ,in is the error limit, Indicates the total power of the electric energy storage system. If the initial judgment conditions for the scene trigger are met, it indicates that the electric-hydrogen hybrid energy storage system is in a steady and normal operating state. Otherwise, it indicates that the hydrogen energy storage system is in a shutdown state or the hydrogen energy storage system has transient fluctuations.

[0192] Step 2.3, based on the first Boolean variable, the second Boolean variable and the scene triggering initial judgment condition of the electric-hydrogen hybrid energy storage system, determine the control conditions of the operation scene of the electric-hydrogen hybrid energy storage system, wherein the control conditions include scene maintenance conditions and scene switching conditions.

[0193] In a non-limiting preferred embodiment, the operation scenarios of the electric-hydrogen hybrid energy storage system include, but are not limited to: normal operation scenario, hydrogen energy storage startup scenario, electric energy storage warning scenario;

[0194] Specifically, the scene switching conditions of the normal operation scene include: when the scene trigger initial judgment condition is met, and the first Boolean variable 1, the second Boolean variable is 0.

[0195] Specifically, the scene maintenance condition of the normal operation scene includes: when the scene switching condition of the normal operation scene is met, and the first Boolean variable Always 1, second Boolean variable Always 0.

[0196] In the normal operation scenario, the photovoltaic power generation / load changes suddenly, but the hydrogen storage working mode remains unchanged, and the energy storage system works within the operating constraints, in which the hydrogen energy storage is responsible for supporting the load and the electric energy storage is responsible for smoothing the fluctuation. In this scenario, it is considered that the electric energy storage system The change is not big, and the situation of over-limit leading to warning is not considered, but its balance will be considered in this scenario, because the system operation state is relatively stable at this time, and the prediction and optimization will be more accurate. In this scenario, the Boolean variable Always 1, Always 0. When the hydrogen energy storage system needs to be shut down for maintenance or switch working mode, it will enter the hydrogen energy storage startup scene.

[0197] Specifically, the scene switching conditions of the hydrogen energy storage startup scene include: when the scene trigger initial judgment conditions are met, and the second Boolean variable 0, the first Boolean variable after the delay Changes from 0 to 1.

[0198] Specifically, the scene maintenance conditions of the hydrogen energy storage startup scene include: when the scene switching conditions of the hydrogen energy storage startup scene are met, and the first Boolean variable Always 1, second Boolean variable Always 0.

[0199] Before entering the hydrogen energy storage startup scenario Set to 0. There are two possible hydrogen energy storage startup situations: 1) Cold start: After long-term maintenance or long-term shutdown, the URFC system needs to be started from a cold state, which takes about 30 seconds; 2) Hot start: Due to a sudden change in load demand or photovoltaic power generation, the URFC system needs to switch the working mode. The hot start time is about 3 seconds. At this time, the equipment consumes 5% of the rated power to maintain the preheating state. When the gas concentration, temperature and other conditions are adjusted, the hot start is completed. After the cold / hot start is completed, that is, after a certain delay Set to 1.

[0200] Specifically, the scene switching conditions from the hydrogen energy storage startup scene to the electric energy storage warning scene include: when the scene switching conditions of the hydrogen energy storage startup scene are met but the scene triggering initial judgment conditions are not met, and the second Boolean variable Changes from 0 to 1.

[0201] During the cold / hot start of the URFC system, the electric energy storage system alone supports the load, which may cause changes and triggers an alert, If it changes from 1 to 0 and the initial judgment conditions for triggering the scene are not met, the electric energy storage warning scene will be entered.

[0202] Specifically, the scene switching conditions from the electric energy storage warning scene to the normal operation scene include: when the scene switching conditions of the hydrogen energy storage startup scene are met and the scene triggering initial judgment conditions are met, and the second Boolean variable From 1 to 0. In this scenario, Set to 1. At this point, the URFC system is within the power constraint and generates or absorbs more power to support the load and restore ,avoid Exceed the safety range, enter the protection state and lose the ability to respond quickly.

[0203] When the second Boolean variable From 1 to 0, and meet the requirements of the electric energy storage system When the status warning conditions are met, the hydrogen energy storage startup scene is switched to the electric energy storage warning scene; when the electric energy storage system Status warning condition, and the second Boolean variable When it changes from 0 to 1, it switches from the electric energy storage warning scene to the normal operation scene. Restore to and hour, Set to 0, the electric energy storage system returns to normal operation and enters the normal operation scenario.

[0204] Step 3: According to the performance indicators of the electric energy storage system, the performance indicators of the hydrogen energy storage system and the control conditions of the operation scenario of the electric-hydrogen hybrid energy storage system, a first target model representing the optimal performance of the electric energy storage system and a second target model representing the optimal performance of the hydrogen energy storage system are respectively established; and a control model of the electric-hydrogen coupling system is established with the first target model, the second target model and the joint constraints.

[0205] The cost function is a function that measures the difference between the model prediction value and the true value. In a non-limiting preferred embodiment, the cost function of the electric energy storage system and the cost function of the hydrogen energy storage system have different performance indicators according to the responsibilities of each system.

[0206] The cost function model satisfies the following relationship:

[0207] ,

[0208] ,

[0209] In the formula, To optimize the output vector, At the sampling time No. The cost function of a distributed generation is At the sampling time No. The quadratic coefficient matrix of the cost function of distributed generation, At the sampling time No. The first-order coefficient matrix of the cost function of distributed generation, For the The coefficient matrix of the inequality constraints for distributed generation, For the The constant matrix of inequality constraints for distributed generation, For the The coefficient matrix of the equality constraints for distributed generation, For the The constant matrix of the equality constraints for distributed generation,

[0210] Specifically, step 3 includes:

[0211] Step 3.1, based on the cost function model, according to the performance indicators of the electric energy storage system and the control conditions of the operation scenario of the electric-hydrogen hybrid energy storage system, establish a first target model that represents the optimal performance of the electric energy storage system;

[0212] Specifically, the performance indicators of the electric energy storage system include, but are not limited to: average voltage recovery indicator, battery power state balance indicator, output power recovery indicator, battery power state recovery indicator and control performance indicator;

[0213] Based on the cost function model, the linear weighted method is adopted, and the first Boolean variable and the second Boolean variable are introduced. The first objective model that characterizes the optimal performance of the electric energy storage system satisfies the following relationship:

[0214] ,

[0215] In the formula, At the sampling time No. The cost function of an electric energy storage system is , , , , All are The weight of the cost function of each electric energy storage system; The number of samples in the time domain is controlled for prediction;

[0216] middle, At the sampling time No. The average output voltage of the energy storage system, is the system rated voltage, the first term is the number of samples in the predictive control time domain Neidi The average output voltage recovery value of each electric energy storage system is the predicted value of the average voltage recovery index;

[0217] middle, At the sampling time No. The SOC of an electric energy storage system, At the sampling time No. The SOC of the energy storage system Electric energy storage system The balance is updated only with the predicted values ​​transmitted from the neighboring energy storage system, which depends on the Communication channel constants ; At the sampling time No. The electric energy storage system and The sparse communication factor between the energy storage systems, the energy storage system set , is the number of electric energy storage systems; by introducing the control conditions of the operation scenario of the electric-hydrogen hybrid energy storage system, the second item is only considered when the hydrogen energy storage system is not shut down. At this time, the electric energy storage system only bears high-frequency loads. The balancing control will not have much impact on the stability of the entire microgrid system, and can avoid the excessive computational complexity of the distributed model predictive control in complex situations. The second item is the battery power state balancing indicator;

[0218] middle, At the sampling time No. The output power of the electric energy storage system is obtained by introducing the control conditions of the operation scenario of the electric-hydrogen hybrid energy storage system. The third term represents the output power of the electric energy storage system. The minimum output power of an energy storage system; when the energy storage system is operating normally, it does not bear low-frequency loads while smoothing fluctuations, and the energy storage system acts as a penalty item in the early warning recovery state, which plays a role in limiting the excessive recovery power in multi-objective optimization. The third item is the output power recovery index;

[0219] middle, For the prediction time No. The average SOC value of the energy storage system is is the recovery reference value of SOC; the fourth item is Electric energy storage system The recovery item is a battery power status recovery indicator used to prevent Exceeding the safety limit;

[0220] middle, At the sampling time No. The fifth item is the rate of change of the secondary control signal of the energy storage system. The minimum value of the control action change rate of an electric energy storage system is the control performance indicator;

[0221] Step 3.2, based on the cost function model, according to the performance indicators of the hydrogen energy storage system and the control conditions of the operation scenario of the electric-hydrogen hybrid energy storage system, establish a second objective model that represents the optimal performance of the hydrogen energy storage system;

[0222] Specifically, the performance indicators of the hydrogen energy storage system include, but are not limited to: average voltage recovery indicators, control performance indicators;

[0223] Based on the cost function model, the linear weighted method is adopted, and the first Boolean variable and the second Boolean variable are introduced. The second objective model that characterizes the optimal performance of the hydrogen energy storage system satisfies the following relationship:

[0224] ,

[0225] In the formula, At the sampling time No. The cost function of a hydrogen energy storage system is: , All are The weight of the cost function of each hydrogen energy storage system;

[0226] middle, At the sampling time No. The average output voltage of the hydrogen energy storage system, is the system rated voltage, the first term is the number of samples in the predictive control time domain Neidi The average output voltage recovery value of each hydrogen energy storage system is the predicted value of the average voltage recovery index;

[0227] middle, At the sampling time No. The second term is the change rate of the secondary control signal of the hydrogen energy storage system. The minimization value of the control action change rate of a hydrogen energy storage system is the control performance indicator.

[0228] Step 3.3, based on the sparse communication consistency algorithm, perform SOC balancing optimization between the electric energy storage systems, and update the first target model with the optimized battery state of charge balancing index;

[0229] Specifically, the consensus algorithm based on sparse communication realizes the electric energy storage system Balance can make each electric energy storage system in the best storage and release state, thereby improving the overall efficiency of the system.

[0230] Under an ideal communication network, based on the sparse communication consensus algorithm, the SOC balancing optimization between the electric energy storage systems satisfies the following relationship:

[0231] ,

[0232] In the formula, At the sampling time No. The predicted value of the SOC of an electric energy storage system, At the sampling time No. The actual value of the SOC of the energy storage system, For the The electric energy storage system and Sparse communication factor between electric energy storage systems; is the convergence coefficient.

[0233] Step 3.4, establish the control model of the electric-hydrogen coupling system with the updated first objective model, second objective model and joint constraints.

[0234] Step 4, iteratively solve the electric-hydrogen coupling system control model to obtain the operating parameter prediction values ​​and control parameter prediction values ​​of the electric-hydrogen hybrid energy storage system; based on the operating parameter prediction values ​​and the control parameter prediction values, control the electric-hydrogen hybrid energy storage system.

[0235] Specifically, the control model of the electric-hydrogen coupling system is solved iteratively to first obtain the predicted time The control parameter prediction values ​​of the electric-hydrogen hybrid energy storage system include: prediction time No. The rate of change of the secondary control signal of the energy storage system and prediction time No. The change rate of the secondary control signal of a hydrogen energy storage system ; Then based on the predicted time The predicted value of the control parameter of the electric-hydrogen hybrid energy storage system is iteratively solved to solve the control model of the electric-hydrogen coupling system and obtain the predicted time The predicted values ​​of the operating parameters of the electric-hydrogen hybrid energy storage system include: No. Prediction of the average output voltage of an energy storage system , predict the time No. Predicted value of the output voltage of a distributed power source , predict the time No. The type is The predicted value of the average SOC value of the distributed power supply , predict the time No. The type is Prediction value of distributed power supply SOC , predict the time No. The predicted value of the output power of distributed generation ;in, .

[0236] Based on the predicted values ​​of operating parameters and control parameters, under normal operating conditions, electric and hydrogen energy storage can support high and low frequency loads respectively, reducing the number of battery charge and discharge times and extending life. When the hydrogen energy storage is shut down, the electric energy storage alone supports the load and maintains system stability.

[0237] By combining URFC and lithium batteries, an isolated DC microgrid with electric-hydrogen hybrid energy storage was constructed to simulate and verify the control strategy mentioned in the present invention. The verification scenarios include: normal operation scenario (scenario 1), hydrogen energy storage startup scenario (scenario 2), and electric energy storage warning scenario (scenario 3).

[0238] The detailed working status of hybrid energy storage in three scenarios is as follows:

[0239] Scenario 1: Normal operation scenario

[0240] In this scenario, the photovoltaic power generation / load changes suddenly, but the hydrogen storage working mode remains unchanged, and the energy storage system works within the operating constraints, in which the hydrogen energy storage is responsible for supporting the load and the electric energy storage is responsible for smoothing the fluctuation. In this scenario, it is considered that the electric energy storage system The change is not big, and the situation of over-limit leading to warning is not considered, but its balance will be considered in this scenario, because the system operation state is relatively stable at this time, and the prediction and optimization will be more accurate. In this scenario, the Boolean variable Always 1, Always 0. When the hydrogen energy storage system needs to shut down for maintenance or switch working mode, it will enter scenario 2.

[0241] Scenario 2: Hydrogen energy storage startup scenario

[0242] In this scenario, the Boolean variable Set to 0. There are two possible startup situations: 1) Cold start: After long-term maintenance or long-term shutdown, the URFC system needs to start from a cold state, which takes about 30 seconds; 2) Hot start: Due to sudden changes in load demand or photovoltaic power generation, the URFC system needs to switch working modes. The hot start time is about 3 seconds. At this time, the equipment consumes 5% of the rated power to maintain the preheating state. When the gas concentration, temperature and other conditions are adjusted, the hot start is completed. After the cold / hot start is completed, Set to 1. During the cold / hot start of the URFC system, the electric energy storage system alone supports the load, which may cause Changes and triggers an alert, Boolean variable Set to 1 to enter scene 3.

[0243] Scenario 3: Electric energy storage warning scenario

[0244] In this scenario, the Boolean variable Set to 1. At this point, the URFC system is within the power constraint and generates or absorbs more power to support the load and restore ,avoid Beyond the safety range, it enters the protection state and loses the ability to respond quickly. Restore to and hour, After reset, the electric energy storage system returns to normal operation and enters scene 1.

[0245] Figure 2 is the simulation result of the deep charging area of ​​battery energy storage. Set three electric energy storages (Bat1, Bat2, Bat3 in Figure 2 (a) represent three electric energy storages, Bat_sum is the sum of the three energy storages, PV is the photovoltaic device, and Load is the load) The initial values ​​are 79.9%, 79.5%, and 79.1%, respectively. The corresponding microgrid unit output power (Average) in the battery energy storage deep charging area is shown in Figure 2 (a), the dispatchable DG output voltage and average output voltage are shown in Figure 2 (b), and the battery charge state is shown in Figure 2 (c). Observe Figure 2, t=0~2s is the URFC system cold start of scenario 2; at t=2s, the URFC system cold start is set to complete and enter the fuel cell mode; t=2~6s is scenario 1, where the light radiation intensity is modified from 600W / m at t=4s. 2 Up to 980 W / m 2At this time, the battery energy storage responds quickly to smooth the fluctuation, and the URFC responds slowly; at t=6s, the load power is changed from 52kW to 12kW, and the URFC power drops rapidly until it shuts down. Therefore, t=6~9s is the hot start of the URFC system in scenario 2. At this time, the URFC system enters the transition state from the fuel cell mode to the electrolysis hydrogen production mode, continuously consumes 3kW of electricity, and completes the hot start at t=9s. Exceeding the threshold The battery system warning is triggered when the battery system is in the state of being ... At t=13s, manually cancel the warning state and re-enter scene 1. The consensus is carried out again until equilibrium is achieved.

[0246] Figure 3 is the simulation result of the deep discharge area of ​​the battery energy storage. The initial SOCe values ​​of the three energy storages (Bat1, Bat2, and Bat3 in Figure 3 (a)) are set to 20.1%, 20.5%, and 20.9%, respectively. The corresponding microgrid unit output power in the deep discharge area of ​​the battery energy storage is shown in Figure 3 (a), the dispatchable DG output voltage and the average output voltage are shown in Figure 3 (b), and the battery charge state is shown in Figure 3 (c). Observe Figure 3, t=0~2s is the cold start of the URFC system in scenario 2; at t=2s, the URFC system is set to complete the cold start and enter the electrolysis hydrogen production mode; t=2~6s is scenario 1, where the light radiation intensity is modified from 1040 W / m at t=4s. 2 Up to 560 W / m 2 At this time, the battery energy storage responds quickly to smooth the fluctuation, and the URFC responds slowly; at t=6s, the load power is changed from 22kW to 48kW, and the URFC power drops rapidly until it shuts down; therefore, t=6~9s is the hot start of the URFC system in scenario 2. At this time, the URFC system enters the transition state of switching from electrolysis hydrogen production mode to fuel cell mode, and completes the hot start at t=9s. Lower than Threshold conditions trigger the battery system warning; t=9~13s is scenario 3, Restore; manually cancel the warning state at t=13s and re-enter scene 1. The consensus is carried out again until equilibrium is achieved.

[0247] During the entire test cycle, the electric-hydrogen hybrid energy storage system reduced the number of charge and discharge cycles of lithium batteries and extended the battery life by about 25%. The voltage stability of the system was significantly improved, and the DC bus voltage fluctuation range was reduced to less than 1.5%. Through the coordination of the DMPC controller, the electric energy storage system quickly responded to high-frequency loads, and the hydrogen energy storage system provided stable power support during low-frequency loads, verifying the advantages of the proposed control strategy in electric-hydrogen hybrid energy storage applications.

[0248] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0249] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the above. The computer-readable storage medium used herein is not to be interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0250] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0251] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect through the Internet). In some embodiments, by using the state information of the computer-readable program instructions to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit may execute the computer-readable program instructions, thereby implementing various aspects of the present disclosure.

[0252] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A control method for an electric-hydrogen coupling system based on distributed model predictive control, applicable to an electric-hydrogen hybrid energy storage system, wherein the electric-hydrogen hybrid energy storage system comprises an electric energy storage system and a hydrogen energy storage system; characterized in that: include: Collect the operation data of the electric-hydrogen hybrid energy storage system, establish the physical layer model and control layer model of the electric-hydrogen hybrid energy storage system; based on the physical layer model and control layer model of the electric-hydrogen hybrid energy storage system, establish the joint constraint conditions of the electric-hydrogen hybrid energy storage system; wherein, the physical layer model satisfies the following relationship: In the formula, For the period No. The output power of distributed power sources is For the period No. The output voltage of the distributed power supply is For the period No. The current on the DC bus side of a distributed power supply, For the period No. The voltage estimation value of the line coupling resistance corresponding to each distributed power source satisfies: , For the The line coupling resistance corresponding to each distributed power source; The control layer models include: electric energy storage dynamic model, hydrogen energy storage dynamic model, electric energy storage state model, hydrogen energy storage state model; Based on droop control, the dynamic model of electric energy storage is established according to the dynamic behavior of electric energy storage, satisfying the following relationship: In the formula, is the system rated voltage, For the period No. The voltage setting value of each energy storage based on droop control, For the period No. The output power of the electric energy storage The droop coefficient, For the period No. The electric energy storage is used to realize the secondary control signal of average voltage recovery, battery state of charge balance, and battery state of charge recovery; Based on VSG control, a hydrogen energy storage dynamic model is established according to the dynamic behavior of hydrogen energy storage, satisfying the following relationship: In the formula, is the system rated power, For the period No. The output power of hydrogen energy storage is For the period No. The voltage setting value of each hydrogen storage is based on the VSG control. For the period No. The hydrogen energy storage is used to realize the secondary control signal of average voltage recovery, hydrogen storage tank capacity state balance, and hydrogen storage tank capacity state recovery. is the damping coefficient, is the virtual inertia; The electric energy storage state model satisfies the following relationship: In the formula, For the period Battery charge status, For the initial period Battery charge status, is the battery output power, is the battery capacity, is the battery voltage, which remains unchanged during a calculation period; The hydrogen energy storage state model satisfies the following relationship: In the formula, For the period The capacity status of the hydrogen storage tank, For the initial period The capacity status of the hydrogen storage tank, and are the gas constant and Faraday constant, respectively. is the number of electrons transferred per reaction, is the hydrogen storage tank temperature, is the volume of the hydrogen storage tank, is the upper limit of hydrogen storage tank pressure, is the hydrogen production efficiency, Output power for the hydrogen storage tank, is the URFC voltage, which remains unchanged during a calculation period; The joint constraints of the electric-hydrogen hybrid energy storage system include: equality constraints and inequality constraints; among them, the equality constraints include: process constraints of the average output voltage, terminal constraints of the average output voltage, average SOC constraints of the electric energy storage, and average SOC constraints of the hydrogen energy storage; the inequality constraints include: constraints of the output voltage of the distributed power source, constraints of the output power of the distributed power source, SOC safety range, constraints of the control action change rate, and constraints of the response speed; The process constraint of the output voltage average value satisfies the following relationship: In the formula, At the sampling time No. The average value of the output voltage of the distributed power supply, At the sampling time No. The output voltage of the distributed power supply is At the sampling time Characterization The first distributed power source and the The constant of the communication channel between distributed generation sources, At the sampling time No. The output voltage of a distributed power supply; It is a collection of distributed power sources; The terminal constraint condition of the output voltage average value satisfies the following relationship: In the formula, At the sampling time No. The average value of the output voltage of the distributed power supply, The number of samples in the time domain is controlled for prediction; Based on the local approximation algorithm, the average SOC constraint conditions of the electric energy storage and the average SOC constraint conditions of the hydrogen energy storage both satisfy the following relationship: In the formula, At the sampling time No. The type is The average SOC value of the distributed power supply, The first distributed power source and the The types of distributed power sources are the same. represents the electric energy storage system, Represents hydrogen energy storage system, distributed power source collection , electric energy storage system collection , Hydrogen Energy Storage System Collection , is the number of electric energy storage systems, is the number of distributed generation; The constraints of the distributed power supply output voltage satisfy the following relationship: In the formula, , For the The lower and upper limits of the output voltage of a distributed power supply, At the sampling time No. The output voltage of a distributed power supply; The constraints of the distributed power output power satisfy the following relationship: In the formula, , Respectively The lower and upper limits of the output power of a distributed power generation unit, At the sampling time No. Output power of distributed power sources; The SOC safety range satisfies the following relationship: In the formula, , Respectively The type is The lower and upper limits of the distributed power supply SOC, At the sampling time No. The type is SOC of distributed power supply; The constraint condition of the change rate of the secondary control signal satisfies the following relationship: In the formula, , Respectively The type is The lower and upper limits of the rate of change of the secondary control signal of the distributed power supply, At the sampling time No. The type is The rate of change of the secondary control signal of the distributed power source; The response speed constraint condition satisfies the following relationship: In the formula, , Respectively The lower and upper limits of the response speed of a distributed power source, At the sampling time No. The response speed of each distributed power source; Establish control conditions for the operation scenarios of the electric-hydrogen hybrid energy storage system; According to the performance indicators of the electric energy storage system, the performance indicators of the hydrogen energy storage system and the control conditions of the operation scenario of the electric-hydrogen hybrid energy storage system, a first target model representing the optimal performance of the electric energy storage system and a second target model representing the optimal performance of the hydrogen energy storage system are respectively established; a control model of the electric-hydrogen coupling system is established with the first target model, the second target model and the joint constraint conditions; The control model of the electric-hydrogen coupling system is solved iteratively to obtain the predicted values ​​of the operating parameters and the predicted values ​​of the control parameters of the electric-hydrogen hybrid energy storage system; based on the predicted values ​​of the operating parameters and the predicted values ​​of the control parameters, the electric-hydrogen hybrid energy storage system is controlled.

2. The method for controlling an electric-hydrogen coupling system based on distributed model predictive control according to claim 1, characterized in that: Collect the operating data of the electric-hydrogen hybrid energy storage system and establish the physical layer model and control layer model of the electric-hydrogen hybrid energy storage system, including: Based on system graph theory, the physical layer and control layer of the electric-hydrogen hybrid energy storage system are established; the control layer adopts a hierarchical control architecture; The operating data of the electric-hydrogen hybrid energy storage system is collected to establish a physical layer model of the electric-hydrogen hybrid energy storage system and a control layer model of the electric-hydrogen hybrid energy storage system.

3. The method for controlling an electric-hydrogen coupling system based on distributed model predictive control according to claim 1, characterized in that: Based on the physical layer model and control layer model of the electric-hydrogen hybrid energy storage system, the joint constraints of the electric-hydrogen hybrid energy storage system are established, including: Discretize the physical layer model and the control layer model; Based on the discretized physical layer model, electric energy storage dynamic model and hydrogen energy storage dynamic model, the joint constraints of the electric-hydrogen hybrid energy storage system are established.

4. The method for controlling an electric-hydrogen coupling system based on distributed model predictive control according to claim 1, characterized in that: Establish control conditions for the operation scenario of the electric-hydrogen hybrid energy storage system, including: The first Boolean variable is used as a start-stop control variable of the hydrogen energy storage system, and the second Boolean variable is used as a SOC warning variable of the electric energy storage system; Establish the initial judgment conditions for scenario triggering of the electric-hydrogen hybrid energy storage system; Based on the first Boolean variable, the second Boolean variable and the scene triggering initial judgment condition of the electric-hydrogen hybrid energy storage system, the control conditions of the operation scene of the electric-hydrogen hybrid energy storage system are determined, wherein the control conditions include scene maintenance conditions and scene switching conditions.

5. The method for controlling an electric-hydrogen coupling system based on distributed model predictive control according to claim 4, characterized in that: When the first Boolean variable is 1, it indicates that the hydrogen energy storage system is started, and when the first Boolean variable is 0, it indicates that the hydrogen energy storage system is shut down; When the second Boolean variable is 1, it indicates that the SOC of the electric energy storage system is in a warning state, and when the second Boolean variable is 0, it indicates that the SOC of the electric energy storage system is normal; The initial judgment conditions for triggering the scenario of the electric-hydrogen hybrid energy storage system are: ,in is the error limit, Indicates the total power of the electric energy storage system; if the initial judgment conditions for the scenario trigger are met, it indicates that the electric-hydrogen hybrid energy storage system is in a steady-state normal operation state; otherwise, it indicates that the hydrogen energy storage system is in a shutdown state or the hydrogen energy storage system has transient fluctuations.

6. The method for controlling an electric-hydrogen coupling system based on distributed model predictive control according to claim 5, characterized in that: The operation scenarios of the electric-hydrogen hybrid energy storage system include: normal operation scenario, hydrogen energy storage startup scenario, and electric energy storage warning scenario; The scene switching conditions of the normal operation scene include: when the scene trigger initial judgment condition is met, and the first Boolean variable is 1 and the second Boolean variable is 0; The scene maintenance condition of the normal operation scene includes: when the scene switching condition of the normal operation scene is met, and the first Boolean variable is 1 and the second Boolean variable is 0; The scene switching conditions of the hydrogen energy storage startup scene include: when the initial judgment condition of the scene trigger is met, and the second Boolean variable is 0, the first Boolean variable changes from 0 to 1 after a delay; The scene maintenance condition of the hydrogen energy storage startup scene includes: when the scene switching condition of the hydrogen energy storage startup scene is met, and the first Boolean variable is 1 and the second Boolean variable is 0; The scene switching condition from the hydrogen energy storage startup scene to the electric energy storage warning scene includes: when the scene switching condition of the hydrogen energy storage startup scene is met but the scene triggering initial judgment condition is not met, and the second Boolean variable changes from 0 to 1; The scene switching conditions from the electric energy storage warning scene to the normal operation scene include: when the scene switching conditions of the hydrogen energy storage startup scene are met and the scene triggering initial judgment conditions are met, and the second Boolean variable changes from 1 to 0.

7. The method for controlling an electric-hydrogen coupling system based on distributed model predictive control according to claim 1, characterized in that: According to the performance indicators of the electric energy storage system, the performance indicators of the hydrogen energy storage system and the control conditions of the operation scenario of the electric-hydrogen hybrid energy storage system, a first target model representing the optimal performance of the electric energy storage system and a second target model representing the optimal performance of the hydrogen energy storage system are established respectively, including: Based on the cost function model, according to the performance indicators of the electric energy storage system and the control conditions of the operation scenario of the electric-hydrogen hybrid energy storage system, a first objective model that characterizes the optimal performance of the electric energy storage system is established; Based on the cost function model, according to the performance indicators of the hydrogen energy storage system and the control conditions of the operation scenario of the electric-hydrogen hybrid energy storage system, a second objective model that characterizes the optimal performance of the hydrogen energy storage system is established; Based on the sparse communication consistency algorithm, the battery state of charge between each electric energy storage system is optimized, and the first target model is updated with the optimized battery state of charge balance index; The control model of the electric-hydrogen coupling system is established based on the updated first objective model, second objective model and joint constraints.

8. The method for controlling an electric-hydrogen coupling system based on distributed model predictive control according to claim 7, characterized in that: The performance indicators of the electric energy storage system include: average voltage recovery indicator, battery power state balance indicator, output power recovery indicator, battery power state recovery indicator and control performance indicator; Based on the cost function model, the linear weighted method is adopted, and the first Boolean variable and the second Boolean variable are introduced. The first objective model that characterizes the optimal performance of the electric energy storage system satisfies the following relationship: In the formula, At the sampling time No. The cost function of an electric energy storage system is , , , , All are The weight of the cost function of each electric energy storage system; The number of samples in the prediction control time domain; the electric energy storage system collection , is the number of electric energy storage systems; the first Boolean variable As the start-stop control variable of the hydrogen energy storage system, the second Boolean variable As a SOC warning variable for electric energy storage systems; middle, At the sampling time No. The average output voltage of the energy storage system, is the system rated voltage, the first term is the number of samples in the predictive control time domain Neidi The average output voltage recovery value of each electric energy storage system is the predicted value of the average voltage recovery index; middle, At the sampling time No. The SOC of an electric energy storage system, At the sampling time No. The SOC of the energy storage system Electric energy storage system The balance is updated only with the predicted values ​​transmitted from the neighboring energy storage system, which depends on the Communication channel constants ; At the sampling time No. The electric energy storage system and The sparse communication factor between the energy storage systems, the energy storage system set , is the number of electric energy storage systems; the second is the battery state of charge balance indicator; middle, At the sampling time No. The output power of the electric energy storage system is obtained by introducing the control conditions of the operation scenario of the electric-hydrogen hybrid energy storage system. The third term represents the output power of the electric energy storage system. The minimum output power of the energy storage system, and the third item is the output power recovery index; middle, For the prediction time No. The average SOC value of the energy storage system is is the recovery reference value of SOC; the fourth item is Electric energy storage system The recovery item is a battery power status recovery indicator; middle, At the sampling time No. The fifth item is the change rate of the secondary control signal of the electric energy storage system. The minimum value of the control action change rate of an electric energy storage system is a control performance indicator.

9. The method for controlling an electric-hydrogen coupling system based on distributed model predictive control according to claim 8, characterized in that: The performance indicators of hydrogen energy storage systems include: average voltage recovery indicators, control performance indicators; Based on the cost function model, the linear weighted method is adopted, and the first Boolean variable and the second Boolean variable are introduced. The second objective model that characterizes the optimal performance of the hydrogen energy storage system satisfies the following relationship: In the formula, At the sampling time No. The cost function of a hydrogen energy storage system is: , All are The weight of the cost function of each hydrogen energy storage system; middle, At the sampling time No. The average output voltage of the hydrogen energy storage system, is the system rated voltage, the first term is the number of samples in the predictive control time domain Neidi The average output voltage recovery value of each hydrogen energy storage system is the predicted value of the average voltage recovery index; middle, At the sampling time No. The second term is the change rate of the secondary control signal of the hydrogen energy storage system. The minimization value of the control action change rate of a hydrogen energy storage system is the control performance indicator.

10. The method for controlling an electric-hydrogen coupling system based on distributed model predictive control according to claim 9, characterized in that: Based on the sparse communication consensus algorithm, the SOC balance optimization between each electric energy storage system satisfies the following relationship: In the formula, At the sampling time No. The predicted value of the SOC of an electric energy storage system, At the sampling time No. The actual value of the SOC of the energy storage system, For the The electric energy storage system and Sparse communication factor between electrical energy storage systems; is the convergence coefficient; At the sampling time Characterization The first distributed power source and the The constant of the communication channel between distributed power sources.

11. The method for controlling an electric-hydrogen coupling system based on distributed model predictive control according to claim 1, characterized in that: Iteratively solve the control model of the electric-hydrogen coupling system to obtain the predicted values ​​of the operating parameters and control parameters of the electric-hydrogen hybrid energy storage system, including: Iteratively solve the control model of the electric-hydrogen coupling system to obtain the predicted time The control parameter prediction values ​​of the electric-hydrogen hybrid energy storage system include: prediction time No. The rate of change of the secondary control signal of the energy storage system and prediction time No. The change rate of the secondary control signal of a hydrogen energy storage system ; Based on the prediction time The predicted value of the control parameter of the electric-hydrogen hybrid energy storage system is iteratively solved to solve the control model of the electric-hydrogen coupling system and obtain the predicted time The predicted values ​​of the operating parameters of the electric-hydrogen hybrid energy storage system include: No. Prediction of the average output voltage of an energy storage system , predict the time No. Predicted value of the output voltage of a distributed power source , prediction time No. The type is The predicted value of the average SOC value of the distributed power supply , prediction time No. The type is Prediction value of distributed power supply SOC , prediction time No. The predicted value of the output power of distributed generation ;in, , Controls the number of time-domain samples for prediction.

12. A control system for an electric-hydrogen coupling system based on distributed model predictive control, characterized in that: include: Joint constraint condition establishment module, operation scenario control condition establishment module, electric-hydrogen coupling system control model establishment module, and prediction value solution module; The joint constraint condition establishment module is used to collect the operation data of the electric-hydrogen hybrid energy storage system and establish the physical layer model and control layer model of the electric-hydrogen hybrid energy storage system; based on the physical layer model and control layer model of the electric-hydrogen hybrid energy storage system, the joint constraint condition of the electric-hydrogen hybrid energy storage system is established; wherein the physical layer model satisfies the following relationship: In the formula, For the period No. The output power of distributed power sources is For the period No. The output voltage of the distributed power supply is For the period No. The current on the DC bus side of a distributed power supply, For the period No. The voltage estimation value of the line coupling resistance corresponding to each distributed power source satisfies: , For the The line coupling resistance corresponding to each distributed power source; The control layer models include: electric energy storage dynamic model, hydrogen energy storage dynamic model, electric energy storage state model, hydrogen energy storage state model; Based on droop control, the dynamic model of electric energy storage is established according to the dynamic behavior of electric energy storage, satisfying the following relationship: In the formula, is the system rated voltage, For the period No. The voltage setting value of each energy storage based on droop control, For the period No. The output power of the electric energy storage The droop coefficient, For the period No. The electric energy storage is used to realize the secondary control signal of average voltage recovery, battery state of charge balance, and battery state of charge recovery; Based on VSG control, a hydrogen energy storage dynamic model is established according to the dynamic behavior of hydrogen energy storage, satisfying the following relationship: In the formula, is the system rated power, For the period No. The output power of hydrogen energy storage is For the period No. The voltage setting value of each hydrogen storage is based on the VSG control. For the period No. The hydrogen energy storage is used to realize the secondary control signal of average voltage recovery, hydrogen storage tank capacity state balance, and hydrogen storage tank capacity state recovery. is the damping coefficient, is the virtual inertia; The electric energy storage state model satisfies the following relationship: In the formula, For the period Battery charge status, For the initial period Battery charge status, is the battery output power, is the battery capacity, is the battery voltage, which remains unchanged during a calculation period; The hydrogen energy storage state model satisfies the following relationship: In the formula, For the period The capacity status of the hydrogen storage tank, For the initial period The capacity status of the hydrogen storage tank, and are the gas constant and Faraday constant, respectively. is the number of electrons transferred per reaction, is the hydrogen storage tank temperature, is the volume of the hydrogen storage tank, is the upper limit of hydrogen storage tank pressure, is the hydrogen production efficiency, Output power for the hydrogen storage tank, is the URFC voltage, which remains unchanged during a calculation period; The joint constraints of the electric-hydrogen hybrid energy storage system include: equality constraints and inequality constraints; among them, the equality constraints include: process constraints of the average output voltage, terminal constraints of the average output voltage, average SOC constraints of the electric energy storage, and average SOC constraints of the hydrogen energy storage; the inequality constraints include: constraints of the output voltage of the distributed power source, constraints of the output power of the distributed power source, SOC safety range, constraints of the control action change rate, and constraints of the response speed; The process constraint of the output voltage average value satisfies the following relationship: In the formula, At the sampling time No. The average value of the output voltage of the distributed power supply, At the sampling time No. The output voltage of the distributed power supply is At the sampling time Characterization The first distributed power source and the The constant of the communication channel between distributed generation sources, At the sampling time No. The output voltage of a distributed power supply; It is a collection of distributed power sources; The terminal constraint condition of the output voltage average value satisfies the following relationship: In the formula, At the sampling time No. The average value of the output voltage of the distributed power supply, The number of samples in the time domain is controlled for prediction; Based on the local approximation algorithm, the average SOC constraint conditions of the electric energy storage and the average SOC constraint conditions of the hydrogen energy storage both satisfy the following relationship: In the formula, At the sampling time No. The type is The average SOC value of the distributed power supply, The first distributed power source and the The types of distributed power sources are the same. represents the electric energy storage system, Represents hydrogen energy storage system, distributed power source collection , electric energy storage system collection , Hydrogen Energy Storage System Collection , is the number of electric energy storage systems, is the number of distributed generation; The constraints of the distributed power supply output voltage satisfy the following relationship: In the formula, , For the The lower and upper limits of the output voltage of a distributed power supply, At the sampling time No. The output voltage of a distributed power supply; The constraints of the distributed power output power satisfy the following relationship: In the formula, , Respectively The lower and upper limits of the output power of a distributed power generation unit, At the sampling time No. Output power of distributed power sources; The SOC safety range satisfies the following relationship: In the formula, , Respectively The type is The lower and upper limits of the distributed power supply SOC, At the sampling time No. The type is SOC of distributed power supply; The constraint condition of the change rate of the secondary control signal satisfies the following relationship: In the formula, , Respectively The type is The lower and upper limits of the rate of change of the secondary control signal of the distributed power supply, At the sampling time No. The type is The rate of change of the secondary control signal of the distributed power source; The response speed constraint condition satisfies the following relationship: In the formula, , Respectively The lower and upper limits of the response speed of a distributed power source, At the sampling time No. The response speed of each distributed power source; An operation scenario control condition establishment module is used to establish control conditions for the operation scenarios of the electric-hydrogen hybrid energy storage system; The electric-hydrogen coupling system control model establishment module is used to establish a first target model representing the optimal performance of the electric energy storage system and a second target model representing the optimal performance of the hydrogen energy storage system according to the performance indicators of the electric energy storage system, the performance indicators of the hydrogen energy storage system and the control conditions of the electric-hydrogen hybrid energy storage system operation scenario; and establish the electric-hydrogen coupling system control model with the first target model, the second target model and the joint constraint conditions; The prediction value solving module is used to iteratively solve the electric-hydrogen coupling system control model to obtain the operating parameter prediction values ​​and control parameter prediction values ​​of the electric-hydrogen hybrid energy storage system; based on the operating parameter prediction values ​​and control parameter prediction values, the electric-hydrogen hybrid energy storage system is controlled.

13. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 11.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 11 are implemented.

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