CN119024757A

CN119024757A - Control method and system of electric-hydrogen coupling system based on distributed model predictive control

Info

Publication number: CN119024757A
Application number: CN202411513902.5A
Authority: CN
Inventors: 钱轶群; 余晋宇; 杨依林; 陈少霞; 张宇磊; 于天佑; 华佳楠; 鲁涛; 王子强; 周荔丹; 姚钢
Original assignee: State Grid Shanghai Electric Power Co Ltd
Current assignee: State Grid Shanghai Electric Power Co Ltd
Priority date: 2024-10-29
Filing date: 2024-10-29
Publication date: 2024-11-26
Anticipated expiration: 2044-10-29
Also published as: CN119024757B

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

Electric hydrogen coupling system control method and system based on distributed model predictive control

Technical Field

The invention belongs to the technical field of new energy system control, and particularly relates to an electric-hydrogen coupling system control strategy research based on distributed model predictive control, which is used for designing and realizing a control strategy based on distributed model predictive control for electric-hydrogen hybrid energy storage.

Background

As renewable energy and dc load permeability continue to rise, the application of dc micro-grids has become more widespread. Meanwhile, the importance of the electric-hydrogen hybrid energy storage system is obviously improved due to high transmission cost in remote areas. The energy storage system is connected to effectively support the stable operation of the electro-hydrogen hybrid energy storage system, and the new energy consumption capacity is improved. Hybrid energy storage systems typically consist of high energy density energy storage and high power density energy storage, which can combine mass storage with fast response compared to single type energy storage. The hydrogen energy storage has the characteristics of high energy density, long service life, easy storage and transportation and the like, and is an important energy carrier for large-capacity storage. Compared with a monomer system with a traditional hydrogen energy storage fuel cell and an electrolytic hydrogen production device separated, the combined type regenerated fuel cell (unitized regenerative fuel cell, URFC) system is highly integrated, and the reversible electric pile is utilized to realize the bidirectional conversion of electric energy and hydrogen, so that the system has the advantage of higher energy density. In the scene of quick regulation and frequent start-stop, the URFC can be combined with a lithium battery with high power density to form an electric-hydrogen hybrid energy storage system, so that high-capacity quick response is realized. In order to fully exert the advantages of hybrid energy storage, stabilize power fluctuation and ensure system stability, the prior art is controlled based on a distributed, centralized and distributed coordination strategy respectively.

The distributed coordination control method is realized based on a local controller, and comprises a method for realizing hybrid electricity-hydrogen energy storage coordination based on a common direct current bus voltage, wherein high and low frequency loads are matched with an energy storage response speed, but the bus voltage always has deviation. Meanwhile, due to lack of communication links, the distributed control is difficult to realize a complex control strategy, the calculation complexity of the distributed control is rapidly increased along with the increase of the types and the number of the devices, and the application range is limited to a certain extent. The centralized control comprises a central processor control and a local processor control, comprises a variable weight model prediction control strategy, changes the power reference value of the energy storage along with the network controller in real time, and can maintain the good SOCe of the battery without considering the unstable voltage caused by the difference of the response speeds of the electric energy storage and the hydrogen energy storage. And the deep reinforcement learning method is adopted to solve the problem of coordination of electricity and hydrogen, but the calculation load is large, and the real-time operation is difficult. In addition, centralized control needs to ensure real-time and rapid communication between the central processor and the local processor, and has the risk of single-point failure, and the reliability is relatively low. Compared with distributed and centralized control, the distributed control has the advantages that each controller works independently, single-point fault risk is avoided, redundancy is high, voltage indifferent control can be achieved, a schedulable source system is regarded as an agent in the distributed control, each scheduling source controller is linked through a communication network, local information is received, data is collected from adjacent agents, and cooperative control can be achieved. The electro-hydrogen storage power allocation is determined based on a marginal cost consensus (margin cost consensus, MCC) strategy, achieving minimal instantaneous power generation costs, but not considering lithium battery life.

Disclosure of Invention

In order to solve the defects in the prior art, the invention provides a control method and a control system of an electric hydrogen coupling system based on distributed model predictive control, and aims at solving the problems that a single type of energy storage unit is difficult to meet the effects of stabilizing new energy fluctuation, storing redundant electric energy and the like due to the limitations of response speed, capacity and the like, and provides a method for combining a URFC with an electric energy storage system to form short-time and long-time hybrid energy storage application; meanwhile, controllers based on Distributed Model Predictive Control (DMPC) are respectively designed for the electric subsystem and the hydrogen subsystem, so that the aims of supporting low-frequency load by hydrogen energy storage and supporting high-frequency load by electric energy storage are fulfilled, the times of charge and discharge of the electric energy storage are reduced, the service life of a lithium battery is prolonged, the quick response and high-capacity supporting function of the hybrid energy storage system are fully exerted, and the new energy absorbing capacity of the electric hydrogen hybrid energy storage system is improved.

The invention adopts the following technical scheme.

The invention provides an electric hydrogen coupling system control method based on distributed model predictive control, which is suitable for 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; comprising the following steps:

Acquiring operation data of the electro-hydrogen hybrid energy storage system, and establishing a physical layer model and a control layer model of the electro-hydrogen hybrid energy storage system; establishing a joint constraint condition of the electro-hydrogen hybrid energy storage system based on a physical layer model and a control layer model of the electro-hydrogen hybrid energy storage system;

Establishing control conditions of an operation scene of the electro-hydrogen hybrid energy storage system;

According to the performance index of the electric energy storage system, the performance index of the hydrogen energy storage system and the control condition of the operation scene 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; establishing an electric hydrogen coupling system control model by using the first target model, the second target model and the joint constraint condition;

iteratively solving an electric hydrogen coupling system control model to obtain an operation parameter predicted value and a control parameter predicted value of the electric hydrogen hybrid energy storage system; and controlling the electric hydrogen hybrid energy storage system based on the operation parameter predicted value and the control parameter predicted value.

Preferably, collecting operation data of the electro-hydrogen hybrid energy storage system, and establishing a physical layer model and a control layer model of the electro-hydrogen hybrid energy storage system, including:

based on a system graph theory, establishing a physical layer and a control layer of the electro-hydrogen hybrid energy storage system; wherein the control layer adopts a layered control architecture;

Acquiring operation data of the electro-hydrogen hybrid energy storage system to establish a physical layer model of the electro-hydrogen hybrid energy storage system and a control layer model of the electro-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;

preferably, the physical layer model satisfies the following relation:

，

in the formula, To be in a period ofFirst, theThe output power of the distributed power supply,To be in a period ofFirst, theThe output voltage of the distributed power supply is,To be in a period ofFirst, theThe current on the dc bus side of the distributed power supply,To be in a period ofFirst, theThe voltage estimation values of the line coupling resistors corresponding to the distributed power supplies meet the following conditions:， Is the first And the line coupling resistances correspond to the distributed power supplies.

Preferably, based on droop control, an electric energy storage dynamic model is built according to the dynamic behavior of electric energy storage, and the following relation is satisfied:

，

in the formula, For the rated voltage of the system,To be in a period ofFirst, theThe individual electrical energy stores are based on droop controlled voltage set points,To be in a period ofFirst, theElectric energy storage output powerIs used for the sag factor of the (c) for the (c),For a period of timeFirst, theThe secondary control signals are used for realizing average voltage recovery, battery state of charge equalization 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, and the following relational expression is satisfied:

，

in the formula, For the rated power of the system,To be in a period ofFirst, theThe output power of the hydrogen stored energy is equal to that of the hydrogen stored energy,To be in a period ofFirst, theThe individual hydrogen storage is based on the VSG controlled voltage set point,To be in a period ofFirst, theThe hydrogen storage is used for realizing 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 of the material, and,Is a virtual inertia;

the electric energy storage state model satisfies the following relation:

，

in the formula, To be in a period ofIs used for controlling the state of charge of the battery,To be in the initial periodIs used for controlling the state of charge of the battery,For the output power of the battery,For battery capacity (in Ah),For battery voltage, constant over a calculation period;

the hydrogen storage state model satisfies the following relationship:

，

in the formula, To be in a period ofIs provided with a hydrogen storage tank capacity state,To be in the initial periodIs provided with a hydrogen storage tank capacity state,AndThe gas constant and the faraday constant are respectively given,For each number of electron transfers to be reacted,For the temperature of the hydrogen storage tank,For the volume of the hydrogen storage tank,Is the upper pressure limit of the hydrogen storage tank,In order to produce hydrogen with efficiency,For the output power of the hydrogen storage tank,For the URFC voltage, it is constant for one calculation period.

Preferably, establishing the joint constraint condition of the electro-hydrogen hybrid energy storage system based on the physical layer model and the control layer model of the electro-hydrogen hybrid energy storage system comprises:

performing discrete processing on the physical layer model and the control layer model;

establishing a joint constraint condition of the electric-hydrogen hybrid energy storage system based on the discretized physical layer model, the electric energy storage dynamic model and the hydrogen energy storage dynamic model; the joint constraints of the electro-hydrogen hybrid energy storage system include: equality constraints and inequality constraints;

Wherein the equality constraint includes: a process constraint condition of an output voltage average value, a terminal constraint condition of the output voltage average value, an electric energy storage SOC average value constraint condition and a hydrogen energy storage SOC average value constraint condition; inequality constraints include: constraints of the output voltage of the distributed power supply, constraints of the output power of the distributed power supply, SOC safety range, constraints of controlling action change rate and constraints of response speed.

Preferably, the process constraints of the output voltage average value satisfy the following relation:

，

in the formula, To at the sampling timeFirst, theThe average value of the output voltages of the distributed power supplies,To at the sampling timeFirst, theThe output voltage of the distributed power supply is,To at the sampling timeCharacterization of the first embodimentIndividual distributed power supplies and the firstConstant of the communication channel between the distributed power supplies,To at the sampling timeFirst, theA plurality of distributed power supply output voltages; is a distributed power supply set;

The terminal constraint condition of the output voltage average value satisfies the following relation:

，

in the formula, To at the sampling timeFirst, theThe average value of the output voltages of the distributed power supplies,The number of samples in the control time domain for prediction;

based on a local approximation algorithm, the electric energy storage SOC average constraint condition and the hydrogen energy storage SOC average constraint condition both meet the following relation:

，

in the formula, To at the sampling timeFirst, theOf the type ofSOC average value of distributed power supply of (a)Individual distributed power supplies and the firstThe type of the individual distributed power sources is the same,An electrical energy storage system is shown and described,Representing a hydrogen storage system, distributed power collectionElectric energy storage system setHydrogen storage system set，For the number of electrical energy storage systems,Is the number of distributed power sources.

Preferably, the constraint condition of the output voltage of the distributed power supply satisfies the following relation:

，

in the formula, 、Is the firstA lower limit and an upper limit of the output voltage of the distributed power supply,To at the sampling timeFirst, theA plurality of distributed power supply output voltages;

Constraint conditions of output power of the distributed power supply meet the following relation:

，

in the formula, 、Respectively the firstA lower limit and an upper limit for the output power of the distributed power supply,To at the sampling timeFirst, theThe output power of each distributed power supply;

SOC safety range satisfies the following relation:

，

in the formula, 、Respectively the firstOf the type ofLower and upper limits of the distributed power SOC,To at the sampling timeFirst, theOf the type ofSOC of the distributed power supply;

Constraint conditions of the change rate of the secondary control signal meet the following relation:

，

in the formula, 、Respectively the firstOf the type ofLower and upper limits of the rate of change of the secondary control signal of the distributed power supply,To at the sampling timeFirst, theOf the type ofThe rate of change of the secondary control signal of the distributed power supply;

5) And the constraint condition of response speed satisfies the following relation:

，

in the formula, 、Respectively the firstA lower limit and an upper limit of the response speed of the distributed power supply,To at the sampling timeFirst, theThe response speed of the individual distributed power supplies.

Preferably, establishing control conditions of an operation scene of the electro-hydrogen hybrid energy storage system comprises the following steps:

Taking the first Boolean variable as a start-stop control variable of the hydrogen energy storage system, and taking the second Boolean variable as an SOC early-warning variable of the electric energy storage system;

establishing a scene triggering initial judgment condition of the electro-hydrogen hybrid energy storage system;

and determining control conditions of the operation scene of the electro-hydrogen hybrid energy storage system based on the first Boolean variable, the second Boolean variable and the scene trigger initial judgment condition of the electro-hydrogen hybrid energy storage system, wherein the control conditions comprise a scene maintenance condition and a scene switching condition.

Preferably, a first boolean variable of 1 indicates that the hydrogen storage system is started and a first boolean variable of 0 indicates that the hydrogen storage system is stopped;

The second Boolean variable is 1 to indicate that the SOC of the electric energy storage system is normal, and the second Boolean variable is 0 to indicate that the SOC of the electric energy storage system is normal;

The scene triggering initial judgment condition of the electric-hydrogen hybrid energy storage system is that WhereinAs a result of the error limit value,Representing the total power of the electrical energy storage system; if the scene triggering initial judgment condition is met, the electric-hydrogen hybrid energy storage system is in a steady state normal operation state, otherwise, the hydrogen energy storage system is in a shutdown state or transient fluctuation occurs to the hydrogen energy storage system.

Preferably, the electro-hydrogen hybrid energy storage system operating scenarios include, but are not limited to: normal operation scene, hydrogen energy storage starting scene, electric energy storage early warning scene;

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

The scene maintenance conditions of the normal operation scene include: when the scene switching condition of the normal operation scene is met, the first Boolean variable is 1, and the second Boolean variable is 0;

The scene switching conditions of the hydrogen energy storage starting scene include: when the scene triggering initial judgment condition is met, the second Boolean variable is 0, and the first Boolean variable is changed from 0 to 1 after time delay;

The scene maintenance conditions of the hydrogen storage start scene include: when the scene switching condition of the hydrogen energy storage starting scene is met, the first Boolean variable is 1, and the second Boolean variable is 0;

the scene switching conditions from the hydrogen energy storage starting scene to the electric energy storage early warning scene comprise: when the scene switching condition of the hydrogen energy storage starting scene is met but the scene triggering initial judging condition is not met, the second Boolean variable is changed from 0 to 1;

The scene switching conditions from the electric energy storage early warning scene to the normal operation scene comprise: when the scene switching condition of the hydrogen energy storage starting scene is met and the scene triggering initial judging condition is met, the second Boolean variable is changed from 1 to 0.

Preferably, according to the performance index of the electric energy storage system, the performance index of the hydrogen energy storage system and the control condition of the operation scene 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 the method comprises the following steps:

Based on the cost function model, a first target model representing the optimal performance of the electric energy storage system is established according to the performance index of the electric energy storage system and the control condition of the operation scene of the electric hydrogen hybrid energy storage system;

based on the cost function model, establishing a second target model representing the optimal performance of the hydrogen energy storage system according to the performance index of the hydrogen energy storage system and the control condition of the operation scene of the electro-hydrogen hybrid energy storage system;

based on a consistency algorithm of sparse communication, carrying out battery state of charge equalization optimization among all the electric energy storage systems, and updating a first target model by using the battery state of charge equalization index after optimization;

And establishing an electro-hydrogen coupling system control model by using the updated first target model, the updated second target model and the updated joint constraint condition.

Preferably, the performance indicators of the electrical energy storage system include: average voltage recovery index, battery state of charge equalization index, output power recovery index, battery state of charge recovery index, and control performance index;

Based on a cost function model, a linear weighting method is adopted, a first Boolean variable and a second Boolean variable are introduced, and a first target model which characterizes the optimal performance of the electric energy storage system is established to meet the following relational expression:

，

in the formula, To at the sampling timeFirst, theThe cost function of the individual electrical energy storage systems,、、、、Are all the firstThe weight of the cost function of the individual electrical energy storage systems; The number of samples in the control time domain for prediction; electric energy storage system set ，Is the number of electrical energy storage systems; first Boolean variableAs a start-stop control variable for the hydrogen storage system, a second Boolean variableAs an SOC early warning variable of the electric energy storage system;

In the process, To at the sampling timeFirst, theThe average value of the output voltages of the individual electrical energy storage systems,The first term is the number of samples in the predictive control time domain for the system nominal voltageInner firstThe average value recovery value of the output voltage of the individual electric energy storage system is a predicted value of an average voltage recovery index;

In the process, To at the sampling timeFirst, theThe SOC of the individual electrical energy storage system,To at the sampling timeFirst, theSOC of the individual electric energy storage system, noOf an electric energy storage systemEqualization is updated only with predictions transmitted from adjacent electrical energy storage systems, which depend on at the sampling instantConstant of communication channel；To at the sampling timeFirst, theElectric energy storage system and the firstSparse communication factor between individual electrical energy storage systems, collection of electrical energy storage systems，Is the number of electrical energy storage systems; the second term is a battery state of charge equalization indicator;

In the process, To at the sampling timeFirst, theThe third item represents the third item of the output power of the electric energy storage system by introducing the control conditions of the operation scene of the electric hydrogen hybrid energy storage systemThe minimum value of the output power of the electric energy storage system, and the third item is an output power recovery index;

In the process, To at the predicted timeFirst, theThe average value of the SOCs of the individual electrical energy storage systems,A recovery reference value for the SOC; fourth item is the firstOf an electric energy storage systemA recovery term, which is a battery state of charge recovery index;

In the process, To at the sampling timeFirst, theSecond-level control signal change rate of individual electric energy storage system, fifth item is firstThe minimum value of the control action change rate of the individual electric energy storage systems is the control performance index.

Preferably, the performance indicators of the hydrogen storage system include: average voltage recovery index and control performance index;

based on a cost function model, a linear weighting method is adopted, a first Boolean variable and a second Boolean variable are introduced, and a second target model which represents the optimal performance of the hydrogen energy storage system is established to meet the following relational expression:

，

in the formula, To at the sampling timeFirst, theA cost function of the individual hydrogen storage systems,、Are all the firstThe weight of the cost function of the hydrogen storage system;

In the process, To at the sampling timeFirst, theThe average value of the output voltages of the hydrogen storage systems,The first term is the number of samples in the predictive control time domain for the system nominal voltageInner firstThe average value recovery value of the output voltage of each hydrogen energy storage system is a predicted value of an average voltage recovery index;

In the process, To at the sampling timeFirst, theSecond-order control signal change rate of hydrogen energy storage system, second-order is first-orderThe minimum value of the control action change rate of the hydrogen energy storage system is a control performance index.

Preferably, based on a consistency algorithm of sparse communication, the following relation is satisfied by performing SOC balance optimization between the electric energy storage systems:

，

in the formula, To at the sampling timeFirst, theA predicted value of SOC of the individual electrical energy storage system,To at the sampling timeFirst, theThe actual value of SOC of the individual electrical energy storage systems,Is the firstElectric energy storage system and the firstSparse communication factors between the individual electrical energy storage systems; is a convergence coefficient; to at the sampling time Characterization of the first embodimentIndividual distributed power supplies and the firstConstant of the communication channel between the distributed power supplies.

Preferably, iteratively solving the electro-hydrogen coupling system control model to obtain an operating parameter predictor and a control parameter predictor of the electro-hydrogen hybrid energy storage system comprises:

iteratively solving an electro-hydrogen coupling system control model to obtain a predicted time A control parameter predictive value for an electro-hydrogen hybrid energy storage system, comprising: predicting time of dayFirst, theRate of change of secondary control signal for individual electrical energy storage systemsAnd predicting the momentFirst, theSecond-order control signal rate of change for individual hydrogen storage systems；

Based on the predicted time of dayIteratively solving an electric hydrogen coupling system control model by using control parameter predictive value of an electric hydrogen hybrid energy storage system to obtain a predictive momentAn operational parameter predictive value for an electro-hydrogen hybrid energy storage system comprising: predicting time of dayFirst, thePredictive value of average value of output voltages of individual electric energy storage systemsPredicting a time of dayFirst, thePredictive value of output voltage of each distributed power supplyPredicting a time of dayFirst, theOf the type ofPredictive value of a distributed power supply SOC average valuePredicting a time of dayFirst, theOf the type ofPredictive value of distributed power supply SOCPredicting a time of dayFirst, thePredictive value of output power of distributed power supply; Wherein, ，The number of samples in the time domain is controlled for prediction.

The invention also provides an electric hydrogen coupling system control system based on distributed model predictive control, which comprises: the system comprises a joint constraint condition establishment module, an operation scene control condition establishment module, an electro-hydrogen coupling system control model establishment module and a predicted value solving module;

The combined constraint condition establishing module is used for acquiring the operation data of the electro-hydrogen hybrid energy storage system and establishing a physical layer model and a control layer model of the electro-hydrogen hybrid energy storage system; establishing a joint constraint condition of the electro-hydrogen hybrid energy storage system based on a physical layer model and a control layer model of the electro-hydrogen hybrid energy storage system;

the operation scene control condition establishing module is used for establishing control conditions of an operation scene of the electric-hydrogen hybrid energy storage system;

The electric hydrogen coupling system control model building module is used for building 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 index of the electric energy storage system, the performance index of the hydrogen energy storage system and the control condition of the operation scene of the electric hydrogen hybrid energy storage system; establishing an electric hydrogen coupling system control model by using the first target model, the second target model and the joint constraint condition;

The predicted value solving module is used for iteratively solving the control model of the electro-hydrogen coupling system to obtain the predicted value of the operation parameter and the predicted value of the control parameter of the electro-hydrogen hybrid energy storage system; and controlling the electric hydrogen hybrid energy storage system based on the operation parameter predicted value and the control parameter predicted value.

A terminal comprising a processor and a storage medium; the storage medium is used for storing instructions; the processor is configured to operate in accordance with the instructions to perform the steps of the method.

A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of a method.

Compared with the prior art, the method at least comprises the step of taking hydrogen energy which takes importance in a future energy system into consideration, and provides a distributed model predictive control method of electric-hydrogen hybrid energy storage aiming at an electric-hydrogen hybrid energy storage system, wherein the characteristics of high response speed of the electric energy storage and high capacity density of the hydrogen energy storage are fully utilized, and the electric energy storage is designed to support high-frequency load and the hydrogen energy storage is designed to support low-frequency load under normal working conditions, so that the charge and discharge times of the electric energy storage are reduced, and the service life of the electric energy storage is prolonged. Meanwhile, the cold and hot starting characteristics of the hydrogen energy storage are also considered, and the electric energy storage is designed to independently support the load in the switching process of the hydrogen energy storage mode, so that the effectiveness of the provided control strategy under different working conditions is realized. In addition, the invention designs a virtual synchronous machine (VSG) control for hydrogen energy storage, which is more in line with the characteristic of slow response speed of the hydrogen energy storage, thereby improving the inertia and stability of the system. Therefore, efficient coordination of hybrid energy storage is achieved.

Drawings

Fig. 1 is a flowchart of an electro-hydrogen coupling system control method based on distributed model predictive control according to the present invention.

Fig. 2 is a simulation result diagram of a deep charging area of battery energy storage in an embodiment of the present invention, where fig. 2 (a) is a simulation result diagram of output power of a micro grid unit under the deep charging area of battery energy storage, fig. 2 (b) is a simulation result diagram of output voltage and average output voltage of a schedulable distributed power supply under the deep charging area of battery energy storage, and fig. 2 (c) is a state of charge of the battery under the deep charging area of battery energy storage;

Fig. 3 is a simulation result diagram of a deep discharge area of a battery energy storage according to an embodiment of the present invention, where fig. 3 (a) is a simulation result diagram of output power of a micro grid unit under the deep discharge area of the battery energy storage, fig. 3 (b) is a simulation result diagram of output voltage and average output voltage of a schedulable distributed power supply under the deep discharge area of the battery energy storage, and fig. 3 (c) is a state of charge of the battery under the deep discharge area of the battery energy storage.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. The described embodiments of the application are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art without inventive faculty, are within the scope of the application, based on the spirit of the application.

The invention provides an electro-hydrogen coupling system control method based on distributed model predictive control, which is suitable for an electro-hydrogen hybrid energy storage system, as shown in figure 1, and comprises the following steps:

Step 1, collecting operation data of an electro-hydrogen hybrid energy storage system, and establishing a physical layer model and a control layer model of the electro-hydrogen hybrid energy storage system; based on a physical layer model and a control layer model of the electro-hydrogen hybrid energy storage system, establishing a joint constraint condition of the electro-hydrogen hybrid energy storage system.

Specifically, step 1 includes:

Step 1.1, establishing a physical layer and a control layer of an electro-hydrogen hybrid energy storage system based on a system graph theory; wherein the control layer adopts a layered control architecture;

The electro-hydrogen hybrid energy storage system comprises a photovoltaic, a battery, an integrated renewable fuel cell (unitized regenerative fuel cell, URFC), a load, an interface converter of the load and the interface converter, and a distributed control unit, and is connected to an island direct current micro-grid bus through the interface converter and a filter. The photovoltaic is a non-schedulable distributed power supply (distributedgeneration, DG) and works in a network-following mode under the tracking control of the maximum power point so as to maximally utilize renewable energy. The lithium battery and the URFC are schedulable DGs and work in a networking mode under a hierarchical control framework so as to realize functions of accurate voltage recovery, power distribution and the like.

Specifically, the electro-hydrogen hybrid energy storage system includesThe DGs, DG sets are expressed asDG setIs an electric energy storage system setAnd a hydrogen storage system setOf (3), whereinFor the number of the electric energy storage systems, satisfy. Communication topology for an electro-hydrogen hybrid energy storage systemIs undirected, the set of communication links is represented as; Wherein the adjacency matrixBy characterization of the firstIndividual distributed power supplies and the firstConstant of communication channel between distributed power sourcesThe composition is that,Indicating the presence of a communication channel,Indicating that the communication channel is not present; thus, the Laplace matrix in the undirected graph satisfies the following relationship:

，

in the formula, Represent the firstThe number of neighbor nodes of the individual node, wherein,First, theThe corresponding node isA distributed power supply, which satisfies。

Step 1.2, establishing a physical layer model of the electro-hydrogen hybrid energy storage system according to operation data of the electro-hydrogen hybrid energy storage system;

Specifically, in the physical layer of the electro-hydrogen hybrid energy storage system, DG is connected to a DC bus through a DC/DC interface converter, a capacitive filter, a line coupling resistor, and the like. The physical layer collects state quantity signals in real time and sends the state quantity signals to the control layer for calculation according to the first step The schedulable DG output power establishes a physical layer model, and the following relation is satisfied:

，

Step 1.3, establishing a control layer model of the electro-hydrogen hybrid energy storage system according to the operation data of the electro-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;

In a primary control layer of the electric-hydrogen hybrid energy storage system, electric energy storage and hydrogen energy storage are designed to be net-structured control, droop control and virtual synchronous generator (virtual synchronous generator, VSG) control are respectively adopted, virtual inertia is provided for an island micro-grid, and the support voltage is stable and the power supply is reliable. The V-P characteristic curves of the sagging control and the VSG control are characterized as affine functions; current-voltage control, droop control or VSG control, sine wave pulse width modulation constitute the primary control layer. Since the current-voltage control is faster than the droop control or VSG control, the dynamic behavior of each energy storage in the primary control layer is mainly determined by the droop control or VSG control.

Specifically, based on droop control, an electric energy storage dynamic model is established according to the dynamic behavior of electric energy storage, and the following relation is satisfied:

，

in the formula, Is a voltage rating value of the system,To be in a period ofFirst, theThe individual electrical energy stores are based on droop controlled voltage set points,To be in a period ofFirst, theElectric energy storage output powerIs used for the sag factor of the (c) for the (c),For a period of timeFirst, theThe electric energy storage is used for realizing secondary control signals of average voltage recovery, battery state of charge equalization and battery state of charge recovery.

Specifically, based on VSG control, a hydrogen energy storage dynamic model is established according to the dynamic behavior of hydrogen energy storage, and the following relational expression is satisfied:

，

in the formula, For the rated power of the system,To be in a period ofFirst, theThe output power of the hydrogen stored energy is equal to that of the hydrogen stored energy,To be in a period ofFirst, theThe individual hydrogen storage is based on the VSG controlled voltage set point,To be in a period ofFirst, theThe hydrogen storage is used for realizing 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 of the material, and,Is a virtual inertia.

The control layers of the electro-hydrogen hybrid energy storage system include, but are not limited to, a primary control layer and a secondary control layer, so that both the electro-hydrogen energy storage dynamic model and the hydrogen energy storage dynamic model of the electro-hydrogen hybrid energy storage system include secondary control signals.

The electric energy storage state model satisfies the following relation:

，

in the formula, To be in a period ofIs used for controlling the state of charge of the battery,To be in the initial periodIs used for controlling the state of charge of the battery,For the output power of the battery,For battery capacity (in Ah),Is the battery voltage, which is constant for a calculated period.

The hydrogen storage state model satisfies the following relationship:

，

The energy storage state model comprises: an electrical energy storage state model, a hydrogen energy storage state model; therefore, the energy storage state model satisfies the following relation:

，

in the formula, For the electric energy storage coefficient, satisfy；Is hydrogen energy storage coefficient, meets；

By usingAnd characterizing the energy storage state model.

Step 1.4, establishing joint constraint conditions of the electro-hydrogen hybrid energy storage system based on a physical layer model and a control layer model of the electro-hydrogen hybrid energy storage system;

Specifically, step 1.4 includes:

Step 1.4.1, performing discrete processing on a physical layer model and a control layer model;

The physical layer model is discretized to obtain the following relational expression:

，

in the formula, 、Respectively at sampling moments、First, theThe output power of the distributed power supply,、Respectively at sampling moments、First, theThe output voltage of the distributed power supply is,Is the firstThe corresponding line coupling resistances of the distributed power supplies,To at the sampling timeFirst, theAnd the voltage estimation value of the line coupling resistor corresponding to each distributed power supply.

The electric energy storage dynamic model and the hydrogen energy storage dynamic model are discretized to obtain the following relational expression:

，

in the formula, 、Respectively at sampling moments、First, theThe individual electrical energy stores are based on droop controlled voltage set points,、Respectively at sampling moments、First, theThe power is output by the electric energy storage,To at the sampling timeFirst, theSecondary control signal for average voltage recovery, battery state of charge equalization, and battery state of charge recoveryThe amount of change in (2) at any one sampling instantSatisfy the following requirements；

To at the sampling timeFirst, theThe individual hydrogen storage is based on the VSG controlled voltage set point,To at the sampling timeFirst, theThe output power of the hydrogen energy storage is equal to the output power of the hydrogen energy storage,To at the sampling timeFirst, theThe hydrogen energy storage is used for realizing average voltage recovery, capacity state balance of the hydrogen storage tank and secondary control signal of capacity state recovery of the hydrogen storage tankThe amount of change in (2) at any one sampling instantSatisfy the following requirements；

Conversion of system control problem into electric energy storage secondary control signal change rateAnd hydrogen storage secondary control signal change rateThe control optimization problem of (2) and the elimination of steady-state errors are realized.

Step 1.4.2, establishing a joint constraint condition of the electro-hydrogen hybrid energy storage system based on the discretized physical layer model, the electro-energy storage dynamic model and the hydrogen energy storage dynamic model; the joint constraints of the electro-hydrogen hybrid energy storage system include: equality constraints and inequality constraints;

Wherein the equality constraint includes: a process constraint condition of an output voltage average value, a terminal constraint condition of the output voltage average value, an electric energy storage SOC average value constraint condition and a hydrogen energy storage SOC average value constraint condition;

specifically, establishing an equality constraint for an electro-hydrogen hybrid energy storage system includes:

1) At the sampling time Respectively obtaining the first time as the reference timeOutput voltage of distributed power supplyFirst, theOf the type ofSOC values of distributed power supplies of (a)First, theOutput power of distributed power supply、Is the firstOf the type ofWherein the voltage of the distributed power supply of (c) is,Represents an electric energy storage capacity, and the electric energy storage capacity,Represents hydrogen storage; and each numerical value of the reference moment is a model predictive control initial constraint value.

2) The process constraint condition of the output voltage average value satisfies the following relation:

，

in the formula, To at the sampling timeFirst, theThe average value of the output voltages of the distributed power supplies,To at the sampling timeFirst, theThe output voltage of the distributed power supply is,To at the sampling timeCharacterization of the first embodimentIndividual distributed power supplies and the firstConstant of the communication channel between the distributed power supplies,To at the sampling timeFirst, theA plurality of distributed power supply output voltages; is a distributed power supply set.

3) The terminal constraint condition of the output voltage average value satisfies the following relation:

，

in the formula, To at the sampling timeFirst, theThe average value of the output voltages of the distributed power supplies,The number of samples in the time domain is controlled for prediction.

Indicating that the output voltage average value converges to the rated voltage value at the end of the predictive control time domain.

4) The constraint condition of the electric energy storage SOC average value and the constraint condition of the hydrogen energy storage SOC average value all meet the following relation:

，

in the formula, To at the sampling timeFirst, theOf the type ofIs provided with a distributed power Supply (SOC) value,Is of the type ofIs provided for the power supply of the power system,For the switching period of the switch-on and switch-off period,To at the sampling timeFirst, theThe output power of each distributed power supply;

，

It can be seen that the process constraint of the output voltage average, the electric energy storage SOC average constraint and the hydrogen energy storage SOC average constraint all depend on the constants of the communication channel。

To limit the solution space and improve the transient response of the controller, establishing inequality constraints for the electro-hydrogen hybrid energy storage system includes: constraint conditions of output voltage of the distributed power supply, constraint conditions of output power of the distributed power supply, SOC safety range, constraint conditions of control action change rate and constraint conditions of response speed;

1) Constraint conditions of output voltage of the distributed power supply meet the following relation:

，

2) Constraint conditions of output power of the distributed power supply meet the following relation:

，

3) And the SOC safety range satisfies the following relation:

，

4) Constraint conditions of the change rate of the secondary control signals meet the following relational expression:

，

And 2, establishing control conditions of an operation scene of the electro-hydrogen hybrid energy storage system.

Specifically, step 2 includes:

Step 2.1, with a first Boolean variable As a start-stop control variable of the hydrogen energy storage system, a second Boolean variable is adoptedAs an SOC early warning variable of the electric energy storage system; the following relations are satisfied:

，

In a non-limiting preferred embodiment, an upper hysteresis comparator and a lower hysteresis comparator are provided, High threshold greater than upper hysteresis comparatorOr less than the low threshold of the lower hysteresis comparatorWhen the electric energy storage system enters the early warning stateNot less than the low threshold of the lower hysteresis comparatorAnd is not greater than the high threshold of the upper hysteresis comparatorAnd when the early warning is released, frequent switching of the early warning state of the electric energy storage system is avoided.

Step 2.2, establishing a scene triggering initial judgment condition of the electro-hydrogen hybrid energy storage system;

Specifically, the scene trigger initial judgment condition is WhereinAs a result of the error limit value,Representing the total power of the electrical energy storage system. If the scene triggering initial judgment condition is met, the electric-hydrogen hybrid energy storage system is in a steady state normal operation state, otherwise, the hydrogen energy storage system is in a shutdown state or transient fluctuation occurs to the hydrogen energy storage system.

And 2.3, determining control conditions of an operation scene of the electro-hydrogen hybrid energy storage system based on the first Boolean variable, the second Boolean variable and the scene trigger initial judgment condition of the electro-hydrogen hybrid energy storage system, wherein the control conditions comprise a scene maintenance condition and a scene switching condition.

In a non-limiting preferred embodiment, the electro-hydrogen hybrid energy storage system operating scenarios include, but are not limited to: normal operation scene, hydrogen energy storage starting scene, electric energy storage early warning scene;

specifically, the scene switching conditions of the normal operation scene include: when the scene triggering initial judgment condition is met, a first Boolean variable Is 1, a second Boolean variableIs 0.

Specifically, the scene maintenance conditions of the normal operation scene include: when meeting the scene switching condition of the normal operation scene, and a first Boolean variableAlways 1, second Boolean variableAlways 0.

Under a normal operation scene, the photovoltaic power generation/load is suddenly changed, but the hydrogen storage working mode is unchanged, and the energy storage system works in operation constraint, wherein the hydrogen storage is responsible for supporting the load, and the electric energy storage is responsible for stabilizing the fluctuation; in this scenario it is considered an electrical energy storage systemThe change is not large, the early warning situation caused by out-of-limit is not considered, but the balance is considered in the scene, because the running state of the system is relatively stable, and the prediction and optimization are more accurate. In this scenario, the Boolean variableIs always 1 to be used for the treatment of the steel wire,Always 0. When the hydrogen energy storage system needs to be shut down for maintenance or the working mode is switched, a hydrogen energy storage starting scene can be entered.

Specifically, the scene switching conditions of the hydrogen storage start scene include: when the scene triggering initial judgment condition is met, and a second Boolean variableIs 0 and is delayed to be the first Boolean variableFrom 0 to 1.

Specifically, the scene maintenance conditions of the hydrogen storage start scene include: when meeting the scene switching condition of the hydrogen energy storage starting scene, and a first Boolean variableAlways 1, second Boolean variableAlways 0.

Before entering the hydrogen storage start-up scenarioAnd setting 0. There are two possible hydrogen storage start-up scenarios: 1) Cold start: after long-term maintenance or long-time shutdown, the URFC system needs to be started from a cold state for about 30 seconds; 2) And (3) hot start: due to sudden changes in load demand or photovoltaic power generation, the URFC system needs to switch modes of operation, with a warm start time of about 3s, at which time the device consumes 5% of its rated power to maintain a warm-up state. After the conditions of gas concentration, temperature and the like are adjusted, the hot start is completed. After cold/hot start is completed, i.e. after a certain delayAnd (3) setting 1.

Specifically, the scene switching conditions from the hydrogen energy storage starting scene to the electric energy storage early warning scene include: when the scene switching condition of the hydrogen energy storage starting scene is met but the scene triggering initial judgment condition is not met, a second Boolean variable is obtainedFrom 0 to 1.

The electrical energy storage system alone supports the load during cold/hot start-up of the URFC system, all of which may result inThe change is performed and the early warning is triggered,And (3) changing from 1 to 0, and entering an electric energy storage early warning scene when the scene triggering initial judgment condition is not met.

Specifically, the scene switching conditions from the electric energy storage early warning scene to the normal operation scene include: when the scene switching condition of the hydrogen energy storage starting scene is met and the scene triggering initial judgment condition is met, a second Boolean variable is obtainedFrom 1 to 0. In this case the number of the light sources to be used in the scene,And (3) setting 1. At this point, the URFC system is within the power constraint range, delivering or absorbing more power to support the load and recoverAvoidingOut of the safe range, enter a protected state and lose the ability to respond quickly.

When the second Boolean variableFrom 1 to 0 and satisfying an electrical energy storage systemWhen the state early warning condition is met, switching from a hydrogen energy storage starting scene to an electric energy storage early warning scene; when not meeting the requirements of the electric energy storage systemStatus early warning condition, and second Boolean variableWhen 0 is changed into 1, the electric energy storage early warning scene is switched to the normal operation scene. When (when)Recovery toAndIn the time-course of which the first and second contact surfaces,And setting 0, and restoring the electric energy storage system to a normal operation state and entering a normal operation scene.

Step 3, respectively establishing 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 index of the electric energy storage system, the performance index of the hydrogen energy storage system and the control condition of the operation scene of the electric hydrogen hybrid energy storage system; and establishing an electric hydrogen coupling system control model by using the first target model, the second target model and the joint constraint condition.

The cost function is a function that measures the difference between the model predicted value and the true value, and in a non-limiting preferred embodiment, the cost function of the electrical energy storage system and the cost function of the hydrogen energy storage system have different performance indicators, respectively, according to the responsibilities assumed by each system.

The cost function model satisfies the following relationship:

，

in the formula, In order to optimize the output vector of the output vector,To at the sampling timeFirst, theA cost function of the individual distributed power supplies,To at the sampling timeFirst, theA quadratic coefficient matrix of the cost function of the distributed power supply,To at the sampling timeFirst, theA matrix of coefficients of the first order of the cost function of the distributed power supply,Is the firstA coefficient matrix constrained by the inequality of the distributed power supplies,Is the firstA constant matrix constrained by the inequality of the distributed power supplies,Is the firstAn equality constrained coefficient matrix for the individual distributed power supplies,Is the firstA constant matrix constrained by the equation for the distributed power sources,

Specifically, step 3 includes:

Step 3.1, based on a cost function model, establishing a first target model representing the optimal performance of the electric energy storage system according to the performance index of the electric energy storage system and the control condition of the operation scene of the electric hydrogen hybrid energy storage system;

In particular, performance metrics of an electrical energy storage system include, but are not limited to: average voltage recovery index, battery state of charge equalization index, output power recovery index, battery state of charge recovery index, and control performance index;

，

in the formula, To at the sampling timeFirst, theThe cost function of the individual electrical energy storage systems,、、、、Are all the firstThe weight of the cost function of the individual electrical energy storage systems; The number of samples in the control time domain for prediction;

In the process, To at the sampling timeFirst, theThe SOC of the individual electrical energy storage system,To at the sampling timeFirst, theSOC of the individual electric energy storage system, noOf an electric energy storage systemEqualization is updated only with predictions transmitted from adjacent electrical energy storage systems, which depend on at the sampling instantConstant of communication channel；To at the sampling timeFirst, theElectric energy storage system and the firstSparse communication factor between individual electrical energy storage systems, collection of electrical energy storage systems，Is the number of electrical energy storage systems; by introducing the control conditions of the operation scene of the electric hydrogen hybrid energy storage system, the second item is only considered in the non-stop state of the hydrogen energy storage system, the electric energy storage system only bears high-frequency load,The balance control does not have great influence on the stability of the whole micro-grid system, so that the excessive calculation complexity based on the distributed model prediction control under the complex condition can be avoided, and the second term is a battery state of charge balance index;

In the process, To at the sampling timeFirst, theThe third item represents the third item of the output power of the electric energy storage system by introducing the control conditions of the operation scene of the electric hydrogen hybrid energy storage systemA minimum value of the output power of the individual electrical energy storage systems; when the electric energy storage system normally operates, the fluctuation is stabilized, the bearing of low-frequency load is not carried out, the electric energy storage system is used as a punishment item in an early warning recovery state, the effect of limiting the excessive recovery power in multi-objective optimization is achieved, and the third item is an output power recovery index;

In the process, To at the predicted timeFirst, theThe average value of the SOCs of the individual electrical energy storage systems,A recovery reference value for the SOC; fourth item is the firstOf an electric energy storage systemA recovery term, which is a battery state of charge recovery index for preventingExceeding the safety range;

In the process, To at the sampling timeFirst, theSecond-level control signal change rate of individual electric energy storage system, fifth item is firstThe minimum value of the control action change rate of the individual electric energy storage systems is a control performance index;

Step 3.2, based on the cost function model, establishing a second target model representing the optimal performance of the hydrogen energy storage system according to the performance index of the hydrogen energy storage system and the control condition of the operation scene of the electro-hydrogen hybrid energy storage system;

In particular, performance metrics of hydrogen energy storage systems include, but are not limited to: average voltage recovery index and control performance index;

，

Step 3.3, carrying out SOC balance optimization among all the electric energy storage systems based on a consistency algorithm of sparse communication, and updating a first target model by using an optimized battery state of charge balance index;

specifically, sparse communication-based consistency algorithm implementation electric energy storage system The balance can ensure that each electric energy storage system is in the optimal storage and release state, thereby improving the overall efficiency of the system.

Under an ideal communication network, based on a consistency algorithm of sparse communication, carrying out SOC balance optimization among all the electric energy storage systems to meet the following relation:

，

in the formula, To at the sampling timeFirst, theA predicted value of SOC of the individual electrical energy storage system,To at the sampling timeFirst, theThe actual value of SOC of the individual electrical energy storage systems,Is the firstElectric energy storage system and the firstSparse communication factors between the individual electrical energy storage systems; Is the convergence coefficient.

And 3.4, establishing an electric hydrogen coupling system control model by using the updated first target model, the updated second target model and the updated joint constraint condition.

Step 4, iteratively solving an electro-hydrogen coupling system control model to obtain an operation parameter predicted value and a control parameter predicted value of the electro-hydrogen hybrid energy storage system; and controlling the electric hydrogen hybrid energy storage system based on the operation parameter predicted value and the control parameter predicted value.

Specifically, iteratively solving an electro-hydrogen coupling system control model to obtain a predicted time firstA control parameter predictive value for an electro-hydrogen hybrid energy storage system, comprising: predicting time of dayFirst, theRate of change of secondary control signal for individual electrical energy storage systemsAnd predicting the momentFirst, theSecond-order control signal rate of change for individual hydrogen storage systems; Then based on the predicted timeIteratively solving an electric hydrogen coupling system control model by using control parameter predictive value of an electric hydrogen hybrid energy storage system to obtain a predictive momentAn operational parameter predictive value for an electro-hydrogen hybrid energy storage system comprising: predicting time of dayFirst, thePredictive value of average value of output voltages of individual electric energy storage systemsPredicting a time of dayFirst, thePredictive value of output voltage of each distributed power supplyPredicting a time of dayFirst, theOf the type ofPredictive value of a distributed power supply SOC average valuePredicting a time of dayFirst, theOf the type ofPredictive value of distributed power supply SOCPredicting a time of dayFirst, thePredictive value of output power of distributed power supply; Wherein, 。

Based on the operation parameter predicted value and the control parameter predicted value, the electric energy storage and the hydrogen energy storage respectively support high-frequency and low-frequency loads under normal working conditions, the charge and discharge times of the battery are reduced, the service life is prolonged, and the electric energy storage independently supports the loads under the hydrogen energy storage stopping working conditions, so that the stability of the system is maintained.

The URFC and the lithium battery are combined to construct an island direct current micro-grid containing electric-hydrogen hybrid energy storage, and simulation verification is carried out on the control strategy. The verification scenario includes: normal operation scene (scene 1), hydrogen energy storage starting scene (scene 2), electric energy storage early warning scene (scene 3).

The three scene hybrid energy storage detailed working states are as follows:

Scene 1: normal operation scenario

In this scenario, the photovoltaic power generation/load is suddenly changed, but the hydrogen storage working mode is unchanged, and the energy storage systems all work within the operation constraint, wherein the hydrogen storage is responsible for supporting the load and the electric storage is responsible for stabilizing the fluctuation; in this scenario it is considered an electrical energy storage systemThe change is not large, the early warning situation caused by out-of-limit is not considered, but the balance is considered in the scene, because the running state of the system is relatively stable, and the prediction and optimization are more accurate. In this scenario, the Boolean variableIs always 1 to be used for the treatment of the steel wire,Always 0. Scenario 2 may be entered when the hydrogen storage system requires shutdown maintenance or a switch of modes of operation.

Scene 2: hydrogen energy storage start-up scenario

In this scenario, the Boolean variableAnd setting 0. There are two possible start-up situations: 1) Cold start: after long-term maintenance or long-time shutdown, the URFC system needs to be started from a cold state for about 30 seconds; 2) And (3) hot start: due to sudden changes in load demand or photovoltaic power generation, the URFC system needs to switch modes of operation, with a warm start time of about 3s, at which time the device consumes 5% of its rated power to maintain a warm-up state. After the conditions of gas concentration, temperature and the like are adjusted, the hot start is completed. After the completion of the cold/hot start-up,And (3) setting 1. The electrical energy storage system alone supports the load during cold/hot start-up of the URFC system, all of which may result inChange and trigger early warning, boolean variablePut 1, enter scene 3.

Scene 3: electric energy storage early warning scene

In this scenario, the Boolean variableAnd (3) setting 1. At this point, the URFC system is within the power constraint range, delivering or absorbing more power to support the load and recoverAvoidingOut of the safe range, enter a protected state and lose the ability to respond quickly. When (when)Recovery toAndIn the time-course of which the first and second contact surfaces,Resetting, and restoring the electric energy storage system to a normal running state to enter a scene 1.

Fig. 2 is a simulation result of the deep charge area of the battery energy storage. Providing three electric energy stores (such as Bat1, bat2, bat3 in fig. 2 (a) respectively represent three electric energy stores, bat_sum is the sum of the three energy stores, PV is a photovoltaic device, load is a Load)Initial values are 79.9%,79.5%,79.1%, respectively, the output power (Average) of the micro-grid unit corresponding to the deep charging area of the battery energy storage is shown in fig. 2 (a), the schedulable DG output voltage and the Average output voltage are shown in fig. 2 (b), and the state of charge of the battery is shown in fig. 2 (c). Observing the cold start of the URFC system in the view of FIG. 2, wherein t=0-2 s is scene 2; setting the cold start of the URFC system to be completed when t=2s, and entering a fuel cell mode; t=2 to 6s is scene 1, wherein the illumination radiation intensity is modified from 600W/m ² to 980W/m ² when t=4 s, and the battery energy storage rapid response stabilizes fluctuation and the URFC slowly responds; at t=6s the modified load power is from 52kW to 12kW, the urfc power drops rapidly until shut down. Therefore, t=6-9 s is the hot start of the URFC system in scene 2, at this time, the URFC system enters a transition state from a fuel cell mode to an electrolysis hydrogen production mode switching state, continuously consumes 3kW of electric energy, and completes the hot start at t=9 s, and occurs in the periodExceeding a threshold valueTriggering early warning of a battery system, wherein t=9 to 13s is scene 3,Recovery is performed. Manually releasing the early warning state at t=13 s, re-entering scenario 1,Again consensus is performed until equalization is complete.

Fig. 3 is a simulation result of the deep discharge area of the battery energy storage. The initial values of SOCe of three electric energy storages (such as Bat1, bat2 and Bat3 in fig. 3 (a)) are respectively 20.1%,20.5% and 20.9%, the output power of the micro-grid unit corresponding to the deep discharge area of the battery energy storage is shown in fig. 3 (a), the output voltage of the schedulable DG and the average output voltage are shown in fig. 3 (b), and the state of charge of the battery is shown in fig. 3 (c). Observing the cold start of the URFC system in the figure 3, wherein t=0-2 s is scene 2; setting the cold start of the URFC system to be completed when t=2s, and entering an electrolytic hydrogen production mode; t=2 to 6s is scene 1, wherein the illumination radiation intensity is modified from 1040W/m ² to 560W/m ² when t=4 s, and the battery energy storage rapid response stabilizes fluctuation and the URFC slowly responds; modifying the load power from 22kW to 48kW at t=6s, the urfc power dropping rapidly until shut down; therefore, t=6-9 s is the hot start of the URFC system in scene 2, at this time, the URFC system enters a transition state from the electrolytic hydrogen production mode to the fuel cell mode switching, and the hot start is completed when t=9s, and the period appearsBelow is lower thanTriggering early warning of a battery system under the condition of a threshold value; t=9 to 13s is scene 3,Recovering; manually releasing the early warning state at t=13 s, re-entering scenario 1,Again consensus is performed until equalization is complete.

In the whole test period, the electric hydrogen hybrid energy storage system reduces the charge and discharge cycle times of the lithium battery and prolongs the service life of the battery by about 25 percent. The voltage stability of the system is obviously improved, and the voltage fluctuation range of the direct current bus is reduced to be within 1.5%. Through coordination of the DMPC controller, the electrical energy storage system responds rapidly to high frequency loads, the hydrogen energy storage system provides stable electrical support during low frequency loads, and the advantages of the proposed control strategy in an electrical-hydrogen hybrid energy storage application are verified.

The present disclosure may be a system, method, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.

The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.

The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as SMALLTALK, C ++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.

Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.

Claims

1. The control method of the electric hydrogen coupling system based on the distributed model predictive control is 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 by comprising the following steps:

2. The method for controlling an electro-hydrogen coupling system based on predictive control of a distributed model as set forth in claim 1,

Acquiring operation data of the electro-hydrogen hybrid energy storage system, and establishing a physical layer model and a control layer model of the electro-hydrogen hybrid energy storage system, wherein the method comprises the following steps of:

Acquiring operation data of the electro-hydrogen hybrid energy storage system to establish a physical layer model of the electro-hydrogen hybrid energy storage system and a control layer model of the electro-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.

3. The method for controlling an electro-hydrogen coupling system based on predictive control of a distributed model as set forth in claim 2,

And the physical layer model satisfies the following relation:

，

4. The method for controlling an electro-hydrogen coupling system based on predictive control of a distributed model as set forth in claim 3,

Based on droop control, an electric energy storage dynamic model is established according to the dynamic behavior of electric energy storage, and the following relation is satisfied:

，

the electric energy storage state model satisfies the following relation:

，

the hydrogen storage state model satisfies the following relationship:

，

5. The method for controlling an electro-hydrogen coupling system based on predictive control of a distributed model as set forth in claim 4,

Based on a physical layer model and a control layer model of the electro-hydrogen hybrid energy storage system, establishing joint constraint conditions of the electro-hydrogen hybrid energy storage system, including:

6. The method for controlling an electrical hydrogen coupling system based on predictive control of a distributed model as claimed in claim 5,

The process constraint condition of the output voltage average value satisfies the following relation:

，

7. The method for controlling an electrical hydrogen coupling system based on predictive control of a distributed model as claimed in claim 6,

Constraint conditions of output voltage of the distributed power supply meet the following relation:

，

SOC safety range satisfies the following relation:

，

8. The method for controlling an electro-hydrogen coupling system based on predictive control of a distributed model as set forth in claim 1,

Establishing control conditions of an operation scene of the electro-hydrogen hybrid energy storage system, wherein the control conditions comprise:

9. The method for controlling an electro-hydrogen coupling system based on predictive control of a distributed model as set forth in claim 8,

The first Boolean variable is 1, which indicates that the hydrogen energy storage system is started, and the first Boolean variable is 0, which indicates that the hydrogen energy storage system is stopped;

10. The method for controlling an electro-hydrogen coupling system based on predictive control of a distributed model as set forth in claim 9,

The operation scene of the electro-hydrogen hybrid energy storage system comprises: normal operation scene, hydrogen energy storage starting scene, electric energy storage early warning scene;

11. The method for controlling an electro-hydrogen coupling system based on predictive control of a distributed model as set forth in claim 1,

According to the performance index of the electric energy storage system, the performance index of the hydrogen energy storage system and the control condition of the operation scene 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 the method comprises the following steps:

12. The method for controlling an electro-hydrogen coupling system based on predictive control of a distributed model as set forth in claim 11,

The performance indexes of the electric energy storage system comprise: average voltage recovery index, battery state of charge equalization index, output power recovery index, battery state of charge recovery index, and control performance index;

，

in the formula, To at the sampling timeFirst, theThe cost function of the individual electrical energy storage systems,、、、、Are all the firstThe weight of the cost function of the individual electrical energy storage systems; The number of samples in the control time domain for prediction; electric energy storage system set ，Is the number of electrical energy storage systems; first Boolean variableAs a start-stop control variable for the hydrogen storage system, a second Boolean variableAs an SOC early warning variable of the electric energy storage system; In the process, To at the sampling timeFirst, theThe average value of the output voltages of the individual electrical energy storage systems,The first term is the number of samples in the predictive control time domain for the system nominal voltageInner firstThe average value recovery value of the output voltage of the individual electric energy storage system is a predicted value of an average voltage recovery index;

13. The method for controlling an electro-hydrogen coupling system based on predictive control of a distributed model as set forth in claim 12,

The performance indexes of the hydrogen energy storage system comprise: average voltage recovery index and control performance index;

，

14. The method for controlling an electro-hydrogen coupling system based on predictive control of a distributed model as set forth in claim 13,

Based on a consistency algorithm of sparse communication, SOC balance optimization among all the electric energy storage systems meets the following relation:

，

15. The method for controlling an electro-hydrogen coupling system based on predictive control of a distributed model as set forth in claim 1,

Iteratively solving the control model of the electro-hydrogen coupling system to obtain an operation parameter predicted value and a control parameter predicted value of the electro-hydrogen hybrid energy storage system, comprising:

16. An electro-hydrogen coupling system control system based on distributed model predictive control, comprising: the system comprises a joint constraint condition establishment module, an operation scene control condition establishment module, an electro-hydrogen coupling system control model establishment module and a predicted value solving module;

17. A terminal comprising a processor and a storage medium; the method is characterized in that:

The storage medium is used for storing instructions;

The processor being operative according to the instructions to perform the steps of the method of any one of claims 1-15.

18. A computer readable storage medium having stored thereon a computer program, characterized in that the program when executed by a processor realizes the steps of the method according to any of claims 1-15.