Energy scheduling method based on DeePC and micro-grid off-grid operation system

Abstract

The embodiment of the specification discloses a kind of energy scheduling method based on DeePC and micro-grid off-grid operation system. Including: the output power of multiple power generation subsystems in different time periods and the energy storage value of battery energy storage subsystem are acquired;According to the output power obtained, the control input sequence of the micro-grid off-grid operation system is generated, and according to the energy storage value obtained, the energy storage output sequence of the micro-grid off-grid operation system is generated;Determine the power reference trajectory of multiple power generation subsystems and battery energy storage subsystem in corresponding period, and input the control input sequence, energy storage output sequence and power reference trajectory into the micro-grid system energy management optimization model established based on DeePC algorithm, to calculate the optimal control input sequence of the micro-grid off-grid operation system in corresponding period;According to the optimal control input sequence, the power of multiple power generation subsystems is adjusted in corresponding period.

Description

TECHNICAL FIELD

[0001] One or more embodiments of the present specification relate to the technical field of microgrid, in particular to a DeePC-based energy scheduling method and a microgrid off-grid operation system. BACKGROUND

[0002] For a new energy microgrid, in order to improve renewable energy consumption, ensure load power supply capacity, and coordinate green electricity and green hydrogen production and utilization, the key lies in the optimization operation and energy management of the microgrid in the real-time operation stage.

[0003] In related technologies, an accurate model is established based on operation data, and multi-step decision optimization control is performed through model state and output, such as MPC (Model Predictive Control) and its improved schemes. However, when facing system parameter changes (for example, degradation or update of energy storage system), such methods cannot give accurate energy control, and system modeling and parameter identification must be performed again. SUMMARY

[0004] The embodiments of the present specification provide a DeePC-based energy scheduling method and a microgrid off-grid operation system, and the technical solutions are as follows:

[0005] In a first aspect, the embodiments of the present specification provide a DeePC-based energy scheduling method applied to a microgrid off-grid operation system, wherein the microgrid off-grid operation system includes a plurality of power generation subsystems and a battery energy storage subsystem, and includes:

[0006] Obtaining output power of the plurality of power generation subsystems and energy storage value of the battery energy storage subsystem in different time periods;

[0007] Generating a control input sequence of the microgrid off-grid operation system according to the obtained output power, and generating an energy storage output sequence of the microgrid off-grid operation system according to the obtained energy storage value;

[0008] Determining power reference trajectories of the plurality of power generation subsystems and the battery energy storage subsystem in corresponding time periods, and inputting the control input sequence, the energy storage output sequence, and the power reference trajectories into a microgrid system energy management optimization model established based on a DeePC algorithm to calculate optimal control input sequences of the microgrid off-grid operation system in the corresponding time periods;

[0009] Adjusting power of the plurality of power generation subsystems according to the optimal control input sequences in the corresponding time periods.

[0010] In a second aspect, the embodiments of the present specification provide a micro-grid off-grid operation system, the micro-grid off-grid operation system comprising a plurality of power generation subsystems and a battery energy storage subsystem, comprising:

[0011] an acquisition unit configured to acquire output power of the plurality of power generation subsystems and energy storage values of the battery energy storage subsystem in different time periods;

[0012] a generation unit configured to generate a control input sequence of the micro-grid off-grid operation system according to the acquired power difference values and generate an energy storage output sequence of the micro-grid off-grid operation system according to the acquired energy storage values;

[0013] a calculation unit configured to determine power reference trajectories of the plurality of power generation subsystems and the battery energy storage subsystem in corresponding time periods, and input the control input sequence, the energy storage output sequence and the power reference trajectories into a micro-grid system energy management optimization model established based on a DeePC algorithm to calculate optimal control input sequences of the micro-grid off-grid operation system in the corresponding time periods;

[0014] an adjustment unit configured to perform power adjustment on the plurality of power generation subsystems according to the optimal control input sequences in the corresponding time periods.

[0015] In a third aspect, the embodiments of the present specification provide an electronic device comprising a processor and a memory; the processor is connected to the memory; the memory is configured to store executable program codes; the processor runs a program corresponding to the executable program codes by reading the executable program codes stored in the memory, so as to execute the steps of the DeePC-based energy scheduling method in the first aspect of the above embodiments.

[0016] In a fourth aspect, the embodiments of the present specification provide a computer storage medium storing a plurality of instructions, the instructions being adapted to be loaded by a processor and executed to perform the steps of the DeePC-based energy scheduling method in the first aspect of the above embodiments.

[0017] The technical solutions provided by some embodiments of the present specification have at least the following beneficial effects:

[0018] DeePC is used to replace MPC, an optimization method is adopted, the state estimation and prediction process is implicitly included in the optimization constraint, data-driven predictive control is achieved, and then the energy scheduling of the micro-grid is achieved. On the one hand, the energy scheduling of the micro-grid can be achieved without establishing an accurate prediction model, which not only reduces the cost but also improves the efficiency; on the other hand, the influence of the change of system parameters on the energy scheduling is avoided, and the accuracy of the energy scheduling is improved. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present specification, the drawings required to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present specification, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0020] Figure 1 is a flowchart of an energy scheduling method based on DeePC provided by an embodiment of the present specification.

[0021] Figure 2 is a block diagram of a micro-grid off-grid operation system provided by an embodiment of the present specification.

[0022] Figure 3 is a structural schematic diagram of an electronic device provided by an embodiment of the present specification. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present specification will be described clearly and completely in combination with the drawings in the embodiments of the present specification.

[0024] In the present specification, the terms “first”, “second”, and the like in the description and claims and the above drawings are used to distinguish different objects, and are not used to describe a specific order. In addition, the term “comprising” and any variation thereof is intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed or can optionally include other steps or units inherent to the process, method, product or device.

[0025] The energy scheduling method based on DeePC will be described in detail in combination with one or more embodiments.

[0026] For a new energy micro-grid, in order to improve renewable energy consumption, ensure load power supply capacity, coordinate green electricity and green hydrogen production and utilization, the key lies in the optimization operation and energy management of the real-time operation stage of the micro-grid.

[0027] In the related art, an accurate model is established based on operation data, and multi-step decision optimization control is performed through the model state and output, such as MPC (Model Predictive Control) and its improved scheme. However, when facing system parameter changes (such as energy storage system degradation or update), such methods cannot give accurate energy control, and system modeling and parameter identification must be performed again.

[0028] To address the problems in the prior art, the present specification provides an energy scheduling method of DeePC and a micro-grid off-grid operation system.

[0029] DeePC (Deep Predictive Control) algorithm is an advanced control strategy combining deep learning and model predictive control (MPC). The algorithm learns the dynamic model of the system through deep learning technology and uses this model for prediction and control. The core idea of DeePC algorithm is data-driven model learning and predictive control. Among them, data-driven model learning: unlike traditional control methods based on physical models, DeePC algorithm learns the dynamic behavior of the system from data without explicit mathematical models; predictive control: the algorithm uses the learned model for multi-step prediction and optimizes the control input to drive the system to achieve the desired trajectory or state.

[0030] The main steps include: data collection: collect input-output data from the actual system or simulation; model learning: use deep learning networks (such as convolutional neural networks, recurrent neural networks, etc.) to learn the dynamic model of the system; state estimation: during control, use the learned model to estimate the state of the system; optimization problem construction: construct an optimization problem aimed at minimizing the prediction error and meeting the control objectives and constraints; solve the optimization problem: obtain a series of control inputs by solving the optimization problem; execute control: apply the first calculated control input to the system and repeat the process in the next control cycle.

[0031] Please refer to Figure 1 , Figure 1 The present specification provides a flowchart of an energy scheduling method based on DeePC, as shown in Figure 1 The energy scheduling method based on DeePC is applied to a micro-grid off-grid operation system, which includes multiple power generation subsystems and a battery energy storage subsystem, and can at least include the following steps:

[0032] Step 102, obtain the output power of the multiple power generation subsystems and the energy storage value of the battery energy storage subsystem in different time periods.

[0033] A microgrid is a small-scale power system that can operate independently or in parallel with the main grid. Microgrids combine distributed generation resources such as solar photovoltaics, wind turbines, fuel cells, micro hydroelectricity, and energy storage systems with loads and power electronics to optimize energy use and management. Microgrids contain multiple distributed energy resources that are typically located near the loads. Microgrids are often equipped with energy storage systems such as batteries for storing excess energy and releasing it when needed. Microgrids use power electronics such as inverters, converters, and switches to control the flow and conversion of energy. Microgrids can operate independently in island mode when disconnected from the main grid. Microgrids can easily add or remove generation and energy storage resources as needed.

[0034] In an embodiment, the plurality of electronic systems comprises at least one of: a photovoltaic electronic system, a wind power electronic system, a fuel cell electronic system, a micro turbine electronic system, a battery energy storage subsystem, an electrolytic hydrogen production subsystem.

[0035] The energy storage value of the battery energy storage subsystem is the state of charge of the batteries in the subsystem.

[0036] Step 104, generating a control input sequence of the microgrid off-grid operation system according to the obtained output power, and generating an energy storage output sequence of the microgrid off-grid operation system according to the obtained energy storage value.

[0037] In an embodiment, the generating a control input sequence of the microgrid off-grid operation system according to the obtained output power, and generating an energy storage output sequence of the microgrid off-grid operation system according to the obtained energy storage value comprises: inputting the obtained output power and the obtained energy storage value into a pre-constructed linear discrete model, and generating the control input sequence and the energy storage output sequence according to the output of the linear discrete model; wherein the linear discrete model comprises a first output power of a current time period, a second output power compared to a previous time period of the current time period, and a power adjustment amount of the current time period, and the control input sequence is generated based on the power adjustment amounts of different time periods.

[0038] The embodiment will be introduced below in combination with formulas:

[0039] The microgrid off-grid operation system involved includes a photovoltaic electronic system (PV), a wind power electronic system (WTG), a fuel cell electronic system (FC), a micro turbine electronic system (MT), a battery energy storage subsystem (Bat), an electrolytic hydrogen production subsystem (EL), a power conversion power electronic device, and a load (Load).

[0040] The linear discrete model of the microgrid off-grid system is as follows:

[0041] ;

[0042] ;

[0043] ;

[0044] Typically, parameter matrices A and B are 1, due to S0C Bat (k+1) and P Bat (k), SOC Bat Since (k) is related, A must have one dimension that is not 1, and matrix C is [0 0 0 0 1]. P MT (k) represents the output power of the micro gas turbine (micro gas turbine generator-electronic system), P FC (k) represents the output power of the fuel cell (fuel cell power generation system), P EL (k) represents the input power of the alkaline electrolyzer (electrolysis hydrogen production subsystem), P MT (k) Output power of the battery (battery energy storage subsystem), SOC Bat (k) represents the state of charge of the battery (battery energy storage subsystem), which reflects the remaining capacity of the battery. P MT (k) represents the power regulation of the micro gas turbine at time k. P FC (k) Power regulation of the fuel cell, P EL (k) Power adjustment of the electrolytic cell, P Bat (k) represents the power regulation of the battery.

[0045] The control input sequence is The energy storage output sequence is The historical data sequence obtained by combining the two is .

[0046] In one embodiment, the method further includes: performing data processing on the control input sequence and the energy storage output sequence according to the Hankel matrix to facilitate the calculation of the optimal control input sequence.

[0047] Assuming the historical data sequence length is T, the initial sample size is... The total time range is H, the operation step size is s, and the prediction step size is N.

[0048] Generate input / output trajectories based on historical data sequences. The formula is as follows:

[0049] ;

[0050] Next, the past data and the future data are divided.

[0051] Suppose the divided past data is U p and Y p , which are used to estimate the initial condition of the state; the divided future data U f and Y f , which are used to predict the future trajectory. U p is the front row of the generation matrix , U f is the back N rows of the generation matrix ; Y p is the front row of the generation matrix , Y f is the back N rows of the generation matrix . The combination is expressed as follows:

[0052] .

[0053] is a Hankel matrix, and the generation of the matrix is as follows:

[0054] ;

[0055] wherein T needs to be greater than , and the "5" here corresponds to the 4 power adjustment amounts in the aforementioned u(k). If there are 3 power adjustment amounts in u(k), then "4" here, that is, the number of power adjustment amounts plus 1. is the rank of the system observability matrix, and in actual use, a history data sequence of a sufficient number of time periods is collected.

[0056] Step 106, determine the power reference trajectory of the plurality of power generation subsystems and the battery energy storage subsystem in the corresponding time period, and input the control input sequence, the energy storage output sequence, and the power reference trajectory into the microgrid system energy management optimization model established based on the DeePC algorithm to calculate the optimal control input sequence of the microgrid off-grid operation system in the corresponding time period.

[0057] The power reference trajectory is the trajectory composed of the output power of each power generation subsystem and the energy storage value of the battery energy storage subsystem in each time period, which is expected by the management personnel of the microgrid offline operation system.

[0058] In an embodiment, the micro-grid system energy management optimization model is provided with a constraint set of the control input sequence and the energy storage output sequence in advance, and the calculation process of the optimal control input sequence includes: solving the DeePC optimization problem under the condition that the control input sequence and the energy storage output sequence satisfy the constraint set, and the result of the solving is the optimal control input sequence.

[0059] Further, the optimization constraint in the DeePC optimization problem includes: the square of the difference between the energy storage output sequence and the power reference trajectory in the same period, and the square of the control input sequence.

[0060] Initialization of related parameters of the DeePC optimization problem: assuming that the power reference trajectory is , the output weight matrix is Q, the input weight matrix is R, and the initial trajectory is .

[0061] According to the above parameters and related data, a micro-grid system energy management model is established as shown in the following formula:

[0062]

[0063] Among them, the first formula in the model is the optimization constraint, and the constraint set of the output is as follows:

[0064]

[0065] Among them, the adjustment power of each power generation system and the upper and lower limits of the energy storage value are set in advance by the management personnel of the micro-grid offline operation system.

[0066] The DeePC optimization problem is established, and iterative solving is performed to obtain the control micro-grid management method, and the steps are as follows:

[0067] Step 1061, in the total time range H, the optimization problem is solved every operation step s; step 1062, the optimization problem in formula (6) is solved to obtain ; step 1063, the optimal control input sequence is calculated; step 1064, in the t to t+s time period, the control input sequence is applied to the system; step 1065, the initial time step t is moved to t+s, the latest input and output observation values are obtained, and are assigned to ; step 1066, the process of step 1061 to step 1065 is repeated in the t+s time step.

[0068] adjusting power of the plurality of power electronic systems according to the optimal control input sequence in the corresponding time period.

[0069] In an embodiment, the control input sequence is generated based on power adjustment amounts of different time periods; and the adjusting power of the plurality of power electronic systems according to the optimal control input sequence in the corresponding time period comprises: calculating output power of the corresponding time period according to the power adjustment amount of the corresponding time period and output power of the last time period.

[0070] In this embodiment, DeePC is used to replace MPC, and an optimization method is used to implicitly include state estimation and prediction process in optimization constraints, so as to realize data-driven predictive control and further realize energy scheduling of the microgrid. On the one hand, the energy scheduling of the microgrid can be realized without establishing an accurate prediction model, which not only reduces the cost but also improves the efficiency; on the other hand, the influence of the change of system parameters on the energy scheduling is avoided, and the accuracy of the energy scheduling is improved.

[0071] Please refer to Figure 2 , Figure 2 A block diagram of a microgrid off-grid operation system is provided in the embodiments of the present specification. The microgrid off-grid operation system comprises a plurality of power electronic systems and a battery energy storage subsystem, comprising:

[0072] An acquisition unit 202 is configured to acquire output power of the plurality of power electronic systems and energy storage values of the battery energy storage subsystem in different time periods;

[0073] A generation unit 204 is configured to generate a control input sequence of the microgrid off-grid operation system according to the acquired power difference values, and generate an energy storage output sequence of the microgrid off-grid operation system according to the acquired energy storage values;

[0074] A calculation unit 206 is configured to determine power reference trajectories of the plurality of power electronic systems and the battery energy storage subsystem in corresponding time periods, and input the control input sequence, the energy storage output sequence and the power reference trajectories into a microgrid system energy management optimization model established based on a DeePC algorithm, to calculate an optimal control input sequence of the microgrid off-grid operation system in the corresponding time period;

[0075] An adjustment unit 208 is configured to adjust power of the plurality of power electronic systems according to the optimal control input sequence in the corresponding time period.

[0076] Optionally, the generation unit 204 is specifically configured to:

[0077] inputting the obtained output power and the obtained energy storage value into a pre-constructed linear discrete model, and generating the control input sequence and the energy storage output sequence according to an output of the linear discrete model; wherein the linear discrete model comprises a first output power of a current time period, a second output power of a previous time period compared to the current time period, and a power adjustment amount of the current time period, and the control input sequence is generated based on the power adjustment amounts of different time periods.

[0078] Optionally, the micro-grid system energy management optimization model further comprises:

[0079] The processing unit 210 is configured to perform data processing on the control input sequence and the energy storage output sequence according to a Hankel matrix, so as to calculate the optimal control input sequence.

[0080] Optionally, the micro-grid system energy management optimization model is pre-provided with a constraint set of the control input sequence and the energy storage output sequence, and the calculation process of the optimal control input sequence comprises:

[0081] In a case where the control input sequence and the energy storage output sequence satisfy the constraint set, a DeePC optimization problem is solved, and a result of the solving is the optimal control input sequence.

[0082] Optionally, the optimization constraint in the DeePC optimization problem comprises: a square of a difference between the energy storage output sequence and the power reference trajectory in a same time period, and a square of the control input sequence.

[0083] Optionally, the control input sequence is generated based on the power adjustment amounts of different time periods; and the adjusting unit 208 is specifically configured to:

[0084] calculate the output power of a corresponding time period according to the power adjustment amount of the corresponding time period and the output power of a previous time period.

[0085] Optionally, the plurality of power generation subsystems comprise at least one of the following: a photovoltaic power generation subsystem, a wind power generation subsystem, a fuel cell power generation subsystem, a micro gas turbine power generation subsystem, a battery energy storage subsystem, and an electrolytic hydrogen production subsystem.

[0086] In this embodiment, DeePC is used to replace MPC, an optimization method is adopted, a state estimation and a prediction process are implicitly included in an optimization constraint, data-driven prediction control is realized, and then energy scheduling of a micro-grid is realized. On one hand, the energy scheduling of the micro-grid can be realized without establishing an accurate prediction model, which not only reduces the cost, but also improves the efficiency; on the other hand, the influence of system parameter changes on the energy scheduling is avoided, and the accuracy of the energy scheduling is improved.

[0087] The various embodiments in the specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments. In particular, the embodiment of the energy scheduling device based on DeePC is described simply because it is basically similar to the embodiment of the energy scheduling method based on DeePC, and the relevant parts can be referred to the description of the method embodiment.

[0088] Please refer to Figure 3 The electronic device 300 shown in the embodiment of the specification provides a structural schematic diagram of an electronic device.

[0089] As Figure 3 The electronic device 300 can include at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0090] The communication bus 302 can be used to realize the connection and communication of the above-mentioned components.

[0091] The user interface 303 can include a key, and the optional user interface can also include a standard wired interface, a wireless interface.

[0092] The network interface 304 can include but is not limited to a Bluetooth module, an NFC module, a Wi-Fi module, etc.

[0093] The processor 301 can include one or more processing cores. The processor 301 connects various parts in the entire electronic device 300 through various interfaces and lines, executes various functions of the electronic device 300 and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be realized by at least one of the hardware forms of DSP, FPGA, and PLA. The processor 301 can integrate one or a combination of CPU, GPU, and modem. Among them, the CPU mainly processes the operating system, user interface, and application program; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 301, but be realized by a separate chip.

[0094] The memory 305 can include a RAM and can also include a ROM. Optionally, the memory 305 includes a non-transitory computer-readable medium. The memory 305 can be used to store instructions, programs, codes, code sets, or instruction sets. The memory 305 can include a program storage area and a data storage area, where the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, etc.; the data storage area can store data involved in the various method embodiments above, etc. The memory 305 can also optionally be at least one storage device located away from the aforementioned processor 301. The memory 305, as a computer storage medium, can include an operating system, a network communication module, a user interface module, and a DeePC-based energy scheduling application. The processor 301 can be used to invoke the DeePC-based energy scheduling application stored in the memory 305 and perform the steps of the DeePC-based energy scheduling mentioned in the foregoing embodiments.

[0095] The embodiments of the present specification also provide a computer-readable storage medium, which stores instructions, when the instructions run on a computer or a processor, cause the computer or the processor to perform the steps of one or more of the embodiments shown above. The various component modules of the electronic device described above, if implemented in the form of software function units and sold or used as independent products, can be stored in the computer-readable storage medium. Figure 1 The various component modules of the electronic device described above, if implemented in the form of software function units and sold or used as independent products, can be stored in the computer-readable storage medium.

[0096] In the above embodiments, all or part of the methods can be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part of the methods can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the specification are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in or transmitted by a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center through a wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, data center, etc. that includes one or more available media sets. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a digital versatile disc (DVD)), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0097] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by a computer program instructing related hardware, which can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. The storage medium includes ROM, RAM, magnetic or optical disc, and other program code storage media. In the case of no conflict, the technical features in the embodiments and the embodiments can be combined arbitrarily.

[0098] The above-described embodiments are merely described as the preferred embodiments of the specification, and do not limit the scope of the specification. Without departing from the design spirit of the specification, various modifications and improvements of the technical solutions of the specification made by a person of ordinary skill in the art should fall within the protection scope of the claims of the specification.

Claims

1. A DeePC-based energy dispatching method applied to off-grid microgrid systems, characterized in that, The off-grid microgrid system includes multiple power generation subsystems and battery energy storage subsystems, including: The output power of the multiple power generation systems and the energy storage value of the battery energy storage subsystem are obtained at different time periods. The control input sequence of the off-grid microgrid operation system is generated based on the obtained output power, and the energy storage output sequence of the off-grid microgrid operation system is generated based on the obtained energy storage value. The power reference trajectories of the multiple power generation systems and the battery energy storage subsystem are determined in the corresponding time period. The control input sequence, the energy storage output sequence, and the power reference trajectory are then input into the microgrid system energy management optimization model established based on the DeePC algorithm to calculate the optimal control input sequence of the off-grid operation system of the microgrid in the corresponding time period. The power of the multiple power generation systems is adjusted according to the optimal control input sequence during the corresponding time period; The step of generating the control input sequence of the off-grid microgrid system based on the obtained output power, and generating the energy storage output sequence of the off-grid microgrid system based on the obtained energy storage value, includes: The obtained output power and the obtained energy storage value are input into a pre-constructed linear discrete model, and the control input sequence and the energy storage output sequence are generated based on the output of the linear discrete model; wherein, the linear discrete model includes the first output power of the current time period, the second output power of the previous time period compared to the current time period, and the power adjustment amount of the current time period, and the control input sequence is generated based on the power adjustment amount of different time periods.

2. The energy scheduling method based on DeePC according to claim 1, characterized in that, Also includes: The control input sequence and the energy storage output sequence are processed according to the Hankel matrix to facilitate the calculation of the optimal control input sequence.

3. The energy scheduling method based on DeePC according to claim 1, characterized in that, The microgrid system energy management optimization model pre-sets a constraint set for the control input sequence and the energy storage output sequence. The calculation process for the optimal control input sequence includes: Given that the control input sequence and the energy storage output sequence satisfy the constraint set, the DeePC optimization problem is solved, and the result of the solution is the optimal control input sequence.

4. The energy scheduling method based on DeePC according to claim 3, characterized in that, The optimization constraints in the DeePC optimization problem include: the square of the difference between the energy storage output sequence and the power reference trajectory in the same time period, and the square of the control input sequence.

5. The energy scheduling method based on DeePC according to claim 1, characterized in that, The control input sequence is generated based on the power adjustment amount at different time periods; The step of adjusting the power of the multiple power generation systems according to the optimal control input sequence during the corresponding time period includes: The output power for the corresponding time period is calculated based on the power adjustment amount for the corresponding time period and the output power for the previous time period.

6. The energy scheduling method based on DeePC according to claim 1, characterized in that, The plurality of power generation systems include at least one of the following: photovoltaic power generation system, wind power power generation system, fuel cell power generation system, micro gas turbine power generation system, battery energy storage subsystem, and electrolysis hydrogen production subsystem.

7. A microgrid off-grid operation system, characterized in that, The off-grid microgrid system includes multiple power generation subsystems and battery energy storage subsystems, including: The acquisition unit is used to acquire the output power of the multiple power generation systems and the energy storage value of the battery energy storage subsystem at different time periods; The generation unit is used to generate the control input sequence of the off-grid operation system of the microgrid based on the obtained output power, and to generate the energy storage output sequence of the off-grid operation system of the microgrid based on the obtained energy storage value. The calculation unit is used to determine the power reference trajectory of the multiple power generation systems and the battery energy storage subsystem in the corresponding time period, and input the control input sequence, the energy storage output sequence and the power reference trajectory into the microgrid system energy management optimization model established based on the DeePC algorithm, so as to calculate the optimal control input sequence of the off-grid operation system of the microgrid in the corresponding time period; An adjustment unit is used to adjust the power of the plurality of generator systems according to the optimal control input sequence during a corresponding time period; The generation unit is specifically used for: The obtained output power and the obtained energy storage value are input into a pre-constructed linear discrete model, and the control input sequence and the energy storage output sequence are generated based on the output of the linear discrete model; wherein, the linear discrete model includes the first output power of the current time period, the second output power of the previous time period compared to the current time period, and the power adjustment amount of the current time period, and the control input sequence is generated based on the power adjustment amount of different time periods.

8. An electronic device, characterized in that, Including the processor and memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code stored in the memory to perform the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 6.

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