A sequential production simulation method for the integration of electricity and hydrogen

Abstract

The present invention provides a timing production simulation method for the integration of electricity and hydrogen. The steps are as follows: constructing an energy supply framework model for a new energy power station, decomposing the original hydrogen load sequence by the variational mode decomposition method, establishing an electricity-hydrogen integration operation model with the operation cost as the objective through the energy supply framework model, and performing sparse dimensionality reduction and linear programming solution on it, and solving the output timing production simulation results by the conjugate gradient asynchronous parallel computing method. The present invention constructs a timing production simulation model for the integration of electricity and hydrogen with a hybrid time scale, and uses the low-frequency component obtained by decomposing the hydrogen load to plan the adaptive step length of the hydrogen energy storage scheduling period, so that it cooperates with the real-time balanced electric energy to ensure the stability of the power output of the power station on a long time scale and improve the operation economy; then, aiming at the sparse dimensionality reduction of the large-scale electricity-hydrogen integration matrix, the parallel computing of the conjugate gradient method is used to not only speed up the solution speed but also ensure the accuracy of the simulation results.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy, and particularly relates to a timing production simulation method for the integration of electricity and hydrogen. Background Art

[0002] The new energy power station integrating electricity and hydrogen not only realizes the coupling between electric energy and hydrogen energy by using hydrogen production electrolyzers and hydrogen fuel cells, but its deep connotation refers to the reasonable operation of an electric-hydrogen multi-energy storage system composed of electrolyzers, fuel cells, hydrogen storage devices, and electrochemical energy storage to achieve flexible interaction conversion and collaborative complementary storage between electricity and hydrogen, and effectively solve the problem of unstable energy supply of wind and solar power generation at the long-time scale in the new energy power station.

[0003] At present, the prior art has studied the optimal operation of new energy power stations from aspects such as technical and economic feasibility analysis, carbon emission reduction, and improvement of new energy consumption. The new energy power station can achieve energy transfer through long-term hydrogen storage to solve the problem of long-term power shortage from several days to several seasons. However, hydrogen storage tanks are limited by the high costs brought about by frequent scheduling and fluctuations in hydrogen production. Short-term energy storage, such as electrochemical energy storage, is good at alleviating short-term power fluctuations but is not suitable for long-term energy transmission. The integration of electricity and hydrogen in new energy power stations combines the advantages of long-term and short-term energy storage systems, can solve the mismatch problem between the actual output and the power generation plan, and hydrogen energy storage does not require real-time balancing.

[0004] Existing time-series production simulation studies simulate electricity and hydrogen on the same time scale, failing to fully reflect the operating characteristics of hydrogen as a long-term energy storage solution. Further, although the introduction of hydrogen energy expands the single electricity production simulation to the combined production simulation of electricity-hydrogen multi-energy flows, the energy flow matrix of the electricity-hydrogen integration is often a large sparse matrix, mostly manifested as a large band matrix with a narrow bandwidth. A band matrix refers to a matrix in which only the elements near the diagonal can be non-zero, while the elements in other positions are all zero. The characteristic of this matrix structure is that it has fewer non-zero elements, so less resources are occupied during storage and calculation, which gives it great advantages in solving practical problems. Although the storage and calculation required for solving the linear equations of a band matrix are smaller than those of a general matrix linear equation system, when solving the linear equations of a band matrix, the current mainstream direct solution methods, such as the LU decomposition algorithm and the QR decomposition algorithm, are often inefficient due to the need to process a large number of zero elements. Further, the time span of time-series production simulation is relatively long, such as the annual operating time reaching 8760h, making the problem difficult to solve. Therefore, the existing literature on the solution technology for the time dimension of production simulation mostly falls into sequential solution and rolling solution. Sequential solution means that after splitting the production simulation period into multiple shorter time segments in the time dimension, the production simulation problems of each sub-segment are solved sequentially. Among them, the operating state at the end time period of the previous sub-segment is used as the initial operating state of the next sub-segment, so as to satisfy the coupling constraints between sub-problems and form a complete and feasible production simulation result. Rolling solution means that when calculating the optimization problem of a certain time segment, several subsequent time steps can be appropriately considered. However, to satisfy the coupling constraints between time segments, rolling solution needs to calculate step by step, and its serial calculation structure limits the calculation efficiency. In addition, there are also options to directly study the typical curves within the time period or perform dimensionality reduction through time series clustering to replace the complete medium- and long-term simulation.

[0005] Therefore, at the level of existing technologies, the following main problems are summarized:

[0006] 1. Simple electricity-hydrogen operation model: Most existing studies model and solve the heterogeneous energy flows of electricity and hydrogen on the same time scale, unable to reflect the advantages of long-term hydrogen energy storage and scheduling. Different from the real-time balanced electricity load, the hydrogen load does not need to meet the hourly balance, and there is still a lack of research on the hybrid time scale operation of new energy power stations based on time-series production simulation;

[0007] 2. Complex electricity-hydrogen integration matrix model: The introduction of hydrogen energy expands the single electricity production simulation to the combined production simulation of electricity-hydrogen multi-energy flows, and there is a technical gap in the research on how to perform sparse transformation and dimensionality reduction processing on the electricity-hydrogen integration energy flow matrix;

[0008] 3. There is a contradiction between computational efficiency and accuracy: Due to the strong time-series characteristics of renewable energy generation, the commonly used methods of rolling solution and selecting typical scenarios for production simulation often introduce significant errors in the results.

[0009] In summary, these problems pose higher requirements for the electro-hydrogen integration model of new energy power stations and its solution algorithms, and it is necessary to optimize and improve the electro-hydrogen integration scenario simulation method for new energy power stations. Summary of the Invention

[0010] In view of the problems existing in the prior art, the present invention provides a time-series-based electro-hydrogen integration new energy power station simulation method, which can effectively improve the speed of the algorithm while ensuring the solution accuracy.

[0011] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0012] The present invention provides a time-series production simulation method for electro-hydrogen integration, which includes the following steps:

[0013] Construct an energy supply framework model for a new energy power station based on electro-hydrogen integration technology;

[0014] Collect the hydrogen load data of the new energy power station, and establish a time-series-based original hydrogen load sequence through the hydrogen load data;

[0015] Decompose the original hydrogen load sequence by the variational mode decomposition method, and the formula is:

[0016] ;

[0017] In the formula, is the original hydrogen load sequence, k is the modal serial number, with a value of 1 - K ; t is the time index, and the subscript h represents the hydrogen identifier; is the set of hydrogen load subsequence components after variational mode decomposition, is the center frequency of the subsequence component, is the gradient calculation, is the impulse function, is the convolution calculation symbol;

[0018] After decomposition, a set of hydrogen load subsequence components including high-frequency subsequence components and low-frequency subsequence components is obtained;

[0019] Based on the low-frequency subsequence component, divide the adaptive step length of the hydrogen energy storage scheduling period of the new energy power station;

[0020] Establish an electricity-hydrogen integration operation model with the target of operating cost through the energy supply framework model, and input the set of hydrogen load subsequences and the adaptive step size of the hydrogen energy storage scheduling period of the new energy power station to obtain the electricity-hydrogen integration matrix of the electricity-hydrogen integration operation model;

[0021] Simplify the electricity-hydrogen integration matrix by the sparse dimensionality reduction method and obtain a simplified matrix equation set;

[0022] Linearize the simplified matrix equation set by the linear programming solution method, and solve the linearized simplified matrix equation set by the conjugate gradient asynchronous parallel computing method, and output the time series production simulation result.

[0023] Optionally, decompose the original hydrogen load sequence by the variational mode decomposition method, and the formula is:

[0024] ;

[0025] In the formula, is the original hydrogen load sequence, k is the mode number, taking values from 1 to K ; t is the time index, and the subscript h represents the hydrogen identifier; is the set of hydrogen load subsequences after variational mode decomposition, is the center frequency of the subsequence component, is the gradient calculation, is the impulse function, is the convolution calculation symbol.

[0026] Optionally, the energy supply framework model of the new energy power station includes renewable energy power generation equipment, hydrogen-electricity conversion equipment, electric energy storage equipment and hydrogen storage equipment.

[0027] Optionally, the objective function of the electricity-hydrogen integration operation model is:

[0028] ;

[0029] In the formula, is the annual comprehensive cost of the new energy power station, is the operation and maintenance cost, is the penalty cost for renewable energy power generation curtailment, is the penalty cost for insufficient hydrogen supply, is the life degradation cost of the electrolyzer stack, is the life degradation cost of the hydrogen fuel cell stack, is the life degradation cost of the electric energy storage equipment.

[0030] Optionally, the objective function of the electricity-hydrogen integration operation model further includes:

[0031] ;

[0032] wherein, is the grid friendliness, is the output power smoothness of the new energy power station, is the alignment degree between the output of the new energy power station and the planned power generation.

[0033] Optionally, constraint conditions are constructed based on the objective function, and the constraint conditions and the objective function form the electricity-hydrogen integrated operation model;

[0034] The constraint conditions include hydrogen storage constraint, electricity storage constraint, capacity constraint, renewable energy constraint, operation constraint and reliability constraint;

[0035] The renewable energy constraint is the limited curtailment of wind energy and light energy;

[0036] The reliability constraint includes the proportional range of the output power of the new energy power station to the demand power.

[0037] Optionally, the electricity-hydrogen integration matrix is a discrete state space matrix;

[0038] The simplification by the sparse dimensionality reduction method includes the following steps:

[0039] The linearized electricity-hydrogen integration matrix is linearly transformed into a banded matrix, and the banded matrix is divided into a block tridiagonal matrix;

[0040] The block tridiagonal matrix is decomposed by a matrix decomposition algorithm, and the simplified matrix equation set is extracted from the decomposition result.

[0041] Optionally, the matrix decomposition algorithm includes a recursive banded matrix diagonalization decomposition algorithm.

[0042] Optionally, the linear programming solution method is the big M method, and the electricity-hydrogen integration matrix of the electricity-hydrogen integrated operation model is linearized by introducing a variable method.

[0043] Optionally, the conjugate gradient asynchronous parallel calculation method includes the following steps:

[0044] The time series data of a preset time is divided into a plurality of consecutive sub-periods, and each sub-period includes a preset coupling period;

[0045] The residual vector, search direction vector and penalty factor of each sub-period are initialized;

[0046] The conjugate gradient iteration is performed in parallel for each initialized sub-period, and the current step size is calculated and the state variables are updated;

[0047] Synchronize the device states of two adjacent sub - periods at the coupling period;

[0048] Adaptively adjust the penalty factor based on the residual norm, and update the search direction vector;

[0049] Iteratively calculate by the iterative method until the residual norms of all sub - periods reach the preset convergence tolerance.

[0050] Optionally, the adaptively adjusting the penalty factor based on the residual norm includes the following steps:

[0051] Dynamically update the penalty factor according to the ratio of the residual norms of two adjacent iterations.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] The present invention constructs a hybrid time - scale electro - hydrogen fusion time - series production simulation model, and uses the low - frequency component solved by decomposing the hydrogen load to plan the adaptive step - size of the hydrogen energy storage scheduling period, so that it cooperates with the real - time balanced electric energy to ensure the stability of the output power of the station on a long - time scale and improve the operation economy; then based on the annual time - series production simulation, a sparse dimensionality reduction method for the large - scale electro - hydrogen fusion matrix is proposed, which is beneficial to reducing the solving difficulty of the model; finally, a parallel computing method based on the conjugate gradient method is proposed. By decomposing the long period into multiple sub - periods for distributed parallel solution, it not only speeds up the solving speed but also ensures the accuracy of the simulation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0055] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0057] It should be noted that like reference numerals and letters denote like items in the following figures, and thus, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0058] It is worth noting that the methods used in the present invention are all conventional methods unless otherwise specified; the raw materials and devices used are all conventional commercially available products unless otherwise specified, and their sources are not specifically limited.

[0059] It should also be noted that for the convenience of understanding in the specific embodiments of the present invention, the method steps are described in a certain order, but those skilled in the art can adjust the order of the steps according to actual needs, so this cannot be used as a limiting condition; further, in the description of the following specific embodiments, without special explanation, min and max in the upper and lower subscripts of each parameter should be understood with reference to common interpretations as representing the minimum and maximum values of the corresponding parameter at a certain time or within a certain period of time.

[0060] As Figure 1 shown, this embodiment provides a timing production simulation method for the integration of electricity and hydrogen, including the following steps:

[0061] 1. Construct an energy supply framework model for a new energy power station based on the technology of integrating electricity and hydrogen;

[0062] First, construct its operation framework according to the system composition and connection mode of the new energy power station. In the system of the new energy power station in this embodiment, renewable energy power generation is integrated, and at the same time, the actual conversion and storage between the power and water power generation energy carriers are utilized to meet the power load and water power generation requirements of the chemical industry or the transportation department. Specifically, at the input source port, renewable energy such as wind energy and solar energy is collected and regulated through wind turbines (WTs) and photovoltaic panels (PV) to generate electricity. This electricity provides power for the water electrolysis process in the electrolyzer stack to produce green hydrogen. Given the limited hydrogen production capacity of a single electrolyzer, a stack of multiple electrolyzers connected in series or parallel is assembled to meet the required production capacity. The generated hydrogen is pressurized by a compressor and stored in a hydrogen storage tank (HST) for long-term use. During periods of shortage of renewable energy, such as when there is no wind or cloudy days, the hydrogen can be converted back to electricity through the hydrogen FC. During system operation, due to the relatively low efficiency of the electricity-hydrogen-electricity conversion path, short-term power fluctuations are mainly managed by the BES rather than the FC. The integration of this electricity-hydrogen coupling and conversion path improves operational flexibility and ensures reliable multi-energy supply for renewable energy power plants.

[0063] Collect the hydrogen load data of the new energy power station, establish the original hydrogen load sequence based on time series through the hydrogen load data, and then decompose the original hydrogen load sequence by the variational mode decomposition method. Among them, the variational mode decomposition (VMD) is an adaptive and completely non-recursive method for modal variation and signal processing. By performing frequency domain analysis on non-stationary signals, complex signals are decomposed into multiple harmonic signals. The overall framework of the variational mode decomposition is a variational problem that minimizes the sum of the estimated bandwidths of each mode, where it is assumed that each mode is a finite bandwidth with different center frequencies. Use VMD to decompose the original hydrogen load sequence to obtain the high-frequency sub-sequence and the low-frequency sub-sequence . Among them, the high-frequency component can capture the high-frequency noise or rapid changes in the hydrogen load data; the low-frequency component can retain the trend of the data, reduce the noise interference of individual data points, and the adaptive step size of the hydrogen energy storage scheduling period of the new energy power station can be divided based on the low-frequency sub-sequence. It cooperates with the real-time balanced electric energy to ensure the stability of the power output of the station on a long time scale and improve the operating economy. Obtain the single-sided spectrum of the sub-sequence through the Hilbert transform and multiply it by the operator to shift the center band of the sub-sequence to the corresponding baseband. Estimate the bandwidth using the norm of the demodulation gradient, and adaptively divide the adjustment period step size of the sub-sequence according to the different bandwidths, as follows:

[0064] (1)

[0065] In the formula, is the set of hydrogen load sub-sequences after VMD decomposition, is the low-frequency sub-sequence, is the high-frequency sub-sequence; is the center frequency of the sub-sequence component, is the low-frequency center frequency, is the high-frequency center frequency; is the gradient calculation, is the impulse function, is the convolution calculation symbol.

[0066] 2. Establish an electro-hydrogen integrated operation model with the goal of balancing operation cost and grid connection friendliness through the energy supply framework model;

[0067] The electro-hydrogen integrated operation model of the renewable energy power station (REPS) based on the annual time series production simulation aims to minimize the annual comprehensive cost of the new energy power station and improve its grid connection friendliness , specifically as follows,

[0068] (2)

[0069] (3)

[0070] In the formula, is the annual comprehensive cost of the new energy power station, is the operation and maintenance cost, is the penalty cost for the curtailment of renewable energy power generation, is the penalty cost for insufficient hydrogen supply, is the life degradation cost of the electrolyzer stack, is the life degradation cost of the hydrogen fuel cell stack, is the life degradation cost of the electrical energy storage device; consists of two key aspects: the output power smoothness of the REPS and the alignment degree between the REPS output and the REPS planned power generation , in order to improve the grid connection friendliness, it is necessary to reduce the power fluctuation and power deviation. Therefore, the smaller the calculated value of the grid connection friendliness in the objective function, the better, so the minimum value is also taken.

[0071] Specifically, the operation and maintenance cost including the battery energy storage (BES) and hydrogen energy storage (HES) is as follows:

[0072] (4)

[0073] (5)

[0074] (6)

[0075] In the formula, and respectively represent the operation and maintenance costs of BES and HES; / / / are the operation and maintenance cost coefficients of BES / HST / electrolyzer stack / hydrogen fuel cell (FC); is the rated capacity of BES; / / / represent the investment costs per unit capacity of BES / HST / electrolyzer stack / FC; is the rated capacity of HST; are the rated powers of the electrolyzer stack and FC respectively.

[0076] If the electricity generated by wind and solar energy cannot be fully consumed, it will lead to energy waste. Therefore, punitive measures are taken for the curtailment of wind and solar energy. The penalty cost for this energy curtailment can be calculated as follows:

[0077] (7)

[0078] where is the penalty cost per unit of renewable energy abandoned; represents the wind and solar power generation abandoned by REPS at time t .

[0079] If REPS cannot fully meet the hydrogen load demand, curtailment is required, and the penalty cost for this energy shortage can be expressed as follows:

[0080] (8)

[0081] where is the penalty cost for reducing the hydrogen load per unit; represents the reduction in the hydrogen load of REPS at time t .

[0082] is determined by two factors: the peak-valley difference and the standard deviation of the output power curve, as follows:

[0083] (9)

[0084] (10)

[0085] (11)

[0086] (12)

[0087] (13)

[0088] where is the average output power value of the power station during the corresponding time period t at time T , is the output power during the corresponding time period t at time T , is the planned power generation power during the corresponding time period t at time T .

[0089] 2.1 Add constraint conditions;

[0090] Build constraint conditions based on the objective function, and form an electric-hydrogen integrated operation model by combining the constraint conditions with the objective function; among them, the constraint conditions include hydrogen storage constraint, electricity storage constraint, capacity constraint, renewable energy constraint, operation constraint and reliability constraint.

[0091] 2.1.1 Hydrogen storage constraint: The generalized hydrogen storage system includes the stages of hydrogen production, storage and utilization. The operation models of the electrolyzer stack, HST, FC and electric compressor, considering the electro-hydrogen conversion, can be expressed as follows.

[0092] (14)

[0093] (15)

[0094] (16)

[0095] (17)

[0096] (18)

[0097] (19)

[0098] (20)

[0099] (21)

[0100] (22)

[0101] (23)

[0102] (24)

[0103] (25)

[0104] In the formula, is the electrolysis power of the electrolyzer at time t ; is the electrolysis power of the electrolyzer stack at time t ; is the number of electrolyzers in the electrolyzer stack; represents the state of energy (SOE) of the hydrogen storage tank (Hydrogen storage tank, HST) at time t ; is the internal pressure of the HST, is the internal pressure of the HST at time t ; is the rated capacity of the HST; represents the molecular weight of hydrogen; and are binary variables that represent the hydrogen charging and discharging states of the HST at time t respectively; specifically, 1 indicates that the HST is in the hydrogen charging state or the discharging state, and 0 indicates that the HST is not in the operating state. The bandwidth is estimated using the norm of the demodulation gradient, and the low-frequency component sequence is adaptively partitioned according to the different bandwidths for the adjustment time step. and are binary variables that represent the hydrogen storage and hydrogen release behaviors of the HST respectively; is consistent with the state within the scheduling period; represents the imbalance state during the d-th scheduling period, where a negative value indicates a power shortage and the HST is used for energy consumption; D represents a set of scheduling periods. Equations (22) and (23) limit the HST to only one state within the same scheduling period. Equation (24) describes the power required to compress hydrogen into the HST. Equation (25) indicates that the outlet pressure of the compressor is limited within the safe operating boundary and usually, the inlet and outlet pressures of the compressor are set artificially to and respectively; / is the hydrogen storage / hydrogen release rate of the HST; is the operating efficiency of the compressor; is the compression power of the compressor; is the specific heat ratio; is the compressor inlet temperature; is the specific heat of hydrogen; / is the hydrogen storage / output efficiency of the HST.

[0105] 2.1.2 Electricity storage constraint: Considering the state of charge (SOC) and the charge and discharge rate, the electricity storage dynamics of the BES can be formulated as follows:

[0106] (26)

[0107] (27)

[0108] (28)

[0109] (29)

[0110] (30)

[0111] (31)

[0112] In the formula, represents the energy state of the BES at time t ; is the self-discharge rate of the BES; , Θ, and Γ are fitting parameters based on experimental data; and are binary variables representing the working state of the BES; / is the charging / discharging power of the BES; / is the charging / discharging efficiency of the BES;.

[0113] 2.1.3 Renewable energy constraint: The renewable energy constraint is the limited reduction of wind energy and light energy. The reduction of wind energy and solar energy should comply with the following constraints:

[0114] (32)

[0115] In the formula, in the range of [0, 1], represents the reduction coefficient of wind energy and solar energy power generation.

[0116] 2.1.4 Operation constraint: Both the electrolyzer and the FC are key electro-hydrogen coupling devices in the REPS. Their input / output working power cannot exceed their rated power and these two devices cannot be in the working state simultaneously.

[0117] (33)

[0118] (34)

[0119] (35)

[0120] In the formula, and are binary variables, representing the working states of the electrolyzer stack and the FC at time t respectively. Specifically, 0 indicates that the device is in the shutdown state, and 1 indicates that the device is in the operating state; are the rated powers of the electrolyzer stack and the FC respectively; is the output electric power of the FC.

[0121] 2.1.5 Reliability constraint: According to the energy supply reliability requirements of renewable energy power plants, including the proportion range of the output power and demand power of new energy stations, thus, the hydrogen load reduced in each time period should be restricted as follows:

[0122] (36)

[0123] (37)

[0124] In the formula, represents the reliability coefficient of hydrogen supply within the range of [0, 1].

[0125] Step 2: Modeling of the electricity-hydrogen integration matrix and its sparse dimensionality reduction method

[0126] 2.2 Modeling of the electricity-hydrogen integration matrix for new energy power stations;

[0127] Input the set of hydrogen load subsequences and the adaptive step size of the hydrogen energy storage scheduling period of the new energy power station to obtain the electricity-hydrogen integration matrix of the electricity-hydrogen integration operation model.

[0128] First, a discrete state space matrix M is constructed, which encapsulates the interconnected topology and energy conversion paths. M The incidence matrix from the input port to the branch X , the incidence matrix from the output port to the branch Y , and the internal energy conversion matrix Z . In Z , is the hydrogen-to-electricity conversion efficiency of the FC. In addition, the state vector V t represents the steady-state energy input and output of the REPS device in each operation cycle. In V t , / is the output power of the WTs / PV at time t ; and are the input electrolysis power and hydrogen production rate of the electrolyzer stack at time t ; is the input hydrogen rate of the FC; is the hydrogen of the REPS. Therefore, the input-output vector L t meets the input port requirements of the REPS for wind and solar power generation, as well as the output port requirements for the output power and the hydrogen load . The above matrix is simplified and shown as follows:

[0129] (38)

[0130] (39)

[0131] (40)

[0132] (41)

[0133] (42)

[0134] (43)

[0135] 3. Solving Model;

[0136] 3.1 Model Preprocessing. The electro-hydrogen integration matrix of the electro-hydrogen integration operation model is linearized through the linear programming solution method, and the linearized electro-hydrogen integration matrix is obtained. Among them, the linear programming solution method is the big M method, and the electro-hydrogen integration matrix of the electro-hydrogen integration operation model is linearized by introducing variable method.

[0137] Specifically, the REPS electro-hydrogen energy storage collaborative optimization configuration model is classified as a mixed-integer non-linear programming problem. The non-linearity mainly comes from equations (15), (19), (20), (26), (29), (30), (38) and (39), which significantly increase the complexity of solving the configuration model. Specifically, the coupling between the planning and operation variables introduces non-linearity in equations (15) and (26). To achieve linearization, by introducing two variables and to reformulate equations (15) and (26), these two variables represent the mass of hydrogen stored in the HST and the electricity stored in the BES at time t. In addition, the non-linearity in equations (19), (20), (29), (30), (38) and (39) is due to the interaction between the planning variables and the operation binary variables. To solve this problem, the big M method is applied, and a sufficiently large positive number M is introduced to manage the non-linearity as follows:

[0138] (44)

[0139] (45)

[0140] (46)

[0141] (47)

[0142] (48)

[0143] (49)

[0144] (50)

[0145] (51)

[0146] (52)

[0147] (53)

[0148] (54)

[0149] (55)

[0150] (56)

[0151] (57)

[0152] (58)

[0153] (59)

[0154] 3.2 Model dimensionality reduction processing, simplify the linearized electro-hydrogen fusion matrix through sparse dimensionality reduction method, and obtain a simplified matrix equation set.

[0155] Specifically, the linearized electro-hydrogen fusion matrix is linearly transformed into a band matrix, and the band matrix is divided into a block tridiagonal matrix; the discrete state space matrix is decomposed in a block form, and the recursive band diagonalization matrix decomposition algorithm (DS decomposition algorithm) is introduced to simplify the decomposed tridiagonal matrix region, and the simplified matrix equation set is extracted for iterative solution, which is beneficial to reducing the solution difficulty of the model. Among them, the band matrix is a special type of matrix, and its non-zero elements are concentrated in the band region near the main diagonal. For the decomposition of the band matrix, the recursive band diagonalization matrix decomposition algorithm is a method that uses the recursive strategy to perform diagonalization decomposition on the band matrix. Based on the matrix sparse dimensionality reduction method of DS decomposition, from the discrete state space matrix established above M It is observed that it can be linearly transformed into a band matrix through the determinant, and all of them can be divided into block tridiagonal matrices. Assuming that the number of diagonal blocks on the diagonal is p, then the order of each band diagonal block ( j = 1, …, p ) is (or approximately n / p). For the partitioned partitions, ( j = 1, …, p - 1 ) and ( j = 2, …, p ) are block matrices on the subdiagonal coupled with the diagonal blocks on the diagonal, specifically:

[0156] (60)

[0157] In the formula, and The orders of are all , and the matrices and contain a large number of zero elements, as shown below:

[0158] (61)

[0159] In the formula, and are k-order matrices.

[0160] Based on the above block pattern, the discrete state space matrix M is subjected to DS decomposition, and the specific decomposition is as follows,

[0161] (62)

[0162] In the formula, represents an identity matrix with a matrix order of and , while and The columns where the non-zero elements of are located (also known as spikes) form a tall and narrow submatrix of size and The expressions of

[0163] (63)

[0164] and can be obtained by solving the matrix equation,

[0165] (64)

[0166] In the formula, and are the jth partitions of the state vector V t and the input-output vector L t respectively; and are and submatrices respectively.

[0167] The block tridiagonal matrix is decomposed by the matrix decomposition algorithm, and the simplified matrix equation set is extracted from the decomposition result. Specifically, in the extraction of the simplified matrix equation set, the discrete state space matrix M After DS decomposition, the solution of equation (63) can be reduced to two steps, that is, and Solve The vector obtained by solving the system of linear equations in will be used as the right-hand vector of the system of linear equations for solving . When solving the system of linear equations for , by using the special diagonal block form of matrix , the system of linear equations is transformed into p systems of linear equations with smaller coefficient matrices of lower order for solution, which will greatly reduce the computational complexity.

[0168] Solving the system of linear equations is further simplified to solving a system of linear equations with a smaller coefficient matrix of lower order, as follows,[[]]

[0169] (65)

[0170] where is composed of k rows directly above and below each block of matrix .

[0171] In fact, the spike matrices and can also be partitioned as:[[]]

[0172] (66)

[0173] (67)

[0174] where , , and , , are respectively the top k rows, the middle and the bottom k rows of , and here similarly, corresponding to and and there are:[[]]

[0175] (68)

[0176] Extract the simplified system of linear equations (65) from , which only contains , , and the top and bottom k rows. The coefficient matrix of this simplified system of linear equations contains p - 1 diagonal diagonal blocks, and its i-th diagonal block is denoted as , and the corresponding sub-diagonal block is denoted as , , the state vector block and the right - hand vector block in the reduced linear equations are expressed as .

[0177] 3.3 Model solution: Solve the simplified matrix equations based on the conjugate gradient asynchronous parallel computing method and output the time - series production simulation results.

[0178] Among them, the conjugate gradient asynchronous parallel computing method includes the following steps:

[0179] Divide the time - series data at a preset time into multiple consecutive sub - periods, and each sub - period contains a preset coupling period;

[0180] Specifically, the parallel computing method based on the conjugate gradient method proposes a parallel computing algorithm of the conjugate gradient method that only requires limited information interaction. The entire period of time - series production simulation is divided into multiple smaller consecutive sub - periods. The concept of associated periods is introduced, and the sub - period problems after solution do not lose the time - series property. By taking the iteration directions of variables SOC and SOE as the conjugate gradient directions for iteration and using a dynamically adaptive adjusted penalty factor to accelerate the iteration interaction to reach a consensus, the states of the electrical energy storage SOC and the hydrogen storage tank SOE are obtained.

[0181] To decompose the time - series production problem into multiple smaller consecutive sub - period problems, first introduce auxiliary variables related to the electrical energy storage SOC and the hydrogen energy storage SOE , , and to decouple the original SOC and SOE values between sub - periods i and j of the new energy power station. Then the day - time consistency constraint can be reshaped as

[0182] (69)

[0183] In the formula, is the time - series set; is the set of coupling period points.

[0184] To ensure the time - series property between sub - periods i and j of the new energy power station, its local optimal operation objective function can be rewritten as

[0185] (70)

[0186] Among them, and are the auxiliary vectors of SOC and SOE between sub - periods i and j of the new energy power station respectively; are the penalty factors related to the consistency constraints of electrical energy storage and hydrogen storage at the k - th decoupling point respectively.

[0187] At At the +1-th iteration, the new energy power station uses the obtained at the -th iteration and to select a set of search directions = , = -A , where A is a positive definite matrix, and is a conjugate vector group with respect to A. If the one-dimensional search has been performed in the direction for times and is obtained, the next step is to determine such that the direction can make reach a consensus with faster. After is determined, the descent algorithm of equations (71) and (72) is used to obtain .

[0188] Therefore, the steps also include initializing the residual vector, search direction vector, and penalty factor for each sub-period;

[0189] Performing conjugate gradient iteration on each initialized sub-period in parallel, calculating the current step size, and updating the state variables;

[0190] Synchronizing the device states of two adjacent sub-periods at the coupling period;

[0191] Adapting the penalty factor based on the residual norm and updating the search direction vector; Optionally, dynamically updating the penalty factor according to the ratio of the residual norms of two adjacent iterations;

[0192] Iteratively calculating by the iterative method until the residual norms of all sub-periods reach the preset convergence tolerance.

[0193] The specific iterative process is as follows:

[0194] Initialization: The electro-hydrogen energy storage auxiliary vector of the new energy power station , , calculate the initial residual = -A , the convergence deviation value , the number of iterations = 1

[0195] Iteration: Calculate the search direction: Use the formula to calculate the search direction coefficient , and use the formula to calculate the search direction ;

[0196] Calculation step size: Use the formula for calculation;

[0197] Update : ;

[0198] Update the residual : ;

[0199] Convergence judgment: Judge whether it satisfies ;

[0200] Update the iteration number: = + 1;

[0201] Output the result: , , ;

[0202] Update and ;

[0203] (71)

[0204] (72)

[0205] (73)

[0206] (74)

[0207] (75)

[0208] In the formula, when ( , A ) = 0, , and ([[]] , ) = ([[]] , ) = 0, that is . Since = are orthogonal to each other, so there is at least one zero vector in , which can make + = 0, that is, convergence is achieved.

[0209] During the iteration process, the conjugate gradient asynchronous parallel computing method can overcome the problem of unstable fluctuations in the search direction near convergence. Through the orthogonality between conjugate search directions, the convergence speed of the iteration is accelerated. While using the coupling time period to ensure the operation timing, parallel computing is used to accelerate the solution speed.

[0210] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than a limitation on the protection scope of the present invention. Any simple modification or equivalent replacement made by those of ordinary skill in the art to the technical solution of the present invention shall not depart from the essence and scope of the technical solution of the present invention.

Claims

1. A method for simulating the sequential production of electric hydrogen fusion, characterized in that: The steps include: Construct an energy supply framework model for new energy stations based on electric-hydrogen fusion technology; Collecting hydrogen load data of the new energy station, and establishing an original hydrogen load sequence based on time series through the hydrogen load data; The original hydrogen load sequence is decomposed by the variational mode decomposition method, and the formula is: ； In the formula, is the original hydrogen loading sequence, k is the mode number, with a value of 1- K ; t is the time index, subscript h It stands for Hydrogen Logo; is the set of hydrogen load subsequences after variational mode decomposition, is the center frequency of the subsequence component, is the gradient calculation, is the impulse function, Compute symbols for convolution; After decomposition, a hydrogen load subsequence set including a high-frequency component subsequence and a low-frequency component subsequence is obtained; An adaptive step size for dividing the hydrogen energy storage scheduling period of the new energy station based on the low-frequency component subsequence; An electric-hydrogen fusion operation model with an operation cost as a target is established through the energy supply framework model, and the hydrogen load subsequence set and the adaptive step size of the hydrogen energy storage scheduling period of the new energy station are input to obtain an electric-hydrogen fusion matrix of the electric-hydrogen fusion operation model; Simplifying the electric-hydrogen fusion matrix by a sparse dimensionality reduction method and obtaining a simplified matrix equation group; Linearize the simplified matrix equations by a linear programming solution method, solve the linearized simplified matrix equations by a conjugate gradient asynchronous parallel computing method, and output a sequential production simulation result; The conjugate gradient asynchronous parallel computing method comprises the following steps: Dividing the time series data of a preset time into a plurality of continuous sub-periods, and each of the sub-periods includes a preset coupling period; Initializing the residual vector, search direction vector and penalty factor of each sub-period; Perform conjugate gradient iterations in parallel for each initialized sub-period, calculate the current step size and update the state variables; Synchronizing device states of two adjacent sub-periods in the coupling period; Adaptively adjusting the penalty factor based on the residual norm, and updating the search direction vector; in the adaptively adjusting the penalty factor based on the residual norm, dynamically updating the penalty factor according to the residual norm ratio of two adjacent iterations; The residual norms of all sub-periods are iteratively calculated through an iterative method until they reach the preset convergence tolerance.

2. The method for simulating the sequential production of electric hydrogen fusion according to claim 1, characterized in that: The energy supply framework model of the new energy station includes renewable energy power generation equipment, hydrogen-to-electricity conversion equipment, electrical energy storage equipment and hydrogen storage equipment.

3. The method for simulating the sequential production of electric hydrogen fusion according to claim 2, characterized in that: The objective function of the electric-hydrogen fusion operation model is: ； In the formula, is the annual comprehensive cost of the new energy station, For operation and maintenance costs, Penalty costs for renewable energy generation cuts, Penalty costs for insufficient hydrogen supply, is the lifetime degradation cost of the electrolyzer stack, is the lifetime degradation cost of the hydrogen fuel cell stack, It is the lifetime degradation cost of the electric energy storage equipment.

4. The method for simulating the sequential production of electric hydrogen fusion according to claim 3, characterized in that: The objective function of the electric-hydrogen fusion operation model also includes: ； In the formula, To be friendly to netizens, is the output power smoothness of the new energy station, It is the alignment between the output of renewable energy sites and the planned power generation.

5. The method for simulating the sequential production of electric hydrogen fusion according to claim 4, characterized in that: Constructing constraint conditions based on the objective function, and combining the constraint conditions and the objective function to form the electric-hydrogen fusion operation model; The constraints include hydrogen storage constraints, electricity storage constraints, capacity constraints, renewable energy constraints, operation constraints and reliability constraints; The renewable energy constraint is a limited reduction amount of wind energy and solar energy; The reliability constraint includes a ratio range of the output power of the new energy station to the required power.

6. The method for simulating the sequential production of electric hydrogen fusion according to claim 1, characterized in that: The electric-hydrogen fusion matrix is ​​a discrete state space matrix; The sparse dimensionality reduction method simplification includes the following steps: The linearized electric-hydrogen fusion matrix is ​​transformed into a band matrix by linear transformation, and the band matrix is ​​divided into block tridiagonal matrices; The block tridiagonal matrix is ​​decomposed by a matrix decomposition algorithm, and the simplified matrix equation group is extracted from the decomposition result.

7. The method for simulating the sequential production of electric hydrogen fusion according to claim 6, characterized in that: The matrix decomposition algorithm includes a recursive banded angularization matrix decomposition algorithm.

8. The method for simulating the sequential production of electric hydrogen fusion according to claim 1, characterized in that: The linear programming solution method is the big M method, and the electric-hydrogen fusion matrix of the electric-hydrogen fusion operation model is linearized by introducing a variable method.

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