Electricity-hydrogen energy storage collaborative configuration method for new energy stations based on time-series production simulation

Abstract

The present invention provides a method for coordinated configuration of electric-hydrogen energy storage in new energy stations based on time-series production simulation, which includes the following steps: constructing a degradation model of hydrogen-electricity conversion equipment in new energy stations, and constructing an electric-hydrogen coordinated configuration model of new energy stations based on the degradation model and time-series production simulation; linearizing the electric-hydrogen coordinated configuration model by a linear programming solution method, and obtaining a linearized result; simplifying the linearized result by a matrix transformation method, and obtaining a calculation matrix; solving the calculation matrix by an iterative method, and obtaining an optimal solution, thereby obtaining an electric-hydrogen energy storage coordinated configuration scheme for new energy stations. The present invention quantitatively analyzes the impact of the degradation model on the life of various types of equipment and the impact on the operating cost of the station, thereby constructing a more complete electric-hydrogen coordinated storage configuration model, and then solving it based on a linear programming solution method, a matrix transformation method, and an iterative method to reduce the difficulty of solving the model while ensuring accuracy, and quickly obtain the optimal configuration scheme.

Description

Technical Field

[0001] The present invention relates to the field of new energy technologies, and in particular to a method for coordinated configuration of electric and hydrogen energy storage in new energy stations based on time-series production simulation. Background Art

[0002] In recent years, with the rapid development of new energy, its high volatility has gradually attracted people's attention. As a result, a variety of technical means have been developed to alleviate the impact of its output volatility on the power grid. One of the ways is to combine traditional power generation systems and / or configure new energy stations composed of energy storage units to achieve stable output.

[0003] In specific new energy sites, energy storage units come in various forms. Among them, electric energy storage has the characteristics of fast two-way regulation, strong short-time charging and discharging capabilities, and good controllability. It can provide fast frequency regulation and inertia support for new energy bases, and at the same time make up for the problem of insufficient DC transmission capacity caused by wind and solar fluctuations.

[0004] However, in the long-term operation, electric energy storage faces problems such as capacity attenuation, increased internal resistance, and reduced efficiency. In the prior art, relevant research has been carried out on the life prediction and degradation modeling of electrochemical energy storage units in electric energy storage units, and the life of electric energy storage has been extended by methods such as temperature management and optimization of charging and discharging strategies. Furthermore, the reasonable configuration of the electric hydrogen multi-energy storage system composed of hydrogen storage devices and electrochemical energy storage can realize flexible interactive conversion and synergistic complementary storage of electric hydrogen to broaden the flexible operation boundary of the energy supply system and ensure the sustainable and stable supply of multiple energies. Among them, the electrolyzer is a key equipment in the process of hydrogen production by electrolysis of water. In previous studies on the capacity configuration and operation analysis of electrolyzers, the hydrogen production efficiency of the electrolyzer was mostly assumed to be a fixed value. However, in actual operation, the hydrogen production efficiency of the electrolyzer does not remain unchanged, and further related studies only consider the hydrogen production efficiency of the electrolyzer to be related to the operating power. In different life stages of the electrolyzer, the "power-efficiency" hydrogen production characteristics of the electrolyzer vary due to component aging, such as catalyst poisoning, electrode passivation, and proton exchange membrane degradation. A constant hydrogen production efficiency or a hydrogen production efficiency that is only related to power does not conform to the actual situation and cannot accurately evaluate the hydrogen production output. The hydrogen production efficiency of the electrolyzer decreases with the increase of operating time, forming a "time-power-efficiency" hydrogen production characteristic.

[0005] At present, facing multiple application scenarios such as peak-valley arbitrage, peak-shaving and frequency regulation, smoothing power fluctuations and promoting the consumption of new energy, technical personnel have carried out research on the optimal configuration of electrochemical energy storage at new energy stations, expanding from a single economic optimal configuration target to multi-type planning that considers power supply reliability, peak-shaving / frequency regulation effects, voltage stability margins and multiple auxiliary service value enhancements. However, the research mainly focuses on the optimal configuration of electrochemical energy storage such as lithium batteries and batteries, and the time span of sequential production simulation is relatively long, such as an annual operating time of 8760h, which makes the problem difficult to solve. The problem of coordinated configuration of electric and hydrogen energy storage at new energy stations based on sequential production simulation is essentially a high-dimensional, multi-constrained, and multi-objective system optimization problem. Although there are corresponding optimization methods in the existing technology, there are still many problems. Specifically, the traditional configuration model often leads to large fluctuations in the output power of new energy stations and serious degradation of electrolyzers through the use of simple modeling; the traditional solution method usually adopts rolling solution or uses typical scenarios for production simulation. For renewable energy with strong timing characteristics, the disadvantage of this method is that it can only consider the timing within a limited time, and the configuration result solved is often a local optimum rather than a global optimum.

[0006] As a result, the optimal configuration of new energy station capacity has become a new research hotspot at home and abroad, covering unified planning framework, economic and technical feasibility analysis, and green hydrogen economic preparation. The sequential production simulation method that considers time coupling constraints such as equipment ramping and energy storage can simulate the optimal operation process of new energy stations to analyze the rationality of energy storage configuration.

[0007] In summary, these problems put forward higher requirements for the electric-hydrogen coordinated configuration model and solution algorithm of new energy stations, and it is also necessary to optimize and improve the electric-hydrogen energy storage configuration method of new energy stations. Summary of the invention

[0008] In view of the problems existing in the prior art, the present invention provides a new energy station energy storage configuration method based on time series that comprehensively considers cost issues, which effectively improves the speed of the algorithm while ensuring the accuracy of the solution.

[0009] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0010] The present invention provides a method for coordinated configuration of electric and hydrogen energy storage in a new energy station based on time-series production simulation, which comprises the following steps:

[0011] Constructing a degradation model of hydrogen-to-electricity conversion equipment in a new energy station; the hydrogen-to-electricity conversion equipment includes an electrolyzer, a fuel cell, and an electrical energy storage device;

[0012] Based on the degradation model and the time-series production simulation, an electric-hydrogen collaborative configuration model of the new energy station is constructed; the construction method of the electric-hydrogen collaborative configuration model is:

[0013] Constructing a discrete state space matrix based on the framework structure and operation mode of the new energy station;

[0014] The objective function of the electric-hydrogen synergistic configuration model based on time series is constructed by the discrete state space matrix and the degradation model; the objective function includes the operating cost of the new energy station, and the formula is:

[0015] ;

[0016] In the formula, is the annual comprehensive cost of the new energy station, is the equivalent annual construction cost, 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, The lifetime degradation cost of the electric energy storage equipment;

[0017] Constructing constraint conditions based on the objective function, and combining the constraint conditions and the objective function to form the electric-hydrogen collaborative configuration model;

[0018] Linearizing the electric-hydrogen synergistic configuration model by a linear programming solution method and obtaining a linearization result; simplifying the linearization result by a matrix transformation method and obtaining a calculation matrix; solving the calculation matrix by an iterative method and obtaining an optimal solution;

[0019] Based on the optimal solution, a coordinated configuration plan for electric and hydrogen energy storage in new energy stations is obtained.

[0020] Optionally, the method for constructing a degradation model of the hydrogen-to-electricity conversion device comprises the following steps:

[0021] Construct the electrolyzer life degradation model, fuel cell life degradation model and electric energy storage device life degradation model respectively;

[0022] The degradation model of the hydrogen-to-electricity conversion device is obtained through a model and data fusion method.

[0023] Optionally, the electrolytic cell life degradation model is constructed based on a data fitting method and Faraday's law.

[0024] Optionally, the fuel cell life degradation model is constructed based on a data fitting method and a fuel cell degradation rate.

[0025] Optionally, the cumulative capacity loss is predicted based on the Arrhenius model, and the life degradation model of the electric energy storage device is calculated.

[0026] Optionally, the objective function also includes a calculation function of user friendliness, and the formula is:

[0027] ;

[0028] 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.

[0029] Optionally, the constraints include hydrogen storage constraints, electricity storage constraints, capacity constraints, renewable energy constraints, operation constraints and reliability constraints;

[0030] The renewable energy constraint is a limited reduction amount of wind energy and solar energy;

[0031] The reliability constraint includes a ratio range of the output power of the new energy station to the required power.

[0032] Optionally, the linear programming solution method is the big M method, and the electric-hydrogen synergistic configuration model is linearized by introducing a variable method.

[0033] Optionally, the matrix transformation method is a matrix QR transformation, which transforms the linearization result into the product of an orthogonal unitary matrix and an upper triangular matrix.

[0034] Optionally, solving the calculation matrix by an iterative method comprises the following steps:

[0035] Setting initial values ​​for at least some parameters and variables of the calculation matrix;

[0036] The optimal solution is solved by an adaptive multiplier distributed parallel computing method based on inertial acceleration.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The present invention quantitatively analyzes the impact of degradation models on the life of various types of equipment and the impact on station operating costs, thereby constructing a more complete electric-hydrogen collaborative storage configuration model, and then solves it based on linear programming methods, matrix transformation methods and iterative methods to reduce the difficulty of solving the model while ensuring accuracy, and quickly obtain the optimal configuration plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0040] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0043] 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, and their sources are not specifically limited unless otherwise specified.

[0044] It should also be noted that in order to facilitate 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 change the order of the steps according to actual needs, and therefore this cannot be used as a restriction. It should be further noted that in the description of the following specific embodiments, the min and max in the superscripts and subscripts of each parameter should be understood as representing the minimum and maximum values ​​of the parameter corresponding to the time or a period of time unless otherwise specified.

[0045] like Figure 1 As shown, this embodiment provides a method for coordinated configuration of electric and hydrogen energy storage in a new energy station based on time-series production simulation, comprising the following steps:

[0046] First, build its operation framework based on the system composition and connection method of the new energy station.

[0047] 1. Construct a degradation model of hydrogen-to-electricity conversion equipment in new energy stations. The method for constructing a degradation model of hydrogen-to-electricity conversion equipment includes the following steps:

[0048] Construct the electrolyzer life degradation model, fuel cell life degradation model and electric energy storage device life degradation model respectively;

[0049] 1.1 Electrolyzer life degradation model: The electrolyzer life degradation model is constructed based on the data fitting method and Faraday's law.

[0050] Specifically, under direct current electrolysis conditions, the efficiency of hydrogen production by alkaline water electrolysis depends largely on the fluctuation of the electrolysis voltage. Due to the change in voltage level, the electrode reaction is dynamically affected, which can reduce the performance of the electrolyzer and reduce the hydrogen production efficiency. Specifically, the reduction in hydrogen production efficiency is mainly affected by three voltage fluctuation parameters: frequency f ,amplitude A and the DC component of the output voltage By fitting a large amount of experimental data under normal conditions (0°C, atmospheric pressure), the hydrogen production rate can be obtained and input electrolysis power The formula is as follows:

[0051] (1)

[0052] (2)

[0053] In the formula, Indicates the rated voltage of the electrolytic cell; and is an empirical parameter in hydrogen production data.

[0054] The relationship between hydrogen production rate and input electrolysis power can be derived from equations (1) and (2), as follows:

[0055] (3)

[0056] In the formula, is the rated power of the electrolyzer; The operating status of the electrolyzer can be divided into four categories, namely shutdown, fluctuation, rated and overload.

[0057] According to Faraday's law, the hydrogen production efficiency Defined as measured Theoretical hydrogen production rate The ratio between them is as follows:

[0058] (4)

[0059] (5)

[0060] In the formula, is the molar volume of an ideal gas under normal conditions; is the theoretical thermodynamic decomposition voltage of water; F is the Faraday number; and are the charges of hydrogen and oxygen respectively.

[0061] Furthermore, by combining equations (3) to (5), the hydrogen production efficiency under different operating conditions can be obtained: The piecewise function of

[0062] (6)

[0063] Generally speaking, hydrogen production efficiency reflects the lifetime degradation of the electrolyzer, so the degradation rate of the electrolyzer under different operating conditions It can be defined according to its efficiency characteristic curve:

[0064] (7)

[0065] (8)

[0066] In the formula, and Indicates that the electrolytic cell is i Unit degradation coefficient and corresponding operating duration under various operating conditions.

[0067] Therefore, electrolyzer stacks consisting of multiple cells in series should be built to ensure sufficient hydrogen production, and the lifetime degradation cost of the electrolyzer stack According to the degradation rate The calculation is as follows:

[0068] (9)

[0069] (10)

[0070] In the formula, The number of units representing the electrolyser stack; is the operating cost per unit time of the electrolyzer under conditions of fluctuating input power; and is the fluctuation tolerance factor of the electrolytic cell; , and The incremental cost factor representing the life degradation caused by electrolysis power fluctuation; is a binary variable that indicates the on / off state of the electrolytic stack.

[0071] 1.2 Hydrogen fuel cell (FC) life degradation model; A fuel cell life degradation model is constructed based on data fitting method and fuel cell degradation rate.

[0072] Specifically, since the lifetime degradation of a fuel cell is a nonlinear and multi-physics process, which is usually manifested as a decrease in the fuel cell voltage, it is difficult to obtain an accurate model that fully conforms to reality. Here, the degradation rate The parameter variables are formulated based on the combined theoretical model and experimental data as follows:

[0073] (11)

[0074] In the formula, is the initial voltage of the hydrogen fuel cell; and correlation coefficients with voltage degradation under constant and dynamic operating conditions, respectively; s is a function of time. When the load changes, the FC undergoes transient processes, including gas diffusion and water accumulation. Due to unfavorable operating conditions, these changes may lead to accelerated degradation of electrolytic components such as catalysts. This degradation process usually occurs in a logarithmic manner.

[0075] Generally, a hydrogen fuel cell stack is composed of multiple individual cells connected in series. Therefore, the lifetime degradation cost of a fuel cell stack can be understood as the lifetime degradation cost of its electrolyzer stack. , can be calculated based on the degradation rate The calculation is as follows:

[0076] (12)

[0077] (13)

[0078] In the formula, is the degradation cost coefficient of hydrogen fuel cell per unit time; , and is the incremental cost coefficient of fuel cell lifetime degradation; and is the boundary value of the degradation degree; is the number of hydrogen fuel cells assembled in the stack; is a binary variable representing the switching state of the hydrogen fuel cell stack.

[0079] 1.3 Battery Energy Storage (BES) equipment life degradation model; Based on the Arrhenius model (Arrhenius model, acceleration model), the cumulative capacity loss is predicted and the battery energy storage equipment life degradation model is calculated.

[0080] According to the Arrhenius model, the cumulative capacity loss of electric energy storage It can be estimated as follows:

[0081] (14)

[0082] (15)

[0083] (16)

[0084] In the formula, B is the pre-exponential factor; is the activation energy; z is the power law factor, is relative to a given B and Equivalent ampere-hour throughput; R is the gas constant; Te represents absolute temperature; is the normalized discharge power of the battery energy storage; , , and are empirical coefficients obtained by fitting experimental data.

[0085] Therefore, the lifetime degradation cost of the electric energy storage device can be understood as the lifetime degradation cost of the electrolyzer stack. , can be formulated according to the capacity throughput principle of BES as follows:

[0086] (17)

[0087] In the formula, is the cost coefficient per unit capacity loss.

[0088] Therefore, three models are obtained through the model and data fusion method, and together constitute the desired degradation model of the hydrogen-to-electricity conversion equipment.

[0089] 2. Construct an electric-hydrogen coordinated configuration model for new energy stations based on the degradation model and temporal production simulation (TPS).

[0090] The construction method of the electric-hydrogen synergistic configuration model includes the following steps:

[0091] 2.1 Construct a discrete state space matrix based on the framework structure and operation mode of the new energy station;

[0092] Among them, the framework structure of the renewable energy power station (REPS): The renewable energy power generation system integrates renewable energy generation while utilizing the actual conversion and storage between electricity and hydropower energy carriers to meet the power load and hydropower demand of the chemical industry or transportation sector. Specifically, at the input source port, renewable energy such as wind and solar energy are collected and regulated through wind turbines (WTs) and photovoltaic panels (PV) to generate electricity. This electricity powers the water electrolysis process within the electrolyzer stack to produce green hydrogen. Given the limited hydrogen production capacity of a single electrolyzer, a stack of multiple electrolyzers in series or parallel is assembled to meet the required production capacity. The produced hydrogen is pressurized by a compressor and stored in a hydrogen storage tank (HST) for long-term use. During periods of renewable energy shortage, such as no wind or cloudy days, it can be converted back to electricity through hydrogen FC. During system operation, short-term power fluctuations are mainly managed by BES rather than FC due to the relatively low efficiency of the electricity-hydrogen-electricity conversion path. This integration of electricity-hydrogen coupling and conversion paths increases operational flexibility and ensures a reliable multi-energy supply for renewable energy power plants.

[0093] An energy hub modeling method is developed to describe the process of hydrogen production, conversion, storage and supply under steady-state operation of renewable energy power generation system. Specifically, a discrete state space matrix is ​​constructed. , which encapsulates the interconnected topology and energy conversion paths. The incident matrix from the input port to the branch , output port to branch incident matrix and the internal energy conversion matrix Composition. middle, / is the charge / discharge efficiency of the BES; / is the hydrogen storage / output efficiency of HST; is the hydrogen-to-electricity conversion efficiency of the FC; is the operating efficiency of the compressor. In addition, the state vector represents the steady-state energy input and output of the REPS device in each operating cycle. middle / It's in time t Output power of WTs / PV at time ; and It's time t Input electrolysis power and hydrogen production rate of the electrolyzer stack at 1 : is the compression power of the compressor; / is the hydrogen storage / release rate of HST; is the output electrical power of FC, is the input hydrogen rate of FC; / is the charging / discharging power of BES; / is the output power / hydrogen of REPS. Therefore, the input-output vector Meet REPS requirements for wind and solar power input ports, as well as output power and hydrogen load The output port requirements of the above matrix are simplified as follows:

[0094] (18)

[0095] (19)

[0096] (20)

[0097] (twenty one)

[0098] (twenty two)

[0099] (twenty three)

[0100] 2.2 Construct the objective function of the time-series-based electric-hydrogen synergistic configuration model through discrete state space matrix and degradation model;

[0101] Among them, the objective function is the operating cost of the new energy station, and the function target is the minimum value; further, the objective function also includes the calculation function of the network friendliness. Specifically, the operating cost adopts the annual comprehensive operating cost, that is, the electric hydrogen storage synergistic configuration model of the new energy station based on the annual TPS aims to minimize the annual comprehensive cost of the new energy station. , and improve its friendliness to netizens , as follows:

[0102] (twenty four)

[0103] (25)

[0104] In the formula, Including equivalent annual construction costs , operation and maintenance costs , Penalty costs for curtailing renewable energy generation , Penalty costs for insufficient hydrogen supply . It consists of two key aspects: REPS output power smoothness and the degree of alignment between REPS output and REPS planned generation In order to improve the goodness of the network, it is necessary to reduce the power fluctuation and power deviation. Therefore, the smaller the calculated value of the goodness of the network in the objective function, the better, so the minimum value is also taken.

[0105] Specifically, The equivalent annual construction cost including BES and hydrogen energy storage (HES) is calculated as follows:

[0106] (26)

[0107] (27)

[0108] (28)

[0109] In the formula, and denote the equivalent annualized construction costs of BES and HES respectively; is the rated capacity of BES; / / / Represents the investment cost per unit capacity of BES / HST / electrolyzer / FC; and are the discount rates for BES and HES respectively; / / / Represents the expected life of BES / HST / electrolyzer / FC; Indicates the rated capacity of the HST; are the rated powers of the electrolyzer stack and FC respectively.

[0110] Including the operation and maintenance costs of BES and HES, the formula is as follows:

[0111] (29)

[0112] (30)

[0113] (31)

[0114] In the formula, and represent the operation and maintenance costs of BES and HES respectively; / / / It is the operation and maintenance cost coefficient of BES / HST / electrolyzer / FC.

[0115] 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 reduction of wind and solar energy. The penalty cost of such energy reduction can be calculated as follows:

[0116] (32)

[0117] In the formula, is the penalty cost per unit of renewable energy forgone; Indicates at time t The amount of wind and solar power that is discarded by REPS.

[0118] If the REPS cannot fully meet the hydrogen load demand, it needs to be reduced, and the penalty cost of this energy shortfall can be expressed as follows:

[0119] (33)

[0120] In the formula, is the penalty cost per unit of hydrogen load reduction; Represents REPS at time t The reduction in hydrogen loading.

[0121] Determined by two factors: peak-to-valley difference and the standard deviation of the output power curve , as shown below:

[0122] (34)

[0123] (35)

[0124] (36)

[0125] (37)

[0126] (38)

[0127] In the formula, For the field station time t The average output power value.

[0128] In order to achieve effective configuration, this paper adopts the weighted summation method to transform the multi-objective problem into a single-objective problem for solution, namely:

[0129] (39)

[0130] (40)

[0131] In the formula, is the normalized result, and Respectively represent and To accurately reflect the weight distribution, each sub-objective function must be normalized, usually by dividing each objective by its maximum value.

[0132] 2.3 Construct constraints based on the objective function, and combine the constraints and the objective function to form an electric-hydrogen collaborative configuration model.

[0133] Among them, the constraints include hydrogen storage constraints, electricity storage constraints, capacity constraints, renewable energy constraints, operation constraints and reliability constraints, as follows:

[0134] 2.3.1 Hydrogen storage constraints: The general hydrogen storage system includes hydrogen production, storage and utilization stages. The operation model of the electrolyzer stack, HST, FC and electric compressor, taking into account the electricity-hydrogen conversion, can be expressed as follows:

[0135] (41)

[0136] (42)

[0137] (43)

[0138] (44)

[0139] (45)

[0140] (46)

[0141] (47)

[0142] (48)

[0143] (49)

[0144] (50)

[0145] In the formula, It's in timet The electrolytic power of the electrolyzer, It's in time t The electrolysis power of the electrolyzer stack; Indicates HST at time t State of energy (SOE); is the internal pressure of HST, For in time t The internal pressure of the HST; is the rated capacity of the HST; represents the molecular weight of hydrogen; and is a binary variable, representing HST at time t The charging and discharging state of hydrogen; is the specific heat capacity of hydrogen; is the compressor inlet temperature; is the specific heat ratio. Specifically, 1 means that the HST is in a hydrogen charging or discharging state, and 0 means that the HST is not in a working state. The outlet pressure of the compressor is limited to the safe operating boundary. and Usually, the inlet and outlet pressures of the compressor are set manually, respectively. and .

[0146] 2.3.2 Storage Constraints: Considering the state of charge (SOC) and charge and discharge rates, the storage dynamics of BES can be formulated as follows:

[0147] (51)

[0148] (52)

[0149] (53)

[0150] (54)

[0151] (55)

[0152] (56)

[0153] In the formula, Indicates that BES is at time t energy state; is the self-discharge rate of BES; , Θ and Γ are fitting parameters based on experimental data; and It is a binary variable, indicating the working status of BES.

[0154] 2.3.3 Capacity constraints: The configuration capacity limits of HST, electrolyzer stack, FC and BES shall meet the following requirements:

[0155] (57)

[0156] (58)

[0157] (59)

[0158] (60)

[0159] 2.3.4 Renewable energy constraints: Due to the sustainable development needs of renewable energy systems, renewable energy constraints are the limited reduction of wind and solar energy, that is, the reduction of wind and solar energy The following constraints should be observed:

[0160] (61)

[0161] In the formula, In the range of [0,1], it means wind energy and solar energy The curtailment factor of power generation.

[0162] 2.3.5 Operation Constraints: Electrolyzers and FCs are both key electricity-hydrogen coupling devices in REPS. Their input / output operating power cannot exceed their rated power. , , and the two devices cannot be in working state at the same time, so the expression of the constraint condition is as follows:

[0163] (62)

[0164] (63)

[0165] (64)

[0166] In the formula, and are binary variables representing time t The working status of the electrolyzer stack and FC. Specifically, 0 means the device is in shutdown state, and 1 means the device is in operation state.

[0167] 2.3.6 Reliability constraints: Reliability constraints include the output power of new energy stations and required power According to the energy supply reliability requirements of renewable energy power plants, the hydrogen load reduction in each time period should be limited as follows:

[0168] (65)

[0169] (66)

[0170] In the formula, The reliability coefficient of hydrogen supply is expressed in the range of [0,1]; For time t of hydrogen load demand.

[0171] 3. Solve the electricity-hydrogen synergistic configuration model.

[0172] 3.1 Linearize the electric-hydrogen synergistic configuration model through linear programming solution method and obtain the linearization results;

[0173] Among them, the linear programming solution method is the big M method, and the electric-hydrogen synergistic configuration model is linearized by introducing the variable method, and the calculation matrix is ​​obtained. The specific steps are as follows:

[0174] The REPS electric-hydrogen energy storage coordinated optimization configuration model is classified as a mixed integer nonlinear programming problem. The nonlinearity mainly comes from the previous equations (43), (47), (48), (52), (55), (56), (63) and (64), which significantly increase the complexity of solving the configuration model. Specifically, the coupling between the planning and operating variables introduces nonlinearity in equations (43) and (52). In order to achieve linearization, two variables are introduced and To restate equations (43), (52), (45) and (54), these two variables represent the t The mass of hydrogen stored in the HST and the electricity stored in the BES. In addition, the nonlinearity in equations (47), (48), (55), (56), (63), and (64) is due to the interaction between the planning variables and the operational binary variables. To address this problem, the large M method is applied, introducing a sufficiently large positive number M to manage nonlinearity as follows:

[0175] (67)

[0176] (68)

[0177] (69)

[0178] (70)

[0179] (71)

[0180] (72)

[0181] (73)

[0182] (74)

[0183] (75)

[0184] (76)

[0185] (77)

[0186] (78)

[0187] (79)

[0188] (80)

[0189] (81)

[0190] (82)

[0191] 3.2 Use matrix transformation method to simplify linearization results;

[0192] Specifically, the matrix transformation adopts the matrix Givens-QR transformation (matrix QR transformation).

[0193] The Givens-QR transform is designed to convert a discrete state space matrix with dimension m×n into Convert to sparse matrix , for efficient iterative calculation. First, the matrix Represented as an orthogonal unitary matrix and the upper triangular matrix The product of is as follows:

[0194] (83)

[0195] The process then involves constructing a series of 2×2 unitary matrices To perform the Givens rotation, the matrix is ​​updated iteratively In each update, the newly constructed 2×2 unitary matrix The matrix The two rows of the corresponding matrix and the identity matrix q After each update, the matrix The first element of the second row in is set to zero. Through multiple updates, The lower triangular part of is completely zeroed, and the upper triangular matrix is ​​obtained At the same time, the identity matrix q is transformed into a unitary matrix , it can also be passed through all Obtained by matrix multiplication.

[0196] The detailed update process of the matrix is ​​as follows:

[0197] The matrix The diagonal elements in the first column are set to always be the first element of the two-element vector. Then, the matrix Givens rotation is applied to the non-diagonal elements in the first column. After each rotation, the values ​​of the diagonal elements will change as shown in the following formula:

[0198] (84)

[0199] (85)

[0200] (86)

[0201] In the formula, and They are and The conjugation of.

[0202] In addition, once the matrix All elements below the diagonal in the first column of are set to zero, excluding The Givens QR transform continues with the first row and first column of and The dimensions of are reduced to ( m -1)×( n -1) and ( m -1)× n This process will continue until The row or column dimension of is reduced to 1, at which point the Givens transform stops. As a result, is transformed into an upper triangular matrix , the unitary matrix It can be expressed as all The matrix product is calculated as follows:

[0203] (87)

[0204] (88)

[0205] Furthermore, during each Givens-QR transformation, the unitary matrix To update the matrix It is worth noting that the same column of data in different rows must participate in the same update step, but the updates of different columns of data in the same row do not interfere with each other, so the calculation of a row of data that does not interfere with each other can be updated in parallel, and the multiplication operation of the two matrices can be converted into a process of multiplying the vectors and summing them respectively, and then the calculation matrix is ​​obtained. The summation result is as follows:

[0206] (89)

[0207] 3.3 Solve the calculation matrix through iteration method and obtain the optimal solution;

[0208] Specifically, this embodiment adopts an adaptive multiplier distributed parallel computing method based on inertial acceleration. That is, an inertial acceleration adaptive multiplier distributed parallel computing algorithm that only requires limited information interaction, divides the entire time period of the time series production simulation into multiple smaller continuous sub-periods, introduces the concept of associated time periods, and solves the sub-period problems without losing the time series. By linearly combining the first two iteration points of the variables SOC and SOE, that is, extrapolating to obtain the iteration point of the new step, the iterative interaction is accelerated to reach a consensus, and the state of the electric energy storage SOC and the hydrogen storage tank SOE is obtained.

[0209] To decompose the time-series production problem into multiple smaller continuous sub-period problems, we first introduce auxiliary variables related to the SOC of electric energy storage and hydrogen energy storage. , , and ( , , , ) to decouple the sub-periods of new energy stations i and j The original SOC value during the day can be reshaped as follows:

[0210] (90)

[0211] In the formula, A collection of scheduling periods.

[0212] New energy station sub-time period i The augmented Lagrangian function L i And its local energy autonomous subproblem can be expressed as:

[0213] (91)

[0214] in, =[ , ]and =[ , ] are the new energy stations in the sub-periods i The self-optimized results are consistent with the new energy stations in the sub-period j Indirect energy storage, Auxiliary vectors and Lagrange multiplier vectors for hydrogen energy storage; =[ , ] is the new energy station in the sub-period j The self-optimized results are consistent with the new energy stations in the sub-period i Auxiliary vectors for inter-electric energy storage and hydrogen energy storage; and is the penalty factor of the consistency constraint; is a set of sub-periods. The key iterative process of the algorithm is summarized as follows:

[0215] Algorithm: Adaptive multiplier distributed parallel computing method based on inertial acceleration;

[0216] Initialization: Electric hydrogen energy storage auxiliary vector for new energy stations , and Lagrange multipliers , set the consistency penalty factor , convergence deviation value and Iterations =1;

[0217]

[0218] for new energy stations in sub-periods ;

[0219] New energy stations use electric hydrogen energy storage to assist variables , decoupling the daily timing constraints (43) and (52) of the primary hydrogen;

[0220] In sub-period i and j The local optimal solution of the solver is obtained separately, and formulas (92), (94) are used and ;

[0221] In the τ+1 iteration, the linear combination of the first two iteration points of formula (93) and (95) is used to extrapolate the new iteration point and ;

[0222] Based on the obtained and Using formula (96) to update, we get ;

[0223] Calculate residuals and , update the penalty factor using formula (97) ;

[0224] Update number of iterations: = +1;

[0225] end for;

[0226] end while;

[0227] Output: , , , ;

[0228] 1) Update and ;

[0229] exist +1 iteration, the new energy station in the sub-period i and j Use in The obtained and To solve its local subproblem and update its local decision variables and , use the first two iteration points to perform linear combination, that is, extrapolate to get the next iteration point and , as shown below:

[0230] (92)

[0231] (93)

[0232] (94)

[0233] (95)

[0234] In the formula, and Sub-periods i and j The set of relaxation factors.

[0235] 2) Update ;

[0236] The Lagrange multiplier receives the iterated and , updated using formula (96) , the formula is as follows:

[0237] (96)

[0238] 3) Dynamic adaptive adjustment of penalty factors;

[0239] During the algorithm iteration process, it is found that when approaching the minimum point, since the search direction is consistent with the current gradient direction, the gradual decrease of the gradient value will slow down the convergence speed. It can be accelerated by adjusting the penalty factor, but too large a penalty factor may slow down the convergence of the dual residual, and too small a penalty factor will slow down the convergence of the original residual. Therefore, this embodiment adopts a penalty factor dynamic adjustment method to make the convergence faster and more stable. The formula is as follows:

[0240] (97)

[0241] (98)

[0242] (99)

[0243] In the formula, is the acceleration factor; is the iteration number threshold; is the growth coefficient.

[0244] The updated state parameters of the electric energy storage SOC and the hydrogen storage tank SOE are brought into the electric-hydrogen coordinated configuration model, and the optimal solution is solved. Then, the electric-hydrogen energy storage coordinated configuration plan for the new energy site is obtained based on the optimal solution.

[0245] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions of the technical solution of the present invention by ordinary technicians in this field do not deviate from the essence and scope of the technical solution of the present invention.

Claims

1. A method for coordinated configuration of electric and hydrogen energy storage in new energy stations based on time-series production simulation, characterized in that: The steps include: Constructing a degradation model of hydrogen-to-electricity conversion equipment in a new energy station; the hydrogen-to-electricity conversion equipment includes an electrolyzer, a fuel cell, and an electrical energy storage device; Based on the degradation model and the time-series production simulation, an electric-hydrogen collaborative configuration model of the new energy station is constructed; the construction method of the electric-hydrogen collaborative configuration model is: Constructing a discrete state space matrix based on the framework structure and operation mode of the new energy station; The objective function of the electric-hydrogen synergistic configuration model based on time series is constructed by the discrete state space matrix and the degradation model; the objective function includes the operating cost of the new energy station, and the formula is: ； In the formula, is the annual comprehensive cost of the new energy station, is the equivalent annual construction cost, 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, The lifetime degradation cost of the electric energy storage equipment; The objective function also includes a calculation function for user friendliness, and the formula is: ； In the formula, To be friendly to netizens, is the output power smoothness of the new energy station, The alignment between the output of the renewable energy site and the planned power generation; The formula for the output power smoothness of the new energy station is: ； In the formula, is the peak-to-valley difference, is the standard deviation of the output power curve; Constructing constraint conditions based on the objective function, and combining the constraint conditions and the objective function to form the electric-hydrogen collaborative configuration model; The electric-hydrogen synergistic configuration model is linearized by a linear programming solution method to obtain a linearization result; the linearization result is simplified by a matrix transformation method to obtain a calculation matrix; the calculation matrix is ​​solved by an iterative method to obtain an optimal solution; the iterative method for solving the calculation matrix includes the following steps: Setting initial values ​​for at least some parameters and variables of the calculation matrix; The optimal solution is solved by an adaptive multiplier distributed parallel computing method based on inertial acceleration mode; the entire time period of the time series production simulation is divided into multiple continuous sub-time periods, and associated time periods are introduced; Based on the optimal solution, a coordinated configuration plan for electric and hydrogen energy storage in new energy stations is obtained.

2. The method for coordinated configuration of electric and hydrogen energy storage in new energy stations based on time-series production simulation according to claim 1 is characterized in that: The method for constructing a degradation model of a hydrogen-to-electricity conversion device comprises the following steps: Construct the electrolyzer life degradation model, fuel cell life degradation model and electric energy storage device life degradation model respectively; The degradation model of the hydrogen-to-electricity conversion device is obtained through a model and data fusion method.

3. The method for coordinated configuration of electric and hydrogen energy storage in new energy stations based on time-series production simulation according to claim 2 is characterized in that: The electrolytic cell life degradation model is constructed based on data fitting method and Faraday's law.

4. The method for coordinated configuration of electric and hydrogen energy storage in new energy stations based on time-series production simulation according to claim 2 is characterized in that: The fuel cell life degradation model is constructed based on a data fitting method and a fuel cell degradation rate.

5. The method for coordinated configuration of electric and hydrogen energy storage in new energy stations based on time-series production simulation according to claim 2 is characterized in that: The accumulated capacity loss is predicted based on the Arrhenius model, and the life degradation model of the electric energy storage device is calculated.

6. The method for coordinated configuration of electric and hydrogen energy storage in new energy stations based on time-series production simulation according to claim 1 is characterized in that: 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.

7. The method for coordinated configuration of electric and hydrogen energy storage in new energy stations based on time-series production simulation according to claim 1 is characterized in that: The linear programming solution method is the big M method, and the electric-hydrogen synergistic configuration model is linearized by introducing a variable method.

8. The method for coordinated configuration of electric and hydrogen energy storage in new energy stations based on time-series production simulation according to claim 7 is characterized in that: The matrix transformation method is a matrix QR transformation, which transforms the linearization result into the product of an orthogonal unitary matrix and an upper triangular matrix.

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