Saline-alkali soil electro-hydrogen energy storage capacity distribution method considering photovoltaic power fluctuation stabilization

Abstract

The invention discloses a method for distributing electricity hydrogen energy storage capacity of saline-alkali soil by considering photovoltaic power fluctuation stabilization, and relates to the technical field of energy distribution of energy storage systems. According to the main technical scheme, the distribution method for the electricity hydrogen energy storage capacity of the saline-alkali soil considering photovoltaic power fluctuation stabilization comprises the following steps that an original power sequence is decomposed into IMF of all orders and a residual item through an EMD method, and a fluctuation limit value is reasonably determined; reconstructing each order of intrinsic mode function, and performing superposition calculation on each component of a low-frequency band and a high-frequency band according to a preset frequency index so as to obtain a low-frequency component and a high-frequency component; grid connection is carried out on the low-frequency component, and the high-frequency component is transmitted to the hybrid energy storage system for stabilizing; and a hybrid energy storage system considering the environmental influence of the saline-alkali soil is constructed. The method is mainly used for energy distribution of the energy storage system.

Description

Technical Field

[0001] This invention relates to the field of energy distribution technology for energy storage systems, and in particular to a method for distributing the capacity of hydrogen energy storage in saline-alkali land that takes into account the smoothing of photovoltaic power fluctuations. Background Technology

[0002] Driven by the global energy transition, power distribution networks are evolving from traditional unidirectional centralized power supply to diversified distributed grid connection. Xinjiang boasts abundant solar energy resources, but photovoltaic output is highly random and non-stationary due to factors such as weather and dust storms, placing higher demands on grid connection quality and frequency regulation capabilities. Simultaneously, the saline-alkali environment exacerbates equipment corrosion and degradation, and incurs additional costs such as water treatment, posing challenges to the reliability and economics of single energy storage solutions. Therefore, there is an urgent need to construct an electric-hydrogen hybrid energy storage system to achieve synergy between power-type and energy-type energy storage.

[0003] While existing research has applied technologies such as data prediction, robust optimization, and digital twins to distribution network dispatching, it still lacks an integrated modeling and optimization framework for the multi-coupled scenarios of "strong photovoltaic fluctuations + saline-alkali land environment + electricity-hydrogen energy storage". In particular, it is insufficient in the selection of power decomposition thresholds, construction of typical daily scenarios, lifetime-economic collaborative evaluation, and environmental adaptability modeling. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method for allocating the capacity of electric hydrogen storage in saline-alkali land that takes into account the smoothing of photovoltaic power fluctuations. The main purpose is to provide a method for allocating the capacity of electric hydrogen storage in saline-alkali land that takes into account the smoothing of photovoltaic power fluctuations, thereby improving grid connection quality through multi-objective optimization and taking into account both life-cycle cost and operational reliability.

[0005] To achieve the above objectives, the present invention mainly provides the following technical solutions:

[0006] This invention provides a method for allocating hydrogen energy storage capacity in saline-alkali land that takes into account the smoothing of photovoltaic power fluctuations. The method includes the following steps:

[0007] The original power sequence is decomposed into IMFs of various orders and a residual term using the EMD method, and fluctuation limits are reasonably determined.

[0008] The intrinsic mode functions of each order are reconstructed, and the components of the low-frequency band and the high-frequency band are superimposed according to the preset frequency index to obtain the low-frequency component and the high-frequency component.

[0009] Low-frequency components are connected to the grid, while high-frequency components are transmitted to a hybrid energy storage system for smoothing.

[0010] Construct a hybrid energy storage system that takes into account the environmental impact of saline-alkali land.

[0011] Furthermore, the original power sequence is decomposed into eigenmode functions of each order and a residual term, the expression of which is:

[0012]

[0013] Among them, c i (t) is the i-th order intrinsic mode function, r n (t) represents the residual component, and n represents the decomposition order.

[0014] Furthermore, the reasonable determination of fluctuation limits includes:

[0015] The fluctuation limit can be expressed as a function in the following form:

[0016]

[0017] Calculate the volatility ΔP for each IMF component. i Its expression is:

[0018]

[0019] Calculate the fluctuation S of each IMF component. i Its expression is:

[0020]

[0021] Where N is the number of data points;

[0022] Set fluctuation limit S th The expression is as follows:

[0023] S th =Percentile(S i ,90%) (5)

[0024] The determined fluctuation limit S th The EMD decomposition results are applied to typical days to verify and adjust the fluctuation limits.

[0025] Furthermore, the reconstruction of the intrinsic mode functions of each order, and the superposition calculation of the components in the low-frequency and high-frequency bands according to the preset frequency index, to obtain the low-frequency and high-frequency components, includes:

[0026] High-frequency reconstruction, based on the EMD decomposition results, involves sequentially superimposing IMF components of each order from top to bottom to generate high-frequency reconstruction components of each order. The specific reconstruction method is as follows:

[0027] Among them, P HF,i (t) represents the i-th order high-frequency reconstruction component, and p is the order of the high-frequency reconstruction;

[0028] Low-frequency reconstruction involves superimposing IMF components of various orders from bottom to top based on the EMD decomposition results to generate low-frequency reconstructed components of various orders. The specific reconstruction method is as follows:

[0029] Among them, P LF,i (t) represents the i-th order low-frequency reconstruction component, N is the total IMF order, and q is the order of low-frequency reconstruction.

[0030] Furthermore, the construction of the hybrid energy storage system taking into account the environmental impact of saline-alkali land includes:

[0031] Construct an economic model;

[0032] Construct a technical performance model;

[0033] Construct the objective function and its constraints.

[0034] Furthermore, the construction of the economic model includes:

[0035] Construct a power cost prediction model;

[0036] Construct a water consumption cost prediction model;

[0037] Construct a hydrogen levelized cost prediction model that includes lifetime decay.

[0038] Furthermore, the construction technology performance model includes:

[0039] Construct a dynamic model of the electrolyzer;

[0040] Construct a pressure model for the hydrogen storage tank;

[0041] Construct a battery degradation model in saline-alkali land.

[0042] Furthermore, the construction objective function and its constraints include:

[0043] Constructing the objective function: The optimization objective is concretized into two mathematical objective functions:

[0044]

[0045] Where X is the decision variable vector, P electrolyzer C represents the installed power (kW) of the electrolytic cell. battery V represents the battery energy storage capacity (kWh). storage Indicates hydrogen storage capacity (m³) 3 ), P operation (t) represents the system operating power (kW) at time t, f1(X)=LCOH(X) represents the levelized hydrogen cost target (yuan / kg), and f2(X)=-L(X) represents the negative value of the system lifetime target (years), which is convenient for minimization.

[0046] The system satisfies the following constraints: total power balance constraint, battery state of charge constraint, electrolyzer start / stop constraint, and battery charge / discharge constraint.

[0047] This invention proposes a method for allocating electro-hydrogen energy storage capacity in saline-alkali land that considers photovoltaic power fluctuation smoothing. The method uses EMD (Enhanced Motion Decomposition) to adaptively decompose photovoltaic output, then sets reasonable fluctuation limits to connect low-frequency components to the grid and transmit high-frequency components to a hybrid energy storage system for smoothing, reducing the operating pressure on the energy storage system from the source. Then, it uses bidirectional reconstruction to select component combinations as "grid-storage" allocation pairs, ensuring grid connection smoothness and energy storage economics. Finally, it constructs a hybrid energy storage system that considers the environmental impact of saline-alkali land, achieving a multi-dimensional coupling characterization of environment, equipment, and economy. By constructing an objective function and its constraints, it achieves effective grid power smoothing and high-frequency fluctuation suppression, and by increasing the proportion of hydrogen energy storage, it mitigates electrochemical energy storage degradation, enhancing system reliability and improving life-cycle economics in the harsh environment of saline-alkali land. Attached Figure Description

[0048] Figure 1 A flowchart illustrating a method for allocating hydrogen energy storage capacity in saline-alkali land, taking into account photovoltaic power fluctuation mitigation, as provided in an embodiment of the present invention;

[0049] Figure 2 A flowchart of a hybrid energy storage system considering the environmental impact of saline-alkali land is provided as an embodiment of the present invention;

[0050] Figure 3 This is a schematic diagram of a hybrid energy storage system provided in an embodiment of the present invention;

[0051] Figure 4 The flowchart illustrates a solution for a method to allocate the capacity of hydroelectric storage in saline-alkali land, taking into account the smoothing of photovoltaic power fluctuations, as provided in an embodiment of the present invention. Detailed Implementation

[0052] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0053] Hybrid Energy Storage System (HESS) is a technology that optimizes energy by combining different energy storage technologies. It is mainly used in fields such as grid frequency regulation and energy storage power stations.

[0054] Empirical Mode Decomposition (EMD) is an adaptive signal processing method specifically designed for analyzing nonlinear and non-stationary signals. Its core idea is to decompose complex signals into several Intrinsic Mode Functions (IMFs) through iterative selection, with each IMF representing a wave component at a different scale in the signal.

[0055] Fine to Coarse (F2C) reconstruction is a key technology in signal processing and system simulation. It mainly involves the sampling, filtering and recovery process of high-frequency signals. Its core objective is to accurately reconstruct the original continuous signal through discrete samples, while suppressing high-frequency noise or aliasing distortion.

[0056] Low-frequency reconstruction (Coarse to Fine, C2F) is a hierarchical processing strategy widely used in signal processing, computer vision, and pattern recognition. Its core idea is to achieve information optimization from coarse to fine through multi-level progressive refinement.

[0057] This invention provides a method for allocating hydrogen energy storage capacity in saline-alkali land that takes into account the smoothing of photovoltaic power fluctuations. The method includes the following steps:

[0058] The original power sequence is decomposed into IMFs of various orders and a residual term using the EMD method, and fluctuation limits are reasonably determined.

[0059] The intrinsic mode functions of each order are reconstructed, and the components of the low-frequency band and the high-frequency band are superimposed according to the preset frequency index to obtain the low-frequency component and the high-frequency component.

[0060] Low-frequency components are connected to the grid, while high-frequency components are transmitted to a hybrid energy storage system for smoothing.

[0061] Construct a hybrid energy storage system that takes into account the environmental impact of saline-alkali land.

[0062] This invention proposes a method for allocating electro-hydrogen energy storage capacity in saline-alkali land that considers photovoltaic power fluctuation smoothing. The method uses EMD (Enhanced Motion Decomposition) to adaptively decompose photovoltaic output, then sets reasonable fluctuation limits to connect low-frequency components to the grid and transmit high-frequency components to a hybrid energy storage system for smoothing, reducing the operating pressure on the energy storage system from the source. Then, it uses bidirectional reconstruction to select component combinations as "grid-storage" allocation pairs, ensuring grid connection smoothness and energy storage economics. Finally, it constructs a hybrid energy storage system that considers the environmental impact of saline-alkali land, achieving a multi-dimensional coupling characterization of environment, equipment, and economy. By constructing an objective function and its constraints, it achieves effective grid power smoothing and high-frequency fluctuation suppression, and by increasing the proportion of hydrogen energy storage, it mitigates electrochemical energy storage degradation, enhancing system reliability and improving life-cycle economics in the harsh environment of saline-alkali land.

[0063] Example 1

[0064] like Figures 1 to 4 As shown, this embodiment of the invention provides a method for allocating the capacity of hydroelectric storage in saline-alkali land that takes into account the smoothing of photovoltaic power fluctuations. The method includes the following steps:

[0065] 101. The original power sequence is decomposed into IMFs of various orders and a residual term using the EMD method.

[0066] To address the characteristics of strong randomness and prominent non-stationarity in photovoltaic power output signals, empirical mode decomposition (EMD) is adopted as the core decomposition method. EMD is an adaptive time-frequency processing technique, particularly suitable for nonlinear and non-stationary signals. It can decompose the original power sequence into multiple eigenmode functions and a residual term, the expression of which is:

[0067]

[0068] Among them, c i (t) is the i-th order intrinsic mode function, r n (t) represents the residual component, and n represents the decomposition order. In this embodiment, the fluctuations of four typical days are sorted, and the median is selected as the typical day for the experiment. Based on the preliminary experimental analysis, the decomposition order n = 4 is obtained, that is, four high and low frequencies are extracted.

[0069] 102. Determine fluctuation limits reasonably.

[0070] To ensure the stability of photovoltaic power output and effectively mitigate photovoltaic fluctuations, determining reasonable fluctuation limits is a crucial step. These fluctuation limits distinguish between power fluctuations requiring mitigation by energy storage systems and those that can be directly connected to the grid. The setting of this threshold directly impacts the grid connection quality, operational economy, and lifespan of the energy storage equipment. The fluctuation limits can be expressed as a function as follows:

[0071]

[0072] Fluctuation limit S th Used to distinguish between high-fluctuation and low-fluctuation components in photovoltaic power output; specifically, when the power fluctuation S... i Exceeding the limit S th When the frequency fluctuation is high, it indicates that the fluctuation band needs to be smoothed by an energy storage system to ensure grid connection quality; conversely, it can be directly connected to the grid, reducing the burden on the energy storage system and thus optimizing the overall system's economy and efficiency. The fluctuation limit S... th The determination needs to be based on the statistical characteristics of photovoltaic output and the technical parameters of the energy storage system. By analyzing the power fluctuation distribution on a typical day and combining it with the system's response capability, a reasonable limit is determined, as follows:

[0073] Calculate the volatility ΔP for each IMF component. i Its expression is:

[0074]

[0075] Calculate the fluctuation S of each IMF component. i Its expression is:

[0076]

[0077] Where N is the number of data points;

[0078] By analyzing the fluctuations S of various IMF components over four typical days i Statistical analysis was conducted to obtain the distribution of fluctuations under different IMF orders, thereby quantifying the contribution of each IMF order component to the overall power fluctuation.

[0079] Based on the statistical analysis results, combined with the technical parameters of the energy storage system and the grid connection requirements, the fluctuation limit S was determined. th This study selects the 90th percentile of the power fluctuation distribution as the fluctuation limit to ensure that the vast majority of fluctuations are within the limit range, and sets the fluctuation limit S. th The expression is as follows:

[0080] S th =Percentile(S i ,90%) (5)

[0081] The determined fluctuation limit S th The EMD decomposition results were applied to typical days to verify and adjust the fluctuation limits. i Exceeding the system's set grid connection threshold S th In this case, the fluctuation is considered to need to be balanced through the energy storage system and recorded as 1; otherwise, it can be directly connected to the grid and recorded as 0. That is:

[0082]

[0083] To optimize the performance of the energy storage system, reduce the burden on energy storage devices, and avoid increasing system complexity and computational burden by processing each IMF component separately, the energy storage task is divided into two parts: high-frequency energy storage and low-frequency grid connection. The low-frequency component is directly connected to the grid, while the high-frequency component is managed as a power task of the Hybrid Energy Storage System (HESS). Its mathematical expression is:

[0084]

[0085] in, and P HESS,n Let these represent the original photoelectric power signal, the direct grid-connected component, and the HESS power task at time n, respectively.

[0086] 103. The reconstructing of the intrinsic mode functions of each order involves superimposing the components of the low-frequency and high-frequency bands according to a preset frequency index to obtain the low-frequency and high-frequency components.

[0087] The IMF reconstruction process, based on preset frequency parameters, superimposes the components of the low-frequency and high-frequency bands to obtain the low-frequency and high-frequency components. The reconstruction methods mainly include two types: high-frequency reconstruction (Fine to Coarse, F2C) and low-frequency reconstruction (Coarse to Fine, C2F), as detailed below:

[0088] High-frequency reconstruction, based on the EMD decomposition results, involves sequentially superimposing IMF components of each order from top to bottom to generate high-frequency reconstruction components of each order. The specific reconstruction method is as follows:

[0089] Among them, P HF,i (t) represents the i-th order high-frequency reconstruction component, and p is the order of the high-frequency reconstruction;

[0090] Low-frequency reconstruction involves superimposing IMF components of various orders from bottom to top based on the EMD decomposition results to generate low-frequency reconstructed components of various orders. The specific reconstruction method is as follows:

[0091] Among them, P LF,i (t) represents the i-th order low-frequency reconstruction component, N is the total IMF order, and q is the order of low-frequency reconstruction.

[0092] like Figure 2 As shown, after the signal decomposition and reconstruction are completed, efficient management of optoelectronic power can be achieved by formulating a reasonable power allocation strategy.

[0093] 104. Construct a hybrid energy storage system that takes into account the environmental impact of saline-alkali land.

[0094] 1041. Construct an economic model.

[0095] The economic model includes electricity cost prediction models, water consumption cost prediction models, capital cost prediction models, operating cost prediction models, and hydrogen levelized cost prediction models including lifetime degradation. Among these, the electricity cost prediction model is one of the key factors in evaluating the economics of the electric-hydrogen energy storage system. This study uses historical electricity price data from Xinjiang and combines it with a future electricity price trend prediction model to predict the electricity cost during the system's operation. The electricity cost C... ele Represented as:

[0096]

[0097] Among them, P t The price of electricity at time t (yuan / kWh) is derived from publicly available data from Xinjiang Power Company. t The power demand (kW) at time t is obtained through prediction of the system load curve. Δt is the time step (hours), typically taken as 1 hour. T is the total number of time steps in system operation.

[0098] In the water consumption cost prediction model, high-salinity and alkaline water treatment is a crucial component of the electro-hydrogen energy storage system, involving equipment investment and operation and maintenance costs. Water consumption cost C water It can be broken down into the cost of capital C. cap and operating costs C op :

[0099] C water =C cap +C op (11)

[0100] The capital cost prediction model is as follows:

[0101]

[0102] Among them, C i Let F be the unit capital cost (yuan / unit) of the i-th type of water treatment equipment. i Let i be the number of devices of type i.

[0103] The operating cost prediction model is as follows:

[0104]

[0105] Operating costs C op This includes energy consumption, chemical usage, and labor costs, of which C energy,i =E i ·p energy E represents the energy consumption cost of the i-th processing technology. i For energy consumption (kWh), p energy For electricity price, C chem,i The cost of chemical reagents is based on process requirements and market prices. labor,i Labor costs are determined based on the complexity of the process and the level of labor wages.

[0106] The levelized cost prediction model for hydrogen production, incorporating lifetime decay, is a key indicator for evaluating the economic viability of hydrogen production. It considers initial investment, operating costs, maintenance expenses, and cost amortization over the system's lifespan. A lifetime decay factor D is introduced. t The expression for LCOH is:

[0107]

[0108] Among them, C t The total cost over time t, including electricity cost C. ele,t Water consumption cost C water,t Other operating and maintenance costs. t Let r be the amount of hydrogen produced in time t (kg). r is the discount rate, determined based on the project financing cost. T is the total system lifespan (years).

[0109] Lifetime decay factor D t A linear decay model is typically used.

[0110]

[0111] This model reflects the trend of system performance decreasing over time.

[0112] 1042. Construct a technical performance model.

[0113] The technical performance models mainly include the dynamic model of the electrolyzer, the pressure model of the hydrogen storage tank, and the battery degradation model in saline-alkali land.

[0114] The dynamic model of the electrolyzer is as follows:

[0115] The hydrogen production H of the electrolyzer changes with time due to temperature T and corrosion loss, which can be expressed as:

[0116]

[0117] Where k(T) is the temperature-dependent electrolysis efficiency coefficient, expressed by the Arrhenius equation:

[0118]

[0119] I is the current density of the electrolytic cell (A / m³). 2 ), D corrosion The hydrogen loss rate due to corrosion (kg / h) is given by A, which is the pre-exponential factor reflecting the reaction rate constant, and E is the hydrogen loss rate due to corrosion. a R is the activation energy (J / mol), and R is the gas constant (8.314 J / (mol·K)).

[0120] The dynamic equation for the change of pressure P inside a hydrogen storage tank over time under underground storage conditions is as follows:

[0121]

[0122] Where R is the gas constant (8.314 J / (mol·K)), T is the absolute temperature (K), and V is the volume of the hydrogen storage tank (m³). 3N is the number of moles of hydrogen stored (mol), and α is the leakage coefficient (1 / h), which reflects the leakage rate of the hydrogen storage tank. The pressure control of the hydrogen storage tank is crucial for maintaining system stability. This model comprehensively considers the physical properties of the storage medium and the influence of the underground environment.

[0123] A battery degradation model in saline-alkali soil: When a battery is used in a saline-alkali environment, the degradation of its capacity C over time can be expressed as follows:

[0124]

[0125] Where, k deg f is the degradation rate constant (1 / year). env (S) is the environmental factor influence function, where S represents salinity. It is assumed that the environmental factors have a linear relationship with the degradation rate.

[0126] f env (S)=βS (18)

[0127] The degeneracy equation can then be expressed as:

[0128]

[0129] The parameter β reflects the intensity of the effect of salinity on the degradation rate.

[0130] 1043. Construct the objective function and its constraints.

[0131] Constructing the objective function: The optimization objective is concretized into two mathematical objective functions:

[0132]

[0133] Where X is the decision variable vector, P electrolyzer C represents the installed power (kW) of the electrolytic cell. battery V represents the battery energy storage capacity (kWh). storage Indicates hydrogen storage capacity (m³) 3 ), P operation (t) represents the system operating power (kW) at time t, f1(X)=LCOH(X) represents the levelized hydrogen cost target (yuan / kg), and f2(X)=-L(X) represents the negative value of the system lifetime target (years), which is convenient for minimization.

[0134] The system satisfies the following constraints: total system power balance constraint, battery state of charge constraint, electrolyzer start / stop constraint, and battery charge / discharge constraint. The total system power balance constraint is as follows:

[0135] P grid (t)+P battery,discharge (t)-P battery,charge(t)=P electrolyzer (t)+P auxiliary (t)(20)

[0136] P grid (t) represents the electricity obtained from the grid at time t, P battery,discharge (t) represents the battery's discharge power at time t.

[0137] P battery,charge (t) represents the charging power of the battery at time t, P electrolyzer (t) represents the electricity consumed by the electrolyzer at time t, P auxiliary (t) represents the power consumed by the auxiliary equipment at time t;

[0138] The battery state of charge constraints are as follows:

[0139]

[0140] Where SOC(t) and SOC(t+1) represent the state of charge of the battery at times t and t+1, respectively, and η charge η discharge C represents the battery's charge and discharge efficiency, respectively. bottery This represents the battery's rated capacity, and Δt is the time step.

[0141] The start-up and shutdown constraints for the electrolytic cell are as follows:

[0142]

[0143] Where z(t) represents the operating state of the electrolyzer at time t (0 indicates shutdown, 1 indicates operation), P max P min These are the maximum and minimum operating power of the electrolytic cell, respectively.

[0144] Battery charge / discharge constraints: To ensure battery safety and equipment lifespan during charging and discharging, constraints must be placed on charging / discharging power and behavior.

[0145]

[0146] in These represent the maximum allowable charging and discharging power of the battery, respectively.

[0147] This invention proposes a method for allocating hydrogen energy storage capacity in saline-alkali land, taking into account photovoltaic power fluctuation smoothing. It adaptively decomposes photovoltaic output using EMD, sets fluctuation limits based on the 90th percentile of the fluctuation, and then, by setting reasonable fluctuation limits, connects low-frequency components to the grid while transmitting high-frequency components to a hybrid energy storage system for smoothing, reducing the operating pressure of the energy storage system from the source. Then, it uses bidirectional reconstruction to select component combinations as "grid-storage" allocation pairs, ensuring grid smoothness and energy storage economics. Finally, it constructs a hybrid energy storage system that considers the environmental impact of saline-alkali land, achieving a multi-dimensional coupled representation of environment, equipment, and economy. It also jointly considers electricity prices, water treatment costs, and levelized hydrogen production costs taking into account lifespan degradation, and introduces the electrolyzer temperature-efficiency relationship, hydrogen storage pressure-leakage characteristics, and a battery saline-alkali degradation model. This approach achieves a multi-dimensional coupled representation of environment, equipment, and economy. It proposes a multi-objective capacity configuration and operation optimization method for HESS (Heated System for Energy Storage) with the goals of minimizing lifetime degradation, achieving levelized cost of hydrogen production, and maximizing system lifetime. This method constrains power balance, state of charge, electrolyzer start-up and shutdown, and safe operation domains for hydrogen storage. A genetic algorithm is used to solve the Pareto solution set, and the optimal solution is selected based on engineering preferences. A typical day-driven annual scenario reduction technique is implemented. By performing K-means clustering on annual photovoltaic data and constructing a typical day using the median within each cluster, the computational complexity of optimization is significantly reduced while preserving fluctuation characteristics. Applications show that under a 100MW fluctuation limit, this method effectively smooths grid-connected power and suppresses high-frequency fluctuations. By increasing the proportion of hydrogen energy storage, it mitigates the degradation of electrochemical energy storage, enhances system reliability in harsh saline-alkali environments, and improves overall lifecycle economics.

[0148] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for allocating saline soil electro-hydrogen energy storage capacity accounting for photovoltaic power fluctuation smoothing, characterized in that, The method comprises the following steps: decompose the original power sequence into IMFs and a residual term by EMD method, and reasonably determine the fluctuation limit value; reconstruct the IMFs, superimpose the components in the low and high frequency bands according to the preset frequency index, and obtain the low and high frequency components; connect the low frequency component to the grid, and transmit the high frequency component to the hybrid energy storage system for smoothing; construct the hybrid energy storage system considering the influence of saline-alkali environment.

2. The method according to claim 1, wherein the original power sequence is decomposed into IMFs and a residual term, and the expression is: the reasonable determination of the fluctuation limit value comprises: where c i (t) is the i-th order intrinsic modal function, r n (t) is the residual component, and n is the decomposition order.

3. The method of Claim 2, wherein the method is characterized by, the fluctuation limit value can be expressed as a function as follows: wherein N is the number of data points; The fluctuation amount ΔP is calculated for each IMF component i whose expression is: calculating the fluctuation amount S of each IMF component i whose expression is the reconstruction of the IMFs, the superimposition of the components in the low and high frequency bands according to the preset frequency index, and the obtaining of the low and high frequency components comprise: Setting a fluctuation limit value S th satisfies the following condition, the expression of which is: S th =Percentile(S i ,90%) (5) The determined fluctuation limit S th The EMD decomposition results applied to each typical day, the verification and adjustment of fluctuation limit.

4. The method of Claim 1, wherein, the construction of the hybrid energy storage system considering the influence of saline-alkali environment comprises: High frequency reconstruction, according to the results of EMD decomposition, each order IMF component is stacked from top to bottom to generate each order high frequency reconstruction component, and the specific reconstruction method is as follows: where P HF,i (t) denotes the i-th order high frequency reconstructed component, p is the order of the high frequency reconstruction; Low frequency reconstruction, according to the result of EMD decomposition, sequentially stack each order IMF component from bottom to top to generate each order low frequency reconstruction component, and the specific reconstruction method is as follows: where P LF,i (t) denotes the i-th order low frequency reconstructed component, N is the total number of IMFs, and q is the order of the low frequency reconstruction.

5. The method of Claim 1, wherein, construction of an economic model; construction of a technical performance model; construction of an objective function and its constraints. the construction of the economic model comprises:

6. The method of Claim 5, wherein, construction of a power cost prediction model; construction of a water consumption cost prediction model; construction of a hydrogen gas equalization cost prediction model with life attenuation. the construction of the technical performance model comprises:

7. The method of Claim 5, wherein, construction of an electrolytic cell dynamic model; construction of a hydrogen storage tank pressure model; construction of a battery degradation model in saline-alkali environment. the construction of the objective function and its constraints comprises:

8. The method of Claim 5, wherein the method is characterized by, construction of an objective function, which specifically defines two mathematical objective functions as optimization targets: minF(X)=[f1(X),f2(X)] (6) the system satisfies the following constraints: system total power balance constraint, battery state of charge constraint, electrolytic cell start-stop constraint, and battery charging and discharging constraint. X = [P electrolyzer ,C battery ,V storage ,P operation (t)] (7) where X is the decision variable vector, P electrolyzer represents the installed power of electrolytic cell (kW), C battery represents the battery energy storage capacity (kWh), V storage represents the hydrogen storage capacity (m 3 ), P operation represents the system operating power at time t (kW), represents the target of the flat hydrogen cost (yuan / kg), f2(X)=-L(X) represents the negative value of the system life target (years), which is convenient for minimization processing; ​

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