Power allocation method for electro-hydrogen hybrid energy storage system in microgrid, and device and medium

Abstract

Disclosed in the present application are a power allocation method for an electro-hydrogen hybrid energy storage system in a microgrid, and a device and a medium. The electro-hydrogen hybrid energy storage system comprises an electrochemical energy storage system and a hydrogen energy storage system. The method comprises: decomposing the current operating power of a microgrid that meets given requirements into a power to be stabilized and a grid-connection power required for controlling the microgrid to connect to the grid; with the goal of minimizing the operating costs and in view of the current SOC value of the electrochemical energy storage system and the current SOH value of the hydrogen energy storage system, allocating the power to be stabilized into a first optimal charge and discharge power required for controlling the operation of the electrochemical energy storage system and a second optimal charge and discharge power required for controlling the operation of the hydrogen energy storage system; and while controlling an overall operating state of the microgrid, on the basis of a protection strategy determined by means of the power to be stabilized and a preset SOC working range and the current SOC value of the electrochemical energy storage system, adjusting the SOC state of the electrochemical energy storage system. The present application can meet energy requirements of microgrids in different scenarios.

Description

Power distribution method, device and medium for electric-hydrogen hybrid energy storage system in micro-grid TECHNICAL FIELD

[0001] The present application relates to the technical field of micro-grid, in particular to a power distribution method, device and medium for an electric-hydrogen hybrid energy storage system in a micro-grid. BACKGROUND

[0002] In a new power system mainly based on renewable energy, the use of multiple energy storage technologies (such as electrochemical energy storage technology and hydrogen energy storage technology) can improve the flexibility and stability of the power grid, solve the intermittent and unstable problems of renewable energy such as wind power, improve energy utilization efficiency, and reduce energy waste.

[0003] In the operation process of the power system, different types of energy storage devices need to be flexibly distributed according to real-time energy demand, but the existing energy storage system power distribution method is usually based on simple rules or static strategies, such as fixed proportion distribution or priority-based distribution, without fully considering the real-time operating state of the energy storage device, such as the SOC value of the electrochemical energy storage device and the SOH value of the hydrogen energy storage device, resulting in insufficient flexibility and efficiency of power distribution, and it is also difficult to make the overall performance of the power system optimal.

[0004] SUMMARY

[0005] The present application provides a power distribution method, device and medium for an electric-hydrogen hybrid energy storage system in a micro-grid, to solve one or more technical problems existing in the prior art, and at least provide a beneficial choice or create conditions.

[0006] In a first aspect, a power distribution method for an electric-hydrogen hybrid energy storage system in a micro-grid is provided, the electric-hydrogen hybrid energy storage system comprising an electrochemical energy storage system and a hydrogen energy storage system, and the method comprising:

[0007] obtaining a current operating power of the micro-grid, a current SOC value of the electrochemical energy storage system, and a current SOH value of the hydrogen energy storage system;

[0008] when the absolute value of the current operating power is greater than a preset power threshold, decomposing the current operating power according to grid-connected demand to obtain a grid-connected power and a power to be suppressed;

[0009] distributing the power to be suppressed to obtain a first optimal charging and discharging power of the electrochemical energy storage system and a second optimal charging and discharging power of the hydrogen energy storage system, in combination with the current SOC value and the current SOH value, with the objective of minimizing operating cost;

[0010] determine a protection strategy according to the to-be-suppressed power and preset SOC working range and current SOC value of the electrochemical energy storage system;

[0011] control the micro-grid to be connected to the grid according to the grid-connected power, control the electrochemical energy storage system to operate according to the first optimal charging and discharging power, control the hydrogen energy storage system to operate according to the second optimal charging and discharging power, and adjust the SOC state of the electrochemical energy storage system according to the protection strategy.

[0012] Further, the current operating power of the micro-grid is obtained by the following way:

[0013] obtaining the power generation of the photovoltaic system in the micro-grid and the power consumption of the direct-current load in the micro-grid, and taking the difference between the power generation and the power consumption as the current operating power of the micro-grid.

[0014] Further, the decomposition of the current operating power according to the grid-connected demand to obtain the grid-connected power and the to-be-suppressed power comprises:

[0015] processing the current operating power by using a variational mode decomposition algorithm, and optimizing the mode decomposition number and the penalty factor required by the variational mode decomposition algorithm by using a grey wolf algorithm in the processing process to obtain a plurality of optimal modal components;

[0016] dividing the plurality of optimal modal components according to the grid-connected demand to obtain a plurality of low-frequency optimal modal components and a plurality of high-frequency optimal modal components;

[0017] synthesizing the plurality of low-frequency optimal modal components to obtain the grid-connected power;

[0018] synthesizing the plurality of high-frequency optimal modal components to obtain the to-be-suppressed power.

[0019] Further, the allocation of the to-be-suppressed power to obtain the first optimal charging and discharging power of the electrochemical energy storage system and the second optimal charging and discharging power of the hydrogen energy storage system comprises:

[0020] determining a fitness function with the minimum operating cost as the target;

[0021] taking the first charging and discharging power of the electrochemical energy storage system and the second charging and discharging power of the hydrogen energy storage system as the target variables to be optimized;

[0022] determining a power balance constraint condition according to the target variables and the to-be-suppressed power;

[0023] determining an SOC constraint condition according to the target variable and the current SOC value;

[0024] determining an SOH constraint condition according to the target variable and the current SOH value;

[0025] iteratively optimizing the target variable by using an improved genetic algorithm according to the fitness function, the power balance constraint condition, the SOC constraint condition and the SOH constraint condition, and introducing an adaptive probability function for cross and mutation operations in each iteration process, so as to obtain a first optimal charging and discharging power of the electrochemical energy storage system and a second optimal charging and discharging power of the hydrogen energy storage system.

[0026] Further, the power balance constraint condition comprises: limiting the sum of the first charging and discharging power of the electrochemical energy storage system and the second charging and discharging power of the hydrogen energy storage system to be the power to be smoothed.

[0027] Further, the operation cost is solved by the following expression:

[0028] In the formula, C use is the operation cost, is a unit power cost of the electrochemical energy storage system, is a unit capacity cost of the electrochemical energy storage system, is a unit power cost of the hydrogen energy storage system, is a unit capacity cost of the hydrogen energy storage system, T s is a sampling period, P ba_set (t) is the first charging and discharging power of the electrochemical energy storage system, P he_set (t) is the second charging and discharging power of the hydrogen energy storage system.

[0029] Further, the preset SOC working range of the electrochemical energy storage system is composed of a high SOC range, a normal SOC range and a low SOC range, and the determination of the protection strategy according to the power to be smoothed and the preset SOC working range and the current SOC value of the electrochemical energy storage system comprises:

[0030] when the power to be smoothed is greater than zero and the current SOC value falls within the high SOC range, the protection strategy is determined as: controlling the electrochemical energy storage system to discharge until the SOC value drops to the normal SOC range, and the electrical energy generated by the electrochemical energy storage system is supplied to the hydrogen energy storage system;

[0031] when the power to be suppressed is less than zero and the current SOC value falls within the low SOC range, determining a protection strategy as: controlling the electrochemical energy storage system to charge until the SOC value rises to the normal SOC range, the electrical energy required by the electrochemical energy storage system being provided by the hydrogen energy storage system.

[0032] Further, the method further comprises: when the absolute value of the current operating power is less than or equal to a preset power threshold, keeping the operating state of the microgrid unchanged.

[0033] In a second aspect, a computer device is provided, comprising a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the power allocation method for the electro-hydrogen hybrid energy storage system in the microgrid as described in the first aspect.

[0034] In a third aspect, a computer readable storage medium is provided, storing a computer program, which, when executed by a processor, implements the power allocation method for the electro-hydrogen hybrid energy storage system in the microgrid as described in the first aspect.

[0035] The present application has at least the following beneficial effects: by using the variational mode decomposition algorithm optimized by the gray wolf algorithm to reasonably decompose the current operating power of the microgrid, the situation of missing important frequency feature information can be avoided. By fully considering the SOC value of the electrochemical energy storage system and the SOH value of the hydrogen energy storage system, the power to be suppressed of the electro-hydrogen hybrid energy storage system is flexibly and efficiently allocated, and in this process, the improved genetic algorithm is introduced and the operating cost is minimized as the optimization target to improve the power allocation accuracy, so as to maximize the energy demand of the microgrid in different scenarios, so that the overall performance of the microgrid reaches the best. By adjusting the overall operating state of the microgrid while enabling the protection strategy, the real-time SOC value of the electrochemical energy storage system is always within the normal SOC range to maintain the service life and working stability of the lithium battery as much as possible, and at the same time, the frequent start phenomenon of the PEM electrolytic tank and the hydrogen fuel cell in the hydrogen energy storage system can be reduced. BRIEF DESCRIPTION OF DRAWINGS

[0036] The accompanying drawings are included to provide a further understanding of the technical scheme of the present application, and constitute a part of the specification, and together with the embodiments of the present application, are used to explain the technical scheme of the present application, and do not constitute a limitation on the technical scheme of the present application.

[0037] Fig. 1 is a schematic diagram of the composition of the microgrid in the embodiment of the present application;

[0038] Fig. 2 is a flowchart of a power allocation method for an electro-hydrogen hybrid energy storage system in a microgrid in the embodiment of the present application;

[0039] FIG. 3 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0040] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0041] It should be noted that although the logical order is shown in the flowchart, in some cases, the steps shown or described can be performed in an order different from that in the flowchart. The terms "first", "second", and the like in the specification and claims and the above-described drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence, and it should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in those orders other than those illustrated or described herein.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0043] In addition, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the present application. One skilled in the relevant art will recognize, however, that the technical solutions of the present application can be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other cases, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the present application.

[0044] The flowchart shown in the accompanying drawings is only an exemplary illustration, and does not necessarily include all contents and operations / steps, nor does it necessarily have to be executed in the order described. For example, some operations / steps can be further decomposed, and some operations / steps can be combined or partially combined, so the actual execution order can be changed according to the actual situation.

[0045] First, some of the terms involved in the present application are explained as follows:

[0046] Variational Mode Decomposition (VMD) is an adaptive signal decomposition method that can decompose a time series data into multiple intrinsic mode functions (IMFs) with specific center frequency and specific bandwidth. Each IMF can be regarded as an oscillation mode of the original signal, and they collectively constitute the complete representation of the original signal.

[0047] Grey Wolf Optimizer (GWO) is an optimization algorithm inspired by the behavior of grey wolves in nature. It mainly simulates the social hierarchy and hunting strategy of grey wolf groups, which are divided into four roles: Alpha, Beta, Delta and Omega. This hierarchical structure helps the grey wolf group organize hunting and resource allocation in an efficient manner. In the algorithm, Alpha represents the optimal solution, Beta represents the sub-optimal solution, Delta represents the third optimal solution, and Omega follows the guidance of these leaders to explore the search space. The entire algorithm process mainly refers to the cooperative hunting method of grey wolf groups, gradually approaching the optimal solution through tracking, surrounding and attacking prey.

[0048] Genetic Algorithm (GA) is a kind of optimization algorithm that simulates biological evolution to find the optimal solution. It mainly relies on genetic, mutation and natural selection mechanisms to perform efficient iterative search for problem solving. The basic idea of the algorithm is to represent the solution of the problem as multiple different individuals, then evaluate the fitness of each individual according to the definition of the fitness function and determine its probability in reproduction, through operations such as crossover and mutation, new individuals replace the original individuals, continue to compete with other generated individuals, this process is iterated until the optimal solution is found or the iteration limit is reached.

[0049] Please refer to FIG. 1, which is a schematic diagram of the composition of a microgrid in an embodiment of the present application. The microgrid specifically includes an energy router, a DC bus, and a photovoltaic system, an electric-hydrogen hybrid energy storage system and a DC load connected to the DC bus via the energy router. The electric-hydrogen hybrid energy storage system includes a hydrogen energy storage system and an electrochemical energy storage system. The hydrogen energy storage system includes a PEM (Proton Exchange Membrane) electrolytic cell, a hydrogen storage tank and a hydrogen fuel cell. Lithium batteries are commonly used in the electrochemical energy storage system.

[0050] Basically, the energy router comprises a first DC / DC converter, a second DC / DC converter, a third DC / DC converter, a fourth DC / DC converter, a bidirectional DC / DC converter and a bidirectional DC / AC converter, the photovoltaic system is connected to the DC bus through the first DC / DC converter, the PEM electrolyzer is connected to the DC bus through the second DC / DC converter, the PEM electrolyzer is connected to the hydrogen storage tank, the hydrogen storage tank is connected to the hydrogen fuel cell, the hydrogen fuel cell is connected to the DC bus through the third DC / DC converter, the DC load is connected to the DC bus through the fourth DC / DC converter, the electrochemical energy storage system is connected to the DC bus through the bidirectional DC / DC converter, and the DC bus is connected to the external AC bus through the bidirectional DC / AC converter; the energy router can realize electrical isolation, voltage conversion and bidirectional flow of electric energy, provide a "plug and play" standardized interface for different levels and different forms of sources and loads, and can adjust the voltage and current of each converter in real time.

[0051] In practical application, the photovoltaic system converts the solar energy captured by itself into electric energy through photovoltaic effect, the electric energy is directly incorporated into the DC bus after being converted by the first DC / DC converter, thereby the PEM electrolyzer can be powered, the DC load can be powered, and the electrochemical energy storage system can be used for storage; the electric energy provided by the DC bus is used to power the PEM electrolyzer to realize consumption after being converted in voltage level by the second DC / DC converter, and the hydrogen gas produced by the PEM electrolyzer during operation is stored by the hydrogen storage tank; the hydrogen fuel cell can convert the hydrogen gas stored in the hydrogen storage tank into electric energy, and the electric energy is incorporated into the DC bus to realize compensation after being converted in voltage level by the third DC / DC converter; the electric energy provided by the DC bus is input to the electrochemical energy storage system for storage to realize consumption after being converted in voltage level by the bidirectional DC / DC converter; the electrochemical energy storage system incorporates the electric energy stored therein into the DC bus to realize compensation after being converted in voltage level by the bidirectional DC / DC converter; the electric energy provided by the DC bus is used to power the DC load to realize consumption after being converted in voltage level by the fourth DC / DC converter; the bidirectional DC / AC converter can be understood as a grid-connected port, which is used to realize AC / DC conversion and bidirectional flow of electric energy between the DC bus and the external AC bus. It should be noted that the microgrid can be configured in an industrial park, a residential area, an island power station and a backup station or other places.

[0052] On this basis, FIG. 2 is a flowchart of a power distribution method of an electro-hydrogen hybrid energy storage system in a microgrid provided by an embodiment of the present application, the method comprising the following steps:

[0053] In step S110, the current operating power of the microgrid, the current SOC value of the electrochemical energy storage system and the current SOH value of the hydrogen energy storage system are obtained.

[0054] Step S120, judging whether the absolute value of the current operating power of the micro-grid is greater than a preset power threshold; if yes, executing step S130; if no, executing step S170;

[0055] Step S130, decomposing the current operating power of the micro-grid according to the grid-connection demand, to obtain a grid-connection power and a power to be suppressed;

[0056] Step S140, distributing the power to be suppressed in combination with the current SOC value of the electrochemical energy storage system and the current SOH value of the hydrogen energy storage system, to obtain a first optimal charging / discharging power of the electrochemical energy storage system and a second optimal charging / discharging power of the hydrogen energy storage system, with the objective of minimizing the operating cost;

[0057] Step S150, determining a protection strategy according to the power to be suppressed, and the preset SOC working range and the current SOC value of the electrochemical energy storage system;

[0058] Step S160, controlling the micro-grid to be grid-connected according to the grid-connection power, controlling the electrochemical energy storage system to operate according to the first optimal charging / discharging power, controlling the hydrogen energy storage system to operate according to the second optimal charging / discharging power, and adjusting the SOC state of the electrochemical energy storage system according to the protection strategy;

[0059] Step S170, keeping the operating state of the micro-grid unchanged.

[0060] In some embodiments, the current operating power of the micro-grid mentioned in step S110 is obtained by subtracting the power consumption of the DC load from the power generation of the photovoltaic system.

[0061] By comparing the current operating power of the micro-grid with the preset power threshold after determining the current operating power of the micro-grid, it can be determined whether the micro-grid needs to be grid-connected and whether the current operating state of the electro-hydrogen hybrid energy storage system needs to be adjusted, further ensuring the overall operating stability of the micro-grid.

[0062] In some embodiments, the implementation process of step S130 includes but is not limited to the following:

[0063] Step S131, processing the current operating power of the micro-grid by a variational mode decomposition algorithm, and optimizing the penalty factor and the number of mode decompositions used in the variational mode decomposition algorithm by a grey wolf algorithm during the processing, to finally generate a plurality of optimal mode components, each of which represents different frequency component information in the current operating power of the micro-grid;

[0064] Step S132, divide the plurality of optimal modal components according to grid-connection requirements to obtain a plurality of high-frequency optimal modal components and a plurality of low-frequency optimal modal components; wherein the grid-connection requirements include a regulation on the frequency range of signals allowed to be received by the external main power grid for the purpose of grid-connection stability;

[0065] Step S133, synthesize the plurality of low-frequency optimal modal components to obtain grid-connection power;

[0066] Step S134, synthesize the plurality of high-frequency optimal modal components to obtain power to be suppressed.

[0067] In the above step S131, the optimal penalty factor and the optimal modal decomposition number are obtained by introducing the grey wolf algorithm for parameter optimization, which can better control the bandwidth and center frequency of each modal component decomposed, thereby more accurately mining the internal characteristics of the current operating power of the microgrid.

[0068] More specifically, regarding the processing of the current operating power of the microgrid by the variational modal decomposition algorithm mentioned in the above step S131, the corresponding implementation process includes but is not limited to the following:

[0069] Step A1, set the basic parameters used in the variational modal decomposition algorithm, including sampling window width, sampling window type, sampling frequency, penalty factor and modal decomposition number;

[0070] Step A2, perform Hilbert transform on the current operating power of the microgrid to obtain the one-sided spectrum of each modal function as follows:

[0071] In the formula, p k (t) is the modal function, A is the one-sided spectrum, δ(t) is the Dirac function, j is the imaginary symbol, t is the time, and * is the convolution operator symbol;

[0072] Step A3, adjust the estimated center frequency of the analytic signal corresponding to each modal function, and modulate the spectrum of each modal function to the corresponding base frequency band:

[0073] In the formula, B is the base frequency band, ω k is the center frequency, and e is the exponential function;

[0074] Step A4, to ensure that the sum of the estimated bandwidths of all modal functions is minimum, the sum of all modal functions should be equal to the current operating power of the microgrid, i.e. to determine the constraint variational problem as follows:

[0075] Where: {p k} = {p1, p2, …, pK},{ω k}={ω1,ω2,…,ω K};

[0076] where K is the number of modal decomposition, i.e., the number of all modal functions, is the gradient of the demodulation signal, f is the current operating power of the microgrid, is the norm symbol;

[0077] Step A5, the above constraint variational problem is converted into a non-constraint variational problem by introducing a Lagrange multiplier and combining a penalty factor, which can be represented by an augmented Lagrange expression:

[0078] where L is the augmented Lagrange function, λ is the Lagrange multiplier, γ is the penalty factor, and <·> is the vector inner product;

[0079] Step A6, the above non-constraint variational problem is solved by using the ADMM algorithm (Alternate Direction Method of Multipliers, multiplier operator alternating direction method), and the extreme point in the above augmented Lagrange expression is determined by alternately updating and λ n+1 , so as to realize the decomposition of the current operating power of the microgrid into several optimal modal components.

[0080] It should be noted that in the above step A6, the above non-constraint variational problem is converted to the ω frequency domain by using the Parseval / Plancherel Fourier equidistant transformation method, and then the update values of and in the simplified ω frequency domain are determined by combining the Hermitian symmetry principle, and the formula is:

[0081] where is the Wiener filter of the current residual component , and is the center frequency of .

[0082] More specifically, regarding the optimization of the penalty factor and the modal decomposition number used in the variational modal decomposition algorithm by the grey wolf algorithm in the above step S131, the corresponding implementation process includes but is not limited to the following:

[0083] Step B1, set the basic parameters required to be applied to the grey wolf algorithm, including the population size and the maximum number of iterations, take the penalty factor and the number of modal decomposition as the target variables required to be optimized by the grey wolf algorithm, determine the fitness function used in the iterative optimization process, and the fitness function is preferably set as the average correlation coefficient between all modal components and the current operating power of the micro-grid;

[0084] Step B2, randomly initialize the grey wolf population, so that the initial positions of different grey wolf individuals represent different initial target variable values;

[0085] Step B3, according to the initial position of each grey wolf, the current operating power of the micro-grid is subjected to variational modal decomposition, and the initial fitness value corresponding to each grey wolf is calculated, the initial positions of the three grey wolves with the best initial fitness values are obtained and saved, and the three grey wolves are defined as the optimal wolf leader, the optimal wolf deputy and the optimal wolf consultant respectively;

[0086] Step B4, in the tth iteration process, the following mathematical expression is used to update the position of the current grey wolf population:

[0087] In the formula, is the position of the grey wolf individual after the t+1th iteration, is the position of the grey wolf individual after the tth iteration, is the position of the prey after the tth iteration, and is a coefficient vector, is a parameter, and are random numbers in the interval [0, 1], t max is the maximum number of iterations;

[0088] Step B5, according to the current position of each grey wolf, the current operating power of the micro-grid is subjected to variational modal decomposition, and the current fitness value corresponding to each grey wolf is calculated, so as to re-determine the optimal wolf leader, the optimal wolf deputy and the optimal wolf consultant and save their current positions and corresponding current fitness values;

[0089] Step B6, judge whether the maximum number of iterations is reached; if yes, output the current position of the latest saved optimal wolf leader as the optimal target variable value, which includes the optimal penalty factor and the optimal number of modal decomposition; if not, assign t+1 to t, and return to the above step B4;

[0090] It should be noted that the above step B4 is executed from t=1.

[0091] In some embodiments, the implementation of step S140 includes but is not limited to the following:

[0092] Step S141, taking the minimum operation cost as the optimization target, and defining the fitness function as:

[0093] wherein, C use is the operation cost, is the unit power cost of the electrochemical energy storage system, is the unit power cost of the hydrogen energy storage system, is the unit capacity cost of the electrochemical energy storage system, is the unit capacity cost of the hydrogen energy storage system, T s is the sampling period, P ba_set (t) is the first charge-discharge power of the electrochemical energy storage system, P he_set (t) is the second charge-discharge power of the hydrogen energy storage system, f(t) is the fitness function;

[0094] Step S142, taking the first charge-discharge power of the electrochemical energy storage system and the second charge-discharge power of the hydrogen energy storage system as the target variables to be optimized for the improved genetic algorithm;

[0095] Step S143, based on the target variables and the power to be smoothed, defining the power balance constraint condition as:

[0096] wherein, P hess (t) is the power to be smoothed, P ba_set (t) is the first charge-discharge power of the electrochemical energy storage system, P he_set (t) is the second charge-discharge power of the hydrogen energy storage system, is the charging power of the lithium battery, is the discharging power of the lithium battery, is the power generation power of the hydrogen fuel cell, is the power consumption power of the PEM electrolyzer, is the minimum charging power of the lithium battery, is the maximum charging power of the lithium battery, is the minimum discharging power of the lithium battery, is the maximum discharging power of the lithium battery, is the minimum power generation power of the hydrogen fuel cell, is the maximum power generation power of the hydrogen fuel cell, is the minimum power consumption power of the PEM electrolyzer, is the maximum power consumption power of the PEM electrolyzer;

[0097] In step S144, based on the target variable and the current SOC value of the electrochemical energy storage system, the SOC constraint condition is defined as:

[0098] In the formula, SOC(t) is the SOC value of the electrochemical energy storage system after running according to the first charging and discharging power, [SOC min , SOC max ] is the preset SOC working range of the electrochemical energy storage system, SOC min is the minimum SOC value of the electrochemical energy storage system, SOC max is the maximum SOC value of the electrochemical energy storage system, SOC(t-1) is the current SOC value of the electrochemical energy storage system, E ba_r is the rated capacity of the lithium battery;

[0099] In step S145, based on the target variable and the current SOH value of the hydrogen energy storage system, the SOH constraint condition is defined as:

[0100] In the formula, SOH(t) is the SOH value of the hydrogen energy storage system after running according to the second charging and discharging power, [SOH min , SOH max ] is the preset SOH working range of the hydrogen energy storage system, SOH min is the minimum SOH value of the hydrogen energy storage system, SOH max is the maximum SOH value of the hydrogen energy storage system, SOH(t-1) is the current SOH value of the hydrogen energy storage system, is the gas inlet amount of the hydrogen storage tank, is the gas outlet amount of the hydrogen storage tank, is the maximum hydrogen storage amount of the hydrogen storage tank, is the hydrogen volume under the ideal gas equation, I el is the working current of the PEM electrolyzer, F is the Faraday constant, R is the ideal gas constant, T is the working temperature of the PEM electrolyzer, p is the working pressure of the PEM electrolyzer, U ideal_el is the ideal working voltage of the PEM electrolyzer, U fc is the working voltage of the hydrogen fuel cell;

[0101] In step S146, based on the power balance constraint condition, the SOC constraint condition, the SOH constraint condition and the fitness function, the target variable is iteratively optimized by the improved genetic algorithm, and the adaptive probability function is introduced for crossover and mutation in each iterative optimization process, so as to obtain the first optimal charging and discharging power of the electrochemical energy storage system and the second optimal charging and discharging power of the hydrogen energy storage system.

[0102] In step S146, the crossover and mutation are performed by introducing the adaptive probability function, which can avoid the local optimum of the algorithm and allow fine search to improve the quality of the solution.

[0103] More specifically, regarding the iterative optimization of the target variables by the improved genetic algorithm mentioned in step S146, the corresponding implementation process includes but is not limited to the following:

[0104] Step S146.1, set the basic parameters required for the improved genetic algorithm, including the population size and the maximum number of iterations, and take the first charge and discharge power of the electrochemical energy storage system and the second charge and discharge power of the hydrogen energy storage system as the target variables to be optimized by the improved genetic algorithm;

[0105] Step S146.2, randomly initialize the population, so that the initial positions of different individuals represent different initial target variable values and at the same time satisfy the power balance constraint condition, the SOC constraint condition and the SOH constraint condition;

[0106] Step S146.3, in the tthiteration process, calculate the fitness value corresponding to each individual according to the position of each individual and sort them, select the individuals with the largest fitness value accounting for 10% of the total as elite individuals and limit them from participating in crossover and mutation, and select the remaining individuals accounting for 90% of the total as non-elite individuals;

[0107] Step S146.4, introduce the following adaptive probability function to perform crossover and mutation on the non-elite individuals to obtain new individuals:

[0108] In the formula, P c is the crossover probability, is the upper limit value of the crossover probability, is the lower limit value of the crossover probability, P m is the mutation probability, is the upper limit value of the mutation probability, is the lower limit value of the mutation probability, e is the natural constant, approximately equal to 2.718, f(t) is the fitness value corresponding to the individual after the tthiteration, is the average fitness value corresponding to the population after the tthiteration, f max (t) is the maximum fitness value corresponding to the population after the tthiteration;

[0109] Step S146.5, form a new generation of population from the elite individuals and the new individuals, and then select all individuals that satisfy the power balance constraint condition, the SOC constraint condition and the SOH constraint condition to form a feasible optimal new generation of population;

[0110] Step S146.6, determining whether the maximum iteration number is reached; if yes, selecting the individual with the maximum fitness value from the current optimal new generation population, taking the current position of the individual as the optimal target variable value, and outputting the optimal target variable value, which includes the first optimal charge and discharge power of the electrochemical energy storage system and the second optimal charge and discharge power of the hydrogen energy storage system; if no, assigning t+1 to t, and returning to step S146.3 above;

[0111] It should be noted that step S146.3 above is executed from t=1.

[0112] In some embodiments, the present application proposes to further divide the preset SOC working range [SOC min ,SOC max ] of the electrochemical energy storage system into three disjoint ranges, i.e., a low SOC range [SOC min ,SOC he-fc ], a normal SOC range [SOC he-fc ,SOC he-el ], and a high SOC range (SOC he-el ,SOC max ], SOC he-el is a maximum critical SOC value, and SOC he-fc is a minimum critical SOC value, and in combination with the to-be-suppressed power and the current SOC value of the electrochemical energy storage system, to formulate a protection strategy required to be enabled when adjusting the overall operation state of the microgrid, i.e., the implementation content of step S150 above is as follows:

[0113] (1) When it is identified that the to-be-suppressed power is greater than zero, and the current SOC value of the electrochemical energy storage system is within the high SOC range (SOC he-el ,SOC max ], it is determined that the electrochemical energy storage system is currently in a charging state and the PEM electrolyzer is currently in an operating state, and at this time, the corresponding protection strategy is formulated as follows: controlling the electrochemical energy storage system to perform a discharging operation until the SOC value thereof drops to the normal SOC range [SOC he-fc ,SOC he-el ], which can be understood as the SOC value of the electrochemical energy storage system dropping to SOC he-el , and the discharging can be stopped, and the electrical energy generated by the operation of the electrochemical energy storage system is supplied to the hydrogen energy storage system in real time to maintain the operation of the PEM electrolyzer;

[0114] (2) When it is identified that the to-be-suppressed power is less than zero, and the current SOC value of the electrochemical energy storage system is within the low SOC range [SOC min ,SOC he-fc) and the hydrogen fuel cell is currently in a running state, at this time, a corresponding protection strategy is formulated: control the electrochemical energy storage system to perform a charging operation until the SOC value of the electrochemical energy storage system rises to the normal SOC range [SOC he-fc ,SOC he-el ] can be understood as the SOC value of the electrochemical energy storage system rising to SOC he-fc , that is, the charging can be stopped, and the electrical energy required for the operation of the electrochemical energy storage system can be supplied by the hydrogen energy storage system in real time while maintaining the operation of the hydrogen fuel cell.

[0115] In the embodiment of the present application, by using the variational mode decomposition algorithm optimized by the grey wolf algorithm to reasonably decompose the current operating power of the microgrid, the situation of missing important frequency feature information can be avoided. By fully considering the SOC value of the electrochemical energy storage system and the SOH value of the hydrogen energy storage system, the to-be-suppressed power of the electric-hydrogen hybrid energy storage system is flexibly and efficiently distributed, and in this process, the improved genetic algorithm is introduced and the minimum operating cost is taken as the optimization target to improve the power distribution accuracy, so as to maximize the energy demand of the microgrid in different scenarios, so that the overall performance of the microgrid reaches the best. By adjusting the overall operating state of the microgrid while enabling the protection strategy, the real-time SOC value of the electrochemical energy storage system is always within the normal SOC range to maintain the service life and working stability of the lithium battery as much as possible, and at the same time, the frequent start phenomenon of the PEM electrolytic tank and the hydrogen fuel cell in the hydrogen energy storage system can be reduced.

[0116] In addition, the embodiment of the present application also provides a computer readable storage medium, and the computer readable storage medium stores a computer program. When the computer program is executed by a processor, the power distribution method of the electric-hydrogen hybrid energy storage system in the microgrid in the above-mentioned embodiment is realized. The computer readable storage medium includes but is not limited to any type of disk (including floppy disk, hard disk, optical disk, CD-ROM and magneto-optical disk), ROM (Read-Only Memory), RAM (Random Access Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, magnetic card or optical card. That is, the storage device includes any medium that stores or transmits information in a readable form by a device (such as a computer, a mobile phone, etc.), which can be a read-only memory, a magnetic disk or an optical disk, etc.

[0117] In addition, FIG. 3 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present application. The computer device includes a processor 220, a memory 230, an input unit 240, a display unit 250, and the like. Those skilled in the art can understand that the device structure shown in FIG. 3 does not constitute a limitation on all devices, and can include more or fewer components than shown, or combine certain components. The memory 230 can be used to store a computer program 210 and various functional modules. The processor 220 runs the computer program 210 stored in the memory 230, thereby performing various functional applications and data processing of the device. The memory can be an internal memory or an external memory, or include an internal memory and an external memory. The internal memory can include a read-only memory (ROM), a programmable ROM (PROM), an electrically programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a flash memory, or a random access memory. The external memory can include a hard disk, a floppy disk, a USB memory, a magnetic tape, and the like. The memory 230 disclosed in the embodiments of the present application includes but is not limited to the above-mentioned types of memory. The memory 230 disclosed in the embodiments of the present application is only by way of example and not as a limitation.

[0118] The input unit 240 is used to receive the input of signals and receive the keyword input by the user. The input unit 240 can include a touch panel and other input devices. The touch panel can collect the touch operation of the user thereon or nearby (such as the operation of the user on or near the touch panel with a finger, a stylus, or any suitable object or accessory), and drive the corresponding connection device according to the pre-set program; the other input devices can include but are not limited to one or more of a physical keyboard, function keys (such as play control buttons, on-off buttons, etc.), a trackball, a mouse, a joystick, and the like. The display unit 250 can be used to display the information input by the user or the information provided to the user and various menus of the terminal device. The display unit 250 can take the form of a liquid crystal display, an organic light-emitting diode, and the like. The processor 220 is the control center of the terminal device, which connects all parts of the device through various interfaces and lines, executes the software program and / or module stored in the memory 230, and calls the data stored in the memory 230, to perform various functions and process data.

[0119] As an embodiment, the computer device includes a processor 220, a memory 230, and a computer program 210, wherein the computer program 210 is stored in the memory 230 and configured to be executed by the processor 220, and the computer program 210 is configured to perform the power distribution method of the micro-grid hydrogen hybrid energy storage system in one of the above embodiments.

[0120] Those of ordinary skill in the art will understand that all or a portion of the steps, functions, modules or units in the methods disclosed above and in the systems, devices, apparatuses can be implemented as software, firmware, hardware, or any suitable combination thereof.

[0121] The terms "include", "has" and any variations thereof in the specification and in the claims of the application are intended to cover a non-exclusive inclusion, for example, a process, method, system, product or apparatus that includes a list of steps or units can not necessarily be limited to those listed steps or units but can include other not-listed steps or units inherent to such process, method, product or apparatus.

[0122] In this application, it should be understood that "at least one" means one or more, "multiple" means two or more. "And / or" is used to describe the association relationship of the associated objects, which means that there can be three relationships, for example, "A and / or B" can represent three cases of only A, only B and A and B existing at the same time, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the front and rear associated objects. "At least one of the following" or similar expressions means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b or c can represent a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b and c can be single or multiple.

[0123] Although the present application has been described in considerable detail and with reference to several aforementioned embodiments, it is not intended to limit the present application to any of these details or embodiments or any special embodiment, but rather it is intended to cover all the alternatives and modifications as can be included within the scope of the present application as defined by the appended claims, considering the prior art. Furthermore, the foregoing description has been written in terms of presently contemplated embodiments of the application to provide those skilled in the art with the information needed to make and use the application, and it will be understood that it is directed to encompass all changes and modifications of the application that fall within the scope of the appended claims.

Claims

1. A power distribution method of an electricity-hydrogen hybrid energy storage system in a micro-grid, the electricity-hydrogen hybrid energy storage system comprising an electrochemical energy storage system and a hydrogen energy storage system, the method comprising: obtaining a current operating power of the micro-grid, a current SOC value of the electrochemical energy storage system, and a current SOH value of the hydrogen energy storage system; when an absolute value of the current operating power is greater than a preset power threshold, decomposing the current operating power according to grid-connection requirements to obtain a grid-connection power and a power to be smoothed; distributing the power to be smoothed to obtain a first optimal charging and discharging power of the electrochemical energy storage system and a second optimal charging and discharging power of the hydrogen energy storage system, in combination with the current SOC value and the current SOH value, with a target of minimizing operating cost; determining a protection strategy according to the power to be smoothed, a preset SOC working range of the electrochemical energy storage system, and the current SOC value; controlling the micro-grid to be connected to the grid according to the grid-connection power, controlling the electrochemical energy storage system to operate according to the first optimal charging and discharging power, controlling the hydrogen energy storage system to operate according to the second optimal charging and discharging power, and adjusting the SOC state of the electrochemical energy storage system according to the protection strategy.

2. The power distribution method of the hybrid energy storage system for electric hydrogen in a microgrid according to claim 1, wherein, The current operating power of the micro-grid is obtained by: obtaining a power generation of a photovoltaic system in the micro-grid and a power consumption of a direct current load in the micro-grid, and taking a difference between the power generation and the power consumption as the current operating power of the micro-grid.

3. The power distribution method of the hybrid energy storage system for electric hydrogen in a microgrid according to claim 1, wherein, The decomposition of the current operating power according to grid-connection requirements to obtain a grid-connection power and a power to be smoothed comprises: processing the current operating power by using a variational mode decomposition algorithm, and optimizing a mode decomposition number and a penalty factor required by the variational mode decomposition algorithm by using a grey wolf algorithm in the processing process to obtain a plurality of optimal modal components; dividing the plurality of optimal modal components according to grid-connection requirements to obtain a plurality of optimal modal components of low frequency and a plurality of optimal modal components of high frequency; synthesizing the plurality of optimal modal components of low frequency to obtain the grid-connection power; synthesizing the plurality of optimal modal components of high frequency to obtain the power to be smoothed.

4. The power distribution method of the hybrid energy storage system for electric hydrogen in a microgrid according to claim 1, wherein, The distribution of the power to be smoothed to obtain the first optimal charging and discharging power of the electrochemical energy storage system and the second optimal charging and discharging power of the hydrogen energy storage system, in combination with the current SOC value and the current SOH value, with a target of minimizing operating cost comprises: determining a fitness function with a target of minimizing operating cost; taking the first charging and discharging power of the electrochemical energy storage system and the second charging and discharging power of the hydrogen energy storage system as target variables to be optimized; determining a power balance constraint condition according to the target variables and the power to be smoothed; determining an SOC constraint condition according to the target variables and the current SOC value; determining an SOH constraint condition according to the target variables and the current SOH value; According to the fitness function, the power balance constraint condition, the SOC constraint condition and the SOH constraint condition, the target variables are iteratively optimized by using an improved genetic algorithm, and in each iteration process, a self-adaptive probability function is introduced for cross and mutation operations, so as to obtain the first optimal charging and discharging power of the electrochemical energy storage system and the second optimal charging and discharging power of the hydrogen energy storage system.

5. The power distribution method of the hybrid energy storage system for electric hydrogen in a microgrid according to claim 4, wherein, The power balance constraint condition comprises: limiting the sum of the first charging and discharging power of the electrochemical energy storage system and the second charging and discharging power of the hydrogen energy storage system to be the power to be smoothed.

6. The power distribution method of claim 1, wherein, The operating cost is solved by the following expression: wherein C use is the operating cost, for the unit power cost of the electrochemical energy storage system, for the unit capacity cost of the electrochemical energy storage system, for the hydrogen storage energy system, T is the cost per unit capacity of the hydrogen energy storage system s P is the sampling period ba_set P (t) is the first charge-discharge power of the electrochemical energy storage system he_set P (t) is the second charge-discharge power of the hydrogen energy storage system 7. The method for power distribution of the hybrid energy storage system of Claim 1, wherein, The preset SOC working range of the electrochemical energy storage system is composed of a high SOC range, a normal SOC range and a low SOC range, and the determination of the protection strategy according to the power to be smoothed and the preset SOC working range and the current SOC value of the electrochemical energy storage system comprises: When the power to be smoothed is greater than zero and the current SOC value falls within the high SOC range, the protection strategy is determined as: controlling the electrochemical energy storage system to discharge until the SOC value drops to the normal SOC range, and the electrical energy generated by the electrochemical energy storage system is supplied to the hydrogen energy storage system; When the power to be smoothed is less than zero and the current SOC value falls within the low SOC range, the protection strategy is determined as: controlling the electrochemical energy storage system to charge until the SOC value rises to the normal SOC range, and the electrical energy generated by the electrochemical energy storage system The required electrical energy is provided by the hydrogen energy storage system.

8. The power distribution method of the hybrid energy storage system for electric hydrogen in a microgrid according to claim 1, wherein, The method further comprises: when the absolute value of the current operating power is less than or equal to a preset power threshold, keeping the operating state of the microgrid unchanged.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the power distribution method of the electro-hydrogen hybrid energy storage system in the microgrid according to any one of claims 1 to 8.

10. A computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the power distribution method of the electro-hydrogen hybrid energy storage system in the microgrid according to any one of claims 1 to 8.

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