Scheduling method and system of hybrid energy system based on multi-objective optimization

Abstract

The invention discloses a scheduling method and system of a hybrid energy system based on multi-objective optimization, and the method comprises the following steps: obtaining user demand power, and providing the user demand power by a plurality of piles of fuel cells in the hybrid energy system, the lithium battery provides the difference power between the actual demand power of the hybrid energy system and the user demand power; according to the user demand power and the range of the optimal working power of the multi-stack fuel cells, determining a number threshold value of the fuel cells needing to work in each time period; and in each time period, carrying out power distribution on all the fuel cell combinations within the number threshold value, and obtaining an optimal fuel cell combination by taking minimization of attenuation of the hybrid energy system as a target. The method provided by the invention not only can optimize the power distribution of the hybrid energy system, but also has higher operation efficiency and better attenuation consistency.

Description

Technical Field

[0001] The present invention relates to the technical field of fuel cells, and in particular to a scheduling method and system for a hybrid energy system based on multi-objective optimization. Background Art

[0002] With the widespread use of fossil fuels, climate change issues are becoming increasingly severe, and the demand for developing clean energy to replace traditional fuels is increasing. As a clean energy technology, proton exchange membrane fuel cells (PEM) have shown great potential in applications such as microgrid power generation due to their high power density, suitable operating temperature and easy expansion. This type of battery generates electricity through the reaction of hydrogen and oxygen, while only generating water and heat, and is seen as an important part of future energy solutions. Existing proton exchange membrane fuel cells have not yet been widely commercialized, mainly due to the durability of the battery, so they are often built into hybrid energy systems with other energy sources. Fuel cell-based hybrid energy systems include two or more energy systems, usually including multiple stacks of fuel cells, batteries or supercapacitors. In a multi-energy system, how to reasonably allocate the energy and power of different energy systems becomes the key to improving the overall system efficiency.

[0003] At present, the energy management strategies (EMS) of hybrid energy systems are mainly divided into three categories: rule-based, optimization-based and learning-based. Rule-based EMS is widely used in practical applications because of its easy implementation and good real-time performance. Dynamic programming (DP), as one of the optimization-based strategies, can theoretically provide the global optimal solution and is often used as a benchmark for other strategies. The learning-based method attempts to optimize energy management through techniques such as machine learning.

[0004] Although these strategies have improved the energy efficiency of hybrid energy systems to a certain extent, they still have limitations. In multi-stack fuel cell systems (MFCS), existing power allocation strategies mainly focus on minimizing fuel consumption and improving operating efficiency, while ignoring the battery attenuation management and recovery characteristics during the start-stop phase. These strategies fail to fully consider the economy, durability, and adaptability of the battery health status, resulting in efficiency and life bottlenecks in the actual operation of the system. Therefore, a new EMS and control method is needed to simplify the operating complexity, reduce system fluctuations, extend the life of the MFCS, and achieve consistency in attenuation. Summary of the invention

[0005] The present invention proposes a scheduling method and system for a hybrid energy system based on multi-objective optimization, which solves the problem that the existing energy management strategy does not take into account the attenuation of multiple fuel cell stacks during use.

[0006] In order to solve the above technical problems, the present invention provides a scheduling method for a hybrid energy system based on multi-objective optimization, comprising the following steps:

[0007] Step S1: obtaining the power required by the user, wherein the multiple fuel cells in the hybrid energy system provide the power required by the user, and the lithium battery provides the difference between the actual power required by the hybrid energy system and the power required by the user;

[0008] Step S2: determining a threshold value of the number of fuel cells that need to work in each time period according to the user's required power and the range of the optimal working power of the multiple fuel cell stacks;

[0009] Step S3: In each time period: power is allocated to all battery stack combinations within the quantity threshold, with the goal of minimizing the attenuation of the hybrid energy system to obtain the optimal battery stack combination.

[0010] Preferably, obtaining the user demand power in step S1 includes the following steps: clustering the power consumption on the user side according to the historical operation data of the hybrid energy system to obtain the user demand power under a variety of typical working conditions, calculating the similarity between the current working condition and each typical working condition, and taking the user demand power under the typical working condition with the highest similarity to the current working condition as the user demand power for the current working condition.

[0011] Preferably, clustering the power consumption of the user side to obtain the user demand power under various typical working conditions comprises the following steps:

[0012] Step S11: Randomly select U initial cluster centers;

[0013] Step S12: Calculate the distance between each data point in the user-side power consumption and each of the cluster centers, assign each data point to the nearest cluster center, and obtain multiple clusters;

[0014] Step S13: Calculate the mean of all data points in each cluster, and use the mean to update the centroid of the cluster;

[0015] Step S14: Update the number U of cluster centers, and repeat steps S12 to S13;

[0016] Step S15: Calculate the clustering index under each type of U respectively, take the U with the minimum clustering index as the optimal number of clusters, and calculate the clustering index S as follows:

[0017]

[0018] N u =N s +1

[0019] In the formula, α1, α2, and α3 represent the weights of each clustering index; e u,ave is the average relative error; C u is the complexity of the working condition; N u Divide the battery volume; X u is the number of data points in the u-th cluster; represents the normalized relative error average, operating condition complexity and battery division amount; x u (i) is the i-th data point in the u-th cluster; Z u is the u-th centroid; P cul (t) is the power consumption of the user at time t; N s is the total number of fuel cell stacks; T is the total time of historical operation data collected;

[0020] Step S16: Utilize a dynamic programming algorithm to constrain the output power range and state of charge of the lithium battery and optimize the clustering results.

[0021] Preferably, step S2 comprises the following steps:

[0022] Step S21: Calculate the minimum number of stacks m required to meet the user's power requirements based on the user's required power and the rated maximum power of a single stack. opt,min :

[0023]

[0024] In the formula, for j i time The number of working battery stacks; P is the maximum power of the battery stack when it is operating at high efficiency; d (j i ) is j i The power demanded by users at any given moment; To round up;

[0025] Step S22: Calculate the maximum number of stacks m required to meet the user's power requirements based on the user's required power and the rated minimum power of a single stack. opt,max :

[0026]

[0027] In the formula, for j i time The number of working stacks; It is the minimum power when the stack is operating at high efficiency; To round down;

[0028] Step S23: Obtaining a threshold value of the number of battery stacks that need to be operated in each time period according to the minimum number of battery stacks and the maximum number of battery stacks [m opt,min ,m opt,max ].

[0029] Preferably, step S3 comprises the following steps:

[0030] Step S31: Acquire all battery stack combinations within a threshold value of the number of battery stacks that need to be operated;

[0031] Step S32: establishing a multi-objective optimization function including attenuation consistency and maximum work efficiency;

[0032] Step S33: solving the multi-objective optimization function, and performing power allocation on each battery stack in all battery stack combinations;

[0033] Step S34: Calculate the attenuation value of each battery stack combination, and select the battery stack combination with the smallest attenuation value as the optimal battery stack combination.

[0034] Preferably, the expression of the attenuation consistency in step S32 is:

[0035]

[0036] In the formula, J dec1 is the decay consistency function; for j i The load distribution ratio of the kth stack at the moment; m is j i The total number of battery stacks working at any given time; for j i The attenuation value of the kth battery stack at the moment; R 0,k is the initial resistance value of the kth battery stack; for j i The cumulative attenuation value of the kth battery stack at the moment; for j i The average attenuation value of multiple fuel cell stacks at the moment.

[0037] Preferably, when solving the multi-objective optimization function in step S33, a constraint condition is also set, and the expression of the constraint condition is:

[0038]

[0039] In the formula, for j i The load distribution ratio of the kth stack at the moment; N s is the total number of fuel cell stacks; P fc,min is the minimum power of the battery stack; P fc,max is the maximum power of the battery stack; γ kis the load distribution ratio of the kth battery stack; P fc,k is the output power of the kth battery stack.

[0040] Preferably, the expression for maximizing the working efficiency in step S32 is:

[0041]

[0042] In the formula, J dec2 is the work efficiency function; for j i The load distribution ratio of the kth stack at the moment; η total is the total efficiency of the hybrid energy system; P d (j i ) is j i The user demand power at the moment; N s is the total number of fuel cell stacks; η k is the efficiency of the kth battery stack; a0, a1, a2, a3, and a4 are polynomial fitting parameters respectively; P fc,k is the net output power of the kth battery stack.

[0043] Preferably, the calculation of the attenuation value of each battery stack combination in step S34 includes the following three cases:

[0044] (1) When the kth battery stack is at j i+1 The start and stop status at the moment and j i When the start and stop states are consistent at the same time and are in high power operation state, let R k (j+1)=ΔR ΔL +ΔR L , where R k (j+1) is the number of cells in the kth battery stack at j i+1 Attenuation value at time, ΔR ΔL The fuel cell is in high power operation Δt L Attenuation value, ΔR L For fuel cells to operate at high power Δt L The cumulative attenuation value of the kth battery stack is i+1 The start and stop status at the moment and j i When the start / stop state is consistent at the moment and it is in the stop / idle state, i The attenuation value at time j i+1 The decay value at the moment;

[0045] (2) When the kth battery stack is at j i+1 Moment by j i When the stop state at the moment is changed to the running state, let ΔR k (j+1)=ΔR ss +ΔR ΔL+ΔR L , where ΔR ss is the attenuation increment caused by the start and stop of the fuel cell; when the kth stack is at j i+1 Moment by j i When the idle state at the time is changed to the running state, let ΔR k (j+1)=ΔR ΔL +ΔR L ;

[0046] (3) When the kth battery stack is at j i+1 Moment by j i When the running state at the moment changes to the idling state, let ΔR k (j+1)=ΔR L +ΔR id , where ΔR id is the attenuation increment caused by the fuel cell idling; when the kth stack is at j i+1 Moment by j i When the running state at the moment is changed to the stopped state, let ΔR k (j+1)=ΔR ss -ΔR ir , where ΔR ir is the irreversible decay value of the fuel cell.

[0047] The present invention also provides a scheduling system for a hybrid energy system based on multi-objective optimization, which is implemented based on the above-mentioned scheduling method for a hybrid energy system based on multi-objective optimization, and includes a data acquisition module, an energy management module, a power allocation module and a status monitoring module;

[0048] The data acquisition module is used to collect and store the power demand data of the hybrid energy system and the operating parameter data of multiple fuel cells and lithium batteries in real time;

[0049] The energy management module is used to distribute power to the multiple fuel cell stacks and lithium batteries according to the data collected by the data collection module, so that the multiple fuel cell stacks and lithium batteries can jointly meet the power demand of the hybrid energy system;

[0050] The power distribution module is used to distribute the power demand allocated by the energy management module to the multiple fuel cell stacks to each fuel cell stack, so as to achieve load balancing between different fuel cell stacks and maximize the efficiency and minimize the attenuation of the hybrid energy system;

[0051] The state monitoring module is used to monitor the operating state of the hybrid energy system in real time and detect abnormal situations.

[0052] The benefits of the present invention include at least:

[0053] 1. By determining the threshold number of fuel cells that need to work in each time period, the number of starts and stops of the stack is reduced, unnecessary stack starts are avoided, and the overall energy consumption of the system and the wear of the stack are reduced;

[0054] 2. By minimizing the degradation of the hybrid energy system, it helps to extend the service life of the fuel cell stack, reduce maintenance costs, and improve system reliability;

[0055] In summary, the method of the present invention not only dynamically adjusts the working combination and power distribution of the fuel cell stack according to real-time data, improves the adaptability of the system to changing conditions, and ensures efficient operation under different working conditions, but also comprehensively considers the attenuation, durability and economy of the fuel cell stack and battery, and realizes multi-objective optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A schematic diagram of a method flow chart of an embodiment of the present invention;

[0057] Figure 2 A schematic diagram of a framework of convergence control of a hybrid energy system according to an embodiment of the present invention;

[0058] Figure 3 is an equivalent circuit diagram of a lithium battery according to an embodiment of the present invention;

[0059] Figure 4 A schematic diagram of the dependence of the decay rate of a fuel cell on the power load demand in an embodiment of the present invention;

[0060] Figure 5 A schematic diagram of the attenuation change of a fuel cell in the start-stop stage in an embodiment of the present invention;

[0061] Figure 6 Schematic diagram of the efficiency curve of a fuel cell in an embodiment of the present invention;

[0062] Figure 7 A schematic diagram of constraint-dynamic programming of an energy management strategy according to an embodiment of the present invention;

[0063] Figure 8 A flowchart of convergence control of multiple fuel cell stacks in an embodiment of the present invention;

[0064] Fig. 9 It is a schematic diagram of event-based decision-making under a dynamic load curve according to an embodiment of the present invention;

[0065] Fig.10 A flow chart for calculating the number of cells that need to work in a plurality of fuel cell stacks according to an embodiment of the present invention;

[0066] Fig.11 A flow chart of determining the attenuation value of a fuel cell in an embodiment of the present invention;

[0067] Fig.12 It is a schematic diagram of attenuation changes of a fuel cell in an idle and shutdown state according to an embodiment of the present invention. DETAILED DESCRIPTION

[0068] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the protection scope of the present invention.

[0069] like Figure 1 As shown, an embodiment of the present invention provides a scheduling method for a hybrid energy system based on multi-objective optimization, comprising the following steps:

[0070] Step S1: establishing a decay model of a hybrid energy system including multiple fuel cell stacks.

[0071] like Figure 2 As shown, taking into account issues such as energy loss and power distribution under actual working conditions, the attenuation model of the hybrid energy system established in the embodiment of the present invention includes the dynamic working condition demand power on the user side, the SOC of the lithium battery, the attenuation, durability and efficiency of the fuel cell.

[0072] The attenuation model of lithium batteries is described by the internal resistance model, such as Figure 3 As shown, where u oc , R bat and u dc They represent the open circuit voltage, internal resistance and DC bus voltage of the lithium battery respectively, I bat is the current of the lithium battery. The state of charge (SOC) indicates the ratio between the remaining energy of the lithium battery and its fully charged energy, where the expression of the change rate of the battery SOC, battery current and DC bus voltage is:

[0073]

[0074] In the formula, is the first-order derivative of the state of charge of the lithium battery at time t; η bat is the efficiency of lithium battery; I bat (t) is the current of the lithium battery at time t; Q bat is the nominal capacity of the lithium battery; u oc (SOC) is the open circuit voltage of the lithium battery in the charging state; R bat (SOC) is the internal resistance of the lithium battery in the charging state; P bat (t) is the power of the lithium battery at time t, which is negative when charging and positive when discharging; u dc is the output voltage of the lithium battery.

[0075] The attenuation of the fuel cell includes the start-stop cycle attenuation, load change cycle attenuation, idle attenuation and high power load attenuation, and its expression is:

[0076] ΔR=ΔR L +ΔR ΔL +ΔR ss +ΔR id ;

[0077] Where ΔR is the overall deterioration of the resistance of the multi-stack fuel cell; ΔR L ΔR is the resistance attenuation increment caused by the inherent attenuation phenomenon related to the load in multiple fuel cell stacks; ΔL ΔR is the attenuation increment caused by load changes in multiple fuel cell stacks; ss ΔR is the attenuation increment caused by the start-stop operation in the multi-stack fuel cell; id is the attenuation increment caused by idle operation in multiple fuel cell stacks.

[0078] Figure 4 is the dependence of the fuel cell decay rate on the power load demand, given by Figure 4 It can be seen that ΔR L The attenuation rate D(P) is not the minimum when the demand current is the lowest (P L,min ) or maximum (P L,max ) when the minimum value is obtained, but when the standard operating power density (P L,nom ) is the smallest. Therefore, the expression of the empirical average resistance attenuation function D(P) relative to the load is:

[0079] D(P)=a(P L (t)-P L,nom ·A fc ) 2 +b;

[0080]

[0081] Where a and b are attenuation coefficients; P L (t) is the output power of the fuel cell at time t; P L,nom is the power density under standard working condition; P L,min , P L,max are the minimum and maximum values ​​of power density respectively; A fc is the electrode area of ​​the fuel cell.

[0082] right Figure 4 The values ​​of a and b can be obtained by performing polynomial fitting on the curve in .

[0083] When the fuel cell works at a high power density for a period of time Δt LWhen the attenuation changes, ΔR L The expression is:

[0084] ΔR L =D(P L (t))·Δt L .

[0085] The load power demand in actual working conditions changes with time, that is, P(t)≠P(t-1). L The resulting resistance decay ΔR ΔL Calculated by the following formula:

[0086] ΔR ΔL =D ΔL ΔP L =D ΔL |P L (t)-P L (t-1)|;

[0087] Where D ΔL is the decay rate of the fuel cell output power change.

[0088] When the fuel cell is in idle operation for a period of time Δt id When the resistance decreases, ΔR id ΔR id for:

[0089] ΔR id =D id ·Δt id ;

[0090] Among them, D id Indicates the decay rate that causes decay when the fuel cell is idling.

[0091] The resistance decay ΔR caused by each start and stop of the fuel cell ss for:

[0092] ΔR ss =R0D ss ;

[0093] Among them, D ss It indicates the decay rate caused by each start and stop of the fuel cell; R0 indicates the resistance of the stack in the initial state.

[0094] Figure 5 The schematic diagram of the attenuation change of the fuel cell during the start-stop stage. Due to the redistribution of the internal membrane water and the recovery of the catalyst point in the fuel cell, the voltage value of the fuel cell when it is turned on again will be higher than the voltage at the last shutdown time, that is, U off1 <U on2 , and the resistance also decreases to a certain extent, which is shown as Roff1 >R on2 The irreversible degradation rate of the fuel cell can be described by the linear regression of the voltage value after the recovery step, as Figure 5 Middle dotted line U ir As shown. When the current density is different, the irreversible voltage degradation rate G ir There is a difference. The greater the current density, the greater the degradation rate, and vice versa. When the fuel cell runs from ton1 to ton2 at a current density j, the irreversible voltage loss ΔU ir for:

[0095] ΔU ir =G ir (j)·(t on2 -t on1 ).

[0096] Therefore, the irreversible decay rate of the average resistance of the fuel cell is D ir and attenuation value ΔR ir for:

[0097]

[0098] The output power of multiple fuel cell stacks must meet the power requirements of all auxiliary equipment, such as air compressors, water circulation pumps, etc. The air compressor has the largest power demand among the auxiliary equipment, so the power requirements of subsequent auxiliary equipment are all represented by the air compressor. The remaining deliverable part is called the net power of the fuel cell, marked as P fc The efficiency curve of a fuel cell is as follows: Figure 6 As shown, the fuel cell is at the maximum efficiency point η max The power at The region where the fuel cell operates at high efficiency is defined as At this time, the efficiency of the fuel cell is always maintained above 45%, that is, η fc ≥0.45. In order to improve the overall efficiency of the system, the working point of the fuel cell should be inclined to this area. When the fuel cell cannot meet the power demand of the system d When it is allowed to exceed But it cannot exceed P fc,max .

[0099] The efficiency of the proton exchange membrane fuel cell has a certain functional relationship with its output power. Through data fitting, it can be known that the curve characteristics of this function can be characterized by a 4th-order polynomial, as follows:

[0100]

[0101] The total efficiency of the system is:

[0102]

[0103] Where N s is the total number of fuel cell stacks; γ k represents the power ratio of the kth battery stack; η k Represents the operating efficiency of the kth battery stack.

[0104] Step S2: distributing power to multiple fuel cell stacks and lithium batteries at the upper layer.

[0105] Specifically, in order to simplify the complexity of the working conditions of the multi-stack fuel cells and extend the service life of the multi-stack fuel cells, it is necessary to reduce the number of starts and stops and power changes of the multi-stack fuel cells. Therefore, the embodiment of the present invention performs power allocation of the multi-stack fuel cells and lithium batteries in the upper energy management stage. The multi-stack fuel cells provide a stable power P after system clustering. d , the lithium battery provides the difference power P between the actual working condition and the clustered bat , multiple fuel cells and lithium batteries work together to meet the system's load power requirement P L ,Right now:

[0106]

[0107] Where P cul represents the power that needs to be provided by MFCS after clustering through DC / DC, η DC Indicates the operating efficiency of the DC / DC converter.

[0108] In the embodiment of the present invention, clustering is used to identify the power consumption status of the system user side. The characteristic parameters are classified according to their similarity, and the characteristic parameters are assigned to the category with higher similarity. There is no need to predefine the classification criteria in the clustering process, and clustering automatically classifies the fragments according to the characteristics of the fragments themselves. The purpose of classification is to simplify the dynamic working conditions of multiple fuel cells. At the same time, in order to meet the working conditions of the lithium battery, certain constraints need to be set. Therefore, the embodiment of the present invention proposes a constrained clustering method K-DP-Means.

[0109] The purpose of K-Means clustering is to classify the working conditions on the user side into U categories, with the centroid of each category being Z, which includes the following steps:

[0110] Step S21: Randomly select U initial cluster centers.

[0111] Step S22: Calculate the Euclidean distance between each data point in the user-side power consumption and each cluster center, assign each data point to the nearest cluster center, and obtain multiple clusters.

[0112] The expression of the Euclidean distance between the e-th data point x(e) and the e-th centroid Z(e) is:

[0113]

[0114] In the formula, x f (e), Z f (e) are the values ​​of the data point x(e) and the centroid Z(e) in the fth dimension respectively; I is the dimension of the data point.

[0115] Step S23: Calculate the mean of all data points in each cluster, and use the mean to update the centroid of the cluster.

[0116] The expression for calculating the mean of each cluster is:

[0117]

[0118] Where, X u is the set of data points of the u-th cluster; (X v ) is the number of data points in the set.

[0119] Step S24: In [1,N s +10], and repeat steps S22 to S23 until the termination condition is met, that is, the cluster centroids no longer change significantly.

[0120] Step S25: Calculate the clustering index under each type of U respectively, and take the U with the minimum clustering index as the optimal number of clusters.

[0121] Clustering indicators include the average relative error after clustering e v，ave 、Complexity of working conditions C u and the number of batteries divided by N u , the expression for calculating the clustering index S is:

[0122]

[0123] N u =N s +1

[0124] In the formula, α1, α2, and α3 represent the weights of each clustering index; e u,ave is the average relative error; C u is the complexity of the working condition; N u Divide the battery volume; X u is the number of data points in the u-th cluster; represents the normalized relative error average, operating condition complexity and battery division amount; x u (i) is the i-th data point in the u-th cluster; Z u is the u-th centroid; P cul(t) is the power consumption of the user at time t; N s is the total number of fuel cell stacks; T is the total time of the historical operation data collected.

[0125] When evaluating the clustering effect, N needs to be considered s Because different numbers of fuel cells can provide the required power P d,max are different, so the division amount N determined by the number of batteries should be u Considered as a judging indicator.

[0126] Step S26: Utilize a dynamic programming algorithm to constrain the output power range and state of charge of the lithium battery and optimize the clustering results.

[0127] Based on the above K-Means clustering, P d According to the clustering results, they are divided into U categories. Figure 7 The figure shows a schematic diagram of the embodiment of the present invention using a dynamic programming algorithm to constrain the global state, wherein the node value J of each node is node and the transfer value J at the previous moment travel The expression is:

[0128]

[0129] Among them, w a and w b Represents the constraint penalty term; SOC v is the state of charge of the vth battery stack; SOC min , SOC max Respectively represent the minimum and maximum value of the stack charge state; P bat,min , P bat,max They are the minimum and maximum output power of lithium batteries respectively; inf means infinity.

[0130] From the above formula, we can get all the paths between each node and the previous moment. t,v With O t-1,v If they are the same, then O t,v , O t-1,v Denote as the optimal path between this point and the previous moment, where O t,v P represents the time t d =Z v If there is no common point, select from the data point closest to the category. Among the 6 nodes at any time t, at least one O t,v The constraints are satisfied and the path value is the same as that of the previous moment, so as to obtain the classification results that satisfy the constraints under all paths.

[0131] Step S3: performing convergence control on the fuel cell stacks in the plurality of fuel cell stacks at the lower layer.

[0132] like Figure 8 FIG. 1 is a flow chart of convergence control according to an embodiment of the present invention. s A multi-stack fuel cell system with m fuel cells, where m fuel cells are in an operated state and output power, and n fuel cells maintain a state where the output power is 0. Where m and n remain constant in all decision cycles, the total number of fuel cells in the multi-stack fuel cell is N s =m+n. In the lower convergence control, the load distribution of any combination of m operating fuel cells is first calculated, and then the fuel cell switching is performed by selecting the fuel cell combination that minimizes the deterioration of the future multi-stack fuel cells. Considering the dynamic load curve of the fuel cell, the embodiment of the present invention adopts the following Fig. 9 The decision process based on load change events is shown in Figure 1. Whenever a load change event is detected, a new load distribution decision is triggered. For example, at decision time j1, a change in the load demand level is detected, and the load to be distributed changes from P to L0 Change to P L1 At the same time, at the decision time j1, it is also necessary to prepare for the subsequent time t dec,1 The fuel cell attenuation model is used to estimate the N caused by load amplitude and load variation. s Finally, the decision strategy makes a choice by balancing the consistency between the various stacks of the multi-stack fuel cell.

[0133] Specifically, the steps include:

[0134] Step S31: Determine a threshold value of the number of fuel cell stacks that need to be operated.

[0135] In the decision time period i ,j i+1 ], there are m battery stacks running and n battery stacks stopped, where m, n ≤ Ns, and it is assumed that m and n are in [j i ,j i+1 ] remains unchanged. Then the m working batteries must meet the required power condition, that is:

[0136]

[0137] like Fig.10 The figure shows a flow chart of determining the number of working fuel cell stacks in a plurality of fuel cell stacks in an embodiment of the present invention. Within the range of optimal efficiency, the optimal number of working fuel cell stacks is The range is Among them, at any decision time j i , Indicated in The number of working batteries under the conditions, The maximum power when the battery stack is operating at high efficiency; Indicated in The number of working batteries under the conditions, It is the minimum power when the battery stack is operating at high efficiency. and m opt,max The expression is:

[0138]

[0139] Where P d (j i ) is j i The actual power of the battery stack at the moment; To round up; To round down.

[0140] Step S32: Obtain all battery stack combinations within a quantity threshold.

[0141] Select m fuel cell stacks from a plurality of fuel cell stacks with Ns fuel cells, and we have possible combinations, among which represents m(j i ) dimension, and For example, for a five-fuel stack with five operating fuel cells, Ns = 5, and m opt,min (j i )=2、m opt,max (j i )=3, then m(j i )=[2,3], there are 20 possible combinations, listed as:

[0142]

[0143] In the formula, represents m(j i ) in the working state of the fuel cell of the hth combination of the rth category, where When the working state is 1, it means that the corresponding fuel cell is running, otherwise it means that the stack is shut down. For example, when r = 1 and h = 1, the first Among them, fc1 and fc2 are operating fuel cells, and fc3, fc4, and fc5 are stopped fuel cells.

[0144] Step S33: Establish a multi-objective optimization function including attenuation consistency and maximum work efficiency.

[0145] For each battery stack combination, the optimization problem J is defined dec1 and J dec2The duration between load change events is considered random, while in the decision stage it can be requested from the fuel cell operator or predicted by costly estimation techniques or unknown. When the event duration is unknown, the expected event duration t estimated by the multi-stack fuel cell model can be used. dec,1 Used in decision-making process.

[0146] According to the above decision-making principle, the load distribution strategy can be formalized as a constrained nonlinear optimization problem: dec1 and J dec2 :

[0147]

[0148] In the formula, J dec1 is the decay consistency function; for j i The load distribution ratio of the kth stack at time j i to i+1 The power of the fuel cell working during this period is for j i The attenuation value of the kth battery stack at the moment; R 0,k is the initial resistance value of the kth battery stack; for j i The cumulative attenuation value of the kth battery stack at the moment; for j i The average attenuation value of multiple fuel cell stacks at the moment.

[0149]

[0150] In the formula, J dec2 is the work efficiency function; η total is the total efficiency of the hybrid energy system; P k (j i ) is j i The output power of the kth battery stack at the moment; η k is the efficiency of the kth battery stack; P fc,k is the net output power of the kth battery stack.

[0151] The constraint expression is:

[0152]

[0153] In the formula, γ k is the load distribution ratio of the kth battery stack; P fc,k is the output power of the kth battery stack. The optimal condition is that the battery stack is in the power range of optimal efficiency. When this condition cannot be met, When the fuel cell is required to provide higher power, the must conditions must be met.

[0154] Step S34: Solve the multi-objective optimization function and allocate power to each battery stack in all battery stack combinations.

[0155] Step S35: Calculate the attenuation value of each battery stack combination, and select the battery stack combination with the smallest attenuation value as the optimal battery stack combination.

[0156] In order to select the optimal stack combination, the second attenuation criterion J that integrates the system attenuation evolution caused by each combination must be calculated. dec3 ,like Fig.11 The embodiment of the present invention is shown in FIG. dec3 Schematic diagram of the process of selecting the optimal battery stack combination. For each combination x h , can be calculated based on the decision time j i+1 When the combination is inserted or not inserted into the multiple fuel cell stacks, the degradation of each combination is determined. i+1 The existing situation is the same as the previous decision time j i Subtract the situation of the battery stack fc k The attenuation is: 1or-1 three cases, among which Indicates j i The optimal operation combination at the moment:

[0157] (1) When the kth battery stack is at j i+1 The start and stop status at the moment and j i When the start and stop states are consistent at the same time, the stack fc k In j i+1 Time and j i The expected cumulative decay corresponding to the time is marked as and If the battery stack is in the stopped / idling state, the kth battery stack is i The attenuation value at time j i+1 The attenuation value at this moment When the kth battery stack is at j i+1 The start and stop status at the moment and j i When the start and stop states are consistent at all times and the system is in high power operation state,

[0158] (2) When the kth battery stack is at j i+1 Moment by j i When the stop state is changed to the running state, the battery stack fc k In j i+1 The expected cumulative decay at time t is denoted by The change of the stack from the stop state to the running state is caused by the load size and start-stop factors. When the kth battery stack is at j i+1 Moment by j i When the idle state at the moment is changed to the running state,

[0159] (3) When the kth battery stack is at j i+1 Moment by j i When the running state at the moment changes to the stopped state, the battery stack fc k In j i+1 The expected cumulative decay at time t is denoted by After the fuel cell is turned off, it enters the reversible recovery process, so the attenuation decreases. Therefore, it is necessary to determine when the attenuation value is -1, fc k Whether to enter the idle state or the shutdown state, the judgment needs to be based on j i to i+1 Which attenuation has the least impact in the time period and which one is selected. Fig.12 As shown, when t(j i+1 -j i ) = t nod When the idle state and the shutdown state have the same attenuation value, that is, ΔR id =ΔR ir , t nod Indicates the time intersection when the shutdown and standby operation attenuation values ​​are the same. Then when t(j i+1 -j i )<t nod When k Enter the idle state, otherwise fc k Shutdown, while fc k The recovery rate after shutdown is related to the load power in the previous stage, so t nod It will happen like Fig.12 The changes in t' nod and t' n ' od Therefore, when the kth battery stack is at j i+1 Moment by j i When the running state at the moment changes to the idling state, When the kth battery stack is at j i+1 Moment by j i When the running state at the moment changes to the stopped state, Where ΔR ir is the irreversible decay value of the fuel cell.

[0160] Therefore, the decision criterion J dec3 It can be expressed as:

[0161]

[0162] The present invention discloses a scheduling method for a hybrid energy system based on multi-objective optimization, which can not only optimize the power distribution among hybrid energy sources, but also has higher operating efficiency and better attenuation consistency, thereby realizing convergent control of a high-efficiency and consistent hybrid power generation system.

[0163] An embodiment of the present invention also discloses a scheduling system for a hybrid energy system based on multi-objective optimization, which is implemented based on the above-mentioned scheduling method for a hybrid energy system based on multi-objective optimization, and includes a data acquisition module, an energy management module, a power allocation module and a status monitoring module.

[0164] The data acquisition module is used to collect and store the power demand data of the hybrid energy system and the operating parameter data of multiple fuel cells and lithium batteries in real time.

[0165] The energy management module is used to distribute power to multiple fuel cell stacks and lithium battery stacks according to the data collected by the data acquisition module, so that the multiple fuel cell stacks and lithium battery stacks can jointly meet the power requirements of the hybrid energy system.

[0166] The power distribution module is used to distribute the power requirements of multiple fuel cell stacks allocated by the energy management module to each fuel cell stack, thereby achieving load balancing between different fuel cell stacks and maximizing the efficiency and minimizing the attenuation of the hybrid energy system.

[0167] The status monitoring module is used to monitor the operating status of the hybrid energy system in real time and detect abnormal situations.

[0168] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. Only the preferred embodiments of the present invention are expressed. The description is more specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. As long as there is no contradiction in the combination of these technical features, they should be considered as the scope recorded in this specification.

[0169] It should be noted that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these modifications and improvements all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A scheduling method for a hybrid energy system based on multi-objective optimization, characterized in that: The following steps are involved: Step S1: obtaining the power required by the user, wherein the multiple fuel cells in the hybrid energy system provide the power required by the user, and the lithium battery provides the difference between the actual power required by the hybrid energy system and the power required by the user; Step S2: determining a threshold value of the number of fuel cells that need to work in each time period according to the user's required power and the range of the optimal working power of the multiple fuel cell stacks; Step S3: In each time period: power is allocated to all battery stack combinations within the quantity threshold, with the goal of minimizing the attenuation of the hybrid energy system to obtain the optimal battery stack combination.

2. The scheduling method of a hybrid energy system based on multi-objective optimization according to claim 1 is characterized in that: The acquisition of user demand power described in step S1 includes the following steps: clustering the power consumption on the user side according to the historical operation data of the hybrid energy system to obtain the user demand power under a variety of typical working conditions, calculating the similarity between the current working condition and each typical working condition, and taking the user demand power under the typical working condition with the highest similarity to the current working condition as the user demand power for the current working condition.

3. The scheduling method of a hybrid energy system based on multi-objective optimization according to claim 2 is characterized in that: The step of clustering the power consumption at the user side to obtain the power demanded by the user under various typical working conditions comprises the following steps: Step S11: Randomly select U initial cluster centers; Step S12: Calculate the distance between each data point in the user-side power consumption and each of the cluster centers, assign each data point to the nearest cluster center, and obtain multiple clusters; Step S13: Calculate the mean of all data points in each cluster, and use the mean to update the centroid of the cluster; Step S14: Update the number U of cluster centers, and repeat steps S12 to S13; Step S15: Calculate the clustering index under each type of U respectively, take the U with the minimum clustering index as the optimal number of clusters, and calculate the clustering index S as follows: N u =N s +1 In the formula, α1, α2, and α3 represent the weights of each clustering index; e u,ave is the average relative error; C u is the complexity of the working condition; N u Divide the battery volume; X u is the number of data points in the u-th cluster; represents the normalized relative error average, operating condition complexity and battery division amount; x u (i) is the i-th data point in the u-th cluster; Z u is the u-th centroid; P cul (t) is the power consumption of the user at time t; N s is the total number of fuel cell stacks; T is the total time of historical operation data collected; Step S16: Utilize a dynamic programming algorithm to constrain the output power range and state of charge of the lithium battery and optimize the clustering results.

4. The scheduling method of a hybrid energy system based on multi-objective optimization according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: Calculate the minimum number of stacks m required to meet the user's power requirements based on the user's required power and the rated maximum power of a single stack. opt,min : In the formula, for j i time The number of working battery stacks; P is the maximum power of the battery stack when it is operating at high efficiency; d (j i ) is j i The power demanded by users at any given moment; To round up; Step S22: Calculate the maximum number of stacks m required to meet the user's power requirements based on the user's required power and the rated minimum power of a single stack. opt,max : In the formula, for j i time The number of working stacks; It is the minimum power when the stack is operating at high efficiency; To round down; Step S23: Obtaining a threshold value of the number of battery stacks that need to be operated in each time period according to the minimum number of battery stacks and the maximum number of battery stacks [m opt,min ,m opt,max ].

5. The scheduling method of a hybrid energy system based on multi-objective optimization according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: Acquire all battery stack combinations within a threshold value of the number of battery stacks that need to be operated; Step S32: establishing a multi-objective optimization function including attenuation consistency and maximum work efficiency; Step S33: solving the multi-objective optimization function, and performing power allocation on each battery stack in all battery stack combinations; Step S34: Calculate the attenuation value of each battery stack combination, and select the battery stack combination with the smallest attenuation value as the optimal battery stack combination.

6. The scheduling method of a hybrid energy system based on multi-objective optimization according to claim 5 is characterized in that: The expression of the attenuation consistency in step S32 is: In the formula, J dec1 is the decay consistency function; for j i The load distribution ratio of the kth stack at the moment; m is j i The total number of battery stacks working at any time; for j i The attenuation value of the kth battery stack at the moment; R 0,k is the initial resistance value of the kth battery stack; for j i The cumulative attenuation value of the kth battery stack at the moment; for j i The average attenuation value of multiple fuel cell stacks at the moment.

7. The scheduling method of a hybrid energy system based on multi-objective optimization according to claim 5 is characterized in that: When solving the multi-objective optimization function in step S33, constraints are also set, and the expression of the constraints is: In the formula, for j i The load distribution ratio of the kth stack at the moment; N s is the total number of fuel cell stacks; P fc,min is the minimum power of the battery stack; P fc,max is the maximum power of the battery stack; γ k is the load distribution ratio of the kth battery stack; P fc,k is the output power of the kth battery stack.

8. The scheduling method of a hybrid energy system based on multi-objective optimization according to claim 5 is characterized in that: The expression for maximizing the working efficiency in step S32 is: In the formula, J dec2 is the work efficiency function; for j i The load distribution ratio of the kth stack at the moment; η total is the total efficiency of the hybrid energy system; P d (j i ) is j i The user demand power at the moment; N s is the total number of fuel cell stacks; η k is the efficiency of the kth battery stack; a0, a1, a2, a3, and a4 are polynomial fitting parameters respectively; P fc,k is the net output power of the kth battery stack.

9. The scheduling method of a hybrid energy system based on multi-objective optimization according to claim 5, characterized in that: The calculation of the attenuation value of each battery stack combination in step S34 includes the following three cases: (1) When the kth battery stack is at j i+1 The start and stop status at the moment and j i When the start and stop states are consistent at the same time and are in high power operation state, let R k (j+1)=ΔR ΔL +ΔR L , where R k (j+1) is the number of cells in the kth battery stack at j i+1 Attenuation value at time, ΔR ΔL The fuel cell is in high power operation Δt L Attenuation value, ΔR L For fuel cells to operate at high power Δt L The cumulative attenuation value of the kth battery stack is i+1 The start and stop status at the moment and j i When the start / stop state is consistent at the moment and it is in the stop / idle state, i The attenuation value at time j i+1 The decay value at the moment; (2) When the kth battery stack is at j i+1 Moment by j i When the stop state at the moment is changed to the running state, let ΔR k (j+1)=ΔR ss +ΔR ΔL +ΔR L , where ΔR ss is the attenuation increment caused by the start and stop of the fuel cell; when the kth stack is at j i+1 Moment by j i When the idle state at the time is changed to the running state, let ΔR k (j+1)=ΔR ΔL +ΔR L ; (3) When the kth battery stack is at j i+1 Moment by j i When the running state at the moment changes to the idling state, let ΔR k (j+1)=ΔR L +ΔR id , where ΔR id is the attenuation increment caused by the fuel cell idling; when the kth stack is at j i+1 Moment by j i When the running state at the moment is changed to the stopped state, let ΔR k (j+1)=ΔR ss -ΔR ir , where ΔR ir is the irreversible decay value of the fuel cell.

10. A scheduling system for a hybrid energy system based on multi-objective optimization, implemented based on a scheduling method for a hybrid energy system based on multi-objective optimization as claimed in any one of claims 1 to 9, characterized in that: It includes data acquisition module, energy management module, power distribution module and status monitoring module; The data acquisition module is used to collect and store the power demand data of the hybrid energy system and the operating parameter data of multiple fuel cells and lithium batteries in real time; The energy management module is used to distribute power to the multiple fuel cell stacks and lithium battery stacks according to the data collected by the data collection module, so that the multiple fuel cell stacks and lithium battery stacks can jointly meet the power demand of the hybrid energy system; The power distribution module is used to distribute the power demand allocated by the energy management module to the multiple fuel cell stacks to each fuel cell stack, so as to achieve load balancing between different fuel cell stacks and maximize the efficiency and minimize the attenuation of the hybrid energy system; The state monitoring module is used to monitor the operating state of the hybrid energy system in real time and detect abnormal situations.

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