A method and system for collaborative optimization of a multi-stack fuel cell hydrogen energy comprehensive power supply system

Abstract

The application provides a multi-stack fuel cell hydrogen energy comprehensive power supply system cooperative optimization method and system, optimal working points of each stack are obtained through offline calculation; source and load are predicted in a prediction time domain, a first-stage optimization is performed with the sum of working costs in the prediction time domain and optimal working point tracking performance indexes of each stack being minimum as an objective, and optimal outputs of each device in the prediction time domain are obtained; wherein the working costs include: system device operation and maintenance costs, wind and light abandonment penalty costs, grid interaction costs and stack performance degradation costs; source and load are predicted in a control time domain, a second-stage optimization is performed with the optimal output of the first stage as a benchmark value, with the sum of deviations of the optimal output of the second stage from the benchmark value and output fluctuations of each stack being minimum as an optimization objective function, and final optimal scheduling results are obtained; the application improves the system economy, and improves the overall working efficiency of the stack and the system life.

Description

Technical Field

[0001] This invention relates to the field of hydrogen energy comprehensive utilization technology, and in particular to a collaborative optimization method and system for a multi-fuel cell hydrogen energy integrated power supply system. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Against the backdrop of numerous real-world problems such as the depletion and severe pollution of traditional fossil fuels, hydrogen energy, with its advantages of being clean, efficient, and safe, has become one of the most promising future energy sources. Furthermore, proton exchange membrane fuel cells (PEMFCs), with their advantages of high power density, low operating temperature, rapid response, and safety and reliability, have become a key core device for hydrogen energy utilization.

[0004] Hydrogen-based integrated energy supply systems, as a new type of regional energy supply system, introduce hydrogen energy as a key element into traditional integrated energy systems. Using fuel cells as the core device, they distribute, convert, and store hydrogen, electricity, and heat to achieve integrated multi-energy supply. However, in high-power load scenarios, single fuel cells suffer from low output power, poor durability, and low reliability. Therefore, it is necessary to construct multi-stack fuel cell systems to address these issues.

[0005] However, the inventors discovered the following pressing problems with the multi-stack fuel cell hydrogen energy integrated power supply system:

[0006] (1) At present, the application scenarios of multi-stack fuel cell systems are mostly in vehicle environments, and there are no applications in the field of integrated energy supply. Furthermore, the source load fluctuation and randomness of the input and output of the hydrogen energy integrated energy supply system are strong. The coupling of hydrogen, electricity and heat in the system exacerbates the difficulty of coordinated operation of multi-stack fuel cell systems.

[0007] (2) Most existing multi-stack operation strategies are based on power distribution, and most adopt average power distribution strategy or step-by-step power distribution strategy, without comprehensively considering factors such as power, efficiency, safety and lifespan of multi-stack fuel cells. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention provides a collaborative optimization method and system for a multi-stack fuel cell hydrogen energy integrated power supply system. It comprehensively considers the rated power, efficiency, voltage drop, and hydrogen consumption rate of the fuel cell stacks, obtains the optimal operating point of each fuel cell stack offline, and performs online rolling optimization based on the MPC (Model Predictive Control) framework. This improves the system's economy while enhancing the overall operating efficiency of the fuel cell stacks and the system's lifespan.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] The first aspect of this invention provides a collaborative optimization method for a multi-fuel cell hydrogen energy integrated power supply system.

[0011] A collaborative optimization method for a multi-fuel cell integrated hydrogen energy supply system includes:

[0012] The optimal operating point of each fuel cell stack is obtained by offline calculation based on the parameter data of each fuel cell stack.

[0013] Source load prediction is performed within the prediction time domain. Based on the source load prediction results, the first stage of optimization is carried out with the goal of minimizing the sum of the operating cost and the tracking performance index of the optimal operating point of each stack within the prediction time domain, so as to obtain the optimal output of each device within the prediction time domain. The operating cost includes: system equipment operation and maintenance cost, wind and solar curtailment penalty cost, grid interaction cost, and stack performance degradation cost.

[0014] Source load prediction is performed in the control time domain. Based on the source load prediction results, the optimal output of the first stage is used as the benchmark value. The objective function is to minimize the deviation between the optimal output of the second stage and the benchmark value, as well as the sum of the output fluctuations of each stack. The second stage optimization is then performed to obtain the final optimized scheduling result.

[0015] As a further limitation of the first aspect of the present invention, the control time domain is divided into multiple time periods using a set rolling optimization granularity, and only the output of each device in the first time period is sent to the field for execution as the final optimization result;

[0016] The optimization results of the first stage are fed back and corrected based on real-time source load data, and the optimization is rolled forward according to the settings to obtain the final optimization result.

[0017] As a further limitation of the first aspect of the present invention, the optimal operating point of each fuel cell stack is obtained by offline calculation based on the parameter data of each fuel cell stack, including:

[0018] Obtain the input hydrogen quantity, output voltage, output current, unidirectional DC / DC output voltage, and output current of each fuel cell stack;

[0019] The acquired data were normalized and fitted to obtain the polarization curve, efficiency curve, hydrogen consumption curve, and offset of the actual power of the fuel cell stack relative to the rated power of the fuel cell stack for each fuel cell stack.

[0020] Based on the results of normalization and fitting, the comprehensive performance expression of the fuel cell stack is obtained. The minimum value of the comprehensive performance expression is obtained by using the gradient descent method, and this minimum value is taken as the power of the optimal operating point of the fuel cell stack.

[0021] As a further limitation of the first aspect of the present invention, the optimal operating point performance index of a certain fuel cell stack is: the difference between the output power of the fuel cell stack and the optimal operating point power of the fuel cell, multiplied by the penalty coefficient for the real-time power of the stack deviating from the optimal operating point.

[0022] The optimal operating point tracking performance index for each fuel cell stack is the sum of the optimal operating point performance indices for each fuel cell stack.

[0023] As a further limitation of the first aspect of the present invention, the output fluctuation of a certain fuel cell stack is: the difference between the output power of the fuel cell stack at the current moment and the output power of the fuel cell stack at the previous moment, multiplied by the penalty coefficient of the power fluctuation of this stack.

[0024] The output fluctuation of each fuel cell stack is the sum of the output fluctuations of each fuel cell stack.

[0025] The second aspect of this invention provides a collaborative optimization system for a multi-fuel cell hydrogen energy integrated power supply system.

[0026] A collaborative optimization system for a multi-fuel cell hydrogen energy integrated power supply system, characterized in that it includes:

[0027] The offline calculation module is configured to perform offline calculations based on the parameter data of each fuel cell stack to obtain the optimal operating point of each fuel cell stack.

[0028] The online optimization module is configured as follows:

[0029] Source load prediction is performed within the prediction time domain. Based on the source load prediction results, the first stage of optimization is carried out with the goal of minimizing the sum of the operating cost and the tracking performance index of the optimal operating point of each stack within the prediction time domain, so as to obtain the optimal output of each device within the prediction time domain. The operating cost includes: system equipment operation and maintenance cost, wind and solar curtailment penalty cost, grid interaction cost, and stack performance degradation cost.

[0030] Source load prediction is performed in the control time domain. Based on the source load prediction results, the optimal output of the first stage is used as the benchmark value. The objective function is to minimize the deviation between the optimal output of the second stage and the benchmark value, as well as the sum of the output fluctuations of each stack. The second stage optimization is then performed to obtain the final optimized scheduling result.

[0031] As a further limitation of the second aspect of the present invention, the offline calculation module performs offline calculations based on the parameter data of each fuel cell stack to obtain the optimal operating point of each fuel cell stack, including:

[0032] Obtain the input hydrogen quantity, output voltage, output current, unidirectional DC / DC output voltage, and output current of each fuel cell stack;

[0033] The acquired data were normalized and fitted to obtain the polarization curve, efficiency curve, hydrogen consumption curve, and offset of the actual power of the fuel cell stack relative to the rated power of the fuel cell stack for each fuel cell stack.

[0034] Based on the results of normalization and fitting, the comprehensive performance expression of the fuel cell stack is obtained. The minimum value of the comprehensive performance expression is obtained by using the gradient descent method, and this minimum value is taken as the power of the optimal operating point of the fuel cell stack.

[0035] As a further limitation of the second aspect of the present invention, in the online optimization module, the control time domain is divided into multiple time periods using a set rolling optimization granularity, and only the output of each device in the first time period is sent to the field for execution as the final optimization result;

[0036] The optimization results of the first stage are fed back and corrected based on real-time source load data, and the optimization is rolled forward according to the settings to obtain the final optimization result.

[0037] The third aspect of this invention provides a collaborative optimization system for a multi-stack fuel cell hydrogen energy integrated power supply system.

[0038] A collaborative optimization system for a multi-fuel cell hydrogen energy integrated power supply system includes:

[0039] The wind power system, photovoltaic system, lithium battery system, electrolyzer system and multi-stack fuel cell system are connected to the AC bus through power electronic converters to supply electrical load and can interact with the power grid;

[0040] The AC bus is connected to the electric refrigeration unit and the heat pump respectively, and is used to supply power to the electric refrigeration unit and the heat pump.

[0041] Electric refrigeration and absorption chillers are used to supply cooling loads, and both electric refrigeration units and absorption chillers are connected to cold storage units.

[0042] Heat pumps and waste heat recovery devices are used to supply heat loads. Both heat pumps and waste heat recovery devices are connected to heat storage devices. Hydrogen storage tanks are used to store hydrogen produced by electrolyzers and to provide hydrogen for multi-stack fuel cell systems.

[0043] The multi-stack fuel cell system adopts a parallel topology. Each stack is connected to the DC bus through a unidirectional DC / DC converter, and the multi-stack fuel cell system is connected to the AC bus through a DC / AC converter.

[0044] The heat pump, waste heat recovery device, and thermal storage device are all connected to the absorption chiller, and the multi-stack fuel cell system is connected to the waste heat recovery device.

[0045] It also includes a control terminal, which is connected to a wind power system, a photovoltaic system, a lithium battery system, an electrolyzer system, a multi-stack fuel cell system, an electric refrigeration device, an absorption chiller, a cold storage device, a heat pump, a waste heat recovery device, and a thermal storage device, respectively, for executing the process of the collaborative optimization method for the multi-stack fuel cell hydrogen energy integrated power supply system described in the first aspect of the present invention.

[0046] The fourth aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the collaborative optimization method for a multi-fuel cell hydrogen energy integrated power supply system as described in the first aspect of the present invention.

[0047] The fifth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, when the processor executes the program, it implements the steps in the collaborative optimization method for a multi-stack fuel cell hydrogen energy integrated supply system as described in the first aspect of the present invention.

[0048] Compared with the prior art, the beneficial effects of the present invention are:

[0049] This invention innovatively proposes a collaborative optimization method for a multi-stack fuel cell hydrogen energy integrated supply system. By applying the multi-stack fuel cell system to the hydrogen energy integrated supply system, the system's energy utilization efficiency is improved. The method comprehensively considers the stack's rated power, efficiency, voltage drop, and hydrogen consumption rate. It adopts an "offline-online" two-layer collaborative optimization approach, offline to obtain the optimal operating point of each stack, and online rolling optimization based on the MPC (Model Predictive Control) framework. This improves the system's economy while enhancing the overall stack efficiency and system lifespan.

[0050] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0051] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0052] Figure 1 This is a flowchart illustrating the collaborative optimization method for a multi-fuel cell hydrogen energy integrated power supply system provided in Embodiment 1 of the present invention.

[0053] Figure 2 This is a flowchart illustrating the collaborative optimization system of the multi-fuel cell hydrogen energy integrated power supply system provided in Embodiment 2 of the present invention.

[0054] Figure 3 This is a schematic diagram of the collaborative optimization system of the multi-stack fuel cell hydrogen energy integrated power supply system provided in Embodiment 3 of the present invention. Detailed Implementation

[0055] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0056] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0057] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0058] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0059] Example 1:

[0060] like Figure 1 As shown, Embodiment 1 of the present invention provides a collaborative optimization method for a multi-fuel cell hydrogen energy integrated power supply system, comprising the following processes:

[0061] S1: Offline calculation, completing the offline solution for the optimal operating point of each stack in the multi-stack fuel cell system. The specific steps are as follows:

[0062] S1.1: Collect the input hydrogen quantity, output voltage, output current, and unidirectional DC / DC output voltage and output current of each fuel cell stack.

[0063] S1.2: The acquired data is normalized and fitted to obtain the characteristic curves of each fuel cell stack, including polarization curves, efficiency curves, and hydrogen consumption curves. The polarization curve can be represented as a power-voltage drop curve ΔU = f1(P), the efficiency curve as η = f2(P), and the hydrogen consumption curve as S = f3(P). Simultaneously, the normalized offset of the actual power of the fuel cell stack relative to its rated power, ΔP = f4(P) = (PP... e ) 2 .

[0064] S1.3: Taking into account the rated power, efficiency, voltage drop, and hydrogen consumption rate of the fuel cell stack, the comprehensive performance expression of the fuel cell stack is obtained:

[0065] F=f(P)=α1f1(P)+α2[1-f2(P)]+α3f3(P)+α4f4(P) (1)

[0066] Where, α i (i = 1, 2, 3, 4) are the weighting coefficients for voltage drop, efficiency, hydrogen consumption, and rated power offset, respectively.

[0067] S1.4: The minimum value of the univariate nonlinear function F = f(P) is obtained by using the gradient descent method, and the corresponding power P0 is obtained, which is the optimal operating point of the stack.

[0068] S1.5: Assess the lifespan and health status of each fuel cell stack.

[0069]

[0070] Where δ represents the degree of performance degradation of the fuel cell stack, and ΔV represents the voltage drop when the fuel cell stack is currently outputting its rated current. It can be expressed as the rated voltage V0 of the fuel cell stack before degradation and the output voltage V of the fuel cell stack at its rated current. i The larger the δ value, the greater the performance degradation of the fuel cell stack; the closer it is to 0, the better its performance.

[0071] S2: Online optimization, employing a two-stage online rolling optimization based on the MPC framework, yields the output plan for each device in the multi-fuel cell hydrogen energy integrated power supply system. The specific steps are as follows:

[0072] S2.1: Establish a mathematical model for a multi-fuel cell integrated hydrogen energy supply system.

[0073] System energy balance equation:

[0074]

[0075] Among them, P wind P represents the power of the wind turbine. pv For photovoltaic power, P cha P dis These represent the charging and discharging power of the lithium battery, P. buy P sell P represents the power purchased and sold in interaction with the power grid. fci P represents the output power of each fuel cell stack. el P is the input power of the electrolytic cell. hp For the input power of the heat pump, P ref P is the input power of the electric chiller. load This refers to the electrical load power.

[0076] System hydrogen energy balance equation:

[0077]

[0078] Where η el For the efficiency of the electrolyzer, η fci For the electrical efficiency of each fuel cell stack, P sto The net power for charging and discharging hydrogen from the hydrogen storage tank is positive when charging and negative when discharging.

[0079] System thermal balance equation:

[0080] Q hr +Q hp +Q dis =Q load +Q ac +Q cha (5)

[0081] Among them, Q hr Q represents the heat release power of the waste heat recovery device. hp For the heat pump to output heat power, Q ac The heat power input to the absorption chiller, Q cha Q dis The respective heat charge / discharge power of the thermal storage device, Q load This represents the heat load power.

[0082]

[0083] Among them, Q fc For the thermal power of a multi-stack fuel cell system, i.e., the heat input to the waste heat recovery device, η hr For the efficiency of the waste heat recovery device, η he For multi-stack fuel cell systems, the output thermal-electric power ratio (COP) is... hp The coefficient of performance (COP) of the heat pump.

[0084] System cold energy balance equation:

[0085] C ac +C ref +C dis =C cha +C load (7)

[0086] Among them, C ac For the refrigeration power of an absorption refrigeration machine, C ref C represents the refrigeration power of an electric refrigeration unit. cha C dis To charge and discharge the cooling power of the cold storage device, C load This is the power of the cooling load.

[0087]

[0088] Among them, COP ac The coefficient of performance (COP) for absorption cooling is... refThe coefficient of performance (COP) of an electric refrigeration system.

[0089] S2.2: Obtain source load data, including wind and solar power output, and cooling, heating and power load power; load system fixed parameters, including electricity purchase and sale prices, unit operation and maintenance costs of each equipment, equipment capacity, and equipment efficiency; load system initial state, including the initial SOC of lithium batteries, hydrogen storage tanks, and thermal and cold storage devices.

[0090] S2.3: The first stage of the online optimization layer, in the prediction time domain N p Within this framework, the power output of wind and solar power, as well as the demand for cooling, heating, and electricity loads, are predicted. Based on the prediction results, and with the objectives of system economy and optimal operating point tracking performance of each fuel cell stack within the prediction time domain, the operation scheduling plan for each device within the prediction time domain is solved, as follows:

[0091] Predicting time domain N p The time interval was set to 4 hours, and the prediction time granularity was 15 minutes.

[0092] Based on historical source load data, a short-term forecasting method is used to predict source load within a given forecasting time domain.

[0093] Objective function expression:

[0094]

[0095] J dev For system equipment operation and maintenance costs, J was To mitigate the penalties for curtailing wind and solar power, J net For grid interaction costs, J loss For the cost of fuel cell stack performance degradation, J trace Track performance indicators for the optimal operating point of each fuel cell stack.

[0096]

[0097] Where, μ i ,T i The decay rate and continuous operating time of each fuel cell stack, δ fc These are the prices of fuel cell stacks, V fc_eol This refers to the output voltage decay at the end of the fuel cell stack's lifespan, which is 10% of the stack's rated output voltage.

[0098]

[0099] Among them, K i K is the penalty coefficient for the real-time power deviation of each fuel cell stack from the optimal operating point. i The value of K is related to the performance degradation rate of each fuel cell stack; the greater the degradation rate, the higher the value of K. i The larger the value of P, the better. i0 Let be the optimal operating point power of the i-th fuel cell stack.

[0100] Based on the above objective function and source load prediction results, the CPLEX solver is called in the Matlab / YALMIP toolbox to obtain the optimal output of each device in the prediction time domain, and then sent to the second stage of online optimization.

[0101] Specifically, within the prediction time domain (4 hours), the output of each device is solved in 15-minute increments, and its time series can be represented as (0:00, 0:15, 0:30, 0:45, 1:00, 1:15, ..., 3:45, 4:00).

[0102] S2.4: The second stage of the online optimization layer, in the control time domain N c Within this framework, using the optimal output of the first stage as the baseline value, and taking the minimum deviation from the baseline value and the minimum output fluctuation of each stack as the optimization objective function, a rolling optimization scheduling model in the control time domain is established, and the final optimization scheduling result is obtained by solving the model, as follows:

[0103] Control time domain N c The timeframe was set to 15 minutes, and the rolling optimization granularity was set to 5 minutes.

[0104] Based on real-time source load data, source load prediction is performed using an ultra-short-term prediction method within a given control time domain.

[0105] Objective function expression:

[0106]

[0107] Among them, P Ⅰ P represents the optimized output results of each device in the first phase. Ⅱ D represents the optimized output results of each device in the second phase. i D is the penalty coefficient for power fluctuations in each fuel cell stack. i The value of D is also related to the performance degradation rate of each fuel cell stack; the greater the degradation rate, the lower the value of D. i The larger the value of K, the better. i D i The value selection rule ensures that when there are differences in the performance and health status of each fuel cell stack, the fuel cell stack with a poor health status will have less output and smaller fluctuations, thereby extending its lifespan.

[0108] Rolling optimization, based on the above objective function and source load prediction results, can be performed by calling the CPLEX solver in the Matlab / YALMIP toolbox (using licensed software, or other licensed solver software can be used instead) to obtain the optimal output of each device in the control time domain (3×5min), and only the output of each device in the first 5min is sent to the field for execution as the final optimization result; the optimization results of the first stage are fed back and corrected based on real-time source load data, and rolling optimization is performed forward with a time granularity of 5min to obtain the final optimization result.

[0109] Specifically, the second-stage control time domain (15min) is based on the solution of the first stage, rolling forward in 5-minute increments. The detailed steps are as follows: the equipment output is obtained at (0:00, 0:05, 0:10; 0:15), but only the result of (0:00-0:05) is sent to the field as the optimal result. Then, it rolls forward 5 minutes to obtain the equipment output at (0:05, 0:10; 0:15, 0:20), but only the result of (0:05-0:10) is sent to the field as the optimal result, and so on, until the final optimized result is obtained.

[0110] Example 2:

[0111] Embodiment 2 of the present invention provides a collaborative optimization system for a multi-stack fuel cell hydrogen energy integrated power supply system, characterized in that it includes:

[0112] The offline calculation module is configured to perform offline calculations based on the parameter data of each fuel cell stack to obtain the optimal operating point of each fuel cell stack.

[0113] The online optimization module is configured as follows:

[0114] Source load prediction is performed within the prediction time domain. Based on the source load prediction results, the first stage of optimization is carried out with the goal of minimizing the sum of the operating cost and the tracking performance index of the optimal operating point of each stack within the prediction time domain, so as to obtain the optimal output of each device within the prediction time domain. The operating cost includes: system equipment operation and maintenance cost, wind and solar curtailment penalty cost, grid interaction cost, and stack performance degradation cost.

[0115] Source load prediction is performed in the control time domain. Based on the source load prediction results, the optimal output of the first stage is used as the benchmark value. The objective function is to minimize the deviation between the optimal output of the second stage and the benchmark value, as well as the sum of the output fluctuations of each stack. The second stage optimization is then performed to obtain the final optimized scheduling result.

[0116] The working method of the system is the same as the steps of the collaborative optimization method for the multi-stack fuel cell hydrogen energy integrated power supply system provided in Example 1, and will not be repeated here.

[0117] Example 3:

[0118] like Figure 3 As shown, Embodiment 3 of the present invention provides a collaborative optimization system for a multi-stack fuel cell hydrogen energy integrated power supply system, comprising:

[0119] The wind power system, photovoltaic system, lithium battery system, electrolyzer system and multi-stack fuel cell system are connected to the AC bus through power electronic converters to supply electrical load and can interact with the power grid;

[0120] The AC bus is connected to the electric refrigeration unit and the heat pump respectively, and is used to supply power to the electric refrigeration unit and the heat pump.

[0121] Electric refrigeration and absorption chillers are used to supply cooling loads, and both electric refrigeration units and absorption chillers are connected to cold storage units.

[0122] Heat pumps and waste heat recovery devices are used to supply heat loads. Both heat pumps and waste heat recovery devices are connected to heat storage devices. Hydrogen storage tanks are used to store hydrogen produced by electrolyzers and to provide hydrogen for multi-stack fuel cell systems.

[0123] The multi-stack fuel cell system adopts a parallel topology, with each stack connected to the DC bus via a unidirectional DC / DC converter, enabling independent control of each stack and improving the overall stability and reliability of the fuel cell system; the multi-stack fuel cell system is connected to the AC bus via a DC / AC converter.

[0124] The heat pump, waste heat recovery device, and thermal storage device are all connected to the absorption chiller, and the multi-stack fuel cell system is connected to the waste heat recovery device.

[0125] It also includes a control terminal, which is connected to the wind power system, photovoltaic system, lithium battery system, electrolyzer system, multi-stack fuel cell system, electric refrigeration device, absorption chiller, cold storage device, heat pump, waste heat recovery device and heat storage device respectively, and is used to execute the process of the collaborative optimization method of multi-stack fuel cell hydrogen energy integrated power supply system described in Embodiment 1 of the present invention.

[0126] Example 4:

[0127] Embodiment 4 of the present invention provides a computer-readable storage medium storing a program thereon, which, when executed by a processor, implements the steps in the collaborative optimization method for a multi-stack fuel cell hydrogen energy integrated power supply system as described in Embodiment 1 of the present invention.

[0128] Example 5:

[0129] Embodiment 5 of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the collaborative optimization method for a multi-stack fuel cell hydrogen energy integrated power supply system as described in Embodiment 1 of the present invention.

[0130] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A collaborative optimization method for a multi-fuel cell hydrogen energy integrated power supply system, characterized in that, include: The optimal operating point of each fuel cell stack is obtained by offline calculation based on the parameter data of each fuel cell stack. Source load prediction is performed within the prediction time domain. Based on the source load prediction results, the first stage of optimization is carried out with the goal of minimizing the sum of the operating cost and the tracking performance index of the optimal operating point of each stack within the prediction time domain, so as to obtain the optimal output of each device within the prediction time domain. The operating cost includes: system equipment operation and maintenance cost, wind and solar curtailment penalty cost, grid interaction cost, and stack performance degradation cost. In the control time domain, source load prediction is performed. Based on the source load prediction results, the optimal output of the first stage is used as the benchmark value. The objective function is to minimize the deviation between the optimal output of the second stage and the benchmark value, as well as the sum of the output fluctuations of each stack. The second stage optimization is then performed to obtain the final optimized scheduling result. Based on the parameter data of each fuel cell stack, offline calculations are performed to obtain the optimal operating point of each fuel cell stack, including: Obtain the input hydrogen quantity, output voltage, output current, unidirectional DC / DC output voltage, and output current of each fuel cell stack; The acquired data were normalized and fitted to obtain the polarization curve, efficiency curve, hydrogen consumption curve, and offset of the actual power of the fuel cell stack relative to the rated power of the fuel cell stack for each fuel cell stack. Based on the results of normalization and fitting, the comprehensive performance expression of the electric stack is obtained. The minimum value of the comprehensive performance expression of the electric stack is obtained by using the gradient descent method. This minimum value is taken as the power of the optimal operating point of the electric stack. The optimal operating point tracking performance index of a certain fuel cell stack is: the difference between the output power of the fuel cell stack and the optimal operating point power of the fuel cell, multiplied by the penalty coefficient for the real-time power deviation of the stack from the optimal operating point. The optimal operating point tracking performance index for each fuel cell stack is the sum of the optimal operating point performance indices for each fuel cell stack. The output fluctuation of a certain fuel cell stack is: the difference between the output power of the fuel cell stack at the current moment and the output power of the fuel cell stack at the previous moment, multiplied by the penalty coefficient for the power fluctuation of this stack. The output fluctuation of each fuel cell stack is the sum of the output fluctuations of each fuel cell stack.

2. The collaborative optimization method for a multi-fuel cell hydrogen energy integrated supply system as described in claim 1, characterized in that, The control time domain is divided into multiple time periods using a set rolling optimization granularity, and only the output of each device in the first time period is sent to the field for execution as the final optimization result; The optimization results of the first stage are fed back and corrected based on real-time source load data, and the optimization is rolled forward according to the settings to obtain the final optimization result.

3. A collaborative optimization system for a multi-fuel cell hydrogen energy integrated supply system, characterized in that, include: The offline calculation module is configured to perform offline calculations based on the parameter data of each fuel cell stack to obtain the optimal operating point of each fuel cell stack. The online optimization module is configured as follows: Source load prediction is performed within the prediction time domain. Based on the source load prediction results, the first stage of optimization is carried out with the goal of minimizing the sum of the operating cost and the tracking performance index of the optimal operating point of each stack within the prediction time domain, so as to obtain the optimal output of each device within the prediction time domain. The operating cost includes: system equipment operation and maintenance cost, wind and solar curtailment penalty cost, grid interaction cost, and stack performance degradation cost. In the control time domain, source load prediction is performed. Based on the source load prediction results, the optimal output of the first stage is used as the benchmark value. The objective function is to minimize the deviation between the optimal output of the second stage and the benchmark value, as well as the sum of the output fluctuations of each stack. The second stage optimization is then performed to obtain the final optimized scheduling result. In the offline calculation module, offline calculations are performed based on the parameter data of each fuel cell stack to obtain the optimal operating point of each stack, including: Obtain the input hydrogen quantity, output voltage, output current, unidirectional DC / DC output voltage, and output current of each fuel cell stack; The acquired data were normalized and fitted to obtain the polarization curve, efficiency curve, hydrogen consumption curve, and offset of the actual power of the fuel cell stack relative to the rated power of the fuel cell stack for each fuel cell stack. Based on the results of normalization and fitting, the comprehensive performance expression of the electric stack is obtained. The minimum value of the comprehensive performance expression of the electric stack is obtained by using the gradient descent method. This minimum value is taken as the power of the optimal operating point of the electric stack. The optimal operating point tracking performance index of a certain fuel cell stack is: the difference between the output power of the fuel cell stack and the optimal operating point power of the fuel cell, multiplied by the penalty coefficient for the real-time power deviation of the stack from the optimal operating point. The optimal operating point tracking performance index for each fuel cell stack is the sum of the optimal operating point performance indices for each fuel cell stack. The output fluctuation of a certain fuel cell stack is: the difference between the output power of the fuel cell stack at the current moment and the output power of the fuel cell stack at the previous moment, multiplied by the penalty coefficient for the power fluctuation of this stack. The output fluctuation of each fuel cell stack is the sum of the output fluctuations of each fuel cell stack.

4. The collaborative optimization system for a multi-fuel cell hydrogen energy integrated supply system as described in claim 3, characterized in that, In the online optimization module, the control time domain is divided into multiple time periods using a set rolling optimization granularity, and only the output of each device in the first time period is sent to the field for execution as the final optimization result; The optimization results of the first stage are fed back and corrected based on real-time source load data, and the optimization is rolled forward according to the settings to obtain the final optimization result.

5. A collaborative optimization system for a multi-fuel cell hydrogen energy integrated supply system, characterized in that, include: The wind power system, photovoltaic system, lithium battery system, electrolyzer system and multi-stack fuel cell system are connected to the AC bus through power electronic converters to supply electrical load and can interact with the power grid; The AC bus is connected to the electric refrigeration unit and the heat pump respectively, and is used to supply power to the electric refrigeration unit and the heat pump. Electric refrigeration and absorption chillers are used to supply cooling loads, and both electric refrigeration units and absorption chillers are connected to cold storage units. Heat pumps and waste heat recovery devices are used to supply heat loads. Both heat pumps and waste heat recovery devices are connected to heat storage devices. Hydrogen storage tanks are used to store hydrogen produced by electrolyzers and to provide hydrogen for multi-stack fuel cell systems. The multi-stack fuel cell system adopts a parallel topology. Each stack is connected to the DC bus through a unidirectional DC / DC converter, and the multi-stack fuel cell system is connected to the AC bus through a DC / AC converter. The heat pump, waste heat recovery device, and thermal storage device are all connected to the absorption chiller, and the multi-stack fuel cell system is connected to the waste heat recovery device. It also includes a control terminal, which is connected to a wind power system, a photovoltaic system, a lithium battery system, an electrolyzer system, a multi-stack fuel cell system, an electric refrigeration device, an absorption chiller, a cold storage device, a heat pump, a waste heat recovery device, and a thermal storage device, respectively, for executing the process of the collaborative optimization method for the multi-stack fuel cell hydrogen energy integrated power supply system as described in claim 1 or 2.

6. A computer-readable storage medium having a program stored thereon, characterized in that, When executed by the processor, the program implements the steps in the collaborative optimization method for a multi-stack fuel cell hydrogen energy integrated power supply system as described in claim 1 or 2.

7. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the collaborative optimization method for a multi-stack fuel cell hydrogen energy integrated power supply system as described in claim 1 or 2.

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