Series-parallel hydrogen production cluster hierarchical control method based on day-ahead-day two-stage optimization

Abstract

The invention provides a series-parallel hydrogen production cluster hierarchical control method based on day-ahead-day two-stage optimization, and the method comprises the steps: building a first-stage model with the maximization of the adjustable capability and the optimization of the economical efficiency of a hydrogen production system as targets; taking renewable energy output tracking precision and controlled quantity fluctuation minimization as targets, and constructing a second-stage model; combining the first-stage model and the second-stage model to construct a day-ahead-day-intra-day two-stage layered control framework of the wind-solar off-grid hydrogen production system; and carrying out renewable energy local consumption and economic operation control of the wind and light off-grid hydrogen production system by adopting the day-ahead-day two-stage wind and light off-grid hydrogen production system hierarchical control framework. According to the method, dual optimization of renewable energy consumption and system economy is achieved in a large-scale renewable energy hydrogen production cluster, a complex optimization problem is decomposed into sub-problems capable of being efficiently solved through hierarchical processing, and control precision and calculation efficiency are both considered.

Description

Technical Field

[0001] This invention relates to the field of renewable energy hydrogen production system control technology, and in particular to a hierarchical control method for hybrid hydrogen production clusters based on day-ahead and intraday two-stage optimization. Background Technology

[0002] With the large-scale application of renewable energy power generation technologies, hydrogen production has become an effective solution for the local consumption of renewable energy output. Current research on hydrogen production system control often simplifies electrolyzers to devices with constant efficiency and load range, neglecting the impact of dynamic parameters such as pressure on hydrogen production efficiency, resulting in low tracking accuracy. The models are overly simplified, lacking a hierarchical control architecture, making it difficult to characterize the state transitions and adjustment characteristics of electrolyzers under fluctuating renewable energy inputs. This hinders refined management from day-ahead planning to real-time intraday adjustments and makes it difficult to cope with instantaneous fluctuations in renewable energy output. For ALK-PEM hybrid hydrogen production clusters, the large capacity and low cost advantages of ALK and the rapid response characteristics of PEM are not fully utilized, limiting the system's adjustment capabilities. Furthermore, the complexity of solving nonlinear models makes it difficult to meet the computational requirements for real-time control of hydrogen production systems. Summary of the Invention

[0003] In view of this, the present invention provides a hierarchical control method for hybrid hydrogen production clusters based on day-to-day and intraday two-stage optimization to solve the above problems.

[0004] This invention provides a hierarchical control method for hybrid hydrogen production clusters based on a two-stage optimization approach from day-ahead to intraday, comprising: constructing a first-stage model with the goal of maximizing the adjustability and economic efficiency of the hydrogen production system; constructing a second-stage model with the goal of minimizing the tracking accuracy of renewable energy output and the fluctuation of control quantities; combining the first-stage model and the second-stage model to construct a hierarchical control framework for the wind-solar off-grid hydrogen production system in the day-ahead to intraday phases; and using the hierarchical control framework for the wind-solar off-grid hydrogen production system in the day-ahead to intraday phases to perform on-site consumption of renewable energy and economic operation control of the wind-solar off-grid hydrogen production system.

[0005] In another implementation of the present invention, the randomness of renewable energy fluctuations is further included by converting the effects of temperature and power on efficiency of the ALK and PEM electrolyzers into quantifiable constraints.

[0006] In another implementation of the present invention, the constraints of the first stage model include system energy balance, electrolytic cell start-up and shutdown interval, and upper and lower power limits.

[0007] In another implementation of the present invention, the objective function of the first-stage model is expressed as:

[0008]

[0009] in, For the system's hydrogen sales revenue, C op For system equipment operation and maintenance costs, C st For the start-up and shutdown costs of the electrolytic cell, C el C is the cost of reduced lifespan of the electrolyzer. pu The cost of penalties for abandoning wind and solar power.

[0010] In another implementation of the present invention, the revenue from hydrogen sales by the system is expressed as:

[0011]

[0012] in, The price per unit mass of hydrogen. The system produces hydrogen volume. This represents the density of hydrogen gas.

[0013] The system equipment operation and maintenance cost is expressed as follows:

[0014]

[0015] Where T is the scheduling cycle, N is the number of alkaline electrolyzers, and M is the number of PEM electrolyzers.

[0016] The start-up and shutdown costs and lifespan depreciation costs of the electrolytic cell are expressed as follows:

[0017]

[0018] Among them, c su For the start-up and shutdown costs of the electrolytic cell, C el For the cost of depreciation over life, f el This is the cost factor for power fluctuations in the electrolytic cell.

[0019] The penalty cost of curtailing wind and solar power is expressed as follows:

[0020]

[0021] Among them, c pu,wt and c pu,pv The cost of curtailing wind and solar power per unit and Contribute to wind power and solar power forecasting.

[0022] In another implementation of the present invention, the construction of the second-stage model with the goal of minimizing the tracking accuracy of renewable energy output and the fluctuation of control quantity includes: constructing an upper-level optimization model with the goal of maximizing the renewable energy power absorption rate and dynamically adjusting the real-time operating power of the module; constructing a lower-level optimization model with the goal of minimizing the deviation between the actual power and the reference plan, while taking into account the hydrogen production revenue and equipment life cost; constructing a Lagrangian function and using KKT conditions to transform the bi-level optimization problem of the upper-level optimization model and the lower-level optimization model into a single-level quadratic programming problem to obtain the second-stage model.

[0023] In another implementation of the present invention, the constraints of the upper-level optimization model include cluster power balance and module dynamic operation restrictions.

[0024] Another aspect of the present invention provides a hierarchical control system for a hybrid hydrogen production cluster based on day-ahead and intraday two-stage optimization, comprising: a first-stage model construction module: constructing a first-stage model with the goal of maximizing the adjustability and economic efficiency of the hydrogen production system; a second-stage model construction module: constructing a second-stage model with the goal of minimizing the tracking accuracy of renewable energy output and the fluctuation of control quantities; a hierarchical control framework construction module: combining the first-stage model and the second-stage model to construct a hierarchical control framework for the wind-solar off-grid hydrogen production system in the day-ahead and intraday two-stage phases; and a coordinated control module: using the hierarchical control framework for the wind-solar off-grid hydrogen production system in the day-ahead and intraday two-stage phases to perform on-site consumption of renewable energy and economic operation control of the wind-solar off-grid hydrogen production system.

[0025] In another aspect, the present invention provides an electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of a hierarchical control method for a hybrid hydrogen production cluster based on day-to-day two-stage optimization as described in any of the preceding claims.

[0026] In another aspect, the present invention provides a computer storage medium, characterized in that the computer storage medium stores a computer program, which, when executed by a processor, implements the steps of a hierarchical control method for a hybrid hydrogen production cluster based on day-to-day two-stage optimization as described in any of the preceding claims.

[0027] This invention presents a hierarchical control method for hybrid hydrogen production clusters based on day-ahead and intraday two-stage optimization. It proposes a two-stage hierarchical control architecture. The first stage uses a probabilistic model to handle the uncertainties of renewable energy and generate an economic operation reference plan with adjustment margins. The second stage constructs a bi-layer optimization model based on real-time data, achieving high-precision tracking of wind and solar power and improving system economics through rolling optimization. This framework utilizes the complementary characteristics of the large capacity and low cost of ALK electrolyzers and the fast response of PEM electrolyzers, using the hybrid hydrogen production module as the basic control unit. Hierarchical optimization resolves the contradiction between accuracy and efficiency inherent in traditional single-model approaches. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. By reading the detailed description of the embodiments below, the advantages and benefits of the solutions will become clear to those skilled in the art. The accompanying drawings are only for illustrating preferred embodiments and are not intended to limit the present invention. In the accompanying drawings:

[0029] Figure 1 This is a schematic diagram of a hierarchical control method for hybrid hydrogen production clusters based on day-to-day and intraday two-stage optimization, according to an embodiment of the present invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art should fall within the protection scope of the present invention.

[0031] Figure 1 A schematic diagram of a hierarchical control method for hybrid hydrogen production clusters based on day-to-day and intraday two-stage optimization is provided for an embodiment of the present invention, as shown below. Figure 1 As shown, this embodiment mainly includes:

[0032] S101. With the goal of maximizing the adjustability and economic efficiency of the hydrogen production system, a first-stage model is constructed.

[0033] S102. With the goal of minimizing the tracking accuracy of renewable energy output and the fluctuation of control quantities, a second-stage model is constructed.

[0034] S103. Combining the first-stage model and the second-stage model, construct a hierarchical control framework for the wind-solar off-grid hydrogen production system in two stages: day-ahead and intraday.

[0035] S104. The two-stage wind-solar off-grid hydrogen production system of the day-to-day phase is adopted to carry out the local consumption of renewable energy and the economic operation control of the wind-solar off-grid hydrogen production system.

[0036] This invention presents a hierarchical control method for hybrid hydrogen production clusters based on day-ahead and intraday two-stage optimization. It proposes a two-stage hierarchical control architecture. The first stage uses a probabilistic model to handle the uncertainties of renewable energy and generate an economic operation reference plan with adjustment margins. The second stage constructs a bi-layer optimization model based on real-time data, achieving high-precision tracking of wind and solar power and improving system economics through rolling optimization. This framework utilizes the complementary characteristics of the large capacity and low cost of ALK electrolyzers and the fast response of PEM electrolyzers, using the hybrid hydrogen production module as the basic control unit. Hierarchical optimization resolves the contradiction between accuracy and efficiency inherent in traditional single-model approaches.

[0037] In another implementation of the present invention, the randomness of renewable energy fluctuations is further included by converting the effects of temperature and power on efficiency of the ALK and PEM electrolyzers into quantifiable constraints.

[0038] For example, considering the effect of temperature on efficiency in ALK and PEM electrolyzers, a heat balance correlation model is established.

[0039] The temperature of the electrolyzer is affected by the heat generated by the water electrolysis reaction under different input power. First, a thermodynamic model characterizing the thermal energy balance of the electrolyzer is established as follows:

[0040]

[0041] In the formula, and These are the temperature changes at time t for the ALK and PEM electrolyzers, respectively. and The electrolytic cell generates heat during the reaction. For electrolytic cells and heat recovery. Determined by the electrolytic cell's factory design parameters. ρ ALK and These are the specific heat capacity, electrolyte density, and electrolyte volume of the alkaline electrolytic cell electrolyte, respectively. ρ PEM and These are the specific heat capacity, electrolyte density, and electrolyte volume of the PEM electrolyzer, respectively. The heat lost is calculated using the following formula:

[0042]

[0043] In the formula, and The equivalent thermal resistances between the ALK and PEM electrolyzers and the environment are T, respectively.a The ambient temperature.

[0044] The heat generation and transfer efficiency inside the electrolytic cell can be approximated as 1. A thermodynamic characteristic model can be established to estimate its internal temperature.

[0045]

[0046] The electrolysis of water consumes a large amount of electrical energy and generates a large amount of heat energy while producing hydrogen.

[0047]

[0048] In the formula, and The operating power of the ALK and PEM electrolyzers are respectively. and The hydrogen production power from electrolysis is related to the electrolysis efficiency, that is:

[0049]

[0050] In the formula, η alk and η pem The hydrogen production efficiency of alkaline electrolyzers and PEM electrolyzers.

[0051] Combining equations (1) and (10), the formula for calculating the electrolytic cell temperature under the change in input power per unit time is as follows:

[0052]

[0053]

[0054] Simplifying the coefficients, we get:

[0055]

[0056] The power-efficiency curves of ALK and PEM electrolyzers were fitted using a piecewise linearization method, taking into account the dynamic changes in Faraday efficiency and voltage efficiency.

[0057] During the operation of alkaline electrolyzers and PEM electrolyzers, the hydrogen production efficiency depends on the Faraday current efficiency and voltage efficiency, as shown in formula (15).

[0058]

[0059] In the formula, η alk and η pem η represents the hydrogen production efficiency of alkaline electrolyzers and PEM electrolyzers. e_alk and η e_pem For voltage efficiency, η f_alk and η f_pemThis is the Faraday current efficiency.

[0060] The voltage efficiency is related to the thermal neutral voltage of the electrolytic cell and the input voltage, as shown in formula (16).

[0061]

[0062] In the formula, U alk and U pem U represents the terminal voltage of the alkaline electrolyzer and the PEM electrolyzer. th For thermal neutral voltage, U th The value is typically 1.481V.

[0063] Faraday current efficiency describes the current loss during the operation of an electrolyzer. The Faraday current efficiency of alkaline electrolyzers and PEM electrolyzers can be calculated using empirical formulas.

[0064]

[0065] In the formula, I alk / A represents the current density, and f1 and f2 are current loss parameters.

[0066] State transition and power regulation constraints: Define the shutdown, standby, production, and overload states of the electrolytic cell and the transition conditions.

[0067] The above analysis shows that the operating status of the electrolyzer is affected by temperature and input power. When the input power is less than the starting power and the temperature is less than the minimum operating temperature, the electrolyzer is in a shutdown state. At this time, the input power is used for heat generation, and the hydrogen production power is zero. Under current technological conditions, the starting power of the ALK electrolyzer is... Generally the rated power 10% of the starting temperature Generally above 60℃.

[0068] Under shutdown conditions, the ALK electrolytic cell meets the following shutdown operation characteristic constraints:

[0069]

[0070] PEM electrolyzer start-up power Generally the rated power 5% of the starting temperature Generally above 50℃.

[0071] Under shutdown conditions, the PEM electrolyzer meets the following shutdown operation characteristic constraints:

[0072]

[0073] As the input power increases, the electrodes of the electrolytic cell reach the minimum reaction power. During shutdown, the heat generated by the input power causes the electrolytic cell temperature to reach the minimum reaction temperature, at which point the electrolytic cell enters the production state. The ALK electrolytic cell meets the operating characteristics constraints under normal production conditions.

[0074]

[0075] In the formula, This is the maximum safe operating temperature allowed for ALK electrolyzers, typically set to 80℃.

[0076] Maximum safe operating temperature of PEM electrolyzer The typical operating temperature is 80℃, and its normal production operating characteristics are constrained as follows:

[0077]

[0078] In addition, a minimum downtime interval needs to be set to prevent frequent start-stop cycles from damaging the electrode materials of the electrolytic cell. For ALK electrolytic cells, this is typically set to 15 minutes.

[0079]

[0080] Equation (23) is the minimum downtime constraint. This is a 0-1 variable representing the shutdown status of the electrolytic cell; a value of 1 indicates that the cell is in a shutdown state. This is the shortest downtime interval for an alkaline electrolyzer.

[0081] The shutdown state transition constraints and minimum shutdown interval constraints for PEM electrolyzers are shown below. PEM electrolyzers are typically set to 20 minutes.

[0082]

[0083] Equation (24) is the minimum downtime constraint. This is a 0-1 variable representing the shutdown status of the electrolytic cell; a value of 1 indicates that the cell is in a shutdown state. This is the shortest downtime interval for the PEM electrolyzer.

[0084] As photovoltaic input further increases, when the input power exceeds the rated power of the electrolyzer, the electrolyzer enters an overload state. However, if the overload duration exceeds the maximum overload tolerance time of the electrolyzer, it will damage the electrode materials and cause equipment failure. Therefore, the overload duration needs to be limited. The overload ranges for ALK and PEM electrolyzers do not exceed 150% and 130% of the rated power, respectively. Therefore, the overload operation constraints for ALK and PEM electrolyzers are as follows:

[0085]

[0086] In the formula, These represent the maximum operating power of the ALK and PEM electrolyzers, respectively.

[0087] Prolonged overload can damage the electrode materials of the electrolytic cell, therefore an overload time limit must be set. For ALK electrolytic cells, if the overload condition exceeds 30 minutes, the electrolytic cell needs to be reduced to below the rated power to recover. Overloading is not permitted again within the specified minimum recovery time of 20 minutes.

[0088]

[0089] Equation (27) represents the maximum overload time constraint and overload recovery constraint for the ALK electrolytic cell. This is the maximum overload time of the ALK electrolyzer. This is the shortest time it takes for the ALK part to re-enter the overload state after recovering from the overload state to the normal state. and These are 0-1 type variables representing the overload and normal production states of the alkaline electrolyzer, respectively. A value of 1 indicates that the electrolyzer is in that state.

[0090] For PEM electrolyzers, if an overload condition persists for more than 20 minutes, the electrolyzer must be restored by reducing the power to below the rated power. It must not be overloaded again within the specified minimum recovery time of 15 minutes. Overload and recovery constraints apply.

[0091]

[0092] Equation (28) represents the maximum overload time constraint and overload recovery constraint of the PEM portion, and represents the overload recovery constraint of the PEM portion. This is the shortest time it takes for the PEM part to return to the overload state after recovering from the overload state to the normal state. and These are 0-1 type variables representing the overload and normal production states of the PEM electrolyzer, respectively. A value of 1 indicates that the electrolyzer is in that state.

[0093] When the input power of the electrolyzer drops below the starting power, but the temperature remains above the minimum reaction temperature, the electrolyzer enters standby mode. In this mode, the electrolyzer does not produce hydrogen; the input power is used for heat generation and heating to maintain the reaction temperature, ensuring that the electrolyzer can be switched to production mode at any time. During this stage, the ALK electrolyzer meets the standby mode operation constraints:

[0094]

[0095] The PEM electrolytic cell meets the standby operation constraints:

[0096]

[0097] Under normal and overload conditions, the power adjustment of the electrolyzer must not exceed the maximum adjustable range of the electrolyzer's power. The maximum change in input power per second for alkaline and PEM electrolyzers is 20% and 40% of the rated power, respectively.

[0098]

[0099] In the formula, and The maximum power change per second for alkaline and PEM electrolyzers are set to 20% and 40% of the rated power, respectively.

[0100] In another implementation of the present invention, the constraints of the first stage model include system energy balance, electrolytic cell start-up and shutdown interval, and upper and lower power limits.

[0101] For example, the first-stage model is used to optimize the start-up and shutdown plan of the electrolyzer, including constraints such as system energy balance, electrolyzer start-up and shutdown interval, and upper and lower power limits. It uses a specific algorithm to predict daytime wind and solar power output, and combines weather type classification and time series feature extraction to improve prediction accuracy.

[0102] In another implementation of the present invention, the primary objective of the first-stage model is to optimize the power allocation of the electrolyzer after knowing the predicted local wind and solar power output, so as to maximize the system's hydrogen production. The objective function is expressed as:

[0103]

[0104] in, For the system's hydrogen sales revenue, C op For system equipment operation and maintenance costs, C st For the start-up and shutdown costs of the electrolytic cell, C el C is the cost of reduced lifespan of the electrolyzer. pu The cost of penalties for abandoning wind and solar power.

[0105] For example, a Gaussian mixture model (GMM) is used to describe the multimodal distribution characteristics of wind and solar power prediction errors. A large number of error samples are generated through sequential Monte Carlo sampling techniques to construct a probabilistic model with confidence constraints. This method overcomes the limitations of traditional deterministic models, transforming the randomness of renewable energy fluctuations into quantifiable constraints, ensuring that the optimization results can cover the power fluctuation range in actual operation.

[0106] With the goal of minimizing the total lifecycle cost of the hydrogen production system, this model comprehensively considers module operation and maintenance costs, lifespan depreciation costs, and hydrogen production revenue. Constraints include cluster power balance constraints (based on day-ahead renewable energy forecasts) and module operating status constraints (covering transition boundaries and minimum start-stop interval requirements for states such as shutdown, production, and overload). This model is used to determine the optimal start-stop schedule and basic power allocation scheme for the hydrogen production modules.

[0107] The Big M method is used to transform nonlinear constraints into linear constraints, and the module power-efficiency curve is piecewise linearized, transforming the original problem into a mixed-integer programming model. The global optimum is quickly obtained using the CPLEX solver, and the generated economic start-up and shutdown reference plan provides the basis for a long-term operational strategy in the second phase, ensuring the system has a margin of adjustment to cope with renewable energy fluctuations.

[0108] In another implementation of the present invention, the revenue from hydrogen sales by the system is expressed as:

[0109]

[0110] in, The price per unit mass of hydrogen. The system produces hydrogen volume. This represents the density of hydrogen gas.

[0111] The system equipment operation and maintenance cost is expressed as follows:

[0112]

[0113] Where T is the scheduling cycle, N is the number of alkaline electrolyzers, and M is the number of PEM electrolyzers.

[0114] The start-up and shutdown costs and lifespan depreciation costs of the electrolytic cell are expressed as follows:

[0115]

[0116] Among them, c su For the start-up and shutdown costs of the electrolytic cell, C el For the cost of depreciation over life, f el This is the cost factor for power fluctuations in the electrolytic cell.

[0117] To maximize the absorption of renewable energy, a penalty cost for wind and solar curtailment is introduced, as shown in the following formula:

[0118]

[0119] Among them, c pu,wt and c pu,pv The cost of curtailing wind and solar power per unit and Contribute to wind power and solar power forecasting.

[0120] In another implementation of the present invention, the construction of the second-stage model with the goal of minimizing the tracking accuracy of renewable energy output and the fluctuation of control quantity includes: constructing an upper-level optimization model with the goal of maximizing the renewable energy power absorption rate and dynamically adjusting the real-time operating power of the module; constructing a lower-level optimization model with the goal of minimizing the deviation between the actual power and the reference plan, while taking into account the hydrogen production revenue and equipment life cost; constructing a Lagrangian function and using KKT conditions to transform the bi-level optimization problem of the upper-level optimization model and the lower-level optimization model into a single-level quadratic programming problem to obtain the second-stage model.

[0121] For example, the upper layer is a real-time renewable energy consumption optimization layer, which dynamically adjusts the real-time operating power of the modules to maximize the renewable energy power consumption rate. By minimizing the deviation between the renewable energy power and the total module power in the control time domain, it ensures that renewable energy is utilized as much as possible and reduces curtailment. Constraints include cluster power balance (renewable energy output equals the sum of module operating power and curtailed power) and module dynamic operation limitations (such as power regulation rate).

[0122] The lower layer is the operational economic optimization layer, aiming to minimize the deviation between actual power and the reference plan, while also considering hydrogen production revenue and equipment lifespan costs. By constructing a Lagrangian function and utilizing KKT conditions, the bi-layer optimization problem is transformed into a single-layer quadratic programming model, achieving coordinated optimization of real-time power allocation and economic costs, ensuring that the system maintains economical operation while accurately tracking renewable energy.

[0123] A closed-loop control process of "prediction-optimization-execution-feedback" is established: real-time acquisition of renewable energy data and module status; prediction of short-term renewable energy trajectories using deep learning algorithms; inputting the prediction results into a two-layer model to solve for the optimal control sequence, executing only the first step instruction and discarding the remaining sequence; repeating the above process after new data updates, forming a second-level rolling optimization cycle. This mechanism achieves adaptive response to instantaneous fluctuations in renewable energy by continuously updating prediction information and optimization objectives.

[0124] Controlling time-domain parameters: The settings of control domain step size and delay step size affect computational efficiency and tracking accuracy. By balancing the two, the light waste rate can be controlled at an extremely low level while ensuring real-time performance.

[0125] Weighting factor adjustment: By adjusting the weight ratio of renewable energy tracking error to economic cost, the absorption accuracy and operating cost are balanced under different operating scenarios, avoiding excessive pursuit of tracking accuracy that leads to a surge in costs.

[0126] Module capacity ratio: Based on the differences in characteristics between ALK and PEM, the capacity ratio between the two is optimized. While taking advantage of the low cost of ALK, the fast response capability of PEM is used to improve the system regulation performance, thereby achieving a balance between economy and dynamic performance.

[0127] Specifically, a dynamic model of the hydrogen production system is constructed using state-space representation, considering the interaction of state variables, control variables, and disturbance variables. With the objectives of minimizing renewable energy output tracking accuracy and control fluctuations, nonlinear constraints are linearized, and the optimal control command is quickly obtained through a solver.

[0128] When the alkaline electrolytic cell operates within the range of 20%-120% of its rated power, its per-unit input power and efficiency exhibit a linear decreasing trend. Fitting the efficiency curve using a linear function yields the following relationship:

[0129] η alk =c1ρ+c2 (34)

[0130] In the formula, c1 and c2 are linear fitting parameters.

[0131] The power-efficiency relationship of the PEM electrolyzer is treated using a piecewise linearization method, where η is... pem The constant segment is divided into X segments, which are then linearized using the following formula:

[0132]

[0133] In the formula, η pem,x For the operating efficiency of the PEM electrolyzer in segment x, W x The x-th segment of the PEM electrolyzer is the operating state. Formula (35) represents the constraint between the actual efficiency of the PEM electrolyzer and the efficiency after piecewise linearization. Formula (36) restricts the efficiency at each moment to be in only one segment. Formula (37) constrains the power of the PEM electrolyzer in segment x.

[0134] By introducing auxiliary variables to linearize the terms containing absolute values, the fluctuating power of the electrolytic cell is defined. and Then formula (19) can be transformed into:

[0135]

[0136] Accordingly, the absolute value term is linearized as follows:

[0137]

[0138] The two-stage collaborative control mechanism employs a two-stage approach. The first stage uses probabilistic optimization to reserve sufficient power adjustment margin for the second stage, ensuring that the module's adjustable range covers the renewable energy prediction error range. The second stage dynamically corrects the reference plan based on real-time data, compensating for prediction deviations through rolling optimization. The two stages, through the bidirectional transmission of module status information and power reference values, form a collaborative system where "long-term economic planning guides short-term real-time control, and short-term control feedback optimizes long-term planning." This mechanism achieves dual optimization of renewable energy consumption and system economy in large-scale renewable energy hydrogen production clusters. By using hierarchical processing, it decomposes complex optimization problems into efficiently solvable sub-problems, balancing control accuracy and computational efficiency.

[0139] The real-time adjustment mechanism dynamically allocates corrective power based on the error between the measured and predicted values ​​of renewable energy output at short intervals, according to the adjustable capacity of the electrolyzer.

[0140] In another implementation of the present invention, the constraints of the upper-level optimization model include cluster power balance and module dynamic operation restrictions.

[0141] Existing technologies mainly suffer from the following problems: traditional control methods do not adequately consider the thermodynamic and efficiency characteristics of electrolyzers, and cannot accurately capture the impact of power changes on the hydrogen production process; single optimization models struggle to balance tracking accuracy and computational efficiency, resulting in poor real-time performance and adaptability when faced with drastic fluctuations in renewable energy output; the lack of dynamic adjustment mechanisms for collaborative control strategies of multiple types of electrolyzers leads to insufficient system operation economy and stability; furthermore, although model predictive control can handle prediction errors to some extent, its robustness in complex and variable scenarios is relatively limited, which may cause the hydrogen production system to deviate from its optimal state, increasing operating costs and risks.

[0142] The method of this invention achieves high-precision tracking of renewable energy output and economical operation of hydrogen production systems by constructing a hybrid module model that considers thermodynamic characteristics and combining it with specific prediction algorithms and model predictive control.

[0143] Beneficial effects include:

[0144] Improved tracking accuracy: Significantly improves photovoltaic utilization compared to traditional methods, and greatly reduces the amount of wasted solar power under different operating conditions.

[0145] Economic optimization: Through hierarchical control and parameter optimization, the system's hydrogen production revenue is increased by a certain percentage compared to traditional methods, while operation and maintenance costs are reduced and total revenue is increased.

[0146] Enhanced adaptability: It can adapt to sudden weather changes and geographical differences, and maintain stable operation even under conditions of large fluctuations in renewable energy output, reducing the number of electrolyzer start-ups and shutdowns.

[0147] Computational efficiency advantage: After linearization, the solution time is controlled in a short time, which meets the requirements of real-time control and significantly improves the solution efficiency compared with nonlinear models.

[0148] Another aspect of the present invention provides a hierarchical control system for hybrid hydrogen production clusters based on day-to-day and intraday two-stage optimization, comprising:

[0149] The first-stage model construction module aims to maximize the adjustability and economic efficiency of the hydrogen production system, and constructs the first-stage model.

[0150] The second-stage model building module aims to build a second-stage model with the goal of minimizing the tracking accuracy of renewable energy output and the fluctuation of control quantities.

[0151] Hierarchical control framework construction module: Combining the first-stage model and the second-stage model, a hierarchical control framework for the wind and solar off-grid hydrogen production system in the day-ahead and intraday phases is constructed.

[0152] Coordination and control module: The hierarchical control framework of the wind and solar off-grid hydrogen production system in the two stages of day-ahead and intraday is adopted to control the local consumption of renewable energy and the economic operation of the wind and solar off-grid hydrogen production system.

[0153] This invention presents a hierarchical control system for hybrid hydrogen production clusters based on day-ahead and intraday two-stage optimization. It proposes a two-stage hierarchical control architecture. The first stage uses a probabilistic model to handle the uncertainties of renewable energy and generate an economical operation reference plan with adjustment margins. The second stage constructs a bi-layer optimization model based on real-time data, achieving high-precision tracking of wind and solar power and improving system economics through rolling optimization. This framework utilizes the complementary characteristics of the large capacity and low cost of ALK electrolyzers and the fast response of PEM electrolyzers, using the hybrid hydrogen production module as the basic control unit. Hierarchical optimization resolves the contradiction between accuracy and efficiency inherent in traditional single-model approaches.

[0154] In another aspect of the present invention, the electronic device includes: a processor, a memory, and a communication bus and a communication interface.

[0155] in:

[0156] The processor, memory, and communication interface communicate with each other via a communication bus.

[0157] A communication interface is used to communicate with other electronic devices or servers.

[0158] The processor is used to execute programs, specifically, to perform any of the steps of the hybrid hydrogen production cluster hierarchical control method based on day-to-day two-stage optimization in the above embodiments.

[0159] Specifically, the program may include program code, which includes computer operation instructions.

[0160] The processor may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The one or more processors included in the smart device may be processors of the same type, such as one or more CPUs; or they may be processors of different types, such as one or more CPUs and one or more ASICs.

[0161] Memory is used to store programs. Memory may include high-speed RAM, and may also include non-volatile memory, such as at least one disk drive.

[0162] Specifically, the program can be used to cause the processor to execute the steps of any of the day-ahead-intraday two-stage optimized hybrid hydrogen production cluster hierarchical control methods described in the embodiments. The specific implementation of each step in the program can be found in the corresponding descriptions of the steps and units executed in any of the day-ahead-intraday two-stage optimized hybrid hydrogen production cluster hierarchical control methods described above, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described equipment and modules can be referred to the corresponding process descriptions in the foregoing method embodiments.

[0163] An exemplary embodiment of this application also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to perform the methods of various embodiments of this application.

[0164] The methods described above according to embodiments of the present invention can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD-ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code originally stored on a remote recording medium or a non-transitory machine-readable medium and subsequently stored on a local recording medium, downloaded via a network. Thus, the methods described herein can be processed by software stored on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components (e.g., RAM, ROM, flash memory, etc.) capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods described herein. Furthermore, when a general-purpose computer accesses code used to implement the methods shown herein, the execution of the code transforms the general-purpose computer into a dedicated computer for executing the methods shown herein.

[0165] Specific embodiments of the present invention have now been described. Other embodiments are within the scope of the appended claims. In some cases, the actions described in the claims can be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result.

[0166] It should be noted that all directional indications (such as up, down, left, right, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship between the components in a certain specific order (as shown in the figure). If the specific order changes, the directional indication will also change accordingly.

[0167] In the description of this invention, the terms "first" and "second" are used only for convenience in describing different components or names, and should not be construed as indicating or implying a sequential relationship, relative importance, or implicitly specifying the number of technical features indicated. Thus, a feature defined with "first" and "second" may explicitly or implicitly include at least one of that feature.

[0168] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0169] It should be noted that although specific embodiments of the present invention have been described in detail with reference to the accompanying drawings, this should not be construed as limiting the scope of protection of the present invention. Various modifications and variations that can be made by those skilled in the art without inventive effort within the scope described in the claims still fall within the scope of protection of the present invention.

[0170] The examples of the embodiments of the present invention are intended to concisely illustrate the technical features of the embodiments of the present invention, so that those skilled in the art can intuitively understand the technical features of the embodiments of the present invention, and are not intended to be an improper limitation of the embodiments of the present invention.

[0171] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A hierarchical control method for hybrid hydrogen production clusters based on day-to-day and intraday two-stage optimization, characterized in that, include: With the goal of maximizing the adjustability and optimizing the economy of the hydrogen production system, a first-stage model is constructed. The second-stage model is constructed with the goal of minimizing the tracking accuracy of renewable energy output and the fluctuation of control quantities. Combining the first-stage model and the second-stage model, a hierarchical control framework for the wind-solar off-grid hydrogen production system in two stages, day-ahead and intraday, is constructed. The hierarchical control framework of the wind-solar off-grid hydrogen production system in two stages, namely day-ahead and day-intraday, is adopted to carry out the local consumption of renewable energy and the economic operation control of the wind-solar off-grid hydrogen production system.

2. The method according to claim 1, characterized in that, Also includes: Based on the influence of temperature and power on efficiency in ALK and PEM electrolyzers, the randomness of renewable energy fluctuations is transformed into quantifiable constraints.

3. The method according to claim 2, characterized in that, The constraints of the first-stage model include system energy balance, electrolyzer start-up and shutdown intervals, and upper and lower power limits.

4. The method according to claim 3, characterized in that, The objective function of the first-stage model is expressed as: in, For the system's hydrogen sales revenue, C op For system equipment operation and maintenance costs, C st For the start-up and shutdown costs of the electrolytic cell, C el C is the cost of reduced lifespan of the electrolyzer. pu The cost of penalties for abandoning wind and solar power.

5. The method according to claim 4, characterized in that, The revenue from hydrogen sales by the system is expressed as follows: in, The price per unit mass of hydrogen. The system produces hydrogen volume. The density of hydrogen gas; The system equipment operation and maintenance cost is expressed as follows: Where T is the scheduling cycle, N is the number of alkaline electrolyzers, and M is the number of PEM electrolyzers; The start-up and shutdown costs and lifespan depreciation costs of the electrolytic cell are expressed as follows: Among them, c su For the start-up and shutdown costs of the electrolytic cell, C el For the cost of depreciation over life, f el This is the cost factor for power fluctuations in the electrolytic cell. The penalty cost of curtailing wind and solar power is expressed as follows: Among them, c pu,wt and c pu,pv The cost of curtailing wind and solar power per unit and Contribute to wind power and solar power forecasting.

6. The method according to claim 1, characterized in that, The second-stage model, aimed at minimizing the tracking accuracy and control fluctuations of renewable energy output, includes: With the goal of maximizing the renewable energy power absorption rate, an upper-level optimization model is constructed to dynamically adjust the real-time operating power of the modules; With the goal of minimizing the deviation between actual power and the reference plan, while taking into account both hydrogen production revenue and equipment lifespan costs, a lower-level optimization model is constructed. A Lagrangian function is constructed, and the KKT conditions are used to transform the bi-level optimization problem of the upper-level optimization model and the lower-level optimization model into a single-level quadratic programming problem, thus obtaining the second-stage model.

7. The method according to claim 6, characterized in that, The constraints of the upper-level optimization model include cluster power balance and module dynamic operation restrictions.

8. A hierarchical control system for hybrid hydrogen production clusters based on day-to-day and intraday two-stage optimization, characterized in that, include: The first-stage model building module aims to maximize the adjustability and economic efficiency of the hydrogen production system, and builds the first-stage model. The second-stage model building module aims to build the second-stage model with the goal of minimizing the tracking accuracy of renewable energy output and the fluctuation of control quantities. Layered control framework construction module: Combining the first-stage model and the second-stage model, a layered control framework for the wind and solar off-grid hydrogen production system in the day-to-day and intraday two-stage phases is constructed. Coordination and control module: The hierarchical control framework of the wind and solar off-grid hydrogen production system in the two stages of day-ahead and intraday is adopted to control the local consumption of renewable energy and the economic operation of the wind and solar off-grid hydrogen production system.

9. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the hybrid hydrogen production cluster hierarchical control method based on day-to-day two-stage optimization as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The computer storage medium stores a computer program, which, when executed by a processor, implements the steps in the hierarchical control method for hybrid hydrogen production clusters based on day-to-day two-stage optimization as described in any one of claims 1 to 7.

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