Electrolytic bath self-adaptive energy management and control method and system applied to wide power fluctuation

Abstract

The invention discloses an electrolytic bath self-adaptive energy management and control method and system applied to wide power fluctuation, and relates to the technical field of energy management and control, and the method comprises the following steps: S1, based on external prediction information and internal demands, generating a predefined power instruction curve through rolling optimization of a model prediction control algorithm, the optimal economical efficiency or the highest energy utilization rate is achieved, and the comprehensive operation performance of the electrolytic cell under the wide power fluctuation condition is remarkably improved by constructing a three-layer intelligent control framework of'predictive scheduling-self-adaptive coordination-rapid execution '. The upper layer adopts a model predictive control algorithm, combines external weather, electricity price and internal hydrogen production demand, generates a power instruction curve with optimal economic and energy efficiency in a rolling manner, and realizes prospective energy scheduling; a multivariable self-adaptive coordination mechanism is introduced into the middle layer, and the multivariable self-adaptive coordination mechanism comprises power-flow dynamic matching, temperature-pressure feedforward-feedback stabilization control and hydrogen-oxygen differential pressure sliding mode robust control.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of energy management and control, in particular to an electrolytic cell adaptive energy management and control method and system applied to wide power fluctuation. BACKGROUND

[0002] The electrolytic cell adaptive energy management and control system realizes efficient and stable operation of the electrolytic cell under renewable energy power supply fluctuation through integration of intelligent algorithms, real-time data acquisition and multi-level control strategies.

[0003] According to the patent name: an adaptive energy management control method of a hybrid clean energy system (patent publication number: CN118054477A, patent publication date: May 17, 2024), the following steps are included: a hybrid clean energy system model is established, including solid oxide fuel cell SOFC, wind turbine, proton exchange membrane PEM electrolytic cell, high-pressure hydrogen storage tank, transformer and other modules. An intelligent decision-making model based on deep reinforcement learning DRL and Markov decision process MDP is constructed, and is trained through a deep Q network DQN; an Internet of Things IoT software and hardware simulation platform is constructed, the intelligent decision-making model is called for adaptive control, and an optimal management method is obtained, and the feasibility of the energy system management method in actual application is also verified.

[0004] However, based on the above-mentioned prior art, the existing electrolytic cell adaptive energy management and control method and system applied to wide power fluctuation still have the following problems. When the current electrolytic water hydrogen production system is coupled with renewable energy (such as photovoltaic and wind power), the core challenge is that the input power is intermittent, random and fluctuates dramatically. Traditional control strategies (such as fixed parameter PID control and simple start-stop control) will lead to: 1. Low efficiency: the electrolytic cell operates under non-rated conditions, and the energy consumption ratio increases significantly.

[0005] 2. Life attenuation: frequent power surges, sudden drops, and emergency starts and stops cause irreversible mechanical and chemical damage to the core components of the electrolytic cell stack and diaphragm.

[0006] 3. Safety risk: rapid power changes can cause internal temperature, pressure and gas-liquid balance to lose control, especially hydrogen-oxygen differential pressure fluctuation, which greatly increases the risk of explosion.

[0007] 4. Abandoning wind and light: to avoid the above problems, the system has to limit the power input range and cannot fully absorb fluctuating renewable energy. Therefore, the application provides an electrolytic cell adaptive energy management and control method and system applied to wide power fluctuation. SUMMARY

[0008] In view of the deficiencies of the prior art, the present application provides a kind of applied to wide power fluctuation electrolytic cell adaptive energy management and control method and system, solve the current electrolytic water hydrogen production system when coupled with renewable energy (such as photovoltaic, wind power), face core challenge: input power has intermittence, randomness, dramatic volatility.Traditional control strategy (such as fixed parameter PID control, simple start-stop control) can lead to: 1. Low efficiency: electrolytic cell runs in non-rated operating condition, energy consumption ratio is significantly increased.

[0009] 2. Life attenuation: frequent power surge, emergency start-stop, cause irreversible mechanical and chemical damage to electrolytic cell stack, diaphragm and other core components.

[0010] 3. Safety risk: power rapid change is easy to cause system internal temperature, pressure, gas-liquid balance out of control, especially hydrogen-oxygen differential pressure fluctuation, greatly increase the risk of explosion.

[0011] 4. Abandon wind and light: to avoid the above problems, system has to limit power input range, cannot completely consume fluctuating renewable energy.

[0012] To achieve the above purposes, the present application is realized by the following technical solutions: a kind of applied to wide power fluctuation electrolytic cell adaptive energy management and control method, comprising the following steps: S1: based on external prediction information and internal demand, pre-defined power instruction curve is generated by model predictive control algorithm rolling optimization, to realize economic optimization or energy utilization rate is highest; S2: based on the pre-defined power instruction curve, multivariable adaptive coordinated control is carried out, including power-flow dynamic matching control, temperature-pressure collaborative stabilization control and hydrogen-oxygen differential pressure robust control; S3: through the rapid response control of device layer, the fluctuating power is quickly, overshoot-free tracked, and stable direct current is provided for electrolytic cell.

[0013] Preferably, the power-flow dynamic matching control includes: A1: the dynamic relationship model of electrolytic cell current density and optimal flow is established; A2: according to the current value calculated from input power, the speed of circulating pump or the opening of water inlet valve is dynamically adjusted, so that the flow and reaction rate maintain the optimal ratio.

[0014] Preferably, the temperature-pressure collaborative stabilization control adopts feedforward-feedback compound control: B1: feedforward control is based on power instruction change in advance adjusts cooling system and back pressure regulating valve; B2: The feedback control adopts a fuzzy adaptive PID control algorithm of multiple input and multiple output, and fine adjustment is performed according to real-time temperature and pressure feedback.

[0015] Preferably, the robust control of the hydrogen-oxygen differential pressure adopts a sliding mode variable structure control algorithm: C1: Taking the hydrogen-oxygen differential pressure as a core control target, the hydrogen-oxygen differential pressure is strictly controlled in a safety window; C2: The control has the highest priority and can dynamically cover and correct the middle-layer pressure control instruction.

[0016] Preferably, the fast response control of the device layer adopts an adaptive sliding mode control algorithm, and the fast and non-overshoot tracking of fluctuating direct-current power is realized through a power electronic converter.

[0017] The application further discloses an electrolytic cell adaptive energy management and control system applied to wide power fluctuation, which comprises: a physical layer comprising a renewable energy power generation device, a power conversion unit, an electrolytic cell, an auxiliary system and a sensor network; an intelligent control layer comprising: an upper-layer energy management system comprising a weather forecast module, a power grid price module and a load demand module for generating a predefined power instruction curve through a model predictive control (MPC) algorithm based on external prediction information and internal demand; a middle-layer adaptive coordination control layer comprising a safety constraint condition module, an electrolytic cell power monitor module, a power distribution module and an optimization algorithm engine for performing multivariate adaptive coordination control based on the predefined power instruction curve, including dynamic matching control of power-flow, cooperative stabilization control of temperature-pressure and robust control of hydrogen-oxygen differential pressure; a lower-layer device control layer comprising a power electronic conversion device and a cooling system for realizing fast tracking of fluctuating power through fast response control.

[0018] Preferably, the power distribution module comprises: a power-flow control submodule for adjusting a circulating pump or a water inlet valve in real time according to a dynamic relationship model of current density and optimal flow; a temperature-pressure control submodule for performing feedforward-feedback compound control; a differential pressure control submodule for performing sliding mode variable structure control to maintain the hydrogen-oxygen differential pressure in a safety range.

[0019] Preferably, the power electronic conversion device adopts an adaptive sliding mode control algorithm to realize fast response and stable output of fluctuating power.

[0020] The application provides a kind of electrolytic cell adaptive energy management and control method and system applied to wide power fluctuation.Compared with prior art, it has the following beneficial effects: 1、The electrolytic cell adaptive energy management and control method and system applied to wide power fluctuation, by constructing the three-layer intelligent control architecture of "predictive scheduling-adaptive coordination-fast execution", the comprehensive operation performance of electrolytic cell under wide power fluctuation conditions is significantly improved. The upper layer adopts model predictive control algorithm, combines external weather, electricity price and internal hydrogen demand, and rolls to generate the power instruction curve of economic and energy efficiency optimization, realizes the forward-looking energy scheduling; The middle layer introduces a multivariable adaptive coordination mechanism, including power-flow dynamic matching, temperature-pressure feedforward-feedback stabilization control and hydrogen-oxygen differential pressure sliding mode robust control, effectively solving the control problem of multivariable strong coupling system; The lower layer adopts adaptive sliding mode control to realize the fast power tracking of power electronic equipment. The system can maintain the efficient and stable operation of electrolytic cell, significantly improve the energy utilization rate, prolong the service life of equipment, and fundamentally eliminate the safety risk caused by hydrogen-oxygen differential pressure exceeding.

[0021] 2、The electrolytic cell adaptive energy management and control method and system applied to wide power fluctuation, through power-flow dynamic matching model, real-time adjustment of circulating pump and valve, ensure that the reactant supply and reaction rate are always matched, improve the working condition adaptability and energy efficiency level; Temperature-pressure adopts feedforward-feedback compound control, effectively suppresses the thermal and pressure fluctuation caused by power mutation, and ensures the stable operation of the system in the efficient interval; The specially designed independent high-priority differential pressure controller adopts sliding mode variable structure algorithm, has strong robustness and fast response ability, can dynamically cover the middle layer instruction, ensures that the differential pressure is always in the safety window, greatly improves the intrinsic safety level of the system. The method has self-learning and adaptive ability, can cope with equipment aging and external environment change, and has good engineering applicability.

[0022] 3、The electrolytic cell adaptive energy management and control method and system applied to wide power fluctuation, through smooth power instruction and multivariable collaborative control, the system effectively avoids the mechanical and chemical damage of electrolytic cell caused by frequent power sudden change, significantly prolongs the service life of core components; The intelligent hierarchical control structure realizes the coordinated optimization of multiple time scales and multiple targets, and considers economy, efficiency and safety, and has strong industrial application prospect. The scheme provides a complete, reliable and adaptive energy management and control solution for renewable energy hydrogen production system, and has good popularization value and social benefits. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 The system block diagram of the application is shown in the figure; Figure 2 The multilevel control architecture of the application is shown in the figure; Figure 3 An energy scheduling architecture diagram of the present application; Figure 4 An adaptive coordinated control architecture diagram of the present application; Figure 5 An adaptive sliding mode control architecture diagram of the present application. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0025] Please refer to Figures 1-5 The present application provides a technical solution: An electrolytic cell adaptive energy management and control method applied to wide power fluctuation, comprising the following steps: S1: Based on external prediction information and internal demand, a pre-defined power instruction curve is generated by model predictive control algorithm rolling optimization to achieve economic optimization or highest energy utilization rate; S2: Based on the pre-defined power instruction curve, multi-variable adaptive coordinated control is carried out, including power-flow dynamic matching control, temperature-pressure collaborative stabilization control and hydrogen-oxygen differential pressure robust control; S3: Through the rapid response control of the device layer, the fluctuating power is rapidly and non-overshoot tracked, and stable direct current is provided for the electrolytic cell.

[0026] The system receives external prediction information (future hours of light / wind speed intensity, power grid price curve) and internal demand (planned hydrogen production amount), and generates a pre-defined power instruction curve for a future period (such as 15 minutes) through model predictive control (MPC) algorithm rolling optimization. The curve pursues economic optimization (more hydrogen production when the price is low) or the highest energy utilization rate (as much renewable energy as possible is consumed), and avoids known periods of severe power reduction, providing smooth power set value for the middle layer control.

[0027] MPC algorithm objective function (Minimize J): Constraint conditions (physical rules that must be followed): 1. Power balance constraint: P_ele(k) + P_grid(k) = P_ren(k) 2. Electrolytic cell capacity constraint: P_ele_min <= P_ele(k) <= P_ele_max 3. Smooth constraint: |P ele(k) - P ele(k-1)| <= ΔP max ele Decision variables: P ele(k) represents electrolyzer power instruction, KW; P grid(k) represents grid interaction power, KW; External prediction and input: P ren(k) is renewable energy predicted power, KW, C price(k) is future electricity price, yuan / kwh, V H2 is hydrogen value, yuan / kwh; Intermediate variables: P prod(k), power for hydrogen production, KW; Weights and parameters (pre-set values): w1 is economic weight coefficient, w2 is hydrogen production benefit weight coefficient, w3 is environmental weight coefficient; P ele_min is electrolyzer power lower limit, KW; P ele_max is electrolyzer power lower limit, KW; ΔP max ele is electrolyzer maximum power ramp rate, KW / min; ΔT is sampling time, min.

[0028] In the embodiment, the power-flow dynamic matching control comprises: A1: establishing a dynamic relationship model of electrolyzer current density and optimal flow rate; A2: dynamically adjusting the rotating speed of the circulating pump or the opening degree of the water inlet valve according to the current value calculated based on the input power, so that the flow rate and the reaction rate are kept in optimal matching.

[0029] If the power changes, the gas production rate changes. If the water flow rate (PEM) is constant, it will lead to insufficient supply of reactants or retention of products, resulting in decreased efficiency and danger. The present application establishes a dynamic relationship model of electrolyzer current density and optimal flow rate. The controller dynamically adjusts the rotating speed of the circulating pump or the opening degree of the water inlet valve according to the current value calculated based on the input power, so that the flow rate is always kept in optimal matching with the reaction rate.

[0030] Dynamic relationship model: In the formula, Q optimal(J) is the optimal water flow rate corresponding to the current density J, L / min; C0, C1... are model coefficients, C0 represents the basic flow rate, C1*J represents the flow rate demand in linear relationship with the current density, C1*J², C1*J³ represent high-order terms of the current density.

[0031] In the embodiment, the temperature-pressure cooperative stabilization control adopts a feedforward-feedback composite control: B1: The feedforward control adjusts the cooling system and the back pressure regulating valve in advance based on the power instruction change; B2: Feedback control adopts fuzzy adaptive PID control algorithm of multiple input and multiple output, and fine-tuning is performed according to real-time temperature and pressure feedback.

[0032] If the power change causes the heat production change, the temperature is affected; the temperature change affects the gas solubility, membrane performance and working pressure, which is a strong coupling process. The present application controls the temperature and pressure as a multivariable coupling system.

[0033] A feedforward-feedback compound control is adopted: Feedforward: When the power instruction changes greatly, the cooling system (such as adjusting the cooling water valve opening) and the back pressure regulating valve are pre-actuated to offset the expected thermal and pressure disturbance.

[0034] Feedforward control law: When the power increases (ΔP_ele > 0): ΔV_cool_ff = k_{ff11} * ΔP_ele, k_{ff11} should be a positive number, meaning that the cooling valve needs to be opened in advance to offset the expected temperature rise; ΔV_back_ff = k_{ff21} * ΔP_ele, k_{ff21} should also be a positive number, the back pressure valve needs to be opened in advance to offset the expected pressure rise; when the power decreases, the opposite is also true.

[0035] Feedback: An advanced fuzzy adaptive PID control algorithm of multiple input and multiple output is adopted, and the cooling system and the pressure regulating system are fine-tuned according to real-time temperature and pressure feedback, so that the working condition is accurately stabilized in the high efficiency interval.

[0036] Fuzzy adaptive PID control algorithm: In the formula, K_p,T(K), K_i,T(K), K_d,T(K) are parameters adjusted by the fuzzy system online in real time.

[0037] In this embodiment, the robust control of the hydrogen-oxygen differential pressure adopts a sliding mode variable structure control algorithm: C1: The hydrogen-oxygen differential pressure is taken as the core control target, and is strictly controlled within the safety window; C2: The control has the highest priority and can dynamically cover and correct the middle-layer pressure control instruction.

[0038] Fluctuating power is easy to cause imbalance of hydrogen and oxygen gas production rates, causing pressure to be out of sync and the differential pressure to exceed the standard. The present application designs an independent, high-priority sliding mode variable structure control differential pressure safety controller, whose output can dynamically cover and correct the middle-layer pressure control instruction.

[0039] The controller takes the hydrogen-oxygen differential pressure as the core control target, and strictly controls it in a small safety window. Once the differential pressure approaches the threshold, the safety controller will ignore other optimization targets and preferentially adjust the valves on both sides to force the differential pressure to return to the safety zone.

[0040] Controller algorithm formula: In the formula, u is the controller output, % or mA; b is the control gain, e = △P = P_H2-P_O2, Kpa; λ is the sliding surface convergence rate, 1 / s; ė is the error change rate, kpa / s; s is the sliding surface variable, kpa / s; K is the sliding surface adjustment gain, 1 / s; η is the switching gain, kpa / s²; sat(s / Φ) is the saturation function; and ˆf is the system dynamic estimation value, kpa / s².

[0041] In the embodiment, the fast response control of the device layer adopts an adaptive sliding mode control algorithm, and the power electronic converter is used to realize fast and non-overshoot tracking of fluctuating DC power.

[0042] The power electronic converter receives the power instruction of the middle layer, adopts an adaptive sliding mode control algorithm, realizes fast and non-overshoot tracking of fluctuating DC power, and provides high-quality and stable DC power for the electrolytic cell.

[0043] The application further discloses an adaptive energy management and control system applied to a wide power fluctuation electrolytic cell, which comprises: A physical layer comprising a renewable energy power generation device, a power conversion unit, an electrolytic cell, an auxiliary system and a sensor network; An intelligent control layer comprising: An upper layer energy management system comprising a weather forecast module, a power grid price module and a load demand module, which is used to generate a predefined power instruction curve based on external prediction information and internal demand through a model prediction control (MPC) algorithm; A middle layer adaptive coordination control layer comprising a safety constraint condition module, an electrolytic cell power monitor module, a power distribution module and an optimization algorithm engine, which is used to perform multivariate adaptive coordination control based on the predefined power instruction curve, including dynamic matching control of power-flow, collaborative stabilization control of temperature-pressure and robust control of hydrogen-oxygen differential pressure; A lower layer device control layer comprising a power electronic conversion device and a cooling system, which is used to realize fast tracking of fluctuating power through fast response control.

[0044] In the embodiment, the power distribution module comprises: A power-flow control submodule, which is used to adjust a circulating pump or a water inlet valve in real time according to a dynamic relationship model of current density and optimal flow; A temperature-pressure control sub-module is configured to perform feedforward-feedback compound control. A differential pressure control sub-module is configured to perform sliding mode variable structure control to maintain the hydrogen-oxygen differential pressure within a safe range.

[0045] In this embodiment, the power electronic conversion device adopts an adaptive sliding mode control algorithm to achieve fast response to fluctuating power and stable output.

[0046] Meanwhile, the contents not described in detail in this specification all belong to the prior art known to those skilled in the art.

[0047] It should be noted that, in this document, the relationship terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such a process, method, article or device.

[0048] Although the embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. An adaptive energy management and control method for electrolyzers with wide power fluctuations, comprising the following steps: S1: Based on external forecast information and internal demand, a predefined power command curve is generated through rolling optimization of the model predictive control algorithm to achieve optimal economic efficiency or the highest energy utilization rate. S2: Based on the predefined power command curve, perform multivariable adaptive coordinated control, including dynamic matching control of power-flow rate, coordinated stabilization control of temperature-pressure, and robust control of hydrogen-oxygen differential pressure; S3: Through rapid response control at the equipment level, it achieves fast, overshoot-free tracking of fluctuating power, providing stable DC power to the electrolytic cell.

2. The adaptive energy management and control method for electrolyzers with wide power fluctuations according to claim 1, characterized in that: The dynamic power-flow matching control includes: A1: Establish a dynamic relationship model between the current density of the electrolytic cell and the optimal flow rate; A2: Based on the current value calculated from the input power, dynamically adjust the speed of the circulating pump or the opening of the inlet valve to maintain the optimal ratio between flow rate and reaction rate.

3. The adaptive energy management and control method for electrolyzers with wide power fluctuations according to claim 1, characterized in that: The temperature-pressure coordinated stabilization control adopts a feedforward-feedback composite control: B1: Feedforward control adjusts the cooling system and back pressure regulating valve in advance based on changes in power command; B2: The feedback control adopts a fuzzy adaptive PID control algorithm with multiple inputs and multiple outputs, and makes fine adjustments based on real-time temperature and pressure feedback.

4. The adaptive energy management and control method for electrolyzers with wide power fluctuations according to claim 1, characterized in that: The robust control of the hydrogen-oxygen differential pressure employs a sliding mode variable structure control algorithm: C1: The hydrogen-oxygen differential pressure is the core control target, and it is strictly controlled within a safe window. C2: The control has the highest priority and can dynamically override and correct the mid-level pressure control commands.

5. The adaptive energy management and control method for electrolyzers with wide power fluctuations according to claim 1, characterized in that: The fast response control of the device layer adopts an adaptive sliding mode control algorithm, which realizes fast and overshoot-free tracking of fluctuating DC power through a power electronic converter.

6. An adaptive energy management and control method for electrolyzers with wide power fluctuations, employing the adaptive energy management and control system for electrolyzers with wide power fluctuations as described in any one of claims 1-5, characterized in that: include: The physical layer includes renewable energy power generation devices, power conversion units, electrolyzers, auxiliary systems, and sensor networks; The intelligent control layer includes: The upper-level energy management system, including the weather forecast module, grid price module, and load demand module, is used to generate predefined power command curves based on external forecast information and internal demand through the model predictive control (MPC) algorithm. The middle layer adaptive coordination control layer includes a safety constraint module, an electrolyzer power monitor module, a power allocation module, and an optimization algorithm engine. It is used to perform multivariate adaptive coordination control based on the predefined power command curve, including dynamic matching control of power-flow rate, coordinated stabilization control of temperature-pressure, and robust control of hydrogen-oxygen differential pressure. The lower-level equipment control layer, including power electronic conversion equipment and cooling systems, is used to achieve rapid tracking of fluctuating power through fast response control.

7. The adaptive energy management and control system for electrolyzers with wide power fluctuations according to claim 6, characterized in that: The power distribution module includes: The power-flow control submodule is used to adjust the circulating pump or inlet valve in real time based on the dynamic relationship model between current density and optimal flow rate. The temperature-pressure control submodule is used to perform feedforward-feedback composite control; The differential pressure control submodule is used to perform sliding mode variable structure control to maintain the hydrogen-oxygen differential pressure within a safe range.

8. The adaptive energy management and control system for electrolyzers with wide power fluctuations according to claim 6, characterized in that: The power electronic conversion equipment adopts an adaptive sliding mode control algorithm to achieve rapid response and stable output to fluctuating power.

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