Method and system for coupling control of wind and solar abandoned electricity consumption and multi-acid liquid flow battery electric synthesis

Abstract

The present application belongs to the field of renewable energy storage and green electrosynthesis scheduling technology, and particularly relates to a wind and light abandoned electricity consumption multi-acid liquid flow battery electrosynthesis coupling control method and system. The method constructs a double-layer wind and light power prediction model containing short-term and long-term prediction sub-models, outputs a power curve based on weather and historical power data, and calculates the abandoned electricity ratio in combination with the station load. According to the abandoned electricity ratio, the power automatic distribution of the energy storage charging and electrosynthesis unit is executed in stages, and the mode is switched without impact through a ramp function and PID adjustment. When the electrosynthesis is in a steady state, the electrolyte concentration, circulation flow and current density are used as variables to construct a double-constraint optimization algorithm with the maximum energy storage capacity and product yield as the joint target, and the optimal process parameters are iteratively solved and executed. The adipic acid yield and purity are simultaneously detected online, and when they do not meet the standards, the parameters are automatically corrected and returned to optimization until the standards are met, forming a closed-loop regulation and control of prediction, grading, optimization and detection. The present application can simultaneously realize the collaborative optimization of energy storage capacity, product yield and energy consumption, the stable utilization rate of abandoned electricity and product indicators, and smooth mode switching.

Description

Technical Field

[0001] This invention belongs to the field of renewable energy storage and green electricity synthesis scheduling technology, specifically involving a method and system for coupling control of electricity synthesis in multi-acid flow batteries for wind and solar power curtailment. Background Technology

[0002] Driven by the "dual-carbon" strategic goals, my country's installed capacity of renewable energy sources such as wind and solar power has continued to expand. However, due to the contradiction between the volatility and intermittency of wind and solar power output and the grid's absorption capacity, the problem of renewable energy curtailment is becoming increasingly prominent. According to industry statistics, the amount of wind and solar power curtailed nationwide reached 33.9 billion kilowatt-hours in 2023, with curtailment rates approaching 20% ​​in some regions. Curtailment absorption has become a key bottleneck restricting the large-scale grid connection of renewable energy. Against this backdrop, converting surplus curtailed electricity into high-value-added chemicals is one of the important ways to achieve local absorption of renewable energy and improve the utilization rate of renewable energy.

[0003] Flow batteries, as an electrochemical energy storage technology, offer advantages such as long cycle life, high safety, and independent design of power and capacity, making them highly advantageous in large-scale, long-term energy storage applications. Indirect electrosynthesis technology, on the other hand, utilizes electrical energy to drive chemical reactions, transforming basic chemical raw materials into high-value-added fine chemicals. Coupled with flow battery energy storage systems and indirect electrosynthesis systems, surplus power can be used to drive electrosynthesis reactions during periods of low grid load, achieving both power utilization and economic value creation. This has become a research direction for the cross-fertilization of energy storage and chemical engineering.

[0004] However, the existing technologies for coordinated control of flow battery energy storage systems and indirect electrosynthesis systems still have the following shortcomings: Firstly, the energy storage system and the electrosynthesis system are equipped with independent control systems, and the switching between their operating modes relies on manual operation. Due to the significant process inertia of the electrosynthesis system, direct switching between energy storage and electrosynthesis modes can cause instantaneous power fluctuations that deviate from optimal electrosynthesis reaction conditions, leading to increased side reactions, decreased product selectivity, and even equipment damage. This makes it impossible to quickly respond to instantaneous power fluctuations in wind and solar power output. Therefore, existing systems lack a mode-coupled control mechanism capable of achieving rapid and smooth switching between energy storage and electrosynthesis.

[0005] Secondly, the existing system adopts a fixed-ratio allocation mode for the curtailed power, without setting a hierarchical power scheduling strategy. When the curtailed power fluctuates, the system cannot automatically adjust the load allocation ratio between energy storage charging and electrosynthesis operation according to the different real-time curtailment surplus levels, resulting in limited curtailment absorption capacity. During periods of high curtailment power, there is still power waste, while during periods of low curtailment power, problems such as frequent start-ups and shutdowns of the electrosynthesis system may occur.

[0006] Finally, in existing systems, the electrolyte operating parameters (such as concentration and flow rate) and electrosynthesis process parameters (such as current density and reaction conditions) of the flow battery are controlled separately by their respective independent control loops, lacking a synergistic optimization mechanism. This decentralized control approach cannot simultaneously achieve synergistic optimization of multiple indicators such as maximizing the energy storage capacity of the flow battery, maximizing the yield of the target product, and minimizing system energy consumption within the same control framework, making it difficult to achieve optimal overall operating efficiency of the energy storage and electrosynthesis system. Summary of the Invention

[0007] This invention provides a coupled control method and system for the electrosynthesis of wind and solar power curtailment using multi-acid flow batteries, in order to solve the technical problems in the prior art, such as the independent control of two systems for energy storage and electrosynthesis, the need for manual switching, low curtailment efficiency, the need to simultaneously maximize energy storage capacity, and the contradiction between high yield and low energy consumption of cyclohexanone and adipic acid.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: A method for the electrosynthesis and coupling control of wind and solar power curtailment using multi-acid flow batteries includes the following steps: A two-layer wind and solar power prediction model is constructed, which includes a short-term prediction sub-model and a long-term prediction sub-model. Power prediction is performed on weather data and historical power data respectively to obtain short-term power generation curves and long-term power generation curves. The short-term power generation curves and long-term power generation curves are compared with the actual load data of power plants to obtain the curtailment ratio. Based on the curtailment ratio, the curtailment conditions are classified and judged according to the preset power threshold. Based on the judgment results of the curtailment conditions, corresponding hierarchical power allocation is carried out. During the hierarchical power allocation process, the change of the curtailment ratio is monitored in real time. When the change of the curtailment ratio causes the curtailment condition level to change, a ramp function is used to limit the rate of change of power allocation command during the switching process of different conditions. At the same time, the flow rate of the stack cooling water is adjusted in real time through the proportional-integral-derivative controller. Using the concentration of polyacid electrolyte, electrolyte circulation flow rate, and stack current density as adjustable variables, and constructing a dual-constraint objective function with the energy storage capacity and product yield of the flow battery as joint optimization objectives, the adjustable variables are iteratively solved to obtain the optimal concentration setpoint, optimal flow rate setpoint, and optimal current density setpoint under the current operating conditions. The system is then adjusted based on the optimal concentration setpoint, optimal flow rate setpoint, and optimal current density setpoint under the current operating conditions.

[0009] In the dual-layer wind and solar power prediction model, both the short-term prediction sub-model and the long-term prediction sub-model adopt the temporal fusion Transformer algorithm. The attention mechanism is used to model the temporal dependency between wind speed, irradiance, temperature characteristics in weather data and historical power operation data. The short-term prediction sub-model and the long-term prediction sub-model run in a rolling manner. According to the preset rolling cycle, the latest measured data is included in the model input window, and the oldest outdated data is removed. The prediction calculation is then re-executed.

[0010] The process of obtaining the curtailment ratio specifically involves: calculating the difference between the short-term and long-term power generation curves and the current actual load data of the power plants to obtain the surplus curtailment power of the power plants; using the ratio of the surplus curtailment power of the power plants to the total installed capacity of the system as the curtailment ratio; comparing the predicted curtailment data output by the model with the actual curtailment metering data of the power plants, and using the minimization of the root mean square error as the optimization criterion to perform rolling correction on the model parameters of the time-series fusion Transformer algorithm.

[0011] The method involves classifying power curtailment conditions based on the curtailment ratio and a preset power threshold. According to the classification results, corresponding tiered power allocation is performed: when the curtailment ratio is less than a first preset threshold, it is determined to be a mild curtailment condition, prioritizing the charging of the energy storage unit and starting the electrosynthesis unit with low power; when the curtailment ratio is between the first and second preset thresholds, it is determined to be a moderate curtailment condition, controlling the charging of the energy storage unit and the synchronous operation of the electrosynthesis unit, and dynamically adjusting the electrolyte circulation flow rate according to real-time power fluctuations; when the curtailment ratio is greater than the second preset threshold, it is determined to be a severe curtailment condition, and all curtailed power is supplied to the electrosynthesis unit.

[0012] The method of using a ramp function to limit the rate of change of power allocation command during the switching process of different operating conditions specifically involves using a ramp function to smooth the rate of change of power allocation command determined by the hierarchical power allocation, so that the power smoothly transitions from the current operating power to the operating power corresponding to the target operating condition in a linear and gradual manner during the switching transition period.

[0013] The product yield includes the yield of the target product adipic acid and the yield of the intermediate product cyclohexanone; the dual-constraint objective function uses the flow battery energy storage capacity and the yields of cyclohexanone and adipic acid as joint optimization objectives to maximize the product yield while ensuring the usable capacity of the energy storage system.

[0014] The dual-constraint objective function also takes minimizing system energy consumption as the optimization objective. The system energy consumption is determined based on the stack current density and electrolyte circulation flow rate. By optimizing the stack current density and electrolyte circulation flow rate, the system can simultaneously achieve the minimum energy consumption while meeting the requirements of energy storage capacity and product yield.

[0015] After adjusting the optimal concentration, flow rate, and current density settings under the current operating conditions, the method further includes: online detection of the electrosynthetic product to obtain the current product yield and purity values; comparing the current product yield and purity values ​​with preset qualified thresholds; if the current adipic acid yield is lower than the yield qualified threshold or the current adipic acid purity is lower than the purity qualified threshold, the product indicators are determined to be substandard; and adaptively adjusting the polyacid electrolyte concentration setting and the stack current density setting based on the direction of the yield deviation and purity deviation, and iteratively optimizing again.

[0016] If the current adipic acid yield is not lower than the yield qualification threshold and the current adipic acid purity is not lower than the purity qualification threshold, then the product indicators are deemed to meet the standards, the current operating parameters are maintained and the process continues, and the current operating data is fed back to the dual-layer wind and solar power prediction model. The entire process of wind and solar prediction and curtailment calculation, curtailment condition classification and layered power allocation, multi-acid electrolyte electrosynthesis optimization, and product detection and judgment is executed iteratively.

[0017] A coupled control system for the electrosynthesis of multi-acid electrolyte in wind and solar curtailment includes a dual-layer wind and solar power prediction module, a layered power allocation and mode-switching-free switching module, and a multi-acid electrolyte electrosynthesis optimization module. The dual-layer wind and solar power prediction module is used to build a dual-layer wind and solar power prediction model that includes a short-term prediction sub-model and a long-term prediction sub-model. It performs power prediction on weather data and historical power data respectively to obtain short-term power generation curves and long-term power generation curves. The short-term power generation curves and long-term power generation curves are compared with the actual load data of the power plants to obtain the curtailment ratio. The layered power allocation and mode-switching module is used to classify the curtailment conditions based on the curtailment ratio and a preset power threshold, and to perform corresponding layered power allocation according to the curtailment condition classification results. During the layered power allocation process, the changes in the curtailment ratio are monitored in real time. When the change in the curtailment ratio causes a change in the curtailment condition level, a ramp function is used to limit the rate of change of the power allocation command during the switching process of different conditions. At the same time, the proportional-integral-derivative controller is used to adjust the stack cooling water flow rate in real time. The polyacid electrolyte electrosynthesis optimization module is used to construct a dual-constraint objective function with polyacid electrolyte concentration, electrolyte circulation flow rate, and stack current density as adjustable variables, and with the energy storage capacity and product yield of the flow battery as joint optimization objectives. The module iteratively solves the adjustable variables to obtain the optimal concentration setpoint, optimal flow rate setpoint, and optimal current density setpoint under the current operating conditions, and adjusts the system based on these optimal values.

[0018] Compared with the prior art, the present invention has the following beneficial effects: This invention constructs a dual-layer wind and solar power prediction model, utilizing short-term and long-term prediction sub-models to predict power at different time scales. The prediction results are then compared with the actual load of the power plants to obtain the curtailment ratio, providing accurate feedforward data for subsequent tiered power allocation. Based on this, the curtailment ratio is categorized according to preset power thresholds, and differentiated power allocation strategies are implemented for mild, moderate, and severe curtailment conditions. This allows the load allocation ratio between energy storage charging and power synergy operation to automatically adjust based on the real-time curtailment surplus, effectively improving the ability to absorb fluctuating curtailment power over a wide range. This avoids the problems of frequent start-ups and shutdowns of the power synergy system during low curtailment periods and wasted power during high curtailment periods, which are common under fixed-ratio allocation models.

[0019] Furthermore, during the tiered power allocation process, when changes in the curtailment ratio lead to changes in the curtailment operating condition level, this invention employs a ramp function to limit the rate of change of power allocation commands during the switching process between different operating conditions. This allows the power to smoothly transition from the current operating power to the operating power corresponding to the target operating condition in a linear, gradual manner. Simultaneously, a proportional-integral-derivative (PID) controller adjusts the stack cooling water flow rate in real time to control the stack temperature fluctuation rate. This mode switching mechanism effectively overcomes the problems of reaction condition deviation and increased side reactions caused by sudden power changes in the electrosynthesis system due to process inertia. It achieves rapid and shock-free mode switching between energy storage and electrosynthesis, ensuring the stable and continuous operation of the system under fluctuating wind and solar power output conditions.

[0020] Furthermore, using the concentration of the polyacid electrolyte, the electrolyte circulation flow rate, and the stack current density as adjustable variables, a dual-constraint objective function with the joint optimization objectives of flow battery energy storage capacity and product yield is constructed and iteratively solved to obtain the optimal process parameter setpoints under the current operating conditions, which are then adjusted accordingly. By incorporating the energy storage capacity and product yield indicators into the same optimization framework, and simultaneously controlling the system energy consumption through the optimization constraints of stack current density and electrolyte circulation flow rate, the coordinated optimization of the three indicators of energy storage capacity, product yield, and energy consumption is achieved. This solves the problem in existing technologies where the electrolyte operating parameters and electrosynthesis process parameters of flow batteries are controlled separately by their respective independent control loops, lacking a coordinated mechanism.

[0021] Furthermore, by conducting online detection of the electrosynthesis products, the yield and purity values ​​of adipic acid are obtained and compared with preset qualified thresholds. When the product indicators fail to meet the standards, the electrolyte concentration and current density settings are adaptively adjusted according to the direction of deviation, and the optimization is iterated again. When the product indicators meet the standards, the current operating data is fed back to the dual-layer wind and solar power prediction model to form a closed loop. This product detection and closed-loop feedback mechanism enables the system to have online self-correction capabilities, continuously maintaining product quality that meets requirements, and continuously improving power prediction accuracy and overall control quality through data feedback, thus realizing automatic closed-loop optimized operation of the entire energy storage and electrosynthesis process. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of a method for electrosynthesis and coupling control of wind and solar power curtailment using multi-acid flow batteries, as described in an embodiment of the present invention. Detailed Implementation

[0023] To further understand the content of this invention, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are merely illustrative and not limiting of the invention.

[0024] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0025] Example 1 This embodiment provides a multi-mode coupling control method for a flow battery energy storage and indirect electrosynthesis system, mainly including the following steps: A two-layer wind and solar power prediction model is constructed, which includes a short-term prediction sub-model and a long-term prediction sub-model. Power prediction is performed on weather data and historical power data respectively to obtain short-term power generation curves and long-term power generation curves. The short-term power generation curves and long-term power generation curves are compared with the actual load data of power plants to obtain the curtailment ratio. Based on the curtailment ratio, the curtailment conditions are classified and judged according to the preset power threshold. Based on the judgment results of the curtailment conditions, corresponding hierarchical power allocation is carried out. During the hierarchical power allocation process, the change of the curtailment ratio is monitored in real time. When the change of the curtailment ratio causes the curtailment condition level to change, a ramp function is used to limit the rate of change of power allocation command during the switching process of different conditions. At the same time, the flow rate of the stack cooling water is adjusted in real time through the proportional-integral-derivative controller. Using the concentration of polyacid electrolyte, electrolyte circulation flow rate, and stack current density as adjustable variables, and constructing a dual-constraint objective function with the energy storage capacity and product yield of the flow battery as joint optimization objectives, the adjustable variables are iteratively solved to obtain the optimal concentration setpoint, optimal flow rate setpoint, and optimal current density setpoint under the current operating conditions. The system is then adjusted based on the optimal concentration setpoint, optimal flow rate setpoint, and optimal current density setpoint under the current operating conditions.

[0026] Combination Figure 1 The flowchart shown illustrates the method as follows: First, a two-layer wind and solar power prediction model is constructed, comprising a short-term prediction sub-model and a long-term prediction sub-model. This model takes weather data and historical power data as input and outputs short-term and long-term power generation curves, respectively. The difference between these curves and the actual load data of the power plants is calculated to obtain the surplus power curtailment. The curtailment ratio is then calculated based on the ratio of this surplus power curtailment to the total installed capacity of the system. Simultaneously, the predicted curtailment data output by the model is compared with the actual curtailment data from the power plants. Error feedback is used to optimize and correct the model parameters, thereby improving the accuracy of subsequent predictions.

[0027] After obtaining the curtailment rate, the curtailment rate is classified according to a preset power threshold, and a corresponding hierarchical power automatic allocation strategy is executed. Specifically, when the curtailment rate is less than 10%, it is determined to be a mild curtailment condition, and the energy storage unit is prioritized for charging, while the electrosynthesis unit is started at a low power to consume a small amount of curtailed power; when the curtailment rate is between 10% and 30%, it is determined to be a moderate curtailment condition, and the energy storage unit is controlled to charge synchronously with the electrosynthesis unit, and the electrolyte circulation flow rate is dynamically adjusted according to real-time power fluctuations; when the curtailment rate is greater than 30%, it is determined to be a severe curtailment condition, and all curtailed power is supplied to the electrosynthesis unit for the preparation of adipic acid.

[0028] After completing the above-mentioned hierarchical automatic power allocation, a mode switching operation is performed. Due to the inherent power response inertia of the electrosynthetic system, a ramp function is used to reduce the rate of power change during the mode switching process, and the stack cooling water flow rate is adjusted through a proportional-integral-derivative controller to ensure low temperature fluctuation rate of the stack, thereby achieving shock-free mode switching.

[0029] After the mode switch is completed, the system operates under the electrosynthesis condition corresponding to the current allocation strategy. At this time, a multi-acid electrolyte electrosynthesis optimization algorithm is constructed to optimize and control key process parameters. This algorithm uses multi-acid electrolyte concentration, electrolyte circulation flow rate, and stack current density as adjustable variables, employing a dual-constraint objective function to jointly optimize the flow battery's energy storage capacity and product yield. The adjustable variables are iteratively solved using the dual-constraint objective function to obtain the optimal concentration setpoint, optimal flow rate setpoint, and optimal current density setpoint under the current operating condition. These setpoints are then distributed to the corresponding actuators, enabling the electrosynthesis system to simultaneously meet the coordinated optimization of energy storage capacity, product yield, and energy consumption.

[0030] After executing the polyacid electrolyte electrosynthesis optimization algorithm, the electrosynthesis products are monitored online to obtain the current adipic acid yield and purity values. These values ​​are then compared with preset yield and purity thresholds. If the product indicators are deemed substandard, the system automatically corrects the polyacid electrolyte concentration and stack current density settings, and returns to the polyacid electrolyte electrosynthesis optimization algorithm for iterative optimization until the product indicators meet the requirements. If the product indicators are deemed satisfactory, the system maintains the current operating parameters and continues execution, feeding the current operating data back to the dual-layer wind and solar power prediction model. This iterative process of prediction, grading, optimization, and detection is repeated to achieve automatic control of energy storage and electrosynthesis.

[0031] Example 2 Based on the multi-mode coupling control method for a flow battery energy storage and indirect electrosynthesis system described in Example 1, this embodiment further describes its specific optimized control method as follows: The dual-layer wind and solar power prediction model comprises a short-term prediction sub-model and a long-term prediction sub-model. The short-term prediction sub-model has a prediction time domain of 15 minutes to capture short-term power fluctuation trends, while the long-term prediction sub-model has a prediction time domain of 24 hours to capture the power change situation throughout the day. Both sub-models employ the temporal fusion Transformer algorithm, using an attention mechanism to model the temporal dependencies between features such as wind speed, irradiance, and temperature in weather data and historical power operation data, outputting short-term and long-term power generation curves respectively. To ensure that the prediction results are continuously updated according to changes in actual operating conditions, the short-term and long-term prediction sub-models operate in a rolling manner, continuously incorporating the latest acquired measured data into the model input window according to a preset rolling cycle, while discarding the oldest outdated data and re-performing the prediction calculation. The difference between the two power prediction curves and the current actual load data of the power plants is calculated to obtain the surplus power of the power plants; further, the ratio of surplus power to the total installed capacity of the system is used as the power curtailment ratio. Meanwhile, the predicted power curtailment data output by the model is compared with the actual power curtailment metering data of the power plants. The model parameters of the time-series fusion Transformer algorithm are continuously adjusted by minimizing the root mean square error as the optimization criterion, thereby continuously improving the subsequent prediction accuracy.

[0032] After obtaining the percentage of abandoned electricity, the system classifies it according to preset power thresholds, including a first preset threshold and a second preset threshold. The first and second preset thresholds can be adaptively adjusted according to parameters such as system capacity and energy storage configuration. In this embodiment, the first preset threshold is 10%, and the second preset threshold is 30%. Specifically, when the percentage of abandoned electricity is less than 10%, it is determined to be a mild power abandonment condition. The system prioritizes charging the energy storage unit and starts the electrosynthesis unit with a small power to consume a small amount of abandoned electricity. When the percentage of abandoned electricity is between 10% and 30%, it is determined to be a moderate power abandonment condition. The system controls the charging of the energy storage unit and the electrosynthesis unit to operate synchronously, and dynamically adjusts the electrolyte circulation flow rate according to real-time power fluctuations. When the percentage of abandoned electricity is greater than 30%, it is determined to be a severe power abandonment condition. The system supplies all abandoned power to the electrosynthesis unit for the preparation of adipic acid.

[0033] When switching between different power curtailment conditions, due to the inherent power response inertia of the electrosynthetic system, the system employs a ramp function to smoothly constrain the power change rate, ensuring that the power transitions smoothly from the current operating power to the target operating power in a linear and gradual manner during the switching transition period, thereby suppressing sudden power surges during switching. Simultaneously, the stack cooling water flow rate is adjusted in real time through a proportional-integral-derivative controller to keep the stack temperature fluctuation rate within a preset range, ensuring a smooth transition without any shocks during the entire mode switching process.

[0034] The optimization algorithm for polyacid electrolyte electrosynthesis uses three parameters as adjustable variables: polyacid electrolyte concentration, electrolyte circulation flow rate, and stack current density. In this embodiment, the product yield includes cyclohexanone yield and adipic acid yield, where cyclohexanone is an intermediate product in the adipic acid synthesis process, and adipic acid is the final target product. The algorithm constructs a dual-constraint objective function with the joint optimization objective of maximizing the flow battery energy storage capacity and the yields of cyclohexanone and adipic acid, and solves the three adjustable variables collaboratively. It should be noted that the system energy consumption index is determined based on the stack current density and electrolyte circulation flow rate: the stack current density directly affects the power consumption level of the electrosynthesis process, and the electrolyte circulation flow rate directly affects the operating energy consumption of the circulation pump. By optimizing the constraints of the above two variables, the system can simultaneously minimize energy consumption while meeting the requirements of energy storage capacity and product yield, thereby achieving synergistic optimization of the three indicators of energy storage capacity, product yield, and energy consumption. After the iterative solution is completed, the obtained optimal concentration setpoint, optimal flow rate setpoint, and optimal current density setpoint are sent to the corresponding actuators to enable the electrosynthesis system to operate in the optimal process state.

[0035] During the operation of the electrosynthesis system, the product is monitored online to obtain the current adipic acid yield and purity values, which are then compared with preset yield and purity thresholds, respectively. If the current adipic acid yield or purity value is lower than the threshold, the product is deemed substandard. Based on the deviation direction between the current adipic acid yield and purity values ​​and their respective thresholds, the system adaptively adjusts the polyacid electrolyte concentration and stack current density settings. It then returns to the polyacid electrolyte electrosynthesis optimization algorithm for iterative optimization until the product meets the requirements. If the current adipic acid yield is not lower than the yield qualification threshold and the current adipic acid purity is not lower than the purity qualification threshold, then the product indicators are deemed to meet the standards. The system maintains the current operating parameters and continues to execute, and feeds back the current operating data to the dual-layer wind and solar power prediction model. The system iteratively executes the entire process of dual-layer wind and solar power prediction and curtailment calculation, curtailment power classification judgment, multi-acid electrolyte electrosynthesis optimization algorithm, and product detection judgment, thereby realizing automatic closed-loop control of energy storage and electrosynthesis.

[0036] Example 3 This embodiment uses the Huaneng 2.5kW / 4h multi-acid flow battery electrosynthesis demonstration system, integrating wind, solar, and energy storage, as a carrier to specifically implement the multi-mode coupling control method described in the previous embodiment. The demonstration system is equipped with a multi-acid flow battery stack with a rated power of 2.5kW and an energy storage duration of 4 hours. The indirect electrosynthesis unit uses phenol as a raw material and a platinum-based catalyst as the working electrode, with adipic acid as the target product. In this embodiment, the system sets the multi-acid electrolyte concentration control range to 0.1–0.5 mol / L, the target energy storage capacity to 13 Ah / L, and configures a power curtailment grading threshold, corresponding to mild curtailment conditions (curtailment rate less than 10%), moderate curtailment conditions (curtailment rate between 10% and 30%), and severe curtailment conditions (curtailment rate greater than 30%).

[0037] During operation, three typical scenarios were simulated sequentially: mild power curtailment (approximately 7%), moderate power curtailment (approximately 20%), and severe power curtailment (approximately 45%). In each scenario, the system accurately identified the curtailment level and executed the corresponding power allocation strategy. The mode switching process between the three operating conditions employed a ramp function to constrain the power change rate, and a proportional-integral-derivative (PID) controller adjusted the stack cooling water flow rate to keep the stack temperature fluctuation within ±2℃, achieving a smooth, shock-free switching.

[0038] During steady-state operation of the electrosynthesis unit, the polyacid electrolyte electrosynthesis optimization algorithm uses polyacid electrolyte concentration, electrolyte circulation flow rate, and stack current density as adjustable variables. The algorithm aims to maximize the energy storage capacity of the flow battery while simultaneously optimizing the yields of cyclohexanone and adipic acid. Iteratively solves for the optimal process parameter settings. In the online detection phase, the acceptable thresholds for adipic acid yield are set at 98%, purity at 99.5%, and cyclohexanone yield at 81%. In this implementation, the initial detection yield of adipic acid was 96.2%, and the purity was 98.8%, both below the acceptable levels. The system automatically adjusted the concentration and current density settings based on the deviation direction and triggered a new algorithm iteration. After two iterations, the adipic acid yield reached 98.5%, and the purity reached 99.6%, meeting the requirements. The system maintained the current parameters and continued operation, feeding back data to the dual-layer wind and solar power prediction model, forming a closed-loop cycle.

[0039] After 120 hours of continuous operation using the above method, this embodiment uses the Huaneng wind-solar-storage integrated 2.5kW / 4h multi-acid flow battery electrosynthesis demonstration system as a carrier to implement the above multi-mode coupling control method. The results show that the curtailment utilization rate reached 96.3%, the average adipic acid yield reached 98.5%, the average cyclohexanone yield reached 82.6%, the energy storage capacity was stably maintained at 13.1 Ah / L, and the comprehensive energy consumption per unit product was reduced by about 12% compared with the fixed power allocation mode. The above results indicate that the method of the present invention can simultaneously achieve synergistic optimization of the three indicators of energy storage capacity, product yield, and energy consumption in a practical system, and achieve rapid and shock-free mode switching between multiple curtailment conditions.

[0040] Example 4 This embodiment provides a multi-mode coupling control system for a flow battery energy storage and indirect electrosynthesis system, which mainly includes a dual-layer wind and solar power prediction module, a layered power distribution and mode shock-free switching module, and a multi-acid electrolyte electrosynthesis optimization module. The dual-layer wind and solar power prediction module is used to build a dual-layer wind and solar power prediction model, which includes a 15-minute short-term prediction sub-model and a 24-hour long-term prediction sub-model. The dual-layer wind and solar power prediction model adopts the time-series fusion Transformer algorithm, takes meteorological forecast data and historical power operation data as input, and outputs short-term power generation prediction curves and long-term power generation prediction curves respectively. The difference between the short-term power generation prediction curves and long-term power generation prediction curves and the current actual load data of the power plants is calculated to obtain the predicted curtailment power. At the same time, the predicted curtailment power is compared with the actual curtailment metering data of the power plants, and the model parameters are rolled over through error feedback. The curtailment ratio is calculated based on the ratio of the predicted curtailment power to the total installed capacity of the system.

[0041] The layered power allocation and shockless mode switching module is used to classify the curtailment rate according to a preset power threshold and execute the corresponding layered automatic power allocation strategy: when the curtailment rate is less than 10%, it is determined to be a mild surplus condition, and the energy storage unit is prioritized for charging and the electrosynthesis unit is started with low power; when the curtailment rate is between 10% and 30%, it is determined to be a moderate surplus condition, and the energy storage charging and electrosynthesis are controlled to run synchronously and the electrolyte circulation flow rate is dynamically adjusted; when the curtailment rate is greater than 30%, it is determined to be a severe surplus condition, and all the curtailed power is supplied to the electrosynthesis unit to prepare adipic acid. During the layered power allocation process, the change of the curtailment rate is monitored in real time. When the change of the curtailment rate causes a change in the curtailment condition level, when switching between different surplus conditions, a ramp function is used to smooth the power change rate to reduce the power surge amplitude at the moment of switching. At the same time, the proportional-integral-derivative controller is used to adjust the stack cooling water flow rate in real time to control the stack temperature fluctuation rate to not exceed the preset range, so as to achieve a shockless transition during the mode switching process.

[0042] The polyacid electrolyte electrosynthesis optimization module is used to construct a dual-constraint objective function with the polyacid electrolyte concentration, electrolyte circulation flow rate and stack current density as adjustable variables, and the liquid phase energy storage capacity index and product yield index as joint optimization objectives. The optimal concentration set value, optimal flow rate set value and optimal current density set value under the current operating conditions are obtained through iterative solution, and the above set values ​​are sent to the corresponding actuators to make the electrosynthesis system operate in the optimal process state.

[0043] In a preferred embodiment, the system further includes a product detection and closed-loop feedback module. This module, after the polyacid electrolyte electrosynthesis optimization module issues optimization parameters, performs online detection of the electrosynthesized product to obtain the current adipic acid yield and purity values. The current adipic acid yield and purity values ​​are then compared with preset yield and purity thresholds. If the product indicators are deemed substandard, the polyacid electrolyte concentration and stack current density settings are automatically corrected, triggering the polyacid electrolyte electrosynthesis optimization module to iterate and optimize again until the product indicators meet the requirements. If the product indicators are deemed satisfactory, the current operating parameters are maintained, and the current operating data is fed back to the dual-layer wind and solar power prediction module, initiating the next round of prediction, grading, optimization, and detection in a closed-loop cycle, achieving fully automatic coordinated control of energy storage and electrosynthesis.

[0044] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for electrosynthesis coupling control of wind and solar power curtailment and multi-acid flow battery, characterized in that, Includes the following steps: A two-layer wind and solar power prediction model is constructed, which includes a short-term prediction sub-model and a long-term prediction sub-model. Power prediction is performed on weather data and historical power data respectively to obtain short-term power generation curves and long-term power generation curves. The short-term power generation curves and long-term power generation curves are compared with the actual load data of power plants to obtain the curtailment ratio. Based on the curtailment ratio, the curtailment conditions are classified and judged according to the preset power threshold. Based on the judgment results of the curtailment conditions, corresponding hierarchical power allocation is carried out. During the hierarchical power allocation process, the change of the curtailment ratio is monitored in real time. When the change of the curtailment ratio causes the curtailment condition level to change, a ramp function is used to limit the rate of change of power allocation command during the switching process of different conditions. At the same time, the flow rate of the stack cooling water is adjusted in real time through the proportional-integral-derivative controller. Using the concentration of polyacid electrolyte, electrolyte circulation flow rate, and stack current density as adjustable variables, and constructing a dual-constraint objective function with the energy storage capacity and product yield of the flow battery as joint optimization objectives, the adjustable variables are iteratively solved to obtain the optimal concentration setpoint, optimal flow rate setpoint, and optimal current density setpoint under the current operating conditions. The system is then adjusted based on the optimal concentration setpoint, optimal flow rate setpoint, and optimal current density setpoint under the current operating conditions.

2. The method for electrosynthesis and coupling control of wind and solar power curtailment and multi-acid flow battery according to claim 1, characterized in that, In the dual-layer wind and solar power prediction model, both the short-term prediction sub-model and the long-term prediction sub-model adopt the temporal fusion Transformer algorithm. The attention mechanism is used to model the temporal dependency between wind speed, irradiance, temperature characteristics in weather data and historical power operation data. The short-term prediction sub-model and the long-term prediction sub-model run in a rolling manner. According to the preset rolling cycle, the latest measured data is included in the model input window, and the oldest outdated data is removed. The prediction calculation is then re-executed.

3. The method for electrosynthesis and coupling control of wind and solar power curtailment and multi-acid flow battery according to claim 2, characterized in that, The process of obtaining the curtailment ratio specifically involves: calculating the difference between the short-term and long-term power generation curves and the current actual load data of the power plants to obtain the surplus curtailment power of the power plants; using the ratio of the surplus curtailment power of the power plants to the total installed capacity of the system as the curtailment ratio; comparing the predicted curtailment data output by the model with the actual curtailment metering data of the power plants, and using the minimization of the root mean square error as the optimization criterion to perform rolling correction on the model parameters of the time-series fusion Transformer algorithm.

4. The method for electrosynthesis and coupling control of wind and solar power curtailment and multi-acid flow battery according to claim 1, characterized in that, The method involves classifying and judging the power curtailment conditions based on the curtailment ratio and a preset power threshold. Based on the judgment results, corresponding tiered power allocation is performed. Specifically: when the curtailment ratio is less than a first preset threshold, it is determined to be a mild curtailment condition, and the energy storage unit is prioritized for charging, while the electrosynthesis unit is started with low power. When the curtailment ratio is between the first and second preset thresholds, it is determined to be a moderate curtailment condition, and the energy storage unit is controlled to charge synchronously with the electrosynthesis unit, while the electrolyte circulation flow rate is dynamically adjusted according to real-time power fluctuations. When the percentage of abandoned power exceeds the second preset threshold, it is determined to be a severe power abandonment condition, and all abandoned power is supplied to the power synthesis unit.

5. The method for electrosynthesis and coupling control of wind and solar power curtailment and multi-acid flow battery according to claim 4, characterized in that, The method of using a ramp function to limit the rate of change of power allocation command during the switching process of different operating conditions specifically involves using a ramp function to smooth the rate of change of power allocation command determined by the hierarchical power allocation, so that the power smoothly transitions from the current operating power to the operating power corresponding to the target operating condition in a linear and gradual manner during the switching transition period.

6. The method for electrosynthesis and coupling control of wind and solar power curtailment and multi-acid flow battery according to claim 1, characterized in that, The product yield includes the yield of the target product adipic acid and the yield of the intermediate product cyclohexanone; the dual-constraint objective function uses the flow battery energy storage capacity and the yields of cyclohexanone and adipic acid as joint optimization objectives to maximize the product yield while ensuring the usable capacity of the energy storage system.

7. The method for electrosynthesis coupling control of wind and solar power curtailment and multi-acid redox flow battery according to claim 6, characterized in that, The dual-constraint objective function also takes minimizing system energy consumption as the optimization objective. The system energy consumption is determined based on the stack current density and electrolyte circulation flow rate. By optimizing the stack current density and electrolyte circulation flow rate, the system can simultaneously achieve the minimum energy consumption while meeting the requirements of energy storage capacity and product yield.

8. The method for electrosynthesis and coupling control of wind and solar power curtailment and multi-acid flow battery according to claim 6, characterized in that, After adjusting the optimal concentration, flow rate, and current density settings under the current operating conditions, the method further includes: online detection of the electrosynthetic product to obtain the current product yield and purity values; comparing the current product yield and purity values ​​with preset qualified thresholds; if the current adipic acid yield is lower than the yield qualified threshold or the current adipic acid purity is lower than the purity qualified threshold, the product indicators are determined to be substandard; and adaptively adjusting the polyacid electrolyte concentration setting and the stack current density setting based on the direction of the yield deviation and purity deviation, and iteratively optimizing again.

9. The method for electrosynthesis and coupling control of wind and solar power curtailment and multi-acid flow battery according to claim 8, characterized in that, If the current adipic acid yield is not lower than the yield qualification threshold and the current adipic acid purity is not lower than the purity qualification threshold, then the product indicators are deemed to meet the standards, the current operating parameters are maintained and the process continues, and the current operating data is fed back to the dual-layer wind and solar power prediction model. The entire process of wind and solar prediction and curtailment calculation, curtailment condition classification and layered power allocation, multi-acid electrolyte electrosynthesis optimization, and product detection and judgment is executed iteratively.

10. A wind and solar power curtailment and multi-acid flow battery electrosynthesis coupling control system, based on the wind and solar power curtailment and multi-acid flow battery electrosynthesis coupling control method according to any one of claims 1 to 9, characterized in that, It includes a dual-layer wind and solar power prediction module, a layered power allocation and mode-switching-free module, and a polyacid electrolyte electrosynthesis optimization module; The dual-layer wind and solar power prediction module is used to build a dual-layer wind and solar power prediction model that includes a short-term prediction sub-model and a long-term prediction sub-model. It performs power prediction on weather data and historical power data respectively to obtain short-term power generation curves and long-term power generation curves. The short-term power generation curves and long-term power generation curves are compared with the actual load data of the power plants to obtain the curtailment ratio. The layered power allocation and mode-switching module is used to classify the curtailment conditions based on the curtailment ratio and a preset power threshold, and to perform corresponding layered power allocation according to the curtailment condition classification results. During the layered power allocation process, the changes in the curtailment ratio are monitored in real time. When the change in the curtailment ratio causes a change in the curtailment condition level, a ramp function is used to limit the rate of change of the power allocation command during the switching process of different conditions. At the same time, the proportional-integral-derivative controller is used to adjust the stack cooling water flow rate in real time. The polyacid electrolyte electrosynthesis optimization module is used to construct a dual-constraint objective function with polyacid electrolyte concentration, electrolyte circulation flow rate, and stack current density as adjustable variables, and with the energy storage capacity and product yield of the flow battery as joint optimization objectives. The module iteratively solves the adjustable variables to obtain the optimal concentration setpoint, optimal flow rate setpoint, and optimal current density setpoint under the current operating conditions, and adjusts the system based on these optimal values.

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