Water-gas coupling energy storage integrated control method and system

Abstract

The invention provides a water-gas coupling energy storage integrated control method and system, and relates to the technical field of water-gas coupling energy storage, and the method comprises the steps: collecting water path operation parameters and gas path operation parameters; constructing a water-gas collaborative operation model; inputting the real-time system state and the power grid demand signal into the water-gas collaborative operation model; according to the power collaborative allocation scheme, generating a target control instruction, and controlling an energy storage system to execute energy storage or release operation; wherein the target control instruction is connected with an execution equipment water pump of the water path and an execution equipment air compressor adjusting valve of the air path, and the target control instruction is executed and used for intelligent control over the flow, the pressure and the valve opening degree of the water and gas execution equipment. The technical problem that the overall energy storage efficiency is not high due to the fact that water pump energy storage and compressed air energy storage lack cooperative scheduling and are independently operated in the prior art is solved, and the water-gas coupling energy storage efficiency is improved through water-gas coupling operation optimization.

Description

Technical Field

[0001] This application relates to the field of water-gas coupled energy storage technology, specifically to an integrated control method and system for water-gas coupled energy storage. Background Technology

[0002] In existing water-air coupled energy storage systems, the two energy storage media, water and air, are typically managed using a decoupled control architecture. This means the pumped-storage section and the compressed-air storage section each have their own independent control loops and operating targets, only undergoing simple power command allocation or sequential start-stop based on fixed logic. Due to the lack of an effective coordinated scheduling mechanism, the energy storage and release strategies cannot be dynamically adjusted according to grid demand, resulting in low overall energy storage efficiency. Especially when grid load fluctuates significantly, pumped-storage systems may experience over-storage or energy waste when meeting load requirements due to rapid changes in power demand, while compressed-air storage systems may fail to respond promptly to grid load changes due to independent operation, further exacerbating the imbalance in energy utilization.

[0003] In summary, the existing technology suffers from the problem of low overall energy storage efficiency due to the lack of coordinated scheduling between water pump energy storage and compressed air energy storage, which operate independently. Summary of the Invention

[0004] The purpose of this application is to provide an integrated control method and system for water-air coupled energy storage, in order to solve the technical problem in the prior art where the lack of coordinated scheduling between water pump energy storage and compressed air energy storage, and their independent operation, leads to low overall energy storage efficiency.

[0005] To achieve the above objectives, this application provides an integrated control method and system for water-gas coupled energy storage.

[0006] Firstly, this application provides a water-gas coupled energy storage integrated control method, which is implemented through a water-gas coupled energy storage integrated control system. The method includes: collecting water circuit operating parameters of the pumped hydro storage section and air circuit operating parameters of the compressed air energy storage section; constructing a water-gas coordinated operation model based on the water circuit and air circuit operating parameters; inputting real-time system status and grid demand signals into the water-gas coordinated operation model, and outputting an optimal power coordination allocation scheme by solving a multi-objective optimization problem in a rolling manner; generating target control commands to coordinate the actions of the water pump in the water circuit and the air compressor in the air circuit according to the power coordination allocation scheme, and controlling the energy storage system to perform energy storage or release operations; wherein the target control commands are connected to the regulating valves of the water pump in the water circuit and the air compressor in the air circuit, and the execution of the target control commands is used for intelligent control of the flow rate, pressure, and valve opening of the water-gas actuators.

[0007] Optionally, the water circuit operating parameters include: reservoir water level, pipeline water pressure, water flow velocity, water pump speed, bearing vibration value, and water temperature; the gas circuit operating parameters include: gas pressure of the gas storage device, air temperature, inlet and outlet flow rate of the compressor / expander, speed, efficiency, and humidity of the inner wall of the gas storage device.

[0008] Optionally, a dynamic model for water circuit operation is constructed based on the water circuit operating parameters; a dynamic model for gas circuit operation is constructed based on the gas circuit operating parameters; according to the law of conservation of mass and energy, the dynamic model for water circuit operation and the dynamic model for gas circuit operation are coupled and interacted to construct a water-gas coupling interaction model; using the water-gas coupling interaction model as a predictor, a strategy for maximizing the benefits of water circuit operating parameters and gas circuit operating parameters is searched to construct the water-gas coordinated operation model.

[0009] Optionally, using the water-gas coupling interaction model as a predictor, an optimization control framework is embedded to construct the water-gas coordinated operation model. The water-gas coordinated operation model is configured to: perform rolling optimization to obtain the optimal coordinated control sequence with operating economy, equipment lifespan, and grid command tracking accuracy as comprehensive optimization objectives. The comprehensive optimization objective is expressed through a multi-objective function, which includes at least a grid power command tracking error term, an operating energy consumption cost term, and an equipment mechanical fatigue accumulation term. In each control cycle, with the current system state as the initial value and grid demand and market signals as inputs, the future state is predicted based on the water-gas coupling interaction model, and the future control sequence that optimizes the multi-objective function is solved as the output of the optimal power coordinated allocation scheme.

[0010] Optionally, based on the water-gas coupling interaction model, the water-gas path coupling strength of the current system is analyzed; according to the characteristics of the water-gas path coupling strength and the power grid demand signal, the weight coefficients of each item in the multi-objective function or the constraints of the optimization problem are dynamically adjusted so that the optimization orientation of the water-gas coordinated operation model is adapted to the physical coupling state and task requirements of the current system.

[0011] Optionally, based on the water-air coupling interaction model, the amplitude of the change in air pressure caused by the change in water flow rate per unit time is identified to obtain a first change amplitude; based on the water-air coupling interaction model, the amplitude of the change in water pressure caused by the change in air flow rate per unit time is identified to obtain a second change amplitude; the geometric mean of the first change amplitude and the second change amplitude is calculated to obtain the water-air path coupling strength.

[0012] Optionally, when the water-gas path coupling strength reaches a first threshold and the grid demand signal changes drastically, the weight of the grid command tracking error term in the multi-objective function is increased, and the constraint on the amplitude of system coupling pressure fluctuation is strengthened to form a rapid response optimization orientation; when the water-gas path coupling strength is lower than a second threshold and the amplitude of the grid demand signal is large and stable, the weight of the operating energy consumption cost term in the multi-objective function is increased to form an economic energy storage optimization orientation.

[0013] Optionally, under the guidance of rapid response optimization, the generation of the target control command includes a feedforward compensation component based on the physical field of the pressure wave, used to offset the pressure shock caused by the rapid action of the gas path on the water path subsystem through pipeline inertia; wherein, using the water-gas coupling interaction model, the pressure shock waveform generated by the rapid action of the gas path on the water path through pipeline inertia under the power collaborative allocation scheme is predicted; while issuing a power command to the gas path actuator, a reverse compensation command is issued in advance to the water path actuator based on the pressure shock waveform to actively offset the pressure shock.

[0014] Optionally, the water-air coordinated operation model is a hierarchical nested decision model structure, including an inner water-air coupling interaction model and an outer predictive control optimizer. The inner water-air coupling interaction model simulates and predicts the water system, the air system, and the dynamic interaction between them through pressure and flow variables in real time. The outer predictive control optimizer is based on the inner predictor and solves the multi-objective optimization problem in each control cycle. The output power coordination allocation scheme is used as the input boundary condition of the water-air coupling interaction model to drive the state prediction of the next cycle.

[0015] Secondly, this application also provides a water-air coupled energy storage integrated control system for executing the water-air coupled energy storage integrated control method as described in the first aspect. The water-air coupled energy storage integrated control system includes: a parameter acquisition module for acquiring the water circuit operation parameters of the pumped hydro storage section and the air circuit operation parameters of the compressed air energy storage section; a model building module for constructing a water-air coordinated operation model based on the water circuit operation parameters and the air circuit operation parameters; a multi-objective optimization module for inputting real-time system status and grid demand signals into the water-air coordinated operation model, and outputting the optimal power coordination allocation scheme by rolling the solution of the multi-objective optimization problem; and a coordinated control module for generating target control commands to coordinate the actions of the water pump in the water circuit and the air compressor in the air circuit according to the power coordination allocation scheme, controlling the energy storage system to perform energy storage or release operations. The target control commands are connected to the regulating valves of the water pump in the water circuit and the air compressor in the air circuit, and executing the target control commands for intelligent control of the flow rate, pressure, and valve opening of the water-air actuators.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages: By collecting the water circuit operating parameters of the pumped hydro storage section and the air circuit operating parameters of the compressed air energy storage section, a water-air coordinated operation model is constructed based on these parameters. Real-time system status and grid demand signals are input into the water-air coordinated operation model, and a multi-objective optimization problem is solved through rolling optimization to output the optimal power coordination allocation scheme. Based on the power coordination allocation scheme, target control commands are generated to coordinate the actions of the water pumps in the water circuit and the air compressors in the air circuit, controlling the energy storage system to perform energy storage or release operations. These target control commands are connected to the regulating valves of the water pumps in the water circuit and the air compressors in the air circuit, executing the target control commands for intelligent control of the flow rate, pressure, and valve opening of the water and air actuators. In other words, by constructing a water-air coordinated operation model and implementing model prediction and rolling optimization, dynamic coordinated allocation of water and air power is achieved, improving overall energy storage efficiency and enabling rapid response to grid load fluctuations, ensuring grid stability and power supply reliability.

[0017] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the integrated control method for water-air coupled energy storage in this application.

[0020] Figure 2 This is a schematic diagram of the integrated water-air coupled energy storage control system of this application.

[0021] Figure labeling: Parameter acquisition module 11, model building module 12, multi-objective optimization module 13, collaborative control module 14. Detailed Implementation

[0022] This application provides an integrated control method and system for water-gas coupled energy storage, solving the technical problem of low overall energy storage efficiency caused by the lack of coordinated scheduling between water pump energy storage and compressed air energy storage, resulting in their independent operation. By constructing a water-gas coordinated operation model and implementing model prediction and rolling optimization, dynamic coordinated allocation of water and gas power is achieved, improving overall energy storage efficiency and enabling rapid response to grid load fluctuations, ensuring grid stability and power supply reliability.

[0023] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0024] Example 1, please refer to the appendix. Figure 1 This application provides an integrated control method for water-gas coupled energy storage, wherein the integrated control method for water-gas coupled energy storage is applied to an integrated control system for water-gas coupled energy storage, and the integrated control method for water-gas coupled energy storage specifically includes the following steps: Collect the water circuit operating parameters of the pumped hydro storage section and the air circuit operating parameters of the compressed air energy storage section.

[0025] Furthermore, this application also includes the following steps: the water circuit operating parameters include: reservoir water level, pipeline water pressure, water flow velocity, water pump speed, bearing vibration value and water temperature; the gas circuit operating parameters include: gas pressure of the gas storage device, air temperature, inlet and outlet flow rate of the compressor / expander, speed, efficiency and humidity of the inner wall of the gas storage device.

[0026] Specifically, based on the physical processes, energy conversion stages, and safety monitoring requirements of the two subsystems—pumped hydro storage and compressed air energy storage—a comprehensive sensor network was meticulously designed and deployed. The pumped hydro storage subsystem is a complete loop in the energy storage system using water as the energy carrier and working medium. During energy storage, it acts as an electrical load, driving a pump to draw water from a low-level reservoir to a high-level reservoir, converting electrical energy into the gravitational potential energy of the water for storage. During energy release, it acts as a power generation device, with the high-level water driving a turbine to generate electricity, converting potential energy back into electrical energy. The compressed air energy storage subsystem is a complete loop in the energy storage system using air as the energy carrier and working medium. During energy storage, a compressor compresses air and injects it into an air storage device, such as an underground salt cavern, abandoned mine shaft, or high-pressure storage tank, converting electrical energy into the internal energy of the air for storage. During energy release, the high-pressure air is released to drive an expander to generate electricity.

[0027] For water systems, high-precision sensors are installed at key nodes, such as water level gauges in reservoirs to continuously monitor the energy storage potential level; pressure transmitters are installed at key parts of pressure pipelines, such as the inlet of the spiral casing and after elbows, to monitor water pressure and assess pipeline stress and hydraulic losses; electromagnetic or ultrasonic flow meters are installed on the main pipeline to accurately measure water velocity and flow rate; speed encoders are installed on the main shaft of water pumps or turbines to provide real-time feedback on their rotational speed; vibration acceleration sensors are installed on the main bearing housing to capture mechanical vibration signals to predict the health status of the equipment; and temperature sensors are installed at the inlet or cooling water circuit to monitor water temperature changes.

[0028] For the gas path, meticulous planning is also implemented. For example, absolute pressure transmitters are installed on the top of the gas storage device and at key pipeline nodes to monitor the pressure of the core energy storage medium; platinum resistance temperature sensors are installed in the same locations to measure air temperature; gas mass flow meters are installed at the compressor inlet and expander outlet; speed sensors are installed on the main shaft; and the operating efficiency is calculated in real time based on the thermodynamic characteristic curves by measuring the pressure, temperature, and flow rate at the compressor / expander inlet and outlet; and explosion-proof humidity sensors are installed on the inner wall of the gas storage device to monitor the internal ambient humidity.

[0029] All sensors are connected via an industrial fieldbus network, such as Industrial Ethernet, and data is synchronously acquired and transmitted to the central controller. The data acquisition system samples at a preset fixed frequency; for example, high-frequency sampling of 100 milliseconds is used for rapidly changing pressure and speed, while medium-low frequency sampling of 1 second is used for slowly changing water level and humidity. The raw data is preprocessed, including filtering, outlier removal, and timestamp alignment, and finally fused into a time-synchronized, uniformly formatted multidimensional system state vector, namely the water circuit operating parameters and the air circuit operating parameters.

[0030] Water system operating parameters describe information such as water flow, pressure, velocity, and rotational speed in a pump-storage system, reflecting the working status of the pumped-storage section. These parameters include reservoir water level, pipeline water pressure, water flow velocity, pump rotational speed, bearing vibration, and water temperature. For example, a radar level gauge reading of 525.3m corresponds to an effective reservoir capacity of approximately 1.5 million cubic meters; a pressure transmitter reading before the pump inlet is 6.8 MPa; and an ultrasonic flow meter on the main pipeline displays a water flow velocity of 2.1 m / s, corresponding to a flow rate of approximately 3.7 m³ / s. 3 The water pump spindle encoder measured a rotational speed of 428 r / min, the vibration sensor at the water pump drive end bearing measured an effective value of 1.5 mm / s, which is within the excellent range, and the temperature sensor showed a water temperature of 15℃.

[0031] Compressed air energy storage describes information such as air flow, temperature, pressure, and compressor operating status within the compressed air energy storage section, reflecting the operational status of the compressed air energy storage section. This includes the air pressure of the storage device, air temperature, compressor / expander inlet and outlet flow rates, speed, efficiency, and humidity of the storage device's inner wall. For example, the pressure sensor reading at the top of an underground salt cavern gas storage facility is 8.2 MPa, the temperature sensor at the same location shows an air temperature of 42°C, the compressor outlet mass flow meter reading is 28.5 kg / s, and the compressor speed is 6550 r / min. Based on the inlet and outlet parameters—inlet pressure 0.1 MPa, temperature 25°C, and outlet pressure 8.5 MPa—the real-time calculated compressor adiabatic efficiency is 0.78. The humidity sensor on the inner wall of the salt cavern shows a relative humidity of 65%, indicating good sealing and moderate air dryness.

[0032] By analyzing the operating parameters of the water and air circuits, it can be determined whether the current system is operating normally. For example, if the bearing vibration value of the water pump is within the normal range and the difference between water and air temperature is small, it indicates that the equipment is operating smoothly. Similarly, if the air pressure and air temperature of the air storage device are within the expected range, it indicates that the air circuit is operating normally. By collecting the operating parameters of the water and air circuits, the water pump energy storage and compressed air energy storage systems can be precisely monitored, enabling rapid adjustment and optimization.

[0033] A water-gas coordinated operation model is constructed based on the water circuit operation parameters and the gas circuit operation parameters.

[0034] Furthermore, this application also includes the following steps: constructing a dynamic model of waterway operation based on the waterway operation parameters; constructing a dynamic model of gasway operation based on the gasway operation parameters; coupling and interacting the dynamic model of waterway operation and the dynamic model of gasway operation according to the law of conservation of mass and energy to construct a water-gas coupling interaction model; using the water-gas coupling interaction model as a predictor, searching for a synergistic strategy to maximize the benefits of the waterway operation parameters and the gasway operation parameters to construct the water-gas coordinated operation model.

[0035] Furthermore, this application also includes the following steps: using the water-gas coupling interaction model as a predictor, embedding an optimization control framework, and constructing the water-gas coordinated operation model, wherein the water-gas coordinated operation model is configured to: perform rolling optimization to obtain the optimal coordinated control sequence with operating economy, equipment life and grid command tracking accuracy as comprehensive optimization objectives, wherein the comprehensive optimization objective is expressed through a multi-objective function, wherein the multi-objective function includes at least a grid power command tracking error term, an operating energy consumption cost term and an equipment mechanical fatigue accumulation term; wherein, in each control cycle, with the current system state as the initial value and grid demand and market signals as input, the future state is predicted based on the water-gas coupling interaction model, and the future control sequence that makes the multi-objective function optimal is solved as the output of the optimal power coordinated allocation scheme.

[0036] Specifically, based on the waterway operating parameters, the physical structure and working principle of the waterway system are analyzed to construct a dynamic model of waterway operation, including reservoir water level dynamics, pipeline equations, and pump / turbine characteristic equations. The dynamic model of waterway operation is a mathematical model used to describe the operation process of the pump storage section, simulating the changes in variables such as water flow, pressure, velocity, and pump speed over time.

[0037] The dynamic model of waterway operation comprises several core modules, such as the reservoir module, the pressure pipeline module, and the pump / turbine unit module. The reservoir module calculates water level changes based on the inflow-outflow difference through integration; for example, when the reservoir area is fixed, the rate of water level change equals the net flow rate divided by the reservoir area. The pressure pipeline module uses water hammer equations or simplified lumped parameter models to describe pressure fluctuations caused by water flow inertia, frictional resistance, and pipe wall elasticity. The pump / turbine unit module focuses on establishing the relationship between head, flow rate, speed, and efficiency. Static characteristics are typically obtained by polynomial or spline interpolation fitting based on performance curves provided by the manufacturer, combined with the inertial equations of rotating components to describe their dynamic speed. Model parameters, such as pipeline friction coefficient, water hammer velocity, unit characteristic curve coefficients, and moment of inertia, are identified and determined through design data, equipment nameplate parameters, and historical operating data.

[0038] Similarly, based on the gas path operating parameters, a dynamic model of gas path operation is established, including the gas state equation of the gas storage device, the characteristic equation of the compressor / expander, and the gas flow equation in the pipeline. The dynamic model of gas path operation is a mathematical model used to describe the operation process of the compressed air energy storage section, simulating the changes of variables such as airflow, pressure, temperature, flow rate, and compressor speed at different points in time. The dynamic model of gas path operation includes several core modules, such as the compressor / expander unit module, the gas storage device module, and the connecting pipeline module. The compressor / expander unit module establishes a linear relationship between pressure ratio, reduced flow rate, speed, and efficiency, usually based on manufacturer curve fitting, and considers the mechanical inertia of the rotor. The gas storage device module is treated as a control volume, applying mass conservation and energy conservation equations. Mass conservation describes that the rate of change of air mass equals the difference between the inlet and outlet mass flow rates. Energy conservation is more critical, requiring consideration of the enthalpy of the inlet and outlet working fluid, heat exchange with the outside environment, and changes in the internal gas state. The gas state is determined by the real gas state equation, such as the van der Waals equation or the more precise PR equation, relating pressure, temperature, and density. For large underground gas storage facilities, a simplified model of heat transfer in the rock mass also needs to be established; for connecting pipeline modules, considering the compressibility of the gas, a one-dimensional unsteady flow model or a simplified volume-inertia model should be adopted.

[0039] The input data for the dynamic waterway operation model includes reservoir water level, pipeline water pressure, water flow velocity, pump speed, bearing vibration value, and water temperature. Output data includes dynamic behaviors such as water flow velocity, pressure changes, and pump power output. Fluid dynamics models, including Bernoulli's equation, are used to describe water flow behavior, considering factors such as water flow friction, pipeline shape and size, and pump operating curves to calculate changes in water velocity, pressure, and flow rate. Reservoir water level, pipeline water pressure, and pump speed are used as inputs, and the model calculates dynamic behaviors such as water flow velocity and pressure changes. The water flow state will continuously adjust pump operating conditions through a feedback mechanism to maintain the system's optimal operating state.

[0040] The input data for the dynamic model of the gas path operation includes the gas pressure of the gas storage device, air temperature, inlet and outlet flow rates, speed, efficiency of the compressor / expander, and humidity of the inner wall of the gas storage device; the output data includes air flow rate, gas pressure changes, and dynamic behaviors such as compressor energy efficiency; based on the ideal gas law and thermodynamic laws, a mathematical model of the airflow compression and expansion process is established to simulate the changes in gas pressure, temperature, and flow rate of the compressor and expander under different operating conditions. By inputting the operating parameters of the gas path, such as gas pressure, temperature, flow rate, and speed, thermodynamic and fluid dynamic formulas are used to calculate the behavior of air in the gas storage device, as well as the energy conversion during the compression and expansion processes.

[0041] Based on the laws of conservation of mass and energy, the dynamic models of the water and air circuits are coupled and interact. Due to the close coupling between the water and air systems, changes in water flow affect changes in air pressure, and conversely, the release of compressed air from the air system also affects the water flow rate and pump operation. According to the laws of conservation of mass and energy, the coupled interaction model links changes in water and air flow rates to ensure optimal balance in the energy conversion processes of the two systems. The water-air coupling interaction model describes how changes in the water-side state affect the air-side, and vice versa, thus simulating the combined dynamic behavior of the entire coupled system.

[0042] Based on the conservation of mass and energy, the following coupling equations are established: the force balance equation is: air chamber pressure = water column static pressure (water density × gravitational acceleration × water column height) + residual pressure head in the pipeline; the volume continuity equation is: air chamber volume change rate = -water flow rate. These two equations dynamically link the water level and flow rate output from the water circuit model with the air pressure and volume output from the air circuit model, forming a closed, unified set of differential-algebraic equations, i.e., a water-air coupled interactive model. This model can simulate the entire process, such as increasing the pump power leading to a rise in water level, a decrease in air chamber volume, and ultimately, an increase in air pressure.

[0043] The water-air coupling interaction model is used as a predictor and integrated into a model predictive control algorithm framework, which typically includes a state estimator, a predictive model, and an optimization solver. The optimization control framework is an advanced control strategy framework whose core principle is to use the system's dynamic model to predict its behavior over a future period. It calculates a series of optimal control commands by repeatedly solving a finite-time optimization problem online, but only implements the first command in the sequence. This allows for balancing multiple dimensions and objectives, and making optimal decisions. The optimization control framework dynamically adjusts its operation based on the current system state, grid demand, and market signals.

[0044] The water-gas coordinated operation model is designed to dynamically optimize control strategies within different time periods, outputting the optimal power coordination allocation scheme, including operating commands for water pumps and compressors, flow rate regulation, and pressure regulation. Within each control cycle, the strategy is adjusted based on the current system state and grid demand, and future states are predicted, ensuring that the system can promptly adjust energy storage or release according to grid load fluctuations and changes in market signals.

[0045] With operational economy, equipment lifespan, and grid command tracking accuracy as comprehensive optimization objectives, operational economy is the sum of the products of the total system input power and the current and predicted electricity prices within the future forecast time domain, i.e., pump power + compressor power + estimated electricity cost. Minimizing operational economy can guide the system to store more energy when electricity prices are low and generate more electricity or reduce electricity consumption when prices are high. Equipment lifespan maintenance is usually quantified as a penalty for changes in control variables, i.e., summing the squares of their increments or second-order differences. Minimizing this can smooth equipment operation, reduce mechanical stress and thermal cycling fatigue, and extend equipment lifespan. Grid command tracking accuracy is the sum of the squares of the differences between the total system output power and grid dispatch commands within the future forecast time domain. Minimizing this ensures accurate grid service.

[0046] In each control cycle, real-time data from all sensors is read and, after state estimation, used as the precise initial value for the current system state. The latest grid demand and market signals are acquired, including the net power curve to be provided in the next stage and time-of-use electricity price forecasts for the next few hours. Starting from the current system state, a set of future control sequences is input into the water-gas coupling interaction model, and a rapid simulation is run to predict the evolution trajectory of all system states over a period of time under this set of control actions. The optimization solver begins working, searching through all feasible control sequence spaces, repeatedly calling the prediction model for simulation evaluation, and finding the specific control sequence that minimizes the above multi-objective function value. The solution process must strictly adhere to all hard constraints, such as equipment power limits, safe gas storage pressure ranges, and reservoir water level limits. Once the optimal sequence is found, the controller extracts only the first control instruction in the sequence, i.e., the optimal power coordination allocation scheme that needs to be executed immediately, such as setting the water pump power to 85 MW and the compressor power to -60 MW (the negative sign represents power generation), and sends it to the underlying speed governor and valve controller for execution. Wait 5 seconds, and the next control cycle begins. Repeat the above steps so that the controller can continuously use the latest feedback information to correct and optimize the trajectory, and has natural robustness to model errors and unknown disturbances.

[0047] For example, the turbine can be started immediately and rapidly increased to full load, while the compressor is stopped or the turbine and expander can be coordinated to increase power at different inclines. For each attempt, the internal coupling model quickly predicts the changes in air pressure and water level over the next 10 minutes. The optimizer eventually finds an optimal solution, determining that at an air pressure of 7.8 MPa, the gas system can immediately provide greater power and higher efficiency, while the water system takes approximately 90 seconds to go from rest to full load. Therefore, the first instruction of the optimal coordinated control sequence is to immediately set the expander power to 120 MW, while simultaneously starting the turbine and setting its power to 80 MW within the first 5 seconds, for a total power of exactly 200 MW. This scheme was selected because it tracks the grid command with a tracking error term of 0; it utilizes the current air pressure to generate electricity directly, avoiding the delay of consuming electricity to pump water before generating electricity, achieving economic efficiency during the current low electricity price period; and it sets a gentle starting incline for the turbine, reducing mechanical shock and resulting in a lower fatigue accumulation term.

[0048] By employing a water-gas coordinated operation model, energy storage and release strategies are adjusted based on grid demand signals to minimize tracking errors in grid power commands and ensure efficient and stable system operation. Through multi-objective optimization, operational energy consumption is optimized while meeting grid demand, reducing unnecessary energy losses and lowering overall operating costs.

[0049] Furthermore, this application also includes the following steps: based on the water-gas coupling interaction model, analyze and obtain the water-gas path coupling strength of the current system; according to the characteristics of the water-gas path coupling strength and the power grid demand signal, dynamically adjust the weight coefficients of each item in the multi-objective function or adjust the constraints of the optimization problem so that the optimization orientation of the water-gas coordinated operation model is adapted to the physical coupling state and task requirements of the current system.

[0050] Furthermore, this application also includes the following steps: based on the water-air coupling interaction model, identifying the amplitude of the change in air pressure caused by the change in water flow rate per unit time, and obtaining a first change amplitude; based on the water-air coupling interaction model, identifying the amplitude of the change in water pressure caused by the change in air flow rate per unit time, and obtaining a second change amplitude; calculating the geometric mean of the first change amplitude and the second change amplitude to obtain the water-air path coupling strength.

[0051] Furthermore, this application also includes the following steps: when the water-gas path coupling strength reaches a first threshold and the power grid demand signal changes drastically, the weight of the power grid command tracking error term in the multi-objective function is increased, and the constraint on the amplitude of system coupling pressure fluctuation is strengthened to form a rapid response optimization orientation; when the water-gas path coupling strength is lower than a second threshold and the amplitude of the power grid demand signal is large and stable, the weight of the operating energy consumption cost term in the multi-objective function is increased to form an economic energy storage optimization orientation.

[0052] Specifically, in each control cycle, when the system is in a quasi-steady state or on a known dynamic trajectory, a virtual perturbation test is performed within the control software. Based on the current state of the water-air coupling interaction model, keeping all control inputs of the air path constant, a small standard incremental change is applied to the main control actuator of the water path. Using the current system state as the initial condition, a dynamic simulation of one control cycle is performed using this model. After the simulation, the changes in water flow rate and air pressure are recorded, and the first change amplitude is calculated: First change amplitude = (Change in air pressure / Rated air pressure) / (Change in water flow rate / Rated water pressure). Dividing the relative change by the rated value is for normalization, making the result dimensionless, representing the normalized relative change in air pressure caused by a unit change in the water path control variable. The first change amplitude is the magnitude of the change in the key state variable of the air path ultimately caused by the system coupling physical mechanism when the main control variable of the water path undergoes a one-unit standard change per unit time, resulting in a corresponding change in the water path flow rate. It quantifies the influence of the water path action on the air path state.

[0053] Within the same control cycle, another virtual perturbation test is executed immediately or in parallel. Keeping all control inputs to the water circuit constant, a small, standard incremental change is applied to the main control actuator of the air circuit, such as an increase in power of 0.5% of its rated value. A control cycle is simulated using a water-air coupling interaction model, recording the changes in air circuit flow rate and the changes in key water circuit pressure. The second change amplitude is then calculated: Second change amplitude = (Change in water circuit pressure / Rated water circuit pressure) / (Change in air circuit flow rate / Rated air circuit flow rate), representing the relative change in normalized water circuit pressure caused by a unit change in air circuit control variable. The second change amplitude quantifies the influence of air circuit actions on the water circuit state, quantifying the change in key water circuit state caused by the system coupling physical mechanism when the main control variable of the air circuit undergoes a one-unit standard change, resulting in a corresponding change in air circuit flow rate.

[0054] To comprehensively reflect the strength of the bidirectional influence using a single index and avoid distortion due to extremely weak influence in one direction, a geometric mean is used for calculation, i.e., sqrt(first change amplitude × second change amplitude). The geometric mean considers both amplitudes equally, and when either value is zero, the coupling strength is zero, consistent with physical intuition; that is, if there is no influence in either direction, it is decoupled. The calculated water-gas path coupling strength is a dimensionless positive number, its magnitude directly reflecting the sensitivity or tightness of the energy and mass transfer channels between the water and gas subsystems under current operating conditions. A higher water-gas path coupling strength indicates a tighter water-gas coupling, and a more significant impact of one's actions on the other's state.

[0055] For example, on a simulation platform of a large-scale water-air coupled energy storage power station, the rated power of the water pump is 150 MW, the rated power of the compressor is 100 MW, the rated water pressure is 6 MPa, and the rated air pressure is 8 MPa. The simulation platform sets the system to operate under medium load, with the water pump pumping water at 90 MW and the compressor compressing air at 60 MW. The current water pressure is 5.2 MPa, and the air pressure is 7.0 MPa. In the model, the water pump power command is increased by 0.75 MW, which is 0.5% of the rated value, and the model simulation lasts for 5 seconds. The simulation results show that the water flow rate increased by approximately 0.12 cubic meters per second, and the air pressure rose from 7.000 MPa to 7.003 MPa. The change in air pressure is calculated as 0.003 MPa, and the change in water pressure is calculated as 0.75 MW. Therefore, the first change amplitude is (0.003 / 8) / (0.75 / 150) = 0.075. In the model, the compressor power command was increased by 0.5 MW, or 0.5% of the rated value, and the simulation lasted for 5 seconds. The simulation results showed that the gas flow rate increased by approximately 0.8 kg / s, and the water pressure rose slightly from 5.200 MPa to 5.201 MPa. The calculated change in water pressure was 0.001 MPa, and the change in gas flow rate was 0.5 MW, resulting in a second change amplitude of 0.0333. The calculated water-gas path coupling strength was 0.05, indicating that at the current operating point, the water-gas coupling is at a moderate to weak strength. This may be because the current combination of water level and gas pressure results in a relatively stable water-gas interface, with minimal volumetric-pressure transmission effects. The system operating point was changed to a high water level and low air pressure state, i.e., water pressure 6.5 MPa and air pressure 6.0 MPa. The above test was repeated, and the first change amplitude was 0.15, the second change amplitude was 0.12, and the coupling strength was 0.134. At the new operating point, the coupling strength was significantly enhanced, rising from 0.05 to 0.134, which is in line with physical expectations. This is because the air path is more sensitive to volume changes under low air pressure, and the water flow is more sensitive to pressure changes under high water level.

[0056] Calculating the coupling strength and quantifying the degree of mutual influence between the water and gas paths helps determine the adjustment range of the control strategy, ensuring efficient collaboration between the two systems during the operation of the water pump and compressor, and avoiding energy waste or system instability. By balancing the coupling effect between the water and gas paths, energy utilization efficiency can be maximized, unnecessary energy loss can be reduced, and the overall energy efficiency of the system can be improved.

[0057] The first threshold is a relatively high value. When the coupling strength reaches or exceeds this value, it indicates that the dynamic interaction between the water and gas subsystems is very close, and the action of one will rapidly and significantly affect the other. The second threshold is a relatively low value. When the coupling strength is below this value, it indicates that the dynamic coupling effect between the two subsystems is weak, and the mutual interference is small. These two thresholds need to be determined based on the physical design parameters of the specific system and a large number of simulation experiments.

[0058] When the coupling strength of the water-gas path reaches the first threshold and the grid demand signal changes drastically, the system immediately switches to a fast response optimization orientation. The controller automatically increases the weight coefficient of the grid command tracking error term in the multi-objective function, significantly increasing the priority of keeping up with grid commands. The optimizer will allow for higher operating power consumption or slightly more drastic equipment actions to minimize power deviation. Simultaneously, the controller strengthens the constraints on the amplitude of system coupling pressure fluctuations. The upper limits of the gas pressure change rate of the gas storage device and / or the water pressure change rate of key pipelines are tightened in the constraints of the optimization problem. This prevents drastic pressure fluctuations caused by the inertia difference between water and gas during the pursuit of fast power tracking, which could lead to water hammer, surge, or overpressure risks. These adjustments collectively shape the fast response orientation. Under this configuration, the control commands solved by the optimizer will tend to assign more instantaneous power regulation tasks to the faster responding component, typically the gas expander that can be quickly adjusted via valves or the water pump with lower inertia, while strictly limiting the overall action amplitude to ensure that critical internal system pressures remain stable while rapidly following grid commands, preventing instability.

[0059] When the coupling strength of the water-gas path is below the second threshold, and the amplitude of the grid demand signal is large and stable, the controller automatically increases the weight coefficient of the operating energy cost term in the multi-objective function, such as increasing it from the base value of 0.3 to 0.6. The controller fully utilizes the advantage of low coupling strength to plan the most economical operating points for both components. It makes decisions closely in conjunction with real-time and predicted electricity price signals. During off-peak electricity price periods, it instructs the water pumps and compressors to simultaneously operate at full load with maximum efficiency for energy storage; during peak electricity price periods, it instructs the turbines and expanders to generate electricity in the optimal combination. Due to the low coupling strength, this strategy of each pursuing its local maximum economic point avoids significant efficiency losses or safety risks due to mutual interference, thereby achieving maximum economic benefits at the system level.

[0060] Based on the characteristics of the grid demand signal and the coupling strength of the water-gas path, the weight coefficients or constraints of each item in the multi-objective function during the optimization process are dynamically adjusted. Optimization orientation refers to adjusting the optimization strategy and objectives according to the current system state. When the system coupling is strong, priority is given to rapid response to grid demand; when grid demand is stable, priority is given to reducing energy consumption and equipment fatigue accumulation. Adjusting the optimization orientation enables the system to better adapt to different operating states and task requirements.

[0061] The water-gas coordinated operation model immediately reconstructs the current optimization problem based on the adjusted weight coefficients and constraint set. Then, using the current system state as the initial value, and based on the water-gas coupling interaction model, it solves for the future control sequence that minimizes the new objective function under the constraints of the constraint set, thus obtaining the optimal power coordinated allocation scheme. By dynamically adjusting the weight coefficients and constraints in the multi-objective function, and flexibly adjusting the optimization orientation according to the characteristics of water-gas path coupling strength and grid demand, the system's response capability and stability are effectively improved.

[0062] Furthermore, this application also includes the following steps: the water-air coordinated operation model is a hierarchical nested decision model structure, including an inner water-air coupling interaction model and an outer predictive control optimizer; the inner water-air coupling interaction model simulates and predicts in real time the water system, the air system, and the dynamic interaction process between them through pressure and flow variables; the outer predictive control optimizer, based on the inner predictor, solves the multi-objective optimization problem in each control cycle, and the output power coordination allocation scheme serves as the input boundary condition of the water-air coupling interaction model to drive the state prediction of the next cycle.

[0063] Specifically, the water-air coordinated operation model adopts a hierarchical nested decision model structure, including an inner water-air coupling interaction model and an outer predictive control optimizer. This hierarchical nested decision model structure is a clear layered design pattern in the software architecture of the water-air coordinated operation model, separating and nesting modules responsible for different functions: the inner layer focuses on high-fidelity physical process simulation, acting as the prediction engine; the outer layer focuses on prediction-based high-level decision optimization, acting as the decision-making brain. The inner layer model is the foundational tool for the outer layer optimizer, while the outer layer provides action instructions and task objectives to the inner layer. The nesting of the two forms a tight closed loop from decision-making to simulation verification and then to new decisions.

[0064] The inner-layer water-air coupling interaction model simulates and predicts the dynamic interaction process between the water and air circuits in real time, including the operating status of the water pump and compressor, and their mutual influence through variables such as flow rate and pressure. The inner-layer model calculates the current state of the water-air system by collecting real-time data, such as water pump flow rate and air circuit pressure. At startup, the inner-layer water-air coupling interaction model is initialized, and its parameters are set according to the design data. Subsequently, in each control cycle, these real measurements are used to correct and update all its internal state variables, keeping the digital twin synchronized with the real physical system and ensuring the accuracy of the prediction starting point.

[0065] The outer predictive control optimizer relies on the output of the inner model and performs rolling optimization within each control cycle. Based on grid demand and the current system state, the outer predictive control optimizer finds the optimal balance among multiple objectives and outputs the optimal power allocation scheme. Optimization objectives typically include: tracking error of grid commands, operational energy efficiency, equipment lifespan, and mechanical fatigue accumulation. Within each control cycle, the optimizer calculates the optimal control strategy for future times based on the current system state and grid demand. The outer predictive control optimizer begins operation, receiving the current system state, grid demand, and market signals. Based on a pre-defined multi-objective function and a series of constraints, and supported by the predictive capabilities provided by the inner model, it performs rolling optimization. By comparing the degree of conformity between the predicted system behavior under different schemes and the optimization objectives, the optimizer ultimately finds an optimal power allocation scheme.

[0066] After performing rolling optimization, the outer optimizer outputs a power coordination allocation scheme, which is the optimal solution considering the results of multi-objective optimization. This scheme serves as the input boundary condition for the inner water-air coupling interaction model, influencing the state prediction and control decisions in the next cycle. The inner model and the outer optimizer form a closed-loop system through feedback and iteration. At the end of each control cycle, the outer optimizer adjusts the power coordination allocation scheme for the next cycle based on the output of the inner model, ensuring continuous system optimization and responsiveness to grid demands.

[0067] Once the outer optimizer determines the optimal solution, it sends the power setpoint of the first control cycle as a target control command to the physical actuators in the field, such as water pumps, compressors, and frequency converters. Simultaneously, it inputs this command as a boundary condition to the inner water-air coupling interaction model. The inner model then starts from the currently synchronized state and, after applying this new control input, runs a forward simulation to predict the system's state at the end of the next control cycle. This prediction, along with the actual measurement values ​​fed back from the system in the next cycle, is used for state correction and optimization calculations in the next cycle. After the control cycle ends, new sensor data arrives, and the process returns to the first step. At this point, the inner model corrects the state using the new data, while the outer optimizer, starting from this new, more realistic state and considering potentially updated grid demands, re-solves the optimization problem and outputs a new optimal solution. This process is repeated continuously to achieve rolling optimization and feedback correction.

[0068] By employing a hierarchical nested decision-making model, multiple objectives are optimized simultaneously, avoiding the limitations of optimizing a single objective. Each objective is fully considered and weighed during the optimization process, thereby ensuring optimal performance of the entire system in multiple aspects.

[0069] The real-time system status and grid demand signals are input into the water-gas coordinated operation model. By solving a multi-objective optimization problem in a rolling manner, the optimal power coordinated allocation scheme is output.

[0070] Specifically, acquiring real-time system status involves obtaining a complete data vector that comprehensively characterizes the operational status of the water-gas coupled energy storage system at a given moment, after being collected and preprocessed by a sensor network. This includes static energy storage levels and dynamic process quantities. Grid demand signals are control commands from the higher-level dispatch center or the electricity market, typically expressed as active power values ​​and their time requirements. These signals reflect the amount of power the grid needs to obtain from the energy storage system at a specific moment and may vary with fluctuations in grid load.

[0071] The real-time system state and grid demand signals are input into the state estimator of the water-gas coordinated operation model. The state estimator uses model information to make optimal estimates of measurements that may contain noise or delays, outputting a more accurate current state as a precise starting point for optimization. The goal of multi-objective optimization problems is to simultaneously optimize multiple indicators, such as grid power command tracking accuracy, system energy efficiency, equipment lifespan, and mechanical fatigue. To ensure the system can operate efficiently while meeting grid demands, a balance must be found among these multiple objectives.

[0072] The rolling solution process occurs within each control cycle, meaning the system solves for the optimal power allocation scheme in real time based on the current state and grid demand within each cycle. During the rolling solution process, the power output of the water pumps and compressors is adjusted according to the real-time input grid demand and system state. For example, if the grid demand signal increases, the water pump output power is increased and the operating state of the gas compressor is adjusted to ensure that the overall power output meets grid requirements. Through the rolling solution of the multi-objective optimization problem, the system arrives at an optimal power allocation scheme, determining the power output values ​​of the water pumps and compressors to meet grid demand, while simultaneously optimizing other objectives, such as reducing system energy consumption and extending equipment lifespan.

[0073] The output power allocation scheme serves as the input boundary condition for the water-gas coordinated operation model, influencing state prediction and control decisions in the next control cycle. Through continuous adjustment and optimization, power allocation is precisely adjusted within each control cycle to respond to changes in grid demand and system state. By continuously solving a multi-objective optimization problem, the power allocation scheme is adjusted in real time based on grid demand and the water-gas system state. By optimizing the power allocation between pumps and compressors, energy storage efficiency is maximized and energy loss is reduced.

[0074] Based on the power coordination allocation scheme, target control commands are generated to coordinate the operation of the water pump in the water circuit and the air compressor in the air circuit, thereby controlling the energy storage system to perform energy storage or release operations.

[0075] The target control command is connected to the water pump in the water circuit and the air compressor regulating valve in the air circuit. The target control command is executed to intelligently control the flow rate, pressure, and valve opening of the water and air actuators.

[0076] Specifically, after obtaining the optimal power coordination allocation scheme, specific control commands are generated for the water pump and compressor based on this scheme. The main contents of the target control commands include requirements for the power output, flow regulation, pressure control, and valve opening adjustment of the water pump and compressor. In other words, the power coordination allocation scheme output by the water-air coordinated operation model is a power command. However, the underlying speed governor or valve controller usually does not directly accept the power command, but rather accepts the speed, opening, or flow setpoints. Based on the power-speed-flow-head comprehensive characteristic curve of the water pump / turbine, the required water pump / turbine speed setpoint or guide vane opening setpoint is derived from the given power setpoint and the current actual head by looking up a table. Similarly, based on the characteristic curve of the compressor / expander, the required speed setpoint or intake valve opening setpoint is derived from the power setpoint and the current inlet and outlet pressures (or pressure ratios) and temperature conditions.

[0077] The converted setpoints for the water and air circuits are synchronously sent to the water pump and air compressor, the actuators for the water circuit and air circuit, respectively. Upon receiving the target control command, the water pump and compressor begin executing the command, adjusting the flow rate, pressure, and valve opening. The control system monitors the execution effect in real time and makes fine adjustments as needed. In the water circuit, the water pump's speed and valve opening are adjusted according to the target control command. For example, when an increase in water flow is required, the water pump's speed may be increased, or the water pump's valve may open wider, thereby increasing the water flow. In the air circuit, the air compressor's regulating valves are adjusted according to pressure changes. For example, when an increase in air circuit pressure is required, the air compressor will increase its speed and increase the intake air volume to achieve the pressure increase.

[0078] The target control commands are connected to the water pumps in the water circuit and the air compressors in the air circuit, and are transmitted to the pumps and compressors via an intelligent control system. Upon receiving the control commands, the actuators adjust the flow rate, pressure, and valve opening according to the requirements. The ultimate goal of the target control commands is to precisely control the flow rate of the medium and the pressure at key points in the pipeline by adjusting the actions of the actuators; these controls are achieved through specific actions such as valve opening.

[0079] Through precise target control commands, the water pump and compressor can accurately adjust parameters such as power output, flow rate, and pressure in each control cycle, optimizing energy storage and release efficiency and reducing energy loss. The target control commands achieve precise power coordination between the water pump and compressor, preventing independent or uncoordinated operation. By coordinating and adjusting water flow and air pressure, efficient operation is maintained even in dynamic environments.

[0080] Furthermore, this application also includes the following steps: Under the guidance of rapid response optimization, the generation of the target control command includes a feedforward compensation component based on the pressure wave physical field, used to offset the pressure shock caused by the rapid action of the gas path on the water path subsystem through pipeline inertia; wherein, using the water-gas coupling interaction model, the pressure shock waveform generated by the rapid action of the gas path on the water path through pipeline inertia under the power cooperative allocation scheme is predicted; while issuing a power command to the gas path actuator, a reverse compensation command is issued in advance to the water path actuator based on the pressure shock waveform to actively offset the pressure shock.

[0081] Specifically, under the guidance of rapid response optimization, after the cooperative operation model solves for the optimal power cooperative allocation scheme, it is necessary to evaluate the potential internal dynamic effects of this scheme. When the scheme includes rapid actions of the pneumatic actuators, such as a large opening of the expander valve within 1 second to increase power, the control system will initiate a dedicated prediction subroutine. Using a water-air coupling interaction model, with the current system state as the initial condition, and the power allocation scheme including this rapid action as input, high-frequency short-time dynamic simulations are performed, such as simulating the next 2 seconds. The simulation will focus on outputting the pressure change curves over time at key water circuit measuring points, which are the predicted pressure impact waveforms, revealing the details of the dynamic pressure disturbances caused by the pneumatic actions on the water circuit through pipeline inertia and fluid coupling.

[0082] Based on the dynamic characteristics of the water circuit subsystem, the actions required to generate a pressure change equal in magnitude but opposite in direction to the predicted impact waveform are calculated, such as pump speed control commands, guide vane opening, or the opening of a dedicated regulating valve. Since the system is nonlinear, a simplified model or empirical gain may be used for mapping. For example, assuming the predicted impact waveform is a positive pulse with the pressure rising and then recovering, the reverse compensation command would be a negative pulse, first reducing the pressure setpoint or flow rate, and then recovering.

[0083] The calculated reverse compensation command is superimposed on the basic water circuit control command originally output by the cooperative operation model for power distribution, forming the final target control command sent to the water circuit actuator. In terms of timing, the feedforward compensation command needs to be issued before the pressure shock caused by the air circuit action reaches the water circuit measuring point, in order to overcome the response delay of the actuator itself and achieve precise cancellation.

[0084] At the same time the control system issues a rapid action command to the pneumatic actuator, it also issues a water circuit target control command with feedforward compensation to the water circuit actuator. When the pressure wave generated by the pneumatic action propagates to the water circuit through the pipeline, the reverse pressure wave generated by the water circuit actuator, which has already acted in advance, happens to meet it. The two cancel each other out or are greatly weakened, thereby protecting the water circuit equipment and maintaining stable water circuit pressure, providing a stable internal environment for rapid and accurate power tracking.

[0085] By introducing feedforward compensation components and reverse compensation commands, measures are taken in advance to reduce pressure surges in the water system under conditions of rapid changes in the gas path, ensuring that the energy storage system can respond quickly and accurately to changes in grid demand. When there is a coupling effect between the gas and water paths, pressure fluctuations can lead to system instability. By predicting and compensating for pressure surges, equipment damage or system instability caused by excessive pressure fluctuations can be effectively avoided, improving the overall reliability and safety of the system. Feedforward and reverse compensation make the coordination between the gas and water paths more precise, ensuring that energy waste or improper regulation does not occur due to inertia when they work together, thereby improving energy storage efficiency.

[0086] In summary, the integrated water-gas coupled energy storage control method provided in this application has the following technical advantages: By collecting the water circuit operating parameters of the pumped hydro storage section and the air circuit operating parameters of the compressed air energy storage section, a water-air coordinated operation model is constructed based on these parameters. Real-time system status and grid demand signals are input into the water-air coordinated operation model, and a multi-objective optimization problem is solved through rolling optimization to output the optimal power coordination allocation scheme. Based on the power coordination allocation scheme, target control commands are generated to coordinate the actions of the water pumps in the water circuit and the air compressors in the air circuit, controlling the energy storage system to perform energy storage or release operations. These target control commands are connected to the regulating valves of the water pumps in the water circuit and the air compressors in the air circuit, executing the target control commands for intelligent control of the flow rate, pressure, and valve opening of the water and air actuators. In other words, by constructing a water-air coordinated operation model and implementing model prediction and rolling optimization, dynamic coordinated allocation of water and air power is achieved, improving overall energy storage efficiency and enabling rapid response to grid load fluctuations, ensuring grid stability and power supply reliability.

[0087] Example 2: Based on the same inventive concept as the integrated water-gas coupled energy storage control method in Example 1, this application also provides an integrated water-gas coupled energy storage control system. Please refer to the appendix. Figure 2 The integrated water-gas coupled energy storage control system includes: The parameter acquisition module 11 is used to acquire the water circuit operation parameters of the pumped hydro storage section and the air circuit operation parameters of the compressed air energy storage section; the model construction module 12 is used to construct a water-air coordinated operation model based on the water circuit operation parameters and the air circuit operation parameters; the multi-objective optimization module 13 is used to input the real-time system status and grid demand signals into the water-air coordinated operation model, and output the optimal power coordination allocation scheme by solving the multi-objective optimization problem in a rolling manner; the coordinated control module 14 is used to generate target control commands to coordinate the actions of the water pump in the water circuit and the air compressor in the air circuit according to the power coordination allocation scheme, and control the energy storage system to perform energy storage or release operations; wherein, the target control commands are connected to the regulating valves of the water pump in the water circuit and the air compressor in the air circuit, and the target control commands are executed for intelligent control of the flow rate, pressure and valve opening of the water and air actuators.

[0088] Furthermore, the parameter acquisition module 11 in the water-air coupled energy storage integrated control system is also used for: the water circuit operating parameters include: reservoir water level, pipeline water pressure, water flow velocity, water pump speed, bearing vibration value and water temperature; the gas circuit operating parameters include: gas storage device pressure, air temperature, compressor / expander inlet and outlet flow rate, speed, efficiency and humidity of the inner wall of the gas storage device.

[0089] Furthermore, the model building module 12 in the water-gas coupled energy storage integrated control system is also used for: building a water circuit operation dynamic model based on the water circuit operation parameters; building a gas circuit operation dynamic model based on the gas circuit operation parameters; coupling and interacting the water circuit operation dynamic model and the gas circuit operation dynamic model according to the law of conservation of mass and energy to build a water-gas coupled interaction model; and using the water-gas coupled interaction model as a predictor to search for a synergistic strategy that maximizes the benefits of the water circuit operation parameters and the gas circuit operation parameters to build the water-gas coordinated operation model.

[0090] Furthermore, the model building module 12 in the integrated water-gas coupled energy storage control system is also used to: use the water-gas coupled interaction model as a predictor, embed an optimization control framework, and construct the water-gas coordinated operation model. The water-gas coordinated operation model is configured to: perform rolling optimization to obtain the optimal coordinated control sequence with operating economy, equipment life and grid command tracking accuracy as comprehensive optimization objectives. The comprehensive optimization objective is expressed through a multi-objective function, which includes at least a grid power command tracking error term, an operating energy consumption cost term, and an equipment mechanical fatigue accumulation term. In each control cycle, with the current system state as the initial value and grid demand and market signals as inputs, the future state is predicted based on the water-gas coupled interaction model, and the future control sequence that makes the multi-objective function optimal is solved as the output of the optimal power coordinated allocation scheme.

[0091] Furthermore, the model building module 12 in the integrated water-gas coupled energy storage control system is also used to: analyze and obtain the water-gas path coupling strength of the current system based on the water-gas coupled interaction model; and dynamically adjust the weight coefficients of each item in the multi-objective function or adjust the constraints of the optimization problem according to the characteristics of the water-gas path coupling strength and the power grid demand signal, so that the optimization orientation of the water-gas coordinated operation model is adapted to the physical coupling state and task requirements of the current system.

[0092] Furthermore, the model building module 12 in the water-gas coupled energy storage integrated control system is also used to: based on the water-gas coupled interaction model, identify the amplitude of the gas pressure change caused by the change in water flow rate per unit time, and obtain a first change amplitude; based on the water-gas coupled interaction model, identify the amplitude of the water pressure change caused by the change in gas flow rate per unit time, and obtain a second change amplitude; calculate the geometric mean of the first change amplitude and the second change amplitude to obtain the water-gas path coupling strength.

[0093] Furthermore, the model building module 12 in the integrated water-gas coupled energy storage control system is also used to: increase the weight of the grid command tracking error term in the multi-objective function and strengthen the constraint on the amplitude of system coupling pressure fluctuation when the water-gas path coupling strength reaches a first threshold and the grid demand signal changes drastically, thereby forming a rapid response optimization orientation; and increase the weight of the operating energy consumption cost term in the multi-objective function when the water-gas path coupling strength is lower than a second threshold and the grid demand signal amplitude is large and stable, thereby forming an economical energy storage optimization orientation.

[0094] Furthermore, the model building module 12 in the water-gas coupled energy storage integrated control system is also used to: under the guidance of rapid response optimization, the generation of the target control command includes a feedforward compensation component based on the pressure wave physical field, which is used to offset the pressure impact on the water subsystem caused by the rapid action of the gas path through the pipeline inertia; wherein, using the water-gas coupled interaction model, the pressure impact waveform generated by the rapid action of the gas path through the pipeline inertia on the water path is predicted under the power collaborative allocation scheme; while issuing power commands to the gas path actuator, a reverse compensation command is issued in advance to the water path actuator based on the pressure impact waveform to actively offset the pressure impact.

[0095] Furthermore, the model building module 12 in the integrated water-gas coupled energy storage control system is also used for: the water-gas coordinated operation model is a hierarchical nested decision model structure, including an inner water-gas coupled interaction model and an outer predictive control optimizer; the inner water-gas coupled interaction model simulates and predicts the water system, the gas system, and the dynamic interaction process between them through pressure and flow variables in real time; the outer predictive control optimizer is based on the inner predictor and solves the multi-objective optimization problem in each control cycle, and the output power coordinated allocation scheme is used as the input boundary condition of the water-gas coupled interaction model to drive the state prediction of the next cycle.

[0096] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The water-gas coupled energy storage integrated control method and specific examples in the aforementioned embodiment one are also applicable to the water-gas coupled energy storage integrated control system of this embodiment. Through the foregoing detailed description of the water-gas coupled energy storage integrated control method, those skilled in the art can clearly understand the water-gas coupled energy storage integrated control system of this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0097] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0098] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A water-gas coupled energy storage integrated control method, characterized in that, include: Collect the water circuit operation parameters of the pumped-storage section and the air circuit operation parameters of the compressed-air energy storage section; Based on the water circuit operating parameters and the gas circuit operating parameters, a water-gas coordinated operation model is constructed; The real-time system status and grid demand signals are input into the water-gas coordinated operation model. By solving the multi-objective optimization problem in a rolling manner, the optimal power coordinated allocation scheme is output. Based on the power coordination allocation scheme, target control commands are generated to coordinate the actions of the water pump in the water circuit and the air compressor in the air circuit, thereby controlling the energy storage system to perform energy storage or release operations. The target control command is connected to the water pump in the water circuit and the air compressor regulating valve in the air circuit. The target control command is executed to intelligently control the flow rate, pressure, and valve opening of the water and air actuators.

2. The integrated control method for water-gas coupled energy storage according to claim 1, characterized in that, The water circuit operating parameters include: reservoir water level, pipeline water pressure, water flow velocity, water pump speed, bearing vibration value, and water temperature; the gas circuit operating parameters include: gas pressure of the gas storage device, air temperature, compressor / expander inlet and outlet flow rates, speed, efficiency, and humidity of the inner wall of the gas storage device.

3. The integrated control method for water-gas coupled energy storage according to claim 2, characterized in that, Based on the water circuit operating parameters and the gas circuit operating parameters, a water-gas coordinated operation model is constructed, including: Based on the waterway operation parameters, a dynamic model of waterway operation is constructed; Based on the gas path operating parameters, a dynamic model of gas path operation is constructed; Based on the law of conservation of mass and energy, the dynamic model of water circuit operation and the dynamic model of gas circuit operation are coupled and interacted to construct a water-gas coupling interaction model. Using the water-air coupling interaction model as a predictor, a collaborative strategy to maximize the benefits of water circuit operating parameters and air circuit operating parameters is searched to construct the water-air coordinated operation model.

4. The integrated control method for water-gas coupled energy storage according to claim 3, characterized in that, Constructing the water-air coordinated operation model includes: Using the water-gas coupling interaction model as a predictor, an optimization control framework is embedded to construct the water-gas coordinated operation model. The water-gas coordinated operation model is configured to: perform rolling optimization to obtain the optimal coordinated control sequence with the comprehensive optimization objectives of operating economy, equipment life and grid command tracking accuracy as comprehensive optimization objectives. The comprehensive optimization objective is expressed by a multi-objective function, which includes at least the grid power command tracking error term, the operating energy consumption cost term and the equipment mechanical fatigue accumulation term. In each control cycle, the current system state is used as the initial value, and the grid demand and market signals are used as inputs. The future state is predicted based on the water-gas coupling interaction model, and the future control sequence that optimizes the multi-objective function is solved as the output of the optimal power cooperative allocation scheme.

5. The integrated control method for water-gas coupled energy storage according to claim 4, characterized in that, Also includes: Based on the aforementioned water-air coupling interaction model, the water-air path coupling strength of the current system is analyzed and obtained. Based on the characteristics of the water-gas path coupling strength and the power grid demand signal, the weight coefficients of each item in the multi-objective function or the constraints of the optimization problem are dynamically adjusted so that the optimization orientation of the water-gas coordinated operation model is adapted to the current physical coupling state and task requirements of the system.

6. The integrated control method for water-gas coupled energy storage according to claim 5, characterized in that, Based on the aforementioned water-air coupling interaction model, the water-air path coupling strength of the current system is analyzed and obtained, including: Based on the water-air coupling interaction model, the amplitude of the air pressure change caused by the change in water flow rate per unit time is identified, and the first change amplitude is obtained. Based on the aforementioned water-air coupling interaction model, the amplitude of water pressure change caused by gas flow rate change per unit time is identified, and the second change amplitude is obtained. The geometric mean of the first and second change amplitudes is calculated to obtain the water-gas path coupling strength.

7. The integrated control method for water-gas coupled energy storage according to claim 5, characterized in that, Based on the characteristics of the water-gas path coupling strength and the power grid demand signal, the weight coefficients of each term in the multi-objective function or the constraints of the optimization problem are dynamically adjusted, including: When the water-gas path coupling strength reaches the first threshold and the power grid demand signal changes drastically, the weight of the power grid command tracking error term in the multi-objective function is increased, and the constraint on the amplitude of system coupling pressure fluctuation is strengthened to form a rapid response optimization orientation. When the water-gas path coupling strength is lower than the second threshold and the power grid demand signal amplitude is large and stable, the weight of the operating energy consumption cost term in the multi-objective function is increased to form an economic energy storage optimization guide.

8. The integrated control method for water-gas coupled energy storage according to claim 7, characterized in that, Under the guidance of rapid response optimization, the generation of the target control command includes a feedforward compensation component based on the pressure wave physical field, which is used to offset the pressure shock to the water subsystem caused by the rapid action of the gas path through the pipeline inertia. Among them, using the water-air coupling interaction model, it is predicted that under the power cooperative allocation scheme, the rapid action of the air path will generate a pressure impact waveform on the water path through the pipeline inertia; While issuing a power command to the pneumatic actuator, a reverse compensation command is issued to the water actuator in advance based on the pressure shock waveform to actively counteract the pressure shock.

9. The integrated control method for water-gas coupled energy storage according to claim 4, characterized in that, The water-air coordinated operation model is a hierarchical nested decision model structure, including an inner water-air coupling interaction model and an outer predictive control optimizer. The inner water-air coupling interaction model simulates and predicts the water system, the air system, and the dynamic interaction between them through pressure and flow variables in real time. The outer predictive control optimizer is based on the inner predictor and solves the multi-objective optimization problem in each control cycle. The output power coordination allocation scheme is used as the input boundary condition of the water-air coupling interaction model to drive the state prediction of the next cycle.

10. A water-gas coupled energy storage integrated control system, characterized in that, The step of implementing the integrated water-gas coupled energy storage control method according to any one of claims 1 to 9, wherein the integrated water-gas coupled energy storage control system comprises: The parameter acquisition module is used to collect the water circuit operation parameters of the pumped storage section and the air circuit operation parameters of the compressed air energy storage section. The model building module is used to build a water-air coordinated operation model based on the water circuit operation parameters and the air circuit operation parameters. The multi-objective optimization module is used to input the real-time system status and power grid demand signal into the water-gas coordinated operation model, and output the optimal power coordinated allocation scheme by solving the multi-objective optimization problem in a rolling manner. The collaborative control module is used to generate target control commands to coordinate the actions of the water pump in the water circuit and the air compressor in the air circuit according to the power collaborative allocation scheme, and to control the energy storage system to perform energy storage or release operations. The target control command is connected to the water pump in the water circuit and the air compressor regulating valve in the air circuit. The target control command is executed to intelligently control the flow rate, pressure, and valve opening of the water and air actuators.

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