Water-gas coupling energy storage integrated control method and system

Abstract

The application provides a water-gas coupling energy storage integrated control method and system, relates to the technical field of water-gas coupling energy storage, and the method comprises the following steps: collecting waterway operation parameters and airway operation parameters; constructing a water-gas collaborative operation model; inputting real-time system states and power grid demand signals into the water-gas collaborative operation model; generating target control instructions according to a power collaborative distribution scheme, and controlling the energy storage system to perform energy storage or release operation; wherein the target control instructions are connected with a water pump, which is an execution equipment of the waterway, and an air compressor regulating valve, which is an execution equipment of the airway, the target control instructions are executed, and the target control instructions are used for intelligent control of the flow, pressure and valve opening degree of the water-gas execution equipment. The application solves the technical problem that the overall energy storage efficiency is not high due to the lack of collaborative scheduling of water pump energy storage and compressed air energy storage, independent operation of each other in the prior art, and improves the water-gas coupling energy storage efficiency through water-gas coupling operation optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water-gas coupling energy storage, in particular to a water-gas coupling energy storage integrated control method and system. BACKGROUND

[0002] In the existing water-gas coupling energy storage system, the two sets of energy storage media of the water circuit and the gas circuit are usually managed by a decoupled control architecture, that is, the pumped storage part and the compressed air energy storage part have independent control loops and operation targets, and only simple power instruction distribution or sequential start-stop based on fixed logic is performed. Due to the lack of effective collaborative scheduling mechanism, the strategy of energy storage and release cannot be dynamically adjusted according to the demand of the power grid, resulting in low overall energy storage efficiency. Especially when the load of the power grid fluctuates greatly, the pumped storage may overstore or waste energy due to the rapid change of power demand when meeting the load, and the compressed air energy storage may not respond to the change of the load of the power grid in time due to independent operation, further exacerbating the imbalance of energy utilization.

[0003] In summary, the prior art has the technical problem of low overall energy storage efficiency due to the lack of collaborative scheduling of pumped storage and compressed air energy storage. SUMMARY

[0004] The purpose of the present application is to provide a water-gas coupling energy storage integrated control method and system to solve the technical problem of low overall energy storage efficiency due to the lack of collaborative scheduling of pumped storage and compressed air energy storage in the prior art.

[0005] In order to achieve the above-mentioned purpose, the present application provides a water-gas coupling energy storage integrated control method and system.

[0006] In a first aspect, the present application provides a water-gas coupling energy storage integrated control method, which is realized by a water-gas coupling energy storage integrated control system, wherein the water-gas coupling energy storage integrated control method comprises: collecting water circuit operation parameters of a pumped storage part and gas circuit operation parameters of a compressed air energy storage part; constructing a water-gas collaborative operation model according to the water circuit operation parameters and the gas circuit operation parameters; inputting real-time system state and power grid demand signal into the water-gas collaborative operation model, and outputting an optimal power collaborative distribution scheme by solving a multi-objective optimization problem; generating target control instructions for the actions of the water pump of the water circuit and the air compressor of the gas circuit according to the power collaborative distribution scheme, and controlling the energy storage system to perform energy storage or release operation; wherein the target control instructions are connected with the water pump of the water circuit and the air compressor regulating valve of the gas circuit, and the target control instructions are used for intelligent control of the flow, pressure and valve opening degree of the water-gas execution equipment.

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

[0008] Optionally, a water circuit operation dynamic model is constructed based on the water circuit operation parameters; a gas circuit operation dynamic model is constructed based on the gas circuit operation parameters; the water circuit operation dynamic model and the gas circuit operation dynamic model are coupled and interacted according to the law of conservation of mass and energy, to construct a water-gas coupled interaction model; the water-gas coupled interaction model is taken as a predictor to search for a benefit maximization coordination strategy of water circuit operation parameters and gas circuit operation parameters, to construct the water-gas coordinated operation model.

[0009] Optionally, the water-gas coupled interaction model is taken as a predictor to embed an optimization control framework, to construct the water-gas coordinated operation model, which is configured to take operation economy, equipment life and power grid instruction tracking accuracy as comprehensive optimization objectives, to perform rolling optimization solution to obtain an optimal coordinated control sequence, wherein the comprehensive optimization objectives are expressed by a multi-objective function, and the multi-objective function at least contains a tracking error term of a power grid power instruction, an operation energy consumption cost term and an equipment mechanical fatigue accumulation term; and in each control period, a future state is predicted based on the water-gas coupled interaction model, taking a current system state as an initial value and taking a power grid demand and a market signal as inputs, to solve a future control sequence that makes the multi-objective function optimal, as an optimal power coordination distribution scheme output.

[0010] Optionally, based on the water-gas coupled interaction model, a water-gas path coupling strength of a current system is analyzed; and according to characteristics of the water-gas path coupling strength and the power grid demand signal, a weight coefficient of each term in the multi-objective function or a constraint condition of an optimization problem is dynamically adjusted, to adapt optimization orientation of the water-gas coordinated operation model to a physical coupling state and a task demand of the current system.

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

[0012] Optionally, when the water-gas path coupling strength reaches a first threshold value and the power grid demand signal changes sharply, the weight of the power grid instruction tracking error term in the multi-objective function is increased, and the constraint on the system coupling pressure fluctuation amplitude is strengthened, forming a fast response optimization guide; when the water-gas path coupling strength is lower than a second threshold value, and the power grid demand signal amplitude is large and stable, the weight of the operation energy consumption cost term in the multi-objective function is increased, forming an economic energy storage optimization guide.

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

[0014] Optionally, the water-gas coordination operation model is a hierarchical nested decision model structure, including an inner layer water-gas coupling interaction model and an outer layer predictive control optimizer; the inner layer water-gas coupling interaction model is used to simulate and predict the dynamic interaction process between the water path system, the gas path system and the pressure and flow variables generated therebetween in real time, and the outer layer predictive control optimizer is based on the inner layer predictor to solve a multi-objective optimization problem at each control cycle, and outputs a power coordination distribution scheme as an input boundary condition of the water-gas coupling interaction model to drive the state prediction of the next cycle.

[0015] In a second aspect, the application also provides a water-gas coupling energy storage integrated control system for executing the water-gas coupling energy storage integrated control method as described in the first aspect, wherein the water-gas coupling energy storage integrated control system comprises: a parameter acquisition module for acquiring water path operation parameters of the pumped storage part and gas path operation parameters of the compressed air energy storage part; a model construction module for constructing a water-gas coordination operation model according to the water path operation parameters and the gas path operation parameters; a multi-objective optimization module for inputting real-time system state and power grid demand signal into the water-gas coordination operation model, solving a multi-objective optimization problem by rolling, and outputting an optimal power coordination distribution scheme; a coordination control module for generating a target control instruction for coordinating the action of the water pump of the water path and the air compressor of the gas path according to the power coordination distribution scheme, and controlling the energy storage system to perform energy storage or release operation; wherein the target control instruction is connected with the execution equipment water pump of the water path and the execution equipment air compressor valve of the gas path, and the execution of the target control instruction is used for intelligent control of the flow, pressure and valve opening degree of the water-gas execution equipment.

[0016] The one or more technical solutions provided in the application have at least the following technical effects or advantages:

[0017] By collecting waterway operation parameters of the pumped storage part and airway operation parameters of the compressed air energy storage part, a water-air coordinated operation model is constructed according to the waterway operation parameters and the airway operation parameters; real-time system state and grid demand signals are input into the water-air coordinated operation model, a multi-objective optimization problem is solved by rolling, and an optimal power coordination distribution scheme is output; according to the power coordination distribution scheme, target control instructions for the actions of the water pump of the coordinated waterway and the air compressor of the airway are generated, and the energy storage system is controlled to perform energy storage or release operation; wherein the target control instructions are connected with the water pump of the waterway and the air compressor regulating valve of the airway, and the target control instructions are executed for intelligent control of the flow, pressure and valve opening degree of the water-air execution equipment. That is, by constructing the water-air coordinated operation model and implementing model predictive rolling optimization, water-air power dynamic coordination distribution is realized, the overall energy storage efficiency is improved, the grid load fluctuation can be quickly responded, and the stability and power supply reliability of the grid are ensured.

[0018] The above description is only a summary of the technical solutions of the application. In order to enable the technical means of the application to be more clearly understood, and to be implemented according to the content of the description, and in order to enable the above and other purposes, characteristics and advantages of the application to be more apparent and easy to understand, the specific embodiments of the application are described below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the application, nor is it used to limit the scope of the application. Other features of the application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the application or the prior art, the drawings needed in the embodiments or the prior art description will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and other drawings can be obtained by those skilled in the art without creating laborious work on the basis of the provided drawings.

[0020] Figure 1 The flowchart of the water-air coupled energy storage integrated control method of the application.

[0021] Figure 2 The structural schematic diagram of the water-air coupled energy storage integrated control system of the application.

[0022] Explanation of reference signs: parameter acquisition module 11, model construction module 12, multi-objective optimization module 13, coordinated control module 14. DETAILED DESCRIPTION

[0023] The application provides a water-gas coupled energy storage integrated control method and system, solves the technical problem that the overall energy storage efficiency is not high due to the lack of collaborative scheduling of water pump energy storage and compressed air energy storage, and independent operation of each other in the prior art. By constructing a water-gas collaborative operation model and implementing model prediction rolling optimization, water-gas power dynamic collaborative distribution is realized, the overall energy storage efficiency is improved, the stability and power supply reliability of the power grid can be ensured by quickly responding to power grid load fluctuations.

[0024] In the following, the technical solutions in the application will be described clearly and completely with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the application, rather than all the embodiments of the application. It should be understood that the application is not limited by the example embodiments described herein. Based on the embodiments of the application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the application. In addition, it should be noted that, for the convenience of description, only parts related to the application are shown in the drawings, not all.

[0025] Embodiment one, please refer to the accompanying drawings Figure 1 The application provides a water-gas coupled energy storage integrated control method, which is applied to a water-gas coupled energy storage integrated control system, and specifically includes the following steps:

[0026] Collecting waterway operation parameters of the pumped storage part and airway operation parameters of the compressed air energy storage part.

[0027] Further, the application further includes the following steps: the waterway operation parameters include reservoir water level, pipeline water pressure, water flow speed, water pump rotating speed, bearing vibration value and water temperature; the airway operation parameters include gas pressure of the gas storage device, air temperature, inlet and outlet flow of the compressor / expander, rotating speed, efficiency and humidity of the inner wall of the gas storage device.

[0028] Specifically, according to the physical process, energy conversion link and safety monitoring requirement of each of the pumped storage part and the compressed air energy storage part, a comprehensive sensor network is carefully designed and deployed. The pumped storage part is a complete circuit in the energy storage system, taking water as an energy carrier and working medium. In the energy storage process, the water pump is driven to pump water from a low reservoir to a high reservoir, converting electric energy into gravitational potential energy of water for storage; in the energy release process, the high water drives the water turbine to rotate to generate electricity, converting potential energy back to electric energy. The compressed air energy storage part refers to a complete circuit in the energy storage system, taking air as an energy carrier and working medium. In the energy storage process, the compressor is driven to compress and inject air into the gas storage device, such as underground salt cavern, abandoned mine or high-pressure storage tank, converting electric energy into internal energy of air for storage; in the energy release process, the high-pressure air is released to drive the expander to rotate to generate electricity.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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℃.

[0033] The compressed air energy storage is to describe the air flow, temperature, pressure, compressor working state and other information in the compressed air energy storage part, reflect the running status of the compressed air energy storage part, including the air pressure, air temperature, compression / expansion machine inlet and outlet flow, rotating speed, efficiency and the wall humidity in the air storage device. For example, the underground salt cavern air storage library top pressure sensor reading is 8.2 MPa, the temperature sensor at the same position shows that the air temperature is 42℃, the compressor outlet mass flow meter reading is 28.5 kg / s, the compressor rotating speed is 6550 r / min, according to the inlet and outlet parameters, that is, the inlet pressure is 0.1 MPa, the temperature is 25℃, the outlet pressure is 8.5 MPa, the real-time calculated compressor adiabatic efficiency is 0.78, and the salt cavern wall humidity sensor shows that the relative humidity is 65%, which indicates that the sealing performance is good, and the air dryness is moderate.

[0034] By analyzing the water route operation parameters and the gas route operation parameters, it is determined whether the current system operation is in a normal state. For example, the bearing vibration value of the water pump is within the normal range, the water temperature and the gas temperature have small difference, which indicates that the equipment runs stably, and the air pressure and the air temperature of the air storage device are also within the expected range, which indicates that the gas route part runs normally. By collecting the water route and the gas route operation parameters, the water pump energy storage and the compressed air energy storage system can be accurately monitored, and rapid adjustment and optimization can be realized.

[0035] According to the water route operation parameters and the gas route operation parameters, a water-gas collaborative operation model is constructed.

[0036] Further, the application further includes the following steps: based on the water route operation parameters, a water route operation dynamic model is constructed; based on the gas route operation parameters, a gas route operation dynamic model is constructed; according to the law of conservation of mass and energy, the water route operation dynamic model and the gas route operation dynamic model are coupled and interacted to construct a water-gas coupled interaction model; the water-gas coupled interaction model is taken as a predictor to search for a benefit maximization collaborative strategy of water route operation parameters and gas route operation parameters, and the water-gas collaborative operation model is constructed.

[0037] Further, the application further includes the following steps: taking the water-gas coupled interaction model as a predictor, embedding an optimization control framework to construct the water-gas collaborative operation model, the water-gas collaborative operation model is configured to take the operation economy, equipment life and grid instruction tracking accuracy as the comprehensive optimization target, perform rolling optimization solution to obtain the optimal collaborative control sequence, wherein the comprehensive optimization target is expressed by a multi-objective function, the multi-objective function at least contains a tracking error term of the grid power instruction, an operation energy consumption cost term and a device mechanical fatigue accumulation term; wherein in each control period, taking the current system state as the initial value, taking the grid demand and market signal as the input, predicting the future state based on the water-gas coupled interaction model, solving the future control sequence that makes the multi-objective function best as the optimal power collaborative distribution scheme output.

[0038] Specifically, according to the waterway operation parameters, the physical structure and working principle of the waterway system are analyzed, and a waterway operation dynamic model is constructed, including reservoir water level dynamics, pipeline equation, water pump / turbine characteristic equation, etc. The waterway operation dynamic model is a mathematical model used to describe the operation process of the water pump energy storage part, simulating the changes of variables such as water flow, pressure, flow rate, water pump speed, etc. with time.

[0039] The waterway operation dynamic model includes several core modules, such as the reservoir module, the pressure pipeline module, and the water pump / turbine unit module. The reservoir module calculates the water level change through integration according to the inflow and outflow difference. For example, when the reservoir area is fixed, the water level change rate is equal to the net flow divided by the reservoir area. The pressure pipeline module uses the water hammer equation or a simplified lumped parameter model to describe the pressure fluctuations caused by water flow inertia, friction resistance, and pipe wall elasticity. The water pump / turbine unit module establishes the relationship between head-flow-speed-efficiency, which is usually based on the performance curve provided by the manufacturer for polynomial or spline interpolation fitting to obtain static characteristics, and combines the inertia equation of the rotating part to describe its speed dynamics. Model parameters, such as pipeline friction coefficient, water hammer wave speed, unit characteristic curve coefficient, and moment of inertia, are determined through design data, equipment nameplate parameters, and historical operation data identification.

[0040] Similarly, based on the gas path operation parameters, a gas path operation dynamic model is established, including the gas state equation of the gas storage device, the characteristic equation of the compressor / expander, and the pipeline gas flow equation. The gas path operation dynamic model is a mathematical model used to describe the operation process of the compressed air energy storage part, simulating the changes of variables such as gas flow, pressure, temperature, flow rate, and compressor speed at different time points. The gas path operation dynamic model 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, equivalent flow rate, speed, and efficiency, which is usually based on manufacturer curve fitting and considers the mechanical inertia of the rotor. The gas storage device module is treated as a control body, and mass conservation and energy conservation equations are applied. Mass conservation describes the change rate of air mass equal to the difference between the incoming and outgoing mass flow rates. Energy conservation is more critical, as it needs to consider the enthalpy of the incoming and outgoing working fluid, heat exchange with the outside world, and the change in internal gas state. The gas state is related to pressure, temperature, and density by a real gas state equation, such as the Van der Waals equation or a more accurate PR equation. For large underground gas storage, a simplified model of rock heat transfer needs to be established. The connecting pipeline module considers the compressibility of the gas and uses a one-dimensional unsteady flow model or a simplified volume-inertia model.

[0041] The input data of the water route operation dynamic model includes reservoir water level, pipeline water pressure, water flow velocity, water pump rotating speed, bearing vibration value and water temperature; the output data is dynamic behavior such as water flow velocity, pressure change and water pump power output; Bernoulli equation and other fluid dynamics models are used to describe water flow behavior, considering factors such as water flow friction, pipeline shape and size, and water pump working curve, to calculate water flow velocity, pressure and flow rate change. The reservoir water level, pipeline water pressure and water pump rotating speed are taken as inputs, and the water flow velocity, pressure change and other dynamic behaviors are calculated by the model. The water flow state will continuously adjust the water pump operating conditions through a feedback mechanism to maintain the optimal working state of the system.

[0042] The input data of the gas route operation dynamic model includes gas storage device gas pressure, air temperature, compressor / expander inlet and outlet flow rate, rotating speed, efficiency and gas storage device inner wall humidity; the output data is dynamic behavior such as air flow rate, gas pressure change and compressor energy efficiency; based on the ideal gas state equation and thermodynamic laws, a mathematical model of gas flow compression and expansion process is established to simulate the gas pressure, temperature and flow rate change of the compressor and expander under different working conditions. By inputting the operating parameters of the gas route such as gas pressure, temperature, flow rate, rotating speed, etc., thermodynamic and fluid mechanics formulas are used to calculate the behavior of air in the gas storage device and the energy conversion in the compression and expansion process.

[0043] According to the law of conservation of mass and energy, the water route operation dynamic model and the gas route operation dynamic model are coupled and interacted. Due to the close coupling relationship between the water route and the gas route system, the change of water flow will affect the change of gas pressure, and vice versa, the release of compressed air in the gas route system will also affect the flow rate and water pump operating state of the water route. According to the law of conservation of mass and energy, the coupled and interactive model of water route and gas route links the changes of water flow and air flow, ensuring that the energy conversion process of the two systems reaches the best balance. The water-gas coupled and interactive model describes how the water side state change affects the gas side and how the gas side state change affects the water side, so as to simulate the joint dynamic behavior of the entire coupled system.

[0044] According to the conservation of mass and energy, the coupled equations are established: the force balance equation is gas chamber pressure = water column static pressure (water density x gravitational acceleration x water column height) + pipeline residual pressure head; the volume continuity equation is gas chamber volume change rate = -water flow rate. Through these two equations, the water level, flow rate output by the water route model and the gas pressure, volume output by the gas route model are dynamically linked to form a closed and unified differential-algebraic equation set, i.e. the water-gas coupled and interactive model, which can simulate the whole process such as increasing the water pump power to pump water, causing the water level to rise, the gas chamber volume to decrease and the gas pressure to rise.

[0045] The water-gas coupled interaction model is integrated into a model predictive control algorithm framework, which usually includes a state estimator, a prediction model, and an optimization solver. The optimization control framework is an advanced control strategy framework whose core principle is to predict the behavior of the system in the future by using the dynamic model of the system, and to calculate a series of optimal control instructions by repeatedly solving an optimization problem in a finite time domain online, but only implementing the first instruction in the sequence, allowing for balancing between multiple dimensions and multiple objectives and making optimal decisions. The optimization control framework dynamically adjusts the system in combination with the current system state and grid demand and market signals.

[0046] The water-gas coordinated operation model is designed to dynamically optimize the control strategy in different time periods, and outputs the optimal power coordination allocation scheme, including the operation instructions, flow, and pressure regulation of the water pump and compressor. In each control period, the strategy is adjusted according to the current system state and grid demand, and the future state is predicted to ensure that the system can adjust the energy storage or release energy in a timely manner according to the fluctuations in grid load and changes in market signals.

[0047] The operation economy, equipment life, and grid instruction tracking accuracy are taken as comprehensive optimization objectives. The operation economy is the sum of the total input power of the system and the product of the current and predicted electricity price in the future prediction time domain, i.e., the pump power + compressor power and the expected electricity cost. Minimizing the operation economy can guide the system to store more energy when the electricity price is low and to generate more electricity or reduce electricity consumption when the electricity price is high. Equipment life maintenance is usually quantified as a penalty for the change amplitude of the control variable, i.e., the square sum of the first-order difference or the second-order difference. Minimizing this can smooth the device action, reduce mechanical stress and thermal cycle fatigue, and prolong the service life of the device. The grid instruction tracking accuracy is the square sum of the difference between the total output power of the system and the grid dispatching instruction in the future prediction time domain. Minimizing this can ensure accurate service to the grid.

[0048] In each control cycle, the real-time data of all sensors are read and used as the exact initial values of the current state of the system after state estimation. The latest grid demand and market signals are obtained, such as the net power curve to be provided in the next stage and the time-of-use price prediction 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 fast simulation is run to predict the evolution trajectory of all system states in the future under this set of control actions. The optimization solver starts to work in the space of all feasible control sequences, repeatedly calling the prediction model for simulation evaluation, to find the specific control sequence that minimizes the above multi-objective function value. The solving process must strictly follow all hard constraints, such as device power upper limit, gas storage pressure safety range, reservoir water level limit, etc. Once the optimal sequence is found, the controller only extracts 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 megawatts and the compressor power to -60 megawatts, with the negative sign representing power generation, and issuing it to the underlying speed governor and valve controller for execution. Wait for 5 seconds, and the next control cycle begins, repeating the previous steps so that the controller can continuously use the latest feedback information to correct the optimization trajectory, and has natural robustness to model errors and unknown disturbances.

[0049] For example, immediately start the water turbine and quickly increase to full load, while letting the compressor shut down or letting the water turbine and expander increase power at different slopes. For each attempt, the internal coupling model quickly predicts the gas pressure and water level changes in the next 10 minutes. The optimizer eventually finds an optimal solution, determining that at 7.8 megapascal gas pressure, the gas system can immediately provide more power and is more efficient, while the water system needs about 90 seconds from static to full load. Therefore, the first instruction of the optimal coordinated control sequence is to immediately set the expander power to 120 megawatts, start the water turbine and set its power to 80 megawatts in the first 5 seconds, and the total power is exactly 200 megawatts. This scheme is selected because it tracks the grid command with a tracking error of 0; it uses the current gas pressure to generate electricity directly, avoiding the delay of first consuming electricity to pump water and then generating electricity, and achieving economy at the current low electricity price period; it sets a gentle start-up slope for the water turbine, reducing mechanical impact and fatigue accumulation.

[0050] Through the water-gas coordinated operation model, the energy storage and release strategy is adjusted according to the grid demand signal to minimize the tracking error of the grid power command, ensuring that the system operates efficiently and stably. Through multi-objective optimization, while meeting the grid demand, the operation energy consumption is optimized, unnecessary energy loss is reduced, and the overall operation cost is reduced.

[0051] Further, the application further comprises the following steps: based on the water-gas coupling interaction model, analyzing to obtain the water-gas path coupling strength of the current system; according to the water-gas path coupling strength and the characteristics of the power grid demand signal, dynamically adjusting the weight coefficients of each term in the multi-objective function or adjusting the constraint conditions 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 demand of the current system.

[0052] Further, the application further comprises the following steps: based on the water-gas coupling interaction model, identifying the waterway flow change caused by the gas path pressure change amplitude in unit time to obtain a first change amplitude; based on the water-gas coupling interaction model, identifying the waterway flow change caused by the gas path flow change amplitude in unit time to obtain a second change amplitude; calculating the geometric mean of the first change amplitude and the second change amplitude to obtain the water-gas path coupling strength.

[0053] Further, the application further comprises the following steps: when the water-gas path coupling strength reaches a first threshold value and the power grid demand signal changes sharply, increasing the weight of the power grid instruction tracking error term in the multi-objective function, and strengthening the constraint on the system coupling pressure fluctuation amplitude, forming a fast response optimization orientation; when the water-gas path coupling strength is lower than a second threshold value, and the power grid demand signal amplitude is large and stable, increase the weight of the operation energy consumption cost term in the multi-objective function, form an economic energy storage optimization orientation.

[0054] Specifically, in each control cycle, when the system is on a quasi-steady state or a known dynamic trajectory, a virtual perturbation test is performed once inside the control software. Based on the current state of the water-gas coupling interaction model, keep all control inputs of the gas path unchanged, and apply a small standard increment change to the main control actuator of the water path. Using the model, a dynamic simulation of the control cycle length is performed with the current system state as the initial condition. After the simulation is completed, the change amount of the waterway flow and the change amount of the gas path pressure are recorded, and then the first change amplitude is calculated, the first change amplitude=(change amount of gas path pressure / nominal value of gas path pressure) / (change amount of waterway flow / nominal value of waterway pressure), using the relative change amount divided by the nominal value is to normalize, so that the result is dimensionless, representing the normalized gas path pressure relative change caused by the unit waterway control amount change. The first change amplitude is the change amplitude of the key state variable of the gas path ultimately caused by the system coupling physical mechanism when the main control variable of the water path changes by a unit standard change, resulting in a corresponding change in waterway flow. The influence of waterway action on the gas path state is quantified.

[0055] In the same control cycle, another virtual perturbation test is performed immediately or in parallel. Keeping all control inputs of the water path unchanged, a small standard increment is applied to the main control actuator of the gas path, such as increasing its power by 0.5% of its rated value. A control cycle is simulated using the water-gas coupling interaction model, and the change in the gas path flow rate and the change in the key pressure of the water path are recorded. Then the second change amplitude is calculated, which is (change in water path pressure / rated value of water path pressure) / (change in gas path flow rate / rated value of gas path flow rate). It represents the normalized relative change in the water path pressure caused by a unit change in the gas path control variable. The second change amplitude is the change amplitude of the key state variable of the water path caused by the system coupling physical mechanism when the main control variable of the gas path changes by a unit of standard change, which leads to the corresponding change in the gas path flow rate. It quantifies the influence of the gas path action on the water path state.

[0056] In order to use a single index to comprehensively reflect the strength of the two-way influence and avoid distortion of the index due to extremely weak influence in one direction, the geometric mean is used for calculation, i.e. sqrt(first change amplitude x second change amplitude). The geometric mean considers both amplitudes equally, and when either value is zero, the coupling strength is zero, which conforms to the physical intuition that if there is no influence in either direction, it is decoupled. The calculated water-gas path coupling strength is a dimensionless positive number, and its size directly reflects the sensitivity or tightness of the energy and mass transfer channels between the water and gas subsystems under the current operating condition. The larger the water-gas path coupling strength, the tighter the water-gas coupling, and the more significant the influence of the action of one side on the state of the other side.

[0057] Exemplarily, in a simulation platform of a certain large-scale water-air coupled energy storage power station, the water pump has a rated power of 150 megawatts, the compressor has a rated power of 100 megawatts, the rated water path pressure is 6 megapascals, and the rated air path pressure is 8 megapascals. The simulation platform sets the system to operate at medium load, the water pump pumps water at 90 megawatts, the compressor compresses air at 60 megawatts, the current water path pressure is 5.2 megapascals, and the air path pressure is 7.0 megapascals. In the model, the water pump power instruction is increased by 0.75 megawatts, i.e., 0.5% of the rated value, and the model is simulated for 5 seconds. The simulation results show that the water path flow rate increases by about 0.12 cubic meters per second, and the air path pressure rises from 7.000 megapascals to 7.003 megapascals. The change in the air path pressure is calculated to be 0.003 MPa, and the change in the water path pressure is calculated to be 0.75 MW. Therefore, the first change amplitude is (0.003 / 8) / (0.75 / 150)=0.075. In the model, the compressor power instruction is increased by 0.5 megawatts, i.e., 0.5% of the rated value, and the model is simulated for 5 seconds. The simulation results show that the air path flow rate increases by about 0.8 kilograms per second, and the water path pressure rises from 5.200 megapascals to 5.201 megapascals. The change in the water path pressure is calculated to be 0.001 MPa, and the change in the air path flow rate is calculated to be 0.5 MW. Therefore, the second change amplitude is 0.0333. The water-air path coupling strength is calculated to be 0.05, indicating that at the current operating point, the water-air coupling is at a medium to weak strength. The possible reason is that the combination of the current water level and air pressure makes the water-air interface relatively stable, and the volume-pressure transmission effect between them is not intense. Changing the system operating point to a high water level and low air pressure state, i.e., the water path pressure is 6.5 megapascals and the air path pressure is 6.0 megapascals, the above test is repeated, and the first change amplitude is 0.15, the second change amplitude is 0.12, and the coupling strength is 0.134. At the new operating point, the coupling strength is significantly enhanced from 0.05 to 0.134, which is consistent with the physical expectation, because the air path is more sensitive to volume changes under low air pressure, and the water flow is also more sensitive to pressure changes under high water level.

[0058] By calculating the coupling strength, the degree of interaction between the water path and the air path is quantified, which helps to determine the adjustment range of the control strategy and ensure that the two systems can work efficiently and collaboratively during the operation of the water pump and the compressor, avoiding energy waste or system instability. By balancing the coupling effect between the water path and the air path, the energy utilization efficiency can be maximized, unnecessary energy loss can be reduced, and the overall energy efficiency of the system can be improved.

[0059] The first threshold is a higher value, and when the coupling strength reaches or exceeds this value, it indicates that the dynamic interaction between the water and air subsystems is very close, and the action of one will quickly and significantly affect the other. The second threshold is a lower value, and when the coupling strength is lower than 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 according to the physical design parameters of the specific system and a large number of simulation experiments.

[0060] When the water-gas path coupling strength reaches the first threshold and the grid demand signal changes sharply, switch to fast response optimization orientation immediately. The controller automatically increases the weight coefficient of the grid instruction tracking error term in the multi-objective function, and the priority of following the grid instruction target is greatly improved. The optimizer will not hesitate to allow higher operating power consumption or slightly more intense device action to minimize power deviation. At the same time, the controller strengthens the constraint on the fluctuation amplitude of the system coupling pressure. In the constraint conditions of the optimization problem, the upper limit value of the gas pressure change rate of the gas storage device and / or the water pressure change rate of the key pipeline is tightened. Preventing the pressure from fluctuating sharply due to the difference in water and gas inertia when pursuing fast power tracking, causing water hammer, surge or overpressure risk. The above adjustments collectively shape the fast response orientation. The control instructions solved by the optimizer under this configuration will tend to make the side with faster response, usually the gas turbine or smaller inertia water pump that can be quickly adjusted by the valve, undertake more instantaneous power regulation tasks, while strictly limiting the overall action amplitude to ensure that while quickly following the grid instruction, the key pressure inside the system is maintained stable, avoiding instability.

[0061] When the water-gas path coupling strength is below the second threshold and the grid demand signal amplitude is large and smooth, the controller automatically increases the weight coefficient of the operating energy consumption cost term in the multi-objective function, such as increasing it from the base value of 0.3 to 0.6. The controller takes full advantage of the low coupling strength to plan the most economical operating point for each. Make decisions in close conjunction with real-time and predicted electricity price signals. In the electricity price valley period, instruct the water pump and compressor to simultaneously store energy at the highest efficiency power point; in the electricity price peak period, instruct the water turbine and expander to generate power at the optimal combination. Due to the low coupling strength, this strategy of each pursuing the local highest economic point will not cause significant efficiency loss or safety risk due to mutual interference, thereby achieving maximum economic benefit at the system level.

[0062] According to the characteristics of the grid demand signal and the water-gas path coupling strength, dynamically adjust the weight coefficients or constraint conditions in the multi-objective function in the optimization process. Optimization orientation refers to adjusting the optimization strategy and target according to the current system state. When the system coupling is strong, prioritize fast response to grid demand; when the grid demand is smooth, prioritize reducing energy consumption and device fatigue accumulation. The adjustment of optimization orientation enables the system to better adapt to different operating states and task requirements.

[0063] The water-gas collaborative operation model immediately reconstructs the current optimization problem according to the adjusted weight coefficients and the constraint condition set. Then, based on the water-gas coupling interaction model, the future control sequence that minimizes the new target function is solved under the constraint condition set, with the current system state as the initial value, to obtain the optimal power collaborative allocation scheme. By dynamically adjusting the weight coefficients in the multi-objective function and the constraint conditions, the optimization orientation is flexibly adjusted according to the characteristics of the water-gas path coupling strength and the power grid demand, thereby effectively improving the response capability and stability of the system.

[0064] Further, the application further includes the following steps: the water-gas collaborative operation model is a hierarchical nested decision model structure, including an inner water-gas coupling interaction model and an outer predictive control optimizer; the inner water-gas coupling interaction model is used to simulate and predict the dynamic interaction process between the waterway system, the gas path system and the dynamic interaction process generated by the pressure and flow variables therebetween in real time, and the outer predictive control optimizer is based on the inner predictor to solve the multi-objective optimization problem in each control cycle, and the output power collaborative allocation scheme is used as the input boundary condition of the water-gas coupling interaction model to drive the state prediction in the next cycle.

[0065] Specifically, the water-gas collaborative operation model is a hierarchical nested decision model structure, including an inner water-gas coupling interaction model and an outer predictive control optimizer. The hierarchical nested decision model structure is a clear hierarchical design pattern adopted by the water-gas collaborative operation model in the software architecture, which separates and nests the modules responsible for different functions: the inner layer focuses on high-fidelity physical process simulation and plays the role of a prediction engine; the outer layer focuses on high-level decision optimization based on prediction and plays the role of a decision-making brain. The inner model is the basic tool of the outer optimizer, and the outer layer provides action instructions and task targets for the inner layer, and the two are nested to form a closed loop from decision-making to simulation verification to new decision-making.

[0066] The inner water-gas coupling interaction model simulates and predicts the dynamic interaction process between the waterway and the gas path in real time, including the operating state of the water pump and the compressor, and the mutual influence of the two through flow, pressure and other variables. The inner model calculates the current state of the water-gas system by collecting real-time data such as water pump flow, gas path pressure, etc. At startup, the inner water-gas coupling interaction model is initialized, and its parameters have been set according to the design data. Subsequently, in each control cycle, these real measurement values are used to correct and update all state variables inside it, so that the digital twin keeps pace with the real physical system, ensuring the accuracy of the prediction starting point.

[0067] The outer layer predictive control optimizer relies on the output of the inner layer model and performs rolling optimization at each control cycle. The outer layer predictive control optimizer finds the best balance among multiple objectives based on grid demand and current system state, and outputs the optimal power coordination allocation scheme. The optimization objectives usually include: tracking error of grid commands, operating energy efficiency, equipment life, mechanical fatigue accumulation, etc. At each control cycle of the system, the optimizer calculates the optimal control strategy at the future time based on the current system state and grid demand. The outer layer predictive control optimizer starts to work, receives the current system state, grid demand and market signals, and based on a preset multi-objective function and a series of constraints, performs rolling optimization solution under the support of the prediction ability provided by the inner layer model. By comparing the predicted system behavior under different schemes with the compliance degree of the optimization objectives, the optimizer finally finds an optimal power coordination allocation scheme.

[0068] After the outer layer optimizer performs rolling optimization solution, it outputs a power coordination allocation scheme, which is the optimal scheme considering the multi-objective optimization results, as the input boundary condition of the inner layer water-gas coupling interaction model, affecting the state prediction and control decision of the next cycle. The inner layer model and the outer layer optimizer form a closed loop system through feedback and iteration. At the end of each control cycle, the outer layer optimizer adjusts the power coordination allocation scheme of the next cycle according to the output of the inner layer model, ensuring continuous optimization and response to grid demand of the system.

[0069] Once the outer layer optimizer determines the optimal scheme, the power set value in the first control cycle of the scheme is used as the target control command to be issued to the physical execution mechanism on site, such as water pump, compressor frequency converter, and as the input boundary condition to be input to the inner layer water-gas coupling interaction model. The inner layer model then runs a forward simulation with the current synchronized state as the starting point after applying the new control input, and predicts the state of the system at the end of the next control cycle. This prediction result, together with the actual measurement value fed back from the system in the next cycle, is used for state correction and optimization calculation in the next cycle. After the control cycle ends, new sensor data arrives, and the process returns to the first step. At this time, the inner layer model corrects the state with the new data, and the outer layer optimizer takes this new state closer to the true state as the starting point, combines the possibly updated grid demand, and re-solves the optimization problem to output a new optimal scheme, which is repeated to realize rolling optimization and feedback correction.

[0070] By using a hierarchical nested decision model, multiple objectives are optimized simultaneously, avoiding the limitations of single objective optimization. Each objective is fully considered and weighed in the optimization process, ensuring the best performance of the entire system in multiple aspects.

[0071] The real-time system state and the power grid demand signal are input into the water-air coordinated operation model, and an optimal power coordination allocation scheme is output by solving a multi-objective optimization problem in a rolling manner.

[0072] Specifically, the real-time system state is obtained. At a certain time, a complete data vector that can comprehensively represent the operating conditions of the water-air coupled energy storage system is collected and pre-processed by a sensor network, including static energy storage level, dynamic process quantity, etc. The power grid demand signal is a control instruction from a higher dispatching center or a power market, usually in the form of active power value and its time requirement. The power grid demand signal reflects the amount of power that the power grid needs to obtain from the energy storage system at a specific time, which may change with the fluctuation of power grid load.

[0073] The real-time system state and the power grid demand signal are input into the state estimator of the water-air coordinated operation model. The state estimator uses model information to optimally estimate the measurement values that may have noise or delay, and outputs a more accurate current state as the accurate starting point for optimization. The multi-objective optimization problem aims to optimize multiple indicators at the same time, such as power grid power instruction tracking accuracy, system energy efficiency, equipment life and mechanical fatigue, etc. In order to ensure that the system can operate efficiently while meeting the power grid demand, a balance must be found among multiple objectives.

[0074] The rolling solution process is performed in each control period, i.e., the system solves the optimal power coordination allocation scheme in real time according to the current state and the power grid demand in each period. During the rolling solution process, the power output of the water pump and the compressor is adjusted according to the real-time input of the power grid demand and the system state. For example, if the power grid demand signal rises, the water pump output power is increased and the working state of the air compressor is adjusted to ensure that the overall power output meets the requirements of the power grid. Through the rolling solution of the multi-objective optimization problem, the system obtains an optimal power coordination allocation scheme, which determines the power output values of the water pump and the compressor to meet the power grid demand and optimize other objectives such as reducing system energy consumption and prolonging equipment service life, etc.

[0075] The output power coordination allocation scheme will be used as the input boundary condition of the water-air coordinated operation model, affecting the state prediction and control decision of the next control period. Through continuous adjustment and optimization, the power allocation is accurately adjusted in each control period to respond to changes in the power grid demand and the system state. Through the rolling solution of the multi-objective optimization problem, the power allocation scheme is adjusted in real time according to the power grid demand and the water-air system state, and the power allocation between the water pump and the air compressor is optimized to maximize the energy storage efficiency and reduce energy loss.

[0076] According to the power coordination allocation scheme, target control instructions for coordinating the actions of the water pump of the water path and the air compressor of the air path are generated, and the energy storage system is controlled to perform energy storage or release operations.

[0077] The target control instruction is connected with the water pump and the air compressor regulating valve of the air path execution equipment, and the target control instruction is executed to intelligently control the flow, pressure, and valve opening of the water and air execution equipment.

[0078] Specifically, after obtaining the optimal power collaborative allocation scheme, specific control instructions are generated for the water pump and the compressor according to the scheme. The main content of the target control instruction includes the power output, flow regulation, pressure control, and valve opening adjustment of the water pump and the compressor. That is, the power collaborative allocation scheme output by the water-air collaborative operation model is a power instruction. However, the speed regulator or valve controller at the bottom level usually does not directly accept the power instruction, but accepts the speed, opening, or flow set value. According to the power-speed-flow-lift comprehensive characteristic curve of the water pump / turbine, the water pump / turbine speed set value or guide vane opening set value required to achieve the given power set value and the current actual water head is obtained by table lookup. Similarly, according to the characteristic curve of the compressor / expander, the speed set value or inlet valve opening set value required to achieve the given power set value and the current inlet and outlet pressure (or pressure ratio) and temperature condition is obtained by table lookup.

[0079] The specific set values of the water path and the air path obtained by conversion are synchronously issued to the water pump of the water path execution equipment and the air compressor of the air path execution equipment. After the target control instruction is issued, the water pump and the compressor start to execute the instruction to adjust the flow, pressure, and valve opening, respectively. The control system will monitor the execution effect in real time and make fine adjustments as needed. In the water path part, the speed and valve opening of the water pump will be adjusted according to the target control instruction. For example, when the water flow needs to be increased, the speed of the water pump may be increased, or the valve of the water pump will be opened to a larger opening, thereby increasing the water flow. In the air path part, the regulating valve of the air compressor will be adjusted according to the pressure change requirement. For example, when the air path pressure needs to be increased, the air compressor will increase the speed and increase the inlet air volume to achieve the pressure increase.

[0080] The target control instruction is connected with the water pump and the air compressor regulating valve of the air path execution equipment, and the target control instruction is transmitted to the execution equipment of the water pump and the compressor through the intelligent control system. After receiving the control instruction, the execution equipment will adjust the flow, pressure, and valve opening according to the requirements of the instruction. The ultimate goal of the target control instruction is to accurately control the medium flow in the pipeline and the pressure at the key node by adjusting the action of the execution equipment, and these controls are achieved through specific actions such as valve opening.

[0081] Through precise target control instructions, the water pump and the compressor can accurately adjust the power output, flow, pressure and other parameters in each control cycle, optimizing the efficiency of energy storage and release, and reducing energy loss. The target control instructions achieve precise power collaborative distribution between the water pump and the compressor, avoiding independent work or uncoordinated operation of the two. Through coordinated adjustment of water flow and air pressure, efficient operation is maintained in a dynamic environment.

[0082] Further, the application further includes the following steps: under the guidance of fast response optimization, the generation of the target control instruction includes a feedforward compensation component based on a pressure wave physical field, used to offset the pressure impact on the water path subsystem caused by the pipeline inertia of the rapid action of the air path; wherein, using the water-air coupling interaction model, the pressure impact waveform on the water path caused by the pipeline inertia of the rapid action of the air path under the power collaborative distribution scheme is predicted; while issuing the power instruction to the air path executive mechanism, a reverse compensation instruction is issued in advance to the water path executive device based on the pressure impact waveform, to actively offset the pressure impact.

[0083] Specifically, under the guidance of fast response optimization, after the optimal power collaborative distribution scheme is obtained by rolling solution of the collaborative operation model, the internal dynamic effects that the scheme may bring are needed to be evaluated. When the scheme contains rapid action of the air path executive mechanism, such as large opening of the expander valve within 1 second to increase power, the control system will start a special prediction subprogram, using the water-air coupling interaction model, taking the current system state as the initial condition, taking the power distribution scheme containing the rapid action as the input, to perform high-frequency short-time dynamic simulation, such as simulating the future 2 seconds. The simulation will focus on outputting the pressure-time curve of the key water path measuring point, i.e. the predicted pressure impact waveform, revealing the details of the dynamic pressure disturbance on the water path caused by the pipeline inertia and fluid coupling of the air path action.

[0084] According to the dynamic characteristics of the water path subsystem, the action of the water path executive device required to generate a pressure change equal in size and opposite in direction to the predicted impact waveform is calculated, such as the speed regulation instruction, guide vane opening or opening of a special regulating valve of the water pump. Due to the nonlinearity of the system, a simplified model or empirical gain may be used for mapping. For example, assuming that the predicted impact waveform is a positive pulse, the pressure first rises and then recovers, then the reverse compensation instruction is a negative pulse, which first reduces the pressure setting or flow, and then recovers.

[0085] The calculated reverse compensation instruction and the basic water path control instruction originally output by the collaborative operation model for implementing power distribution are superimposed to form the target control instruction finally sent to the water path executive device. In time, the feedforward compensation instruction needs to be issued in advance of the time when the pressure impact caused by the air path action reaches the water path measuring point, in order to overcome the response delay of the executive mechanism and achieve accurate offset.

[0086] The control system issues a water path target control instruction superimposed with feedforward compensation to the water path execution device at the same time as issuing a fast action instruction to the gas path execution mechanism. When the pressure wave generated by the gas path action propagates through the pipeline to the water path, the water path execution device has already acted in advance to generate a reverse pressure wave that meets it exactly, and the two cancel each other out or are greatly weakened, thereby protecting the water path device and maintaining the stability of the water path pressure, providing a smooth internal environment for fast and accurate tracking of power.

[0087] By introducing a feedforward compensation component and a reverse compensation instruction, measures are taken in advance to reduce pressure impact on the water path system in the case of rapid changes in the gas path, ensuring that the energy storage system can quickly and accurately respond to changes in grid demand. When there is a coupling effect between the gas path and the water path, pressure fluctuations can cause instability in the system. By predicting and compensating for pressure impact, damage to equipment or system instability caused by excessive pressure fluctuations is effectively avoided, improving the reliability and safety of the overall system. Feedforward compensation and reverse compensation make the coordination between the gas path and the water path more accurate, ensuring that when they work together, energy waste or improper adjustment due to inertia effects is avoided, thereby improving energy storage efficiency.

[0088] In summary, the water-gas coupled energy storage integrated control method provided by the present application has the following technical effects:

[0089] By collecting water path operation parameters of the pumped storage part and gas path operation parameters of the compressed air energy storage part; according to the water path operation parameters and the gas path operation parameters, a water-gas coordinated operation model is constructed; the real-time system state and the grid demand signal are input into the water-gas coordinated operation model, and a multi-objective optimization problem is solved by rolling, to output an optimal power coordination distribution scheme; according to the power coordination distribution scheme, a target control instruction for coordinating the actions of the water pump of the water path and the air compressor of the gas path is generated, and the energy storage system is controlled to perform energy storage or release operation; wherein the target control instruction is connected with the execution equipment water pump of the water path and the execution equipment air compressor regulating valve of the gas path, and the target control instruction is executed for intelligent control of the flow, pressure and valve opening of the water-gas execution equipment. That is, by constructing a water-gas coordinated operation model and implementing model predictive rolling optimization, water-gas power dynamic coordination distribution is realized, overall energy storage efficiency is improved, and rapid response to grid load fluctuations is achieved to ensure the stability and power supply reliability of the grid.

[0090] Embodiment two, based on the same inventive concept as the water-gas coupled energy storage integrated control method in the aforementioned embodiment one, the present application also provides a water-gas coupled energy storage integrated control system, please refer to the attached Figure 2 , the water-gas coupled energy storage integrated control system comprises:

[0091] The parameter acquisition module 11 is configured to acquire waterway operation parameters of the pumped storage part and airway operation parameters of the compressed air energy storage part; the model construction module 12 is configured to construct a water-air collaborative operation model according to the waterway operation parameters and the airway operation parameters; the multi-objective optimization module 13 is configured to input real-time system states and power grid demand signals into the water-air collaborative operation model, and output an optimal power collaborative distribution scheme by solving a multi-objective optimization problem in a rolling manner; and the collaborative control module 14 is configured to generate target control instructions for actions of a water pump of the waterway and an air compressor of the airway according to the power collaborative distribution scheme, and control the energy storage system to perform energy storage or release operations; wherein the target control instructions are connected with an execution device, i.e., the water pump of the waterway and an air compressor valve of the airway, and are used for intelligent control of flow, pressure and valve opening degree of the water-air execution device.

[0092] Further, the parameter acquisition module 11 in the water-air coupled energy storage integrated control system is further configured to: the waterway operation parameters include reservoir water level, pipeline water pressure, water flow speed, water pump rotating speed, bearing vibration value and water temperature; and the airway operation parameters include gas storage device gas pressure, air temperature, compressor / expander inlet and outlet flow, rotating speed, efficiency and gas storage device inner wall humidity.

[0093] Further, the model construction module 12 in the water-air coupled energy storage integrated control system is further configured to: construct a waterway operation dynamic model based on the waterway operation parameters; construct an airway operation dynamic model based on the airway operation parameters; couple and interact the waterway operation dynamic model and the airway operation dynamic model according to the law of conservation of mass and energy, and construct a water-air coupled interaction model; and use the water-air coupled interaction model as a predictor to search for a benefit maximization collaborative strategy of waterway operation parameters and airway operation parameters, and construct the water-air collaborative operation model.

[0094] Further, the model construction module 12 in the water-air coupled energy storage integrated control system is further configured to: use the water-air coupled interaction model as a predictor, embed an optimization control framework, and construct the water-air collaborative operation model, which is configured to: take operation economy, device life and power grid instruction tracking accuracy as comprehensive optimization targets, perform rolling optimization solving, and obtain an optimal collaborative control sequence, wherein the comprehensive optimization targets are expressed by a multi-objective function, and the multi-objective function at least includes a tracking error term of a power grid power instruction, an operation energy consumption cost term and a device mechanical fatigue accumulation term; and in each control period, a current system state is taken as an initial value, power grid demand and market signals are taken as inputs, future states are predicted based on the water-air coupled interaction model, a future control sequence that makes the multi-objective function optimal is solved as an optimal power collaborative distribution scheme and is output.

[0095] Further, the model construction module 12 in the water-gas coupling energy storage integrated control system is further used for: based on the water-gas coupling interaction model, analyzing to obtain the water-gas path coupling strength of the current system; according to the water-gas path coupling strength and the characteristics of the power grid demand signal, dynamically adjusting the weight coefficients of each term in the multi-objective function or adjusting the constraint conditions of the optimization problem, so that the optimization orientation of the water-gas collaborative operation model is adapted to the physical coupling state and task demand of the current system.

[0096] Further, the model construction module 12 in the water-gas coupling energy storage integrated control system is further used for: based on the water-gas coupling interaction model, identifying the waterway flow change caused by the gas path pressure change amplitude in unit time to obtain a first change amplitude; based on the water-gas coupling interaction model, identifying the waterway flow change caused by the gas path flow change amplitude in unit time to obtain a second change amplitude; calculating the geometric mean of the first change amplitude and the second change amplitude to obtain the water-gas path coupling strength.

[0097] Further, the model construction module 12 in the water-gas coupling energy storage integrated control system is further used for: when the water-gas path coupling strength reaches a first threshold value and the power grid demand signal changes sharply, increasing the weight of the power grid instruction tracking error term in the multi-objective function, and strengthening the constraint on the system coupling pressure fluctuation amplitude, forming a fast response optimization orientation; when the water-gas path coupling strength is lower than a second threshold value, and the power grid demand signal amplitude is large and smooth, increase the weight of the operation energy consumption cost term in the multi-objective function, form an economic energy storage optimization orientation.

[0098] Further, the model construction module 12 in the water-gas coupling energy storage integrated control system is further used for: under the fast response optimization orientation, the generation of the target control instruction contains a feedforward compensation component based on the pressure wave physical field, which is used to offset the pressure impact on the waterway subsystem caused by the pipeline inertia of the gas path fast action; wherein, by using the water-gas coupling interaction model, the pressure impact waveform generated by the pipeline inertia of the gas path fast action on the waterway under the power coordination distribution scheme is predicted; while issuing the power instruction to the gas path executive mechanism, based on the pressure impact waveform, the reverse compensation instruction is issued to the waterway executive device in advance, and the pressure impact is actively offset.

[0099] Further, the model construction module 12 in the water-gas coupling energy storage integrated control system is further configured to: the water-gas coordination operation model is a hierarchical nested decision model structure, including an inner layer water-gas coupling interaction model and an outer layer predictive control optimizer; the inner layer water-gas coupling interaction model is used to simulate and predict the dynamic interaction process between the waterway system, the gas system and the pressure and flow variables generated therebetween in real time, and the outer layer predictive control optimizer is based on the inner layer predictor to solve a multi-objective optimization problem in each control cycle, and output a power coordination distribution scheme as an input boundary condition of the water-gas coupling interaction model to drive the state prediction in the next cycle.

[0100] The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The water-gas coupling energy storage integrated control system in the present embodiment is also applicable to the water-gas coupling energy storage integrated control method and specific examples in the foregoing embodiment one, and the water-gas coupling energy storage integrated control system in the present embodiment can be clearly known by those skilled in the art through the foregoing detailed description of the water-gas coupling energy storage integrated control method. Therefore, for the sake of brevity of the specification, it will not be described in detail here.

[0101] The above description of disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0102] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application also intends to include these 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. 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 operation parameters and air circuit operation parameters is searched to construct the water-air coordinated operation model. 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 makes the multi-objective function optimal is solved as the output of the optimal power cooperative allocation scheme. 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, 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. Calculate the geometric mean of the first change amplitude and the second change amplitude to obtain the water-gas path coupling strength; 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.

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 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.

4. The integrated control method for water-gas coupled energy storage according to claim 3, 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.

5. The integrated control method for water-gas coupled energy storage according to claim 2, 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.

6. 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 5, 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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