Hydroelectric energy storage system optimization scheduling method considering energy storage and water consumption rate

Abstract

The invention belongs to the field of power system dispatching control, and relates to a hydroelectric energy storage system optimal dispatching method considering energy storage and water consumption rate, which comprises the following steps: constructing a hydroelectric generating set water consumption characteristic model and a battery energy storage life loss cost model; establishing an optimization model by taking minimization of the comprehensive operation cost of the system as a target and taking power balance and hydroelectric-energy storage coupling operation as core constraints; solving the optimization model by adopting a double-layer frame combining day-ahead optimization scheduling and intra-day rolling correction, and generating a combined scheduling instruction of hydropower and energy storage; issuing the generated joint dispatching instruction to a hydropower station automatic power generation control system and a battery energy storage energy management system for execution, and collecting actual operation data to perform feedback correction on the model; on the premise that the power grid dispatching requirement is met, the utilization efficiency of water energy resources is improved, attenuation of the service life of an energy storage battery is delayed, and economical operation of a water-electricity-energy storage combined system is achieved.

Description

Technical Field

[0001] This invention relates to the field of power system dispatch and control, and specifically discloses an optimized dispatch method for hydropower energy storage systems that takes into account energy storage and water consumption rate. Background Technology

[0002] The high proportion of renewable energy sources such as wind and solar power has exacerbated grid fluctuations, posing significant challenges to flexibility. Given the limitations of pumped storage and electrochemical energy storage in terms of scale, economics, and regulation duration, exploring and reshaping the regulation potential of existing conventional hydropower is key to solving this problem. For hydropower units, the addition of energy storage systems can improve the ability of hydropower plants to smooth out fluctuations in renewable energy output, participate in grid peak and frequency regulation, and achieve spatiotemporal energy transfer. However, current technologies have not yet achieved coordinated operation of hydropower and energy storage, and its dispatching methods still require in-depth research. In existing hydropower station operations, to meet grid dispatching demands, unit output often needs frequent adjustments, deviating from the optimal water consumption rate. This results in consuming more water resources for the same power generation, or reducing power generation for the same water consumption time, leading to a waste of hydropower resources. Simultaneously, the configured energy storage battery systems are often only used for low-storage, high-generation or fluctuation-smoothing purposes, and the dispatching method is not coupled with the inherent operating efficiency of the hydropower plant. Existing scheduling methods often overlook the lifespan reduction costs caused by frequent battery charging and discharging, only considering short-term economic benefits. In the long run, however, the total cost increases due to battery replacement.

[0003] Therefore, this invention aims to provide an optimized scheduling method for hydropower energy storage systems that takes into account energy storage and water consumption rate. By actively replacing or absorbing the output of the battery, the hydropower unit can operate stably at the optimal water consumption rate for as long as possible. The battery life loss is explicitly quantified in the optimization model, and the overall economic efficiency of the hydropower-energy storage combined system is optimized over a long period of time. Summary of the Invention

[0004] The purpose of this invention is to provide an optimized scheduling method for hydropower energy storage systems that considers both energy storage and water consumption rate. The problem addressed is: by constructing a collaborative optimization model that couples the battery energy storage lifespan degradation cost with the water consumption characteristics of hydropower, and designing a hierarchical solution strategy, the aim is to maintain hydropower units operating within a high-efficiency range as much as possible, while simultaneously balancing system operational economy and equipment durability. The specific solution is as follows:

[0005] An optimized scheduling method for a hydropower energy storage system considering energy storage and water consumption rate includes: Step 1, constructing a water consumption characteristic model for hydropower units and a battery energy storage lifespan loss cost model; Step 2, establishing an optimization model with the goal of minimizing the overall system operating cost and power balance and hydropower-energy storage coupled operation as the core constraints; the overall system operating cost includes battery lifespan loss cost and hydropower efficiency penalty cost; Step 3, using a two-layer framework combining day-ahead optimized scheduling and intraday rolling correction to solve the optimization model and generate joint scheduling instructions for hydropower and energy storage; Step 4, issuing the generated joint scheduling instructions to the hydropower station's automatic generation control system and battery energy storage management system for execution, and collecting actual operating data to provide feedback correction for the hydropower unit water consumption characteristic model and the battery energy storage lifespan loss cost model.

[0006] Further, Step 1 includes Step 1.1, establishing a water consumption characteristic model for the hydropower unit: inputting the relationship curve data between unit output and water consumption rate under different operating heads, and solving the optimal water consumption rate interval for each head based on the fitting model, including: constructing an initial hydropower unit water consumption characteristic model; the initial hydropower unit water consumption characteristic model is a cubic polynomial; obtaining fitting parameters through unit efficiency tests and / or historical operating data; the fitting parameters include water consumption rate, output, and head; fitting the fitting parameters through the initial hydropower unit water consumption characteristic model to obtain the hydropower unit water consumption characteristic model; obtaining the minimum water consumption rate point by solving the partial derivative of water consumption with unit output at each head in the hydropower unit water consumption characteristic model; calculating the water consumption of the optimal output interval based on the minimum water consumption rate point and the set buoyancy ratio; substituting the water consumption of the optimal output interval into the hydropower unit water consumption characteristic model to obtain the optimal output interval at each head.

[0007] Furthermore, the water consumption characteristic model of the hydropower unit is as follows: ; in, The water consumption per unit of power generation for a hydropower unit; The power output of the hydropower unit; q is the water consumption per unit of power generation; H is the operating head; , , , , , , , , and These are the initial, first, second, third, fourth, fifth, sixth, seventh, eighth, and ninth fitting coefficients, respectively.

[0008] Furthermore, Step 1 includes Step 1.2, which establishes a battery energy storage lifespan loss cost model: based on the battery discharge depth and cycle number, a lifespan decay model is established, and the capacity decay caused by a single charge-discharge cycle is converted into an equivalent per-kilowatt-hour lifespan loss cost.

[0009] Furthermore, the equivalent power lifetime loss cost is: ; in, Cost per unit throughput of battery over its lifetime; This refers to the initial investment cost of the battery. The cycle lifetime at a specific depth of discharge (DoD); The rated capacity of the battery is represented by DoD; the depth of discharge is represented by DoD.

[0010] Furthermore, Step 2 includes: Step 2.1, Construct the objective optimization function: ; in, Let R be the objective function; C be the total operating cost; t be the time period variable; and T be the total number of time periods. The electricity price for period t; The output of the hydropower unit during time period t; The energy storage battery provides power for time period t; This refers to the charging and discharging time; Costs related to battery lifespan degradation; This is the penalty coefficient for hydropower efficiency; Penalize costs for hydropower efficiency; Step 2.2, set operating constraints; operating constraints include power balance constraints, hydropower unit operating constraints, and battery energy storage constraints; The power balance constraint is: ; in, The output of the hydropower unit during time period t; To provide power to the energy storage battery during time period t; The total output plan of the joint system issued by the dispatcher; To take the absolute value; δ is the allowable deviation threshold; The operating constraints of the hydropower unit are: ; in, and These are the maximum and minimum output limits for hydropower stations, respectively. Battery energy storage constraints are: ; in, and These represent the lowest and highest states of charge of the energy storage battery, respectively. and These represent the state of charge of the energy storage battery during time period t and time period t+1, respectively. and These are the maximum discharge power and maximum charging power of the energy storage battery, respectively. This refers to the rated capacity of the energy storage battery.

[0011] Furthermore, the costs associated with battery life loss and hydroelectric efficiency penalties are as follows: ; ; in, Costs related to battery lifespan degradation; Cost per unit of battery life loss; To provide power to the energy storage battery during time period t; To take the absolute value; This refers to the charging and discharging time; The cost is the penalty for hydropower efficiency; k is the penalty coefficient; This is a function representing the deviation of the current unit output from the optimal range. The output of the hydropower unit during time period t; This is the optimal interval.

[0012] Furthermore, the formula for calculating the deviation function is as follows: ; in, Here, P is the deviation function; P is the unit output. For the optimal interval, ; This represents the minimum output value within the optimal range; This represents the maximum output value within the optimal range; To take the absolute value.

[0013] Furthermore, Step 3 includes: Step 3.1, based on the hydropower dispatch instructions, using an optimization algorithm to solve the optimization model to obtain the daily charging and discharging plan of the battery energy storage and the hydropower output plan; Step 3.2, based on the minute-level load forecast deviation and the actual operating status of the equipment, correcting the energy storage battery output through intraday rolling to obtain the joint dispatch instructions.

[0014] Further, Step 4 includes: Step 4.1, sending joint dispatch instructions to the hydropower station's automatic power generation control system and the battery energy storage management system for execution; Step 4.2, synchronously collecting hydropower unit operating data and energy storage battery operating data to obtain feedback data; hydropower unit operating data includes the actual output of the hydropower unit, instantaneous water consumption, and reservoir water level; energy storage battery operating data includes the actual charging and discharging power of the energy storage battery and the battery state of charge; Step 4.3, periodically comparing the actual operating efficiency with the model's expected value, and using the feedback data to adaptively correct the parameters of the hydropower unit water consumption characteristic model and the battery energy storage life loss cost model.

[0015] The present invention has the following advantages and beneficial effects: The present invention provides a multi-objective optimization scheduling method for hydropower energy storage systems that takes into account energy storage lifespan and water consumption rate. By constructing a collaborative optimization model that couples the battery energy storage lifespan decay cost with the water consumption characteristics of hydropower, and designing a hierarchical solution strategy, the method can enable hydropower units to operate in a high-efficiency range as much as possible, while taking into account the system's operating economy and equipment durability.

[0016] This invention establishes a quantitative model of the water consumption characteristics of hydropower units and the lifespan degradation of battery energy storage, constructs an optimized scheduling model with the goal of minimizing the overall system cost, and employs a dual-timescale solution strategy of day-ahead optimization plus intraday rolling. The final output is a sequence of joint scheduling instructions that allows hydropower units to operate within their optimal efficiency range as much as possible while mitigating grid fluctuations. This invention can improve the efficiency of hydropower resource utilization, delay the degradation of energy storage battery life, and achieve economical operation of the hydropower-energy storage combined system while meeting grid scheduling requirements. Attached Figure Description

[0017] Figure 1 This is an exemplary flowchart of an optimized scheduling method for a hydropower energy storage system that takes into account energy storage and water consumption rate, as proposed in this invention. Figure 2 The typical water consumption rate surface fitting effect and the optimal water consumption rate operating range obtained by the method proposed in this invention are shown. Figure 3 Typical hydropower output and battery charge / discharge power curves obtained by the method proposed in this invention; Figure 4 The optimal solution obtained by the method proposed in this invention is located at the running point of the typical water consumption rate surface. Figure 5 An exemplary flowchart for closed-loop adaptive correction of model parameters provided by the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0019] Figure 1 This is an exemplary flowchart of an optimized scheduling method for a hydropower energy storage system that considers both energy storage and water consumption rate, as proposed in this invention. Figure 1 As shown, an optimized scheduling method for a hydropower energy storage system that takes into account energy storage and water consumption rate includes the following steps: Step 1: Establish an optimized scheduling model; the optimized scheduling model includes a hydropower unit water consumption characteristic model and a battery energy storage lifespan loss cost model. In some embodiments, Step 1 includes the following steps: Step 1.1: Establish a water consumption characteristic model for the hydropower unit. Input the relationship curve data between unit output and water consumption rate under different operating heads, and solve for the optimal water consumption rate range under each head based on the fitting model. In some embodiments, Step 1.1 includes the following: Constructing an initial water consumption characteristic model for the hydropower unit; the initial water consumption characteristic model for the hydropower unit is a cubic polynomial. Obtain fitting parameters through unit efficiency tests and / or historical operating data; the fitting parameters include water consumption rate q, output, and water consumption rate q. And the head H. To ensure the fitting effect, a cubic polynomial can be used with the addition of cross terms for head and unit output. The fitting parameters are fitted using the initial hydropower unit water consumption characteristic model to obtain the hydropower unit water consumption characteristic model. The hydropower unit water consumption characteristic model is used to represent the nonlinear functional relationship between water consumption rate and output and head. For a certain head, the expression of the hydropower unit water consumption characteristic model with respect to unit output is: ; in, The water consumption per unit of power generation for a hydropower unit; The output of the hydropower unit is (MW); q is the water consumption per unit of power generation ( H represents the operating head (m). , , , , , , , , and These are the initial, first, second, third, fourth, fifth, sixth, seventh, eighth, and ninth fitting coefficients, respectively. The partial derivatives of water consumption at each head with respect to the unit's output are calculated in the hydropower unit's water consumption characteristic model. The minimum water consumption rate point is obtained. Based on the minimum water consumption rate point and the set buoyancy ratio ε, the water consumption of the optimal output range is calculated. Setting the buoyancy ratio refers to a pre-set threshold for determining the optimal output range of the hydropower unit. For example, the buoyancy ratio could be set to 0.03. The water consumption within the optimal output range is then input into the hydropower unit's water consumption characteristic model, and the optimal output range at each head is obtained using the cubic root-finding formula. .

[0020] Step 1.2, Establish a battery energy storage lifespan degradation cost model: Based on the battery's depth of discharge and cycle count, establish a lifespan degradation model, and convert the capacity decay caused by a single charge-discharge cycle into an equivalent per-kilowatt-hour lifespan degradation cost. For example, the equivalent number of cycles can be calculated based on a counting method and a semi-empirical aging model considering the depth of discharge, and then converted into cost. The formula for calculating the equivalent per-kilowatt-hour lifespan degradation cost is: ; in, Cost per unit throughput of battery over its lifetime (RMB / kWh); The initial investment cost of the battery (RMB); The cycle lifetime at a specific depth of discharge (DoD); The rated capacity of the battery is (kWh); DoD is the depth of discharge.

[0021] Step 2: Construct a collaborative optimization scheduling model: With the goal of minimizing the overall system operating cost, and power balance and hydropower-energy storage coupled operation as core constraints, an optimization model is established. The overall system operating cost includes battery life loss cost and hydropower efficiency penalty cost. In some embodiments, Step 2 includes the following steps: Step 2.1: Constructing a Comprehensive Economic Optimization Objective: Establish an objective function that maximizes the system's net revenue within the scheduling cycle. The comprehensive revenue is the difference between total power generation and sales revenue and the total system operating cost. The total system operating cost includes at least the battery lifespan loss cost and the virtual efficiency penalty cost incurred due to the hydropower units deviating from their optimal efficiency operation. The objective optimization function is as follows: ; in, Let R be the objective function; C be the total operating cost; t be the time period variable; and T be the total number of time periods. The electricity price for period t; The output of the hydropower unit during time period t; To provide power to the energy storage battery during time period t; This refers to the charging and discharging time; Costs related to battery lifespan degradation; This is the penalty coefficient for hydropower efficiency; Penalize costs for hydropower efficiency; To contribute to the overall system.

[0022] ; ; in, Costs related to battery lifespan degradation; Cost per unit of battery life loss; To provide power to the energy storage battery during time period t; To take the absolute value; This refers to the charging and discharging time; The cost is the penalty for hydropower efficiency; k is the penalty coefficient; This is a function representing the deviation of the current unit output from the optimal range. The output of the hydropower unit during time period t; This represents the optimal operating range. This guiding scheduling scheme aims to ensure that hydropower units operate within their most efficient operating range.

[0023] The formula for calculating the deviation function of the current unit output from the optimal range is: ; in, Here, P is the deviation function; P is the unit output. For the optimal interval, ; This represents the minimum output value within the optimal range; This represents the maximum output value within the optimal range; To take the absolute value; Step 2.2: Set system operating constraints; operating constraints include power balance constraints, hydropower unit operating constraints, and battery energy storage constraints. The power balance constraints are: ; in, The output of the hydropower unit during time period t; To provide power to the energy storage battery during time period t; The total power output plan (MW) of the joint system issued by the dispatcher; δ is the absolute value; δ is the allowable deviation threshold.

[0024] The operating constraints of the hydropower unit are: ; in, and These are the maximum and minimum output limits for hydropower stations, respectively. Battery energy storage constraints are: ; in, and These represent the lowest and highest states of charge of the energy storage battery, respectively. and These represent the state of charge of the energy storage battery during time period t and time period t+1, respectively. and These are the maximum discharge power and maximum charging power of the energy storage battery, respectively. This refers to the rated capacity of the energy storage battery.

[0025] Step 3: Dual-Time-Scale Model Solving and Command Generation: A two-layer framework combining day-ahead optimal scheduling and intraday rolling correction is employed to solve the optimization model and generate joint scheduling commands for hydropower and energy storage. In some embodiments, Step 3 includes the following steps: Step 3.1: Based on hydropower dispatch instructions An optimization algorithm is used to solve the optimization model, and the day-ahead charge and discharge plan for battery energy storage is obtained. Hydropower output plan For example, based on load and water inflow forecast data for the next tens of hours, an intelligent optimization algorithm is used to solve the model established in Step 2, obtaining the planned output curve of the hydropower unit and the daily charge / discharge plan of the battery storage. Specifically, the total daily output plan of hydropower is used as the hydropower dispatch instruction; the load situation for the next 24 hours is predicted; the daily water inflow is predicted; and a particle swarm optimization algorithm is used, incorporating the objective optimization function and operational constraints, to comprehensively balance the hydropower output and energy storage charge / discharge, obtaining the output of hydropower during flat load periods and peak load periods, as well as the full allocation of battery energy storage.

[0026] Step 3.2: Based on minute-level load forecast deviations and actual equipment operating status, energy storage battery output is adjusted through intraday rolling corrections to fine-tune the day-ahead schedule, resulting in joint dispatch instructions. The optimization objective is to minimize the overall cost deviation at the current moment. For example, intraday rolling corrections are performed with a period of Δt = 15 minutes, based on ultra-short-term load forecast deviations. By using a predictive control framework to solve a short-time domain optimization problem, the battery output is rapidly corrected, with the correction amount... We need to minimize the increase in multi-objective costs over the next few hours.

[0027] Step 4: Dispatch Command Execution and Closed-Loop Feedback: The generated joint dispatch commands are sent to the hydropower station's automatic generation control system and battery energy storage management system for execution, and actual operating data is collected for feedback correction. In some embodiments, Step 4 includes the following steps: Step 4.1: Send the joint dispatch command to the hydropower station's Automatic Generation Control (AGC) system and Battery Energy Management System (BMS) respectively. For example, send the hydropower output command to the hydropower station's AGC system and the energy storage battery output command to the battery BMS system, and control the equipment to execute it.

[0028] Step 4.2: Synchronously collect the operating data of the hydropower unit and the energy storage battery to obtain feedback data. For example, the monitoring system can collect the actual output, instantaneous water consumption, and reservoir water level of the hydropower unit, as well as the actual charging and discharging power and state of charge of the energy storage battery.

[0029] Step 4.3: Periodically compare the actual operating efficiency with the model's expected value, and use feedback data to adaptively correct the parameters of the hydropower unit's water consumption characteristic model and the battery storage lifespan loss cost model. For example, periodically calculate the actual average water consumption rate and compare it with the predicted value. If the deviation continues to exceed the threshold, refit the coefficients in the hydropower unit's water consumption characteristic model using the latest operating data. , , , , , , , , and The battery energy storage lifespan loss cost model parameters are updated to achieve model self-adaptation. Specifically, a correction process is executed weekly to form a closed loop: the actual average water consumption rate is calculated and compared with the predicted value of the water consumption characteristic model constructed in Step 1; at the same time, it is evaluated whether the battery capacity decay rate meets expectations; it is judged whether the deviation meets the standard—if the water consumption rate deviation continues to exceed the deviation threshold, or the battery decay deviates from the expectation; then correction is performed: the coefficients a0~a9 of the water consumption model are refitted, and the battery unit throughput lifespan loss cost is updated; the updated model parameters are substituted into Step 1~Step 3 for the next round of scheduling optimization to achieve adaptive iteration of the model.

[0030] Example 1: Taking a single hydropower station with an installed capacity of 300MW and equipped with a 30MW / 60MWh lithium iron phosphate battery energy storage system as an example for day-ahead dispatching, the specific implementation steps are as follows: Historical operating data of the hydropower station was collected, including records of unit output and water consumption under different water heads. A water consumption characteristic model was obtained through cubic polynomial fitting, and the water consumption rate surface was plotted as follows. Figure 2 As shown, the optimal output range under each water head is determined.

[0031] Battery technical parameters are collected, and the cycle life curve is provided by the manufacturer. The cost per unit throughput lifespan is calculated by combining this with the equivalent per-unit-hour lifespan loss cost. The electricity price curve λ(t) is set using time-of-use pricing.

[0032] The scheduling cycle is based on 96 time periods (15-minute intervals) and the scheduling curve. Based on the battery's state of charge, a comprehensive economic objective function is established as shown in the objective optimization function.

[0033] Set the output range of the hydropower unit, such as =40, =300. An optimization algorithm is used to solve the day-ahead optimization model, obtaining the planned output of the hydropower units and the energy storage charging and discharging schedule. Every 15 minutes during the day, based on the ultra-short-term load forecast deviation and actual SOC state, and with the next 4 hours as the rolling time domain, the local optimization problem is solved to correct the energy storage output. The final typical output curve given by the algorithm is shown below. Figure 3 As shown, the optimized hydropower output's trajectory on a typical water consumption rate surface is as follows: Figure 4 As shown, during peak load periods, energy storage discharges to assist hydropower in increasing total output; during off-peak load periods, energy storage charges to absorb excess hydropower, keeping the hydropower unit output within its optimal range as much as possible.

[0034] The optimized unit output plan is sent to the hydropower station AGC system, and the energy storage output plan is sent to the battery BMS system for execution.

[0035] Real-time data collection of actual unit output, water consumption, battery power, and SOC is performed to calculate the actual average water consumption rate and compare it with model predictions. Model parameter calibration is conducted weekly: if the water consumption rate deviation consistently exceeds 5%, the coefficients of the hydropower unit's water consumption characteristic model are refitted; if the battery capacity degradation rate deviates from expectations, the lifespan loss cost per unit throughput of the battery is updated based on experimental or manufacturer data. The closed-loop adaptive process is as follows: Figure 5 As shown.

[0036] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the scheduling of a hydropower energy storage system that considers energy storage and water consumption rate, characterized in that, include: Step 1: Construct a water consumption characteristic model for hydropower units and a lifespan loss cost model for battery energy storage. Step 2: With the goal of minimizing the overall system operating cost, and with power balance and hydropower-energy storage coupled operation as the core constraints, an optimization model is established. The overall system operating cost includes battery life loss cost and hydropower efficiency penalty cost. Step 3: Using a two-layer framework that combines day-ahead optimization scheduling with intraday rolling correction, the optimization model is solved to generate joint scheduling instructions for hydropower and energy storage. Step 4: The generated joint dispatch instructions are sent to the hydropower station's automatic power generation control system and battery energy storage management system for execution, and actual operating data is collected to provide feedback correction for the hydropower unit's water consumption characteristic model and the battery energy storage life loss cost model.

2. The optimized scheduling method for hydropower energy storage systems considering energy storage and water consumption rate according to claim 1, characterized in that, Step 1 includes Step 1.1, establishing a water consumption characteristic model for the hydropower unit: inputting the relationship curve data between unit output and water consumption rate under different operating heads, and solving for the optimal water consumption rate range under each head based on the fitting model, including: An initial water consumption characteristic model for the hydropower unit is constructed; the initial water consumption characteristic model for the hydropower unit is a cubic polynomial. The fitting parameters are obtained through unit efficiency tests and / or historical operating data; the fitting parameters include water consumption rate, output, and head. The water consumption characteristic model of the hydropower unit is obtained by fitting the fitting parameters through the initial water consumption characteristic model of the hydropower unit. The point of minimum water consumption rate is obtained by solving the partial derivative of water consumption with respect to unit output at each head in the water consumption characteristic model of hydropower unit. Based on the minimum water consumption rate point and the set buoyancy ratio, calculate the water consumption of the optimal output range; By substituting the water consumption of the optimal output range into the water consumption characteristic model of the hydropower unit, the optimal output range under each head is obtained.

3. The optimized scheduling method for hydropower energy storage systems considering energy storage and water consumption rate according to claim 2, characterized in that, The water consumption characteristic model of hydropower units is as follows: ； in, The water consumption per unit of power generation for a hydropower unit; The power output of the hydropower unit; q is the water consumption per unit of power generation; H is the operating head; , , , , , , , , and These are the initial, first, second, third, fourth, fifth, sixth, seventh, eighth, and ninth fitting coefficients, respectively.

4. The optimized scheduling method for hydropower energy storage systems considering energy storage and water consumption rate according to claim 1, characterized in that, Step 1 includes Step 1.2, establishing a battery energy storage life loss cost model: based on the battery discharge depth and cycle number, a life decay model is established, and the capacity decay caused by a single charge-discharge cycle is converted into an equivalent energy life loss cost.

5. The optimized scheduling method for hydropower energy storage systems considering energy storage and water consumption rate according to claim 4, characterized in that, The equivalent power lifetime loss cost is: ； in, Cost per unit throughput of battery over its lifetime; This refers to the initial investment cost of the battery. The cycle lifetime at a specific depth of discharge (DoD); The rated capacity of the battery is represented by DoD; the depth of discharge is represented by DoD.

6. The optimized scheduling method for hydropower energy storage systems considering energy storage and water consumption rate according to claim 1, characterized in that, Step 2 includes: Step 2.1, Construct the objective optimization function: ； in, Let R be the objective function; C be the total operating cost; t be the time period variable; and T be the total number of time periods. The electricity price for period t; The output of the hydropower unit during time period t; To provide power to the energy storage battery during time period t; This refers to the charging and discharging time; Costs related to battery lifespan degradation; This is the hydropower efficiency penalty coefficient; Penalize costs for hydropower efficiency; Step 2.2, set operating constraints; operating constraints include power balance constraints, hydropower unit operating constraints, and battery energy storage constraints; The power balance constraint is: ； in, The output of the hydropower unit during time period t; To provide power to the energy storage battery during time period t; The total output plan of the joint system issued by the dispatcher; To take the absolute value; δ is the allowable deviation threshold; The operating constraints of the hydropower unit are: ； in, and These are the maximum and minimum output limits for hydropower stations, respectively. Battery energy storage constraints are: ； in, and These represent the lowest and highest states of charge of the energy storage battery, respectively. and These represent the state of charge of the energy storage battery during time period t and time period t+1, respectively. and These are the maximum discharge power and maximum charging power of the energy storage battery, respectively. This refers to the rated capacity of the energy storage battery.

7. The optimized scheduling method for hydropower energy storage systems considering energy storage and water consumption rate according to claim 6, characterized in that, The costs of battery life loss and hydroelectric efficiency penalties are as follows: ； ； in, Costs related to battery lifespan degradation; Cost per unit of battery life loss; To provide power to the energy storage battery during time period t; To take the absolute value; This refers to the charging and discharging time; The cost is the penalty for hydropower efficiency; k is the penalty coefficient; This is a function representing the deviation of the current unit output from the optimal range. The output of the hydropower unit during time period t; This is the optimal interval.

8. The optimized scheduling method for hydropower energy storage systems considering energy storage and water consumption rate according to claim 7, characterized in that, The formula for calculating the deviation function is: ； in, Here, P is the deviation function; P is the unit output. For the optimal interval, ; This represents the minimum output value within the optimal range; This represents the maximum output value within the optimal range; To take the absolute value.

9. The optimized scheduling method for a hydropower energy storage system considering energy storage and water consumption rate according to claim 1, characterized in that, Step 3 includes: Step 3.1: Based on the hydropower dispatch instructions, an optimization algorithm is used to solve the optimization model to obtain the day-ahead charging and discharging plan for battery energy storage and the hydropower output plan; Step 3.2: Based on minute-level load forecast deviation and actual equipment operating status, the output of energy storage batteries is corrected through intraday rolling adjustment to obtain joint dispatch instructions.

10. The optimized scheduling method for a hydropower energy storage system considering energy storage and water consumption rate according to claim 1, characterized in that, Step 4 includes: Step 4.1: Send the joint dispatch command to the hydropower station's automatic power generation control system and battery energy storage management system for execution. Step 4.2: Simultaneously collect the hydropower unit's operating data and the energy storage battery's operating data to obtain feedback data; the hydropower unit's operating data includes the actual output of the hydropower unit, instantaneous water consumption, and reservoir water level; the energy storage battery's operating data includes the actual charging and discharging power of the energy storage battery and the battery's state of charge. Step 4.3: Periodically compare the actual operating efficiency with the model's expected value, and use the feedback data to adaptively correct the parameters of the hydropower unit's water consumption characteristic model and the battery storage life loss cost model.

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