A method for optimized charge and discharge control of MWh-level aqueous sodium-ion battery energy storage system suitable for peak-valley electricity pricing

Abstract

This invention discloses an optimized charge and discharge control method for MWh-level aqueous sodium-ion battery energy storage systems suitable for peak-valley electricity pricing, belonging to the field of energy storage system technology. The method first collects peak-valley and flat-valley electricity prices over a continuous pricing cycle, quantifying a set of characteristic parameters including price fluctuation entropy, peak-valley price difference gradient, and price trend weighting coefficients. Then, it establishes a capacity decay factor and a dynamic safe charge and discharge threshold range based on the real-time battery status. Subsequently, it constructs an optimization model with multi-dimensional safety constraints, aiming to maximize economic benefits, and solves for the optimal charge and discharge power using the Lagrange multiplier method. Finally, based on real-time operating parameters, it generates control commands through dual correction of state of charge and voltage deviation. This method deeply couples electricity price characteristics with battery status, balancing arbitrage profits and equipment safety, improving control accuracy and real-time performance, adapting to the characteristics of MWh-level aqueous sodium-ion batteries, and providing technical support for the large-scale application of large-capacity energy storage systems.

Description

Technical Field

[0001] This invention relates to the field of energy storage system technology, specifically to a method for optimizing the charge and discharge control of an MWh-level aqueous sodium-ion battery energy storage system suitable for peak-valley electricity pricing. Background Technology

[0002] Driven by dual-carbon goals, new energy power generation technologies such as wind power and photovoltaics are rapidly becoming widespread. However, these energy sources have inherent drawbacks such as intermittency and high volatility, posing challenges to the safe and stable operation of the power grid. As a core component for energy storage and flexible dispatch, energy storage systems are playing an increasingly important role. Among them, MWh-level large-capacity energy storage systems have become a key focus in the energy storage field due to their ability to meet the large-scale application needs of power grids, such as peak shaving and valley filling, frequency regulation, and voltage regulation. Aqueous sodium-ion batteries, with their advantages of low cost, high safety, good environmental compatibility, and excellent charge-discharge rate, have significant application potential in large-capacity energy storage scenarios compared to lithium batteries, and are gradually becoming one of the core candidate technologies in this field.

[0003] The implementation of peak-valley electricity pricing policies has provided energy storage systems with a commercial path to increase revenue through arbitrage via "charging during off-peak hours and discharging during peak hours," serving as a key driver for the commercialization of energy storage projects. However, existing MWh-level aqueous sodium-ion battery energy storage systems still have many limitations, making it difficult to fully adapt to actual application needs: First, the electricity price characteristics are represented in a one-sided manner. Most control methods rely solely on the absolute difference between peak and off-peak electricity prices, ignoring crucial information such as the severity of price fluctuations, the trend of price difference changes, and recent electricity price dynamics, resulting in a lack of precision in the control strategy's response to price fluctuations. Second, battery status is disconnected from electricity price response. Existing methods do not fully consider the impact of electricity price fluctuations on battery capacity degradation, and the safe charging and discharging threshold range often uses fixed values, failing to adaptively adjust according to electricity price dynamics and the real-time degradation status of the battery, making it prone to... This can lead to overcharging or over-discharging, resulting in wasted capacity and shortened battery lifespan. Thirdly, the optimization objectives and constraints are not well-designed. Some solutions only pursue maximum economic benefits, neglecting the cost of battery capacity degradation, the impact of charging and discharging power fluctuations on the power grid, or failing to cover key safety indicators such as voltage change rate, leading to insufficient system stability. Fourthly, there is a lack of adaptability and real-time performance. Existing control technologies mostly use the control logic of lithium batteries, without specifically optimizing for the capacity degradation characteristics and safe operating boundaries of aqueous sodium-ion batteries. Furthermore, some optimization algorithms rely on numerical iteration, resulting in slow response speeds and difficulty in meeting the real-time control requirements of MWh-level energy storage systems.

[0004] Therefore, how to construct a charge and discharge optimization control method that can deeply couple the characteristics of electricity price and battery state, take into account both economic benefits and operational safety, and adapt to the characteristics of MWh-level aqueous sodium-ion batteries has become the key to breaking through the existing technical bottlenecks and promoting the large-scale application of large-capacity aqueous sodium-ion battery energy storage systems. It has important engineering value and practical significance. Summary of the Invention

[0005] The purpose of this invention is to provide a charging and discharging optimization control method for MWh-level aqueous sodium-ion battery energy storage systems suitable for peak-valley electricity pricing. For MWh-level aqueous sodium-ion battery energy storage systems, this method collects and quantifies peak-valley and flat-valley electricity prices over a continuous electricity price cycle to obtain a set of electricity price fluctuation characteristic parameters. Combined with real-time battery state parameters, the method derives the capacity decay factor and safe charging and discharging threshold range. Then, it constructs a charging and discharging power optimization model with the goal of maximizing economic benefits and battery safety as constraints, and solves for the optimal power using the Lagrange multiplier method. Finally, it dynamically corrects the optimal power based on the real-time operating parameters of the energy storage system, generating the final control command to achieve charging and discharging control that balances efficiency and safety.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for optimizing the charge and discharge control of an MWh-level aqueous sodium-ion battery energy storage system suitable for peak-valley electricity pricing includes the following steps:

[0008] S1: Collect peak, valley, and flat electricity price sequences within several consecutive electricity price cycles, and obtain a set of electricity price fluctuation characteristic parameters, including electricity price fluctuation entropy, peak-valley price difference gradient, and electricity price trend weight coefficient, through quantitative calculation.

[0009] S2: Based on the aforementioned feature parameter set and combined with the real-time state parameters of the aqueous sodium-ion battery, calculate the battery's usable capacity decay factor and safe charge / discharge threshold range;

[0010] S3: Using the set of characteristic parameters, available capacity decay factor and safe charge and discharge threshold range as input, construct a charge and discharge power optimization model with the goal of maximizing economic benefits and the battery safety state as constraint, and solve for the optimal charge and discharge power using the Lagrange multiplier method;

[0011] S4: Collect the real-time voltage, state of charge, and charging / discharging power of the energy storage system. Combine the optimal charging / discharging power with the corresponding state of charge and voltage reference values. Dynamically adjust the optimal charging / discharging power through dual correction logic of state of charge and voltage deviation. Generate the final charging / discharging control command and send it to the charging / discharging controller.

[0012] Furthermore, step S1 specifically includes: acquiring continuous data. The peak, valley, and flat electricity price sequences within each electricity price cycle, with the variables for each sequence corresponding to the [number]th cycle. At that moment, The value range is 1 to The set of characteristic parameters of electricity price fluctuations was obtained through quantitative calculation. This parameter set Specifically, it includes the entropy of electricity price fluctuations. Peak-valley price gradient and electricity price trend weighting coefficient Three core parameters;

[0013] Among them, electricity price fluctuation entropy By combining the proportion of peak-valley electricity price difference at each time point with the negative of its logarithmic term, and superimposing the average of the product of the ratio of flat-period electricity price to peak-valley electricity price and the arctangent of the peak-valley electricity price ratio, the degree of electricity price fluctuation is quantified; peak-valley price difference gradient. The trend of peak-valley price difference is quantified by weighting the difference between the year-on-year change rate of peak-valley electricity prices over consecutive time periods and the exponential term, and then adding the square root of the square of the deviations of the peak-valley price ratio from the mean. The electricity price trend weighting coefficient is used. By using an index term based on the comparison of time period percentages with the average peak-valley electricity price, weights are allocated according to the percentage of each index term at each time period to highlight the impact of recent electricity price trends.

[0014] Furthermore, step S2 specifically includes: using the feature parameter set obtained in step S1 Based on this, the battery's usable capacity decay factor is calculated by combining real-time battery state parameters. and safe charge / discharge threshold range;

[0015] Among them, the available capacity attenuation factor By using the initial attenuation factor Based on the benchmark, the exponential term of the electricity price characteristic parameters is corrected, the product of the electricity price trend weight and the SOC fluctuation is added, and the logical growth term related to the service cycle is included to quantify the battery capacity degradation state; the safe charge and discharge threshold range is based on the battery's initial maximum and minimum charge and discharge voltage, and the correction term is constructed by combining the degradation factor and the electricity price characteristic parameters, and the trigonometric function adjustment term is added to determine the safe voltage range that adapts to electricity price fluctuations.

[0016] Furthermore, step S3 specifically includes: taking the feature parameter set Ω from step S1 and the available capacity decay factor λ and safe charge / discharge threshold range obtained from step S2 as input conditions, constructing a charge / discharge power optimization model with the goal of maximizing economic benefits and the constraint of battery safety state, and obtaining the optimal charge / discharge power by solving through optimization logic;

[0017] The core logic of the objective function is to maximize the total economic benefit, which is the difference between the weighted peak-valley electricity price revenue and the capacity decay cost, minus the smoothing cost of charging and discharging power fluctuations. The constraints include four items: first, the voltage is within the safe threshold range; second, the charging and discharging power does not exceed the maximum limit of the system; third, the state of charge is maintained within the safe range and dynamically updated according to the charging and discharging efficiency parameters; and fourth, the voltage change rate does not exceed the constraint value of the appropriate electricity price characteristics.

[0018] The constrained optimization problem is solved by using the Lagrange multiplier method. The analytical solution of the optimal charge and discharge power is determined by combining the electricity price weighted revenue, the power smoothing adjustment amount and the battery state partial derivative constraint, and the optimal charge and discharge power is determined by the ratio of the numerator and denominator.

[0019] Furthermore, step S4 specifically includes: collecting real-time operating parameters of the energy storage system, including real-time voltage, real-time state of charge, and real-time charging and discharging power; comparing the optimal charging and discharging power obtained in step S3 with the above real-time operating parameters; and obtaining the final charging and discharging control command through correction logic.

[0020] The correction logic for the final charge and discharge control command is as follows: based on the optimal charge and discharge power, a dual correction term is constructed by combining the state of charge deviation and voltage deviation, and the power is dynamically adjusted through product operation to generate a control command that adapts to the real-time state.

[0021] Furthermore, the electricity price fluctuation entropy The formula for calculation is:

[0022] ;

[0023] In the formula, Entropy of electricity price fluctuations; Let be a time variable, and its value range be . arrive This corresponds to a specific time point within a single electricity price cycle; This represents the total number of consecutively collected electricity price data cycles. For the first Peak electricity prices at any given time; For the first Off-peak electricity prices at specific times; It is a local constant to prevent the numerator or denominator from being zero in the formula; For the first The flat-rate electricity price at any given time; It is the arctangent function.

[0024] Furthermore, the peak-valley price difference gradient The formula for calculation is:

[0025] ;

[0026] In the formula, The peak-valley price difference gradient; For time variables; This represents the total number of consecutively collected electricity price data cycles. For the first Peak electricity prices at any given time; For the first Peak electricity prices at any given time; For the first Off-peak electricity prices at specific times; For the first Off-peak electricity prices at specific times; The periodic average of the peak-valley electricity price ratio, i.e. .

[0027] Furthermore, the electricity price trend weighting coefficient The formula for calculation is:

[0028] ;

[0029] In the formula, For the first The electricity price trend weighting coefficient at each time point, the sum of the weights for all times is: ; For time variables; This represents the total number of consecutively collected electricity price data cycles. Percentage of time; For the first Peak electricity prices at any given time; For the first Off-peak electricity prices at specific times; For the first Average peak and off-peak electricity prices at any given time; It is the periodic benchmark value of the peak-valley electricity price average, that is, the average value of the peak-valley average over the entire period.

[0030] Furthermore, the formula for calculating the available capacity attenuation factor λ is:

[0031] ;

[0032] In the formula, The available capacity attenuation factor has a range of values. The closer The smaller the attenuation; This is the initial capacity degradation factor of the battery, typically set to a value of [value missing]. ; , , As an empirical coefficient, it was obtained by selecting aqueous sodium-ion batteries with different attenuation levels, conducting cyclic charge-discharge tests under different peak and valley electricity price fluctuation scenarios, collecting test data, and fitting the data using the least squares method. Entropy of electricity price fluctuations; The peak-valley price difference gradient; For the first The weighting coefficient for the electricity price trend at any given time; For the first The fluctuation of the state of charge at time t, i.e. , For real-time state of charge, This is the reference value for the state of charge; The percentage of fluctuations in the state of charge; This refers to the initial service time of the battery. This refers to the battery's service life. For logical growth functions, the output range is... ;

[0033] Safe charge and discharge threshold range [ , The formula for calculating ] is:

[0034] ;

[0035] ;

[0036] In the formula, For the first The maximum safe charging and discharging voltage at any given time; This refers to the battery's initial maximum charge / discharge voltage. For the first Minimum safe charge / discharge voltage at any given time; This refers to the initial minimum charge / discharge voltage of the battery. This is the available capacity decay factor; The peak-valley price difference gradient; , To be , Transform into The correction factor for the interval; Entropy of electricity price fluctuations; For time variables; This represents the total number of consecutively collected electricity price cycles.

[0037] Furthermore, the final charge / discharge control command The following modified formula is used to obtain:

[0038] ;

[0039] In the formula, For the first The final charge / discharge control command at any given moment; For the first The optimal charging and discharging power at any given time; , Here, is the correction coefficient, and is an empirical constant determined based on the system response characteristics; For the first Reference value of state of charge at any given time; For the first Real-time value of the state of charge at any given moment; , These represent the maximum and minimum values ​​of the safe range for the state of charge. For the first The real-time voltage value at any given moment; For the first Voltage reference value at any given time; , The minimum and maximum safe charging and discharging voltages.

[0040] The core working mechanism of the charge and discharge optimization control method for the MWh-level aqueous sodium-ion battery energy storage system is as follows:

[0041] First, the fluctuation characteristics are perceived and quantified through electricity price data: The system collects peak, valley, and flat electricity price sequences for multiple consecutive electricity price cycles. Through entropy calculation, gradient analysis, and weight allocation, the entropy of electricity price fluctuation is extracted to quantify the intensity of fluctuation, the peak-valley price difference gradient represents the trend of price difference change, and the electricity price trend weight coefficient highlights the impact of recent electricity prices, forming a set of feature parameters that comprehensively reflect the dynamics of electricity prices, providing core input basis for subsequent optimization.

[0042] Secondly, based on the set of electricity price characteristic parameters, and combined with the battery's real-time state of charge, voltage and other operating parameters, the impact of electricity price fluctuations, SOC fluctuation effects and battery service life are calculated and integrated to quantify the available capacity decay factor. On the other hand, a correction term is constructed by combining the decay factor and electricity price characteristics, and a trigonometric function is superimposed to adjust the dynamic range of safe charging and discharging voltage thresholds that are adapted to electricity price fluctuations, so as to achieve dynamic matching between electricity price changes and battery safety boundaries.

[0043] Furthermore, a constrained charging and discharging power optimization model is established: taking the electricity price characteristic parameters, capacity decay factor, and safety threshold as inputs, the objective is to maximize the "weighted peak-valley electricity price revenue minus capacity decay cost and power smoothing cost". At the same time, four constraints are included: voltage safety, power limit, SOC safety range, and voltage change rate. The constrained optimization problem is solved by the Lagrange multiplier method to obtain the analytical solution of the optimal charging and discharging power that takes into account both benefits and safety, and to clarify the ideal charging and discharging intensity at different times.

[0044] Finally, precise control is achieved through real-time dynamic correction: the system continuously collects real-time operating parameters of the energy storage system, such as voltage, SOC, and charging / discharging power, compares the reference value and real-time value corresponding to the optimal charging / discharging power, constructs dual correction terms for SOC deviation and voltage deviation, dynamically adjusts the optimal power through product operation, generates the final control command adapted to the current battery state and electricity price scenario, and sends it to the charging / discharging controller for execution.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] 1. This invention extracts multi-dimensional electricity price characteristic parameters such as electricity price fluctuation entropy and peak-valley price difference gradient, and deeply couples them with the real-time SOC and capacity decay status of the battery to form a model. It dynamically adjusts the safe charging and discharging threshold range and optimizes the target weight, so that the control strategy can accurately respond to the demand for peak-valley electricity price arbitrage and adapt to the battery decay law, avoiding battery overcharging and discharging damage caused by drastic fluctuations in electricity prices.

[0047] 2. This invention constructs a multi-dimensional set of electricity price characteristic parameters through entropy calculation, gradient analysis, and weight allocation. At the same time, it introduces logical growth terms and trigonometric function adjustment terms to quantify battery capacity decay and safety boundaries. Compared with traditional single-parameter characterization, it can more comprehensively capture the dynamics of electricity prices and changes in battery state, providing a more reliable input basis for charge and discharge optimization.

[0048] 3. The optimization model of this invention takes the maximization of weighted peak-valley revenue, attenuation cost, and smoothing cost as its core objective, while incorporating four constraints: voltage safety, power limit, SOC range, and voltage change rate. The analytical solution is obtained by using the Lagrange multiplier method. While improving peak-valley arbitrage revenue, it effectively reduces battery loss and suppresses the impact of power fluctuations on the power grid, achieving synergistic optimization of economic benefits and equipment safety.

[0049] 4. This invention collects parameters such as real-time voltage, SOC, and charging / discharging power, constructs a dual correction term for SOC deviation and voltage deviation, and dynamically adjusts the optimal charging / discharging power. This can quickly compensate for deviations caused by model errors and real-time operating condition fluctuations, and generate control commands adapted to the current state. Compared with static control, this invention significantly improves control accuracy and system operation stability.

[0050] 5. This invention, through experimentation, fits the empirical coefficients suitable for this type of battery, optimizes the calculation logic of capacity decay factor and safety threshold, and makes the control strategy more in line with the operating characteristics of MWh-level aqueous sodium-ion batteries, filling the technical gap in precise charge and discharge control of this type of large-capacity energy storage system.

[0051] 6. This invention derives the analytical solution for optimal charging and discharging power using the Lagrange multiplier method, eliminating the need for complex iterative calculations and enabling rapid output of control results. Combined with a real-time dynamic correction mechanism, it can adapt to rapid changes in the state of the power grid and battery, making it more suitable for engineering applications. Attached Figure Description

[0052] Figure 1 This invention provides an optimized charge and discharge control method for an MWh-level aqueous sodium-ion battery energy storage system suitable for peak-valley electricity pricing. Detailed Implementation

[0053] The technical solutions of the present invention will now be described in detail with reference to the accompanying drawings.

[0054] like Figure 1 As shown, a method for optimizing the charge and discharge control of an MWh-level aqueous sodium-ion battery energy storage system suitable for peak-valley electricity pricing includes the following steps:

[0055] S1: Collect peak, valley, and flat electricity price sequences within several consecutive electricity price cycles, and obtain a set of electricity price fluctuation characteristic parameters, including electricity price fluctuation entropy, peak-valley price difference gradient, and electricity price trend weight coefficient, through quantitative calculation.

[0056] S2: Based on the aforementioned feature parameter set and combined with the real-time state parameters of the aqueous sodium-ion battery, calculate the battery's usable capacity decay factor and safe charge / discharge threshold range;

[0057] S3: Using the set of characteristic parameters, available capacity decay factor and safe charge and discharge threshold range as input, construct a charge and discharge power optimization model with the goal of maximizing economic benefits and the battery safety state as constraint, and solve for the optimal charge and discharge power using the Lagrange multiplier method;

[0058] S4: Collect the real-time voltage, state of charge, and charging / discharging power of the energy storage system. Combine the optimal charging / discharging power with the corresponding state of charge and voltage reference values. Dynamically adjust the optimal charging / discharging power through dual correction logic of state of charge and voltage deviation. Generate the final charging / discharging control command and send it to the charging / discharging controller.

[0059] Furthermore, step S1 specifically includes: acquiring continuous data. The peak, valley, and flat electricity price sequences within each electricity price cycle, with the variables for each sequence corresponding to the [number]th cycle. At that moment, The value range is 1 to The set of characteristic parameters of electricity price fluctuations was obtained through quantitative calculation. This parameter set Specifically, it includes the entropy of electricity price fluctuations. Peak-valley price gradient and electricity price trend weighting coefficient Three core parameters;

[0060] Among them, electricity price fluctuation entropy By combining the proportion of peak-valley electricity price difference at each time point with the negative of its logarithmic term, and superimposing the average of the product of the ratio of flat-period electricity price to peak-valley electricity price and the arctangent of the peak-valley electricity price ratio, the degree of electricity price fluctuation is quantified; peak-valley price difference gradient. The trend of peak-valley price difference is quantified by weighting the difference between the year-on-year change rate of peak-valley electricity prices over consecutive time periods and the exponential term, and then adding the square root of the square of the deviations of the peak-valley price ratio from the mean. The electricity price trend weighting coefficient is used. By using an index term based on the comparison of time period percentages with the average peak-valley electricity price, weights are allocated according to the percentage of each index term at each time period to highlight the impact of recent electricity price trends.

[0061] Furthermore, step S2 specifically includes: using the feature parameter set obtained in step S1 Based on this, the battery's usable capacity decay factor is calculated by combining real-time battery state parameters. and safe charge / discharge threshold range;

[0062] Among them, the available capacity attenuation factor By using the initial attenuation factor Based on the benchmark, the exponential term of the electricity price characteristic parameters is corrected, the product of the electricity price trend weight and the SOC fluctuation is added, and the logical growth term related to the service cycle is included to quantify the battery capacity degradation state; the safe charge and discharge threshold range is based on the battery's initial maximum and minimum charge and discharge voltage, and the correction term is constructed by combining the degradation factor and the electricity price characteristic parameters, and the trigonometric function adjustment term is added to determine the safe voltage range that adapts to electricity price fluctuations.

[0063] Furthermore, step S3 specifically includes: taking the feature parameter set Ω from step S1 and the available capacity decay factor λ and safe charge / discharge threshold range obtained from step S2 as input conditions, constructing a charge / discharge power optimization model with the goal of maximizing economic benefits and the constraint of battery safety state, and obtaining the optimal charge / discharge power by solving through optimization logic;

[0064] The core logic of the objective function is to maximize the total economic benefit, which is the difference between the weighted peak-valley electricity price revenue and the capacity decay cost, minus the smoothing cost of charging and discharging power fluctuations. The constraints include four items: first, the voltage is within the safe threshold range; second, the charging and discharging power does not exceed the maximum limit of the system; third, the state of charge is maintained within the safe range and dynamically updated according to the charging and discharging efficiency parameters; and fourth, the voltage change rate does not exceed the constraint value of the appropriate electricity price characteristics.

[0065] The constrained optimization problem is solved by using the Lagrange multiplier method. The analytical solution of the optimal charge and discharge power is determined by combining the electricity price weighted revenue, the power smoothing adjustment amount and the battery state partial derivative constraint, and the optimal charge and discharge power is determined by the ratio of the numerator and denominator.

[0066] Furthermore, step S4 specifically includes: collecting real-time operating parameters of the energy storage system, including real-time voltage, real-time state of charge, and real-time charging and discharging power; comparing the optimal charging and discharging power obtained in step S3 with the above real-time operating parameters; and obtaining the final charging and discharging control command through correction logic.

[0067] The correction logic for the final charge and discharge control command is as follows: based on the optimal charge and discharge power, a dual correction term is constructed by combining the state of charge deviation and voltage deviation, and the power is dynamically adjusted through product operation to generate a control command that adapts to the real-time state.

[0068] Furthermore, the electricity price fluctuation entropy The formula for calculation is:

[0069] ;

[0070] In the formula, Entropy of electricity price fluctuations; Let be a time variable, and its value range be . arrive This corresponds to a specific time point within a single electricity price cycle; This represents the total number of consecutively collected electricity price data cycles. For the first Peak electricity prices at any given time; For the first Off-peak electricity prices at specific times; It is a local constant to prevent the numerator or denominator from being zero in the formula; For the first The flat-rate electricity price at any given time; It is the arctangent function.

[0071] Furthermore, the peak-valley price difference gradient The formula for calculation is:

[0072] ;

[0073] In the formula, The peak-valley price difference gradient; For time variables; This represents the total number of consecutively collected electricity price data cycles. For the first Peak electricity prices at any given time; For the first Peak electricity prices at any given time; For the first Off-peak electricity prices at specific times; For the first Off-peak electricity prices at specific times; The periodic average of the peak-valley electricity price ratio, i.e. .

[0074] Furthermore, the electricity price trend weighting coefficient The formula for calculation is:

[0075] ;

[0076] In the formula, For the first The electricity price trend weighting coefficient at each time point, the sum of the weights for all times is: ; For time variables; This represents the total number of consecutively collected electricity price data cycles. Percentage of time; For the first Peak electricity prices at any given time; For the first Off-peak electricity prices at specific times; For the first Average peak and off-peak electricity prices at any given time; It is the periodic benchmark value of the peak-valley electricity price average, that is, the average value of the peak-valley average over the entire period.

[0077] Furthermore, the formula for calculating the available capacity attenuation factor λ is:

[0078] ;

[0079] In the formula, The available capacity attenuation factor has a range of values. The closer The smaller the attenuation; This is the initial capacity degradation factor of the battery, typically set to a value of [value missing]. ; , , As an empirical coefficient, it was obtained by selecting aqueous sodium-ion batteries with different attenuation levels, conducting cyclic charge-discharge tests under different peak and valley electricity price fluctuation scenarios, collecting test data, and fitting the data using the least squares method. Entropy of electricity price fluctuations; The peak-valley price difference gradient; For the first The weighting coefficient for the electricity price trend at any given time; For the first The fluctuation of the state of charge at time t, i.e. , For real-time state of charge, This is the reference value for the state of charge; The percentage of fluctuations in the state of charge; This refers to the initial service time of the battery. This refers to the battery's service life. For logical growth functions, the output range is... ;

[0080] Safe charge and discharge threshold range [ , The formula for calculating ] is:

[0081] ;

[0082] ;

[0083] In the formula, For the first The maximum safe charging and discharging voltage at any given time; This refers to the battery's initial maximum charge / discharge voltage. For the first Minimum safe charge / discharge voltage at any given time; This refers to the initial minimum charge / discharge voltage of the battery. This is the available capacity decay factor; The peak-valley price difference gradient; , To be , Transform into The correction factor for the interval; Entropy of electricity price fluctuations; For time variables; This represents the total number of consecutively collected electricity price cycles.

[0084] Furthermore, the final charge / discharge control command The following modified formula is used to obtain:

[0085] ;

[0086] In the formula, For the first The final charge / discharge control command at any given moment; For the first The optimal charging and discharging power at any given time; , Here, is the correction coefficient, and is an empirical constant determined based on the system response characteristics; For the first Reference value of state of charge at any given time; For the first Real-time value of the state of charge at any given moment; , These represent the maximum and minimum values ​​of the safe range for the state of charge. For the first The real-time voltage value at any given moment; For the first Voltage reference value at any given time; , Minimum and maximum safe charge / discharge voltage

[0087] The application example selects peak, valley, and flat electricity price data for 30 consecutive electricity price cycles. Through quantitative calculations, it obtains the electricity price fluctuation entropy reflecting the severity of price fluctuations, the peak-valley price difference gradient reflecting the trend of price difference changes, and the electricity price trend weighting coefficient highlighting the impact of recent electricity prices, forming a complete set of characteristic parameters for electricity price fluctuations. Based on this parameter set, combined with operating parameters such as the real-time state-of-charge voltage of the MWh-level aqueous sodium-ion battery, the usable capacity decay factor is calculated to be 0.95. Simultaneously, the maximum and minimum safe charging and discharging voltages adapted to the current electricity price fluctuations are determined, providing a safety boundary for subsequent control. Using these parameters as input, a charging and discharging power optimization model is constructed with the goal of maximizing economic benefits and including voltage safety power limits, state-of-charge range, and voltage change rate constraints. Solving using the Lagrange multiplier method yields the optimal charging and discharging power at different times, such as the optimal charging power at 2 AM during the valley period being 90% of the system's maximum limit, and the optimal discharging power at 3 PM during the peak period being 85% of the system's maximum limit. The system continuously collects real-time voltage, real-time state of charge, and real-time charging and discharging power, compares the optimal power with the corresponding reference value, and fine-tunes the charging power to 88% when the real-time state of charge is slightly lower than the reference value. When the voltage deviates slightly, it is further optimized through exponential adjustment, generates the final charging and discharging control command and sends it to the charging and discharging controller, so as to achieve precise charging and discharging control that balances economic benefits and battery safety.

Claims

1. A method for optimizing charge and discharge control of an MWh-level aqueous sodium-ion battery energy storage system suitable for peak-valley electricity pricing, characterized in that, Includes the following steps: S1: Collect peak, valley, and flat electricity price sequences within several consecutive electricity price cycles, and obtain a set of electricity price fluctuation characteristic parameters, including electricity price fluctuation entropy, peak-valley price difference gradient, and electricity price trend weight coefficient, through quantitative calculation. S2: Based on the aforementioned feature parameter set and combined with the real-time state parameters of the aqueous sodium-ion battery, calculate the battery's usable capacity decay factor and safe charge / discharge threshold range; S3: Using the set of characteristic parameters, available capacity decay factor and safe charge and discharge threshold range as input, construct a charge and discharge power optimization model with the goal of maximizing economic benefits and the battery safety state as constraint, and solve for the optimal charge and discharge power using the Lagrange multiplier method; S4: Collect the real-time voltage, state of charge, and charging / discharging power of the energy storage system. Combine the optimal charging / discharging power with the corresponding state of charge and voltage reference values. Dynamically adjust the optimal charging / discharging power through dual correction logic of state of charge and voltage deviation. Generate the final charging / discharging control command and send it to the charging / discharging controller.

2. The method for optimizing charge and discharge control of an MWh-level aqueous sodium-ion battery energy storage system suitable for peak-valley electricity pricing, as described in claim 1, is characterized in that... Step S1 specifically includes: acquiring continuous data. The peak, valley, and flat electricity price sequences within each electricity price cycle, with the variables for each sequence corresponding to the [number]th cycle. At that moment, The value range is 1 to The set of characteristic parameters of electricity price fluctuations was obtained through quantitative calculation. This parameter set Specifically, it includes the entropy of electricity price fluctuations. Peak-valley price gradient and electricity price trend weighting coefficient Three core parameters; Among them, electricity price fluctuation entropy By combining the proportion of peak-valley electricity price difference at each time point with the negative of its logarithmic term, and superimposing the average of the product of the ratio of flat-period electricity price to peak-valley electricity price and the arctangent of the peak-valley electricity price ratio, the degree of electricity price fluctuation is quantified; peak-valley price difference gradient. The trend of peak-valley price difference is quantified by weighting the difference between the year-on-year change rate of peak-valley electricity prices over consecutive time periods and the exponential term, and then adding the square root of the square of the deviations of the peak-valley price ratio from the mean. The electricity price trend weighting coefficient is used. By using an index term based on the comparison of time period percentages with the average peak-valley electricity price, weights are allocated according to the percentage of each index term at each time period to highlight the impact of recent electricity price trends.

3. The method for optimizing charge and discharge control of an MWh-level aqueous sodium-ion battery energy storage system suitable for peak-valley electricity pricing, as described in claim 1, is characterized in that... Step S2 specifically includes: using the feature parameter set obtained in step S1 Based on this, the battery's usable capacity decay factor is calculated by combining real-time battery state parameters. and safe charge / discharge threshold range; Among them, the available capacity attenuation factor By using the initial attenuation factor Based on the benchmark, the exponential term of the electricity price characteristic parameters is corrected, and the product of the electricity price trend weight and the SOC fluctuation is added. Then, the logical growth term related to the service cycle is included to quantify the battery capacity degradation state. The safe charge and discharge threshold range is based on the battery's initial maximum and minimum charge and discharge voltages, and the correction term is constructed by combining the degradation factor and the electricity price characteristic parameters. The trigonometric function adjustment term is added to determine the safe voltage range that adapts to electricity price fluctuations.

4. The method for optimizing charge and discharge control of an MWh-level aqueous sodium-ion battery energy storage system suitable for peak-valley electricity pricing, as described in claim 1, is characterized in that... Step S3 specifically includes: taking the feature parameter set Ω from step S1 and the available capacity decay factor λ and safe charge / discharge threshold range obtained from step S2 as input conditions, constructing a charge / discharge power optimization model with the goal of maximizing economic benefits and the constraint of battery safety state, and obtaining the optimal charge / discharge power by solving through optimization logic; The core logic of the objective function is to maximize the total economic benefit, which is the difference between the weighted peak-valley electricity price revenue and the capacity decay cost, minus the smoothing cost of charging and discharging power fluctuations. The constraints include four items: first, the voltage is within the safe threshold range; second, the charging and discharging power does not exceed the maximum limit of the system; third, the state of charge is maintained within the safe range and dynamically updated according to the charging and discharging efficiency parameters; and fourth, the voltage change rate does not exceed the constraint value of the appropriate electricity price characteristics. The constrained optimization problem is solved by using the Lagrange multiplier method. The analytical solution of the optimal charge and discharge power is determined by combining the electricity price weighted revenue, the power smoothing adjustment amount and the battery state partial derivative constraint, and the optimal charge and discharge power is determined by the ratio of the numerator and denominator.

5. The method for optimizing charge and discharge control of an MWh-level aqueous sodium-ion battery energy storage system suitable for peak-valley electricity pricing, as described in claim 1, is characterized in that... Step S4 specifically includes: collecting real-time operating parameters of the energy storage system, including real-time voltage, real-time state of charge, and real-time charging and discharging power; comparing the optimal charging and discharging power obtained in step S3 with the above real-time operating parameters; and obtaining the final charging and discharging control command through correction logic. The correction logic for the final charge and discharge control command is as follows: based on the optimal charge and discharge power, a dual correction term is constructed by combining the state of charge deviation and voltage deviation, and the power is dynamically adjusted through product operation to generate a control command that adapts to the real-time state.

6. A method for optimizing charge and discharge control of an MWh-level aqueous sodium-ion battery energy storage system suitable for peak-valley electricity pricing, as described in claim 1 or 2, characterized in that... The electricity price fluctuation entropy The formula for calculation is: ； In the formula, Entropy of electricity price fluctuations; Let be a time variable, and its value range be . arrive This corresponds to a specific time point within a single electricity price cycle; This represents the total number of consecutively collected electricity price data cycles. For the first Peak electricity prices at any given time; For the first Off-peak electricity prices at specific times; It is a local constant to prevent the numerator or denominator from being zero in the formula; For the first The flat-rate electricity price at any given time; It is the arctangent function.

7. A method for optimizing charge and discharge control of an MWh-level aqueous sodium-ion battery energy storage system suitable for peak-valley electricity pricing, as described in claim 1 or 2, characterized in that... The peak-valley price difference gradient The formula for calculation is: ； In the formula, The peak-valley price difference gradient; For time variables; This represents the total number of consecutively collected electricity price data cycles. For the first Peak electricity prices at any given time; For the first Peak electricity prices at any given time; For the first Off-peak electricity prices at specific times; For the first Off-peak electricity prices at specific times; The periodic average of the peak-valley electricity price ratio, i.e. .

8. A method for optimizing charge and discharge control of an MWh-level aqueous sodium-ion battery energy storage system suitable for peak-valley electricity pricing, as described in claim 1 or 2, characterized in that... The electricity price trend weighting coefficient The formula for calculation is: ； In the formula, For the first The electricity price trend weighting coefficient at each time point, the sum of the weights for all times is: ; For time variables; This represents the total number of consecutively collected electricity price data cycles. Percentage of time; For the first Peak electricity prices at any given time; For the first Off-peak electricity prices at specific times; For the first Average peak and off-peak electricity prices at any given time; It is the periodic benchmark value of the peak-valley electricity price average, that is, the average value of the peak-valley average over the entire period.

9. A method for optimizing charge and discharge control of a MWh-level aqueous sodium-ion battery energy storage system suitable for peak-valley electricity pricing, as described in claim 1 or 3, characterized in that... The formula for calculating the available capacity attenuation factor λ is: ； In the formula, The available capacity attenuation factor has a range of values. The closer The smaller the attenuation; This is the initial capacity degradation factor of the battery, typically set to a value of [value missing]. ; , , As an empirical coefficient, it was obtained by selecting aqueous sodium-ion batteries with different degradation levels, conducting cyclic charge-discharge tests under different peak and valley electricity price fluctuation scenarios, collecting test data, and fitting the data using the least squares method. Entropy of electricity price fluctuations; The peak-valley price difference gradient; For the first The weighting coefficient for the electricity price trend at any given time; For the first The fluctuation of the state of charge at time t, i.e. , For real-time state of charge, This is the reference value for the state of charge; The percentage of fluctuations in the state of charge; This refers to the initial service time of the battery. This refers to the battery's service life. For logical growth functions, the output range is... ; Safe charge and discharge threshold range [ , The formula for calculating ] is: ； ； In the formula, For the first The maximum safe charging and discharging voltage at any given time; This refers to the battery's initial maximum charge / discharge voltage. For the first Minimum safe charge / discharge voltage at any given time; This refers to the initial minimum charge / discharge voltage of the battery. This is the available capacity decay factor; The peak-valley price difference gradient; , To be , Transform into The correction factor for the interval; Entropy of electricity price fluctuations; For time variables; This represents the total number of consecutively collected electricity price cycles.

10. A method for optimizing charge and discharge control of an MWh-level aqueous sodium-ion battery energy storage system suitable for peak-valley electricity pricing, as described in claim 1 or 4, characterized in that... The final charge and discharge control command The following modified formula is used to obtain: ； In the formula, For the first The final charge / discharge control command at any given moment; For the first The optimal charging and discharging power at any given time; , Here, is the correction coefficient, and is an empirical constant determined based on the system response characteristics; For the first Reference value of state of charge at any given time; For the first Real-time value of the state of charge at any given moment; , These represent the maximum and minimum values ​​of the safe range for the state of charge. For the first The real-time voltage value at any given moment; For the first Voltage reference value at any given time; , The minimum and maximum safe charging and discharging voltages.

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