Multi-source cooperative control method and device, computer equipment and computer program product

Abstract

The invention is suitable for the technical field of electrochemical energy storage, and provides a multi-source cooperative control method and device, computer equipment and a computer program product, and the method comprises the steps: obtaining basic parameters of a power grid, a hybrid energy storage system and a new energy power generation system; obtaining prediction parameters of the electric power energy system based on the historical working conditions and the prediction model; according to the preset initial investment amount, the cycle life of the hybrid energy storage system and the basic parameters and working conditions of the electric power energy system, calculating the unit energy charging and discharging cost of the hybrid energy storage system; based on the unit energy charging and discharging cost, the time step length, the power grid billing time-of-use electricity price and the prediction parameters of the hybrid energy storage system and the planned charging and discharging power of the hybrid energy storage system, an objective function with the optimal economical efficiency as an objective is constructed, the objective function is solved, and the optimal planned charging and discharging power of the hybrid energy storage system in each time period is obtained. According to the method, multi-dimensional data is combined, an objective function with the optimal economical efficiency is constructed and solved, and a hybrid energy storage charging and discharging strategy is automatically obtained.

Description

Technical Field

[0001] This application belongs to the field of electrochemical energy storage technology, and in particular relates to multi-source coordinated control methods, devices, computer equipment and computer program products. Background Technology

[0002] Electrochemical energy storage has become a core energy storage technology for the construction of new power systems due to its advantages such as high energy density, fast response capability, high scalability, easy deployment, mature industrial chain, and continuously decreasing cost of large-scale application. Among them, lithium battery energy storage, with its high energy density, efficient charging and discharging, and millisecond-level response speed, is suitable for grid frequency regulation and short-term energy storage scenarios. However, it has drawbacks such as thermal runaway risk, low cycle life, and significant capacity decay at low temperatures. Moreover, its high dependence on imported lithium resources leads to prominent supply chain risks. Vanadium redox flow storage, on the other hand, achieves intrinsic safety through water-based electrolytes, has a longer cycle life, and combines capacity and power decoupling characteristics, making it suitable for long-term energy storage. Furthermore, China's vanadium resource reserves ensure supply chain security. However, its lower energy density results in a large size, system efficiency is only 70% to 75%, and it needs to be strictly controlled within the temperature range of 5-45℃.

[0003] Existing technologies still face the following challenges: First, a single energy storage mode struggles to meet the grid's multidimensional demands for peak shaving, frequency regulation, storage duration, and lifespan. Furthermore, lithium batteries and vanadium redox flow storage have shortcomings in terms of resource supply chain risks and environmental adaptability. Second, for a single energy storage mode, existing energy storage control strategies rely on manually set fixed charge-discharge strategies based on historical data (such as peak-valley arbitrage strategies under time-of-use pricing). While feasible when loads are stable and grid safety margins are sufficient, these strategies fail to realize their full potential and pose grid safety risks when facing complex scenarios such as sudden load changes and increased volatility in renewable energy sources. Third, although combining lithium batteries with vanadium redox flow storage can create a hybrid energy storage system that complements short-term frequency regulation and long-term support, this hybrid system not only faces the drawbacks of fixed control strategies in a single energy storage mode but also challenges in ensuring the rationality of coordination and cooperation mechanisms among the various energy storage systems and optimizing resource allocation. Summary of the Invention

[0004] This application provides a multi-source collaborative control method, apparatus, computer equipment, and computer program product, which can solve the collaborative control problem of hybrid energy storage systems.

[0005] In a first aspect, embodiments of this application provide a multi-source coordinated control method. This method is applied to a power energy system comprising a hybrid energy storage system, a new energy power generation system, and electrical loads, wherein the power energy system is connected to the power grid. The method includes: Acquire basic parameters of the power grid, hybrid energy storage system, and new energy power generation system; the hybrid energy storage system includes lithium battery energy storage system and vanadium redox flow storage system; the basic parameters are inherent system properties or externally given fixed parameters, including at least the time-of-use electricity price for grid billing. ; The predicted parameters of the power energy system are obtained based on historical operating conditions and prediction models; the predicted parameters include at least the predicted power of the new energy power generation system. and load forecast power ; Based on the preset initial investment amount and the cycle life of the hybrid energy storage system, as well as the basic parameters and operating conditions of the power energy system, calculate the unit energy charging and discharging cost of the hybrid energy storage system; Based on the unit energy charge / discharge cost and time step of the hybrid energy storage system Time-of-use pricing for electricity grid billing The predicted parameters and the planned charge and discharge power of the hybrid energy storage system are used to construct an objective function with the goal of optimal economic efficiency. Solving the objective function yields the optimal planned charge and discharge power of the hybrid energy storage system for each time period.

[0006] Secondly, embodiments of this application provide a multi-source coordinated control device, which is applied to a power energy system including a hybrid energy storage system, a new energy power generation system, and an electrical load, wherein the power energy system is connected to the power grid; the device includes: The basic parameter acquisition module is used to acquire basic parameters of the power grid, the hybrid energy storage system, and the new energy power generation system; the hybrid energy storage system includes a lithium battery energy storage system and a vanadium redox flow battery energy storage system; the basic parameters are inherent system properties or externally given fixed parameters, and at least include the grid billing time-of-use tariff. ; The prediction parameter acquisition module is used to acquire prediction parameters of the power energy system based on historical operating conditions and prediction models; the prediction parameters include at least the predicted power of the new energy power generation system. and load forecast power ; The charging and discharging cost calculation module is used to calculate the unit energy charging and discharging cost of the hybrid energy storage system based on the preset initial investment amount, the cycle life of the hybrid energy storage system, and the basic parameters and operating conditions of the power energy system. The strategy generation module is used to determine the unit energy charge / discharge cost and time step of the hybrid energy storage system. Time-of-use pricing for electricity grid billing The predicted parameters and the planned charge and discharge power of the hybrid energy storage system are used to construct an objective function with the goal of optimal economic efficiency. Solving the objective function yields the optimal planned charge and discharge power of the hybrid energy storage system for each time period.

[0007] Thirdly, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in any of the first aspects above.

[0008] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the method described in any of the first aspects above.

[0009] Fifthly, embodiments of this application provide a computer program product that, when run on a computer device, causes the computer device to perform the method described in any one of the first aspects above.

[0010] The beneficial effects of the embodiments in this application compared with the prior art are: This application employs a hybrid energy storage system, effectively addressing the shortcomings of single energy storage systems and comprehensively considering the multi-dimensional needs of the power grid. Furthermore, this application combines multi-dimensional data such as basic parameters, operating conditions, and predicted parameters of the power energy system to construct and solve an objective function with optimal economic efficiency as the optimization goal, deriving a hybrid energy storage charging and discharging strategy (i.e., the optimal planned charging and discharging power of the hybrid energy storage system at each time period), thereby achieving automated control of the hybrid energy storage system. Compared to traditional fixed control strategies, the strategy derived in this application is more flexible, more adaptable to complex scenarios, and more optimized in terms of resource allocation, greatly improving overall efficiency.

[0011] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart illustrating a multi-source collaborative control method provided in an embodiment of this application; Figure 2This is a schematic diagram of the structure of a multi-source collaborative control device provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0014] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0015] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0016] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0017] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0018] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0019] References to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0020] The technical solutions in the embodiments of this application will be described in detail below.

[0021] This application applies to the following scenarios: a power energy system, comprising a hybrid energy storage system consisting of a lithium battery energy storage system and a vanadium redox flow storage system, a new energy power generation system, and electrical loads. This power energy system is connected to the power grid, and the grid sets a power limit for system access. The new energy power generation system must be fully consumed locally and is prohibited from feeding back into the grid.

[0022] For the above application scenarios, this application aims to automatically output the optimal charging and discharging strategy of the hybrid energy storage system while ensuring the safety of the power energy system, so as to achieve the goal of optimal economic efficiency of the whole system.

[0023] Figure 1 The diagram illustrates a multi-source cooperative control method according to an embodiment of this application. This is an example and not a limitation; the method can be applied to computer devices. Figure 1 As shown, the method includes the following steps: Step 101: Obtain the basic parameters of the power grid, hybrid energy storage system, and new energy power generation system.

[0024] Basic parameters are either inherent system properties or externally given fixed parameters. In some embodiments, the basic parameters to be considered for the power energy system include those of the power grid, hybrid energy storage system, and new energy power generation system. For example, the specific basic parameters of the power grid, hybrid energy storage system, and new energy power generation system are as follows: 1. Basic parameters of the power grid: 1) Maximum safe power supported by the power grid

[0025] This parameter refers to the maximum input / output power limit (usually in kW or MW) that the power grid can withstand within a specific time period. Exceeding this limit may lead to safety hazards such as grid frequency overruns, line overloads, or equipment damage. This parameter is typically set by the grid operator based on the grid topology, load characteristics, and stability requirements to constrain the charging and discharging power of energy storage systems and the grid-connected power of new energy sources, ensuring the safe operation of the power grid.

[0026] 2) Time-of-use pricing for grid billing

[0027] The power grid typically divides electricity into peak, off-peak, and valley periods based on different electricity pricing standards (usually expressed in yuan / kWh). For example, electricity prices are higher during peak hours and lower during valley hours.

[0028] 2. Basic parameters of hybrid energy storage system: 1) Capacity of lithium battery energy storage system Rated power

[0029] capacity The total energy that a lithium battery energy storage system can store (usually measured in kWh or MWh) represents its maximum available energy reserve.

[0030] Rated power The maximum charge / discharge power (usually measured in kW or MW) allowed for continuous operation of a lithium battery energy storage system reflects its instantaneous power output capability.

[0031] 2) Capacity of the all-vanadium redox flow storage system Rated power

[0032] capacity Vanadium redox flow storage system (VDS) is the total energy that it can store (usually measured in kWh or MWh), representing its maximum available energy reserve.

[0033] Rated power The maximum charge / discharge power (usually in kW or MW) allowed for continuous operation of a vanadium redox flow storage system reflects its instantaneous power output capability.

[0034] 3) The SOC operating range set for the lithium battery energy storage system

[0035] The State of Charge (SOC) operating range refers to the range of charge levels that an energy storage system (such as lithium batteries and flow batteries) is allowed to operate in practice, usually expressed as a percentage (e.g., 20%–90%). Its core significance lies in balancing the performance, lifespan, and safety of the energy storage system by limiting the depth of charge and discharge (DoD).

[0036] 4) State of Charge (SOC) operating space of the all-vanadium redox flow storage system

[0037] The definition of this parameter can be found in the relevant descriptions of lithium battery energy storage systems, and will not be repeated here.

[0038] 3. Basic parameters of new energy power generation system: Total installed capacity of new energy power sources

[0039] This parameter refers to the total rated installed capacity (usually in kW or MW) of all new energy power generation devices (such as photovoltaic and wind power) in the system, reflecting the maximum theoretical power generation capacity of the new energy power generation system. This parameter is used to assess the output potential of the new energy power generation system and serves as an important basis for predicting the available power generation of the new energy power generation system in the energy storage system dispatch strategy.

[0040] Based on the above basic parameters, other parameters (hereinafter referred to as calculation parameters) can be further calculated. The calculation of some parameters requires consideration of the operating conditions of the power energy system. The calculation parameters involved in the embodiments of this application are as follows: Calculation parameter 1: The actual SOC variation range of the lithium battery energy storage system The calculation formula is as follows:

[0041] Calculation parameter 2: Actual energy storage capacity used by the lithium battery energy storage system

[0042] This parameter refers to the effective capacity (usually measured in kWh or MWh) that a lithium-ion battery energy storage system can actually utilize within a set SOC (State of Charge) range. The calculation formula is:

[0043] Calculation parameter 3: Energy efficiency of lithium battery energy storage system : Energy efficiency is actually affected by many factors, including temperature, charging and discharging power, and the internal structure of the energy storage system. Quantitative evaluation based on multiple factors is a complex process. In one embodiment, the energy efficiency of a lithium-ion battery energy storage system is defined as the ratio of the actual discharge capacity to the actual chargeable capacity at rated power. For example, energy efficiency is quantitatively determined through the operation of an actual energy storage system. Under constant rated power and ambient temperature, the time required for the lithium-ion battery energy storage system to charge from 0% to 100% SOC is calculated as follows: The time it takes for the SOC to discharge from 100% to 0% is measured as follows: The energy efficiency of the lithium battery energy storage system The calculation formula is as follows:

[0044] Calculation parameter 4: SOC variation range in actual use of vanadium redox flow storage system The calculation formula is as follows:

[0045] Calculation parameter 5: Actual energy storage capacity of the vanadium redox flow storage system The calculation formula is as follows:

[0046] Calculation parameter 6: Energy efficiency of the all-vanadium redox flow storage system The calculation method is equivalent to solving the energy efficiency problem of a lithium battery energy storage system. Under constant rated power and ambient temperature, the time required for a vanadium redox flow storage system to charge from 0% SOC to 100% is calculated as follows: The time it takes for the SOC to discharge from 100% to 0% is measured as follows: The energy efficiency of the vanadium redox flow storage system is then... The calculation formula is as follows:

[0047] Step 102: Obtain the predicted parameters of the power energy system based on historical operating conditions and prediction models.

[0048] In this embodiment of the application, the prediction parameters include at least the predicted power of the new energy power generation system and the predicted load power. Specifically: Predicted power of new energy power generation system In some embodiments, time-series data of new energy (photovoltaic, wind power, etc.) power generation for future periods can be generated based on historical operating condition data and prediction models (such as machine learning algorithms). The time granularity of the prediction data includes, but is not limited to, a basic granularity of 15 minutes. For example, with a granularity of 15 minutes, the predicted power of the new energy power generation system... The daily count is 96 points; if the granularity is 5 minutes, the daily count is 288 points.

[0049] Load forecast power Similar to the calculation method for new energy power generation, in some embodiments, the power load time series data for future periods can be generated based on historical operating condition data and prediction models. The time granularity of the prediction data includes, but is not limited to, a basic granularity of 15 minutes.

[0050] Step 103: Calculate the unit energy charging and discharging cost of the hybrid energy storage system based on the preset initial investment amount, the cycle life of the hybrid energy storage system, and the basic parameters and operating conditions of the aforementioned power energy system.

[0051] From an economic optimization perspective, it is necessary to calculate the operating cost of hybrid energy storage systems (lithium-ion battery energy storage systems and vanadium redox flow storage systems), which is based on amortizing and predicting the energy storage usage cost over its entire life cycle. The cycle life of energy storage is highly related to its characteristics and depth of charge / discharge. Given a predetermined depth of charge / discharge, let the initial investment be M, the energy storage capacity be E, the cycle life be N (which can be provided by the manufacturer), and the depth of charge / discharge be... Energy storage efficiency is Without considering the time cost of investment funds, the charging and discharging cost per unit energy (e.g., kWh) of any energy storage system can be calculated using the following formula: Because the charging and discharging efficiency of an energy storage system is affected by factors such as temperature, wiring, and auxiliary equipment, under the same system environment and a defined depth of charge and discharge, the charging power consumption at the front-end metering point of the energy storage system is taken as the optimal value. Actual discharge of the energy storage system Simultaneously calculate the actual stored capacity of the energy storage system at this charge / discharge depth. It can be concluded that: Energy storage system charging efficiency ; Energy storage system discharge efficiency ; Therefore, the total amount of electricity actually charged and discharged in a single complete cycle of the energy storage system is: ; The cost per unit of energy (e.g., per kWh) for charging and discharging an energy storage system is: ; Therefore, the unit energy charge / discharge cost of lithium battery energy storage systems and vanadium redox flow storage systems in hybrid energy storage systems can be calculated. , .

[0052] The State of Charge (SOC) variation range and depth of charge / discharge essentially refer to the same core concept: the range of changes in the state of charge (SOC) of an energy storage system during actual charging and discharging. The SOC variation range emphasizes the actual range of changes in the battery's SOC during a charge-discharge cycle. The depth of charge / discharge describes a key parameter to consider when calculating unit energy cost, explicitly indicating the proportion of the energy storage system's rated capacity actually used in a complete charge-discharge cycle. Both refer to the magnitude of the actual change in the SOC of the energy storage system within a single charge-discharge cycle.

[0053] Step 104: Based on the unit energy charge / discharge cost and time step of the hybrid energy storage system. Time-of-use pricing for electricity grid billing Based on the predicted parameters and the planned charge and discharge power of the hybrid energy storage system, an objective function with the goal of optimal economic efficiency is constructed. By solving the objective function, the optimal planned charge and discharge power of the hybrid energy storage system for each time period is obtained.

[0054] This application aims for optimal economic efficiency by constructing a multi-constraint optimization model of the hybrid energy storage system and employing a linear programming algorithm to solve for the planned charge-discharge power strategy. The charge-discharge strategy of the hybrid energy storage system to be solved is equivalent to solving for the planned charge-discharge power of the hybrid energy storage system, such as solving for the planned charge-discharge power of a lithium battery energy storage system or a vanadium redox flow storage system based on a preset time dimension. , The specific process is as follows: 1. Establish the objective function In this embodiment of the application, an objective function for strategy optimization is established with the goal of achieving economic efficiency in electricity consumption and electricity costs. Let the time dimension step size (i.e., the time step size) be... If the power value is positive during the discharge of new energy power sources and energy storage, and negative during the charging of energy storage, then the economically optimal objective function is:

[0055] The objective function aims to find its minimum value.

[0056] 2. Establish constraints In this embodiment, the physical and logical constraints of the entire system are used as the system's constraints to establish system constraints. Combined with operations research optimization algorithms, such as linear programming, the model of the entire system is established and solved.

[0057] For example, the constraints include at least one of the following: Energy storage systems are subject to mutual exclusion constraints for charging and discharging. At any given time, the same energy storage system is only allowed to be in either charging or discharging state, and simultaneous charging and discharging is prohibited. Energy storage system charging and discharging power constraints: the charging and discharging power of each energy storage system shall not exceed the rated power to ensure that the equipment operates within a safe operating range and prevent overload damage. The operating range of the SOC of the energy storage system is constrained. The operating range of each energy storage SOC must be within the set range to prevent overcharging / over-discharging from causing lifespan degradation or thermal runaway risks. The optimization starting SOC of each energy storage system is set to the minimum SOC to ensure that the initial state is controllable. Power constraints on new energy power generation systems: the power of new energy sources shall not exceed the installed capacity. ; Power grid constraints: The maximum power grid output shall not exceed the maximum supported safe power. ; Local absorption constraints for new energy power generation systems mean that the minimum grid power is 0. Power balance constraints require the system to satisfy the real-time power balance equation: Grid power = ; The energy storage system's charge and discharge balance constraint requires the system to meet the following formulas: Energy storage system discharge capacity = Energy storage system discharge depth corresponding capacity * Energy storage system discharge efficiency; Energy storage system charging capacity = Energy storage system charging depth corresponding capacity / Energy storage system charging efficiency. Physical constraints on the relationship between the change in energy storage SOC, including charge and discharge losses, and the charge and discharge power and quantity.

[0058] 3. Solving the objective function The objective function and constraints are transformed into a linear programming model, and the optimal power for each time period is solved using operations research optimization algorithms (such as the simplex method and interior point method) to form a charging and discharging strategy.

[0059] First, the constraints established in step 2 are transformed into mathematical formulas relating the variables. Then, the constraints and objective function are substituted into a linear programming algorithm to solve for the optimal collaborative control strategy data for the lithium battery energy storage system and the vanadium redox flow storage system based on a specific time scale and granularity. In some embodiments, simultaneously, based on the constraint that the new energy power generation system cannot be connected to the grid, the control data of the new energy power generation system can be solved. In the entire power energy system, the power and capacity of each energy storage system are determined, and the predicted power sequences of the electricity load and the new energy power generation system are obtained through pre-set prediction technology. With the upper limit of the grid connection safety power determined, a linear programming model is constructed by integrating the objective function of the entire system and various constraints. Then, an operations research optimization algorithm, represented by linear programming, is applied to solve for the optimal hybrid energy storage charging and discharging strategy.

[0060] This application provides a basic solution process for a linear programming algorithm, which specifically includes the following steps: 1) Import the library file containing the linear programming algorithm into the system; 2) Create problem instances and define the optimization direction of linear programming problems, such as solving the problem of minimizing economic efficiency; 3) Define decision variables and set their upper and lower boundaries and variable types, such as the charging and discharging power of lithium battery energy storage systems and the charging and discharging power of vanadium redox flow energy storage systems mentioned above. 4) Set the objective function and constraints as described above; 5) Call the linear programming solver to calculate the optimal value.

[0061] This completes the work on... Figure 1 The description of the multi-source cooperative control method shown.

[0062] based on Figure 1 The method shown in this application can achieve the following technical effects: This application employs a hybrid energy storage system, effectively addressing the shortcomings of single energy storage systems and comprehensively considering the multi-dimensional needs of the power grid. Furthermore, this application combines multi-dimensional data such as basic parameters, operating conditions, and predicted parameters of the power energy system to construct and solve an objective function with optimal economic efficiency as the optimization goal, deriving a hybrid energy storage charging and discharging strategy (i.e., the optimal planned charging and discharging power of the hybrid energy storage system at each time period), thereby achieving automated control of the hybrid energy storage system. Compared to traditional fixed control strategies, the strategy derived in this application is more flexible, more adaptable to complex scenarios, and more optimized in terms of resource allocation, greatly improving overall efficiency.

[0063] In one optional embodiment, traditional hybrid energy storage control uses a fixed ratio to allocate medium-frequency power (e.g., mechanically setting lithium batteries and flow batteries to each bear 50%), ignoring the real-time health status of the batteries. This leads to accelerated lithium plating in lithium batteries due to prolonged high-frequency and medium-frequency tasks, while cross-contamination of the electrolyte in flow batteries intensifies under continuous low-frequency tasks, ultimately shortening the system lifespan. To address these issues, a dynamic allocation mechanism that adapts to health status is introduced, including: Step a1: Predict the power of the new energy power generation system. Perform wavelet packet transform to decompose it into high-frequency power components. Intermediate frequency component power and low-frequency component power .

[0064] Step a2, based on the health status of the lithium battery and the health status of flow batteries Calculate the frequency band entropy weight And obtain the normalized frequency band entropy weights. .

[0065] Among them, the health status of lithium batteries and the health status of flow batteries For real-time health status, existing technologies can be used to obtain the health status of the lithium battery. and the health status of flow batteries .

[0066] in, , This indicates the frequency band energy percentage of the target frequency band, including high-frequency component power. Frequency band energy percentage Intermediate frequency component power Frequency band energy percentage and low-frequency component power Frequency band energy percentage ,in, This indicates that the frequency band is limited to the current frequency band only. The proportion of energy in a given frequency band, such as calculating the power of high-frequency components. Frequency band energy percentage hour, Indicates high frequency band, Shannon entropy represents the high-frequency band; Indicates all frequency bands When calculating the frequency band energy percentage, the denominator should be... Traversing high, mid, and low frequency bands, Shannon entropy encompassing all frequency bands; Indicates the target frequency band Shannon entropy in the frequency band Represents the reciprocal of the Shannon entropy of the frequency band; Indicates the charge / discharge efficiency of a flow battery; Indicates the charge and discharge efficiency of a lithium battery; This represents the band Shannon entropy for all frequency bands. , This represents the Shannon entropy in the mid-frequency band. This represents the Shannon entropy in the low-frequency band. Shannon entropy represents the high-frequency band; It is a health regulation index.

[0067] Among them, the frequency band entropy weights of high, medium and low frequency bands are obtained. Then, the normalized frequency band entropy weights are calculated according to the existing normalization algorithm. , , Traversing high, mid, and low frequency bands, This represents the sum of the frequency band entropy weights for all frequency bands.

[0068] Step a3: Allocate all high-frequency component power to the lithium battery energy storage system; allocate all low-frequency component power to the vanadium redox flow storage system; and allocate the medium-frequency component power proportionally... Allocation, of which, , It is a health regulation index.

[0069] Among these, all high-frequency component power is allocated to the lithium battery energy storage system, providing... , where here For high-frequency band entropy weights Normalized frequency band entropy weights.

[0070] All low-frequency power components are allocated to the vanadium redox flow storage system, for , where For low-frequency band entropy weights Normalized frequency band entropy weights.

[0071] The intermediate frequency component power is proportional The power allocation for the lithium battery energy storage system is as follows: The total power capacity of the vanadium redox flow storage system is: ; .

[0072] In this embodiment, wavelet packet transform is used to predict the power of the new energy power generation system. Decomposed into high-frequency component power Intermediate frequency component power and low-frequency component power Based on the matching relationship between frequency band characteristics and battery response capabilities, high-frequency components are directionally allocated to the lithium battery to leverage its millisecond-level response advantage, low-frequency components are fully allocated to the flow battery to utilize its long-term regulation capability, and mid-frequency components are allocated dynamically. By implementing a dual-battery hybrid response, the response mismatch problem caused by traditional frequency mixing and allocation is eliminated, the frequency modulation error is reduced, and the response delay and ineffective loss caused by frequency band mismatch are avoided.

[0073] Optionally, a lithium plating risk model can be established as follows: ;in, This indicates the degree of degradation in the health status of the lithium battery; It represents the baseline decay coefficient, reflecting the inherent decay characteristics of the battery chemistry system; Indicates the lithium plating sensitivity coefficient; The peak variance-to-peak ratio (PVR) indicates the degree of drastic fluctuation in high-frequency loads. , Represents the power of high-frequency components The sequence variance, Represents the power of high-frequency components Peak value of absolute value Indicates the duration of a high-frequency task.

[0074] Among them, high A scenario (e.g., >0.8) indicates frequent load spikes, accelerating lithium-ion deposition and increasing the risk of lithium plating. Low In scenarios such as <0.3, the load fluctuation is gradual, and the risk of lithium plating is negligible.

[0075] in, , The reference lithium plating sensitivity coefficient is the inherent sensitivity parameter of a lithium battery to load shock at a standard temperature (25°C). Indicates the core temperature of the battery (°C); This represents the temperature sensitivity coefficient (typical value 0.02 / ℃). For every 10℃ increase in temperature, Increased by 20% to enhance lithium plating punishment under high-temperature conditions.

[0076] Among them, the lithium plating risk model Predict lifespan decline.

[0077] Among them, an electrolyte degradation model was established. , ;in, This indicates the electrolyte degradation index of a flow battery, quantifying the degree of electrolyte cross-contamination caused by low-frequency loads (the higher the value, the more severe the degradation). This represents the time integration operator, which calculates the cumulative effect from system startup time (0) to the current time (t). Represents the reconstruction coefficients; Indicates the electricity price coefficient; Spectral density representing the power of the low-frequency component; Indicates frequency, such as low-frequency fluctuation frequency (range 0.001~0.1Hz). This represents the exponential decay term, which suppresses the influence of high-frequency components. The larger the value, the faster the decay. Indicates the electrolyte penetration attenuation coefficient; This represents the maximum permissible degradation index.

[0078] Among them, when If the value exceeds a preset threshold, such as 0.05 / month, the weight of the high-frequency component allocation is reduced to 50%–70% of the original value, and the response ratio of the flow battery in the mid-frequency band is increased to 80%, thereby reducing the impact of high-rate discharge on the lithium battery and suppressing the risk of lithium plating.

[0079] when Greater than the preset threshold At that time, a pulsed charge rebalancing is initiated, transferring 10% of the low-frequency components to the lithium battery energy storage system, forcing ions to be evenly distributed, reducing vanadium ion penetration, and allowing the lithium battery to share some of the low-frequency tasks, thus avoiding long-term laminar flow operation of the flow battery.

[0080] At the same time, update the health regulation index. , Among them, the updated health regulation index back, Updated accordingly; Dynamic updates Real-time adjustments, minute-level optimization of power allocation strategies, and automatic reduction of damaging tasks to extend battery life when health deteriorates.

[0081] In this process, a health loss penalty term is embedded into the original objective function: ,in, This indicates the cost of replacing a lithium battery per unit capacity. This indicates the unit maintenance cost of a flow battery. This represents the health loss weighting coefficient, a regulating factor that balances economic efficiency and lifespan (e.g., 0.25).

[0082] In this embodiment of the application, the objective function is embedded and The long-term economic impact of battery life degradation is quantified into immediate operating costs, and weighted by coefficients. This model achieves a dynamic trade-off between short-term economic efficiency and long-term equipment lifespan. Traditional optimization models typically focus only on electricity purchase costs (the first term) and direct charge / discharge benefits (the second term), while this model introduces a third term—capacity degradation (associated with replacement costs). ) and maintenance needs (related maintenance costs) This is transformed into an economic penalty directly coupled with operational decisions, enabling the optimization algorithm to proactively avoid high-loss conditions (such as deep charge and discharge) when scheduling charging and discharging power. This allows the algorithm to endogenously extend battery life and reduce total lifecycle costs while minimizing daily total costs, thereby improving the sustainability of system planning.

[0083] Optionally, the basic parameters also include the actual output of the new energy power generation system. The prediction parameters also include short-term prediction power. and long-term predicted power The method further includes: Based on the actual output of the new energy power generation system and short-term predicted power Calculate the short-term forecast error ; Based on the actual output of the new energy power generation system and long-term predicted power Calculate the long-term cumulative error ; According to the short-term prediction error and long-term cumulative error Perform time-scale coupling compensation.

[0084] Among them, the actual output of new energy sources is a time-varying function that changes in real time, so It can also be written as Ultra-short-term power prediction is a time-dependent prediction sequence, therefore... It can also be written as .but .

[0085] in, Where t represents the current time and defines the upper limit of integration; This indicates the length of the sliding window and sets the error accumulation period (e.g., ≥2h). Indicates the time variable of integration; Represents the partial derivative operator; express Always putting in actual effort; express Long-term predicted power at time point; It represents the time derivative.

[0086] in, This usually refers to rapid fluctuations on the order of seconds to minutes (e.g., within 15 minutes), caused by cloud cover, sudden changes in wind speed, etc. This typically refers to systematic deviations lasting longer than an hour (e.g., within a 2-hour sliding window), caused by factors such as prolonged cloudy weather or inherent biases in the prediction model. Lithium-ion batteries offer high power density and fast response (milliseconds), but have limited cycle life, making them suitable for handling short-term, high-frequency fluctuations. Flow batteries, on the other hand, offer high energy density and slow response (minutes), but have long cycle life, making them suitable for handling long-term, persistent deviations.

[0087] The short-term prediction error is calculated. and long-term cumulative error Then, time-scale coupling compensation is performed, including the second-level response of the lithium battery and the hour-level response of the flow battery.

[0088] Among them, lithium batteries have a second-level response time: Hourly response time of flow batteries: `sat()` is a saturation function that limits the compensation power to within the lithium battery's capability range, providing hard limiting protection and ensuring the compensation power does not exceed the physical limits of the lithium battery. The exponential term prioritizes response within the SOC median region. This indicates the lithium battery compensation power, used for real-time error mitigation output. Indicates the maximum charging power; Indicates the maximum discharge power; This represents the attenuation coefficient, which controls the deviation of SOC from the penalty intensity. This indicates the state of charge (SOC) of the lithium battery, representing the real-time percentage of battery capacity. Specifically, it addresses... ,when At 50%, there is no attenuation (optimal operating point); When λ = 30% or 70% (λ = 5), the compensation power decreases to 60%; When the power is 20% or 80%, the compensation power decreases to 37%.

[0089] In this embodiment, overcharging / over-discharging is prevented based on a saturation function (hard constraint), and SOC soft protection (automatic derating when exceeding limits) is achieved based on exponential decay; priority is given to... =Operates around 50% (the region with the longest cycle life), automatically reduces power during deep charge and discharge (to reduce the risk of lithium plating); when When it approaches 50%, the 100% response error occurs. When the error reaches 20%, it only responds to 37% of the error, thus achieving dynamic response. A dual-insurance mechanism (hard limiting + soft degradation) resolves the conflict between lithium battery safety and lifespan.

[0090] Among them, the hourly response time of the flow battery is: ,in, To compensate for the activation condition, it is triggered only when the accumulated error exceeds the threshold; To compensate for the power of the flow battery and eliminate long-term accumulated errors; This is the capacity coupling coefficient; This is the power warm-up coefficient, used to control the saturation start point; This represents the state of charge (SOC) of the flow battery, showing the real-time percentage of its capacity. The ideal operating point is used for the optimal response location; It is a smoothing coefficient that controls the slope of the transition zone.

[0091] Among these methods, a triple nonlinear coupling mechanism is used to achieve safe and economical compensation for the flow battery. Linear driving term. The cumulative error of the response is expressed by the fourth power inverse radical. Superlinear saturation (full compensation for small errors, derating and shock prevention for large errors), followed by the hyperbolic tangent function. Achieving a smooth transition at the SOC boundary (50% operating point shutdown compensation protection lifetime, asymptotic full response in the high SOC region). Ultimately, the following effects are achieved: automatic derating for large errors (fourth-order suppression), soft transition at the SOC boundary (tanh function) for safety protection; zero compensation at the 50% SOC operating point (reducing electrolyte cross-contamination) for extended lifespan; linear compensation for small errors (lowest cost per kilowatt-hour), and near-100% response in the high SOC region during peak electricity prices (maximizing arbitrage) for economic self-adaptation.

[0092] In this embodiment, high-frequency short-term errors are compensated by a lithium battery with a second-level response. In a short period of time (e.g., 15 minutes) Zeroing out. This addresses the issue of solar power curtailment / limited power supply caused by the lag in response of flow batteries in traditional solutions, preventing short-term errors from propagating to long-term scales. The hourly response of flow batteries eliminates low-frequency, long-term accumulated errors. Over a long period of time (e.g., 2 hours) Zeroing out mitigates the limitation of insufficient lithium battery capacity to support long-term compensation, eliminating systemic biases. Based on the above short- and long-term elimination, a time-decoupled error elimination chain is formed. Rapid lithium battery compensation. To prevent it from accumulating Slow compensation of flow batteries This prevents lithium batteries from running out of capacity due to prolonged use.

[0093] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0094] Corresponding to the multi-source cooperative control method described in the above embodiments, Figure 2 A schematic diagram of a multi-source coordinated control device according to an embodiment of this application is shown. For ease of explanation, only the parts relevant to the embodiment of this application are shown. This device is applied to a power energy system that includes a hybrid energy storage system, a new energy power generation system, and electrical loads, and this power energy system is connected to the power grid.

[0095] Reference Figure 2 The device includes a basic parameter acquisition module 201, a prediction parameter acquisition module 202, a charge / discharge cost calculation module 203, and a strategy generation module 204. Wherein: The basic parameter acquisition module 201 is used to acquire basic parameters of the power grid, the hybrid energy storage system, and the new energy power generation system; the hybrid energy storage system includes a lithium battery energy storage system and a vanadium redox flow storage system; the basic parameters are inherent system attributes or externally given fixed parameters, and at least include the grid billing time-of-use tariff. ; The prediction parameter acquisition module 202 is used to acquire prediction parameters of the power energy system based on historical operating conditions and prediction models; the prediction parameters include at least the predicted power of the new energy power generation system. and load forecast power ; The charging and discharging cost calculation module 203 is used to calculate the unit energy charging and discharging cost of the hybrid energy storage system based on the preset initial investment amount, the cycle life of the hybrid energy storage system, and the basic parameters and operating conditions of the power energy system. Strategy generation module 204 is used to generate strategies based on the unit energy charge / discharge cost and time step of the hybrid energy storage system. Time-of-use pricing for electricity grid billing The predicted parameters and the planned charge and discharge power of the hybrid energy storage system are used to construct an objective function with the goal of optimal economic efficiency. Solving the objective function yields the optimal planned charge and discharge power of the hybrid energy storage system for each time period.

[0096] It should be noted that the information interaction and execution process between the above-mentioned devices / units / modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0097] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0098] This application also provides a computer device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor, when executing the computer program, implements the steps in any of the above method embodiments.

[0099] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0100] This application provides a computer program product that, when run on a computer device, enables the computer device to implement the steps described in the various method embodiments above.

[0101] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Figure 3 As shown, the computer device of this embodiment includes: at least one processor 30 ( Figure 3 (Only one is shown in the image), memory 31, and computer program 32 stored in the memory 31 and executable on the at least one processor 30, wherein the processor 30 executes the computer program 32 to implement the steps in any of the above-described visual programming method embodiments.

[0102] The computer device may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will understand that... Figure 3 The examples of computer devices are merely examples and do not constitute a limitation on computer devices. They may include more or fewer components than shown in the illustration, or combinations of certain components, or different components. For example, they may also include input / output devices, network access devices, etc.

[0103] The processor 30 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0104] In some embodiments, the memory 31 may be an internal storage unit of the computer device, such as a hard drive or memory. In other embodiments, the memory 31 may be an external storage device of the computer device, such as a plug-in hard drive, smart media card (SMC), secure digital card (SD), flash card, etc. Furthermore, the memory 31 may include both internal and external storage units of the computer device. The memory 31 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 31 can also be used to temporarily store data that has been output or will be output.

[0105] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a device / computer equipment, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0106] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0107] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0108] In the embodiments provided in this application, it should be understood that the disclosed apparatus / computer devices and methods can be implemented in other ways. For example, the apparatus / computer device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0109] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0110] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A multi-source cooperative control method, characterized in that, This method is applied to a power energy system that includes a hybrid energy storage system, a new energy power generation system, and electrical loads, and that the power energy system is connected to the power grid; the method includes: Acquire basic parameters of the power grid, hybrid energy storage system, and new energy power generation system; the hybrid energy storage system includes lithium battery energy storage system and vanadium redox flow storage system; the basic parameters are inherent system properties or externally given fixed parameters, including at least the time-of-use electricity price for grid billing. ; The predicted parameters of the power energy system are obtained based on historical operating conditions and prediction models; the predicted parameters include at least the predicted power of the new energy power generation system. and load forecast power ; Based on the preset initial investment amount and the cycle life of the hybrid energy storage system, as well as the basic parameters and operating conditions of the power energy system, calculate the unit energy charging and discharging cost of the hybrid energy storage system; Based on the unit energy charge / discharge cost and time step of the hybrid energy storage system Time-of-use pricing for electricity grid billing The predicted parameters and the planned charge and discharge power of the hybrid energy storage system are used to construct an objective function with the goal of optimal economic efficiency. Solving the objective function yields the optimal planned charge and discharge power of the hybrid energy storage system for each time period.

2. The method as described in claim 1, characterized in that, The basic parameters of the power grid include: Maximum supported safe power of the power grid Time-of-use pricing and grid billing ; The basic parameters of the hybrid energy storage system include: Capacity of lithium battery energy storage system Rated power of lithium battery energy storage system The capacity of the all-vanadium redox flow storage system Rated power of vanadium redox flow storage system The SOC operating range set for lithium battery energy storage systems The SOC operating space set for the all-vanadium redox flow storage system ; The basic parameters of the new energy power generation system include: Total installed capacity of new energy power sources .

3. The method as described in claim 2, characterized in that, The method further includes: Based on the basic parameters and operating conditions of the power energy system, the following calculation parameters are obtained: The actual SOC variation range of lithium battery energy storage systems The actual energy storage capacity used in lithium battery energy storage systems Energy efficiency of lithium battery energy storage systems The actual SOC variation range of vanadium redox flow storage system Actual energy storage capacity of vanadium redox flow storage system Energy efficiency of vanadium redox flow storage system .

4. The method as described in claim 1, characterized in that, The unit energy charge / discharge cost of any energy storage system in the hybrid energy storage system is calculated as follows: The initial investment is set to M, the energy storage capacity of the energy storage system is E, the cycle life of the energy storage system is N, and the depth of charge / discharge is [missing information]. Energy storage efficiency is ; Under a defined depth of charge and discharge, obtain the charging power consumption at the front-end metering point of the energy storage system. The actual discharge of the energy storage system And calculate the actual stored capacity of the energy storage system at this charge / discharge depth. ; The total amount of energy actually charged and discharged in a single complete cycle of an energy storage system is: Among them, the charging efficiency of the energy storage system Discharge efficiency of energy storage system ; The cost per unit of energy for charging and discharging an energy storage system is: .

5. The method as described in claim 1, characterized in that, The objective function is: ； in, , Lithium-ion battery energy storage systems and vanadium redox flow batteries are based on planned charge and discharge power over a preset time period. , The unit energy charge / discharge cost for lithium battery energy storage systems and vanadium redox flow energy storage systems, respectively. The objective function aims to find its minimum value.

6. The method as described in claim 1, characterized in that, Solving the objective function includes: Constraints are established, and the objective function and constraints are transformed into a linear programming model. The optimal planned charging and discharging power of the hybrid energy storage system for each time period is solved using an operations research optimization algorithm.

7. The method as described in claim 6, characterized in that, The constraints include at least one of the following: The constraints include mutual exclusion of charging and discharging of energy storage systems, charging and discharging power constraints of energy storage systems, SOC operating range constraints of energy storage systems, power constraints of new energy power generation systems, power constraints of power grids, local absorption constraints of new energy power generation systems, power balance constraints, charging and discharging energy balance constraints of energy storage systems, and physical constraints on the relationship between the change of energy storage SOC and charging and discharging power and energy, including charging and discharging losses.

8. The method as described in claim 1, characterized in that, The method further includes: Predicted power of the new energy power generation system Perform wavelet packet transform to decompose it into high-frequency component power, mid-frequency component power and low-frequency component power; Based on the health status of lithium batteries and the health status of flow batteries Calculate the frequency band entropy weight And obtain the normalized frequency band entropy weights. ; All high-frequency power is allocated to the lithium battery energy storage system; all low-frequency power is allocated to the vanadium redox flow battery energy storage system; and medium-frequency power is allocated proportionally. Allocation, of which, , It is a health regulation index.

9. The method as described in claim 1, characterized in that, The basic parameters also include the actual output of the new energy power generation system. The prediction parameters also include short-term prediction power. and long-term predicted power The method further includes: Based on the actual output of the new energy power generation system and short-term predicted power Calculate the short-term forecast error ; Based on the actual output of the new energy power generation system and long-term predicted power Calculate the long-term cumulative error ; According to the short-term prediction error and long-term cumulative error Perform time-scale coupling compensation.

10. A multi-source collaborative control device, characterized in that, This device is applied to a power energy system that includes a hybrid energy storage system, a new energy power generation system, and electrical loads, and that the power energy system is connected to the power grid; the device includes: The basic parameter acquisition module is used to acquire basic parameters of the power grid, the hybrid energy storage system, and the new energy power generation system; the hybrid energy storage system includes a lithium battery energy storage system and a vanadium redox flow battery energy storage system; the basic parameters are inherent system properties or externally given fixed parameters, and at least include the grid billing time-of-use tariff. ; The prediction parameter acquisition module is used to acquire prediction parameters of the power energy system based on historical operating conditions and prediction models; the prediction parameters include at least the predicted power of the new energy power generation system. and load forecast power ; The charging and discharging cost calculation module is used to calculate the unit energy charging and discharging cost of the hybrid energy storage system based on the preset initial investment amount, the cycle life of the hybrid energy storage system, and the basic parameters and operating conditions of the power energy system. The strategy generation module is used to determine the unit energy charge / discharge cost and time step of the hybrid energy storage system. Time-of-use pricing for electricity grid billing The predicted parameters and the planned charge and discharge power of the hybrid energy storage system are used to construct an objective function with the goal of optimal economic efficiency. Solving the objective function yields the optimal planned charge and discharge power of the hybrid energy storage system for each time period.

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