Hybrid energy storage system capacity optimization configuration method and electronic equipment

Abstract

According to the hybrid energy storage system capacity optimization configuration method and the electronic equipment provided by the invention, the problem of how to integrate an electrochemical energy storage battery, advanced adiabatic compressed air energy storage (AA-CAES) and a hydrogen storage system to construct an AA-CAES fused hybrid energy storage system capacity optimization configuration model in the prior art is solved; according to the technical scheme, the method comprises the steps of constructing a multi-energy complementary system architecture, selecting typical daily data by applying a K-means clustering technology in combination with a cumulative fluctuation quantity median, establishing an advanced adiabatic compressed air energy storage system model, and constructing a hybrid energy storage system capacity optimization configuration model containing the advanced adiabatic compressed air energy storage system by taking the maximum daily comprehensive net income as an objective function. According to the invention, the randomness and volatility of wind and light output can be effectively suppressed, and the reliability and economy of system power supply can be considered at the same time; a hydrogen storage device is arranged and a hydrogen selling channel is introduced, so that the overall income of the system is improved.

Description

Technical Field

[0001] This invention relates to the field of energy storage technology, and in particular to a method for optimizing the capacity configuration of a hybrid energy storage system and an electronic device. Background Technology

[0002] Guided by the "dual carbon" goal, vigorously promoting the large-scale development and efficient utilization of renewable energy sources such as wind and solar power has become a core path for energy system transformation. Wind and solar power generation are affected by natural conditions, resulting in significant intermittent power output and power fluctuations, making it difficult to match the power generation curve with the electricity load in real time. This instability puts pressure on the real-time balance of the grid after grid connection, increases the difficulty of peak shaving, and causes wind and solar power curtailment. Based on the current energy transformation situation, energy storage technology, with its efficient and flexible adjustment characteristics, is showing an increasingly significant role. Energy storage can effectively suppress the randomness and volatility of renewable energy generation, improve the grid's capacity to accept renewable energy, help the system achieve peak shaving targets, and provide support for the stable, reliable, and safe operation of the power system. However, various energy storage components have their own advantages and limitations, and a single energy storage technology often cannot fully cover the diverse needs of the power system. Hybrid energy storage systems, by integrating two or more energy storage technologies with differences in energy density and complementary performance characteristics, can meet the dual requirements for energy density and power density. Therefore, further research is needed on how to effectively integrate the synergistic gain effects of multiple energy storage technologies, accurately optimize their capacity configuration, and thus achieve simultaneous enhancement of system economic operation and power supply reliability.

[0003] Among various energy storage technologies, pumped hydro storage and compressed air storage (CHS) are particularly suitable for grid-level large-capacity, long-cycle energy dispatch scenarios due to their large system scale and long storage duration. However, pumped hydro power plants not only have high initial investment costs but also heavily rely on specific geographical conditions, requiring the construction of upper and lower reservoirs with significant elevation differences and close proximity, which limits their large-scale application. In mineral-rich areas such as Northwest my country, there are numerous closed mines, and these abandoned underground spaces provide natural gas storage sites for CHS systems. Converting these spaces into gas storage facilities can not only achieve the recycling of underground resources and improve the absorption capacity of new energy sources but also significantly reduce the construction cost of CHS systems. Energy storage batteries are flexible in adjustment and respond quickly, capable of meeting short-term, high-frequency power support needs. The significant environmental advantages and high energy density characteristics of hydrogen energy make it one of the key directions for future energy system transformation. Electrolysis of water can convert surplus electricity that cannot be immediately absorbed into hydrogen energy. The generated hydrogen can be used to generate electricity through fuel cells, or transported externally via pipelines or vehicles, thus enabling the effective utilization of wind and solar power resources on a broader spatial and temporal scale.

[0004] Existing research on constructing multi-energy complementary systems generally adopts a model based on traditional power sources, coupled with one or two energy storage technologies. However, research on hybrid energy storage systems that primarily utilize new energy sources and integrate multiple energy storage forms such as gas, electricity, and hydrogen remains limited. How to integrate electrochemical energy storage batteries, advanced adiabatic compressed air energy storage (AA-CAES), and hydrogen storage systems to construct a capacity optimization configuration model for a hybrid energy storage system incorporating AA-CAES is a technical problem that needs to be solved. Summary of the Invention

[0005] To address the problem of how existing technologies can integrate electrochemical energy storage batteries, advanced adiabatic compressed air energy storage (AA-CAES), and hydrogen storage systems to construct a capacity optimization configuration model for a hybrid energy storage system incorporating AA-CAES, the present invention aims to provide a capacity optimization configuration method and electronic device for a hybrid energy storage system.

[0006] To address the aforementioned technical problems, in a first aspect, according to some embodiments, the present invention provides a method for optimizing the capacity configuration of a hybrid energy storage system, comprising:

[0007] A multi-energy complementary system architecture is constructed, the system including an energy storage battery system, a compressed air energy storage system, and an electric-hydrogen conversion system, and the operation strategies of the energy storage battery system, the compressed air energy storage system, and the hydrogen storage system are determined.

[0008] Typical daily data are selected by combining K-means clustering technology with the median of cumulative fluctuations. This includes: dividing the original data of a year into data clusters using the K-means clustering method based on the historical data of the whole year, calculating the sum of the fluctuations of all days under different scenarios, obtaining the median by sorting, and selecting the days corresponding to the median as typical days of that scenario, as data for wind power, photovoltaics and load.

[0009] An advanced adiabatic compressed air energy storage system model is established. With the objective function of maximizing daily comprehensive net income, a capacity optimization configuration model for a hybrid energy storage system incorporating advanced adiabatic compressed air energy storage is constructed. The optimization configuration model includes: an advanced adiabatic compressed air energy storage system model and a hybrid energy storage system capacity optimization configuration model incorporating advanced adiabatic compressed air energy storage. The hybrid energy storage system capacity optimization configuration model incorporating advanced adiabatic compressed air energy storage includes constraints on energy storage batteries, compressed air energy storage systems, and hydrogen energy systems.

[0010] Optionally, in some embodiments, the advanced adiabatic compressed air energy storage system model includes:

[0011] The mathematical model for the AA-CAES charging and discharging process, expressed by Formula 1, is as follows:

[0012] (1)

[0013] In the formula: and These are defined as the total power consumption and power generation of all compressors and expanders within time step t, respectively. and This corresponds to the ambient air mass flow rate entering the compressor and the high-pressure air mass flow rate being sent to the expander during the same time period; The specific heat capacity ratio of air; Represents the ideal gas coefficient; in terms of system structure, and These represent the total number of stages for the compressor and expander, respectively. and This represents the isentropic efficiency of the k-th level device; among the temperature parameters, and These are the inlet air temperatures of the k-th stage compressor and expander, respectively, and their outlet temperatures are correspondingly... and ; and These are defined as the design compression ratio and expansion ratio of the k-th stage compressor and expander, respectively.

[0014] The rate of change of air pressure over time and the instantaneous air pressure value are expressed using Formula 2, as follows:

[0015] (2)

[0016] in, and These represent the rate of change of gas pressure in the gas storage chamber during time period t and the gas pressure value at the end of that time period, respectively. This is the initial air pressure; and These represent the air temperature inside the gas storage chamber and the wall temperature, respectively. The volume of the gas storage chamber; This is the unit scheduling duration (taken as 1 hour); and It is the comprehensive heat transfer coefficient, used to describe the heat exchange process caused by natural convection and forced convection;

[0017] The thermal storage power and heating work of AA-CAES are expressed by Formula 3, as follows:

[0018] (3)

[0019] in: and These represent the recovered heat power and consumed heat power of AA-CAES during time period t, respectively. and These are the high and low temperature heat exchange medium temperatures, respectively. The isobaric specific heat capacity of air;

[0020] Based on the first law of thermodynamics, the energy of a thermal storage tank is conserved per unit time. The rate of change of energy within the tank is equal to the difference between the net heat inflow and the heat dissipation of the system. This relationship can be described by the following equation:

[0021]

[0022] In the formula: The heat storage loss rate within a unit scheduling period; and These represent the heat storage capacity of the thermal storage tank at time t and time t−1, respectively. The former is calculated from the latter after heat loss correction.

[0023] Optionally, in some embodiments, the construction of a capacity optimization configuration model for a hybrid energy storage system including advanced adiabatic compressed air energy storage, with the objective function of maximizing daily comprehensive net revenue, specifically includes:

[0024] With the core optimization objective of maximizing daily net profit, the objective function is expressed as shown in Formula 5, as follows:

[0025] (5)

[0026] In the formula: Daily net income; Total daily revenue; , , and These are daily investment costs, operation and maintenance costs, replacement costs, and depreciation costs, respectively.

[0027] The system's daily overall revenue is expressed using Formula Six, as follows:

[0028] (6)

[0029] In the formula: The actual power delivered by the system at time t; Power deviation; , and These are the prices for electricity sales, hydrogen sales, and power deviation penalties, respectively. Let be the amount of hydrogen delivered by the system at time t; To optimize the duration of each day, take one day's formula: The actual power delivered by the system at time t; Power deviation; , and These are the prices for electricity sales, hydrogen sales, and power deviation penalties, respectively. Let be the amount of hydrogen delivered by the system at time t; To optimize the duration of a day, we take one day as 24 hours.

[0030] The daily investment cost of the system is expressed using Formula 7, as follows:

[0031] (7)

[0032] In the formula: The system's operating lifespan; The system discount rate; These represent the investment costs of five subsystems: wind power, photovoltaic power, energy storage batteries, compressed air energy storage systems, and water electrolysis hydrogen production systems. The calculation method is expressed by Formula 8, as follows:

[0033] (8)

[0034] In the formula: the unit capacity cost of wind power and photovoltaic power are respectively denoted as... and In the compressed air energy storage subsystem, the unit capacity cost of the compressor, expander, and thermal storage tank is defined as follows: , and The unit volume construction cost of the gas storage chamber is The unit investment cost of the power and capacity of the battery energy storage system is respectively... and The text states that the hydrogen energy subsystem comprises three parts: an electrolyzer, a fuel cell, and a hydrogen storage device, with unit capacity costs of [missing information]. , and The corresponding capacity variables in the model include: wind power installed capacity. Photovoltaic installed capacity Battery power With capacity compressor rated power Expander rated power Thermal storage tank capacity and the volume of the gas storage chamber The hydrogen energy component includes the capacity of the electrolyzer. Fuel cell capacity With hydrogen storage capacity ;

[0035] In the economic analysis of this model, the system's daily operation and maintenance costs, equipment replacement costs, and depreciation costs are key factors. This application only considers the battery replacement cost and lifespan, and the specific calculation method for its cost items is as follows:

[0036] (9)

[0037] In the formula: This indicates the kth time the battery has been replaced. This is the operation and maintenance cost coefficient; This refers to the actual lifespan of the battery. This represents the total number of battery replacements. ,calculate Time Round up; This indicates the kth time the battery has been replaced. This refers to the residual value coefficient.

[0038] The actual power delivered by the system is calculated using Formula 10, as follows:

[0039] (10)

[0040] In the formula: , , and These represent the compression power, expansion power generation, hydrogen production power, and fuel cell power generation at time t, respectively. and These represent the charging power and discharging power of the battery at the same moment, respectively. and These correspond to the wind power generation and photovoltaic power generation at time t, respectively;

[0041] The power deviation is calculated using Formula 11, as follows:

[0042] (11)

[0043] In the formula: The load at time t;

[0044] The system power deviation rate is expressed by Formula XII, as follows:

[0045] (12);

[0046] In the formula, This represents the system power deviation rate.

[0047] Optionally, in some embodiments, the energy storage battery constraint includes:

[0048] The operating boundaries of the energy storage battery during charging and discharging are jointly limited by its maximum allowable charging power and maximum allowable discharging power, expressed by Formula Thirteen, as follows:

[0049] (13)

[0050] In the formula: This is a charging state variable; a value of 1 indicates charging. This is a discharge state variable; a value of 1 indicates discharge.

[0051] The state of charge and discharge constraints of the energy storage battery are expressed by Formula Fourteen, as follows:

[0052] (14)

[0053] The state of charge constraint is expressed by Formula 15, as follows:

[0054] (15)

[0055] In the formula: and These correspond to the lower and upper limits of the permissible operating range under charged conditions, respectively. This represents the state of charge of the battery at time t; , These represent the battery's charging efficiency and discharging efficiency, respectively.

[0056] The state-of-charge constraints at the beginning and end of the energy storage battery scheduling cycle are expressed by Formula Sixteen, as follows:

[0057] (16)

[0058] In the formula: Indicates the allowable capacity deviation; and These represent the state of charge of the battery at the beginning and end of the scheduling cycle, respectively.

[0059] The cycle life of energy storage batteries is expressed using Formula 17, as follows:

[0060] (17)

[0061] In the formula: The lifetime decay index obtained by fitting; Represents the depth of discharge in actual operation; This indicates the number of cycles required to achieve 100% depth of discharge. This indicates the total number of cycles a battery can complete within its lifespan.

[0062] Equivalent cycle number at 100% depth of discharge The conversion relationship is expressed using Formula 18, as follows:

[0063] (18)

[0064] Battery daily equivalent full cycle count It is represented by Formula Nineteen, as follows:

[0065] (19)

[0066] Cyclic state constraints are represented by Formula 20, as follows:

[0067] (20)

[0068] In the formula: It is a key variable characterizing the cycle state; when its value is 1, it indicates that a complete charge-discharge cycle has been completed. Indicates the depth of discharge of the battery; This corresponds to the real-time state of charge of the battery;

[0069] The charging and discharging processes of a battery are mutually exclusive in operation, a characteristic expressed by Equation 21, as follows:

[0070] (twenty one)

[0071] In the formula: and All are binary state identifier variables, representing the state variables during the charging process. The time t indicates that the battery is in a charging state at time t; When the time is right, it indicates that the battery is in a discharging state.

[0072] The charging and discharging states of a battery exhibit a continuous temporal characteristic, and their dynamic evolution must satisfy the constraints of Equation 22 to ensure that the operating mode at time t is consistent with the state at time t-1.

[0073] (twenty two)

[0074] In the formula: This is a charging state variable; a value of 1 indicates charging. This is a discharge state variable; a value of 1 indicates discharge.

[0075] The number of times a battery completes one charge-discharge cycle is determined by the state transition from discharge to charging within adjacent time periods, as defined in Formula 23, as follows:

[0076] (twenty three);

[0077] In the formula, This represents the battery's cycle state variable; a value of 1 indicates that one cycle has been completed.

[0078] Optionally, in some embodiments, the constraints of the compressed air energy storage system specifically include:

[0079] The planning constraints for each nominal value of the AA-CAES system are expressed using Formula 24, as follows:

[0080] (twenty four)

[0081] In the formula: , The lower and upper limits of the rated compression power that the compressor is allowed to be configured with are specified respectively; , The reasonable range of values ​​for the rated expansion power of the expander was defined; , Together, they defined the feasible range for the gas storage chamber volume; , This defines the minimum and maximum allowable values ​​for the rated thermal storage capacity of the thermal storage tank;

[0082] The operational constraints for the compression and expansion phases of AA-CAES are expressed by Formula 25, as follows:

[0083] (25)

[0084] In the formula: and These are binary state variables, representing the operating states of the compressor and expander during time period t, respectively. A value of 1 indicates operation, and a value of 0 indicates shutdown. and These are the wide operating condition adjustment coefficients for the compressor and expander, respectively, used to set the allowable power range of the equipment under partial load.

[0085] The operational constraints of the AA-CAES gas storage chamber are expressed using Formula 26, as follows:

[0086] (26)

[0087] In the formula and These represent the upper and lower limits of the gas pressure in the gas storage chamber, respectively.

[0088] The operating constraints of the AA-CAES thermal storage tank are expressed by Formula 27, as follows:

[0089] (27)

[0090] In the formula: The maximum thermal storage power that the system can receive within a unit scheduling period is defined. This is the maximum allowable heat power output within the same time period; The minimum heat capacity ratio that a thermal storage tank must maintain is specified as a safe operating parameter.

[0091] Optionally, in some embodiments, the constraints of the hydrogen energy system specifically include:

[0092] The constraint for hydrogen production in the electrolyzer is shown in Formula 28, as follows:

[0093]

[0094] In the formula: For hydrogen production efficiency; The amount of hydrogen produced by the electrolyzer at time t;

[0095] The operating range constraints of the electrolytic cell are defined by Formula 29, as follows:

[0096]

[0097] In the formula: and These represent the upper and lower limit coefficients of the electrolytic cell output, respectively; This is a binary variable that characterizes the operating status of an electrolytic cell. When its value is 1, it indicates that the electrolytic cell is in operation.

[0098] The power generation constraint of fuel cells is given by Equation 30, as follows:

[0099]

[0100] In the formula: For the power generation efficiency of fuel cells; The amount of hydrogen consumed by the fuel cell during time period t;

[0101] The output power of the fuel cell during time period t must meet the following operating range limitations, as shown in Formula 31, as follows:

[0102]

[0103] In the formula: and These are the maximum and minimum technical output coefficients of the fuel cell, respectively. A binary variable representing the operating state of a fuel cell;

[0104] The hydrogen storage capacity of the hydrogen storage device should fall within the range of Formula 32 at any given time t:

[0105]

[0106] In the formula: This represents the actual amount of hydrogen stored in the hydrogen storage device at time t;

[0107] The energy conservation relationship of a hydrogen storage device is defined by the following equilibrium equation, as shown in Formula 33, as follows:

[0108]

[0109] In the formula: This represents the amount of hydrogen exported during that period.

[0110] The amount of hydrogen that a hydrogen storage device can deliver per unit time period is limited by the following delivery capacity, as shown in Formula 34:

[0111]

[0112] In the formula: This indicates the maximum external transmission capacity of the hydrogen storage device within a unit of time period.

[0113] Optionally, in some embodiments, the operating strategy of the energy storage battery system specifically includes:

[0114] During periods when wind farms or photovoltaic power stations experience power curtailment, energy storage batteries are permitted to charge, but discharging is prohibited.

[0115] When wind and solar power generation is in normal operation and there is no power curtailment, the authorized energy storage battery releases electrical energy into the system.

[0116] The criteria for determining the state of power abandonment are defined by the constraint conditions in Formula 35, as follows:

[0117] (35)

[0118] In the formula: For wind power installed capacity, For photovoltaic installed capacity, and The wind power generation and photovoltaic power generation at time t, respectively. As an indicator of the state of power curtailment, This indicates that power wasted; θ is a very small positive number introduced to maintain numerical stability. This represents the normalized theoretical power generation of wind power at time t. Let be the normalized theoretical power generation of photovoltaic power at time t;

[0119] The energy storage battery operation strategy constraints are as follows:

[0120] (36);

[0121] in, For battery power, and These represent the charging power and discharging power of the battery at the same moment, respectively.

[0122] Optionally, in some embodiments, the compressed air energy storage system operation strategy and the hydrogen storage system operation strategy specifically include:

[0123] The compressed air energy storage system operation strategy includes: when wind and solar power curtailment occurs, the system starts the compressor to consume excess electrical energy to generate high-pressure air, and the expansion power generation stage is suspended.

[0124] During periods when there is no power wastage, the compressor stops working, allowing the expander to generate electricity using the stored high-pressure air;

[0125] The hydrogen storage system operation strategy includes: during periods of power curtailment, the electrolyzer is started to convert the remaining electricity into hydrogen for storage, and the fuel cell is prohibited from generating electricity;

[0126] During periods when there is no power wastage, the electrolyzer stops producing hydrogen, allowing the fuel cell to generate electricity using the stored hydrogen.

[0127] The criteria for determining the state of power abandonment are defined by the constraint conditions in Formula 35, as follows:

[0128] (35)

[0129] In the formula: For wind power installed capacity, For photovoltaic installed capacity, and The wind power generation and photovoltaic power generation at time t, respectively. As an indicator of the state of power curtailment, This indicates that power wasted; θ is a very small positive number introduced to maintain numerical stability.

[0130] The compressed air energy storage operation strategy is subject to the following constraints:

[0131] (37)

[0132] in, The rated power of the compressor. This refers to the compressor's rated power. Let be the compression power at time t. Let be the expansion power generation at time t;

[0133] The operating strategy constraints for the hydrogen storage system are as follows:

[0134] (38)

[0135] in, and These represent the hydrogen production power and fuel cell power generation power at time t, respectively. This refers to the capacity of the electrolytic cell.

[0136] In a second aspect, embodiments of the present invention also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method described in any of the first aspects above.

[0137] Thirdly, according to embodiments of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the method described in any of the first aspects above.

[0138] The above-described technical solution of the present invention has at least the following beneficial technical effects:

[0139] Through the above design scheme, the present invention can bring the following beneficial effects: The present invention constructs a capacity optimization configuration model for a gas-electricity-hydrogen hybrid energy storage system integrating advanced adiabatic compressed air energy storage. In new energy power generation systems lacking conventional power support, it can effectively suppress the randomness and fluctuation of wind and solar power output, and simultaneously take into account the system's power supply reliability and economy; configuring hydrogen storage devices and introducing hydrogen sales channels improves the overall system revenue; and formulating a coordinated operation strategy for new energy and diversified energy storage effectively reduces the operation frequency of energy storage equipment without significantly affecting the overall system revenue, thus avoiding lifespan degradation caused by frequent start-stop operations. Attached Figure Description

[0140] To more clearly illustrate the technical solutions in the embodiments of the present invention or in the conventional art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0141] Figure 1 This invention provides a method for optimizing the capacity configuration of a hybrid energy storage system.

[0142] Figure 2 A structural diagram of a hybrid energy storage system provided in an embodiment of the present invention;

[0143] Figure 3 A structural diagram of an advanced insulated compressed air energy storage system for converting abandoned mines, provided in an embodiment of the present invention;

[0144] Figure 4 The annual wind, solar, and load output curves provided for embodiments of the present invention;

[0145] Figure 5 Typical daily curves of wind, solar, and load provided for embodiments of the present invention;

[0146] Figure 6 This is a diagram showing the operation and output of Scheme 1 provided in an embodiment of the present invention;

[0147] Figure 7 This is a diagram showing the operation and output of Scheme 2 provided in an embodiment of the present invention;

[0148] Figure 8 This is a diagram showing the operation and output of Scheme 3 provided in this embodiment of the invention;

[0149] Figure 9 This is a diagram showing the operation and output of Scheme 4 provided in an embodiment of the present invention;

[0150] Figure 10 A graph showing the variation in operational revenue for different hydrogen energy transmission capacities provided in this embodiment of the invention;

[0151] Figure 11 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0152] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0153] Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the present invention.

[0154] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0155] Addressing the shortcomings of existing research, this invention comprehensively considers the technical characteristics of electrochemical energy storage batteries, advanced adiabatic compressed air energy storage (AA-CAES), and hydrogen storage systems, constructs a capacity optimization configuration model for a hybrid energy storage system integrating AA-CAES, and formulates a collaborative operation strategy for new energy sources and multiple energy storage systems.

[0156] like Figure 1 and Figure 2 As shown, this embodiment of the invention provides a method for optimizing the capacity configuration of a hybrid energy storage system, including:

[0157] S1. Construct a multi-energy complementary system architecture, the system including an energy storage battery system, a compressed air energy storage system, and an electric-hydrogen conversion system, and determine the operation strategies of the energy storage battery system, the compressed air energy storage system, and the hydrogen storage system.

[0158] S2. Use K-means clustering technology combined with the median of cumulative fluctuations to select typical daily data, including: based on the historical data of the whole year, use the K-means clustering method to divide the original data of the year into data clusters, count the sum of the fluctuations of all days under different scenarios, obtain the median by sorting, and select the days corresponding to the median as typical days of that scenario, as wind power, photovoltaic and load data.

[0159] S3. Establish an advanced adiabatic compressed air energy storage system model. With the objective function of maximizing the daily comprehensive net benefit, construct a capacity optimization configuration model for a hybrid energy storage system containing advanced adiabatic compressed air energy storage. The optimization configuration model includes: an advanced adiabatic compressed air energy storage system model and a hybrid energy storage system capacity optimization configuration model containing advanced adiabatic compressed air energy storage. The hybrid energy storage system capacity optimization configuration model containing advanced adiabatic compressed air energy storage includes constraints on energy storage batteries, compressed air energy storage systems, and hydrogen energy systems.

[0160] The following is a detailed explanation.

[0161] Step 1: Construct the overall architecture of the multi-energy complementary system, and use K-means clustering technology combined with the median of cumulative fluctuations to select typical daily data.

[0162] This system couples wind and solar power generation with multiple subsystems, including battery energy storage, advanced adiabatic compressed air energy storage (AA-CAES), and water electrolysis for hydrogen production. Within the system, wind and solar power serve as the electricity supply; the battery and compressed air energy storage work in tandem to effectively mitigate the intermittency and volatility of renewable energy output, ensuring real-time balance between power generation and load; and the water electrolysis for hydrogen production further enables efficient utilization of surplus wind and solar power, enhancing the overall system's flexibility and energy efficiency.

[0163] To address the high computational complexity and significant time consumption associated with full-time simulation when analyzing large-scale wind, solar, and load data, K-means clustering is employed for scenario reduction. However, this method has limitations when dealing with extreme values ​​in wind, solar, and load sequences. Because the algorithm is sensitive to outliers, the centroids of clusters obtained by traditional K-means clustering tend to deviate from the actual data distribution. To better resist the interference of extreme data, the median of cumulative fluctuations is calculated to determine the typical daily data for each scenario. The specific steps are as follows:

[0164] 1) Based on the historical data for the whole year, the K-means clustering method was used to divide the original data of the year into 7 data clusters. To ensure that the wind, solar and load data obtained by clustering meet the time correlation, the original wind, solar and load data first need to be concatenated into a one-dimensional vector with a length of 24h×3 before performing the clustering operation.

[0165] 2) Calculate the sum of the fluctuations of all days under different scenarios, and then obtain the median by sorting. Select the day corresponding to the median as the typical day of this type of scenario, and use it as a representative 7-day wind power, photovoltaic and load data in a year.

[0166] Step 2: Transform the abandoned mine into a gas storage chamber, establish an advanced adiabatic compressed air energy storage system model, and construct a capacity optimization configuration model for a hybrid energy storage system that includes advanced adiabatic compressed air energy storage, with the objective function of maximizing the daily comprehensive net income. Figure 3 Structural diagram of an advanced insulated compressed air energy storage system for the renovation of abandoned mines.

[0167] (1) Model of advanced adiabatic compressed air energy storage system

[0168] Mathematical model of the AA-CAES charging and discharging process:

[0169] (1)

[0170] In the formula: and These are defined as the total power consumption and power generation of all compressors and expanders within time step t, respectively. and This corresponds to the ambient air mass flow rate entering the compressor and the high-pressure air mass flow rate being sent to the expander during the same time period; The specific heat capacity ratio of air; Represents the ideal gas coefficient; in terms of system structure, and These represent the total number of stages for the compressor and expander, respectively. and This represents the isentropic efficiency of the k-th level device; among the temperature parameters, and These are the inlet air temperatures of the k-th stage compressor and expander, respectively, and their outlet temperatures are correspondingly... and ; and These are defined as the design compression ratio and expansion ratio of the k-th stage compressor and expander, respectively.

[0171] Rate of change of air pressure over time and instantaneous air pressure value:

[0172] (2)

[0173] In the formula: and These represent the rate of change of gas pressure in the gas storage chamber during time period t and the gas pressure value at the end of that time period, respectively. This is the initial air pressure; and These represent the air temperature inside the gas storage chamber and the wall temperature, respectively. The volume of the gas storage chamber; This is the unit scheduling duration (taken as 1 hour); and It is the comprehensive heat transfer coefficient, used to describe the heat exchange process caused by natural convection and forced convection.

[0174] Thermal storage capacity and heating capacity of AA-CAES:

[0175] (3)

[0176] In the formula: and These represent the recovered heat power and consumed heat power of AA-CAES during time period t, respectively. and These are the high and low temperature heat exchange medium temperatures, respectively. This is the isobaric specific heat capacity of air.

[0177] Based on the first law of thermodynamics, the energy of a thermal storage tank is conserved per unit time. The rate of change of energy within the tank is equal to the difference between the net heat inflow and the heat dissipation of the system. This relationship can be described by the following equation:

[0178] (4)

[0179] In the formula: The heat storage loss rate within a unit scheduling period; and These represent the heat storage capacity of the thermal storage tank at time t and time t−1, respectively. The former is calculated from the latter after heat loss correction.

[0180] (2) Capacity optimization configuration model of hybrid energy storage system with advanced adiabatic compressed air energy storage

[0181] In this invention, the core optimization objective is to maximize the daily overall net profit, and its objective function is expressed as follows:

[0182] (5)

[0183] In the formula: Daily net income; Total daily revenue; , , and These are daily investment costs, operation and maintenance costs, replacement costs, and depreciation costs.

[0184] System daily total revenue:

[0185] (6) In the formula: The actual power delivered by the system at time t; Power deviation; , and These are the prices for electricity sales, hydrogen sales, and power deviation penalties, respectively. Let be the amount of hydrogen delivered by the system at time t; To optimize the duration of each day, take one day's formula: The actual power delivered by the system at time t; Power deviation; , and These are the prices for electricity sales, hydrogen sales, and power deviation penalties, respectively. Let be the amount of hydrogen delivered by the system at time t; The daily optimization duration is calculated using a 24-hour period. System daily investment cost:

[0186] (7)

[0187] In the formula: The system's operating lifespan; The system discount rate; These represent the investment costs of five subsystems: wind power, photovoltaic power, energy storage batteries, compressed air energy storage systems, and water electrolysis hydrogen production systems. The calculation method is as follows:

[0188] (8)

[0189] In the formula: the unit capacity cost of wind power and photovoltaic power are respectively denoted as... and In the compressed air energy storage subsystem, the unit capacity cost of the compressor, expander, and thermal storage tank is defined as follows: , and The unit volume construction cost of the gas storage chamber is The unit investment cost of the power and capacity of the battery energy storage system is respectively... and The text states that the hydrogen energy subsystem comprises three parts: an electrolyzer, a fuel cell, and a hydrogen storage device, with unit capacity costs of [missing information]. , and The corresponding capacity variables in the model include: wind power installed capacity. Photovoltaic installed capacity Battery power With capacity compressor rated power Expander rated power Thermal storage tank capacity and the volume of the gas storage chamber The hydrogen energy component includes the capacity of the electrolyzer. Fuel cell capacity With hydrogen storage capacity .

[0190] In the economic analysis of this model, the system's daily operation and maintenance costs, equipment replacement costs, and depreciation costs are key factors. This paper only considers the battery replacement cost and lifespan. The specific calculation methods for these cost items are as follows:

[0191] (9)

[0192] In the formula: This indicates the kth time the battery has been replaced. This is the operation and maintenance cost coefficient; This refers to the actual lifespan of the battery. This represents the total number of battery replacements. ,calculate Time Round up; This indicates the kth time the battery has been replaced. This is the residual value coefficient.

[0193] Actual power delivered by the system:

[0194] (10)

[0195] In the formula: , , and These represent the compression power, expansion power generation, hydrogen production power, and fuel cell power generation at time t, respectively. and These represent the charging power and discharging power of the battery at the same moment, respectively. and These correspond to the wind power generation and photovoltaic power generation at time t, respectively.

[0196] Power deviation:

[0197] (11)

[0198] In the formula: Let t be the load at time t.

[0199] System power deviation rate:

[0200] (12)

[0201] 1) Constraints of energy storage batteries:

[0202] The operating boundaries of an energy storage battery during charging and discharging are jointly defined by its maximum permissible charging power and maximum permissible discharging power.

[0203] (13)

[0204] In the formula: This is a charging state variable; a value of 1 indicates charging. This is a discharge state variable; a value of 1 indicates discharge.

[0205] The state of charge / discharge constraints of the energy storage battery are:

[0206] (14)

[0207] State of charge constraints:

[0208] (15)

[0209] In the formula: and These correspond to the lower and upper limits of the permissible operating range under charged conditions, respectively. This represents the state of charge of the battery at time t; , These represent the battery's charging efficiency and discharging efficiency, respectively.

[0210] Energy storage battery scheduling cycle start and end state of charge constraints:

[0211] (16)

[0212] In the formula: Indicates the allowable capacity deviation; and These represent the state of charge of the battery at the beginning and end of the scheduling cycle, respectively.

[0213] Energy storage battery cycle life:

[0214] (17)

[0215] In the formula: The lifetime decay index obtained by fitting; Represents the depth of discharge in actual operation; This indicates the number of cycles required to achieve 100% depth of discharge. This indicates the total number of cycles a battery can complete within its lifespan.

[0216] Equivalent cycle number at 100% depth of discharge The conversion relationship is as follows:

[0217] (18)

[0218] Battery daily equivalent full cycle count for:

[0219] (19)

[0220] Cyclic state constraints:

[0221] (20)

[0222] In the formula: It is a key variable characterizing the cycle state; when its value is 1, it indicates that a complete charge-discharge cycle has been completed. Indicates the depth of discharge of the battery; This corresponds to the real-time state of charge of the battery.

[0223] The charging and discharging processes of a battery are mutually exclusive in operation. This characteristic is defined by the following mathematical relationship:

[0224] (twenty one)

[0225] In the formula: and All are binary state identifier variables, representing the state variables during the charging process. The time t indicates that the battery is in a charging state at time t; When the time is right, it indicates that the battery is in a discharging state.

[0226] The charging and discharging states of a battery exhibit a continuous temporal characteristic. Its dynamic evolution must satisfy the following constraints to ensure that the operating mode at time t is consistent with the state at time t-1:

[0227] (twenty two)

[0228] In the formula: This is a charging state variable; a value of 1 indicates charging. This is a discharge state variable; a value of 1 indicates discharge.

[0229] The number of times a battery completes one charge-discharge cycle is determined by the transition from discharge to charging state within adjacent time periods, as defined below:

[0230] (twenty three)

[0231] 2) Constraints of compressed air energy storage systems:

[0232] The planning constraints for each nominal value of the AA-CAES system are as follows:

[0233] (twenty four)

[0234] In the formula: , The lower and upper limits of the rated compression power that the compressor is allowed to be configured with are specified respectively; , The reasonable range of values ​​for the rated expansion power of the expander was defined; , Together, they defined the feasible range for the gas storage chamber volume; , This defines the minimum and maximum allowable values ​​for the rated thermal storage capacity of the thermal storage tank.

[0235] The operational constraints for the compression and expansion phases of AA-CAES are as follows:

[0236] (25)

[0237] In the formula: and These are binary state variables, representing the operating states of the compressor and expander during time period t, respectively. A value of 1 indicates operation, and a value of 0 indicates shutdown. and These are the wide operating condition adjustment coefficients for the compressor and expander, respectively, used to set the allowable power range of the equipment under partial load.

[0238] The operating constraints of the AA-CAES gas storage chamber are as follows:

[0239] (26)

[0240] In the formula and These represent the upper and lower limits of the gas pressure in the gas storage chamber, respectively.

[0241] AA-CAES thermal storage tank operating constraints:

[0242] (27)

[0243] In the formula: The maximum thermal storage power that the system can receive within a unit scheduling period is defined. This is the maximum allowable heat power output within the same time period; The minimum heat capacity ratio that a thermal storage tank must maintain is specified as a safe operating parameter.

[0244] 3) Constraints of hydrogen energy systems

[0245] Constraints of hydrogen production via electrolyzer:

[0246] (28)

[0247] In the formula: For hydrogen production efficiency; The amount of hydrogen produced by the electrolyzer at time t is the amount produced.

[0248] Operating range constraints of electrolytic cells:

[0249] (29)

[0250] In the formula: and These represent the upper and lower limit coefficients of the electrolytic cell output, respectively; This is a binary variable that characterizes the operating status of an electrolytic cell. When its value is 1, it indicates that the electrolytic cell is in operation.

[0251] Fuel cell power generation constraints:

[0252] (30)

[0253] In the formula: For the power generation efficiency of fuel cells; This represents the amount of hydrogen consumed by the fuel cell during time period t.

[0254] The output power of the fuel cell during time period t must meet the following operating range limitations:

[0255] (31)

[0256] In the formula: and These are the maximum and minimum technical output coefficients of the fuel cell, respectively. A binary variable representing the operating state of a fuel cell.

[0257] The hydrogen storage capacity of the hydrogen storage device should fall within the following range at any given time t:

[0258] (32)

[0259] In the formula: This represents the actual amount of hydrogen stored in the hydrogen storage device at time t.

[0260] The energy conservation relationship of a hydrogen storage device is defined by the following equilibrium equation:

[0261] (33)

[0262] In the formula: This represents the amount of hydrogen exported during that period.

[0263] The amount of hydrogen that a hydrogen storage device can deliver per unit time period is limited by the following delivery capacity:

[0264] (34)

[0265] In the formula: This indicates the maximum external transmission capacity of the hydrogen storage device within a unit of time period.

[0266] Step 3: In order to avoid the impact of frequent start-stop of energy storage equipment on its lifespan, this invention proposes a collaborative operation strategy of new energy and diversified energy storage.

[0267] This paper constructs a set of operating strategies and constraints for energy storage systems that are closely related to the curtailment status of wind and solar power. The specific control schemes for each subsystem are as follows:

[0268] 1) Energy Storage Batteries: During periods of curtailment at wind farms or photovoltaic power plants, the system allows energy storage batteries to charge while prohibiting them from discharging. When wind and solar power generation is normal and there is no curtailment, the energy storage batteries are authorized to release energy back into the system. This operational strategy is based on two main considerations: First, given the already abundant renewable energy generation, it avoids further exacerbating the power imbalance in the grid by preventing energy storage batteries from discharging. Second, by reducing the frequency of charge-discharge cycles, it lowers energy losses during operation, thereby extending battery life.

[0269] 2) Compressed air energy storage: When wind and solar power curtailment occurs, the system can start the compressor to consume excess electrical energy to generate high-pressure air, but the expansion power generation link needs to be suspended; conversely, during periods without curtailment, the compressor should stop working, and the expander is allowed to use the stored high-pressure air to generate electricity.

[0270] 3) Electricity-hydrogen conversion system: During periods of power curtailment, the electrolyzer is allowed to start, converting surplus electricity into hydrogen for storage, while the fuel cell is prohibited from generating electricity; during periods without power curtailment, the electrolyzer stops producing hydrogen, and the fuel cell is allowed to generate electricity using the stored hydrogen. The core of this strategy is that the overall efficiency of the secondary electricity-hydrogen-electricity conversion process is relatively low, avoiding high-loss energy conversion during non-curtailment periods.

[0271] When implementing these operational strategies, the first step is to accurately determine whether wind and solar power are in a state of curtailment. The criteria for determining curtailment status are defined by the following constraints:

[0272] (35)

[0273] In the formula: For the curtailment status indicator factor ( (This indicates that power wasted); θ is a very small positive number introduced to maintain numerical stability.

[0274] The constraints of the energy storage battery operation strategy are as follows:

[0275] (36)

[0276] The constraints of the compressed air energy storage operation strategy are as follows:

[0277] (37)

[0278] The operational constraints of the hydrogen storage system are as follows:

[0279] (38)

[0280] To further illustrate the effectiveness of this invention and the efficient solution of the optimization problem, this paper utilizes the YALM toolbox in the MATLAB software platform to call the GUROBI solver to solve the objective function. Since the GUROBI solver does not have the capability to directly handle high-order (quadratic and higher) nonlinear terms, to ensure the model's solvability, the high-order nonlinear equations involved in this paper need to be equivalently transformed into linear or quadratic forms. Specifically, in equation (2), its nonlinear terms can be linearized by introducing the physical relationship between the change in gas mass and the change in gas pressure in the storage chamber. This aims to reconstruct the original problem into one that GUROBI can efficiently solve. The transformation process is as follows:

[0281] (39)

[0282] In the formula: and Let represent the rate of change of mass of the gas storage chamber during time period t and its instantaneous mass at time t, respectively, with the former being the time derivative of the latter.

[0283] Therefore, the constraints on the upper and lower limits of gas pressure in the original model can be equivalently transformed into constraints on the gas mass in the gas storage chamber. The specific transformation relationship is as follows:

[0284] (40)

[0285] Specific example: The capacity optimization configuration analysis of the hybrid energy storage system with advanced adiabatic compressed air energy storage provided by this invention includes the following:

[0286] (1) Example background and parameter settings

[0287] This invention uses historical operational data from a region in Northwest my country as an example for simulation. The normalized power curve for the entire year is shown below. Figure 4The system has an installed capacity of 160 MW for both wind and solar power, with a maximum load of 110 MW. To reduce the dimensionality of the raw data and extract typical operating characteristics, the K-means clustering algorithm was used to cluster the standardized wind-solar-load dataset into 7 representative scenarios. By solving for the median of the cumulative fluctuation, a representative typical day was selected from each cluster scenario. The probability of typical days is shown in Table 1. The complete data can be found in [link to table]. Figure 5 During the simulation, the operating cycle for each typical day was set to 24 hours, with a 1-hour time step. Regarding lifespan, the hybrid energy storage system's operating life was consistent with that of the wind and solar power plant (20 years), with the energy storage batteries undergoing a one-time replacement in the 10th year. The main parameters involved in system modeling are detailed in Tables 2-6.

[0288] Table 1 Typical Number of Days and Probability

[0289]

[0290] Table 2 Wind Power, Solar Power and Economic Parameters

[0291]

[0292] Table 3 Economic and Operating Parameters of Energy Storage Batteries

[0293]

[0294] Table 4 Construction Costs and Parameters of AA-CAES

[0295]

[0296] Table 5 AA-CAES System Capacity Planning Parameters

[0297]

[0298] Table 6 Hydrogen Storage System Parameters

[0299]

[0300] (2) Solving the optimization model

[0301] To systematically analyze the effectiveness of capacity configuration schemes for hybrid energy storage systems, this study planned six different comparative schemes, the specific composition of which is detailed in Table 7.

[0302] Table 7 Comparison Schemes

[0303]

[0304] The first four schemes are used to analyze the results under different energy storage structures. Scheme 5 and Scheme 4 are compared to analyze the impact of hydrogen sales. Scheme 6 and Scheme 4 are compared to analyze the impact of operation strategies.

[0305] Let's take a typical day 3 as an example for a specific analysis.

[0306] 1) The optimized configuration results under different energy storage structures are shown in Table 8.

[0307] Table 8 Optimization configuration results under different energy storage structures

[0308]

[0309] In the first scenario, the system adopts differentiated operation strategies for different stages of wind and solar power generation: During the off-peak hours of wind and solar power output from 17:00 to midnight the following day, to compensate for the power shortage, the system mainly relies on the coordinated discharge of energy storage batteries and fuel cells to maintain power balance, thus requiring a large-capacity battery system; while during the surplus hours of wind and solar power generation from 00:00 to 17:00, to effectively utilize the surplus power, the system simultaneously starts battery charging and electrolyzer hydrogen production processes, accordingly equipped with electrolyzers and hydrogen storage facilities of appropriate scale. Although the water electrolysis hydrogen production process brings some hydrogen sales revenue, the high initial investment cost of key equipment such as batteries and electrolyzers results in poor overall net profit performance of the system. At the same time, the system increases revenue through curtailment of electricity, resulting in a high curtailment rate. For specific operational details, see [link to details]. Figure 6 .

[0310] In Option 2, to reduce costs, the system tends to use lower-cost compressed air energy storage, thus reducing the scale of fuel cell deployment. However, due to minimum and maximum output limitations in the energy storage and release processes, compressed air energy storage systems cannot fully meet load demands during certain periods, resulting in significant power deviations and poor reliability. For example, between 19:00 and 21:00, the system's power generation does not meet load requirements. Because both hydrogen storage and compressed air energy storage systems have low efficiency in energy storage and release, the large system scale leads to high investment costs, and the power deviation penalty further results in negative net returns, making it uneconomical. See [link to specific operational details] for details. Figure 7 .

[0311] In Option 3, the energy storage battery and compressed air energy storage form a complementary system. When wind and solar power output is insufficient (17:00-24:00), the battery and expander discharge to supplement the power supply. When wind and solar power output is excessive (00:00-17:00), the battery charging and compressed air energy storage are used to absorb the excess power. The system has good overall reliability and a net profit of 80,200 yuan, demonstrating high economic benefits. However, compared to Option 4, there is still a 0.16% power deviation, and the net profit is lower than Option 4 due to the lack of hydrogen sales. For specific operational details, see [link to details]. Figure 8 .

[0312] Option 4, building upon Options 1 to 3, further integrates energy storage batteries, advanced insulated compressed air energy storage, and a hydrogen storage system into a hybrid energy storage system. Compared to Option 1, Option 4 introduces advanced insulated compressed air energy storage with lower investment costs, significantly improving the system's net revenue. Compared to Option 2, Option 4 fully leverages the flexible adjustment and precise tracking of small load changes of energy storage batteries, achieving a significant increase in daily net revenue while improving power supply reliability and reducing the power deviation rate by 2.47%. Compared to Option 3, Option 4 utilizes surplus wind and solar power resources for water electrolysis to produce hydrogen, which can be used for fuel cell power generation and generate additional revenue through hydrogen production and sales. Therefore, Option 4's daily comprehensive net revenue is 21,700 yuan higher than Option 3, and the power deviation rate is reduced by 0.16%. When wind and solar power generation is insufficient, the expansion joints of the energy storage batteries, fuel cells, and compressed air energy storage system work together to supply power to meet load demand; while when wind and solar power generation is excessive, the system utilizes energy storage battery charging, compressed air energy storage, and the start-up of the electrolyzer to produce hydrogen in a coordinated manner to absorb excess power. For specific operational details, see [link to details]. Figure 9 .

[0313] 2) Analysis of the impact of hydrogen sales on the system

[0314] Based on the operational benefits shown in Table 9, the capacities of the electric, gas, and hydrogen energy storage devices in the multi-energy complementary system are 261.45 MWh, 4.62 × 10⁻⁶ MWh, and 4.62 × 10⁻⁶ MWh, respectively. 5 The system achieves its highest daily net revenue of RMB 101,900 when the volume is 500.38 m³ and the weight is 500.38 kg. Under this configuration, the revenue from electricity sales and hydrogen sales are RMB 550,600 and RMB 90,600 respectively, with hydrogen sales contributing 14.13% of the total revenue.

[0315] For comparison, Table 9 also lists the operating revenue and capacity configuration under the mode relying solely on electricity sales (Scheme 5). Under the pure electricity sales mode: the energy storage capacity decreases from 261.45 MWh to 132.07 MWh, while the gas storage chamber volume increases from 4.62 × 10⁻⁶ m³ / h. 5 m³ increased to 8.40 × 10 5 The hydrogen storage capacity also decreased from 500.38 kg to 183.03 kg. Consequently, the system's daily net revenue decreased from 101,900 yuan to 86,100 yuan. The main reason for this is that, in the absence of revenue from hydrogen sales, the system tends to configure gas storage devices with lower costs and higher energy conversion rates, resulting in a significant increase in gas storage volume and a corresponding decrease in hydrogen storage capacity.

[0316] In the electricity-only sales model, the system reduces the scale of energy storage batteries to lower investment costs and achieve higher net profits. This results in periods where load demand cannot be fully met, leading to a 0.13% power matching deviation. Because of this power deviation penalty, the revenue from electricity sales is also lower than in the electricity + hydrogen sales model.

[0317] Table 9 System operation results under different modes

[0318]

[0319] Figure 10 The study demonstrates the relationship between system operating revenue and maximum daily hydrogen transmission capacity. It shows that for every 0.5 million Nm³ increase in maximum daily hydrogen transmission capacity, the system's hydrogen sales revenue increases by an average of RMB 15,100, and the daily net revenue increases by an average of RMB 1,300. When the transmission capacity reaches 30,000 Nm³, the proportion of hydrogen sales revenue in total revenue increases to 14.13%. This analysis indicates that, under current hydrogen price conditions, external hydrogen transmission has become an important source of revenue for multi-energy complementary systems. Rational allocation of hydrogen storage facilities and expansion of hydrogen sales channels have a significant effect on improving the overall economic efficiency of the system.

[0320] 3) Impact analysis of energy storage operation strategy

[0321] Tables 10 and 11 compare and analyze the system capacity configuration and key equipment operating indicators under two scenarios: with and without the proposed operating strategy. The data comparison shows that without this operating strategy, the system tends to require a larger gas storage chamber (an increase of 3.5 × 10⁻⁶). 4 This approach adds a hydrogen storage device (m³) and a hydrogen storage unit (an increase of 1124 kg), but simultaneously reduces the configuration capacity of the electrochemical energy storage battery (a decrease of 51.73 MWh). Under this configuration, although the system's net revenue improves by 0.36 million yuan (an increase of 3.53%), the annual equivalent full charge-discharge cycles of the energy storage power station also increase significantly by 39.54 times (an increase of 20.32%), and the annual operating time of the electrolyzer is extended by 21.04 hours. These results demonstrate that the proposed strategy can effectively reduce the operating frequency of energy storage equipment without significantly affecting the overall system revenue, thus helping to delay lifespan reduction caused by excessive operation.

[0322] Table 10 Optimization configuration results of the system under different operating strategies

[0323]

[0324] Table 11 System performance under different operating strategies

[0325]

[0326] According to embodiments of the present invention, an electronic device 1100 is also provided, such as... Figure 11 As shown, it includes a memory 1101, a processor 1102, and a computer program stored in the memory 1101 and executable on the processor. When the processor 1102 executes the program, it implements the steps of the method described in any of the above embodiments.

[0327] According to embodiments of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the method described in any of the above embodiments.

[0328] This invention also provides a computer program product, including a computer program stored in a computer-readable storage medium; when a processor of an electronic device reads the computer program from the computer-readable storage medium, the processor executes the computer program, causing the electronic device to perform the steps of any of the methods described in the above embodiments.

[0329] Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0330] It should be understood that the specific embodiments described above are merely illustrative or explanatory of the principles of the invention and do not constitute a limitation thereof. Therefore, any modifications, equivalent substitutions, improvements, etc., made without departing from the spirit and scope of the invention should be included within the protection scope of the invention. Furthermore, the appended claims are intended to cover all variations and modifications falling within the scope and boundaries of the appended claims, or equivalent forms of such scope and boundaries.

Claims

1. A method for optimizing the capacity configuration of a hybrid energy storage system, characterized in that, include: A multi-energy complementary system architecture is constructed, the system including an energy storage battery system, a compressed air energy storage system, and an electric-hydrogen conversion system, and the operation strategies of the energy storage battery system, the compressed air energy storage system, and the hydrogen storage system are determined. Typical daily data are selected by combining K-means clustering technology with the median of cumulative fluctuations. This includes: dividing the original data of a year into data clusters using the K-means clustering method based on the historical data of the whole year, calculating the sum of the fluctuations of all days under different scenarios, obtaining the median by sorting, and selecting the days corresponding to the median as typical days of that scenario, as data for wind power, photovoltaics and load. An advanced adiabatic compressed air energy storage system model is established. With the objective function of maximizing daily comprehensive net income, a capacity optimization configuration model for a hybrid energy storage system incorporating advanced adiabatic compressed air energy storage is constructed. The optimization configuration model includes: an advanced adiabatic compressed air energy storage system model and a hybrid energy storage system capacity optimization configuration model incorporating advanced adiabatic compressed air energy storage. The hybrid energy storage system capacity optimization configuration model incorporating advanced adiabatic compressed air energy storage includes constraints on energy storage batteries, compressed air energy storage systems, and hydrogen energy systems.

2. The method according to claim 1, characterized in that, The advanced adiabatic compressed air energy storage system model includes: The mathematical model for the AA-CAES charging and discharging process, expressed by Formula 1, is as follows: (1) In the formula: and These are defined as the total power consumption and power generation of all compressors and expanders within time step t, respectively. and This corresponds to the ambient air mass flow rate entering the compressor and the high-pressure air mass flow rate being sent to the expander during the same time period; The specific heat capacity ratio of air; Represents the ideal gas coefficient; in terms of system structure, and These represent the total number of stages for the compressor and expander, respectively. and This represents the isentropic efficiency of the k-th level device; among the temperature parameters, and These are the inlet air temperatures of the k-th stage compressor and expander, respectively, and their outlet temperatures are correspondingly... and ; and These are defined as the design compression ratio and expansion ratio of the k-th stage compressor and expander, respectively. The rate of change of air pressure over time and the instantaneous air pressure value are expressed using Formula 2, as follows: (2) in, and These represent the rate of change of gas pressure in the gas storage chamber during time period t and the gas pressure value at the end of that time period, respectively. This is the initial air pressure; and These represent the air temperature inside the gas storage chamber and the wall temperature, respectively. The volume of the gas storage chamber; This is the unit scheduling duration (taken as 1 hour); and It is the comprehensive heat transfer coefficient, used to describe the heat exchange process caused by natural convection and forced convection; The thermal storage power and heating work of AA-CAES are expressed by Formula 3, as follows: (3) in: and These represent the recovered heat power and consumed heat power of AA-CAES during time period t, respectively. and These are the high and low temperature heat exchange medium temperatures, respectively. The isobaric specific heat capacity of air; Based on the first law of thermodynamics, the energy of a thermal storage tank is conserved per unit time. The rate of change of energy within the tank is equal to the difference between the net heat inflow and the heat dissipation of the system. This relationship can be described by the following equation: ； in: The heat storage loss rate within a unit scheduling period; and These represent the heat storage capacity of the thermal storage tank at time t and time t−1, respectively. The former is calculated from the latter after heat loss correction.

3. The method according to claim 1, characterized in that, The aforementioned model for optimizing the capacity configuration of a hybrid energy storage system, including advanced adiabatic compressed air energy storage, is constructed with the objective function of maximizing daily net comprehensive income. Specifically, it includes: With the core optimization objective of maximizing daily net profit, the objective function is expressed as shown in Formula 5, as follows: (5) In the formula: Daily net income; Total daily revenue; , , and These are daily investment costs, operation and maintenance costs, replacement costs, and depreciation costs, respectively. The system's daily overall revenue is expressed using Formula Six, as follows: (6) In the formula: The actual power delivered by the system at time t; Power deviation; , and These are the prices for electricity sales, hydrogen sales, and power deviation penalties, respectively. Let be the amount of hydrogen delivered by the system at time t; To optimize the duration of each day, take one day's formula: The actual power delivered by the system at time t; Power deviation; , and These are the prices for electricity sales, hydrogen sales, and power deviation penalties, respectively. Let be the amount of hydrogen delivered by the system at time t; To optimize the duration of a day, we take one day as 24 hours. The daily investment cost of the system is expressed using Formula 7, as follows: (7) In the formula: The system's operating lifespan; The system discount rate; These represent the investment costs of five subsystems: wind power, photovoltaic power, energy storage batteries, compressed air energy storage systems, and water electrolysis hydrogen production systems. The calculation method is expressed by Formula 8, as follows: (8) In the formula: the unit capacity cost of wind power and photovoltaic power are respectively denoted as... and In the compressed air energy storage subsystem, the unit capacity cost of the compressor, expander, and thermal storage tank is defined as follows: , and The unit volume construction cost of the gas storage chamber is The unit investment cost of the power and capacity of the battery energy storage system is respectively... and The text states that the hydrogen energy subsystem comprises three parts: an electrolyzer, a fuel cell, and a hydrogen storage device, with unit capacity costs of [missing information]. , and The corresponding capacity variables in the model include: wind power installed capacity. Photovoltaic installed capacity Battery power With capacity compressor rated power , compressor rated power Thermal storage tank capacity and the volume of the gas storage chamber The hydrogen energy component includes the capacity of the electrolyzer. Fuel cell capacity With hydrogen storage capacity ; In the economic analysis of this model, the system's daily operation and maintenance costs, equipment replacement costs, and depreciation costs are key factors. This application only considers the battery replacement cost and lifespan, and the specific calculation method for its cost items is as follows: (9) In the formula: This indicates the kth time the battery has been replaced. This is the operation and maintenance cost coefficient; This refers to the actual lifespan of the battery. This represents the total number of battery replacements. ,calculate Time Round up; This indicates the kth time the battery has been replaced. This refers to the residual value coefficient. The actual power delivered by the system is calculated using Formula 10, as follows: (10) In the formula: , , and These represent the compression power, expansion power generation, hydrogen production power, and fuel cell power generation at time t, respectively. and These represent the charging power and discharging power of the battery at the same moment, respectively. and These correspond to the wind power generation and photovoltaic power generation at time t, respectively; The power deviation is calculated using Formula 11, as follows: (11) In the formula: The load at time t; The system power deviation rate is expressed by Formula XII, as follows: (12)； In the formula, This represents the system power deviation rate.

4. The method according to claim 1, characterized in that, The energy storage battery constraints include: The operating boundaries of the energy storage battery during charging and discharging are jointly limited by its maximum allowable charging power and maximum allowable discharging power, expressed by Formula Thirteen, as follows: (13) In the formula: This is a charging state variable; a value of 1 indicates charging. This is a discharge state variable; a value of 1 indicates discharge. The state of charge and discharge constraints of the energy storage battery are expressed by Formula Fourteen, as follows: (14) The state of charge constraint is expressed by Formula 15, as follows: (15) In the formula: and These correspond to the lower and upper limits of the permissible operating range under charged conditions, respectively. This represents the state of charge of the battery at time t; , These represent the battery's charging efficiency and discharging efficiency, respectively. The state-of-charge constraints at the beginning and end of the energy storage battery scheduling cycle are expressed by Formula Sixteen, as follows: (16) In the formula: Indicates the allowable capacity deviation; and These represent the state of charge of the battery at the beginning and end of the scheduling cycle, respectively. The cycle life of energy storage batteries is expressed using Formula 17, as follows: (17) In the formula: The lifetime decay index obtained by fitting; Represents the depth of discharge in actual operation; This indicates the number of cycles required to achieve 100% depth of discharge. This indicates the total number of cycles a battery can complete within its lifespan. Equivalent cycle number at 100% depth of discharge The conversion relationship is expressed using Formula 18, as follows: (18) Battery daily equivalent full cycle count It is represented by Formula Nineteen, as follows: (19) Cyclic state constraints are represented by Formula 20, as follows: (20) In the formula: It is a key variable characterizing the cycle state; when its value is 1, it indicates that a complete charge-discharge cycle has been completed. Indicates the depth of discharge of the battery; This corresponds to the real-time state of charge of the battery; The charging and discharging processes of a battery are mutually exclusive in operation, a characteristic expressed by Equation 21, as follows: (21) In the formula: and All are binary state identifier variables, which are state variables during the charging process; when The time t indicates that the battery is in a charging state at time t; When the time is right, it indicates that the battery is in a discharging state; The charging and discharging states of a battery exhibit a continuous temporal characteristic, and their dynamic evolution must satisfy the constraints of Equation 22 to ensure that the operating mode at time t is consistent with the state at time t-1. (22) In the formula: This is a charging state variable; a value of 1 indicates charging. This is a discharge state variable; a value of 1 indicates discharge. The number of times a battery completes one charge-discharge cycle is determined by the state transition from discharge to charging within adjacent time periods, as defined in Formula 23, as follows: (23)； In the formula, This represents the battery's cycle state variable; a value of 1 indicates that one cycle has been completed.

5. The method according to claim 1, characterized in that, The constraints of the compressed air energy storage system specifically include: The planning constraints for each nominal value of the AA-CAES system are expressed using Formula 24, as follows: (24) In the formula: , The lower and upper limits of the rated compression power that the compressor is allowed to be configured with are specified respectively; , The reasonable range of values ​​for the rated expansion power of the expander was defined; , Together, they defined the feasible range for the gas storage chamber volume; , This defines the minimum and maximum allowable values ​​for the rated thermal storage capacity of the thermal storage tank; The operational constraints for the compression and expansion phases of AA-CAES are expressed by Formula 25, as follows: (25) In the formula: and These are binary state variables, representing the operating states of the compressor and expander during time period t, respectively. A value of 1 indicates operation, and a value of 0 indicates shutdown. and These are the wide operating condition adjustment coefficients for the compressor and expander, respectively, used to set the allowable power range of the equipment under partial load; The operational constraints of the AA-CAES gas storage chamber are expressed using Formula 26, as follows: (26) In the formula, and These represent the upper and lower limits of the gas pressure in the gas storage chamber, respectively. The operating constraints of the AA-CAES thermal storage tank are expressed by Formula 27, as follows: (27) In the formula: The maximum thermal storage power that the system can receive within a unit scheduling period is defined. This is the maximum allowable heat power output within the same time period; The minimum heat capacity ratio that a thermal storage tank must maintain is specified as a safe operating parameter.

6. The method according to claim 1, characterized in that, The constraints of the hydrogen energy system specifically include: The constraint for hydrogen production in the electrolyzer is shown in Formula 28, as follows: ； In the formula: For hydrogen production efficiency; The amount of hydrogen produced by the electrolyzer at time t; The operating range constraints of the electrolytic cell are defined by Formula 29, as follows: (29) In the formula: and These represent the upper and lower limit coefficients of the electrolytic cell output, respectively; A binary variable representing the operating status of an electrolytic cell; when its value is 1, it indicates that the electrolytic cell is in operation. The power generation constraint of fuel cells is given by Equation 30, as follows: ； In the formula: For the power generation efficiency of fuel cells; The amount of hydrogen consumed by the fuel cell during time period t; The output power of the fuel cell during time period t must meet the following operating range limitations, as shown in Formula 31, as follows: ； In the formula: and These are the maximum and minimum technical output coefficients of the fuel cell, respectively. A binary variable representing the operating state of a fuel cell; The hydrogen storage capacity of the hydrogen storage device should fall within the range of Formula 32 at any given time t: ； In the formula: This represents the actual amount of hydrogen stored in the hydrogen storage device at time t; The energy conservation relationship of a hydrogen storage device is defined by the following equilibrium equation, as shown in Formula 33, as follows: ； In the formula: This represents the amount of hydrogen exported during that period. The amount of hydrogen that a hydrogen storage device can deliver per unit time period is limited by the following delivery capacity, as shown in Formula 34: ； In the formula: This indicates the maximum external transmission capacity of the hydrogen storage device within a unit of time period.

7. The method according to claim 1, characterized in that, The operation strategy of the energy storage battery system specifically includes: During periods when wind farms or photovoltaic power stations experience power curtailment, energy storage batteries are permitted to charge, but discharging is prohibited. When wind and solar power generation is in normal operation and there is no power curtailment, the authorized energy storage battery releases electrical energy into the system. The criteria for determining the state of power abandonment are defined by the constraint conditions in Formula 35, as follows: (35) In the formula: For wind power installed capacity, For photovoltaic installed capacity, and The wind power generation and photovoltaic power generation at time t, respectively. As an indicator of the state of power curtailment, This indicates that power wasted; θ is a very small positive number introduced to maintain numerical stability. This represents the normalized theoretical power generation of wind power at time t. Let be the normalized theoretical power generation of photovoltaic power at time t; The energy storage battery operation strategy constraints are as follows: (36) in, For battery power, and These represent the charging power and discharging power of the battery at the same moment, respectively.

8. The method according to claim 1, characterized in that, The operation strategies for the compressed air energy storage system and the hydrogen storage system specifically include: The compressed air energy storage system operation strategy includes: when wind and solar power curtailment occurs, the system starts the compressor to consume excess electrical energy to generate high-pressure air, and the expansion power generation stage is suspended. During periods when there is no power wastage, the compressor stops working, allowing the expander to generate electricity using the stored high-pressure air; The hydrogen storage system operation strategy includes: during periods of power curtailment, the electrolyzer is started to convert the remaining electricity into hydrogen for storage, and the fuel cell is prohibited from generating electricity; During periods when there is no power wastage, the electrolyzer stops producing hydrogen, allowing the fuel cell to generate electricity using the stored hydrogen. The criteria for determining the state of power abandonment are defined by the constraint conditions in Formula 35, as follows: (35) In the formula: For wind power installed capacity, For photovoltaic installed capacity, and The wind power generation and photovoltaic power generation at time t, respectively. As an indicator of the state of power curtailment, This indicates that power wasted; θ is a very small positive number introduced to maintain numerical stability. The compressed air energy storage operation strategy is subject to the following constraints: (37) in, The rated power of the compressor. This refers to the compressor's rated power. Let be the compression power at time t. Let be the expansion power generation at time t; The operating strategy constraints for the hydrogen storage system are as follows: (38) in, and These represent the hydrogen production power and fuel cell power generation power at time t, respectively. This refers to the capacity of the electrolytic cell.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method according to any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-8.

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