A medium- to long-term optimization operation method for a regional integrated energy system that considers the physical properties of hydrogen and its multi-mode utilization.

Abstract

This invention discloses a long-term optimized operation method for a regional integrated energy system considering the physical properties of hydrogen and multi-mode utilization. The steps are as follows: 1) Clustering source-load data using a clustering algorithm to obtain k typical days and the correspondence between typical days and actual operating days, k = h(r); 2) Establishing an equipment operation model based on equipment operating parameters; 3) Establishing a hydrogen utilization model under medium- and long-term multi-mode utilization; 4) Linearizing the equipment operation model; 5) Establishing a long-term optimized operation model of the Energy System (IES) considering LHS (Local Hybridization System); 8) Obtaining real-time operating data of the energy system and inputting it into the long-term optimized operation model of the IES considering LHS to obtain a long-term optimized operation scheme for the energy system. This invention can be widely applied to the evaluation of new energy AGC (Automatic Generation Control) power plants with different installed capacities, in different regions, and with different frequency regulation performance. It can provide a useful reference for the operation control of new energy participating in the secondary frequency regulation of the power system and the distribution of revenue in the auxiliary frequency regulation market.

Description

Technical Field

[0001] This invention relates to the field of new energy AGC scheduling, specifically a long-term optimization operation method for a regional integrated energy system that considers the physical properties of hydrogen and multi-mode utilization. Background Technology

[0002] Integrated energy systems (IES) enable the synergy of multiple energy sources, including electricity, gas, and heat, improving energy efficiency and promoting the absorption of new energy sources. They are considered a major form of future energy systems. Simultaneously, to achieve the "dual carbon" goal (carbon reduction and emission reduction), a high proportion of new energy grid connection is considered a fundamental characteristic of future energy systems. However, due to the uncertainty and seasonality of new energy output, the demand for flexible resource regulation in the power system is increasing. Configuring large-capacity energy storage systems (such as pumped hydro storage, compressed air storage, hydrogen storage, and electrochemical storage) is considered an effective method to improve system flexibility.

[0003] Among all energy storage technologies, hydrogen energy storage has received widespread attention. On the one hand, hydrogen energy, as a clean secondary energy carrier, can be easily converted into other forms of energy using technologies such as electricity-to-gas, gas-to-electricity, and methanation, and has broad application prospects in the fields of power, heat, and transportation. On the other hand, hydrogen energy storage can serve as a long-term, large-capacity seasonal hydrogen storage (SHS) system, which can address the seasonal imbalance between renewable energy output and load demand. Compared with seasonal energy storage methods such as pumped hydro storage, compressed air storage, and seasonal thermal storage, SHS offers greater diversity in structure and conversion methods, making it more suitable as an IES (Environmentally Integrated Gas Storage) seasonal energy storage system.

[0004] Existing literature has extensively studied the economic benefits and technical feasibility of SHS (Supply-Side Container Load) configurations. However, current technologies typically simulate SHS operation on an annual scale, resulting in significant computational burdens for full-time simulations. To reduce computational load, time-series load aggregation methods are commonly used to obtain representative typical time periods, simulating a few typical periods to simulate the entire time period. However, traditional typical time period aggregation methods model each period independently, failing to reflect the long-term, cross-time-period operation characteristics of energy storage. Furthermore, current SHS applications primarily focus on power-to-gas-to-power (P2G2P) models, with limited research considering multi-mode hydrogen utilization over long time scales.

[0005] Hydrogen energy can be utilized in various modes within an energy ecosystem (IES). Besides P2G2P, existing research has extensively studied the economic and technical feasibility of hydrogen injection through natural gas pipelines and hydrogen supply from refueling stations, but it hasn't been effectively integrated with the Sustainable Energy Supply System (SHS). SHS has significant advantages in promoting multi-mode hydrogen utilization. Firstly, due to the low conversion efficiency of hydrogen, the traditional P2G2P model is economically inefficient; it only becomes economically viable when the electricity sales price is more than 10 times the electricity purchase price. Secondly, hydrogen demand exhibits seasonal characteristics. For hydrogen supply from refueling stations, similar to the operating characteristics of electric vehicles, hydrogen fuel cell vehicles have higher hydrogen demand in summer and winter, and lower demand in spring and autumn. For hydrogen injection through natural gas pipelines, the main demand comes from gas and heat loads, both of which are higher in winter. Therefore, integrating SHS with seasonal hydrogen supply is crucial for the future promotion of hydrogen energy applications.

[0006] The aforementioned study did not consider the inherent properties of hydrogen. Because hydrogen is the smallest substance in terms of relative molecular mass on Earth, its transportation and storage are quite difficult. Currently, there are three commonly used hydrogen storage technologies: compressed hydrogen, liquid hydrogen, and solid-state hydrogen storage, with compressed hydrogen being the most mainstream and technologically mature. Based on the end-user's demand for hydrogen pressure, hydrogen storage tanks can be broadly categorized into high-pressure hydrogen storage (HHS) and low-pressure hydrogen storage (LHS). Hydrogen exhibits different properties at different pressures; under high pressure, hydrogen deviates significantly from the calculations based on the ideal gas law. When the hydrogen pressure reaches 30 MPa, the actual gas content deviates by 20% from the calculations based on the ideal gas law. Ignoring this deviation will affect the safe operation of the hydrogen storage system. Furthermore, due to its relatively low density, compressing hydrogen requires more energy compared to other gases. Therefore, it is necessary to establish hydrogen storage models and compressor power consumption models that consider the actual physical properties of hydrogen. Summary of the Invention

[0007] The purpose of this invention is to provide a long-term optimized operation method for a regional integrated energy system that considers the physical properties of hydrogen and multi-mode utilization, comprising the following steps:

[0008] 1) Obtain historical operating data of the energy system.

[0009] The operating data of the energy system includes new energy output, electricity load, gas load, heat load, and hydrogen load.

[0010] 2) Cluster the source load data using a clustering algorithm to obtain k typical days and the correspondence between typical days and actual operating days, k = h(r).

[0011] 3) Obtain operating parameters of energy system equipment.

[0012] 4) Establish an equipment operation model based on the equipment operating parameters.

[0013] The equipment operation models include high-pressure hydrogen storage equipment operation models, low-pressure hydrogen storage equipment operation models, and compressor operation models.

[0014] The operating model of the high-pressure hydrogen storage device is shown below:

[0015]

[0016]

[0017]

[0018]

[0019]

[0020]

[0021] In the formula, This represents the hydrogen storage and release volume (g) at a typical daily k-time in a high-pressure hydrogen storage tank. The values ​​represent the pressure and hydrogen mass of the high-pressure hydrogen storage tank at time g on a typical day (k). N g This represents the total number of moments within the day. This represents the mass of hydrogen gas at time g+1 on a typical day (k). These represent the upper and lower limits of the high-pressure hydrogen storage tank pressure at time k and time g on a typical day. This represents the maximum hydrogen storage and release capacity of the high-pressure hydrogen storage tank. For high-pressure hydrogen storage tanks, the 0-1 state variables for hydrogen storage and release are... A value of 1 indicates that the hydrogen storage tank is filled with hydrogen at time k on a typical day; otherwise, it is not filled with hydrogen. The pressure of the high-pressure hydrogen storage tank is at time k on a typical day at 1 o'clock.

[0022] The steps for establishing an operational model for a high-pressure hydrogen storage device include:

[0023] a) Establish a hydrogen production system model, namely:

[0024]

[0025]

[0026]

[0027]

[0028]

[0029] In the formula, These represent the hydrogen production mass and power consumption of g at time k on a typical day. These represent the maximum and minimum power consumption of the water electrolysis device, respectively. For the 0-1 state variables of the water electrolysis device, A value of 1 indicates that the water electrolysis device is in the on state at that moment; otherwise, it is in the off state. This is a variable for starting and stopping the water electrolysis unit. or This indicates that the water electrolysis device started at that moment. or This indicates that the water electrolysis unit has stopped at this moment. ED,cycle η represents the maximum number of times the water electrolysis unit can be started and stopped in a single day. ED,H Here are the calculation parameters; Δg is the time difference;

[0030] b) Establish a real gas state model for the hydrogen storage tank based on the van der Waals equation, namely:

[0031]

[0032]

[0033] In the formula, Let G be the pressure of the high-pressure hydrogen storage tank and the mass of hydrogen at time g; R is the ideal gas constant, and T is the mass of hydrogen. H M represents the gas temperature. H V is the relative molecular mass of hydrogen. HHS denoted as HHS volume; a and b are Van der Waals coefficients, which are corrections for the attractive and repulsive forces between hydrogen molecules, respectively. These are parameters for mass calculation;

[0034] c) Establish the corrected real gas formula for hydrogen under high pressure, namely:

[0035]

[0036] d) Based on the real gas formula of hydrogen under high pressure, establish an operation model for high-pressure hydrogen storage equipment.

[0037] The operating model of the low-pressure hydrogen storage device is shown below:

[0038]

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] In the formula, The hydrogen storage pressure and hydrogen storage mass of the LHS at time g on the operating day r. These represent the incremental hydrogen storage, hydrogen storage, and hydrogen release of LHS at time k on a typical day. N represents the initial hydrogen storage mass of the LHS on day r. r For time scale. V LHS For LHS volume. This represents the maximum hydrogen storage and release capacity of the LHS. For the hydrogen storage and release state variables of LHS. The upper and lower limits of the hydrogen storage pressure of LHS at time g on the operating day are given.

[0049] The compressor operating model is shown below:

[0050]

[0051]

[0052] In the formula, These represent the hydrogen storage capacity and hydrogen release capacity of the hydrogen storage tank at time g, respectively. These represent the maximum hydrogen storage and hydrogen release rates of the hydrogen storage tank, respectively. These represent the 0-1 state variables of the hydrogen storage tank at time g, representing hydrogen storage and hydrogen release. When the value is 1, the hydrogen storage tank is in hydrogen storage mode; otherwise, it is not. In the formula, T in These represent compressor power consumption and temperature, respectively; γ is the heat capacity ratio of the compressed gas; p in p out These are the input and output power, respectively. η is the amount of hydrogen compressed by the compressor. comp This refers to the compressor's operating efficiency.

[0053] 5) Establish a hydrogen utilization model under medium- and long-term multi-mode.

[0054] The hydrogen utilization model under multiple modes in the medium and long term is as follows:

[0055]

[0056]

[0057] HHV mix =ω H HHV H +(1-ω H HHV NG (29)

[0058]

[0059] In the formula, These represent the flow rates of mixed hydrogen gas in the mixed hydrogen-natural gas pipeline, the flow rate of hydrogen injected into the hydrogen energy system, the flow rate of hydrogen purchased from the hydrogen energy market, and the flow rate of natural gas purchased from the upstream gas grid, respectively. H This refers to the hydrogen injection ratio. HHV mix HHV H HHV NG These are the calorific values ​​of the mixed hydrogen gas, hydrogen gas, and natural gas, respectively. ρ H This is the conversion factor for hydrogen mass and volume. These represent the mass of mixed hydrogen gas in the mixed hydrogen natural gas pipeline and the mass of hydrogen injected into the hydrogen energy system, respectively.

[0060] 6) Linearize the equipment operation model.

[0061] The equipment operation model is linearized to obtain the linear operation model of the high-pressure hydrogen storage equipment and the linear operation model of the compressor.

[0062] The linear model for the operation of high-pressure hydrogen storage equipment is as follows:

[0063]

[0064]

[0065] θ i+1 ≤η i ≤θ i (33)

[0066] 0≤θ i ≤1,η i ∈{0,1} (34)

[0067] In the formula, N p m is the number of linearized pieces. p,i It is the breakpoint; θ i Reflects At the position of the i-th segment interval, η iFor binary variables, equation (37) is used to ensure the continuity of segmented intervals.

[0068] The linear model of compressor operation is shown below:

[0069]

[0070]

[0071]

[0072]

[0073]

[0074]

[0075] In the formula, W x m are continuous variables. It is a binary variable. As an auxiliary variable, equation (35) uses the continuous variable W x Discretization, that is, using p-bit binary numbers Let y represent the resolution; equations (36) to (40) are based on the Big M method to reduce the nonlinear term mW. x Perform linearization representation. M is a positive number. For continuous variable W x The upper and lower limits.

[0076] 7) With the goal of minimizing the system's annual operating cost, establish a medium- to long-term optimization operation model for the IES that takes into account LHS.

[0077] The long-term optimization model of the IES for LHS is shown below:

[0078]

[0079]

[0080]

[0081]

[0082] In the formula, D0 and N k N g These represent the total number of days in a year, the number of typical days, and the number of moments within a day, respectively. Ψ(k) is the probability that typical day k occurs throughout the year. C buy The system's energy purchase cost includes the cost of electricity, gas, and hydrogen. This refers to the unit price for purchasing electricity from the superior power grid, gas from the superior gas grid, and hydrogen from the hydrogen energy market. This includes hydrogen flow rate interacting with the gas grid and the quality of hydrogen purchased from the hydrogen energy market. For electrical energy to interact with the power grid, when When τ indicates that electricity is purchased from a higher-level power grid. grid τ NG τ H These represent the unit carbon emissions from purchasing electricity, gas, and hydrogen, respectively. em The price of carbon emissions. em The cost of system carbon emissions. C om This is for system operation and maintenance costs. These represent the electrical energy output by the gas turbine and the fan of the water electrolysis unit, respectively. These represent the unit power consumption and maintenance costs of gas turbines, water electrolysis devices, high-pressure hydrogen storage equipment, low-pressure hydrogen storage equipment, wind turbines, hydrogen fuel cells, and gas boilers, respectively. This indicates the mass of hydrogen gas.

[0083] 8) Obtain real-time operating data of the energy system and input it into the long-term optimization operation model of the IES that takes LHS into account to obtain the long-term optimization operation scheme of the energy system.

[0084] The technical effects of this invention are undeniable. This invention can be widely applied to the evaluation of new energy AGC new energy power stations with different installed capacities, different regions and different frequency regulation performance. It can provide a useful reference for the operation control of new energy participating in the secondary frequency regulation of the power system and the distribution of revenue in the auxiliary frequency regulation market. Attached Figure Description

[0085] Figure 1 This is an IEEE 33 hydrogen energy system simulation network.

[0086] Figure 2 A map of source-load energy throughout the year;

[0087] Figure 3 For LHS annual hydrogen storage quality;

[0088] Figure 4 This represents the typical intraday pressure of a high-pressure hydrogen storage tank. Detailed Implementation

[0089] The present invention will be further described below with reference to embodiments, but it should not be construed that the scope of the present invention is limited to the following embodiments. Various substitutions and modifications made based on ordinary technical knowledge and common practices in the art without departing from the above-described technical concept of the present invention should be included within the scope of protection of the present invention.

[0090] Example 1:

[0091] See Figure 1 , 23, 4, A long-term optimization operation method for a regional integrated energy system considering the physical properties of hydrogen and multi-mode utilization, comprising the following steps:

[0092] 1) Obtain historical operating data of the energy system.

[0093] The operating data of the energy system includes new energy output, electricity load, gas load, heat load, and hydrogen load.

[0094] 2) Cluster the source load data using a clustering algorithm to obtain k typical days and the correspondence between typical days and actual operating days, k = h(r).

[0095] 3) Obtain operating parameters of energy system equipment.

[0096] 4) Establish an equipment operation model based on the equipment operating parameters.

[0097] The equipment operation models include high-pressure hydrogen storage equipment operation models, low-pressure hydrogen storage equipment operation models, and compressor operation models.

[0098] The operating model of the high-pressure hydrogen storage device is shown below:

[0099]

[0100]

[0101]

[0102]

[0103]

[0104]

[0105] In the formula, This represents the hydrogen storage and release volume (g) at a typical daily k-time in a high-pressure hydrogen storage tank. The values ​​represent the pressure and hydrogen mass of the high-pressure hydrogen storage tank at time g on a typical day (k). N g This represents the total number of moments within the day. This represents the mass of hydrogen gas at time g+1 on a typical day (k). These represent the upper and lower limits of the high-pressure hydrogen storage tank pressure at time k and time g on a typical day. This represents the maximum hydrogen storage and release capacity of the high-pressure hydrogen storage tank. For high-pressure hydrogen storage tanks, the 0-1 state variables for hydrogen storage and release are... A value of 1 indicates that the hydrogen storage tank is filled with hydrogen at time k on a typical day; otherwise, it is not filled with hydrogen. This refers to the pressure of the high-pressure hydrogen storage tank at the first moment of day k. The pressure of the high-pressure hydrogen storage tank is at time k on a typical day at 1 o'clock.

[0106] The steps for establishing an operational model for a high-pressure hydrogen storage device include:

[0107] a) Establish a hydrogen production system model, namely:

[0108]

[0109]

[0110]

[0111]

[0112]

[0113] In the formula, These represent the hydrogen production mass and power consumption of g at time k on a typical day. These represent the maximum and minimum power consumption of the water electrolysis device, respectively. For the 0-1 state variables of the water electrolysis device, A value of 1 indicates that the water electrolysis device is in the on state at that moment; otherwise, it is in the off state. This is a variable for starting and stopping the water electrolysis unit. or This indicates that the water electrolysis device started at that moment. or This indicates that the water electrolysis unit has stopped at this moment. ED,cycle η represents the maximum number of times the water electrolysis unit can be started and stopped in a single day. ED,H The state variable is 0-1; Δg is the time difference;

[0114] b) Establish a real gas state model for the hydrogen storage tank based on the van der Waals equation, namely:

[0115]

[0116]

[0117] In the formula, Let G be the hydrogen storage pressure and hydrogen mass in the high-pressure hydrogen storage tank at time g; R is the ideal gas constant, and T is the hydrogen mass. H M represents the gas temperature. H V is the relative molecular mass of hydrogen. HHS denoted as HHS volume; a and b are Van der Waals coefficients, which are corrections for the attractive and repulsive forces between hydrogen molecules, respectively. These are parameters for mass calculation;

[0118] c) Establish the corrected real gas formula for hydrogen under high pressure, namely:

[0119]

[0120] d) Based on the real gas formula of hydrogen under high pressure, establish an operation model for high-pressure hydrogen storage equipment.

[0121] The operating model of the low-pressure hydrogen storage device is shown below:

[0122]

[0123]

[0124]

[0125]

[0126]

[0127]

[0128]

[0129]

[0130]

[0131]

[0132] In the formula, The hydrogen storage pressure and hydrogen storage mass of the LHS at time g on the operating day r. These represent the incremental hydrogen storage, hydrogen storage, and hydrogen release of LHS at time k on a typical day. N represents the initial hydrogen storage mass of the LHS on day r. r For time scale. V LHS For LHS volume. This represents the maximum hydrogen storage and release capacity of the LHS. For the hydrogen storage and release state variables of LHS. The upper and lower limits of the hydrogen storage pressure of LHS at time g on the operating day are given.

[0133] The compressor operating model is shown below:

[0134]

[0135]

[0136] In the formula, These represent the hydrogen storage capacity and hydrogen release capacity of the hydrogen storage tank at time g, respectively. These represent the maximum hydrogen storage and hydrogen release rates of the hydrogen storage tank, respectively. Let g represent the 0-1 state variables of the hydrogen storage tank at time g, specifically the hydrogen storage and hydrogen release states. Taking the hydrogen storage state as an example, when... When the value is 1, the hydrogen storage tank is in hydrogen storage mode; otherwise, it is not. In the formula, T in These represent compressor power consumption and temperature, respectively; γ is the heat capacity ratio of the compressed gas; p in p out These are the input and output power, respectively. η is the amount of hydrogen compressed by the compressor. comp This refers to the compressor's operating efficiency.

[0137] 5) Establish a hydrogen utilization model under medium- and long-term multi-mode.

[0138] The hydrogen utilization model under multiple modes in the medium and long term is as follows:

[0139]

[0140]

[0141] HHV mix =ω H HHV H +(1-ω H HHV NG (29)

[0142]

[0143] In the formula, These represent the flow rates of mixed hydrogen gas in the mixed hydrogen-natural gas pipeline, the flow rate of hydrogen injected into the hydrogen energy system, the flow rate of hydrogen purchased from the hydrogen energy market, and the flow rate of natural gas purchased from the upstream gas grid, respectively. H This refers to the hydrogen injection ratio. HHV mix HHV H HHV NG These are the calorific values ​​of the mixed hydrogen gas, hydrogen gas, and natural gas, respectively. ρ H This is the conversion factor for hydrogen mass and volume. These represent the mass of mixed hydrogen gas in the mixed hydrogen natural gas pipeline and the mass of hydrogen injected into the hydrogen energy system, respectively.

[0144] 6) Linearize the equipment operation model.

[0145] The equipment operation model is linearized to obtain the linear operation model of the high-pressure hydrogen storage equipment and the linear operation model of the compressor.

[0146] HHS and The relationship exhibits a nonlinear functional relationship. To simplify the calculation, an incremental piecewise linearization method is used to linearize the equation, resulting in the following piecewise linear approximation equation for pressure.

[0147]

[0148]

[0149] θ i+1 ≤η i ≤θ i (33)

[0150] 0≤θ i ≤1,η i ∈{0,1} (34)

[0151] In the formula, N p m is the number of linearized pieces. p,i It is the breakpoint; θ i Reflects At the position of the i-th segment interval, η i For binary variables, equation (37) is used to ensure the continuity of segmented intervals.

[0152] The compression ratio changes dynamically with variations in inlet and outlet pressures, thus affecting compression power consumption. Considering the dynamic compression ratio, the compressor power consumption model contains a nonlinear term W, which is the product of two continuous variables. x To simplify calculations, the nonlinear term is linearized using the binary method and the Big M method as follows.

[0153]

[0154]

[0155]

[0156]

[0157]

[0158]

[0159] In the formula, W x m are continuous variables. It is a binary variable. As an auxiliary variable, equation (35) uses the continuous variable W x Discretization, that is, using p-bit binary numbers Let y represent the resolution; equations (36) to (40) are based on the Big M method to reduce the nonlinear term mW. x Perform linearization representation. M is a positive number. For continuous variable W x The upper and lower limits.

[0160] 7) With the goal of minimizing the system's annual operating cost, establish a medium- to long-term optimization operation model for the IES that takes into account LHS.

[0161] The long-term optimization model of the IES for LHS is shown below:

[0162]

[0163]

[0164]

[0165]

[0166] In the formula, D0 and N k N g These represent the total number of days in a year, the number of typical days, and the number of moments within a day, respectively. Ψ(k) is the probability that typical day k occurs throughout the year. C buy The system's energy purchase cost includes the cost of electricity, gas, and hydrogen. This refers to the unit price for purchasing electricity from the superior power grid, gas from the superior gas grid, and hydrogen from the hydrogen energy market. This includes hydrogen flow rate interacting with the gas grid and the quality of hydrogen purchased from the hydrogen energy market. For electrical energy to interact with the power grid, when When τ indicates that electricity is purchased from a higher-level power grid. grid τ NG τ H These represent the unit carbon emissions from purchasing electricity, gas, and hydrogen, respectively. em The price of carbon emissions. em The cost of system carbon emissions. C om This is for system operation and maintenance costs. These represent the electrical energy output by the gas turbine and the fan of the water electrolysis unit, respectively. These represent the unit power consumption and maintenance costs of gas turbines, water electrolysis devices, high-pressure hydrogen storage equipment, low-pressure hydrogen storage equipment, wind turbines, hydrogen fuel cells, and gas boilers, respectively.

[0167] 8) Obtain real-time operating data of the energy system and input it into the long-term optimization operation model of the IES that takes LHS into account to obtain the long-term optimization operation scheme of the energy system.

[0168] Example 2:

[0169] A long-term optimization operation method for a regional integrated energy system considering the physical properties of hydrogen and multi-mode utilization is presented in Example 1. The distribution network model of the energy system is as follows:

[0170]

[0171]

[0172]

[0173]

[0174]

[0175]

[0176] In the formula, equation (1) represents the relationship between node voltage and branch active and reactive power; equation (2) represents the voltage constraint of the balancing node; equations (3) and (4) represent the active and reactive power constraints of the node. i and Q i Equation (5) represents the active and reactive power of node i, respectively; Equation (6) represents the upper and lower limits of node voltage; Equation (7) represents the line transmission power constraint after linearization of the internal approximation constraint.

[0177] Example 3:

[0178] A method for long-term optimized operation of a regional integrated energy system that considers the physical properties of hydrogen and multi-mode utilization, comprising the following steps:

[0179] 1) Determine the research subjects and the research time scale N r ;

[0180] 2) Obtain 8760 hours of annual source load data for the region, including new energy output, electricity load, gas load, heat load, and hydrogen load;

[0181] 3) The k-clustering algorithm is used to cluster the source load data throughout the year to obtain k typical days and their correspondence with the actual operating days k = h(r);

[0182] 4) Obtain operating parameters of various types of power generation, power consumption, and energy storage equipment in the region, including alkaline electrolyzers, HHS, LHS, hydrogen fuel cells, gas turbines, and gas boilers;

[0183] 5) Based on the characteristics of the equipment, establish its operation model on a medium- to long-term scale. For equipment such as LHS that can be adjusted in the medium to long term, establish a medium- to long-term operation model based on an improved typical day; for HHS equipment, establish a high-pressure hydrogen storage tank hydrogen storage model that considers the real gas characteristics of hydrogen under high pressure; for other equipment, establish its intraday operation model.

[0184] The hydrogen production system model is shown below:

[0185]

[0186]

[0187]

[0188]

[0189]

[0190] Equations (1) and (2) represent the operating constraints of the water electrolysis device. These represent the hydrogen production mass and electricity consumption at time k on a typical day, respectively. These are the maximum and minimum power consumption of the water electrolysis device, respectively. The state variable is 0-1 for the water electrolysis device; a value of 1 indicates that the device is in the on state at that moment. Equations (3) to (5) are constraints on the number of times the water electrolysis device can be started and stopped, to avoid irreversible damage to the electrodes caused by frequent start and stop. N is a 0-1 variable representing the start / stop of the water electrolysis device. A value of 1 indicates that the water electrolysis device is started or stopped at that moment. ED,cycle This represents the maximum number of times the water electrolysis unit can be started and stopped in a single day.

[0191] Typical hydrogen refueling stations supply hydrogen at pressures of 35 MPa or 70 MPa, with 35 MPa currently being the mainstream. Due to the high pressure, a hydrogen buffer system (HHS) is usually required. High-pressure hydrogen storage tanks can store hydrogen at pressures up to approximately 20 MPa. Because hydrogen under high pressure deviates from the characteristics of an ideal gas, corrections are necessary.

[0192]

[0193]

[0194] In the formula, Let G be the hydrogen storage pressure and hydrogen mass in the high-pressure hydrogen storage tank at time g; R is the ideal gas constant, and T is the hydrogen mass. H M represents the gas temperature. H V is the relative molecular mass of hydrogen. HHS denoted as HHS volume; a and b are Van der Waals coefficients, which are corrections for the attractive and repulsive forces between hydrogen molecules, respectively.

[0195]

[0196] Therefore, the relationship between the internal pressure of the HHS and the hydrogen mass is non-linear. Furthermore, considering the high hydrogen storage pressure and the risk of leakage, the HHS is not suitable for long-term storage. Ignoring losses during hydrogen conversion, the daily operating model of the HHS can be expressed as:

[0197]

[0198]

[0199]

[0200]

[0201]

[0202]

[0203] In the formula, equation (9) represents the temporal constraint on the hydrogen storage quality of the high-pressure hydrogen storage tank. Let N be the hydrogen storage and release amount at time k on a typical day in the high-pressure hydrogen storage tank; Equation (10) represents the safety operation constraints of the HHS; Equation (11) ensures that the initial and final states of the HHS are equal throughout the day, guaranteeing that the HHS can still operate safely on the second day, where N... g =24; Equations (12) to (14) are the constraints for hydrogen storage and release in high-pressure hydrogen storage tanks. This represents the maximum hydrogen storage and release capacity of the high-pressure hydrogen storage tank. The 0-1 state variables are used for hydrogen storage and release in high-pressure hydrogen storage tanks.

[0204] The hydrogen storage pressure of an LHS (Liquid-source heat pump) is typically 2–5 MPa. When the storage pressure is less than 10 MPa, the deviation between hydrogen and ideal gas characteristics is almost negligible. Furthermore, due to the relatively low storage pressure, the risk of hydrogen leakage is low, making it suitable for long-term energy storage. Therefore, LHS can operate not only intraday but also across days and seasons. Considering the computational complexity, this paper adopts an improved modeling method for a typical day, as follows:

[0205]

[0206]

[0207]

[0208]

[0209]

[0210]

[0211]

[0212]

[0213]

[0214]

[0215] Equation (15) shows the relationship between the hydrogen storage capacity and the hydrogen storage pressure in an LHS. The hydrogen storage pressure and mass of the LHS at time g on the operating day are given by equations (16) to (20), which represent the typical daily operating constraints of the energy storage equipment. These represent the increase in hydrogen storage (g), hydrogen storage, and hydrogen release at time k on a typical day for the LHS. It is worth noting that... This does not represent the actual hydrogen storage capacity of the LHS; it is recorded as 0 at the beginning of a typical day. Equations (21) to (22) are the daytime operation constraints for the energy storage equipment. Let N be the initial hydrogen storage mass of LHS on day r. The initial hydrogen storage mass of LHS for two consecutive days can be obtained by equation (21). The correspondence between r and k is k = h(r), which can be obtained by clustering method. Equation (22) shows that the hydrogen storage mass of LHS is equal at the initial and final times throughout the entire time period, where N r =365; Equations (23) and (24) represent the actual hydrogen storage capacity of the LHS and the constraints for safe operation of the LHS.

[0216] The compressor operating model is shown below:

[0217]

[0218]

[0219] In the formula, These represent the hydrogen storage capacity and hydrogen release capacity of the hydrogen storage tank at time g, respectively. These represent the maximum hydrogen storage and hydrogen release rates of the hydrogen storage tank, respectively. Let g represent the 0-1 state variables of the hydrogen storage tank at time g, specifically the hydrogen storage and hydrogen release states. Taking the hydrogen storage state as an example, when... When the value is 1, the hydrogen storage tank is in a hydrogen storage state; otherwise, it is not.

[0220] 6) Establish a multi-mode utilization model for hydrogen in the medium and long term, including P2G2P, hydrogen injection through natural gas pipelines, and hydrogen supply from hydrogen refueling stations;

[0221] Injecting hydrogen into natural gas pipelines allows for hydrogen transport without additional investment, making it an effective method for large-scale hydrogen consumption. Currently, low-pressure gas distribution networks operate at pressures of 0.1–0.5 MPa, allowing water electrolysis units to safely inject generated hydrogen into the natural gas pipeline network via a hydrogen injection system. However, injecting excessive amounts of hydrogen can cause hydrogen embrittlement, necessitating control of the injection volume. Assuming uniform hydrogen distribution within the natural gas pipeline, the hydrogen-injected natural gas pipeline can be described as follows:

[0222]

[0223]

[0224] HHV mix =ω H HHV H +(1-ω H HHV NG (29)

[0225]

[0226] In the formula, These represent the flow rates of mixed hydrogen gas in the mixed hydrogen-natural gas pipeline, hydrogen injected into the hydrogen energy system, hydrogen purchased from the hydrogen energy market, and natural gas purchased from the upstream gas network, respectively, in cubic meters (m³). 3 / h;ω H Regarding the hydrogen injection ratio, studies have shown that the theoretical maximum volumetric hydrogen injection ratio in natural gas pipelines can reach as high as 20%; HHV mix HHV H HHV NG These are the calorific values ​​of the mixed hydrogen gas, hydrogen gas, and natural gas, respectively; Equation (30) is the equivalent conversion of hydrogen mass and volume, ρ H This is the conversion factor for hydrogen mass and volume.

[0227] 7) The above models are linearized using a linearization modeling method, mainly including the high-pressure hydrogen storage tank hydrogen storage model and the compressor dynamic linear model. Considering the real gas characteristics of hydrogen and seasonal hydrogen storage, the long-term optimization operation of a regional integrated energy system is a large-scale mixed-integer nonlinear optimization problem. To reduce the difficulty of solving this model, its nonlinear parts need to be linearized. In equation (8), pressure and mass are nonlinear relationships, which are processed using incremental piecewise linearization. This method can also handle equations (30) and (31), as follows:

[0228]

[0229]

[0230] θ i+1 ≤η i ≤θ i (33)

[0231] 0≤θ i ≤1,η i ∈{0,1} (34)

[0232] Equations (32) and (33) contain a nonlinear term Wm, which is linearized using the binary method and the Big M method, as follows:

[0233]

[0234]

[0235]

[0236]

[0237]

[0238]

[0239] 8) To minimize the system's annual operating cost, a long-term optimization model of the IES considering LHS is established. The model is built using the Yalmip toolbox and solved using the Gurobi solver. The optimization objective is to minimize the annual operating cost of the IES, i.e.:

[0240]

[0241]

[0242]

[0243]

[0244] In the formula, equation (41) is the objective function, D0, N k N g These represent the total number of days in the year, the number of typical days, and the number of moments within a day, respectively; Ψ(k) is the probability that typical day k occurs throughout the year; Equation (42) represents the system's energy purchase cost, including electricity purchase cost, gas purchase cost, and hydrogen purchase cost. This refers to the unit price for purchasing electricity from the superior power grid, gas from the superior gas grid, and hydrogen from the hydrogen energy market. To prevent electricity from being fed back into the superior power grid and impacting its operation, penalties are imposed on electricity fed back into the grid. For electrical energy to interact with the power grid, when "At that time" indicates that electricity is purchased from the upstream power grid. An auxiliary variable is introduced. And add constraints Since the objective function is to minimize energy purchase cost, Alternative Equivalent to the original problem. Equation (43) represents the system's carbon emission cost, τ grid τ NG τ H These represent the unit carbon emissions from purchasing electricity, gas, and hydrogen, respectively. em The carbon emission price is given by equation (44). The system operation and maintenance cost is given by equation (44). This indicates the mass of hydrogen gas.

[0245] In addition, this embodiment uses the Lindisflow AC power flow model to model the distribution network, as follows:

[0246]

[0247]

[0248]

[0249]

[0250]

[0251]

[0252] In the formulas, equation (45) represents the relationship between node voltage and branch active and reactive power; equation (46) represents the voltage constraint of the balancing node; equations (47) and (48) represent the active and reactive power constraints of the node. i and Q i Equation (49) represents the active and reactive power of node i, respectively; Equation (50) represents the upper and lower limits of node voltage; Equation (50) represents the line transmission power constraint after linearization of the internal approximation constraint.

[0253] Example 4:

[0254] See Figure 1 This embodiment uses a comprehensive energy testing system to perform simulations of the methods described in embodiments 1-3. The annual wind power and load data are sourced from a region in North China. Source-load data and h(i) for 12 typical days are obtained using the k-means clustering algorithm. The parameters in the simulation are shown in the table below. The electricity price uses time-of-use pricing; specific data is shown in Table 1. The natural gas price is 3.5 yuan / m³. 3 The price of hydrogen purchased from the hydrogen energy market is 50 yuan / kg, ω H It is 20%.

[0255] 1) The research object is a comprehensive energy system in a certain region of North China, and the research time scale is one year;

[0256] 2) Obtain 8760 hours of annual source load data for the region, including new energy output, electricity load, gas load, heat load, and hydrogen load;

[0257] 3) The k-clustering algorithm was used to cluster the source load data for the whole year, resulting in k typical days and their correspondence with the actual operating days, k = h(r). The aggregated typical days are shown below. Figure 2 ;

[0258] Figure 2This is a map showing the daily energy distribution of the region throughout the year (hydrogen load is converted to isothermal value). North China is a typical subtropical monsoon climate region with distinct seasonal characteristics. Electricity load is higher in summer and winter and lower in spring and autumn; gas and heat loads are higher in winter and lower in other seasons; according to the operating pattern of hydrogen fuel cell electric vehicles, hydrogen load is higher in summer and winter and lower in spring and autumn; wind power output is higher in spring and winter and lower in summer.

[0259] 4) Obtain operating parameters for various types of power generation, power consumption, and energy storage equipment in the region, including alkaline electrolyzers, HHS, LHS, hydrogen fuel cells, gas turbines, and gas boilers. System simulation parameters are shown in the table below:

[0260] Table 1 System-related parameters

[0261]

[0262] 5) Based on the characteristics of the equipment, establish its operation model on a medium- to long-term scale. For equipment such as LHS that can be adjusted in the medium to long term, establish a medium- to long-term operation model based on an improved typical day; for HHS equipment, establish a high-pressure hydrogen storage tank hydrogen storage model that considers the real gas characteristics of hydrogen under high pressure; for other equipment, establish its intraday operation model.

[0263] 6) Establish a multi-mode utilization model for hydrogen in the medium and long term, including P2G2P, hydrogen injection through natural gas pipelines, and hydrogen supply from hydrogen refueling stations;

[0264] 7) The above models are linearized using a linearization modeling method, which mainly includes the high-pressure hydrogen storage tank hydrogen storage model and the compressor dynamic linear model.

[0265] 8) With the goal of minimizing the annual operating cost of the system, establish a medium- and long-term optimization operation model of IES that takes into account LHS, and use the Yalmip toolbox to model the model, and call the Gurobi solver to solve the model. The optimization objective is to minimize the annual operating cost of IES.

[0266] A. To verify the impact of multi-mode hydrogen utilization on the operational results of IES systems with a high proportion of new energy sources in the medium and long term, this paper uses three scenarios for analysis and illustration, as follows:

[0267] Case 1: SHS and hydrogen multi-mode utilization are not considered;

[0268] Case 2: Consider SHS, but only the P2G2P mode;

[0269] Case 3: Consider SHS and multi-mode utilization;

[0270] Table 2. Impact of Multi-Energy Utilization Mode on System Operation

[0271]

[0272] *Cost unit: RMB 10,000; Curtailment volume unit: MWh

[0273] The annual operating costs for the three scenarios are shown in Table 2, and the LHS hydrogen storage capacity variation curve is shown in Figure 2. Figure 3 .Depend on Figure 3 As can be seen, in scenarios 2 and 3, the LHS stores hydrogen in spring and autumn and releases hydrogen in summer and winter, exhibiting obvious seasonal characteristics. Table 2 shows that the total costs for the three scenarios are RMB 14.572 million, RMB 14.37 million, and RMB 12.148 million, respectively, with wind curtailment volumes of 384.1 MWh, 217.8 MWh, and 12.6 MWh, respectively. Compared to scenarios 1 and 2, the proposed method reduces operating costs by 16.6% and 15.5%, respectively, and wind curtailment by 96.7% and 94.2%, respectively, demonstrating significant advantages. Therefore, multi-mode utilization of hydrogen energy can effectively reduce the total operating cost of the system and improve the level of new energy consumption.

[0274] Compared to scenarios 1 and 2, the proposed method has the lowest hydrogen purchase cost, at only 84,000 yuan. This is because scenarios 1 and 2 rely solely on the external hydrogen market for hydrogen supply, while in scenario 3, the LHS (Liquid Hydrogen Fuel Cell) can store excess hydrogen during peak renewable energy seasons. When hydrogen production capacity is insufficient, it supports multi-mode utilization of hydrogen energy, thereby reducing dependence on the external hydrogen market and lowering hydrogen purchase costs. The proposed method has the highest electricity purchase cost because the large-scale hydrogen production from water electrolysis requires significant electricity. Scenario 2 has the lowest electricity and gas purchase costs, primarily because it only considers the P2G2P (Plug-in to Gas to Power) model, using hydrogen fuel cells to consume all hydrogen for combined heat and power (CHP), reducing electricity and heat demand and thus lowering system electricity and gas purchase costs.

[0275] B. This paper uses two scenarios to analyze the impact of the actual gas properties of hydrogen on HHS operation. The pressure changes of the high-pressure hydrogen storage tank in each typical day under the two scenarios are as follows: Figure 4 As shown:

[0276] Case 4: The pressure of the high-pressure hydrogen storage tank is evaluated using the ideal gas equation;

[0277] Case 5: The pressure of the high-pressure hydrogen storage tank is evaluated using the real gas formula;

[0278] Depend on Figure 4It can be seen that modeling the pressure of the high-pressure hydrogen storage tank based on ideal gas characteristics deviates somewhat from the characteristics of a real gas, and the deviation increases with the pressure. Furthermore, on typical days with high hydrogen load demand, using ideal gas characteristics may result in the actual pressure of the HHS exceeding 20 MPa, reaching a maximum of 23 MPa, exceeding the maximum allowable pressure by 15%, which will have a certain impact on the safe operation of the HHS. Therefore, not using real gas characteristics will result in an overestimation of the hydrogen mass that the HHS can hold, causing the actual operating pressure of the HHS to exceed the allowable pressure. Using the linear HHS model considering real gas characteristics as presented in this paper, the pressure of the high-pressure hydrogen storage tank can be accurately characterized, ensuring the safe operation of the HHS within the specified pressure range.

[0279] C. This paper uses three case studies to illustrate the impact of different compressor modeling methods on system operation. The results are shown in the table below:

[0280] Case 6: Compressor power consumption is not considered;

[0281] Case 7: Fixed compression ratio;

[0282] Case 8: Consider dynamic compression ratio;

[0283] Table 3. Impact of compressor power consumption in three scenarios

[0284]

[0285] *Cost unit: RMB 10,000; Power consumption unit: MWh

[0286] As shown in the table above, considering compressor power consumption has a relatively small impact on system operation. However, for hydrogen production systems, compressor power consumption accounts for 5.9% and 7.9% of the total power consumption, respectively. Comparing cases 7 and 8, it can be seen that considering the dynamic compression ratio reduces compressor power consumption by 25.1%. This is because, after considering the dynamic changes in the high-pressure hydrogen storage tank pressure, the compression pressure of the front compressor is the actual HHS pressure, which is smaller than the fixed compression ratio. Therefore, the power consumption of the front compressor decreases. Similarly, the power consumption of the rear compressor increases, while the power consumption of the LHS compressor remains almost unchanged. Overall, considering the dynamic changes in the high-pressure hydrogen storage tank pressure reduces compressor power consumption.

Claims

1. A method for long-term optimal operation of a regional integrated energy system considering physical properties of hydrogen and multi-mode utilization, characterized in that, The method comprises the following steps: 1) obtaining historical operation data of an energy system; 2) clustering source-load data by using a clustering algorithm to obtain k typical days and a corresponding relationship between the typical days and real operation days k=h(r); 3) obtaining equipment operation parameters of the energy system; 4) establishing an equipment operation model according to the equipment operation parameters; 5) establishing a hydrogen utilization model in a medium and long term multi-mode; 6) performing linearization processing on the equipment operation model; 7) taking the minimum annual operation cost of the system as an objective, and establishing an IES medium and long term optimization operation model considering LHS; 8) obtaining real-time operation data of the energy system, and inputting the real-time operation data into the IES medium and long term optimization operation model considering LHS to obtain an IES medium and long term optimization operation scheme; In the equipment operation model, the high-pressure hydrogen storage equipment operation model is as follows: wherein, is the hydrogen storage and release amount of the high-pressure hydrogen storage tank at the typical day k time g; is the pressure and hydrogen mass of the high-pressure hydrogen storage tank at the typical day k time g; N g is the total number of times within a day; is the hydrogen mass at the typical day k time g+1; are the upper and lower limits of the pressure of the high-pressure hydrogen storage tank at the typical day k time g, respectively; is the maximum hydrogen storage and release amount of the high-pressure hydrogen storage tank, is the 0-1 state variable of the hydrogen storage and release of the high-pressure hydrogen storage tank, is 1, indicating that the hydrogen storage tank is charged with hydrogen at the typical day k time g, otherwise it is not charged with hydrogen; is the pressure of the high-pressure hydrogen storage tank at the typical day k time 1; The hydrogen utilization model in the medium and long term multi-mode is as follows: HHV mix = ω H HHV H + (1 - ω H ) HHV NG (29) In the formula, respectively are the flow rate of hydrogen mixed gas in the hydrogen mixed natural gas pipeline, the flow rate of hydrogen injected by the hydrogen energy system, the flow rate of hydrogen purchased from the hydrogen energy market, and the flow rate of natural gas purchased from the upper gas network; ω H is the hydrogen injection ratio; HHV mix , HHV H , HHV NG respectively are the hydrogen mixed gas heat value, the hydrogen heat value, and the natural gas heat value; ρ H is the hydrogen mass and volume conversion coefficient; respectively are the mass of hydrogen mixed gas in the hydrogen mixed natural gas pipeline and the mass of hydrogen injected by the hydrogen energy system; The linearization processing is performed on the equipment operation model to obtain a high-pressure hydrogen storage equipment operation linear model and a compressor operation linear model; The high-pressure hydrogen storage equipment operation linear model is as follows: θ i+1 ≤η i ≤θ i (33) 0 < θ i ≤ 1, η i ∈ {0, 1} (34) where N p is the number of linearization segments, m p,i is the segment point; θ i reflects the position in the i-th segment interval, η i is a binary variable; The compressor operation linear model is as follows: where W x is a continuous variable, is a binary variable, is an auxiliary variable, and equation (35) discretizes the continuous variable W x into a p-bit binary number with y being the resolution; equations (36)-(40) linearize the non-linear term mW x based on the large-M method; M is a positive number; is the upper and lower bounds of the continuous variable W x ; The IES medium and long term optimization operation model considering LHS is as follows: where D0, N k , and N g are the total number of days in a year, the number of days in a typical day, and the number of time points in a day, respectively; Ψ(k) is the probability of a typical day k in a year; C buy is the system energy purchase cost, including electricity purchase cost, gas purchase cost, and hydrogen purchase cost, is the unit price of electricity purchased from the upper grid, gas purchased from the upper gas grid, and hydrogen purchased from the hydrogen energy market; is the hydrogen flow interacting with the gas grid and the hydrogen quality purchased from the hydrogen energy market; is the electricity interacting with the grid, and when , it represents electricity purchased from the upper grid; τ grid , τ NG , and τ H are the unit carbon emissions of electricity purchase, gas purchase, and hydrogen purchase, respectively, and c em is the carbon emission price; C em is the system carbon emission cost; C om is the system operation and maintenance cost; represent the electricity output of the gas turbine, the water electrolysis device, and the fan, respectively; represent the unit power consumption operation and maintenance cost of the gas turbine, the water electrolysis device, the high-pressure hydrogen storage equipment, the low-pressure hydrogen storage equipment, the fan, the hydrogen fuel cell, and the gas boiler, respectively; represents the hydrogen quality.

2. The long-term optimized operation method for a regional integrated energy system considering the physical properties of hydrogen and multi-mode utilization as described in claim 1, characterized in that: The operation data of the energy system comprises new energy output, electric load, gas load, heat load and hydrogen load. 3.The method of claim 1, wherein the method further comprises: determining the hydrogen production amount based on the hydrogen production cost and the hydrogen production amount; and determining the hydrogen storage amount based on the hydrogen storage cost and the hydrogen storage amount. The equipment operation model comprises a high-pressure hydrogen storage equipment operation model, a low-pressure hydrogen storage equipment operation model and a compressor operation model. 4.The method of claim 1, wherein, The steps of establishing the high-pressure hydrogen storage equipment operation model comprise: 1) establishing a hydrogen production system model, namely: wherein, respectively the hydrogen production mass and the electricity consumption at time g of typical day k; respectively the maximum and minimum power consumption of the water electrolysis device; is a 0-1 state variable of the water electrolysis device, is 1 if the water electrolysis device is in the on state at that time, otherwise it is in the off state; is a start-stop variable of the water electrolysis device, or represents that the water electrolysis device is started at that time; or represents that the water electrolysis device is stopped at that time;N ED,cycle is the maximum start-stop number of the water electrolysis device in a day;HHV H , is the hydrogen heat value;η ED,H is a calculation parameter;Δg is the time difference; 2) establishing a real gas state model of a hydrogen storage tank based on the Van der Waals equation, namely: In the formula, Pghis the hydrogen storage pressure of the high-pressure hydrogen storage tank at time g, and the hydrogen mass; R is the ideal gas constant, T H T is the gas temperature, M H M is the relative molecular mass of hydrogen, V HHS V is the HHS volume; a and b are the Van der Waals coefficients, which are the correction quantities of hydrogen intermolecular attraction and repulsion, respectively; is a mass calculation parameter; 3) establishing a real gas formula of hydrogen under high pressure after correction, namely: 4) establishing a high-pressure hydrogen storage equipment operation model based on the real gas formula of hydrogen under high pressure. 5.The method of claim 3, wherein, The low-pressure hydrogen storage equipment operation model is as follows: wherein, is the hydrogen storage pressure and hydrogen storage mass of LHS at time g on day r of operation; are the hydrogen storage increment, hydrogen storage mass, and hydrogen release mass of LHS at time g on typical day k, respectively; is the initial hydrogen storage mass of LHS on day r; N r is the time scale; V LHS is the volume of LHS; is the maximum hydrogen storage and hydrogen release mass of LHS; is the hydrogen storage and hydrogen release state variable of LHS; is the upper and lower limit of hydrogen storage pressure of LHS at time g on day r of operation.

6. The method for long-term optimal operation of a regional integrated energy system considering hydrogen physical properties and multi-mode utilization according to claim 3, characterized in that, The compressor operation model is as follows: wherein T in respectively the compressor power consumption, temperature; γ is the specific heat capacity of the compressed gas; p in , p out respectively the input, output power; is the amount of hydrogen compressed by the compressor; η comp for the compressor to work efficiently.

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