HIES optimization scheduling method containing source load uncertainty and hydrogen energy multi-element production and use

Abstract

An HIES optimization scheduling method containing source load uncertainty and hydrogen energy multi-element production and use comprises the steps that S1, a hydrogen-containing comprehensive energy system (HIES) model containing hydrogen energy multi-element production and use is established, and the model comprises a hydrogen energy multi-element production and use equipment model, a carbon capture device (CCS) model and a storage battery (ES) and heat storage tank (HS) model; s2, on the basis of the HIES model established in the step S1, a deterministic optimization scheduling model is established, the model takes the minimum total cost as the target, and the total cost comprises the energy purchase cost, the operation and maintenance cost, the stepped carbon transaction cost, the energy abandoning cost, the carbon sequestration cost and the blue hydrogen purification cost; establishing an electric power balance constraint, a thermal power balance constraint, a gas energy balance constraint and a hydrogen energy balance constraint; and S5, carrying out uncertainty HIES optimization scheduling analysis. The problems of energy abandoning, carbon emission and high total cost caused by a single hydrogen energy production and utilization mode in the HIES in the prior art are solved, and meanwhile reference is provided for decision makers to deal with uncertainty factors of the HIES.

Description

TECHNICAL FIELD

[0001] The present application relates to a hydrogen energy multiple production HIES optimization scheduling method with source load uncertainty. BACKGROUND

[0002] IES (integrated energy system) couples multiple energy forms, has the advantages of improving energy utilization efficiency and reducing carbon emissions. Hydrogen energy, as a low-carbon and clean secondary energy, is coupled with hydrogen in the traditional integrated energy system to form a hydrogen-containing integrated energy system (HIES), which can realize the complementation of hydrogen energy, electricity and heat energy. Hydrogen energy is divided into green hydrogen, gray hydrogen and blue hydrogen according to production form. Green hydrogen is obtained by electrolysis of water, and the hydrogen production process has no carbon emissions; gray hydrogen is obtained from fossil fuels, and the process has high carbon emissions; blue hydrogen is obtained by using carbon adsorption technology on the basis of gray hydrogen, but the production process still has certain carbon emissions.

[0003] In the current research on HIES, the hydrogen production method is mostly electrolytic hydrogen, and the utilization method of hydrogen energy is mostly hydrogen methane. These methods improve the economy and low carbon of HIES to some extent. However, the existing technology has many defects and deficiencies. Electrolytic water to produce green hydrogen has become a research hotspot at home and abroad due to its low carbon, but its cost is higher than that of fossil energy hydrogen which is widely used and mature in technology, which restricts the promotion of hydrogen energy in HIES. Studies have shown that the mixed hydrogen production method of blue hydrogen and green hydrogen can improve the economy and low carbon benefit of HIES, but it does not consider the flexible proportion of blue hydrogen and green hydrogen supply hydrogen load, which may lead to all hydrogen load being supplied by blue hydrogen, resulting in an increase in carbon emissions of HIES. And in most studies, the utilization method of hydrogen energy is relatively single, which reduces the economic and carbon emission benefits of HIES. In summary, under the uncertainty of wind, light and load, the current research is less involved in the optimization scheduling analysis of HIES considering hydrogen energy multiple production. SUMMARY

[0004] The purpose of the present application is to provide a hydrogen energy multiple production HIES optimization scheduling method with source load uncertainty, to solve the problem of high cost and high carbon emission caused by single hydrogen production and utilization method in HIES in the prior art, and to provide a reference for decision makers to deal with the uncertainty factors of HIES.

[0005] In order to solve the above problems, the technical scheme of the present application is as follows:

[0006] The HIES optimization scheduling method with source load uncertainty and hydrogen energy multiple production includes the following steps:

[0007] S1: Establish a hydrogen-containing integrated energy system (HIES) model with hydrogen energy multiple production, which includes constructing hydrogen energy multiple production equipment model, carbon capture device (CCS) model, and electric storage (ES) and heat storage tank (HS) model;

[0008] S2: Based on the HIES model established in step S1, a deterministic optimization scheduling model is established. The model aims to minimize the total cost, which includes energy purchase cost, operation and maintenance cost, tiered carbon trading cost, energy curtailment cost, carbon sequestration cost, and blue hydrogen purification cost. Electric power balance constraints, thermal power balance constraints, gas energy balance constraints, and hydrogen energy balance constraints are also established.

[0009] S3: Considering the uncertainty of source load, an uncertainty optimization scheduling model is established using the information gap decision theory (IGDT) based on the entropy weight method. IGDT is divided into risk avoidance (RA) and opportunity seeking (OS) strategies.

[0010] S4: Perform deterministic HIES optimization scheduling analysis;

[0011] S5: Perform uncertain HIES optimization scheduling analysis.

[0012] Furthermore, in step S1, the hydrogen energy multi-product equipment model includes a green hydrogen production equipment (EL), a blue hydrogen production equipment (G2H), an adjustable electrothermal ratio hydrogen-blended gas turbine (HGT), a hydrogen-blended gas boiler (HGB), a green hydrogen fuel cell (GHFC), a blue hydrogen fuel cell (BHFC), a green hydrogen storage tank (GHS), a blue hydrogen storage tank (BHS), and a hydrogen methanation (MR) model; the carbon capture device (CCS) model is established to reduce system carbon emissions, and the battery (ES) and thermal storage tank (HS) models are established to cope with fluctuations in electrical and thermal energy in the system.

[0013] Furthermore, in step S2, the formula for calculating energy purchase cost is:

[0014]

[0015] In the formula: c e,t ,c g,t These are the electricity and gas prices for time period t, respectively. The amount of electricity purchased from the power grid during time period t; The gas purchase volume during time period t.

[0016] Furthermore, in step S2, the formula for calculating the operation and maintenance cost is as follows:

[0017]

[0018] In the formula: The maintenance coefficient of device i; The output power of device i.

[0019] Furthermore, in step S2, the calculation formula for the tiered carbon trading cost is determined according to different carbon emission ranges, specifically as follows:

[0020]

[0021] In the formula: I t and C CO2,t These represent the carbon trading volume and carbon trading cost for period t, respectively. t =I total,t -I pe,t I total,t and I pe,t λ represents the system's actual carbon emissions and carbon allowances during time period t; l represents the carbon trading base price; α represents the length of the carbon emission interval; and α represents the price growth rate.

[0022] Furthermore, in step S2, the hydrogen energy balance constraint is divided into blue hydrogen balance and green hydrogen balance because the production and storage of blue hydrogen and green hydrogen in the system are independent of each other, as expressed below:

[0023]

[0024] In the formula: and These represent the amount of hydrogen released from the GHS during time period t; and The amounts of green hydrogen consumed by HGT, HGB, GHFC, and hydrogen load during time period t are respectively. and These represent the amount of hydrogen released from the BHS during time period t; and These represent the amount of blue hydrogen consumed by HGT, HGB, BHFC, and hydrogen load, respectively. Let t be the hydrogen load during time period t.

[0025] Furthermore, in step S3, the deviation coefficient weights of each uncertain factor are calculated using the entropy weight method, and the mathematical expression is as follows:

[0026]

[0027] a is the normalized value of j in time period t; j,t H represents the proportion of j in time period t; j Let J be the information entropy of j; K be the number of j, which is taken as 5 in this invention; w j The deviation coefficient weight is j.

[0028] Furthermore, in step S3, under the RA strategy, the mathematical model is as follows:

[0029]

[0030] In the formula: α1 is the total deviation coefficient; C is the total system operating cost; α1 is the robust deviation coefficient under the RA strategy; C0 is the optimal cost of deterministic model optimization scheduling; p j,t The actual value of j in time period t; Let j be the predicted value for time period t; is the deviation coefficient of j; WT is wind power, PV is photovoltaic, E is electrical load, Q is thermal load, and H is hydrogen load.

[0031] Furthermore, in step S3, under the OS strategy, the mathematical model is as follows:

[0032]

[0033] Where: φ is the total deviation coefficient; C is the total system operating cost; α2 is the robust deviation coefficient under the OS strategy; C0 is the optimal cost of deterministic model optimization scheduling; p j,t The actual value of j in time period t; φ is the predicted value of j in time period t; j is the deviation coefficient of j; WT is wind power, PV is photovoltaic, E is electrical load, Q is thermal load, and H is hydrogen load.

[0034] Furthermore, in step S4, the deterministic HIES optimization scheduling analysis sets up four schemes, namely:

[0035] Option 1: Includes hydrogen production by electricity, CCS device, MR, hydrogen fuel cell, gas turbine and gas boiler, to build a traditional HIES combining electricity to gas and CCS;

[0036] Option 2: Based on Option 1, add HGT and HGB to construct a green hydrogen multi-utilization HIES;

[0037] Option 3: Based on Option 2, add a blue hydrogen production and application module to build a HIES with a green hydrogen-blue hydrogen ratio supply;

[0038] Option 4: Based on Option 3, adopt a coordinated utilization mechanism of blue hydrogen and green hydrogen, that is, blue hydrogen and green hydrogen are supplied in a flexible ratio in the hydrogen load, with the minimum proportion of green hydrogen being 0.3.

[0039] The beneficial effects of this invention are as follows:

[0040] 1. This invention introduces hydrogen-blended gas turbines and hydrogen-blended gas boilers with adjustable electrothermal ratios to operate in conjunction with gas-to-hydrogen equipment, forming a hydrogen energy multi-utilization system (HIES). This reduces the system's total cost, curtailment cost, and carbon emissions. Compared to traditional HIES, the introduction of HGTs and HGBs with adjustable electrothermal ratios allows hydrogen energy to replace a portion of gas energy, converts excess wind and solar power into hydrogen energy, and increases the power generation of HGTs by increasing their electrothermal ratio. This not only reduces curtailment costs but also lowers electricity purchase costs and carbon trading costs.

[0041] 2. The method of this invention optimizes the supply ratio of blue hydrogen and green hydrogen in the hydrogen load, forming a multi-use hydrogen energy production and utilization model (HIES) with hydrogen energy diversification equipment. This makes the supply of hydrogen energy through electricity and heat more flexible, reducing wind and solar power curtailment and further lowering the total cost, energy purchase cost, and carbon emissions of HIES. The more flexible supply of green and blue hydrogen enables HIES to achieve coordinated operation of multi-use hydrogen energy production and utilization, and also reduces the cost of blue hydrogen purification, improving its economic efficiency and low-carbon characteristics.

[0042] 3. This method utilizes an IGDT-based model constructed using the entropy weight method to construct a HIES optimal scheduling model under risk aversion and opportunity seeking strategies. It obtains the scheduling strategies and optimal costs of HIES under different risk attitudes. Through sensitivity analysis, it provides a reference for decision-makers in formulating HIES scheduling arrangements. In the RA strategy, the deviation coefficient is directly proportional to the total cost of the system. As the deviation factor increases, the system's risk-bearing capacity increases, but the cost also increases. In the OS strategy, as the deviation factor increases, the system's deviation coefficient and total cost are inversely proportional. Reduced energy purchase costs lower the total cost. Decision-makers can weigh economics and uncertainty to select an appropriate deviation factor. Attached Figure Description

[0043] The invention will be further described below with reference to the accompanying drawings:

[0044] Figure 1 This is an overall flowchart of the method of the present invention.

[0045] Figure 2 This is a structural diagram of the HIES of the present invention.

[0046] Figure 3 This is a solution diagram of the overall model of the present invention.

[0047] Figure 4 This is a prediction diagram of wind, solar, and electric / thermal hydrogen loads in an embodiment of the present invention.

[0048] Figure 5 This is a cost comparison chart of different risk attitudes in the HIES of this invention.

[0049] Figure 6 Cost comparison chart of different risk attitudes in HIES of this invention Detailed Implementation

[0050] 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.

[0051] like Figure 1 As shown, an optimal scheduling method for a hydrogen-containing integrated energy system considering source-load uncertainty and diversified hydrogen production and utilization is implemented according to the following steps:

[0052] S1: Establish a HIES model for hydrogen energy multi-product generation; first, construct a device model for hydrogen energy multi-product generation; second, construct a CCS device; finally, construct models for other devices. The HIES structure diagram is as follows: Figure 2 As shown, the specific process includes:

[0053] S1.1: Construct a multi-functional hydrogen energy production and application equipment model, including green hydrogen production equipment (EL), blue hydrogen production equipment (G2H), adjustable electrothermal ratio hydrogen-blended gas turbine (HGT), hydrogen-blended gas boiler (HGB), green hydrogen fuel cell (GHFC), blue hydrogen fuel cell (BHFC), green hydrogen storage tank (GHS), blue hydrogen storage tank (BHS), and hydrogen methanation (MR) model;

[0054] S1.2: To reduce system carbon emissions, a carbon capture device (CCS) model is established;

[0055] S1.3: To cope with the fluctuations in electrical and thermal energy in the system, establish battery (ES) and thermal storage tank (HS) models;

[0056] S2: Establish a deterministic HIES optimization scheduling model, including its objective function and constraints:

[0057] Furthermore, in S2, based on the HIES constructed in S1, a deterministic HIES optimal scheduling model is established. This model comprehensively considers the energy purchase cost, operation and maintenance cost, tiered carbon trading cost, energy curtailment cost, carbon sequestration cost, and blue hydrogen purification cost of HIES. A low-carbon economic optimal scheduling model for HIES with the minimum total cost is constructed, as shown in the following expression:

[0058]

[0059] In the formula: C represents the total cost; C buy,t C yw,t C CO2,t C waste,t C fc,t and C PSA,tThese are the energy purchase cost, operation and maintenance cost, carbon trading cost, energy curtailment cost, carbon sequestration cost, and blue hydrogen purification cost for time period t.

[0060] Energy purchase cost

[0061]

[0062] In the formula: c e,t ,c g,t These are the electricity and gas prices for time period t, respectively. The amount of electricity purchased from the power grid during time period t; The gas purchase volume during time period t.

[0063] Operation and maintenance costs

[0064]

[0065] In the formula: P represents the maintenance coefficient of device i; t i The output power of device i.

[0066] Tiered carbon trading costs

[0067] In traditional carbon trading mechanisms, carbon trading prices are typically fixed. If carbon emissions exceed the allowance, a fixed price is charged for the excess emissions. A tiered carbon trading mechanism, however, is an improved method that sets multiple emission tiers, with the cost of carbon emissions gradually increasing for each tier. The model is shown below:

[0068]

[0069] In the formula: I t and C CO2,t These represent the carbon trading volume and carbon trading cost for period t, respectively. t =I total,t -I pe,t I total,t and I pe,t λ represents the system's actual carbon emissions and carbon allowances during time period t; l represents the carbon trading base price; α represents the length of the carbon emission interval; and α represents the price growth rate.

[0070] The main carbon emission sources in HIES are HGT (Hybrid Gas Tariff), HGB (Hybrid Gas Tariff), and the equivalent carbon emissions from purchasing electricity from the main grid. Its carbon emission quota model is as follows:

[0071]

[0072] In the formula: and These represent the carbon allowances for electricity and gas purchases during time period t; χ buy and χ gThese are the carbon emission coefficients for electricity purchases, HGT, and HGB, respectively. Let t be the electrical power output by HGT during time period t; and These represent the thermal power output of HGT and HGB during time period t, respectively.

[0073] The actual carbon emissions are the total CO2 emissions during system operation, expressed as...

[0074]

[0075] In the formula: I buy,t ,I HGT,t ,I HGB,t and I′ G2H,t These represent the actual carbon emissions from electricity purchases during period t, HGT and HGB, and the carbon adsorption capacity of G2H, respectively; κ buy ,κ HGT and κ HGB These are the actual carbon emission factors for electricity purchase, HGT, and HGB, respectively; κ G2H The carbon adsorption rate of G2H; μ capt The carbon capture efficiency of CCS.

[0076] Energy curtailment cost

[0077] C waste,t =c qf P qf,t +c qg P qg,t (7);

[0078] In the formula: c qf and c qg These are the penalty coefficients for wind curtailment and solar curtailment, respectively; P qf,t and P qg,t These represent the power of wind and solar power curtailed during time period t.

[0079] Carbon sequestration costs

[0080] C fc,t =c fc I fc,t (8);

[0081] In the formula: c fc This represents the carbon sequestration price coefficient.

[0082] Blue hydrogen purification cost

[0083]

[0084] In the formula: c PSA The price coefficient for blue hydrogen purification.

[0085] After establishing the deterministic HIES optimization model, electrical power balance constraints, thermal power balance constraints, gas energy balance constraints, and hydrogen energy balance constraints are established for it.

[0086] Electric power balance constraints

[0087]

[0088] In the formula: These represent the electrical power consumed by EL and CCS during time period t, respectively. and These represent the electrical load during time period t, the electrical power output by GHFC, the electrical power output by BHFC, and the charging and discharging power of ES, respectively.

[0089] Thermal power balance constraint

[0090]

[0091] In the formula: and The figures represent the thermal power output of GHFC, the thermal power output of BHFC, the thermal load, and the charge / discharge thermal power of HS during time period t.

[0092] Gas energy balance constraint

[0093]

[0094] In the formula: and These represent the gas production of MR, the gas consumption of HGT, the gas consumption of HGB, and the gas consumption of G2H during time period t.

[0095] Hydrogen energy balance constraints

[0096] Since the production and storage of blue hydrogen and green hydrogen in the system are independent, the hydrogen energy supply and demand balance can be decomposed into blue hydrogen balance and green hydrogen balance, as shown in the following expressions:

[0097]

[0098] In the formula: and These represent the amount of hydrogen released from the GHS during time period t; and The amounts of green hydrogen consumed by HGT, HGB, GHFC, and hydrogen load during time period t are respectively. and These represent the amount of hydrogen released from the BHS during time period t; and These represent the amount of blue hydrogen consumed by HGT, HGB, BHFC, and hydrogen load, respectively. Let t be the hydrogen load during time period t.

[0099] S3: To address the uncertainty of wind, solar, and hydrothermal hydrogen loads, an uncertain HIES optimal scheduling model is established using the information gap decision theory (IGDT) based on the entropy weight method. The IGDT model comprises risk aversion (RA) and opportunity seeking (OS) strategies. The specific method is as follows:

[0100] IGDT typically uses an envelope model to model the deviation coefficients of system uncertainties, as shown in the following expression:

[0101]

[0102] In the formula: p j,t , and are the actual value and predicted value of j in time period t, respectively; is the deviation coefficient of j; j is the wind, solar, and electric heating hydrogen load.

[0103] For the deviation coefficient optimization problem involving multiple uncertainties, this invention employs the entropy weight method to calculate the weight of each uncertainty's deviation coefficient. Its mathematical expression is as follows:

[0104]

[0105] In the formula: a is the normalized value of j in time period t; j,t H represents the proportion of j in time period t; j Let J be the information entropy of j; K be the number of j, which is taken as 5 in this invention; w j The deviation coefficient weight is j.

[0106] Under the RA strategy, decision-makers must ensure that the total system operating cost does not exceed the expected scheduling cost, while simultaneously avoiding the impact of the deviation coefficient of uncertainties on optimal system scheduling. In other words, under the RA strategy, the system aims to maximize the deviation coefficient. A larger deviation coefficient indicates a stronger ability to bear risk, but the corresponding cost is also higher. The mathematical model is as follows:

[0107]

[0108] In the formula: C0 is the optimal cost of the deterministic model for optimized scheduling; α1 is the robust bias coefficient under the RA strategy.

[0109] As can be seen from equation (16), the optimization model based on IGDT is a two-layer model, and as... As it increases, its cost will also increase. Once determined, its maximum operating cost occurs when the parameter uncertainty is... When necessary, a two-layer model can be converted into a single-layer model, as follows:

[0110]

[0111] Similarly, under the OS strategy, decision-makers believe that scheduling can be optimized to reduce costs and minimize the risks to the system. In other words, under the opportunity-seeking strategy, the system's optimization objective is minimization. Its mathematical model is as follows:

[0112]

[0113] For the HIES model proposed in this invention, the CPLEX solver in MATLAB is selected to solve the model. Since there are mixed integer nonlinear functions such as Equation (4) and Equation (6), the piecewise linearization method is used to linearize the nonlinear model before solving it.

[0114] S4: Conduct deterministic HIES optimization scheduling analysis. Under deterministic conditions, four schemes were set up to verify the low-carbon and economical nature of the proposed hydrogen-containing multi-product HIES, specifically:

[0115] Option 1: Considering hydrogen production by electricity, CCS device, MR, hydrogen fuel cell, gas turbine and gas boiler, construct a traditional HIES combining electricity to gas and CCS;

[0116] Option 2: Based on Option 1, introduce HGT and HGB to construct a green hydrogen multi-utilization HIES;

[0117] Option 3: Based on Option 2, introduce the production and application of blue hydrogen to build a HIES with a green hydrogen-blue hydrogen ratio;

[0118] Option 4: Based on Option 3, consider an improved scenario of coordinated utilization of blue hydrogen and green hydrogen, that is, flexible supply of blue hydrogen and green hydrogen in the hydrogen load, with the minimum proportion of green hydrogen being 0.3.

[0119] To verify the effectiveness of the proposed deterministic model, numerical examples were conducted using typical daily data from a certain region, setting up the aforementioned four schemes. The system's scheduling optimization cycle was taken as 24 hours, with a time interval of 1 hour. The wind and solar power and electricity / heat / hydrogen load predictions were as follows: Figure 4 As shown in Table 1, the parameters of the HIES system are shown in Table 2, the parameters and coefficients of the other equipment are shown in Table 3, and the electricity price and gas price are shown in Table 4. The ramp-up limits of HGT and HGB are 400kW, and the operation and maintenance coefficient is 0.07 for all equipment except energy storage equipment, which is 0.017.

[0120] Table 1 Main Equipment Parameters

[0121]

[0122] Table 2 Energy Storage Parameters

[0123]

[0124]

[0125] Table 3 Energy Storage Parameters

[0126]

[0127] Table 4 Energy Storage Parameters

[0128]

[0129] Table 5. HIES Costs (RMB) for Deterministic Solutions

[0130]

[0131] As shown in Table 5, compared with Scheme 1, Scheme 2 reduced the total cost, electricity purchase cost, curtailment cost, and carbon emissions by 3.76%, 19.16%, 55.14%, and 22.96%, respectively. The high total cost and carbon emissions are due to the relatively singular use of hydrogen energy in traditional HIES, which leads to higher electricity purchase and curtailment costs. Scheme 2 introduces HGT and HGB with adjustable electrothermal ratios. Hydrogen energy replaces a portion of gas energy and converts excess wind and solar power into hydrogen energy for utilization. In addition, increasing the electrothermal ratio of HGT increases its power generation, which not only reduces curtailment costs but also lowers electricity purchase and carbon trading costs. Compared to Option 2, Option 3 reduces total cost, electricity purchase cost, and carbon emissions by 6.27%, 41.34%, and 24.29%, respectively. This is because the green-to-blue hydrogen ratio supply method used in Option 3 diversifies hydrogen production methods within the HIES (Higher Energy Environment). In Option 2, some hydrogen production requiring water electrolysis is replaced by lower-cost blue hydrogen, thus reducing electricity purchase costs. Furthermore, the blue hydrogen production process involves carbon adsorption, further reducing carbon emissions, although gas purchase costs increase accordingly. Additionally, the ratio supply method leads to sufficient wind power at night and reduced green hydrogen supply during off-peak hours, resulting in increased wind curtailment in Option 3. Compared to Scheme 2 and Scheme 3, Scheme 4 reduces total costs by 11.17% and 5.23%, electricity purchase costs by 52.82% and 19.57%, curtailment costs by 10.53% and 61.06%, and carbon emissions by 9.79% and 6.27%. Moreover, compared to Scheme 3, Scheme 4 reduces blue hydrogen purification costs by 22.71%. Because Scheme 4 proposes a more flexible green and blue hydrogen supply method, it enables HIES to achieve coordinated operation of diversified hydrogen production and utilization, thereby further reducing the cost, carbon emissions, and curtailment of wind and solar energy, and improving its economic efficiency and low-carbon characteristics.

[0132] S5: Perform uncertain HIES optimization scheduling analysis, the specific method is as follows:

[0133] In actual operation of HIES, wind and solar power output and load demand are often uncertain. Therefore, to consider the impact of uncertainty on the operating cost of the HIES system, uncertainty optimization scheduling analysis is performed based on Scheme 4 as the basic scenario. The total cost of HIES under Scheme 4 is 28058.92 yuan. Calculations show that the weights of wind and solar power and power / thermal / hydrogen loads are 0.213, 0.182, 0.206, 0.202, and 0.197, respectively.

[0134] Different risk attitudes can influence the scheduling strategy of HIES. Therefore, S5 analyzes the impact of the deviation factor on the total cost and deviation coefficient of the system, increasing the deviation factor from 0 to 0.2 in increments of 0.025. The trends of the system's deviation coefficient and total cost as a function of the deviation factor are shown below. Figure 5 and Figure 6 As shown. By Figure 5 It is known that in the RA strategy, the deviation coefficient is proportional to the total cost of the system. This is because, in a risk-averse strategy, as the deviation factor increases, the decrease in wind and solar power output and the increase in electricity, heat, and hydrogen load bring greater risks to the system, leading to increased energy purchase costs and thus increased total costs. Figure 6 It is known that in an OS strategy, as the deviation factor increases, the system's deviation coefficient and total cost are inversely proportional. This leads to a decrease in the system's energy purchase cost, thereby reducing the total cost. Therefore, decision-makers need to rationally select the system's deviation factor based on a balance between the system's economics and its uncertainties.

[0135] The embodiments described in this specification are merely examples of implementations of the inventive concept. The scope of protection of this invention should not be considered as limited to the specific forms stated in the embodiments. The scope of protection of this invention also extends to equivalent technical means that can be conceived by those skilled in the art based on the inventive concept.

Claims

1. A HIES optimal scheduling method for hydrogen energy multiple generation and utilization with source-load uncertainty, characterized by: Includes the following steps; S1: Establish a hydrogen-containing integrated energy system (HIES) model with multiple hydrogen energy production and utilization. The model includes the construction of hydrogen energy multiple production and utilization equipment models, carbon capture device (CCS) models, and battery (ES) and thermal storage tank (HS) models. S2: Based on the HIES model established in step S1, a deterministic optimization scheduling model is established. The model aims to minimize the total cost, which includes energy purchase cost, operation and maintenance cost, tiered carbon trading cost, energy curtailment cost, carbon sequestration cost, and blue hydrogen purification cost. Electric power balance constraints, thermal power balance constraints, gas energy balance constraints, and hydrogen energy balance constraints are also established. S3: Considering the uncertainty of source load, an uncertainty optimization scheduling model is established using the information gap decision theory (IGDT) based on the entropy weight method. IGDT is divided into risk avoidance (RA) and opportunity seeking (OS) strategies. S4: After establishing the uncertainty model in step S3, perform deterministic HIES optimization scheduling analysis; S5: Based on the analysis results of step S4, perform uncertain HIES optimization scheduling analysis.

2. The HIES optimization scheduling method for hydrogen energy multi-generation and utilization with source-load uncertainty as described in claim 1, characterized in that: In step S1, the hydrogen energy multi-product equipment model includes a green hydrogen production equipment (EL), a blue hydrogen production equipment (G2H), an adjustable electrothermal ratio hydrogen-blended gas turbine (HGT), a hydrogen-blended gas boiler (HGB), a green hydrogen fuel cell (GHFC), a blue hydrogen fuel cell (BHFC), a green hydrogen storage tank (GHS), a blue hydrogen storage tank (BHS), and a hydrogen methanation (MR) model; the carbon capture device (CCS) model is established to reduce system carbon emissions, and the battery (ES) and thermal storage tank (HS) models are established to cope with fluctuations in electrical and thermal energy in the system.

3. The HIES optimization scheduling method for hydrogen energy multi-generation and utilization with source-load uncertainty as described in claim 1, characterized in that: In step S2, the formula for calculating energy purchase cost is: In the formula: c e,t ,c g,t These are the electricity and gas prices for time period t, respectively. The amount of electricity purchased from the power grid during time period t; The gas purchase volume during time period t.

4. The HIES optimization scheduling method for hydrogen energy multi-generation and utilization with source-load uncertainty as described in claim 1, characterized in that: In step S2, the formula for calculating the operation and maintenance cost is: In the formula: P represents the maintenance coefficient of device i; t i The output power of device i.

5. The HIES optimization scheduling method for hydrogen energy multi-generation and utilization with source-load uncertainty as described in claim 1, characterized in that: In step S2, the calculation formula for the tiered carbon trading cost is determined according to different carbon emission ranges, specifically as follows: In the formula: I t and C CO2,t These represent the carbon trading volume and carbon trading cost for period t, respectively. t =I total,t -I pe,t I total,t and I pe,t λ represents the system's actual carbon emissions and carbon allowances during time period t; l represents the carbon trading base price; α represents the length of the carbon emission interval; and α represents the price growth rate.

6. The HIES optimization scheduling method for hydrogen energy multi-generation and utilization with source-load uncertainty as described in claim 1, characterized in that: In step S2, the hydrogen energy balance constraint is divided into blue hydrogen balance and green hydrogen balance because the production and storage of blue hydrogen and green hydrogen in the system are independent of each other, as expressed below: In the formula: and These represent the amount of hydrogen released from the GHS during time period t; and The amounts of green hydrogen consumed by HGT, HGB, GHFC, and hydrogen load during time period t are respectively. and These represent the amount of hydrogen released from the BHS during time period t; and These represent the amount of blue hydrogen consumed by HGT, HGB, BHFC, and hydrogen load, respectively. Let t be the hydrogen load during time period t.

7. The HIES optimal scheduling method for hydrogen energy multi-generation with source-load uncertainty as described in claim 1, characterized in that: In step S3, the deviation coefficient weights of each uncertain factor are calculated using the entropy weight method. The mathematical expression is as follows: a is the normalized value of j in time period t; j,t H represents the proportion of j in time period t; j Let J be the information entropy of j; K be the number of j, which is taken as 5 in this invention; w j The deviation coefficient weight is j.

8. The HIES optimal scheduling method for hydrogen energy multi-generation and utilization with source-load uncertainty as described in claim 1, characterized in that: In step S3, under the RA strategy, the mathematical model is as follows: In the formula: α1 is the total deviation coefficient; C is the total system operating cost; α1 is the robust deviation coefficient under the RA strategy; C0 is the optimal cost of deterministic model optimization scheduling; p j,t The actual value of j in time period t; Let j be the predicted value for time period t; is the deviation coefficient of j; WT is wind power, PV is photovoltaic, E is electrical load, Q is thermal load, and H is hydrogen load.

9. The HIES optimal scheduling method for hydrogen energy multi-generation with source-load uncertainty as described in claim 1, characterized in that: In step S3, under the OS strategy, the mathematical model is as follows: Where: φ is the total deviation coefficient; C is the total system operating cost; α2 is the robust deviation coefficient under the OS strategy; C0 is the optimal cost of deterministic model optimization scheduling; p j,t The actual value of j in time period t; φ is the predicted value of j in time period t; j is the deviation coefficient of j; WT is wind power, PV is photovoltaic, E is electrical load, Q is thermal load, and H is hydrogen load.

10. The HIES optimization scheduling method for hydrogen energy multi-generation and utilization with source-load uncertainty as described in claim 1, characterized in that: In step S4, the deterministic HIES optimization scheduling analysis sets up four schemes, namely: Option 1: Includes hydrogen production by electricity, CCS device, MR, hydrogen fuel cell, gas turbine and gas boiler, to build a traditional HIES combining electricity to gas and CCS; Option 2: Based on Option 1, add HGT and HGB to construct a green hydrogen multi-utilization HIES; Option 3: Based on Option 2, add a blue hydrogen production and application module to build a HIES with a green hydrogen-blue hydrogen ratio supply; Option 4: Based on Option 3, adopt a coordinated utilization mechanism of blue hydrogen and green hydrogen, that is, blue hydrogen and green hydrogen are supplied in a flexible ratio in the hydrogen load, with the minimum proportion of green hydrogen being 0.3.

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