Hydrogen-containing IES low-carbon day-ahead optimization scheduling method for improving flexibility
By building a two-stage three-layer robust optimization model in H2-IES, optimizing the equipment operating range and energy purchase situation, the problems of volatility and instability of the power system are solved, and efficient low-carbon and flexibility evaluation and improvement are achieved.
Patent Information
- Application Number
- CN202411660594.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-20
- Publication Date
- 2025-02-25
AI Technical Summary
With the increase in renewable energy access to the power grid, the volatility and instability of power systems have increased. Traditional single power systems are difficult to meet complex energy demands and carbon emission reduction goals, and a low-carbon energy system scheduling method that can improve flexibility is needed.
A low-carbon, recently optimized scheduling method for hydrogen-containing integrated energy system (H2-IES) is proposed. By constructing a two-stage three-layer robust optimization model for min-max-min, it simulates the game process between the output of renewable energy units and decision makers, optimizes the equipment operation range and energy purchase situation, and realizes low-carbon and flexibility assessment.
Through this method, the low carbonity and flexibility of H2-IES can be evaluated more accurately, the flexible regulation capabilities of the system can be improved, the ability to absorb renewable energy, and carbon emissions can be reduced.
Smart Images
Figure CN119514796A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to a technology in the field of energy system scheduling, specifically a method for optimizing the scheduling of low-carbon day-ahead hydrogen-containing IES with improved flexibility. Background Art
[0002] With the large-scale access of renewable energy such as wind and solar energy to the power grid, the volatility and instability of the power system have increased. The integrated energy system (IES) achieves efficient utilization of multiple energy sources through the coordinated dispatch of multiple energy sources such as electricity, natural gas, heat and hydrogen energy. With the low-carbon transformation of the energy structure, the traditional single power system is difficult to meet the increasingly complex energy demand and carbon reduction targets. IES has become an important development direction for the future energy system due to its multi-energy complementarity and coordinated optimization. Hydrogen energy, as a clean and efficient secondary energy source, has the advantage of zero carbon emissions. In IES, hydrogen energy can be produced and stored through the electrolysis of water from renewable energy. As a flexible means of regulating the system, it can not only balance energy supply and demand, but also significantly reduce carbon emissions. In addition, hydrogen energy can be widely used in transportation, industry and other fields to further promote the low-carbonization of the system. Summary of the invention
[0003] In view of the above-mentioned deficiencies in the prior art, the present invention proposes a method for optimizing the day-ahead scheduling of low-carbon hydrogen-containing IES with improved flexibility, and realizes optimized scheduling by evaluating the low carbon and flexibility of H2-IES.
[0004] The present invention is achieved through the following technical solutions:
[0005] The present invention relates to a method for optimizing the scheduling of low-carbon hydrogen-containing IES with improved flexibility, comprising:
[0006] Step 1) simulate the game process between the output of renewable energy units and decision makers, and construct a min-max-min two-stage three-layer robust optimization model to obtain the optimal operating range of the equipment and the situation of purchasing energy from the superior energy network.
[0007] Step 2) In the first stage, based on the worst scenario set of renewable energy output, the optimal operation range of the equipment and the energy purchase from the upper energy network are solved and passed to the second stage.
[0008] Step 3) In the max problem in the second stage, the equipment operation range and energy purchase situation obtained in the first stage are used as constraints to simulate and generate the worst scenario of renewable energy unit output under the constraints, that is, to try to maximize the comprehensive operating cost of H2-IES.
[0009] Step 4) Based on the dispatch results obtained by the H2-IES day-ahead optimization dispatch model, the low-carbon and flexibility evaluation of H2-IES can be performed. BRIEF DESCRIPTION OF THE DRAWINGS
[0010] Figure 1 It is a flow chart of the present invention.
[0011] Figure 2 This is a schematic diagram of decoupling of time coupling constraints;
[0012] Figure 3 This is a schematic diagram of the flexible adjustment capability of the equipment;
[0013] Figure 4 It is a schematic diagram of the NC&CG algorithm flow;
[0014] Figure 5 It is a basic parameter diagram of the embodiment;
[0015] Figure 6 Optimize the result graph for the model;
[0016] Figure 7 It is the energy balance diagram of the energy subsystem of the comprehensive energy system;
[0017] Figure 8 This is the low carbon assessment result diagram;
[0018] Fig. 9 This is a diagram of the flexibility assessment results. DETAILED DESCRIPTION
[0019] like Figure 1 As shown, this embodiment relates to a method for optimizing the day-ahead scheduling of low-carbon hydrogen-containing IES with improved flexibility, including:
[0020] Step 1: Establish the H2-IES day-ahead optimization scheduling model, including: objective function: Where: C energy It represents the energy purchase cost of the integrated energy system when it purchases energy from the upper energy grid; C om represents the operation and maintenance cost of equipment in the integrated energy system park; C pur Represents the penalty cost for curtailing wind and solar power within the integrated energy system park.
[0021] The energy purchase cost C when purchasing energy from the upper energy grid energy Including the cost of electricity purchased from the upper power grid and the cost of hydrogen purchased from the upper hydrogen network, the expression is: in: The unit price of electricity purchased from the upper power grid; The unit price of hydrogen purchased from the upper hydrogen network; The power of electricity purchased from the upper power grid; The power of purchasing hydrogen energy from the upper-level hydrogen network.
[0022] The operation and maintenance cost of the equipment in the integrated energy system park is C om Including the operation and maintenance costs of renewable energy output units, electrolyzer equipment, electric heating equipment, hydrogen fuel cell units, power storage equipment, heat storage equipment, and hydrogen storage equipment, expressed as: in: χ PW , χ PV , χ EL , χ HFC , χ EB , χ ES , χ HS , They respectively represent the power and unit power operation and maintenance cost of wind power generation, photovoltaic power generation units, electrolyzers, hydrogen fuel cells, electric boilers, electricity storage equipment, heat storage equipment, and hydrogen storage equipment.
[0023] The penalty cost C for abandoning wind and solar power in the integrated energy system park pur That is, to punish the renewable energy output that is not consumed in the park, so as to promote the park to increase the renewable energy consumption rate in the park, the expression is: Where: c pur is the penalty cost per unit for wind and solar power abandonment; ΔP PW t , ΔP PV t They respectively represent the abandoned wind power and abandoned solar power in the park.
[0024] The constraints of the H2-IES day-ahead optimization scheduling model include:
[0025] Outer constraints: To ensure that the optimized interval satisfies all necessary constraints and can be adjusted arbitrarily within the obtained interval without exceeding the limit, it is necessary to decouple the constraints involving the time coupling relationship of the equipment in the integrated energy system park. By introducing the upper and lower bounds of the equipment operation interval, the specific scheduling value of the energy conversion equipment is relaxed to the operation interval enclosed by the upper and lower bounds, and it is ensured that the equipment meets the climbing capacity constraint when operating in this interval; similarly, the specific storage capacity variable of the original energy storage equipment is also changed to an interval variable, and it is ensured that it meets the operation constraints of the energy storage equipment. The schematic diagram of the decoupling principle of the time coupling constraint is shown in Figure 2 shown.
[0026] The operating range constraints of the electrolyzer include: in: are the upper and lower bounds of the electric power input to the electrolyzer during period t respectively.
[0027] Hydrogen fuel cell operating range constraints include:
[0028] in: They are the upper and lower bounds of the electric power output of the hydrogen fuel cell during period t.
[0029] Electric boiler operation range constraints include: in: They are the upper and lower bounds of the electric power input to the electric boiler equipment during period t.
[0030] The operating range constraints of power storage equipment include: in: They are the upper and lower bounds of the storage capacity of the energy storage device in period t respectively.
[0031] Thermal storage equipment operation range constraints include: in: are the upper and lower bounds of the heat storage capacity of the heat storage equipment in period t respectively.
[0032] The operating range constraints of hydrogen storage equipment include:
[0033] The upper energy network purchase energy constraints include: in: is the maximum value of electricity purchased from the upper power grid during period t; It is the maximum value of hydrogen purchased from the upper hydrogen network during period t.
[0034] The uncertainty of renewable energy unit output is considered in the modeling of integrated energy systems, and the box-type uncertainty set is used to model the wind and solar output: z PW,+ t +z PW,- t ≤1, z PV,+ t +z PV,- t ≤1, in: is the day-ahead forecast value of wind power output during period t; PW,+ t 、z PW,- t is a binary variable, used to represent the fluctuation of wind power output during period t; PW max,t , P PW min,t are the maximum and minimum predicted wind power output in period t respectively; Γ PW is the time smoothing coefficient of wind power, which is used to reduce the conservatism of the model; is the day-ahead forecast value of photovoltaic output during period t; PV,+ t 、z PV,- t is a binary variable, used to represent the fluctuation of photovoltaic output during period t; P PV max,t , P PV min,t are the maximum and minimum predicted photovoltaic output in period t respectively; Γ PV is the time smoothing coefficient of photovoltaics, which is used to reduce the conservatism of the model.
[0035] The inner constraints include:
[0036] Electrolyser operating constraints, including:
[0037] Hydrogen fuel cell operating constraints, including:
[0038] Electric boiler operation constraints include:
[0039] Operation constraints of power storage equipment include:
[0040] Hydrogen storage equipment operating constraints, including:
[0041] Energy system power balance constraints, including:
[0042] Based on the H2-IES day-ahead optimization dispatch model, its low carbon and flexibility can be evaluated. The low carbon nature of H2-IES can be quantitatively evaluated through indicators such as the renewable energy consumption rate and green electricity ratio in the park, and the flexibility of H2-IES can be intuitively evaluated through the obtained optimal operation range of the equipment.
[0043] Step 2: The present invention uses the green electricity ratio and renewable energy consumption rate in the H2-IES park as quantitative indicators to evaluate the low-carbon situation of the park, specifically including:
[0044] 2.1) Green electricity ratio: The green electricity sources in H2-IES include the consumption of wind and solar power output and the power output of hydrogen fuel cells. The expression of the green electricity ratio in the park is as follows: Where: r green is the proportion of green electricity in H2-IES.
[0045] 2.2) Renewable energy consumption rate: Where: r reg is the renewable energy consumption rate in H2-IES.
[0046] Step 3: Figure 3 As shown in the figure, after evaluating the park flexibility based on the adjustable range of equipment operation intervals within H2-IES, the H2-IES day-ahead optimization scheduling model is further transformed into a compact form.
[0047] The equipment includes an electrolyzer device with unidirectional flexible adjustment capability, a hydrogen fuel cell unit, an electric boiler, and an electricity storage device with bidirectional flexible adjustment capability.
[0048] The flexibility of the electrolyser equipment is evaluated in the following ways: in: It is the flexible adjustment capability of the electrolyzer equipment, that is, the difference between the upper limit of the optimal operating range of the input electric power of the electrolyzer equipment and the specific scheduling value of the input electric power of the electrolyzer in a certain scenario.
[0049] The flexibility of the hydrogen fuel cell unit is evaluated in the following ways: in: It is the flexible adjustment capability of the hydrogen fuel cell unit, that is, the difference between the upper limit of the optimal operating range of the hydrogen fuel cell output power and the specific scheduling value of the hydrogen fuel cell output power in a certain scenario.
[0050] The flexibility of the electric boiler is evaluated in the following ways: in: It is the flexible adjustment capability of the electric boiler equipment, that is, the difference between the upper limit of the optimal operating range of the electric boiler equipment input power and the specific scheduling value of the electric boiler input power in a certain scenario.
[0051] The flexibility of the energy storage device is evaluated in the following ways: in: They are the upward / downward flexible adjustment capabilities of the power storage device, that is, they correspond to the difference between the upper limit of the optimal operating range of the SOC of the power storage device and the specific scheduling value of the SOC of the power storage device in a certain scenario, and the difference between the specific scheduling value of the SOC of the power storage device in the scenario and the lower limit of the optimal operating range of the SOC.
[0052] The H2-IES day-ahead optimization scheduling model is further transformed into a compact form as follows: st.Ay≥b,Ex+Gz≥f-Ru-Dy, z∈{0,1}where: y is the decision variable of the first stage; u is the uncertainty variable; x is the continuous decision variable of the second stage; z is the binary decision variable of the second stage; c, d, g are the coefficient matrices of the objective function; A, b, E, G, f, R, D are the coefficient matrices of the constraint conditions.
[0053] First, based on the C&CG algorithm, the H2-IES day-ahead optimization scheduling model is converted into an outer main problem and outer sub-problems, where the outer main problem MP is converted to: st.Ay≥b,η≥dx o +gz o ,1≤o≤I out ,Ex o +Gz o ≥f-Ru o* -Dy,1≤o≤I out , z o ∈{0,1},1≤o≤I out , where: η is the newly added decision variable in the outer master problem MP; x o 、z o is the decision variable added in the oth iteration of the outer subproblem SP; u o* is the optimal solution u of the outer subproblem SP solved in the oth iteration and fed back to the outer main problem MP; out is the number of iterations of the outer layer.
[0054] The outer subproblem SP is transformed into: z∈{0,1}, where: The optimal solution of y obtained by solving the outer main problem MP.
[0055] Since the outer subproblem SP contains continuous decision variables x and binary decision variables z, this subproblem belongs to MILP problem and cannot be directly transformed and solved using strong duality theory. First, separate x and z and transform them, specifically:
[0056] At this time, the minimization problem in the constraint condition is an LP model. In order to facilitate the solution, the KKT condition can be used to convert this minimization problem into a feasibility problem. On this basis, the outer subproblem SP is converted into a new main and subproblem and solved again using the C&CG algorithm to construct the inner main problem mp and the inner subproblem sp, specifically: st.θ≤dx i +gz i* ,1≤i≤I in , 1≤i≤I in , ET μ i ≤d T ,1≤i≤I in , x i (d T -E T μ i )=0,1≤i≤I in , 1≤i≤I in , where: θ is the newly added decision variable in the inner main problem mp; x i Add a new decision variable for the inner subproblem sp at the i-th iteration; z i* is the optimal solution of z that is solved by the inner subproblem sp in the i-th iteration and fed back to the inner main problem mp; in is the number of iterations of the inner layer; μ i is the dual variable introduced in the KKT condition.
[0057] 1≤i≤I in ,in: The optimal solution of u obtained by solving the inner master problem.
[0058] Step 4: Figure 4 As shown in the figure, the two-stage three-layer robust optimization model is solved by the NC&CG algorithm, which includes:
[0059] 4.1) Set initialization parameters: LB o =LB i =-∞,UB o =UB i =-∞,I out =I in =0;
[0060] 4.2) Solve the outer main problem MP and obtain the optimal solution Update outer C&CG lower bounds: When the convergence condition is met at this time, return And end the iteration: UB o -LB o <ε o ;
[0061] 4.3) Solve the inner main problem mp and get the optimal solution Update the inner C&CG upper bound: If the convergence condition is met at this time, return And go to step 4.6): UB i -LB i <ε i ;
[0062] 4.4) Solve the inner subproblem sp and get the optimal solution Update the inner C&CG lower bound: When the convergence condition is met at this time, return And proceed to step 4.6);
[0063] 4.5) When the inner layer does not reach the convergence condition, add a new decision variable And add I to the inner main question mp in +1 corresponding to the constraint condition, I in =I in +1, return to step 4.3);
[0064] 4.6) When the inner layer reaches the convergence condition, the outer layer C&CG upper bound is updated: When the convergence condition of the formula is satisfied at this time, it returns And the iteration ends. If the outer layer does not reach the convergence condition, a new decision variable is added And the new I in the outer main problem MP out +1 corresponding to the constraint condition, I out =I out +1, return to step 4.2).
[0065] After specific practical experiments, in the environment of matlab2023a, a park including wind turbines, electrolyzer equipment, hydrogen fuel cells, electric boilers, power storage equipment, heat storage equipment, and hydrogen storage equipment was simulated. The parameters of each device are shown in Table 1 and Table 2. The optimization period T = 24h, and the scheduling time interval Δt = 1h. The wind turbine data is as follows Figure 5 As shown in (a), the uncertainty of wind power output is 10% of its predicted value. The electrical load, thermal load, and hydrogen load in the integrated energy system are shown in Figure 5 As shown in (b), the unit price of energy purchased from the upper energy network is Figure 5 (c) as shown.
[0066] Table 1 Energy supply equipment parameters
[0067] Table 2 Energy storage equipment parameters
[0068] In this embodiment, the YALMIP tool is used for modeling under the MATLAB 2022a compilation environment, and the GUROBI mathematical solver is called for solving.
[0069] 1) Model optimization results
[0070] The model optimization results are shown in Figure 6 As shown, Figure 6(a)-(f) correspond to the optimal operating ranges of electrolyzer, hydrogen fuel cell, electric boiler, power storage equipment, heat storage equipment, and hydrogen storage equipment, respectively. The actual dispatch of equipment based on the wind power output forecast value scenario is shown in Figure 6 The black curves in (a)-(f) are shown. Figure 6 (g)-(f) show the situation where the integrated energy system purchases energy from the upper-level energy grid.
[0071] Through Figure 6 The upper and lower bounds of the interval (a)-(f) are shown. The optimal operating interval obtained by the model can effectively ensure that any working point of the equipment will not exceed the limit of the equipment operation constraints. According to the obtained optimal operating interval of the equipment, the equipment operation point can be adjusted more flexibly according to the actual scenario. The whole system has better flexibility and can absorb renewable energy with uncertainty, thereby further improving the low-carbon nature of the park. Among them, the electrolyzer has the ability to climb quickly and flexibly, so its optimal operating interval is the area surrounded by the envelope of the minimum input power and the maximum input power of the equipment, which can respond quickly to uncertainty. Configuring the electrolyzer can effectively help the integrated energy system improve its flexible adjustment ability.
[0072] The energy balance of each energy subsystem in the integrated energy system park based on the wind power output forecast value is as follows Figure 7 As shown. Figure 7 (a) During the periods of high wind power output from 1:00 to 8:00 and 21:00 to 24:00, the electrolyzer can utilize its flexible electricity-hydrogen conversion characteristics to convert surplus wind power into hydrogen energy, which can be used by hydrogen fuel cells to heat the thermal load or stored in hydrogen storage equipment for use during the day. At the same time, the energy storage equipment also stores electricity during the periods of high wind power output, and releases it during the peak electricity load and high electricity price periods during the day. The integrated energy system park purchases electricity from the upper power grid during the period of low wind power output from 9:00 to 20:00. During this period, the electricity demand in the park is jointly supplied by the upper power grid, wind power, hydrogen fuel cells, and energy storage equipment. Figure 7 (b) The heat demand of the park can be met by hydrogen fuel cells, while electric boilers and heat storage equipment can be used to provide heat energy for the heat load in the park. Figure 7 (c) It can be seen that the hydrogen production of the electrolyzer is more significant during the periods of 1:00-6:00 and 22:00-24:00 when the wind power output is relatively large. During the period of daytime when the wind power output is relatively low, the park needs to purchase hydrogen energy from the upper hydrogen network, and at the same time use hydrogen storage to release hydrogen energy to meet the hydrogen energy demand in the park.
[0073] 2) Low carbon assessment
[0074] Based on the actual dispatch of equipment in the integrated energy system park ( Figure 7 ) to calculate the green power ratio and wind curtailment volume of the park, such as Figure 8 As shown. Figure 8 As shown in (a), during the periods of 1:00-8:00 and 21:00-24:00 when the wind power output is relatively large, the green electricity ratio of the integrated energy system park reaches 100%. Combining the wind power output and the purchase of electricity from the upper power grid, it can be seen that during the period of 9:00-20:00, the wind power in the park is relatively small, and the park purchases some electricity from outside to meet the power demand in the park. This part of electricity does not belong to the defined green electricity category, so the green electricity ratio has decreased. Figure 8 (b) The integrated energy system park constructed can absorb wind power well, with only a small amount of wind power abandoned during the period of 1:00-3:00. Wind power is absorbed during other scheduling periods, which can ensure the green and low-carbon nature of the park.
[0075] 3) Flexibility Assessment
[0076] like Fig. 9 As shown in the figure, the optimal equipment operation status and equipment scheduling plan obtained based on the model ( Figure 6 (a)-(d)), the flexible adjustment range of the electrolyzer, hydrogen fuel cell, electric boiler, and energy storage equipment can be obtained. When the equipment is operated at any working point within this flexible adjustment range, it can meet the energy demand of multiple loads in the park and will not adversely affect the reliability and stability of the park's energy supply.
[0077] like Fig. 9 As shown in (a), during the period of 1:00-6:00, the electrolyzer needs to absorb the surplus wind power in the park, so the operating point is at a higher level, and the flexible adjustment range at this time is relatively small. In the remaining scheduling periods, the flexible adjustment range of the electrolyzer is relatively abundant, which can provide the system with unidirectional flexible adjustment capabilities. Fig. 9 As shown in (b), the hydrogen fuel cell is in a high-level operating state at night. At the same time, it is limited by the equipment climbing constraints, and the flexible adjustment range is relatively small. It has good one-way flexible adjustment capability in the rest of the scheduling period. Fig. 9 (c) is the flexible adjustment range of the electric boiler. During the period of 1:00-5:00, the electric boiler has a certain flexible adjustment range, but in the rest of the time periods, it has a smaller flexible adjustment range because it works at a high level. Fig. 9 (d) shows the flexible adjustment range of the energy storage equipment. As shown, the flexible adjustment range of the energy storage is smaller at the beginning and end of the scheduling cycle. This is because the energy storage needs to meet the energy conservation constraints at the beginning and end of the scheduling. It has a larger flexible adjustment range in the time periods of 4:00-12:00 and 16:00-22:00, and can provide the system with two-way flexible adjustment capabilities through charging and discharging.
[0078] Compared with the prior art, this invention proposes a two-stage three-layer robust optimization model based on the optimal operation range of the equipment, taking into account the uncertainty of renewable energy output in the park and the impact of time coupling constraints on equipment operation, and obtains more accurate H2-IES low-carbon and flexibility quantitative evaluation results. The NC&CG algorithm is used to efficiently solve the model, and a converged solution is obtained through collaborative iterative optimization of the inner and outer layers.
[0079] The above-mentioned specific implementation can be partially adjusted in different ways by those skilled in the art without departing from the principle and purpose of the present invention. The protection scope of the present invention shall be based on the claims and shall not be limited by the above-mentioned specific implementation. Each implementation scheme within its scope shall be subject to the constraints of the present invention.
Claims
1. A method for optimizing the scheduling of low-carbon hydrogen-containing IES with improved flexibility, characterized in that: By simulating the game process between the output of renewable energy units and decision makers, a min-max-min two-stage three-layer robust optimization model is constructed to obtain the optimal operating range of the equipment and the energy purchase situation from the superior energy network. In the first stage, based on the worst scenario set of renewable energy output, the optimal operating range of the equipment and the energy purchase situation from the superior energy network are solved and passed to the second stage; in the max problem in the second stage, the equipment operating range and energy purchase situation obtained in the first stage are used as constraints to simulate the worst scenario of renewable energy unit output under the constraints, that is, to maximize the comprehensive operating cost of H2-IES; based on the scheduling results obtained by the H2-IES day-ahead optimization scheduling model, H2-IES can be evaluated for low carbon and flexibility.
2. The method for optimizing the low-carbon day-ahead scheduling of hydrogen-containing IES with improved flexibility according to claim 1 is characterized in that: include: Step 1: Establish the H2-IES day-ahead optimization scheduling model, including: objective function: Where: C energy It represents the energy purchase cost of the integrated energy system when it purchases energy from the upper energy grid; C om represents the operation and maintenance cost of equipment in the integrated energy system park; C pur It represents the penalty cost of curtailing wind and solar power in the integrated energy system park; Step 2, the present invention uses the green electricity ratio and renewable energy consumption rate in the H2-IES park as quantitative indicators to evaluate the low-carbon situation of the park; Step 3: After evaluating the flexibility of the park based on the adjustable range of the equipment operation interval in H2-IES, the H2-IES day-ahead optimization scheduling model is further transformed into a compact form; The equipment includes an electrolyzer device with unidirectional flexible adjustment capability, a hydrogen fuel cell unit, an electric boiler, and an electric storage device with bidirectional flexible adjustment capability; Step 4: Solve the two-stage three-layer robust optimization model using the NC&CG algorithm.
3. The method for optimizing the low-carbon day-ahead scheduling of hydrogen-containing IES with improved flexibility according to claim 2 is characterized in that: The energy purchase cost C when purchasing energy from the upper energy grid energy Including the cost of electricity purchased from the upper power grid and the cost of hydrogen purchased from the upper hydrogen network, the expression is: in: The unit price of electricity purchased from the upper power grid; The unit price of hydrogen purchased from the upper hydrogen network; The power of electricity purchased from the upper power grid; The power of hydrogen purchased from the upper hydrogen network; The operation and maintenance cost of the equipment in the integrated energy system park is C om Including the operation and maintenance costs of renewable energy output units, electrolyzer equipment, electric heating equipment, hydrogen fuel cell units, power storage equipment, heat storage equipment, and hydrogen storage equipment, expressed as: in: χ PW , χ PV , χ EL , χ HFC , χ EB , χ ES , χ HS , Respectively represent the power and unit power operation and maintenance cost of wind power generation, photovoltaic generator set, electrolyzer, hydrogen fuel cell, electric boiler, power storage equipment, heat storage equipment, and hydrogen storage equipment; The penalty cost C for abandoning wind and solar power in the integrated energy system park pur That is, to punish the renewable energy output that is not consumed in the park, so as to promote the park to increase the renewable energy consumption rate in the park, the expression is: Where: c pur is the penalty cost per unit for wind and solar curtailment; ΔP PW t , ΔP PV t They respectively represent the abandoned wind power and abandoned solar power in the park.
4. The method for optimizing the low-carbon day-ahead scheduling of hydrogen-containing IES with improved flexibility according to claim 2 is characterized in that: The outer constraints in the constraints of the H2-IES day-ahead optimization scheduling model are as follows: to ensure that the optimized interval satisfies all necessary constraints and can be adjusted arbitrarily within the obtained interval without exceeding the limit, it is necessary to decouple the constraints involving the time coupling relationship of the equipment in the integrated energy system park, and by introducing the upper and lower bounds of the equipment operation interval, the specific scheduling value of the energy conversion equipment is relaxed to the operation interval enclosed by the upper and lower bounds, and the equipment is guaranteed to meet the climbing capacity constraint when operating within the interval; similarly, the specific storage capacity variable of the original energy storage equipment is also changed to an interval variable, and it is guaranteed to meet the operation constraint of the energy storage equipment; The operating range constraints of the electrolyzer include: in: are the upper and lower bounds of the electric power input to the electrolyzer during period t, respectively; Hydrogen fuel cell operating range constraints include: in: are the upper and lower bounds of the output power of the hydrogen fuel cell during period t, respectively; Electric boiler operation range constraints include: in: are the upper and lower bounds of the electric power input to the electric boiler equipment during period t; The operating range constraints of power storage equipment include: in: are the upper and lower bounds of the storage capacity of the power storage device in period t respectively; Thermal storage equipment operation range constraints include: in: are the upper and lower bounds of the heat storage capacity of the heat storage equipment in period t respectively; The operating range constraints of hydrogen storage equipment include: The upper energy network purchase energy constraints include: in: is the maximum value of electricity purchased from the upper power grid during period t; is the maximum value of hydrogen purchased from the upper hydrogen network during period t; The uncertainty of renewable energy unit output is considered in the modeling of integrated energy systems, and the box-type uncertainty set is used to model the wind and solar output: z PW,+ t +z PW,- t ≤1, z PV,+ t +z PV,- t ≤1, in: is the day-ahead forecast value of wind power output during period t; PW,+ t 、z PW,- t is a binary variable, used to represent the fluctuation of wind power output during period t; PW max,t , P PW min,t are the maximum and minimum predicted wind power output in period t respectively; Γ PW is the time smoothing coefficient of wind power, which is used to reduce the conservatism of the model; is the day-ahead forecast value of photovoltaic output during period t; PV,+ t 、z PV,- t is a binary variable, used to represent the fluctuation of photovoltaic output during period t; P PV max,t , P PV min,t are the maximum and minimum predicted photovoltaic output in period t respectively; Γ PV is the time smoothing coefficient of photovoltaics, which is used to reduce the conservatism of the model.
5. The method for optimizing the low-carbon day-ahead scheduling of hydrogen-containing IES with improved flexibility according to claim 2 is characterized in that: The inner constraints in the constraints of the H2-IES day-ahead optimization scheduling model include: Electrolyser operating constraints, including: Hydrogen fuel cell operating constraints, including: Electric boiler operation constraints include: Operation constraints of power storage equipment include: Hydrogen storage equipment operating constraints, including: Energy system power balance constraints, including:
6. The method for optimizing the low-carbon day-ahead scheduling of hydrogen-containing IES with improved flexibility according to claim 2 is characterized in that: The step 2 specifically includes: 2.1) Green electricity ratio: The green electricity sources in H2-IES include the consumption of wind and solar power output and the power output of hydrogen fuel cells. The expression of the green electricity ratio in the park is as follows: Where: r green is the proportion of green electricity in H2-IES; 2.2) Renewable energy consumption rate: Where: r reg is the renewable energy consumption rate in H2-IES.
7. The method for optimizing the low-carbon day-ahead scheduling of hydrogen-containing IES with improved flexibility according to claim 2 is characterized in that: The flexibility of the electrolyser equipment is evaluated in the following ways: in: The flexible adjustment capability of the electrolyzer equipment is the difference between the upper limit of the optimal operation range of the input power of the electrolyzer equipment and the specific scheduling value of the input power of the electrolyzer in a certain scenario; The flexibility of the hydrogen fuel cell unit is evaluated in the following ways: in: is the flexible adjustment capability of the hydrogen fuel cell unit, that is, the difference between the upper limit of the optimal operating range of the hydrogen fuel cell output power and the specific dispatching value of the hydrogen fuel cell output power in a certain scenario; The flexibility of the electric boiler is evaluated in the following ways: in: The flexible adjustment capability of the electric boiler equipment is the difference between the upper limit of the optimal operation range of the electric boiler equipment input power and the specific dispatch value of the electric boiler input power in a certain scenario; The flexibility of the energy storage device is evaluated in the following ways: in: They are the upward / downward flexible adjustment capabilities of the power storage device, that is, they correspond to the difference between the upper limit of the optimal operating range of the SOC of the power storage device and the specific scheduling value of the SOC of the power storage device in a certain scenario, and the difference between the specific scheduling value of the SOC of the power storage device in the scenario and the lower limit of the optimal operating range of the SOC.
8. The method for optimizing the low-carbon day-ahead scheduling of hydrogen-containing IES with improved flexibility according to claim 2 is characterized in that: The H2-IES day-ahead optimization scheduling model is further transformed into a compact form as follows: st.Ay≥b,Ex+Gz≥f-Ru-Dy, z∈{0,1}, where: y is the decision variable of the first stage; u is the uncertainty variable; x is the continuous decision variable of the second stage; z is the binary decision variable of the second stage; c, d, g are the coefficient matrices of the objective function; A, b, E, G, f, R, D are the coefficient matrices of the constraint conditions.
9. The method for optimizing the low-carbon day-ahead scheduling of hydrogen-containing IES with improved flexibility according to claim 2 is characterized in that: The transformation specifically includes: First, based on the C&CG algorithm, the H2-IES day-ahead optimization scheduling model is converted into an outer main problem and outer sub-problems, where the outer main problem MP is converted to: st.Ay≥b,η≥dx o +gz o ,1≤o≤I out ,Ex o +Gz o ≥f-Ru o* -Dy,1≤o≤I out , z o ∈{0,1},1≤o≤I out , where: η is the newly added decision variable in the outer master problem MP; x o 、z o is the decision variable added in the oth iteration of the outer subproblem SP; u o* is the optimal solution of u that is solved in the oth iteration of the outer subproblem SP and fed back to the outer main problem MP; out is the number of iterations of the outer layer; The outer subproblem SP is transformed into: z∈{0,1}, where: To find the optimal solution of y obtained by solving the outer master problem MP; Since the outer subproblem SP contains continuous decision variables x and binary decision variables z, this subproblem belongs to the MILP problem and cannot be directly transformed and solved using the strong duality theory. First, x and z are separated and transformed as follows: At this time, the minimization problem in the constraint condition is an LP model. In order to facilitate the solution, the KKT condition is used to convert this minimization problem into a feasibility problem. On this basis, the outer subproblem SP is converted into a new main and subproblem and solved again using the C&CG algorithm to construct the inner main problem mp and the inner subproblem sp. Specifically: st.θ≤dx i +gz i* ,1≤i≤I in , x i (d T -E T μ i )=0,1≤i≤I in , Among them: θ is the newly added decision variable in the inner main problem mp; x i Add a new decision variable for the inner subproblem sp at the i-th iteration; z i* is the optimal solution of z that is solved by the inner subproblem sp in the i-th iteration and fed back to the inner main problem mp; in is the number of iterations of the inner layer; μ i is the dual variable introduced in the KKT condition; in: The optimal solution of u obtained by solving the inner master problem.
10. The method for optimizing the low-carbon day-ahead scheduling of hydrogen-containing IES with improved flexibility according to claim 2 is characterized in that: The step 4 specifically includes: 4.1) Set initialization parameters: LB o =LB i =-∞,UB o =UB i =-∞,I out =I in =0; 4.2) Solve the outer main problem MP and obtain the optimal solution Update outer C&CG lower bounds: When the convergence condition is met at this time, return And end the iteration: UB o -LB o <ε o ; 4.3) Solve the inner main problem mp and get the optimal solution Update the inner C&CG upper bound: If the convergence condition is met at this time, return And go to step 4.6): UB i -LB i <ε i ; 4.4) Solve the inner subproblem sp and get the optimal solution Update the inner C&CG lower bound: When the convergence condition is met at this time, return And proceed to step 4.6); 4.5) When the inner layer does not reach the convergence condition, add a new decision variable And add I to the inner main question mp in +1 corresponding to the constraint condition, I in =I in +1, return to step 4.3); 4.6) When the inner layer reaches the convergence condition, the outer layer C&CG upper bound is updated: When the convergence condition of the formula is satisfied at this time, it returns And end the iteration. If the outer layer does not reach the convergence condition, add a new decision variable And the new I in the outer main problem MP out +1 corresponding to the constraint condition, I out =I out +1, return to step 4.2).