Multi-time-scale low-carbon flexible scheduling method based on integrated energy system

Abstract

A multi-time-scale low-carbon flexible scheduling method based on an integrated energy system comprises the following steps: constructing a day-ahead scheduling robust optimization model, and based on a worst scene set of renewable energy output and flexible adjustment requirements, solving to obtain an optimal operation interval of equipment and power interacting with a superior energy network; simulating and generating the worst scene of the output and flexible adjustment requirements of the wind and light generator set by taking the optimal operation interval of the equipment and the interaction power of the superior energy network as constraint conditions; the minimum operation cost of the integrated energy system is taken as an optimization target, and the actual scheduling value and the upward / downward flexible adjustment capacity of each device in the park are determined according to the optimal operation interval of the device and the interaction power of the device and the superior energy network as constraint conditions and the worst scene of the output and flexible adjustment demand of the wind and light generator set. And constructing an intra-day scheduling model, and performing single-period optimization in each scheduling period according to the optimization solution result of the day-ahead scheduling robust optimization model and the actual observation value of the uncertainty factor. According to the method, the flexible resources of the integrated energy system can be reasonably distributed between energy and flexible adjustment services, and meanwhile, an efficient scheduling strategy which reasonably balances operation economy, low-carbon performance and flexibility is formulated by considering the carbon transaction cost on the carbon transaction market.

Description

Technical Field

[0001] The present invention relates to a technology in the field of power system dispatching, and in particular to a multi-time scale low-carbon flexible dispatching method based on an integrated energy system. Background Art

[0002] The integrated energy system (H2-IES) with wind, solar and hydrogen as the main energy sources integrates wind power generation, photovoltaic power generation, electrolyzers, hydrogen fuel cells, electric boilers, energy storage and other equipment. This combination is typical and representative. H2-IES has a high proportion of zero-carbon energy, integrates multiple energy conversion technologies, and has multiple flexible resources. It is a multi-energy coupling system with both low-carbon and flexible characteristics. Summary of the invention

[0003] In view of the above-mentioned deficiencies in the prior art, the present invention proposes a multi-time-scale low-carbon flexible scheduling method based on an integrated energy system, which can reasonably allocate the flexibility resources of the integrated energy system between energy and flexible adjustment services, while taking into account the carbon trading costs in the carbon trading market to formulate an efficient scheduling strategy that reasonably balances operating economy, low carbon and flexibility.

[0004] The present invention is achieved through the following technical solutions:

[0005] The present invention relates to a multi-time scale low-carbon flexible scheduling method based on an integrated energy system, comprising:

[0006] Step 1: Construct a robust optimization model for day-ahead scheduling, and based on the worst scenario set of renewable energy output and flexible adjustment demand, solve the optimal operation range of the equipment and the power of interaction with the upper-level energy grid;

[0007] Step 2: Using the optimal operation range of the equipment obtained in step 1 and the power of interaction with the upper energy grid as constraints, simulate and generate the worst scenario of wind and solar power generator output and flexible adjustment demand;

[0008] Step 3: Taking the lowest operating cost of the integrated energy system as the optimization goal, determine the actual scheduling value of each device in the park and the upward / downward flexible adjustment capacity based on the optimal operating range of the equipment obtained in step 1 and the power of interaction with the upper-level energy network as constraints and the worst scenario of the wind and solar power generator output and flexible adjustment demand obtained in step 2.

[0009] Step 4: construct an intraday scheduling model, and perform single-period optimization in each scheduling period according to the actual observed values ​​of the uncertainty factors based on the optimization solution results of the day-ahead scheduling robust optimization model obtained in step 3.

[0010] The day-ahead scheduling robust optimization model includes:

[0011] 1) Objective function Refers to: C energy The transaction costs between the integrated energy system park and the upper energy grid; is the carbon trading cost of the park; C om is the operation and maintenance cost of the equipment in the park; C ab U is the penalty cost for abandoning wind and solar power in the park; FRP Compensation for the integrated energy system providing flexible regulation services to the upper power grid: The unit price of purchasing and selling electricity from the upper grid; The unit price of hydrogen purchased from the upper hydrogen network; The power of purchasing and selling electricity from the upper power grid; The power of hydrogen energy purchased from the upper hydrogen network (already converted); The amount by which the park's actual carbon emissions exceed the free carbon quota during period t; is the unit price of carbon trading cost; χ PW , χ PV , χ EL , χ HFC , χ EB , χ ES , χ HS , χ hS They are 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; c ab is the penalty cost per unit for wind and solar power abandonment; ΔP PW t , ΔP PV t They are the abandoned wind and solar power in the park respectively; It is the compensation unit price given by the upper power grid for the flexible adjustment service provided to the integrated energy system. This value can be set according to the threshold of the flexible adjustment demand curve. ru and rd are the upward / downward flexible adjustment capacity provided by the equipment, respectively. The equipment with the flexible adjustment capacity provided is marked in the lower right corner.

[0012] 2) 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 operating interval, the specific scheduling value of the energy conversion equipment is relaxed to the operating interval enclosed by the upper and lower bounds, and it is ensured that the equipment meets the climbing capacity constraint when operating within 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 operating constraints of the energy storage equipment.

[0013] Electrolyzer operation range constraints: in, are the upper and lower bounds of the electric power input to the electrolyzer during period t, respectively; They are the upper and lower limits of the electric energy input to the electrolyzer respectively; are the maximum values ​​of the downward and upward climbing of the electrolytic cell respectively;

[0014] Hydrogen fuel cell operating range constraints: in, are the upper and lower bounds of the output power of the hydrogen fuel cell during period t, respectively; They are the upper and lower limits of the output power of the hydrogen fuel cell respectively; They are the maximum values ​​of the hydrogen fuel cell climbing downward and upward respectively;

[0015] Electric boiler operation range constraints: in, are the upper and lower bounds of the electric power input to the electric boiler equipment during period t; They are the upper and lower limits of electric energy input to the electric boiler respectively; They are the maximum values ​​of the downward and upward climbing of the electric boiler respectively;

[0016] Constraints on the operation range of power storage equipment: in, are the upper and lower limits of the storage capacity of the storage device in time period t; η ESc , η ESd are the efficiency of charging and discharging power of the power storage device respectively; are the maximum values ​​of the electric power charged into and released from the electric storage device respectively; are the maximum and minimum values ​​of the power storage capacity of the power storage device respectively;

[0017] Thermal storage equipment operation range constraints: in, are the upper and lower bounds of the heat storage capacity of the heat storage equipment in period t; η HSc , η HSd are the heat storage and heat release efficiencies of the heat storage equipment respectively; are the maximum values ​​of heat storage and heat release power of the heat storage equipment respectively; are the maximum and minimum values ​​of heat storage capacity of the heat storage equipment respectively;

[0018] Hydrogen storage equipment operating range constraints: in, are the upper and lower limits of the hydrogen storage capacity of the hydrogen storage equipment during period t; η hSc , η hSd They are the efficiency of storing and releasing hydrogen energy of hydrogen storage equipment; are the maximum values ​​of hydrogen storage and release power of hydrogen storage equipment respectively; They are respectively the maximum and minimum values ​​of the hydrogen storage capacity of the hydrogen storage equipment.

[0019] Superior energy online shopping can be restricted: in, is the maximum value of electricity purchased from the upper power grid during period t; is the maximum value of electricity sold to the upper grid during period t; It is the maximum value of hydrogen purchased from the upper hydrogen network during period t.

[0020] 3) Middle-level constraints: In the modeling of the integrated energy system, the uncertainty of renewable energy output and the uncertainty of the flexible adjustment requirements proposed by the upper power grid are considered, and the wind and solar output and flexible adjustment requirements are modeled using a box-type uncertainty set: Wind and solar output uncertainty constraints: in: is the day-ahead forecast value of wind power output during period t; PW,+ t 、z PW,- t is a binary variable, which is 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, which is 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.

[0021] Flexible adjustment of demand uncertainty constraints: in: is the day-ahead forecast value of the upper-level power grid’s upward flexible regulation demand during period t; FRU,+ t 、z FRU,- t is a binary variable used to flexibly adjust the fluctuation of demand upward during period t; FRU max,t 、FRU min,t They are the maximum and minimum values ​​of the demand forecast value that can be adjusted upward flexibly during period t; Γ FRUThe time smoothing coefficient for upward flexible adjustment of demand is used to reduce the conservatism of the model; is the day-ahead forecast value of the flexible downward adjustment demand of the upper power grid during period t; FRD,+ t 、z FRD,- t It is a binary variable, which is used to flexibly adjust the fluctuation of demand downward in period t; FRD max,t 、FRD min,t They are the maximum and minimum values ​​of the demand forecast value that can be adjusted downward flexibly during period t; Γ FRD The time smoothing coefficient of demand is flexibly adjusted downward to reduce the conservatism of the model.

[0022] 4) Inner constraints: Consider the internal operating constraints of the integrated energy system when it provides flexibility regulation services to the upper power grid by coordinating the dispatch of electrolyzer equipment, hydrogen fuel cells, electric boilers, and power storage equipment: Electrolyzer operation constraints: in: is the hydrogen energy output by the electrolyzer in period t; η EL is the energy conversion efficiency of the electrolyzer; is the electric energy input to the electrolytic cell during period t; The downward flexible adjustment capacity of the electrolyzer equipment during period t;

[0023] Hydrogen fuel cell operation constraints: in: are the hydrogen energy input, electrical energy and thermal energy output of the hydrogen fuel cell during period t respectively; They are the total thermoelectric efficiency, hydrogen-electricity, and hydrogen-heat energy conversion efficiency of hydrogen fuel cells; κ min , κ max They are the minimum and maximum values ​​of the thermoelectric ratio of hydrogen fuel cells; is the upward flexible adjustment capacity of the hydrogen fuel cell during period t;

[0024] Electric boiler operation constraints: in: are the heat energy output and the electrical energy input of the electric boiler in period t respectively; η EB is the energy conversion efficiency of the electric boiler; is the downward flexible adjustment capacity of the electric boiler during period t;

[0025] Power storage equipment operation constraints: in: is the storage capacity of the power storage device in period t; are the electric power charged and released by the power storage device in period t; η ESc , η ESd are the efficiency of charging and discharging power of the power storage device respectively; is a binary variable constraining the charging and discharging state of the energy storage device; They are the upward / downward flexible adjustment capacity of the power storage device in the charging state and the upward / downward flexible adjustment capacity in the discharging state during the period t respectively;

[0026] Hydrogen storage equipment operation constraints: in: is the hydrogen storage capacity of the hydrogen storage equipment in period t; are the hydrogen power stored and released by the hydrogen storage equipment in period t; η hSc , η hSd They are the efficiency of storing and releasing hydrogen energy of hydrogen storage equipment; A binary variable constraining the hydrogen storage and release states of the hydrogen storage device;

[0027] Thermal storage equipment operation constraints: in: is the heat storage capacity of the heat storage equipment in period t; are the heat power stored and released by the heat storage equipment in period t; η HSc , η HSd are the heat storage and heat release efficiencies of the heat storage equipment respectively; It is a binary variable constraining the heat storage and release state of the heat storage device;

[0028] Energy system power balance constraints: Flexible adjustment of balance constraints: Where: ΔFRU t , ΔFRD t They are respectively the shortage of the integrated energy system in period t when providing upward / downward flexible adjustment services to the superior power grid.

[0029] After the day-ahead scheduling stage, the day-ahead scheduling results are imported into the next step of intraday scheduling to provide a reference for subsequent scheduling.

[0030] Furthermore, each scheduling period is optimized based on the optimization results of the day-ahead scheduling model and the actual observed values ​​of the uncertainty factors in the intraday stage, and an intraday scheduling plan is formulated with a time scale of 15 minutes.

[0031] The intraday scheduling model includes:

[0032] 1) Objective function:

[0033] 2) Constraints: When the integrated energy system is in operation, the electrolyzer equipment, hydrogen fuel cells, electric boilers, and energy storage equipment involve time coupling relationships, the uncertainty of renewable energy output and the uncertainty of flexible regulation requirements proposed by the upper power grid, and the flexibility regulation services provided by each device to the upper power grid are similar to those in the day-ahead stage. The outer, middle, and inner constraints can be directly referenced. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a flow chart of the present invention;

[0035] Figure 2 Output curve diagram of renewable energy unit in the embodiment of the present invention;

[0036] Figure 3 A flexible regulation service demand curve diagram of the upper power grid in an embodiment of the present invention;

[0037] Figure 4 It is a curve diagram of electric load, heat load and hydrogen load in the comprehensive energy system in the embodiment of the present invention;

[0038] Figure 5 It is a curve diagram of the interactive unit price of the upper energy network in an embodiment of the present invention;

[0039] Figure 6 A graph showing the carbon emission factor of the upper energy grid in an embodiment of the present invention;

[0040] Figure 7 Schematic diagram of the optimal operation range of the electrolyzer in the day-ahead scheduling stage in an embodiment of the present invention;

[0041] Figure 8 Schematic diagram of the optimal operation range of a hydrogen fuel cell in the day-ahead scheduling stage in an embodiment of the present invention;

[0042] Fig. 9 Schematic diagram of the optimal operation range of an electric boiler in the day-ahead dispatching stage in an embodiment of the present invention;

[0043] Fig.10 Schematic diagram of the optimal operation range of the power storage device in the day-ahead scheduling stage in an embodiment of the present invention;

[0044] Fig.11 Schematic diagram of the optimal operation range of the electrolyzer in the intraday scheduling stage in an embodiment of the present invention;

[0045] Fig.12 Schematic diagram of the optimal operating range of a hydrogen fuel cell during the intraday scheduling phase in an embodiment of the present invention;

[0046] Fig.13 Schematic diagram of the optimal operation range of an electric boiler in the intraday scheduling stage in an embodiment of the present invention;

[0047] Fig.14 Schematic diagram of the optimal operating range of the power storage device in the intraday scheduling stage in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] like Figure 1As shown, a multi-time-scale low-carbon flexible scheduling method based on an integrated energy system involved in this embodiment includes:

[0049] 1) For the day-ahead dispatch model, in the first stage, based on the worst scenario set of renewable energy output and flexible adjustment demand, the optimal operation range of the equipment and the power of interaction with the upper energy grid are solved and passed to the second stage;

[0050] 2) In the max problem in the second stage, the equipment operation range obtained in the first stage and the power interacting with the upper-level energy grid are used as constraints to simulate the worst scenario of wind and solar power generation unit output and flexible adjustment demand under the constraints; the min problem in the second stage is based on the optimization results of the max problems in the first and second stages, with the lowest H2-IES comprehensive operating cost as the optimization goal, to determine the actual dispatch value of each equipment in the park and the upward / downward flexible adjustment capacity;

[0051] 3) The information exchanged between the first and second stages in the day-ahead scheduling will affect the optimization results of each other's decision variables, so it is necessary to solve the two-stage problem collaboratively. The optimization results obtained by solving the day-ahead scheduling model can be directly applied to the intraday scheduling problem. Since the day-ahead scheduling has decoupled the time coupling constraints, in the intraday scheduling, it is only necessary to optimize the single period in each scheduling period according to the actual observed values ​​of the uncertainty factors.

[0052] The present invention uses the proposed method to conduct a power system dispatch experiment, and the constructed example is as follows: The H2-IES park includes wind turbines, photovoltaic generators, electrolyzers, hydrogen fuel cell units, electric boilers, power storage equipment, heat storage equipment, and hydrogen storage equipment. The parameters of each device are shown in Table 1 and Table 2. The optimization period T = 24h, and the dispatch time interval Δt = 1h. The renewable energy unit data is as follows Figure 2 As shown in Figure 2, the uncertainty of its output is 10% of the predicted value. The flexible regulation service demand of the upper power grid is as follows: Figure 3 As shown in Figure 2, the uncertainty of flexible adjustment service demand is 10% of its predicted value. The electrical load, thermal load, and hydrogen load in the integrated energy system are shown in Figure 2. Figure 4 As shown in the figure, the unit price of energy purchase and sale with the upper energy grid is as follows: Figure 5 As shown in Figure 2, the carbon emission factor of the upper energy grid is as follows: Figure 6 shown.

[0053] Table 1 Energy supply equipment parameters

[0054] Table 2 Energy storage equipment parameters

[0055] like Figure 7-Figure 10 As shown in the figure, the low-carbon flexible day-ahead dispatch result of the integrated energy system park generated by this method is shown. In order, the optimal operating ranges of the electrolyzer, hydrogen fuel cell, electric boiler, and power storage equipment in the integrated energy system park are shown. Among them, the black solid line is the power curve without considering the flexible reserve, the black dotted line is the equipment power curve after considering the flexible reserve capacity, and the envelope area of ​​the black solid line and the black dotted line is the flexible reserve range of the equipment.

[0056] The traditional intraday rolling scheduling method is limited by time coupling constraints and needs to track the day-ahead scheduling results, which makes its adjustable range relatively limited. However, because the model proposed in the present invention is based on the optimal operating range results of the equipment, there is no need to consider the time coupling constraints during intraday scheduling. It can be adjusted arbitrarily within the equipment operating range obtained by the day-ahead scheduling, and has greater flexibility.

[0057] like Figure 11-Figure 14 As shown in FIG. 1 , the optimal operating range of each device in the optimization result of the above-mentioned day-ahead scheduling model is obtained according to the value of the uncertainty factor in the intraday scheduling stage and the minimum comprehensive operating cost is taken as the optimization goal to obtain the intraday scheduling result.

[0058] By comparing the results of day-ahead scheduling and intra-day scheduling, it can be seen that the adjustment range of the method proposed in the present invention during intra-day scheduling does not need to be limited to the specific output value of the day-ahead scheduling equipment. The equipment operating point can be adjusted arbitrarily within the equipment operating range according to the actual value of the uncertainty factor, which has better flexibility.

[0059] The present invention constructs a multi-time scale optimization scheduling model for the day-ahead and intraday that considers the participation of the integrated energy system in the main energy market, the ancillary service market, and the carbon market. The day-ahead scheduling model is a two-stage three-layer robust optimization scheduling model that considers the uncertainty of renewable energy output and flexible adjustment demand. The optimal operation range of the equipment and the specific equipment scheduling value and flexible standby situation of the corresponding scenario can be obtained through the collaborative optimization solution between the two-stage models. In the intraday scheduling stage, according to the actual values ​​of the uncertainty factors and the optimal operation range of the equipment obtained by the day-ahead scheduling, the scheduling arrangement of the equipment is flexibly adjusted with the minimum comprehensive operating cost as the objective function, thereby obtaining the intraday scheduling scheme. The day-ahead scheduling model proposed in the present invention is essentially a two-stage three-layer MILP problem, which needs to be solved based on the NC&CG algorithm and combined with the KKT condition. Since the day-ahead scheduling has decoupled the time coupling constraint, the intraday scheduling model only needs to optimize the single period based on the day-ahead scheduling result, that is, the working point can be adjusted arbitrarily within the obtained optimal operation range of the equipment, and the system will have better flexibility. Finally, through the example, the effectiveness of the multi-time scale low-carbon flexible scheduling model and method of the integrated energy system under the market environment proposed by the present invention is verified.

[0060] 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 multi-time scale low-carbon flexible scheduling method based on an integrated energy system, characterized in that: By constructing a day-ahead dispatching robust optimization model, and based on the worst-case scenario set of renewable energy output and flexible regulation demand, the optimal operating range of the equipment and the power of interaction with the upper-level energy grid are solved; with the optimal operating range of the equipment and the power of interaction with the upper-level energy grid as constraints, the worst-case scenario of wind and solar power generation unit output and flexible regulation demand is simulated and generated; Taking the lowest operating cost of the integrated energy system as the optimization goal, the actual scheduling value of each device in the park and the upward / downward flexible adjustment capacity are determined according to the optimal operating range of the equipment and the power of interaction with the superior energy network as constraints and the worst scenario of wind and solar power generator output and flexible adjustment demand, and an intraday scheduling model is constructed. According to the optimization solution results of the day-ahead scheduling robust optimization model, single-period optimization is performed in each scheduling period according to the actual observed values ​​of uncertainty factors.

2. The multi-time-scale low-carbon flexible scheduling method based on an integrated energy system according to claim 1 is characterized in that: The objective function of the day-ahead scheduling robust optimization model is Refers to: C energy is the transaction cost between the integrated energy system park and the upper energy grid; C CO2 is the carbon trading cost of the park; C om is the operation and maintenance cost of the equipment in the park; C ab U is the penalty cost for abandoning wind and solar power in the park; FRP Compensation for the integrated energy system providing flexible regulation services to the upper power grid: The unit price of purchasing and selling electricity from the upper grid; The unit price of hydrogen purchased from the upper hydrogen network; The power of purchasing and selling electricity from the upper power grid; The power of hydrogen energy purchased from the upper hydrogen network (already converted); is the amount by which the actual carbon emissions of the park exceed the free carbon quota in period t; θ0 is the unit price of carbon trading cost; χ PW , χ PV , χ EL , χ HFC , χ EB , χ ES , χ HS , χ hS They are 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; c ab is the penalty cost per unit for wind and solar power abandonment; ΔP PW t , ΔP PV t They are the abandoned wind and solar power in the park respectively; It is the compensation unit price given by the upper power grid for the flexible adjustment service provided to the integrated energy system. This value can be set according to the threshold of the flexible adjustment demand curve. ru and rd are the upward / downward flexible adjustment capacity provided by the equipment, respectively. The equipment with the flexible adjustment capacity provided is marked in the lower right corner.

3. The multi-time-scale low-carbon flexible scheduling method based on an integrated energy system according to claim 1 or 2 is characterized in that: The constraints of the day-ahead scheduling robust optimization model include: Outer constraints: To ensure that the optimized interval meets 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 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 constraints of the energy storage equipment; Middle-level constraints: In the modeling of the integrated energy system, the uncertainty of renewable energy output and the uncertainty of the flexible regulation requirements proposed by the upper power grid are considered, and the box-type uncertainty set is used to model the wind and solar output and flexible regulation requirements; Inner constraints: Consider the internal operating constraints of the integrated energy system when it provides flexibility regulation services to the upper power grid by coordinating the dispatch of electrolyzer equipment, hydrogen fuel cells, electric boilers, and energy storage equipment.

4. The multi-time-scale low-carbon flexible scheduling method based on an integrated energy system according to claim 3 is characterized in that: The outer constraints include: Electrolyzer operation range constraints: in, are the upper and lower bounds of the electric power input to the electrolyzer during period t, respectively; They are the upper and lower limits of the electric energy input to the electrolyzer respectively; are the maximum values ​​of the downward and upward climbing of the electrolytic cell respectively; Hydrogen fuel cell operating range constraints: in, are the upper and lower bounds of the output power of the hydrogen fuel cell during period t, respectively; They are the upper and lower limits of the output power of the hydrogen fuel cell respectively; They are the maximum values ​​of the hydrogen fuel cell climbing downward and upward respectively; Electric boiler operation range constraints: in, are the upper and lower bounds of the electric power input to the electric boiler equipment during period t; They are the upper and lower limits of electric energy input to the electric boiler respectively; They are the maximum values ​​of the downward and upward climbing of the electric boiler respectively; Constraints on the operation range of power storage equipment: in, are the upper and lower limits of the storage capacity of the storage device in time period t; η ESc , η ESd are the efficiency of charging and discharging power of the power storage device respectively; The maximum values ​​of the electric power charged into and released from the electric storage device respectively; are the maximum and minimum values ​​of the power storage capacity of the power storage device respectively; Thermal storage equipment operation range constraints: in, are the upper and lower bounds of the heat storage capacity of the heat storage equipment in period t; η HSc , η HSd are the heat storage and heat release efficiencies of the heat storage equipment respectively; are the maximum values ​​of heat storage and heat release power of the heat storage equipment respectively; are the maximum and minimum values ​​of heat storage capacity of the heat storage equipment respectively; Hydrogen storage equipment operating range constraints: in, are the upper and lower limits of the hydrogen storage capacity of the hydrogen storage equipment during period t; η hSc , η hSd They are the efficiency of storing and releasing hydrogen energy of hydrogen storage equipment; are the maximum values ​​of hydrogen storage and release power of hydrogen storage equipment respectively; are the maximum and minimum values ​​of hydrogen storage capacity of the hydrogen storage equipment respectively; Constraints for purchasing energy online: in, is the maximum value of electricity purchased from the upper power grid during period t; is the maximum value of electricity sold to the upper grid during period t; It is the maximum value of hydrogen purchased from the upper hydrogen network during period t.

5. The multi-time-scale low-carbon flexible scheduling method based on an integrated energy system according to claim 3 is characterized in that: The middle-level constraints include: Uncertainty constraints on wind and solar power output: in: is the day-ahead forecast value of wind power output during period t; PW,+ t 、z PW,- t is a binary variable, which is 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, which is 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 PV, which is used to reduce the conservatism of the model; Flexible adjustment of demand uncertainty constraints: in: is the day-ahead forecast value of the upper-level power grid’s upward flexible regulation demand during period t; FRU,+ t 、z FRU,- t is a binary variable used to flexibly adjust the fluctuation of demand upward during period t; FRU max,t 、FRU min,t They are the maximum and minimum values ​​of the demand forecast value that can be adjusted upward flexibly during period t; Γ FRU The time smoothing coefficient for upward flexible adjustment of demand is used to reduce the conservatism of the model; is the day-ahead forecast value of the flexible downward adjustment demand of the upper power grid during period t; FRD,+ t 、z FRD,- t It is a binary variable, which is used to flexibly adjust the fluctuation of demand downward in period t; FRD max,t 、FRD min,t They are the maximum and minimum values ​​of the demand forecast value that can be adjusted downward flexibly during period t; Γ FRD The time smoothing coefficient of demand is flexibly adjusted downward to reduce the conservatism of the model.

6. The multi-time-scale low-carbon flexible scheduling method based on an integrated energy system according to claim 3 is characterized in that: The inner constraints specifically include: Electrolyzer operation constraints: in: is the hydrogen energy output by the electrolyzer in period t; η EL is the energy conversion efficiency of the electrolyzer; is the electric energy input to the electrolytic cell during period t; The downward flexible adjustment capacity of the electrolyzer equipment during period t; Hydrogen fuel cell operation constraints: in: are the hydrogen energy input, electrical energy and thermal energy output of the hydrogen fuel cell during period t respectively; They are the total thermoelectric efficiency, hydrogen-electricity, and hydrogen-heat energy conversion efficiency of hydrogen fuel cells; κ min , κ max They are the minimum and maximum values ​​of the thermoelectric ratio of hydrogen fuel cells; is the upward flexible adjustment capacity of the hydrogen fuel cell during period t; Electric boiler operation constraints: in: are the heat energy output and the electrical energy input of the electric boiler in period t respectively; η EB is the energy conversion efficiency of the electric boiler; is the downward flexible adjustment capacity of the electric boiler during period t; Power storage equipment operation constraints: in: is the storage capacity of the power storage device in period t; are the electric power charged and released by the power storage device in period t; η ESc , η ESd are the efficiency of charging and discharging power of the power storage device respectively; is a binary variable constraining the charging and discharging state of the energy storage device; They are the upward / downward flexible adjustment capacity of the power storage device in the charging state and the upward / downward flexible adjustment capacity in the discharging state during the period t respectively; Hydrogen storage equipment operation constraints: in: is the hydrogen storage capacity of the hydrogen storage equipment in period t; are the hydrogen power stored and released by the hydrogen storage equipment in period t; η hSc , η hSd They are the efficiency of storing and releasing hydrogen energy of hydrogen storage equipment; A binary variable constraining the hydrogen storage and release states of the hydrogen storage device; Thermal storage equipment operation constraints: in: is the heat storage capacity of the heat storage equipment in period t; are the heat power stored and released by the heat storage equipment in period t; η HSc , η HSd are the heat storage and heat release efficiencies of the heat storage equipment respectively; It is a binary variable constraining the heat storage and release state of the heat storage device; Energy system power balance constraints: Flexible adjustment of balance constraints: Where: ΔFRU t , ΔFRD t They are respectively the shortage of the integrated energy system in period t when providing upward / downward flexible adjustment services to the superior power grid.

7. The multi-time-scale low-carbon flexible scheduling method based on an integrated energy system according to claim 1 is characterized in that: After the day-ahead scheduling stage, the day-ahead scheduling results are imported into the next step of intraday scheduling to provide a reference for subsequent scheduling.

8. The multi-time-scale low-carbon flexible scheduling method based on an integrated energy system according to claim 7 is characterized in that: Each scheduling period is optimized based on the optimization results of the day-ahead scheduling model and the actual observed values ​​of the uncertainty factors in the intraday stage to formulate an intraday scheduling plan with a time scale of 15 minutes.

9. The multi-time-scale low-carbon flexible scheduling method based on an integrated energy system according to claim 1 is characterized in that: The objective function of the intraday scheduling model is: Constraints: When the integrated energy system is in operation, the electrolyzer equipment, hydrogen fuel cells, electric boilers, and energy storage equipment involve time coupling relationships, the uncertainty of renewable energy output and the uncertainty of flexible regulation requirements proposed by the upper-level power grid, and the flexibility regulation services provided by each device to the upper-level power grid are similar to those in the day-ahead stage. The outer, middle, and inner constraints are directly quoted.

10. The multi-time-scale low-carbon flexible scheduling method based on an integrated energy system according to any one of claims 1 to 9, characterized in that: include: 1) For the day-ahead dispatch model, in the first stage, based on the worst scenario set of renewable energy output and flexible adjustment demand, the optimal operation range of the equipment and the power of interaction with the upper energy grid are solved and passed to the second stage; 2) In the second stage, the max problem uses the equipment operation range obtained in the first stage and the power interacting with the upper energy grid as constraints to simulate and generate the worst scenario of wind and solar power generator output and flexible adjustment demand under the constraints; The min problem in the second stage is based on the optimization results of the max problems in the first and second stages. The optimization goal is to minimize the comprehensive operating cost of H2-IES, determine the actual scheduling value of each device in the park, and flexibly adjust the capacity upward / downward; 3) The information exchanged between the first and second stages in day-ahead scheduling will affect the optimization results of each other’s decision variables. Therefore, it is necessary to solve the two-stage problem collaboratively. The optimization results obtained by solving the day-ahead scheduling model can be directly applied to the intraday scheduling problem. Since the day-ahead scheduling has decoupled the time coupling constraints, in intraday scheduling, it is only necessary to optimize the single period in each scheduling period according to the actual observed values ​​of the uncertainty factors.

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