Scheduling method adaptive to multi-time-scale rolling between wind and light fluctuation and maximum hydrogen yield

Abstract

The invention discloses a scheduling method adaptive to multi-time-scale rolling between wind-light fluctuation and maximum hydrogen yield. The scheduling method specifically comprises a wind-light power generation prediction model, an electrolytic tank group, a power model of a storage battery pack, a price type demand response model and the like. In the working process, electric energy input caused by wind and light is included, wind and light fluctuation is responded through the hydrogen load demand, it is ensured that the hydrogen consumption is increased when there is more wind and more light, the hydrogen consumption is reduced when there is less wind and less light while electricity is converted into hydrogen, and the effect of flexibly adapting to volatility is achieved. Electricity-hydrogen conversion is achieved through a storage battery-electrolytic cell system, the wind and light abandoning rate and the electricity abandoning rate of new energy are reduced, the consumption level of renewable energy is improved, economic and stable operation of the system is guaranteed, and meanwhile the maximum hydrogen production amount is obtained. According to the method, the adaptability and the flexibility of the renewable energy hydrogen production system are enhanced, the economical efficiency and the prediction accuracy are improved, efficient consumption of renewable energy is promoted, and carbon peak reaching and carbon neutralization targets are supported to be achieved.

Description

Technical Field

[0001] The present invention belongs to the field of new energy technology, and specifically relates to a multi-time scale rolling optimization scheduling method that can flexibly adapt to wind and solar fluctuations and maximize hydrogen production. Background Art

[0002] In recent years, distributed renewable energy power generation technologies represented by wind power and photovoltaics have developed rapidly. China has made it clear that by 2030, the total installed capacity of wind power and solar power generation will reach more than 1.2 billion kilowatts, and renewable energy will enter a new stage of high-quality leapfrogging development. At the same time, in the context of the world's requirements for carbon peak and carbon neutrality, hydrogen energy, known as the ultimate energy of the 21st century, is the only choice for today's ideal renewable energy products.

[0003] However, the volatility of clean energy generation such as wind and light will bring many problems to the dispatching and operation of microgrid systems. Moreover, as the penetration rate of renewable energy increases, the prediction errors of wind and light power generation are also relatively large, making it more difficult for the power system to achieve safe, reliable and economical operation. Therefore, the development of new energy hydrogen production systems faces the difficult problem of both high-level consumption and hydrogen production.

[0004] In this technical context, a multi-time scale optimization scheduling strategy, namely the day-ahead-intraday optimization method, is proposed. The day-ahead optimization gives a preliminary output plan for each flexible and dispatchable resource, optimizes the output of each resource in real time and rollingly during the day, and then modifies the day-ahead scheduling plan. Combined with the battery and hydrogen load demand response, the power fluctuations of renewable energy are flexibly adapted on the supply and demand sides, respectively. On the basis of meeting the scheduling plan for maximizing hydrogen production, the output of renewable energy is more adaptable. Summary of the invention

[0005] In order to further solve the problem that it is difficult to balance the volatility and hydrogen production of renewable energy hydrogen production systems, the present invention discloses a multi-time scale rolling scheduling method that adapts to wind and solar fluctuations and maximum hydrogen production, introduces electrolyzer hydrogen production and battery regulation, and constructs a renewable energy electricity-to-hydrogen conversion system; combined with price-based hydrogen load demand response, the hydrogen production power is flexibly adapted to the fluctuation of renewable energy power; with the maximum hydrogen production as the objective function, considering multiple time scales from day one to day one, the best adaptation scheduling is optimized in a rolling manner, which ensures the maximization, refinement and flexibility of hydrogen production while adapting to the volatility of wind and solar power.

[0006] The scheduling method that adapts to the multi-time scale rolling between wind and solar fluctuations and the maximum hydrogen production includes two scheduling stages: day-ahead and intraday. The specific scheduling method includes the following steps:

[0007] S1: Construct an integrated energy system model of electricity-hydrogen conversion with wind power, photovoltaic resources, energy storage devices, electrolyzer groups and hydrogen load demand response.

[0008] S2: In the day-ahead dispatching stage, the one-hour wind and solar forecast data are used as the input data for optimizing the day-ahead dispatching model. The time-of-use hydrogen price is designed based on the output of renewable energy. The hydrogen price is used to guide users to adjust their hydrogen demand, respond to changes in the system operating status, and adapt to wind and solar fluctuations.

[0009] S3: On this basis, 24 hours is selected as a scheduling cycle, and the maximum hydrogen production of the electrolyzer is taken as the optimization goal. Considering the output constraints, capacity constraints, power balance and other constraints of wind turbines, photovoltaic units, electrolytic cell groups and battery groups, a day-ahead optimization scheduling model is constructed, and the optimal solution in the model is solved through the corresponding mathematical model of the MATLAB platform, and finally the optimal scheduling plan for the day-ahead is obtained.

[0010] S4: In the intraday stage, 10-minute wind and light forecast data are used. Based on this data and considering the demand response, a real-time rolling optimization strategy is adopted in the intraday stage to correct the day-ahead scheduling plan. The model uses 4 hours as a scheduling cycle and 10 minutes as the scheduling time scale for rolling optimization. The minimum variance of the adjustment amount relative to the day-ahead scheduling result is used as the objective function. The S3 day-ahead optimization solution method is also used to calculate the model, and finally an optimized scheduling result with better adaptability is obtained.

[0011] In the multi-time scale rolling scheduling method proposed in the present invention to adapt to the fluctuations of wind and solar power and the maximum hydrogen production, a wind and solar power generation prediction model, a power model of the electrolytic cell group and the battery group are established respectively, and a price-type demand response model is established. As well as an objective function of maximizing hydrogen production and corresponding constraints, the optimal scheduling result of the electric-hydrogen conversion system is solved by combining the multi-time rolling optimization of the day before and within the day. While ensuring the maximization of hydrogen production, a broad and reasonable scheduling plan is given in advance, which flexibly adapts to the volatility brought by wind and solar power generation and greatly improves the flexibility and accuracy of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is an overall framework diagram of the electricity-hydrogen conversion comprehensive energy system model of the present invention.

[0013] The characteristics of the present invention and the beneficial effects it produces are that the proposed wind, solar and hydrogen multi-scale rolling optimization scheduling method, through multi-time scale optimization on the day before and within the day, combined with real-time rolling strategy and price-based demand response, achieves the maximization of hydrogen production under the fluctuating conditions of wind and solar power generation, promotes the efficient consumption of renewable energy, and enhances the adaptability and flexibility of the system; compared with other optimization schemes, the method of the present invention takes into account both economy and prediction accuracy, highlights its superiority in the field of hydrogen production from renewable energy, and supports the realization of carbon peak and carbon neutrality goals. DETAILED DESCRIPTION

[0014] The method of the present invention is further explained below with reference to the accompanying drawings and through specific examples. It should be noted that this calculation example is a narrative description for explaining the modeling steps and is not intended to limit the scope of protection of the present invention.

[0015] In view of the problem that it is difficult to balance the volatility of renewable energy hydrogen production systems and hydrogen production, the present invention provides a scheduling method that adapts to the multi-time scale rolling between wind and solar fluctuations and maximum hydrogen production, which includes two scheduling stages: day-ahead and intraday (in order to simplify the description in the text, the day-ahead stage is sometimes referred to as day-ahead; the intraday stage is referred to as intraday). The scheduling method includes the following steps:

[0016] S1: Construct an integrated energy system model of electricity-hydrogen conversion with wind power, photovoltaic resources, energy storage devices, electrolyzer groups and hydrogen load demand response.

[0017] S2: In the day-ahead dispatch stage, the one-hour wind and solar forecast data are used as the input data for optimizing the day-ahead dispatch model. The time-of-day hydrogen price is designed based on the output of renewable energy, and the hydrogen price is used to guide users to adjust their hydrogen demand, respond to changes in system operating status, and adapt to wind and solar fluctuations.

[0018] S3: On this basis, 24 hours is selected as a scheduling cycle, and the maximum hydrogen production of the electrolyzer is taken as the optimization goal. Considering the output constraints, capacity constraints, power balance and other constraints of wind turbines, photovoltaic units, electrolytic cell groups and battery groups, a day-ahead optimization scheduling model is constructed, and the corresponding mathematical model of the MATLAB platform is used to solve the optimal solution in the model, and finally the day-ahead optimal scheduling plan is obtained.

[0019] S4: In the intraday stage, more accurate 10-minute wind and light forecast data are used. Based on this data and considering demand response, a real-time rolling optimization strategy is adopted in the intraday stage to correct the day-ahead scheduling plan. The model uses 4 hours as a scheduling cycle and 10 minutes as the scheduling time scale for rolling optimization, with the minimum variance of the adjustment amount relative to the day-ahead scheduling result as the objective function. The S3 day-ahead optimization solution method is also used to calculate the model, and finally a more adaptable optimization scheduling result is obtained.

[0020] Specifically, the establishment of the comprehensive energy system model in step S1 includes:

[0021] The integrated energy system for electricity-to-hydrogen conversion consists of power models of electrolytic cell groups and battery groups. The electric load is supplied by a wind and solar forecast model, and the hydrogen generated by the system is used or stored downstream as hydrogen load.

[0022] The renewable energy power generation prediction module consists of photovoltaic power generation and wind power generation.

[0023] (1) Photovoltaic power model:

[0024] P pv (t) = [1-θ pv (TT ref )]η pv N pv A pv I pv (t) (1)

[0025] (2) Wind power model:

[0026]

[0027] Demand response (DR) means that when the electricity market price rises or the system reliability decreases, the load user changes the electricity consumption strategy after receiving the direct compensation notice of induced load reduction or the electricity price increase notice from the power supplier, so as to reduce or shift the electricity load in a certain period of time, thereby achieving a benign interaction between the user and the power grid.

[0028] The price-based demand response is applied to the hydrogen load, and load regulation on the hydrogen demand side is achieved by constructing time-of-day hydrogen prices and different loads.

[0029] (1) Modeling of interruptible load demand response

[0030] Different loads react differently to the same price. Price-based demand response loads are divided into interruptible loads (CL) and transferable loads (SL). Interruptible loads are determined by judging the price changes before and after DR, and then deciding whether to interrupt their own loads. The price demand elasticity matrix is ​​used to describe the demand response DR characteristics. The element e in the mth row and nth column of the elasticity matrix E(m,n) is m,n It represents the price elasticity coefficient between row m and column n. The mathematical model is:

[0031]

[0032] Change in interruptible load after demand response ΔP CL,m_e for:

[0033]

[0034] (2) Transferable load demand response modeling

[0035] The principle of transferable load is that users can flexibly adjust energy consumption time according to their own needs and real-time hydrogen price information, so that the hydrogen load under high hydrogen price is transferred to the period of low and flat hydrogen price. The transferable change ΔP after demand response is SL,m_e The mathematical model is:

[0036]

[0037] The battery plays a role in regulating the electrical load through flexible charging and discharging. Its power model is as follows:

[0038]

[0039] The electrolytic cell uses power provided by renewable energy to electrolyze water to produce hydrogen. Based on the power input, the efficiency of the electrolytic cell (η ele ) and the higher heating value (HHV) of hydrogen, calculate the hydrogen Δm produced by the electrolytic cell at time step Δt:

[0040]

[0041] Step S2: input of wind and solar power forecast power and adaptation of hydrogen price in response to demand. Step S3: determination of the objective function and constraints of the day-ahead phase, including the day-ahead dispatch model.

[0042] Day-ahead scheduling must comprehensively consider the maximization of hydrogen production by the electric-hydrogen conversion system and the flexible adaptation of new energy sources. Therefore, the objective function is to maximize hydrogen production while meeting the level of demand response that adapts to wind and solar fluctuations.

[0043] (1) Objective function:

[0044]

[0045] (2) Equipment operation constraints: The equipment is limited by rated capacity and rated power.

[0046]

[0047] To ensure stable operation of the equipment and extend its service life, the battery needs to meet the following constraints:

[0048]

[0049] (3) Hydrogen load constraint: Hydrogen is generated by electrolysis of water in an electrolytic cell, part of which is used downstream, and the rest is stored in a hydrogen storage tank for standby use.

[0050]

[0051] (4) Energy balance constraint: The electric bus is a node in the power system that connects wind and solar generators and other equipment. It is used to centrally transmit electric energy and ensure that electricity is efficiently and stably distributed from the power generation point to various equipment.

[0052]

[0053] The determination of the intraday stage objective function and constraints in step S4 includes the intraday stage scheduling model.

[0054] Intraday optimization refers to dynamic optimization in the form of rolling optimization real-time tracking, and adjusting the day-ahead plan to ensure that the scheduling strategy is presented in the best form. Therefore, compared with the day-ahead single optimization scheduling, rolling optimization scheduling has better reliability and adaptability, which is used to reduce the uncertainty caused by system modeling, external interference, and prediction errors and their impact on scheduling results.

[0055] (1) Objective function:

[0056] Taking into account the deviation between the day-ahead optimized scheduling plan and the actual operation caused by the long-term prediction error in the day-ahead stage and the changes in the climate environment, the intraday scheduling is adjusted based on the newly obtained ultra-short-term wind and light forecast data.

[0057] In order to give full play to the guiding significance of the day-ahead scheduling plan, the intraday rolling optimization minimizes the adjustment amount compared to the day-ahead. In the objective function, the adjustment amount is used as a penalty function, so the intraday optimization objective function is to minimize the sum of the penalty variances of the adjustment amount within the rolling scheduling period.

[0058] C Total =min(C punish_PEM +C punish_Bat ) (13)

[0059]

[0060]

[0061] (2) Intraday climbing constraints:

[0062] The constraints involved in the demand response model, wind turbines, photovoltaic units, and battery groups are exactly the same as those set for the day-ahead scheduling. However, in the optimization scheduling process of the shorter time scale in the intraday stage, the ramp constraints of the electrolytic cell group need to be considered. The constraints are as follows:

[0063]

[0064] Where P PEM_d The upper limit of the ramp rate of the electrolytic cell group; -P PEM_d It is the lower limit of the ramp rate of the electrolytic cell group.

[0065] Specific calculation example

[0066] Taking a county in Zhangjiakou City, Hebei Province as the research object, the meteorological data of wind power and photovoltaic power generation in the area were collected, and the typical day was taken as the research object of the present invention. The equipment parameters involved in the model are shown in Table 1. The basic hydrogen price in the market at different time periods and the time-of-use hydrogen price adapted to the wind and solar power output are shown in Table 2.

[0067] The wind speed v(t), light intensity Ipv (t) External data such as key equipment parameters are read into MATLAB, and corresponding model strategies are selected according to different scenarios. Under the operating constraints of various equipment, the hydrogen load measurement demand response is constructed. Through day-ahead optimization scheduling, the day-ahead scheduling result is used as the intraday plan, and the final multi-time scale optimization scheduling result is obtained through rolling optimization, which satisfies the maximum hydrogen production under the flexible adaptation of wind and solar fluctuations.

[0068] Table 1 Parameters of each equipment in the model

[0069]

[0070]

[0071] Table 2 Basic hydrogen prices in different periods of the market and time-sharing hydrogen prices adapted to wind and solar power output

[0072]

[0073] In order to verify the effectiveness of the multi-time-scale rolling scheduling method proposed in the present invention to adapt to wind and solar fluctuations and maximum hydrogen production, a simulation model of two time scales, day-ahead and intraday, was constructed based on MATLAB R2023b, and the following four scenarios were set up for comparative analysis, as shown in Table 3:

[0074] Table 3 Case studies in different scenarios and optimized results

[0075]

[0076]

[0077] The results show that, through the comparison of the first two scenarios, although smaller-scale power prediction and regulation will lead to an increase in hydrogen production, larger-capacity batteries are required for peak shaving and valley filling. Comparison between Scenario 2 and Scenario 3 shows that adding multiple time scales can not only provide a broader and more reasonable scheduling plan in the day-ahead stage, but also increase the hydrogen production. However, both still require larger-capacity batteries for regulation, which is not conducive to the flexible and stable operation of the system. Therefore, on the basis of considering multiple time scales, Scenario 4 introduces the demand response on the hydrogen load side, ensuring that while a broad and reasonable scheduling plan is given in the day-ahead stage of system operation, the intra-day stage is reasonably allocated and scheduled according to the day-ahead stage plan, so that the battery capacity and the number of cycles are significantly reduced, thereby greatly reducing the dependence on batteries, and the hydrogen production is also maintained at a relatively high level. So far, the strategy adopted in Scenario 4 has improved the flexibility and accuracy of the system, and also highlighted the superiority of the scheme of the present invention.

[0078] As one of the embodiments, the specific calculation process is:

[0079] 1. Combine historical wind and solar data with (1) and (2) to establish a wind and solar power model, and obtain wind and solar power results at the 1-hour and 10-minute scales as inputs for the day-ahead and intraday models, respectively.

[0080] 2. Combined with the hydrogen load valley and peak demand elasticity matrix E(m,n):

[0081]

[0082] Combining the basic hydrogen price in the market at different time periods and the time-of-use hydrogen price adapted to the wind and solar power output in Table 2, the demand elasticity matrix E is constructed. CL (m,n) and E SL (m,n), according to (4) and (5), the change in interruptible load ΔP is obtained CL,m_e and the change in transferable load ΔP SL,m_e , Finally, the minimum hydrogen production power for downstream use is obtained by adapting to the fluctuations of wind and solar power at different times.

[0083] 3. In MATLAB, mathematical models of the battery and electrolytic cell are established according to formulas (6) and (7), and constraints of formulas (9)-(12) are created. The wind and solar power at a scale of 1 h is input, and the parameters in Table 1 are substituted to optimize and solve the objective function (8). The hydrogen production is obtained by satisfying all constraints. The maximum value of .

[0084] 4. Keep the constraints of the day-ahead optimization formulas (9)-(12) unchanged. Since the intraday time scale is shortened to 10 minutes, the electrolytic cell needs to establish a small-scale ramp constraint formula (16); the day-ahead optimization is used to obtain the maximum hydrogen production. Electrolytic cell power P PEM , battery power P Bat As the standard, the electrolytic cell power P is adjusted within a day according to formulas (14) and (15). PEM_0 , battery power P Bat_0 , combined with the penalty adjustment coefficient in Table 1, the day-ahead-intraday constraints and the 10-minute wind and solar power input intraday, the optimization objective (13), i.e., the total penalty amount C adjusted intraday, is Total The minimum solution is a rolling cycle of 4 hours and only the optimization result of the first 10-minute scale is retained. The optimization is then rolled back and solved in sequence until 144 10-minute scales are solved. So far, the multi-time scale scheduling results that adapt to the fluctuation of wind and solar power and maximize hydrogen production are obtained.

[0085] 5. List different comparison scenarios, selectively use the above method to establish the corresponding mathematical model and input and output conditions, and then use the battery capacity, battery equivalent cycle number, and total hydrogen production adapted to different schemes as indicators to conduct a comparative analysis of the control variables.

Claims

1. A multi-time-scale rolling scheduling method that adapts to wind and solar fluctuations and maximum hydrogen production, including two scheduling stages: day-ahead and intra-day, characterized by: The scheduling method includes the following steps: S1: Construct an integrated energy system model for electricity-hydrogen conversion with wind power, photovoltaic resources, energy storage devices, electrolyzer groups and hydrogen load demand response; S2: In the day-ahead dispatch stage, the one-hour wind and solar forecast data are used as the input data for optimizing the day-ahead dispatch model. The time-of-day hydrogen price is designed based on the output of renewable energy. The hydrogen price is used to guide users to adjust their hydrogen demand, respond to changes in system operating status, and adapt to wind and solar fluctuations. S3: On this basis, 24 hours is selected as a scheduling cycle, and the maximum hydrogen production of the electrolyzer is taken as the optimization goal. Considering the output constraints, capacity constraints, power balance and other constraints of wind turbines, photovoltaic units, electrolytic cell groups and battery groups, a day-ahead optimization scheduling model is constructed, and the optimal solution in the model is solved through the corresponding mathematical model of the MATLAB platform, and finally the day-ahead optimal scheduling plan is obtained; S4: In the intraday stage, 10-minute wind and light forecast data are used. Based on this data and considering the demand response, a real-time rolling optimization strategy is adopted in the intraday stage to correct the day-ahead scheduling plan. The model uses 4 hours as a scheduling cycle and 10 minutes as the scheduling time scale for rolling optimization. The minimum variance of the adjustment amount relative to the day-ahead scheduling result is used as the objective function. The S3 day-ahead optimization solution method is also used to calculate the model, and finally an optimized scheduling result with better adaptability is obtained.

2. The scheduling method for adapting wind and solar fluctuations and multi-time scale rolling between maximum hydrogen production according to claim 1 is characterized by: The establishment of the comprehensive energy system model in S1 specifically includes: The integrated energy system for electricity-hydrogen conversion consists of power models of electrolytic cell groups and battery groups. The electricity load is supplied by the wind and solar forecast model. The hydrogen generated by the system is used or stored downstream as hydrogen load. The renewable energy power generation prediction module consists of photovoltaic power generation and wind power generation. (1) Photovoltaic power model: P pv (t)=[1-θ pv (T-T ref )]η pv N pv A pv I pv (t) (1) Where P pv (t) is the photovoltaic power output power at time t, θ pv is the generator efficiency temperature parameter, T is the battery operating temperature, T ref is the ambient reference temperature, η pv is the photovoltaic power generation efficiency, I pv (t) is the solar radiation intensity at time t, N pv is the number of photovoltaic panels, A pv is the single panel area, (2) Wind power model: Where P w (t) is the wind turbine impeller power output at time t, v(t) is the wind speed at time t, v c is the cut-in wind speed, v r is the rated wind speed, v f is the cut-out wind speed, ρ is the air density, C p is the wind energy utilization coefficient, R w is the radius of the wind wheel, is the rated power of the wind turbine generator set, Demand response refers to the situation when the electricity market price rises or the system reliability decreases, and the load user changes the electricity consumption strategy after receiving the direct compensation notice of induced load reduction or the electricity price increase notice from the power supplier, so as to reduce or shift the electricity load in a certain period of time, thereby achieving a benign interaction between the user and the power grid.

3. The scheduling method for adapting wind and solar fluctuations and multi-time scale rolling between maximum hydrogen production according to claim 1 is characterized by: Apply price-based demand response to the hydrogen load, and implement load regulation on the hydrogen demand side by building time-based hydrogen prices and different loads. (1) Modeling of interruptible load demand response Different loads react differently to the same price. Price-based demand response loads are divided into interruptible loads and transferable loads. Interruptible loads decide whether to interrupt their own loads by judging the price changes before and after demand response. The price demand elasticity matrix is ​​used to describe the demand response characteristics. The element e in the mth row and nth column of the elasticity matrix E(m,n) is m,n Represents the price elasticity coefficient between row m and column n, and the mathematical model is: Where ΔP L,m_e is the change in hydrogen load before and after the response at time m, ΔP L,m_e0 is the original amount of hydrogen load; Δρ n is the change in hydrogen price before and after demand response at time n; ρ n_0 is the basic hydrogen price in the market at time n, and the change in load that can be interrupted after demand response ΔP CL,m_e for: Where P CL,m_e0 is the load that can be interrupted at the time of m rows; E CL (m,n) is the interruptible load price demand elasticity matrix; ρ n The hydrogen price adjusted to suit the wind and solar power output at time n; (2) Transferable load demand response modeling The principle of transferable load is that users can flexibly adjust energy consumption time according to their own needs and real-time hydrogen price information, so that the hydrogen load under high hydrogen price is transferred to the period of low and flat hydrogen price. The transferable change ΔP after demand response is SL,m_e The mathematical model is: Where P SL,m_e0 is the original transferable hydrogen load at time m; E SL (m,n) is the transferable hydrogen load price demand elasticity matrix, The battery regulates the electrical load by charging and discharging, and its power model is as follows: In the formula, θ bat is the self-discharge rate of the battery, is the charging efficiency of the battery, is the discharge efficiency, is the charging power of the battery at time t, is the discharge power of the battery at time t, Δt is the time step, S bat (t) is the storage capacity at time t, SOC bat (t) is the state of charge at time t, is the rated capacity at time t, The electrolytic cell uses power provided by renewable energy to electrolyze water to produce hydrogen. Based on the power input, the efficiency of the electrolytic cell and the higher calorific value of hydrogen, the hydrogen Δm produced by the electrolytic cell at time step Δt is calculated:

4. The scheduling method for adapting wind and solar fluctuations and multi-time scale rolling between maximum hydrogen production according to claim 1 is characterized by: The input of wind and solar power forecast power in step S2 and the adaptation of hydrogen price in response to demand, the determination of the objective function and constraints of the day-ahead stage in step S3, including the day-ahead dispatch model, Day-ahead dispatching must comprehensively consider the maximum hydrogen production of the electric-hydrogen conversion system and the adaptation of new energy sources. Therefore, under the condition of meeting the level of demand response adaptation to wind and solar fluctuations, the objective function is to maximize hydrogen production. (1) Objective function: Where f1 is the day-ahead scheduling objective function, The hydrogen production of the electrolyzer, (2) Equipment operation constraints: The equipment is limited by rated capacity and rated power. Where P i (t) represents the output power of i at time t, represents the rated capacity of i, i represents wind turbine and photovoltaic unit, To ensure stable operation of the equipment and extend its service life, the battery needs to meet the following constraints: In the formula, represents the charging state of the battery pack at time t, represents the discharge state of the battery pack at time t, (3) Hydrogen load constraint: Hydrogen is generated by electrolysis of water in an electrolytic cell, part of which is used downstream, and the rest is stored in a hydrogen storage tank for standby use. In the formula, represents the minimum hydrogen production power for downstream use at time t, (4) Energy balance constraint: The electric bus is a node in the power system that connects wind and solar generators and other equipment. It is used to centralize power transmission and ensure that power is efficiently and stably distributed from the power generation point to various devices. Where P E (t) is the electrical load of the comprehensive energy system at time t.

5. The scheduling method for adapting wind and solar fluctuations and multi-time scale rolling between maximum hydrogen production according to claim 1 is characterized by: The determination of the intra-day stage objective function and constraint conditions in step S4 of claim 1 includes an intra-day stage scheduling model, Intraday optimization refers to dynamic optimization in the form of rolling optimization real-time tracking, and adjusting the day-ahead plan to ensure that the scheduling strategy is presented in the best form. Therefore, the rolling optimization scheduling result has better reliability and adaptability than the result of one-time optimization scheduling, which is used to reduce the uncertainty caused by system modeling, external interference, and prediction error and its impact on the scheduling result; (1) Objective function: Taking into account the deviation between the day-ahead optimization scheduling plan and the actual operation caused by the long-term prediction error and the change of climate environment in the day-ahead stage, the intraday scheduling is adjusted according to the newly obtained ultra-short-term prediction data of wind and light. In order to give full play to the guiding significance of the day-ahead scheduling plan, the intraday rolling optimization minimizes the adjustment amount compared with the day-ahead. The adjustment amount is used as a penalty function in the objective function. Therefore, the intraday optimization objective function is to minimize the sum of the penalty variances of the adjustment amount within the rolling scheduling period. C Total =min(C punish_PEM +C punish_Bat ) (13) In the formula, C Total is the intraday scheduling objective function, C Punish_PEM is the penalty cost of the electrolytic cell group, C Punish_Bat Penalty fee for battery pack, Where ΔP PEM is the total amount of adjustment of the electrolytic cell group compared with the day-ahead dispatch plan, ΔP Bat is the total amount of adjustment of the battery group compared with the day-ahead dispatch plan, λ PEM is the penalty adjustment coefficient of the electrolytic cell, λ Bat is the penalty adjustment coefficient of the battery, P PEM_0 (t) is the electric power of the electrolytic cell group in a day, P Bat_0 (t) are the electric power of the battery pack in a day, (2) Intraday climbing constraints: The constraints involved in the demand response model, wind turbines, photovoltaic units, and battery groups are exactly the same as those set for the day-ahead scheduling. However, in the optimization scheduling process of the shorter time scale in the intraday stage, the ramp constraints of the electrolytic cell group need to be considered. The constraints are as follows: Where P PEM_d is the upper limit of the ramp rate of the electrolytic cell group; -P PEM_d It is the lower limit of the ramp rate of the electrolytic cell group.

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