A risk prediction-based electric-hydrogen coupling system optimization scheduling method and system

Abstract

The application discloses a kind of based on risk prediction's electric hydrogen coupling system optimization scheduling method and system, belong to electric hydrogen coupling system optimization scheduling technical field, establish based on risk prediction's optimization scheduling model include photovoltaic power generation operating cost, wind power generation operating cost, hydrogen production operating cost, energy conversion risk cost, storage risk cost and electricity transaction income, considering the impact of the uncertainty and instability of renewable energy on electric hydrogen coupling system, introduce energy conversion risk cost and storage risk cost, can significantly control energy conversion risk and storage risk, ensure that electric hydrogen coupling system safely and stably operates under renewable energy fluctuation.In the case of sharp changes in renewable energy power generation, by adjusting the input power of the electric hydrogen coupling system, the energy conversion risk cost is kept at a low level;When the pressure of hydrogen storage tank changes greatly, the optimization scheduling strategy can timely adjust the hydrogen storage and release strategy, reduce the storage risk cost.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of optimal scheduling of electric-hydrogen coupling systems, and in particular to an optimal scheduling method and system for electric-hydrogen coupling systems based on risk prediction. BACKGROUND

[0002] An electric-hydrogen coupling system uses electricity generated by renewable energy sources to produce hydrogen through water electrolysis, thereby achieving energy storage and conversion. It is a system that converts electricity into hydrogen energy. Due to the intermittent and unstable nature of renewable energy sources such as wind and solar energy, when the amount of electricity generated exceeds the immediate demand, the excess electricity can be converted into hydrogen energy for storage through water electrolysis. This helps to balance the load of the power grid and improves the utilization rate of renewable energy. Hydrogen energy, as a highly efficient and clean secondary energy carrier, can be applied in various fields. Although the electric-hydrogen coupling system has many advantages, due to the flammable and explosive nature of hydrogen gas and its low safety factor, existing technologies only consider the risk caused by storage pressure. For example, the patent with publication number CN118821501B discloses an optimal scheduling method and system for electric-hydrogen coupling systems based on life attenuation and risk, which establishes a linearized safety risk quantification model for hydrogen storage tanks. Based on the conditional value-at-risk method, the uncertainty of the source and load of the electric-hydrogen coupling system equipment operation model is processed, and an electric-hydrogen coupling system scheduling model considering the conditional value-at-risk is established. The objective function is constructed with the lowest running cost, life attenuation cost, and safety risk cost as the target, the constraint conditions are determined, and the optimal scheduling of the electric-hydrogen coupling system is realized. However, in the actual system operation process, not only the storage risk needs to be considered, but also the risk caused by the fluctuation of electric-to-hydrogen efficiency. SUMMARY

[0003] The purpose of the present application is to provide an optimal scheduling method and system for electric-hydrogen coupling systems based on risk prediction, which solves the above technical problems.

[0004] To achieve the above purpose, the present application provides an optimal scheduling method for electric-hydrogen coupling systems based on risk prediction, the specific steps are as follows:

[0005] Step S1: Obtain the data of the renewable energy system, the power grid system, and the electric-hydrogen coupling system;

[0006] Step S2: Based on the data obtained in step S1, an optimal scheduling model based on risk prediction is established, and the optimal scheduling model is as follows:

[0007] ;

[0008] Wherein, is the minimum function, is the photovoltaic power generation operation cost, is the wind power generation operation cost, is the hydrogen production operation cost, a risk cost of energy conversion, a risk cost of storage, a profit of electricity transaction;

[0009] Step S3: a constraint function of the optimization scheduling model is established by the data obtained in step S1, and the constraint function includes a power balance constraint function, a renewable energy system constraint function, and an electricity-hydrogen coupling system constraint function;

[0010] Step S4: the optimization scheduling model is solved by using the constraint function to obtain an optimization scheduling strategy.

[0011] Preferably, the renewable energy system includes a photovoltaic power generation part and a wind power generation part.

[0012] Preferably, in step S2, the photovoltaic power generation operation cost calculation formula is as follows:

[0013] ;

[0014] wherein, a photovoltaic maintenance cost coefficient, a photovoltaic power generation amount in a scheduling period;

[0015] the wind power generation operation cost calculation formula is as follows:

[0016] ;

[0017] wherein, a wind maintenance cost coefficient, a wind power generation amount in a scheduling period.

[0018] Preferably, in step S2, the hydrogen production operation cost calculation formula is as follows:

[0019] ;

[0020] wherein, an input power of the electricity-hydrogen coupling system, a consumable cost required for producing a unit volume of hydrogen, a hydrogen production maintenance cost coefficient, a hydrogen production amount, a scheduling period, a sampling time interval.

[0021] Preferably, the energy conversion risk cost calculation formula is as follows:

[0022] ;

[0023] wherein, and These represent the maximum and minimum electrical power input to the electro-hydrogen coupling system within a scheduling cycle. The average electrical power input to the electro-hydrogen coupling system within one scheduling cycle, The first matching coefficient;

[0024] The formula for calculating storage risk costs is as follows:

[0025] ;

[0026] in, This is the current hydrogen storage pressure. The current hydrogen storage pressure change rate, The sampling time interval, This is the second matching coefficient.

[0027] Preferably, in step S2, the formula for calculating the revenue from electricity transactions is as follows:

[0028] ;

[0029] in, for Real-time electricity price at any given moment for The transaction coefficient at any given time, when The value is -1 when purchasing electricity at any time. The value is 1 when electricity is sold at any time. The value is 0 when no transaction has occurred. For the scheduling period, for Real-time trading power.

[0030] Preferably, the power balance constraint function is as follows:

[0031] ;

[0032] in, This represents the actual output power of photovoltaic power generation. This represents the actual output power of wind power generation. For the power output of the power grid, This represents the charging and discharging power of the energy storage battery. It is positive when the energy storage battery is charging and negative otherwise. The input power of the electro-hydrogen coupling system. The output power of the electro-hydrogen coupling system. This represents the power of the grid load.

[0033] Preferably, the constraint function for the renewable energy system is as follows:

[0034] ;

[0035] To predict the maximum output power of wind power generation, Predict the maximum output power for photovoltaic power generation.

[0036] Preferably, the constraint function for the electro-hydrogen coupling system is as follows:

[0037] ;

[0038] in, and The minimum and maximum input power settings for the electro-hydrogen coupling system. The input power of the electro-hydrogen coupling system; and They are respectively The energy storage state coefficient and release state coefficient of the electro-hydrogen coupling system at any time, and the value range is 0-1; and They are respectively Momentary hydrogen storage power and Hydrogen release power at any given moment; and These are the maximum power for hydrogen storage and the maximum power for hydrogen release, respectively. and They are respectively Time and Hydrogen stores energy at all times. For maximum hydrogen storage energy; and These are the hydrogen storage conversion efficiency and the hydrogen release conversion efficiency, respectively.

[0039] A system for optimizing the scheduling of an electro-hydrogen coupling system based on risk prediction includes:

[0040] The data acquisition module is used to acquire data from renewable energy systems, power grid systems, and electro-hydrogen coupling systems.

[0041] The model building module is used to build an optimized scheduling model based on risk prediction. The optimized scheduling model includes the operating costs of photovoltaic power generation, wind power generation, hydrogen production, energy conversion risk costs, storage risk costs, and electricity trading revenue.

[0042] The optimization module solves the constructed risk prediction-based optimization scheduling model through constraint functions to obtain the optimal scheduling strategy and achieve optimal scheduling of the electric-hydrogen coupling system.

[0043] Therefore, the application adopts the above-mentioned risk prediction-based electric-hydrogen coupling system optimization scheduling method and system, which has the beneficial effects that: in the case that the power balance constraint is always satisfied, the optimization strategy obtained by solving the optimization scheduling model based on risk prediction reasonably schedules renewable energy generation, energy storage battery charging and discharging, and operation of the electric-hydrogen coupling system, ensuring reliable power supply of the power grid load. The optimization scheduling method effectively improves the accommodation capacity of renewable energy, reduces the phenomena of curtailed wind power and curtailed solar power, can significantly control energy conversion risk and storage risk, and ensures safe and stable operation of the electric-hydrogen coupling system under renewable energy fluctuation. When the renewable energy generation power changes sharply, the input power of the electric-hydrogen coupling system is adjusted, so that the energy conversion risk cost is kept at a low level; when the pressure of the hydrogen storage tank changes greatly, the optimization scheduling strategy can timely adjust the hydrogen storage and release strategy, reducing the storage risk cost.

[0044] The technical solutions of the application will be further described in detail below with reference to the drawings and embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 A flow chart of the risk prediction-based electric-hydrogen coupling system optimization scheduling method of the application;

[0046] Figure 2 A system principle block diagram of the application;

[0047] Figure 3 A simulation test cost column chart of each part of the application. DETAILED DESCRIPTION

[0048] In the description of the application, it should be noted that the terms "upper", "lower", "inner", "outer" and the like indicate the orientation or positional relationship shown in the drawings, or the orientation or positional relationship commonly used when the product of the application is used, and are only for the convenience of describing the application and simplifying the description, and do not indicate or imply that the indicated device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the application. In the description of the application, it should be noted that, unless otherwise explicitly specified and limited, the terms "arrangement", "installation", "connection" should be understood broadly, for example, it can be fixedly connected, or detachably connected, or integrally connected; it can be mechanically connected, or electrically connected; it can be directly connected, or indirectly connected through an intermediate medium, or it can be the communication between two elements inside. For those skilled in the art, the specific meaning of the above-mentioned terms in the application can be understood according to the specific circumstances.

[0049] The embodiments of the application will be described in detail below with reference to the drawings.

[0050] Example 1

[0051] As shown in Figure 1 A risk prediction-based electric-hydrogen coupling system optimization scheduling method, the specific steps are as follows:

[0052] Step S1: Obtain the data of renewable energy system, power grid system and electric-hydrogen coupling system. The renewable energy system includes photovoltaic power generation part and wind power generation part. Other power generation systems such as thermal power system can be added according to actual situation.

[0053] Step S2: Based on the data obtained in step S1, an optimization scheduling model based on risk prediction is established, and the optimization scheduling model is as follows:

[0054] ;

[0055] Among them, is the minimum function, is the photovoltaic power generation operation cost, is the wind power generation operation cost, is the hydrogen production operation cost, is the energy conversion risk cost, is the storage risk cost, is the electricity transaction income.

[0056] The calculation formula of photovoltaic power generation operation cost is as follows:

[0057] ;

[0058] Among them, is the photovoltaic maintenance cost coefficient, is the photovoltaic power generation amount in a scheduling period;

[0059] The calculation formula of wind power generation operation cost is as follows:

[0060] ;

[0061] Among them, is the wind power maintenance cost coefficient, is the wind power generation amount in a scheduling period.

[0062] The calculation formula of hydrogen production operation cost is as follows:

[0063] ;

[0064] Among them, is the input power of electric-hydrogen coupling system, is the cost of consumables required for producing unit volume of hydrogen, is the hydrogen production maintenance cost coefficient, is the hydrogen production amount, For the scheduling period, This represents the sampling time interval.

[0065] The safety factors considered in this embodiment include energy conversion risk and storage risk.

[0066] Because renewable energy systems are significantly affected by environmental factors and exhibit uncertainty and instability, they pose a certain impact on electro-hydrogen coupling systems. Fluctuations in the electrical power input to the electro-hydrogen coupling system also introduce certain risks. The formula for calculating the energy conversion risk cost is as follows:

[0067] ;

[0068] in, and These represent the maximum and minimum electrical power input to the electro-hydrogen coupling system within a scheduling cycle. The average electrical power input to the electro-hydrogen coupling system within one scheduling cycle, This is the first matching coefficient.

[0069] When the hydrogen pressure inside a hydrogen storage tank changes frequently or significantly, the tank material will be subjected to cyclic stress loading and unloading. Long-term, significant pressure changes lead to material fatigue, reducing its mechanical strength and increasing the risk of rupture. Therefore, the risk factors are not only related to the real-time hydrogen storage pressure but also to the rate of pressure change. The formula for calculating storage risk costs is as follows:

[0070] ;

[0071] in, This is the current hydrogen storage pressure. This represents the current hydrogen storage pressure change rate. The sampling time interval, This is the second matching coefficient.

[0072] The formula for calculating electricity trading revenue is as follows:

[0073] ;

[0074] in, for Real-time electricity price at any given moment for The transaction coefficient at any given time, when The value is -1 when purchasing electricity at any time. The value is 1 when electricity is sold at any time. The value is 0 when no transaction has occurred. For the scheduling period, for Real-time trading power.

[0075] Step S3: Establish constraint functions for the optimized scheduling model using the data obtained in step S1. The constraint functions include power balance constraint functions, renewable energy system constraint functions, and electric-hydrogen coupling system constraint functions.

[0076] The power balance constraint function is as follows:

[0077] ;

[0078] in, This represents the actual output power of photovoltaic power generation. This represents the actual output power of wind power generation. Power output to the power grid This represents the charging and discharging power of the energy storage battery. It is positive when the energy storage battery is charging and negative otherwise. The input power of the electro-hydrogen coupling system. The output power of the electro-hydrogen coupling system. This represents the power of the grid load.

[0079] The constraint functions for the renewable energy system are as follows:

[0080] ;

[0081] The maximum output power of wind power generation is predicted and is related to wind speed; The maximum output power of photovoltaic power generation is predicted and is related to the light intensity.

[0082] The constraint functions for the electro-hydrogen coupling system are as follows:

[0083] ;

[0084] in, and The minimum and maximum input power settings for the electro-hydrogen coupling system. The input power of the electro-hydrogen coupling system; and They are respectively The energy storage state coefficient and release state coefficient of the electro-hydrogen coupling system at any time, and the value range is 0-1; and They are respectively Momentary hydrogen storage power and Hydrogen release power at any given moment; and These are the maximum power for hydrogen storage and the maximum power for hydrogen release, respectively. and They are respectively Time and The maximum hydrogen storage energy, The maximum hydrogen storage energy; And The hydrogen storage conversion efficiency and the hydrogen release conversion efficiency, respectively.

[0085] Step S4: solving the optimization scheduling model by using the constraint function to obtain the optimization scheduling strategy.

[0086] In order to verify the superiority of the embodiment scheme, simulation test is carried out, and the specific test process is as follows:

[0087] I. Simulation scene setting

[0088] 1. Geographic area and time range: Taking a northern city as an example, a typical week in spring is selected as the simulation time range. The region is rich in wind and solar resources, and the industrial and residential electricity demand has obvious weekly variation law.

[0089] 2. Meteorological data: Obtain the hourly wind speed and light intensity data of the week from the local meteorological department. The average wind speed fluctuates between 4-8 m / s, and the light intensity changes between 200-800 W / m².

[0090] 3. Grid data: The real-time electricity price of the grid adopts the time-of-use electricity price mechanism, and the electricity price is 0.8 yuan / kWh during the peak period (8:00-22:00), and the electricity price is 0.3 yuan / kWh during the valley period (22:00-8:00 the next day). The grid load power in the region changes between 50MW-200MW within a week.

[0091] 4. Renewable energy system: The installed capacity of the photovoltaic power generation system is 50MW, and the photovoltaic maintenance cost coefficient is 0.05 yuan / kWh; the installed capacity of the wind power generation system is 30MW, and the wind power maintenance cost coefficient is 0.08 yuan / kWh.

[0092] 5. Electro-hydrogen coupling system: The minimum input power of the hydrogen production equipment is 10MW, and the maximum input power is 30MW; the cost of consumables required for producing unit volume of hydrogen is 4 yuan / m³, the hydrogen production maintenance cost coefficient is 0.03 yuan / kWh, and the hydrogen production efficiency is 0.85; the maximum hydrogen storage energy of the hydrogen storage tank is 1000MWh, the hydrogen storage conversion efficiency is 0.9, and the hydrogen release conversion efficiency is 0.95.

[0093] II. Establishing an optimization scheduling model based on risk prediction

[0094] According to the optimization scheduling method based on risk prediction described above, an optimization scheduling model is constructed in the Python environment by using the PuLP library. And the calculation expressions of photovoltaic power generation operation cost, wind power generation operation cost, hydrogen production operation cost, energy conversion risk cost, storage risk cost and electricity transaction income are defined respectively.

[0095] III. Determining the constraint function

[0096] The power balance constraint, renewable energy system constraint, and electricity-hydrogen coupling system constraint are set. For example, the power balance constraint equation ensures the balance between the actual output power of photovoltaic power generation, the actual output power of wind power generation, the grid output power, the charging and discharging power of the energy storage battery, the input and output power of the electricity-hydrogen coupling system, and the grid load power.

[0097] IV. Optimization solution

[0098] The CBC (Coin-OR Branch and Cut) solver is used to solve the model to obtain the optimized scheduling strategy every hour within a week.

[0099] V. Analysis of simulation results

[0100] The cost of each part is shown in Table 1. Figure 3

[0101] Photovoltaic power generation: The total photovoltaic power generation within a week is 720 MWh, and the operating cost is 0.05 x 720 = 360,000 yuan.

[0102] Wind power generation: The wind power generation within a week is 450 MWh, and the operating cost is 0.08 x 450 = 36,000 yuan.

[0103] Hydrogen production: The hydrogen production is 15,000 m³, and the hydrogen production operating cost is calculated to be 630,000 yuan.

[0104] Energy conversion risk: Due to the reasonable adjustment of the input power of the electricity-hydrogen coupling system by the optimized scheduling strategy, the energy conversion risk cost remains at a low level, totaling 1.2 million yuan per week.

[0105] Storage risk: Through effective control of the pressure change of the hydrogen storage tank, the storage risk cost is 80,000 yuan per week.

[0106] Electricity trading income: According to the time-of-use electricity price mechanism and the optimized electricity trading strategy, the electricity trading income for this week is 500,000 yuan.

[0107] Power balance analysis: Throughout the simulation period, the power balance constraint is always satisfied. Through reasonable scheduling of renewable energy generation, energy storage battery charging and discharging, and electricity-hydrogen coupling system operation, the reliable power supply of the grid load is ensured. For example, during the day when the light is sufficient and the electricity load is low, the excess electricity is used for hydrogen production or charging the energy storage battery; during the night when the electricity load is high and the renewable energy generation is insufficient, the energy storage battery is discharged and the output power of the electricity-hydrogen coupling system is adjusted to ensure power supply.

[0108] ​Renewable energy utilization rate: the actual renewable energy (photovoltaic and wind power) generation capacity accounted for 85% of the potential generation capacity in this week, indicating that the optimal scheduling method effectively improves the consumption capacity of renewable energy and reduces the phenomenon of curtailment of wind and light.

[0109] Embodiment 2

[0110] As shown in Figure 2 A system of a risk prediction-based optimal scheduling method for an electricity-hydrogen coupled system, comprising:

[0111] A data acquisition module for acquiring data of a renewable energy system, a power grid system and an electricity-hydrogen coupled system.

[0112] A model construction module for constructing an optimal scheduling model based on risk prediction, the optimal scheduling model including photovoltaic power generation operation cost, wind power generation operation cost, hydrogen production operation cost, energy conversion risk cost, storage risk cost and electricity transaction revenue.

[0113] An optimization module for solving the constructed optimal scheduling model based on risk prediction through a constraint function to obtain an optimal scheduling strategy and realize optimal scheduling of the electricity-hydrogen coupled system.

[0114] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application but not to limit it, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that: it can still modify or equivalently replace the technical solutions of the present application, and these modifications or equivalent replacements also cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present application.

Claims

1. A risk prediction based optimal scheduling method for power-hydrogen coupled system, characterized in that, The specific steps are as follows: Step S1: Obtain data of the renewable energy system, the power grid system and the electricity-hydrogen coupling system; Step S2: Establish an optimization scheduling model based on risk prediction based on the data obtained in step S1, and the optimization scheduling model is as follows: ； wherein, is the minimum function, is the photovoltaic power generation operation cost, is the wind power generation operation cost, is the hydrogen production operation cost, is the energy conversion risk cost, is the storage risk cost, is the electricity transaction income; The energy conversion risk cost calculation formula is as follows: ； wherein, and Pmax and Pmin are the maximum and minimum electric power input to the electric-hydrogen coupling system in a dispatch period, respectively, Pavg is the average electric power input to the electric-hydrogen coupling system in a dispatch period, is a first matching coefficient; The storage risk cost calculation formula is as follows: ； wherein, is the current hydrogen storage pressure, is the current hydrogen storage pressure conversion speed, is the sampling time interval, is the second matching coefficient; Step S3: Establish a constraint function of the optimization scheduling model through the data obtained in step S1, and the constraint function includes a power balance constraint function, a renewable energy system constraint function and an electricity-hydrogen coupling system constraint function; Step S4: Solve the optimization scheduling model using the constraint function to obtain an optimization scheduling strategy. 2.The risk prediction based optimal scheduling method for electro-hydrogen coupled system according to claim 1, wherein: The renewable energy system includes a photovoltaic power generation part and a wind power generation part. 3.The risk prediction based optimal scheduling method for electro-hydrogen coupled system according to claim 2, wherein: In step S2, the photovoltaic power generation operation cost calculation formula is as follows: ； wherein, is a photovoltaic maintenance cost coefficient, is the photovoltaic power generation amount in a dispatching period; The wind power generation operation cost calculation formula is as follows: ； wherein, is the wind maintenance cost coefficient, is the wind power generation amount in a mobilization period.

4. The method of claim 3, wherein: In step S2, the hydrogen production operation cost calculation formula is as follows: ； wherein, is the input power to the electro-hydrogen coupling system, is the cost of consumables per volume of hydrogen produced, is the coefficient of maintenance cost for hydrogen production, is the amount of hydrogen produced, is the dispatch period, is the sampling time interval.

5. The method of claim 4, wherein: In step S2, the electricity transaction revenue calculation formula is as follows: ； wherein, is the real-time electricity price at time t, is the trading coefficient at time t, which takes the value -1 when electricity is purchased at time t, 1 when electricity is sold at time t, and 0 when no transaction occurs at time t, is the real-time electricity price at time t, is the dispatch period, is the trading power at time t.

6. The electricity-hydrogen coupling system optimization scheduling method based on risk prediction according to claim 5, characterized in that: The power balance constraint function is as follows: ； wherein, is the actual output power of photovoltaic power generation, is the actual output power of wind power generation, is the grid output power, is the charging and discharging power of the energy storage battery, positive when charging the energy storage battery, otherwise negative, is the input power of the electricity-hydrogen coupling system, is the output power of the electricity-hydrogen coupling system, is the grid load power.

7. The electricity-hydrogen coupling system optimization scheduling method based on risk prediction according to claim 6, characterized in that: The renewable energy system constraint function is as follows: ； predict the maximum output power for wind power generation, predict the maximum output power for photovoltaic power generation.

8. The electricity-hydrogen coupling system optimization scheduling method based on risk prediction according to claim 7, characterized in that: The electricity-hydrogen coupling system constraint function is as follows: ； wherein, and are the minimum input power and the maximum input power set for the electrical-hydrogen coupling system, is the input power of the electrical-hydrogen coupling system; and are respectively are the storage state coefficient and the release state coefficient of the electrical-hydrogen coupling system at the moment t, and the value range is 0-1; and are respectively is the hydrogen storage power at the moment t, and is the hydrogen release power at the moment t; and are respectively the hydrogen storage maximum power and the hydrogen release maximum power; and are respectively is the hydrogen storage energy at the moment t, and is the hydrogen storage energy at the moment t; is the maximum hydrogen storage energy; and are respectively the hydrogen storage conversion efficiency and the hydrogen release conversion efficiency.

9. The system for risk prediction based optimal scheduling of electro-hydrogen coupled system according to claim 8, wherein, It includes: A data acquisition module for acquiring data of the renewable energy system, the power grid system and the electricity-hydrogen coupling system; A model construction module for constructing an optimization scheduling model based on risk prediction, which includes photovoltaic power generation operation cost, wind power generation operation cost, hydrogen production operation cost, energy conversion risk cost, storage risk cost and electricity transaction revenue; An optimization module for solving the constructed optimization scheduling model based on risk prediction through a constraint function to obtain an optimization scheduling strategy and realize optimal scheduling of the electricity-hydrogen coupling system.

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