Electric hydrogen coupling system optimization scheduling method and system based on risk prediction

Abstract

The invention discloses an electricity-hydrogen coupling system optimization scheduling method and system based on risk prediction, and belongs to the technical field of electricity-hydrogen coupling system optimization scheduling. An optimized dispatching model based on risk prediction is established and comprises 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, and impact of uncertainty and instability of renewable energy on an electricity-hydrogen coupling system is considered. And the energy conversion risk cost and the storage risk cost are introduced, so that the energy conversion risk and the storage risk can be remarkably controlled, and the safe and stable operation of the electricity-hydrogen coupling system under the condition of renewable energy source fluctuation is ensured. When the power generation power of the renewable energy source changes sharply, the energy conversion risk cost is kept at a low level by adjusting the input power of the electricity-hydrogen coupling system; when the pressure change of the hydrogen storage tank is large, the hydrogen storage and release strategy can be adjusted in time by optimizing the scheduling strategy, and the storage risk cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of optimal scheduling of the electric-hydrogen coupling system, and particularly relates to an optimal scheduling method and system for the electric-hydrogen coupling system based on risk prediction. Background Art

[0002] The electric-hydrogen coupling system uses the electricity generated by renewable energy to produce hydrogen through electrolysis of water, thereby realizing the storage and conversion of energy, and is a system for converting electricity into hydrogen energy. Due to the intermittency and instability of renewable energy such as wind energy and solar energy, when the power generation exceeds the immediate demand, the excess electric energy can be converted into hydrogen energy and stored by electrolyzing water. This helps to balance the grid load and improve the utilization rate of renewable energy. As a high-efficiency and clean secondary energy carrier, hydrogen energy can be applied in multiple fields. Although the electric-hydrogen coupling system has many advantages, since hydrogen is an inflammable and explosive gas with a low safety factor, only the risk brought by the storage pressure is considered in the prior art. For example, the patent with the publication number CN118821501B discloses an optimal scheduling method and system for the electric-hydrogen coupling system based on life attenuation and risk, establishing a linearized safety risk quantification model for the hydrogen storage tank; dealing with the uncertainty of the source load of the equipment operation model of the electric-hydrogen coupling system based on the conditional value at risk method, establishing a scheduling model for the electric-hydrogen coupling system considering the conditional value at risk, constructing an objective function with the lowest operation cost, life attenuation cost, and safety risk cost as the goal, determining the constraint conditions, and realizing the optimal scheduling of the electric-hydrogen coupling system. However, in the actual system operation process, not only the storage risk but also the risk brought by the fluctuation of the electricity-to-hydrogen conversion efficiency need to be considered. Summary of the Invention

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

[0004] To achieve the above purpose, the present invention provides an optimal scheduling method for the electric-hydrogen coupling system based on risk prediction, and the specific steps are as follows: Step S1: Obtain the data of the renewable energy system, the power grid system, and the electric-hydrogen coupling system; Step S2: Establish an optimal scheduling model based on risk prediction according to the data obtained in Step S1. The optimal scheduling model is as follows: ; Wherein, is the minimum function, is the operation cost of photovoltaic power generation, is the operation cost of wind power generation, is the operation cost of hydrogen production, is the energy conversion risk cost, is the storage risk cost, is the electricity trading revenue; Step S3: Establish the constraint functions of the optimal scheduling model based on the data obtained in Step S1. The constraint functions include the power balance constraint function, the renewable energy system constraint function, and the power-to-hydrogen coupling system constraint function; Step S4: Solve the optimal scheduling model using the constraint functions to obtain the optimal scheduling strategy.

[0005] Preferably, the renewable energy system includes a photovoltaic power generation part and a wind power generation part.

[0006] Preferably, in Step S2, the calculation formula for the operating cost of photovoltaic power generation is as follows: ; where, is the photovoltaic maintenance cost coefficient, is the photovoltaic power generation amount within a scheduling period; The calculation formula for the operating cost of wind power generation is as follows: ; where, is the wind power maintenance cost coefficient, is the wind power generation amount within a scheduling period.

[0007] Preferably, in Step S2, the calculation formula for the hydrogen production operating cost is as follows: ; where, is the input power of the power-to-hydrogen coupling system, is the cost of consumables required to produce a unit volume of hydrogen, is the hydrogen production maintenance cost coefficient, is the hydrogen production amount, is the scheduling period, is the sampling time interval.

[0008] Preferably, the calculation formula for the energy conversion risk cost is as follows: ; where, and are respectively the maximum electric power and the minimum electric power input to the power-to-hydrogen coupling system within a scheduling period, is the average electric power input to the power-to-hydrogen coupling system within a scheduling period, is the first matching coefficient; The calculation formula for the storage risk cost is as follows: ; where, is the current hydrogen storage pressure, is the current hydrogen storage pressure change rate, is the sampling time interval, is the second matching coefficient.

[0009] Preferably, in step S2, the electricity trading revenue calculation formula is as follows: ; wherein, is the real-time electricity price at time is the trading coefficient at time. When purchasing electricity at time it takes the value of -1. When selling electricity at time it takes the value of 1. When no transaction occurs at time it takes the value of 0, is the scheduling period, is the trading power at time.

[0010] Preferably, 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 power output of the power grid, is the charge and discharge power of the energy storage battery, which is positive when the energy storage battery is charging and negative otherwise, is the input power of the electric-hydrogen coupling system, is the output power of the electric-hydrogen coupling system, is the power load of the power grid.

[0011] Preferably, the renewable energy system constraint function is as follows: ; is the predicted maximum output power of wind power generation, is the predicted maximum output power of photovoltaic power generation.

[0012] Preferably, the electric-hydrogen coupling system constraint function is as follows: ; wherein, and are the set minimum input power and maximum input power of the electric-hydrogen coupling system, is the input power of the electric-hydrogen coupling system; and are respectively the energy storage state coefficient and release state coefficient of the electric-hydrogen coupling system at time, and the value range is 0-1; and are respectively the hydrogen storage power at time and the hydrogen release power at time; and are respectively the maximum hydrogen storage power and the maximum hydrogen release power; and are respectively time and the hydrogen storage energy at time, is the maximum hydrogen storage energy; and are respectively the hydrogen storage conversion efficiency and the hydrogen release conversion efficiency.

[0013] A system of an optimization scheduling method for an electric-hydrogen coupling system based on risk prediction, comprising: A data acquisition module, configured to acquire data of a renewable energy system, a power grid system, and an electric-hydrogen coupling system; A model construction module, configured to construct an optimization scheduling model based on risk prediction, and the optimization scheduling model includes the operation cost of photovoltaic power generation, the operation cost of wind power generation, the operation cost of hydrogen production, the energy conversion risk cost, the storage risk cost, and the electricity trading revenue; An optimization module, which solves the constructed optimization scheduling model based on risk prediction through constraint functions to obtain an optimization scheduling strategy, and realizes the optimal scheduling of the electric-hydrogen coupling system.

[0014] Therefore, the present invention adopts the above-mentioned optimization scheduling method and system for an electric-hydrogen coupling system based on risk prediction, and the beneficial effects are as follows: Under the condition that the power balance constraint is always satisfied, the optimization strategy obtained by solving the optimization scheduling model based on risk prediction is used to reasonably schedule the operation of renewable energy power generation, energy storage battery charging and discharging, and the electric-hydrogen coupling system, ensuring reliable power supply for the grid load. The optimization scheduling method effectively improves the consumption capacity of renewable energy, reduces the phenomena of abandoned wind and abandoned light, and the method can significantly control the energy conversion risk and storage risk, ensuring the safe and stable operation of the electric-hydrogen coupling system under the condition of renewable energy fluctuations. When the power generation power of renewable energy changes sharply, by adjusting the input power of the electric-hydrogen coupling system, the energy conversion risk cost is maintained 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 strategies to reduce the storage risk cost.

[0015] Next, through the accompanying drawings and embodiments, the technical solutions of the present invention will be further described in detail. Description of the Drawings

[0016] Figure 1 is a flowchart of an optimization scheduling method for an electric-hydrogen coupling system based on risk prediction of the present invention; Figure 2 This is the system principle block diagram of the present invention; Figure 3 This is the bar chart of the costs of various parts of the simulation test of the present invention. Specific implementation manners

[0017] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper", "lower", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the present invention is usually placed during use. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In the description of the present invention, it should also be noted that unless otherwise clearly specified and limited, the terms "set", "installed", "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0018] The following will describe the implementation manners of the present invention in detail with reference to the drawings.

[0019] Embodiment 1 As Figure 1 shown, an optimized scheduling method for an electric-hydrogen coupling system based on risk prediction includes the following specific steps: Step S1: Obtain the data of the renewable energy system, the power grid system, and the electric-hydrogen coupling system. The renewable energy system includes a photovoltaic power generation part and a wind power generation part. Other power generation systems can be added according to actual situations, such as a thermoelectric system, etc.

[0020] Step S2: Establish an optimized scheduling model based on risk prediction based on the data obtained in step S1. The optimized scheduling model is as follows: ; Wherein, is the minimum function, is the operating cost of photovoltaic power generation, is the operating cost of wind power generation, is the operating cost of hydrogen production, is the energy conversion risk cost, is the storage risk cost, is the electricity trading revenue.

[0021] The calculation formula for the operating cost of photovoltaic power generation is as follows: ; Among them, is the photovoltaic maintenance cost coefficient, is the photovoltaic power generation amount within a scheduling period; The calculation formula for the operating cost of wind power generation is as follows: ; Among them, is the wind power maintenance cost coefficient, is the wind power generation amount within a scheduling period.

[0022] The calculation formula for the operating cost of hydrogen production is as follows: ; Among them, is the input power of the power-to-hydrogen coupling system, is the cost of consumables required to produce a unit volume of hydrogen, is the hydrogen production maintenance cost coefficient, is the hydrogen production amount, is the scheduling period, is the sampling time interval.

[0023] The safety factors considered in this embodiment include energy conversion risk and storage risk.

[0024] Since the renewable energy system is greatly affected by environmental factors and has uncertainty and instability, it has a certain impact on the power-to-hydrogen coupling system, and the power fluctuation of the electric power input into the power-to-hydrogen coupling system will also bring certain risks to the power-to-hydrogen coupling system. The calculation formula for the energy conversion risk cost is as follows: ; Among them, and are respectively the maximum electric power and the minimum electric power input into the power-to-hydrogen coupling system within a scheduling period, is the average electric power input into the power-to-hydrogen coupling system within a scheduling period, is the first matching coefficient.

[0025] When the hydrogen pressure in the hydrogen storage tank changes frequently or greatly, the tank body material will bear periodic stress loading and unloading. Long-term large-scale pressure changes will cause material fatigue, reduce its mechanical strength, and increase the risk of rupture. Therefore, the risk factor is not only related to the real-time hydrogen storage pressure, but also related to the pressure change speed. The calculation formula for the storage risk cost is as follows: ; Among them, is the current hydrogen storage pressure, is the current hydrogen storage pressure change speed, is the sampling time interval, is the second matching coefficient.

[0026] The calculation formula for electricity trading revenue is as follows: ; Wherein, is the real-time electricity price at time is the trading coefficient at time When purchasing electricity at time it takes the value of -1, and when selling electricity at time it takes the value of 1, and when no transaction occurs at time it takes the value of 0,

[0027] Step S3: Establish the constraint functions of the optimal scheduling model based on the data obtained in Step S1. The constraint functions include the power balance constraint function, the renewable energy system constraint function, and the electricity-hydrogen coupling system constraint function.

[0028] 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 charge and discharge power of the energy storage battery, which is positive when the energy storage battery is charging and negative otherwise, 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.

[0029] The renewable energy system constraint function is as follows: ; is the predicted maximum output power of wind power generation, which is related to the wind speed; is the predicted maximum output power of photovoltaic power generation, which is related to the light intensity.

[0030] The electricity-hydrogen coupling system constraint function is as follows: ; Wherein, and are the set minimum input power and maximum input power of the electricity-hydrogen coupling system, is the input power of the electricity-hydrogen coupling system; and are respectively The energy storage state coefficient and release state coefficient of the power-to-hydrogen coupling system at a certain moment, and the value range is 0-1; and are respectively the hydrogen storage power at a certain moment and the hydrogen release power at a certain moment; and are respectively the maximum hydrogen storage power and the maximum hydrogen release power; and are respectively the moment and the hydrogen storage energy at a certain moment, is the maximum hydrogen storage energy; and are respectively the hydrogen storage conversion efficiency and the hydrogen release conversion efficiency.

[0031] Step S4: Solve the optimal scheduling model using the constraint function to obtain the optimal scheduling strategy.

[0032] To verify the superiority of the solution in this embodiment, a simulation test is carried out. The specific test process is as follows: I. Simulation scenario setting 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 wind resources and solar energy resources in this area are relatively rich, and the industrial and residential electricity demands have obvious intra-week variation rules.

[0033] 2. Meteorological data: Hourly wind speed and light intensity data for this week are obtained from the local meteorological department. The average wind speed fluctuates between 4-8 m / s, and the light intensity varies between 200-800 W / m².

[0034] 3. Grid data: The real-time electricity price of the grid adopts a time-of-use electricity price mechanism. The electricity price during peak hours (8:00-22:00) is 0.8 yuan / kWh, and the electricity price during off-peak hours (22:00 - 8:00 the next day) is 0.3 yuan / kWh. The grid load power in this area varies between 50 MW - 200 MW within a week.

[0035] 4. Renewable energy system: The installed capacity of the photovoltaic power generation system is 50 MW, and the photovoltaic maintenance cost coefficient is 0.05 yuan / kWh; the installed capacity of the wind power generation system is 30 MW, and the wind power maintenance cost coefficient is 0.08 yuan / kWh.

[0036] 5. Electro-hydrogen coupling system: The minimum input power of the hydrogen production equipment is 10 MW, and the maximum input power is 30 MW; the cost of consumables required to produce a 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 1000 MWh, the hydrogen storage conversion efficiency is 0.9, and the hydrogen release conversion efficiency is 0.95.

[0037] II. Establish an optimization scheduling model based on risk prediction According to the optimization scheduling method based on risk prediction described above, in the Python environment, use the PuLP library to construct an optimization scheduling model. And define the calculation expressions for the operating cost of photovoltaic power generation, the operating cost of wind power generation, the operating cost of hydrogen production, the energy conversion risk cost, the storage risk cost, and the electricity trading revenue respectively.

[0038] III. Determine the constraint functions Set the power balance constraint, the renewable energy system constraint, and the electro-hydrogen coupling system constraint. 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 charge and discharge power of the energy storage battery, the input and output power of the electro-hydrogen coupling system, and the grid load power.

[0039] IV. Optimization solution Use the CBC (Coin-OR Branch and Cut) solver to solve the model to obtain the hourly optimization scheduling strategy within a week.

[0040] V. Analysis of simulation results The costs of each part are as Figure 3 shown.

[0041] Photovoltaic power generation: The total photovoltaic power generation within a week is 720 MWh, and the operating cost is 0.05×720 = 360,000 yuan.

[0042] Wind power generation: The wind power generation within a week is 450 MWh, and the operating cost is 0.08×450 = 360,000 yuan.

[0043] Hydrogen production: The hydrogen production volume is 15000 m³, and the hydrogen production operating cost is calculated to be 630,000 yuan.

[0044] Energy conversion risk: Since the optimization scheduling strategy reasonably adjusts the input power of the electro-hydrogen coupling system, the energy conversion risk cost is kept at a low level, totaling 120,000 yuan within a week.

[0045] Storage risk: By effectively controlling the pressure change of the hydrogen storage tank, the storage risk cost is 80,000 yuan within a week.

[0046] Electric trading revenue: According to the time-of-use electricity price mechanism and the optimized electricity trading strategy, the electric trading revenue for this week is 500,000 yuan.

[0047] Power balance analysis: Throughout the simulation period, the power balance constraint is always satisfied. By reasonably dispatching the operation of renewable energy generation, energy storage battery charging and discharging, and the electro-hydrogen coupling system, reliable power supply for the grid load is ensured. For example, when there is sufficient sunlight and low electricity load during the day, the excess electricity is used for hydrogen production or charging the energy storage battery; when there is a peak electricity consumption at night and insufficient renewable energy generation, the energy storage battery discharges and adjusts the output power of the electro-hydrogen coupling system to guarantee power supply.

[0048] Renewable energy utilization rate: The proportion of the actual power generation of renewable energy (photovoltaic and wind power) in the potential power generation during this week reached 85%, indicating that the optimized dispatching method effectively improves the consumption capacity of renewable energy and reduces the phenomena of wind and light curtailment.

[0049] Embodiment 2 As Figure 2 shown, a system of an optimized dispatching method for an electro-hydrogen coupling system based on risk prediction includes: A data acquisition module for acquiring data of the renewable energy system, the power grid system, and the electro-hydrogen coupling system.

[0050] A model construction module for constructing an optimized dispatching model based on risk prediction, where the optimized dispatching model includes the operation cost of photovoltaic power generation, the operation cost of wind power generation, the operation cost of hydrogen production, the energy conversion risk cost, the storage risk cost, and the electric trading revenue.

[0051] An optimization module for solving the constructed optimized dispatching model based on risk prediction through constraint functions to obtain an optimized dispatching strategy and achieve the optimal dispatching of the electro-hydrogen coupling system.

[0052] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. An optimized scheduling method for an electric-hydrogen coupling system based on risk prediction, characterized in that The specific steps are as follows: Step S1: Obtain data of the renewable energy system, power grid system, and power-to-hydrogen coupling system; Step S2: Based on the data obtained in Step S1, establish an optimization scheduling model based on risk prediction. The optimization scheduling model is as follows: ； Among them, is the minimum function, is the operating cost of photovoltaic power generation, is the operating cost of wind power generation, is the operating cost of hydrogen production, is the energy conversion risk cost, is the storage risk cost, is the revenue from electricity trading; The calculation formula for the energy conversion risk cost is as follows: ； Among them, and are the maximum electric power and the minimum electric power input into the electric-hydrogen coupling system within a scheduling period respectively, is the average electric power input into the electric-hydrogen coupling system within a scheduling period, is the first matching coefficient; The calculation formula for the storage risk cost is as follows: ； wherein, is the current hydrogen storage pressure, is the current hydrogen storage pressure change rate, is the sampling time interval, is the second matching coefficient; Step S3: Establish constraint functions for the optimization scheduling model through the data obtained in Step S1. The constraint functions include a power balance constraint function, a renewable energy system constraint function, and a power-to-hydrogen coupling system constraint function; Step S4: Solve the optimization scheduling model using the constraint functions to obtain an optimization scheduling strategy.

2. The optimization scheduling method of an electric-hydrogen coupling system based on risk prediction according to claim 1, wherein: The renewable energy system includes a photovoltaic power generation part and a wind power generation part.

3. The optimization scheduling method of the electric-hydrogen coupling system based on risk prediction according to claim 2, wherein: In Step S2, the calculation formula for the operating cost of photovoltaic power generation is as follows: ； Among them, is the photovoltaic maintenance cost coefficient, is the photovoltaic power generation amount within a scheduling period; The calculation formula for the operating cost of wind power generation is as follows: ； Among them, is the wind power maintenance cost coefficient, is the wind power generation within a transfer period.

4. The optimization scheduling method of an electric-hydrogen coupling system based on risk prediction according to claim 3, characterized in that: In Step S2, the calculation formula for the hydrogen production operating cost is as follows: ； Among them, is the input power of the power-to-hydrogen coupling system, is the cost of consumables required to produce unit volume of hydrogen, is the hydrogen production maintenance cost coefficient, is the hydrogen production volume, is the scheduling period, is the sampling time interval.

5. The optimized scheduling method for an electric-hydrogen coupling system based on risk prediction according to claim 4, wherein: In Step S2, the calculation formula for the electricity trading revenue is as follows: ； Among them, is the real-time electricity price at time is the trading coefficient at time. When purchasing electricity at time, the value is -1. When selling electricity at time, the value is 1. When no transaction occurs at time, the value is 0. is the scheduling period, is the transaction power at time.

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

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

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

9. A system for an optimized scheduling method of an electric-hydrogen coupling system based on risk prediction according to claim 8, characterized in that, Including: A data acquisition module for obtaining data of the renewable energy system, power grid system, and power-to-hydrogen coupling system; A model construction module for constructing an optimization scheduling model based on risk prediction. The optimization scheduling model includes the operating cost of photovoltaic power generation, the operating cost of wind power generation, the hydrogen production operating cost, the energy conversion risk cost, the storage risk cost, and the electricity trading revenue; An optimization module for solving the constructed optimization scheduling model based on risk prediction through constraint functions to obtain an optimization scheduling strategy and achieve optimal scheduling of the power-to-hydrogen coupling system.

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