Multi-energy complementary and collaborative multi-source hydrogen energy micro-grid low-carbon optimization scheduling method and system

Abstract

The invention discloses a multi-energy complementary and collaborative multi-source hydrogen energy micro-grid low-carbon optimization scheduling method and system, and belongs to the field of micro-grid low-carbon optimization scheduling, and the method comprises the steps: building a comprehensive constraint condition according to the characteristics of a micro-grid, building a multi-source hydrogen energy micro-grid operation total cost model based on the comprehensive constraint condition, and carrying out the multi-source hydrogen energy micro-grid operation total cost model. The total operation cost is minimized through the total operation cost model of the multi-source hydrogen energy micro-grid; and on the basis of the multi-source hydrogen energy micro-grid operation total cost model, defining a system total cost deviation coefficient based on the wind power and photoelectric uncertainty, performing analysis modeling on the wind power and photoelectric power generation uncertainty by adopting an opportunity seeking strategy of an information gap decision theory, and calculating the income brought by the uncertainty through the constructed model. According to the invention, reliable technical support can be provided for optimal scheduling of the micro-grid.

Description

Technical Field

[0001] The present invention relates to the field of low-carbon optimization scheduling of microgrids, and in particular to a low-carbon optimization scheduling method and system for a multi-source hydrogen energy microgrid with multi-energy complementary coordination. Background Art

[0002] Microgrids have the multifunctionality of power generation, transmission and consumption. Through the coordinated operation of their internal components, they can achieve efficient integration of renewable energy and friendly access to existing distribution networks.

[0003] Configuring multiple power sources for complementary operation is an effective means of achieving reliable power supply in microgrids. Currently, scholars at home and abroad have studied the capacity configuration of remote microgrids based on wind, solar, diesel, and storage, primarily powered by thermal power. However, a true transition from highly polluting and energy-intensive power generation to clean and environmentally friendly power generation remains elusive. With the gradual establishment and improvement of global carbon trading markets, carbon emission costs have become a major factor influencing microgrid operation strategies. Considering these factors comprehensively, the challenge of building an economical, clean, and reliable microgrid system is becoming increasingly complex.

[0004] Overall, although the current methods have achieved the integration of renewable energy into existing microgrids to a certain extent, there are still some problems that need to be solved urgently: 1) Most studies rely on deterministic wind and solar power forecast data and lack active optimization of forecast error robustness, which can easily lead to load loss or excessive reliance on electricity purchases in extreme weather or load mutation scenarios; 2) The production strategies of green hydrogen and blue hydrogen are not dynamically coupled with carbon trading costs, making it difficult to balance low-carbon goals and the economic efficiency of hydrogen production; 3) The operating constraints of multiple devices are complex, making it difficult to achieve global optimization. They are often simplified into linear models, which deviate greatly from the actual physical properties, affecting the feasibility of the scheduling scheme. Summary of the Invention

[0005] One of the purposes of the present invention is to provide a multi-source hydrogen energy microgrid low-carbon optimization scheduling method with multi-energy complementary and coordinated efforts, so as to solve the problem in the prior art of lacking active optimization of prediction error robustness, which easily leads to load loss or over-reliance on electricity purchase in extreme weather or load mutation scenarios.

[0006] The present invention is achieved through the following technical solution, a multi-source hydrogen energy microgrid low-carbon optimization scheduling method with multi-energy complementary and coordinated cooperation, comprising the following steps: S100, constructing comprehensive constraints according to the characteristics of the microgrid, constructing a multi-source hydrogen energy microgrid operation total cost model based on the comprehensive constraints shown, and minimizing the total operation cost through the multi-source hydrogen energy microgrid operation total cost model; S200, based on the multi-source hydrogen energy microgrid operation total cost model, based on the uncertainty of wind power and photovoltaic power generation, defining the system total cost deviation coefficient, adopting the opportunity seeking strategy of information gap decision theory to analyze and model the uncertainty of wind power and photovoltaic power generation, and calculating the benefits brought by uncertainty through the constructed model.

[0007] Furthermore, the constraints include: electricity purchase cost, gas turbine power generation cost constraint, energy balance constraint, gas-to-hydrogen equipment cost constraint, hydrogen energy storage system operation constraint, and carbon trading cost; wherein, the gas turbine power generation cost constraint is constructed by considering fuel consumption and power generation power constraint, ramp constraint, gas turbine start-stop constraint and natural gas consumption constraint, integrating these constraints to construct the gas turbine power generation cost constraint; the safe operation constraint is constructed by considering the power exchange of the upper system, wind and solar power abandonment and load loss constraint; the energy balance constraint is constructed based on the two-way flow of electricity and hydrogen energy according to the balance of electricity supply and demand and the balance of hydrogen energy supply and demand; the gas-to-hydrogen equipment cost constraint includes the calculation of real-time power consumption based on voltage efficiency and Faraday efficiency The efficiency model of green hydrogen production by electrolyzer, the blue hydrogen production model by gas hydrogen production equipment, and the polarization characteristic model of proton exchange membrane fuel cell constructed by using the piecewise linearization method to deal with the nonlinear relationship between the output power and current of the fuel cell stack are integrated into the cost constraint of the gas hydrogen production equipment; the operation constraints of the hydrogen energy storage system include: the hydrogen storage capacity limitation of the hydrogen storage device, the upper and lower limits of the storage and release rates, and the consistency constraint of the hydrogen storage cycle, as well as the charge and discharge power ramp rate limitation, the state of charge range constraint and the charge and discharge efficiency model of the battery; the carbon trading cost includes: based on the ladder carbon trading model, a carbon emission evaluation method is constructed to quantify the carbon emissions of the microgrid system during electricity purchase, gas turbine power generation and gas hydrogen production equipment operation.

[0008] Furthermore, the total cost model of multi-source hydrogen energy microgrid operation is expressed as follows:

[0009] ,

[0010] in, is the minimum total operating cost, The cost of purchasing electricity, is the total operating period of each power supply device, For the Gas turbines in The power generation cost at the time, is the number of gas turbines, For the Taiwan Gas Hydrogen Production Equipment The cost of time, is the number of gas-to-hydrogen equipment, for The penalty cost of the moment, The cost of hydrogen sales revenue is It is the tiered carbon trading cost.

[0011] Furthermore, the energy balance constraint is expressed as follows:

[0012] ,

[0013] ,

[0014] in, for The power purchased at the time; For the Wind turbines in The power input to the grid at all times; For the Photovoltaic generators in The power input to the grid at all times; For the Gas turbines in Power generation at the moment; for Moment Fuel cell output power; For the Batteries in Discharge power at the moment; For microgrids The predicted value of electric load at the moment; for The load loss power at the moment; for Enter the time Power of electrolyzer; For the Taiwan Gas Hydrogen Production Equipment Power consumption at all times; For the cth battery Charging power at the moment; 、 、 、 、 、 、 Respectively represent wind The number of power generators, photovoltaic generators, gas turbines, fuel cells, batteries, electrolyzers, and gas-to-hydrogen equipment.

[0015] Furthermore, the carbon trading cost is calculated by the following formula:

[0016] ,

[0017] in, is the cost of tiered carbon trading; It is the base price for carbon trading; is the length of the carbon emission interval; is the growth rate of carbon trading price; is the carbon emission rights trading amount of the microgrid system.

[0018] Furthermore, the uncertainty of wind power and photovoltaic power is expressed as follows:

[0019] ,

[0020] ,

[0021] in, is the wind power generation parameter, is the predicted value of wind power generation parameters, is the photovoltaic power generation parameter, is the predicted value of photovoltaic power generation parameters, is the uncertainty of wind power generation parameters, is the uncertainty of photovoltaic power generation parameters, the set is the output interval of wind power generation under the uncertainty model, and the set is the output range of photovoltaic power generation under the uncertainty model.

[0022] Furthermore, the uncertainties of wind power and photovoltaic power also include the cost of curtailment of wind power and photovoltaic power and the penalty cost of load loss, which can be expressed as follows:

[0023] ,

[0024] in, is the penalty coefficient for load loss; express Load loss during the period; is the wind curtailment penalty coefficient; is the light abandonment penalty coefficient; Indicates the Wind turbines in The wind power curtailment during the period; Indicates the photovoltaic generators The abandoned optical power in a time period.

[0025] On the other hand, the present invention provides a multi-source hydrogen energy microgrid low-carbon optimization scheduling system with multi-energy complementary and coordinated cooperation. The system includes a processor and a memory, and a computer program is stored in the memory. When the computer program is executed by the processor, the multi-source hydrogen energy microgrid low-carbon optimization scheduling method with multi-energy complementary and coordinated cooperation as described above is implemented.

[0026] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0027] 1. Through multi-energy complementary synergy strategy and refined modeling, the present invention reduces wind and solar power abandonment while ensuring the economy and low carbon nature of the microgrid, reduces operating costs, improves the overall efficiency of the system, and provides reliable technical support for the optimized scheduling of the microgrid.

[0028] 2. This invention introduces the IGDT opportunity seeking strategy. Under the premise of ensuring that the expected cost does not deteriorate, it actively pursues the potential benefits brought by the uncertainty of wind and solar power output. By taking greater risks, it obtains the opportunity to reduce costs, thereby optimizing the economic dispatch of the microgrid and improving the flexibility and adaptability of the system.

[0029] 3. This invention combines a stepped carbon trading model to dynamically quantify carbon emissions, incentivize low-carbon operation strategies, significantly reduce the overall carbon emissions of microgrids, promote the efficient use of clean energy, and provide a theoretical basis and practical guidance for the sustainable development of microgrids.

[0030] 4. The present invention significantly improves the economy and low-carbon nature of the microgrid through multi-energy complementary collaborative optimization. By constructing a multi-objective optimization model that includes electricity purchase costs, gas turbine power generation costs, gas-to-hydrogen equipment costs, wind and solar power abandonment and load loss penalty costs, and carbon trading costs, the total operating cost is minimized. At the same time, combined with the ladder carbon trading model, the carbon emissions of the microgrid are dynamically quantified, low-carbon operation strategies are incentivized, and the overall carbon emissions of the microgrid are effectively reduced. In addition, by refining the physical characteristics of modeling equipment, such as fuel cell polarization curves and electrolyzer efficiency, the scheduling deviation caused by traditional linear simplification is avoided, the feasibility and accuracy of the scheduling scheme are improved, and the efficiency and stability of the microgrid in actual operation are ensured.

[0031] 5. The present invention enhances the robustness of the microgrid to the uncertainty of wind and solar power output by introducing the IGDT opportunity-seeking strategy. Traditional methods rely on deterministic wind and solar power forecast data, which makes it difficult to cope with extreme weather or sudden load changes, and can easily lead to load loss or over-reliance on electricity purchases. However, the present invention constructs a wind and solar power output uncertainty model and defines a total cost deviation coefficient. Under the premise of ensuring that the expected cost does not deteriorate, it minimizes uncertainty to pursue potential benefits. This method not only improves the power supply reliability of the microgrid in extreme scenarios, but also avoids over-reliance on deterministic forecast data by actively managing wind and solar power uncertainties, thereby enhancing the flexibility and adaptability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0033] Figure 1 This is a flow chart of the method provided in Example 1 of the present invention.

[0034] Figure 2 This is a typical daily residential electricity load diagram provided by Example 2 of the present invention.

[0035] Figure 3 These are the wind power generation and photovoltaic power generation prediction values ​​provided by Example 2 of the present invention.

[0036] Figure 4 This is a wind and solar power curtailment diagram for Solution 1 provided in Example 2 of the present invention.

[0037] Figure 5 This is a diagram of the power consumption of wind and solar power curtailment, electrolyzer, and gas-to-hydrogen equipment provided in Example 2 of the present invention.

[0038] Figure 6 This is a diagram of the charge and discharge conditions of the battery provided in Example 2 of the present invention.

[0039] Figure 7 A comparison chart of gas turbine power generation, power purchase, electrolyzer power consumption and gas-to-hydrogen equipment power consumption for Scheme 3 and Scheme 4 provided in Example 2 of the present invention.

[0040] Figure 8 This is a cost deviation diagram for wind photovoltaic power generation and gas turbine power generation provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0041] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0042] Example 1

[0043] This paper approaches the certainty and uncertainty of wind and solar power output from two perspectives, designing a reasonable framework for the economically stable operation of a multi-source hydrogen energy microgrid. First, by constructing a wind-solar-hydrogen-storage-gas turbine multi-energy complementary microgrid architecture, a multi-objective optimization operation strategy is proposed, taking into account the electricity purchase cost, gas turbine power generation cost, gas-to-hydrogen equipment cost, wind and solar curtailment and load loss penalty costs, and carbon trading costs. Second, energy balance constraints, gas turbine operation constraints, safety operation constraints, hydrogen production and use equipment operation constraints, and energy storage system operation constraints are considered to ensure the stable operation of the microgrid under various operating conditions. Third, a step-by-step carbon trading model is combined to dynamically quantify carbon emissions, incentivize low-carbon operation strategies, and significantly reduce carbon emissions in the microgrid. Finally, to address the uncertainty of wind and solar power generation, the information gap decision theory (IGDT) is used for modeling. Through opportunity-seeking strategies, uncertainty is minimized, opportunities for cost reduction are obtained, and thus the optimal scheduling of the multi-source hydrogen energy microgrid is achieved.

[0044] Figure 1 A flowchart of a multi-source hydrogen energy microgrid low-carbon optimization scheduling method for multi-energy complementary coordination in this embodiment is shown. As can be seen from the figure, this embodiment includes the following steps:

[0045] Step 1: Construct a microgrid operation optimization method based on a multi-source hydrogen energy refined model.

[0046] The specific steps include:

[0047] Step 1.1: Construct a multi-source hydrogen microgrid operation total cost model that takes into account electricity purchase costs, gas turbine power generation costs, gas-to-hydrogen equipment costs, wind and solar power curtailment and load loss penalty costs, and carbon trading costs to minimize the total operating cost.

[0048] Specifically, the total cost model for the operation of a multi-source hydrogen microgrid can be obtained by integrating the electricity purchase cost, the power generation cost of the gas turbine unit, the cost of gas-to-hydrogen equipment, the penalty costs for wind and solar power curtailment and load loss, and the carbon trading cost into the minimum total operating cost and performing minimization operations. It can be expressed as follows:

[0049] ,

[0050] in,

[0051] is the minimum total operating cost,

[0052] The cost of purchasing electricity, is the total operating period of each power supply device, For the Gas turbines in The power generation cost at the time, is the number of gas turbines, For the Taiwan Gas Hydrogen Production Equipment The cost of time, is the number of gas-to-hydrogen equipment, for The penalty cost of the moment, The cost of hydrogen sales revenue is It is the tiered carbon trading cost.

[0053] Power purchase cost , No. Gas turbines in Power generation cost at the time and Taiwan Gas Hydrogen Production Equipment The cost of a moment can be calculated as follows:

[0054] ,

[0055] ,

[0056] ,

[0057] in, The unit price of electricity; for The power purchased at the time; is the unit price of natural gas; For the Gas turbines in Natural gas consumption during the period; For the Taiwan Gas Hydrogen Production Equipment Natural gas consumption during the period.

[0058] Penalty costs It can be calculated by the following formula:

[0059] ,

[0060] in, is the penalty coefficient for load loss; express Load loss during the period; is the wind curtailment penalty coefficient; is the light abandonment penalty coefficient; Indicates the Wind turbines in The wind power curtailment during the period; Indicates the photovoltaic generators The abandoned optical power in a time period.

[0061] The cost of hydrogen sales revenue can be calculated using the following formula:

[0062] ,

[0063] in, Indicates the quality of hydrogen sold; Indicates the unit price of hydrogen sold.

[0064] Step 1.2: Based on the balance of electricity supply and demand and hydrogen supply and demand, construct an energy balance constraint for the bidirectional flow of electricity and hydrogen. The energy balance constraint is to make the power input into the microgrid during period t equal to the power output from the microgrid.

[0065] Specifically, the energy balance constraint can be expressed as follows:

[0066] ,

[0067] in, for The power purchased at the time; For the Wind turbines in The power input to the grid at all times; For the Photovoltaic generators in The power input to the grid at all times; For the Gas turbines in Power generation at the moment; for Moment Fuel cell output power; For the Batteries in Discharge power at the moment; For microgrids The predicted value of electric load at the moment; for The load loss power at the moment; for Enter the time Power of electrolyzer; For the Taiwan Gas Hydrogen Production Equipment Power consumption at all times; For the cth battery Charging power at the moment; 、 、 、 、 、 、 They represent the number of wind turbines, photovoltaic generators, gas turbines, fuel cells, batteries, electrolyzers, and gas-to-hydrogen equipment respectively.

[0068] Considering the possibility of wind and solar power abandonment in actual situations, the actual power input to the grid by wind turbines and photovoltaic generators can be calculated using the following formula:

[0069] ,

[0070] ,

[0071] in, is the actual power input to the grid by the wind turbine, For the Wind turbines in The power generation at the moment, For the Wind turbines in The wind power curtailment at the moment, is the actual power input to the grid by the photovoltaic generator, For the Photovoltaic generators in The power generation at the moment, For the Photovoltaic generators in The abandoned optical power at the moment.

[0072] exist The hydrogen energy input to the microgrid at a given moment should be equal to the hydrogen energy output from the microgrid, which can be calculated using the following formula:

[0073] ,

[0074] in, for Moment The quality of hydrogen released by each electrolyzer; for Time period The quality of blue hydrogen production from Taiwan Gas’ hydrogen production equipment; For the Hydrogen storage equipment in The mass of hydrogen released at any moment; for During the period The mass of hydrogen consumed by each fuel cell; For the Hydrogen storage equipment in The quality of hydrogen stored at any given moment; For the quality of hydrogen sold.

[0075] Step 1.3: Construct gas turbine operating constraints and system safety operating constraints.

[0076] When constructing the gas turbine operating constraints, the fuel consumption and power generation constraints, ramp constraints, gas turbine start and stop constraints, and natural gas consumption constraints should be considered. These constraints are integrated to construct the gas turbine operating constraints.

[0077] When constructing the system's safe operation constraints, we should consider the power exchange with the upper-level system, wind and solar power abandonment and load loss constraints, and integrate these constraints to construct the system's safe operation constraints.

[0078] Specifically, the relationship model between the fuel consumption of the gas turbine and the power generation can be expressed by the following formula:

[0079] ,

[0080] in, is the natural gas heat rate curve function; 、 、 Indicates the The gas coefficient of the gas turbine; For the The minimum limit of gas turbine output, is the natural gas heat rate.

[0081] The gas turbine output power constraint is:

[0082] ,

[0083] in, and Respectively The minimum and maximum limits of the gas turbine output.

[0084] The relationship between natural gas consumption and gas turbine output power is shown in the following formula:

[0085] ,

[0086] in, Indicates the Gas turbines in Natural gas consumption at any given moment; and Respectively represent Gas turbines in The heat required to turn the machine on and off at all times.

[0087] The heat constraint required for starting and stopping the gas turbine is as follows:

[0088] ,

[0089] ,

[0090] in, and are the costs of starting up and shutting down the gas turbine, respectively; Indicates the Gas turbines in The start and stop status of the moment, "0" means shutdown, "1" means startup, Indicates the Gas turbines in The start and stop status of the moment, "0" means shutdown, and "1" means startup.

[0091] when When , the ramp constraint of the gas turbine is as follows:

[0092] ,

[0093] ,

[0094] in, and Respectively Ramp-up and ramp-down rates of the gas turbines; Indicates the Gas turbines in The start and stop status of the moment, "0" means shutdown, and "1" means startup.

[0095] The start-stop constraints of the gas turbine are as follows:

[0096] ,

[0097] ,

[0098] in, Indicates the Gas turbines in The time that has been running continuously before the moment; Indicates the The minimum continuous operating time required for a gas turbine after startup; Indicates the Gas turbines in Time that has been continuously stopped before time; Indicates the The minimum continuous shutdown time required for a gas turbine after it is stopped.

[0099] The microgrid performs bidirectional energy exchange with the upper-level power grid and gas grid, so it needs to meet the power exchange constraint with the upper-level network as shown in the following formula:

[0100] ,

[0101] in, for The power purchased at the time; and Respectively represent the minimum and maximum value of purchased power.

[0102] When the system power generation cannot match the load demand, it is necessary to cut off part of the load to ensure the normal operation of important loads. Therefore, the proportion of load loss to total load should not exceed the upper and lower limits:

[0103] ,

[0104] in, for The load loss power at the moment; for The load of the moment; It is the upper limit of the ratio of lost load to total load.

[0105] When the system generates too much wind and solar power and the microgrid cannot absorb it, some of the wind and solar power will be abandoned. The proportion of abandoned wind and solar power to its power generation should not exceed the wind and solar power forecast value:

[0106] ,

[0107] .

[0108] Step 1.4: Construct the operating constraints of hydrogen production and use equipment, including an efficiency model for green hydrogen production by an electrolyzer that calculates real-time power consumption based on voltage efficiency and Faraday efficiency, a blue hydrogen production model for gas-to-hydrogen equipment, and a polarization characteristic model for proton exchange membrane fuel cells that uses a piecewise linearization method to handle the nonlinear relationship between stack output power and current.

[0109] Hydrogen is categorized by source as green hydrogen, blue hydrogen, and gray hydrogen. Green hydrogen is produced by water electrolysis, resulting in zero carbon emissions. Gray hydrogen is produced from fossil fuels, offering the lowest cost, but with carbon emissions. Blue hydrogen, based on gray hydrogen, utilizes carbon capture, utilization, and storage technology, resulting in minimal carbon emissions.

[0110] Green hydrogen is produced by electrolysis of water. There is no carbon emission in the hydrogen production process, but it requires a large amount of electricity and water.

[0111] At operating temperature (353K) and pressure (1.01×105Pa), During the period Operating voltage of electrolyzer and operating current As shown in the following formula:

[0112] ,

[0113] in, is the reversible voltage, is the ohmic overvoltage, is the activation overvoltage, These are concentration overvoltages, which depend on the flow of stack current.

[0114] It should be noted that the reversible voltage The open circuit voltage is the minimum ideal voltage required for the thermodynamic electrolysis reaction. The voltage overvoltage is a function of the voltage, and is affected to some extent by pressure, which is 1.23V under standard conditions. The ohmic overvoltage, activation overvoltage, and concentration overvoltage are heavily dependent on the availability of certain electrolytic stack data and parameters, such as the thickness and conductivity of the cell / electrolytic stack material, the moisture content in the membrane, the activation overpotential of the anode and cathode, etc. To solve this problem, the voltage efficiency To evaluate the deviation from the ideal conditions, the electrolytic stack model is constructed as shown below:

[0115] ,

[0116] in, is the thermal neutral voltage, which is 1.48 V under standard conditions, including the heat energy bound due to entropy change.

[0117] According to Faraday's first law, the molar yield of hydrogen is Proportional to the current flowing through the electrolysis stack. In order to take into account the diffusion loss of hydrogen through the membrane and the loss of the downstream hydrogen purification system, the Faraday efficiency is introduced. :

[0118] ,

[0119] ,

[0120] Among them, F=96485 (C / mol) is the Faraday constant; in the proton exchange membrane (PEM) water electrolysis hydrogen production device, ; is the current flowing through the electrolytic stack; For electrolytic cell cross-sectional area; for During the period The unit current density of an electrolytic cell.

[0121] Therefore, the DC hydrogen production efficiency of the electrolyzer is for:

[0122] ,

[0123] in, 、 are the Faradaic efficiency and voltage efficiency, respectively.

[0124] The mass of hydrogen produced by the electrolyzer is:

[0125] ,

[0126] in, is the mass output of hydrogen (kg / s); is the molar production of hydrogen (mol / s); is the molar mass of hydrogen (kg / mol).

[0127] Calculate the power consumption of the electrolyzer through the DC hydrogen production efficiency of the electrolyzer:

[0128] ,

[0129] The power consumption constraint of the electrolytic cell is:

[0130] ,

[0131] in, for The minimum value of the electric power consumed by the electrolytic cell at the moment, for The maximum value of the electric power consumed by the electrolytic cell at that moment.

[0132] when When the electrolyzer power set point deviation Depends on the start / shutdown time of the water electrolysis hydrogen production unit and its slope capability:

[0133] ,

[0134] ,

[0135] in, The duration of each scheduling period; 、 They are the response time for activating and deactivating the water electrolysis hydrogen production device, including communication delay, startup time, etc. 、 are the rising and falling rates of the slope, respectively.

[0136] The hydrogen sales constraints are:

[0137] (34),

[0138] The gas-to-hydrogen equipment uses natural gas to produce blue hydrogen. The present invention adopts a natural gas steam reforming (pressure swing adsorption, PSA) hydrogen production method.

[0139] No. Taiwan Gas Hydrogen Production Equipment The blue hydrogen production during the period is:

[0140] ,

[0141] in, for Time period Blue hydrogen production capacity of Taiwan Gas’ hydrogen production facilities; is the gas-to-hydrogen efficiency; is the lower calorific value of natural gas, which is 10.122kW·h / (Nm 3 ); for Time period Natural gas consumption of Taiwan Gas' hydrogen production equipment; is the lower calorific value of hydrogen, which is 3.539kW·h / (Nm 3 )(Nm 3 That is, the standard volume is the volume of gas at 0°C and standard atmospheric pressure).

[0142] The quality of blue hydrogen production is:

[0143] ,

[0144] in, for Time period The quality of blue hydrogen production from Taiwan Gas’ hydrogen production equipment; is the density of hydrogen, which is 0.899 kg / (Nm 3 ).

[0145] No. Taiwan Gas Hydrogen Production Equipment Power consumption during the time period for:

[0146] ,

[0147] in, is the power consumption coefficient of the gas-to-hydrogen equipment, which is 0.2kW·h / (Nm3).

[0148] The upper and lower limits of blue hydrogen production are:

[0149] ,

[0150] in, 、 They are the upper and lower limits of blue hydrogen production respectively.

[0151] Hydrogen-oxygen fuel cells undergo electrochemical reactions to generate electrical energy and thermal energy. Considering that proton exchange membrane fuel cells generate less heat, the waste heat utilization link of fuel cells is ignored.

[0152] The constraint relationship between the hydrogen mass consumed by hydrogen-oxygen fuel cells and their output power:

[0153] ,

[0154] in, Indicates the efficiency of the fuel cell; for Enter the first The hydrogen quality of each fuel cell; The duration of each scheduling period.

[0155] The output power of the fuel cell is:

[0156] ,

[0157] in, is the stack voltage; is the stack current.

[0158] The output power of the fuel cell stack is nonlinearly related to the current. The piecewise linear method is used to transform Equation (1.31):

[0159] ,

[0160] ,

[0161] ,

[0162] ,

[0163] in, is the number of linearization segments; is the segmentation point; reflects In the The position of the segment interval; It is a binary variable, which ensures the continuity of the segmented interval.

[0164] During the normal operation of the fuel cell, the stack voltage will decrease irreversibly with the increase of current density. This phenomenon is called stack polarization, which includes activation polarization, ohmic polarization and concentration polarization. For the PEMFC stack model, the fuel cell stack voltage is:

[0165] ,

[0166] in, is the stack voltage; is the Nernst voltage; is the activation polarization overvoltage; is the ohmic polarization overvoltage; is the concentration polarization overvoltage.

[0167] From the thermodynamic equation and the standard state entropy change, the Nernst voltage can be obtained as:

[0168] ,

[0169] in, is the Gibbs free energy; is the Faraday constant; is the fuel cell temperature, which is considered to be a constant of 340.0K to ensure that the fuel cell operates in the best state; is the ambient temperature, which is 293.15K; is the standard molar entropy; 、 are the input hydrogen and oxygen partial pressures, respectively; is the gas constant.

[0170] The reason for the activation polarization overvoltage is the energy loss during the transfer of electrons from the anode to the cathode, namely:

[0171] ,

[0172] in, is the stack current; 、 、 、 It is a system parameter obtained by simulating experimental data such as electrochemistry and thermodynamics.

[0173] The reason for the ohmic polarization overvoltage is that the ions need to overcome resistance during migration, that is:

[0174] ,

[0175] in, is the equivalent resistance of protons passing through the membrane, and .

[0176] The reason for concentration polarization overvoltage is that the concentration of reactants decreases during the electrochemical reaction of the battery, resulting in uneven distribution of the electrolyte solution and resistance, namely:

[0177] ,

[0178] in, is the battery operation parameter, take ; The maximum allowable current is 0.12A.

[0179] The operating current constraint is:

[0180] ,

[0181] in, is the minimum allowable current; Indicates the Fuel cells in The start and stop status of the moment, "0" means shutdown, and "1" means startup.

[0182] The output power constraint of hydrogen and oxygen fuel cells is:

[0183] ,

[0184] in, is the minimum output power of the hydrogen-oxygen fuel cell, is the maximum output power of the hydrogen-oxygen fuel cell.

[0185] when When , the fuel cell climbing constraint is:

[0186] ,

[0187] ,

[0188] in, is the fuel cell climbing coefficient.

[0189] Step 1.5: Construct the operating constraints of the energy storage system, including the hydrogen storage capacity limit of the hydrogen storage device, the upper and lower limits of the storage and release rates, and the consistency constraints of the hydrogen storage cycle, as well as the battery's charge and discharge power ramp rate limit, state of charge range constraints, and charge and discharge efficiency model.

[0190] Specifically, hydrogen energy storage equipment constraints include hydrogen storage capacity constraints, upper and lower limit constraints on hydrogen storage rate, upper and lower limit constraints on hydrogen outflow rate, and equipment capacity constraints.

[0191] The relationship between the hydrogen storage capacity of the hydrogen storage device and the hydrogen storage capacity at the previous moment and the hydrogen storage and release rate of the hydrogen storage device is as follows:

[0192] ,

[0193] in, For the Hydrogen storage equipment in The amount of hydrogen stored at a given moment; For the The hydrogen storage capacity of the hydrogen storage device at time t-1, 、 They are the gas release efficiency and gas storage efficiency of the hydrogen storage equipment respectively; and They are Hydrogen storage equipment in The rate of hydrogen storage and release at each moment; is the maximum hydrogen storage capacity of the hydrogen storage equipment; 、 The hydrogen storage equipment is The hydrogen storage coefficient at the time.

[0194] The hydrogen storage capacity of the hydrogen storage equipment at the beginning and end of a time period should be equal:

[0195]

[0196] The upper and lower limits of the rate of hydrogen entering and leaving the hydrogen storage system are as follows:

[0197] ,

[0198] in, 、 Indicates hydrogen storage equipment exist The state of the moment, When it is 1, it means that hydrogen is stored in the hydrogen storage system. When it is 1, it means that hydrogen flows out of the hydrogen storage system; and They are Hydrogen storage equipment in The rate of hydrogen storage and release during the period; and They are The maximum hydrogen storage and release rate of the hydrogen storage device; and They are The minimum hydrogen storage and release rate of a hydrogen storage device.

[0199] The hydrogen storage capacity constraint of the hydrogen storage equipment is:

[0200] ,

[0201] in, 、 are the minimum and maximum hydrogen storage capacities of the hydrogen storage equipment, respectively.

[0202] For battery energy storage, its ramping constraints must be considered, that is, its charging and discharging power should meet the following constraints:

[0203] ,

[0204] ,

[0205] ,

[0206] in, 、 Indicates the Batteries in The state of the moment, When it is 1, it means the battery is charging. When it is 1, it means the battery is discharged; 、 Respectively represent Batteries in The charging and discharging power at each moment; 、 Indicates the minimum power allowed for battery charging and discharging; 、 Indicates the maximum power allowed for battery charging and discharging;

[0207] The battery state of charge constraints are as follows:

[0208] ,

[0209] ,

[0210] in, Indicates the Batteries in State of charge at the moment; and Respectively represent the minimum state of charge and maximum state of charge allowed for the battery; For the Batteries in The amount of electricity at the moment; The maximum capacity of the battery.

[0211] When the battery is charging and discharging, Batteries in Power at all times for:

[0212] ,

[0213] in, and Respectively represent the charge and discharge efficiency of the battery; 、 The battery is The energy storage coefficient at the moment.

[0214] The battery charge at the beginning and end of a time period should be equal, as shown in the following formula:

[0215] ,

[0216] The battery capacity constraint is:

[0217] ,

[0218] in, and They are the minimum and maximum power allowed by the battery.

[0219] Step 1.6: Based on the tiered carbon trading model, a carbon emissions assessment method was constructed to quantify the carbon emissions of the microgrid system during electricity purchase, gas turbine power generation, and gas-to-hydrogen equipment operation. A case study was performed using typical daily residential electricity load, electricity purchase price, and predicted wind and solar power generation values ​​to verify the effectiveness of the model.

[0220] First, it is proposed that carbon emissions come from electricity purchased from the upper power grid, gas turbines, and gas-to-hydrogen equipment, and that the upper power purchase comes from coal-fired power generation. The carbon emission quota model is shown as follows:

[0221] ,

[0222] in, 、 、 、 They are microgrid system, upper power purchase, Gas turbine and Carbon emission quotas for Taiwan Gas' hydrogen production equipment; 、 Indicates the number of gas turbines and gas-to-hydrogen equipment; 、 、 They are the carbon emission quotas per unit electricity consumption of coal-fired units, per unit natural gas consumption of natural gas-fired units, and per unit hydrogen production of gas-to-hydrogen equipment; for The power purchased at the time; For the Gas turbines in Power generation during the time period; for Time period Blue hydrogen production capacity of Taiwan Gas’ hydrogen production facilities; Indicates the total operating time of each power supply.

[0223] Secondly, the actual carbon emission model is constructed as shown below:

[0224] ,

[0225] in, 、 They are the actual carbon emissions of the microgrid system and the power purchased from the upper level; For the Actual carbon emissions of gas turbines; For the Taiwan Gas Hydrogen Production Equipment Actual carbon emissions at the time; 、 and are the carbon emission coefficients of coal-fired units, gas turbines, and gas-to-hydrogen equipment, respectively; Indicates the total operating time of each power supply.

[0226] The carbon trading amount can be calculated based on the carbon emission quota and actual carbon emissions:

[0227] ,

[0228] in, is the carbon emission rights trading amount of the microgrid system.

[0229] The calculation of tiered carbon trading costs is as follows:

[0230] ,

[0231] in, is the cost of tiered carbon trading; It is the base price for carbon trading; is the length of the carbon emission interval; is the growth rate of carbon trading price; is the carbon emission rights trading amount of the microgrid system.

[0232] Step 2: Construct an uncertainty optimization model based on the information gap decision theory (IGDT), which includes the following steps:

[0233] Step 2.1: Based on the microgrid operation optimization method constructed in Step 1, considering the uncertainty of wind power and photovoltaic output, the system total cost deviation coefficient is defined, and the opportunity seeking strategy of information gap decision theory is used to analyze and model the uncertainty of wind power and photovoltaic power generation, and pursue the benefits brought by uncertainty.

[0234] In the model constructed previously, wind and solar power generation forecasts were assumed to be accurate and were substituted as actual values ​​for the solution. However, due to technical limitations, wind farms' forecasts of wind and solar power generation exhibit significant deviations. Furthermore, for confidentiality reasons, dispatchers are unable to obtain accurate and complete wind power data. The IGDT opportunity-seeking strategy model minimizes uncertainty and pursues the benefits of uncertainty, assuming the minimum cost does not exceed the expected cost. However, in order to gain opportunities to reduce costs and take greater risks, this paper adopts the more open IGDT opportunity model to analyze and model wind and solar power generation uncertainty.

[0235] In IGDT, it is assumed that the wind power generation parameters The predicted value is , photovoltaic power generation parameters The predicted value is . and In actual situations, there is uncertainty, and the uncertainty model can be expressed as follows:

[0236] ,

[0237] ,

[0238] in, and The parameters are and uncertainty of Relative to the predicted value The maximum perturbation is ;gather Relative to the predicted value The maximum perturbation is .

[0239] When considering the uncertainty of wind and solar power generation, in order to pursue the benefits brought by uncertainty, the adverse disturbance of wind and solar uncertain parameters is usually minimized. The corresponding mathematical model can be expressed by the following formula:

[0240] ,

[0241] in, is the total system cost obtained under the deterministic model; is the deviation coefficient of system cost.

[0242] Step 2.2: Based on the wind and solar output uncertainty optimization model in step 2.1, verify the benefits brought by pursuing uncertainty and complete the microgrid optimization scheduling.

[0243] Example 2

[0244] In this embodiment, a simulation test of a microgrid operating in grid-connected operation based on the method in Example 1 is disclosed, by taking into account the specific simulation calculation process and results of the impact of wind turbines, photovoltaic generators, electrolyzers, gas-to-hydrogen equipment, hydrogen-oxygen fuel cells, batteries, gas turbines, etc. on the economic dispatch of the microgrid.

[0245] Figure 2 A typical daily residential electricity load diagram in this embodiment is disclosed, and the time-of-use electricity purchase unit prices at 24 moments in the diagram are shown in Table 1. Figure 3 The predicted values ​​of wind power generation and photovoltaic power generation in this embodiment are disclosed.

[0246] Table 1: Time-of-use electricity purchase price

[0247]

[0248] The following simulation analysis of the operation of the microgrid system will be carried out from the five cases shown in Table 2, where "×" indicates not connected to the system; "√" indicates connected to the system.

[0249] Table 2, Scheme 1 to 5 settings

[0250]

[0251] The minimum cost and wind and solar curtailment of Schemes 1 to 5 are simulated by the method disclosed in Example 1 to obtain the cost and wind and solar curtailment load loss table of Schemes 1 to 5 as shown in Table 3.

[0252] Table 3. Costs and load loss of Schemes 1 to 5

[0253]

[0254] It should be noted that the generator set in Scheme 1 only includes wind turbines, photovoltaic generators, and gas turbines, with a penetration rate of 50%. Figure 4 The wind and solar curtailment diagram of Solution 1 of this embodiment is shown.

[0255] Scheme 2 builds on Scheme 1 by adding hydrogen production from an electrolyzer and gas-to-hydrogen equipment, power generation from a hydrogen-oxygen fuel cell, hydrogen storage from a hydrogen storage system, and hydrogen sales. Simulations show that Scheme 2's minimum total cost is 298.7951 yuan less than Scheme 1's. Figure 5 The following diagram shows the power consumption of wind and solar power curtailment and electrolyzer and gas-to-hydrogen equipment in Scheme 2 of this embodiment. Figure 4 and Figure 5 The addition of the hydrogen energy system reduced both wind and solar curtailment in the microgrid. Compared to Option 1, the addition of the hydrogen-to-electricity conversion system allows for more efficient regulation of system power generation. When wind and solar output exceeds the load, excess electricity is converted to hydrogen via an electrolyzer and stored in the hydrogen storage system, reducing penalty costs for curtailment. When wind and solar output falls below the load, the excess hydrogen is discharged via a hydrogen-oxygen fuel cell, and the excess hydrogen can be sold to reduce overall costs. Compared to producing green hydrogen through water electrolysis using an electrolyzer, producing blue hydrogen using a gas-based hydrogen generator (GHG) produces carbon emissions using natural gas, resulting in both natural gas and carbon emission costs. The GHGG consumes only electricity, but consumes far more electricity to produce the same amount of hydrogen than a GHGG system. Therefore, when wind and solar power are sufficient, the electrolyzer consumes electricity to produce hydrogen. During the relatively high electricity price period of 10:00 AM to 8:00 PM, the GHGG replaces the electrolyzer to produce hydrogen. Its power consumption curve exhibits a characteristic of "starting when wind and solar power are insufficient and shutting down when wind and solar power are sufficient," complementing electrolyzer-based hydrogen production.

[0256] Scheme 3 adds three batteries with a maximum capacity of 70 kWh to Scheme 2. Simulations show that wind and solar curtailment are reduced to zero, and the minimum total cost is reduced by 238.1133 yuan compared to Scheme 2. Compared to Scheme 2, the batteries in Scheme 3 can store the wind and solar power output that is not fully converted by the electrolyzer. The batteries also have higher energy storage and discharge efficiencies and larger capacity than hydrogen-to-electricity conversion systems. They discharge when the generator set's power generation is less than the system's required load. Figure 6 The diagram shows the charge and discharge status of the battery of this embodiment.

[0257] Plan 4 adds carbon trading to Plan 3. Through simulation, it is found that its minimum total cost increases by 94.0174 yuan compared with Plan 3. Figure 7A comparison chart of gas turbine power generation, electricity purchase, electrolyzer power consumption, and gas-to-hydrogen equipment power consumption for Schemes 3 and 4 of this embodiment is shown. As can be seen from the figure, in Scheme 4, gas turbine power generation and hydrogen production equipment consume relatively little power, while electricity purchase is relatively high. After carbon trading is introduced, gas turbine power generation, electricity purchase, and hydrogen production from gas-to-hydrogen equipment will all incur carbon trading costs. Therefore, when optimizing the system's minimum cost solution, gas turbine power generation, electricity purchase, and hydrogen production from gas-to-hydrogen equipment will be minimized. However, the relative increase in electricity purchase in Scheme 4 is due to the relatively higher carbon trading costs incurred by gas turbine power generation. Under constant load conditions, gas turbine power generation decreases, electrolyzer and gas-to-hydrogen equipment power consumption decreases, and electricity purchase increases.

[0258] In Scheme 4, in order to further explore the impact of carbon trading prices and carbon trading cost models on the economic dispatch of microgrids, the carbon trading base price is raised from 0.2 yuan to 0.5 yuan, and the number of tiered carbon trading cost segments is set to 3, 4, and 5, respectively, and simulation analyses are performed separately.

[0259] Table 4 shows the costs, gas turbine power generation, and electricity purchases when the carbon trading base price is 0.2 yuan and 0.5 yuan. A comparative analysis shows that when the carbon trading base price is low, the carbon trading cost is relatively low when the sum of gas turbine power generation and electricity purchases is constant. However, when the sum of gas turbine power generation and electricity purchases under economic operation is relatively large, the carbon trading cost is relatively small compared to the total cost.

[0260] Table 4. Cost, gas turbines, and electricity purchases at different base prices

[0261]

[0262] Through simulation, it is found that when the number of segments of the tiered carbon trading cost is 3, 4, and 5, the costs and the power generation of the generator are roughly the same. Therefore, the different number of segments of the tiered carbon trading cost has little effect on the economic dispatch of the microgrid.

[0263] Scheme 5 adds a deviation coefficient of system cost to Scheme 4 The IGDT opportunity-seeking strategy model was simulated and found to have a minimum total cost of 72.4909 yuan less than that of Scheme 4. Table 5 compares the minimum cost, carbon trading, wind and solar power generation, and gas turbine power generation of Schemes 4 and 5. Scheme 5's minimum total cost is approximately 72.49 yuan less than that of Scheme 4, and its carbon trading cost is reduced by approximately 5.42 yuan, indicating that the introduction of the IGDT model effectively reduces system costs. Furthermore, Scheme 5 reduces gas turbine power generation and increases wind and solar power generation. This is because the IGDT opportunity-seeking strategy model can increase wind and solar power generation within the allowable deviation coefficient, reducing reliance on high-carbon emission sources and thus reducing gas turbine power generation and costs.

[0264] Table 5. Comparison between Scheme 4 and Scheme 5

[0265]

[0266] In Scheme 5, in order to further explore the impact of the IGDT model on the economic dispatch of the microgrid, a series of different system cost deviation coefficients of 0, 0.1, 0.2, 0.3, 0.4, and 0.5 were set for simulation analysis. Figure 8 The cost deviation diagram of wind photovoltaic power generation and gas turbine power generation is shown. It can be seen from the figure that as With the increase of cost, the system's tolerance for cost deviation increases, wind power generation gradually decreases, while photovoltaic power generation increases, the total wind and solar power generation shows an upward trend, and gas turbine power generation shows a fluctuating but overall downward trend. The larger the value, the greater the fluctuation of wind and solar power generation is allowed, thus adjusting the output of each power generation unit more flexibly, giving priority to the use of more economical photovoltaic power generation, and reducing the dependence on gas turbines. As the uncertainty increases, the system can accept greater uncertainty, thereby finding a lower-cost solution in the optimization process, resulting in a further reduction in total cost. However, as the uncertainty of the system increases, the risk it bears also increases.

[0267] The simulation results in Example 2 demonstrate that the model framework for low-carbon optimal scheduling of a multi-source hydrogen microgrid, as proposed in Example 1, first considers electricity purchase costs, gas turbine power generation costs, gas-to-hydrogen equipment costs, wind and solar curtailment and load loss penalty costs, and carbon trading costs, thereby proposing a multi-objective optimization operation strategy. Secondly, it considers energy balance constraints, gas turbine operation constraints, safety operation constraints, hydrogen production and use equipment operation constraints, and energy storage system operation constraints to ensure the stable operation of the microgrid under various operating conditions. Thirdly, it combines a step-by-step carbon trading model to dynamically quantify carbon emissions, incentivize low-carbon operation strategies, and significantly reduce the microgrid's carbon emissions. Finally, it employs information gap decision theory (IGDT) to model the uncertainty of wind and solar power generation, minimizing uncertainty through an opportunity-seeking strategy to identify cost reduction opportunities. Finally, by comparing five different schemes, it analyzes the impact of hydrogen energy systems, batteries, carbon trading, and the IGDT model on the microgrid's operation and economic scheduling. The results validate the effectiveness of the proposed framework and method. The proposed method helps researchers optimize the economic dispatch and low-carbon operation of microgrids while effectively dealing with the uncertainty of wind and solar power output, improving the flexibility and stability of the system and achieving better operating results.

[0268] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A multi-energy complementary and coordinated multi-source hydrogen energy microgrid low-carbon optimization scheduling method, characterized in that: The optimization scheduling method includes: S100, constructing comprehensive constraints according to the characteristics of the microgrid, and constructing a multi-source hydrogen energy microgrid operation total cost model based on the comprehensive constraints shown. Minimizing the total operating cost through the multi-source hydrogen energy microgrid total operating cost model; S200, based on the total cost model of multi-source hydrogen energy microgrid operation and based on the uncertainty of wind power and photovoltaic power, define the total cost deviation coefficient of the system, The opportunity seeking strategy of information gap decision theory is used to analyze and model the uncertainty of wind power and photovoltaic power generation, and the benefits brought by uncertainty are calculated through the constructed model.

2. The multi-energy complementary and coordinated multi-source hydrogen energy microgrid low-carbon optimization scheduling method according to claim 1 is characterized in that: The constraints include: electricity purchase cost, gas turbine power generation cost constraint, energy balance constraint, gas-to-hydrogen equipment cost constraint, hydrogen energy storage system operation constraint, and carbon trading cost; The gas turbine power generation cost constraint is constructed by considering fuel consumption and power generation constraints, ramp constraints, gas turbine start and stop constraints, and natural gas consumption constraints, and integrating these constraints to obtain the gas turbine power generation cost constraint. The safe operation constraints are constructed by taking into account the upper system power exchange, wind and solar power curtailment and load loss constraints; The energy balance constraint is constructed based on the bidirectional flow of electricity and hydrogen energy according to the balance of electricity supply and demand and the balance of hydrogen energy supply and demand; The cost constraints of the gas-to-hydrogen equipment include an efficiency model for green hydrogen production by an electrolyzer based on voltage efficiency and Faraday efficiency to calculate real-time power consumption, and a blue hydrogen production model for gas-to-hydrogen equipment. The polarization characteristic model of the proton exchange membrane fuel cell is constructed by using the piecewise linearization method to deal with the nonlinear relationship between the stack output power and current. Integrate the efficiency model of green hydrogen production, the blue hydrogen production model of gas hydrogen production equipment, and the polarization characteristic model of proton exchange membrane fuel cells into the cost constraint of gas hydrogen production equipment; The operational constraints of the hydrogen energy storage system include: the hydrogen storage capacity limit of the hydrogen storage device, the upper and lower limits of the storage and release rates, and the consistency constraints of the hydrogen storage cycle; as well as the battery charge and discharge power ramp rate limit, state of charge range constraint, and charge and discharge efficiency model; Carbon trading costs include: building a carbon emission evaluation method based on a tiered carbon trading model to quantify the carbon emissions of the microgrid system during electricity purchase, gas turbine power generation, and gas-to-hydrogen equipment operation.

3. The multi-energy complementary and coordinated multi-source hydrogen energy microgrid low-carbon optimization scheduling method according to claim 1 is characterized in that: The total cost model of multi-source hydrogen energy microgrid operation is expressed by the following formula: ， in, is the minimum total operating cost, The cost of purchasing electricity, is the total operating period of each power supply device, For the Gas turbines in The power generation cost at the time, is the number of gas turbines, For the Taiwan Gas Hydrogen Production Equipment The cost of time, is the number of gas-to-hydrogen equipment, for The penalty cost of the moment, The cost of hydrogen sales revenue is is the tiered carbon trading cost.

4. The multi-energy complementary and coordinated multi-source hydrogen energy microgrid low-carbon optimization scheduling method according to claim 2 is characterized in that: The energy balance constraint is expressed by the following formula: ， ， in, for The power purchased at the time; For the Wind turbines in The power input to the grid at all times; For the Photovoltaic generators in The power input to the grid at all times; For the Gas turbines in Power generation at the moment; for Moment Fuel cell output power; For the Batteries in Discharge power at the moment; For microgrids The predicted value of electric load at the moment; for The load loss power at the moment; for Enter the time Power of electrolyzer; For the Taiwan Gas Hydrogen Production Equipment Power consumption at all times; For the cth battery Charging power at the moment; 、 、 、 、 、 、 Respectively represent wind The number of power generators, photovoltaic generators, gas turbines, fuel cells, batteries, electrolyzers, and gas-to-hydrogen equipment.

5. The multi-energy complementary and coordinated multi-source hydrogen energy microgrid low-carbon optimization scheduling method according to claim 2 is characterized in that: The carbon trading cost is calculated by the following formula: ， in, is the tiered carbon trading cost; It is the base price for carbon trading; is the length of the carbon emission interval; is the growth rate of carbon trading price; is the carbon emission rights trading amount of the microgrid system.

6. The multi-energy complementary and coordinated multi-source hydrogen energy microgrid low-carbon optimization scheduling method according to claim 1 is characterized in that: The uncertainty of wind power and photovoltaic power is expressed by the following formula: ， ， in, is the wind power generation parameter, is the predicted value of wind power generation parameters, is the photovoltaic power generation parameter, is the predicted value of photovoltaic power generation parameters, is the uncertainty of wind power generation parameters, is the uncertainty of photovoltaic power generation parameters, the set is the output interval of wind power generation under the uncertainty model, and the set is the output range of photovoltaic power generation under the uncertainty model.

7. The multi-energy complementary and coordinated multi-source hydrogen energy microgrid low-carbon optimization scheduling method according to claim 1 is characterized in that: The uncertainties of wind power and photovoltaic power also include the cost of wind and photovoltaic curtailment and load loss penalty, which can be expressed as follows: ， in, is the penalty coefficient for load loss; express Load loss during the period; is the wind curtailment penalty coefficient; is the light abandonment penalty coefficient; Indicates the Wind turbines in The wind power curtailment during the period; Indicates the photovoltaic generators The abandoned optical power in a time period.

8. A multi-energy complementary and coordinated multi-source hydrogen energy microgrid low-carbon optimization scheduling system, characterized by: The optimization scheduling system includes: processor; The memory stores a computer program, which, when executed by a processor, implements the multi-energy complementary and coordinated multi-source hydrogen energy microgrid low-carbon optimization scheduling method according to any one of claims 1 to 7.

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