Low-carbon-emission light-storage-hydrogen support micro-grid capacity configuration optimization method and low-carbon-emission light-storage-hydrogen support micro-grid capacity configuration optimization system

Abstract

The invention belongs to the technical field of microgrid capacity configuration, and discloses a capacity configuration optimization method and system for a low-carbon-emission light-storage-hydrogen support microgrid, and the method comprises the steps: calculating the cost of a photovoltaic panel, the cost of a hydrogen energy storage unit, and the cost of a battery energy storage unit; based on the cost, under the transaction of the carbon right, constructing a target function with the minimum unit cost meeting the hydrogen transaction demand; constructing a carbon transaction constraint; and under operation constraints including a carbon transaction constraint, solving the objective function to obtain a carbon weight which needs to be purchased by the enterprise at each moment and optimal capacity configuration of each device in the light-storage-hydrogen support microgrid. According to the method, the optimal capacity configuration of the micro-grid structure jointly supported by light, storage and hydrogen is used as a technical route, on the premise of ensuring the hydrogen demand supply, the carbon emission reduction incentive policy is considered during cost calculation, the corresponding carbon transaction constraint is added, low-cost and high-efficiency green hydrogen preparation is realized, the energy storage effect is good, and the adjustability is high.

Description

Technical Field

[0001] The present invention belongs to the technical field of microgrid capacity configuration, and more specifically, relates to a capacity configuration optimization method and system for a low-carbon-emission photovoltaic-storage-hydrogen-supported microgrid. Background Art

[0003] In the face of carbon emission quota policies and the rise of the carbon economy, zero-carbon-emission hydrogen production (green hydrogen) is an important transformation strategy for grey hydrogen enterprises to solve production restriction problems and actively participate in carbon trading. In order to reduce the carbon emission quotas consumed by enterprises in manufacturing grey hydrogen, it is necessary to manufacture green hydrogen to replace grey hydrogen. In the prior art, a hydrogen energy-supported microgrid that outputs green electricity and has a zero-carbon advantage is generally used for the preparation of green hydrogen. However, in the existing capacity optimization configuration schemes of hydrogen energy-supported microgrids, carbon emission reduction policies and carbon trading requirements are not considered, resulting in the inability to achieve the optimal capacity optimization configuration with low carbon emissions, leading to a relatively high cost of green hydrogen production; and the existing hydrogen energy-supported microgrid has a relatively simple structure, resulting in a relatively low efficiency of green hydrogen production. Summary of the Invention

[0004] In view of the above deficiencies or improvement requirements of the prior art, the present invention provides a capacity configuration optimization method and system for a low-carbon-emission photovoltaic-storage-hydrogen-supported microgrid, aiming to achieve the optimal configuration of the capacity of the photovoltaic-storage-hydrogen-supported microgrid under carbon trading requirements, reduce the cost of green hydrogen production, and improve the efficiency of green hydrogen production.

[0005] To achieve the above object, the present invention provides a capacity configuration optimization method for a low-carbon-emission photovoltaic-storage-hydrogen-supported microgrid. The photovoltaic-storage-hydrogen-supported microgrid includes a photovoltaic panel, a battery energy storage unit, and a hydrogen energy storage unit. The hydrogen energy storage unit is used to prepare green hydrogen under the power supply of the photovoltaic panel and the battery energy storage unit and supply it to a solid oxide fuel cell. The photovoltaic panel is also used to supply power to the battery energy storage unit. The capacity configuration optimization method includes:

[0006] Calculating the cost C PV of the photovoltaic panel, the cost of the hydrogen energy storage unit, and the cost C BESS of the battery energy storage unit; under the trading of carbon rights, constructing an objective function with the minimum unit cost to meet the hydrogen trading demand: Wherein, C C is the cost for the enterprise to purchase carbon rights, C C = Price C ∑ t ct(t)L, ct(t) is the amount of carbon rights that the enterprise needs to purchase at time t, ct(t) ≤ 0, Price C is the carbon price, L is the whole life cycle of the photovoltaic-storage-hydrogen-supported microgrid; D(t) is the hydrogen demand of the enterprise at time t;

[0007] Construct carbon trading constraints: Among them, is the rated carbon emission of the enterprise, P(t) is the hydrogen production amount of the hydrogen production equipment in the hydrogen energy storage unit at time t; Q(t) is the hydrogen consumption amount of the solid oxide fuel cell in the hydrogen energy storage unit at time t; λ EC and λ FC are the incentive coefficients of the hydrogen production equipment and the solid oxide fuel cell respectively, λ EC > 0, λ FC > 0;

[0008] Solve the objective function under the operation constraints including carbon trading constraints to obtain the carbon credit amount ct(t) that the enterprise needs to purchase at time t and the optimal capacity configuration of each device in the photovoltaic-storage-hydrogen supported microgrid.

[0009] Further, the upper limit λ EC of the incentive coefficient λ of the hydrogen production equipment EC_max is determined as follows:

[0010] Under the upper limit incentive coefficient λ EC_max of the hydrogen production equipment, determine the carbon credit amount rewarded for producing a unit of green hydrogen by the hydrogen production equipment; taking the carbon credit amount rewarded for the unit of green hydrogen being equal to the carbon emission reduction amount of the unit of green hydrogen as the optimization goal, obtain the upper limit incentive coefficient λ EC_max ;

[0011] Under the set incentive coefficient λ EC of the hydrogen production equipment, the determination method of the incentive coefficient λ FC of the solid oxide fuel cell is as follows:

[0012] Initialize λ FC as a quantity λ FC _min lower than the preset threshold, start iteration from λ FC _min. In the current round, based on the current value of λ FC and the set incentive coefficient λ EC of the hydrogen production equipment, solve the objective function, and judge whether the number of current solid oxide fuel cells in the obtained optimal capacity configuration is 0. If so, increase the value of the current λ FC , perform the next round of iteration until the number of solid oxide fuel cells is not 0, stop the iteration, and obtain the incentive coefficient λ EC of the solid oxide fuel cell under the set incentive coefficient λ FC of the hydrogen production equipment.

[0013] Further, it also includes preparing grey hydrogen by catalytic partial oxidation, and the amount of grey hydrogen prepared by the catalytic partial oxidation is less than the amount of green hydrogen prepared by the photovoltaic-storage-hydrogen supported microgrid;

[0014] In the scenario of preparing a mixture of gray hydrogen and green hydrogen, the corresponding objective function is:

[0015]

[0016] where C CPOX represents the cost of catalytic partial oxidation to produce gray hydrogen;

[0017] The corresponding carbon trading constraint is:

[0018]

[0019] where represents the carbon emission rate of catalytic partial oxidation at time t, and N CPOX represents the number of catalytic partial oxidation devices.

[0020] Furthermore, the cost C PV of the photovoltaic panel is:

[0021]

[0022] where γ PV，cap and γ PV，OM are the capital cost coefficient and the operation and maintenance cost coefficient of the photovoltaic panel respectively; N PV is the number of photovoltaic panels, and P PV (t) is the power generation power of the photovoltaic panel at time t;

[0023] The cost C BESS of the battery energy storage unit is:

[0024]

[0025] where γ BESS，OM and γ BESS，cap are the operation and maintenance cost coefficient and the capital cost coefficient of the battery energy storage unit respectively, N BESS is the number of battery energy storage units, P BESS (t) is the power generation power of the battery energy storage unit at time t, and |·| represents the absolute value operation; L 1 is the life cycle of the battery energy storage unit;

[0026] The cost of the hydrogen energy storage unit is:

[0027]

[0028] C EC，cap =γ EC，cap N EC

[0029]

[0030] C FC，cap = γ FC，cap N FC

[0031]

[0032] where γ EC，cap and γ EC，OM are the capital cost coefficient and operation and maintenance cost coefficient of the hydrogen production equipment respectively; N EC is the number of hydrogen production equipment; γ FC，cap and γ FC，OM are the capital cost coefficient and operation and maintenance cost coefficient of the solid oxide fuel cell respectively; N FC is the number of solid oxide fuel cells, P FC (t) is the power of the solid oxide fuel cell consuming hydrogen at time t; γ HT，cap and γ HT，OM are the capital cost coefficient and operation and maintenance cost coefficient of the hydrogen storage tank in the hydrogen energy storage unit respectively; is the rated hydrogen storage capacity of the hydrogen storage tank; L 2 and L 3 and L 4 are the life cycles of the hydrogen production equipment, solid oxide fuel cell, and hydrogen storage tank respectively.

[0033] Furthermore, the operation constraint further includes the state transition equation constraint of the hydrogen energy storage unit:

[0034]

[0035] where LOH(t) represents the hydrogen storage capacity of the hydrogen storage tank at time t, represents the hydrogen production rate of the hydrogen production equipment at time t, represents the hydrogen consumption rate of a single solid oxide fuel cell.

[0036] Furthermore, the cost C CPOX of catalytic partial oxidation to produce gray hydrogen is:

[0037]

[0038] where γ CPOX，cap is the capital cost coefficient of catalytic partial oxidation, is the daily hydrogen production amount limit of catalytic partial oxidation; is the reaction rate of methane in catalytic partial oxidation, is the reaction rate of oxygen in catalytic partial oxidation; is the operation and maintenance cost coefficient of methane, is the operation and maintenance cost coefficient of oxygen; L 5For the life cycle of the catalytic partial oxidation equipment.

[0039] Furthermore, the operation constraint also includes the state transition equation constraint of the hydrogen energy storage unit:

[0040]

[0041] where LOH(t) represents the hydrogen storage capacity of the hydrogen storage tank at time t, represents the hydrogen production rate of the hydrogen production equipment at time t, represents the hydrogen consumption rate of a single solid oxide fuel cell; represents the hydrogen production rate of the catalytic partial oxidation at time t.

[0042] The present invention also provides a capacity configuration optimization system for a low-carbon emission optical-storage-hydrogen supported microgrid, including a computer-readable storage medium and a processor;

[0043] The computer-readable storage medium is used to store executable instructions;

[0044] The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the capacity configuration optimization method described in any one of the above.

[0045] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the capacity configuration optimization method described in any one of the above.

[0046] The present invention also provides a computer program product, including a computer program, and when the computer program runs on a computer, it causes the computer to execute the capacity configuration optimization method described in any one of the above.

[0047] Generally speaking, through the above technical solutions conceived by the present invention, the following beneficial effects can be achieved:

[0048] (1) The microgrid structure supported by the integration of solar, energy storage, and hydrogen in this invention. The introduction of photovoltaic panels can supply electrical energy to both the battery energy storage unit and the hydrogen energy storage unit simultaneously, without the need for grid power supply, avoiding power loss, and leveraging the complementary advantages of battery energy storage and hydrogen energy storage to improve the efficiency of green hydrogen production in the microgrid. The capacity configuration optimization method of this invention takes into account the carbon emission reduction incentive policy on the premise of ensuring the supply of hydrogen trading demand. That is, through the carbon trading market, enterprises can trade carbon emission allowances and sell the remaining allowances on the market. By using the microgrid structure supported by the integration of solar, energy storage, and hydrogen in this invention to produce green hydrogen, the production volume of grey hydrogen can be reduced, carbon emissions can be lowered, enabling enterprises to have surplus carbon rights. When there are surplus carbon rights, carbon trading is carried out, and the carbon trading cost is negative (ct(t) ≤ 0). In the objective function constructed to minimize the unit cost to meet hydrogen trading demand, the cost C C for enterprises to purchase carbon rights is negative, which can reduce the total cost. Under the constructed carbon trading constraints, solving this objective function can obtain the optimal capacities of various devices in the solar, energy storage, and hydrogen-supported microgrid and the amount of carbon rights that enterprises need to purchase (i.e., the optimal amount of carbon rights sold by enterprises) under this optimal capacity configuration. This invention simultaneously considers hydrogen trading demand and carbon trading demand, produces green hydrogen through the microgrid structure supported by the integration of solar, energy storage, and hydrogen, enabling enterprises to have surplus carbon rights, and trading the surplus carbon rights, making the cost of purchasing carbon rights negative to reduce the total cost.

[0049] (2) Further, considering that hydrogen production equipment is the equipment directly responsible for reducing carbon emissions, increasing the number of hydrogen production equipment helps reduce carbon emissions and can, to a certain extent, lower the cost of green hydrogen production. However, since the increase in the number of hydrogen production equipment will correspondingly increase the equipment cost, therefore, the determination of the incentive coefficient λ EC of hydrogen production equipment is crucial. This coefficient balances the increase in equipment cost brought about by the increase in the number of hydrogen production equipment and the reduction in the cost of green hydrogen production. And as the consumption end of hydrogen, the increase in the number of solid oxide fuel cells will, to a certain extent, promote the use of hydrogen production equipment, improve hydrogen utilization rate and thus reduce costs. Still, it is necessary to consider balancing the increase in the cost of solid oxide fuel cell equipment, that is, the determination of the incentive coefficient λ FC of solid oxide fuel cells. Based on the above considerations, this invention provides the corresponding determination methods for the incentive coefficients λ EC and λ FC . Under the determined incentive coefficients λ EC and λ FC , it can ensure the minimum cost under the premise of meeting hydrogen trading demand and carbon emission reduction policies.

[0050] (3) Further, in order to meet the relatively high hydrogen trading demand under certain conditions, the present invention replaces the commonly used SR (steam reforming) for producing gray hydrogen with catalytic partial oxidation, designs a hybrid hydrogen production system (simultaneously producing green hydrogen and gray hydrogen). Catalytic partial oxidation has the advantages of small equipment footprint, low operating cost, and low energy consumption. Compared with the commonly used steam reforming, it has lower carbon emissions, higher reaction rate, and flexible fuel adaptability.

[0051] All in all, the present invention takes the optimal capacity configuration of the microgrid structure supported by light-storage-hydrogen as the technical route. On the premise of ensuring the supply of hydrogen demand, the carbon emission reduction incentive policy is considered in the cost calculation, and the corresponding carbon trading constraint is added, realizing the preparation of green hydrogen with low cost and high efficiency, good energy storage effect, and high adjustability. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is the microgrid structure supported by light-storage-hydrogen in the embodiment of the present invention.

[0053] Figure 2 It is a schematic diagram of the capacity configuration optimization method of the light-storage-hydrogen supported microgrid in the embodiment of the present invention.

[0054] Figure 3 It is the relationship diagram between the carbon amount (green hydrogen amount), the incentive coefficient λ of the hydrogen production equipment EC and the unit cost in the embodiment of the present invention.

[0055] Figure 4 It is the hybrid hydrogen production system under the carbon emission reduction policy in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0057] Embodiment 1

[0058] As Figure 1 shown, the light-storage-hydrogen supported microgrid in the embodiment of the present invention includes: a photovoltaic panel PV, a battery energy storage unit BESS, and a hydrogen energy storage unit HESS; the photovoltaic panel PV is used to provide electrical energy for the battery energy storage unit BESS and the hydrogen energy storage unit HESS to meet the hydrogen production demand by electrolyzing water; the battery energy storage unit BESS is used to provide electrical energy for the hydrogen energy storage unit HESS; the hydrogen energy storage unit HESS is used to prepare and store green hydrogen, and provide green hydrogen for the solid oxide fuel cell SOFC.

[0059] As Figure 2 shown, the method for optimizing the capacity configuration of a low-carbon emission photovoltaic-storage-hydrogen supported microgrid provided in the embodiment of the present invention includes:

[0060] Calculating the cost C PV of the photovoltaic panel PV, the cost of the hydrogen energy storage unit and the cost C BESS of the battery energy storage unit;

[0061] Under the carbon rights trading, constructing an objective function min LCOH with the minimum unit cost to meet the hydrogen trading demand:

[0062]

[0063] where C C is the cost for the enterprise to purchase carbon rights, and C C = Price C ∑t ct(t)L, Price C is the carbon price, which is taken according to experience; ct(t) is the amount of carbon rights that the enterprise needs to purchase at time t, which is a decision variable. When the enterprise has surplus carbon rights, carbon trading is carried out, ct(t) ≤ 0, L is the whole life cycle of the photovoltaic-storage-hydrogen supported microgrid; D(t) is the hydrogen demand of the enterprise at time t;

[0064] Constructing carbon trading constraints:

[0065]

[0066] where is the rated carbon emission of the enterprise, ∑ t N EC P EC (t) is the hydrogen production amount of the hydrogen production equipment in the hydrogen energy storage unit, N EC is the number of hydrogen production equipment, which is a decision variable, P EC (t) is the power of the hydrogen production equipment at time t, which is a decision variable; ∑ t N FC P FC (t) is the hydrogen consumption amount of the solid oxide fuel cell in the hydrogen energy storage unit, N FC is the number of solid oxide fuel cells, which is a decision variable, P FC (t) is the power of the solid oxide fuel cell consuming hydrogen at time t, which is a decision variable; λ EC and λ FC are the incentive coefficients of the hydrogen production equipment and the solid oxide fuel cell respectively, λ EC > 0, λ FC > 0, with the unit of kg / kW;

[0067] Under the operating constraints including carbon trading constraints, an optimization algorithm is used to solve the above objective function, and the optimal capacity configuration of each device in the photovoltaic-storage-hydrogen supported microgrid and the amount of carbon rights ct(t) that the enterprise needs to purchase at time t are obtained. Among them, the optimal capacity configuration of each device includes the power generation power P BESS (t) of the battery energy storage unit at time t, the hydrogen production power P EC (t) of the hydrogen production device at time t, the power P FC (t) of the solid oxide fuel cell consuming hydrogen at time t, and the quantity of each device.

[0068] In the embodiment of the present invention, considering that the hydrogen production device is a device that directly causes carbon emissions reduction, increasing the quantity of hydrogen production devices helps to reduce carbon emissions and can reduce the cost of green hydrogen production to a certain extent. However, since the increase in the quantity of hydrogen production devices will correspondingly increase the equipment cost, therefore, the determination of the incentive coefficient λ EC is crucial, and this coefficient balances the increase in equipment cost brought about by the increase in the quantity of hydrogen production devices and the reduction in the cost of green hydrogen production; while the solid oxide fuel cell is the consumption end of hydrogen, the increase in the quantity of solid oxide fuel cells will promote the use of hydrogen production devices to a certain extent, improve the hydrogen utilization rate and thus reduce the cost, but still need to consider balancing the increase in the cost of solid oxide fuel cell devices, that is, the determination of the incentive coefficient λ FC .

[0069] Based on the above considerations, in the embodiment of the present invention, a determination method of the incentive coefficients λ EC and λ FC is provided. Specifically, the determination method of the upper limit λ EC of the incentive coefficient λ of the hydrogen production device is as follows: EC_max Under the upper limit incentive coefficient λ

[0070] of the hydrogen production device, determine the amount of carbon rights rewarded for producing a unit of green hydrogen by the hydrogen production device; taking the amount of carbon rights rewarded for a unit of green hydrogen being equal to the carbon emission reduction amount of a unit of green hydrogen as the optimization objective, the upper limit incentive coefficient λ EC_max of the hydrogen production device is obtained; in the embodiment of the present invention, the calculated upper limit incentive coefficient λ EC_max of the hydrogen production device is λ EC_max = 2.28 kg / kW. As Figure 3 shown, when λ EC_max = 2.28 kg / kW, the amount of carbon rights rewarded for a unit of green hydrogen is equal to the carbon emission reduction amount of a unit of green hydrogen. At this time, if the incentive coefficient λ EC is continued to be increased to reduce the cost, for example, the cost is reduced to 29 yuan / kg, the incentive coefficient λ EC is 2.4 kg / kW, that is, the amount of carbon rights rewarded for a unit of green hydrogen has exceeded the theoretical carbon emission reduction amount of a unit of green hydrogen, which is not in line with the theory.

[0071] When setting the excitation coefficient λ of the hydrogen production equipment EC (in the embodiment of the present invention, λ EC = 2 kg / kW is taken), the determination method of the excitation coefficient λ of the solid oxide fuel cell is as follows: FC

[0072] Initialize λ FC to a quantity λ FC _min lower than the preset threshold. In the embodiment of the present invention, Start iteration from λ FC _min. In the current round, based on the current value of λ FC and the set excitation coefficient λ of the hydrogen production equipment EC , solve the objective function, and determine whether the number of current solid oxide fuel cells in the obtained optimal capacity configuration is 0. If so, increase the current value of λ FC , and perform the next round of iteration until the number of solid oxide fuel cells is not 0, then stop the iteration to obtain the excitation coefficient λ of the solid oxide fuel cell under the set excitation coefficient λ of the hydrogen production equipment EC . As shown in Table 1 below, it is the optimal configuration when the excitation coefficient λ of the hydrogen production equipment EC FC = 2 kg / kW. EC

[0073] Table 1 Optimal configuration and performance when the excitation coefficient λ EC = 2 kg / kW

[0074]

[0075] In the embodiment of the present invention, the photovoltaic panel PV is a power generation device that converts solar energy into electrical energy. The cost C of the photovoltaic panel PV PV includes capital cost and operation and maintenance cost.

[0076] The capital cost C PV，cap (unit: yuan) is calculated by the following formula:

[0077] C PV，cap = γ PV，cap N PV

[0078] where γ PV，cap is the capital cost coefficient of the photovoltaic panel PV, which is taken according to experience; N PV is the number of photovoltaic panels PV.

[0079] The operation and maintenance cost C PV，OM (unit: yuan) is related to the operating state of the photovoltaic panel PV and can be expressed by the following formula:

[0080] C​​PV，OM = γ PV，OM ∑ t N PV P PV (t)

[0081] where γ PV，OM is the operation and maintenance cost coefficient of the PV panel, which is determined based on experience, and P PV (t) is the power generation of the PV panel at time t and is a decision variable.

[0082] In summary, the total cost C of the PV panel PV is (unit: yuan):

[0083] C PV = C PV，cap + C PV，OM

[0084] In the embodiments of the present invention, the characteristic curve of the PV panel is related to the irradiance and the ambient temperature. Therefore, the power generation P of the PV panel PV (t) is a function of the solar irradiance and the ambient temperature and can be expressed as:

[0085]

[0086] where is the rated power of the PV panel, IR is the solar irradiance, and N T is the temperature coefficient of the PV panel. T C is the temperature of the PV panel and can be obtained by the following formula:

[0087]

[0088] where T air is the ambient temperature and NOCT is the standard operating temperature of the PV panel.

[0089] As a preferred implementation, the battery energy storage unit is modeled. The battery energy storage unit, as a controllable energy storage system, can either discharge or charge, but cannot discharge and charge simultaneously, and one of the two states must be zero. The following formula represents the common relationship between the charge and discharge power of the battery energy storage unit:

[0090] P BESS，ddis (t) × P BESS，ch (t) = 0

[0091] min{P BESS，dis (t), P BESS，ch (t)} = 0

[0092] where P BESS，dis (t) is the discharge power of the battery energy storage unit at time t, and PBESS，ch (t) is the charging power of the battery energy storage unit at time t.

[0093] The state of charge (SOC) of the battery energy storage unit represents the electrical storage, as shown in the following formula:

[0094]

[0095] Among them, SOC(t) represents the state of charge of the battery energy storage unit at time t, η BESS，dis is the BESS discharge efficiency, η BESS，ch is the BESS charging efficiency, C r is the rated capacity of a single energy storage battery. In the embodiments of the present invention, the energy storage battery is a lithium battery, and Δt represents the time interval between two adjacent scheduling cycles.

[0096] In the embodiments of the present invention, the cost C of the battery energy storage unit BESS includes the operation and maintenance cost and the capital cost.

[0097] Among them, the daily operation and maintenance cost C BESS，OM is determined by the power flow, as shown in the following formula:

[0098]

[0099] Among them, γ BESS，OM is the operation and maintenance cost coefficient of the battery energy storage unit, which is taken according to experience, N BESS is the number of battery energy storage units, P BESS (t) is the power generation power of the battery energy storage unit at time t, and |·| represents the absolute value operation. L 1 is the life cycle of the battery energy storage unit.

[0100] The capital cost C BESS，cap is as shown in the following formula:

[0101] C BESS，cap =γ BESS，cap N BESS

[0102] Among them, γ BESS，cap is the capital cost coefficient of the battery energy storage unit, which is taken according to experience.

[0103] Therefore, the cost C of the battery energy storage unit BESS is:

[0104] C BESS =C BESS，cap +C BESS，OM

[0105] In the embodiment of the present invention, the hydrogen energy storage unit includes an electrolyzer (EC), a solid oxide fuel cell (SOFC), and a high-pressure gaseous hydrogen storage tank (HT). The electrolyzer prepares hydrogen and stores it in the high-pressure gaseous hydrogen storage tank, and the solid oxide fuel cell consumes the hydrogen stored in the tank. e The electrolyzer can electrolyze water with the excess power in the microgrid to generate hydrogen and oxygen. In the present invention, an alkaline electrolytic cell is selected as the research object. In the microgrid, the electrolyzer consumes power to produce hydrogen and stores it in the hydrogen storage unit. Therefore, it is necessary to model the power and hydrogen production rate of the electrolyzer, and at the same time, it is necessary to model the cost of the electrolyzer. The following formula is the hydrogen production rate expression of the electrolyzer at time t:

[0106] where, P

[0107]

[0108] (t) is the hydrogen production power of the electrolyzer at time t, η EC is the hydrogen production efficiency of the electrolytic hydrogen production device, and Hhv is the high heating value of hydrogen. EC

[0109] The capital cost C EC，cap and the operation and maintenance cost C EC，OM of the electrolyzer are respectively:

[0110] C EC，cap =γ EC，cap N EC

[0111]

[0112] where, γ EC，cap is the capital cost coefficient of the electrolyzer, which is obtained according to experience, N EC is the number of electrolyzers, γ EC，OM is the operation and maintenance cost coefficient of the electrolyzer, which is obtained according to experience. L 2 is the life cycle of the electrolyzer.

[0113] The solid oxide fuel cell SOFC outputs power to the power grid by consuming hydrogen. Therefore, it is necessary to establish the relationship between the output power of the SOFC and the hydrogen consumption rate.

[0114] The hydrogen consumption rate of a single SOFC (unit: mol / s) is:

[0115]

[0116] where, Is is the stack current of the SOFC, n is the number of cell units constituting the stack, and F is the Faraday constant with a value of 96485 C / mol.

[0117] The capital cost C of the SOFC FC，cap and the operation and maintenance cost C FC，OM are respectively:

[0118] C FC，cap = γ FC，cap N FC

[0119]

[0120] where γ FC，cap is the capital cost coefficient of the SOFC, which is taken according to experience, and N FC is the number of SOFCs, and γ FC , OM is the operation and maintenance cost coefficient of the SOFC, which is taken according to experience, and P FC (t) is the power of hydrogen consumption of the SOFC at time t. L 3 is the life cycle of the SOFC.

[0121] The hydrogen storage model of the high-pressure gaseous hydrogen storage tank is:

[0122] S(t + Δt) = S(t) + (R in (t) - R out (t)) × Δt

[0123] where S(t) and S(t + Δt) are the hydrogen storage amounts at time t and t + Δt respectively, and R in (t) and R out (t) are the hydrogen input rate and output rate of the hydrogen storage tank at time t respectively.

[0124] The cost of the high-pressure gaseous hydrogen storage tank includes the capital cost C HT，cap and the operation and maintenance cost C HT，OM :

[0125]

[0126] where is the rated hydrogen storage capacity of the hydrogen storage tank, and γ HT，OM is the operation and maintenance cost coefficient of the hydrogen storage tank, which is taken according to experience; γ HT，cap is the capital cost coefficient of the hydrogen storage tank, which is taken according to experience; L 4 is the life cycle of the hydrogen storage tank.

[0127] Therefore, in the embodiments of the present invention, the cost of the hydrogen energy storage unit is:

[0128]

[0129] As a preferred implementation, the operation constraints of the optical-storage-hydrogen supported microgrid in the embodiments of the present invention further include: the relevant constraints of the battery energy storage unit, the hydrogen energy storage unit, and the photovoltaic panel PV.

[0130] The relevant constraints of the battery energy storage unit include the output power range constraint and the transfer equation constraint of the state of charge of the energy storage between two adjacent time points:

[0131]

[0132] SoC_min ≤ SOC(t + 1) = SOC(t) + [P BESS，ch (t)n ch -P BESS，dis (t)η dis / C r ≤ SoC_max

[0133] Wherein, represents the rated upper limit power of the battery energy storage unit; represents the rated upper limit power of the hydrogen production equipment; SoC_min and SoC_max respectively represent the minimum value and the maximum value of SoC.

[0134] The relevant constraints of the hydrogen energy storage unit include: the state transfer equation constraint of the hydrogen energy storage unit:

[0135]

[0136] Wherein, LOH(t) represents the hydrogen storage capacity of the hydrogen storage tank at time t.

[0137] The relevant constraints of the photovoltaic panel PV include: the constraint of the maximum light curtailment rate. Specifically, due to the excessive abundance of renewable energy, light curtailment may occur. In order to stimulate the consumption capacity of the microgrid system for renewable energy, the minimum consumption rate (the constraint of the maximum light curtailment rate) is set as follows:

[0138]

[0139] Wherein, P cur (t) is the light curtailment rate, is the rated light curtailment rate.

[0140] As another preferred implementation, in some application scenarios, in order to meet the relatively high hydrogen trading demand under certain conditions, it also includes preparing grey hydrogen by catalytic partial oxidation (CPOX) to flexibly fill the hydrogen demand gap when the production volume of green hydrogen cannot meet the demand. In the embodiments of the present invention, the amount of green hydrogen prepared by the optical-storage-hydrogen supported microgrid is greater than the amount of grey hydrogen prepared by catalytic partial oxidation.

[0141] As shown Figure 3 in the figure, it is a hybrid hydrogen production system under the carbon emission reduction policy in the embodiment of the present invention. In this scenario, the corresponding objective function is:

[0142]

[0143] Among them, C CPOX represents the cost of producing gray hydrogen by catalytic partial oxidation (CPOX).

[0144] In the embodiment of the present invention, catalytic partial oxidation is used to produce hydrogen instead of the commonly used steam reforming (SR). Catalytic partial oxidation to produce hydrogen is a technology that produces hydrogen through catalytic partial oxidation reactions. Its reactions usually require high temperatures (600 - 900 °C) and catalysts to proceed. Commonly used catalysts include platinum, palladium, nickel, etc. These catalysts can promote the partial oxidation reaction and reduce the energy required for the reaction. The general reaction formula of CPOX is:

[0145] CH 4 +0.5O 2 →CO + 2H 2

[0146] The cost of producing gray hydrogen by CPOX also includes capital cost and operation and maintenance cost. The capital cost C CPOX，cap is calculated as follows:

[0147]

[0148] Among them, is the daily hydrogen production limit of catalytic partial oxidation, and γ CPOX，cap is the capital cost coefficient of catalytic partial oxidation.

[0149] From the general reaction formula of CPOX, its operation and maintenance cost C CPOX，OM is composed of methane and oxygen together:

[0150]

[0151] Among them, is the reaction rate of methane in CPOX, is the reaction rate of oxygen in CPOX; is the methane operation and maintenance cost coefficient, is the oxygen operation and maintenance cost coefficient, which is taken according to experience. L 5 is the life cycle of the catalytic partial oxidation equipment.

[0152] In this scenario (producing green hydrogen and gray hydrogen simultaneously), the state transition equation constraint of the hydrogen energy storage unit is:

[0153]

[0154] wherein, N CPOX represents the quantity of catalytic partial oxidation, and represents the hydrogen production rate of catalytic partial oxidation at time t.

[0155] In this scenario, the corresponding carbon trading constraint is:

[0156]

[0157] ct(t) ≤ 0

[0158] wherein, represents the total carbon emissions at time t, and represents the carbon emission rate of catalytic partial oxidation at time t.

[0159] In the present invention, in the microgrid structure supported by the combination of light, energy storage and hydrogen, the introduction of the photovoltaic panel PV can provide electrical energy for both the battery energy storage unit BESS and the hydrogen energy storage unit HESS at the same time, and there is no need to use grid power supply, avoiding power loss, and giving play to the complementary advantages of battery energy storage and hydrogen energy storage, improving the efficiency of green hydrogen production.

[0160] The capacity configuration optimization method of the present invention takes into account the carbon emission reduction incentive policy on the premise of ensuring the supply of hydrogen trading demand, that is, through the carbon trading market, enterprises can trade carbon emission quotas and sell the remaining quotas on the market. Green hydrogen is prepared through the microgrid structure supported by the combination of light, energy storage and hydrogen in the present invention to reduce the production amount of grey hydrogen, so that enterprises have surplus carbon rights. When there is surplus carbon rights, carbon trading is carried out, ct(t) ≤ 0, that is, the carbon trading cost is negative. In the objective function constructed to minimize the unit cost to meet the hydrogen trading demand, the cost C of the enterprise to purchase carbon rights C is negative, which can reduce the total cost; under the constructed carbon trading constraint, solving this objective function can obtain the optimal capacity of each device in the light, energy storage and hydrogen supported microgrid and the amount of carbon rights that the enterprise needs to purchase (that is, the optimal amount of carbon rights sold by the enterprise) under this optimal capacity configuration. The present invention takes into account both hydrogen trading demand and carbon trading demand, prepares green hydrogen through the microgrid structure supported by the combination of light, energy storage and hydrogen, so that enterprises have surplus carbon rights, and conducts carbon trading on the surplus carbon rights, making the cost of purchasing carbon rights negative, which can reduce the total cost.

[0161] The present invention takes the optimal capacity configuration of the microgrid structure supported by the combination of light, energy storage and hydrogen as the technical route. On the premise of ensuring the supply of hydrogen demand, the carbon emission reduction incentive policy is considered in the cost calculation, and the corresponding carbon trading constraint is added, realizing low-cost and high-efficiency green hydrogen production, with good energy storage effect and high adjustability.

[0162] Instead of steam reforming (SR), catalytic partial oxidation (CPOX) is used to produce gray hydrogen, and a hybrid hydrogen production system (producing green hydrogen and gray hydrogen simultaneously) is designed. Catalytic partial oxidation has the advantages of small equipment footprint, low operating cost, and low energy consumption. Compared with the commonly used steam reforming, it has lower carbon emissions, higher reaction rate, and flexible fuel adaptability.

[0163] The present invention is applicable to hydrogen production enterprises to construct a photovoltaic-battery energy storage-hydrogen energy storage supported microgrid in areas with high photovoltaic endowment and perform capacity optimization configuration.

[0164] Example 2

[0165] An embodiment of the present invention provides a capacity configuration optimization system for a photovoltaic-storage-hydrogen supported microgrid, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the capacity configuration optimization method for the photovoltaic-storage-hydrogen supported microgrid in the above-mentioned Example 1 are realized.

[0166] The related technical solutions are the same as above and will not be elaborated here.

[0167] Example 3

[0168] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the capacity configuration optimization method for the photovoltaic-storage-hydrogen supported microgrid in the above-mentioned Example 1 are realized.

[0169] Specifically, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0170] The related technical solutions are the same as above and will not be elaborated here.

[0171] Example 4

[0172] An embodiment of the present application provides a computer program product, including a computer program. When the computer program runs on a computer, the computer is enabled to execute the steps of the capacity configuration optimization method for the photovoltaic-storage-hydrogen supported microgrid in the above-mentioned Example 1.

[0173] The related technical solutions are the same as above and will not be elaborated here.

[0174] Those skilled in the art can easily understand that the above are only the preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A capacity configuration optimization method for a low-carbon photovoltaic-storage-hydrogen supported microgrid, characterized in that: The light-storage-hydrogen supporting microgrid includes photovoltaic panels, battery energy storage units and hydrogen energy storage units. The hydrogen energy storage unit is used to prepare green hydrogen under the power supply of the photovoltaic panels and the battery energy storage unit and supply it to the solid oxide fuel cell. The photovoltaic panels are also used to power the battery energy storage unit. The capacity configuration optimization method includes: Calculate the cost of photovoltaic panels C PV , the cost of hydrogen storage units and the cost of the battery storage unit C BESs ; Under the carbon rights trading, the objective function is constructed to minimize the unit cost to meet the hydrogen trading demand: Among them, C C The cost of purchasing carbon rights for enterprises, C C =Price C ∑ t ct(t)L, ct(t) is the amount of carbon rights that the enterprise needs to purchase at time t, ct(t)≤0, Price C is the carbon price, L is the full life cycle of the photovoltaic-storage-hydrogen supported microgrid; D(t) is the hydrogen demand of the enterprise at time t; Constructing carbon trading constraints: in, is the enterprise's rated carbon emissions; P(t) is the hydrogen production of the hydrogen production equipment in the hydrogen energy storage unit at time t; Q(t) is the hydrogen consumption of the solid oxide fuel cell in the hydrogen energy storage unit at time t; λ EC and λ FC are the excitation coefficients of the hydrogen production equipment and solid oxide fuel cell, respectively, EC >0,λ FC >0; The objective function is solved under operational constraints including carbon trading constraints to obtain the carbon weight ct(t) that the enterprise needs to purchase at time t and the optimal capacity configuration of each device in the photovoltaic-storage-hydrogen supported microgrid.

2. The capacity configuration optimization method according to claim 1, characterized in that: The excitation coefficient λ of the hydrogen production equipment EC Upper limit λ EC_max The method of determining is: The upper limit incentive coefficient λ of the hydrogen production equipment EC_max Under this condition, the carbon weight rewarded by the hydrogen production equipment for producing unit green hydrogen is determined; the upper limit incentive coefficient λ of the hydrogen production equipment is obtained by taking the carbon weight rewarded by the unit green hydrogen and the carbon emission reduction per unit green hydrogen as the optimization goal. EC_max ; When setting the excitation coefficient λ of the hydrogen production equipment EC Under the above conditions, the excitation coefficient λ of the solid oxide fuel cell FC The method of determining is: Initialize λ FC is a quantity λ lower than the preset threshold FC _min, from λ FC _min starts the iteration, in the current round, based on the current λ FC The value of and the excitation coefficient λ of the hydrogen production equipment are set EC Under the condition, solve the objective function, determine whether the number of current solid oxide fuel cells in the optimal capacity configuration obtained by solving is 0, and if so, increase the current λ FC The value of is set, and the next round of iteration is performed until the number of the solid oxide fuel cell is not 0, and the iteration is stopped to obtain the excitation coefficient λ of the hydrogen production equipment. EC The excitation coefficient λ of the solid oxide fuel cell under FC .

3. The capacity configuration optimization method according to claim 1 or 2, characterized in that: It also includes preparing grey hydrogen by catalytic partial oxidation, and the amount of grey hydrogen prepared by the catalytic partial oxidation is less than the amount of green hydrogen prepared by the light-storage-hydrogen supported microgrid; In the scenario of mixed production of grey hydrogen and green hydrogen, the corresponding objective function is: Among them, C CPOX represents the cost of producing grey hydrogen by catalytic partial oxidation; The corresponding carbon trading constraints are: in, represents the carbon emission rate of catalytic partial oxidation at time t, N CPOX Indicates the number of catalytic partial oxidation equipment.

4. The capacity configuration optimization method according to claim 1 or 2, characterized in that: The cost of the photovoltaic panel C PV for: Among them, γ PV,cap , γ PV,OM are the capital cost coefficient and operation and maintenance cost coefficient of photovoltaic panels respectively; N PV is the number of photovoltaic panels, P PV (t) is the power generated by the photovoltaic panel at time t; The cost of the battery energy storage unit C BESS for: Among them, γ BESS,OM , γ BESS,cap are the operation and maintenance cost coefficient and capital cost coefficient of the battery energy storage unit, N BESS is the number of battery storage units, P BESS (t) is the power generation of the battery energy storage unit at time t, |·| represents the absolute value operation; L1 is the life cycle of the battery energy storage unit; The cost of the hydrogen storage unit for: C EC，cap =c EC,cap N EC C FC，cap =c FC,cap N FC Among them, γ EC，cap , γ EC,OM are the capital cost coefficient and operation and maintenance cost coefficient of hydrogen production equipment respectively; N EC is the number of hydrogen production equipment; γ FC,cap , γ FC,OM The capital cost coefficient and operation and maintenance cost coefficient of solid oxide fuel cells respectively; N FC is the number of solid oxide fuel cells, P FC (t) is the power of hydrogen consumed by the solid oxide fuel cell at time t; γ HT,cap , Y HT,OM They are the capital cost coefficient and operation and maintenance cost coefficient of the hydrogen storage tank in the hydrogen energy storage unit respectively; is the rated hydrogen storage capacity of the hydrogen storage tank; L2, L3, and L4 are the life cycles of the hydrogen production equipment, solid oxide fuel cell, and hydrogen storage tank, respectively.

5. The capacity configuration optimization method according to claim 4, characterized in that: The operation constraints also include the state transfer equation constraints of the hydrogen energy storage unit: Wherein, LOH(t) represents the hydrogen storage capacity of the hydrogen storage tank at time t, represents the hydrogen production rate of the hydrogen production equipment at time t, Represents the hydrogen consumption rate of a single solid oxide fuel cell.

6. The capacity configuration optimization method according to claim 3, characterized in that: The cost of preparing grey hydrogen by catalytic partial oxidation C CPOX for: Among them, γ CPOX,cap is the capital cost factor for catalytic partial oxidation, The daily hydrogen production limit for catalytic partial oxidation; is the reaction rate of methane in the catalytic partial oxidation, is the reaction rate of oxygen in the catalytic partial oxidation; is the methane operation and maintenance cost coefficient, is the oxygen operation and maintenance cost coefficient; L5 is the life cycle of the catalytic partial oxidation equipment.

7. The capacity configuration optimization method according to claim 6, characterized in that: The operation constraints also include the state transfer equation constraints of the hydrogen energy storage unit: Wherein, LOH(t) represents the hydrogen storage capacity of the hydrogen storage tank at time t, represents the hydrogen production rate of the hydrogen production equipment at time t, Represents the hydrogen consumption rate of a single solid oxide fuel cell; represents the hydrogen production rate of catalytic partial oxidation at time t.

8. A capacity configuration optimization system for a low-carbon photovoltaic-storage-hydrogen supported microgrid, characterized in that: comprising a computer readable storage medium and a processor; The computer-readable storage medium is used to store executable instructions; The processor is used to read the executable instructions stored in the computer-readable storage medium to execute the capacity configuration optimization method described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the capacity configuration optimization method according to any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that The method comprises a computer program, which, when executed on a computer, enables the computer to execute the capacity configuration optimization method according to any one of claims 1 to 7.

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