Method and system for constructing energy system comprising hydrogen energy router in multiple scenes

Abstract

The invention discloses a method and system for constructing an energy system containing a hydrogen energy router in multiple scenes, and belongs to the field of electrical engineering, and the method comprises the steps: building an input-output incidence matrix for the energy system containing the hydrogen energy router through an energy hub matrix modeling method, and carrying out the linear processing of the input-output incidence matrix; establishing an optimization model by taking the minimum sum of the investment construction cost and the operation cost of the energy system as a target and combining the equipment capacity constraint, the input and output constraint limited by the input-output incidence matrix after linearization processing, the electricity storage, oxygen storage and hydrogen storage operation constraint and the energy conversion capacity constraint; and solving the optimization model to obtain topological structures and capacities of the energy systems in different scenes of upstream, middle and downstream of the watershed. According to the method, starting from multiple scenes, a standard structure design normal form of an energy-quality coupling system for inputting various renewable energy sources into various energy-loaded chemical outputs is provided.

Description

Technical Field

[0001] This invention belongs to the field of electrical engineering, and more specifically, relates to a method and system for constructing an energy system including a hydrogen energy router in multiple scenarios. Background Art

[0002] High-quality development of renewable energy is an urgent requirement for low-carbon energy transition, climate governance, and environmental protection. Hydrogen, with its high energy density and flexible conversion characteristics, is widely recognized as an ideal secondary energy source. Chemical production requires over 30 million tons of hydrogen annually, currently primarily derived from fossil fuel decomposition. To achieve decarbonization in chemical production, a comprehensive shift towards green hydrogen is necessary. Green hydrogen chemistry is a new type of productive force supporting the low-carbon transition of the energy and chemical industries.

[0003] Green hydrogen chemistry offers a new approach to the high-quality development of renewable energy and, by achieving zero-carbon fuels in the energy consumption sector, has broad application prospects. However, the flexible and efficient conversion of renewable energy power generation from hydropower, wind power, and solar power into hydrogen, and further synthesis of energy-intensive chemicals such as methanol, methane, and ammonia, still faces many challenges. Previous research on electro-hydrogen coupling systems has mainly focused on the energy balance of electro-hydrogen conversion, but research on the mass-energy balance matching of "electro-green hydrogen-chemical" coupling systems is still insufficient. Current structural designs are only for single scenarios and lack flexible topology design processes and methods for coupling systems that can adapt to multiple scenarios, which urgently need improvement. Summary of the Invention

[0004] To address the shortcomings and improvement needs of existing technologies, this invention provides a method and system for constructing an energy system including a hydrogen energy router in multiple scenarios. Its purpose is to reduce the energy storage buffer requirements of the coupled electro-hydrogen production section and the chemical synthesis section, improve the conversion efficiency of the electro-hydrogen-energy-carrying chemical system, and enrich the application scenarios of the electro-hydrogen-energy-carrying chemical coupling system.

[0005] To achieve the above objectives, according to one aspect of the present invention, a method for constructing an energy system including a hydrogen energy router in multiple scenarios is provided, comprising: establishing an input-output correlation matrix for the energy system including the hydrogen energy router using an energy hub matrix modeling method, and linearizing the input-output correlation matrix; establishing an optimization model with the objective of minimizing the sum of the investment and construction costs and operating costs of the energy system, combined with equipment capacity constraints, input-output constraints limited by the linearized input-output correlation matrix, energy storage, oxygen storage, and hydrogen storage operation constraints, and energy conversion capacity constraints; and solving the optimization model to obtain the topology and capacity of the energy system in different scenarios in the upper, middle, and lower reaches of the watershed.

[0006] Furthermore, the method of using the energy hub matrix modeling approach to establish an input-output correlation matrix for an energy system containing hydrogen energy routers specifically includes: searching all branches in the energy system; for each branch, if the starting sequence and ending sequence of the branch are not adjacent, adding virtual nodes at the missing node locations between the starting sequence and the ending sequence; splitting the energy system into multiple energy routers, and constructing a topological correlation matrix for each energy router based on the energy conversion and node order in the energy system after adding virtual nodes, to form the input-output correlation matrix.

[0007] Furthermore, the energy system is divided into N energy routers, where N ≥ 3; the input-output equation of the first energy router is:

[0008] L1=C1P input

[0009] The input-output equation for the nth energy router is:

[0010] L n =C n L n-1

[0011] The input-output equation for the Nth energy router is:

[0012] L out =C N L N-1

[0013] Where n = 2, 3, ..., N-1, L1 is the output of the first energy router, L n L n-1 L N-1 Let C1 be the output of the nth, n-1th, and N-1th energy routers, respectively, and let C1 be the topological correlation matrix of the first energy router. n C N The topological correlation matrices of the nth and Nth energy routers are respectively, P input L is the input to the energy system. out This is the output of the energy system.

[0014] Furthermore, the input-output correlation matrix is ​​as follows:

[0015] C total =C N ×C N-1 ...×C1

[0016] Among them, C total This is the input-output correlation matrix.

[0017] Furthermore, the investment and construction costs are as follows:

[0018]

[0019] κ=r(1+r) n / ((1+r) n -1)

[0020] Among them, F inv Let κ be the investment and construction cost, and z be the annual cost conversion factor. WE z BG z ES z OS z HS z PSA z CCU z AS z SML z SM z SA These represent 0-1 variables indicating whether or not an electrolysis water unit, biomass gasification unit, energy storage unit, oxygen storage unit, hydrogen storage unit, pressure swing adsorption unit, carbon capture unit, air separation unit, methanol synthesis unit, methane synthesis unit, and ammonia synthesis unit are installed. The unit capacity investment costs for the following units are respectively: water electrolysis unit, biomass gasification unit, energy storage unit, oxygen storage unit, hydrogen storage unit, pressure swing adsorption unit, carbon capture unit, air separation unit, methanol synthesis unit, methane synthesis unit, and ammonia synthesis unit. WE 、c BG 、c ES 、c OS 、c HS 、c PSA 、c CCU 、c AS 、c SML 、c SM 、c SA The planned capacities are respectively for water electrolysis unit, biomass gasification unit, energy storage unit, oxygen storage unit, hydrogen storage unit, pressure swing adsorption unit, carbon capture unit, air separation unit, methanol synthesis unit, methane synthesis unit, and ammonia synthesis unit, where r is the interest rate and n is the investment payback period.

[0021] Furthermore, the operating cost is:

[0022] F ope =F main +F raw

[0023]

[0024]

[0025]

[0026] Among them, F ope For the aforementioned operating cost, F main For maintenance costs, F raw Let ν represent the cost of raw materials, ν represent the number of days in a year, s represent the scenario, t represent time, and ω(s) represent the probability of scenario s. These are the unit operating and maintenance costs for photovoltaic, hydropower, wind power, water electrolysis, biomass gasification, energy storage, oxygen storage, hydrogen storage, pressure swing adsorption, carbon capture, air separation, methanol synthesis, methane synthesis, and ammonia synthesis units, respectively. These represent the photovoltaic power output, hydropower output, and wind power output at time t under scenario s, respectively. Let be the electrical power consumed by the water electrolysis device at time t under scenario s. These represent the energy storage charging and discharging power at time t in scenario s, respectively. These represent the charging and discharging flow rates of the oxygen storage device at time t in scenario s. These represent the charging and discharging flow rates of the hydrogen storage device at time t in scenario s, respectively. These represent the prices of biomass, nitrogen, and carbon dioxide at time t in scenario s. These represent the electrical power consumed by the pressure swing adsorption unit, the methanol synthesis unit, the methane synthesis unit, and the ammonia synthesis unit at time t under scenario s, respectively. These represent the prices per unit of biomass, nitrogen, and carbon dioxide at time t in scenario s.

[0027] Furthermore, the operational constraints for energy storage, oxygen storage, and hydrogen storage include: Energy storage operational constraints:

[0028]

[0029]

[0030]

[0031]

[0032]

[0033]

[0034] Oxygen storage operation constraints:

[0035]

[0036]

[0037]

[0038]

[0039]

[0040]

[0041] Hydrogen storage operation constraints:

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] in, Let η be the capacity of the energy storage device at time t in scenario s. ES_in η ES_out These are the energy storage charging and discharging efficiencies, respectively. These represent the energy storage charging and discharging power at time t in scenario s, respectively. Configure capacity for energy storage devices in scenario s. These represent the capacity of the energy storage device at the initial and final moments in scenario s, respectively. Let ε be the change in energy storage power at time t under scenario s. ES This refers to the energy storage and dissipation power coefficient. Let be the capacity of the oxygen storage device at time t in scenario s. Δt represents the charging and discharging flow rates of the oxygen storage device at time t under scenario s, k2 is the unit conversion factor, and Δt is the time interval. Configure the capacity of the oxygen storage device in scenario s. These represent the oxygen storage device's capacity at the initial and final moments in scenario s, respectively. Let ε be the change in oxygen storage at time t under scenario s. OS This is the oxygen storage and release power coefficient. Let be the capacity of the hydrogen storage device at time t in scenario s. Do not specify the charging and discharging flow rates of the hydrogen storage device at time t under scenario s. Configure the capacity of the hydrogen storage device in scenario s. These represent the capacities of the hydrogen storage device at the initial and final moments in scenario s, respectively. Let ε be the change in hydrogen storage at time t under scenario s. HSThis is the hydrogen storage and release power coefficient.

[0049] According to another aspect of the present invention, an energy system construction system including a hydrogen energy router in multiple scenarios is provided, comprising: a processor; and a memory storing a computer-executable program, wherein when the program is executed by the processor, the processor causes the processor to perform the energy system construction method including a hydrogen energy router in multiple scenarios as described above.

[0050] According to another aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the energy system construction method including a hydrogen energy router in multiple scenarios as described above.

[0051] In summary, the above-described technical solutions of this invention can achieve the following beneficial effects: A method for constructing an energy system containing hydrogen energy routers in multiple scenarios is provided. An input-output correlation matrix is ​​established for the energy system containing hydrogen energy routers using the energy hub matrix modeling method. When establishing optimization objectives and constraints, data from different scenarios in the upper, middle, and lower reaches of the watershed are considered. Therefore, the final designed topology can provide a standard design paradigm for energy-mass coupling systems with multiple renewable energy inputs and multiple energy-carrying chemical outputs. This reduces the system's demand for energy storage devices and improves the system's energy conversion efficiency through complementary oxygen utilization, supporting the large-scale application of green hydrogen chemistry. Attached Figure Description

[0052] Figure 1 A flowchart illustrating a method for constructing an energy system including a hydrogen energy router in multiple scenarios, as provided in an embodiment of the present invention.

[0053] Figure 2 A diagram illustrating the steps of standardized topology hierarchical modeling for a hydrogen-powered router provided in this embodiment of the invention.

[0054] Figure 3 A diagram illustrating the energy system structure of a hydrogen-powered router provided in an embodiment of the present invention;

[0055] Figure 4 This is a system diagram showing the arrangement of real and virtual nodes according to an embodiment of the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0057] In this invention, the terms "first," "second," etc. (if present) in the invention and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0058] Example 1

[0059] A method for constructing an energy system incorporating a hydrogen-powered router across multiple scenarios. (See also...) Figures 1-4 This embodiment provides a detailed description of the energy system construction method including a hydrogen energy router in multiple scenarios. The method includes operations S1-S3, such as... Figure 1 As shown.

[0060] Operation S1 uses the energy hub matrix modeling method to establish an input-output correlation matrix for the energy system containing hydrogen energy routers, and then performs linearization processing on the input-output correlation matrix.

[0061] According to an embodiment of the present invention, an input-output correlation matrix is ​​established for an energy system containing a hydrogen energy router using the energy hub matrix modeling method, specifically including the following sub-operations S11-S13.

[0062] In sub-operation S11, all branches in the energy system are searched.

[0063] In suboperation S12, for each branch, if the starting sequence and the ending sequence of the branch are not adjacent, a virtual node is added at the point where the node between the starting sequence and the ending sequence is missing.

[0064] In sub-operation S13, the energy system is split into multiple energy routers. Based on the energy conversion and node order in the energy system after adding virtual nodes, a topology correlation matrix is ​​constructed for each energy router to form an input-output correlation matrix.

[0065] Specifically, the energy system is divided into N energy routers, where N ≥ 3. The input-output equation of the first energy router is:

[0066] L1=C1P input

[0067] The input-output equation for the nth energy router is:

[0068] L n =C n L n-1

[0069] The input-output equation for the Nth energy router is:

[0070] L out =C N L N-1

[0071] Where n = 2, 3, ..., N-1, L1 is the output of the first energy router, L n L n-1 L N-1 Let C1 be the output of the nth, n-1th, and N-1th energy routers, respectively, and let C1 be the topological correlation matrix of the first energy router. n C N The topological correlation matrices of the nth and Nth energy routers are respectively, P input As the input to the energy system, L out For the output of the energy system.

[0072] The input-output correlation matrix is ​​as follows:

[0073] C total =C N ×C N-1 ...×C1

[0074] Among them, C total This is the input-output correlation matrix.

[0075] The specific process of establishing the input-output correlation matrix is ​​as follows: Figure 2 As shown. First, search each branch in the hydrogen-powered router energy system to find the starting sequence (sequence M) and ending sequence (sequence N) of that branch. Check if the starting and ending node sequences of each branch are adjacent. If they are not adjacent, add virtual nodes at the missing points. Specifically, if NM > 1, insert (NM-1) virtual nodes in that branch until all branches are modified. Second, draw a directed graph to arrange the energy conversion and aggregation point order in the hydrogen-powered router energy system. Finally, split the hydrogen-powered router energy system into multiple energy routers, define the topological association matrix of each energy router, and then obtain the input-output association matrix of the entire energy system.

[0076] by Figure 3 Taking the energy system shown as an example, the modeling process of the input-output correlation matrix is ​​explained. This energy system model includes photovoltaic (PV), wind power (WT), hydropower (HP), water electrolysis unit (WE), biomass gasification unit (BG), pressure swing adsorption unit (PSA), energy storage unit (ES), oxygen storage unit (OS), hydrogen storage unit (HS), carbon capture unit (CCU), air separation unit (AS), methanol synthesis unit (SML), methane synthesis unit (SM), and ammonia synthesis unit (SA).

[0077] according to Figure 3 After adding virtual nodes and arranging the order of real and virtual nodes, as follows: Figure 4 As shown. From Figure 4As can be seen, energy storage is negative in charging mode and positive in discharging mode, and can be regarded as additional input energy. The node sequence of the energy system of the hydrogen-powered router is as follows: Figure 4 As shown. Figure 4 In this system, V1-V54 are virtual nodes with an efficiency of 1. Furthermore, the total sequence number of the energy system is 10.

[0078] After arranging the different nodes in the correct order, the energy system model of this hydrogen-powered router is transformed into 10 energy router models. For each energy router model, there is no coupling between the nodes. Therefore, the input and output equations of these 10 energy router models can be easily derived and converted into linear models using the variable substitution method. The process of establishing the correlation matrix is ​​as follows.

[0079] The input-output equations for the first energy router model are:

[0080] L1=C1P input

[0081]

[0082] L in the following text m correspond Figure 4 The output at position m, such as L 10 correspond Figure 4 Output at position 10; P HP 、P PV 、P WT 、P ES These are the input power of hydropower, photovoltaic power, wind power, and energy storage, respectively; N OS N HS These are the flow rates of the oxygen storage and hydrogen storage input systems, respectively.

[0083] The input-output equations for the second energy router model are:

[0084] L2=C2L1

[0085]

[0086] Where x1, x2, ..., x7 are the scheduling factors of D2, and D2 is the electrical bus.

[0087] The input-output equations for the third energy router model are:

[0088] L3 = C3L2

[0089]

[0090] in, These are the coefficients for hydrogen and oxygen production via water electrolysis, respectively; η CCUThe power consumption coefficient of the carbon capture device; These are the correlation coefficients between the oxygen and nitrogen obtained by the air separation device and the power consumption, respectively.

[0091] The input-output equations for the fourth energy router model are:

[0092] L4 = C4L3

[0093]

[0094] Where x8 and x9 are the scheduling factors of D4, and D4 is the carbon dioxide bus.

[0095] The input-output equations for the fifth energy router model are:

[0096] L5 = C5L4

[0097]

[0098] Where, η BG It is the hydrogen production coefficient of the biomass gasification unit.

[0099] The input-output equations for the sixth energy router model are:

[0100] L6 = C6L5

[0101]

[0102] Where, η PSA_E , These are the power consumption coefficient and adsorption efficiency of the pressure swing adsorption (PSA) device, respectively. The input-output equations for the 7th energy router model are:

[0103] L7 = C7L6

[0104]

[0105] The input-output equations for the 8th energy router model are:

[0106] L8 = C8L7

[0107]

[0108] The input-output equations for the 9th energy router model are:

[0109] L9 = C9L8

[0110]

[0111] The input-output equations for the 10th energy router model are:

[0112] Lout =C 10 L9

[0113]

[0114] Therefore, the correlation matrix of the entire energy system can be represented by the following formula:

[0115] L out =C total P input

[0116] C total =C 10 ×C9×C8...×C1

[0117] The input-output correlation matrix established above exhibits nonlinearity, which leads to computational difficulties. Therefore, the input-output correlation matrix is ​​linearized to facilitate the construction of a mixed-integer linear optimization model later.

[0118] Still with Figure 3 and Figure 4 Taking the energy system shown as an example, the correlation matrices of the 2nd, 4th, and 9th energy router models are linearized as follows:

[0119] x1+x2+x3+x4+x5+x6+x7=1

[0120] 0≤x1,x2,x3,x4,x5,x6,x7≤1

[0121]

[0122]

[0123]

[0124]

[0125]

[0126]

[0127]

[0128]

[0129]

[0130]

[0131]

[0132]

[0133]

[0134]

[0135]

[0136]

[0137]

[0138] 0≤x8,x9≤1

[0139]

[0140]

[0141]

[0142]

[0143]

[0144]

[0145] x 10 +x 11 +x 12 +x 13 =1

[0146] 0≤x 10 ,x 11 ,x 12 ,x 13 ≤1

[0147]

[0148]

[0149]

[0150]

[0151]

[0152]

[0153]

[0154]

[0155]

[0156]

[0157] Operation S2 aims to minimize the sum of investment and construction costs and operating costs of the energy system. It combines equipment capacity constraints, input-output constraints limited by the linearized input-output correlation matrix, energy storage, oxygen storage, and hydrogen storage operation constraints, and energy conversion capacity constraints to establish an optimization model.

[0158] Before executing operation S2, it is necessary to collect data on water, wind, and solar resources, as well as electricity, hydrogen, and chemical demand data in different scenarios in the upper, middle, and lower reaches of the basin. The collected data should be processed and analyzed for subsequent optimization model building.

[0159] Wind and solar resource data acquisition: Based on the acquired data on wind speed, irradiance, and temperature at the research sites, wind speed-power and irradiance-power physical models are established to simulate the output power of wind and solar power, which serves as input for the optimization model. Furthermore, when conducting short-term optimization scheduling studies, it is difficult to analyze each directly simulated long-sequence of wind and solar power output individually. Therefore, a scenario reduction technique is used to select representative typical daily power output scenarios.

[0160] Hydropower station data acquisition: Collect information on the installed capacity and main operating parameters of hydropower stations that have been built or are planned in different areas of the river basin through planning data or cooperation.

[0161] The objective function for optimizing the model is:

[0162] minF inv +F ope

[0163] Investment and construction costs F inv for:

[0164]

[0165] Where κ is the annual cost conversion factor, z WE z BG z ES z OS z HS z PSA z CCU z AS z SML z SM z SA These represent 0-1 variables indicating whether or not an electrolysis water unit, biomass gasification unit, energy storage unit, oxygen storage unit, hydrogen storage unit, pressure swing adsorption unit, carbon capture unit, air separation unit, methanol synthesis unit, methane synthesis unit, and ammonia synthesis unit are installed. The unit capacity investment costs for the following units are respectively: water electrolysis unit, biomass gasification unit, energy storage unit, oxygen storage unit, hydrogen storage unit, pressure swing adsorption unit, carbon capture unit, air separation unit, methanol synthesis unit, methane synthesis unit, and ammonia synthesis unit. WE 、c BG 、c ES 、c OS 、c HS 、c PSA 、c CCU 、c AS 、c SML 、c SM 、c SA The planned capacities are respectively for water electrolysis unit, biomass gasification unit, energy storage unit, oxygen storage unit, hydrogen storage unit, pressure swing adsorption unit, carbon capture unit, air separation unit, methanol synthesis unit, methane synthesis unit, and ammonia synthesis unit, where r is the interest rate and n is the investment payback period.

[0166] Operating cost F ope for:

[0167] F ope =F main +F raw

[0168]

[0169]

[0170]

[0171] Among them, F main For maintenance costs, F raw Let ν represent the cost of raw materials, ν represent the number of days in a year, s represent the scenario, t represent time, and ω(s) represent the probability of scenario s. These are the unit operating and maintenance costs for photovoltaic, hydropower, wind power, water electrolysis, biomass gasification, energy storage, oxygen storage, hydrogen storage, pressure swing adsorption, carbon capture, air separation, methanol synthesis, methane synthesis, and ammonia synthesis units, respectively. These represent the photovoltaic power output, hydropower output, and wind power output at time t under scenario s, respectively. Let be the electrical power consumed by the water electrolysis device at time t under scenario s. These represent the energy storage charging and discharging power at time t in scenario s, respectively. These represent the charging and discharging flow rates of the oxygen storage device at time t in scenario s. These represent the charging and discharging flow rates of the hydrogen storage device at time t in scenario s, respectively. These represent the prices of biomass, nitrogen, and carbon dioxide at time t in scenario s. These represent the electrical power consumed by the pressure swing adsorption unit, the methanol synthesis unit, the methane synthesis unit, and the ammonia synthesis unit at time t under scenario s, respectively. These represent the prices per unit of biomass, nitrogen, and carbon dioxide at time t in scenario s.

[0172] The equipment capacity constraint is:

[0173] z WE c WE_min ≤c WE ≤z WE c WE_max

[0174] z BG c BG_min ≤c BG ≤z BG c BG_max

[0175] z ES c ES w min ≤c ES ≤z ES c ES_max

[0176] z OS c OS_min ≤c OS ≤z OS c OS_max

[0177] z HS c HS_min ≤c HS ≤z HS c HS_max

[0178] z PSA c PSA_min ≤c PSA ≤z PSA c PSA_max

[0179] z CCU c CCU_min ≤c CCU ≤z CCU c CCU_max

[0180] z AS c AS_min ≤c AS ≤z AS c AS_max

[0181] z SML c SML_min ≤c SML ≤zSML c SML_max

[0182] z SM c SM_min ≤c SM ≤z SM c SM_max

[0183] z SA c SA_min ≤c SA ≤z SA c SA_max

[0184] Among them, c X_min 、c X_max Configure the minimum and maximum capacity values ​​for device X, where X is WE, BG, ES, OS, HS, PSA, CCU, AS, SML, SM, or SA.

[0185] The operational constraints for energy storage, oxygen storage, and hydrogen storage include: energy storage operational constraints, oxygen storage operational constraints, and hydrogen storage operational constraints.

[0186] The constraints for energy storage operation are:

[0187]

[0188]

[0189]

[0190]

[0191]

[0192]

[0193] The constraints for oxygen storage operation are:

[0194]

[0195]

[0196]

[0197]

[0198]

[0199]

[0200] The constraints for hydrogen storage operation are:

[0201]

[0202]

[0203]

[0204]

[0205]

[0206]

[0207] in, Let η be the capacity of the energy storage device at time t in scenario s. ES_in η ES_out These are the energy storage charging and discharging efficiencies, respectively. These represent the energy storage charging and discharging power at time t in scenario s, respectively. Configure capacity for energy storage devices in scenario s. These represent the capacity of the energy storage device at the initial and final moments in scenario s, respectively. Let ε be the change in energy storage power at time t under scenario s. ES This refers to the energy storage and dissipation power coefficient. Let be the capacity of the oxygen storage device at time t in scenario s. Δt represents the charging and discharging flow rates of the oxygen storage device at time t under scenario s, k2 is the unit conversion factor, and Δt is the time interval. Configure the capacity of the oxygen storage device in scenario s. These represent the oxygen storage device's capacity at the initial and final moments in scenario s, respectively. Let ε be the change in oxygen storage at time t under scenario s. OS This is the oxygen storage and release power coefficient. Let be the capacity of the hydrogen storage device at time t in scenario s. Do not specify the charging and discharging flow rates of the hydrogen storage device at time t under scenario s. Configure the capacity of the hydrogen storage device in scenario s. These represent the capacities of the hydrogen storage device at the initial and final moments in scenario s, respectively. Let ε be the change in hydrogen storage at time t under scenario s. HS This is the hydrogen storage and release power coefficient.

[0208] The energy conversion capacity constraint is:

[0209]

[0210]

[0211]

[0212]

[0213]

[0214]

[0215]

[0216]

[0217] in, Let be the electrical power consumed by the water electrolysis device at time t under scenario s. The mass flow rate entering the pressure swing adsorption unit. Let be the methanol demand at time t under scenario s. Let be the methane demand at time t under scenario s. Let be the ammonia synthesis demand at time t under scenario s.

[0218] Operation S3 is used to solve the optimization model and obtain the topology and capacity of the energy system under different scenarios in the upper, middle and lower reaches of the watershed.

[0219] Specifically, for example, by using the commercial software GUROBI on the MATLAB platform to solve optimization models, the topology and capacity of energy systems in different scenarios in the upper, middle and lower reaches of the watershed are determined, so as to construct energy systems based on the obtained topology and capacity.

[0220] Taking the 2035 scenario of the lower reaches of the Yalong River as an example, the topology construction and capacity configuration of the energy system are carried out. Table 1 shows the investment parameters of each device. The maximum planned capacities of the water electrolysis unit, biomass gasification unit, energy storage unit, oxygen storage unit, hydrogen storage unit, pressure swing adsorption unit, carbon capture unit, air separation unit, methanol synthesis unit, methane synthesis unit, and ammonia synthesis unit are set at 5000MW, 600000kg / h, 2000MWh, and 100000Nm, respectively. 3 800000Nm 3 900000Nm 3 / h, 5000000Nm 3 / h, 3000000Nm 3 / h, 1000000Nm 3 / h, 4000000Nm 3 / h, 3000000Nm 3 / h.

[0221] Table 1

[0222] Device invest Device invest Electrolyzed water $615,000 / MW Carbon capture device $60 / ton Biomass gasification 7789$ / kg / h Air separation device <![CDATA[2000$ / m 3 ]]> Energy storage 232160$ / MWh Methanol Synthesis Unit $800 / ton oxygen storage <![CDATA[200$ / m 3 ]]> Methane synthesis unit $1000 / ton gas storage <![CDATA[167$ / m 3 ]]> Ammonia synthesis unit $1200 / ton Pressure Swing Adsorption <![CDATA[500$ / m 3 ]]> — —

[0223] Taking the Yalong River Basin as an example, the development of wind and solar power in the Yalong River Basin is still in its initial stage. At present, only a few pilot projects have been built in the lower reaches of the Yalong River. The measured data of wind and solar power is insufficient to support the research on the multi-energy complementary operation mode. Therefore, this embodiment will use a data platform with high industry recognition (Xihe Big Data Platform) to obtain numerical simulation data of wind and solar power, and conduct research on multi-energy complementarity in the basin based on this.

[0224] Hydropower Station Data Acquisition: According to the Yalong River Basin planning data, the upstream section has planned 10 cascade hydropower stations: Wenbo Temple (60,000 kW installed capacity), Munengda (220,000 kW installed capacity), Geni (220,000 kW installed capacity), Muluo (160,000 kW installed capacity), Renda (400,000 kW installed capacity), Linda (144,000 kW installed capacity), Le'an (99,000 kW installed capacity), Xinlong (220,000 kW installed capacity), Gongke (400,000 kW installed capacity), and Jiaxi (360,000 kW installed capacity), with a total output of 2,283,000 kW. The midstream section includes the planned Lianghekou (3,000,000 kW installed capacity) and Yagen First-Level Hydropower Station (…). The Yalong River has six cascade hydropower stations with a total installed capacity of 11,845,000 kW: 270,000 kW, Yagen Second-Level Hydropower Station (1,080,000 kW), Lenggu Hydropower Station (2,575,000 kW), Mengdigou Hydropower Station (2,400,000 kW), Yangfanggou Hydropower Station (1,500,000 kW), and Kala Hydropower Station (1,020,000 kW). Downstream, five cascade hydropower stations are planned: Jinping First-Level Hydropower Station (3,600,000 kW), Jinping Second-Level Hydropower Station (4,800,000 kW), Guandi Hydropower Station (2,400,000 kW), Ertan Hydropower Station (3,300,000 kW), and Tongzilin Hydropower Station (600,000 kW), with a total installed capacity of 14,700,000 kW. This method yields the structural and configuration scheme for the downstream Yalong River.

[0225] Example 2

[0226] A system for constructing an energy system including a hydrogen energy router in multiple scenarios includes: a processor; and a memory storing a computer-executable program. When the program is executed by the processor, it causes the processor to execute the aforementioned method for constructing an energy system including a hydrogen energy router in multiple scenarios. The related technical solutions are the same as in Embodiment 1 and will not be repeated here.

[0227] Example 3

[0228] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned method for constructing an energy system including a hydrogen-powered router in multiple scenarios. The related technical solutions are the same as in Embodiment 1 and will not be repeated here.

[0229] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for constructing an energy system including a hydrogen energy router in multiple scenarios, characterized in that: include: Using the energy hub matrix modeling method, an input-output correlation matrix is ​​established for the energy system including the hydrogen energy router, and the input-output correlation matrix is ​​linearized; Taking the minimum sum of the investment and construction cost and the operation cost of the energy system as the goal, an optimization model is established by combining equipment capacity constraints, input and output constraints defined by the input-output association matrix after linearization, power storage, oxygen storage, and hydrogen storage operation constraints, and energy conversion capacity constraints; The optimization model is solved to obtain the topological structure and capacity of the energy system in different scenarios in the upper, middle and lower reaches of the basin.

2. The method for constructing an energy system including a hydrogen energy router in multiple scenarios as described in claim 1, characterized in that: The energy hub matrix modeling method is used to establish an input-output association matrix for the energy system including the hydrogen energy router, specifically including: Search all branches in said energy system; For each branch, if the starting sequence and the ending sequence of the branch are not adjacent, a virtual node is added at the missing node between the starting sequence and the ending sequence; The energy system is split into a plurality of energy routers, and a topological association matrix is ​​constructed for each of the energy routers according to energy conversion and node sequence in the energy system after adding virtual nodes, so as to form the input-output association matrix.

3. The method for constructing an energy system including a hydrogen energy router in multiple scenarios as described in claim 2, characterized in that: Splitting the energy system into N energy routers, N ≥ 3; The input-output equation of the first energy router is: L1=C1P input The input-output equation of the nth energy router is: L n =C n L n-1 The input-output equation of the Nth energy router is: L out =C N L N-1 Where n = 2, 3, ..., N-1, L1 is the output of the first energy router, L n , L n-1 , L N-1 are the outputs of the nth, n-1th, and N-1th energy routers respectively. C1 is the topological association matrix of the first energy router. n , C N are the topological association matrices of the nth and Nth energy routers, respectively, input is the input of the energy system, L out is the output of the energy system.

4. The method for constructing an energy system including a hydrogen energy router in multiple scenarios as described in claim 3, characterized in that: The input-output association matrix is: C total =C N ×C N-1 ...×C1 Among them, C total is the input-output association matrix.

5. The method for constructing an energy system including a hydrogen energy router in multiple scenarios according to any one of claims 1 to 4, characterized in that: The investment and construction costs are: κ=r(1+r) n / ((1+r) n -1) Among them, F inv is the investment and construction cost, κ is the annual cost conversion coefficient, z WE 、z BG 、z ES 、z OS 、z HS 、z PSA 、z CCU 、z AS 、z SML 、z SM 、z SA 0-1 variables indicating whether a water electrolysis device, a biomass gasification device, a power storage device, an oxygen storage device, a hydrogen storage device, a pressure swing adsorption device, a carbon capture device, an air separation device, a synthetic methanol device, a synthetic methane device, and a synthetic ammonia device are installed. The unit capacity investment costs of water electrolysis device, biomass gasification device, power storage device, oxygen storage device, hydrogen storage device, pressure swing adsorption device, carbon capture device, air separation device, synthetic methanol device, synthetic methane device, and synthetic ammonia device are respectively, c WE 、c BG 、c ES 、c OS 、c HS 、c PSA 、c CCU 、c AS 、c SML 、c SM 、c SA They are the planned capacities of water electrolysis device, biomass gasification device, power storage device, oxygen storage device, hydrogen storage device, pressure swing adsorption device, carbon capture device, air separation device, synthetic methanol device, synthetic methane device and synthetic ammonia device, r is the interest rate and n is the investment recovery period.

6. The method for constructing an energy system including a hydrogen energy router in multiple scenarios according to any one of claims 1 to 4, characterized in that: The operating costs are: F ope =F main +F raw Among them, F ope is the operating cost, F main is the operation and maintenance cost, F raw is the raw material cost, ν is the number of days in a year, s is the scene, t is the time, ω(s) is the probability of scene s, They are the unit operation and maintenance costs of photovoltaic, hydropower, wind power, water electrolysis device, biomass gasification device, power storage device, oxygen storage device, hydrogen storage device, pressure swing adsorption device, carbon capture device, air separation device, synthetic methanol device, synthetic methane device, and synthetic ammonia device. are the photovoltaic output, hydropower output, and wind power output at time t in scenario s, is the electric power consumed by the water electrolysis device at time t in scenario s, are the energy storage charging and discharging power at time t in scenario s, are the charging and discharging flow rates of the oxygen storage device at time t in scenario s, are the charging and discharging flow rates of the hydrogen storage device at time t under scenario s, are the prices of biomass, nitrogen, and carbon dioxide at time t in scenario s, are the power consumed by the pressure swing adsorption unit, the methanol synthesis unit, the methane synthesis unit, and the ammonia synthesis unit at time t in scenario s, respectively. are the prices of biomass, nitrogen and carbon dioxide per unit at time t in scenario s, respectively.

7. The method for constructing an energy system including a hydrogen energy router in multiple scenarios as claimed in claim 1, characterized in that: The electricity, oxygen and hydrogen storage operation constraints include: Power storage operation constraints: Oxygen storage operation constraints: Hydrogen storage operation constraints: in, is the capacity of the power storage device at time t in scenario s, η ES_in , η ES_out are the energy storage charging and discharging efficiency, are the energy storage charging and discharging power at time t in scenario s, Configure the capacity of the power storage device under scenario s, are the capacities of the power storage device at the initial and final moments under scenario s, is the change in energy storage power at time t under scenario s, ε ES is the power storage and discharge power coefficient, is the capacity of the oxygen storage device at time t in scenario s, are the charging and discharging flow rates of the oxygen storage device at time t in scenario s, k2 is the unit conversion coefficient, Δt is the time interval, Configure the capacity of the oxygen storage device under scenario s, are the capacities of the oxygen storage device at the initial and final moments under scenario s, is the change in oxygen storage at time t under scenario s, ε OS is the oxygen storage and release power coefficient, is the capacity of the hydrogen storage device at time t under scenario s, The charging and discharging flow rates of the hydrogen storage device at time t in scenario s are respectively Configure the capacity of the hydrogen storage device under scenario s, are the capacities of the hydrogen storage device at the initial and final moments under scenario s, is the change in hydrogen storage at time t under scenario s, ε HS is the hydrogen storage and release power coefficient.

8. An energy system construction system including a hydrogen energy router in multiple scenarios, characterized in that: include: processor; A memory storing a computer executable program, wherein when the program is executed by the processor, the processor executes an energy system construction method including a hydrogen energy router in multiple scenarios as 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, an energy system construction method including a hydrogen energy router in multiple scenarios as described in any one of claims 1-7 is implemented.

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