Urban power grid multi-objective scheduling method and device and computer equipment

Abstract

The application relates to a kind of urban power grid multi-objective scheduling method, device, computer equipment, storage medium and computer program product.The method comprises: connecting hydrogen energy automobile into original urban power grid operation system, and obtaining updated urban power grid operation system;Determine electrical hydrogen coupling unit from the updated urban power grid operation system;Respectively, a corresponding mathematical model is established for each subunit in the electrical hydrogen coupling unit;According to the operation cost parameters of each module of the updated urban power grid operation system, determine the first objective function, and according to the energy supply parameters of each subunit of the electrical hydrogen coupling unit, determine the second objective function, generate a multi-objective optimization scheduling model according to the first objective function and the second objective function;Solve the multi-objective optimization scheduling model to obtain a target scheduling strategy.The scheduling strategy required by the urban power grid target can be accurately obtained.

Description

Technical Field

[0001] This application relates to the field of cloud computing technology, and in particular to a method, apparatus, computer equipment, storage medium and computer program product for multi-objective scheduling of urban power grids. Background Technology

[0002] With the development of urban power grids, a large number of new energy vehicles have been connected to urban power grids. The integration of these vehicles helps balance the load on urban power grids and reduce energy waste and carbon emissions. Currently, in terms of the flexibility settings for connecting hydrogen fuel cell vehicles to urban power grids, the minimum economic requirements are usually met first. Based on these minimum requirements, factors such as water electrolysis devices, thermal power units, hydrogen-blended gas turbines, and hydrogen fuel cells are comprehensively considered to improve the flexibility of the urban power grid. However, this approach to flexibility settings cannot achieve the target requirements and cannot meet real-world needs. Summary of the Invention

[0003] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for multi-objective dispatching of urban power grids to address the aforementioned technical problems.

[0004] Firstly, this application provides a multi-objective scheduling method for urban power grids. The method includes:

[0005] By connecting hydrogen fuel cell vehicles to the original urban power grid operating system, an updated urban power grid operating system is obtained. The updated urban power grid operating system includes an urban power grid module, a natural gas grid module, and a user unit module, and there are energy conversion relationships between the modules.

[0006] An electrical-hydrogen coupling unit is determined from the updated urban power grid operation system; wherein, the electrical-hydrogen coupling unit includes sub-units corresponding to each stage between hydrogen energy production and use;

[0007] Establish corresponding mathematical models for each subunit in the electrical hydrogen coupling unit;

[0008] Based on the updated operating cost parameters of each module of the urban power grid operation system, a first objective function is determined, and based on the energy supply parameters of each subunit of the electrical-hydrogen coupling unit, a second objective function is determined. Based on the first objective function and the second objective function, a multi-objective optimization scheduling model is generated.

[0009] The multi-objective optimization scheduling model is solved to obtain the updated target resource scheduling strategy for the urban power grid operation system.

[0010] In one embodiment, determining the electrical hydrogen coupling unit from the updated urban power grid operating system includes:

[0011] Based on the updated urban power grid operation system, the hydrogen energy production stage, storage stage, hydrogen blending stage, and usage stage are divided to obtain the sub-units corresponding to each stage between hydrogen energy production and usage.

[0012] Based on the sub-units corresponding to each stage, the electrical hydrogen coupling unit is identified.

[0013] In one embodiment, the subunit includes at least one of the following: a hydrogen production subunit, a hydrogen storage subunit, a hydrogen mixing subunit, and a hydrogen utilization subunit. The step of establishing corresponding mathematical models for each subunit in the electro-hydrogen coupling unit includes:

[0014] When the subunit is a hydrogen production subunit, the volumetric flow rate of hydrogen produced by the water electrolysis device in the hydrogen production subunit is obtained based on the efficiency of the water electrolysis device, the input electrical power of the water electrolysis device, and the higher calorific value of hydrogen. The volumetric flow rate of hydrogen produced by the water electrolysis device is then used as the mathematical model of the hydrogen production subunit.

[0015] When the subunit is a hydrogen storage subunit, the hydrogen volume of the hydrogen storage subunit is obtained based on the volumetric flow rate of hydrogen allowed to be injected into the hydrogen storage subunit, the efficiency of hydrogen injection into the hydrogen storage subunit, the total volumetric flow rate of hydrogen extracted by the hydrogen storage subunit, and the efficiency of hydrogen extraction by the hydrogen storage subunit. The hydrogen volume of the hydrogen storage subunit is used as the mathematical model of the hydrogen storage subunit.

[0016] When the subunit is a mixed hydrogen subunit, the mixed hydrogen natural gas calorific value of the mixed hydrogen subunit is obtained based on the mixed hydrogen ratio, the higher calorific value of natural gas, and the higher calorific value of hydrogen. The mixed hydrogen natural gas calorific value is then used as the mathematical model of the mixed hydrogen subunit.

[0017] When the subunit is a hydrogen-using subunit, the output power of the hydrogen fuel cell is obtained based on the efficiency of the hydrogen fuel cell in the hydrogen-using subunit, the hydrogen volumetric flow rate required by the hydrogen fuel cell, and the higher calorific value of hydrogen. The hydrogen fuel volumetric flow rate supplied by the hydrogen refueling station to the hydrogen-powered vehicle is obtained based on the maximum capacity of the hydrogen storage tank of the hydrogen-powered vehicle in the hydrogen-using subunit, the initial fuel volume of the hydrogen-powered vehicle, the number of cycle starting stations of the hydrogen-powered vehicle, the driving distance of the vehicle route, the hydrogen consumption volume per kilometer of the hydrogen-powered vehicle, the fuel threshold of the hydrogen-powered vehicle, the initial departure time of the hydrogen-powered vehicle, the road distance between each two stations of the hydrogen-powered vehicle, the design speed of each level of road, the number of vehicles on the road, the design traffic flow of the road, and the total number of hydrogen-powered vehicles supplied by the hydrogen refueling station. The output power of the hydrogen fuel cell and the hydrogen fuel volumetric flow rate supplied by the hydrogen refueling station to the hydrogen-powered vehicle are used as the mathematical model of the hydrogen-using subunit.

[0018] In one embodiment, the power supply parameters of each subunit include at least one of the following: hydrogen-mixed gas turbine flexibility parameters, water electrolysis device flexibility parameters, and hydrogen fuel cell flexibility parameters. Determining the second objective function based on the power supply parameters of each subunit of the electro-hydrogen coupling unit includes:

[0019] Based on the upper and lower limits of the output of the hydrogen-mixed gas turbine, and the output sampling value of the hydrogen-mixed gas turbine in the user unit, the flexibility parameters of the hydrogen-mixed gas turbine are determined.

[0020] Based on the power consumption sampling value of the water electrolysis device, the maximum volume of the hydrogen storage subunit, the hydrogen volume sampling value of the hydrogen storage subunit, the hydrogen injection efficiency of the hydrogen storage subunit, the efficiency of the water electrolysis device, and the high calorific value of hydrogen, the flexibility parameters of the water electrolysis device are determined.

[0021] Based on the upper limit of the output power of the hydrogen fuel cell, the lower limit of the output power of the hydrogen fuel cell, the output power sampling value of the hydrogen fuel cell, the hydrogen volume sampling value of the hydrogen storage subunit, the efficiency of the hydrogen fuel cell, the efficiency of hydrogen extraction by the hydrogen storage subunit, and the high calorific value of hydrogen, the flexibility parameters of the hydrogen fuel cell are obtained.

[0022] The second objective function is obtained based on the flexibility parameters of the hydrogen-mixed gas turbine, the flexibility parameters of the water electrolysis device, and the flexibility parameters of the hydrogen fuel cell.

[0023] In one embodiment, a second objective function is obtained based on the flexibility parameters of the hydrogen-mixed gas turbine, the water electrolysis device, and the hydrogen fuel cell, including:

[0024] Based on the upper limit of output of thermal power unit, the lower limit of output of thermal power unit, the upward ramp rate of thermal power unit, the downward ramp rate of thermal power unit, and the output sampling value of thermal power unit, the flexibility parameters of thermal power unit are obtained.

[0025] The second objective function is obtained based on the flexibility parameters of the hydrogen-mixed gas turbine, the water electrolysis device, the hydrogen fuel cell, and the thermal power unit.

[0026] In one embodiment, solving the multi-objective optimization scheduling model to obtain the target scheduling strategy includes:

[0027] Single-objective optimization is performed on the first objective function and the second objective function respectively to obtain the extreme points;

[0028] Based on the extreme points, the Utopia Line is obtained;

[0029] The utopian line is divided to obtain each dividing point, and the target constraint conditions are obtained based on the vectors pointing to each dividing point and the vectors pointing to the extreme points.

[0030] Based on the operational constraints corresponding to the urban power grid module, the natural gas grid module, and the user unit module, the operational constraints of the urban power grid operation system are determined.

[0031] Based on the mathematical models established for each subunit, the target constraints, and the operational constraints, the second objective function is subjected to single-objective optimization processing to obtain a set of target scheduling strategies.

[0032] Secondly, this application also provides a multi-objective dispatching device for urban power grids. The device includes:

[0033] The vehicle access module is used to connect hydrogen fuel cell vehicles to the original urban power grid operating system to obtain an updated urban power grid operating system. The updated urban power grid operating system includes an urban power grid module, a natural gas grid module, and a user unit module, and there are energy conversion relationships between the modules.

[0034] A unit determination module is used to determine an electrical-hydrogen coupling unit from the updated urban power grid operation system; wherein the electrical-hydrogen coupling unit includes sub-units corresponding to each stage between hydrogen energy production and use;

[0035] The model building module is used to build corresponding mathematical models for each sub-unit in the electrical hydrogen coupling unit;

[0036] The scheduling model module is used to determine a first objective function based on the updated operating cost parameters of each module of the urban power grid operation system, and to determine a second objective function based on the energy supply parameters of each subunit of the electrical-hydrogen coupling unit, and to generate a multi-objective optimization scheduling model based on the first objective function and the second objective function.

[0037] The target scheduling module is used to solve the multi-objective optimization scheduling model to obtain the updated target resource scheduling strategy for the urban power grid operation system.

[0038] In one embodiment, the unit determining module includes:

[0039] The phase submodule is used to divide the hydrogen energy production phase, storage phase, hydrogen blending phase, and usage phase based on the updated urban power grid operation system, and obtain the sub-units corresponding to each phase between hydrogen energy production and usage.

[0040] The confirmation submodule is used to confirm the electrical hydrogen coupling unit based on the sub-units corresponding to each stage.

[0041] In one embodiment, the model building module includes:

[0042] The hydrogen production submodule is used to obtain the volumetric flow rate of hydrogen produced by the water electrolysis device in the hydrogen production submodule based on the efficiency of the water electrolysis device, the input electrical power of the water electrolysis device, and the higher calorific value of hydrogen, when the submodule is a hydrogen production submodule. The volumetric flow rate of hydrogen produced by the water electrolysis device is used as the mathematical model of the hydrogen production submodule.

[0043] The hydrogen storage submodule is used to obtain the hydrogen volume of the hydrogen storage subunit when the subunit is a hydrogen storage subunit, based on the volumetric flow rate of hydrogen allowed to be injected into the hydrogen storage subunit, the efficiency of hydrogen injection into the hydrogen storage subunit, the total volumetric flow rate of hydrogen extracted by the hydrogen storage subunit, and the efficiency of hydrogen extraction by the hydrogen storage subunit, and to use the hydrogen volume of the hydrogen storage subunit as the mathematical model of the hydrogen storage subunit.

[0044] The hydrogen mixing submodule is used to obtain the calorific value of the mixed hydrogen natural gas of the mixed hydrogen subunit based on the hydrogen mixing ratio, the higher calorific value of natural gas, and the higher calorific value of hydrogen when the subunit is a mixed hydrogen subunit, and to use the calorific value of the mixed hydrogen natural gas as the mathematical model of the mixed hydrogen subunit.

[0045] The hydrogen-using submodule, when the subunit is a hydrogen-using subunit, is used to obtain the output power of the hydrogen fuel cell based on the efficiency of the hydrogen fuel cell in the hydrogen-using subunit, the required hydrogen volumetric flow rate of the hydrogen fuel cell, and the higher calorific value of hydrogen. It also uses the maximum capacity of the hydrogen storage tank of the hydrogen-powered vehicle in the hydrogen-using subunit, the initial fuel volume of the hydrogen-powered vehicle, the number of cycle starting stations of the hydrogen-powered vehicle, the driving distance of the vehicle route, the hydrogen consumption volume per kilometer of the hydrogen-powered vehicle, the fuel threshold of the hydrogen-powered vehicle, the initial departure time of the hydrogen-powered vehicle, the road distance between every two stations of the hydrogen-powered vehicle, the design speed of each road level, the number of vehicles on the road, the design traffic flow of the road, and the total number of hydrogen-powered vehicles supplied by the hydrogen refueling station to obtain the hydrogen fuel volumetric flow rate supplied by the hydrogen refueling station. The output power of the hydrogen fuel cell and the hydrogen fuel volumetric flow rate supplied by the hydrogen refueling station are used as the mathematical model of the hydrogen-using subunit.

[0046] In one embodiment, the scheduling model module includes:

[0047] The hydrogen-mixed gas turbine submodule is used to determine the flexibility parameters of the hydrogen-mixed gas turbine based on the upper limit of the output of the hydrogen-mixed gas turbine, the lower limit of the output of the hydrogen-mixed gas turbine, and the output sampling value of the hydrogen-mixed gas turbine of the user unit.

[0048] The water electrolysis device submodule is used to determine the flexibility parameters of the water electrolysis device based on the sampled power consumption of the water electrolysis device, the maximum volume of the hydrogen storage subunit, the sampled hydrogen volume of the hydrogen storage subunit, the efficiency of hydrogen injection in the hydrogen storage subunit, the efficiency of the water electrolysis device, and the high calorific value of hydrogen.

[0049] The hydrogen fuel cell submodule is used to obtain the flexibility parameters of the hydrogen fuel cell based on the upper limit of the hydrogen fuel cell output power, the lower limit of the hydrogen fuel cell output power, the output power sampling value of the hydrogen fuel cell, the hydrogen volume sampling value of the hydrogen storage subunit, the efficiency of the hydrogen fuel cell, the efficiency of hydrogen extracted by the hydrogen storage subunit, and the high calorific value of hydrogen.

[0050] The objective function submodule is used to obtain a second objective function based on the flexibility parameters of the hydrogen-mixed gas turbine, the flexibility parameters of the water electrolysis device, and the flexibility parameters of the hydrogen fuel cell.

[0051] In one embodiment, the second objective function submodule includes:

[0052] The thermal power unit submodule obtains the thermal power unit flexibility parameters based on the thermal power unit's upper output limit, lower output limit, upward ramp rate, downward ramp rate, and output sampling value.

[0053] The second objective function submodule is used to obtain the second objective function based on the flexibility parameters of the hydrogen-mixed gas turbine, the flexibility parameters of the water electrolysis device, the flexibility parameters of the hydrogen fuel cell, and the flexibility parameters of the thermal power unit.

[0054] In one embodiment, the target scheduling module includes:

[0055] The single-objective optimization submodule is used to perform single-objective optimization on the first objective function and the second objective function respectively to obtain the extreme points;

[0056] The Utopia Line submodule is used to obtain the Utopia Line based on the extreme points;

[0057] The target constraint submodule is used to divide the utopian line to obtain each dividing point, and to obtain the target constraint conditions based on the vectors pointing to each dividing point and the vectors pointing to the extreme points.

[0058] The operation constraint submodule is used to determine the operation constraint conditions of the urban power grid operation system based on the operation constraint conditions corresponding to the urban power grid module, the natural gas grid module, and the user unit module, respectively.

[0059] The target scheduling submodule is used to perform single-objective optimization processing on the second objective function based on the corresponding mathematical model established by each subunit, the target constraints, and the operational constraints, to obtain a set of target scheduling strategies.

[0060] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the urban power grid multi-objective scheduling method as described in any one of the embodiments of this disclosure.

[0061] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the urban power grid multi-objective scheduling method as described in any one of the embodiments of this disclosure.

[0062] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the urban power grid multi-objective scheduling method as described in any one of the embodiments of this disclosure.

[0063] The aforementioned urban power grid multi-objective scheduling method, device, computer equipment, storage medium, and computer program product, by connecting hydrogen-powered vehicles to the original urban power grid operating system, identifies electrical-hydrogen coupling units in the updated urban power grid operating system from hydrogen energy production to consumption stages, establishes a model, determines the first objective function based on the operating cost of each module of the urban power grid, determines the second objective function based on the energy supply parameters of each sub-unit of the electrical-hydrogen coupling unit, generates and solves a multi-objective optimization scheduling model, and obtains the target scheduling strategy. This satisfies the urban power grid's needs for both the first and second objectives. The integration of hydrogen-powered vehicles into the urban power grid operating system effectively leverages the coordinated multi-energy complementarity of the urban power grid system, effectively alleviates the pressure on the urban power grid's flexible reserve, improves the urban power grid's flexible adjustment capability, and enhances the stability of the urban power grid. Attached Figure Description

[0064] Figure 1 This is a flowchart illustrating a multi-objective scheduling method for urban power grids in one embodiment;

[0065] Figure 2 This is a flowchart illustrating the process of determining an electrical hydrogen coupling unit in one embodiment;

[0066] Figure 3 This is a flowchart illustrating the process of establishing a mathematical model for an electrical hydrogen coupling unit in one embodiment.

[0067] Figure 4 This is a schematic diagram of the first process for determining the second objective function in one embodiment;

[0068] Figure 5 This is a schematic diagram of the second process for determining the second objective function in one embodiment;

[0069] Figure 6 This is a schematic diagram illustrating the process of solving a multi-objective optimization scheduling model in one embodiment;

[0070] Figure 7 This is a flowchart illustrating the multi-objective scheduling method for urban power grids in one embodiment;

[0071] Figure 8 This is a structural block diagram of a multi-objective dispatching device for urban power grids in one embodiment;

[0072] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0073] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0074] In one embodiment, such as Figure 1 As shown, a multi-objective scheduling method for urban power grids is provided, including the following steps:

[0075] Step S100: Connect the hydrogen-powered vehicle to the original urban power grid operating system to obtain an updated urban power grid operating system; wherein, the updated urban power grid operating system includes an urban power grid module, a natural gas grid module, and a user unit module, and there are energy conversion relationships between the modules.

[0076] In one exemplary embodiment, the integration of hydrogen fuel cell vehicles into the existing urban power grid operating system may include establishing an urban power grid operating system for hydrogen energy integration by tightly coupling the natural gas grid and user units, taking into account technical routes such as hydrogen production by electricity, hydrogen-to-natural gas blending, hydrogen fuel cell operation, hydrogen use at hydrogen refueling stations, and demand response technology.

[0077] In one exemplary embodiment, the urban power grid operating system to which hydrogen fuel cell vehicles are connected can be composed of a city power grid, a natural gas grid, and user units coupled together. The city power grid may include equipment such as thermal power units, wind farms, water electrolysis devices, and hydrogen storage tanks; the natural gas grid may include equipment such as natural gas equipment, gas booster boxes, follow-up flow mixers, and compressed natural gas depressurization stations; and the user units may include equipment such as hydrogen-blended gas turbines, hydrogen fuel cells, photovoltaic-storage 5G base stations, and hydrogen refueling stations.

[0078] In one exemplary embodiment, the urban power grid operation system to which the hydrogen energy vehicle is connected may include: when the power grid has surplus electricity, an electrolysis device produces hydrogen gas, realizing the conversion of electrical energy flow and hydrogen energy flow; part of the hydrogen gas produced by the electrolysis device is stored in a hydrogen storage tank and supplied to the user unit, and part of it is injected into the natural gas network in a certain proportion to form mixed hydrogen natural gas, realizing the conversion of hydrogen energy flow and gas energy flow; the mixed hydrogen natural gas is supplied to the mixed hydrogen gas turbine inside the user unit to generate electricity, realizing the conversion of gas energy flow and electrical energy flow; when the user unit has a power shortage or is in a peak electricity consumption period, the hydrogen fuel cell uses hydrogen to generate electricity, realizing the conversion of hydrogen energy flow and electrical energy flow.

[0079] Step S200: Determine the electrical-hydrogen coupling unit from the updated urban power grid operation system; wherein the electrical-hydrogen coupling unit includes sub-units corresponding to each stage between hydrogen energy production and use.

[0080] In one exemplary embodiment, the electrical hydrogen coupling unit may be established by devices such as a water electrolysis device, a hydrogen fuel cell, a hydrogen-mixed gas turbine, a hydrogen storage tank, and a hydrogen refueling station.

[0081] In one exemplary embodiment, the electrical-hydrogen coupling unit may contain electrical load, hydrogen load, and gas load, and the load demand may be met by energy provided by thermal power units, wind farms, hydrogen-mixed gas turbines, hydrogen fuel cells, hydrogen-mixed natural gas, etc.

[0082] Step S300: Establish corresponding mathematical models for each subunit in the electrical hydrogen coupling unit.

[0083] In one exemplary embodiment, the electrical hydrogen coupling unit can be divided into a hydrogen production unit, a hydrogen storage unit, a hydrogen-to-natural gas mixing technology unit, and a hydrogen consumption unit, and its operating characteristics can be modeled by combining the hydrogen volume flow balance relationship between each unit.

[0084] Step S400: Determine a first objective function based on the updated operating cost parameters of each module of the urban power grid operation system, and determine a second objective function based on the energy supply parameters of each subunit of the electrical-hydrogen coupling unit. Generate a multi-objective optimization scheduling model based on the first objective function and the second objective function.

[0085] In one exemplary embodiment, the first objective function can be obtained according to the following formula:

[0086] minF1=min(C e +C g +C h +C us (1)

[0087] Where F1 is the first objective function; C eC represents the operating cost of the power system. g The operating cost of the natural gas system; C h The operating cost of the electrical hydrogen coupling unit; C us The operating cost of the user unit.

[0088] In one exemplary embodiment, the operating cost of the power system can be obtained according to the following formula:

[0089]

[0090] Among them, C e C represents the operating cost of the power system. PG C is the cost of generating electricity from thermal power units. PW C is the operating cost of the wind farm. buy The cost of purchasing electricity from the upper-level power grid for a user unit; L is the number of user units; T is the number of time periods in the dispatch cycle; f g (P G,g,t () represents the fuel cost that thermal power unit g needs to pay in time period t; Where a g b g c g ζ represents the fuel cost coefficient for thermal power unit g; G represents the number of thermal power units; W represents the number of wind farms; PW The operation and maintenance cost per unit of wind power output; P PW,w,t The actual power output of the w-th wind farm at time t; Time-of-use pricing; Let t be the power purchased by user unit l from the urban power distribution network at time t.

[0091] In one exemplary embodiment, the operating cost of the natural gas system can be obtained according to the following formula:

[0092]

[0093] Among them, C g λ represents the operating cost of the natural gas system; T represents the number of time periods in the scheduling cycle; I represents the number of nodes in the natural gas network; λ sg Cost of gas supply; Δt represents the gas supply flow rate injected into node i of the natural gas network at time t; Δt is the difference between two adjacent scheduling times.

[0094] In one exemplary embodiment, the operating cost of the electro-hydrogen coupling unit can be obtained according to the following formula:

[0095]

[0096] Among them, C h The operating cost of the electrical hydrogen coupling unit; CP2G C is the operating cost of the water electrolysis unit; HFC For the operating cost of hydrogen fuel cells; C HV For the operating costs of hydrogen refueling stations; C ST Operating costs of the hydrogen storage unit; ζ P2G The operating cost per unit of electricity consumed by the water electrolysis equipment; L is the number of user units; T is the number of time periods in the scheduling cycle; P P2G,n,t ζ represents the input electrical power of the water electrolysis device in the nth hydrogen production unit at time t; N represents the number of hydrogen production subunits, hydrogen storage subunits, and hydrogen mixing subunits; HFC Operating cost per unit of electricity generated by a hydrogen fuel cell; P HFC,l,t Let ζ be the output power of the hydrogen fuel cell in user unit l at time t; HV The operating cost per unit of hydrogen refueling at a hydrogen refueling station; Let ζ be the hydrogen volumetric flow rate required by the hydrogen refueling station of the l-th user unit at time t; ST The operating cost of injecting and extracting a unit volume of hydrogen into the hydrogen storage unit; Let t be the volumetric flow rate of hydrogen extracted from the nth hydrogen storage unit and injected into the natural gas pipeline; Let t be the volumetric flow rate of hydrogen extracted from the nth hydrogen storage unit to supply the hydrogen-using unit; Let t be the volumetric flow rate of injected hydrogen in the nth hydrogen storage unit at time t; Δt is the difference between two adjacent scheduling times.

[0097] In one exemplary embodiment, the user unit operating cost can be obtained according to the following formula:

[0098]

[0099] Among them, C us The operating cost of the user unit; C dr Demand response cost for adjustable electrical loads; C 5G The operating cost of a photovoltaic-storage 5G base station; C HT L represents the gas purchase cost from the natural gas system for the hydrogen-mixed gas turbine; T represents the number of user units; and T represents the number of time slots in the scheduling cycle. The value at time t for reducing the load participating in demand response for user unit l; Let τ be the value of the transferable load of user unit l at time t participating in demand response; c Price for compensation of reduced electrical load per unit power; τ t The compensation price per unit power of electrical load transferred out; P PV,l,t The actual output of the distributed photovoltaic unit at time t of the user unit base station; The operation and maintenance cost of the distributed photovoltaic unit at the base station; P CH,l,tThe charging power of the backup battery energy storage at time t for user unit base station; P DIS,l,t The discharge power of the backup battery energy storage at time t for user unit base station; ζ ESS The operation and maintenance cost of charging and discharging power per unit of backup battery energy storage for base stations; Let ζ be the total power consumption of the 5G base station of user unit l at time t; B Energy consumption cost per unit power consumption of 5G base stations; λ hg,t The time-of-use gas price for the hydrogen-mixed gas turbine to purchase gas from the natural gas system; V HT,l,t The volume of mixed hydrogen natural gas required for the user unit's mixed hydrogen gas turbine to generate electricity at time t.

[0100] In an exemplary embodiment, the multi-objective optimization scheduling model may consider urban power grid operation constraints, natural gas grid operation constraints, and user unit operation constraints. The urban power grid constraints may include power balance constraints, thermal power unit operation constraints, and wind farm operation constraints. The natural gas grid operation constraints may include pipeline flow and node pressure constraints, node energy and component balance constraints, gas source supply constraints, and compressor constraints. The user unit operation constraints may include user unit internal power balance constraints, adjustable power load demand response constraints, photovoltaic-storage 5G base station operation constraints, hydrogen-blended gas turbine operation constraints, and hydrogen fuel cell and hydrogen-powered vehicle operation constraints.

[0101] Step S500: Solve the multi-objective optimization scheduling model to obtain the updated target resource scheduling strategy for the urban power grid operation system.

[0102] In one exemplary embodiment, solving the multi-objective optimization scheduling model may include solving the multi-objective optimization scheduling model using a normalization method. For example, the method transforms the multi-objective optimization scheduling model into solving a series of single-objective problems to obtain the Pareto front of the model, and selects a suitable compromise solution from the Pareto front using the approximation ideal solution ranking method.

[0103] In the aforementioned multi-objective scheduling method for urban power grids, hydrogen-powered vehicles are integrated into the original urban power grid operating system. Electrical-hydrogen coupling units are identified within the updated urban power grid operating system, representing the stages from hydrogen energy production to consumption. A model is established, and a first objective function is determined based on the operating costs of each module of the urban power grid. A second objective function is determined based on the energy supply parameters of each sub-unit of the electrical-hydrogen coupling unit. A multi-objective optimization scheduling model is then generated and solved to obtain the target scheduling strategy. This method satisfies the urban power grid's requirements for both the first and second objectives. The integration of hydrogen-powered vehicles into the urban power grid operating system effectively leverages the coordinated multi-energy complementarity of the urban power grid system, effectively alleviating the pressure on the urban power grid's flexible reserve, improving the urban power grid's flexible adjustment capability, and enhancing the stability of the urban power grid.

[0104] In one embodiment, such as Figure 2 As shown, step S200 includes:

[0105] Step S201: Based on the updated urban power grid operation system, the hydrogen energy production stage, storage stage, hydrogen blending stage, and usage stage are divided to obtain the sub-units corresponding to each stage between hydrogen energy production and usage.

[0106] Step S202: Based on the sub-units corresponding to each stage, confirm the electrical hydrogen coupling unit.

[0107] In one exemplary embodiment, partitioning the updated urban power grid operation system may include partitioning the various devices in the urban power grid module, the natural gas network module, and the user unit module within the urban power grid operation system.

[0108] In one exemplary embodiment, the sub-units corresponding to each stage between the production and use of hydrogen energy may include a hydrogen production unit, a hydrogen storage unit, a hydrogen blending unit, and a hydrogen consumption unit. The hydrogen production unit may include a water electrolysis device, the hydrogen storage unit may include a cluster of hydrogen storage tanks, the hydrogen blending unit may include a natural gas pipeline, and the hydrogen consumption unit may include a hydrogen fuel cell, a hydrogen-powered vehicle, etc.

[0109] In this embodiment, by dividing the hydrogen energy system into stages in the urban power grid operation system and determining the electrical-hydrogen coupling unit based on each sub-unit, the model constructed by the coupling unit can be made more comprehensive and intuitive, realizing refined modeling of the entire hydrogen energy process and comprehensively and intuitively reflecting the relationship between the various hydrogen energy processes in the urban power grid system.

[0110] In one embodiment, such as Figure 3 As shown, step S300 includes:

[0111] Step S301: When the subunit is a hydrogen production subunit, the volumetric flow rate of hydrogen produced by the water electrolysis device is obtained based on the efficiency of the water electrolysis device, the input power of the water electrolysis device, and the higher calorific value of hydrogen in the hydrogen production subunit, and the volumetric flow rate of hydrogen produced by the water electrolysis device is used as the mathematical model of the hydrogen production subunit.

[0112] Step S302: When the subunit is a hydrogen storage subunit, the hydrogen volume of the hydrogen storage subunit is obtained based on the allowable volumetric flow rate of hydrogen injected into the hydrogen storage subunit, the efficiency of hydrogen injection into the hydrogen storage subunit, the total volumetric flow rate of hydrogen extracted by the hydrogen storage subunit, and the efficiency of hydrogen extraction by the hydrogen storage subunit. The hydrogen volume of the hydrogen storage subunit is then used as the mathematical model of the hydrogen storage subunit.

[0113] Step S303: When the subunit is a mixed hydrogen subunit, the mixed hydrogen natural gas calorific value of the mixed hydrogen subunit is obtained based on the mixed hydrogen ratio, the higher calorific value of natural gas, and the higher calorific value of hydrogen. The mixed hydrogen natural gas calorific value is then used as the mathematical model of the mixed hydrogen subunit.

[0114] Step S304: When the subunit is a hydrogen-using subunit, the output power of the hydrogen fuel cell is obtained based on the efficiency of the hydrogen fuel cell in the hydrogen-using subunit, the hydrogen volumetric flow rate required by the hydrogen fuel cell, and the higher calorific value of hydrogen. The hydrogen fuel volumetric flow rate supplied by the hydrogen refueling station to the hydrogen-using subunit is obtained based on the maximum capacity of the hydrogen storage tank of the hydrogen-powered vehicle in the hydrogen-using subunit, the initial fuel volume of the hydrogen-powered vehicle, the number of cycle starting stations of the hydrogen-powered vehicle, the driving distance of the vehicle route, the hydrogen consumption volume per kilometer of the hydrogen-powered vehicle, the fuel threshold of the hydrogen-powered vehicle, the initial departure time of the hydrogen-powered vehicle, the road distance between each two stations of the hydrogen-powered vehicle, the design speed of each level of road, the number of vehicles on the road, the design traffic flow of the road, and the total number of hydrogen-powered vehicles supplied by the hydrogen refueling station. The output power of the hydrogen fuel cell and the hydrogen fuel volumetric flow rate supplied by the hydrogen refueling station to the hydrogen-powered vehicle are used as the mathematical model of the hydrogen-using subunit.

[0115] In one exemplary embodiment, a mathematical model can be established for the hydrogen production subunit based on the following formula:

[0116]

[0117] in, Let η be the volumetric flow rate of hydrogen produced by the water electrolysis unit in the nth hydrogen production subunit at time t; P2G The efficiency of the water electrolysis device; P P2G,n,t Let t be the input electrical power of the water electrolysis device in the nth hydrogen production subunit; The calorific value of hydrogen is given by Δt; Δt is the difference between two adjacent scheduling times; P P2G,n,max Let t be the upper limit of the input power of the nth hydrogen production single-eye water electrolysis device.

[0118] In one exemplary embodiment, a mathematical model for the hydrogen storage subunit can be established according to the following formula:

[0119]

[0120] in, The variable is 0-1, representing the decision variable for the hydrogen injection process of the nth hydrogen storage unit at time t; The variable is 0-1, representing the decision variable for the process of hydrogen extraction by the nth hydrogen storage unit at time t; Let be the volumetric flow rate of injected hydrogen in the nth hydrogen storage sub-unit at time t; Let t be the maximum volume of hydrogen that can be injected into the hydrogen storage subunit at time t; Let be the total hydrogen volumetric flow rate extracted from the nth hydrogen storage subunit at time t; Let t be the maximum volumetric flow rate of hydrogen that the hydrogen storage subunit is allowed to extract at time t; Let t be the volumetric flow rate of hydrogen extracted from the nth hydrogen storage subunit and injected into the natural gas pipeline; V is the volumetric flow rate of hydrogen extracted from the nth hydrogen storage sub-unit at time t to supply the hydrogen supply sub-unit; ST,n,t Let η be the hydrogen volume of the nth hydrogen storage subunit at time t; in Efficiency of hydrogen injection into the hydrogen storage subunit; η out The efficiency of hydrogen extraction from the hydrogen storage subunit; V ST,min V is the minimum volume of the hydrogen storage subunit; ST,max This represents the maximum volume of the hydrogen storage subunit.

[0121] In one exemplary embodiment, the upper limit of the hydrogen mixing ratio can be set to 10%, and a constraint model for the hydrogen mixing ratio of the hydrogen mixing subunit can be established according to the following formula.

[0122]

[0123] in, Let be the hydrogen mixing ratio of natural gas network node i at time t;

[0124] A mathematical model for the mixed-hydrogen subunit can be established based on the following formula.

[0125]

[0126] in, Let be the calorific value of the hydrogen-mixed natural gas at node i of the natural gas network at time t; Let ξ be the hydrogen mixing ratio at node i of the natural gas network at time t; HHVGas It has a high calorific value for natural gas; It has a high calorific value for hydrogen.

[0127] In one exemplary embodiment, the output power of the hydrogen fuel cell can be obtained according to the following formula:

[0128]

[0129] Among them, P HFC,l,t η is the output power of the hydrogen fuel cell in user unit l at time t; HFC For the efficiency of hydrogen fuel cells; Let t be the hydrogen volumetric flow rate required by the hydrogen fuel cell of user unit l at time t; The calorific value of hydrogen is given by Δt; Δt is the difference between two adjacent scheduling times; P HFC,min This represents the lower limit of the output power of a hydrogen fuel cell; P HFC,max ΔP represents the upper limit of the output power of a hydrogen fuel cell. HFC,max This represents the maximum ramp power of the hydrogen fuel cell.

[0130] In one exemplary embodiment, hydrogen fuel cell vehicles are mostly commercial vehicles such as buses. These vehicles can have relatively fixed operating scenarios and travel routes. Assuming the hydrogen refueling station of user unit l is located at the starting point of the hydrogen fuel cell vehicle's travel route, when the z-th hydrogen fuel cell vehicle of user unit l completes the k-th cycle and returns to the starting point, the time it takes for the hydrogen fuel cell vehicle to arrive at the refueling station and the remaining fuel volume can be obtained according to the following formula.

[0131]

[0132]

[0133]

[0134] Among them, v t Let be the average vehicle speed on the road during time period t; λ1, λ2, λ3, and γ are regression parameters and correction coefficients, which are related to road grade; v0 is the design speed for each road grade; F t Let F0 be the traffic flow rate of the road during time period t; F0 is the designed traffic flow rate of the road. The time it takes for a hydrogen fuel cell vehicle to arrive at a hydrogen refueling station; Let x be the initial time of departure of the z-th hydrogen fuel cell vehicle in user unit l; D is the number of bus stops the vehicle passes through during its journey; x d Let be the road distance from the (d-1)th station to the dth station along the route; Δt represents the average speed of vehicles on the road during the (d-1)th stop along the route. d-1 The time spent waiting at the (d-1)th station; η represents the remaining fuel volume of the hydrogen fuel cell vehicle; η is the energy consumption coefficient of the vehicle under abnormal driving conditions, including energy loss caused by the load work required by the engine during start-up, acceleration, and braking. Let be the initial fuel volume of the z-th hydrogen fuel cell vehicle in user unit l, which can be simulated using Monte Carlo simulation to generate initial random fuel numbers; k is the k-th cycle state of the hydrogen fuel cell vehicle; s is the distance traveled along the vehicle route; ΔV HV This refers to the volume of hydrogen consumed per kilometer by a hydrogen-powered vehicle.

[0135] In one exemplary embodiment, when the remaining fuel in a hydrogen fuel cell vehicle is less than a certain value, hydrogen refueling is performed upon reaching a hydrogen refueling station. The hydrogen refueling status of the hydrogen fuel cell vehicle can be obtained according to the following formula:

[0136]

[0137] in, For hydrogen fuel cell vehicles, the hydrogen refueling status This indicates that the z-th hydrogen fuel cell vehicle in user unit l needs to refuel during time period t at the end of the k-th cycle; V represents the remaining fuel volume of a hydrogen fuel cell vehicle. set The fuel threshold for hydrogen fuel cell vehicles; This refers to the time it takes for a hydrogen fuel cell vehicle to arrive at a hydrogen refueling station.

[0138] In one exemplary embodiment, the volumetric flow rate of hydrogen fuel supplied to hydrogen-powered vehicles by a hydrogen refueling station can be obtained according to the following formula:

[0139]

[0140] in, Z represents the volumetric flow rate of hydrogen fuel supplied to hydrogen-powered vehicles by the hydrogen refueling station for user unit l during time period t; Z represents the total number of hydrogen-powered vehicles supplied by the hydrogen refueling station. The maximum volume for hydrogen storage in hydrogen fuel cell vehicles; The remaining fuel volume of a hydrogen fuel cell vehicle; The hydrogen refueling status of a hydrogen fuel cell vehicle; Δt is the difference between two adjacent scheduling times; when Time t represents the time when the hydrogen fuel cell vehicle returns to the starting point after the kth cycle, and the remaining fuel in the hydrogen fuel cell vehicle is less than or equal to the fuel threshold. set It requires hydrogenation, therefore the hydrogenation state is... when and Time: This indicates that time period t is the time when the hydrogen fuel cell vehicle returns to the starting station after the kth cycle, but the remaining fuel in the hydrogen fuel cell vehicle is greater than the fuel threshold V. set No hydrogen is needed, therefore the hydrogenated state is... This indicates that time period t is not the time when the hydrogen fuel cell vehicle returns to the starting station at the end of the kth cycle. Since the hydrogen fuel cell vehicle has not returned to the refueling station and does not refuel, its refueling status is [not specified].

[0141] In one exemplary embodiment, the electrical hydrogen coupling unit can maintain the following balance: the volumetric flow rate of hydrogen produced by the hydrogen production subunit is equal to the sum of the volumetric flow rates of hydrogen stored in the natural gas network by the hydrogen storage subunit; the volumetric flow rate of hydrogen consumed by the hydrogen subunit should be balanced with the volumetric flow rate of a portion of hydrogen extracted by the hydrogen storage subunit; and the volumetric flow rate of hydrogen injected into the natural gas network is equal to the volumetric flow rate of hydrogen injected into the natural gas pipeline by the hydrogen production subunit and the volumetric flow rate of another portion of hydrogen extracted from the hydrogen storage subunit.

[0142] In one exemplary embodiment, the electro-hydrogen coupling unit can satisfy the balance constraint in the following formula:

[0143]

[0144]

[0145]

[0146] in, Where is the volumetric flow rate of hydrogen produced by the water electrolysis device of the nth hydrogen production subunit at time t; The volumetric flow rate of hydrogen injected into the hydrogen storage subunit at time t for the nth hydrogen production subunit; Let L be the volumetric flow rate of hydrogen injected into the natural gas pipeline by the nth hydrogen production subunit at time t; L is the number of user units. Let t be the hydrogen volumetric flow rate required by the hydrogen fuel cell of user unit l at time t; Let N be the hydrogen volumetric flow rate required by the hydrogen refueling station of the l-th user unit at time t; N is the number of hydrogen production sub-units, hydrogen storage sub-units, and hydrogen mixing sub-units. Let t be the volumetric flow rate of hydrogen extracted from the nth hydrogen storage sub-unit to supply the hydrogen supply sub-unit; Let t be the volumetric flow rate of hydrogen extracted from the nth hydrogen storage subunit and injected into the natural gas pipeline; Let t be the total volumetric flow rate of hydrogen injected into the natural gas network by the nth hydrogen production subunit and hydrogen storage subunit at time t.

[0147] In this embodiment, by establishing mathematical models for each subunit of the electrical-hydrogen coupling unit based on the parameters of the subunits, the relationship between each link and hydrogen in the electrical-hydrogen coupling unit can be comprehensively and intuitively reflected, realizing refined modeling of the entire hydrogen energy process; at the same time, by considering factors such as the change in the calorific value of the mixed hydrogen natural gas and the hydrogen mixing ratio, the scheduling results are made more accurate.

[0148] In one embodiment, such as Figure 4 As shown, step S400 includes:

[0149] Step S401: Determine the flexibility parameters of the hydrogen-mixed gas turbine based on the upper limit of the output of the hydrogen-mixed gas turbine, the lower limit of the output of the hydrogen-mixed gas turbine, and the output sampling value of the hydrogen-mixed gas turbine of the user unit.

[0150] Step S402: Determine the flexibility parameters of the water electrolysis device based on the sampled power consumption of the water electrolysis device, the maximum volume of the hydrogen storage subunit, the sampled hydrogen volume of the hydrogen storage subunit, the efficiency of hydrogen injection into the hydrogen storage subunit, the efficiency of the water electrolysis device, and the high calorific value of hydrogen.

[0151] Step S403: Based on the upper limit of the output power of the hydrogen fuel cell, the lower limit of the output power of the hydrogen fuel cell, the output power sampling value of the hydrogen fuel cell, the hydrogen volume sampling value of the hydrogen storage subunit, the efficiency of the hydrogen fuel cell, the efficiency of hydrogen extraction by the hydrogen storage subunit, and the high calorific value of hydrogen, the flexibility parameters of the hydrogen fuel cell are obtained.

[0152] Step S404: Based on the flexibility parameters of the hydrogen-mixed gas turbine, the flexibility parameters of the water electrolysis device, and the flexibility parameters of the hydrogen fuel cell, a second objective function is obtained.

[0153] In one exemplary embodiment, the flexibility index of the hydrogen-mixed gas turbine can be obtained according to the following formula:

[0154]

[0155] Among them, F HT For the flexibility index of hydrogen-mixed gas turbines; The flexibility index provided for the hydrogen-mixed gas turbine of the user unit; L is the number of user units; The reduced flexibility index provided for the hydrogen-mixed gas turbine of the l-user unit; P HT,max This represents the upper limit of the output of the hydrogen-mixed gas turbine; P HT,min This represents the lower limit of the output of a hydrogen-mixed gas turbine; P HT,l The output P of the hydrogen-mixed gas turbine in the l-user unit HT,l,t A vector from 0 to 24h.

[0156] In one exemplary embodiment, the flexibility index of the water electrolysis device can be obtained according to the following formula:

[0157]

[0158] Among them, F P2G N represents the flexibility index of the water electrolysis unit; N is the number of hydrogen production subunits, hydrogen storage subunits, and hydrogen mixing subunits. The upscaling flexibility index for the water electrolysis unit in the nth hydrogen production unit; P is the down-adjustment flexibility index for the water electrolysis unit in the nth hydrogen production unit; P2G,n P represents the electrical power consumption of the water electrolysis unit in the nth hydrogen production unit. P2G,n,t A vector from 0 to 24h; V ST,max V is the maximum volume of the hydrogen storage unit. ST,n V represents the hydrogen volume of the nth hydrogen storage unit. ST,n,t A vector from 0 to 24h; η P2G The efficiency of the water electrolysis device; η in The efficiency of hydrogen injection into the hydrogen storage unit; Hydrogen has a high calorific value; P P2G,n,max Δt represents the upper limit of the input electrical power of the water electrolysis device of the nth hydrogen production unit at time t; Δt is the difference between two adjacent scheduling times.

[0159] In one exemplary embodiment, the flexibility of a hydrogen fuel cell can be obtained according to the following formula:

[0160]

[0161] Among them, F HFC L represents the flexibility index of hydrogen fuel cells; L is the number of user units. The increased flexibility index provided for hydrogen fuel cells in user units; The reduced flexibility index provided for the l user unit hydrogen fuel cell; P HFC,max P represents the upper limit of the output power of a hydrogen fuel cell. HFC,l The output P of the hydrogen fuel cell for the user unit HFC,l,t A vector from 0 to 24h; V ST,i The hydrogen volume V of the hydrogen storage unit i connected to user unit l ST,i,t A vector from 0 to 24h; η HFC For the efficiency of hydrogen fuel cells; η out The efficiency of hydrogen extraction from the hydrogen storage unit; Hydrogen has a high calorific value; P HFC,min Δt represents the lower limit of the output power of the hydrogen fuel cell; Δt is the difference between two adjacent scheduling times.

[0162] In this embodiment, by calculating the power supply parameters of each subunit in the electrical hydrogen coupling unit, the flexibility parameters of each subunit are obtained sequentially, and finally the second objective function is obtained. This allows for a more accurate assessment of the flexibility of the urban power grid. At the same time, through hydrogen fuel cells, water electrolysis devices, and hydrogen-mixed gas turbines, the adjustable load of the urban power grid is reflected more effectively, enabling flexible dispatching of resources to users.

[0163] In one embodiment, such as Figure 5 As shown, step S404 includes:

[0164] Step S441: Based on the upper limit of output of the thermal power unit, the lower limit of output of the thermal power unit, the upward ramp rate of the thermal power unit, the downward ramp rate of the thermal power unit, and the output sampling value of the thermal power unit, obtain the flexibility parameters of the thermal power unit.

[0165] Step S442: Based on the flexibility parameters of the hydrogen-mixed gas turbine, the water electrolysis device, the hydrogen fuel cell, and the thermal power unit, obtain the second objective function.

[0166] In one exemplary embodiment, the flexibility index of a thermal power unit can be obtained according to the following formula:

[0167]

[0168] Among them, F G G represents the flexibility index of thermal power units; G is the number of thermal power units. The flexibility index provided for thermal power units; These represent the downward adjustment flexibility indicators provided by thermal power units; P G,max P represents the upper limit of the output of the g-th thermal power unit; G,min R is the lower limit of the output of the g-th thermal power unit; d,g R is the downward ramp rate of the g-th thermal power unit; u,g P represents the upward gradient rate of the g-th thermal power unit. G,g P represents the output of the g-th thermal power unit. G,g,t A vector from 0 to 24h.

[0169] In one exemplary embodiment, the second objective function can be obtained according to the following formula:

[0170] minF2=min-(F G +F HT +F P2G +F HFC ) (twenty three)

[0171] Where F2 is the second objective function; F G For the flexibility index of thermal power units; F HT For the flexibility index of hydrogen-mixed gas turbines; F P2G For the flexibility index of water electrolysis device; F HFC This is an indicator of the flexibility of hydrogen fuel cells.

[0172] In this embodiment, by confirming the flexibility index of thermal power units, a second objective function is obtained through optimization. This makes the second objective function more accurate with the final scheduling result. At the same time, by using the sum of the flexibility of thermal power units, the flexibility of hydrogen-mixed gas turbines, the flexibility of water electrolysis devices, and the flexibility of hydrogen fuel cells as the second objective index, the flexible adjustment capability of the urban power grid is more effectively reflected.

[0173] In one embodiment, such as Figure 6 As shown, step S500 includes:

[0174] Step S501: Perform single-objective optimization on the first objective function and the second objective function respectively to obtain the extreme points.

[0175] Step S502: Obtain the Utopia Line based on the extreme points.

[0176] Step S503: Divide the Utopia line to obtain each dividing point, and obtain the target constraint conditions based on the vectors pointing to each dividing point and the vectors pointing to the extreme points.

[0177] Step S504: Determine the operating constraints of the urban power grid system based on the operating constraints corresponding to the urban power grid module, the natural gas grid module, and the user unit module.

[0178] Step S505: Based on the mathematical model, target constraints, and operational constraints established for each sub-unit, single-objective optimization processing is performed on the second objective function to obtain a set of target scheduling strategies.

[0179] In one exemplary embodiment, performing single-objective optimization on the first objective function and the second objective function to obtain the extreme point may include: for the urban power grid multi-objective scheduling model, considering only the single-objective optimization of the first objective function while keeping the constraints unchanged, obtaining the intraday economic scheduling cost; simultaneously, substituting the optimization results of each variable into the second objective function to obtain the second objective index; and using the daily economic scheduling cost optimized by the first objective and the corresponding second objective index as the first extreme point; for the urban power grid multi-objective scheduling model, considering only the single-objective optimization of the second objective function while keeping the constraints unchanged, obtaining the flexibility index; substituting each variable into the first objective function to obtain the first objective index; and using the second objective index optimized by the second objective and the corresponding first objective index as the second extreme point.

[0180] In one exemplary embodiment, the space of two extreme points is normalized and connected to obtain a Utopia line, and the Utopia line is divided equally according to a preset threshold to generate a threshold number of equally spaced dividing points.

[0181] In one exemplary embodiment, the solution of the model can be normalized according to the following formula:

[0182]

[0183] Among them, the This is the result after normalization. The first target indicator after standardization; F1 is the normalized second objective index; F2 is the model's solution for the first objective index; F3 is the model's solution for the second objective index. The first objective indicator is the result of single-objective optimization of the first objective. The second objective index is obtained by substituting the parameters of the first objective index, which is the result of single-objective optimization of the first objective, into the second objective function. The second objective indicator is the result of single-objective optimization of the second objective. The first objective index is obtained by substituting the parameters of the second objective index, which is the result of single-objective optimization of the second objective, into the first objective function.

[0184] In one exemplary embodiment, the division of the Utopia line to obtain each division point can be derived according to the following formula:

[0185]

[0186] in, is the segmentation point obtained at a certain threshold; j is the sequence of segmentation points; a is the preset segmentation threshold.

[0187] In one exemplary embodiment, the operational constraints of the urban power grid module may include power balance constraints, thermal power unit operational constraints, wind farm operational constraints, etc.; the natural gas grid operational constraints may include pipeline flow and node gas pressure constraints, node energy and component balance constraints, gas source supply constraints, etc.; user unit constraints may include internal power balance constraints, power load demand response constraints, photovoltaic-storage 5G base station operational constraints, hydrogen-mixed gas turbine operational constraints, etc.

[0188] In one exemplary embodiment, the power balance of the urban power grid can be obtained based on the electricity consumption of water electrolysis devices, the output of thermal power units, the actual output of wind farms and photovoltaic power stations, the electricity purchases of user units, and the electrical load of the urban distribution network; the power balance can be obtained according to the following formula.

[0189]

[0190] Where G represents the number of thermal power units; P G,g,t P represents the output of the g-th thermal power unit at time t; W represents the number of wind farms; P represents the output of the g-th thermal power unit at time t.PW,w,t Let w be the actual power output of the w-th wind farm at time t; L is the number of user units. P represents the power purchased by user unit l from the urban power distribution network at time t; N represents the number of hydrogen production subunits, hydrogen storage subunits, and hydrogen mixing subunits; P2G,n,t L represents the input electrical power of the water electrolysis device in the nth hydrogen production unit at time t. ud,t Let t be the electrical load of the city's power grid at time t.

[0191] In one exemplary embodiment, the operating constraints of the thermal power unit can be obtained according to the following formula:

[0192]

[0193] Among them, P G,min P is the lower limit of the output of the g-th thermal power unit; G,g,t P represents the output of the g-th thermal power unit at time t; G,min R is the upper limit of the output of the g-th thermal power unit; d,g R is the downward ramp rate of the g-th thermal power unit; u,g Let be the upward ramp rate of the g-th thermal power unit; Δt is the difference between two adjacent scheduling times.

[0194] In one exemplary embodiment, wind farm operating constraints can be obtained according to the following formula:

[0195]

[0196] Among them, P PW,w,t The actual power output of the w-th wind farm at time t; The predicted power output of the w-th wind farm at time t.

[0197] In one exemplary embodiment, nodal pressure and pipeline flow rate are related. Due to the influence of the natural gas network structure, the flow of hydrogen-mixed natural gas needs to meet the constraints of pipeline flow rate and nodal pressure during the flow process. This can be modeled using the Weymouth equation, and the pipeline flow rate and nodal pressure constraints can be obtained by modeling according to the following formula.

[0198]

[0199] ε ij,t =sgn(q ij,t (30)

[0200] -q ij,max ≤q ij,t ≤q ij,max (31)

[0201] p i,min ≤p i,t ≤pi,max (32)

[0202] q ij,t Let σ be the pipeline flow rate between natural gas network nodes i and j at time t; ij ε represents the pipeline transmission parameters between natural gas network nodes i and j; ij,t Let p be the directional parameter of the natural gas network pipeline at time t, taking values ​​of -1, 0, and 1; i,t Let q be the pressure value of node i in the natural gas network at time t; ij,max p represents the upper limit of pipeline flow between natural gas network nodes i and j at time t; i,max p represents the upper limit of the pressure value for node i in the natural gas network. i,min This represents the lower limit of the pressure value for node i in the natural gas network.

[0203] In one exemplary embodiment, the energy and component balance of the natural gas network nodes is related to the gas source point, the hydrogen mixing point, and other relevant nodes. The system is complex, and the energy and component flows within the system are directional. The flow rate of the hydrogen-mixed natural gas pipeline flowing into the natural gas network nodes is defined as positive. The node energy and component balance constraints can be obtained according to the following formula.

[0204]

[0205]

[0206]

[0207] in, Let be the volumetric flow rate of hydrogen injected into node i of the natural gas network at time t. Hydrogen has a high calorific value; ξ is the gas supply flow rate injected into node i of the natural gas network at time t; HHVGas It has a high calorific value for natural gas; u ij,t q is the positive direction indicator of the actual hydrogen-mixed natural gas pipeline flow between nodes i and j in the natural gas network at time t; ij,t Let be the pipeline flow rate between natural gas network nodes i and j at time t; Let L be the calorific value of the hydrogen-mixed natural gas at node i of the natural gas network at time t; q (j,i) represents the set of all pipelines with a reference direction from node j to i in the natural gas network; L q (i,j) represents the set of all pipelines with reference directions from node i to j in the natural gas network; γ ij,t V is the reverse direction identifier for the actual hydrogen-mixed natural gas pipeline flow between nodes i and j in the natural gas network at time t; DL,i,t Let V be the conventional gas load of natural gas network node i at time t; HT,i,t The volume of mixed hydrogen natural gas consumed by the mixed hydrogen gas turbine connected to node i of the natural gas grid; xc,i,t Let ε represent the proportion of gas component c at node i in the natural gas network at time t. When c = CH4, H2, the corresponding gases are methane and hydrogen, respectively. ij,t Let be the directional parameters of the natural gas network pipeline at time t, taking values ​​of -1, 0, and 1.

[0208] In one exemplary embodiment, the gas supply constraint can be obtained according to the following formula:

[0209]

[0210] in, This is the lower limit of the gas supply flow rate. Let t be the gas supply flow rate injected into node i of the natural gas network. This is the upper limit of the gas supply flow rate.

[0211] In one exemplary embodiment, the compressor constraint can be obtained according to the following formula:

[0212]

[0213] Among them, K min This represents the lower limit of the compression ratio; p i,t K represents the pressure value of node i in the natural gas network at time t. max This represents the upper limit of the compression ratio.

[0214] In one exemplary embodiment, user unit operating constraints can be obtained according to the following formula:

[0215]

[0216] Among them, P HT,l,t The electrical power output of the hydrogen-mixed gas turbine in user unit l at time t; P represents the power purchased by user unit l from the urban power distribution network at time t. PV,l,t P represents the actual output of the distributed photovoltaic unit at time t of the user unit base station; DIS,l,t P represents the discharge power of the backup battery of the user unit base station at time t. HFC,l,t L represents the output power of the hydrogen fuel cell in user unit l at time t. us,l,t P represents the electrical load of user unit l at time t. CH,l,t The charging power of the backup battery energy storage for user unit base station at time t; Let t be the total power consumption of the 5G base station of user unit l at time t.

[0217] In one exemplary embodiment, the electrical load demand response takes into account both electrical load reduction and electrical load transfer in the time dimension within the scheduling cycle. The electrical load of a user unit can be divided into three parts: fixed electrical load, reducible electrical load, and transferable electrical load. Among these, the fixed load does not participate in the demand response.

[0218] In one exemplary embodiment, the electrical load constraint of the user unit can be obtained according to the following formula:

[0219]

[0220] Among them, L us,l,t Let l be the electrical load of user unit l at time t; The fixed electrical load of user unit l at time t; The reducible electrical load for user unit l at time t; Let l be the alternative electrical load for user unit t at time t.

[0221] In one exemplary embodiment, the user unit can withstand a certain degree of load interruption and power reduction; by partially or completely reducing the reducible load power to meet the overall energy consumption target of the user unit, the reducible load constraint can be obtained according to the following formula.

[0222]

[0223] in, The value after the load demand response can be reduced for user unit l at time t; The reducible electrical load for user unit l at time t; The value at time t for reducing the load participating in demand response for user unit l; The upper limit of the load that can be reduced for a user unit at time t.

[0224] In one exemplary embodiment, transferable load may refer to the transfer of system energy consumption over a specific time period, but the total transferable load must remain constant within the scheduling cycle. The constraint on transferable load can be obtained according to the following formula.

[0225]

[0226] in, The value of the transferable load demand response for user unit l at time t; Let l be the value of the load that can be transferred to participate in demand response at time t for user unit l; It is a 0-1 variable, representing the state of the transferable load of user unit l at time t; It is a 0-1 variable, representing the state of the transferable load of user unit l at time t; Let l be the power transferred into the user unit at time t; Let l be the transferable load output of user unit l at time t; This represents the upper limit of the amount of transferable load from a user unit participating in the demand response at time t. This represents the lower limit of the amount of transferable load of a user unit participating in the demand response at time t.

[0227] In one exemplary embodiment, the photovoltaic-storage 5G base station consists of distributed photovoltaic units, backup battery energy storage, and base station equipment, possessing abundant flexible and adjustable resources. The main unit processing constraints to be considered for the distributed photovoltaic units of the base station can be obtained according to the following formula.

[0228]

[0229] Among them, P PV,l,t The actual output of the distributed photovoltaic unit at time t of the user unit base station; The predicted output of the distributed photovoltaic unit at time t for user unit base station.

[0230] In an exemplary embodiment, the constraints of the backup battery energy storage can be obtained based on the discharge state constraints, charge / discharge cycle constraints, charge / discharge power constraints, and energy storage constraints of the base station backup battery energy storage. The operational constraints of the backup battery energy storage can be obtained according to the following formula.

[0231]

[0232] in, , which is a 0-1 variable, represents the charging state of the backup battery energy storage of user unit base station at time t; N is a 0-1 variable, representing the discharge state of the backup battery energy storage of user unit base station at time t; T is the number of time periods in the scheduling cycle; N ESS The maximum number of charge-discharge cycles for base station backup battery energy storage; P CH,l,t The charging power of the backup battery energy storage at time t for user unit base station; P DIS,l,t P represents the discharge power of the backup battery of the user unit base station at time t. CH,max The upper limit of charging power for backup battery energy storage in base stations; P DIS,max The upper limit of discharge power for backup battery energy storage in base stations; η CH The charging efficiency for energy storage of backup batteries for base stations; η DIS Discharge efficiency of backup batteries for base stations; E ESS,l,t The capacity of the backup battery for the base station at time t; E ESS,max Δt represents the upper limit of the backup battery energy storage capacity of the base station; Δt is the difference between two adjacent scheduling times.

[0233] In one exemplary embodiment, the base station equipment mainly refers to the base station's operating power consumption, which can include static power consumption and dynamic power consumption. Static power consumption is independent of the number of users accessing the base station and represents the basic power consumption during normal base station operation. Dynamic power consumption is directly proportional to the number of users accessing the base station, and the base station power consumption operating constraints can be obtained according to the following formula.

[0234]

[0235] in, The total power consumption of the 5G base station of user unit l at time t; Let l be the static power consumption of the 5G base station of user unit l at time t; Let β represent the dynamic power consumption of the 5G base station of user unit l at time t; β represents the energy efficiency coefficient of the base station. This represents the maximum dynamic power consumption of the base station at time t.

[0236] In one exemplary embodiment, the hydrogen-mixed gas turbine unit is an important coupling device between the urban power distribution network and the natural gas network, and the operating constraints of the hydrogen-mixed gas turbine can be obtained according to the following formula.

[0237]

[0238] Among them, P HT,l,t For the output of the hydrogen-mixed gas turbine of the l user unit; η HT The power generation efficiency of a hydrogen-mixed gas turbine; V HT,l,t The volume of mixed hydrogen natural gas required for the user unit's mixed hydrogen gas turbine to generate electricity at time t; P represents the calorific value of the hydrogen-mixed natural gas at the access node of the natural gas grid for the user unit's hydrogen-mixed gas turbine at time t. HT,max This represents the upper limit of the output of the hydrogen-mixed gas turbine; P HT,min This represents the lower limit of the output of a hydrogen-mixed gas turbine.

[0239] In one exemplary embodiment, the operational constraints of the hydrogen fuel cell and hydrogen refueling station within the user unit can be obtained based on mathematical models of the hydrogen fuel cell and hydrogen refueling station.

[0240] In one exemplary embodiment, the solution of multi-objective optimization Jupiter by the normalization method to the constraint method can be transformed into the solution of a series of single-objective problems, and the optimal solution obtained by solving each single-objective problem constitutes the optimal solution set.

[0241] In one exemplary embodiment, the following target constraints can be obtained based on the vectors pointing to each of the segmentation points and the vectors pointing to the extreme points.

[0242]

[0243] in, A vector pointing from one extreme point to the other extreme point; These are the coordinates pointing to the solutions of the normalized model; Let be the vector pointing to the j-th segmentation point.

[0244] In one exemplary embodiment, based on the objective constraints, the mathematical models of the hydrogen production subunit, the hydrogen storage subunit, the hydrogen mixing subunit, the hydrogen utilization subunit, the power balance constraints, the thermal power unit operation constraints, the wind farm operation constraints, the pipeline flow and node gas pressure constraints, the node energy and component balance constraints, the gas supply constraints, the user unit internal power balance constraints, the power load demand response constraints, the photovoltaic-storage 5G base station operation constraints, and the hydrogen mixing gas turbine operation constraints, a single-objective optimization model is performed on the second objective function at each segmentation point to obtain the Pareto front, i.e., the set of objective scheduling strategies.

[0245] In one exemplary embodiment, the set of target scheduling strategies can be sorted using an approximation ideal solution sorting method to obtain a compromise target scheduling strategy for the urban power grid target scheduling model.

[0246] In one exemplary embodiment, the set of target scheduling strategies obtained from the model solution can be used as an evaluation set to form an evaluation matrix.

[0247]

[0248] Where M is the number of target scheduling strategies in the target scheduling strategy set, i.e., the number of evaluation individuals; m is the evaluation individual index; C is the number of objective functions or indicators; F m，c This represents the c-th indicator value for the m-th individual being evaluated.

[0249] The evaluation matrix is ​​normalized according to the following formula.

[0250]

[0251] The following normalized evaluation matrix is ​​further obtained.

[0252]

[0253] The following index weights were obtained using the entropy weight method.

[0254] in

[0255] Among them, H c Let be the entropy value of the c-th index;

[0256] Define entropy weight using the following formula.

[0257]

[0258] Constructing the weight matrix yields the following matrix.

[0259]

[0260] The value matrix was calculated, resulting in the following matrix.

[0261]

[0262] The following formulas are obtained by calculating the positive and negative ideal solutions:

[0263]

[0264]

[0265] Calculate the Euclidean distance from each evaluated individual to the positive and negative ideal solutions, and obtain the following formula:

[0266]

[0267] The relative closeness of each evaluated individual is calculated using the following formula:

[0268]

[0269] The target scheduling strategy that yields the highest relative progress is the compromise.

[0270] In this embodiment, by solving the target constraints, the operating constraints of the urban power grid system, and the mathematical models of each sub-unit in the electrical-hydrogen coupling unit, the multi-objective optimization scheduling model is obtained, and the target scheduling strategy is obtained. This strategy can not only ensure the economy of the urban power grid, but also fully utilize the flexible backup capabilities of the power sources and loads within the urban power grid, making the target scheduling strategy more accurate.

[0271] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0272] In one exemplary embodiment, the urban power grid multi-objective scheduling method can be as follows: Figure 7The flowchart is as follows:

[0273] Collect basic parameters of urban power grid, natural gas grid, user unit, wind power processing forecast data, and electricity and natural gas load forecast data respectively;

[0274] The collected data is then input into the program that invokes the strategy model;

[0275] Establish an urban power grid operation framework for the integration of hydrogen fuel cell vehicles;

[0276] By establishing the relationship between each unit in the urban power grid operation framework and hydrogen energy, an operational characteristic model of the electrical-hydrogen coupling unit is established.

[0277] An economic-flexible multi-objective optimization scheduling model for urban power grids is established based on the urban power grid operation architecture and the electrical-hydrogen coupling unit model.

[0278] By solving the urban power grid economic-flexible multi-objective optimization scheduling model using the normalization method, an intraday optimization scheduling strategy for urban power grids with hydrogen fuel cell vehicles is obtained.

[0279] Based on the same inventive concept, this application also provides an urban power grid multi-objective scheduling device for implementing the urban power grid multi-objective scheduling method described above. The solution provided by this device is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more embodiments of the urban power grid multi-objective scheduling device provided below can be found in the limitations of the urban power grid multi-objective scheduling method described above, and will not be repeated here.

[0280] In one embodiment, such as Figure 8 As shown, a multi-objective scheduling device 100 for urban power grids is provided, comprising: a vehicle access module 101, a unit determination module 102, a model building module 103, a scheduling model module 104, and an objective scheduling module 105, wherein:

[0281] The vehicle access module is used to connect hydrogen fuel cell vehicles to the original urban power grid operating system to obtain an updated urban power grid operating system. The updated urban power grid operating system includes an urban power grid module, a natural gas grid module, and a user unit module, and there are energy conversion relationships between the modules.

[0282] A unit determination module is used to determine an electrical-hydrogen coupling unit from the updated urban power grid operation system; wherein the electrical-hydrogen coupling unit includes sub-units corresponding to each stage between hydrogen energy production and use;

[0283] The model building module is used to build corresponding mathematical models for each sub-unit in the electrical hydrogen coupling unit;

[0284] The scheduling model module is used to determine a first objective function based on the updated operating cost parameters of each module of the urban power grid operation system, and to determine a second objective function based on the energy supply parameters of each subunit of the electrical-hydrogen coupling unit, and to generate a multi-objective optimization scheduling model based on the first objective function and the second objective function.

[0285] The target scheduling module is used to solve the multi-objective optimization scheduling model to obtain the updated target resource scheduling strategy for the urban power grid operation system.

[0286] In one embodiment, the unit determining module includes: a stage submodule and a confirmation submodule, wherein:

[0287] The phase submodule is used to divide the hydrogen energy production phase, storage phase, hydrogen blending phase, and usage phase based on the updated urban power grid operation system, and obtain the sub-units corresponding to each phase between hydrogen energy production and usage.

[0288] The confirmation submodule is used to confirm the electrical hydrogen coupling unit based on the sub-units corresponding to each stage.

[0289] In one embodiment, the model building module includes: a hydrogen production submodule, a hydrogen storage submodule, a hydrogen mixing submodule, and a hydrogen utilization submodule, wherein:

[0290] The hydrogen production submodule is used to obtain the volumetric flow rate of hydrogen produced by the water electrolysis device in the hydrogen production submodule based on the efficiency of the water electrolysis device, the input electrical power of the water electrolysis device, and the higher calorific value of hydrogen, when the submodule is a hydrogen production submodule. The volumetric flow rate of hydrogen produced by the water electrolysis device is used as the mathematical model of the hydrogen production submodule.

[0291] The hydrogen storage submodule is used to obtain the hydrogen volume of the hydrogen storage subunit when the subunit is a hydrogen storage subunit, based on the volumetric flow rate of hydrogen allowed to be injected into the hydrogen storage subunit, the efficiency of hydrogen injection into the hydrogen storage subunit, the total volumetric flow rate of hydrogen extracted by the hydrogen storage subunit, and the efficiency of hydrogen extraction by the hydrogen storage subunit, and to use the hydrogen volume of the hydrogen storage subunit as the mathematical model of the hydrogen storage subunit.

[0292] The hydrogen mixing submodule is used to obtain the calorific value of the mixed hydrogen natural gas of the mixed hydrogen subunit based on the hydrogen mixing ratio, the higher calorific value of natural gas, and the higher calorific value of hydrogen when the subunit is a mixed hydrogen subunit, and to use the calorific value of the mixed hydrogen natural gas as the mathematical model of the mixed hydrogen subunit.

[0293] The hydrogen-using submodule, when the subunit is a hydrogen-using subunit, is used to obtain the output power of the hydrogen fuel cell based on the efficiency of the hydrogen fuel cell in the hydrogen-using subunit, the required hydrogen volumetric flow rate of the hydrogen fuel cell, and the higher calorific value of hydrogen. It also uses the maximum capacity of the hydrogen storage tank of the hydrogen-powered vehicle in the hydrogen-using subunit, the initial fuel volume of the hydrogen-powered vehicle, the number of cycle starting stations of the hydrogen-powered vehicle, the driving distance of the vehicle route, the hydrogen consumption volume per kilometer of the hydrogen-powered vehicle, the fuel threshold of the hydrogen-powered vehicle, the initial departure time of the hydrogen-powered vehicle, the road distance between each two stations of the hydrogen-powered vehicle, the design speed of each road level, the number of vehicles on the road, the design traffic flow of the road, and the total number of hydrogen-powered vehicles supplied by the hydrogen refueling station to obtain the hydrogen fuel volumetric flow rate supplied by the hydrogen refueling station. The output power of the hydrogen fuel cell and the hydrogen fuel volumetric flow rate supplied by the hydrogen refueling station are used as the mathematical model of the hydrogen-using subunit.

[0294] In one embodiment, the scheduling model module includes: a hydrogen-mixed gas turbine submodule, a water electrolysis device submodule, a hydrogen fuel cell submodule, and an objective function submodule, wherein:

[0295] The hydrogen-mixed gas turbine submodule is used to determine the flexibility parameters of the hydrogen-mixed gas turbine based on the upper limit of the output of the hydrogen-mixed gas turbine, the lower limit of the output of the hydrogen-mixed gas turbine, and the output sampling value of the hydrogen-mixed gas turbine of the user unit.

[0296] The water electrolysis device submodule is used to determine the flexibility parameters of the water electrolysis device based on the sampled power consumption of the water electrolysis device, the maximum volume of the hydrogen storage subunit, the sampled hydrogen volume of the hydrogen storage subunit, the efficiency of hydrogen injection in the hydrogen storage subunit, the efficiency of the water electrolysis device, and the high calorific value of hydrogen.

[0297] The hydrogen fuel cell submodule is used to obtain the flexibility parameters of the hydrogen fuel cell based on the upper limit of the hydrogen fuel cell output power, the lower limit of the hydrogen fuel cell output power, the output power sampling value of the hydrogen fuel cell, the hydrogen volume sampling value of the hydrogen storage subunit, the efficiency of the hydrogen fuel cell, the efficiency of hydrogen extracted by the hydrogen storage subunit, and the high calorific value of hydrogen.

[0298] The objective function submodule is used to obtain a second objective function based on the flexibility parameters of the hydrogen-mixed gas turbine, the flexibility parameters of the water electrolysis device, and the flexibility parameters of the hydrogen fuel cell.

[0299] In one embodiment, the second objective function submodule includes: a thermal power unit submodule and a second objective function submodule, wherein:

[0300] The thermal power unit submodule obtains the thermal power unit flexibility parameters based on the thermal power unit's upper output limit, lower output limit, upward ramp rate, downward ramp rate, and output sampling value.

[0301] The second objective function submodule is used to obtain the second objective function based on the flexibility parameters of the hydrogen-mixed gas turbine, the flexibility parameters of the water electrolysis device, the flexibility parameters of the hydrogen fuel cell, and the flexibility parameters of the thermal power unit.

[0302] In one embodiment, the target scheduling module includes: a single-target optimization submodule, a utopian line submodule, a target constraint submodule, a runtime constraint submodule, and a target scheduling submodule, wherein:

[0303] The single-objective optimization submodule is used to perform single-objective optimization on the first objective function and the second objective function respectively to obtain the extreme points;

[0304] The Utopia Line submodule is used to obtain the Utopia Line based on the extreme points;

[0305] The target constraint submodule is used to divide the utopian line to obtain each dividing point, and to obtain the target constraint conditions based on the vectors pointing to each dividing point and the vectors pointing to the extreme points.

[0306] The operation constraint submodule is used to determine the operation constraint conditions of the urban power grid operation system based on the operation constraint conditions corresponding to the urban power grid module, the natural gas grid module, and the user unit module, respectively.

[0307] The target scheduling submodule is used to perform single-objective optimization processing on the second objective function based on the corresponding mathematical model established by each subunit, the target constraints, and the operational constraints, to obtain a set of target scheduling strategies.

[0308] Each module in the aforementioned urban power grid multi-objective dispatching device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0309] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores urban power grid data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a multi-objective scheduling method for the urban power grid.

[0310] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0311] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data shall comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0312] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0313] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0314] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A multi-objective scheduling method for urban power grids, characterized in that, The method includes: By connecting hydrogen fuel cell vehicles to the original urban power grid operating system, an updated urban power grid operating system is obtained. The updated urban power grid operating system includes an urban power grid module, a natural gas grid module, and a user unit module, and there are energy conversion relationships between the modules. An electrical-hydrogen coupling unit is determined from the updated urban power grid operation system; wherein the electrical-hydrogen coupling unit includes sub-units corresponding to each stage between hydrogen energy production and use; the sub-units include at least one of the following: hydrogen production sub-unit, hydrogen storage sub-unit, hydrogen blending sub-unit, and hydrogen use sub-unit; When the subunit is a hydrogen production subunit, the volumetric flow rate of hydrogen produced by the water electrolysis device in the hydrogen production subunit is obtained based on the efficiency of the water electrolysis device, the input electrical power of the water electrolysis device, and the higher calorific value of hydrogen. The volumetric flow rate of hydrogen produced by the water electrolysis device is then used as the mathematical model of the hydrogen production subunit. When the subunit is a hydrogen storage subunit, the hydrogen volume of the hydrogen storage subunit is obtained based on the volumetric flow rate of hydrogen allowed to be injected into the hydrogen storage subunit, the efficiency of hydrogen injection into the hydrogen storage subunit, the total volumetric flow rate of hydrogen extracted by the hydrogen storage subunit, and the efficiency of hydrogen extraction by the hydrogen storage subunit. The hydrogen volume of the hydrogen storage subunit is used as the mathematical model of the hydrogen storage subunit. When the subunit is a mixed hydrogen subunit, the mixed hydrogen natural gas calorific value of the mixed hydrogen subunit is obtained based on the mixed hydrogen ratio, the higher calorific value of natural gas, and the higher calorific value of hydrogen. The mixed hydrogen natural gas calorific value is then used as the mathematical model of the mixed hydrogen subunit. When the subunit is a hydrogen-using subunit, the output power of the hydrogen fuel cell is obtained based on the efficiency of the hydrogen fuel cell in the hydrogen-using subunit, the hydrogen volumetric flow rate required by the hydrogen fuel cell, and the higher calorific value of hydrogen. The hydrogen fuel volumetric flow rate supplied by the hydrogen refueling station to the hydrogen-powered vehicle is obtained based on the maximum capacity of the hydrogen storage tank of the hydrogen-powered vehicle in the hydrogen-using subunit, the initial fuel volume of the hydrogen-powered vehicle, the number of cycle starting stations of the hydrogen-powered vehicle, the driving distance of the vehicle route, the hydrogen consumption volume per kilometer of the hydrogen-powered vehicle, the fuel threshold of the hydrogen-powered vehicle, the initial departure time of the hydrogen-powered vehicle, the road distance between each two stations of the hydrogen-powered vehicle, the design speed of each level of road, the number of vehicles on the road, the design traffic flow of the road, and the total number of hydrogen-powered vehicles supplied by the hydrogen refueling station. The output power of the hydrogen fuel cell and the hydrogen fuel volumetric flow rate supplied by the hydrogen refueling station to the hydrogen-powered vehicle are used as the mathematical model of the hydrogen-using subunit. Based on the updated operating cost parameters of each module of the urban power grid operation system, a first objective function is determined, and based on the energy supply parameters of each subunit of the electrical-hydrogen coupling unit, a second objective function is determined. Based on the first objective function and the second objective function, a multi-objective optimization scheduling model is generated. The multi-objective optimization scheduling model is solved to obtain the updated target resource scheduling strategy for the urban power grid operation system.

2. The method according to claim 1, characterized in that, The process of determining the electrical hydrogen coupling unit from the updated urban power grid operation system includes: Based on the updated urban power grid operation system, the hydrogen energy production stage, storage stage, hydrogen blending stage, and usage stage are divided to obtain the sub-units corresponding to each stage between hydrogen energy production and usage. Based on the sub-units corresponding to each stage, the electrical hydrogen coupling unit is identified.

3. The method according to claim 1, characterized in that, The power supply parameters of each subunit include at least one of the following: hydrogen-mixed gas turbine flexibility parameters, water electrolysis device flexibility parameters, and hydrogen fuel cell flexibility parameters. The determination of the second objective function based on the power supply parameters of each subunit of the electro-hydrogen coupling unit includes: Based on the upper and lower limits of the output of the hydrogen-mixed gas turbine, and the output sampling value of the hydrogen-mixed gas turbine in the user unit, the flexibility parameters of the hydrogen-mixed gas turbine are determined. Based on the power consumption sampling value of the water electrolysis device, the maximum volume of the hydrogen storage subunit, the hydrogen volume sampling value of the hydrogen storage subunit, the hydrogen injection efficiency of the hydrogen storage subunit, the efficiency of the water electrolysis device, and the high calorific value of hydrogen, the flexibility parameters of the water electrolysis device are determined. Based on the upper limit of the output power of the hydrogen fuel cell, the lower limit of the output power of the hydrogen fuel cell, the output power sampling value of the hydrogen fuel cell, the hydrogen volume sampling value of the hydrogen storage subunit, the efficiency of the hydrogen fuel cell, the efficiency of hydrogen extraction by the hydrogen storage subunit, and the high calorific value of hydrogen, the flexibility parameters of the hydrogen fuel cell are obtained. The second objective function is obtained based on the flexibility parameters of the hydrogen-mixed gas turbine, the flexibility parameters of the water electrolysis device, and the flexibility parameters of the hydrogen fuel cell.

4. The method according to claim 3, characterized in that, Based on the flexibility parameters of the hydrogen-mixed gas turbine, the water electrolysis device, and the hydrogen fuel cell, a second objective function is obtained, including: Based on the upper limit of output of thermal power unit, the lower limit of output of thermal power unit, the upward ramp rate of thermal power unit, the downward ramp rate of thermal power unit, and the output sampling value of thermal power unit, the flexibility parameters of thermal power unit are obtained. The second objective function is obtained based on the flexibility parameters of the hydrogen-mixed gas turbine, the water electrolysis device, the hydrogen fuel cell, and the thermal power unit.

5. The method according to claim 1, characterized in that, Solving the multi-objective optimization scheduling model to obtain the target scheduling strategy includes: Single-objective optimization is performed on the first objective function and the second objective function respectively to obtain the extreme points; Based on the extreme points, the Utopia Line is obtained; The utopian line is divided to obtain each dividing point, and the target constraint conditions are obtained based on the vectors pointing to each dividing point and the vectors pointing to the extreme points. Based on the operational constraints corresponding to the urban power grid module, the natural gas grid module, and the user unit module, the operational constraints of the urban power grid operation system are determined. Based on the mathematical models established for each subunit, the target constraints, and the operational constraints, the second objective function is subjected to single-objective optimization processing to obtain a set of target scheduling strategies.

6. A multi-objective dispatching device for urban power grids, characterized in that, The device includes: The vehicle access module is used to connect hydrogen fuel cell vehicles to the original urban power grid operating system to obtain an updated urban power grid operating system. The updated urban power grid operating system includes an urban power grid module, a natural gas grid module, and a user unit module, and there are energy conversion relationships between the modules. A unit determination module is used to determine an electrical-hydrogen coupling unit from the updated urban power grid operation system; wherein, the electrical-hydrogen coupling unit includes sub-units corresponding to each stage between hydrogen energy production and use; the sub-units include at least one of the following: hydrogen production sub-unit, hydrogen storage sub-unit, hydrogen blending sub-unit, and hydrogen use sub-unit; The model building module is used to obtain the volumetric flow rate of hydrogen produced by the water electrolysis device in the hydrogen production subunit based on the efficiency of the water electrolysis device, the input power of the water electrolysis device, and the higher calorific value of hydrogen, when the subunit is a hydrogen production subunit. The volumetric flow rate of hydrogen produced by the water electrolysis device is then used as the mathematical model of the hydrogen production subunit. When the subunit is a hydrogen storage subunit, the hydrogen volume of the hydrogen storage subunit is obtained based on the volumetric flow rate of hydrogen allowed to be injected into the hydrogen storage subunit, the efficiency of hydrogen injection into the hydrogen storage subunit, the total volumetric flow rate of hydrogen extracted by the hydrogen storage subunit, and the efficiency of hydrogen extraction by the hydrogen storage subunit. The hydrogen volume of the hydrogen storage subunit is used as the mathematical model of the hydrogen storage subunit. When the subunit is a mixed hydrogen subunit, the mixed hydrogen natural gas calorific value of the mixed hydrogen subunit is obtained based on the mixed hydrogen ratio, the higher calorific value of natural gas, and the higher calorific value of hydrogen. The mixed hydrogen natural gas calorific value is then used as the mathematical model of the mixed hydrogen subunit. When the subunit is a hydrogen-using subunit, the output power of the hydrogen fuel cell is obtained based on the efficiency of the hydrogen fuel cell in the hydrogen-using subunit, the required hydrogen volumetric flow rate of the hydrogen fuel cell, and the higher calorific value of hydrogen. The output power is also determined based on the maximum capacity of the hydrogen storage tank in the hydrogen-powered vehicle within the hydrogen-using subunit, the initial fuel volume of the hydrogen-powered vehicle, the number of cycle start stations of the hydrogen-powered vehicle, the driving distance of the vehicle route, the hydrogen consumption per kilometer of the hydrogen-powered vehicle, the fuel threshold of the hydrogen-powered vehicle, the initial departure time of the hydrogen-powered vehicle, the road distance between every two stations of the hydrogen-powered vehicle, the design speed of each road class, the traffic volume on the road, and the road... The design considers the traffic flow and the total number of hydrogen fuel vehicles supplied by the hydrogen refueling station to obtain the volumetric flow rate of hydrogen fuel supplied by the hydrogen refueling station to the hydrogen fuel vehicles. The output power of the hydrogen fuel cell and the volumetric flow rate of hydrogen fuel supplied by the hydrogen refueling station to the hydrogen fuel vehicles are used as the mathematical model scheduling model module of the hydrogen-using sub-unit. This module is used to determine the first objective function based on the updated operating cost parameters of each module of the urban power grid operation system, and to determine the second objective function based on the energy supply parameters of each sub-unit of the electrical-hydrogen coupling unit. Based on the first objective function and the second objective function, a multi-objective optimization scheduling model is generated. The target scheduling module is used to solve the multi-objective optimization scheduling model to obtain the updated target resource scheduling strategy for the urban power grid operation system.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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