Multi-objective scheduling method and apparatus for urban power grid, and computer device

Abstract

The present application relates to a multi-objective scheduling method and apparatus for an urban power grid, a computer device, a storage medium, and a computer program product. The method comprises: connecting hydrogen energy vehicles to an original urban power grid operation system to obtain an updated urban power grid operation system; determining an electrical-hydrogen coupling unit from the updated urban power grid operation system; establishing a corresponding mathematical model for each subunit in the electrical-hydrogen coupling unit; determining a first objective function on the basis of operation cost parameters of respective modules of the updated urban power grid operation system, determining a second objective function on the basis of energy supply parameters of each subunit of the electrical-hydrogen coupling unit, and generating a multi-objective optimization and scheduling model on the basis of the first objective function and the second objective function; and solving the multi-objective optimization and scheduling model to obtain a target scheduling strategy.

Description

Urban power grid multi-objective dispatching method, device and computer equipment

[0001] Related applications

[0002] This application claims priority to Chinese patent application number 2023114498539, filed on November 2, 2023, entitled “Multi-objective dispatching method, device and computer equipment for urban power grids,” the entire text of which is incorporated herein by reference. Technical Field

[0003] The present 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 Art

[0004] With the development of urban power grids, new energy vehicles are being connected on a large scale. This integration helps balance the load on urban grids, reducing energy waste and carbon emissions. Currently, when designing the flexibility for hydrogen vehicles connected to urban grids, the approach typically prioritizes meeting minimum economic requirements. Based on this minimum, factors such as water electrolysis systems, thermal power units, hybrid gas turbines, and hydrogen fuel cells are comprehensively considered to improve the flexibility of the urban grid. However, this flexibility approach falls short of the target requirements and fails to meet real-world needs.

[0005] Summary of the Invention

[0006] According to various embodiments of the present application, a method, apparatus, computer device, computer-readable storage medium, and computer program product for multi-objective scheduling of an urban power grid are provided.

[0007] In a first aspect, the present application provides a multi-objective scheduling method for an urban power grid. The method comprises:

[0008] Connecting hydrogen energy vehicles to the original urban power grid operation system results in an updated urban power grid operation system; wherein the updated urban power grid operation system includes an urban power grid module, a natural gas network module, and a user unit module, and each module has an energy conversion relationship;

[0009] Determining an electric-hydrogen coupling unit from the updated urban power grid operation system; wherein the electric-hydrogen coupling unit includes subunits corresponding to each stage between the production and use of hydrogen energy;

[0010] Establish corresponding mathematical models for each subunit in the electrical hydrogen coupling unit;

[0011] Determining a first objective function based on the updated operating cost parameters of each module of the urban power grid operation system, and determining a second objective function based on the energy supply parameters of each subunit of the electric-hydrogen coupling unit, and generating a multi-objective optimization scheduling model based on the first objective function and the second objective function;

[0012] The multi-objective optimization scheduling model is solved to obtain the updated urban power grid operation system target resource scheduling strategy.

[0013] In one embodiment, determining the electric hydrogen coupling unit from the updated urban power grid operation system includes:

[0014] Based on the updated urban power grid operation system, the hydrogen energy production stage, storage stage, hydrogen mixing stage and use stage are divided to obtain subunits corresponding to each stage from hydrogen energy production to use;

[0015] According to the subunits corresponding to each stage, the electrical hydrogen coupling unit is confirmed.

[0016] In one embodiment, the subunits include at least one of the following: a hydrogen production subunit, a hydrogen storage subunit, a hydrogen mixing subunit, and a hydrogen use subunit, and establishing a corresponding mathematical model for each subunit in the electrical hydrogen coupling unit includes:

[0017] In the case where the subunit is a hydrogen production subunit, the volume flow rate of hydrogen produced by the water electrolysis device is obtained according to the efficiency of the water electrolysis device in the hydrogen production subunit, the input power of the water electrolysis device, and the high calorific value of hydrogen, and the volume flow rate of hydrogen produced by the water electrolysis device is used as the mathematical model of the hydrogen production subunit;

[0018] In the case where the subunit is a hydrogen storage subunit, the hydrogen volume of the hydrogen storage subunit is obtained based on the volume flow rate of hydrogen allowed to be injected into the hydrogen storage subunit, the efficiency of hydrogen injection of the hydrogen storage subunit, the total volume flow rate of hydrogen produced by the hydrogen storage subunit, and the efficiency of hydrogen production of the hydrogen storage subunit, and the hydrogen volume of the hydrogen storage subunit is used as the mathematical model of the hydrogen storage subunit;

[0019] In the case where the subunit is a hydrogen mixing subunit, the hydrogen-mixed natural gas calorific value of the hydrogen mixing subunit is obtained according to the hydrogen mixing ratio, the natural gas higher calorific value and the hydrogen higher calorific value of the hydrogen mixing subunit, and the hydrogen-mixed natural gas calorific value is used as the mathematical model of the hydrogen mixing subunit;

[0020] In the case where 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 volume flow rate of hydrogen required by the hydrogen fuel cell, and the high calorific value of hydrogen. The volume flow rate of hydrogen fuel supplied to the hydrogen energy vehicle by the hydrogen refueling station is obtained based on the maximum capacity of the hydrogen storage bottle of the hydrogen energy vehicle in the hydrogen-using subunit, the initial fuel volume of the hydrogen energy vehicle, the number of cycle starting stations of the hydrogen energy vehicle, the driving distance of the vehicle route, the volume of hydrogen consumption per kilometer of the hydrogen energy vehicle, the fuel threshold of the hydrogen energy vehicle, the initial departure time of the hydrogen energy vehicle, the road distance between every two stations of the hydrogen energy vehicle, the design speed of each grade of road, the number of vehicles on the road, the design traffic flow of the road, and the total number of hydrogen energy vehicles supplied by the hydrogen refueling station. The output power of the hydrogen fuel cell and the volume flow rate of hydrogen fuel supplied to the hydrogen energy vehicle by the hydrogen refueling station are used as the mathematical model of the hydrogen-using subunit.

[0021] In one embodiment, the energy supply parameters of each subunit include at least one of the following: a hydrogen-mixed gas turbine flexibility parameter, a water electrolysis device flexibility parameter, and a hydrogen fuel cell flexibility parameter. Determining the second objective function based on the energy supply parameters of each subunit of the electric-hydrogen coupling unit includes:

[0022] Determine the flexibility parameter of the hydrogen-mixed gas turbine according to the output upper limit and the output lower limit of the hydrogen-mixed gas turbine and the output sampling value of the hydrogen-mixed gas turbine of the user unit;

[0023] Determine the flexibility parameter of the water electrolysis device 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 efficiency of hydrogen injection of the hydrogen storage subunit, the efficiency of the water electrolysis device and the high calorific value of hydrogen;

[0024] The flexibility parameter of the hydrogen fuel cell is obtained according to the upper limit of the hydrogen fuel cell output power, the lower limit of the hydrogen fuel cell output power, the output 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 production by the hydrogen storage subunit and the high calorific value of hydrogen;

[0025] A second objective function is obtained based on the flexibility parameter of the hydrogen-mixed gas turbine, the flexibility parameter of the water electrolysis device and the flexibility parameter of the hydrogen fuel cell.

[0026] In one embodiment, a second objective function is obtained based on the flexibility parameter of the hydrogen-mixed gas turbine, the flexibility parameter of the water electrolysis device, and the flexibility parameter of the hydrogen fuel cell, including:

[0027] According to the output upper limit of the thermal power unit, the output lower limit of the thermal power unit, the upward climbing rate of the thermal power unit, the downward climbing rate of the thermal power unit and the output sampling value of the thermal power unit, the flexibility parameter of the thermal power unit is obtained;

[0028] A second objective function is obtained 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.

[0029] In one embodiment, solving the multi-objective optimization scheduling model to obtain a target scheduling strategy includes:

[0030] Perform single-objective optimization processing on the first objective function and the second objective function respectively to obtain extreme points;

[0031] According to the extreme points, the Utopia line is obtained;

[0032] The Utopia line is segmented to obtain segmentation points, and target constraint conditions are obtained according to vectors pointing to the segmentation points and vectors pointing to the extreme points;

[0033] Determining the operation constraint conditions of the city power grid operation system according to the operation constraint conditions corresponding to the city power grid module, the natural gas network module and the user unit module respectively;

[0034] A corresponding mathematical model, the target constraint conditions and the operation constraint conditions are established according to each subunit, and a single-objective optimization process is performed on the second objective function to obtain a target scheduling strategy set.

[0035] In one embodiment, the acquisition of the city power grid operation system to which the hydrogen energy vehicle is connected includes coupling the city power grid, the natural gas grid and the user unit.

[0036] In one embodiment, solving the multi-objective optimization scheduling model to obtain the updated urban power grid operation system target resource scheduling strategy includes:

[0037] The multi-objective optimization scheduling model is solved by using a normalization method to obtain the updated urban power grid operation system target scheduling strategy.

[0038] In one embodiment, the operation constraints of the urban power grid module include: electric power balance constraints, thermal power unit operation constraints, and wind farm operation constraints.

[0039] In one embodiment, the operating constraints of the natural gas network module include pipeline flow and node gas pressure constraints, node energy and component balance constraints, and gas source supply constraints.

[0040] In one embodiment, the operating constraints of the user unit module include internal electric power balance constraints of the user unit, electric load demand response constraints, photovoltaic storage 5G base station operating constraints and hybrid hydrogen gas turbine operating constraints.

[0041] In a second aspect, the present application also provides a multi-objective dispatching device for a city power grid. The device comprises:

[0042] The vehicle access module is used to connect the hydrogen energy vehicle to the original urban power grid operation system to obtain an updated urban power grid operation system; wherein the updated urban power grid operation system includes an urban power grid module, a natural gas network module and a user unit module, and an energy conversion relationship exists between the modules;

[0043] A unit determination module is used to determine an electric-hydrogen coupling unit from the updated urban power grid operation system; wherein the electric-hydrogen coupling unit includes subunits corresponding to each stage between the production and use of hydrogen energy;

[0044] A model building module is used to build corresponding mathematical models for each subunit in the electrical hydrogen coupling unit;

[0045] a scheduling model module, configured to determine a first objective function based on the updated operating cost parameters of each module of the urban power grid operation system, and a second objective function based on the energy supply parameters of each subunit of the electric-hydrogen coupling unit, and to generate a multi-objective optimization scheduling model based on the first objective function and the second objective function;

[0046] The target scheduling module is used to solve the multi-objective optimization scheduling model to obtain the updated urban power grid operation system target resource scheduling strategy.

[0047] In one embodiment, the unit determination module includes:

[0048] A stage submodule is used to divide the hydrogen energy production stage, storage stage, hydrogen mixing stage, and use stage based on the updated urban power grid operation system, and obtain subunits corresponding to each stage from hydrogen energy production to use;

[0049] The confirmation submodule is used to confirm the electrical hydrogen coupling unit according to the subunits corresponding to each stage.

[0050] In one embodiment, the model building module includes:

[0051] A hydrogen production submodule, for obtaining, when the subunit is a hydrogen production subunit, a volume flow rate of hydrogen produced by the water electrolysis device according to the efficiency of the water electrolysis device in the hydrogen production subunit, the input power of the water electrolysis device, and the high calorific value of hydrogen, and using the volume flow rate of hydrogen produced by the water electrolysis device as a mathematical model of the hydrogen production subunit;

[0052] A hydrogen storage submodule, for obtaining the hydrogen volume of the hydrogen storage subunit based on the volume 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 volume flow rate of hydrogen produced by the hydrogen storage subunit, and the efficiency of hydrogen production by the hydrogen storage subunit, when the subunit is a hydrogen storage subunit, and using the hydrogen volume of the hydrogen storage subunit as a mathematical model of the hydrogen storage subunit;

[0053] A hydrogen mixing submodule, for obtaining the hydrogen-mixed natural gas calorific value of the hydrogen mixing subunit according to the hydrogen mixing ratio, the natural gas higher calorific value, and the hydrogen higher calorific value of the hydrogen mixing subunit when the subunit is a hydrogen mixing subunit, and using the hydrogen-mixed natural gas calorific value as a mathematical model of the hydrogen mixing subunit;

[0054] The hydrogen submodule is used to obtain the output power of the hydrogen fuel cell according to the efficiency of the hydrogen fuel cell in the hydrogen subunit, the volume flow of hydrogen required by the hydrogen fuel cell, and the high calorific value of hydrogen when the subunit is a hydrogen subunit; and obtain the volume flow of hydrogen fuel supplied to the hydrogen energy vehicle by the hydrogen refueling station according to the maximum capacity of the hydrogen storage bottle of the hydrogen energy vehicle in the hydrogen subunit, the initial fuel volume of the hydrogen energy vehicle, the number of cycle starting stations of the hydrogen energy vehicle, the driving distance of the vehicle route, the volume of hydrogen consumption per kilometer of the hydrogen energy vehicle, the fuel threshold of the hydrogen energy vehicle, the initial departure time of the hydrogen energy vehicle, the road distance between every two stations of the hydrogen energy vehicle, the design speed of each grade of road, the number of vehicles on the road, the design traffic flow of the road, and the total number of hydrogen energy vehicles supplied by the hydrogen refueling station; and the output power of the hydrogen fuel cell and the volume flow of hydrogen fuel supplied to the hydrogen energy vehicle by the hydrogen refueling station are used as the mathematical model of the hydrogen subunit.

[0055] In one embodiment, the scheduling model module includes:

[0056] The hydrogen-mixed gas turbine submodule is used to determine the flexibility parameter of the hydrogen-mixed gas turbine according to the output upper limit of the hydrogen-mixed gas turbine, the output lower limit of the hydrogen-mixed gas turbine, and the output sampling value of the hydrogen-mixed gas turbine of the user unit;

[0057] The water electrolysis device submodule is used to determine the flexibility parameter of the water electrolysis device 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 efficiency of the hydrogen storage subunit hydrogen injection, the efficiency of the water electrolysis device, and the high calorific value of hydrogen;

[0058] The hydrogen fuel cell submodule is used to obtain the hydrogen fuel cell flexibility parameter based on the upper limit of the hydrogen fuel cell output power, the lower limit of the hydrogen fuel cell output power, the output 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 production by the hydrogen storage subunit, and the high calorific value of hydrogen;

[0059] The objective function submodule is used to obtain a second objective function based on the flexibility parameter of the hydrogen-mixed gas turbine, the flexibility parameter of the water electrolysis device, and the flexibility parameter of the hydrogen fuel cell.

[0060] In one embodiment, the second objective function submodule includes:

[0061] The thermal power unit submodule obtains the thermal power unit flexibility parameter based on the thermal power unit output upper limit, the thermal power unit output lower limit, the thermal power unit upward climbing rate, the thermal power unit downward climbing rate, and the thermal power unit output sampling value;

[0062] 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.

[0063] In one embodiment, the target scheduling module includes:

[0064] A single-objective optimization submodule is used to perform single-objective optimization processing on the first objective function and the second objective function respectively to obtain extreme points;

[0065] A Utopia line submodule, configured to obtain a Utopia line according to the extreme points;

[0066] A target constraint submodule is used to segment the Utopia line to obtain segmentation points, and obtain target constraint conditions based on vectors pointing to the segmentation points and vectors pointing to the extreme points;

[0067] An operation constraint submodule, configured to determine the operation constraint conditions of the city power grid operation system according to the operation constraint conditions corresponding to the city power grid module, the natural gas grid module, and the user unit module respectively;

[0068] The target scheduling submodule is used to establish corresponding mathematical models, the target constraints, and the operating constraints according to each subunit, perform single-objective optimization processing on the second objective function, and obtain a target scheduling strategy set.

[0069] In one embodiment, the acquisition of the city power grid operation system to which the hydrogen energy vehicle is connected includes coupling the city power grid, the natural gas grid and the user unit.

[0070] In one embodiment, the target scheduling module includes:

[0071] The multi-objective optimization scheduling model is solved by using a normalization method to obtain the updated urban power grid operation system target scheduling strategy.

[0072] In one embodiment, the operation constraints of the urban power grid module include: electric power balance constraints, thermal power unit operation constraints, and wind farm operation constraints.

[0073] In one embodiment, the operating constraints of the natural gas network module include pipeline flow and node gas pressure constraints, node energy and component balance constraints, and gas source supply constraints.

[0074] In one embodiment, the operating constraints of the user unit module include internal electric power balance constraints of the user unit, electric load demand response constraints, photovoltaic storage 5G base station operating constraints and hybrid hydrogen gas turbine operating constraints.

[0075] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the multi-objective scheduling method for an urban power grid as described in any one of the embodiments of the present disclosure.

[0076] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the multi-objective scheduling method for an urban power grid as described in any one of the embodiments of the present disclosure.

[0077] In a fifth aspect, the present application further provides a computer program product, comprising a computer program that, when executed by a processor, implements the multi-objective scheduling method for an urban power grid as described in any one of the embodiments of the present disclosure.

[0078] The details of one or more embodiments of the present application are set forth in the following drawings and description. Other features, solutions, and advantages of the present application will become apparent from the description, drawings, and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to better describe and illustrate the embodiments and / or examples of the inventions disclosed herein, reference may be made to one or more of the accompanying drawings. The additional details or examples used to describe the accompanying drawings should not be considered to limit the scope of the disclosed inventions, the presently described embodiments and / or examples, and any of the best modes currently understood for these inventions.

[0080] FIG1 is a schematic flow chart of a multi-objective scheduling method for a city power grid according to an embodiment;

[0081] FIG2 is a schematic diagram of a process for determining an electrical hydrogen coupling unit in one embodiment;

[0082] FIG3 is a schematic diagram of a process for establishing a mathematical model of an electrical hydrogen coupling unit in one embodiment;

[0083] FIG4 is a schematic diagram of a first process for determining a second objective function in one embodiment;

[0084] FIG5 is a schematic diagram of a second process for determining a second objective function in one embodiment;

[0085] FIG6 is a schematic diagram of a process for solving a multi-objective optimization scheduling model in one embodiment;

[0086] FIG7 is a schematic diagram of a flow chart of a multi-objective scheduling method for a city power grid according to an embodiment;

[0087] FIG8 is a structural block diagram of a multi-objective dispatching device for a city power grid according to an embodiment;

[0088] FIG9 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0089] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0090] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0091] In one embodiment, as shown in FIG1 , a multi-objective scheduling method for a city power grid is provided, comprising the following steps:

[0092] Step S100: Connect the hydrogen energy vehicle to the original urban power grid operation system to obtain an updated urban power grid operation system; wherein the updated urban power grid operation system includes an urban power grid module, a natural gas network module and a user unit module, and an energy conversion relationship exists between the modules.

[0093] In an exemplary embodiment, the connection of hydrogen energy vehicles to the original urban power grid operation system may include establishing a hydrogen energy access urban power grid operation system by tightly coupling the natural gas grid and user units, and considering technical routes such as electric hydrogen production technology, mixed hydrogen and natural gas technology, hydrogen fuel cell operation, hydrogen use at hydrogen refueling stations, and demand response technology.

[0094] In one exemplary embodiment, the city grid operating system connected to hydrogen-powered vehicles can be composed of a coupled urban grid, natural gas grid, and user units. The urban grid can include equipment such as thermal power units, wind farms, water electrolysis devices, and hydrogen storage tanks; the natural gas grid can include natural gas equipment, gas booster tanks, flow mixers, and compressed natural gas pressure reducing stations; and the user units can include equipment such as hydrogen-blending gas turbines, hydrogen fuel cells, 5G solar-storage base stations, and hydrogen refueling stations.

[0095] In an exemplary embodiment, the urban power grid operation system to which the hydrogen energy vehicle is connected may include: when the system power grid has surplus power, a water electrolysis device produces hydrogen to realize the conversion of electric energy flow and hydrogen energy flow; a portion of the hydrogen produced by the water electrolysis device is stored in a hydrogen storage tank and supplied to the user unit, and a portion is injected into the natural gas grid in a certain proportion to form mixed hydrogen natural gas to realize the conversion of hydrogen energy flow and gas energy flow; the mixed hydrogen natural gas is supplied to the internal hydrogen-mixed gas turbine of the user unit to generate electricity to realize the conversion of gas energy flow and electric energy flow; when the user unit is short of electricity or is at a peak of electricity consumption, the hydrogen fuel cell uses hydrogen to generate electricity to realize the conversion of hydrogen energy flow and electric energy flow.

[0096] Step S200: determining an electric-hydrogen coupling unit from the updated urban power grid operation system; wherein the electric-hydrogen coupling unit includes subunits corresponding to each stage between the production and use of hydrogen energy.

[0097] In an exemplary embodiment, the electrical hydrogen coupling unit can be established by a water electrolysis device, a hydrogen fuel cell, a hydrogen-mixed gas turbine, a hydrogen storage tank, a hydrogen refueling station and other devices.

[0098] In an exemplary embodiment, the electric hydrogen coupling unit may contain electric 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.

[0099] Step S300 : establishing corresponding mathematical models for each subunit in the electrical hydrogen coupling unit.

[0100] In an exemplary embodiment, the electric hydrogen coupling unit can be divided into a hydrogen production unit, a hydrogen storage unit, a hydrogen mixing natural gas technology unit and a hydrogen use unit, and its operating characteristics can be modeled in combination with the hydrogen volume flow balance relationship between the units.

[0101] 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 electric-hydrogen coupling unit; and generate a multi-objective optimization scheduling model based on the first objective function and the second objective function.

[0102] In an exemplary embodiment, the first objective function can be obtained according to the following formula: min F1 = min(C e +C g +C h +C us ) (1)

[0103] Among them, F1 is the first objective function; C e is the operating cost of the power system; C g is the operating cost of the natural gas system; C h is the operating cost of the electric hydrogen coupling unit; C us The operating cost of the user unit.

[0104] In an exemplary embodiment, the operating cost of the power system can be obtained according to the following formula:

[0105] Among them, C e is the operating cost of the power system; C PG is the power generation cost of thermal power units; C PW is the operating cost of the wind farm; C buy is the cost of electricity purchased by the user unit from the upper power grid; L is the number of user units; T is the number of moments in the dispatch cycle; f g (P G,g,t ) is the fuel cost to be paid by thermal power unit g in period t; where a g 、b g 、c g are the fuel cost coefficients of thermal power unit g; G is the number of thermal power units; W is the number of wind farms; ζ PW P is the operation and maintenance cost per unit wind power output; PW,w,t is the actual output of the w-th wind farm at time t; Time-of-use electricity price; is the power purchased by user unit l from the urban distribution network at time t.

[0106] In an exemplary embodiment, the operating cost of the natural gas system can be obtained according to the following formula:

[0107] Among them, C gis the operating cost of the natural gas system; T is the number of hours in the scheduling cycle; I is the number of nodes in the natural gas network; λ sg The cost of gas supply; is the gas supply flow injected into the natural gas grid node i at time t; Δt is the difference between two adjacent scheduling times.

[0108] In an exemplary embodiment, the operating cost of the electric hydrogen coupling unit can be obtained according to the following formula,

[0109] Among them, C h is the operating cost of the electric hydrogen coupling unit; C P2G is the operating cost of the water electrolysis device; C HFC is the operating cost of hydrogen fuel cells; C HV is the operating cost of the hydrogen refueling station; C ST is the operating cost of the hydrogen storage unit; P2G is the operating cost of the unit power consumption of the electrolytic water equipment; L is the number of user units; T is the number of moments in the scheduling cycle; P P2G,n,t is the input power of the water electrolysis device of the nth hydrogen production unit at time t; N is the number of hydrogen production subunits, hydrogen storage subunits and hydrogen mixing subunits; ζ HFC P is the operating cost of hydrogen fuel cell unit power generation; HFC,l,t is the output power of the hydrogen fuel cell of user unit l at time t; HV The operating cost of the hydrogen refueling station per unit hydrogen refueling volume; is the hydrogen volume flow rate required by the lth user unit hydrogen refueling station at time t; ST The operating cost of injecting and extracting unit hydrogen volume flow rate of the hydrogen storage unit; is the volume flow rate of hydrogen produced from the nth hydrogen storage unit and injected into the natural gas pipeline at time t; is the volume flow rate of hydrogen produced from the nth hydrogen storage unit and supplied to the hydrogen unit at time t; is the volume flow rate of hydrogen injected into the nth hydrogen storage unit at time t; Δt is the difference between two adjacent scheduling times.

[0110] In an exemplary embodiment, the user unit operating cost can be obtained according to the following formula:

[0111] Among them, C us is the operating cost of the user unit; C dr is the demand response cost of adjustable electric load; C 5G C is the operating cost of the optical storage 5G base station; HT is the gas purchase cost of the hydrogen-blended gas turbine from the natural gas system; L is the number of user units; T is the time of the scheduling cycle; is the value of load reduction that can be achieved by user unit l at time t to participate in demand response; is the value of the load that can be transferred by user unit l at time t to participate in demand response; τ c is the compensation price for unit power load reduction; τ t P is the compensation price for unit power load transfer; PV,l,t is the actual output of the distributed photovoltaic unit of the user unit base station at time t; P is the operation and maintenance cost of the base station distributed photovoltaic unit per unit output; CH,l,t is the charging power of the backup battery energy storage of the user unit base station at time t; P DIS,l,t is the discharge power of the backup battery of the user unit base station at time t; ESS The operating and maintenance costs of the base station backup battery energy storage unit charging and discharging power; is the total power consumption of the 5G base station of user unit l at time t; B is the energy cost per unit power consumption of 5G base stations; hg,t The time-of-use gas price for purchasing gas from the natural gas system for the hydrogen-blended gas turbine; V HT,l,t is the volume of hydrogen-mixed natural gas required for the hydrogen-mixed gas turbine of user unit l to generate electricity at time t.

[0112] In an exemplary embodiment, the multi-objective optimization scheduling model can take into account urban power grid operation constraints, natural gas grid operation constraints, and user unit operation constraints, among which the urban power grid constraints may include electric power balance constraints, thermal power unit operation constraints, and wind farm operation constraints, among which the natural gas grid operation constraints may include pipeline flow and node gas pressure constraints, node energy and component balance constraints, gas source supply constraints, and compressor constraints, and the user unit operation constraints include user unit internal electric power balance constraints, adjustable electric load demand response constraints, photovoltaic storage 5G base station operation constraints, hybrid hydrogen gas turbine operation constraints, and hydrogen fuel cell and hydrogen energy vehicle operation constraints, among others.

[0113] Step S500 , solving the multi-objective optimization scheduling model to obtain the updated urban power grid operation system target resource scheduling strategy.

[0114] In an exemplary embodiment, solving the multi-objective optimization scheduling model may include using a normalization method to solve the multi-objective optimization scheduling model. For example, the method converts the multi-objective optimization scheduling model into a solution to a series of single-objective problems, thereby obtaining the Pareto front of the model, and selecting a suitable compromise solution from the Pareto front by approximating the ideal solution sorting method.

[0115] In the above-mentioned multi-objective scheduling method for urban power grids, hydrogen-powered vehicles are connected to the original urban power grid operating system. The subunits in the updated urban power grid operating system, from hydrogen energy production to use, are then identified as electric-hydrogen coupling units. A model is then established. The first objective function is determined based on the operating costs of each module of the urban power grid. The second objective function is determined based on the energy supply parameters of each subunit of the electric-hydrogen coupling unit. A multi-objective optimization scheduling model is generated and solved to obtain the target scheduling strategy. This satisfies the urban power grid's requirements for the first and second objectives. The urban power grid operating system connected to hydrogen-powered vehicles effectively plays the role of coordinating multiple energy complementarity in the urban power grid system, effectively alleviating the pressure on the flexible standby of the urban power grid, improving the flexible adjustment capability of the urban power grid, and enhancing the stability of the urban power grid.

[0116] In one embodiment, as shown in FIG2 , step S200 includes:

[0117] Step S201: Based on the updated urban power grid operation system, the hydrogen energy production stage, storage stage, hydrogen mixing stage, and use stage are divided to obtain subunits corresponding to each stage from hydrogen energy production to use.

[0118] Step S202 : confirming the electrical hydrogen coupling unit according to the subunits corresponding to each stage.

[0119] In an exemplary embodiment, dividing the updated urban power grid operation system may include dividing each device in the urban power grid template, the natural gas grid module, and the user unit module in the urban power grid operation system.

[0120] In an exemplary embodiment, the subunits corresponding to each stage between the production and use of the hydrogen energy may include a hydrogen production unit, a hydrogen storage unit, a hydrogen mixing unit and a hydrogen use unit, wherein the hydrogen production unit may include a water electrolysis device, etc., the hydrogen storage unit may include a cluster of hydrogen storage tanks, etc., the hydrogen mixing unit may include a natural gas pipeline, etc., and the hydrogen use unit may include a hydrogen fuel cell, a hydrogen energy vehicle, etc.

[0121] In this embodiment, by dividing the hydrogen energy into stages in the urban power grid operation system and determining the electrical hydrogen coupling unit based on the divided sub-units, the model constructed by the coupling unit can be made more comprehensive and intuitive, and the refined modeling of all aspects of hydrogen energy can be achieved, which can comprehensively and intuitively reflect the relationship between the various aspects of hydrogen energy in the urban power grid system.

[0122] In one embodiment, as shown in FIG3 , the subunits include at least one of the following: a hydrogen production subunit, a hydrogen storage subunit, a hydrogen mixing subunit, and a hydrogen use subunit. Step S300 includes:

[0123] Step S301: When the subunit is a hydrogen production subunit, the volume flow rate of hydrogen produced by the water electrolysis device is obtained according to the efficiency of the water electrolysis device in the hydrogen production subunit, the input power of the water electrolysis device, and the high calorific value of hydrogen, and the volume flow rate of hydrogen produced by the water electrolysis device is used as the mathematical model of the hydrogen production subunit.

[0124] Step S302, when the subunit is a hydrogen storage subunit, the hydrogen volume of the hydrogen storage subunit is obtained according to the volume flow rate of hydrogen allowed to be injected into the hydrogen storage subunit, the efficiency of hydrogen injection of the hydrogen storage subunit, the total volume flow rate of hydrogen produced by the hydrogen storage subunit, and the efficiency of hydrogen production of the hydrogen storage subunit, and the hydrogen volume of the hydrogen storage subunit is used as the mathematical model of the hydrogen storage subunit.

[0125] Step S303, when the subunit is a hydrogen mixing subunit, the hydrogen-mixed natural gas calorific value of the hydrogen mixing subunit is obtained according to the hydrogen mixing ratio, natural gas higher calorific value, and hydrogen higher calorific value of the hydrogen mixing subunit, and the hydrogen-mixed natural gas calorific value is used as the mathematical model of the hydrogen mixing subunit.

[0126] Step S304, when the subunit is a hydrogen-using subunit, the output power of the hydrogen fuel cell is obtained according to the efficiency of the hydrogen fuel cell in the hydrogen-using subunit, the volume flow rate of hydrogen required by the hydrogen fuel cell, and the high calorific value of hydrogen. The volume flow rate of hydrogen fuel supplied to the hydrogen energy vehicle by the hydrogen refueling station is obtained according to the maximum capacity of the hydrogen storage bottle of the hydrogen energy vehicle in the hydrogen-using subunit, the initial fuel volume of the hydrogen energy vehicle, the number of cycle starting stations of the hydrogen energy vehicle, the driving distance of the vehicle route, the volume of hydrogen consumption per kilometer of the hydrogen energy vehicle, the fuel threshold of the hydrogen energy vehicle, the initial departure time of the hydrogen energy vehicle, the road distance between every two stations of the hydrogen energy vehicle, the design speed of each grade of road, the number of vehicles on the road, the design traffic flow of the road, and the total number of hydrogen energy vehicles supplied by the hydrogen refueling station. The output power of the hydrogen fuel cell and the volume flow rate of hydrogen fuel supplied to the hydrogen energy vehicle by the hydrogen refueling station are used as the mathematical model of the hydrogen-using subunit.

[0127] In an exemplary embodiment, a mathematical model can be established for the hydrogen production subunit according to the following formula:

[0128] in, is the volume flow rate of hydrogen produced by the water electrolysis device of the nth hydrogen production subunit at time t; η P2G is the efficiency of the water electrolysis device; P P2G,n,t is the input power of the water electrolysis device of the nth hydrogen production subunit at time t; is the high calorific value of hydrogen; Δt is the difference between two adjacent scheduling times; P P2G,n,max is the upper limit of the input power of the nth single-eye hydrogen-producing water electrolysis device at time t.

[0129] In an exemplary embodiment, a mathematical model can be established for the hydrogen storage subunit according to the following formula:

[0130] in, is a 0-1 variable, representing the decision variable of the hydrogen injection process of the nth hydrogen storage unit at time t; is a 0-1 variable, representing the decision variable of the hydrogen production process of the nth hydrogen storage unit at time t; is the volume flow rate of hydrogen injected into the nth hydrogen storage subunit at time t; The maximum volume of hydrogen allowed to be injected into the hydrogen storage subunit at time t; is the total hydrogen volume flow rate extracted from the nth hydrogen storage subunit at time t; is the maximum hydrogen volume flow rate allowed to be extracted by the hydrogen storage subunit at time t; is the volume flow rate of hydrogen produced from the nth hydrogen storage subunit and injected into the natural gas pipeline at time t; V is the volume flow rate of hydrogen extracted from the nth hydrogen storage subunit to supply the hydrogen subunit at time t; ST,n,t is the hydrogen volume of the nth hydrogen storage subunit at time t; η in The efficiency of hydrogen injection into the hydrogen storage subunit; η out V is the efficiency of hydrogen production from the hydrogen storage subunit; ST,min is the minimum volume of the hydrogen storage subunit; V ST,max is the maximum volume of the hydrogen storage subunit.

[0131] In an exemplary embodiment, the upper limit of the hydrogen mixing ratio may be set to 10%, and a constraint model of the hydrogen mixing ratio may be established for the hydrogen mixing subunit according to the following formula:

[0132] in, is the hydrogen mixing ratio of natural gas grid node i at time t;

[0133] A mathematical model can be established for the hydrogen mixing subunit according to the following formula:

[0134] in, is the calorific value of hydrogen-mixed natural gas at node i in the natural gas grid at time t; is the hydrogen mixing ratio of natural gas grid node i at time t; ξ HHVGas The natural gas has a high calorific value; Hydrogen has a high calorific value.

[0135] In an exemplary embodiment, the output power of the hydrogen fuel cell can be obtained according to the following formula:

[0136] Among them, P HFC,l,t is the output power of the hydrogen fuel cell of user unit l at time t; η HFC for the efficiency of hydrogen fuel cells; is the volume flow of hydrogen required by the hydrogen fuel cell of the user unit l at time t; is the high calorific value of hydrogen; Δt is the difference between two adjacent scheduling times; P HFC,min is the lower limit of the output power of the hydrogen fuel cell; P HFC,max is the upper limit of the output power of the hydrogen fuel cell; ΔP HFC,max is the maximum climbing power of the hydrogen fuel cell.

[0137] In an exemplary embodiment, hydrogen-powered vehicles are mostly commercial vehicles such as buses. Hydrogen-powered vehicles may have relatively fixed operating scenarios and travel routes. Assume that the hydrogen refueling station of user unit 1 is located at the starting point of the hydrogen-powered vehicle's travel route. When the zth hydrogen-powered vehicle of user unit 1 returns to the starting point after completing the kth cycle, the time it takes for the hydrogen-powered vehicle to arrive at the hydrogen refueling station and the remaining fuel volume can be obtained according to the following formula:

[0138] Among them, v t is the average speed of cars on the road during period t; λ1, λ2, λ3 and γ are regression parameters and correction coefficients, which are related to the road grade; v0 is the design speed of each grade of road; F t is the traffic volume of the road during period t; F0 is the designed traffic volume of the road; The time it takes for hydrogen vehicles to arrive at the hydrogen refueling station; is the initial departure time of the zth hydrogen energy vehicle in the lth user unit; D is the number of bus stops passed by the vehicle during its travel; x d is the road distance from the d-1th station to the dth station on the way; is the average speed of cars on the road during the waiting period at the d-1th station; Δt d-1 is the waiting time at the d-1th station; is the remaining fuel volume of the hydrogen energy 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 starting, acceleration, and braking; is the initial fuel volume of the zth hydrogen energy vehicle in the lth user unit, which can be simulated by Monte Carlo simulation to generate a random number of initial fuel; k is the kth cycle state of the hydrogen energy vehicle; s is the driving distance of the vehicle route; ΔV HV It is the volume of hydrogen consumed per kilometer by hydrogen-powered vehicles.

[0139] In an exemplary embodiment, when the remaining fuel of a hydrogen energy vehicle is less than a certain value, hydrogenation is performed after the vehicle reaches a hydrogen refueling station. The hydrogenation state of the hydrogen energy vehicle can be obtained according to the following formula:

[0140] in, The hydrogen refueling status of hydrogen energy vehicles. It means that the zth hydrogen energy vehicle of user unit l needs to be refueled during the t period at the end of the kth cycle; is the remaining fuel volume of the hydrogen energy vehicle; V set The fuel threshold for hydrogen vehicles; It is the time it takes for a hydrogen vehicle to arrive at a hydrogen refueling station.

[0141] In an exemplary embodiment, the volume flow rate of hydrogen fuel supplied to hydrogen energy vehicles by a hydrogen refueling station can be obtained according to the following formula:

[0142] in, is the volume flow of hydrogen fuel supplied to hydrogen energy vehicles by the hydrogen refueling station for user unit l during period t; Z is the total number of hydrogen energy vehicles supplied by the hydrogen refueling station; The maximum volume of hydrogen storage for hydrogen-powered vehicles; is the remaining fuel volume of the hydrogen energy vehicle; is the hydrogenation state of the hydrogen energy vehicle; Δt is the difference between two adjacent scheduling times; when and When t is the time when the hydrogen energy vehicle returns to the starting station after the kth cycle, and the remaining fuel of the hydrogen energy vehicle is less than or equal to the fuel threshold, V set , needs hydrogenation, so the hydrogenation state is when and Time: represents the time period t when the hydrogen energy vehicle returns to the starting station at the end of the kth cycle, but the remaining fuel of the hydrogen energy vehicle is greater than the fuel threshold V set , no hydrogenation is required, so the hydrogenation state is Indicates that the time period t is not the time when the hydrogen energy vehicle returns to the starting station at the end of the kth cycle. The hydrogen energy vehicle does not return to the hydrogen refueling station and does not refuel. Therefore, the hydrogen refueling status is

[0143] In an exemplary embodiment, the electrical hydrogen coupling unit can have the following balance relationship: the volume flow rate of hydrogen produced by the hydrogen production sub-unit is equal to the sum of the volume flow rates of hydrogen entering the hydrogen storage sub-unit and stored in the natural gas network; the volume flow rate of hydrogen consumed by the hydrogen sub-unit should be balanced with the volume flow rate of part of the hydrogen produced by the hydrogen storage sub-unit; the volume flow rate of hydrogen injected into the natural gas network is equal to the volume flow rate of hydrogen injected into the natural gas pipeline by the hydrogen production sub-unit and the volume flow rate of another part of hydrogen produced from the hydrogen storage sub-unit, etc.

[0144] In an exemplary embodiment, the electrical hydrogen coupling unit may satisfy the balance constraint in the following equation,

[0145] in, Wherein is the volume flow rate of hydrogen produced by the water electrolysis device of the nth hydrogen production subunit at time t; is the volume flow rate of hydrogen injected into the hydrogen storage subunit by the nth hydrogen production subunit at time t; is the volume flow rate of hydrogen injected into the natural gas pipeline by the nth hydrogen production sub-unit at time t; L is the number of user units; is the volume flow of hydrogen required by the hydrogen fuel cell of the user unit l at time t; is the hydrogen volume flow rate required by the lth user unit hydrogen refueling station at time t; N is the number of hydrogen production subunits, hydrogen storage subunits and hydrogen mixing subunits; is the volume flow rate of hydrogen extracted from the nth hydrogen storage subunit to supply the hydrogen subunit at time t; is the volume flow rate of hydrogen produced from the nth hydrogen storage subunit and injected into the natural gas pipeline at time t; is the total volume flow of hydrogen injected into the natural gas grid by the nth hydrogen production subunit and hydrogen storage subunit at time t.

[0146] In this embodiment, by adjusting the parameters of the sub-units in the electric-hydrogen coupling unit, a mathematical model is established for each sub-unit of the electric-hydrogen coupling unit, which can comprehensively and intuitively reflect the relationship between each link in the electric-hydrogen coupling unit and hydrogen, and realize refined modeling of all links of hydrogen energy; at the same time, by taking into account factors such as the change in the calorific value of mixed hydrogen natural gas and the hydrogen mixing ratio, the scheduling results are made more accurate.

[0147] In one embodiment, as shown in FIG4 , step S400 includes:

[0148] Step S401: Determine the flexibility parameter of the hydrogen-mixed gas turbine according to the output upper limit and lower limit of the hydrogen-mixed gas turbine and the output sampling value of the user unit hydrogen-mixed gas turbine.

[0149] Step S402, determining the flexibility parameters of the water electrolysis device 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 efficiency of hydrogen injection of the hydrogen storage subunit, the efficiency of the water electrolysis device, and the high calorific value of hydrogen.

[0150] Step S403, obtains the hydrogen fuel cell flexibility parameter based on the upper limit of the hydrogen fuel cell output power, the lower limit of the hydrogen fuel cell output power, the output 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 the hydrogen storage subunit to produce hydrogen, and the high calorific value of hydrogen.

[0151] Step S404: Obtain a second objective function based on the flexibility parameter of the hydrogen-mixed gas turbine, the flexibility parameter of the water electrolysis device, and the flexibility parameter of the hydrogen fuel cell.

[0152] In an exemplary embodiment, the flexibility index of the hydrogen-mixed gas turbine can be obtained according to the following formula:

[0153] Among them, F HT It is the flexibility index of the hydrogen-blended gas turbine; The upper flexibility index provided by the hydrogen-mixed gas turbine of l user unit; L is the number of user units; The downward flexibility index provided for the hydrogen-blended gas turbine of the user unit; HT,max is the upper limit of the output of the hydrogen-mixed gas turbine; P HT,min is the lower limit of the output of the hydrogen-mixed gas turbine; P HT,l Represents the output P of the hydrogen-mixed gas turbine of the user unit HT,l,t A vector from 0 to 24h.

[0154] In an exemplary embodiment, the flexibility index of the water electrolysis device can be obtained according to the following formula:

[0155] Among them, F P2G is the flexibility index of the water electrolysis device; N is the number of hydrogen production subunits, hydrogen storage subunits and hydrogen mixing subunits; The upward flexibility index of the water electrolysis device in the nth hydrogen production unit; is the downward flexibility index of the water electrolysis device in the nth hydrogen production unit; P P2G,n Represents the power consumption P of the water electrolysis device in the nth hydrogen production unit P2G,n,t A vector from 0 to 24h; V ST,max is the maximum volume of the hydrogen storage unit; V ST,n Represents the hydrogen volume V of the nth hydrogen storage unit ST,n,t A vector from 0 to 24h; η P2Gis the efficiency of the water electrolysis device; η in Efficiency of hydrogen injection into the hydrogen storage unit; is the high calorific value of hydrogen; P P2G,n,max is the upper limit of the input power of the water electrolysis device of the nth hydrogen production unit at time t; Δt is the difference between two adjacent scheduling times.

[0156] In an exemplary embodiment, the hydrogen fuel cell flexibility can be obtained according to the following equation,

[0157] Among them, F HFC is the flexibility index of hydrogen fuel cells; L is the number of user units; Increased flexibility for hydrogen fuel cells in user units; The downward flexibility index provided for the hydrogen fuel cell of the user unit; HFC,max is the upper limit of the output power of the hydrogen fuel cell; P HFC,l The output P of the hydrogen fuel cell of the user unit HFC,l,t A vector from 0 to 24h; VST,i is the hydrogen volume V of hydrogen storage unit i connected to user unit l ST,i,t A vector from 0 to 24h; η HFC is the efficiency of the hydrogen fuel cell; η out The efficiency of extracting hydrogen for the hydrogen storage unit; is the high calorific value of hydrogen; P HFC,min is the lower limit of the output power of the hydrogen fuel cell; Δt is the difference between two adjacent scheduling times.

[0158] In this embodiment, the flexibility parameters of each subunit in the electric hydrogen coupling unit are calculated in turn by adjusting the energy supply parameters of each subunit, and finally obtaining the second objective function, which can more accurately obtain the flexibility of the urban power grid. At the same time, through hydrogen fuel cells, water electrolysis devices, and hybrid hydrogen gas turbines, the adjustable load of the urban power grid is more effectively reflected, and flexible calls to users are achieved.

[0159] In one embodiment, as shown in FIG5 , step S404 includes:

[0160] Step S441 , obtaining the flexibility parameter of the thermal power unit according to the output upper limit of the thermal power unit, the output lower limit of the thermal power unit, the upward climbing rate of the thermal power unit, the downward climbing rate of the thermal power unit, and the output sampling value of the thermal power unit.

[0161] Step S442, obtaining a second objective function according to the flexibility parameter of the hydrogen-mixed gas turbine, the flexibility parameter of the water electrolysis device, the flexibility parameter of the hydrogen fuel cell, and the flexibility parameter of the thermal power unit.

[0162] In an exemplary embodiment, the flexibility index of the thermal power unit can be obtained according to the following formula:

[0163] Among them, F G is the flexibility index of thermal power units; G is the number of thermal power units; Provide upper flexibility index for thermal power units; They represent the downward flexibility index provided by thermal power units; P G,max is the upper limit of the output of the g-th thermal power unit; P G,min is the lower limit of the output of the g-th thermal power unit; R d,g is the downward climbing rate of the g-th thermal power unit; R u,g is the upward climbing rate of the g-th thermal power unit; P G,g Represents the output P of the g-th thermal power unit G,g,t A vector from 0 to 24h.

[0164] In an exemplary embodiment, the second objective function can be obtained according to the following formula: min F2 = min-(F G +F HT +F P2G +F HFC ) (twenty three)

[0165] Among them, F2 is the second objective function; F G is the flexibility index of thermal power units; F HT F is the flexibility index of the hydrogen-mixed gas turbine; P2G F is the flexibility index of the water electrolysis device; HFC It is an indicator of the flexibility of hydrogen fuel cells.

[0166] In this embodiment, the flexibility index of the thermal power unit is confirmed and the second objective function is optimized, which can make the second objective function and the final scheduling result more accurate. At the same time, the sum of the flexibility of the thermal power unit, the flexibility of the hybrid hydrogen gas turbine, the flexibility of the water electrolysis device and the flexibility of the hydrogen fuel cell is used as the second target index, which more effectively reflects the flexible adjustment capability of the urban power grid.

[0167] In one embodiment, as shown in FIG6 , step S500 includes:

[0168] Step S501 : performing single-objective optimization processing on the first objective function and the second objective function respectively to obtain extreme points.

[0169] Step S502: Obtaining a Utopia line according to the extreme points.

[0170] Step S503 : segmenting the Utopia line to obtain segmentation points, and obtaining target constraint conditions based on vectors pointing to the segmentation points and vectors pointing to the extreme points.

[0171] Step S504 : determining the operation constraint conditions of the city power grid operation system according to the operation constraint conditions corresponding to the city power grid module, the natural gas grid module, and the user unit module respectively.

[0172] Step S505 , establishing corresponding mathematical models, the target constraints, and the operating constraints according to each subunit, performing single-objective optimization processing on the second objective function, and obtaining a target scheduling strategy set.

[0173] In an exemplary embodiment, performing single-objective optimization processing on the first objective function and the second objective function to obtain extreme points may include: considering the single-objective optimization of the first objective function for the multi-objective dispatching model of the urban power grid, keeping the constraints unchanged, obtaining the intraday economic dispatch cost, and substituting the optimization results of each variable into the second objective function to obtain the second objective index, and taking the daily economic dispatch cost after the first objective optimization and the corresponding second objective index as the first extreme point; considering the single-objective optimization of the second objective function for the multi-objective dispatching model of the urban power grid, keeping the constraints unchanged, obtaining the flexibility index, substituting each variable into the first objective function to obtain the first objective index; and taking the second objective index after the second objective optimization and the corresponding first objective index as the second extreme point.

[0174] In an exemplary embodiment, the spaces of two extreme points are normalized and connected to obtain a Utopia line, and the Utopia line is divided into equal parts according to a preset threshold value to generate a threshold number of equally spaced segmentation points.

[0175] In an exemplary embodiment, the solution to the model may be normalized according to the following equation,

[0176] Among them, the is the result after normalization; It is the first target indicator after normalization; is the second target indicator after normalization; F1 is the solution of the model to the first target indicator; F2 is the solution of the model to the second target indicator; is the first objective indicator after single-objective optimization of the first objective; The second objective indicator is obtained by substituting the parameters of the first objective indicator after single-objective optimization of the first objective into the second objective function; The second objective indicator after single objective optimization of the second objective; The first objective indicator is obtained by substituting the parameters of the second objective indicator after single-objective optimization of the second objective into the first objective function.

[0177] In an exemplary embodiment, the Utopia line is segmented to obtain segmentation points according to the following formula:

[0178] in, is the segmentation point of a certain threshold; j is the sequence of segmentation points; a is the preset segmentation threshold.

[0179] In an exemplary embodiment, the operating constraints of the urban power grid module may include electric power balance constraints, thermal power unit operating constraints, wind farm operating constraints, etc.; the natural gas network operating 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 user unit internal electric power balance constraints, electric load demand response constraints, photovoltaic storage 5G base station operating constraints, hybrid hydrogen gas turbine operating constraints, etc.

[0180] In an exemplary embodiment, the electric power balance of the urban power grid can be obtained based on the electricity consumption of the water electrolysis device, the output of the thermal power unit, the actual output of the wind farm and the photovoltaic power station, the electricity purchased by the user unit, and the electric load of the urban distribution network in the urban power grid; the electric power balance can be obtained according to the following formula:

[0181] Where G is the number of thermal power units; P G,g,t is the output of the g-th thermal power unit at time t; W is the number of wind farms; P PW,w,t is the actual output of the w-th wind farm at time t; L is the number of user units; is the power purchased by the user unit from the urban distribution network at time t; N is the number of hydrogen production subunits, hydrogen storage subunits and hydrogen mixing subunits; P P2G,n,t is the input power of the water electrolysis device of the nth hydrogen production unit at time t; L ud,t is the power load of the urban power grid at time t.

[0182] In an exemplary embodiment, the operation constraints of the thermal power unit can be obtained according to the following formula:

[0183] Among them, P G,min is the lower limit of the output of the g-th thermal power unit; P G,g,t is the output of the g-th thermal power unit at time t; P G,min is the upper limit of the output of the g-th thermal power unit; R d,g is the downward climbing rate of the g-th thermal power unit; R u,gis the upward climbing rate of the g-th thermal power unit; Δt is the difference between two adjacent scheduling times.

[0184] In an exemplary embodiment, the wind farm operation constraints can be obtained according to the following formula:

[0185] Among them, P PW,w,t is the actual output of the w-th wind farm at time t; is the predicted output of the w-th wind farm at time t.

[0186] In an exemplary embodiment, the node gas pressure and pipeline flow are related to each other. Affected by the natural gas network structure, the flow of hydrogen-mixed natural gas needs to meet the constraints of pipeline flow and node gas pressure. The Weymouth equation can be used for modeling. The pipeline flow and node gas pressure constraints can be obtained by modeling according to the following formula: ε ij,t =sgn(q ij,t ) (30) -q ij,max ≤q ij,t ≤q ij,max (31) p i,min ≤p i,t ≤p i,max (32)

[0187] q ij,t is the pipeline flow between nodes i and j in the natural gas grid at time t; σ ij is the pipeline transmission parameter between nodes i and j in the natural gas grid; ε ij,t is the natural gas network pipeline direction parameter at time t, which can be -1, 0, or 1; p i,t is the pressure value of natural gas grid node i at time t; q ij,max is the upper limit of pipeline flow between nodes i and j in the natural gas network at time t; p i,max is the upper limit of the pressure value of the natural gas grid node i; p i,min is the lower limit of the pressure value of natural gas grid node i.

[0188] In an exemplary embodiment, the energy and component balance of a natural gas network node is related to the gas source point, the hydrogen mixing point, and other related nodes. The system is heavy and the energy and component flows in the system are directional. The flow rate of the hydrogen-mixed natural gas pipeline flowing into the natural gas network node is stipulated to be positive. The energy and component balance constraint of the node can be obtained according to the following formula:

[0189] in, is the volume flow of hydrogen injected into the natural gas grid node i at time t; It is the high calorific value of hydrogen; is the gas supply flow rate injected into the natural gas grid node i at time t; ξ HHVGas is the high calorific value of natural gas; u ij,t is the positive direction indicator of the actual hydrogen-mixed natural gas pipeline flow between natural gas network nodes i and j at time t; q ij,t is the pipeline flow between nodes i and j in the natural gas grid at time t; is the calorific value of hydrogen-mixed natural gas at node i in the natural gas grid at time t; L q (j,i) represents the set of all pipelines from natural gas grid node j to i in the reference direction; L q (i, j) represents the set of all pipelines from natural gas grid node i to j in the reference direction; γ ij,t is the reverse direction mark of the actual hydrogen-mixed natural gas pipeline flow between nodes i and j in the natural gas network at time t; V DL,i,t is the conventional gas load of node i in the natural gas grid at time t; V HT,i,t The volume of hydrogen-mixed natural gas consumed by the hydrogen-mixed gas turbine connected to the natural gas grid node i; x c,i,t is the ratio of gas component c at node i in the natural gas grid at time t. When c=CH4,H2, the corresponding gases are methane and hydrogen respectively; ε ij,t is the natural gas network pipeline direction parameter at time t, which takes the values ​​of -1, 0, and 1.

[0190] In an exemplary embodiment, the gas supply constraint can be obtained according to the following formula:

[0191] in, It is the lower limit of gas supply flow rate of gas source; is the gas supply flow rate injected into the natural gas grid node i at time t; It is the upper limit of gas supply flow rate.

[0192] In an exemplary embodiment, the compressor constraint can be obtained according to the following equation,

[0193] Among them, K min is the lower limit of compression ratio; p i,t is the pressure value of natural gas grid node i at time t; K max The upper limit of compression ratio.

[0194] In an exemplary embodiment, the user unit operation constraints can be obtained according to the following formula:

[0195] Among them, P HT,l,t is the electric power output of the hydrogen-mixed gas turbine of user unit l at time t; P is the power purchased by user unit l from the urban distribution network at time t;PV,l,t is the actual output of the distributed photovoltaic unit of the user unit base station at time t; P DIS,l,t is the discharge power of the backup battery energy storage of the user unit base station at time t; P HFC,l,t L is the output power of the hydrogen fuel cell of user unit l at time t; us,l,t is the electric load of user unit l at time t; P CH,l,t is the charging power of the backup battery of the user unit base station at time t; is the total power consumption of the 5G base station of user unit l at time t.

[0196] In an exemplary embodiment, the electric load demand response takes into account the electric load reduction and the electric load transfer in the time dimension within the scheduling cycle, and the user unit electric load can be divided into three parts: fixed electric load, curtailable electric load and transferable electric load, among which the fixed load does not participate in the demand response.

[0197] In an exemplary embodiment, the user unit electrical load constraint can be obtained according to the following formula:

[0198] Among them, L us,l,t is the electric load of user unit l at time t; is the fixed electric load of user unit l at time t; is the curtailable electric load of user unit l at time t; is the replaceable electric load of user unit l at time t.

[0199] In an exemplary embodiment, the user unit can tolerate a certain degree of load interruption and power reduction. By partially or completely reducing the power of the reducible load to meet the overall energy consumption target of the user unit, the reducible load constraint can be obtained according to the following formula:

[0200] in, is the value of load demand response that can be reduced by user unit l at time t; is the curtailable electric load of user unit l at time t; is the value of load reduction that can be achieved by user unit l at time t to participate in demand response; is the upper limit of the load that can be reduced by the user unit at time t.

[0201] In an exemplary embodiment, the transferable load may refer to the transfer of system energy consumption time periods, but the total transferable load must remain unchanged within the scheduling period. The transferable load constraint can be obtained according to the following formula:

[0202] in, is the value of the load demand response of user unit l at time t; is the value of the load that can be transferred by user unit l at time t to participate in demand response; is a 0-1 variable, indicating the state of the transferable load of user unit l at time t; is a 0-1 variable, indicating the state of the transferable load of user unit l at time t; is the power transferred into the transferable load of user unit l at time t; is the power transferred out of the transferable load of user unit l at time t; is the upper limit of the amount of load that can be transferred by the user unit to participate in demand response at time t; It is the lower limit of the amount of load that can be transferred by the user unit to participate in demand response at time t.

[0203] In an exemplary embodiment, a 5G solar-storage base station consists of distributed photovoltaic units, backup battery energy storage, and base station equipment, and has abundant flexible and adjustable resources. The unit processing constraints mainly considered by the distributed photovoltaic units of the base station can be obtained according to the following formula:

[0204] Among them, P PV,l,t is the actual output of the distributed photovoltaic unit of the user unit base station at time t; is the predicted output of the distributed photovoltaic unit of user unit base station at time t.

[0205] In an exemplary embodiment, the backup battery energy storage constraint can be obtained according to the discharge state constraint, charge and discharge number constraint, charge and discharge power constraint, and energy storage constraint of the base station backup battery energy storage, and the operation constraint of the backup battery energy storage can be obtained according to the following formula:

[0206] in, is a 0-1 variable, representing the charging status of the backup battery energy storage at the user unit base station at time t; is a 0-1 variable, representing the discharge state of the backup battery energy storage at the user unit base station at time t; T is the number of moments in the scheduling cycle; N ESS The maximum number of charge and discharge times for the base station backup battery energy storage; P CH,l,t , is the charging power of the backup battery energy storage of the user unit base station at time t; P DIS,l,t is the discharge power of the backup battery energy storage of the user unit base station at time t; P CH,max The upper limit of charging power for base station backup battery energy storage; P DIS,max The upper limit of the discharge power of the base station backup battery energy storage; η CH Charging efficiency of the base station backup battery energy storage; η DISThe discharge efficiency of the backup battery for base station energy storage; E ESS,l,t E is the energy storage capacity of the base station backup battery at time t; ESS,max is the upper limit of the energy storage capacity of the base station backup battery; Δt is the difference between two adjacent scheduling times.

[0207] In an exemplary embodiment, the base station equipment mainly refers to the operating power consumption of the base station, which can include static power consumption and dynamic power consumption. The static power consumption is independent of the number of users connected to the base station and is the basic power consumption of the base station in normal operation. The dynamic power consumption is directly proportional to the number of users connected to the base station. The base station power consumption operation constraint can be obtained according to the following formula:

[0208] in, is the total power consumption of the 5G base station of user unit l at time t; is the static power consumption of the 5G base station of user unit l at time t; is 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; is the maximum dynamic power consumption of the base station at time t.

[0209] In an exemplary embodiment, the hydrogen-gas turbine is an important coupling device between the urban distribution network and the natural gas grid. The operating constraints of the hydrogen-gas turbine can be obtained according to the following formula:

[0210] Among them, P HT,l,t is the output of the hydrogen-mixed gas turbine of the user unit; η HT is the power generation efficiency of the hydrogen-mixed gas turbine; V HT,l,t The volume of hydrogen-mixed natural gas required for the hydrogen-mixed gas turbine of user unit l to generate electricity at time t; P is the calorific value of the hydrogen-mixed natural gas at the access node of the natural gas grid at time t for the hydrogen-mixed gas turbine of user unit l; HT,max is the upper limit of the output of the hydrogen-mixed gas turbine; P HT,min It is the lower limit of the output of the hydrogen-mixed gas turbine.

[0211] In an exemplary embodiment, the operating constraints of the hydrogen fuel cell and the hydrogen refueling station in the user unit can be obtained according to mathematical models of the hydrogen fuel cell and the hydrogen refueling station.

[0212] In an exemplary embodiment, the multi-objective optimization problem can be converted into the solution of a series of single-objective problems by using the normalized normal constraint method, and the optimal solution obtained by solving each single-objective problem constitutes an optimal solution set.

[0213] In an exemplary embodiment, the following target constraint conditions can be obtained based on the vectors pointing to each segmentation point and the vectors pointing to the extreme point:

[0214] in, A vector pointing from one extreme point to another extreme point; is the coordinate of the solution to the normalized model; is the vector pointing to the j-th segmentation point.

[0215] In an exemplary embodiment, based on the target constraints, the mathematical model of the hydrogen production subunit, the mathematical model of the hydrogen storage subunit, the mathematical model of the hydrogen mixing subunit, the mathematical model of the hydrogen use subunit, the electric 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 source supply constraints, the user unit internal electric power balance constraints, the electric load demand response constraints, the photovoltaic storage 5G base station operation constraints, the hydrogen mixing gas turbine operation constraints, etc., a single-objective optimization model is performed on the second objective function at each split point to obtain the Pareto frontier, that is, the target scheduling strategy set.

[0216] In an exemplary embodiment, the target scheduling strategy set may be subjected to an approximate ideal solution sorting method to obtain a compromise target scheduling strategy for the urban power grid target scheduling model.

[0217] In an exemplary embodiment, the target scheduling strategy set obtained by solving the model can be used as an evaluation set to form an evaluation matrix.

[0218] Where M is the number of target scheduling strategies in the target scheduling strategy set, that is, the number of evaluation individuals; m is the evaluation individual number; C is the objective function or the number of indicators; F m,c is the cth indicator value of the mth evaluation individual;

[0219] The evaluation matrix is ​​normalized according to the following formula:

[0220] The following normalized evaluation matrix is ​​further obtained:

[0221] The entropy weight method is used to obtain the following indicator weights:

[0222] in

[0223] Among them, H c is the entropy value of the cth indicator;

[0224] The entropy weight is defined as follows:

[0225] Construct the weight matrix and get the following matrix,

[0226] Calculate the value matrix and get the following matrix,

[0227] Calculating the positive and negative ideal solutions yields the following equation:

[0228] Calculate the Euclidean distance of each evaluation individual to the positive and negative ideal solutions, and get the following formula:

[0229] The relative closeness of each evaluation individual is calculated by the following formula:

[0230] The compromise target scheduling strategy is the one that achieves the greatest relative progress.

[0231] In this embodiment, the multi-objective optimization scheduling model is solved by solving the target constraints, the operating constraints of the urban power grid operation system, and the mathematical models of each sub-unit in the electric-hydrogen coupling unit to obtain the target scheduling strategy, which can not only ensure the economy of the urban power grid but also fully call on the flexible backup capacity of the power supply and load within the urban power grid, making the target calling strategy more accurate.

[0232] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0233] FIG7 is a flow chart of a multi-objective scheduling method for an urban power grid according to an embodiment of the present application. It should be understood that although the steps in the flow chart shown in FIG7 are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction for the execution of these steps, and they can be executed in other orders. Moreover, at least a portion of the steps in FIG7 may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0234] In an exemplary embodiment, the multi-objective scheduling method for the urban power grid can be performed as shown in the flowchart of FIG7 :

[0235] Collect basic parameters of the urban power grid, natural gas grid, user units, wind power processing forecast data, and power and natural gas load forecast data;

[0236] inputting the collected data into a program that calls a policy model;

[0237] Establishing an urban power grid operation framework for hydrogen energy vehicles;

[0238] Through the relationship between each unit and hydrogen energy in the urban power grid operation framework, the operation characteristic model of the electric hydrogen coupling unit is established;

[0239] Through the urban power grid operation framework and the model of the electric-hydrogen coupling unit, an economic-flexible multi-objective optimization scheduling model for the urban power grid is established;

[0240] The urban power grid economic-flexible multi-objective optimization scheduling model is solved by the normalization method, and the intraday optimization scheduling strategy of the urban power grid with hydrogen energy vehicles connected is obtained.

[0241] Based on the same inventive concept, embodiments of the present application also provide an urban power grid multi-objective scheduling device for implementing the aforementioned urban power grid multi-objective scheduling method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the urban power grid multi-objective scheduling device provided below can be found in the above-mentioned limitations of the urban power grid multi-objective scheduling method and will not be further elaborated here.

[0242] In one embodiment, as shown in FIG8 , a multi-objective scheduling device 100 for a city power grid is provided, comprising: a vehicle access module 101 , a unit determination module 102 , a model establishment module 103 , a scheduling model module 104 , and a target scheduling module 105 , wherein:

[0243] The vehicle access module is used to connect the hydrogen energy vehicle to the original urban power grid operation system to obtain an updated urban power grid operation system; wherein the updated urban power grid operation system includes an urban power grid module, a natural gas network module and a user unit module, and an energy conversion relationship exists between the modules;

[0244] A unit determination module is used to determine an electric-hydrogen coupling unit from the updated urban power grid operation system; wherein the electric-hydrogen coupling unit includes subunits corresponding to each stage between the production and use of hydrogen energy;

[0245] A model building module is used to build corresponding mathematical models for each subunit in the electrical hydrogen coupling unit;

[0246] a scheduling model module, configured to determine a first objective function based on the updated operating cost parameters of each module of the urban power grid operation system, and a second objective function based on the energy supply parameters of each subunit of the electric-hydrogen coupling unit, and to generate a multi-objective optimization scheduling model based on the first objective function and the second objective function;

[0247] The target scheduling module is used to solve the multi-objective optimization scheduling model to obtain the updated urban power grid operation system target resource scheduling strategy.

[0248] In one embodiment, the unit determination module includes: a phase submodule and a confirmation submodule, wherein:

[0249] A stage submodule is used to divide the hydrogen energy production stage, storage stage, hydrogen mixing stage, and use stage based on the updated urban power grid operation system, and obtain subunits corresponding to each stage from hydrogen energy production to use;

[0250] The confirmation submodule is used to confirm the electrical hydrogen coupling unit according to the subunits corresponding to each stage.

[0251] In one embodiment, the model building module includes: a hydrogen production submodule, a hydrogen storage submodule, a hydrogen mixing submodule, and a hydrogen use submodule, wherein:

[0252] A hydrogen production submodule, for obtaining, when the subunit is a hydrogen production subunit, a volume flow rate of hydrogen produced by the water electrolysis device according to the efficiency of the water electrolysis device in the hydrogen production subunit, the input power of the water electrolysis device, and the high calorific value of hydrogen, and using the volume flow rate of hydrogen produced by the water electrolysis device as a mathematical model of the hydrogen production subunit;

[0253] A hydrogen storage submodule, for obtaining the hydrogen volume of the hydrogen storage subunit based on the volume 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 volume flow rate of hydrogen produced by the hydrogen storage subunit, and the efficiency of hydrogen production by the hydrogen storage subunit, when the subunit is a hydrogen storage subunit, and using the hydrogen volume of the hydrogen storage subunit as a mathematical model of the hydrogen storage subunit;

[0254] A hydrogen mixing submodule, for obtaining the hydrogen-mixed natural gas calorific value of the hydrogen mixing subunit according to the hydrogen mixing ratio, the natural gas higher calorific value, and the hydrogen higher calorific value of the hydrogen mixing subunit when the subunit is a hydrogen mixing subunit, and using the hydrogen-mixed natural gas calorific value as a mathematical model of the hydrogen mixing subunit;

[0255] The hydrogen submodule is used to obtain the output power of the hydrogen fuel cell according to the efficiency of the hydrogen fuel cell in the hydrogen subunit, the volume flow of hydrogen required by the hydrogen fuel cell, and the high calorific value of hydrogen when the subunit is a hydrogen subunit; and obtain the volume flow of hydrogen fuel supplied to the hydrogen energy vehicle by the hydrogen refueling station according to the maximum capacity of the hydrogen storage bottle of the hydrogen energy vehicle in the hydrogen subunit, the initial fuel volume of the hydrogen energy vehicle, the number of cycle starting stations of the hydrogen energy vehicle, the driving distance of the vehicle route, the volume of hydrogen consumption per kilometer of the hydrogen energy vehicle, the fuel threshold of the hydrogen energy vehicle, the initial departure time of the hydrogen energy vehicle, the road distance between every two stations of the hydrogen energy vehicle, the design speed of each grade of road, the number of vehicles on the road, the design traffic flow of the road, and the total number of hydrogen energy vehicles supplied by the hydrogen refueling station; and the output power of the hydrogen fuel cell and the volume flow of hydrogen fuel supplied to the hydrogen energy vehicle by the hydrogen refueling station are used as the mathematical model of the hydrogen subunit.

[0256] 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:

[0257] The hydrogen-mixed gas turbine submodule is used to determine the flexibility parameter of the hydrogen-mixed gas turbine according to the output upper limit of the hydrogen-mixed gas turbine, the output lower limit of the hydrogen-mixed gas turbine, and the output sampling value of the hydrogen-mixed gas turbine of the user unit;

[0258] The water electrolysis device submodule is used to determine the flexibility parameter of the water electrolysis device 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 efficiency of the hydrogen storage subunit hydrogen injection, the efficiency of the water electrolysis device, and the high calorific value of hydrogen;

[0259] The hydrogen fuel cell submodule is used to obtain the hydrogen fuel cell flexibility parameter based on the upper limit of the hydrogen fuel cell output power, the lower limit of the hydrogen fuel cell output power, the output 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 production by the hydrogen storage subunit, and the high calorific value of hydrogen;

[0260] The objective function submodule is used to obtain a second objective function based on the flexibility parameter of the hydrogen-mixed gas turbine, the flexibility parameter of the water electrolysis device, and the flexibility parameter of the hydrogen fuel cell.

[0261] In one embodiment, the second objective function submodule includes: a thermal power unit submodule and a second objective function submodule, wherein:

[0262] The thermal power unit submodule obtains the thermal power unit flexibility parameter based on the thermal power unit output upper limit, the thermal power unit output lower limit, the thermal power unit upward climbing rate, the thermal power unit downward climbing rate, and the thermal power unit output sampling value;

[0263] 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.

[0264] In one embodiment, the target scheduling module includes: a single-target optimization submodule, a utopia line submodule, a target constraint submodule, an operation constraint submodule, and a target scheduling submodule, wherein:

[0265] A single-objective optimization submodule is used to perform single-objective optimization processing on the first objective function and the second objective function respectively to obtain extreme points;

[0266] A Utopia line submodule, configured to obtain a Utopia line according to the extreme points;

[0267] A target constraint submodule is used to segment the Utopia line to obtain segmentation points, and obtain target constraint conditions based on vectors pointing to the segmentation points and vectors pointing to the extreme points;

[0268] An operation constraint submodule, configured to determine the operation constraint conditions of the city power grid operation system according to the operation constraint conditions corresponding to the city power grid module, the natural gas grid module, and the user unit module respectively;

[0269] The target scheduling submodule is used to establish corresponding mathematical models, the target constraints, and the operating constraints according to each subunit, perform single-objective optimization processing on the second objective function, and obtain a target scheduling strategy set.

[0270] Each module in the aforementioned urban power grid multi-objective dispatching device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0271] Each module in the aforementioned multi-objective dispatching device for a city power grid can be implemented in whole or in part through software, hardware, or a combination thereof. The network interface can be an Ethernet card or a wireless network card, among others. Each module can be embedded in or independent of a server processor in hardware form, or stored in a server memory in software form, allowing the processor to call and execute the corresponding operations of each module.

[0272] As used in this application, the terms "component," "module," and "system" are intended to refer to a computer-related entity, which can be hardware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable code, a thread of execution, a program, and / or a computer. As an illustration, both an application running on a server and a server can be a component. One or more components can reside in a process and / or a thread of execution, and a component can be located within a computer and / or distributed between two or more computers.

[0273] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in Figure 9. The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, memory, and I / O interface are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store urban power grid data. The I / O interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a multi-objective scheduling method for urban power grids.

[0274] Those skilled in the art will understand that the structure shown in Figure 9 is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0275] 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, stored data, displayed data, 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 relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions.

[0276] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may 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 may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0277] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned 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.

[0278] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A multi-objective dispatching method for urban power grid, characterized in that: The method comprises: Connecting hydrogen energy vehicles to the original urban power grid operation system to obtain an updated urban power grid operation system; wherein the updated urban power grid operation system includes an urban power grid module, a natural gas grid module and a user unit module, and there is an energy conversion relationship between the modules; Determine an electric hydrogen coupling unit from the updated urban power grid operation system; wherein the electric hydrogen coupling unit includes subunits corresponding to each stage from the production to the use of hydrogen energy; Establish corresponding mathematical models for each subunit in the electrical hydrogen coupling unit; Determine a first objective function according to the updated operating cost parameters of each module of the urban power grid operation system, and determine a second objective function according to the energy supply parameters of each subunit of the electric hydrogen coupling unit, and generate a multi-objective optimization scheduling model according to the first objective function and the second objective function; The multi-objective optimization scheduling model is solved to obtain the updated urban power grid operation system target resource scheduling strategy.

2. The method according to claim 1, characterized in that: The step of determining the electrical hydrogen coupling unit from the updated urban power grid operation system comprises: Based on the updated urban power grid operation system, the hydrogen energy production stage, storage stage, hydrogen mixing stage and use stage are divided to obtain subunits corresponding to each stage from the production to the use of hydrogen energy; According to the sub-units corresponding to each stage, the electrical hydrogen coupling unit is confirmed.

3. The method according to claim 1, characterized in that The subunits include at least one of the following: a hydrogen production subunit, a hydrogen storage subunit, a hydrogen mixing subunit, and a hydrogen use subunit. The corresponding mathematical models are established for each subunit in the electrical hydrogen coupling unit, including: In the case where the subunit is a hydrogen production subunit, the volume flow rate of hydrogen produced by the water electrolysis device is obtained according to the efficiency of the water electrolysis device in the hydrogen production subunit, the input electrical power of the water electrolysis device and the high calorific value of hydrogen, and the volume flow rate of hydrogen produced by the water electrolysis device is used as the mathematical model of the hydrogen production subunit; In the case where the subunit is a hydrogen storage subunit, the hydrogen volume of the hydrogen storage subunit is obtained according to the volume flow rate of hydrogen allowed to be injected into the hydrogen storage subunit, the efficiency of hydrogen injection of the hydrogen storage subunit, the total volume flow rate of hydrogen produced by the hydrogen storage subunit and the efficiency of hydrogen produced by the hydrogen storage subunit, and the hydrogen volume of the hydrogen storage subunit is used as the mathematical model of the hydrogen storage subunit; In the case where the subunit is a hydrogen mixing subunit, the calorific value of the hydrogen-mixed natural gas of the hydrogen mixing subunit is obtained according to the hydrogen mixing ratio, the high calorific value of the natural gas and the high calorific value of the hydrogen mixing subunit, and the calorific value of the hydrogen-mixed natural gas is used as the mathematical model of the hydrogen mixing subunit; In the case where the subunit is a hydrogen-using subunit, the output power of the hydrogen fuel cell is obtained according to the efficiency of the hydrogen fuel cell in the hydrogen-using subunit, the volume flow rate of hydrogen required by the hydrogen fuel cell and the high calorific value of hydrogen, and the volume flow rate of hydrogen fuel supplied to the hydrogen energy vehicle by the hydrogen filling station is obtained according to the maximum capacity of the hydrogen storage bottle of the hydrogen energy vehicle in the hydrogen-using subunit, the initial fuel volume of the hydrogen energy vehicle, the number of cycle starting stations of the hydrogen energy vehicle, the driving distance of the vehicle route, the volume of hydrogen consumption per kilometer of the hydrogen energy vehicle, the fuel threshold of the hydrogen energy vehicle, the initial time of departure of the hydrogen energy vehicle, the road distance between every two stations of the hydrogen energy vehicle, the design speed of each grade of road, the vehicle volume on the road, the design traffic flow of the road and the total number of hydrogen energy vehicles supplied by the hydrogen filling station, and the output power of the hydrogen fuel cell and the volume flow rate of hydrogen fuel supplied to the hydrogen energy vehicle by the hydrogen filling station are used as the mathematical model of the hydrogen-using subunit.

4. The method according to claim 1, characterized in that: The energy supply parameters of each subunit include at least one of the following: a flexibility parameter of a hydrogen-mixed gas turbine, a flexibility parameter of a water electrolysis device, and a flexibility parameter of a hydrogen fuel cell. The second objective function is determined according to the energy supply parameters of each subunit of the electric hydrogen coupling unit, including: Determine the flexibility parameter of the hydrogen-mixed gas turbine according to the output upper limit and the output lower limit of the hydrogen-mixed gas turbine and the output sampling value of the hydrogen-mixed gas turbine of the user unit; According to the power sampling value of the water electrolysis device, the maximum volume of the hydrogen storage subunit, the hydrogen volume of the hydrogen storage subunit The sampling values, the efficiency of hydrogen injection of the hydrogen storage subunit, the efficiency of the water electrolysis device and the high calorific value of hydrogen are used to determine the flexibility parameters of the water electrolysis device; The flexibility parameter of the hydrogen fuel cell is obtained according to 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 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 the hydrogen storage subunit to extract hydrogen and the high calorific value of hydrogen; A second objective function is obtained according to the flexibility parameter of the hydrogen-mixed gas turbine, the flexibility parameter of the water electrolysis device and the flexibility parameter of the hydrogen fuel cell.

5. The method according to claim 4, characterized in that According to the flexibility parameter of the hydrogen-mixed gas turbine, the flexibility parameter of the water electrolysis device, and the flexibility parameter of the hydrogen fuel cell, a second objective function is obtained, including: According to the output upper limit of the thermal power unit, the output lower limit of the thermal power unit, the upward climbing rate of the thermal power unit, the downward climbing rate of the thermal power unit and the output sampling value of the thermal power unit, the flexibility parameter of the thermal power unit is obtained; A second objective function is obtained according to the flexibility parameter of the hydrogen-mixed gas turbine, the flexibility parameter of the water electrolysis device, the flexibility parameter of the hydrogen fuel cell and the flexibility parameter of the thermal power unit.

6. The method according to claim 1, characterized in that The multi-objective optimization scheduling model is solved to obtain a target scheduling strategy, including: Perform single-objective optimization processing on the first objective function and the second objective function respectively to obtain extreme points; Based on the extreme points, the Utopia line is obtained; The Utopia line is segmented to obtain segmentation points, and target constraint conditions are obtained according to vectors pointing to the segmentation points and vectors pointing to the extreme points; Determining the operation constraint conditions of the city power grid operation system according to the operation constraint conditions corresponding to the city power grid module, the natural gas grid module and the user unit module respectively; According to each sub-unit, a corresponding mathematical model, the target constraint condition and the operation constraint condition are established, and a single-objective optimization process is performed on the second objective function to obtain a target scheduling strategy set.

7. The method according to claim 1, characterized in that The acquisition of the city power grid operation system to which the hydrogen energy vehicle is connected includes coupling the city power grid, the natural gas grid and the user unit.

8. The method according to claim 1, characterized in that The multi-objective optimization scheduling model is solved to obtain the updated urban power grid operation system target resource scheduling strategy, including: The multi-objective optimization scheduling model is solved by using a normalization method to obtain the updated urban power grid operation system target scheduling strategy.

9. The method according to claim 6, characterized in that The operation constraints of the urban power grid module include: electric power balance constraints, thermal power unit operation constraints and wind farm operation constraints.

10. The method according to claim 6, characterized in that The operating constraints of the natural gas network module include pipeline flow and node gas pressure constraints, node energy and component balance constraints, and gas source supply constraints.

11. The method according to claim 6, characterized in that The operating constraints of the user unit module include internal electric power balance constraints of the user unit, electric load demand response constraints, photovoltaic storage 5G base station operating constraints and hydrogen-mixed gas turbine operating constraints.

12. A multi-objective dispatching device for a city power grid, characterized in that: The device comprises: The vehicle access module is used to connect the hydrogen energy vehicle to the original urban power grid operation system to obtain an updated urban power grid operation system; wherein the updated urban power grid operation system includes an urban power grid module, a natural gas grid module and a user unit module, and there is an energy conversion relationship between the modules; A unit determination module, used to determine an electric hydrogen coupling unit from the updated urban power grid operation system; wherein the electric hydrogen coupling unit includes subunits corresponding to each stage between the production and use of hydrogen energy; A model building module, used to build corresponding mathematical models for each subunit in the electrical hydrogen coupling unit; A dispatching model module, used to determine a first objective function according to the updated operating cost parameters of each module of the urban power grid operation system, and to determine a second objective function according to the energy supply parameters of each subunit of the electric hydrogen coupling unit, and to generate a multi-objective optimization dispatching model according to the first objective function and the second objective function; The target scheduling module is used to solve the multi-objective optimization scheduling model to obtain the updated urban power grid operation system target resource scheduling strategy.

13. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 11 are implemented.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 11 are implemented.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 11 are implemented.

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