Multi-time-scale low-carbon operation optimization method and system for electro-hydrogen comprehensive energy system

Abstract

The invention discloses a multi-time-scale low-carbon operation optimization method and system for an electricity-hydrogen comprehensive energy system, and relates to the technical field of comprehensive energy system application, and the method comprises the steps: obtaining the related data of operation units with different single-unit capacities, and calculating the multi-time-scale low-carbon operation of the electricity-hydrogen comprehensive energy system based on the related data of the operation units with different single-unit capacities and the deep coupling of electricity, heat, gas and hydrogen; generating a multi-energy complementary electricity-hydrogen comprehensive energy system model considering deep coupling of electricity, heat, gas and hydrogen; the method comprises the following steps: carrying out travel data sampling based on a random driving behavior modeling and sampling method of an electric vehicle and a hydrogen fuel cell vehicle of Monte Carlo, and generating flexible V2G charging and discharging models of EVs and HFCVs; and an EH-IES multi-time scale low-carbon operation optimization model is generated based on the two models, solving is carried out by taking total cost minimization as a target, output results of various operation devices in the EH-IES are obtained, and the resource utilization efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of application technology of integrated energy systems, and specifically to a multi-time scale low-carbon operation optimization method and system for an electric-hydrogen integrated energy system. Background Art

[0002] The depletion of fossil fuels and the increase in carbon dioxide emissions pose a major threat to the global energy structure. Energy production and consumption patterns must meet the requirements of social development. The transformation of the energy structure is a long process and needs to address the following two major challenges: 1) The uncertainty and volatility of renewable energy with a high penetration rate. How to promote the consumption of renewable energy is a major problem, especially promoting the efficient use of energy to achieve energy conservation and emission reduction. Since the clean electricity and carbon emission policies brought about by a high proportion of renewable energy have promoted the vigorous development of hydrogen energy, how to use hydrogen energy to change the global energy system to overcome environmental problems has become a major challenge; 2) EH-IES supply and demand fluctuations and the accuracy of the output of operating units, uncertainty problems and the adverse effects of power fluctuations on the system, how to mobilize flexible resources to minimize these adverse effects to improve system performance, and how to reduce the impact of renewable energy and load uncertainty on scheduling results has become another major challenge. Summary of the invention

[0003] In order to solve the deficiencies mentioned in the above background technology, the purpose of the present invention is to provide a multi-time scale low-carbon operation optimization method and system for an electric-hydrogen integrated energy system.

[0004] In the first aspect, the purpose of the present invention can be achieved by the following technical solution: a multi-time scale low-carbon operation optimization method for an electric-hydrogen integrated energy system, the method comprising the following steps: A multi-energy complementary EH-IES model taking into account the deep coupling of electricity, heat, gas and hydrogen is established in advance. The EH-IES model is coupled through the traditional IES equipment operation model and the hydrogen production-storage-use link model in hydrogen production and energy storage. The traditional IES equipment operation model includes: an energy conversion equipment model, an energy storage equipment model and a cogeneration equipment model; the hydrogen production-storage-use link model in hydrogen production and energy storage includes: a hydrogen production link model, a hydrogen storage link model and a hydrogen use link model; Pre-establishing a flexible V2G charging and discharging model for EVs and HFCVs, wherein the flexible V2G charging and discharging model for EVs and HFCVs is established based on the driving behaviors of EVs and HFCVs obtained through Monte Carlo simulation; Taking into account the EH-IES model of multi-energy complementarity with deep coupling of electricity, heat, gas and hydrogen and the flexible V2G charging and discharging model of EVs and HFCVs, the corresponding EH-IES low-carbon operation optimization models are generated at four time scales: week, day before, day and real time. As the EH-IES multi-time scale low-carbon operation optimization model, the model is solved with the goal of minimizing the total cost at four time scales: week, day before, day and real time, and the output results of various operating equipment in EH-IES are obtained. The actual output of various operating equipment in EH-IES is calculated based on the output results of various operating equipment. The various operating equipment in EH-IES include: ED, EB, GB, WHB and MET as energy conversion equipment; BT, TS, GS and HS as energy storage equipment; GT and HFC as cogeneration equipment; UG, photovoltaic output PV, natural gas network NGN and hydrogen network HN as energy supply.

[0005] In combination with the first aspect, in some implementations of the first aspect, the method further includes: the traditional IES device operation model includes: Energy conversion equipment models: in, and Energy conversion equipment model exist Input and output power during the time period; and Energy conversion equipment model The upper and lower limits of capacity; and Energy conversion equipment model The upper and lower limits of output power fluctuation; Model for energy conversion equipment conversion efficiency; for Operational state variables of the time period energy conversion equipment model; It is a collection of all energy conversion equipment models in the traditional IES, including electric boiler EB, gas boiler GB, waste heat boiler WHB and methanation reactor MET; For the entire scheduling cycle; Energy storage equipment model: in, is the self-damage rate of the energy storage device model; For the energy storage device model Storage levels for time periods; and are the charging and discharging efficiencies of the energy storage device model respectively; and They are The charging and discharging power of the time-slot energy storage device model; is the scheduling time scale; and They are the upper and lower limits of the charging power of the energy storage device model respectively; and They are the upper and lower limits of the energy storage device model’s energy release power, respectively; and are the upper and lower limits of the capacity of the energy storage device model respectively; and They are Charging and discharging state variables of the time-slot energy storage device model; and They are the energy storage capacity at the beginning and end of the energy storage equipment model cycle in the traditional IES; It is a collection of energy storage equipment models in traditional IES, including energy storage batteries BT, heat storage tanks TS, and gas storage tanks GS; Combined Heat and Power Plant Model: in, and They are Output electricity and heat power of the cogeneration equipment model during the time period; is the comprehensive conversion efficiency of the combined heat and power equipment model; for Input power to the CHP plant model during the period; and are the upper and lower limits of the input power of the cogeneration equipment model, respectively; for Operational state variables of the cogeneration equipment model for the time period; and They are the upper and lower limits of the heat-to-electricity ratio of the cogeneration equipment model; and They are the upper and lower limits of the output power fluctuation of the cogeneration equipment model respectively; A collection of models for all cogeneration equipment in the traditional IES, including gas turbines GT; The hydrogen production-storage-use link model in the hydrogen production and energy storage includes: Hydrogen production model: in, and The electrolytic cell ED is Input and output power during the time period; and They are the upper and lower limits of ED capacity, respectively; and They are the upper and lower limits of ED output power fluctuation respectively; is the conversion efficiency of ED; for The operating status variables of the time period ED; Hydrogen storage link model: in, is the self-damage rate of the hydrogen storage tank HS; For HS Storage levels for time periods; and are the charging and discharging efficiency of HS respectively; and They are The charging and discharging power of HS in the time period; and They are the upper and lower limits of HS hydrogen charging power respectively; and They are the upper and lower limits of HS hydrogen degassing power respectively; and are the lower upper limits of the capacity of HS respectively; and They are Time period HS charging and discharging state variables; and They are the hydrogen storage capacity at the beginning and end of the HS cycle; Hydrogen use link model: in, and They are Output electrical and thermal power of hydrogen fuel cell HFC during the period; is the comprehensive conversion efficiency of HFC; for Input power of HFC during the period; and They are the upper and lower limits of HFC input power respectively; for Operation status variables of HFC in the time period; and They are the upper and lower limits of HFC heat-to-power ratio respectively; and They are the upper and lower limits of HFC output power fluctuation respectively.

[0006] In combination with the first aspect, in some implementations of the first aspect, the method further includes: the driving behaviors of EVs and HFCVs obtained based on the Monte Carlo simulation are obtained based on the random driving behavior modeling and sampling method of EVs and HFCVs based on Monte Carlo, and the random driving behavior modeling and sampling method of EVs and HFCVs based on Monte Carlo is as follows: The travel behavior of vehicles is simulated according to the probability density functions of the start and end charging time of EVs and HFCVs and the daily mileage, and the Monte Carlo simulation method is used to sample travel data: The start and end charging time of EVs satisfy the piecewise normal distribution function, and the probability density functions are and , which is expressed as follows: in, , ; here Indicates the time when EVs start charging.

[0007] in, , ; here Indicates the time when EVs finish charging.

[0008] The daily mileage of EVs approximately satisfies the following lognormal distribution function: : in, , ; here Indicates the daily mileage of EVs in km.

[0009] The initial state of charge of the vehicle when it starts charging and hydrogen is calculated based on the daily mileage of the sampled EVs and HFCVs: in, For the The state of charge of the EV / HFCV; For the The expected power of the EV / HFCV after charging is completed; The power consumption per 100 km of EVs / HFCVs; For the The time when EV / HFCV starts charging; For the Mileage travelled by the EV / HFCV; is the battery capacity of EV / HFCV; Using the Monte Carlo simulation method, according to the probability density function , and To simulate the travel behavior of vehicles, the energy consumption of vehicles before being connected to the grid is estimated based on the mileage and power consumption per 100 km. The Monte Carlo simulation method is modified to ensure that the start charging time is less than the departure time, and the driving behavior of EVs and HFCVs is obtained.

[0010] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: the flexible V2G charging and discharging model of the EVs and HFCVs is as follows: in, and They are Time period The charging and discharging power of an EV; and They are the upper limits of charging and discharging power of EV respectively; and They are Time period The amount of hydrogen charged and discharged by a HFCV; and They are the upper limits of hydrogen filling and discharging of HFCV respectively; and Respectively represent Time period The charging and discharging state variables of EV / HFCV; is the hydrogen-to-electricity conversion coefficient; and They are Time period The charging and discharging power of a HFCV; for The total charging and discharging power of EVs being charged in the system during the time period; is the charging station capacity; for The total amount of hydrogen charged and discharged by the HFCVs being charged in the system during the period; is the capacity of the hydrogen filling station; for The number of EV / HFCVs connected to the system during the time period; and They are the charging and discharging efficiency of EV and HFCV respectively; For the The time for EV / HFCV to complete charging and hydrogen consumption.

[0011] In combination with the first aspect, in certain implementations of the first aspect, the method further includes obtaining the low-carbon characteristics in the EH-IES multi-time scale low-carbon operation optimization model based on a carbon trading mechanism model, and the carbon trading mechanism model consists of a carbon emission quota model and an actual carbon emission model: The carbon emission quota model is as follows: in, and are the initial carbon emission quotas of the upper grid UG, GT, GB, EVs and HFCVs respectively; , and They are the carbon emission quotas obtained from the electricity generated by the unit of electricity consumed by the purchased electricity and the carbon emission quotas obtained from the electricity and heat generated by the unit of natural gas consumed by the natural gas-fired unit; for Power purchased during the time period; is the thermoelectric conversion coefficient; and They are Output electrical and thermal power of GT during the time period; for GB output thermal power during the time period; and They are the mileage per unit of electricity for EV and HFCV respectively; is the marginal carbon emission factor of fuel vehicles; The actual carbon emission model is as follows: in, are the actual carbon emissions of UG, GT and GB and the carbon emissions captured by MET; is the carbon emission factor per unit electricity of UG; for Input power of GB in time period; for Input power in time period GT; is the carbon emission intensity of gas-fired units; is the coefficient of CO2 absorbed per unit CH4 produced, ,in The amount of CO2 required per unit CH4, is the calorific value of natural gas; is the input power of MR.

[0012] In combination with the first aspect, in some implementations of the first aspect, the method further includes: the EH-IES multi-time scale low-carbon operation optimization model constructs an EH-IES model based on a multi-energy complementarity that takes into account the deep coupling of electricity, heat, gas, and hydrogen, and a flexible V2G charging and discharging model for EVs and HFCVs, and generates an EH-IES low-carbon operation optimization model that takes into account flexible V2G on a weekly time scale, and obtains the output of various operating equipment in the EH-IES on a weekly time scale, wherein the various operating equipment in the EH-IES include: ED, EB, GB, WHB, and MET as energy conversion equipment models; BT, TS, GS, and HS as energy storage equipment models; GT and HFC as cogeneration equipment models; UG, PV, NGN, and HN as energy supply; The objective function of the EH-IES low-carbon operation optimization framework considering flexible V2G is as follows: in, and They are the operation and maintenance cost, abandoned solar power cost, carbon emission cost, energy purchase cost, start-up and shutdown cost of EH-IES, and the income from providing spinning reserve; and They are the unit operation and maintenance costs of the energy conversion equipment model, the cogeneration equipment model, and the energy storage equipment model; and are the unit operation and maintenance costs of EV and HFCV respectively; is the penalty coefficient for abandoning light; for The abandoned optical power in the time period; for The photovoltaic output value predicted by the time period; for The photovoltaic power output actually consumed during the period; is the price per unit of carbon emissions; and They are The purchased natural gas and hydrogen power during the period; It is the low calorific value of hydrogen; and They are time-of-use electricity price, natural gas price, hydrogen price and time-of-use unit price for providing backup for the upper power grid; and They are the startup cost coefficient and shutdown cost coefficient of the energy conversion equipment model, energy storage equipment model and cogeneration equipment model respectively.

[0013] In combination with the first aspect, in some implementations of the first aspect, the method further includes: the EH-IES multi-time scale low-carbon operation optimization model is based on the objective function of the EH-IES low-carbon operation optimization framework taking into account flexible V2G, and generates corresponding EH-IES low-carbon operation optimization models at four time scales: week, day before, day and real time, and solves the problem with the goal of minimizing the total cost at the four time scales: week, day before, day and real time, to obtain the output results of various operating equipment in EH-IES, wherein, The objective function of the EH-IES low-carbon operation optimization model at the weekly time scale is the objective function of the EH-IES low-carbon operation optimization framework taking into account flexible V2G; The objective function of the EH-IES low-carbon operation optimization model on the day-ahead time scale is: in, is the adjustment cost of HS in one scheduling cycle; is the initial time of the day-ahead scheduling cycle; is the total number of time periods in the day-ahead scheduling cycle; Adjust the cost factor for HS unit hydrogen charging and discharging; and The time scales are day-ahead and week-ahead respectively. The charging and discharging power of the time period HS; and Respectively in The adjustment of the charging and discharging power of HS in the previous day compared with the weekly time scale; The objective function of the EH-IES low-carbon operation optimization model on a daily time scale is: in, is the adjustment cost of the energy conversion equipment model, the cogeneration equipment model, and the energy storage equipment model except the HS within a dispatch cycle; is the initial time of the intraday scheduling cycle; is the total number of time periods within the daily scheduling cycle; and They are the unit adjustment cost coefficients for the energy conversion equipment model, the cogeneration equipment model, and the energy storage equipment model except HS; and Intraday and day-ahead time scales respectively The input power of the time period energy conversion equipment model, the cogeneration equipment model, and the charging and discharging power of the energy storage equipment model except HS; and Respectively in The adjustment amount of the intraday energy conversion equipment model, the cogeneration equipment model, and the energy storage equipment model except HS compared to the day-ahead time scale; The objective function of the EH-IES low-carbon operation optimization model in real-time time scale is: in, The adjustment cost of purchased energy in one dispatch cycle; is the initial time of the intraday scheduling cycle; is the total number of time periods in the real-time scheduling cycle; and Adjust the cost coefficients for purchased electricity, natural gas, and hydrogen units respectively; and Real-time and intraday time scales respectively The power of purchased electricity, natural gas and hydrogen during the period; For The adjustment amount of real-time purchased energy in the period compared to the intraday time scale.

[0014] Solve the EH-IES multi-time scale low-carbon optimization operation model taking into account flexible V2G, and obtain the EH-IES multi-time scale optimization operation strategy under the objective function of the lowest system comprehensive cost, including the output of various operating equipment, the output adjustment of various operating equipment at different time scales, and the charging and discharging of EVs and HFCVs, hydrogen conditions, etc. In the second aspect, in order to achieve the above purpose, the present invention discloses a multi-time scale low-carbon operation optimization system for an electric hydrogen integrated energy system, including: The data coupling module pre-establishes a multi-energy complementary EH-IES model that takes into account the deep coupling of electricity, heat, gas and hydrogen. The EH-IES model is coupled through the traditional IES equipment operation model and the hydrogen production-storage-use link model in hydrogen production and energy storage. The traditional IES equipment operation model includes: energy conversion equipment model, energy storage equipment model and cogeneration equipment model; the hydrogen production-storage-use link model in hydrogen production and energy storage includes: hydrogen production link model, hydrogen storage link model and hydrogen use link model; A data sampling module is used to pre-establish a flexible V2G charging and discharging model for EVs and HFCVs, wherein the flexible V2G charging and discharging model for EVs and HFCVs is established based on the driving behaviors of EVs and HFCVs obtained through Monte Carlo simulation; The operation optimization module is used to take into account the EH-IES model with deep coupling of electricity, heat, gas and hydrogen and the flexible V2G charging and discharging model of EVs and HFCVs. The corresponding EH-IES low-carbon operation optimization model is generated at four time scales: week, day before, day and real time. As the EH-IES multi-time scale low-carbon operation optimization model, it is solved with the goal of minimizing the total cost at four time scales: week, day before, day and real time, and the output results of various operating equipment in EH-IES are obtained. The actual output of various operating equipment in EH-IES is calculated based on the output results of various operating equipment. The various operating equipment in EH-IES include: ED, EB, GB, WHB and MET as energy conversion equipment; BT, TS, GS and HS as energy storage equipment; GT and HFC as cogeneration equipment; UG, photovoltaic output PV, natural gas network NGN and hydrogen network HN as energy supply.

[0015] In another aspect of the present invention, in order to achieve the above-mentioned purpose, a terminal device is disclosed, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the memory stores a computer program capable of running on the processor, and when the processor loads and executes the computer program, the multi-time scale low-carbon operation optimization method of the electric-hydrogen integrated energy system as described above is adopted.

[0016] Beneficial effects of the present invention: Finally, based on the EH-IES low-carbon operation optimization framework taking into account flexible V2G, the present invention proposes a multi-time scale optimization operation strategy to reduce fluctuations in the supply and demand sides of the system, improve the accuracy of the output of various operating units, and formulate scheduling plans more finely to achieve stable operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative work. Figure 1 It is a schematic flow chart of the method of the present invention; Figure 2 It is a schematic diagram of the framework of the electric-hydrogen integrated energy system used in the embodiment of the present invention; Figure 3 It is a weekly prediction curve diagram of photovoltaic power under four time scales in the embodiment of the present invention; Figure 4 It is a weekly prediction curve diagram of electric load under four scales in an embodiment of the present invention; Figure 5It is a weekly prediction curve diagram of heat load under four scales in the embodiment of the present invention; Figure 6 It is a weekly prediction curve chart of gas load under four scales in the embodiment of the present invention; Figure 7 It is a weekly prediction curve chart of hydrogen load under four scales in the embodiment of the present invention; Figure 8 It is a comparison diagram of the output of hydrogen fuel cells in the day-to-day time scale in the comparative example of the present invention; Fig. 9 It is a comparison diagram of purchased electric energy in the day-ahead-real-time time scale in the comparative example of the present invention; Fig.10 It is a comparison chart of purchased electric energy in the intra-day-real-time time scale in the comparative example of the present invention; Fig.11 It is a comparison chart of the charging and discharging conditions of electric vehicles in the intra-day-real-time time scale in the comparative example of the present invention; Fig.12 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Embodiment 1: like Figure 1 As shown, a multi-time scale low-carbon operation optimization method for an electric-hydrogen integrated energy system comprises the following steps: S101: pre-establishing a multi-energy complementary EH-IES model that takes into account the deep coupling of electricity, heat, gas and hydrogen. The EH-IES model is coupled through a traditional IES equipment operation model and a hydrogen production-storage-use link model in hydrogen production and energy storage. The traditional IES equipment operation model includes: an energy conversion equipment model, an energy storage equipment model and a cogeneration equipment model; the hydrogen production-storage-use link model in hydrogen production and energy storage includes: a hydrogen production link model, a hydrogen storage link model and a hydrogen use link model; The EH-IES model, which takes into account the deep coupling of electricity, heat, gas and hydrogen, includes the traditional IES equipment operation model and the hydrogen production-storage-use link model in hydrogen production and energy storage. The traditional IES equipment operation model includes: Energy conversion equipment models: in, and Energy conversion equipment model exist Input and output power during the time period; and Energy conversion equipment model The upper and lower limits of capacity; and Energy conversion equipment model The upper and lower limits of output power fluctuation; Model for energy conversion equipment conversion efficiency; for Operational state variables of the time period energy conversion equipment model; It is a collection of models of all energy conversion equipment in the traditional IES, including electric boiler (EB), gas boiler (GB), waste heat boiler (WHB) and methanogenization reactor (MET); For the entire scheduling cycle; Energy storage equipment model: in, is the self-damage rate of the energy storage device model; For the energy storage device model Storage levels for time periods; and are the charging and discharging efficiencies of the energy storage device model respectively; and They are The charging and discharging power of the time-slot energy storage device model; is the scheduling time scale; and They are the upper and lower limits of the charging power of the energy storage device model respectively; and They are the upper and lower limits of the energy storage device model’s energy release power, respectively; and are the upper and lower limits of the capacity of the energy storage device model respectively; and They are Charging and discharging state variables of the time-slot energy storage device model; and They are the energy storage capacity at the beginning and end of the energy storage equipment model cycle in the traditional IES; It is a collection of energy storage equipment models in traditional IES, including energy storage batteries (BT), heat storage tanks (TS), and gas storage tanks (GS); Combined Heat and Power Plant Model: in, and They are Output electricity and heat power of the cogeneration equipment model during the time period; is the comprehensive conversion efficiency of the combined heat and power equipment model; for Input power to the CHP plant model during the period; and are the upper and lower limits of the input power of the cogeneration equipment model, respectively; for Operational state variables of the cogeneration equipment model for the time period; and They are the upper and lower limits of the heat-to-electricity ratio of the cogeneration equipment model; and They are the upper and lower limits of the output power fluctuation of the cogeneration equipment model respectively; A collection of models for all CHP equipment in the traditional IES, including gas turbines (GT); The hydrogen production-storage-use link model in the hydrogen production and energy storage includes: Hydrogen production model: in, and The electrolyzer (ED) is Input and output power during the time period; and They are the upper and lower limits of ED capacity, respectively; and They are the upper and lower limits of ED output power fluctuation respectively; is the conversion efficiency of ED; for The operating status variables of the time period ED; Hydrogen storage link model: in, is the self-damage rate of the hydrogen storage tank (HS); For HS Storage levels for time periods; and are the charging and discharging efficiency of HS respectively; and They are The charging and discharging power of HS in the time period; and They are the upper and lower limits of HS hydrogen charging power respectively; and They are the upper and lower limits of HS hydrogen degassing power respectively; and are the lower upper limits of the capacity of HS respectively; and They are Time period HS charging and discharging state variables; and They are the hydrogen storage capacity at the beginning and end of the HS cycle; Hydrogen use link model: in, and They are Output electrical and thermal power of hydrogen fuel cells (HFC) during the period; is the comprehensive conversion efficiency of HFC; for Input power of HFC during the period; and They are the upper and lower limits of HFC input power respectively; for Operation status variables of HFC in the time period; and They are the upper and lower limits of HFC heat-to-power ratio respectively; and They are the upper and lower limits of HFC output power fluctuation respectively.

[0020] S102: pre-establishing a flexible V2G charging and discharging model for EVs and HFCVs, wherein the flexible V2G charging and discharging model for EVs and HFCVs is established based on driving behaviors of EVs and HFCVs obtained through Monte Carlo simulation; The random driving behavior modeling and sampling method of EVs and HFCVs based on Monte Carlo simulation is obtained as follows: The statistical probability method is used to analyze the travel behavior results. The data of HFCVs is relatively small, so the information on their travel behavior in this paper refers to EVs. The start and end charging time of EVs satisfy the piecewise normal distribution function, and the probability density functions are and , which is expressed as follows: in, , ; here Indicates the time when EVs start charging.

[0021] in, , ; here Indicates the time when EVs finish charging.

[0022] The daily mileage of EVs approximately satisfies the following lognormal distribution function: : in, , ; here Indicates the daily mileage of EVs in km.

[0023] The initial state of charge of the vehicle when it starts charging and hydrogen is calculated based on the daily mileage of the sampled EVs and HFCVs: in, For the The state of charge of the EV / HFCV; For the The expected power of the EV / HFCV after charging is completed; The electricity and hydrogen consumption per 100 kilometers for EVs / HFCVs; For the The time for EV / HFCV to start charging and hydrogen; For the Mileage travelled by the EV / HFCV; is the battery capacity of EV / HFCV; Using the Monte Carlo simulation method, according to the above probability density function , and To simulate the travel behavior of vehicles, the energy consumption of vehicles before being connected to the grid is estimated based on the mileage and power consumption per 100 km. The Monte Carlo simulation method is modified to ensure that the start charging time is less than the departure time, and the driving behavior of EVs and HFCVs is obtained.

[0024] The flexible V2G charging and discharging model for EVs and HFCVs is: in, and They are Time period The charging and discharging power of an EV; and They are the upper limits of charging and discharging power of EV respectively; and They are Time period The amount of hydrogen charged and discharged by a HFCV; and They are the upper limits of hydrogen filling and discharging of HFCV respectively; and Respectively represent Time period The charging and discharging state variables of EV / HFCV; is the hydrogen-to-electricity conversion coefficient; and They are Time period The charging and discharging power of a HFCV; for The total charging and discharging power of EVs being charged in the system during the time period; is the charging station capacity; for The total amount of hydrogen charged and discharged by the HFCVs being charged in the system during the period; is the capacity of the hydrogen filling station; for The number of EV / HFCVs connected to the system during the time period; and They are the charging and discharging efficiency of EV and HFCV respectively; For the The time for EV / HFCV to complete charging and hydrogen consumption.

[0025] The low-carbon characteristics in the EH-IES multi-time scale low-carbon operation optimization model are obtained based on the carbon trading mechanism model. The carbon trading mechanism model consists of two parts: a carbon emission quota model and an actual carbon emission model: Carbon emission quota model: in, and are the initial carbon emission quotas of the upper grid (UG), GT, GB, EVs, and HFCVs; , and They are the carbon emission quotas obtained from the electricity generated by the unit of electricity consumed by the purchased electricity and the carbon emission quotas obtained from the electricity and heat generated by the unit of natural gas consumed by the natural gas-fired unit; for Power purchased during the time period; is the thermoelectric conversion coefficient; and They are Output electrical and thermal power of GT during the time period; for GB output thermal power during the time period; and They are the mileage per unit of electricity for EV and HFCV respectively; is the marginal carbon emission factor of fuel vehicles; Actual carbon emission model: in, are the actual carbon emissions of UG, GT and GB and the carbon emissions captured by MET; is the carbon emission factor per unit electricity of UG; for Input power of GB in time period; for Input power in time period GT; is the carbon emission intensity of gas-fired units; is the coefficient of CO2 absorbed per unit CH4 produced, ,in The amount of CO2 required per unit CH4, is the calorific value of natural gas; is the input power of MR.

[0026] S103: Taking into account the EH-IES model with deep coupling of electricity, heat, gas and hydrogen and the flexible V2G charging and discharging model of EVs and HFCVs, the corresponding EH-IES low-carbon operation optimization model is generated at four time scales: week, day before, day and real time. As the EH-IES multi-time scale low-carbon operation optimization model, it is solved with the goal of minimizing the total cost at four time scales: week, day before, day and real time, and the output results of various operating equipment in EH-IES are obtained. The actual output of various operating equipment in EH-IES is calculated based on the output results of various operating equipment. The various operating equipment in EH-IES include: ED, EB, GB, WHB and MET as energy conversion equipment; BT, TS, GS and HS as energy storage equipment; GT and HFC as cogeneration equipment; UG, photovoltaic output PV, natural gas network NGN and hydrogen network HN as energy supply.

[0027] The EH-IES multi-time scale low-carbon operation optimization model is constructed based on the EH-IES model of multi-energy complementarity taking into account the deep coupling of electricity, heat, gas and hydrogen and the flexible V2G charging and discharging model of EVs and HFCVs. On a weekly time scale, an EH-IES low-carbon operation optimization model taking into account flexible V2G is generated to obtain the output of various operating equipment in EH-IES on a weekly time scale. Based on the objective function of the EH-IES low-carbon operation optimization framework taking into account flexible V2G, corresponding EH-IES low-carbon operation optimization models are generated on four time scales: weekly, day-ahead, intraday and real-time. The model is solved with the goal of minimizing the total cost on the four time scales: weekly, day-ahead, intraday and real-time, to obtain the output results of various operating equipment in EH-IES; The various types of operating equipment in the EH-IES specifically include UG, NGN, HN, PV, ED, HFC, EB, GB, GT, WHB, MET, BT, TS, GS and HS; among them, ED, EB, GB, WHB and MET are used as energy conversion equipment models to convert between multiple energy sources; BT, TS, GS and HS are used as energy storage equipment models for real-time balance of electricity, heat, gas and hydrogen power; GT and HFC are used as cogeneration equipment models to adjust the heat and electricity production requirements according to the actual electricity and heat load conditions; UG, NGN, HN and PV are used as energy suppliers to achieve the balance of system electricity, heat, gas and hydrogen supply and demand.

[0028] The objective function of the EH-IES low-carbon operation optimization framework considering flexible V2G is: in, and They are the operation and maintenance cost, abandoned solar power cost, carbon emission cost, energy purchase cost, start-up and shutdown cost of EH-IES, and the income from providing spinning reserve; and They are the unit operation and maintenance costs of the energy conversion equipment model, the cogeneration equipment model, and the energy storage equipment model; and are the unit operation and maintenance costs of EV and HFCV respectively; is the penalty coefficient for abandoning light; for The abandoned optical power in the time period; for The photovoltaic output value predicted by the time period; for The photovoltaic power output actually consumed during the period; is the price per unit of carbon emissions; and They are The purchased natural gas and hydrogen power during the period; It is the low calorific value of hydrogen; and They are time-of-use electricity price, natural gas price, hydrogen price and time-of-use unit price for providing backup for the upper power grid; and They are the startup cost coefficient and shutdown cost coefficient of the energy conversion equipment model, energy storage equipment model and cogeneration equipment model respectively.

[0029] The constraints of the EH-IES low-carbon operation optimization model on a weekly time scale are: The power balance constraints for electricity, heat, gas and hydrogen are: in, and They are The power demand of electricity, heat, gas and hydrogen loads predicted during the time period; and They are The charging and discharging power of BT, TS, GS and HS in the time period; for Output power of time period GT; for Input power in time period GT; and They are The charging and discharging power of EVs during the time period; for Output power of WHB in the time period; and They are Input and output power of time periods EB, GB, and MET; and HFCVs in Charging and discharging power in each time period; Constraints on purchased energy and photovoltaic output are: in, for The upper limit of the capacity of purchased electricity, natural gas and hydrogen during the time period; The input power constraint of the waste heat boiler is: in, for Output thermal power of time period GT; for Input power of WHB during the period.

[0030] Balance constraints between electrolyzer output and hydrogen storage tank input: Spinning reserve capacity constraints: in, and They are The upper limit of spinning reserve that can be provided by BT, GT, HFC and EVs during the period; is the discharge efficiency of BT; for BT capacity during the time period; for The lower limit of BT capacity during the time period; and They are The spinning reserve capacity required for EH-IES and UG during the time period; the time available for generators to increase production and provide spare capacity; for Time period BT discharge state variable; for The upper limit of BT discharge power during the time period; for The running state variables of the time period GT; is the comprehensive conversion efficiency of GT; is the upper limit of GT input power; The upper and lower limits of the GT's output power fluctuation; Capacity constraints of EVs and HFCVs: in, and Respectively The upper and lower limits of the state of charge of EV / HFCV; The maximum number of EVs and HFCVs that can be charged and hydrogenated by EH-IES.

[0031] The EH-IES multi-time scale low-carbon operation optimization model is: The objective function of the EH-IES low-carbon operation optimization model on a weekly time scale is shown in the objective function of the EH-IES low-carbon operation optimization framework taking flexible V2G into account.

[0032] The objective function of the EH-IES low-carbon operation optimization model on the day-ahead time scale is: in, is the adjustment cost of HS in one scheduling cycle; is the initial time of the day-ahead scheduling cycle; is the total number of time periods in the day-ahead scheduling cycle; Adjust the cost factor for HS unit hydrogen charging and discharging; and The time scales are day-ahead and week-ahead respectively. The charging and discharging power of the time period HS; and Respectively in The adjustment of the charging and discharging power of HS in the previous day compared with the weekly time scale; The constraints of the EH-IES low-carbon operation optimization model on the day-ahead time scale are: Similar to the low-carbon operation optimization stage on a weekly time scale, the power balance constraints, energy conversion equipment model, cogeneration equipment model and energy storage equipment model operating constraints in day-ahead scheduling are similar to the low-carbon operation optimization model on a weekly time scale and will not be repeated here.

[0033] in, and They are respectively HS under the day-ahead scheduling time scale The charging and discharging state variables of the time period; and They are respectively HS in the weekly scheduling time scale The charging and discharging state variables of each time period.

[0034] The objective function of the EH-IES low-carbon operation optimization model on a daily time scale is: in, is the adjustment cost of the energy conversion equipment model, the cogeneration equipment model, and the energy storage equipment model except the HS within a dispatch cycle; is the initial time of the intraday scheduling cycle; is the total number of time periods within the daily scheduling cycle; and They are the unit adjustment cost coefficients for the energy conversion equipment model, the cogeneration equipment model, and the energy storage equipment model except HS; and Intraday and day-ahead time scales respectively The input power of the time period energy conversion equipment model, the cogeneration equipment model, and the charging and discharging power of the energy storage equipment model except HS; and Respectively in The adjustment amount of the intraday energy conversion equipment model, the cogeneration equipment model, and the energy storage equipment model except HS compared to the day-ahead time scale; The constraints of the EH-IES low-carbon operation optimization model on the intraday time scale are: Similar to the low-carbon operation optimization stage at the day-ahead time scale, the power balance constraints, energy conversion equipment model operation constraints, and energy storage equipment model operation constraints in the intraday dispatch are similar to the low-carbon operation optimization model at the day-ahead time scale, and will not be repeated here. However, due to the change in the time scale from 1 hour to 15 minutes, the ramp rate and spinning reserve constraints of each energy conversion equipment model need to be adjusted to a certain extent: in, and They are respectively the energy conversion equipment model under the intraday scheduling time scale The running status variables of the time period; and They are respectively the cogeneration equipment model under the intraday scheduling time scale The running status variable of the time period.

[0035] The objective function of the EH-IES low-carbon operation optimization model in real-time time scale is: in, The adjustment cost of purchased energy in one dispatch cycle; is the initial time of the intraday scheduling cycle; is the total number of time periods in the real-time scheduling cycle; and Adjust the cost coefficients for purchased electricity, natural gas, and hydrogen units respectively; and Real-time and intraday time scales respectively The power of purchased electricity, natural gas and hydrogen during the period; For The adjustment amount of real-time purchased energy in the period compared to the intraday time scale.

[0036] The constraints of the EH-IES low-carbon operation optimization model in real-time time scale are: In the low-carbon operation optimization stage under the real-time time scale, since the output of the energy conversion equipment model, the energy storage equipment model and the cogeneration equipment model is substituted into the real-time scheduling with a determined value, there is no need to consider the operation constraints and ramp constraints of the equipment. Other constraints are similar to those within the day, but the time scale is different.

[0037] Use solvers (such as gurobi, cplex, etc.) to solve the EH-IES multi-time scale low-carbon optimization operation model taking into account flexible V2G, and obtain the EH-IES multi-time scale optimization operation strategy under the objective function of lowest system comprehensive cost, including the output of various operating equipment, the output adjustment of various operating equipment at different time scales, and the charging, discharging and hydrogen conditions of EVs and HFCVs.

[0038] Specifically, the present invention is further described below by way of embodiments: Embodiment 2 In this embodiment, a comprehensive energy system containing hydrogen energy is taken as an example to introduce a multi-time scale low-carbon operation optimization method of an electric-hydrogen comprehensive energy system; specifically, the following steps are included: The schematic diagram of the framework of the electric-hydrogen integrated energy system based on the flow of electric energy, thermal energy, hydrogen energy and gas energy is as follows: Figure 2 As shown in the figure, the power demand of the system is provided by UG, PV, GT, HFC and BT. In particular, when the system power supply is insufficient, EVs and HFCVs connected to EH-IES can realize the reverse transmission of electric energy through the flexible V2G mode while meeting their own travel needs, ensuring the reliability of the system power supply and maintaining the stable operation of the system; the heat demand is provided by WHB, EB, GB and TS. In particular, GT and HFC first absorb the generated heat energy through WHB before providing it; the gas demand is provided by P2G devices (hydrogen produced by ED generates methane through MR), GS and purchased natural gas; the supply of hydrogen load comes from two parts: when the power supply of the entire system is in excess, the surplus power is used for hydrogen production by electrolysis of water and purchased hydrogen. HFC can use hydrogen produced by electrolysis of water or purchased hydrogen as fuel to discharge to the system. In addition, electric hydrogen can realize long-term storage of electric energy through the coupling and conversion of ED, HS and HFC. The weekly prediction curves of photovoltaic, electric load, heat load, gas load and hydrogen load under four time scales are shown in the figure below. Figure 3-7As shown, the prediction curve is obtained by adding disturbances to the actual load and photovoltaic curves. The prediction errors of photovoltaic and load curves in the week-ahead, day-ahead, day-in, and real-time scheduling stages decrease in turn. Assuming that the day-ahead prediction error of photovoltaic output is 20%, the day-ahead photovoltaic output prediction curve is the actual photovoltaic output curve plus white noise with an expectation of 0 and a standard deviation of 0.2 (assuming that the prediction error follows a normal distribution). Similarly, assuming that the week-ahead, day-ahead, day-in, and real-time prediction errors of photovoltaic are 30%, 20%, 5%, and 2%, respectively, and the week-ahead, day-ahead, day-in, and real-time prediction errors of load are 8%, 3%, 1%, and 0.5%, respectively, the prediction curve is the actual curve plus the corresponding white noise. The main parameters of the energy conversion equipment model and the cogeneration equipment model within EH-IES are shown in Table 1, the main parameters of the energy storage equipment model are shown in Table 2, the relevant parameters of EV and HFCV are shown in Table 3, and the carbon trading parameters are shown in Table 4; Table 1 Energy conversion equipment model parameters Table 2 Energy storage equipment model parameters Table 3 EV and HFCV related parameters Table 4 Carbon trading parameters According to the operation information of each device in EH-IES, combined with the flow of electric energy, heat energy, gas energy and hydrogen energy, the energy conversion equipment model, energy storage equipment model and cogeneration equipment model operation model are established; based on the travel information of EVs and HFCVs, considering the flexible V2G mode of both, the flexible V2G charging and discharging model of EVs and HFCVs is constructed; Consider the power balance constraints of electricity, heat, gas and hydrogen, the power constraints of purchased energy and photovoltaic output, the input power constraints of waste heat boilers, the balance constraints of electrolyzer output and hydrogen storage tank input, the spinning reserve capacity constraints, and the capacity constraints of EVs and HFCVs; Considering the green and low-carbon benefits and economic characteristics of the electric-hydrogen integrated energy system, the objective function is constructed; Based on the fact that the data prediction accuracy of photovoltaic-electricity, heat, gas and hydrogen loads gradually improves as the time scale decreases, the continuously updated prediction information is used to optimize different time scales while taking into account the economic efficiency of system operation and the efficient utilization of clean energy; In this embodiment, a commercial solver is used to solve the EH-IES multi-time scale low-carbon optimization operation model taking into account flexible V2G, and the EH-IES multi-time scale optimization operation strategy is obtained under the objective function of minimizing the overall system cost. The output conditions of various operating equipment, the output adjustment conditions of various operating equipment at different time scales, and the charging and discharging and hydrogen conditions of EVs and HFCVs are obtained.

[0039] Comparative Example In order to verify the effectiveness of the method proposed in the present invention, the multi-time scale low-carbon operation strategy is temporarily not considered, and a comparative example is now given. In the comparative example: Scheme 1: IES low-carbon operation optimization model under flexible V2G mode without considering hydrogen production and storage (electricity-hydrogen coupling), EVs and HFCVs; Scheme 2: EH-IES low-carbon operation optimization model in flexible V2G mode without considering EVs and HFCVs, and the capacity conservation period of HS is 168 hours (1 week); Scheme 3: Considering the EH-IES low-carbon operation optimization model in the flexible V2G mode of EVs and HFCVs, the capacity conservation period of HS is 24h; Scheme 4: Considering the EH-IES low-carbon operation optimization model in the flexible V2G mode of EVs and HFCVs, the capacity conservation period of HS is 168h; The cost comparison results of the four schemes are shown in Table 5: Table 5 Comparison of scheduling results under various schemes in low-carbon operation optimization on a weekly time scale From the information in the table, we can clearly observe the advantages and potential of electric-hydrogen coupling and vehicle-grid interaction IES. The data in the table can intuitively show that the total cost of the system gradually decreases with the addition of hydrogen energy and flexible V2G mode. By coupling IES with hydrogen energy, ED can convert excess electricity in the system into hydrogen energy when photovoltaic output is surplus, and store it in HS, reducing resource waste and improving the utilization rate of light energy; flexible V2G technology realizes two-way interaction with the power grid. When it is at the peak of renewable energy output, EVs and HFCVs are charged. When photovoltaic output is insufficient and the system needs a lot of energy, both can release energy, improve the absorption of renewable energy, reduce the occurrence of abandoned light, and maintain the supply and demand balance of the system. It can well smooth load fluctuations, effectively reduce the peak-to-valley difference of load, and play the role of peak shaving and valley filling; the difference in the capacity conservation period of HS shows that hydrogen storage is more advantageous in longer storage, and also proves that hydrogen storage has huge development potential in future long-term electricity storage; Considering the multi-time scale low-carbon operation strategy, a comparative example is given. In the comparative example: Scheme 5: Considering only a single time scale, the EH-IES day-ahead low-carbon operation optimization model considering the flexible V2G mode proposed in this paper is adopted; Solution 6: Adopt the EH-IES multi-time scale low-carbon operation optimization model taking into account the flexible V2G mode proposed in the present invention; The output of hydrogen fuel cells on the day-to-day time scale is compared as follows: Figure 8 As shown in the figure, we can clearly observe the difference in the output of HFC in the two time scales of day-ahead and intraday. As the forecast time scale from day-ahead to intraday is reduced, the source-load forecast information is more accurate, and the output of HFC is also adjusted more accurately, making its operation results closer to the actual situation. The same is true for other operating equipment in the system. The comparison of purchased power in the day-ahead-real-time time scale and the comparison of purchased power in the intraday-real-time time scale are shown in the figure below. Fig. 9 and 10 As shown in the figure, the two figures respectively show the comparison of purchased energy in the three time scales of day-ahead-real-time and intraday-real-time. The overall change trend of purchased electricity is small, because most of the errors caused by the prediction can be reduced by the units inside the system first, and the ones that cannot be adjusted inside the system will be adjusted by purchased electricity. As the time scale from day-ahead to intraday and then to real-time becomes smaller and smaller, the prediction information becomes more accurate, so the adjustment of purchased electricity becomes less and less obvious. The comparison of electric vehicle charging and discharging in the intraday-real-time time scale is shown in the figure below. Fig.11 As shown in the figure, it can be seen that with the gradual refinement of the time scale from intraday to real-time, the uncertainty of the source-load forecast information is also gradually reduced. Since the real-time time scale is too small, frequent adjustments to the output of each operating equipment will cause too much loss. Therefore, the charging and discharging plan of EVs can be adjusted in real time to indirectly correct the purchased energy plan, so as to smooth out the power fluctuations caused by the forecast error and make the optimization result more in line with reality.

[0040] As can be seen from the above pictures, under the multi-time scale scheduling strategy, the uncertainty of renewable energy output and various loads is more fully considered. By continuously updating the source-load forecast information, the forecast data is made more accurate, and the output plan of the unit is continuously adjusted, so that the system can suppress fluctuations on the supply and demand side. The scheduling results under multiple time scales are also more reliable and reasonable, and the output plans of various operating equipment are more in line with the actual situation, improving the output accuracy of various operating units, formulating more detailed scheduling plans, and ensuring the stable operation of the system. EVs and HFCVs can give full play to their rapid adjustment capabilities through charging and discharging power to balance the deviations of photovoltaic output and various loads in the intraday and real-time forecast data on ultra-short time scales, thereby alleviating the power adjustment pressure of the energy storage equipment model and energy conversion equipment model in the system in the short-term or even ultra-short time scale, and improving the reliable, safe and stable operation of the system.

[0041] Embodiment 3: Fig.12 As shown, in order to achieve the above-mentioned purpose, the present invention discloses a multi-time scale low-carbon operation optimization system for an electric-hydrogen integrated energy system, comprising: The data coupling module 11 pre-establishes a multi-energy complementary EH-IES model that takes into account the deep coupling of electricity, heat, gas and hydrogen. The EH-IES model is coupled through a traditional IES equipment operation model and a hydrogen production-storage-use link model in hydrogen production and energy storage. The traditional IES equipment operation model includes: an energy conversion equipment model, an energy storage equipment model and a cogeneration equipment model; the hydrogen production-storage-use link model in hydrogen production and energy storage includes: a hydrogen production link model, a hydrogen storage link model and a hydrogen use link model; A data sampling module 12 is used to pre-establish a flexible V2G charging and discharging model for EVs and HFCVs, wherein the flexible V2G charging and discharging model for EVs and HFCVs is established based on the driving behaviors of EVs and HFCVs obtained through Monte Carlo simulation; The operation optimization module 13 is used to take into account the EH-IES model with deep coupling of electricity, heat, gas and hydrogen and the flexible V2G charging and discharging model of EVs and HFCVs, and to generate corresponding EH-IES low-carbon operation optimization models at four time scales: week, day before, day and real time. As the EH-IES multi-time scale low-carbon operation optimization model, it is solved with the goal of minimizing the total cost at four time scales: week, day before, day and real time, and the output results of various operating equipment in EH-IES are obtained. The actual output of various operating equipment in EH-IES is calculated based on the output results of various operating equipment. The various operating equipment in EH-IES include: ED, EB, GB, WHB and MET as energy conversion equipment; BT, TS, GS and HS as energy storage equipment; GT and HFC as cogeneration equipment; UG, photovoltaic output PV, natural gas network NGN and hydrogen network HN as energy supply.

[0042] Based on the same inventive concept, the present invention also provides a computer device, which includes: one or more processors, and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute the program instructions stored in the memory. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is used to implement one or more instructions, specifically for loading and executing one or more instructions in a computer storage medium to implement the above method.

[0043] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium, on which a computer program is stored, and the computer program is executed by the processor to execute the above method. The storage medium can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0044] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0045] The above shows and describes the basic principles, main features and advantages of the present disclosure. Those skilled in the art should understand that the present disclosure is not limited by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present disclosure. Without departing from the spirit and scope of the present disclosure, the present disclosure may have various changes and improvements, and these changes and improvements fall within the scope of the present disclosure to be protected.

Claims

1. A multi-time scale low-carbon operation optimization method for an electric-hydrogen integrated energy system, characterized in that: The method comprises the following steps: A multi-energy complementary EH-IES model taking into account the deep coupling of electricity, heat, gas and hydrogen is established in advance. The EH-IES model is coupled through the traditional IES equipment operation model and the hydrogen production-storage-use link model in hydrogen production and energy storage. The traditional IES equipment operation model includes: an energy conversion equipment model, an energy storage equipment model and a cogeneration equipment model; the hydrogen production-storage-use link model in hydrogen production and energy storage includes: a hydrogen production link model, a hydrogen storage link model and a hydrogen use link model; Pre-establishing a flexible V2G charging and discharging model for EVs and HFCVs, wherein the flexible V2G charging and discharging model for EVs and HFCVs is established based on the driving behaviors of EVs and HFCVs obtained through Monte Carlo simulation; Taking into account the EH-IES model of multi-energy complementarity with deep coupling of electricity, heat, gas and hydrogen and the flexible V2G charging and discharging model of EVs and HFCVs, the corresponding EH-IES low-carbon operation optimization models are generated at four time scales: week, day before, day and real time. As the EH-IES multi-time scale low-carbon operation optimization model, the model is solved with the goal of minimizing the total cost at four time scales: week, day before, day and real time, and the output results of various operating equipment in EH-IES are obtained. The actual output of various operating equipment in EH-IES is calculated based on the output results of various operating equipment. The various operating equipment in EH-IES include: ED, EB, GB, WHB and MET as energy conversion equipment; BT, TS, GS and HS as energy storage equipment; GT and HFC as cogeneration equipment; UG, photovoltaic output PV, natural gas network NGN and hydrogen network HN as energy supply.

2. The multi-time scale low-carbon operation optimization method of the electric-hydrogen integrated energy system according to claim 1 is characterized in that: The traditional IES equipment operation model includes: Energy conversion equipment models: in, and Energy conversion equipment model exist Input and output power during the time period; and Energy conversion equipment model The upper and lower limits of capacity; and Energy conversion equipment model The upper and lower limits of output power fluctuation; Model for energy conversion equipment conversion efficiency; for Operational state variables of the time period energy conversion equipment model; It is a collection of all energy conversion equipment models in the traditional IES, including electric boiler EB, gas boiler GB, waste heat boiler WHB and methanation reactor MET; For the entire scheduling cycle; Energy storage equipment model: in, is the self-damage rate of the energy storage device model; For the energy storage device model Storage levels for time periods; and are the charging and discharging efficiencies of the energy storage device model respectively; and They are The charging and discharging power of the time-slot energy storage device model; is the scheduling time scale; and They are the upper and lower limits of the charging power of the energy storage device model respectively; and They are the upper and lower limits of the energy storage device model’s energy release power, respectively; and are the upper and lower limits of the capacity of the energy storage device model respectively; and They are Charging and discharging state variables of the time-slot energy storage device model; and They are the energy storage capacity at the beginning and end of the energy storage equipment model cycle in the traditional IES; It is a collection of energy storage equipment models in traditional IES, including energy storage batteries BT, heat storage tanks TS, and gas storage tanks GS; Combined Heat and Power Plant Model: in, and They are Output electricity and heat power of the cogeneration equipment model during the time period; is the comprehensive conversion efficiency of the combined heat and power equipment model; for Input power to the CHP plant model during the period; and are the upper and lower limits of the input power of the cogeneration equipment model, respectively; for Operational state variables of the cogeneration equipment model for the time period; and They are the upper and lower limits of the heat-to-electricity ratio of the cogeneration equipment model; and They are the upper and lower limits of the output power fluctuation of the cogeneration equipment model respectively; A collection of models for all cogeneration equipment in the traditional IES, including gas turbines GT; The hydrogen production-storage-use link model in the hydrogen production and energy storage includes: Hydrogen production model: in, and The electrolytic cell ED is Input and output power during the time period; and They are the upper and lower limits of ED capacity, respectively; and They are the upper and lower limits of ED output power fluctuation respectively; is the conversion efficiency of ED; for The operating status variables of the time period ED; Hydrogen storage link model: in, is the self-damage rate of the hydrogen storage tank HS; For HS Storage levels for time periods; and are the charging and discharging efficiency of HS respectively; and They are The charging and discharging power of HS in the time period; and They are the upper and lower limits of HS hydrogen charging power respectively; and They are the upper and lower limits of HS hydrogen degassing power respectively; and are the lower upper limits of the capacity of HS respectively; and They are Time period HS charging and discharging state variables; and They are the hydrogen storage capacity at the beginning and end of the HS cycle; Hydrogen use link model: in, and They are Output electrical and thermal power of hydrogen fuel cell HFC during the period; is the comprehensive conversion efficiency of HFC; for Input power of HFC during the period; and They are the upper and lower limits of HFC input power respectively; for Operation status variables of HFC in the time period; and They are the upper and lower limits of HFC heat-to-power ratio respectively; and They are the upper and lower limits of HFC output power fluctuation respectively.

3. The multi-time scale low-carbon operation optimization method of the electric-hydrogen integrated energy system according to claim 1 is characterized in that: The driving behaviors of EVs and HFCVs obtained based on Monte Carlo simulation are obtained by the random driving behaviors modeling and sampling method of EVs and HFCVs based on Monte Carlo. The random driving behaviors modeling and sampling method of EVs and HFCVs based on Monte Carlo is as follows: The travel behavior of vehicles is simulated according to the probability density functions of the start and end charging time of EVs and HFCVs and the daily mileage, and the Monte Carlo simulation method is used to sample travel data: The start and end charging time of EVs satisfy the piecewise normal distribution function, and the probability density functions are and , which is expressed as follows: in, , ; here Indicates the time when EVs start charging; in, , ; here Indicates the time when EVs end charging; The daily mileage of EVs approximately satisfies the following lognormal distribution function: : in, , ; here It represents the daily mileage of EVs in km; The initial state of charge of the vehicle when it starts charging and hydrogen is calculated based on the daily mileage of the sampled EVs and HFCVs: in, For the The state of charge of the EV / HFCV; For the The expected power of the EV / HFCV after charging is completed; The electricity and hydrogen consumption per 100 kilometers for EVs / HFCVs; For the The time for EV / HFCV to start charging and hydrogen; For the Mileage travelled by the EV / HFCV; is the battery capacity of EV / HFCV; Using the Monte Carlo simulation method, according to the probability density function , and To simulate the travel behavior of vehicles, the energy consumption of vehicles before being connected to the grid is estimated based on the mileage and power consumption per 100 km. The Monte Carlo simulation method is modified to ensure that the start charging time is less than the departure time, and the driving behavior of EVs and HFCVs is obtained.

4. The multi-time scale low-carbon operation optimization method of the electric-hydrogen integrated energy system according to claim 1 is characterized in that: The flexible V2G charging and discharging model of EVs and HFCVs is as follows: in, and They are Time period The charging and discharging power of an EV; and They are the upper limits of charging and discharging power of EV respectively; and They are Time period The amount of hydrogen charged and discharged by a HFCV; and They are the upper limits of hydrogen filling and discharging of HFCV respectively; and Respectively represent Time period The charging and discharging state variables of EV / HFCV; is the hydrogen-to-electricity conversion coefficient; and They are Time period The charging and discharging power of a HFCV; for The total charging and discharging power of EVs being charged in the system during the time period; is the charging station capacity; for The total amount of hydrogen charged and discharged by the HFCVs being charged in the system during the period; is the capacity of the hydrogen filling station; for The number of EV / HFCVs connected to the system during the time period; and They are the charging and discharging efficiency of EV and the charging and discharging efficiency of HFCV respectively; For the The time for EV / HFCV to complete charging and hydrogen consumption.

5. The multi-time scale low-carbon operation optimization method of the electric-hydrogen integrated energy system according to claim 1 is characterized in that: The low-carbon characteristics of the EH-IES multi-time scale low-carbon operation optimization model are obtained based on the carbon trading mechanism model. The carbon trading mechanism model consists of two parts: the carbon emission quota model and the actual carbon emission model. composition: Carbon emission quota model: in, and are the initial carbon emission quotas of the upper grid UG, GT, GB, EVs and HFCVs respectively; , and They are the carbon emission quotas obtained from the electricity generated by the unit of electricity consumed by the purchased electricity and the carbon emission quotas obtained from the electricity and heat generated by the unit of natural gas consumed by the natural gas-fired unit; for Power purchased during the time period; is the thermoelectric conversion coefficient; and They are Output electrical and thermal power of GT during the time period; for GB output thermal power during the time period; and They are the mileage per unit of electricity for EV and HFCV respectively; is the marginal carbon emission factor of fuel vehicles; Actual carbon emission model: in, are the actual carbon emissions of UG, GT and GB and the carbon emissions captured by MET; is the carbon emission factor per unit electricity of UG; for Input power of GB in time period; for Input power in time period GT; is the carbon emission intensity of gas-fired units; is the coefficient of CO2 absorbed per unit CH4 produced, ,in The amount of CO2 required per unit CH4, is the calorific value of natural gas; is the input power of MR.

6. The multi-time scale low-carbon operation optimization method of the electric-hydrogen integrated energy system according to claim 5 is characterized in that: The EH-IES multi-time scale low-carbon operation optimization model is constructed based on the EH-IES model of multi-energy complementarity taking into account the deep coupling of electricity, heat, gas and hydrogen and the flexible V2G charging and discharging model of EVs and HFCVs. On a weekly time scale, an EH-IES low-carbon operation optimization model taking into account flexible V2G is generated to obtain the output of various operating equipment in the EH-IES on a weekly time scale. The various operating equipment in the EH-IES include: ED, EB, GB, WHB and MET as energy conversion equipment models, BT, TS, GS and HS as energy storage equipment models, GT and HFC as cogeneration equipment models, UG, photovoltaic output PV, natural gas network NGN and hydrogen network HN as energy supply; The objective function of the EH-IES low-carbon operation optimization framework considering flexible V2G is as follows: in, and They are the operation and maintenance cost, abandoned solar power cost, carbon emission cost, energy purchase cost, start-up and shutdown cost of EH-IES, and the income from providing spinning reserve; and They are the unit operation and maintenance costs of the energy conversion equipment model, the cogeneration equipment model, and the energy storage equipment model; and are the unit operation and maintenance costs of EV and HFCV respectively; is the penalty coefficient for abandoning light; for The abandoned optical power in the time period; for The photovoltaic output value predicted by the time period; for The actual photovoltaic output consumed during the period; is the price per unit of carbon emissions; and They are The purchased natural gas and hydrogen power during the period; It is the low calorific value of hydrogen; and They are time-of-use electricity price, natural gas price, hydrogen price and time-of-use unit price for providing backup for the upper power grid; and They are the startup cost coefficient and shutdown cost coefficient of the energy conversion equipment model, energy storage equipment model and cogeneration equipment model respectively.

7. The multi-time scale low-carbon operation optimization method of the electric-hydrogen integrated energy system according to claim 6 is characterized in that: The EH-IES multi-time scale low-carbon operation optimization model is based on the objective function of the EH-IES low-carbon operation optimization framework taking into account flexible V2G. The corresponding EH-IES low-carbon operation optimization model is generated at four time scales: weekly, day-ahead, intraday and real-time. The total cost of the four time scales is minimized to obtain the output results of various operating equipment in EH-IES. The objective function of the EH-IES low-carbon operation optimization model at the weekly time scale is the objective function of the EH-IES low-carbon operation optimization framework taking into account flexible V2G; The objective function of the EH-IES low-carbon operation optimization model on the day-ahead time scale is: in, is the adjustment cost of HS in one scheduling cycle; is the initial time of the day-ahead scheduling cycle; is the total number of time periods in the day-ahead scheduling cycle; Adjust the cost factor for HS unit hydrogen charging and discharging; and The time scales are day-ahead and week-ahead respectively. The charging and discharging power of the time period HS; and Respectively in The adjustment of the charging and discharging power of HS in the previous day compared with the weekly time scale; The objective function of the EH-IES low-carbon operation optimization model on a daily time scale is: in, is the adjustment cost of the energy conversion equipment model, the cogeneration equipment model, and the energy storage equipment model except the HS within a dispatch cycle; is the initial time of the intraday scheduling cycle; is the total number of time periods within the daily scheduling cycle; and They are the unit adjustment cost coefficients for the energy conversion equipment model, the cogeneration equipment model, and the energy storage equipment model except HS; and Intraday and day-ahead time scales respectively The input power of the time period energy conversion equipment model, the cogeneration equipment model, and the charging and discharging power of the energy storage equipment model except HS; and Respectively in The adjustment amount of the intraday energy conversion equipment model, the cogeneration equipment model, and the energy storage equipment model except HS compared to the day-ahead time scale; The objective function of the EH-IES low-carbon operation optimization model in real-time time scale is: in, The adjustment cost of purchased energy in one dispatch cycle; is the initial time of the intraday scheduling cycle; is the total number of time periods in the real-time scheduling cycle; and Adjust the cost coefficients for purchased electricity, natural gas, and hydrogen units respectively; and Real-time and intraday time scales The power of purchased electricity, natural gas and hydrogen during the period; For The adjustment amount of real-time purchased energy in the period compared to the intraday time scale.

8. A multi-time-scale low-carbon operation optimization system for an electric-hydrogen integrated energy system, which adopts the multi-time-scale low-carbon operation optimization method for an electric-hydrogen integrated energy system according to any one of claims 1 to 7, characterized in that: include: The data coupling module pre-establishes a multi-energy complementary EH-IES model that takes into account the deep coupling of electricity, heat, gas and hydrogen. The EH-IES model is coupled through the traditional IES equipment operation model and the hydrogen production-storage-use link model in hydrogen production and energy storage. The traditional IES equipment operation model includes: energy conversion equipment model, energy storage equipment model and cogeneration equipment model; the hydrogen production-storage-use link model in hydrogen production and energy storage includes: hydrogen production link model, hydrogen storage link model and hydrogen use link model; A data sampling module is used to pre-establish a flexible V2G charging and discharging model for EVs and HFCVs, wherein the flexible V2G charging and discharging model for EVs and HFCVs is established based on the driving behaviors of EVs and HFCVs obtained through Monte Carlo simulation; The operation optimization module is used to take into account the EH-IES model with deep coupling of electricity, heat, gas and hydrogen and the flexible V2G charging and discharging model of EVs and HFCVs. The corresponding EH-IES low-carbon operation optimization model is generated at four time scales: week, day before, day and real time. As the EH-IES multi-time scale low-carbon operation optimization model, it is solved with the goal of minimizing the total cost at four time scales: week, day before, day and real time, and the output results of various operating equipment in EH-IES are obtained. The actual output of various operating equipment in EH-IES is calculated based on the output results of various operating equipment. The various operating equipment in EH-IES include: ED, EB, GB, WHB and MET as energy conversion equipment; BT, TS, GS and HS as energy storage equipment; GT and HFC as cogeneration equipment; UG, photovoltaic output PV, natural gas network NGN and hydrogen network HN as energy supply.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: The memory stores a computer program that can be run on a processor. When the processor loads and executes the computer program, the multi-time-scale low-carbon operation optimization method of the electric-hydrogen integrated energy system described in any one of claims 1 to 7 is adopted.

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