Multi-time Scale Low-carbon Operation Optimization Method and System for Electric-hydrogen Integrated Energy System

Abstract

The present invention discloses a multi-time scale low-carbon operation optimization method and system for an electric-hydrogen integrated energy system, which relates to the technical field of integrated energy system applications, and includes: obtaining relevant data of operating units with different single-unit capacities, and generating a multi-energy complementary electric-hydrogen integrated energy system model that takes into account the deep coupling of electricity, heat, gas, and hydrogen based on the relevant data of operating units with different single-unit capacities and the deep coupling of electricity, heat, gas, and hydrogen; sampling travel data based on the Monte Carlo-based random driving behavior modeling and sampling method for electric vehicles and hydrogen fuel cell vehicles to generate flexible V2G charge and discharge models for EVs and HFCVs; generating an EH-IES multi-time scale low-carbon operation optimization model based on the above two models, solving it with the goal of minimizing the total cost, and obtaining the output results of various operating equipment within the EH-IES to improve the resource utilization efficiency.

Description

Technical Field

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

[0002] The gradual depletion of fossil fuels and the increase in carbon dioxide emissions pose a major threat to the global energy structure. The energy production and consumption patterns must meet the requirements of social development. The transformation of the energy structure is a long process that needs to address the following two major challenges: 1) The uncertainty and volatility problems of highly penetrated renewable energy. How to promote the consumption of renewable energy is a major problem, especially to promote the efficient utilization of energy to achieve the effect of energy conservation and emission reduction. Due to the clean electricity brought by highly penetrated renewable energy and carbon emission policies, the vigorous development of hydrogen energy has been promoted. How to use hydrogen energy to change the global energy system to overcome environmental problems has become a major challenge; 2) The problems of supply-demand side fluctuations and accurate output of operating units in the EH-IES, the uncertainty problems, and the adverse effects that power fluctuations will have 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 the scheduling results has become another major challenge. Summary of the Invention

[0003] To solve the deficiencies mentioned in the above background technique, 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 through the following technical solutions: A multi-time scale low-carbon operation optimization method for an electric-hydrogen integrated energy system, the method includes the following steps:

[0005] Pre-establish 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 device operation model and the hydrogen production-storage-utilization link model in hydrogen production and energy storage. The traditional IES device operation model includes: an energy conversion device model, an energy storage device model, and a combined heat and power device model; the hydrogen production-storage-utilization link model in hydrogen production and energy storage includes: a hydrogen production link model, a hydrogen storage link model, and a hydrogen utilization link model;

[0006] Pre-establish a flexible V2G charging and discharging model for EVs and HFCVs. Among them, 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;

[0007] The EH-IES model of multi-energy complementarity considering deep coupling of electricity, heat, gas, and hydrogen, and the flexible V2G charging and discharging models of EVs and HFCVs generate corresponding low-carbon operation optimization models for the EH-IES at four time scales: weekly, day-ahead, intra-day, and real-time. As the multi-time scale low-carbon operation optimization model of the EH-IES, it is solved with the goal of minimizing the total cost at the four time scales of weekly, day-ahead, intra-day, and real-time to obtain the output results of various operating equipment within the EH-IES, and the actual output of various operating equipment within the EH-IES is based on the output results of various operating equipment. The various operating equipment within the EH-IES includes: taking ED, EB, GB, WHB, and MET as energy conversion equipment; taking BT, TS, GS, and HS as energy storage equipment; taking GT and HFC as combined heat and power equipment; taking UG, photovoltaic output PV, natural gas network NGN, and hydrogen network HN as energy supplies.

[0008] Combined with the first aspect, in some implementations of the first aspect, the method further includes: The traditional IES equipment operation model includes:

[0009] Energy conversion equipment model:

[0010]

[0011]

[0012]

[0013] Among them, and are respectively the input and output powers of the energy conversion equipment model in the time period; and are respectively the upper and lower limits of the capacity of the energy conversion equipment model ; and are respectively the upper and lower limits of the output power fluctuation of the energy conversion equipment model ; is the conversion efficiency of the energy conversion equipment model ; is the operating state variable of the energy conversion equipment model in the time period; is the set of all energy conversion equipment models within the traditional IES, including the electric boiler EB, gas boiler GB, waste heat boiler WHB, and methanation reactor MET; is the entire scheduling period;

[0014] Energy storage equipment model:

[0015]

[0016]

[0017]

[0018]

[0019]

[0020]

[0021] wherein, is the self-loss rate of the energy storage device model; is the storage level of the energy storage device model during period; and are the charging and discharging efficiencies of the energy storage device model respectively; and are respectively the charging and discharging powers of the energy storage device model during is the scheduling time scale; and are the upper and lower limits of the charging power of the energy storage device model respectively; and are the upper and lower limits of the discharging power of the energy storage device model respectively; and are the upper and lower limits of the capacity of the energy storage device model respectively; and are respectively the charging and discharging state variables of the energy storage device model during and are the initial and final energy storage capacities of the energy storage device model in the traditional IES respectively; is the set of energy storage device models in the traditional IES, including the energy storage battery BT, the thermal storage tank TS, and the gas storage tank GS;

[0022] Combined heat and power equipment model:

[0023]

[0024]

[0025]

[0026]

[0027] wherein, and are respectively the output electric and heat powers of the combined heat and power equipment model during is the comprehensive conversion efficiency of the combined heat and power equipment model; is the input power of the combined heat and power equipment model during the and are respectively the upper and lower limits of the input power of the combined heat and power equipment model; is the operating state variable of the combined heat and power equipment model during the and are respectively the upper and lower limits of the heat - electricity ratio of the combined heat and power equipment model; and are respectively the upper and lower limits of the output electric power fluctuation of the combined heat and power equipment model; is the set of all combined heat and power equipment models in the traditional IES, including gas turbines GT;

[0028] The hydrogen production - hydrogen storage - hydrogen utilization link model in the hydrogen production and energy storage includes:

[0029] Hydrogen production link model:

[0030]

[0031]

[0032]

[0033] Among them, and are respectively the input and output powers of the electrolyzer ED during the period; and are respectively the upper and lower limits of the capacity of ED; and are respectively the upper and lower limits of the output power fluctuation of ED; is the conversion efficiency of ED; is the operating state variable of ED during the

[0034] Hydrogen storage link model:

[0035]

[0036]

[0037]

[0038]

[0039]

[0040]

[0041] Among them, is the self-damage rate of the hydrogen storage tank HS; is the storage level of HS at time period; and are the hydrogen charging and discharging efficiencies of HS respectively; and are respectively the hydrogen charging and discharging powers of HS at time period; and are respectively the upper and lower limits of the hydrogen charging power of HS; and are respectively the upper and lower limits of the hydrogen discharging power of HS; and are respectively the upper and lower limits of the capacity of HS; and are respectively the hydrogen charging and discharging state variables of HS at time period; and

[0042] Hydrogen utilization link model:

[0043]

[0044]

[0045]

[0046]

[0047] Among them, and are respectively the output electrical and thermal powers of the hydrogen fuel cell HFC at is the comprehensive conversion efficiency of HFC; is the input power of HFC at time period; and are respectively the upper and lower limits of the input power of HFC; is time period the operating state variable of HFC; and are respectively the upper and lower limits of the thermoelectric ratio of HFC;

[0048] 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 Monte Carlo simulation are obtained from the Monte Carlo-based stochastic driving behavior modeling and sampling method for EVs and HFCVs. The Monte Carlo-based stochastic driving behavior modeling and sampling method for EVs and HFCVs is as follows:

[0049] Simulate the travel behaviors of vehicles according to the probability density functions of the start and end charging times and the daily driving mileage of EVs and HFCVs, and use the Monte Carlo simulation method to sample travel data:

[0050] The start charging time and end charging time of EVs satisfy a piecewise normal distribution function, and the probability density functions are and , which are expressed as follows:

[0051]

[0052] Where , ; Here represents the start charging moment of EVs.

[0053]

[0054] Where , ; Here represents the end charging moment of EVs.

[0055] The daily driving mileage of EVs approximately satisfies the following lognormal distribution function :

[0056]

[0057] Where , ; Here represents the daily driving mileage of EVs, with the unit of km.

[0058] Calculate the initial state of charge of the vehicle when starting to charge hydrogen according to the daily driving mileage of the sampled EVs and HFCVs:

[0059]

[0060] Where is the state of charge of the th EV / HFCV; is the expected power of the th EV / HFCV after charging; is the power consumption per 100 km of EVs / HFCVs; is the starting charging time of the th EV / HFCV; is the mileage traveled by the th EV / HFCV; is the battery capacity of the EV / HFCV;

[0061] Using the Monte Carlo simulation method, according to the probability density function , and simulate the travel behavior of the vehicle. The energy consumption before the vehicle is connected to the grid is estimated based on the mileage and the power consumption per 100 kilometers. Modify the Monte Carlo simulation method to ensure that the starting charging time is less than the departure time, and obtain the travel behavior of EVs and HFCVs.

[0062] Combined with the first aspect, in some implementations of the first aspect, the method further includes: The flexible V2G charge and discharge model of the EVs and HFCVs is as follows:

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072] where and are respectively the charging and discharging powers of the th EV during the and are respectively the upper limits of the charging and discharging powers of the EV; and are respectively the hydrogen charging and discharging amounts of the th HFCV during the and are respectively the upper limits of the hydrogen charging and discharging amounts of the HFCV; and respectively represent The charging and discharging state variables of the n-th EV / HFCV during the hydrogen-electric conversion coefficient; and are respectively the charging and discharging powers of the n-th HFCV during the is the total charging and discharging power of the EVs being charged in the system during the charging station capacity; is the total hydrogen charging and discharging amount of the HFCVs being hydrogen-charged in the system during the hydrogen charging station capacity; is the number of EV / HFCVs connected to the system during the and are respectively the charging and discharging efficiencies of EVs and the charging and discharging efficiencies of HFCVs; is the ending charging and hydrogen time of the n-th EV / HFCV.

[0073] Combined with the first aspect, in some implementations of the first aspect, the method further includes that 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, and the carbon trading mechanism model consists of two parts: the carbon emission quota model and the actual carbon emission model:

[0074] The carbon emission quota model is as follows:

[0075]

[0076] where and are respectively the initial carbon emission quotas of the superior power grid UG, GT, GB, EVs, and HFCVs; , and are respectively the carbon emission right quotas obtained by generating electric energy from the purchased unit power consumption and the carbon emission right quotas obtained by consuming unit natural gas in the natural gas-fired unit to generate electricity and heat; is the purchased electric power during the thermoelectric conversion coefficient; and are respectively the output electric and heat powers of GT during the is the output heat power of GB during the and are respectively the mileage numbers of EVs and HFCVs per unit of electric energy; is the marginal carbon emission factor of fuel vehicles;

[0077] The actual carbon emission model is as follows:

[0078]

[0079] Among them, are the actual carbon emissions of UG, GT, and GB and the carbon emissions captured by MET respectively; is the carbon emission factor per unit of electricity of UG; is the input power of GB during the is the input power of GT during the is the carbon emission intensity of the gas turbine unit; is the coefficient of CO2 absorbed per unit of CH4 produced, , where is the amount of CO2 required per unit of CH4, is the calorific value of natural gas; is the input power of MR.

[0080] Combined with the first aspect, in some implementation manners of the first aspect, the method further includes: The construction of the EH-IES multi-time scale low-carbon operation optimization model is based on the multi-energy complementary EH-IES model considering the deep coupling of electricity, heat, gas, and hydrogen and the flexible V2G charging and discharging model of EVs and HFCVs. On the weekly time scale, a low-carbon operation optimization model of EH-IES considering flexible V2G is generated to obtain the output of various operating equipment in the EH-IES on the weekly time scale. The various operating equipment in the EH-IES includes: using ED, EB, GB, WHB, and MET as energy conversion equipment models; using BT, TS, GS, and HS as energy storage equipment models; using GT and HFC as combined heat and power equipment models; using UG, PV, NGN, and HN as energy supplies;

[0081] The objective function of the EH-IES low-carbon operation optimization framework considering flexible V2G is as follows:

[0082]

[0083]

[0084]

[0085]

[0086] Among them, and are the operation and maintenance cost, curtailment cost, carbon emission cost, energy purchase cost, start-stop cost of the EH-IES, and the revenue obtained by providing spinning reserve, respectively; and are the unit operation and maintenance costs of the energy conversion equipment model, combined heat and power equipment model, and energy storage equipment model, respectively; and are the unit operation and maintenance costs of EV and HFCV, respectively; is the curtailment penalty coefficient; is the curtailment power during the is the predicted PV output value during the is the actually consumed PV output during the is the unit carbon emission price; and are the purchased natural gas and hydrogen power during the is the lower calorific value of hydrogen; and are the time-of-use electricity price, natural gas price, hydrogen price, and the time-of-use unit price for providing reserve for the superior power grid, respectively; and are the start-up cost coefficient and shutdown cost coefficient of the energy conversion equipment model, energy storage equipment model, and combined heat and power equipment model, respectively.

[0087] Combined with the first aspect, in some implementation manners of the first aspect, the method further includes: The multi-time scale low-carbon operation optimization model of the EH-IES is based on the objective function of the low-carbon operation optimization framework of the EH-IES considering flexible V2G. At the four time scales of week, day-ahead, intra-day, and real-time, the corresponding low-carbon operation optimization models of the EH-IES are generated respectively, and the total cost at the four time scales of week, day-ahead, intra-day, and real-time is minimized to solve for the output results of various operating equipment in the EH-IES. Among them,

[0088] the objective function of the low-carbon operation optimization model of the EH-IES at the week time scale is the objective function of the low-carbon operation optimization framework of the EH-IES considering flexible V2G;

[0089] the objective function of the low-carbon operation optimization model of the EH-IES at the day-ahead time scale is:

[0090]

[0091]

[0092]

[0093]

[0094] Among them, is the adjustment cost of HS within a scheduling period; is the initial moment of the day-ahead scheduling period; is the total number of time periods within the day-ahead scheduling period; is the unit charging and discharging hydrogen adjustment cost coefficient of HS; and are the charging and discharging powers of HS at time periods under the day-ahead and weekly time scales respectively; and are respectively the adjustment amounts of the charging and discharging hydrogen powers of HS at time periods compared with those of the day-ahead HS under the weekly time scale;

[0095] The objective function of the low-carbon operation optimization model of EH-IES under the intraday time scale is:

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102] Among them, is the adjustment cost of the energy conversion equipment model, the combined heat and power equipment model, and the energy storage equipment model except HS within a scheduling period; is the initial moment of the intraday scheduling period; is the total number of time periods within the intraday scheduling period; and are the unit adjustment cost coefficients of the energy conversion equipment model, the combined heat and power equipment model, and the energy storage equipment model except HS respectively; and are the input powers of the energy conversion equipment model, the combined heat and power equipment model, and the charging and discharging powers of the energy storage equipment model except HS at time periods under the intraday and day-ahead time scales respectively; and are respectively the adjustment amounts of the energy conversion equipment model, the combined heat and power equipment model, and the energy storage equipment model except HS at time periods compared with those of the day-ahead under the intraday time scale;

[0103] The objective function of the EH-IES low-carbon operation optimization model under the real-time time scale is as follows:

[0104]

[0105]

[0106]

[0107]

[0108]

[0109] Among them, is the adjustment cost of purchased energy within a scheduling period; is the initial moment of the intraday scheduling period; is the total number of time periods within the real-time scheduling period; and are the unit adjustment cost coefficients of purchased electric energy, natural gas, and hydrogen respectively; and are respectively under the real-time and intraday time scales The power of purchased electric energy, natural gas, and hydrogen in the time period; is at The adjustment amount of real-time purchased energy compared to the intraday time scale in the time period.

[0110] Solve the EH-IES multi-time scale low-carbon optimization operation model considering flexible V2G, and obtain the EH-IES multi-time scale optimization operation strategy under the objective function of the lowest comprehensive cost of the system, including the output of various operating equipment, the output adjustment of various operating equipment under different time scales, and the charging and discharging and hydrogen conditions of EVs and HFCVs, etc. Secondly, 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:

[0111] A data coupling module, which pre-establishes a multi-energy complementary EH-IES model considering 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-utilization 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 combined heat and power equipment model; the hydrogen production-storage-utilization link model in hydrogen production and energy storage includes: a hydrogen production link model, a hydrogen storage link model, and a hydrogen utilization link model;

[0112] A data sampling module is used to pre - establish a flexible V2G charging and discharging model for EVs and HFCVs. Among them, 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.

[0113] An operation optimization module is used to consider 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. At four time scales of week - ahead, day - ahead, intra - day, and real - time, corresponding EH - IES low - carbon operation optimization models are respectively generated. 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 of week - ahead, day - ahead, intra - day, and real - time to obtain the output results of various operating devices within the EH - IES, and the actual output of various operating devices within the EH - IES is carried out based on the output results of various operating devices. The various operating devices within the EH - IES include: using ED, EB, GB, WHB, and MET as energy conversion devices; using BT, TS, GS, and HS as energy storage devices; using GT and HFC as combined heat and power generation devices; using UG, photovoltaic output PV, natural gas network NGN, and hydrogen network HN as energy supplies.

[0114] In another aspect of the present invention, in order to achieve the above - mentioned purpose, a terminal device is disclosed, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores a computer program capable of running on the 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 as described above is adopted.

[0115] The beneficial effects of the present invention:

[0116] Based on the EH - IES low - carbon operation optimization framework considering flexible V2G, the present invention proposes a multi - time - scale optimal operation strategy, reduces the fluctuations on both the supply and demand sides of the system, improves the accuracy of the output of various operating units, can formulate a more refined scheduling plan, and realizes the stable operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0117] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0118] Figure 1 It is a schematic flowchart of the method of the present invention;

[0119] Figure 2It is a schematic diagram of the framework of the electric-hydrogen integrated energy system used in the embodiments of the present invention;

[0120] Figure 3 It is a one-week prediction curve graph of photovoltaic power under 4 time scales in the embodiments of the present invention;

[0121] Figure 4 It is a one-week prediction curve graph of electric load under 4 scales in the embodiments of the present invention;

[0122] Figure 5 It is a one-week prediction curve graph of heat load under 4 scales in the embodiments of the present invention;

[0123] Figure 6 It is a one-week prediction curve graph of gas load under 4 scales in the embodiments of the present invention;

[0124] Figure 7 It is a one-week prediction curve graph of hydrogen load under 4 scales in the embodiments of the present invention;

[0125] Figure 8 It is a comparison graph of the output of hydrogen fuel cells under the day-ahead - intraday time scale in the comparative example of the present invention;

[0126] Figure 9 It is a comparison graph of the purchased electricity under the day-ahead - real-time time scale in the comparative example of the present invention;

[0127] Figure 10 It is a comparison graph of the purchased electricity under the intraday - real-time time scale in the comparative example of the present invention;

[0128] Figure 11 It is a comparison graph of the charging and discharging of electric vehicles under the intraday - real-time time scale in the comparative example of the present invention;

[0129] Figure 12 It is a schematic diagram of the system structure of the present invention. Detailed implementation manners

[0130] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts fall within the protection scope of the present invention.

[0131] Embodiment 1:

[0132] As Figure 1 shown, the multi-time scale low-carbon operation optimization method for the electric-hydrogen integrated energy system includes the following steps:

[0133] S101: Pre - establish a multi - energy complementary EH - IES model considering the deep coupling of electricity, heat, gas, and hydrogen. The EH - IES model is coupled through the traditional IES device operation model and the hydrogen production - storage - utilization link model in hydrogen production and energy storage. The traditional IES device operation model includes: an energy conversion device model, an energy storage device model, and a combined heat and power device model; the hydrogen production - storage - utilization link model in hydrogen production and energy storage includes: a hydrogen production link model, a hydrogen storage link model, and a hydrogen utilization link model.

[0134] The multi - energy complementary EH - IES model considering the deep coupling of electricity, heat, gas, and hydrogen includes the traditional IES device operation model and the hydrogen production - storage - utilization link model in hydrogen production and energy storage.

[0135] The traditional IES device operation model includes:

[0136] Energy conversion device model:

[0137]

[0138]

[0139]

[0140] Among them, and are the input and output powers of the energy conversion device model at time periods respectively. and are the upper and lower limits of the capacity of the energy conversion device model respectively. and are the upper and lower limits of the output power fluctuation of the energy conversion device model respectively. is the conversion efficiency of the energy conversion device model ; is the operation state variable of the energy conversion device model at time periods. is the set of all energy conversion device models in the traditional IES, including an electric boiler (EB), a gas boiler (GB), a waste heat boiler (WHB), and a methanation reactor (MET); is the entire scheduling period;

[0141] Energy storage device model:

[0142]

[0143]

[0144]

[0145]

[0146]

[0147]

[0148] Among them, is the self-loss rate of the energy storage device model; is the storage level of the energy storage device model during period; and are the charging and discharging efficiencies of the energy storage device model respectively; and are respectively the charging and discharging powers of the energy storage device model during the is the scheduling time scale; and are the upper and lower limits of the charging power of the energy storage device model respectively; and are the upper and lower limits of the discharging power of the energy storage device model respectively; and are the upper and lower limits of the capacity of the energy storage device model respectively; and are respectively the charging and discharging state variables of the energy storage device model during the and are the initial and final energy storage capacities of the energy storage device model in the traditional IES respectively; is the set of energy storage device models in the traditional IES, including battery thermal storage (BT), thermal storage tank (TS), and gas storage tank (GS);

[0149] Combined heat and power equipment model:

[0150]

[0151]

[0152]

[0153]

[0154] Among them, and are respectively the output electric and heat powers of the combined heat and power equipment model during the is the comprehensive conversion efficiency of the combined heat and power equipment model; is the input power of the combined heat and power equipment model during the and are the upper and lower limits of the input power of the combined heat and power equipment model, respectively; is the operating status variable of the combined heat and power equipment model during the time period; and are the upper and lower limits of the heat - to - power ratio of the combined heat and power equipment model, respectively; and are the upper and lower limits of the output electric power fluctuation of the combined heat and power equipment model, respectively; is the set of all combined heat and power equipment models in the traditional IES, including gas turbines (GT);

[0155] The hydrogen production - hydrogen storage - hydrogen utilization link model in the hydrogen production and energy storage includes:

[0156] Hydrogen production link model:

[0157]

[0158]

[0159]

[0160] Among them, and are the input and output powers of the electrolyzer (ED) during the time period respectively; and are the upper and lower limits of the capacity of the ED, respectively; and are the upper and lower limits of the output power fluctuation of the ED, respectively; is the conversion efficiency of the ED; is the operating status variable of the ED during the time period;

[0161] Hydrogen storage link model:

[0162]

[0163]

[0164]

[0165]

[0166]

[0167]

[0168] Among them, is the self - loss rate of the hydrogen storage tank (HS); is the storage level of HS during ; and are the hydrogen charging and discharging efficiencies of HS, respectively; and are respectively the hydrogen charging and discharging powers of HS during ; and are respectively the upper and lower limits of the hydrogen charging power of HS; and are respectively the upper and lower limits of the hydrogen discharging power of HS; and are respectively the upper and lower limits of the capacity of HS; and are respectively the charge-discharge state variables of HS during ; and are respectively the initial and final hydrogen storage capacities of HS in a cycle;

[0169] Hydrogen utilization link model:

[0170]

[0171]

[0172]

[0173]

[0174] Among them, and are respectively the output electrical and thermal powers of the hydrogen fuel cell (HFC) during is the comprehensive conversion efficiency of HFC; is the input power of HFC during ; and are respectively the upper and lower limits of the input power of HFC; is the operating state variable of HFC during ; and are respectively the upper and lower limits of the thermoelectric ratio of HFC; ; and are respectively the upper and lower limits of the output electrical power fluctuation of HFC.

[0175] S102: Pre-establish a flexible V2G charge-discharge model for EVs and HFCVs. Among them, the flexible V2G charge-discharge model for EVs and HFCVs is established based on the driving behaviors of EVs and HFCVs obtained through Monte Carlo simulation;

[0176] The driving behaviors of EVs and HFCVs are obtained based on Monte Carlo simulation. The random driving behavior modeling and sampling methods of EVs and HFCVs based on Monte Carlo are as follows:

[0177] The statistical probability method is used to analyze the results of travel behavior. Since the data of HFCVs is relatively scarce, the information on the travel behavior of HFCVs in this paper refers to that of EVs. The start charging time and end charging time of EVs satisfy the piecewise normal distribution function, and the probability density functions are respectively and , which are expressed as follows:

[0178]

[0179] Among them, , ; here represents the start charging moment of EVs.

[0180]

[0181] Among them, , ; here represents the end charging moment of EVs.

[0182] The daily driving mileage of EVs approximately satisfies the following lognormal distribution function :

[0183]

[0184] Among them, , ; here represents the daily driving mileage of EVs, with the unit of km.

[0185] Calculate the initial state of charge of the vehicle when starting to charge and hydrogen according to the daily driving mileage of the sampled EVs and HFCVs:

[0186]

[0187] Among them, is the state of charge of the th EV / HFCV; is the expected power after the th EV / HFCV finishes charging; is the power consumption per 100 km of EVs / HFCVs; is the start charging and hydrogen time of the th EV / HFCV; is the The mileage traveled by an EV / HFCV; is the battery capacity of the EV / HFCV;

[0188] Using the Monte Carlo simulation method, according to the above probability density function 、 and simulate the travel behavior of the vehicle. The energy consumption before the vehicle is connected to the grid is estimated based on the driving mileage and the power consumption per 100 kilometers. Modify the Monte Carlo simulation method to ensure that the start charging time is less than the departure time, and obtain the driving behavior of EVs and HFCVs.

[0189] The flexible V2G charge and discharge model for EVs and HFCVs is:

[0190]

[0191]

[0192]

[0193]

[0194]

[0195]

[0196]

[0197]

[0198]

[0199] Among them, and are respectively the charging and discharging powers of the th EV during the and are respectively the upper limits of the charging and discharging powers of the EV; and are respectively the hydrogen charging and discharging amounts of the th HFCV during the and are respectively the upper limits of the hydrogen charging and discharging amounts of the HFCV; and are respectively used to represent the charging and discharging state variables of the th EV / HFCV during the is the hydrogen-electric conversion coefficient; and are respectively The charging and discharging power of the th HFCV during the time period; The total charging and discharging power of the EVs that are being charged in the system during the time period; is the charging station capacity; is the total hydrogen charging and discharging amount of the HFCVs that are being hydrogenated in the system during the time period; and are the charging and discharging efficiency of the EV and the charging and discharging efficiency of the HFCV, respectively; is the ending charging and hydrogen time of the

[0200] Among them, 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, and the carbon trading mechanism model consists of two parts: the carbon emission quota model and the actual carbon emission model:

[0201] Carbon emission quota model:

[0202]

[0203] Among them, and are the initial carbon emission quotas of the superior power grid (UG), GT, GB, EVs, and HFCVs, respectively; , and are the carbon emission right quotas obtained by generating electric energy from the purchased unit power consumption and the carbon emission right quotas obtained by consuming unit natural gas in the natural gas-fired unit to generate electricity and heat, respectively; is the purchased electric power during the time period; and are the output electric and heat powers of GT during the is the output heat power of GB during the and are the mileage numbers traveled per unit electric quantity of EV and HFCV, respectively; is the marginal carbon emission factor of fuel vehicles;

[0204] Actual carbon emission model:

[0205]

[0206] wherein, are the actual carbon emissions of UG, GT, and GB, and the carbon emissions captured by MET, respectively; is the carbon emission factor per unit of electricity of UG; is the input power of GB during the period; is the input power of GT during the period; is the carbon emission intensity of the gas turbine unit; is the coefficient of CO2 absorbed per unit of CH4 generated, , where is the amount of CO2 required per unit of CH4, is the calorific value of natural gas; is the input power of MR.

[0207] S103: Considering the EH-IES model of multi-energy complementarity with deep coupling of electricity, heat, gas, and hydrogen, and the flexible V2G charging and discharging models of EVs and HFCVs, corresponding EH-IES low-carbon operation optimization models are generated at four time scales: weekly, day-ahead, intra-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: weekly, day-ahead, intra-day, and real-time, to obtain the output results of various operating equipment within the EH-IES, and the actual output of various operating equipment within the EH-IES is based on the output results of various operating equipment; various operating equipment within the EH-IES includes: using ED, EB, GB, WHB, and MET as energy conversion equipment; using BT, TS, GS, and HS as energy storage equipment; using GT and HFC as combined heat and power equipment; using UG, photovoltaic output PV, natural gas network NGN, and hydrogen network HN as energy supplies.

[0208] The construction of the EH-IES multi-time scale low-carbon operation optimization model is based on the EH-IES model of multi-energy complementarity with deep coupling of electricity, heat, gas, and hydrogen, and the flexible V2G charging and discharging models of EVs and HFCVs. At the weekly time scale, an EH-IES low-carbon operation optimization model considering flexible V2G is generated to obtain the output of various operating equipment within the EH-IES at the weekly time scale. Then, based on the objective function of the EH-IES low-carbon operation optimization framework considering flexible V2G, corresponding EH-IES low-carbon operation optimization models are generated at four time scales: weekly, day-ahead, intra-day, and real-time, and are solved with the goal of minimizing the total cost at four time scales: weekly, day-ahead, intra-day, and real-time, to obtain the output results of various operating equipment within the EH-IES;;

[0209] All kinds 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 energies. BT, TS, GS, and HS are used as energy storage equipment models for real-time balancing of electricity, heat, gas, and hydrogen power. GT and HFC are used as combined heat and power equipment models to adjust heat and electricity production demands according to actual electricity and heat load conditions. UG, NGN, HN, and PV are used as energy supplies to achieve the balance of electricity, heat, gas, and hydrogen supply and demand in the system.

[0210] The objective function of the low-carbon operation optimization framework for the EH-IES considering flexible V2G is as follows:

[0211]

[0212]

[0213]

[0214]

[0215] Among them, and are respectively the operation and maintenance costs, curtailment costs, carbon emission costs, energy purchase costs, start-stop costs, and revenues obtained from providing spinning reserve of the EH-IES. and are respectively the unit operation and maintenance costs of the energy conversion equipment model, combined heat and power equipment model, and energy storage equipment model. and are respectively the unit operation and maintenance costs of EV and HFCV. is the curtailment penalty coefficient. is the curtailment power during the is the predicted PV output value during the is the actually consumed PV output during the is the unit carbon emission price. and are respectively the purchased natural gas and hydrogen power during the is the lower calorific value of hydrogen. and are respectively the time-of-use electricity price, natural gas price, hydrogen price, and the time-of-use unit price for providing reserve to the superior power grid. and The start-up cost coefficients and shut-down cost coefficients of the energy conversion equipment model, energy storage equipment model, and combined heat and power equipment model, respectively.

[0216] The constraints of the EH-IES low-carbon operation optimization model on a weekly time scale are as follows:

[0217] The power balance constraints for electricity, heat, gas, and hydrogen are as follows:

[0218]

[0219]

[0220]

[0221]

[0222] Among them, and are respectively the predicted electricity, heat, gas, and hydrogen load power demands in the and are respectively the charging and discharging powers of BT, TS, GS, and HS in the is the output electric power of GT in the is the input power of GT in the and are respectively the charging and discharging powers of EVs in the is the output power of WHB in the and are respectively the input and output powers of EB, GB, and MET in the and are respectively the charging and discharging powers of HFCVs in the time period;

[0223] The constraints on purchased energy and PV output are as follows:

[0224]

[0225]

[0226]

[0227] Among them, is the upper limits of the capacities of purchased electricity, natural gas, and hydrogen in the

[0228] The input power constraint of the waste heat boiler is:

[0229]

[0230] Wherein, is the output heat power of GT in period is the input power of WHB in period

[0231] The balance constraint between the output of the electrolyzer and the input of the hydrogen storage tank:

[0232]

[0233] The spinning reserve capacity constraint:

[0234]

[0235]

[0236]

[0237]

[0238]

[0239] Wherein, and are respectively the upper limits of the spinning reserve that BT, GT, HFC, and EVs can provide in period is the discharge efficiency of BT; is the capacity of BT in period is the lower limit of the capacity of BT in period and are respectively the spinning reserve capacities required by EH-IES and UG in period is the available time for the generator to increase production and provide reserve capacity; is the discharge state variable of BT in period is the upper limit of the discharge power of BT in period is the operating state variable of GT in period is the comprehensive conversion efficiency of GT; is the upper limit of the input power of GT; are the upper and lower limits of the fluctuation of the output electric power of GT;

[0240] The capacity constraints of EVs and HFCVs:

[0241]

[0242]

[0243]

[0244] Among them, and are the upper and lower limits of the state of charge of the th EV / HFCV, respectively. is the maximum number of EVs and HFCVs that EH-IES can accommodate for charging and hydrogen.

[0245] The multi-time scale low-carbon operation optimization model of EH-IES is:

[0246] The objective function of the low-carbon operation optimization model of EH-IES on the weekly time scale is as shown in the objective function of the above-mentioned EH-IES low-carbon operation optimization framework considering flexible V2G.

[0247] The objective function of the low-carbon operation optimization model of EH-IES on the day-ahead time scale is:

[0248]

[0249]

[0250]

[0251]

[0252] Among them, is the adjustment cost of HS within a scheduling period; is the initial moment of the day-ahead scheduling period; is the total number of time periods within the day-ahead scheduling period; is the HS unit charge / discharge hydrogen adjustment cost coefficient; and are the charge and discharge powers of HS at the time period on the day-ahead and weekly time scales, respectively; and are the charge / discharge hydrogen power adjustment amounts of HS at the time period compared with the day-ahead HS on the weekly time scale;

[0253] The constraint conditions of the low-carbon operation optimization model of EH-IES on the day-ahead time scale are:

[0254] Similar to the low-carbon operation optimization stage on a weekly time scale, the power balance constraint, energy conversion equipment model, combined heat and power equipment model, and operation constraints of energy storage equipment model in day-ahead scheduling are all similar to those in the low-carbon operation optimization model on a weekly time scale, and will not be elaborated here.

[0255]

[0256] Among them, and are the charge and discharge state variables of HS during the period on the day-ahead scheduling time scale; and are the charge and discharge state variables of HS during the period on the weekly scheduling time scale.

[0257] The objective function of the low-carbon operation optimization model of EH-IES on the intra-day time scale is:

[0258]

[0259]

[0260]

[0261]

[0262]

[0263]

[0264] Among them, is the adjustment cost of the energy conversion equipment model, combined heat and power equipment model, and energy storage equipment model except HS within a scheduling period; is the initial moment of the intra-day scheduling period; is the total number of time periods within the intra-day scheduling period; and are the unit adjustment cost coefficients of the energy conversion equipment model, combined heat and power equipment model, and energy storage equipment model except HS respectively; and are the input powers of the energy conversion equipment model, combined heat and power equipment model, and the charge and discharge powers of the energy storage equipment model except HS during the period on the intra-day and day-ahead time scales respectively; and are the adjustment amounts of the energy conversion equipment model, combined heat and power equipment model, and energy storage equipment model except HS during the period compared with the day-ahead time scale on the intra-day;

[0265] The constraint conditions of the EH-IES low-carbon operation optimization model on the intraday time scale are as follows:

[0266] Similar to the low-carbon operation optimization stage on the day-ahead time scale, the power balance constraint, the operation constraint of the energy conversion equipment model, the operation constraint of the energy storage equipment model, etc. in the intraday scheduling are all similar to those in the low-carbon operation optimization model on the day-ahead time scale, and will not be elaborated here. However, due to the change in the time scale, from 1 h to 15 min, the ramp rate and spinning reserve constraint of each energy conversion equipment model need to be adjusted accordingly:

[0267]

[0268]

[0269]

[0270] Among them, and are the operation state variables of the energy conversion equipment model in the intraday scheduling time scale at the time period respectively; and are the operation state variables of the combined heat and power equipment model in the intraday scheduling time scale at the time period respectively.

[0271] The objective function of the EH-IES low-carbon operation optimization model on the real-time time scale is:

[0272]

[0273]

[0274]

[0275]

[0276]

[0277] Among them, is the adjustment cost of the purchased energy within a scheduling period; is the initial moment of the intraday scheduling period; is the total number of time periods within the real-time scheduling period; and are the unit adjustment cost coefficients of the purchased electric energy, natural gas, and hydrogen respectively; and are the powers of the purchased electric energy, natural gas, and hydrogen at the time period under the real-time and intraday time scales respectively; is at The adjustment amount of real-time purchased external energy compared to the intraday time scale.

[0278] The constraint conditions of the EH-IES low-carbon operation optimization model under the real-time time scale are as follows:

[0279] In the low-carbon operation optimization stage under the real-time time scale, since the output powers of the energy conversion equipment model, energy storage equipment model, and combined heat and power equipment model are substituted into the real-time scheduling with determined values, the operation constraints and ramp constraints of the equipment do not need to be considered. Other constraints are similar to those in the intraday time scale, except for the time scale difference.

[0280] Use a solver (such as gurobi, cplex, etc.) to solve the EH-IES multi-time scale low-carbon optimal operation model considering flexible V2G, and obtain the EH-IES multi-time scale optimal operation strategy under the objective function of the lowest comprehensive system cost, including the output of various operating equipment, the output adjustment of various operating equipment under different time scales, and the charging / discharging and hydrogen conditions of EVs and HFCVs, etc.

[0281] Specifically, the solution of the present invention will be further elaborated through the following embodiments:

[0282] Embodiment 2

[0283] In this embodiment, taking a comprehensive energy system containing hydrogen energy as an example, a multi-time scale low-carbon operation optimization method for an electric-hydrogen integrated energy system is introduced; specifically, it includes the following steps:

[0284] Based on the power flow, heat flow, hydrogen flow, and gas flow directions, a schematic diagram of the framework of the electric-hydrogen integrated energy system is constructed as Figure 2 shown. The electricity 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 the EH-IES can, on the premise of meeting their own travel needs, realize the reverse transmission of electric energy through the flexible V2G mode, 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 and then provide it; the gas demand is provided by the P2G device (the hydrogen produced by ED is converted into methane through MR), GS, and externally purchased natural gas; the supply of the hydrogen load comes from two parts: when the power supply of the entire system is excessive, the surplus power is used for electrolytic hydrogen production and externally purchased hydrogen. HFC can use the hydrogen produced by electrolytic water or externally purchased hydrogen as fuel to discharge electricity to the system. In addition, electric-hydrogen can realize the long-term storage of electric energy through the coupling and conversion of ED, HS, and HFC. The one-week prediction curves of photovoltaic, electric load, heat load, gas load, and hydrogen load under 4 time scales are as Figures 3 - 7As shown in the figure, the prediction curves are obtained by adding perturbations to the real load and PV curves. The prediction errors of the PV and load curves in the weekly-ahead, day-ahead, intra-day, and real-time scheduling stages decrease in turn. Assuming that the day-ahead prediction error of PV output is 20%, the day-ahead PV output prediction curve is obtained by adding white noise with an expected value of 0 and a standard deviation of 0.2 to the actual PV output curve (assuming that the prediction error follows a normal distribution). Similarly, assuming that the weekly-ahead, day-ahead, intra-day, and real-time prediction errors of PV are 30%, 20%, 5%, and 2% respectively, and the weekly-ahead, day-ahead, intra-day, and real-time prediction errors of the load are 8%, 3%, 1%, and 0.5% respectively, their prediction curves are obtained by adding the corresponding white noise to the actual curves. The main parameters of the energy conversion equipment models and the combined heat and power equipment models inside the 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 EVs and HFCVs are shown in Table 3, and the carbon trading parameters are shown in Table 4;

[0285] Table 1 Parameters of Energy Conversion Equipment Models

[0286]

[0287] Table 2 Parameters of Energy Storage Equipment Models

[0288]

[0289] Table 3 Relevant Parameters of EVs and HFCVs

[0290]

[0291] Table 4 Carbon Trading Parameters

[0292]

[0293] According to the operation information of each device in the EH-IES, combined with the power flow of electricity, heat, gas, and hydrogen energy flow, establish the operation models of energy conversion equipment models, energy storage equipment models, and combined heat and power equipment models; based on the travel information of EVs and HFCVs, considering their flexible V2G modes, construct the flexible V2G charging and discharging models of EVs and HFCVs;

[0294] Consider the power balance constraints of electricity, heat, gas, and hydrogen, the constraints of purchased energy and PV output, the input power constraints of waste heat boilers, the balance constraints of the output of electrolyzers and the input of hydrogen storage tanks, the spinning reserve capacity constraints, and the capacity constraints of EVs and HFCVs;

[0295] Consider the green and low-carbon benefits and economic characteristics of the integrated electricity and hydrogen energy system, and construct the objective function;

[0296] According to the characteristic that the data prediction accuracy of photovoltaic - electricity, heat, gas, and hydrogen loads gradually improves with the reduction of the time scale, the continuously updated prediction information is utilized to optimize at different time scales while taking into account the economy of system operation and the efficient utilization of clean energy.

[0297] In this embodiment, a commercial solver is used to solve the multi - time - scale low - carbon optimal operation model of EH - IES considering flexible V2G. Under the objective function of the lowest comprehensive cost of the system, the multi - time - scale optimal operation strategy of EH - IES is obtained, 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, etc.

[0298] Comparative example

[0299] To verify the effectiveness of the method proposed in the present invention, the multi - time - scale low - carbon operation strategy is temporarily not considered first, and a comparative example is given. In the comparative example:

[0300] Scenario 1: Do not consider the low - carbon operation optimization model of IES without considering hydrogen production energy storage (electro - hydrogen coupling) and the flexible V2G mode of EVs and HFCVs.

[0301] Scenario 2: Do not consider the low - carbon operation optimization model of EH - IES without considering the flexible V2G mode of EVs and HFCVs, and the capacity conservation period of HS is 168 hours (1 week).

[0302] Scenario 3: Consider the low - carbon operation optimization model of EH - IES with the flexible V2G mode of EVs and HFCVs, and the capacity conservation period of HS is 24h.

[0303] Scenario 4: Consider the low - carbon operation optimization model of EH - IES with the flexible V2G mode of EVs and HFCVs, and the capacity conservation period of HS is 168h.

[0304] The cost comparison results of the four scenarios are shown in Table 5:

[0305] Table 5 Comparison of dispatching results under various scenarios in the low - carbon operation optimization of the weekly time scale

[0306]

[0307] From the information in the table, the advantages and potential of the electric-hydrogen coupling and the vehicle-grid interactive IES can be clearly observed. From the data in the table, it can be intuitively found that the total cost of the system gradually decreases with the addition of hydrogen energy and the flexible V2G mode. When IES is coupled with hydrogen energy, ED can convert the excess electric energy in the system into hydrogen energy when the photovoltaic output is surplus and store it in HS, reducing the waste of resources and improving the utilization rate of light energy; the flexible V2G technology realizes two-way interaction with the power grid. When it is at the peak period of renewable energy output, EVs and HFCVs are charged, and when the photovoltaic output is insufficient and the system needs a large amount of energy, both can discharge energy, improving the consumption of renewable energy, reducing the occurrence of light curtailment, maintaining the supply-demand balance of the system, being able to well suppress the load fluctuation, effectively reducing the peak-valley difference of the load, and playing the role of peak shaving and valley filling; the difference in the capacity conservation period of HS shows that hydrogen storage has more advantages in long-term storage, and also proves that hydrogen storage has great development potential in long-term electric energy storage in the future;

[0308] Considering the multi-time scale low-carbon operation strategy, a comparative example is given as follows:

[0309] Scenario 5: Only consider a single time scale and adopt the day-ahead low-carbon operation optimization model of EH-IES considering the flexible V2G mode proposed in this paper;

[0310] Scenario 6: Adopt the multi-time scale low-carbon operation optimization model of EH-IES considering the flexible V2G mode proposed in the present invention;

[0311] The comparison chart of the output of the hydrogen fuel cell under the day-ahead - intra-day time scale is as Figure 8 shown. It can be clearly observed the difference in the output of the HFC under the day-ahead and intra-day time scales. Due to the reduction of the prediction time scale from day-ahead to intra-day, the source-load prediction information is more accurate, and the output of the HFC is also adjusted more precisely, making its operation result closer to the actual situation. The same is true for other operating equipment in the system. The comparison chart of the purchased electric energy under the day-ahead - real-time time scale and the comparison chart of the purchased electric energy under the intra-day - real-time time scale are respectively as Figure 9 and 10 shown. The two charts respectively show the comparison of the purchased energy under the day-ahead - real-time and intra-day - real-time time scales. The overall change trend of the purchased electric energy is small because most of the errors caused by prediction can be reduced by the units within the system first, and the ones that cannot be adjusted within the system will be trimmed by the purchased electric energy. As the time scale from day-ahead to intra-day to real-time becomes smaller and smaller, the prediction information becomes more and more accurate, so the adjustment of the purchased electric energy becomes less and less obvious. The comparison chart of the charging and discharging of electric vehicles under the intra-day - real-time time scale is as Figure 11As shown in the figure, it can be seen that as the time scale gradually becomes more refined from intraday to real-time, the uncertainty of the source-load prediction information also gradually decreases. Since the real-time time scale is too small, the loss of frequently adjusting the output of each operating device is too large. Therefore, by adjusting the charging and discharging plan of EVs in real time, the external power purchase plan can be indirectly corrected to smooth out the power fluctuations caused by prediction errors and make the optimization result more in line with the actual situation.

[0312] As can be seen from the above figure, under the strategy of multi-time scale scheduling, the uncertainty of renewable energy output and various loads is more fully considered. By continuously updating the source-load prediction information, the prediction data becomes more accurate, and the output plan of the unit is continuously adjusted to enable the system to suppress the fluctuations on the supply and demand sides. The scheduling results under multiple time scales are also more reliable and reasonable. The output plans of various operating devices are more in line with the actual situation, improving the accuracy of the output of various operating units and formulating a more refined scheduling plan to ensure the stable operation of the system. EVs and HFCVs can fully utilize their fast regulation capabilities through charging and discharging power to balance the deviations of the photovoltaic output and the ultra-short-term time scale intraday and real-time prediction data of various loads, thereby alleviating the power adjustment pressure of the energy storage device model and the energy conversion device model in the system under short-term and even ultra-short time scales and improving the reliable, safe and stable operation of the system.

[0313] Embodiment 3: As Figure 12 shown, to achieve the above object, the present invention discloses a multi-time scale low-carbon operation optimization system for an electric-hydrogen integrated energy system, including:

[0314] A data coupling module 11, which pre-establishes a multi-energy complementary EH-IES model considering the deep coupling of electricity, heat, gas, and hydrogen. The EH-IES model is coupled through a traditional IES device operation model and a hydrogen production-storage-utilization link model in hydrogen production and energy storage. The traditional IES device operation model includes: an energy conversion device model, an energy storage device model, and a combined heat and power generation device model; the hydrogen production-storage-utilization link model in hydrogen production and energy storage includes: a hydrogen production link model, a hydrogen storage link model, and a hydrogen utilization link model;

[0315] A data sampling module 12, which is used to pre-establish a flexible V2G charging and discharging model for EVs and HFCVs. Among them, 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;

[0316] The operation optimization module 13 is used to generate corresponding low-carbon operation optimization models of the EH-IES for four time scales of week, day-ahead, intra-day, and real-time, based on the multi-energy complementary EH-IES model with deep coupling of electricity, heat, gas, and hydrogen and the flexible V2G charging and discharging models of EVs and HFCVs. As the low-carbon operation optimization model of the EH-IES with multi-time scales, it is solved with the goal of minimizing the total cost for the four time scales of week, day-ahead, intra-day, and real-time, to obtain the output results of various operating devices within the EH-IES, and the actual output of various operating devices within the EH-IES is carried out based on the output results of various operating devices. The various operating devices within the EH-IES include: ED, EB, GB, WHB, and MET as energy conversion devices; BT, TS, GS, and HS as energy storage devices; GT and HFC as combined heat and power devices; and UG, PV output, natural gas network NGN, and hydrogen network HN as energy supplies.

[0317] 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 may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is used to implement one or more instructions, specifically used to load and execute one or more instructions in the computer storage medium to implement the above method.

[0318] It should be further noted that, based on the same inventive concept, the present invention also provides a computer storage medium, on which a computer program is stored, and when the computer program is run by a processor, the above method is executed. The storage medium may adopt any combination of one or more computer-readable media. The computer-readable media may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electro-magnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: an electrical connection having 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, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device.

[0319] 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 connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples.

[0320] The above shows and describes the basic principles, main features, and advantages of the present disclosure. Those skilled in the art of this industry should understand that the present disclosure is not limited by the above embodiments, and the above embodiments and the descriptions in the specification only illustrate the principles of the present disclosure. Without departing from the spirit and scope of the present disclosure, the present disclosure will have various changes and improvements, and these changes and improvements all fall within the scope of the present disclosure claimed.

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 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: weekly, day-ahead, intraday 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: weekly, day-ahead, intraday and real-time, and the output results of various operating equipment in EH-IES are obtained. Based on the output results of various operating equipment, the actual output of various operating equipment in EH-IES is calculated; 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; 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: minC total =min(C ope +C waste +C c +C e +C startup -I re ) Among them, C ope , C waste , C c , C e , C startup and I re 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 light abandonment penalty coefficient; P t PV,cut is the abandoned optical power in period t; P t PV,pre is the photovoltaic output value predicted in period t; P t PV is the photovoltaic power output actually consumed during period t; p C is the price per unit of carbon emissions; P t g and P t q are the purchased natural gas and hydrogen power in period t respectively; q hy It is the low calorific value of hydrogen; c g 、c q 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; is the operating state variable of the energy conversion equipment model during period t, is the operating state variable of the cogeneration equipment model during period t, and They are the charging and discharging state variables of the energy storage device model during period t; and are the initial carbon emission quotas of the upper grid UG, GT, GB, EVs and HFCVs, respectively, and E grid 、E GT 、E GB E MET are the actual carbon emissions of UG, GT and GB and the carbon emissions captured by MET; 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: minC total =min(C ope +C waste +C c +C e +C startup -I re +C Δegq ) ΔP t HS,cha =|P t HS,cha,day-ahead -P t HS,cha,week | ΔP t HS,dis =|P t HS,dis,day-ahead -P t HS,dis,week | Among them, C Δegq is the adjustment cost of HS in a scheduling cycle; t0 is the initial time of the day-ahead scheduling cycle; N T is the total number of time periods in the day-ahead scheduling cycle; P is the cost coefficient for adjusting the unit charging and discharging of HS; t HS,cha,day-ahead , P t HS,cha,week , P t HS,dis,day-ahead and P t HS,dis,week are the charging and discharging powers of HS in period t on the day-ahead and week-ahead time scales respectively; ΔP t HS,cha and ΔP t HS,dis They are the charging and discharging power adjustment of HS in the previous day compared with the weekly time scale in period t; The objective function of the EH-IES low-carbon operation optimization model on a daily time scale is: my C total =min(C ope +C waste +C c +C e +C startup -IN re +C Δegq ) ΔP t y,in =|P t y,in,day-in -P t y,in,day-ahead | ΔP t x,cha =|P t x,cha,day-in -P t x,cha,day-ahead | ΔP t x,dis =|P t x,dis,day-in -P t x,dis,day-ahead | ΔP t m,in =|P t m,in,day-in -P t m,in,day-ahead | Among them, C Δegq is the adjustment cost of the energy conversion equipment model, the cogeneration equipment model, and the energy storage equipment model except HS within a scheduling cycle; t0 is the initial time of the intraday scheduling cycle; N T is the total number of time periods within the daily scheduling cycle; and are the unit adjustment cost coefficients of the energy conversion equipment model, the cogeneration equipment model, and the energy storage equipment model except HS; P t y / m,in,day-in , P t x,cha / dis,day-in , P t y / m,in,day-ahead and P t x,cha / dis,day-ahead are the input power of the energy conversion equipment model and the cogeneration equipment model in the t period in the intraday and day-ahead time scales, as well as the charging and discharging power of the energy storage equipment model except HS; ΔP t y / m,in and ΔP t x,cha / dis They are the adjustment amounts of the intraday energy conversion equipment model, the cogeneration equipment model, and the energy storage equipment model except HS in period t compared with the day-ahead time scale; The objective function of the EH-IES low-carbon operation optimization model in real-time time scale is: my C total =min(C ope +C waste +C c +C e -IN re +C Δegq ) ΔP t grid =|P t grid,hour -P t grid,day-in | ΔP t g =|P t g,hour -P t g,day-in | ΔP t q =|P t q,hour -P t q,day-in | Among them, C Δegq is the adjustment cost of purchased energy in a dispatch cycle; t0 is the initial time of the intraday dispatch cycle; N T is the total number of time periods in the real-time scheduling cycle; and are the unit adjustment cost coefficients for purchased electricity, natural gas and hydrogen respectively; P t grid / g / q,hour and P t grid / g / q,day-in are the power of purchased electricity, natural gas and hydrogen in time period t in real time and intraday time scales respectively; ΔP t grid / g / q is the adjustment amount of real-time purchased energy in period t compared to the intraday time scale.

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: Among them, P t y,in and P t y,out are the input and output power of the energy conversion device model y in period t, respectively; and are the upper and lower limits of the capacity of the energy conversion equipment model y, respectively; and are the upper and lower limits of the output power fluctuation of the energy conversion device model y; η y is the conversion efficiency of the energy conversion device model y; is the operating state variable of the energy conversion equipment model during period t; 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; is the storage level of the energy storage device model in period t; η x,cha and η x,dis are the charging and discharging efficiencies of the energy storage device model respectively; P t x,cha and P t x,dis are the charging and discharging power of the energy storage device model in period t respectively; Δt 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 the charging and discharging state variables of the energy storage device model during period t; 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: P t m,e +P t m,h =η m P t m,in Among them, P t m,e and P t m,h are the output electrical and thermal power of the cogeneration equipment model during period t; η m is the comprehensive conversion efficiency of the cogeneration equipment model; P t m,in is the input power of the cogeneration equipment model during period t; and are the upper and lower limits of the input power of the cogeneration equipment model, respectively; is the operating state variable of the cogeneration equipment model during period t; 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: Among them, P t ED,in and P t ED,out are the input and output power of electrolyzer ED in period t respectively; and They are the upper and lower limits of ED capacity, respectively; and are the upper and lower limits of ED output power fluctuation respectively; η ED is the conversion efficiency of ED; is the operating state variable of ED during period t; Hydrogen storage link model: in, is the self-damage rate of the hydrogen storage tank HS; is the storage level of HS in period t; η HS,cha and η HS,dis are the charging and discharging efficiency of HS respectively; P t HS,cha and P t HS,dis are the charging and discharging power of HS in period t respectively; 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 the HS charging and discharging state variables in period t respectively; and They are the hydrogen storage capacity at the beginning and end of the HS cycle; Hydrogen use link model: P t HFC,e +P t HFC,h =η HFC P t HFC,in Among them, P t HFC,e and P t HFC,h are the output electrical and thermal power of the hydrogen fuel cell HFC during period t; η HFC is the comprehensive conversion efficiency of HFC; P t HFC,in is the input power of HFC during period t; and They are the upper and lower limits of HFC input power respectively; is the operating state variable of HFC during period t; 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 2 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 time of EVs charging satisfy the piecewise normal distribution function, and the probability density functions are f s (t EV ,arr ) and f e (t EV,dep ), which is expressed as follows: Among them, σ s =3.24,u s =8.92; here t EV,arr Indicates the time when EVs start charging; Among them, σ e =3.41,u e =17.47; here t EV,dep Indicates the time when EVs end charging; The daily mileage of EVs approximately satisfies the following lognormal distribution function f m (x EV ): Among them, σ m =1.14,u m =2.98; here x EV 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, is the state of charge of the i-th EV / HFCV; is the expected power of the i-th EV / HFCV after charging is completed; The electricity and hydrogen consumption per 100 kilometers for EVs / HFCVs; The time for starting charging and hydrogen for the i-th EV / HFCV; is the mileage of the i-th EV / HFCV; C EV / HFCV is the battery capacity of EV / HFCV; Using the Monte Carlo simulation method, according to the probability density function f s (t EV / HFCV,arr ), f e (t EV / HFCV,dep ) and f m (x EV / HFCV ) simulates 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 3 is characterized in that: The flexible V2G charging and discharging model of EVs and HFCVs is as follows: in, and are the charging and discharging power of the i-th EV in period t respectively; and They are the upper limits of charging and discharging power of EV respectively; and are the hydrogen filling and discharging amounts of the i-th HFCV during period t, respectively; and They are the upper limits of hydrogen filling and discharging of HFCV respectively; and are the charging and discharging state variables of the i-th EV / HFCV during period t; η H-E is the hydrogen-to-electricity conversion coefficient; and are the charging and discharging power of the i-th HFCV in period t; P t EV,cha / dis is the total charging and discharging power of the EVs being charged in the system during period t; is the charging station capacity; is the total amount of hydrogen charged and discharged by the HFCVs being charged in the system during period t; is the capacity of the hydrogen filling station; is the number of EV / HFCV connected to the system during period t; η EV,cha , η EV,cha , η HFCV,cha and η HFCV,cha They are the charging and discharging efficiency of EV and the charging and discharging efficiency of HFCV respectively; The charging and hydrogen time for the i-th EV / HFCV ends.

5. The multi-time scale low-carbon operation optimization method of the electric-hydrogen integrated energy system according to claim 4 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; γ e , γ ge and γ gh The carbon emission quotas are obtained from the electricity generated by the unit electricity consumption purchased and the carbon emission quotas obtained from the electricity and heat generated by the natural gas-fired units consuming the unit natural gas; P t grid is the purchased electric power in period t; θ h-e is the thermoelectric conversion coefficient; P t GT,e and P t GT,h are the output electrical and thermal power of GT in period t respectively; P t GB,out is the thermal power output of GB during period t; L EV and L HFCV E is the mileage per unit of electricity for EV and HFCV respectively; f is the marginal carbon emission factor of fuel vehicles; Actual carbon emission model: Among them, E grid 、E GT 、E GB E MET are the actual carbon emissions of UG, GT and GB and the carbon emissions captured by MET; E th is the carbon emission factor per unit of electricity of UG; P t GB,in is the input power of GB during period t; P t GT,in is the input power of GT during period t; is the carbon emission intensity of the gas unit; k is the coefficient of CO2 absorbed by the unit CH4 produced, k = α / L HHV , where α is the amount of CO2 required per unit CH4, L HHV is the calorific value of natural gas; P t MET,in is the input power of MR.

6. The multi-time-scale low-carbon operation optimization system of the electric-hydrogen integrated energy system adopts the multi-time-scale low-carbon operation optimization method of the electric-hydrogen integrated energy system according to any one of claims 1 to 5, 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.

7. 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 5 is adopted.

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