Hydrogen-electricity coupling direct-current micro-grid optimization scheduling method considering vehicle-network interaction

Abstract

The invention discloses a hydrogen-electricity coupling direct current micro-grid optimization scheduling method considering vehicle-network interaction. The method comprises the following steps: building a vehicle-network interaction hydrogen electricity coupling direct current micro-grid system; constructing a model and a constraint condition of each subsystem; predicting the total generating capacity of the photovoltaic power and the wind power in a period of time in the future; predicting the basic power load capacity except for the hydrogen fuel cell vehicle in a period of time in the future; meanwhile, calculating the total charging energy E0 required by all hydrogen fuel cell vehicles connected to the micro-grid to reach the corresponding expected SOC value; managing and scheduling the vehicle-network interactive hydrogen-electricity coupling direct current micro-grid system to ensure that the system has sufficient energy to support the power load; and establishing a stability evaluation index of the vehicle-network interactive hydrogen-electricity coupling direct-current micro-grid, and calculating how many hydrogen fuel cell vehicles can meet the energy charging requirements of the surplus electric energy total energy. According to the invention, more hydrogen fuel cell automobiles can be connected to the DC micro-grid system.

Description

Technical Field

[0001] The present invention relates to the technical field of direct current microgrids, and in particular to a method for optimizing the scheduling of a hydrogen-electric coupled direct current microgrid taking vehicle-grid interaction into consideration. Background Art

[0002] With the proposal of the "dual carbon" goal, a new power system dominated by renewable energy has become an important trend for China's future development. Among them, microgrids are widely used for their stability, high efficiency, flexibility, low carbon and reliability. According to the definition of microgrids: This is a collection of loads and micro power sources. The micro power source operates in a system by providing both electricity and heat, and provides the required flexibility, thereby ensuring that the microgrid can operate as an integrated system. In order for a microgrid to be a controlled unit of a large power system, it must also be able to exist independently of the public power grid, that is, in the form of an island.

[0003] Due to the instability of renewable energy generation under microgrid conditions, there are scenarios where sufficient power cannot be provided to the load. Therefore, in order to ensure the normal operation of the microgrid, the microgrid will optimize the scheduling to ensure that the necessary loads obtain sufficient power and prevent power outages on the necessary loads.

[0004] The transformation of traditional fossil fuel vehicles to new energy vehicles reduces carbon dioxide emissions, which is of great significance to promoting energy conservation, emission reduction and environmental pollution. However, in the existing technology, there are few studies that consider the impact of new energy vehicles on microgrids, and there is no research that considers connecting more new energy vehicles to the DC microgrid system. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention proposes a hydrogen-electric coupled DC microgrid optimization scheduling method that takes into account vehicle-grid interaction, so as to achieve the connection of more hydrogen fuel cell vehicles to the DC microgrid system while maintaining a constant power generation and not relying heavily on the public power grid, while ensuring the stable operation of the microgrid.

[0006] To achieve the above object, the present invention designs a hydrogen-electric coupled DC microgrid optimization scheduling method considering vehicle-grid interaction, which is characterized by comprising the following steps:

[0007] S1) building a vehicle-grid interactive hydrogen-electricity coupled DC microgrid system, the system comprising a public grid subsystem, a master controller subsystem, a photovoltaic subsystem, a wind power subsystem, an energy storage battery subsystem, an electrolysis hydrogen production subsystem, a hydrogen fuel cell subsystem, an electricity load subsystem, and a charging pile subsystem, which are respectively connected to the DC bus;

[0008] The hydrogen generated by the electrolysis hydrogen production subsystem is transmitted to the hydrogen storage tank subsystem, the hydrogen output by the hydrogen storage tank subsystem is transmitted to the hydrogen fuel cell subsystem and the hydrogen refueling station subsystem, the hydrogen output by the hydrogen refueling station subsystem is transmitted to the hydrogen fuel cell vehicle charging subsystem, the electric energy generated by the hydrogen fuel cell subsystem is transmitted to the DC bus, and the electric energy output by the charging pile is transmitted to the hydrogen fuel cell vehicle charging subsystem;

[0009] S2) constructing a photovoltaic power generation model and constraints for the photovoltaic power generation subsystem, a wind-solar power generation model and constraints for the wind power generation subsystem, an energy storage operation model and constraints for the energy storage battery subsystem, an electrolyzer operation model and constraints for the electrolysis hydrogen production subsystem, a hydrogen storage tank operation model and constraints for the hydrogen storage tank subsystem, and a hydrogen fuel cell operation model and constraints for the hydrogen fuel cell subsystem;

[0010] S3) inputting historical photovoltaic power generation data and wind power generation data into the master controller subsystem, and predicting the total power generation of photovoltaic and wind power in the future;

[0011] S4) Forecast the basic power load other than hydrogen fuel cell vehicles in the future; at the same time, by collecting user social statistical variables and distributing questionnaires, calculate the required charging energy for the hydrogen fuel cell vehicles currently connected to the microgrid to reach the expected SOC value, and perform a cyclic calculation to finally obtain the total charging energy E required for all hydrogen fuel cell vehicles connected to the microgrid to reach the corresponding expected SOC value 0 ;

[0012] S5) The total power generation of photovoltaic and wind power predicted in step S3), the basic power load predicted in step S4), and the total charging energy E required for all hydrogen fuel cell vehicles connected to the microgrid to reach the corresponding expected SOC value 0 , manage and dispatch the vehicle-grid interactive hydrogen-electric coupled DC microgrid system to ensure that the system has sufficient energy to support the power load;

[0013] S6) Establishing a stability evaluation index for a vehicle-grid interactive hydrogen-electric coupled DC microgrid to evaluate the total charging energy E required for all hydrogen fuel cell vehicles connected to the microgrid in step S4) to reach the corresponding expected SOC value when the predicted total power generation of photovoltaic and wind power remains unchanged. 0 Compared with the charging energy required to fully charge each vehicle, calculate how many hydrogen fuel cell vehicles the surplus electrical energy can meet.

[0014] Furthermore, in S2), the power balance equation of the vehicle-grid interactive hydrogen-electric coupled DC microgrid system is as follows:

[0015]

[0016] In the formula

[0017] Indicates the power purchased from the public grid.

[0018] P PV is the power generated by the distributed energy photovoltaic module,

[0019] P WT is the power generated by distributed energy wind power,

[0020] Refers to the electrical power consumed by the hydrogen electrolyzer.

[0021] Refers to the power of energy storage battery discharge.

[0022] represents the power of electricity sold to the public grid,

[0023] Refers to the total energy power stored in the hydrogen storage tank.

[0024] Refers to the electrical power consumed by hydrogen fuel cells.

[0025] Refers to the power of the energy storage battery charging.

[0026] P Load Refers to the power of the electrical load.

[0027] Furthermore, in S3), the Wasserstein distance is used to measure a Copula function with high accuracy and good geometric characteristics, and the total power generation of photovoltaic and wind power in the future period is predicted through the Copula function.

[0028] Furthermore, in S4), based on the regularity of the time scale of other power loads other than hydrogen fuel cell vehicles, the exponential smoothing method is used to predict the values ​​of other power loads other than hydrogen fuel cell vehicles in the future.

[0029] Furthermore, in S4), the specific steps of calculating the total charging energy required for all hydrogen fuel cell vehicles connected to the microgrid to reach the corresponding expected SOC value are as follows:

[0030] S41) Collecting social statistical variables of current car owners, determining the current car owner categories, and preliminarily understanding the choices of different categories of car owners on different charging scales in the future;

[0031] S42) The current category car owners fill out a questionnaire survey on their daily living habits, and the current car owners are divided into different levels of power anxiety according to the final scores of the questionnaire survey. Based on the current level of power anxiety of the car owners, the hydrogen fuel cell vehicles owned by the car owners are additionally charged with energy after completing the planned itinerary for the day;

[0032] S43) collecting the travel plan of the current car owner in the future, calculating the amount of electricity required to complete the travel plan in the future, and adding the additional energy supplemented by the hydrogen fuel cell vehicle owned by the current car owner in step S42), and finally obtaining the final expected SOC value of the hydrogen fuel cell vehicle owned by the current car owner;

[0033] S44) The master controller subsystem senses the number of hydrogen fuel cell vehicles connected to the microgrid, and repeats steps S41) to S43) to cyclically calculate the total charging energy E required for all hydrogen fuel cell vehicles connected to the microgrid to reach the corresponding expected SOC value. 0 .

[0034] Further, in S5), the specific method for managing and scheduling the vehicle-grid interactive hydrogen-electric coupled DC microgrid system is as follows:

[0035] When the total power generation of photovoltaic and wind power predicted in the future is greater than the total charging energy E of all hydrogen fuel cell vehicles predicted in step S4), 0 When combined with other power loads, the excess power is stored in energy storage batteries, or converted into hydrogen through the electrolysis hydrogen production subsystem and stored in hydrogen storage tanks, so that it can be used when the total power generation of photovoltaic and wind power is insufficient in the future;

[0036] When the total power generation of photovoltaic and wind power in the future is less than the total charging energy E of all hydrogen fuel cell vehicles predicted in step S4), 0 When the power demand is combined with other power loads, the surplus energy in the energy storage battery and hydrogen storage tank is considered. If the surplus energy in the energy storage battery and hydrogen storage tank can meet the power demand, no additional power will be purchased from the public grid.

[0037] When the total power generation of photovoltaic and wind power plus the total energy in the energy storage battery and hydrogen storage tank is less than the total charging energy E of all hydrogen fuel cell vehicles predicted in step S4) 0 When combined with other power loads, electricity is purchased from the public grid to ensure the operation of the necessary loads.

[0038] Further, in S6), the stability evaluation index is represented by the following formula:

[0039]

[0040] In the formula,

[0041] E rst is the total amount of electric energy surplus of the charging scheme proposed in step S4) compared with the scheme where each vehicle is fully charged,

[0042] is the predicted wind power value,

[0043] is the predicted light power value,

[0044] It is expressed as the total amount of electricity that can be generated by the wind and solar power generation equipment within the time step T.

[0045] P Load Based on the predicted power value of the basic power load,

[0046] E u The energy required to fully charge N hydrogen fuel cell vehicles connected to the microgrid,

[0047] E 0 The total charging energy required for N hydrogen fuel cell vehicles connected to the microgrid to reach the corresponding expected SOC value,

[0048] N o The total surplus electrical energy can meet the charging needs of many hydrogen fuel cell vehicles.

[0049] The advantages of the present invention are:

[0050] 1. The present invention builds a car-grid interactive hydrogen-electric coupled DC microgrid system, divides the owner's power anxiety level in detail through the owner's basic information and questionnaires, and replenishes additional energy for the owner's hydrogen fuel cell vehicle on the basis of completing the day's itinerary according to the owner's current power anxiety level, combines the power required by the owner to complete the day's itinerary with the additional energy, and obtains the final expected SOC value of the hydrogen fuel cell vehicle;

[0051] 2. The present invention compares the predicted total wind and solar power generation with the total charging energy required for the final expected SOC value of all hydrogen fuel cell vehicles in the microgrid and the sum of other power loads in the microgrid, so as to achieve the coordinated scheduling optimization of power generation equipment and energy storage equipment stably under various uncertain conditions, meet the charging needs of car owners, and ensure the stable operation of the microgrid;

[0052] 3. Compared with the full charge in the prior art, the present invention establishes a new charging scheme for hydrogen fuel cell vehicles. By obtaining the final expected SOC value of the hydrogen fuel cell vehicle, the energy supply demand of a single hydrogen fuel cell vehicle is reduced, thereby ensuring that more new energy vehicles can be connected to the microgrid system;

[0053] The present invention takes into account the vehicle-grid interactive hydrogen-electric coupled DC microgrid optimization scheduling method, which enables more hydrogen fuel cell vehicles to be connected to the DC microgrid system while maintaining a constant power generation amount and not relying heavily on the public power grid, while ensuring the stable operation of the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A flow chart of the method for optimizing the scheduling of a hydrogen-electricity coupled DC microgrid considering vehicle-grid interaction according to the present invention;

[0055] Figure 2 It is a structural schematic diagram of the vehicle-grid interactive hydrogen-electricity coupled DC microgrid system of the present invention;

[0056] Figure 3 A flow chart for calculating the expected SOC value of a single hydrogen fuel cell vehicle connected to a microgrid in the present invention;

[0057] Figure 4 A flow chart for calculating the total charging energy required for all hydrogen fuel cell vehicles connected to the microgrid to reach the corresponding expected SOC value in the present invention;

[0058] Figure 5 It is a schematic diagram of a questionnaire survey in the present invention;

[0059] Figure 6 It is a schematic diagram of the second questionnaire survey in the present invention;

[0060] Figure 7 Schematic diagram of questionnaire survey 3 in the present invention. DETAILED DESCRIPTION

[0061] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Figure 1 The present invention provides a method for optimizing the scheduling of a hydrogen-electric coupled DC microgrid taking into account vehicle-grid interaction, comprising the following steps:

[0062] S1) Building a vehicle-grid interactive hydrogen-electricity coupled DC microgrid system, the system includes a public power grid subsystem, a master controller subsystem, a photovoltaic power generation subsystem, a wind power generation subsystem, an energy storage battery subsystem, an electrolysis hydrogen production subsystem, a hydrogen fuel cell subsystem, an electricity load subsystem, and a charging pile subsystem, which are respectively connected to the DC bus.

[0063] like Figure 2 As shown, it is a structural schematic diagram of the vehicle-grid interactive hydrogen-electric coupled DC microgrid system.

[0064] The hydrogen generated by the electrolysis hydrogen production subsystem is transmitted to the hydrogen storage tank subsystem, the hydrogen output by the hydrogen storage tank subsystem is transmitted to the hydrogen fuel cell subsystem and the hydrogen refueling station subsystem, the hydrogen output by the hydrogen refueling station subsystem is transmitted to the hydrogen fuel cell vehicle charging subsystem, the electrical energy generated by the hydrogen fuel cell subsystem is transmitted to the DC bus, and the electrical energy output by the charging pile is transmitted to the hydrogen fuel cell vehicle charging subsystem.

[0065] S2) Construct the photovoltaic power generation model and constraints of the photovoltaic power generation subsystem, the wind-solar power generation model and constraints of the wind power generation subsystem, the energy storage operation model and constraints of the energy storage battery subsystem, the electrolyzer operation model and constraints of the electrolysis hydrogen production subsystem, the hydrogen storage tank operation model and constraints of the hydrogen storage tank subsystem, and the hydrogen fuel cell operation model and constraints of the hydrogen fuel cell subsystem.

[0066] Specifically, the photovoltaic power generation model and constraints are as follows:

[0067] P PV (t) = N PV ×I PV (t)×V PV (t)×η PV

[0068]

[0069] In the formula,

[0070] t is the prediction time,

[0071] P PV (t) is the power predicted for the photovoltaic module at time t,

[0072] N PV is the number of photovoltaic modules,

[0073] I PV (t) is the operating current inside the PV module at time t,

[0074] V PV (t) is the operating voltage inside the PV module at time t,

[0075] η PV is the photovoltaic module photoelectric conversion efficiency,

[0076] is the actual value of the photovoltaic module at time t,

[0077] is the predicted value of the PV module at time t.

[0078] The wind and solar power generation model and constraints are as follows:

[0079]

[0080] In the formula,

[0081] ws represents the wind speed at time t,

[0082] P WT (ws) is the power generation of the wind turbine under the wind speed at time t,

[0083] is the rated power of the wind turbine component,

[0084] ws ci is the cut-in wind speed at time t,

[0085] ws co is the cut-out wind speed at time t,

[0086] ws rated is the rated wind speed,

[0087] is the actual value of the wind component at time t,

[0088] is the predicted value of the wind component at time t.

[0089] The energy storage operation model and constraints are as follows:

[0090]

[0091] SOC min ≤SOC(t)≤SOC max

[0092] In the formula,

[0093] SOC(t) represents the battery state of charge of the energy storage battery at time t, and SOC(t-1) represents the battery state of charge of the energy storage battery at time t-1. Indicates the charging efficiency of the energy storage battery.

[0094] represents the charging power of the energy storage battery, Δt represents the charging interval of the energy storage battery,

[0095] Indicates the energy discharge efficiency of the energy storage battery.

[0096] Indicates the energy discharge power of the energy storage battery,

[0097] Specifies the minimum power limit for charging energy storage batteries.

[0098] Specifies the maximum power limit for charging energy storage batteries.

[0099] The minimum power limit for discharging energy storage batteries is specified.

[0100] The maximum power limit for discharging energy storage batteries is specified.

[0101] SOC max The upper limit of the power of the energy storage battery is stipulated to prevent the energy storage battery from being overcharged and causing the battery life to be shortened.

[0102] SOC min The lower limit of the power of the energy storage battery is specified to prevent the energy storage battery from being over-discharged and thus reducing the battery life.

[0103] The electrolyzer operation model and constraints are as follows:

[0104] η EL =η I ×η V

[0105]

[0106] In the formula,

[0107] η EL is the conversion efficiency of the electrolyzer,

[0108] η I is the Faradaic efficiency or current efficiency of the electrolyzer,

[0109] η V is the voltage efficiency,

[0110] is the power per unit time of the electrolytic cell,

[0111] I EL is the unit current of the electrolytic cell,

[0112] V EL is the point voltage of the electrolytic cell,

[0113] For the Ohm point,

[0114] is the reversible point of the electrolytic cell,

[0115] Indicates that the electrolyzer is at the cathode activation point.

[0116] Indicates the activation point of the electrolytic cell at the anode.

[0117] E 0 is the open loop voltage,

[0118] R is the gas constant,

[0119] T EL is the current electrolytic cell temperature,

[0120] F is Faraday's constant,

[0121] i EL is the current density of the electrolytic cell, where I EL =i EL ×A EL ,

[0122] A EL is the electrode cross-sectional area,

[0123] i 0,ca is the current density at the cathode of the electrolytic cell,

[0124] i 0,an is the current density at the anode of the electrolytic cell,

[0125] R Ohm is the ohmic resistance of the electrolytic cell,

[0126] represents the rate of hydrogen generation per unit time,

[0127] is the lower calorific value of hydrogen,

[0128] is the maximum operating power of the electrolyzer,

[0129] is the minimum operating power of the electrolyzer,

[0130] is the on / off state of the fuel cell,

[0131] It is the on / off state of the electrolyzer, and the fuel cell and the electrolyzer cannot be on and off at the same time.

[0132] The operation model and constraints of the hydrogen storage tank are as follows:

[0133]

[0134] SOH min ≤SOH≤SOH max

[0135]

[0136] In the formula,

[0137] SOH is the state of hydrogen in the hydrogen storage tank.

[0138] SOH0 is the initial state of hydrogen in the hydrogen storage tank,

[0139] is the molar mass of hydrogen,

[0140] The amount of hydrogen filled for the nth hydrogen fuel cell vehicle,

[0141] represents the rate of hydrogen generation per unit time,

[0142] is the hydrogen consumption of the hydrogen fuel cell per unit time,

[0143] SOH min is the minimum hydrogen storage capacity of the hydrogen storage tank,

[0144] SOH max is the maximum hydrogen storage capacity of the hydrogen storage tank,

[0145] SOH ch Indicates the speed at which the hydrogen storage tank stores hydrogen.

[0146] Indicates the maximum speed at which the hydrogen storage tank can store hydrogen.

[0147] SOH dis Indicates the speed at which hydrogen is released from the hydrogen storage tank.

[0148] Indicates the maximum speed at which hydrogen is released from a hydrogen storage tank.

[0149] The operation model and constraints of hydrogen fuel cells are as follows:

[0150]

[0151] In the formula,

[0152] is the hydrogen consumption of the hydrogen fuel cell per unit time,

[0153] is the output power of the fuel cell,

[0154] η FC For the efficiency of the fuel cell,

[0155] is the lower calorific value of hydrogen,

[0156] is the minimum output power of the fuel cell,

[0157] is the maximum output power of the fuel cell,

[0158] is the on / off state of the fuel cell,

[0159] It is the on / off state of the electrolyzer, and the fuel cell and the electrolyzer cannot be on and off at the same time.

[0160] The power balance equation of the vehicle-grid interactive hydrogen-electric coupled DC microgrid system is as follows:

[0161]

[0162] In the formula

[0163] Indicates the power purchased from the public grid.

[0164] P PV is the power generated by the distributed energy photovoltaic module,

[0165] P WT is the power generated by distributed energy wind power,

[0166] Refers to the electrical power consumed by the hydrogen electrolyzer.

[0167] Refers to the power of energy storage battery discharge.

[0168] represents the power of electricity sold to the public grid,

[0169] Refers to the total energy power stored in the hydrogen storage tank.

[0170] Refers to the electrical power consumed by hydrogen fuel cells.

[0171] Refers to the power of the energy storage battery charging.

[0172] P Load Refers to the power of the electrical load.

[0173] S3) Inputting historical photovoltaic power generation data and wind power generation data into the master controller subsystem, and predicting the total power generation of photovoltaic and wind power in the future.

[0174] Preferably, the Wasserstein distance is used to measure a Copula function with high accuracy and good geometric characteristics, and the total power generation of photovoltaic and wind power in the future period is predicted by the Copula function.

[0175] Specifically, the future period of time may be one, two, or three days in the future.

[0176] In step S3), the prediction of wind and solar power generation usually establishes the Euclidean distance to select the optimal Copula function. However, compared with the Euclidean distance, the Wasserstein distance performs better in terms of evaluation accuracy and geometric property preservation. By using the Wasserstein distance to measure the accuracy and geometric effect of the Copula function in predicting wind and solar power generation, the Copula function used in this embodiment is as follows

[0177]

[0178] In the formula, the Copula function represents the dependency structure between several related variables, where u and v represent variables related to wind and solar power generation, u and v both represent the cumulative distribution function (CDF) of the marginal distribution, and the values ​​of u and v are usually between [0,1]; α is an important parameter of the Frank-Copula function, which usually determines the intensity and direction of the dependency between the two variables u and v. When α>0, the variables are positively correlated, and the stronger the dependency, the larger the value of α; when α<0, the variables are negatively correlated, and the stronger the dependency, the smaller the value of α; when α=0, the variables are independent.

[0179] S4) Forecast the basic power load other than hydrogen fuel cell vehicles in the future; at the same time, by collecting user social statistical variables and distributing questionnaires, calculate the required charging energy for the hydrogen fuel cell vehicles currently connected to the microgrid to reach the expected SOC value, and perform a cyclic calculation to finally obtain the total charging energy E required for all hydrogen fuel cell vehicles connected to the microgrid to reach the corresponding expected SOC value 0 .

[0180] Specifically, based on the regularity of other power loads except hydrogen fuel cell vehicles on a time scale, the exponential smoothing method is used to predict the values ​​of other power loads except hydrogen fuel cell vehicles in the future.

[0181] Specifically, the future period of time may be one day, two days, or three days in the future. Figure 3 As shown in Figure 2, the specific steps for calculating the total charging energy required for all hydrogen fuel cell vehicles connected to the microgrid to reach the corresponding expected SOC value are as follows:

[0182] S41) Collect social statistical variables of current car owners, determine the current car owner categories, and preliminarily understand the choices of different categories of car owners on different charging scales in the future.

[0183] Collect the owner's age, gender, personal income and other socio-demographic variables. These socio-demographic variables will largely determine the owner's choice in future charging plans. For example, an owner with a higher annual income will usually choose to use the highest power charging scale to save time on charging. Therefore, by collecting this basic information, we can initially understand the tendency towards different charging options.

[0184] S42) The current category of car owners fills out a questionnaire survey on their daily living habits, and the current car owners are divided into different levels of battery anxiety based on the final scores of the questionnaire survey. Based on the current level of battery anxiety of the car owners, the hydrogen fuel cell vehicles owned by the car owners are additionally replenished with energy on the basis of completing the planned itinerary for the day.

[0185] The owner shall fill in Figures 5-6 Different types of car owners get different final scores after filling out the questionnaire. Therefore, we can classify the target car owners according to the final scores by settling the final scores. Those with 41-50 points are classified as those with severe battery anxiety; those with 31-40 points are classified as those with moderate battery anxiety; those with 21-30 points are classified as those with mild battery anxiety; and those with 0-20 points are classified as those with basically no battery anxiety. And the four types of people classified above will have their hydrogen fuel cell vehicles charged additionally on the basis of completing the day's itinerary plan.

[0186] The rules for additional charging are as follows: people classified as having severe battery anxiety will charge an additional 15%; people classified as having moderate battery anxiety will charge an additional 10%; people classified as having mild battery anxiety will charge an additional 5%; people classified as having no battery anxiety will charge an additional 2-3%, to ensure that customers' anxiety is taken care of while completing today's formation.

[0187] S43) collects the travel plan of the current car owner in the future, calculates the power required to complete the travel plan in the future, and adds the additional energy supplemented by the hydrogen fuel cell vehicle owned by the current car owner in step S42), and finally obtains the final expected SOC value of the hydrogen fuel cell vehicle owned by the current car owner.

[0188] After collecting the potential variables of the customer in step S42), let the customer fill in Figure 7 The questionnaire shown is used to collect information about the customer's travel plans for the future. For example, the customer plans to travel 100 km today. The amount of electricity required to complete the 100 km today is calculated, assuming a SOC of 60%. And assuming that the customer is a person with severe battery anxiety, the car will be charged with an additional 20% of energy, that is, it will eventually be charged to 80% of the energy and then stop charging, which reduces 20% of the energy compared to full charging.

[0189] S44) The master controller subsystem senses the number of hydrogen fuel cell vehicles connected to the microgrid, and repeats steps S41) to S43) to cyclically calculate the total charging energy required for all hydrogen fuel cell vehicles connected to the microgrid to reach the corresponding expected SOC value.

[0190] like Figure 4 As shown, it is a flow chart for calculating the total charging energy required for all hydrogen fuel cell vehicles connected to the microgrid to reach the corresponding expected SOC value.

[0191] S5) The total power generation of photovoltaic and wind power predicted in step S3), the basic power load predicted in step S4), and the total charging energy E required for all hydrogen fuel cell vehicles connected to the microgrid to reach the corresponding expected SOC value 0 , manage and dispatch the vehicle-grid interactive hydrogen-electricity coupled DC microgrid system to ensure that the system has sufficient energy to support the power load.

[0192] Specifically, the specific method for managing and dispatching the vehicle-grid interactive hydrogen-electric coupled DC microgrid system is as follows:

[0193] When the total power generation of photovoltaic and wind power predicted in the future is greater than the total charging energy E of all hydrogen fuel cell vehicles predicted in step S4), 0 When combined with other power loads, the excess power is stored in energy storage batteries, or converted into hydrogen through the electrolysis hydrogen production subsystem and stored in hydrogen storage tanks, so that it can be used when the total power generation of photovoltaic and wind power is insufficient in the future;

[0194] When the total power generation of photovoltaic and wind power in the future is less than the total charging energy E of all hydrogen fuel cell vehicles predicted in step S4), 0 When the power demand is combined with other power loads, the surplus energy in the energy storage battery and hydrogen storage tank is considered. If the surplus energy in the energy storage battery and hydrogen storage tank can meet the power demand, no additional power will be purchased from the public grid.

[0195] When the total power generation of photovoltaic and wind power plus the total energy in the energy storage battery and hydrogen storage tank is less than the total charging energy E of all hydrogen fuel cell vehicles predicted in step S4) 0 When combined with other power loads, electricity is purchased from the public grid to ensure the operation of the necessary loads.

[0196] Specifically, the future period of time may be one, two, or three days in the future.

[0197] S6) Establishing a stability evaluation index for a vehicle-grid interactive hydrogen-electric coupled DC microgrid to evaluate the total charging energy E required for all hydrogen fuel cell vehicles connected to the microgrid in step S4) to reach the corresponding expected SOC value when the predicted total power generation of photovoltaic and wind power remains unchanged. 0 Compared with the charging energy required to fully charge each vehicle, calculate how many hydrogen fuel cell vehicles the surplus electrical energy can meet.

[0198] Compared with the full charge in the prior art, the present invention establishes a new charging scheme for hydrogen fuel cell vehicles. By obtaining the final expected SOC value of the hydrogen fuel cell vehicle, the energy supply demand of a single hydrogen fuel cell vehicle is reduced, thereby ensuring that more new energy vehicles can be connected to the microgrid system.

[0199] Specifically, the stability evaluation index is expressed by the following formula:

[0200]

[0201] In the formula,

[0202] E rst is the total amount of electric energy surplus of the charging scheme proposed in step S4) compared with the scheme where each vehicle is fully charged,

[0203] is the predicted wind power value,

[0204] is the predicted light power value,

[0205] It is expressed as the total amount of electricity that can be generated by the wind and solar power generation equipment within the time step T.

[0206] P Load Based on the predicted power value of the basic power load,

[0207] E u The energy required to fully charge N hydrogen fuel cell vehicles connected to the microgrid,

[0208] E 0 The total charging energy required for N hydrogen fuel cell vehicles connected to the microgrid to reach the corresponding expected SOC value,

[0209] N o The total surplus electrical energy can meet the charging needs of many hydrogen fuel cell vehicles.

[0210] The present invention takes into account the vehicle-grid interaction of the hydrogen-electric coupled DC microgrid optimization scheduling method, which can connect more hydrogen fuel cell vehicles to the DC microgrid system while keeping the power generation constant and not relying heavily on the public power grid, while ensuring the stable operation of the microgrid.

[0211] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A method for optimizing the scheduling of a hydrogen-electric coupled DC microgrid considering vehicle-grid interaction, characterized in that: The steps include: S1) building a vehicle-grid interactive hydrogen-electricity coupled DC microgrid system, the system comprising a public grid subsystem, a master controller subsystem, a photovoltaic subsystem, a wind power subsystem, an energy storage battery subsystem, an electrolysis hydrogen production subsystem, a hydrogen fuel cell subsystem, an electricity load subsystem, and a charging pile subsystem, which are respectively connected to the DC bus; The hydrogen generated by the electrolysis hydrogen production subsystem is transmitted to the hydrogen storage tank subsystem, the hydrogen output by the hydrogen storage tank subsystem is transmitted to the hydrogen fuel cell subsystem and the hydrogen refueling station subsystem, the hydrogen output by the hydrogen refueling station subsystem is transmitted to the hydrogen fuel cell vehicle charging subsystem, the electric energy generated by the hydrogen fuel cell subsystem is transmitted to the DC bus, and the electric energy output by the charging pile is transmitted to the hydrogen fuel cell vehicle charging subsystem; S2) constructing a photovoltaic power generation model and constraints for the photovoltaic power generation subsystem, a wind-solar power generation model and constraints for the wind power generation subsystem, an energy storage operation model and constraints for the energy storage battery subsystem, an electrolyzer operation model and constraints for the electrolysis hydrogen production subsystem, a hydrogen storage tank operation model and constraints for the hydrogen storage tank subsystem, and a hydrogen fuel cell operation model and constraints for the hydrogen fuel cell subsystem; S3) inputting historical photovoltaic power generation data and wind power generation data into the master controller subsystem, and predicting the total power generation of photovoltaic and wind power in the future; S4) Forecast the basic power load other than hydrogen fuel cell vehicles in the future; at the same time, by collecting user social statistical variables and distributing questionnaires, calculate the required charging energy for the hydrogen fuel cell vehicles currently connected to the microgrid to reach the expected SOC value, and perform a cyclic calculation to finally obtain the total charging energy E0 required for all hydrogen fuel cell vehicles connected to the microgrid to reach the corresponding expected SOC value; S5) managing and scheduling the vehicle-grid interactive hydrogen-electricity coupled DC microgrid system based on the total photovoltaic and wind power generation predicted in step S3), the basic power load predicted in step S4), and the total charging energy E0 required for all hydrogen fuel cell vehicles connected to the microgrid to reach the corresponding expected SOC value, to ensure that the system has sufficient energy to support the power load; S6) Establishing stability evaluation indicators for the vehicle-grid interactive hydrogen-electric coupled DC microgrid, evaluating the total charging energy E0 required for all hydrogen fuel cell vehicles connected to the microgrid in step S4) to reach the corresponding expected SOC value and comparing it with the charging energy required to fully charge each vehicle, and calculating how many hydrogen fuel cell vehicles can be satisfied by the surplus total electric energy, assuming that the predicted total power generation of photovoltaic and wind power remains unchanged.

2. The method for optimizing the scheduling of a hydrogen-electric coupled DC microgrid considering vehicle-grid interaction according to claim 1 is characterized in that: In S2), the power balance equation of the vehicle-grid interactive hydrogen-electric coupled DC microgrid system is as follows In the formula Indicates the power purchased from the public grid. P PV is the power generated by the distributed energy photovoltaic module, P WT is the power generated by distributed energy wind power, Refers to the electrical power consumed by the hydrogen electrolyzer. Refers to the power of energy storage battery discharge. represents the power of electricity sold to the public grid, Refers to the total energy power stored in the hydrogen storage tank. Refers to the electrical power consumed by hydrogen fuel cells. Refers to the power of the energy storage battery charging. P Load Refers to the power of the electrical load.

3. The method for optimizing the scheduling of hydrogen-electricity coupled DC microgrid considering vehicle-grid interaction according to claim 1 is characterized in that: In S3), the Wasserstein distance is used to measure the Copula function with high accuracy and good geometric characteristics, and the total power generation of photovoltaic and wind power in the future is predicted through the Copula function.

4. The method for optimizing the scheduling of a hydrogen-electric coupled DC microgrid considering vehicle-grid interaction according to claim 3 is characterized in that: In S4), based on the regularity of other power loads other than hydrogen fuel cell vehicles on a time scale, the exponential smoothing method is used to predict the values ​​of other power loads other than hydrogen fuel cell vehicles in the future.

5. The method for optimizing the scheduling of a hydrogen-electric coupled DC microgrid considering vehicle-grid interaction according to claim 4 is characterized in that: In S4), the specific steps of calculating the total charging energy required for all hydrogen fuel cell vehicles connected to the microgrid to reach the corresponding expected SOC value are as follows: S41) Collecting social statistical variables of current car owners, determining the current car owner categories, and preliminarily understanding the choices of different categories of car owners on different charging scales in the future; S42) The current category car owners fill out a questionnaire survey on their daily living habits, and the current car owners are divided into different levels of power anxiety according to the final scores of the questionnaire survey. Based on the current level of power anxiety of the car owners, the hydrogen fuel cell vehicles owned by the car owners are additionally charged with energy after completing the planned itinerary for the day; S43) collecting the travel plan of the current car owner in the future, calculating the amount of electricity required to complete the travel plan in the future, and adding the additional energy supplemented by the hydrogen fuel cell vehicle owned by the current car owner in step S42), and finally obtaining the final expected SOC value of the hydrogen fuel cell vehicle owned by the current car owner; S44) The master controller subsystem senses the number of hydrogen fuel cell vehicles connected to the microgrid, and repeats steps S41) to S43) to cyclically calculate the total charging energy E0 required for all hydrogen fuel cell vehicles connected to the microgrid to reach the corresponding expected SOC value.

6. The method for optimizing the scheduling of a hydrogen-electricity coupled DC microgrid considering vehicle-grid interaction according to claim 1, characterized in that: In S5), the specific method for managing and dispatching the vehicle-grid interactive hydrogen-electric coupled DC microgrid system is as follows: When the predicted total power generation of photovoltaic and wind power in the future is greater than the total charging energy E0 of all hydrogen fuel cell vehicles predicted in step S4) plus other power loads, the excess power is stored in the energy storage battery, or the excess power is converted into hydrogen by the electrolysis hydrogen production subsystem and stored in the hydrogen storage tank, so as to be used when the total power generation of photovoltaic and wind power is insufficient in the future; When the total power generation of photovoltaic and wind power in the future is less than the sum of the total charging energy E0 of all hydrogen fuel cell vehicles predicted in step S4) and other power loads, the surplus energy in the energy storage battery and the hydrogen storage tank is considered to be used. If the surplus energy in the energy storage battery and the hydrogen storage tank can meet the power demand, no additional power is purchased from the public grid; When the total power generation of photovoltaic and wind power and the total energy in the energy storage battery and hydrogen storage tank are less than the sum of the total charging energy E0 of all hydrogen fuel cell vehicles predicted in step S4) and other power loads, electricity is purchased from the public power grid to ensure the operation of the necessary loads.

7. The method for optimizing the scheduling of a hydrogen-electricity coupled DC microgrid considering vehicle-grid interaction according to claim 1, characterized in that: S6), the stability evaluation index is represented by the following formula: In the formula, E rst is the total amount of electric energy surplus of the charging scheme proposed in step S4) compared with the scheme where each vehicle is fully charged, is the predicted wind power value, is the predicted light power value, It is expressed as the total amount of electricity that can be generated by the wind and solar power generation equipment within the time step T. P Load Based on the predicted power value of the basic power load, E u The energy required to fully charge N hydrogen fuel cell vehicles connected to the microgrid, E0 is the total charging energy required for N hydrogen fuel cell vehicles connected to the microgrid to reach the corresponding expected SOC value. N o The total surplus electrical energy can meet the charging needs of many hydrogen fuel cell vehicles.

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