Optimal scheduling method for hydrogen-electric coupled DC microgrid considering vehicle-grid interaction

Abstract

This invention discloses an optimized scheduling method for a hydrogen-electricity coupled DC microgrid considering vehicle-grid interaction. It includes the following steps: constructing a vehicle-grid-interactive hydrogen-electricity coupled DC microgrid system; building models and constraints for each subsystem; predicting the total power generation of photovoltaic and wind power over a future period; predicting the basic electricity load (excluding hydrogen fuel cell vehicles) over a future period; simultaneously calculating the total charging energy E0 required for all hydrogen fuel cell vehicles connected to the microgrid to reach their corresponding expected SOC values; managing and scheduling the vehicle-grid-interactive hydrogen-electricity coupled DC microgrid system to ensure sufficient energy to support the electricity load; establishing a stability evaluation index for the vehicle-grid-interactive hydrogen-electricity coupled DC microgrid, and calculating how many hydrogen fuel cell vehicles the surplus electricity can meet for charging. This invention enables more hydrogen fuel cell vehicles to be connected to the DC microgrid system.

Description

Technical Field

[0001] This invention relates to the field of DC microgrid technology, specifically to an optimized scheduling method for hydrogen-electric coupled DC microgrids that considers vehicle-grid interaction. Background Technology

[0002] With the introduction of the "dual carbon" target, a new power system dominated by renewable energy has become an important trend for China's future development. Microgrids, with their stability, efficiency, flexibility, low carbon footprint, and reliability, are widely used. By definition, a microgrid is a collection of loads and micro-sources. These micro-sources operate within a system, simultaneously providing electricity and heat, and offering the required flexibility to ensure the microgrid functions as an integrated system. As a controlled unit within a large power system, a microgrid must also be able to exist independently of the public power grid, i.e., in an islanded form.

[0003] Due to the instability of renewable energy generation under microgrid conditions, there are scenarios where the load cannot be provided with enough power. Therefore, in order to ensure the normal operation of the microgrid, the microgrid will perform optimized scheduling to ensure that the necessary loads receive enough power and prevent power outages for the necessary loads.

[0004] The transition from traditional fossil fuel vehicles to new energy vehicles reduces carbon dioxide emissions, which is of great significance for promoting energy conservation, emission reduction, and environmental protection. However, existing technologies have limited research considering the impact of new energy vehicles on microgrids, and there is a lack of research on connecting more new energy vehicles to DC microgrid systems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention proposes an optimized scheduling method for hydrogen-electric coupling DC microgrids that considers vehicle-grid interaction. This method enables more hydrogen fuel cell vehicles to be connected to the DC microgrid system while maintaining 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 objectives, this invention designs an optimized scheduling method for hydrogen-electric coupled DC microgrids that considers vehicle-grid interaction, characterized by the following steps:

[0007] S1) Construct a vehicle-grid interactive hydrogen-electric coupling DC microgrid system, the system including a public grid subsystem, a main 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 electrical load subsystem, and a charging pile subsystem, all of which are connected to the DC bus.

[0008] The hydrogen produced by the electrolysis hydrogen production subsystem is transferred to the hydrogen storage tank subsystem. The hydrogen output from the hydrogen storage tank subsystem is transferred to the hydrogen fuel cell subsystem and the hydrogen refueling station subsystem. The hydrogen output from the hydrogen refueling station subsystem is transferred to the hydrogen fuel cell vehicle charging subsystem. The electrical energy generated by the hydrogen fuel cell subsystem is transferred to the DC bus. The electrical energy output from the charging pile is transferred to the hydrogen fuel cell vehicle charging subsystem.

[0009] S2) Construct the photovoltaic power generation model and constraints of the photovoltaic power generation system, the wind and solar power generation model and constraints of the wind power generation system, 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 system, and the hydrogen fuel cell operation model and constraints of the hydrogen fuel cell subsystem.

[0010] S3) Input historical photovoltaic power generation data and wind power generation data into the main controller subsystem, and predict the total power generation of photovoltaic and wind power in the future.

[0011] S4) Predict the basic electricity 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 charging energy required for hydrogen fuel cell vehicles currently connected to the microgrid to reach the expected SOC value, and perform iterative calculations 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.

[0012] S5) By using 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 E0 required for all hydrogen fuel cell vehicles connected to the microgrid to reach the corresponding expected SOC value, the vehicle-grid interactive hydrogen-electric coupling DC microgrid system is managed and scheduled to ensure that the system has sufficient energy to support the power load.

[0013] S6) Establish a stability evaluation index for the vehicle-grid interactive hydrogen-electric coupling DC microgrid. Assess how many hydrogen fuel cell vehicles can meet the charging needs of the surplus electrical energy by comparing the total charging energy E0 required for all hydrogen fuel cell vehicles connected to the microgrid to reach the corresponding expected SOC value in step S4) with the charging energy required for each vehicle to be fully charged.

[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] This indicates the power output purchased from the public power grid.

[0018] P PV The power output of distributed energy photovoltaic modules,

[0019] P WT For the power generation capacity of distributed energy wind power,

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

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

[0022] This indicates the power output sold to the public power grid.

[0023] This refers to the total energy and power stored in the hydrogen storage tank.

[0024] This refers to the electrical power generated by a hydrogen fuel cell consuming hydrogen.

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

[0026] P Load This 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 properties, and the total power generation of photovoltaic and wind power in the future is predicted by the Copula function.

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

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

[0030] S41) Collect social statistical variables of current car owners, determine the current car owner category, and gain a preliminary understanding of the choices of different categories of car owners in the future under different charging scales;

[0031] S42) A questionnaire survey on the daily life habits of current car owners is conducted. Based on the final score of the questionnaire survey, the current car owners are divided into groups according to their level of battery anxiety. Based on the current car owners' level of battery anxiety, the hydrogen fuel cell vehicles owned by the car owners are given additional energy replenishment after completing the daily travel plan.

[0032] S43) Collect the current owner's travel plans for a period of time in the future, calculate the electricity required to complete the travel plans for a period of time in the future, add the additional energy replenished by the hydrogen fuel cell vehicle owned by the current owner in step S42), and finally obtain the final expected SOC value of the hydrogen fuel cell vehicle owned by the current owner.

[0033] S44) The main controller subsystem senses the number of hydrogen fuel cell vehicles connected to the microgrid and repeats steps S41) to S43) to calculate the total charging energy E0 required for all hydrogen fuel cell vehicles connected to the microgrid to reach the corresponding expected SOC value.

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

[0035] When the predicted total power generation from photovoltaic and wind power is greater than the sum of the total charging energy E0 of all hydrogen fuel cell vehicles and other electrical loads predicted in step S4) in the future, the excess electrical energy will be stored in the energy storage battery or converted into hydrogen through the electrolysis hydrogen production subsystem and stored in the hydrogen storage tank for use when the total power generation from photovoltaic and wind power is insufficient in the future.

[0036] If, in the future, the total power generation of photovoltaic and wind power is less than the sum of the total charging energy E0 of all hydrogen fuel cell vehicles predicted in step S4) and other electrical loads, the surplus energy in the energy storage battery and hydrogen storage tank will be considered. If the surplus energy in the energy storage battery and hydrogen storage tank can meet the power demand, no additional electricity will be purchased from the public grid.

[0037] When the total power generation from photovoltaic and wind power, plus the total energy in the energy storage batteries and hydrogen storage tanks, is less than the sum of the total charging energy E0 of all hydrogen fuel cell vehicles predicted in step S4) and other electrical loads, electricity will be purchased from the public grid to ensure the operation of the necessary loads.

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

[0039]

[0040] In the formula,

[0041] E rstCompared to a solution where every vehicle is fully charged, the total surplus electrical energy of the charging scheme proposed in step S4) is...

[0042] For the predicted wind power value,

[0043] For the predicted light energy power value,

[0044] This represents the total amount of electricity that wind and solar power generation equipment can generate within a time step T.

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

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

[0047] E0 represents the total charging energy required for N hydrogen fuel cell vehicles connected to the microgrid to reach their corresponding desired SOC value.

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

[0049] The advantages of this invention are:

[0050] 1. This invention constructs a vehicle-grid interactive hydrogen-electricity coupled DC microgrid system. By using the basic information of vehicle owners and conducting questionnaires, the level of vehicle owners' battery anxiety is classified in detail. Based on the current level of vehicle owners' battery anxiety, the hydrogen fuel cell vehicle owned by the vehicle owner is given additional energy after completing the planned trip for the day. The final expected SOC value of the hydrogen fuel cell vehicle is obtained by combining the battery required for the vehicle owner to complete the trip for the day with the additional energy.

[0051] 2. This 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 electrical loads in the microgrid. This solves the problem of stably achieving coordinated scheduling optimization of power generation equipment and energy storage equipment under various uncertain conditions, meeting the charging needs of vehicle owners, and ensuring the stable operation of the microgrid.

[0052] 3. Compared with the existing technology of fully charged vehicles, the present invention establishes a completely new charging scheme for hydrogen fuel cell vehicles. By obtaining the final expected SOC value of hydrogen fuel cell vehicles, the energy demand of individual hydrogen fuel cell vehicles is reduced, thereby ensuring that more new energy vehicles can be connected to the microgrid system.

[0053] This invention considers a hydrogen-electric coupling DC microgrid optimization scheduling method for vehicle-grid interaction, which enables more hydrogen fuel cell vehicles to be connected to the DC microgrid system while maintaining constant power generation and not relying heavily on the public power grid, and at the same time ensuring the stable operation of the microgrid. Attached Figure Description

[0054] Figure 1 The flowchart is a process for optimizing the scheduling of a hydrogen-electric coupled DC microgrid that considers vehicle-grid interaction, as described in this invention.

[0055] Figure 2 This is a schematic diagram of the hydrogen-electric coupling DC microgrid system with vehicle-grid interaction in this invention;

[0056] Figure 3 This is a flowchart illustrating the calculation of the desired SOC value for a single hydrogen fuel cell vehicle connected to a microgrid in this invention.

[0057] Figure 4 This is a flowchart for calculating the total charging energy required for all hydrogen fuel cell vehicles connected to the microgrid to reach their corresponding desired SOC values ​​in this invention.

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

[0059] Figure 6 This is a schematic diagram of Questionnaire Survey 2 in this invention;

[0060] Figure 7 This is a schematic diagram of the questionnaire survey in this invention. Detailed Implementation

[0061] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. For example... Figure 1 The present invention provides an optimized scheduling method for hydrogen-electric coupled DC microgrids that considers vehicle-grid interaction, comprising the following steps:

[0062] S1) Construct a vehicle-grid interactive hydrogen-electric coupling DC microgrid system, which includes a public grid subsystem, a main 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 electrical load subsystem, and a charging pile subsystem, all of which are connected to the DC bus.

[0063] like Figure 2 The diagram shown is a schematic of a hydrogen-electric coupled DC microgrid system with vehicle-grid interaction.

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

[0065] S2) Construct the photovoltaic power generation model and constraints of the photovoltaic power generation system, the wind and solar power generation model and constraints of the wind power generation system, 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 system, and the hydrogen fuel cell operation model and constraints of the hydrogen fuel cell subsystem.

[0066] Specifically, the photovoltaic power generation model and its 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 predicted time.

[0071] P PV (t) represents the predicted power of the photovoltaic module at time t.

[0072] N PV The number of photovoltaic modules.

[0073] I PV (t) represents the operating current inside the photovoltaic module at time t.

[0074] V PV (t) represents the operating voltage inside the photovoltaic module at time t.

[0075] η PV For the photoelectric conversion efficiency of photovoltaic modules,

[0076] Let t be the actual value of the photovoltaic module.

[0077] Let t be the predicted value of the photovoltaic module at time t.

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

[0079]

[0080] In the formula,

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

[0082] P WT (ws) represents the power generation of the wind turbine at the wind speed at time t.

[0083] This refers to the rated power output of the wind turbine components.

[0084] ws ci Let be the cut-in wind speed at time t.

[0085] ws co Let be the cut-out wind speed at time t.

[0086] ws rated Rated wind speed,

[0087] The actual value of the wind turbine component at time t.

[0088] Let t be the predicted value of the wind turbine 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 state of charge of the energy storage battery at time t, and SOC(t-1) represents the state of charge of the energy storage battery at time t-1. This indicates the charging efficiency of the energy storage battery.

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

[0095] This indicates the energy release efficiency of the energy storage battery.

[0096] This indicates the energy output power of the energy storage battery.

[0097] The regulations specify the minimum power limit for charging energy storage batteries.

[0098] The regulations specify the maximum power limit for charging energy storage batteries.

[0099] The minimum power limit for the discharge of energy storage batteries is specified.

[0100] The regulations specify the maximum power limit for the discharge of energy storage batteries.

[0101] SOC max The regulations specify an upper limit for the capacity of energy storage batteries to prevent overcharging from reducing battery life.

[0102] SOC min A minimum charge limit for energy storage batteries has been set to prevent over-discharge of the batteries, which could shorten their lifespan.

[0103] The electrolytic cell operating model and constraints are as follows:

[0104] η EL =η I ×η V

[0105]

[0106] In the formula,

[0107] η EL For the conversion efficiency of the electrolytic cell,

[0108] η I This refers to the Faraday efficiency or current efficiency of the electrolytic cell.

[0109] η V For voltage efficiency,

[0110] This represents the power per unit time of the electrolytic cell.

[0111] I EL The unit current of the electrolytic cell.

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

[0113] For Ohm to pass the point,

[0114] This is the reversible point of the electrolytic cell.

[0115] This indicates the point where the electrolytic cell has been activated at the cathode.

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

[0117] E0 is the open-loop voltage.

[0118] R is the gas constant.

[0119] T EL This is the current temperature of the electrolytic cell.

[0120] F is the Faraday constant.

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

[0122] A EL The cross-sectional area of ​​the electrode is...

[0123] i 0,ca This represents the current density at the cathode of the electrolytic cell.

[0124] i 0,an This represents the current density at the anode of the electrolytic cell.

[0125] R Ohm The ohmic resistance of the electrolytic cell.

[0126] This indicates the rate at which hydrogen is produced per unit time.

[0127] Because of the low calorific value of hydrogen,

[0128] This is the maximum operating power of the electrolytic cell.

[0129] This represents the minimum operating power of the electrolytic cell.

[0130] This refers to the on / off state of the fuel cell.

[0131] This refers to the on / off state of the electrolyzer, and the fuel cell and electrolyzer cannot be turned on or off simultaneously.

[0132] The operating 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 represents the state of hydrogen gas in the hydrogen storage tank.

[0138] SOH0 represents the initial state of hydrogen gas in the hydrogen storage tank.

[0139] Here is the molar mass of hydrogen.

[0140] The amount of hydrogen needed to refuel the nth hydrogen fuel cell vehicle.

[0141] This indicates the rate at which hydrogen is produced per unit time.

[0142] This represents the amount of hydrogen consumed by a hydrogen fuel cell per unit time.

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

[0144] SOH max This represents the maximum hydrogen storage capacity of the hydrogen storage tank.

[0145] SOH ch This indicates the rate at which the hydrogen storage tank stores hydrogen.

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

[0147] SOH dis This indicates the rate at which hydrogen is released from the hydrogen storage tank.

[0148] This indicates the maximum rate at which hydrogen is released from the hydrogen storage tank.

[0149] The operating model and constraints of the hydrogen fuel cell are as follows.

[0150]

[0151] In the formula,

[0152] This represents the amount of hydrogen consumed by a hydrogen fuel cell per unit time.

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

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

[0155] Because of the low calorific value of hydrogen,

[0156] This represents the minimum output power of the fuel cell.

[0157] This represents the maximum output power of the fuel cell.

[0158] This refers to the on / off state of the fuel cell.

[0159] This refers to the on / off state of the electrolyzer, and the fuel cell and electrolyzer cannot be turned on or off simultaneously.

[0160] The power balance equations for a vehicle-grid interactive hydrogen-electric coupled DC microgrid system are as follows:

[0161]

[0162] In the formula

[0163] This indicates the power output purchased from the public power grid.

[0164] P PV The power output of distributed energy photovoltaic modules,

[0165] P WT For the power generation capacity of distributed energy wind power,

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

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

[0168] This indicates the power output sold to the public power grid.

[0169] This refers to the total energy and power stored in the hydrogen storage tank.

[0170] This refers to the electrical power generated by a hydrogen fuel cell consuming hydrogen.

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

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

[0173] S3) Input historical photovoltaic power generation data and wind power generation data into the main controller subsystem, and predict 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 properties, and the total power generation of photovoltaic and wind power in the future is predicted by the Copula function.

[0175] Specifically, the future period could be the next day, two days, or three days.

[0176] In step S3), predicting the total wind and solar power generation typically involves using Euclidean distance to select the optimal Copula function. However, compared to Euclidean distance, Wasserstein distance performs better in terms of accuracy and geometric property preservation. Using 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 among 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 strength and direction of the dependency between 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) Predict the basic electricity 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 charging energy required for hydrogen fuel cell vehicles currently connected to the microgrid to reach the expected SOC value, and perform iterative calculations 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.

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

[0181] Specifically, the future period could be the next day, two days, or three days. For example, Figure 3 As shown, the specific steps for calculating the total charging energy required for all hydrogen fuel cell vehicles connected to the microgrid to reach their corresponding desired SOC values ​​are as follows:

[0182] S41) Collect social statistical variables of current car owners, determine the current car owner category, and gain a preliminary understanding of the choices of different categories of car owners in the future under different charging scales.

[0183] Collecting social statistical variables such as car owners' age, gender, and personal income is helpful because these variables largely determine the car owner's choices in future charging plans. For example, a car owner with a higher annual income will usually choose to use the highest power charging capacity to save charging time. Therefore, collecting this basic information can provide an initial understanding of their preferences in different charging options.

[0184] S42) A questionnaire survey on the daily life habits of current car owners is conducted. Based on the final score of the questionnaire survey, the current car owners are divided into groups according to their level of battery anxiety. Based on the current car owners' level of battery anxiety, the hydrogen fuel cell vehicles owned by the car owners will be given additional energy after completing the daily travel plan.

[0185] Car owner fills in as follows Figures 5-6 The survey questionnaire yielded different scores for different types of car owners. Therefore, we can categorize the target car owners based on the final scores: those scoring 41-50 are classified as having severe battery anxiety; those scoring 31-40 are classified as having moderate battery anxiety; those scoring 21-30 are classified as having mild battery anxiety; and those scoring 0-20 are classified as having virtually no battery anxiety. Furthermore, based on these four categories, their hydrogen fuel cell vehicles will be recharged after completing their planned daily itinerary.

[0186] The additional charging rules are as follows: people classified as having severe battery anxiety will receive an additional 15% charging; people classified as having moderate battery anxiety will receive an additional 10% charging; people classified as having mild battery anxiety will receive an additional 5% charging; and people classified as having no battery anxiety will receive an additional 2-3% charging, in order to ensure that customers' anxieties are taken into account while completing today's tasks.

[0187] S43) Collect the current owner's travel plans for the next period of time, calculate the electricity required to complete the travel plans for the next period of time, add the additional energy replenished by the hydrogen fuel cell vehicle owned by the current owner in step S42), and finally obtain the final expected SOC value of the hydrogen fuel cell vehicle owned by the current owner.

[0188] After collecting the customer's potential variables in step S42), have the customer fill out... Figure 7 The questionnaire shown is used to collect customers' travel plans for the near future. For example, if a customer plans to drive 100km today, the amount of electricity needed to complete 100km is calculated, assuming a SOC of 60%. Assuming the customer has severe battery anxiety, the car will be charged an additional 20% of its capacity, eventually reaching 80% before stopping, which reduces the energy consumption by 20% compared to a full charge.

[0189] S44) The main controller subsystem senses the number of hydrogen fuel cell vehicles connected to the microgrid and repeats steps S41) to S43) to 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 The diagram shows a flowchart for calculating the total charging energy required for all hydrogen fuel cell vehicles connected to the microgrid to reach their corresponding desired SOC values.

[0191] S5) By using 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 E0 required for all hydrogen fuel cell vehicles connected to the microgrid to reach the corresponding expected SOC value, the vehicle-grid interactive hydrogen-electric coupling DC microgrid system is managed and scheduled to ensure that the system has sufficient energy to support the power load.

[0192] Specifically, the methods for managing and scheduling the vehicle-grid interactive hydrogen-electric coupled DC microgrid system are as follows:

[0193] When the predicted total power generation from photovoltaic and wind power is greater than the sum of the total charging energy E0 of all hydrogen fuel cell vehicles and other electrical loads predicted in step S4) in the future, the excess electrical energy will be stored in the energy storage battery or converted into hydrogen through the electrolysis hydrogen production subsystem and stored in the hydrogen storage tank for use when the total power generation from photovoltaic and wind power is insufficient in the future.

[0194] If, in the future, the total power generation of photovoltaic and wind power is less than the sum of the total charging energy E0 of all hydrogen fuel cell vehicles predicted in step S4) and other electrical loads, the surplus energy in the energy storage battery and hydrogen storage tank will be considered. If the surplus energy in the energy storage battery and hydrogen storage tank can meet the power demand, no additional electricity will be purchased from the public grid.

[0195] When the total power generation from photovoltaic and wind power, plus the total energy in the energy storage batteries and hydrogen storage tanks, is less than the sum of the total charging energy E0 of all hydrogen fuel cell vehicles predicted in step S4) and other electrical loads, electricity will be purchased from the public grid to ensure the operation of the necessary loads.

[0196] Specifically, the future period could be the next day, two days, or three days.

[0197] S6) Establish a stability evaluation index for the vehicle-grid interactive hydrogen-electric coupling DC microgrid. Assess how many hydrogen fuel cell vehicles can meet the charging needs of the surplus electrical energy by comparing the total charging energy E0 required for all hydrogen fuel cell vehicles connected to the microgrid to reach the corresponding expected SOC value in step S4) with the charging energy required for each vehicle to be fully charged.

[0198] Compared with the existing technology of fully charging, the present invention establishes a completely new charging scheme for hydrogen fuel cell vehicles. By obtaining the final expected SOC value of hydrogen fuel cell vehicles, the energy demand of individual hydrogen fuel cell vehicles 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 Compared to a solution where every vehicle is fully charged, the total surplus electrical energy of the charging scheme proposed in step S4) is...

[0203] For the predicted wind power value,

[0204] For the predicted light energy power value,

[0205] This represents the total amount of electricity that wind and solar power generation equipment can generate within a time step T.

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

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

[0208] E0 represents the total charging energy required for N hydrogen fuel cell vehicles connected to the microgrid to reach their corresponding desired SOC value.

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

[0210] This invention considers a hydrogen-electric coupling DC microgrid optimization scheduling method based on vehicle-grid interaction. It enables more hydrogen fuel cell vehicles to be connected to the DC microgrid system while maintaining constant power generation and not relying heavily on the public power grid, and at the same time 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 considered as equivalent replacement methods and are included in the scope of protection of the present invention.

Claims

1. A method for optimal scheduling of hydrogen-electric coupled DC microgrids considering vehicle-grid interaction, characterized in that, Includes the following steps: S1) Construct a vehicle-grid interactive hydrogen-electricity coupled DC microgrid system, which includes a public power grid subsystem, a main 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 electrical load subsystem, and a charging pile system; S2) Constructing constraints for each distributed energy source within a vehicle-grid interactive hydrogen-electric coupled DC microgrid system; S3) Input historical photovoltaic power generation data and wind power generation data into the main controller subsystem, and predict the total power generation of photovoltaic and wind power in the future. S4) Predict the basic electricity 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 charging energy required for hydrogen fuel cell vehicles currently connected to the microgrid to reach the expected SOC value, and perform iterative calculations 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. The specific steps for calculating the total charging energy required for all hydrogen fuel cell vehicles connected to the microgrid to reach their corresponding desired SOC values ​​are as follows. S41) Collect social statistical variables of current car owners, determine the current car owner category, and gain a preliminary understanding of the choices of different categories of car owners in the future under different charging scales; S42) A questionnaire survey on the daily life habits of current car owners is conducted. Based on the final score of the questionnaire survey, the current car owners are divided into groups according to their level of battery anxiety. Based on the current car owners' level of battery anxiety, the hydrogen fuel cell vehicles owned by the car owners are given additional energy replenishment after completing the daily travel plan. S43) Collect the current owner's travel plans for a period of time in the future, calculate the electricity required to complete the travel plans for a period of time in the future, add the additional energy replenished by the hydrogen fuel cell vehicle owned by the current owner in step S42), and finally obtain the final expected SOC value of the hydrogen fuel cell vehicle owned by the current owner. S44) The main controller subsystem senses the number of hydrogen fuel cell vehicles connected to the microgrid and repeats steps S41) to S43) to calculate the total charging energy E0 required for all hydrogen fuel cell vehicles connected to the microgrid to reach the corresponding expected SOC value. S5) Using the total photovoltaic and wind power generation predicted in step S3), the base 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 their corresponding expected SOC values, the vehicle-grid interactive hydrogen-electric coupling DC microgrid system is managed and scheduled to ensure that the system has sufficient energy to support the power load; S6) Establish a stability evaluation index for the vehicle-grid interactive hydrogen-electric coupling DC microgrid, and evaluate how many hydrogen fuel cell vehicles' charging needs can be met by the surplus total electrical energy, compared with the total charging energy required for all hydrogen fuel cell vehicles connected to the microgrid to reach their corresponding expected SOC values ​​in step S4), assuming that the predicted total photovoltaic and wind power generation remains unchanged.

2. The hydrogen-electric coupled DC microgrid optimization scheduling method considering vehicle-grid interaction according to claim 1, 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 This indicates the power output purchased from the public power grid. P PV The power generation of distributed energy photovoltaic modules, P WT For the power generation capacity of distributed energy wind power, This refers to the electrical power consumed by the hydrogen electrolyzer. This refers to the discharge power of the energy storage battery. This indicates the power output sold to the public power grid. This refers to the total energy and power stored in the hydrogen storage tank. This refers to the electrical power generated by a hydrogen fuel cell consuming hydrogen. This refers to the charging power of the energy storage battery. P Load This refers to the power of the electrical load.

3. The hydrogen-electric coupled DC microgrid optimization scheduling method considering vehicle-grid interaction according to claim 1, characterized in that: In S3), the Wasserstein distance is used to measure the Copula function, which has high accuracy and good geometric properties. The Copula function is then used to predict the total power generation of photovoltaic and wind power in the future.

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

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

6. The hydrogen-electric coupled DC microgrid optimization scheduling method considering vehicle-grid interaction according to claim 1, characterized in that: In S6), the stability evaluation index is expressed by the following formula: Where, E rst Compared to a solution where every vehicle is fully charged, the total surplus electrical energy of the charging scheme proposed in step S4) is... For the predicted wind power value, For the predicted light energy power value, This represents the total amount of electricity that wind and solar power generation equipment can generate within a time step T. P Load Based on the predicted power value of the predicted base electrical load, E u The energy required to fully charge N hydrogen fuel cell vehicles connected to the microgrid. E0 represents the total charging energy required for N hydrogen fuel cell vehicles connected to the microgrid to reach their corresponding desired SOC value. N o The total surplus electrical energy can meet the charging needs of how many hydrogen fuel cell vehicles?

Images