Zero-energy-consumption building system electricity-hydrogen-electric vehicle optimization regulation and control method

Abstract

The invention discloses an electricity-hydrogen-electric vehicle optimization regulation and control method for a zero-energy-consumption building system, and relates to the field of optimization operation regulation and control of a zero-energy-consumption building by connecting an electric vehicle to a hydrogen energy micro-grid. The hydrogen energy micro-grid zero-energy-consumption building system connected with the electric vehicle is constructed, and the charging behavior of the electric vehicle is simulated by adopting Monte Carlo; a fast charging mode or a slow charging mode of the electric vehicle is selected according to the charging emergency degree index, and an electric vehicle ordered charging scheduling model is established with the minimum peak-valley load difference; system optimization operation constraints are established, direct use of photovoltaic power generation is the highest priority, residual power generation is stored in a storage battery and a hydrogen energy utilization system, and when the power generation is insufficient, the storage battery or hydrogen energy is converted into electricity through combustion of a fuel cell to supply power to a load; and carrying out optimization solution by taking the minimum energy consumption as an objective function to obtain an operation scheme of each device of the system.

Description

Technical Field

[0001] The present invention belongs to the field of optimizing operation and control of realizing zero-energy buildings by connecting electric vehicles to hydrogen energy microgrids, and specifically relates to an electricity-hydrogen-electric vehicle optimizing control method for a zero-energy building system. Background Art

[0002] The design concept of a zero energy building (ZEB) is to balance the energy generated by renewable energy with the energy required by the building, and basically do not rely on the energy transmitted by the power grid, so as to achieve the goal of zero energy consumption. In the ZEB system, wind power and photovoltaic power can be used for renewable energy generation. At the same time, energy storage is equipped to suppress the uncertainty of wind and solar output. Since hydrogen energy storage has the characteristics of cleanliness, high energy density, and long-term storage, through the coordination of renewable energy and hydrogen energy storage, two-way energy conversion between electricity and hydrogen can be achieved, further improving the utilization rate of renewable energy. Therefore, using wind / light and hydrogen energy to build a ZEB system is an acceptable solution. However, due to the large-scale growth of electric vehicles, ZEB needs to add charging piles to meet the charging needs of electric vehicles. Therefore, it is of great significance to study the design and operation control of the ZEB system considering electric vehicle charging.

[0003] In terms of realizing ZEB with hydrogen energy storage systems and renewable energy, some research results have been achieved at home and abroad. Hasan Mehrjerdi integrated a multi-carrier energy system model including hydropower, wind power, solar energy, methane, carbon dioxide and thermal energy into ZEB. This model minimizes environmental pollution while improving energy elasticity. Based on net zero energy buildings supported by renewable resources (i.e. solar energy, hydropower and fuel cells) and hydrogen storage systems, Hasan Mehrjerdi studied the daily-seasonal operation modes, uncertainties and cogeneration of various renewable resources and energy storage systems, determined the optimal operation configuration of solar energy, hydropower, hydrogen and fuel cells, optimized the coordination of hydrogen storage and fuel cells, and eliminated uncertainty. Wu Di and others from North China Electric Power University proposed an energy management strategy that uses fuzzy logic to allocate electricity to hydrogen storage and electricity storage, and used a multi-objective collaborative optimization method to design the system configuration and its related operations. The proposed energy management strategy improves the comprehensive performance of the system. In addition, Ali Izadi, Sajad Maleki Dastjerdi and others also studied and analyzed zero-energy buildings with hydrogen energy storage in off-grid mode. However, the above operation scheme / control strategy does not consider the situation where electric vehicles are connected to the hydrogen energy microgrid ZEB.

[0004] Although Mostafa Kafaei and Kong Lingguo of Northeast Electric Power University considered electric vehicles in their research, the access of electric vehicles was disorderly or single. In fact, the large-scale growth of electric vehicles will aggravate the peak load, produce a larger load peak-valley difference, and affect the stable operation of hydrogen energy microgrid ZEB. At present, there are a lot of works on the optimization of electric vehicle charging scheduling, which can be mainly divided into decentralized methods and centralized methods. Decentralized methods usually use changes in electricity prices to change the charging behavior of electric vehicles. For example, Tianyang Zhang et al. proposed an intelligent electric vehicle charging system that combines non-monetary incentives with monetary incentives to encourage electric vehicle users to consume more renewable energy. The centralized method collects electric vehicle information through an aggregator and centrally arranges the charging time of electric vehicles. For example, Yanchong Zheng of Southern University of Science and Technology proposed an efficient real-time scheduling method for electric vehicles based on the definition of capacity margin indicators and charging priority indicators. The simulation results show that this method can suppress the increase of peak load of the power grid. Although these methods have achieved the charging optimization of electric vehicles, they have not been combined with the implementation of hydrogen energy microgrid ZEB. Therefore, the present invention constructs a zero-energy building system of a hydrogen microgrid connected to electric vehicles. On the basis of clarifying the interactive mechanism of energy supply and demand of electricity, hydrogen and electric vehicles in the system, a multi-constraint operation optimization objective function of the system is established, and a zero-energy building system electricity-hydrogen-electric vehicle optimization and control method is proposed. Summary of the invention

[0005] In view of the above-mentioned defects of the prior art, the present invention constructs a hydrogen energy microgrid zero-energy building system connected to electric vehicles. On the basis of clarifying the interactive mechanism of energy supply and demand of electricity, hydrogen and electric vehicles in the system, a system multi-constraint operation optimization objective function is established, and a zero-energy building system electricity-hydrogen-electric vehicle optimization and control method is proposed, which realizes the purpose of zero-energy operation and load peak filling of the system.

[0006] To achieve the above object, the present invention provides a method for optimizing and controlling an electric-hydrogen-electric vehicle in a zero-energy building system, comprising the following steps:

[0007] Construct a hydrogen microgrid zero-energy building system connected to electric vehicles, in which photovoltaics are selected as the renewable energy power generation system, the hydrogen energy storage system includes an electrolyzer for hydrogen production, a hydrogen storage tank, a hydrogen fuel cell and an energy storage battery, and the load includes the basic load generated by the building and the electric vehicle load;

[0008] Monte Carlo simulation of electric vehicle charging behavior is used to obtain the arrival time, departure time and state of charge of electric vehicles;

[0009] The fast or slow charging mode of electric vehicles is selected by the charging urgency index, and an orderly charging scheduling model for electric vehicles is established with the minimum peak-valley load difference;

[0010] Establish system optimization operation constraints, with the direct use of photovoltaic power generation as the highest priority, and the remaining power generation stored in batteries and hydrogen energy utilization systems. When the power generation is insufficient, the battery or hydrogen energy is converted into electricity through fuel cell combustion to supply power to the load;

[0011] The optimization solution is performed with minimum energy consumption as the objective function to obtain the operation plan for each device in the system.

[0012] Preferably, the power supply and demand balance relationship satisfied by the system is:

[0013] P PV (t)+P Bd (t)+P FC (t)+P Grid (t) = P Ely (t)+P Basic_load (t)+P EV_load (t)+P Bc (t)

[0014] Among them, P PV is the photovoltaic power (kW), P Bd is the battery discharge power (kW), P FC is the fuel cell discharge power (kW), P Grid is the grid power (kW), P Ely is the hydrogen production power (kW), P Basic_load is the power consumption of the base load (kW), P EV_load is the power consumed by the electric vehicle (kW), P Bc is the battery charging power (kW).

[0015] Preferably, the excess photovoltaic power is used to produce hydrogen in the following relationship:

[0016] sumP excess_PV (t)-sumP Ely (t) = 0

[0017] Among them, P excess_PV (t) is the excess photovoltaic power.

[0018] Preferably, the hydrogen storage capacity and the state of charge of the battery are:

[0019]

[0020] S Bat (t+1)=S Bat (t)-P Bat(t)×Δt; S Bat (t)≥0

[0021] Among them, S MH is the hydrogen storage capacity, S Bat is the battery state of charge, Δt is the operating time step, W MH The hydrogen storage tank parameter is 5.0kWh / Nm 3 , W FC Fuel cell parameters are 1.0kWh / 0.606Nm 3 .

[0022] Preferably, in order to achieve zero carbon emissions, the objective function established with the minimum power consumption of the power grid is:

[0023] Minimum emission = sumP Grid (t) 2 .

[0024] Preferably, the charging behavior of the electric vehicle is in the public charging mode, the arrival time and departure time of the electric vehicle obey the normal distribution, and the 24 hours of a day are divided into M time slots, each time slot is Δt minutes, and the arrival time slot and departure time slot of each electric vehicle are expressed as:

[0025]

[0026] Where N is the number of electric vehicles, i is the index of electric vehicles, and They represent the time slot numbers of the i-th electric vehicle entering and exiting the microgrid, and They represent the arrival time and departure time of the i-th electric car respectively, and Δt represents the length of a time slot.

[0027] Preferably, based on the arrival time slot and leave time slot The entire time slot of electric vehicles connected to the microgrid is calculated as: Charging behavior and charging scheduling strategy are arranged within this time period.

[0028] Preferably, define a binary state variable x i,j , when x i,j =1, it means that the ith electric car is charging in the jth time slot; when x i,j = 0, it means that the i-th electric vehicle is not charged in the j-th time slot; the charging urgency index is defined as:

[0029]

[0030] i=1,2,3……,N; j=1,2,3……,M

[0031] in, represents the slow charging power of electric vehicles, η EV represents the charging efficiency of electric vehicles, represents the lower limit of SOC when the i-th electric vehicle ends charging, represents the SOC of the i-th electric vehicle when it is connected to the microgrid, Indicates the battery capacity of an electric vehicle; when CUI i <0, it means that the charging demand of the i-th electric vehicle is urgent. i When ≥0, it means that the charging demand is not urgent; electric vehicles have two charging modes, namely fast charging mode and slow charging mode. According to the urgency index of the charging demand, the fast charging mode is selected in an emergency, and the slow charging mode is selected when it is not urgent: in, Indicates the fast charging power of electric vehicles.

[0032] Preferably, the total load of the microgrid includes the base load and the charging load of the electric vehicle, and the total load of the jth time slot is expressed as:

[0033]

[0034] in, represents the basic load of the jth time slot, represents the power provided by all electric vehicles in the jth time slot; the goal of charging scheduling is to reduce the peak-valley load difference of the microgrid:

[0035]

[0036] and Indicates maximum and minimum load demands.

[0037] Preferably, the electric vehicle constraints include:

[0038] Fast charging constraints are:

[0039]

[0040] in, represents the maximum SOC requirement; the slow charging constraint is that when the electric vehicle is disconnected from the microgrid, its minimum SOC requirement must be met:

[0041]

[0042] This paper takes zero-energy buildings based on hydrogen energy microgrids as the research object, and considers the access needs of electric vehicles. It proposes a zero-energy building system electricity-hydrogen-electric vehicle optimization control method and provides a system operation optimization scheme, which can achieve the following effects:

[0043] (1) Based on the electric vehicle charging information collected by the aggregator, the present invention simulates the charging behavior of electric vehicles through the Monte Carlo method and selects the charging mode of electric vehicles according to the charging urgency index, which can realize the orderly charging of electric vehicles and achieve the purpose of load peak shifting and valley filling, avoiding the impact of disorderly access on the stability of the microgrid.

[0044] (2) The electric vehicle load and the basic load of the building are connected to the constructed photovoltaic-hydrogen energy microgrid system. The direct use of photovoltaic power generation is the highest priority, and the remaining power generation is stored in the battery and hydrogen energy utilization system. When the power generation is insufficient, the battery or hydrogen-to-electricity is used to supply power to the load. By minimizing energy consumption, the effective operation of the hydrogen energy ZEB system considering electric vehicles is achieved.

[0045] (3) The photovoltaic-hydrogen microgrid system constructed with electric vehicles in mind can meet the growing demand for electric vehicle charging and provide an optimized operation and management plan, providing important support for the construction of new energy parking lot charging infrastructure, thereby achieving the goal of promoting the use of clean energy and promoting sustainable development in the energy field.

[0046] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is the structural diagram of the hydrogen energy microgrid system connected to electric vehicles;

[0048] Figure 2 This is the simulation result diagram of the public mode charging behavior of electric vehicles;

[0049] Figure 3 It is the result diagram of orderly and disorderly charging scheduling of electric vehicles;

[0050] Figure 4 It is the load diagram of electric vehicles under orderly charging;

[0051] Figure 5 It is a photovoltaic output situation diagram;

[0052] Figure 6 It is a 24-hour operation diagram of the photovoltaic-hydrogen microgrid with orderly charging access for electric vehicles;

[0053] Figure 7 It is a diagram of battery charging and discharging power and state of charge;

[0054] Figure 8 Hydrogen storage diagram. DETAILED DESCRIPTION

[0055] The following describes several preferred embodiments of the present invention with reference to the drawings in the specification, so that the technical content is clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the protection scope of the present invention is not limited to the embodiments mentioned in the text.

[0056] The present invention constructs a zero-energy building system of a hydrogen microgrid connected to electric vehicles. On the basis of clarifying the interactive mechanism of energy supply and demand of electricity, hydrogen and electric vehicles in the system, a multi-constraint operation optimization objective function of the system is established, and a zero-energy building system electricity-hydrogen-electric vehicle optimization control method is proposed, which realizes the purpose of zero-energy operation and load peak filling of the system.

[0057] The present invention adopts Monte Carlo simulation of the charging behavior of electric vehicles. On the basis of obtaining charging information such as the arrival time, departure time and state of charge (SOC) of electric vehicles, the fast charging or slow charging mode of electric vehicles is selected by the charging urgency index, and an orderly charging scheduling model of electric vehicles is established with the minimum peak-valley load difference. This step can effectively realize the orderly charging of electric vehicles, so that the electric vehicle load can be smoothly connected to the hydrogen energy microgrid zero-energy building system. Furthermore, the system optimization operation constraints are established, and the direct use of photovoltaic power generation is the highest priority, and the remaining power generation is stored in the battery and hydrogen energy utilization system. When the power generation is insufficient, the battery or hydrogen energy is converted into electricity through fuel cell combustion to supply power to the load. The optimization solution is performed with the minimum energy consumption as the objective function, and the operation plan of each device in the system is obtained. Finally, the system can achieve maximum renewable energy utilization and orderly charging of electric vehicles.

[0058] In order to verify the effectiveness of the proposed method, the system model was established in MATLAB / YALMIP and the CPLEX solver was used for simulation verification. The simulation results show that electric vehicles can be charged in an orderly manner, the load achieves the purpose of peak load shifting, and the hydrogen microgrid zero-energy building system can maximize the use of renewable energy and achieve stable operation with near-zero energy consumption.

[0059] (1) Hydrogen energy microgrid ZEB system connected to electric vehicles

[0060] The hydrogen energy microgrid ZEB system connected to electric vehicles is as follows: Figure 1As shown in the figure, photovoltaic power generation is selected as the renewable energy power generation system because it is easy to install on buildings and parking lots; the hydrogen energy storage system includes an electrolyzer for hydrogen production, a hydrogen storage tank, a hydrogen fuel cell, and an energy storage battery; the load includes the base load generated by buildings and the electric vehicle load. The direct use of photovoltaic power generation has the highest priority. A small amount of excess photovoltaic power is stored in the battery in the short term, and a large amount of excess photovoltaic power is used for hydrogen production and stored in the hydrogen storage tank. The generated hydrogen can be reconverted into electrical energy through the fuel cell. To maximize the self-sufficiency of the building, the microgrid uses grid power only when it cannot meet the user's demand. For example, during periods of low photovoltaic power generation, such as at night or on rainy days, the power is supplied by the battery, the fuel cell, and the grid.

[0061] (2) Constraints for optimal operation of the microgrid

[0062] The system first satisfies the following power supply and demand balance relationship:

[0063] P PV (t) + P Bd (t) + P FC (t) + P Grid (t) = P Ely (t) + P Basic_load (t) + P EV_load (t) + P Bc (t) (1)

[0064] Wherein, P PV is the photovoltaic power (kW), P Bd is the battery discharge power (kW), P FC is the fuel cell discharge power (kW), P Grid is the grid power (kW), P Ely is the hydrogen production power (kW), P Basic_load is the power consumption of the base load such as buildings (kW), P EV_load is the power consumption of electric vehicles (kW), P Bc is the battery charging power (kW).

[0065] The excess photovoltaic power is used for hydrogen production:

[0066] sumP excessPV (t) + sumP Ely (t) = 0 (2)

[0067] Wherein, P excess_PV is the excess photovoltaic power (kW).

[0068] The hydrogen storage volume and the state of charge of the battery are:

[0069]

[0070] S Bat (t+1)=S Bat (t)-P Bat (t)×Δt; S Bat (t)≥0 (4)

[0071] Among them, S MH is the hydrogen storage capacity, S Bat is the battery state of charge, Δt is the operating time step, W MH The hydrogen storage tank parameter is 5.0kWh / Nm 3 , W FC Fuel cell parameters are 1.0kWh / 0.606Nm 3 .

[0072] In order to achieve zero carbon emissions, the following objective function is established with the minimum power consumption of the power grid:

[0073] Minimum emission = sum P Grid (t) 2 (5)

[0074] (3) Electric vehicle dispatch model

[0075] The charging behavior of electric vehicles is in public charging mode. Electric vehicle owners start charging when they arrive at work in the morning and complete charging after get off work. The arrival time and departure time of electric vehicles follow a normal distribution:

[0076]

[0077]

[0078] in,

[0079] In order to improve efficiency during the scheduling process, the scheduling time is usually divided into several time periods. This paper divides 24 hours a day into 96 time slots, each time slot is 15 minutes. Therefore, the arrival time slot and departure time slot of each electric vehicle can be expressed as:

[0080]

[0081] Where N is the number of electric vehicles and i is the index of electric vehicles. and They represent the time slot numbers when the i-th electric vehicle joins and exits the microgrid respectively. and They represent the arrival time and departure time of the i-th electric car respectively. Δt represents the length of a time slot.

[0082] Based on arrival time slot and leave time slot The entire time slot of electric vehicles connected to the microgrid can be calculated:

[0083]

[0084] Charging behavior and charging scheduling strategy are arranged within this time period.

[0085] When electric vehicles are charged disorderly, the load will be increased on the basis of the basic load, resulting in an increase in peak load and a larger load peak-valley difference, which will affect the operation of the system after being connected to the microgrid. By setting the charging urgency index and selecting fast charging and slow charging for electric vehicles, orderly charging of electric vehicles can be achieved.

[0086] Define a binary state variable x i，j , when x i，j =1, it means that the i-th electric car is charging in the j-th time slot; when x i,j =0, it means that the i-th electric car is not charged in the j-th time slot.

[0087] The charging urgency index is defined as:

[0088]

[0089] in, represents the slow charging power of electric vehicles, η EV represents the charging efficiency of electric vehicles, represents the lower limit of SOC when the i-th electric vehicle ends charging, represents the SOC of the i-th electric vehicle when it is connected to the microgrid, Represents the battery capacity of the electric vehicle. The SOC when the i-th EV is disconnected from the microgrid:

[0090]

[0091] When CUI i <0, it means that the charging demand of the i-th electric vehicle is urgent. i When ≥0, it means that the charging demand is not urgent.

[0092] There are two charging modes for electric vehicles, namely fast charging mode and slow charging mode. According to the urgency index of charging demand, choose fast charging mode in emergency situations and slow charging mode in non-emergency situations:

[0093]

[0094] in, Indicates the fast charging power of electric vehicles.

[0095] The total load of the microgrid includes the base load and the charging load of electric vehicles. Therefore, the total load of the jth time slot can be expressed as:

[0096]

[0097] in, represents the basic load of the jth time slot, Represents the power provided by all electric vehicles in the jth time slot.

[0098] The goal of charging scheduling is to reduce the peak-valley load difference of the microgrid:

[0099]

[0100] and Indicates maximum and minimum load demands.

[0101] (4) Electric vehicle constraints

[0102] 1) Fast charging constraints:

[0103]

[0104] The time from the arrival time to the departure time of the electric vehicle, x i，j = 1 ensures that the emergency charging vehicle is always in a charging state. However, continuous charging will lead to overcharging, so the charging demand must be less than the maximum SOC demand. Indicates the time slots where fast charging EV stops charging:

[0105]

[0106] Indicates the maximum SOC requirement.

[0107] 2) Slow charging constraints:

[0108] When an EV is disconnected from the microgrid, its minimum SOC requirement must be met:

[0109]

[0110] (4) Example simulation verification

[0111] In order to verify the effectiveness of the proposed method, this paper selects a parking lot building in a certain area of ​​Ningxia for simulation analysis. The simulation time is 24 hours and the sampling time is 15 minutes. They are uniformly distributed between 0.1 and 0.3, 0.4 and 0.6, and 0.8 and 1.0. Under disordered charging, electric vehicles are charged with slow charging power.

[0112] When the number of electric vehicles is 20, the probability distribution of the arrival and departure times of electric vehicles is as follows: Figure 2 As shown, the red curve is the probability distribution function (PDF) of the arrival and departure time of electric vehicles, and the histogram is the data generated based on the PDF in the Monte Carlo method. It can be seen that the arrival time of electric vehicles is concentrated in the morning working hours, and the departure time is concentrated in the afternoon getting off work. The load results of orderly charging and disorderly charging in the public charging behavior mode are as follows Figure 3 The red curve represents the total load that meets the maximum state of charge of electric vehicles under disordered charging, and the yellow curve represents the total load that meets the minimum state of charge of electric vehicles under disordered charging. The purple curve is the result of orderly charging scheduling. It can be seen that compared with disordered charging, the peak-to-valley difference of load under orderly charging of electric vehicles is effectively reduced, and the total load can be smoothly connected to the microgrid to ensure the stable operation of the microgrid. At this time, the electric vehicle load is as follows Figure 4 shown.

[0113] In the photovoltaic-hydrogen microgrid, the photovoltaic output is as follows Figure 5 As shown in Figure 1, the photovoltaic power plant generates power from 07:00 to 19:00, with a maximum output power of nearly 100kW. Figure 6 As shown in the figure, when the photovoltaic power generation is sufficient, it is directly provided to the building load and the electric vehicle load, and the excess power is used for hydrogen production and energy storage; when the photovoltaic power generation is insufficient, the energy storage and the fuel cell discharge and the grid are used for power supply. At this time, the battery energy storage and hydrogen storage tank hydrogen storage are as follows Figure 7 and Figure 8 The state of charge (S0C) of the battery and the hydrogen content (LOH) of the hydrogen storage tube are both stable between 0.1 and 0.8, avoiding the loss caused by deep charging and discharging.

[0114] Example 1: Zero-energy system control for small commercial buildings

[0115] 1. Project background and system construction

[0116] This example selects a small commercial building located in a city's commercial district as the research object. The building includes office areas, commercial shops, and supporting parking lots. Considering its limited space and high requirements for energy supply stability and clean energy utilization, a hydrogen microgrid zero-energy building system connected to electric vehicles is constructed.

[0117] Photovoltaic power generation systems are installed on the roof of the building and in some areas of the parking lot. High-efficiency monocrystalline photovoltaic panels are used, with a total installed capacity of 100kW. In terms of hydrogen energy storage system, a 100kW electrolyzer is equipped for hydrogen production, and the hydrogen storage tank capacity is 100Nm 3The hydrogen fuel cell has a power of 30kW and a 50kWh battery is installed. The basic load in the building mainly includes lighting, office equipment, air conditioning system, etc., and the peak power is expected to be 30kW. The parking lot can accommodate 30 electric vehicles, including 10 private cars, 15 commercial electric vehicles (such as logistics delivery vehicles) and 5 shared cars.

[0118] 2. Charging behavior simulation and regulation

[0119] Charging behavior simulation is performed based on the usage patterns of different types of electric vehicles. Private car owners usually arrive at the parking lot during working hours (8:00-9:00) and start charging, and leave after get off work (17:00-18:00). Their arrival time and departure time follow a normal distribution, and the parameters are determined based on actual survey data. Commercial electric vehicles have multiple charging time periods during the day according to the delivery task arrangement, such as 10:00-11:00 in the morning and 14:00-15:00 in the afternoon for centralized charging. The usage time of shared cars is more flexible, and the charging demand is simulated through big data analysis of their usage probability in different time periods.

[0120] The Monte Carlo simulation method is used to simulate the charging behavior of each electric vehicle by comprehensively considering factors such as vehicle type, battery capacity (the average battery capacity of private cars is 50kWh, that of commercial electric vehicles is 80kWh, and that of shared cars is 30kWh), and initial state of charge (randomly distributed between 0.1-0.3). The charging mode is selected according to the charging urgency index. For commercial electric vehicles that are about to perform delivery tasks and have low power, the charging urgency index is less than 0, and the fast charging mode (fast charging power is 60kW) is selected; for private cars that stay in the parking lot for a long time, the slow charging mode (slow charging power is 7kW) is selected when the charging urgency index is greater than or equal to 0. In this way, orderly charging of electric vehicles is achieved, avoiding the impact of a large number of electric vehicles on the microgrid caused by fast charging at the same time.

[0121] 3. System optimization operation and energy management

[0122] Establish system optimization operation constraints to ensure the balance of power supply and demand. When photovoltaic power generation is sufficient during the day (9:00-16:00), photovoltaic power generation will give priority to meeting the basic load of the building and the load of electric vehicles being charged, and the excess power will be used to charge the battery and produce hydrogen for storage. For example, at 12:00 noon, the photovoltaic power generation power reaches 80kW, the basic load of the building is 20kW, and there are 10 electric vehicles being charged (total charging power is 30kW). At this time, the excess 30kW of power, 10kW is used to charge the battery, and 20kW is used to produce hydrogen.

[0123] When the photovoltaic power generation is insufficient (such as at night or on cloudy days), the power supply mode is determined according to the battery state of charge and hydrogen storage. If the battery state of charge is high (SOC>0.5), the battery discharge is preferred for power supply; if the battery power is insufficient and the hydrogen storage is sufficient, the hydrogen fuel cell is started for power generation. For example, at 20:00 in the evening, the photovoltaic power generation power is 0, and the basic load of the building is 15kW. At this time, the battery state of charge is 0.4, the hydrogen storage is sufficient, and the hydrogen fuel cell is started to supply power at a power of 10kW. At the same time, the battery discharges at a power of 5kW to meet the building load requirements.

[0124] The particle swarm optimization algorithm is used to optimize the solution with the minimum energy consumption as the objective function. During the optimization process, the impact of different seasons and weather conditions on photovoltaic power generation is considered, and the energy storage and power supply strategies are adjusted according to historical meteorological data and prediction models. In the summer when the sunlight is sufficient, the hydrogen production storage is appropriately increased; in the winter when the sunlight is weak, more attention is paid to the battery power management to ensure the stable operation of the system.

[0125] 4. Electric vehicle restraints and system safety assurance

[0126] For electric vehicle charging constraints, fast charging and slow charging constraints are strictly enforced. During fast charging, ensure that the vehicle is continuously charged from the time it is connected to the charging pile until it leaves, but stop charging when the power reaches the maximum SOC requirement (such as 0.9 for private cars, 0.85 for commercial electric vehicles, and 0.95 for shared cars). For example, a commercial electric vehicle starts fast charging at 10:00. According to its battery capacity and fast charging power, it is expected to reach the maximum SOC requirement at 11:00, at which time the system automatically stops charging.

[0127] During slow charging, the SOC of the electric vehicle is guaranteed to be within the minimum and maximum SOC requirements when it leaves. At the same time, the system is equipped with comprehensive safety protection measures, including a hydrogen leak detection device for the hydrogen energy storage system, which monitors the hydrogen concentration in real time. Once a leak is found, the ventilation equipment is immediately started and an alarm is issued; overvoltage and overcurrent protection is provided for electrical equipment to ensure safe operation of the system.

[0128] 5. Operational results and data analysis

[0129] Through a period of operation monitoring and related data analysis, in this small commercial building's zero-energy consumption system, electric vehicles have achieved orderly charging, the peak-to-valley difference in load has been reduced by more than 30% compared with disorderly charging, and the utilization rate of photovoltaic power generation has reached 100%. Under the condition of optimized operation control, the zero-energy consumption operation goal can basically be achieved.

[0130] Example 2: Zero-energy system control in large residential areas

[0131] 1. Project overview and system construction

[0132] This example uses a large residential community with 500 residents as the research scenario. There is a centralized parking lot in the community that can park 200 electric vehicles, including residents' private cars and some visitors' temporary parking vehicles. In order to achieve the goal of zero energy consumption, a hydrogen energy microgrid system including electric vehicle access is built.

[0133] A photovoltaic power generation system with a total installed capacity of 200kW is installed in suitable areas within the community (such as rooftops, public open spaces, etc.), using polycrystalline silicon photovoltaic panels. The hydrogen energy storage system is equipped with an electrolyzer with a power of 200kW and a capacity of 300Nm 3 The basic load of the community is mainly composed of residents' daily electricity (lighting, household appliances, etc.) and public facilities electricity (elevators, street lights, etc.), with an estimated peak power of 1,300kW.

[0134] 2. Charging behavior simulation and orderly charging implementation

[0135] Considering the daily travel patterns of residents, private car charging behavior is mainly concentrated in the evening after get off work (18:00-22:00) and during the weekend. The Monte Carlo simulation method is used to simulate the charging demand of electric vehicles based on the distribution of residents' working hours (normal distribution, mean 8:30, standard deviation 0.5 hours) and off-get off work time (normal distribution, mean 17:30, standard deviation 1 hour), as well as vehicle battery capacity (average 60kWh) and initial SOC (0.1-0.3 random distribution) and other factors.

[0136] For visitor vehicles, their parking time and charging demand probability are analyzed based on the community access control records and parking lot usage data. The charging mode is determined based on the charging urgency index. For example, for residential vehicles with low battery after get off work and needing to travel long distances the next day, the charging urgency index is less than 0, and the fast charging mode (fast charging power is 70kW) is selected; and for vehicles that stay in the community for a long time on weekends, the slow charging mode (slow charging power is 7kW) is selected. Through reasonable charging scheduling, orderly charging of electric vehicles is achieved, avoiding the pressure on the microgrid caused by a large number of electric vehicles charging at the same time during the peak power consumption at night.

[0137] 3. System optimization operation and energy balance management

[0138] Establish system optimization operation constraints to ensure the balance of power supply and demand. When the photovoltaic power generation is sufficient during the day (9:00-16:00), priority is given to meeting the electricity needs of community public facilities and some residents (such as residents at home during the day), and the excess power is used to charge batteries and produce hydrogen. For example, at 11:00 in the morning, the photovoltaic power generation power is 150kW, the public facility power consumption is 30kW, and there are 30 electric vehicles charging (total charging power is 60kW). At this time, there is 60kW of power remaining, 40kW is used for hydrogen production, and 20kW is used to charge batteries.

[0139] When the photovoltaic power generation is insufficient (such as at night or on cloudy days), power is supplied in the order of battery, hydrogen fuel cell, and power grid. If the battery state of charge is low (SOC<0.3) and the hydrogen storage capacity is sufficient, the hydrogen fuel cell is started to generate electricity; if the hydrogen fuel cell cannot meet the demand or the hydrogen storage capacity is insufficient, a small amount of electricity is obtained from the power grid to supplement. For example, at 23:00 in the evening, the photovoltaic power generation power is 0, the basic load of the community is 100kW, the battery state of charge is 0.2, the hydrogen storage capacity is sufficient, the hydrogen fuel cell supplies power at 30kW, the battery discharges at 20kW, and at the same time obtains 50kW of electricity from the power grid to meet the load demand.

[0140] The optimization solution is performed with the minimum energy consumption as the objective function, and the genetic algorithm is used. In the optimization process, the impact of different seasons (large cooling load in summer and large heating load in winter) and weather conditions on photovoltaic power generation and load demand are fully considered, and the energy storage and power supply strategies are dynamically adjusted. In hot summer weather, photovoltaic power generation is increased for the operation of refrigeration equipment, and energy storage is reasonably arranged; in cold winter weather, priority is given to ensuring the electricity consumption of residents' heating equipment, and the power supply ratio of hydrogen fuel cells and batteries is optimized.

[0141] 4. Electric vehicle constraints and system safety measures

[0142] In terms of electric vehicle charging constraints, fast charging and slow charging rules are strictly enforced. When fast charging, ensure that the vehicle is continuously charged from arrival to departure, while preventing overcharging, and determine the stop charging time based on the vehicle battery characteristics and the maximum SOC requirement (0.95 for private cars). When slow charging, ensure that the SOC of the vehicle meets the requirements when leaving (the minimum SOC is 0.4).

[0143] In terms of system safety, hydrogen leak detection sensors are installed to monitor the safety of the hydrogen energy storage system in real time. Once a leak occurs, the emergency plan is immediately activated, including shutting down related equipment, evacuating personnel, etc. Strict insulation testing and protection are carried out on electrical equipment to prevent leakage accidents.

[0144] 5. Operation effect evaluation and benefit analysis

[0145] After a period of operation monitoring, the zero-energy consumption system of this large residential community has achieved good results. The orderly charging of electric vehicles has reduced the peak-to-valley difference of the community's nighttime electricity load by more than 20%, improving the stability of the microgrid. The utilization rate of photovoltaic power generation has reached 100%, and the charging and discharging efficiency of batteries and hydrogen energy storage systems has remained at a high level. During the one-year operation cycle, through reasonable energy management and optimized regulation, the amount of electricity obtained from the power grid has been greatly reduced, effectively reducing residents' electricity costs, while reducing carbon emissions, providing strong support for the sustainable development of the community. In addition, the system optimizes the distribution of photovoltaic power generation and energy storage strategies based on residents' electricity consumption habits and seasonal changes (such as peak electricity consumption in summer and low electricity consumption in winter), further improving the adaptability and reliability of the system.

[0146] Example 3: Zero-energy system control in industrial parks

[0147] 1. Project scenario and system construction

[0148] This example focuses on a medium-sized industrial park, which includes multiple manufacturing companies, office areas, and a large supporting parking lot for parking company employees' vehicles and logistics vehicles. In order to achieve efficient use of energy and sustainable development, a hydrogen microgrid zero-energy building system connected to electric vehicles is constructed.

[0149] Large-scale photovoltaic power generation systems are installed on the roofs of factory buildings and idle land in the park, with a total installed capacity of 500kW. High-efficiency and durable photovoltaic modules are selected to meet the energy needs of a large number of electrical equipment and electric vehicles in the park. The hydrogen energy storage system is equipped with an electrolyzer with a power of 400kW for hydrogen production, and a hydrogen storage tank with a capacity of 500Nm 3 The hydrogen fuel cell has a power of 200kW and a battery with a capacity of 300kWh. The basic load of the park covers production equipment, lighting systems, office equipment, air conditioning and ventilation systems, etc., with an estimated peak power of 400kW. The parking lot can accommodate 150 electric vehicles, including 100 employee private cars and 50 logistics distribution vehicles.

[0150] 2. Charging behavior simulation and orderly charging strategy

[0151] Develop personalized charging behavior simulation plans for different types of electric vehicles. The charging needs of employees' private cars are mainly concentrated before work (6:00-8:00) and after get off work (17:00-20:00). Their arrival and departure times are simulated according to the company's work system and employee commuting habits. The average battery capacity is 65kWh, and the initial SOC is randomly distributed between 0.1-0.3. Logistics distribution vehicles are charged during the vehicle's idle time according to the cargo distribution plan. There is usually a higher probability of charging during the lunch break (12:00-14:00) and after the afternoon cargo delivery is completed (17:00-19:00). Their battery capacity is large, averaging 100kWh.

[0152] The Monte Carlo simulation method is used to simulate the charging behavior of each electric vehicle by comprehensively considering factors such as vehicle type, battery status, and charging time. The charging mode is selected according to the charging urgency index. For logistics vehicles that are about to perform emergency delivery tasks and have low power, the charging urgency index is less than 0, and the fast charging mode (fast charging power is 100kW) is selected; and for private cars of employees who stay in the park for a long time, the slow charging mode (slow charging power is 7kW) is selected when the charging urgency index is greater than or equal to 0. Through precise charging scheduling, orderly charging of electric vehicles is achieved, effectively balancing the power load in the park.

[0153] 3. System optimization operation and energy distribution management

[0154] Establish strict system optimization operation constraints to ensure real-time balance of power supply and demand. During the peak period of photovoltaic power generation in the daytime (9:00-15:00), photovoltaic power generation first meets the operation of high-power basic loads such as production equipment, and the remaining power is allocated to charging electric vehicles and for energy storage (battery charging and hydrogen production). For example, at 11:00 in the morning, the photovoltaic power generation power reaches 400kW, the production equipment operation consumes 300kW, and there are 20 electric vehicles charging (total charging power is 50kW). At this time, there is 50kW of power remaining, 30kW is used for hydrogen production, and 20kW is used to charge the battery.

[0155] When the photovoltaic power generation is insufficient or there is no light at night, the power supply mode is determined based on factors such as the battery state of charge, hydrogen storage capacity, and grid electricity price. If the battery state of charge is good (SOC>0.4) and the hydrogen storage capacity is sufficient, the battery discharge and hydrogen fuel cell will be used to supply power; if the battery power is low and the grid electricity price is at a low point (such as 23:00 at night-7:00 the next day), some electricity will be obtained from the grid to supplement it, and the hydrogen fuel cell will be started to maintain stable operation of the system. For example, at 22:00 in the evening, the photovoltaic power generation power is 0, the basic load of the park is 200kW, the battery state of charge is 0.3, the hydrogen storage capacity is sufficient, the hydrogen fuel cell supplies power at 50kW, the battery discharges at 30kW, and at the same time obtains 120kW of electricity from the grid at a low price to meet the load demand.

[0156] The optimization solution is carried out with the objective function of minimizing the system operating cost and energy consumption, and an improved ant colony algorithm is used. In the optimization process, the impact of changes in production tasks in different seasons (such as production reduction in some enterprises in summer and overtime production in some enterprises in winter) on load demand and the fluctuation of photovoltaic power generation under weather conditions are fully considered, and the energy storage and power supply strategies are dynamically adjusted. In hot summer weather, photovoltaic power generation is appropriately increased for air conditioning and refrigeration, and hydrogen production and energy storage are reasonably arranged to cope with possible power shortages; in cold winter weather, the power demand of production equipment and heating equipment is prioritized, and the coordinated power supply mode of hydrogen fuel cells and batteries is optimized.

[0157] 4. Electric vehicle charging constraints and system safety assurance

[0158] For the charging process of electric vehicles, fast charging and slow charging constraints are strictly enforced. In fast charging mode, ensure that the vehicle is continuously charged from the time it is connected to the charging pile until it leaves, but automatically stop charging when the power reaches the maximum SOC requirement (such as 0.9 for private cars and 0.85 for logistics vehicles) to prevent overcharging from causing damage to the battery. In slow charging, ensure that the SOC of the electric vehicle meets the minimum SOC requirement (0.4 for private cars and 0.5 for logistics vehicles) when it leaves, ensuring that the vehicle can drive normally.

[0159] To ensure the safe and stable operation of the system, a full range of safety measures are provided. In terms of hydrogen energy storage systems, high-precision hydrogen leak detection sensors are installed to monitor hydrogen concentration in real time. Once a leak is detected, ventilation equipment is immediately started for ventilation, and an alarm is issued to notify relevant personnel to handle it. Electrical equipment and lines are regularly inspected and maintained, and overvoltage, overcurrent, and leakage protection devices are installed to prevent electrical accidents. At the same time, a comprehensive emergency plan is established, and detailed response measures are formulated for possible emergency situations such as fires and explosions to ensure rapid response in the event of an emergency and to ensure the safety of personnel and equipment.

[0160] 5. Operation effect evaluation and comprehensive benefit analysis

[0161] Through long-term operation monitoring and data analysis, the industrial park has achieved remarkable results in reducing grid energy consumption. The orderly charging of electric vehicles has reduced the peak-to-valley difference of the park's electricity load by more than 25%, significantly improving the stability and power supply reliability of the microgrid. The utilization rate of photovoltaic power generation has reached 100%. In one production year, through reasonable energy management and optimized regulation, the park has greatly reduced the amount of electricity obtained from the power grid, reduced carbon emissions, and made positive contributions to the sustainable development and environmental protection of the park. In addition, the system optimizes the distribution and energy storage strategy of photovoltaic power generation according to the company's production plan and seasonal changes (such as peak electricity consumption in summer and maintenance and shutdown of some equipment in winter), further improving the flexibility and adaptability of the system, and providing strong support for the intelligent and green development of the industrial park.

[0162] The preferred specific embodiments of the present invention are described in detail above. It should be understood that ordinary technicians in the field can make many modifications and changes based on the concept of the present invention without creative work. Therefore, all technical solutions that can be obtained by technicians in the technical field based on the concept of the present invention through logical analysis, reasoning or limited experiments on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A zero-energy building system electricity-hydrogen-electric vehicle optimization control method, characterized in that: The following steps are involved: Construct a hydrogen microgrid zero-energy building system connected to electric vehicles, in which photovoltaics are selected as the renewable energy power generation system, the hydrogen energy storage system includes an electrolyzer for hydrogen production, a hydrogen storage tank, a hydrogen fuel cell and an energy storage battery, and the load includes the basic load generated by the building and the electric vehicle load; Monte Carlo simulation of electric vehicle charging behavior is used to obtain the arrival time, departure time and state of charge of electric vehicles; The fast or slow charging mode of electric vehicles is selected by the charging urgency index, and an orderly charging scheduling model for electric vehicles is established with the minimum peak-valley load difference; Establish system optimization operation constraints, with the direct use of photovoltaic power generation as the highest priority, and the remaining power generation stored in batteries and hydrogen energy utilization systems. When the power generation is insufficient, the battery or hydrogen energy is converted into electricity through fuel cell combustion to supply power to the load; The optimization solution is performed with minimum energy consumption as the objective function to obtain the operation plan for each device in the system.

2. The zero-energy building system electricity-hydrogen-electric vehicle optimization control method according to claim 1 is characterized in that: The power supply and demand balance relationship satisfied by the system is: P PV (t)+P Bd (t)+P FC (t)+P Grid (t)=P Ely (t)+P Basic_load (t)+P EV_load (t)+P Bc (t) Among them, P PV is the photovoltaic power (kW), P Bd is the battery discharge power (kW), P FC is the fuel cell discharge power (kW), P Grid is the grid power (kW), P Ely is the hydrogen production power (kW), P Basic_load is the power consumption of the base load (kW), P EV_load is the power consumed by the electric vehicle (kW), P Bc is the battery charging power (kW).

3. The zero-energy building system electricity-hydrogen-electric vehicle optimization control method according to claim 1 is characterized in that: The relationship between excess photovoltaic power used to produce hydrogen is: sumP excess_PV (t)-sumP Ely (t)=0 Among them, P excess_PV (t) is the excess photovoltaic power.

4. The zero-energy building system electricity-hydrogen-electric vehicle optimization control method according to claim 1 is characterized in that: The hydrogen storage capacity and the battery state of charge are: S Bat (t+1)=S Bat (t)-P Bat (t)×Δt;S Bat (t)≥0 Among them, S MH is the hydrogen storage capacity, S Bat is the battery state of charge, Δt is the operating time step, W MH The hydrogen storage tank parameter is 5.0kWh / Nm 3 , W FC Fuel cell parameters: 1.0kWh / 0.606Nm 3 .

5. The zero-energy building system electricity-hydrogen-electric vehicle optimization control method according to claim 1 is characterized in that: In order to achieve zero carbon emissions, the objective function established with the minimum power consumption of the power grid is: Minimum emission=sumP Grid (t) 2 。 6. The zero-energy building system electricity-hydrogen-electric vehicle optimization control method according to claim 1 is characterized in that: The charging behavior of electric vehicles is in the public charging mode. The arrival time and departure time of electric vehicles follow the normal distribution. The 24 hours of a day are divided into M time slots, each time slot is Δt minutes, and the arrival time slot and departure time slot of each electric vehicle are expressed as: Where N is the number of electric vehicles, i is the index of electric vehicles, and They represent the time slot numbers of the i-th electric vehicle entering and exiting the microgrid, and They represent the arrival time and departure time of the i-th electric car respectively, and Δt represents the length of a time slot.

7. The zero-energy building system electricity-hydrogen-electric vehicle optimization control method according to claim 6 is characterized in that: Based on arrival time slot and leave time slot The entire time slot of electric vehicles connected to the microgrid is calculated as: Charging behavior and charging scheduling strategy are arranged within this time period.

8. The zero-energy building system electricity-hydrogen-electric vehicle optimization control method according to claim 1 is characterized in that: Define a binary state variable x i,j , when x i,j =1, it means that the i-th electric car is charging in the j-th time slot; when x i,j = 0, it means that the i-th electric vehicle is not charged in the j-th time slot; the charging urgency index is defined as: in, represents the slow charging power of electric vehicles, η EV represents the charging efficiency of electric vehicles, represents the lower limit of SOC when the i-th electric vehicle ends charging, represents the SOC of the i-th electric vehicle when it is connected to the microgrid, Indicates the battery capacity of an electric vehicle; when CUI i <0, it means that the charging demand of the i-th electric vehicle is urgent. i When ≥0, it means that the charging demand is not urgent; electric vehicles have two charging modes, namely fast charging mode and slow charging mode. According to the urgency index of the charging demand, the fast charging mode is selected in an emergency, and the slow charging mode is selected when it is not urgent: in, Indicates the fast charging power of electric vehicles.

9. The zero-energy building system electricity-hydrogen-electric vehicle optimization control method according to claim 1 is characterized in that: The total load of the microgrid includes the base load and the charging load of electric vehicles. The total load of the jth time slot is expressed as: in, represents the basic load of the jth time slot, represents the power provided by all electric vehicles in the jth time slot; the goal of charging scheduling is to reduce the peak-valley load difference of the microgrid: and Indicates maximum and minimum load demands.

10. The zero-energy building system electricity-hydrogen-electric vehicle optimization control method according to claim 1, characterized in that: Electric vehicle constraints include: Fast charging constraints are: in, Indicates the maximum SOC requirement; The slow charging constraint is that when the electric vehicle is disconnected from the microgrid, its minimum SOC requirement must be met:

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