Electric vehicle charging and discharging scheduling method for interior of building and related device

Abstract

The invention discloses a charging and discharging scheduling method for electric vehicles in a building and a related device. The method comprises the following steps: determining the type of the building, and obtaining the power grid load of the building, the electricity price, the schedulable time of the electric vehicles in the building and the mileage of the electric vehicles; and inputting the data into an electric vehicle ordered charging and discharging optimization scheduling model in the building to obtain a charging demand state of each electric vehicle in the building at each moment, and performing charging and discharging scheduling on the electric vehicles in the building by using the charging demand state. And the technical problem that benefits of a power grid side, a charging station operator side and an electric vehicle user side are not considered is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric vehicle energy management, and particularly relates to a method and related device for electric vehicle charging and discharging scheduling in a building. Background Art

[0002] The new energy industry has been continuously developing, and among them, photovoltaic power generation, as a major clean energy source, has been widely used. In recent years, adding a distributed photovoltaic system to a building to achieve the green and low-carbon operation of the building has gradually become a new model for the green development of the building field. However, due to the volatility and uncertainty of photovoltaic power generation, the phenomenon of abandoned light often occurs. Therefore, there is an urgent need to propose a scheduling method that can improve the consumption rate of photovoltaic power generation in a building.

[0003] With the rapid development of the electric vehicle industry, the supporting electric vehicle charging station (EVCS) industry has also witnessed remarkable growth. The construction and operation issues of EVCS have gradually become the research focus in this field. In recent years, the rise of vehicle-to-building (V2B) technology and photovoltaic (PV) technology has promoted the EVCS industry to enter a new stage of green, low-carbon, and sustainable development. More and more intelligent buildings have started to build charging station systems inside them and are equipped with distributed photovoltaic power generation equipment to form a building photovoltaic charging station system (PVCS). This system combines V2B technology with the loads and distributed energy storage devices in the building to build a building microgrid. Through the real-time optimization of the intelligent building control platform, flexible regulation of the building microgrid can be achieved, the charging load distribution of electric vehicles can be optimized, and the utilization efficiency of renewable energy can be improved.

[0004] The construction and renovation of intelligent buildings are usually concentrated in public buildings with dense population and high electricity demand, such as residential buildings, office buildings, and commercial buildings. Investigations have found that the destinations of vehicle trips often concentrate in these areas, and these buildings have great potential in realizing intelligent transformation and applying V2B technology. Therefore, the construction of PVCS systems in these areas has received more attention and favor from investors.

[0005] In addition, electric vehicles, as an important implementation carrier in the low-carbon field, have attracted much attention. Since electric vehicles have a two-way charging and discharging function, their charging load can be directly involved in the load regulation of the building microgrid as a flexible load in the V2B environment to achieve "peak shaving and valley filling" of the building microgrid and the immediate consumption of photovoltaic power generation. However, this scheduling mode has the following problems: First, due to the disordered access of electric vehicles to the power grid, it is easy to cause an increase in the load fluctuation of the building microgrid; second, the overlapping period of the charging load of electric vehicles and photovoltaic power generation is short, resulting in the occurrence of the phenomenon of abandoned light; third, due to the existence of time-of-use electricity prices, the charging and discharging costs of electric vehicle users increase.

[0006] To address the above problems, many scholars have conducted research on the optimal scheduling of the orderly charging and discharging of electric vehicles by considering three aspects: the building microgrid side, the charging station operator side, and the electric vehicle user side. However, previous studies have rarely considered the interests of all three parties simultaneously. Failing to comprehensively consider the interests of all three parties may lead to sub-optimal distribution of power resources, thereby reducing the energy utilization efficiency of the entire system. Moreover, neglecting the interests of any one party may affect the overall economic benefits. Summary of the Invention

[0007] To solve the problems existing in the prior art, the present invention provides an electric vehicle charging and discharging scheduling method and related device for buildings, aiming to solve the problems of low PV accommodation rate and the lack of consideration for the interests of all three parties on the grid side, the charging station operator side, and the electric vehicle user side.

[0008] To achieve the above object, the present invention provides the following technical solution: An electric vehicle charging and discharging scheduling method for buildings, the steps are as follows: Determine the building type, and obtain the building grid load, electricity price, schedulable time of electric vehicles in the building, and electric vehicle driving mileage; Input the above data into the optimal scheduling model for the orderly charging and discharging of electric vehicles in the building to obtain the charging demand status of each electric vehicle in the building at each moment, and use the charging demand status to schedule the charging and discharging of electric vehicles in the building; The optimal scheduling model for the orderly charging and discharging of electric vehicles in the building uses an objective function that minimizes the building grid load fluctuation, light curtailment rate, and user charging and discharging costs, with the charging and discharging power, state of charge, charging time, discharging time, and number of charge and discharge switching times of each electric vehicle meeting the requirements as constraints; In the optimal scheduling model for the orderly charging and discharging of electric vehicles in the building, the state of charge SOC value at the end of the electric vehicle's journey is obtained based on the electric vehicle driving mileage, and it is compared with the charging threshold to determine whether the electric vehicle needs to be charged or parked; the required charging time of the electric vehicle that needs to be charged is obtained and compared with the schedulable time to determine whether to perform charging and discharging scheduling or only charging.

[0009] Furthermore, the building type includes residential buildings, office buildings, and commercial buildings; the building grid load includes basic load, photovoltaic power generation, and electric vehicle charging load; the schedulable time of electric vehicles in the building is determined according to the travel characteristics of electric vehicles in the building; the electric vehicle driving mileage is obtained by least squares fitting; the steps for obtaining the travel characteristics of electric vehicles in the building are specifically as follows: Obtain the travel data of electric vehicles in different types of buildings; Use the Monte Carlo method to randomly sample the departure and arrival times of electric vehicles at different types of buildings, and fit them with a standard normal distribution curve; Obtain the travel characteristics of electric vehicles in each building; The building types include residential buildings, office buildings, and commercial buildings. The travel characteristics of electric vehicles in the three types of buildings are as follows: The travel characteristics of electric vehicles in residential buildings follow a general normal distribution, and the formula is as follows:

[0010]

[0011] In the formula, and are the mean values of the departure and arrival times of electric vehicles in residential buildings respectively, and are the standard deviations, , ; , ; The travel characteristics of electric vehicles in office buildings follow a general normal distribution, and the formula is as follows:

[0012]

[0013] In the formula, and are the mean values of the departure and arrival times of electric vehicles in office buildings respectively, and are the standard deviations, , ; , ; The departure time of electric vehicles in commercial buildings is represented by a mixture model composed of two sub-Gaussian distributions, and the formulas are as follows:

[0014] In the formula, , ; The arrival time of electric vehicles in commercial buildings is represented by a mixture model composed of two sub-Gaussian distributions, and the formulas are as follows:

[0015] In the formula, , ; Use the least squares method to fit the driving mileage of electric vehicles, specifically as follows:

[0016] In the formula, is the daily driving mileage of the electric vehicle, .

[0017] Furthermore, the calculation process in the optimized scheduling model for the orderly charging and discharging of electric vehicles in the building is as follows: Calculate the power consumption of the electric vehicle using the fitted driving mileage of the electric vehicle. According to the power consumption, obtain the state of charge (SOC) value of the electric vehicle at the end of the journey. When the SOC value at the end of the electric vehicle journey is less than the charging threshold, the charging demand state of the electric vehicle at this moment is charging; otherwise, it is in a parked state. During charging, when the required charging time of the electric vehicle is less than the schedulable time, charge-discharge scheduling is performed; otherwise, only charging is carried out. The charging demand state of each electric vehicle in the building at each moment obtained satisfies the minimum value of the objective function within the constraint conditions.

[0018] Furthermore, the formula for the driving power consumption of the electric vehicle is as follows:

[0019] In the formula, is the driving power consumption of the electric vehicle, W is the power consumption per 100 kilometers of the electric vehicle, ; is the drive efficiency, ; The formula for the SOC value of the electric vehicle at the end of the journey is as follows:

[0020] In the formula, is the SOC of the electric vehicle at the end of the journey, is the SOC at the start of the journey, is the rated capacity of the battery of the electric vehicle; The formula for the charging duration of the electric vehicle is as follows:

[0021] In the formula, is the charging duration of the electric vehicle, is the charging power of the electric vehicle, and 0.95 means that the remaining capacity after charging does not exceed 95% of the rated capacity.

[0022] Furthermore, the objective function of the optimized scheduling model for the orderly charging and discharging of electric vehicles in the building is:

[0023] Among them, ; Among them, the minimum fluctuation of the building power grid load is the first objective function, and the expression is:

[0024] In the formula, is t the base load at time is t the total charging and discharging power of electric vehicles at time and respectively represent t the charging / discharging power of the electric vehicle at time >0 represents charging, and when <0, it represents discharging; is t the PV power generation at time is the average load of the building microgrid; Taking the minimum curtailment rate as the second objective function, the expression is:

[0025] In the formula, needs to meet the following conditions:

[0026] In the formula, is the difference between the PV output power and the electric vehicle charging power, is t the total PV power generation at time is t the total electric vehicle charging power at time; Taking the minimum charging and discharging cost of users as the third objective function, the expression is:

[0027]

[0028] In the formula, is the charging and discharging cost of the electric vehicle user, and are respectively t the charging / discharging power of the n th electric vehicle at time is t the charging cost of the electric vehicle at time, and it adopts time-of-use electricity price; is the peak-time electricity price, is the normal-time electricity price, is the valley-time electricity price; is the peak period, is the normal period, is the valley period; ​​​​Use peak-period electricity prices for discharge compensation; The constraints are: the charging and discharging power and state of charge of each electric vehicle meet the set maximum and minimum limits; the charging time of each electric vehicle meets the charging requirements; the discharge time of each electric vehicle does not exceed the specified maximum discharge duration; the number of charge and discharge switching times for each electric vehicle is less than or equal to the maximum number of charge and discharge switching times allowed during the electric vehicle scheduling period.

[0029] Furthermore, the orderly charging and discharging optimization scheduling model of electric vehicles in buildings is solved by using a PSO algorithm improved by population initialization, particle velocity update strategy and position update strategy; among them, when initializing the population, the initial population of the particle swarm algorithm is an integer; the particle velocity update strategy adopts adaptive inertia weight; and the position update strategy adopts a random walk mechanism.

[0030] The present invention also provides a charging and discharging scheduling system for electric vehicles in buildings, comprising: The data acquisition module is used to determine the building type, obtain the building grid load, electricity price, and the available dispatch time and mileage of electric vehicles in the building; An orderly charging and discharging optimization scheduling strategy acquisition module is used to input the above data into the orderly charging and discharging optimization scheduling model of electric vehicles in the building to obtain the charging demand status of each electric vehicle in the building at any time; An orderly charge and discharge scheduling module, configured to schedule the charge and discharge of electric vehicles in the building using the charging demand status; The optimized scheduling model for orderly charging and discharging of electric vehicles in buildings adopts the objective function of minimizing the load fluctuation of the building grid, the curtailment rate of solar power, and the charging and discharging costs of users, and takes the charging and discharging power, state of charge, charging time, discharging time, and the number of charging and discharging switching of each electric vehicle as constraints. In the optimized scheduling model for orderly charging and discharging of electric vehicles in a building, the state of charge (SOC) value of the electric vehicle at the end of the trip is obtained based on the electric vehicle's mileage, and the SOC value is compared with the charging threshold to determine whether the electric vehicle needs to be charged or stopped; the required charging time of the electric vehicle that needs to be charged is obtained, and the SOC value is compared with the available scheduling time to determine whether charging and discharging scheduling or charging only is performed.

[0031] The present invention also provides a terminal device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for charging and discharging scheduling of electric vehicles in a building are implemented.

[0032] The present invention also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for scheduling the charging and discharging of electric vehicles in a building are implemented.

[0033] The present invention also provides a computer program product including a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for scheduling the charging and discharging of electric vehicles in a building are implemented.

[0034] Compared with the prior art, the present invention has at least the following beneficial effects: The present invention provides a method for scheduling the charging and discharging of electric vehicles in a building. By constructing an optimization scheduling model for the charging and discharging of electric vehicles that takes into account the interests of the building microgrid, the photovoltaic side, and electric vehicle users, the balanced distribution of interests is achieved. This not only helps to reduce the load fluctuation of the power grid, improve the stability and security of the power grid, but also effectively improves the photovoltaic accommodation rate, reduces energy waste, and at the same time reduces the charging costs of electric vehicle users, enhancing the user experience and satisfaction. This method that comprehensively takes into account the interests of multiple parties provides strong support for realizing the sustainable development of the energy field.

[0035] The present invention fits the travel data of electric vehicles in different regions to obtain an accurate mathematical model of travel characteristics, including key information such as the time of leaving and arriving at the building and the driving distance. This information provides a solid foundation for subsequent charging and discharging scheduling. And the present invention establishes an optimization model with the minimum power grid load fluctuation, the minimum light abandonment rate, and the minimum user charging cost, and sets strict constraint conditions. By using an improved PSO integer optimization algorithm to solve the model, the charging and discharging status of each electric vehicle at each moment is obtained, realizing the organic combination of accurate prediction and intelligent scheduling. This accuracy and intelligence not only improve the efficiency of scheduling, but also greatly reduce energy waste and cost expenditure.

[0036] The present invention improves the traditional particle swarm optimization (PSO) algorithm and applies it to the optimization scheduling model for the charging and discharging of electric vehicles, improving the efficiency and accuracy of the solution. And it fully considers the characteristics of different types of buildings and the travel characteristics of electric vehicles, making the scheduling scheme closer to the actual application scenario, with stronger practicability and operability. In addition, the present invention also provides various implementation manners such as a system for scheduling the charging and discharging of electric vehicles in a building, a terminal device, and a computer-readable storage medium, providing convenience for the popularization and application of the technology. The application of these innovative technologies not only promotes the technological progress in the field of scheduling the charging and discharging of electric vehicles, but also provides decision-making support for the future investment and construction of load aggregators, helping to promote the transformation and development of the energy field. Description of the Drawings

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0038] Figure 1 Flow chart of an electric vehicle charging and discharging scheduling method for buildings according to the present invention; Figure 2 Flow chart for solving the scheduling model; Figure 3 Transfer process diagram of an electric vehicle at a certain moment; Figure 4 Curve graph of the number of electric vehicles in different regions; Figure 5 Optimization result graph of orderly charging and discharging of residential buildings. Specific implementation manner

[0039] The present invention will be further described below in conjunction with the drawings and specific implementation manners.

[0040] Please refer to Figure 1 , the present invention provides an electric vehicle charging and discharging scheduling method for buildings, including the following steps: Step 1: The building photovoltaic charging station system determines the type of building where the electric vehicle is located at this time, and obtains the basic load data of the building, the output power of photovoltaic power generation, and the charging and discharging power information of the photovoltaic charging station; the Monte Carlo method is used to extract electric vehicle driving data, covering the time when it leaves and arrives at the charging station, the driving distance d and the value at the start of the journey, etc.; Step 1-1: Analyze the travel characteristics of electric vehicles in different types of buildings; The travel characteristics of electric vehicles in different types of buildings are different, so they need to be studied separately. Data shows that the travel characteristics of electric vehicle owners follow a normal distribution. First, collect the travel data of household electric vehicles in different types of buildings; then use the Monte Carlo method to randomly extract the time samples of the cars leaving and arriving at different types of buildings, and use the standard normal distribution curve to fit them; finally, obtain the travel characteristics of electric vehicles in each building; (1) Travel characteristics of electric vehicles in residential buildings: Using the standard normal distribution curve to fit the departure and arrival times of electric vehicles in residential buildings, it can be seen that they follow a general normal distribution, and the formulas are as follows:

[0041]

[0042] Wherein, and are the mean values of the departure and arrival times of electric vehicles in residential buildings, respectively, and are the standard deviations, , ; , .

[0043] (2) Travel characteristics of electric vehicles in office buildings By fitting the departure and arrival times of electric vehicles in office buildings with a standard normal distribution curve, it can be seen that they follow a general normal distribution, and the formulas are as follows:

[0044]

[0045] Wherein, and are the mean values of the departure and arrival times of electric vehicles in office buildings, respectively, and are the standard deviations, , ; , .

[0046] (3) Travel characteristics of electric vehicles in commercial buildings The departure and arrival times of electric vehicles in the commercial area are volatile and conform to the normal distribution in local areas.

[0047] The departure time of electric vehicles in the commercial area is represented by a mixture model composed of two sub-Gaussian distributions, and the formulas are as follows:

[0048] Wherein, , .

[0049] The arrival time of electric vehicles in the commercial area is represented by a mixture model composed of two sub-Gaussian distributions, and the formulas are as follows:

[0050] Wherein, , .

[0051] Step 1-2: Fit the driving mileage of electric vehicles; The daily driving mileage of the electric vehicle is fitted by the least squares method to obtain the following formula:

[0052] In the formula, is the daily driving mileage of the electric vehicle (km), .

[0053] Step 2: Determine whether charging is required according to the driving data of the electric vehicle; set the charging threshold to 0.85. When at the end of the trip, the electric vehicle is charged, otherwise only a stop process is performed; In Step 2, the power consumption and state of charge (SOC) value of the electric vehicle are calculated using the fitted driving mileage of the electric vehicle; The formula for the power consumption of the electric vehicle during driving is as follows:

[0054] In the formula, (kWh) is the power consumption of the electric vehicle during driving, W (kWh / 100km) is the power consumption per 100 km of the electric vehicle, ; is the drive efficiency, .

[0055] When the electric vehicle reaches the end of the trip, i.e., at the start of charging, the state of charge (SOC) is given by the following formula:

[0056] In the formula, is the SOC at the end of the electric vehicle trip, is the SOC at the start of the trip, is the rated capacity of the electric vehicle battery, .

[0057] The charge and discharge depth of the electric vehicle has a significant impact on the battery life. Generally, it is stipulated that the remaining capacity after the electric vehicle discharges should not be less than 10% of the rated capacity, and the remaining capacity after charging should not exceed 95% of the rated capacity. In addition, the battery life is also greatly related to the number of charge and discharge cycles. Frequent charge and discharge will cause the battery performance to decline. Therefore, the charging threshold is set to 0.85. When the battery power drops to the charging threshold, the electric vehicle directly charges.

[0058] Step 3: If charging is required, calculate the required charging time , and determine whether the charging demand can be met within the schedulable time period . If , then charge and discharge scheduling is performed; otherwise, only the charging operation is executed.

[0059] In step 3, calculate the charge and discharge duration. When an electric vehicle is connected to a charging pile and meets the charging requirements, it starts charging immediately. The charging duration of the electric vehicle is defined as follows:

[0060] In the formula, (h) is the charging duration of the electric vehicle, is the charging power of the electric vehicle, , and 0.95 means that the remaining capacity after charging does not exceed 95% of the rated capacity.

[0061] Therefore, the t moment charging load of electric vehicles in different regions is defined as:

[0062]

[0063] In the formula, (kW) is the unordered charging load of electric vehicles in the t moment i region, , i represents different regions. is the number of electric vehicles in the t moment i region. (kW) represents the charging power of the t th electric vehicle at the j moment, represents the j th electric vehicle's charging demand status. When , it means charging; when , it means no charging is required and it is only a temporary stop; when , it means discharging.

[0064] Step 4: Establish an optimal scheduling model for the orderly charging and discharging of electric vehicles in the building, and use the improved PSO algorithm for iterative operation to obtain the optimal orderly charging and discharging strategy of electric vehicles. The solution flow chart is as Figure 2 shown.

[0065] Step 4-1: Establish the model objective function; (1) Take the minimum fluctuation of the building power grid load as the first objective function; For the building power grid side, the lower the load fluctuation, the more stable its operation. The building power grid load consists of three parts: basic load, photovoltaic power generation, and electric vehicle charging load. The minimization of the building power grid load fluctuation is:

[0066] In the formula, (kW) is t the base load at the (kW) is t the total charge-discharge power of electric vehicles at the and respectively represent t the charge / discharge power of electric vehicles at the When > 0, it represents charging. When (kW) is t the PV power generation at the is the average load of the building microgrid, which is expressed as follows:

[0067] (2) Taking the minimum curtailment rate as the second objective function; The minimum curtailment rate means the maximum PV accommodation rate, that is, the minimum difference between the PV output power and the electric vehicle charging power. Its expression is:

[0068] In the formula, The following conditions need to be satisfied:

[0069] In the formula, (kW) is the difference between the PV output power and the electric vehicle charging power, (kW) is t the total PV power generation at the (kW) is t the total electric vehicle charging power at the

[0070] (3) Taking the minimum user charge-discharge cost as the third objective function; The objective function of the minimum user charge-discharge cost of electric vehicles is expressed as follows:

[0071]

[0072] In the formula, is the charge-discharge cost of electric vehicle users, (kW) and (kW) are respectively t at the n th electric vehicle's charge / discharge power. is tThe charging cost of an electric vehicle at a certain moment, which adopts time-of-use electricity price. is the peak-time electricity price, is the normal-time electricity price, is the valley-time electricity price; is the peak-time period, is the normal-time period, is the valley-time period. The peak-time period electricity price is adopted for discharge compensation.

[0073] (4)The objective function of the model; The above objective functions are linearly weighted and combined into one objective as follows:

[0074] where .

[0075] Step 4-2: Formulate the model constraint conditions; (1)Constraint condition 1; During the scheduling process, the charging and discharging power of each electric vehicle needs to meet its set maximum and minimum limits, as follows:

[0076] In the formula, and are the upper and lower limits of the charging and discharging power of the electric vehicle respectively.

[0077] (2)Constraint condition 2; The state of charge of each electric vehicle should also be maintained within its specified maximum and minimum ranges, specifically as follows:

[0078] In the formula, and are the upper and lower limits of SOC respectively.

[0079] (3)Constraint condition 3; The charging time of each electric vehicle within the adjustable time period needs to meet its charging demand, expressed as:

[0080] In the formula, represents the adjustable time of the i th electric vehicle, that is, the maximum residence time.

[0081] (4)Constraint condition 4; During the user-adjustable time, the discharging time of each electric vehicle should not exceed its specified maximum discharging duration, defined as:

[0082] In the formula, (h) is the maximum continuous discharge duration of an electric vehicle for a single discharge, is the upper limit of 0.95 for SOC, is the discharge threshold. When the electric vehicle will no longer perform discharge operations. is the discharge power of the electric vehicle.

[0083] (5) Constraint condition 5; Since the frequent switching of the charge and discharge states of electric vehicles will have an adverse impact on the battery capacity and life, it is necessary to limit the number of charge and discharge switches of electric vehicles as follows:

[0084] In the formula, is the charge and discharge state of the j th electric vehicle, is the maximum number of charge and discharge switches allowed during the scheduling period of the electric vehicle.

[0085] Step 5: Obtain the ordered charge and discharge strategies of each electric vehicle in each building, and calculate the total charge and discharge load after the ordered scheduling of each building. Finally, the building photovoltaic charging station system regulates the charge and discharge operations of electric vehicles according to the scheduling strategy to achieve the effect of peak shaving and valley filling.

[0086] In step 4, the traditional PSO algorithm was improved from three aspects: population initialization, particle velocity update strategy, and position update strategy, so that the improved PSO algorithm can be applied to the ordered charge and discharge optimization problem in this article; The traditional particle swarm optimization (PSO) algorithm is an optimization algorithm that simulates the foraging behavior of bird flocks and was proposed by Eberhart and Kennedy in 1995. The PSO algorithm aims at the optimization problem and realizes the optimization search by simulating the foraging process of bird flocks in the solution space. The traditional PSO algorithm is for continuous optimization problems, regards the solution as particles in a continuous space, and uses continuous position and velocity for search.

[0087] The orderly charge and discharge optimization problem in this study is an integer programming problem (IP). The decision variables are the charge and discharge states of each electric vehicle at each moment. -1 represents the discharge state, 0 represents the staying state, and 1 represents the charging state. For the integer optimization problem, the solution space needs to be restricted to a discrete set of integer points, that is, both the position and velocity of the particle are integer values. In terms of the update strategy, the traditional PSO algorithm uses parameters such as the inertia weight, individual learning factor, and social learning factor to update the velocity and position of the particle. While for the PSO algorithm in integer programming, a special update strategy needs to be designed to ensure effective search in the integer solution space. For example, techniques such as using integer indexing and integer neighborhood search can be used to control the update of the particle position.

[0088] Preferably, in step 5, the population initialization strategy is improved. The integer programming problem requires that the decision variables all take integer values. Therefore, the initial population of the improved particle swarm algorithm must take integer values. When generating the initial population, in order to make the population cover the entire search space as much as possible, generate N initial individuals randomly according to the following formula.

[0089]

[0090] In the formula is the initial position of the i th individual, N represents the population size, M represents the individual dimension.

[0091]

[0092] In the formula is the value of the th individual in the rd dimension, ]>[[]END]]is a uniform random number between [0, 1].

[0093] In step 5, the velocity update strategy is improved. The velocity update formula of the particles in the traditional PSO algorithm is as follows:

[0094] In the formula is the inertia weight, is the individual extreme value, is the global extreme value, is a random number between.

[0095] To enable the particles to have a better evolution direction, an adaptive inertia weight is introduced to improve the search ability and convergence speed of the algorithm.

[0096] First, set , , , which makes the velocity of the particle move between the individual extreme value and the global extreme value . The particle will gradually approach the feasible region. The magnitude of the parameter can determine whether the velocity of the particle flies towards the individual extreme value or the global extreme value. The change of the value is expressed by the following formula:

[0097] In the formula, is the current iteration number, is the maximum iteration number.

[0098] In addition, in the original formula is replaced by , where represents the sign of , which means that the velocity of the th particle only affects the flight direction of the next generation of particles. The flight magnitude of the next generation of particles is affected by . is a uniformly random integer between. According to the above assumptions, will roughly reflect the size of the feasible region, which makes the displacement of the particle not deviate too far from the feasible region, and the value of can adaptively adjust the search range of the population.

[0099] changes linearly with the iteration number and is determined by the following formula:

[0100] Therefore, the improved velocity update formula is:

[0101] In step 5, the position update strategy is improved; By introducing a random walk mechanism, the exploration ability of the particle is enhanced, which helps to avoid the population falling into local optimum, thereby improving the global search ability of the algorithm. According to the above particle velocity , the update formula for the particle position is obtained as follows:

[0102]

[0103] In the formula, is a uniformly random number between. When When it represents a particle in the positive direction at the -dimensional position is a better evolutionary direction, and vice versa. And when

[0104] The following is an apparatus embodiment of the present invention, which can be used to execute the method embodiment of the present invention. For details not disclosed in the apparatus embodiment, please refer to the method embodiment of the present invention.

[0105] The embodiment of the present invention also provides an electric vehicle charging and discharging scheduling system for a building, including: A data acquisition module, configured to determine the building type, acquire the building power grid load, electricity price, and the schedulable time and driving mileage of electric vehicles in the building; An orderly charging and discharging optimization scheduling strategy acquisition module, configured to input the above data into the orderly charging and discharging optimization scheduling model of electric vehicles in the building to obtain the charging demand status of each electric vehicle in the building at each moment; An orderly charging and discharging scheduling module, configured to perform charging and discharging scheduling on electric vehicles in the building by using the charging demand status; Wherein, the orderly charging and discharging optimization scheduling model of electric vehicles in the building adopts an objective function with the minimum building power grid load fluctuation, light abandonment rate, and user charging and discharging costs, and takes the charging and discharging power, state of charge, charging time, discharging time, and charging and discharging switching times of each electric vehicle meeting the requirements as constraint conditions; In the orderly charging and discharging optimization scheduling model of electric vehicles in the building, the state of charge SOC value at the end of the electric vehicle's journey is obtained according to the driving mileage of the electric vehicle, and it is compared with the charging threshold to judge whether the electric vehicle needs to be charged or parked; the required charging time of the electric vehicle that needs to be charged is obtained, and it is compared with the schedulable time to judge whether to perform charging and discharging scheduling or only charging.

[0106] The embodiment of the present invention also provides a terminal device, which includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the above-mentioned method for scheduling the charging and discharging of electric vehicles in a building; or, when the processor executes the computer program, it implements the functions of each module in the above-mentioned public building cooling load prediction system with adaptive hybrid rime optimization.

[0107] The computer program can be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention.

[0108] The terminal device may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory.

[0109] The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0110] The memory may be used to store the computer program and / or module. The processor realizes various functions of the terminal device by running or executing the computer program and / or module stored in the memory, and by calling the data stored in the memory.

[0111] If the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium.

[0112] Based on such an understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it may also be completed by a computer program instructing relevant hardware. The computer program may be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-described method for scheduling the charging and discharging of electric vehicles in a building can be implemented. Among them, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, an executable file, or some intermediate form, etc.

[0113] The computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, a Read Only Memory (ROM), a Random Access Memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0114] Example 1: Test Results The data used in this example are from three different types of buildings in a certain area, including residential buildings, office buildings, and commercial buildings. Historical base load data and local solar irradiance data of three buildings in June 2020 were collected respectively, and the collection time interval was 15 minutes. The battery capacity of each electric vehicle was set to 58 kWh. By default, the electric vehicle was charged and discharged at a constant power, and the charging and discharging powers were the same, which was 10 kW. The population size of the improved PSO algorithm was 200, and the maximum number of iterations was 500 generations. Calculation of the number of electric vehicles in different regions based on the Markov chain; The Markov process is a stochastic process with no aftereffect. A Markov process with discrete parameters and discrete state space is called a Markov chain. In this process, given the current knowledge or information, the past historical state has nothing to do with predicting the future state.

[0115] In this study, three types of building areas: residential areas, office areas, and commercial areas were targeted, and they were marked as R, O, and C respectively. Then there are three states of the vehicle at a certain moment , and the states can be converted into each other, as Figure 3 shown.

[0116] The transfer of electric vehicles in different regions is random and only related to the current state, independent of the past, which conforms to the Markov process. Then the transfer probability matrix of electric vehicles is expressed as:

[0117] Among them is the transfer probability matrix from time t to time , and the matrix element represents the probability of transferring from state to state during this time period. According to the vehicle data in NHTS, the transfer frequencies of vehicles starting from different regions in each time period were counted, and the transfer frequency matrix of electric vehicles moving among the three types of regions in each time period was obtained. Combining the results in the literature, the electric vehicle state transfer probability matrix at different times of a day was finally obtained as follows:

[0118] Assume that the initial numbers of electric vehicles in residential areas, working areas, and other areas are [60, 20, 40]. According to the transfer probability matrix, the distribution of the number of electric vehicles parked in different regions for 24 hours a day is as Figure 4 shown.

[0119] Based on the Markov transition probability matrix, the number of electric vehicle dockings in different areas over a 24-hour period is determined. However, not all electric vehicles require charging; their charging needs depend on their initial state of charge (SOC) and mileage. Disorderly charging refers to the autonomous charging behavior of electric vehicle users without appropriate intervention by charging station operators. Based on the analysis in step 1-1, the Monte Carlo method is used to extract the departure and arrival times of each electric vehicle. The mileage of each electric vehicle is then calculated using the formulas in steps 1-3 and 1-4 to calculate the power consumption and charging duration of each electric vehicle. The charging load of each electric vehicle is then superimposed to determine the disorderly charging load for different building types. To mitigate the uncertainty caused by the randomness of the extraction, the experiment was repeated 100 times, and the average value was used as the disorderly charging load. Finally, an improved PSO algorithm was used to solve the ordered charging and discharging optimization problem. The load changes for each building type under ordered scheduling were determined, and the results were compared and analyzed.

[0120] The electric vehicle charging electricity price in this embodiment adopts the time-of-use electricity price rule, and the time-of-use electricity price is shown in Table 1. At the same time, in order to encourage electric vehicle users to actively participate in the charging and discharging optimization scheduling, users are given a certain discharge compensation, and the discharge compensation electricity price adopts the peak electricity price.

[0121] Table 1 Time-of-use electricity price and discharge compensation price

[0122] The results of orderly charging and discharging optimization of photovoltaic charging stations in residential buildings are as follows Figure 5 As shown in the figure. It is obvious that compared with the disordered charging load curve, the optimized ordered charging and discharging curve has larger peaks and valleys. The charging peak is no longer concentrated between 18:00 and 24:00 in the evening, but is delayed to between 00:00 and 8:00 in the morning of the next day. This period happens to be the valley period of time-of-use electricity prices, which greatly reduces users' charging costs. In addition, during the peak electricity consumption period in the evening, more electric vehicles participate in discharge regulation, which greatly reduces the peak of the total building load at this time. The optimized total load curve of the residential building also becomes smoother, which is conducive to the stable operation of the power grid. The above results show that when electric vehicles are connected to the orderly charging and discharging control system, the peak load of the building can be shaving and valley filling can be achieved, and the charging cost can be reduced.

[0123] It is worth noting that no charging load and discharge input were generated between 12:00 and 17:00 in the afternoon. This is because there were almost no electric vehicles staying in the residential building during this period, and even fewer electric vehicles participated in the charging and discharging scheduling. Therefore, for the photovoltaic charging station in the residential building, the role of using the charging load to absorb photovoltaic power generation is very limited. Table 2 shows the parameter results of the residential building before and after optimization. It can be seen that compared with disordered charging, the photovoltaic power generation absorption rate did not change after orderly scheduling, which fully proves the limitedness of the charging load in the residential building to absorb photovoltaic power generation. However, other parameters have changed significantly. The disordered charging load volatility changed from 397900 to 49600 after optimization, which is a qualitative change, proving that the optimization model is beneficial to the stable operation of the power grid. In addition, although orderly scheduling led to an increase in charging costs, it also brought generous discharge compensation to electric vehicle users. It can be seen from the table that the discharge compensation and charging costs are almost equivalent, which greatly reduces the total cost after optimization. This also proves that the optimization model has a significant effect on reducing the charging costs of electric vehicle users.

[0124] Table 2 Comparison of Parameter Results of Residential Building Optimization

[0125] Assuming that the initial setting of the electric vehicle ownership in the office area is 90 vehicles, the optimization model is solved to obtain the optimization results as shown in Table 3.

[0126] Table 3 Comparison of Parameter Results of Office Building Optimization

[0127] As can be seen from Table 3, compared with before optimization, the load fluctuation of the optimized building microgrid has decreased slightly, which is beneficial to the stable operation of the power grid. The photovoltaic power generation absorption rate has increased significantly compared with before optimization, which is beneficial to the economic operation of the photovoltaic charging station. In addition, the charging costs of electric vehicle users have increased compared with before optimization, but due to the high discharge subsidy, the total cost of users has decreased significantly.

[0128] Assuming that the initial setting of the electric vehicle ownership in the commercial area is 90 vehicles, the optimization model is solved to obtain the optimization results as shown in Table 4.

[0129] Table 4 Comparison of Parameter Results of Commercial Building Optimization

[0130] As can be seen from Table 4, compared with before optimization, the load fluctuation of the optimized building microgrid has decreased slightly. In addition, the charging cost of electric vehicle users has increased compared with before optimization, but due to the higher discharge subsidy, the total cost of users has decreased significantly. However, the PV power consumption rate has decreased, which is not conducive to the economic operation of the PV charging station.

[0131] In summary, the charging station in residential buildings has the highest potential for optimizing the charging and discharging scheduling of electric vehicles. Although it is still slightly insufficient in improving the PV power consumption rate, it plays a significant role in reducing load fluctuations and reducing the charging costs of users. Therefore, building a charging station in residential buildings has the highest cost performance for safeguarding the interests of all parties. In addition, office buildings also have good potential for optimizing the charging and discharging scheduling, mainly because while safeguarding the interests of electric vehicle users, they can fully consume PV power generation, which has high investment attractiveness for charging station operators in office building construction. However, there are many limitations in optimizing the charging and discharging scheduling in commercial buildings. Although the charging costs of electric vehicle users can be reduced through scheduling optimization, it is at the cost of reducing the PV power consumption rate, which is unacceptable for charging station operators.

[0132] The present invention discloses a method for scheduling the charging and discharging of electric vehicles in a building, which takes into account the interests of the building microgrid, the PV side and electric vehicle users, and establishes an optimized scheduling model for the charging and discharging of electric vehicles considering the interests of the three parties, so as to achieve the purpose of reducing the load fluctuation of the power grid, improving the PV power consumption rate and reducing the charging costs of users, and providing decision support for the future investment and construction of load aggregators.

Claims

1. An electric vehicle charging and discharging scheduling method for buildings, characterized in that, The steps are as follows: Determine the building type, obtain the building power grid load, electricity price, the schedulable time of electric vehicles in the building, and the driving mileage of electric vehicles; Input the above data into the orderly charging and discharging optimization scheduling model of electric vehicles in the building to obtain the charging demand status of each electric vehicle in the building at each moment, and use the charging demand status to schedule the charging and discharging of electric vehicles in the building; The orderly charging and discharging optimization scheduling model of electric vehicles in the building adopts an objective function with the minimum building power grid load fluctuation, light abandonment rate, and user charging and discharging costs, and takes the charging and discharging power, state of charge, charging time, discharging time, and the number of charging and discharging switching times of each electric vehicle meeting the requirements as constraints; In the orderly charging and discharging optimization scheduling model of electric vehicles in the building, the state of charge SOC value at the end of the electric vehicle's journey is obtained according to the driving mileage of the electric vehicle, and it is compared with the charging threshold to judge whether the electric vehicle needs to be charged or parked; the required charging time of the electric vehicle that needs to be charged is obtained and compared with the schedulable time to judge whether to perform charging and discharging scheduling or only charging.

2. The method for scheduling the charging and discharging of electric vehicles in a building according to claim 1, characterized in that The building type includes residential buildings, office buildings, and commercial buildings; the building power grid load includes the basic load, photovoltaic power generation, and the electric vehicle charging load; the schedulable time of electric vehicles in the building is determined according to the travel characteristics of electric vehicles in the building; the driving mileage of electric vehicles is obtained by least squares fitting; the steps for obtaining the travel characteristics of electric vehicles in the building are specifically as follows: Obtain the travel data of electric vehicles in different types of buildings; Use the Monte Carlo method to randomly extract time samples of electric vehicles leaving and arriving at different types of buildings, and fit them with a standard normal distribution curve; Obtain the travel characteristics of electric vehicles in each building; The building type includes residential buildings, office buildings, and commercial buildings, and the travel characteristics of electric vehicles in the three types of buildings are as follows: The travel characteristics of electric vehicles in residential buildings follow a general normal distribution, and the formula is as follows: wherein, and are respectively the mean values of the departure and arrival times of electric vehicles in residential buildings, and are the standard deviations, , ; , ; The travel characteristics of electric vehicles in office buildings follow a general normal distribution, and the formula is as follows: wherein, and are the mean values of the departure and arrival times of electric vehicles in the office building respectively, and are the standard deviations, , ; , ; The leaving time of electric vehicles in commercial buildings is represented by a mixture model composed of two sub-Gaussian distributions, and the formulas are as follows: In the formula, , ; The arrival time of electric vehicles in commercial buildings is represented by a mixture model composed of two sub-Gaussian distributions, and the formulas are as follows: In the formula, , ; Use the least squares method to fit the driving mileage of electric vehicles, specifically as follows: In the formula, is the daily driving range of the electric vehicle, .

3. A method for scheduling the charging and discharging of electric vehicles in a building according to claim 1, characterized in that, The calculation process in the orderly charging and discharging optimization scheduling model of electric vehicles in the building is as follows: Calculate the power consumption of electric vehicles using the fitted driving mileage of electric vehicles, obtain the state of charge SOC value at the end of the electric vehicle's journey according to the power consumption, and when the SOC value at the end of the electric vehicle's journey is less than the charging threshold, the charging demand status of the electric vehicle at this moment is charging, otherwise it is parked; when charging, if the required charging time of the electric vehicle is less than the schedulable time, perform charging and discharging scheduling, otherwise only charge; The obtained charging demand status of each electric vehicle in the building at each moment satisfies the minimum objective function value within the constraints.

4. A method for scheduling the charging and discharging of electric vehicles in a building according to claim 3, characterized in that, The formula for the driving power consumption of electric vehicles is as follows: Wherein, is the power consumption of the electric vehicle during driving, W is the power consumption of the electric vehicle per 100 kilometers, ; is the drive efficiency, ; The formula for the SOC value of an electric vehicle at the end of a journey is as follows: Wherein, is the SOC at the end of the electric vehicle's driving range, is the SOC at the start of the driving range, is the rated capacity of the electric vehicle's battery; The formula for the charging duration of an electric vehicle is as follows: Wherein, is the charging duration of the electric vehicle, is the charging power of the electric vehicle, and 0.95 means that the remaining capacity after charging ends does not exceed 95% of the rated capacity.

5. A method for scheduling the charging and discharging of electric vehicles in a building according to claim 3, characterized in that The objective function of the optimal scheduling model for the orderly charging and discharging of electric vehicles in a building is: Among them, ; Among them, the minimum building grid load fluctuation is the first objective function, and the expression is: In the formula, is t the base load at time is t the total charging and discharging power of electric vehicles at time and respectively represent t the charging / discharging power of electric vehicles at time > 0, representing charging, and when < 0, representing discharging; is t the PV power generation at time is the average load of the building microgrid; Taking the minimum curtailment rate as the second objective function, the expression is: In the formula, the following conditions need to be satisfied: In the formula, is the difference between the photovoltaic output power and the electric vehicle charging power, is t the total photovoltaic power generation at time is t the total electric vehicle charging power at time; Taking the minimum user charging and discharging cost as the third objective function, the expression is: Wherein, is the charging and discharging cost of electric vehicle users, and are respectively t at time n the charging / discharging power of the th t electric vehicle; the charging cost of the electric vehicle at time is the peak time electricity price, is the normal time electricity price, is the valley time electricity price; is the peak time period, is the normal time period, is the valley time period; Discharge compensation is carried out using the peak time period electricity price. The constraints are as follows: The charging and discharging power and state of charge of each electric vehicle comply with the set maximum and minimum limits; the charging time of each electric vehicle meets the charging demand; the discharging time of each electric vehicle does not exceed the specified maximum discharging duration; the number of charging and discharging switching times of each electric vehicle is less than or equal to the maximum number of charging and discharging switching times allowed during the electric vehicle scheduling period.

6. The electric vehicle charging and discharging scheduling method for buildings according to claim 1, wherein The optimal scheduling model for the orderly charging and discharging of electric vehicles in a building is solved using a PSO algorithm improved by population initialization, particle velocity update strategy, and position update strategy; among them, during population initialization, the initial population that takes integers using the particle swarm algorithm is used; the particle velocity update strategy uses an adaptive inertia weight; the position update strategy uses a random walk mechanism.

7. An electric vehicle charging and discharging scheduling system for a building, characterized in that, Including: A data acquisition module for determining the building type and obtaining the building grid load, electricity price, and the schedulable time and driving mileage of electric vehicles in the building; An orderly charging and discharging optimal scheduling strategy acquisition module for inputting the above data into the optimal scheduling model for the orderly charging and discharging of electric vehicles in a building to obtain the charging demand status of each electric vehicle in the building at each moment; An orderly charging and discharging scheduling module for performing charging and discharging scheduling on electric vehicles in the building using the charging demand status; Among them, the optimal scheduling model for the orderly charging and discharging of electric vehicles in the building uses an objective function with the minimum building grid load fluctuation, curtailment rate, and user charging and discharging cost, and takes the charging and discharging power, state of charge, charging time, discharging time, and number of charging and discharging switching times of each electric vehicle meeting the requirements as constraints; In the optimal scheduling model for the orderly charging and discharging of electric vehicles in the building, the state of charge SOC value of the electric vehicle at the end of the journey is obtained based on the driving mileage of the electric vehicle, compared with the charging threshold to judge whether to charge the electric vehicle or make a stop treatment; the required charging time of the electric vehicle that needs to be charged is obtained and compared with the schedulable time to judge whether to perform charging and discharging scheduling or only charging.

8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of a method for scheduling the charging and discharging of electric vehicles in a building as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of a method for scheduling the charging and discharging of electric vehicles in a building as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of a method for scheduling the charging and discharging of electric vehicles in a building as described in any one of claims 1 to 6.

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